Systems and methods for assessment of cell-free RNA based on abundance

Abstract

Systems and methods for sequencing of cell-free RNAs are provided. Targeted sequencing can be performed using a panel of transcripts that are genes of rare abundance within a population of control samples of cell-free nucleic acids.

Description

SYSTEMS AND METHODS FOR ASSESSMENT OF CELL-FREE RNA BASED ON ABUNDANCECROSS REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to U. S. Provisional Application Ser. No.63 / 789,907, entitled “Systems And Methods For Assessment Of Cell-Free Rna Based On Abundance,” filed April 16, 2025, the disclosure of which is hereby incorporated by reference in its entirety.STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

[0002] This invention was made with Government support under contract CA254179 awarded by the National Institutes of Health. The Government has certain rights in the invention.TECHNICAL FIELD

[0003] The disclosure provides description of an improved method for performing sequencing on cell-free RNA.BACKGROUND

[0004] Blood-based liquid biopsies enable non-invasive characterization of health, including detection of biological phenomena such as pregnancy and cancer. Liquid biopsies offer many advantages over tissue biopsies because they are minimally invasive, easily repeated over time, and more accurately reflect the geographic heterogeneity among the cellular sources. In patients with advanced cancer disease, analysis of circulating tumor DNA (ctDNA) is used clinically for non-invasive genotyping. However, full clinical evaluation can benefit from expression-based analyses, such as for distinguishing between tumor types or subtypes.SUMMARY

[0005] In some implementations, a method for sequencing of cell-free RNA comprises performing targeted sequencing. In some embodiments, the sequencing comprisestargeted at cell-free RNA molecules that are infrequently present as cell-free RNA within liquid biopsies of control individuals.

[0006] In one aspect disclosed herein is a method comprising: (a) obtaining a sample comprising a plurality of cell-free nucleic acid molecules, the plurality of cell-free nucleic acid molecules comprising a plurality of cell-free RNA molecules from a biological sample of an individual or a plurality of DNA molecules complementary to the plurality of cell-free RNA molecules; (b) obtaining a panel of nucleic acid molecules that enriches for a subset of transcripts from a whole transcriptome; (c) enriching the sample for the subset by contacting the sample with the panel of nucleic acid molecules, to identify a set of target genes from the enriched subset; and (d) determining, based at least in part on the identified set of target genes, a presence or state of an injury to an organ associated with a therapeutic intervention for a biological condition, wherein the therapeutic intervention is selected from the group consisting of a radiation therapy, a cellular therapy, a non-cellular pharmacologic therapy, and a surgical procedure. In some embodiments, the method comprises determining the presence of the injury to the organ following the therapeutic intervention. In some embodiments, the method comprises determining the state of the injury to the organ following the therapeutic intervention. In some embodiments, the state of the injury comprises progression of the injury to the organ. In some embodiments, the method further comprises: generating sequencing read data of the enriched subset; and aligning the sequencing read data to a reference database to identify at least one member of the set of target genes. In some embodiments, the reference database comprises a reference genome, a reference transcriptome, or both. In some embodiments, the method further comprises determining a gene expression profile of the at least one member of the set of target genes based on the aligning. In some embodiments, the method further comprises excluding, in silico, a gene expression contribution of platelets from the gene expression profile. In some embodiments, the method further comprises determining, based at least in part on the determined presence or state of the injury to the organ, the individual’s response to the therapeutic intervention for the biological condition. In some embodiments, the method further comprises modifying, based at least in part on the determined presence or state of the injury to the organ, the therapeutic intervention for the biological condition of the individual. In someembodiments, the method further comprises subjecting the individual to the modified therapeutic intervention. In some embodiments, the method further comprises determining, based at least in part on the determined presence or state of the injury to the organ, a different therapeutic intervention for the biological condition of the individual. In some embodiments, the method further comprises subjecting the individual to the different therapeutic intervention.

[0007] In another aspect disclosed herein is a method, comprising: (a) identifying an individual comprising an injury to an organ following a therapeutic intervention for a biological condition, wherein the individual was identified as having the injury to the organ based at least in part on: (i) obtaining a sample comprising a plurality of cell-free nucleic acid molecules, the plurality of cell-free nucleic acid molecules comprising a plurality of cell-free RNA molecules from a biological sample of the individual or a plurality of DNA molecules complementary to the plurality of cell-free RNA molecules; (ii) obtaining a panel of nucleic acid molecules that enriches for a subset of transcripts from a whole coding transcriptome; and (iii) enriching the sample for the subset by contacting the sample with the panel of nucleic acid molecules, to identify a set of target genes from the enriched subset, wherein the identified set of target genes is indicative of a presence or state of the injury to the organ; and (b) treating the individual with an alternative therapeutic intervention based at least in part on the identified set of target genes. In some embodiments, the alternative therapeutic intervention comprises at least one regimen modification of the therapeutic intervention. In some embodiments, the alternative therapeutic intervention comprises a different therapeutic intervention for the biological condition of the individual. In some embodiments, the method further comprises discontinuing the therapeutic intervention to the individual.

[0008] In another aspect disclosed herein is a method, comprising: (a) obtaining a sample comprising a plurality of cell-free nucleic acid molecules, the plurality of cell-free nucleic acid molecules comprising a plurality of cell-free RNA molecules from a biological sample of an individual or a plurality of DNA molecules complementary to the plurality of cell-free RNA molecules; (b) obtaining a panel of nucleic acid molecules that enriches for a subset of transcripts from a whole transcriptome; and (c) enriching the sample for the subset by contacting the sample with the panel of nucleic acid molecules, to identify a setof target genes from the enriched subset; (d) generating sequencing read data of the enriched subset; and (e) aligning the sequencing read data to a reference database to identify at least one member of the set of target genes, wherein the identified at least one member is indicative of a presence or state of an injury to an organ associated with a therapeutic intervention for a biological condition in the individual. In some embodiments, the aligning the sequencing read data to the reference database comprises aligning reads from greater than 100, greater than 500, or greater than 1,000 different cfRNA molecules or cDNA molecules complementary to the plurality of cell-free RNA molecules to the reference database. In some embodiments, the reference database comprises greater than 5 kilobases (kb), greater than 100 kb, greater than 1 megabases (Mb), greater than 10 Mb, greater than 100 Mb, greater than 1 gigabase (Gb), or greater than 3 Gb. In some embodiments, the reference database comprises a reference genome, a reference transcriptome, or both. In some embodiments, the method further comprises determining a gene expression profile of the at least one member of the set of target genes based on the aligning. In some embodiments, the method further comprises excluding, in silico, a gene expression contribution of platelets from the gene expression profile.

[0009] In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 5% of a subset of genes identified in Table 1, wherein the subset consists of the genes indicated as “Yes” in the “RAG Capture Panel V2” column of Table 1. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the subset of genes. In some embodiments, at least 5% of the nucleic acid molecules of the panel used for the enrichment of the sample enrich for the subset of genes. In some embodiments, at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the nucleic acid molecules of the panel used for the enrichment of the sample enrich for the subset of genes. In some embodiments, the at least 5% of the subset of genes comprises at least one gene indicated in Table 45. In some embodiments, the at least one gene comprises at least 5%, at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of thegenes indicated in Table 45. In some embodiments, the at least 5% of the subset of genes comprises at least one gene indicated in Table 1 as “Yes” in the “RAG Capture Panel V2” column of and as in the “RAG Capture Panel V1” column. In some embodiments, the at least one gene comprises at least 5%, at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes indicated in Table 1 as “Yes” in the “RAG Capture Panel V2” column of and asin the “RAG Capture Panel V1” column. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 5% of a subset of genes identified in Table 1, wherein the subset consists of the genes indicated as “Yes” in the “RAG Capture Panel V1” column of Table 1. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the subset of genes. In some embodiments, at least 5% of the nucleic acid molecules of the panel used for the enrichment of the sample enrich for the subset of genes. In some embodiments, at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the nucleic acid molecules of the panel used for the enrichment of the sample enrich for the subset of genes. In some embodiments, the panel of nucleic acid molecules that enriches for the subset of transcripts comprises nucleic acids appended to a pull-down tag. In some embodiments, the pull-down tag comprises biotin.

[0010] In some embodiments, the therapeutic intervention comprises the radiation therapy. In some embodiments, the therapeutic intervention comprises the cellular therapy. In some embodiments, the therapeutic intervention comprises the non-cellular pharmacologic therapy. In some embodiments, the non-cellular pharmacologic therapy is selected from the group consisting of a protein therapy, an antibody therapy, a nucleic acid molecule therapy, and a small molecule therapy. In some embodiments, the therapeutic intervention comprises the surgical procedure.

[0011] In some embodiments, the plurality of cell-free nucleic acid molecules comprises the plurality of cell-free RNA molecules. In some embodiments, the plurality ofcell-free nucleic acid molecules comprises the plurality of DNA molecules produced by reverse transcription of the plurality of cell-free RNA molecules.

[0012] In some embodiments, the biological condition is cancer. In some embodiments, the organ is selected from the group consisting of lung, liver, kidney, brain, spine, heart, bone marrow, and organs comprising the hematopoietic system. In some embodiments, the injury is pneumonitis, optionally wherein the injury is radiation-induced pneumonitis.

[0013] In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 5, Table 6, Table 8, Table 9, Table 21, and Table 22. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in the one or more members selected from the group consisting of Table 5, Table 6, Table 8, Table 9, Table 21, and Table 22. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 5. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 5. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 7. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 7. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 6. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 6. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 23. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%,at least 95%, or 100% of the genes identified in Table 23. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 24. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 24. In some embodiments, the panel of nucleic acid molecules targets transcripts of one or more genes associated with one or more cell types selected from the group consisting of lung signaling alveolar epithelial type 2 cell, lung alveolar epithelial type 1 cell, lung alveolar epithelial type 2 cell, lung goblet cell, lung club cell, lung proliferating basal cell, lung platelet megakaryocyte cell, lung proliferating macrophage cell, lung TREM2+ dendritic cell, lung neutrophil cell, lung CD8+ memory effector T cell, lung EREG dendritic cell, lung NK cell, lung classical monocyte cell, lung CD8+ naive T cell, lung B cell, lung CD4+ memory effector T cell, and lung CD4+ naive T cell. In some embodiments, the one or more cell types are selected from the group consisting of lung signaling alveolar epithelial type 2 cell, lung signaling alveolar epithelial type 1 cell, lung alveolar epithelial type 2 cell, lung goblet cell, lung club cell, lung proliferating basal cell, and lung platelet megakaryocyte cell. In some embodiments, the one or more cell types are selected from the group consisting of lung signaling alveolar epithelial type 2 cell, lung signaling alveolar epithelial type 1 cell, lung alveolar epithelial type 2 cell, lung goblet cell, and lung proliferating basal cell. In some embodiments, the identified set of target genes from the enriched subset is sufficient to determine radiation pneumonitis relative to a preradiation baseline, optionally wherein the radiation pneumonitis is symptomatic radiation pneumonitis. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 8. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 8. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 10. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genesidentified in Table 10. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 9. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 9. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 25. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 25. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 26. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 26. In some embodiments, the panel of nucleic acid molecules targets transcripts of one or more genes associated with one or more cell types selected from the group consisting of lung platelet megakaryocyte cell, lung capillary intermediate 1 cell, lung NK cell, lung NK T cell, lung CD8+ naive T cell, lung alveolar fibroblast cell, lung bronchial vessel 1 cell, lung basal cell, lung CD4+ naive T cell, lung adventitial fibroblast cell, lung plasmacytoid dendritic cell, and lung B cell. In some embodiments, the one or more cell types are selected from the group consisting of lung platelet megakaryocyte cell, lung capillary intermediate 1 cell, and lung capillary intermediate 1 cell. In some embodiments, the one or more cell types comprises lung platelet megakaryocyte cell. In some embodiments, the identified set of target genes from the enriched subset is sufficient to determine radiation pneumonitis relative to a post-radiation baseline, optionally wherein the identified set of target genes from the enriched subset is sufficient to determine symptomatic radiation pneumonitis relative to a post-radiation asymptomatic baseline. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 21. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified inTable 21. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 22. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 22. In some embodiments, the panel of nucleic acid molecules targets transcripts of one or more genes associated with immune activation and lymphocyte signaling. In some embodiments, the one or more genes comprise CD79A / B, CD22, or CD3. In some embodiments, the panel of nucleic acid molecules targets transcripts of one or more genes associated with innate inflammation. In some embodiments, the one or more genes comprise CXCR1 or FPR1 / 2. In some embodiments, the panel of nucleic acid molecules targets transcripts of one or more genes associated with lung epithelial function. In some embodiments, the one or more genes comprise SFTPA1 / 2 or DUOXA1. In some embodiments, the panel of nucleic acid molecules targets transcripts of one or more genes associated with tissue remodeling. In some embodiments, the one or more genes comprise ITGA8, SLIT3, or COL14A1. In some embodiments, the identified set of target genes from the enriched subset is sufficient to predict radiation pneumonitis prior to symptom onset. In some embodiments, the determining in (d) exhibits a sensitivity that is greater than that of a control assay by at least about 20%. In some embodiments, the sensitivity is greater than that of the control assay by at least about 40%. In some embodiments, the control assay is based on a measure of lung volume upon exposure to a predetermined radiation dose. In some embodiments, the injury is pneumonitis, optionally wherein the injury is a non-cellular pharmacologic therapy-induced pneumonitis. In some embodiments, the non-cellular pharmacologic therapy comprises administration of at least one antibody. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 11. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 11. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 27. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 10%, atleast 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 27. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 28. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 28. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 29. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 29. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 30. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 30. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 31. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 31. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 32. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 32. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 33. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 33. In some embodiments, the panel of nucleic acid molecules targets transcripts of one or more genes associated with inflammatory response, fatty acid, orMYC. In some embodiments, the one or more genes are associated with inflammatory response or fatty acid. In some embodiments, the panel of nucleic acid molecules targets transcripts of one or more genes associated with lung neutrophil cell, lung classical monocyte cell, lung vascular smooth muscle cell, lung OLR1 + classical monocyte cell, or lung capillary intermediate 2 cell. In some embodiments, the one or more genes are associated with lung neutrophil cell, lung classical monocyte cell, lung vascular smooth muscle cell, or lung OLR1+ classical monocyte cell. In some embodiments, the one or more genes are associated with lung neutrophil cell. In some embodiments, the panel of nucleic acid molecules targets transcripts of one or more genes associated with neutrophil, monocyte, T helper cell, Cajal-Retzius cell, Ductal cell, or hepatocyte. In some embodiments, the one or more genes are associated with neutrophil or monocyte. In some embodiments, the identified set of target genes from the enriched subset is sufficient to determine non-cellular pharmacologic therapy-associated pneumonitis. In some embodiments, the injury is necrosis, optionally wherein the injury is radiation necrosis. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 12, Table 34, Table 35, Table 15, Table 16, Table 18, and Table 19. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 12. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 12. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 14. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 14. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 34. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 34. In some embodiments, the panel of nucleic acid molecules targets transcripts of atleast 5% of genes identified in Table 36. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 36. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 37. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 37. In some embodiments, the panel of nucleic acid molecules targets transcripts of one or more genes associated with one or more cell types selected from the group consisting of T cell, monocyte / macrophage, smooth muscle cell / pericyte, stromal cell, connective tissue cell, mast / basophil, type I pneumocyte, type II pneumocyte, intestinal cell, and hepatocyte. In some embodiments, the one or more cell types are selected from the group consisting of T cell, monocyte / macrophage, smooth muscle cell / pericyte, stromal cell, and connective tissue cell. In some embodiments, the one or more genes are associated with T cell. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 35. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 35. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 42. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 42. In some embodiments, the panel of nucleic acid molecules targets transcripts of one or more genes associated with NOTCH, allograft rejection, MTORC1, heme metabolism, adipogenesis, oxidative phosphorylation (OxPhos), fatty acid, peroxisome, ROS, and MYC. In some embodiments, the one or more genes are associated with NOTCH or allograft. In some embodiments, the identified set of target genes from the enriched subset is sufficient to determine radiation necrosis relative to a post-radiation baseline, optionally wherein the identified set of target genesfrom the enriched subset is sufficient to determine symptomatic radiation necrosis relative to a post-radiation asymptomatic baseline. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 15. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 15. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 17. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 17. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 16. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 16. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 38. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 38. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 39. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 39. In some embodiments, the panel of nucleic acid molecules targets transcripts of one or more genes associated with one or more cell types selected from the group consisting of T cell, monocyte / macrophage, ciliated cell, monocyte / macrophage, B cell, ionocyte, hepatocyte, neutrophil / granulocyte, connective tissue cell, and type I pneumocyte. In some embodiments, the one or more cell types are selected from the group consisting of T cell, monocyte / macrophage, ciliated cell, monocyte / macrophage, and B cell. In some embodiments, the one or more cell types comprise T cell. In someembodiments, the identified set of target genes from the enriched subset is sufficient to determine symptomatic radiation necrosis relative to a asymptomatic radiation necrosis baseline. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 18. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 18. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 20. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 20. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 19. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 19. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 40. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 40. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 41. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 41. In some embodiments, the panel of nucleic acid molecules targets transcripts of one or more genes associated with one or more cell types selected from the group consisting of T cell, B cell, smooth muscle cell / pericyte, ciliated cell, monocyte / macrophage, connective tissue cell, mast / basophil, type I pneumocyte, neutrophil / granulocyte, and hepatocyte. In some embodiments, the one or more cell types are selected from the group consisting of T cell, B cell, smooth muscle cell / pericyte, ciliated cell, and monocyte / macrophage. In someembodiments, the one or more cells comprise T cell. In some embodiments, the identified set of target genes from the enriched subset is sufficient to determine symptomatic radiation necrosis relative to non-radiated brain metastasis baseline. In some embodiments, the therapeutic intervention comprises stereotactic radiosurgery.

[0014] In some embodiments, the organ comprises brain, bone, or lung.

[0015] In some embodiments, at least 5% of the nucleic acid molecules of the panel used for the enrichment of the sample enrich for the genes identified in one of: Table 5; Table 6; Table 8; Table 9; Table 21; Table 22; Table 27; Table 29; Table 32; Table 12; Table 34; Table 35; Table 15; Table 16; Table 18; or Table 19. In some embodiments, at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the nucleic acid molecules of the panel used for the enrichment of the sample enrich for the genes identified in one of: Table 5; Table 6; Table 8; Table 9; Table 21; Table 22; Table 27; Table 29; Table 32; Table 12; Table 34; Table 35; Table 15; Table 16; Table 18; or Table 19.

[0016] In some embodiments, the panel targets a subset of a pluralithy of exons of a target gene of the set, wherein an exon of the subset is identified as showing a higher concordance between an exon-level expression and a gene-level expression as compared to another exon of the plurality of exons. In some embodiments, the exon is identified as showing the highest concordance between the exon-level expression and the gene-level expression within the plurality of exons.

[0017] In some embodiments, the set of target genes comprises genes that are rarely abundant as cell-free RNA transcripts within a plurality of control liquid biopsies. In some embodiments, the plurality of control liquid biopsies is from a cohort of health subjects. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 5%, at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of a subset of genes identified in Table 1, wherein the subset consists of the genes indicated as “Yes” in the “RAG” column of Table 1.

[0018] In another aspect disclosed herein is a nucleic acid enrichment panel, comprising: a plurality of nucleic acid molecules that enriches a sample of a plurality ofcell-free nucleic acid molecules for a subset of transcripts from a whole coding transcriptome, the subset corresponding to at least 5% of the genes identified in: Table 5; Table 6; Table 8; Table 9; Table 21; Table 22; Table 27; Table 29; Table 32; Table 12; Table 34; Table 35; Table 15; Table 16; Table 18; or Table 19.

[0019] In some embodiments, the subset of transcripts corresponds to at least 5% of a subset of genes identified in Table 1, wherein the subset of genes consists of the genes indicated as “Yes” in the “RAG Capture Panel V2” column of Table 1. In some embodiments, the subset of transcripts corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the subset of genes. In some embodiments, at least 5% of the plurality of nucleic acid molecules of the panel used for the enrichment of the sample enrich for the subset of genes. In some embodiments, at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the plurality of nucleic acid molecules of the panel used for the enrichment of the sample enrich for the subset of genes. In some embodiments, the at least 5% of the subset of genes comprises at least one gene indicated in Table 45. In some embodiments, the at least one gene comprises at least 5%, at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes indicated in Table 45. In some embodiments, the at least 5% of the subset of genes comprises at least one gene indicated in Table 1 as “Yes” in the “RAG Capture Panel V2” column of and as in the “RAG Capture Panel V1” column. In some embodiments, the at least one gene comprises at least 5%, at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes indicated in Table 1 as “Yes” in the “RAG Capture Panel V2” column of and as in the “RAG Capture Panel V1” column. In some embodiments, the subset of transcripts corresponds to at least 5% of a subset of genes identified in Table 1, wherein the subset of genes consists of the genes indicated as “Yes” in the “RAG Capture Panel V1” column of Table 1. In some embodiments, the subset of transcripts corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%,or 100% of the subset of genes. In some embodiments, at least 5% of the plurality of nucleic acid molecules of the panel used for the enrichment of the sample enrich for the subset of genes. In some embodiments, at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the plurality of nucleic acid molecules of the panel used for the enrichment of the sample enrich for the subset of genes.

[0020] In some embodiments, the subset corresponds to at least 5% of the genes identified in Table 5. In some embodiments, the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 5. In some embodiments, the subset corresponds to at least 5% of the genes are identified in Table 7. In some embodiments, the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 7. In some embodiments, the subset corresponds to at least 5% of the genes are identified in Table 6. In some embodiments, the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 6. In some embodiments, the subset corresponds to at least 5% of the genes are identified in Table 23. In some embodiments, the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 23. In some embodiments, the subset corresponds to at least 5% of the genes are identified in Table 24. In some embodiments, the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 24. In some embodiments, the subset corresponds to at least 5% of the genes are identified in Table 8. In some embodiments, the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 8. In some embodiments, the subset corresponds to at least 5% of the genes are identified in Table10. In some embodiments, the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 10. In some embodiments, the subset corresponds to at least 5% of the genes are identified in Table 9. In some embodiments, the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 9. In some embodiments, the subset corresponds to at least 5% of the genes are identified in Table 25. In some embodiments, the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 25. In some embodiments, the subset corresponds to at least 5% of the genes are identified in Table 26. In some embodiments, the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 26. In some embodiments, the subset corresponds to at least 5% of the genes are identified in Table 21. In some embodiments, the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 21. In some embodiments, the subset corresponds to at least 5% of the genes are identified in Table 22. In some embodiments, the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 22. In some embodiments, the subset corresponds to at least 5% of the genes are identified in Table 27. In some embodiments, the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 27. In some embodiments, the subset corresponds to at least 5% of the genes are identified in Table 28. In some embodiments, the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 28. In someembodiments, the subset corresponds to at least 5% of the genes are identified in Table 29. In some embodiments, the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 29. In some embodiments, the subset corresponds to at least 5% of the genes are identified in Table 30. In some embodiments, the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 30. In some embodiments, the subset corresponds to at least 5% of the genes are identified in Table 31. In some embodiments, the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 31. In some embodiments, the subset corresponds to at least 5% of the genes are identified in Table 32. In some embodiments, the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 32. In some embodiments, the subset corresponds to at least 5% of the genes are identified in Table 33. In some embodiments, the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 33. In some embodiments, the subset corresponds to at least 5% of the genes are identified in Table 12. In some embodiments, the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 12. In some embodiments, the subset corresponds to at least 5% of the genes are identified in Table 14. In some embodiments, the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 14. In some embodiments, the subset corresponds to at least 5% of the genes are identified in Table 34. In some embodiments, the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%,at least 90%, at least 95%, or 100% of the genes identified in Table 34. In some embodiments, the subset corresponds to at least 5% of the genes are identified in Table 36. In some embodiments, the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 36. In some embodiments, the subset corresponds to at least 5% of the genes are identified in Table 37. In some embodiments, the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 37. In some embodiments, the subset corresponds to at least 5% of the genes are identified in Table 35. In some embodiments, the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 35. In some embodiments, the subset corresponds to at least 5% of the genes are identified in Table 42. In some embodiments, the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 42. In some embodiments, the subset corresponds to at least 5% of the genes are identified in Table 15. In some embodiments, the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 15. In some embodiments, the subset corresponds to at least 5% of the genes are identified in Table 17. In some embodiments, the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 17. In some embodiments, the subset corresponds to at least 5% of the genes are identified in Table 16. In some embodiments, the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 16. In some embodiments, the subset corresponds to at least 5% of the genes are identified in Table 38. In some embodiments, the subset corresponds to at least 10%, at least 20%, at least30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 38. In some embodiments, the subset corresponds to at least 5% of the genes are identified in Table 39. In some embodiments, the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 39. In some embodiments, the subset corresponds to at least 5% of the genes are identified in Table 18. In some embodiments, the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 18. In some embodiments, the subset corresponds to at least 5% of the genes are identified in Table 20. In some embodiments, the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 20. In some embodiments, the subset corresponds to at least 5% of the genes are identified in Table 19. In some embodiments, the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 19. In some embodiments, the subset corresponds to at least 5% of the genes are identified in Table 40. In some embodiments, the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 40. In some embodiments, the subset corresponds to at least 5% of the genes are identified in Table 41. In some embodiments, the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 41.

[0021] In some embodiments, the panel is for determining a presence or state of an injury to an organ associated with a therapeutic intervention for a biological condition.

[0022] In some embodiments, the plurality of cell-free nucleic acid molecules comprises a plurality of cell-free RNA molecules from a biological sample of an individual or a plurality of DNA molecules complementary to the plurality of cell-free RNA molecules.

[0023] In some embodiments, at least 5% of the plurality of nucleic acid molecules used for the enrichment of the sample enrich for the genes identified in one of: Table 5; Table 6; Table 8; Table 9; Table 21; Table 22; Table 27; Table 29; Table 32; Table 12; Table 34; Table 35; Table 15; Table 16; Table 18; or Table 19. In some embodiments, at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the plurality of nucleic acid molecules used for the enrichment of the sample enrich for the genes identified in one of: Table 5; Table 6; Table 8; Table 9; Table 21; Table 22; Table 27; Table 29; Table 32; Table 12; Table 34; Table 35; Table 15; Table 16; Table 18; or Table 19.

[0024] In some embodiments, the nucleic acid enrichment panel targets a subset of a plurality of exons of a target gene, wherein an exon of the subset is identified as showing a higher concordance between an exon-level expression and a gene-level expression as compared to another exon of the plurality of exons. In some embodiments, the exon is identified as showing the highest concordance between the exon-level expression and the gene-level expression within the plurality of exons.

[0025] In some embodiments, the panel of nucleic acid molecules that enriches for the subset of transcripts comprises nucleic acids appended to a pull-down tag. In some embodiments, the the pull-down tag comprises biotin.

[0026] In another aspect disclosed herein is a mixture comprising (1) a nucleic acid enrichment panel described above and (2) a plurality of cell-free nucleic acid molecules from a biological sample of an individual or a plurality of DNA molecules complementary to the plurality of cell-free RNA molecules.

[0027] In another aspect disclosed herein is a method of preparing for sequencing of cell-free RNA, comprising: providing a sample comprising nucleic acids for sequencing, wherein the nucleic acids for sequencing are cell-free RNA or nucleic acids derived from and representative of cell-free RNA; and contacting the sample with a panel of nucleic acid molecules that comprises molecules having sequences from or complement to gene transcripts that are rarely abundant as cell-free RNA molecules within control liquid biopsies such that the panel of nucleic acid molecules anneals with a subset of the nucleic acids for sequencing. In some embodiments, the transcripts that are rarely are defined astranscripts that are expressed within cell-free RNA in less than 30% liquid biopsies of a cohort of control liquid biopsies. In some embodiments, the transcripts that are rarely abundant are defined as transcripts that are expressed within cell-free RNA in less than 5% liquid biopsies of the cohort of control liquid biopsies. In some embodiments, the transcripts that are rarely abundant are defined as transcripts that are in the bottom 60% of genes with respect to normalized expression across with cell-free RNA of a cohort of control liquid biopsies. In some embodiments, the cohort of control liquid biopsies comprises at least 5 liquid biopsies. In some embodiments, the cohort of control liquid biopsies comprises at least 50 liquid biopsies. In some embodiments, the control liquid biopsies are collected from individuals not having one or more the following when the biopsy is collected: an observed pathogenic infection, a diagnosed cancer, a diagnosed metabolic disorder, a diagnosed neurological disorder, a diagnosed immunodeficiency disorder, a diagnosed autoimmune disorder, a diagnosed inflammatory disorder, a diagnosed cardiovascular disorder, a diagnosed renal disorder, a diagnosed hepatic disorder, active pregnancy, a diagnosed pregnancy complication, a diagnosed fetal complication, an organ transplant, active rejection of an organ transplant, obesity, malnourishment, cachexia, and an abnormality on a clinical test.

[0028] In some embodiments, the transcripts that are rarely abundant comprise at least of 50% of transcripts within Table 1. In some embodiments, the transcripts that are rarely abundant comprise at least of 90% of transcripts within Table 1. In some embodiments, the transcripts that are rarely abundant comprise at least of 100% of transcripts within Table 1. In some embodiments, the panel of nucleic acid molecules excludes at least 50% of whole-exome gene transcripts that are not transcripts that are rarely abundant as cell-free RNA molecules. In some embodiments, the panel of nucleic acid molecules excludes at least 90% of whole-exome gene transcripts that are not transcripts that are rarely abundant as cell-free RNA molecules. In some embodiments, the panel of nucleic acid molecules consists of 5000 or fewer gene transcripts in addition to transcripts that are rarely abundant as cell-free RNA molecules. In some embodiments, the panel of nucleic acid molecules consists of 500 or fewer gene transcripts in addition to transcripts that are rarely abundant as cell-free RNA molecules. In some embodiments, the panel of nucleic acid molecules further comprises one or more of: tissue-specifictranscripts, cell-type-specific transcripts, clinically relevant transcripts, B-cell receptor and T-cell receptor transcripts, biomarkers, commonly mutagenized transcripts and a set of control transcripts for normalization between samples. In some embodiments, the biomarkers are associated with one of the following biological characteristics: a medical disorder, pregnancy, a fetal complication, a pregnancy complication, a neoplastic growth, cancer, a particular cancer type, a pathogen infection, immune activation, organ transplant rejection, neurodegeneration, tissue of origin, cell type of origin, or activation of a biochemical pathway. In some embodiments, the panel of nucleic acid molecules are a set of probes for targeted capture hybridization. In some embodiments, the panel of nucleic acid molecules are a set of primers for targeted amplification.

[0029] In some embodiments, the sample of cfRNA is derived from blood, plasma, lymph, mucus, sputum, cerebrospinal fluid, amniotic fluid, urine, or stool.

[0030] In some embodiments, the method further comprises: generating a sequencing library derived from the sample; and performing targeted sequencing of the sequencing library to yield a sequencing result of the cell-free RNA, wherein the sequencing is targeted towards the panel of nucleic acid molecules. In some embodiments, the method further comprises: removing platelet expression from the sequencing result in silico.

[0031] In another aspect disclosed herein is a panel of nucleic acids for targeting transcripts that are rarely abundant as cell-free RNA molecules, the panel comprising: nucleic acid molecules having sequences from or complement to gene transcripts that are rarely abundant as cell-free RNA molecules within control liquid biopsies. In some embodiments, the transcripts that are rarely are defined as transcripts that are expressed within cell-free RNA in less than 30% liquid biopsies of a cohort of control liquid biopsies. In some embodiments, the transcripts that are rarely abundant are defined as transcripts that are expressed within cell-free RNA in less than 5% liquid biopsies of the cohort of control liquid biopsies. In some embodiments, the transcripts that are rarely abundant are defined as transcripts that are in the bottom 60% of genes with respect to normalized expression across with cell-free RNA of a cohort of control liquid biopsies. In some embodiments, the transcripts that are rarely abundant are defined as transcripts that are in the bottom 30% of genes with respect to normalized expression across with cell-free RNA of a cohort of control liquid biopsies. In some embodiments, the transcripts that arerarely abundant as cell-free RNA molecules within control liquid biopsies are defined as transcripts having log transformed and normalized expression values less than zero across a population of control liquid biopsies. In some embodiments, the cohort of control liquid biopsies comprises at least 5 liquid biopsies. In some embodiments, the cohort of control liquid biopsies comprises at least 50 liquid biopsies. In some embodiments, the control liquid biopsies are collected from individuals not having one or more the following when the biopsy is collected: an observed pathogenic infection, a diagnosed cancer, a diagnosed metabolic disorder, a diagnosed neurological disorder, a diagnosed immunodeficiency disorder, a diagnosed autoimmune disorder, a diagnosed inflammatory disorder, a diagnosed cardiovascular disorder, a diagnosed renal disorder, a diagnosed hepatic disorder, active pregnancy, a diagnosed pregnancy complication, a diagnosed fetal complication, an organ transplant, active rejection of an organ transplant, obesity, malnourishment, cachexia, and an abnormality on a clinical test.

[0032] In some embodiments, the transcripts that are rarely abundant comprise at least of 50% of transcripts within Table 1. In some embodiments, the transcripts that are rarely abundant comprise at least of 90% of transcripts within Table 1. In some embodiments, the transcripts that are rarely abundant comprise at least of 100% of transcripts within Table 1. In some embodiments, the panel of nucleic acid molecules excludes at least 50% of whole-exome gene transcripts that are not transcripts that are rarely abundant as cell-free RNA molecules. In some embodiments, the panel of nucleic acid molecules excludes at least 90% of whole-exome gene transcripts that are not transcripts that are rarely abundant as cell-free RNA molecules. In some embodiments, the panel of nucleic acid molecules consists of 5000 or fewer gene transcripts in addition to transcripts that are rarely abundant as cell-free RNA molecules. In some embodiments, the panel of nucleic acid molecules consists of 500 or fewer gene transcripts in addition to transcripts that are rarely abundant as cell-free RNA molecules. In some embodiments, the panel of nucleic acid molecules further comprises one or more of: tissue-specific transcripts, cell-type-specific transcripts, clinically relevant transcripts, B-cell receptor and T-cell receptor transcripts, biomarkers, commonly mutagenized transcripts and a set of control transcripts for normalization between samples. In some embodiments, the biomarkers are associated with one of the following biological characteristics: a medicaldisorder, pregnancy, a fetal complication, a pregnancy complication, a neoplastic growth, cancer, a particular cancer type, a pathogen infection, immune activation, organ transplant rejection, neurodegeneration, tissue of origin, cell type of origin, or activation of a biochemical pathway. In some embodiments, the panel of nucleic acid molecules further comprises a set of control transcripts for normalization between samples. In some embodiments, the panel of nucleic acid molecules are a set of probes for targeted capture hybridization. In some embodiments, the panel of nucleic acid molecules are a set of primers for targeted amplification.

[0033] In another aspect disclosed herein is a computational method of classifying a lung cancer, comprising: receiving, using a computational processing system, a sequencing result of cell-free RNA sample; generating, using the computational processing system, two or more features as input for a computational machine-learning model; classifying or predicting the likelihood, using the computational processing system, whether the cell-free RNA sample includes RNA from a lung cancer by utilizing the two or more features within the computational machine-learning model. In some embodiments, the two or more features comprise one or more transcript expression features and one or more mutation-based features. In some embodiments, the one or more transcript expression features comprises expression of one or more of the following genes: SLC34A2, FOLR1, TUBB, SCEL, CXCL13, CEACAM5, CLDN1, FGG, CCND1, GPR37, PLEKHS1, SLC16A9, FLRT3, AGR2, SEMA7A, VSTM2L, VSTM4, ACTA2, MAL2, or FAM83A. In some embodiments, the one or more mutation-based features comprises one or more of the following mutations: any KRAS mutation or EGFR L858R mutation.

[0034] In another aspect disclosed herein is a method comprising: (a) obtaining a sample comprising a plurality of cell-free nucleic acid molecules, the plurality of cell-free nucleic acid molecules comprising a plurality of cell-free RNA molecules from a biological sample of an individual or a plurality of DNA molecules complementary to the plurality of cell-free RNA molecules; (b) obtaining a panel of nucleic acid molecules that enriches for a subset of transcripts from a whole coding transcriptome; (c) enriching the sample for the subset by contacting the sample with the panel of nucleic acid molecules, to identify a set of target genes from the enriched subset, wherein the panel targets a subset of aplurality of exons of a target gene of the set, wherein an exon of the subset is identified as showing a higher concordance between an exon-level expression and a gene-level expression as compared to another exon of the plurality of exons. In some embodiments, the exon is identified as showing the highest concordance between the exon-level expression and the gene-level expression within the plurality of exons.

[0035] In some embodiments, the plurality of nucleic acid molecules in the panel comprises molecules that enrich for genes that are rarely abundant as cell-free RNA transcripts within a plurality of control liquid biopsies. In some embodiments, the plurality of control liquid biopsies is from a cohort of health subjects. In some embodiments, the plurality of nucleic acid molecules in the panel targets transcripts of at least 5%, at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of a subset of genes identified in Table 1, wherein the subset consists of the genes indicated as “Yes” in the “RAG” column of Table 1.BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The description and claims will be more fully understood with reference to the following figures and data, which are presented as examples of the disclosure and should not be construed as a complete recitation of the scope of the disclosure.

[0037] FIGs. 1A-1L demonstrates example analysis on platelet and non- hematopoietic transcripts that are enriched in cfRNA. FIG. 1A shows an example schematic of a method of cfRNA analysis described elsewhere herein (e.g., the RARE- Seq method). FIG. 1B shows an example differential expression analysis comparing cfRNA and matched leukocyte RNA from healthy donors (n=10 pairs). Significance was determined using Wald test and adjusted using Benjamini-Hochberg (BH) correction. FIG.1C shows an example gene set enrichment analysis using cell type-specific signatures from PanglaoDB12 (n=170 cell types). The top 15 positively and negatively enriched cell types are shown, and the dotted line delineates significant enrichment. FIG. 1D shows example data demonstrating the relationship between centrifugation speed during plasma isolation and cfRNA concentration per mL plasma (n=10 1200G, n=6 1800G, n=162500G). Comparisons were performed using Kruskal-Wallis test. In box plots, the center line denotes the median, the box contains the interquartile range (IQR), and the whiskers denote extrema no farther than 1.5 × IQR. FIG. 1E shows example data demonstrating the relationship between centrifugation speed and platelet-specific gene expression in cfRNA samples from FIG. 1D. Avg log2NX can refer to mean of normalized expression. Comparisons were performed using Kruskal-Wallis test. In box plots, the center line denotes the median, the box contains the interquartile range (IQR), and the whiskers denote extrema no farther than 1.5 x IQR. FIG. 1F shows example principal component analysis (PCA) of cfRNA expression clusters according to spin speed (PC1) and sex (PC2). The top 1,000 variable genes in cfRNA samples from FIG. 1D were used for PCA.FIG. 1G shows example data demonstrating the association between average platelet-specific gene expression and PC1 in cfRNA samples from FIG. 1D. Pearson and Spearman correlation are shown. FIG. 1H shows example data demonstrating the association between average platelet-specific gene expression and PC1 when considering only samples processed using the same centrifugation speed (n=16 2500G).FIG. 1I shows an example schematic of platelet transcript correction approach. Z can refer to z-scores. Zp can refer to z-scores of platelet genes. SVD can refer to singular value decomposition. W can refer to platelet correction factor. FIG. 1J shows example data demonstrating the association between platelet-specific gene expression and PC1, after platelet-directed correction. FIG. 1K shows an example hierarchical clustering of the top 1,000 variable genes in cfRNA. Clustering was performed using Euclidean distance. FIG. 1L shows an example hierarchical clustering of the top 1,000 variable genes, after platelet-directed correction.

[0038] FIGs. 2A-2I demonstrates example analysis that targeting transcripts absent or rarely abundant from healthy cfRNA can improve analytical sensitivity for lung cancer detection. FIG. 2A shows an example schematic depicting the selection of rare abundance genes (RAGs) in plasma. Log2NX can refer to log2 normalized expression.FIG. 2B shows example analysis of tissue-enriched genes that overlap with RAGs. Tissue-enriched genes can be selected as described elsewhere herein. Comparisons were performed using two-sided Fisher’s exact test and adjusted with Benjamini-Hochberg (BH) correction. FIG. 2C shows example analysis of cancer-enriched genesthat overlap with RAGs. Cancer-enriched genes can be selected as described elsewhere herein. Comparisons were performed using two-sided Fisher’s exact test and adjusted with Benjamini-Hochberg (BH) correction. FIG. 2D shows example analysis of unique depth in libraries captured using either whole coding transcriptome or RAG capture panels (n=5 matched libraries). Libraries were downsampled to have equivalent sequencing depth and filtered to include regions captured by both panels. Comparisons were performed using paired two-sided t-test (***P=0.0009). In box plots, the center line denotes the median, the box contains the interquartile range (IQR), and the whiskers denote extrema no farther than 1.5 x IQR. FIG. 2E shows example analysis of number of shared genes detected in libraries in FIG. 2D. The threshold for detection was >1 unique read. Comparisons were performed using paired two-sided t-test (***P=0.0008). In box plots, the center line denotes the median, the box contains the interquartile range (IQR), and the whiskers denote extrema no farther than 1.5 x IQR. FIG. 2F shows an example histogram depicting the difference in unique depth per gene for libraries in FIG. 2D. FIG.2G shows an example schematic of cfRNA enrichment score (ES) analytical framework as described elsewhere herein. FIG. 2H shows example data demonstrating the relationship between cfRNA ES detection rate and lung cancer cell line (NCI-H1975) fraction for spikes captured with the RAG panel (n=3 replicates per spike). The presence of cancer RNA was detected in each spike using an NCI-H1975 cfRNA ES. Logistic regression was used to estimate the limit of detection (LOD95) from in silico spikes (shown as grey circles). Results from in vitro spikes are shown as red triangles. FIG. 2I shows example data depicting empirical LOD95 of NCI-H1975 spikes captured using the whole coding transcriptome panel (n=1 replicate per spike) evaluated using the same approach as FIG. 2F.

[0039] FIGs. 3A-3M demonstrates example analysis for detection of ctRNA in plasma from NSCLC patients. FIG. 3A shows example data of differential expression between lung adenocarcinoma (LUAD) cfRNA (n=139) and non-cancer cfRNA (n=50). Significance was determined using Wald test and Benjamini-Hochberg (BH) correction.FIG. 3B shows example data of average expression of genes enriched in LUAD cfRNA in LUAD tumors (n=385) and meta-reference cfRNA (n=15). Comparison was performed using two-sided Wilcoxon test (****p< 2.2e-16). FIG. 3C shows example gene setenrichment analysis (GSEA) using PanglaoDB12 cell-type signatures. Top 15 positive and negative types are shown, and the dotted line delineates significant enrichment. FIG.3D shows example data of enrichment scores (ES) for LUAD-specific signature (LUAD Sig; n=72 genes) in cfRNA from LUAD patients (n=139), risk-matched (n=26) and healthy (n=24) controls. LDCT can refer to low-dose computed tomography. In box plots, the center line denotes the median, the box contains the interquartile range (IQR), and the whiskers denote extrema no farther than 1.5 x IQR. FIG. 3E shows example analysis for sensitivity of LUAD cfRNA ES detection, summarized by stage. Detection threshold achieving >95% specificity was used. Error bars represent the 95% binomial confidence interval. Stages were compared using Spearman correlation. FIG. 3F shows example data demonstrating the relationship between LUAD cfRNA ES and ctDNA allele frequency (AF) in matched plasma (n=38). ND can refer to not detected. FIG. 3G shows example data of the percentage of matched samples with detectable ctRNA and / or ctDNA. FIG. 3H shows example oncoprint of variants detected in cfRNA from stage IV LUAD patients with >1 tissue- or ctDNA-adjudicated variant (n=136) and controls (n=97). SNV can refer to single nucleotide variant. Indel can refer to insertion / deletion. FIG. 3I shows example data of the percentage of samples with >1 variant detected in cfRNA. In box plots, the center line denotes the median, the box contains the interquartile range (IQR), and the whiskers denote extrema no farther than 1.5 x IQR. FIG. 3J shows example data of the percentage of known variants detected in cfRNA. FIG. 3K shows example data demonstrating the relationship between EGFR expression and variant detection for samples with known EGFR variants. FIG. 3L shows example data to the probability of lung cancer from elastic net (EN) model (Train: n=96 LUAD, 47 Control; Validation: n=52 LUAD, 46 Control). FIG. 3M shows example receiver operating characteristic (ROC) curves summarizing detection using LUAD cfRNA ES and EN probabilities. Area under curve (AUC) was compared with DeLong’s test, ns can refer to not significant.

[0040] FIGs. 4A-4G demonstrates example analysis for detecting mechanisms of resistance to EGFR tyrosine kinase inhibitors in cfRNA. FIG. 4A shows an example summary of a EGFR tyrosine kinase inhibitor (TKI) cohort (n=10 patients, n=24 timepoints). Mechanisms of resistance were determined via tissue biopsy. METamp canrefer toMET amplification. C797S can refer to EGFR C797S SNV. tSCLC can refer to histological transformation to small cell lung cancer. FIG. 4B shows example enrichment scores (ES) for a small cell lung cancer (SCLC) gene signature (SCLC Sig; n=73 genes) in patients with biopsy-proven tSCLC. The threshold representing >95% specificity in controls is shown by the dotted line. In box plots, the center line denotes the median, the box contains the interquartile range (IQR), and the whiskers denote extrema no farther than 1.5 x IQR. FIG. 4C shows an example vignette of patient with tSCLC after EGFR TKI treatment. The left axis depicts ES or elastic net (EN) scores and the right axis depicts the allele frequency (AF) of tumor-adjudicated variants detected in cfRNA. FIG.4D shows an example ES for a MET amplification-specific signature (METamp Sig; n=9 genes) in patients with biopsy-proven MET amplification. In box plots, the center line denotes the median, the box contains the interquartile range (IQR), and the whiskers denote extrema no farther than 1.5 x IQR. FIG. 4E shows an example vignette of patient with emergence of MET amplification after EGFR TKI treatment. FIG.4F shows examples of EGFR C797S allele fraction (AF) in cfRNA from patients with biopsy-proven C797S. FIG. 4G shows an example vignette of patient with emergence of EGFR C797S mutation after EGFR TKI treatment.

[0041] FIGs. 5A-5I demonstrates example applications cfRNA analysis described elsewhere herein (e.g., RARE-Seq). FIG. 5A shows an example analysis of sensitivity of detection of liver hepatocellular carcinoma (LIHC) cfRNA enrichment scores (ES; n=10), pancreatic adenocarcinoma (PAAD) ES (n=10), and prostate adenocarcinoma (PRAD) ES (n=9) using the respective cancer-specific signatures. Detection thresholds achieving >90% specificity after multiple hypothesis correction were used. Error bars represent the 95% binomial confidence interval. FIG. 5B shows an example analysis for accuracy of tissue-of-origin (TOO) determination using cancer-TOO signatures in samples detected by at least one cancer-specific signature (n=105). Shading depicts ES rank for the true cancer type. FIG. 5C shows an example confusion matrix comparing predicted and true cancer type. FIG. 5D shows example data of ES for normal lung tissue signature (n=5 genes) in cfRNA from patients with benign pulmonary conditions (n=77 timepoints from 71 individuals). COPD can refer to chronic obstructive pulmonary disease. COVID can refer to COVID-19 infection. ICU can refer to intensive care unit. ARDS can refer to acuterespiratory distress syndrome. FIG. 5E shows example data demonstrating the relationship between normal lung ES and smoking history. Samples without known pulmonary conditions were compared using two-sided Wilcoxon test (n=42, P=0.00095). In box plots, the center line denotes the median, the box contains the interquartile range (IQR), and the whiskers denote extrema no farther than 1.5 x IQR. FIG. 5F shows example data demonstrating the relationship between normal lung ES and ventilator status. Samples with known pulmonary conditions were compared using two-sided Wilcoxon test (n=35 timepoints from 29 individuals, P=0.0061). In box plots, the center line denotes the median, the box contains the interquartile range (IQR), and the whiskers denote extrema no farther than 1.5 x IQR. FIG. 5G shows an example timecourse depicting COVID-19 mRNA vaccine expression in cfRNA (n=20 timepoints from 5 individuals). Data are presented as mean log fold change above baseline + / - SD. FIG. 5H shows an example gene set enrichment analysis (GSEA) of differentially expressed genes (DEGs) in post-vaccination timepoints. DEGs were determined by likelihood ratio test (LRT), and GSEA significance was determined using fgsea. Top 10 MSigDb Hallmark sets are displayed. FIG. 5I shows an example heatmap showing COVID-19 mRNA vaccine and DEGs in post-vaccination timepoints. Expression was centered and scaled using pre-vaccination levels.

[0042] FIGs. 6A-6G demonstrates example data for optimization of blood collection and cfRNA extraction. FIG. 6A shows an example bioanalyzer trace for cell-free RNA (top) and leukocyte cellular RNA (bottom). FIG. 6B shows an example concentration of cfRNA per mL of plasma in healthy controls measured via quantitative PCR (n=117). Plasma was collected for technical experiments using 2,500G centrifugation for 10 minutes. FIG. 6C shows an example analysis of plasma cfRNA concentrations using different blood collection tubes (BCTs). Ro can refer to Roche Cell-free DNA BCT (n=2). D can refer to Streck Cell-free DNA BCT (n=2). R can refer to Streck RNA Complete BCT (n=5). E can refer to EDTA (n=11 ). ns can refer to not significant. In box plots, the center line denotes the median, the box contains the interquartile range (IQR), and the whiskers denote extrema no farther than 1.5 x IQR. FIG. 6D shows an example analysis of plasma cfRNA concentrations using different extraction methods. T-R can refer to TRIzol-LS + Qiagen RNeasy Kit (n=3). V can refer to QIAamp Viral RNA Kit (n=7). Ro can refer toRoche High Pure Viral RNA Kit (n=3). M can refer to Qiagen miRNeasy Kit (n=5). MPS can refer to Qiagen miRNeasy Serum / Plasma Kit (n=3). T-M can refer to TRIzol-LS + Qiagen miRNeasy Kit (n=7). P can refer to mirVana PARIS Kit (n=2). CCF can refer to QIAamp ccfDNA / RNA Kit (n=4). T-V can refer to TRIzol-LS + QIAamp Viral RNA Kit (n=50). CNA can refer to QIAamp Circulating Nucleic Acid Kit (n=35). ns can refer to not significant; *P < 0.05; **P < 0.01, ***P < 0.001. In box plots, the center line denotes the median, the box contains the interquartile range (IQR), and the whiskers denote extrema no farther than 1.5 × IQR. FIG. 6E shows example data demonstrating the association between hemolysis and cfRNA plasma concentration in healthy controls (n=40). Amount of hemolysis was measured using optical density (OD) at414nm. Pearson and Spearman correlations are shown. FIG. 6F shows example data demonstrating the association between time in -80oC freezer and cfRNA plasma concentration in healthy controls (n=108). FIG. 6G shows example data demonstrating the association between time in -80oC freezer and cfRNA plasma concentration across all cfRNA cohorts analyzed in this study (n=237).

[0043] FIGs. 7A-7K demonstrates example data for optimization of library preparation and capture for cfRNA analysis described elsewhere herein (e.g., RARE-Seq). FIG. 7A shows example data of expression correlation from cfRNA collected in EDTA tubes or Streck RNA Complete tubes (average of n=3 pairs). FIG. 7B shows example data of expression correlation from cfRNA extracted using the TRIzol LS + QIAamp Viral RNA method (T-V) and the QIAamp Circulating Nucleic Acid Kit (CNA) (average of n=3 pairs).FIG. 7C shows example data of expression correlation between cfRNA libraries generated using stranded and non-stranded methods (average of n=3 pairs). FIG. 7D shows an example rarefaction analysis representing the relationship between total and unique sequencing depth for cfRNA libraries from FIG. 7B. The inset plot depicts the percent increase in unique sequencing depth for non-stranded libraries relative to paired stranded libraries. FIG. 7E shows example data of expression correlation between cfRNA libraries generated with and without S1 nuclease end repair (average of n=3 pairs). FIG.7F shows an example rarefaction analysis representing the relationship between total and unique sequencing depth for cfRNA libraries from FIG. 7E. Log2NX can refer to log2 normalized expression. FIG. 7G shows example data demonstrating estimated DNAcontamination after DNase I digestion performed either on or off Qiagen column (n=6 per condition). Conditions were compared using a paired two-sided t-test. In box plots, the center line denotes the median, the box contains the interquartile range (IQR), and the whiskers denote extrema no farther than 1.5 x IQR. FIG. 7H shows example data of expression correlation between cfRNA samples digested on-column and off-column (average of n=6 pairs). FIG. 7I shows an example distribution of RNA biotypes in cfRNA using SMART-Seq (n=3). FIG. 7J shows example data of expression correlation from cfRNA libraries generated using SMART-Seq and whole coding transcriptome RARE-Seq (median of n=3 pairs). Histograms depict the log2NX distributions. The Venn diagram represents the number of coding genes with higher (or equivalent) log2NX in each method. Blue=coding genes, grey=non-coding genes. FIG. 7K shows an example heatmap showing pairwise Pearson correlation between all technical and biological replicates sequenced from a single individual (n=8).

[0044] FIGs. 8A-8I demonstrates example schematics and validation of capture panel targeting rare abundance genes. FIG. 8A shows example data demonstrating the relationship between average gene expression in healthy cfRNA and overall percentage of healthy cfRNA samples with low or absent gene expression (n=50). Red=rare abundance gene (RAGs; n=5,632). Log2NX can refer to log2 normalized expression. FIG.8B shows example data demonstrating the relationship between average gene expression and expression stability measured by Gini coefficient in healthy cfRNA. Yellow=housekeeping genes (n=50). FIG. 8C shows an example schematic depicting genes included in the core modules of the RAG-focused capture panel and examples of optional add-on modules that could be used to supplement RAG-focused capture panel, FIG. 8D shows example data of cumulative frequency of non-small cell lung cancer (NSCLC) patients with one or more variants in genes added to the RAG-focused capture panel because they are recurrently altered or frequently aberrantly expressed in NSCLC (n=122). Top 25 genes are shown. Variant data from the TCGA lung adenocarcinoma and lung squamous cell carcinoma project were downloaded from cBioPortal. FIG. 8E shows example data of expression correlation between whole coding transcriptome and RAG capture for all captured genes (n=5 pairs). FIG. 8F shows example data of expression correlation between whole coding transcriptome and RAG capture forhousekeeping genes (n=5 pairs). FIG. 8G shows an example schematic of platelet transcript correction approach using meta-reference controls with variable platelet expression. This approach was developed for samples captured with the RAG-focused panel which excluded platelet genes. Z can refer to z-score matrix. Zp can refer to z-score matrix of platelet genes. SVD can refer to singular value decomposition. W can refer to platelet correction factor. FIG. 8H shows example data demonstrating the association between platelet-specific gene expression and first principal component (PC1), after platelet transcript correction of cfRNA expression as depicted in FIG. 8G. Controls from FIGs. 1D-1K were used (n=32). FIG. 8I shows example data demonstrating the association between NCI-H1975 spike detection and total sequencing depth. Logistic regression was used to estimate limit of detection (LOD95) of the in silico spikes as described in elsewhere herein.

[0045] FIGs. 9A-9L demonstrates example analysis for detection of lung adenocarcinoma ctRNA. FIG. 9A shows example data demonstrating the relationship between cfRNA concentration (ng per m L plasma) from healthy controls (n=24), low dose computed tomography controls (LDCT, n=26), and lung adenocarcinoma (LUAD, n=50) patients. Comparison was performed using Kruskal-Wallis test. In box plots, the center line denotes the median, the box contains the interquartile range (IQR), and the whiskers denote extrema no farther than 1.5 x IQR. FIG. 9B shows example data demonstrating the relationship between total sequencing depth from healthy controls (n=24), low dose computed tomography controls (LDCT, n=26), and lung adenocarcinoma (LUAD, n=50) patients. Comparison was performed using Kruskal-Wallis test. In box plots, the center line denotes the median, the box contains the interquartile range (IQR), and the whiskers denote extrema no farther than 1.5 x IQR. FIG. 9C shows example data demonstrating the relationship between cfRNA mass used for library preparation from healthy controls (n=24), low dose computed tomography controls (LDCT, n=26), and lung adenocarcinoma (LUAD, n=50) patients. Comparison was performed using Kruskal- Wallis test. In box plots, the center line denotes the median, the box contains the interquartile range (IQR), and the whiskers denote extrema no farther than 1.5 x IQR.FIG. 9D shows example data demonstrating the relationship between unique sequencing depth from healthy controls (n=24), low dose computed tomography controls (LDCT,n=26), and lung adenocarcinoma (LUAD, n=50) patients. Comparison was performed using Kruskal-Wallis test. In box plots, the center line denotes the median, the box contains the interquartile range (IQR), and the whiskers denote extrema no farther than 1.5 x IQR. FIG. 9E shows example analysis of sensitivity of detection using LUAD cfRNA ES with respect to cfRNA mass used for library preparation. FIG. 9F shows example analysis of sensitivity of detection using LUAD cfRNA ES with respect to unique sequencing depth. FIG. 9G shows example analysis of sensitivity of detection using LUAD cfRNA ES with respect to extraction method used. FIG. 9H shows example analysis of sensitivity of detection using LUAD cfRNA ES with respect to capture panel used. FIG. 9I shows example analysis of sensitivity of detection using LUAD cfRNA ES with respect to patient smoking status. FIG. 9J shows example analysis of false positive rate of LUAD cfRNA ES in controls with respect to smoking status (n=47). FIG. 9K shows example analysis of sensitivity of detection using LUAD cfRNA ES with respect to driver oncogene sub-type. FIG. 9L shows example analysis of sensitivity of detection using LUAD cfRNA ES with respect to whether platelet correction was used. Comparison was performed using one-sided Fisher’s exact test.

[0046] FIGs. 10A-10G demonstrates example analysis for genotyping from lung adenocarcinoma ctRNA. FIG. 10A shows an example schematic depicting ctRNA single nucleotide variant (SNV) calling approach. FIG. 10B shows example integrated genomics viewer (IGV) images displaying sequencing reads containing alternative splicing of the MET exon 14. FIG. 10C shows example integrated genomics viewer (IGV) images displaying sequencing reads containing EGFR exon 19 deletion. FIG. 10D shows example count of reads mapped to canonical splice junctions in ROS1 and CD74 gene and to fusion breakpoints for representative sample with tissue-adjudicated ROS1 / CD74 gene fusion. Junctions / breakpoints with >5 aligned reads are displayed. FIG. 10E shows an example pie chart summarizing the percentage of tumor-adjudicated variants detected in ctDNA and / or ctRNA from plasma collected on the same day (n=34 variants). FIG. 10F shows example data of LUAD elastic net (EN) model coefficients (n=23 features total).FIG. 10G shows example data demonstrating the relationship between model training cohort size and 10-fold cross validated model performance. Data are presented as mean values + / - SD of 10CV AUC at each sub-sampling level (n=10 samples per level). Thedotted line represents the 10-fold cross validated (10CV) AUC of the final LUAD EN model.

[0047] FIG. 11 shows an example summary of cancer and non-cancer cfRNA cohorts. LDCT can refer to low-dose computed tomography. ICU can refer to intensive care unit. COVID can refer to COVID-19 infection. VACC can refer to post-COVID-19 mRNA vaccination. LUAD can refer to lung adenocarcinoma. EGFR TKI can refer to treated with epidermal growth factor receptor tyrosine kinase inhibitor. PAAD can refer to pancreatic adenocarcinoma. PRAD can refer to prostate adenocarcinoma. LIHC can refer to liver hepatocellular carcinoma.

[0048] FIGs. 12A-12C shows example data demonstrating depletion of platelet transcripts using centrifugation. FIG. 12A shows an example summary of centrifugation conditions tested (n=3 per condition). FIG. 12B shows an example rarefaction analysis representing the relationship between total and unique sequencing depth for cfRNA libraries from FIG. 12A. FIG, 12C shows example data demonstrating the relationship between centrifugation speed and platelet-specific gene expression in cfRNA libraries from FIG. 12A. Comparisons were performed using two-sided t-test.

[0049] FIG. 13 shows example analyses demonstrating improved sensitivity from cancer signatures defined from cfRNA expression relative to signatures defined from known tissue-based expression. FIG. 13 shows example data demonstrating sensitivity of cfRNA ES detection in cfRNA from LUAD patients (n=139), using gene signatures restricted to genes that have been annotated as specific to normal tissue- or cancertypes. Detection threshold achieving >95% specificity in cfRNA from non-cancer controls (n=50) was used.

[0050] FIG. 14 shows example data demonstrating recovery of custom RNA spike-in controls in RARE-seq libraries. FIG. 14 shows example data depicting correlation between input copies of custom RNA spike-ins and unique molecules recovered by RARE-seq. Each data point represents the average and standard deviation recovered for an individual RNA spike-in across cfRNA libraries, and the line of best fit is shown in gray.

[0051] FIGs. 15A-15H shows example data demonstrating prediction of radiation induced pneumonitis in cfRNA before onset of symptoms. FIG. 15A shows example data of differential gene expression analysis comparing cfRNA expression of samplescollected prior to radiation therapy and during symptomatic radiation pneumonitis (RP).FIG. 15B shows example gene set enrichment analysis (GSEA) in the cohort from FIG.15A, performed using lung cell-type signatures. FIG. 15C shows example data of differential gene expression analysis comparing post-radiation cfRNA from patients who did not develop RP from patients who eventually developed RP. FIG. 15D shows example GSEA in the cohort from FIG. 15C performed using lung cell-type signatures. FIG. 15E shows an example schematic showing radiation pneumonitis (RP) random forest (RF) model training and 10-fold cross-validation to distinguish samples from patients who eventually get RP vs those who do not. FIG. 15F shows example data of feature importance measured as mean absolute SHAP values for the top 20 features in the RP RF model. FIG. 15G shows an example receiver operating characteristic (ROC) curve for the cfRNA RP Predictor. FIG. 15H shows example data demonstrating sensitivity for detecting RP for the cfRNA RP Predictor compared with lung V20 in the same cohort (both at 85% specificity). In FIGs.15B and 15D, * indicates nominal p-value < 0.05 and ** indicates adjusted p-value (FDR) < 0.05.

[0052] FIGs. 16A-16C shows example analyses of gene signatures associated with drug-induced pneumonitis in cfRNA. FIG. 16A shows an example gene set enrichment analysis (GSEA) performed in the immunotherapy pneumonitis cohort (n=9 samples) using Hallmark MSigDB signatures. FIG. 16B shows an example gene set enrichment analysis (GSEA) performed in the immunotherapy pneumonitis cohort (n=9 samples) using lung cell-type signatures. FIG. 16C shows an example gene set enrichment analysis (GSEA) performed in the immunotherapy pneumonitis cohort (n=9 samples) using PanglaoDB cell-type signatures. In FIGs. 16A-16C, * indicates nominal p-value < 0.05 and ** indicates adjusted p-value (FDR) < 0.05.

[0053] FIG. 17A-17G shows example data of gene signatures associated with radiation necrosis in cfRNA. FIG. 17A shows example differential expression analysis between CK-treated patients who do or do not develop symptomatic RN (n=11 CK+SRN, n=13 CK-RN). FIG. 17B shows an example gene set enrichment analysis (GSEA) of differentially expressed genes from FIG. 17A using Tabula Sapiens cell-type specific gene sets curated for cfRNA. FIG. 17C shows an example gene set enrichment analysis (GSEA) of differentially expressed genes from FIG. 17A using Hallmark MsigDBsignatures. FIG. 17D shows example differential expression analysis between CK-treated patients during symptomatic versus non-symptomatic RN (n=11 CK+SRN, n=19 CK+NRN). FIG. 17E shows an example gene set enrichment analysis (GSEA) of differentially expressed genes from FIG. 17D using using Tabula Sapiens cell-type specific gene sets curated for cfRNA. FIG. 17F shows example differential expression analysis between CK-treated patients with symptomatic RN compared to patients with brain metastases not treated with radiation (n=11 CK+SRN, n=10 Brain Mets no RT). FIG.17G shows an example gene set enrichment analysis (GSEA) of differentially expressed genes from FIG. 17F using Tabula Sapiens cell-type specific gene sets curated for cfRNA. In FIGs. 17B, 17C, 17E, and 17G, * indicates nominal p-value < 0.05 and ** indicates adjusted p-value (FDR) < 0.05.

[0054] FIGs. 18A-18C shows example gene set enrichment analyses of cell-type specific gene signatures optimized for cfRNA. FIG. 18A shows an example schematic illustrating the approach used to generate human cell-type-specific gene signatures from Tabula Sapiens single-cell RNA-seq datasets. FIG. 18B shows an example heatmap displaying the expression of the top 50 genes defining each human cell-type-specific signature. FIG. 18C shows an example heatmap of normalized enrichment scores (NES) from pseudo-bulk gene-set enrichment analysis for each custom cell-type signature in the Tabula Sapiens dataset. Significant enrichment is indicated as follows: *P < 0.05, **adjusted P < 0.05.DETAILED DESCRIPTION

[0055] In some aspects disclosed herein are systems and methods for performing sequencing of cell-free RNA (cfRNA) molecules. The systems and methods can comprise a means for perform ing targeted sequencing of the cfRNA in which rare abundance genes (RAGs) are targeted. The systems and methods can comprise the use of a gene panel for selecting genes to be targeted in which the gene panel excludes genes that are abundantly expressed.

[0056] When sequencing cfRNA from a cell-free source, the nucleic acids within that source can be difficult to perform analysis due to the lack of quality nucleic acid molecules. Historically, analysis of cfRNA from a cell-free source focused on micro-RNAs (miRNAs).Expressed nucleic acid molecules of other RNA types (e.g. messenger RNA), however, can greatly enhance detection of biological phenomena and / or medical disorders and thus could have great benefit in the field of diagnostics. Unfortunately, a liquid biopsy from plasma comprises cfRNA molecules that can be derived from sources throughout the body. Processing and / or circulatory biology can result in a high percentage of RNA derived from platelets and hematopoetic cells, drowning out signals from other potential sources. Thus, it can be difficult to use plasma-derived cfRNA for diagnostic purposes, such as the detection of cancer. Recognized herein is a need for new systems and methods to enhance the detection cfRNA from cell-free sources.

[0057] In some embodiments, the systems and methods disclosed herein are directed to sequencing of cfRNA derived from a cell-free source (also referred to as a liquid biopsy or excreta sample). Throughout the disclosure, the term liquid biopsy is utilized and is to refer to sources of cfRNA (inclusive of excreta) and include (but are not limited to) blood, plasma, lymph, cerebrospinal fluid, amniotic fluid, mucus, sputum, urine, and stool. The systems and methods can perform targeted sequencing to enhance the detection of some cfRNA molecules of the liquid biopsy. In some implementations, targeted sequencing comprises targeting cfRNA molecules that are infrequently found within liquid biopsies of control individuals (e.g., healthy individuals). In some implementations, a panel of capture probes or a panel of sets of primers are utilized to target cfRNA of rare abundance genes, and genes abundantly found in plasma are generally not targeted.

[0058] In some embodiments, the systems and methods disclosed herein are directed to transcript panels and their use in methods for targeted sequencing of cfRNA molecules. A transcript panel can refer to a capture-based panel (e.g., ssDNA molecules for hybridization capture) or amplification-based panel (e.g., a set of primers for amplification). In some cases, a transcript panel of nucleic acid probes that enriches for target nucleic acid molecules can comprise nucleic acid probes appended or coupled to a pull-down tag, e.g., for hybridization capture. Non-limiting examples of the pull-down tag can include biotin (e.g., streptavidin as a binding partner) and digoxigenin (e.g., anti-digoxigenin antibody as a binding partner). Accordingly, a transcript panel can be utilized during preparation of a sequencing library to perform targeted sequencing.

[0059] In some implementations, a transcript panel comprises rare abundance genes (RAGs), which are transcripts (including non-coding transcripts) that infrequently have cfRNA molecules expressed within liquid biopsies of healthy individuals. In some implementations, a transcript panel comprises rare abundance genes (RAGs), which are transcripts of the coding transcriptome. Targeting of RAGs can have various benefits, including overcoming the drowning signal provided by cfRNA derived from hematopoietic cells and / or platelets, and also being able to enhance detection of various biological characteristics (including a medical disorder, pregnancy, a fetal complication, a pregnancy complication, a neoplastic growth, cancer, a particular cancer type, a pathogen infection, immune activation, organ transplant rejection, neurodegeneration, tissue of origin, cell type of origin, activation of a biochemical pathway, etc.). In some implementations, a transcript panel excludes abundant genes, which are transcripts frequently or highly detected within liquid biopsies of healthy individuals.

[0060] In some implementations, RAGs are defined by as genes that are expressed or detected below a threshold within cfRNA from control liquid biopsies. Likewise, abundant genes can be defined by as genes that are expressed or detected above a threshold within cfRNA of control liquid biopsies and / or within cellular RNA from whole blood samples. Populations of individuals can be utilized to acquire control liquid biopsies for analysis. In some implementations, a control liquid biopsy is a sample derived from an individual have generally good health (a control liquid biopsy can also be referred to as a healthy liquid biopsy). Generally good health can mean an individual not having one or more the following when the sample is collected: an observed pathogenic infection, a diagnosed cancer, a diagnosed metabolic disorder, a diagnosed neurological disorder, a diagnosed immunodeficiency disorder, a diagnosed autoimmune disorder, a diagnosed inflammatory disorder, a diagnosed cardiovascular disorder, a diagnosed renal disorder, a diagnosed hepatic disorder, active pregnancy, a diagnosed pregnancy complication, a diagnosed fetal complication, having an organ transplant, active rejection of an organ transplant, obesity, malnourishment, cachexia, and having an abnormality on a clinical test. In some implementations, cfRNA are collected to generate diagnostics of a particular disorder. In some implementations in which a diagnostic of particular disorder (or trait) isto be generated, a control liquid biopsy is a biopsy derived from an individual that is not diagnosed with the particular disorder.

[0061] To identify RAGs and abundance genes, any appropriate number of control liquid biopsies can be used to establish which genes are expressed below a threshold within cfRNA from control liquid biopsies. In various instances, the number of control liquid biopsies to identify RAGs is 5 or more control liquid biopsies, 10 or more control liquid biopsies, 15 or more control liquid biopsies, 20 or more control liquid biopsies, 50 or more control liquid biopsies, 100 or more control liquid biopsies, 200 or more control liquid biopsies, 500 or more control liquid biopsies, 1000 or more control liquid biopsies, 2000 or more control liquid biopsies, 5000 or more control liquid biopsies, 10,000 or more control liquid biopsies, 20,000 or more control liquid biopsies, 50,000 or more control liquid biopsies, or 100,000 or more control liquid biopsies.

[0062] Various definitions of RAGs and abundance genes can be utilized, based on expression of genes within cfRNA of control liquid biopsies. In various implementations, RAGs are defined as transcripts that are expressed within cfRNA in less than 30% of control liquid biopsies, RAGs are defined as transcripts that are expressed within cfRNA in less than 20% of control liquid biopsies, RAGs are defined as transcripts that are expressed in less than 15% within cfRNA of control liquid biopsies, RAGs are defined as transcripts that are expressed within cfRNA in less than 10% of control liquid biopsies, RAGs are defined as transcripts that are expressed within cfRNA in less than 5% of control liquid biopsies, RAGs are defined as transcripts that are expressed within cfRNA in less than 2% of control liquid biopsies, or RAGs are defined as transcripts that are expressed within cfRNA in less than 1% of control liquid biopsies. In various implementations, abundance genes are defined as transcripts that are expressed within cfRNA in more than 30% of control liquid biopsies, abundance genes are defined as transcripts that are expressed within cfRNA in more than 20% of control liquid biopsies, abundance genes are defined as transcripts that are expressed within cfRNA in more than 15% of control liquid biopsies, abundance genes are defined as transcripts that are expressed within cfRNA in more than 10% of control liquid biopsies, abundance genes are defined as transcripts that are expressed within cfRNA in more than 5% of control liquid biopsies, abundance genes are defined as transcripts that are expressed withincfRNA in more than 2% of control liquid biopsies, or abundance genes are defined as transcripts that are expressed within cfRNA in more than 1% of control liquid biopsies. In some implementations, a clustering technique is utilized to categorize transcripts that are expressed within control liquid biopsies and not expressed within control liquid biopsies.

[0063] In some implementations, RAGs are defined by having an expression level below a threshold within cfRNA of control liquid biopsies. In some implementations, expression values are normalized for comparison. In some implementations, expression values are normalized and adjusted for platelet variation. In some implementations, expression values are log transformed (e.g., Log2NX) for comparison. In some instances, RAGs are defined as transcripts that have expression values Log2NX less than threshold (e.g., Log2NX < 0). In some implementations, RAGs are defined as transcripts that have the lowest expression values with respect to normalized expression in within cfRNA of the control liquid biopsies. In various implementations, RAGs are defined as transcripts that are in the bottom 60% of genes with respect to normalized expression within cfRNA of control liquid biopsies, RAGs are defined as transcripts that are in the bottom 50% of genes with respect to normalized expression within cfRNA of control liquid biopsies, RAGs are defined as transcripts that are in the bottom 40% of genes with respect to normalized expression within cfRNA of control liquid biopsies, RAGs are defined as transcripts that are in the bottom 30% of genes with respect to normalized expression within cfRNA of control liquid biopsies, RAGs are defined as transcripts that are in the bottom 20% of genes with respect to normalized expression within cfRNA of control liquid biopsies, RAGs are defined as transcripts that are in the bottom 10% of genes with respect to normalized expression within cfRNA of control liquid biopsies, or RAGs are defined as transcripts that are in the bottom 5% of genes with respect to normalized expression within cfRNA of control liquid biopsies. In various implementations, abundance genes are defined as transcripts that are in the top 40% of genes with respect to normalized expression within cfRNA of control liquid biopsies, abundance genes are defined as transcripts that are in the top 50% of genes with respect to normalized expression within cfRNA of control liquid biopsies, abundance genes are defined as transcripts that are in the top 60% of genes with respect to normalized expression within cfRNA of control liquid biopsies, abundance genes are defined as transcripts that are inthe top 70% of genes with respect to normalized expression within cfRNA of control liquid biopsies, abundance genes are defined as transcripts that are in the top 80% of genes with respect to normalized expression within cfRNA of control liquid biopsies, abundance genes are defined as transcripts that are in the top 90% of genes with respect to normalized expression within cfRNA of control liquid biopsies, or abundance genes are defined as transcripts that are in the top 95% of genes with respect to normalized expression within cfRNA of control liquid biopsies.

[0064] Because much of the cfRNA molecules are derived from hematopoetic cells, the expression within healthy cells of whole blood can also be used to define RAGs. In various implementations, RAGs are defined as transcripts that are expressed within cellular RNA in less than 60% of control whole blood samples, RAGs are defined as transcripts that are expressed within cellular RNA in less than 50% of control whole blood samples, RAGs are defined as transcripts that are expressed within cellular RNA in less than 40% of control whole blood samples, RAGs are defined as transcripts that are expressed within cellular RNA in less than 30% of control whole blood samples, RAGs are defined as transcripts that are expressed within cellular RNA in less than 20% of control liquid biopsies, RAGs are defined as transcripts that are expressed within cellular RNA in less than 10% of control liquid biopsies, and RAGs are defined as transcripts that are expressed within cellular RNA in less than 5% of control liquid biopsies. In various implementations, abundance genes are defined as transcripts that are expressed within cellular RNA in more than 60% of control whole blood samples, abundance genes are defined as transcripts that are expressed within cellular RNA in more than 50% of control whole blood samples, abundance genes are defined as transcripts that are expressed within cellular RNA in more than 40% of control whole blood samples, abundance genes are defined as transcripts that are expressed within cellular RNA in more than 30% of control whole blood samples, abundance genes are defined as transcripts that are expressed within cellular RNA in more than 20% of control whole blood samples, abundance genes are defined as transcripts that are expressed within cellular RNA in more than 10% of control whole blood samples, abundance genes are defined as transcripts that are expressed within cellular RNA in more than 5% of control whole blood samples.

[0065] In some implementations, RAGs are defined by being detected below a threshold within cfRNA of liquid biopsies, having an expression level below a threshold within cfRNA of liquid biopsies, and / or being detected below a threshold within cellular RNA of whole blood samples. Accordingly, any threshold as defined herein for presence within a control biopsy can be combined with any threshold as defined herein for expression level. In one example, within the Examples and Data section below, RAGs were defined as transcripts that are expressed in less than 5% of cfRNA derived from control liquid biopsies and that are in the bottom 30% of genes with respect to normalized expression and that are expressed in less than 40% of whole blood samples. To generate a transcript panel, other genes can be included in addition to RAGs for various applications, such as (for example) transcripts having tissue specificity, transcripts having cell-type specificity, transcripts having known clinical relevancy, transcripts having known biological relevancy (e.g., biomarker for a cellular pathway or process or cancer-specific transcripts), and transcripts having known and common mutagenesis profiles such as fusion events and variants.

[0066] Provided in Table 1 is an example of a list of RAGs that were defined as being identified within cfRNA in less than 5% of control liquid biopsies and for which average log2NX was in the bottom 30% of all genes withn cfRNA of liquid biopsies as determined from 50 replicates of 28 healthy controls using the whole exome sequencing (WES) of cell-free RNA (cfRNA). Table 1 also included genes that were defined as being identified in cellular RNA in less than 40% of control whole blood samples as determined from 307 samples of whole blood gene expression data from the Genotype-Tissue Expression (GTEx) project. Provided in Table 1 is an example comparison of RAGs selected under a different threshold (e.g., as compared to International Application Publication No. WO2024238686A2, the disclosure of which is incorporated herein by reference in its entirety).

[0067] In some implementations, a transcript panel comprises nucleic acid molecules for detecting a plurality of RAGs from Table 1. In some implementations, a transcript panel comprises nucleic acid molecules for detecting at least about 1 % of the listed RAGs from Table 1. In some implementations, a transcript panel comprises nucleic acid molecules for detecting at least about 5% of the listed RAGs from Table 1. In some implementations,a transcript panel comprises nucleic acid molecules for detecting at least about 10% of the listed RAGs from Table 1. In some implementations, a transcript panel comprises nucleic acid molecules for detecting at least about 20% of the listed RAGs from Table 1. In some implementations, a transcript panel comprises nucleic acid molecules for detecting at least about 30% of the listed RAGs from Table 1. In some implementations, a transcript panel comprises nucleic acid molecules for detecting at least about 40% of the listed RAGs from Table 1. In some implementations, a transcript panel comprises nucleic acid molecules for detecting at least about 50% of the listed RAGs from Table 1. In some implementations, a transcript panel comprises nucleic acid molecules for detecting at least about 60% of the listed RAGs from Table 1. In some implementations, a transcript panel comprises nucleic acid molecules for detecting at least about 70% of the listed RAGs from Table 1. In some implementations, a transcript panel comprises nucleic acid molecules for detecting at least about 80% of the listed RAGs from Table 1. In some implementations, a transcript panel comprises nucleic acid molecules for detecting at least about 90% of the listed RAGs from Table 1. In some implementations, a transcript panel comprises nucleic acid molecules for detecting 100% of the listed RAGs from Table 1.

[0068] In some implementations, a transcript panel excludes nucleic acid molecules for detecting a plurality of abundance genes, which is any gene of the human genome not listed as a RAG from Table 1. In some implementations, a transcript panel excludes nucleic acid molecules for detecting 30% of all other genes of the human genome not listed as RAGs from Table 1. In some implementations, a transcript panel excludes nucleic acid molecules for detecting 40% of all other genes of the human genome not listed as RAGs from Table 1. In some implementations, a transcript panel excludes nucleic acid molecules for detecting 50% of all other genes of the human genome not listed as RAGs from Table 1. In some implementations, a transcript panel excludes nucleic acid molecules for detecting 60% of all other genes of the human genome not listed as RAGs from Table 1. In some implementations, a transcript panel excludes nucleic acid molecules for detecting 70% of all other genes of the human genome not listed as RAGs from Table 1. In some implementations, a transcript panel excludes nucleic acid molecules for detecting 80% of all other genes of the human genome notlisted as RAGs from Table 1. In some implementations, a transcript panel excludes nucleic acid molecules for detecting 90% of all other genes of the human genome not listed as RAGs from Table 1. In some implementations, a transcript panel excludes nucleic acid molecules for detecting 99% of all other genes of the human genome not listed as RAGs from Table 1.

[0069] A transcript panel can target nucleic acids using a capture technique or an amplification technique. To target cfRNA, in some implementations, the RNA is first converted to cDNA before targeting. And in some implementations, the cfRNA is targeted prior to cDNA conversion. A transcript panel can thus comprise nucleic acid molecules that are complementary to the RNA strand, or to either strand of the cDNA, such that they can anneal and / or hybridize to the RNA or cDNA. In some implementations, a transcript panel is utilized to specifically target particular molecules of RNA and / or of the singlestranded or double-stranded cDNA. In some implementations, a capture-based panel comprises a set of single-stranded or double-stranded nucleic acid probes for hybridization capture of particular molecules of the RNA and / or of particular molecules of single-stranded or double-stranded cDNA. In some implementations, an amplificationbased panel comprises a set of primers for specific reverse-transcription of particular RNA and / or specific amplification of particular molecules of the single-stranded or doublestranded cDNA.

[0070] The sequences of the capture probes and / or primers are complementary to the transcripts that are to be targeted. The capture probes and / or primers do not need perfect complementation, but have enough complementation to its target in order to capture via hybridization or anneal for priming of amplification. Design of probes can be based on any appropriate sequence of the target. For example, a reference database such hg 19 or hg38 can be utilized to design probes or primers for human transcript targets.

[0071] Particular targeting is based on sequence complementation and the genes selected within the panel. In some implementations, the transcript panel particularly targets a set of rare abundance genes (RAGs). In some implementations, the transcript panel excludes genes that are not RAGs (as defined by the criteria or as listed in Table 1). Exclusion of the genes that are not RAGs can facilitate streamlined sequencing protocols and enhance sequencing results (e.g., greater sequencing depth in the targetedsequences as compared to the depth afforded by a whole exome panel). The better sequencing results can allow for better sensitivity, yielding better results in various applications such as cfRNA-based diagnostics.

[0072] In some implementations, a transcript panel excludes at least 50% of whole-exome genes that are not RAGs. In some implementaions, a transcript panel excludes at least 60% of whole-exome genes that are not RAGs. In some implementations, a transcript panel excludes at least 70% of whole-exome genes that are not RAGs. In some implementations, a transcript panel excludes at least 80% of whole-exome genes that are not RAGs. In some implementaions, a transcript panel excludes at least 90% of whole-exome genes that are not RAGs. In some implementations, a transcript panel excludes at least 95% of whole-exome genes that are not RAGs. In some implementations, a transcript panel excludes at least 99% of whole-exome genes that are not RAGs.

[0073] In some implementations, a transcript panel excludes nucleic acid molecules for detecting a plurality of abundance genes, which is any gene of the human genome not listed as a RAG from Table 1. In some implementations, a transcript panel excludes nucleic acid molecules for detecting at least 1 % of all other genes of the human genome not listed as RAGs from Table 1. In some implementations, a transcript panel excludes nucleic acid molecules for detecting at least 5% of all other genes of the human genome not listed as RAGs from Table 1. In some implementations, a transcript panel excludes nucleic acid molecules for detecting at least 10% of all other genes of the human genome not listed as RAGs from Table 1. In some implementations, a transcript panel excludes nucleic acid molecules for detecting at least 20% of all other genes of the human genome not listed as RAGs from Table 1. In some implementations, a transcript panel excludes nucleic acid molecules for detecting at least 30% of all other genes of the human genome not listed as RAGs from Table 1. In some implementations, a transcript panel excludes nucleic acid molecules for detecting at least 40% of all other genes of the human genome not listed as RAGs from Table 1. In some implementations, a transcript panel excludes nucleic acid molecules for detecting at least 50% of all other genes of the human genome not listed as RAGs from Table 1. In some implementations, a transcript panel excludes nucleic acid molecules for detecting at least 60% of all other genes of the human genome not listed as RAGs from Table 1. In some implementations, a transcript panel excludesnucleic acid molecules for detecting at least 70% of all other genes of the human genome not listed as RAGs from Table 1. In some implementations, a transcript panel excludes nucleic acid molecules for detecting at least 80% of all other genes of the human genome not listed as RAGs from Table 1. In some implementations, a transcript panel excludes nucleic acid molecules for detecting at least 90% of all other genes of the human genome not listed as RAGs from Table 1. In some implementations, a transcript panel excludes nucleic acid molecules for detecting at least 99% of all other genes of the human genome not listed as RAGs from Table 1.

[0074] Sequencing protocols and their applications can be enhanced by including a set of control genes, which can provide positive assurance of the sequencing results and facilitate normalization between samples. In some implementations, a transcript panel comprises nucleic acid molecules for detecting a set of control transcripts to provide a normalization between samples. Generally, the set of control transcripts can be any set of transcripts that are commonly detected and having a relatively stable expression level among control liquid biopsies. In some embodiments, a set of control transcripts comprises one or more housekeeping transcripts (e.g., GAPDH, actin, ubiquitin).

[0075] In some implementations, a transcript panel has not more than 1 control gene. In some implementations, a transcript panel has not more than 5 control genes. In some implementations, a transcript panel has not more than 10 control genes. In some implementations, a transcript panel has not more than 20 control genes. In some implementations, a transcript panel has not more than 50 control genes. In some implementations, a transcript panel has not more than 100 control genes. In some implementations, a transcript panel has not more than 500 control genes. In some implementations, a transcript panel has not more than 1000 control genes.

[0076] In some implementations, a transcript panel comprises of 5 or more control genes. In some implementations, a transcript panel comprises of 10 or more control genes. In some implementations, a transcript panel comprises of 20 or more control genes. In some implementations, a transcript panel comprises of 50 or more control genes. In some implementations, a transcript panel comprises of 100 or more control genes. In some implementations, a transcript panel comprises of 500 or more controlgenes. In some implementations, a transcript panel comprises of 1000 or more control genes.

[0077] Sequencing protocols and their applications can be enhanced by assessing genes that provide further insight. For example, if the provision of diagnosing cancer, certain transcripts can provide further diagnostic insight, such as genes that are recurrently mutagenized in cancer. Accordingly, a transcript panel can include additional genes that are not RAGs (as defined by the criteria or as listed in Table 1 ).

[0078] In some implementations, a transcript panel consists of 10 or fewer genes in addition to RAGs. In some implementations, a transcript panel consists of 20 or fewer genes in addition to RAGs. In some implementations, a transcript panel consists of 50 or fewer genes in addition to RAGs. In some implementations, a transcript panel consists of 100 or fewer genes in addition to RAGs. In some implementations, a transcript panel consists of 200 or fewer genes in addition to RAGs. In some implementations, a transcript panel consists of 500 or fewer genes in addition to RAGs. In some implementations, a transcript panel consists of 1000 or fewer genes in addition to RAGs. In some implementations, a transcript panel consists of 2000 or fewer genes in addition to RAGs. In some implementations, a transcript panel consists of 5000 or fewer genes in addition to RAGs. In some implementations, a transcript panel consists of 10,000 or fewer genes in addition to RAGs.

[0079] Various types of genes may be included in addition to RAGs, such as (for example) tissue-specific transcripts, cell-type-specific transcripts, clinically relevant transcripts, biomarker transcripts, commonly mutagenized transcripts, and B-cell and T- cell clone transcripts. In some implementations, the transcript panel targets a set of tissue-specific transcripts. In some implementations, the transcript panel targets a set of cell-type-specific transcripts. In some implementations, the transcript panel targets a set of clinically relevant transcripts. In some implementations, the transcript panel targets a set transcripts that are biomarker transcripts. In some implementations, the transcript panel targets a set of mutagenic events. In some implementations, the transcript panel targets a set of B-cell and T-cell clones.

[0080] In some implementations, a transcript panel comprises nucleic acid molecules for detecting tissue-specific transcripts. A tissue-specific transcript can be a transcript thatis uniquely highly expressed within a particular tissue as compared to its expression in other tissues. In various implementations, a tissue-specific transcript is expressed at least 2-fold within a particular tissue as compared to other tissues, a tissue-specific transcript is expressed at least 3-fold within a particular tissue as compared to other tissues, a tissue-specific transcript is expressed at least 4-fold within a particular tissue as compared to other tissues, a tissue-specific transcript is expressed at least 5-fold within a particular tissue as compared to other tissues, a tissue-specific transcript is expressed at least 6-fold within a particular tissue as compared to other tissues, a tissue-specific transcript is expressed at least 7-fold within a particular tissue as compared to other tissues, a tissue-specific transcript is expressed at least 8-fold within a particular tissue as compared to other tissues, a tissue-specific transcript is expressed at least 9-fold within a particular tissue as compared to other tissues, or a tissue-specific transcript is expressed at least 10-fold within a particular tissue as compared to other tissues. Unique expression can be determined empirically or data derived from a database, such as (for example) data derived from the Genotype-Tissue Expression (GTEx) project, the Human Protein Atlas (HPA).

[0081] In some implementations, a transcript panel comprises nucleic acid molecules for detecting cell-type-specific transcripts. A cell-type-specific transcript is a transcript that is uniquely highly expressed within a particular cell type as compared to its expression in other cell types. In various implementations, a cell-type-specific transcript is expressed at least 2-fold within a particular cell type as compared to other cell types, a cell-type-specific transcript is expressed at least 3-fold within a particular cell type as compared to other cell types, a cell-type-specific transcript is expressed at least 4-fold within a particular cell type as compared to other cell types, a cell-type-specific transcript is expressed at least 5-fold within a particular cell type as compared to other cell types, a cell-type-specific transcript is expressed at least 6-fold within a particular cell type as compared to other cell types, a cell-type-specific transcript is expressed at least 7-fold within a particular cell type as compared to other cell types, a cell-type-specific transcript is expressed at least 8-fold within a particular cell type as compared to other cell types, a cell-type-specific transcript is expressed at least 9-fold within a particular cell type as compared to other cell types, or a cell-type-specific transcript is expressed at least 10-fold within a particularcell type as compared to other cell types. Unique expression can be determined empirically or data derived from a database, such as (for example) data derived from PanglaoDb or Tabula Sapiens.

[0082] In some implementations, a transcript panel comprises nucleic acid molecules for detecting clinically relevant transcripts (e.g., for diagnostic relevance). Clinically relevant transcripts can include transcripts for the diagnosis of medical disorders (e.g., cancer). For example, transcripts of EGFR, KRAS, BRAF, MET, ALK, RET, and ROS1 are useful in the diagnosis non-small cell lung cancer (NSCLC) such as lung adenocarcinomas (LUAD).

[0083] In some implementations, a transcript panel comprises nucleic acid molecules for detecting transcripts associated with a biological characteristics (e.g., biomarker transcripts). Biological characteristics include (but are not limited to) a medical disorder, pregnancy, a fetal complication, a pregnancy complication, a neoplastic growth, cancer, a particular cancer type, a pathogen infection, immune activation, organ transplant rejection, neurodegeneration, tissue of origin, cell type of origin, and activation of a biochemical pathway.

[0084] In some implementations, a transcript panel comprises nucleic acid molecules for detecting transcripts associated with mutagenic events, which may useful for identifying de novo mutagenesis and / or cancer oncogenes. Mutagenic events include (but are not limited to) gene fusions, insertions, deletions, translocations, single nucleotide variants, and splice variants.

[0085] In some implementations, a transcript panel comprises nucleic acid molecules for detecting transcripts associated with detection of B-cell and T-cell clones, which can be useful for tracking immunological activity against particular antigens. A transcript panel can target V(D)J recombination of B-cell receptors and T-cell receptors, and thus identify sequences of B-cell and T-cell clones. In some implementations, clones are detected at a single time point to identify current activity of immunogens (e.g., immunogens of a pathogenic infection, a cancer, a vaccine, or an autoimmune disorder). In some implementations, clones are detected at over a plurality of time points to detect changes of activity of immunogens (e.g., detection of minimal residual disease, cancer treatmentsuccess, waning immunogenicity from vaccination or pathogenic infection, autoimmune flares).

[0086] Several components can be utilized and several steps can be performed to sequence cfRNA molecules obtained in a cell-free sample. A liquid biopsy can be derived from any appropriate biological source, including (but not limited to) blood, plasma, lymph, mucus, sputum, cerebrospinal fluid, amniotic fluid, urine, and stool. To extract RNA, any appropriate kit can be utilized (e.g., QIAamp Circulating Nucleic Acid kit). In some implementations, glycogen is added to the cell-free sample comprising nucleic acids prior to adding to a silica-based column. In some embodiments, the sample also comprises cell-free DNA, which may be utilized in other assessments.

[0087] In some implementations, cell-free nucleic acids are quantified using quantitative Real-Time Polymerase Chain Reaction (qRT-PCR). In some implementations, both RNA and DNA are simultaneously quantified in the cell-free sample. To quantify RNA and DNA are simultaneously, qRT-PCR can be performed in which primers can detect and quantify RNA using a primer set across an intron and primers can detect and quantify DNA using a primer set targeting an untranscribed region of the genome. For quantifying RNA, an intron of commonly expressed gene (e.g., housekeeping gene) may be utilized. Control standards can be utilized to generate standard curves to quantify the nucleic acids. After quantification, the cell-free samples can be split into aliquots for performing DNA and RNA assessments. In some implementations, DNA is digested from the cell-free sample (e.g., DNase digestion) for performing RNA assessments. In some implementations, DNA digestion is performed after elution from a silica column for performing RNA assessments. In some implementations, one or more downstream steps of the sequencing preparation protocol uses a defined amount of material based on the quantification of cell-free RNA.

[0088] In some implementations, cell-free RNA is converted to cDNA. In some implementations, double-stranded cDNA is generated. In some implementations, doublestranded cDNA is treated with a nuclease that removes single-stranded nucleic acid molecules (e.g., S1 endonuclease).

[0089] After capture and / or amplification a set of targets, the library can be further processed (e.g., amplified) and sequenced using high-throughput sequencing. In someimplementations, sequencing is performed to a depth of more than 10,000 reads, sequencing is performed to a depth of more than 10,000 reads, sequencing is performed to a depth of more than 100,000 reads, sequencing is performed to a depth of more than 1,000,000 reads, sequencing is performed to a depth of more than 10,000,000 reads, or sequencing is performed to a depth of more than 100,000,000 reads.

[0090] In some embodiments, sequencing is performed to a depth of at least 1,000,000 reads. In some embodiments, sequencing is performed to a depth of at least 5,000,000 reads. In some embodiments, sequencing is performed to a depth of at least 10,000,000 reads. In some embodiments, sequencing is performed to a depth of less than 50,000,000 reads. In some embodiments, sequencing is performed to a depth of less than 60,000,000 reads. In some embodiments, sequencing is performed to a depth of less than 70,000,000 reads. In some embodiments, sequencing is performed to a depth of less than 80,000,000 reads. In some embodiments, sequencing is performed to a depth of less than 90,000,000 reads. In some embodiments, sequencing is performed to a depth of less than 100,000,000 reads. In some embodiments, sequencing is performed to a depth of less than 150,000,000 reads. In some embodiments, sequencing is performed to a depth of less than 200,000,000 reads. In some embodiments, sequencing is performed to a depth of 5,000,000 to 50,000,000 reads. In some embodiments, sequencing is performed to a depth of 10,000,000 to 50,000,000 reads. In some embodiments, sequencing is performed to a depth of 5,000,000 to 100,000,000 reads. In some embodiments, sequencing is performed to a depth of 10,000,000 to 100,000,000 reads. In some embodiments, sequencing is performed to a depth of 15,000,000 to 40,000,000 reads. In some embodiments, sequencing is performed to a depth of 20,000,000 to 40,000,000 reads.

[0091] After sequencing, various analyses of the sequencing results can be performed to enhance the detection of the targeted cell-free nucleic acid molecules. In some implementations, only transcripts included within the transcript panel are included for downstream analysis. In some implementations, transcript counts are converted to log-transformed counts per million (CPM) to account for library size and transcriptome complexity. In some implementations, transcript counts are normalized to account for transcript size. In some implementations, transcript counts are normalized to account fortranscript count per milliliter of plamsa (or other biofluid). In some implementations, counts are normalized to account for sample-to-sample variation. In some implementations, unwanted variation is removed, such as (for example) removal of variation due to transcript expression associated with platelets.

[0092] It has been recognized herein that platelet derived RNA can confound analysis of cfRNA. Platelets can be found in many types of liquid biopsies. Platelets can be found more readily in liquid biopsies when cellular injury is present. Therefore, platelets can confound assessments of liquid biopsies derived from (for example) blood, plasma, lymph, mucus, sputum, cerebrospinal fluid, amniotic fluid, urine, and stool for assessment of number of biological characteristics (for example) a medical disorder, pregnancy, a fetal complication, a pregnancy complication, a neoplastic growth, cancer, a particular cancer type, a pathogen infection, immune activation, organ transplant rejection, neurodegeneration, tissue of origin, cell type of origin, and activation of a biochemical pathway. In some implementations, a cfRNA sample is centrifuged to remove platelets. In some embodiments, a cfRNA sample is centrifuged to remove platelets by a method described elsewhere herein (e.g., in Example 2). In some embodiments, the cfRNA sample is centrifuged once to remove platelets (e.g., at about 2,500g or at about 4,000g). In some embodiments, the cfRNA sample is centrifuged once at about 2,500g for at least about 10 minutes. In some embodiments, the cfRNA sample is centrifuged once at about 2,500g for at least about 20 minutes. In some embodiments, the cfRNA sample is centrifuged once at about 2,500g for at least about 30 minutes. In some embodiments, the cfRNA sample is centrifuged once at about 2,500g for at least about 40 minutes. In some embodiments, the cfRNA sample is centrifuged once at about 2,500g for at least about 50 minutes. In some embodiments, the cfRNA sample is centrifuged once at about 2,500g for at least about 60 minutes. In some embodiments, the cfRNA sample is centrifuged once at about 4,000g for at least about 10 minutes. In some embodiments, the cfRNA sample is centrifuged once at about 4,000g for at least about 20 minutes. In some embodiments, the cfRNA sample is centrifuged once at about 4,000g for at least about 30 minutes. In some embodiments, the cfRNA sample is centrifuged once at about 4,000g for at least about 40 minutes. In some embodiments, the cfRNA sample is centrifuged once at about 4,000g for at least about 50 minutes. In some embodiments,the cfRNA sample is centrifuged once at about 4,000g for at least about 60 minutes. In some embodiments, the cfRNA sample is centrifuged twice to remove platelets. In some embodiments, the cfRNA sample is centrifuged at about 1,800g then centrifuged at about 4,000g. In some embodiments, the cfRNA sample is centrifuged at about 1,800g for at least about 10 minutes then centrifuged at about 4,000g for at least about 10 minutes. In some embodiments, the cfRNA sample is centrifuged at about 1,800g for at least about 15 minutes then centrifuged at about 4,000g for at least about 15 minutes. In some embodiments, the cfRNA sample is centrifuged at about 1,800g for at least about 20 minutes then centrifuged at about 4,000g for at least about 20 minutes. In some embodiments, the cfRNA sample is centrifuged at about 1,800g for at least about 30 minutes then centrifuged at about 4,000g for at least about 30 minutes. In some embodiments, the cfRNA sample is centrifuged at about 1,600g then centrifuged at about 16,000g. In some embodiments, the cfRNA sample is centrifuged at about 1,600g for at least about 10 minutes then centrifuged at about 16,000g for at least about 10 minutes. In some embodiments, the cfRNA sample is centrifuged at about 1,600g for at least about 15 minutes then centrifuged at about 16,000g for at least about 15 minutes. In some embodiments, the cfRNA sample is centrifuged at about 1,600g for at least about 20 minutes then centrifuged at about 16,000g for at least about 20 minutes. In some embodiments, the cfRNA sample is centrifuged at about 1,600g for at least about 30 minutes then centrifuged at about 16,000g for at least about 30 minutes. In some implementations, platelet expression is removed in silico after sequencing. To remove platelet expression in silico, an appropriate method to remove unwanted variation is utilized (e.g., RUVseq R package). In some implementations, a transcript panel for targeted sequencing specifically excludes expression of genes associated with platelets. Even when a transcript panel for targeted sequencing specifically excludes platelet expression, factors of unwanted variation can be estimated to establish a correction factor correlated with platelet expression. In one example, platelet correction can be performed by ordinary least squares regression of log2NX on the selected factor of unwanted platelet expression.

[0093] In some implementations, the sequencing result is utilized to perform differential transcript expression analysis. In some implementations, differential transcriptexpression analysis can be utilized for comparison of samples (e.g., medical disorder vs. control; e.g., comparison from one time point to other timepoint(s)). Any appropriate method for performing differential transcript expression analysis can be utilized (e.g., DESeq2 R package). In one example, a generalized linear model can be built using sample type as the covariate. Significantly differentially expressed genes can be identified utilizing an appropriate cut-off (e.g., cut-off based on fold change and / or significance). In some embodiments, significantly differentially expressed genes can be identified utilizing a fold change cut-off (e.g., greater than 0 log fold change, greater than 0.5 log fold change, greater than 1 log fold change, greater than 1.5 log fold change, greater than 2 log fold change) and / or a significance cut-off (e.g., adjusted p-value < 0.5, adjusted p-value < 0.1, adjusted p-value < 0.05, adjusted p-value < 0.01). Further downstream analysis can be performed on the differentially expressed genes to gain insight on biological phenomena associated with a sample. For instance, gene set enrichment analysis can be performed, which can be utilized to identify molecular signatures associated with various phenomena.

[0094] In some implementations, the sequencing result is utilized to detect enrichment of expression signatures related to one or more biological characteristics. Many biological characteristics can be identified within a sequencing result via expression signatures. Examples of biological characteristics include (but are not limited to) a medical disorder, pregnancy, a fetal complication, a pregnancy complication, a neoplastic growth, cancer, a particular cancer type, a pathogen infection, immune activation, organ transplant rejection, neurodegeneration, tissue of origin, cell type of origin, and activation of a biochemical pathway. In some implementations, an enrichment score of an expression signature is computed, providing a scaled assessment on whether a biological characteristic is present in the sample. Diagnoses can be established based on various biological characteristics present within the sequencing result. For example, a liquid biopsy can be utilized to assess the health of a cancer patient by assessing the cfRNA for expression signatures related to various health statuses and / or complications.

[0095] In some implementations, the sequencing result is utilized to detect mutagenesis, including somatic or de novo mutagenic events. Examples of mutagenic events that can be detected include (but are not limited to) gene fusions, insertions,deletions, translocations, single nucleotide variants, and splice variants. In some implementations, the sequencing result is utilized to infer copy number status of one or more genes. In some examples, MET gene amplification can be inferred from an expression signature, which is related to targeted therapy resistance in non-small cell lung cancer. Assessment of mutagenesis can be diagnostic and inform treatment options, especially for various cancer types.

[0096] In some implementations, a computational model is trained to predict a categorical status or the likelihood a biological characteristic is present in a cfRNA sample based on the sequencing result. In some implementations, the computational model is trained utilizing the sequencing result directly as input into the model. In some implementations, the computational model utilizes derived features, such as (for example) normalized expression of individual genes, enrichment of one or more gene signatures, enrichment of biochemical pathways, collection of sequence variants, and copy number status. A trained computational model can then be utilized to assess cfRNA sequencing results of patients, such as for use as a diagnostic in a clinical setting.

[0097] For predicting a biological characteristic, any appropriate computational machine-learning model can be utilized. Example of computational models include (but are not limited to) logistic regression, elastic net, LASSO, random forest, XGBoost, and a neural network. Training can be performed using expression data from samples having a biological characteristic and control samples. A cohort of individuals with a known categorical status of a biological phenomenon can have their cfRNA sequenced and processed to train the computational model. In some embodiments, because cfRNA are derived from expression within particular cell types, samples can be from solid tissue or any other source representative of cfRNA. Upon training, sets of features, weights and hyperparameters providing robust predictability can be selected.

[0098] Some embodiments of the systems and methods disclosed elsewhere herein are directed towards classification and / or a prediction of likelihood of a disease or condition (e.g., lung cancer (e.g., lung adenocarcinoma (LUAD))). In some embodiments, a computational method is performed in which feature data derived from a sequencing result are utilized within the computational machine-learning model. Computational models can include (but are not limited to) logistic regression, elastic net, LASSO, randomforest, XGBoost, and a neural network. The sequencing data can be derived from sequencing of cfRNA from a non-cellular source, such as blood, plasma, lymph, urine, and repiratory tract sputum. In some embodiments, the computational model distinguishes whether a sample includes cfRNA derived from a lung cancer or noncancer source (e.g., healthy). Targeted sequencing can be performed as described elsewhere herein, in which targeted sequencing directed towards trasncripts defined as RAGs and / or transcripts useful in the diagnosis NSCLC (e.g, EGFR, KRAS, MET, ALK, RET, and ROS1). In some embodiments, data collection can be specific for the feature data to be entered into the model to perform classification and / or prediction of likelihood of cancer.

[0099] As described in greater detail within the Examples and Supporting Data, several features can be used in the assessment of lung cancer via a computational machine-learning model. An example model found 23 features were beneficial in distinguishing LLIAD and control cfRNA. These features included both transcript expression features and mutation-based features. The transcript expression features selected were (in order of feature coefficient) SLC34A2, FOLR1, TUBB, SCEL, CXCL13, CEACAM5, CLDN1, FGG, CCND1, GPR37, PLEKHS1, SLC16A9, FLRT3, AGR2, SEMA7A, VSTM2L, VSTM4, ACTA2, MAL2, and FAM83A. The mutation-based features selected were any KRAS mutation and EGFR L858R mutation.

[0100] Various computational models can be utilized to classify and / or predict a likelihood of lung cancer from sequencing of a cfRNA sample. In some embodiments, a computational machine-learning model to assess a cfRNA sample comprises one or more transcript expression features. In some embodiments, a computational machine-learning model to assess a cfRNA sample comprises two or more transcript expression features. In some embodiments, a computational machine-learning model to assess a cfRNA sample comprises three or more transcript expression features. In some embodiments, a computational machine-learning model to assess a cfRNA sample comprises four or more transcript expression features. In some embodiments, a computational machine-learning model to assess a cfRNA sample comprises five or more transcript expression features. In some embodiments, a computational machine-learning model to assess a cfRNA sample comprises six or more transcript expression features. In some embodiments, acomputational machine-learning model to assess a cfRNA sample comprises seven or more transcript expression features. In some embodiments, a computational machinelearning model to assess a cfRNA sample comprises eight or more transcript expression features. In some embodiments, a computational machine-learning model to assess a cfRNA sample comprises nine or more transcript expression features. In some embodiments, a computational machine-learning model to assess a cfRNA sample comprises ten or more transcript expression features. In some embodiments, a computational machine-learning model to assess a cfRNA sample comprises fifteen or more transcript expression features. In some embodiments, a computational machinelearning model to assess a cfRNA sample comprises twenty or more transcript expression features. In some embodiments, a computational machine-learning model to assess a cfRNA sample comprises twenty-one or more transcript expression features. In the various embodiments, the transcript expression features comprises transcript expression of one or more transcripts selected from the group consisting of: SLC34A2, FOLR1, TUBB, SCEL,CXCL13, CEACAM5, CLDN1, FGG, CCND1, GPR37, PLEKHS1, SLC16A9, FLRT3, AGR2, SEMA7A, VSTM2L, VSTM4, ACTA2, MAL2, and FAM83A.

[0101] In some embodiments, a computational machine-learning model to assess a cfRNA sample comprises one or more mutation-based features. In some embodiments, a computational machine-learning model to assess a cfRNA sample comprises two or more mutation-based features. In the various embodiments, the mutation-based features detection of one or more mutations selected from the group consisting of: a KRAS mutation and an EGFR mutation (e.g., EGFR L858R mutation).

[0102] In some embodiments, a computational machine-learning model to assess a cfRNA sample comprises one or more transcript expression features and one or more mutation-based features. In some embodiments, a computational machine-learning model to assess a cfRNA sample comprises two or more transcript expression features and one or more mutation-based features. In some embodiments, a computational machine-learning model to assess a cfRNA sample comprises three or more transcript expression features and one or more mutation-based features. In some embodiments, a computational machine-learning model to assess a cfRNA sample comprises four or more transcript expression features and one or more mutation-based features. In someembodiments, a computational machine-learning model to assess a cfRNA sample comprises five or more transcript expression features and one or more mutation-based features. In some embodiments, a computational machine-learning model to assess a cfRNA sample comprises ten or more transcript expression features and one or more mutation-based features. In some embodiments, a computational machine-learning model to assess a cfRNA sample comprises fifteen or more transcript expression features and one or more mutation-based features. In some embodiments, a computational machine-learning model to assess a cfRNA sample comprises twenty or more transcript expression features and one or more mutation-based features. In some embodiments, a computational machine-learning model to assess a cfRNA sample comprises twenty-one or more transcript expression features and one or more mutation-based features. In the various embodiments, the transcript expression features comprises transcript expression of one or more transcripts selected from the group consisting of: SLC34A2, FOLR1, TUBB, SCEL, CXCL13, CEACAM5, CLDN1, FGG, CCND1, GPR37, PLEKHS1, SLC16A9, FLRT3, AGR2, SEMA7A, VSTM2L, VSTM4, ACTA2, MAL2, and FAM83A. In the various embodiments, the mutation-based features detection of one or more mutations selected from the group consisting of: a KRAS mutation and an EGFR mutation (e.g., EGFR L858R mutation).

[0103] In some embodiments, a computational machine-learning model to assess a cfRNA sample comprises one or more transcript expression features and two or more mutation-based features. In some embodiments, a computational machine-learning model to assess a cfRNA sample comprises two or more transcript expression features and two or more mutation-based features. In some embodiments, a computational machine-learning model to assess a cfRNA sample comprises three or more transcript expression features and two or more mutation-based features. In some embodiments, a computational machine-learning model to assess a cfRNA sample comprises four or more transcript expression features and two or more mutation-based features. In some embodiments, a computational machine-learning model to assess a cfRNA sample comprises five or more transcript expression features and two or more mutation-based features. In some embodiments, a computational machine-learning model to assess a cfRNA sample comprises ten or more transcript expression features and two or moremutation-based features. In some embodiments, a computational machine-learning model to assess a cfRNA sample comprises fifteen or more transcript expression features and two or more mutation-based features. In some embodiments, a computational machine-learning model to assess a cfRNA sample comprises twenty or more transcript expression features and two or more mutation-based features. In some embodiments, a computational machine-learning model to assess a cfRNA sample comprises twenty-one or more transcript expression features and two or more mutation-based features. In the various embodiments, the transcript expression features comprises transcript expression of one or more transcripts selected from the group consisting of: SLC34A2, FOLR1, TUBB, SCEL, CXCL13, CEACAM5, CLDN1, FGG, CCND1, GPR37, PLEKHS1, SLC16A9, FLRT3, AGR2, SEMA7A, VSTM2L, VSTM4, ACTA2, MAL2, and FAM83A. In the various embodiments, the mutation-based features detection of one or more mutations selected from the group consisting of: a KRAS mutation and an EGFR mutation (e.g., EGFR L858R mutation).Sequencing Methods and Diagnostics

[0104] In some aspects disclosed herein are methods directed towards performing sequencing of cfRNA. cfRNA sequencing methods can be utilized in a number of diagnostic assessments. Accordingly, an individual can have a liquid biopsy extracted or collected for cfRNA sequencing, which can be prepared utilizing a transcript panel of RAGs. Various biomedical characteristics can be screened for and / or diagnosed via cfRNA sequencing. Based on diagnostic assessments, an individual can be further assessed via clinical evaluations. Diagnostic assessments can also inform treatment options and thus, in some instances, a treatment can be performed by a medical professional, such as a doctor, nurse, dietician, or similar. Sequencing of cfRNA can also be utilized to assess treatment response, treatment outcomes, and / or presence of minimal residual disease.

[0105] A number of sequencing methods can be performed to assess cfRNA. In some embodiments, a sequencing method collects a sample comprising cfRNA and prepares it for targeted sequencing utilizing a panel to detect transcripts of RAGs.

[0106] An example of method for sequencing cfRNA can comprise extracting or collecting a cfRNA sample. In some embodiments, a method for sequencing cfRNA can comprise enriching cfRNA sample with a targeted RAG panel (e.g., a panel of nucleic acid molecules to detect transcripts of one or more RAGs). In some embodiments, a method for sequencing cfRNA can comprise sequencing enriched cfRNA sample to yield a sequencing result.

[0107] In some implementations, the cfRNA sample is (or is derived from) a liquid biopsy. In some implementations, the cfRNA sample is (or is derived from) an excreta sample. In some implementations, the cfRNA sample is (or is derived from) blood, plasma, lymph, mucus, sputum, cerebrospinal fluid, amniotic fluid, urine, or stool.

[0108] In some implementations, the RAG panel comprises a set of genes that are expressed in less than a percentage (e.g., less than 60%, less than 50%, less than 40%, less than 30%, less than 20%, less than 10%, less than 5%, less than 1 %) of a population of control liquid biopsies. In some implementations, the RAG panel comprises a set of genes having an expression level below a threshold within a population of control liquid biopsies. In some implementations, the RAG panel comprises a set of genes that are expressed in less than a percentage of a population of control liquid biopsies and having an expression level below a threshold within the population of control liquid biopsies. In some implementations, the RAG panel comprises one or more genes from Table 1. In some implementations, the RAG panel comprises at least 5%, at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 90%, or 100% of genes from Table 1. In some implementations, the RAG panel comprises nucleic acid molecules for amplifying RAGs. In some implementations, the RAG panel comprises nucleic acid molecules for capturing RAGs via hybridization.

[0109] In some implementations, the method for sequencing cfRNA further comprises collecting a population of control cfRNA samples. In some implementations, the method for sequencing cfRNA further comprises performing sequencing on one or more control cfRNA samples of the population of control cfRNA samples to yield sequencing result. In some implementations, the method for sequencing cfRNA further comprises identifing RAGs from the sequencing results of the one or more control cfRNA samples.

[0110] In some implementations, the control cfRNA samples are extracted or collected from healthy individuals. In some implementations, RAGs are identified as being expressed in less than a percentage (e.g., less than 60%, less than 50%, less than 40%, less than 30%, less than 20%, less than 10%, less than 5%, less than 1 %) of a population of control liquid biopsies. In some implementations, RAGs are identified by having an expression level below a threshold within a population of control liquid biopsies. In some implementations, RAGs are identified as being expressed in less than a percentage of a population of control liquid biopsies and having an expression level below a threshold within the population of control liquid biopsies.

[0111] Sequencing of a cfRNA sample can inform biological characteristics of the individual from which the cfRNA sample was extracted or collected. Diagnostic methods can be utilized for a variety of purposes, such as (for example) screening individuals for a biological characteristic or for biomedical complications, performing a diagnostic for a particular medical disorder, assessing treatment response, assessing treatment outcome, and screening for minimal residual disease. Diagnostic methods can be utilized for an assessment of number of biomedical conditions, such as (for example) a medical disorder, pregnancy, a fetal complication, a pregnancy complication, a neoplastic growth, cancer, a particular cancer type, a pathogen infection, immune activation, organ transplant rejection, neurodegeneration, tissue of origin, cell type of origin, and activation of a biochemical pathway.

[0112] In some implementations, a screening method or a diagnostic comprises extracting or collecting a cfRNA sample. In some implementations, a screening method or a diagnostic comprises enriching cfRNA sample with a targeted RAG panel. In some implementations, a screening method or a diagnostic comprises sequencing enriched cfRNA sample to yield a sequencing result. In some implementations, a screening method or a diagnostic comprises identifying one or more biomedical conditions within the sequencing result.

[0113] In some implementations, cfRNA sample is (or is derived from) blood, plasma, lymph, mucus, sputum, cerebrospinal fluid, amniotic fluid, urine, or stool. In some implementations, the screening method is generalized and assesses a plurality of biomedical condition. In some implementations, the diagnostic method is a particularbiomedical condition. In some implementations, a biomedical condition is identified by gene expression signature. In some implementations, a biomedical condition is identified by a computational model trained to predict the biomedical condition utilizing the sequencing result or features derived from the sequencing result.

[0114] In some implementations, the biomedical condition is pregnancy. In some implementations, the biomedical condition is a fetal complication. In some implementations, the biomedical condition is a pregnancy complication. When assessing a fetal complication or a pregnancy complication, in some implementations, the cfRNA sample is derived amniotic fluid. When assessing a fetal complication or a pregnancy complication, in some implementations, screening is performed at various timepoints throughout a pregnancy. In some implementations, when a fetal complication or a pregnancy complication is identified, further diagnostic procedures are performed, such as (for example) assessments for gestational diabetes, genetic assessment of the fetus, fetal ultrasound, and maternal blood testing. In some implementations, when a fetal complication or a pregnancy complication is identified, a treatment is performed such as (for example) inducing labor, administering a tocolytic medication, and performing a Caesarian delivery.

[0115] In some implementations, the biomedical condition is a pathogenic infection. In some implementations, the biomedical condition is immunological status, such as (for example) a vaccination status or prior pathogenic infection. In some implementations, when assessing a pathogenic infection, the cfRNA sample is also enriched for pathogen sequences. In some implementations, when a pathogenic infection is identified, a treatment is performed (such as) administering an antipathogenic medication (e.g., antibiotic agent, antiviral agent, antiparasitic agent, etc.). In some implementations, the screening method monitors the pathogenic infection, an antipathogenic treatment response, an immunological response, a health condition, or any combination thereof.

[0116] In some implementations, the biomedical condition is immune activation, such as (for example) activation in response to a pathogen, activation in response to an immunization, or activation of an autoimmune disorder. In some implementations, the biomedical condition is inflammation. In some implementations, when the biomedical condition is an autoimmune disorder or inflammation, a treatment is performed such as(for example) administering an immune suppressor, and administering an antiinflammatory agent.

[0117] In some implementations, the biomedical condition is an organ transplant rejection. In some implementations, an organ transplant rejection is identified by gene expression signatures related cytotoxicity, gene expression signatures related to tissue of origin, gene expression signatures related to cell type of origin, genetic sequence that differentiate donor from host, or a combination thereof. In some implementations, the screening method is performed periodically after the host receives the transplant. In some implementations, when organ transplant rejection is identified, further diagnostic procedures are performed such as (for example) tissue biopsy of the organ, and medical imaging of the organ. In some implementations, when organ transplant rejection is identified, a treatment is performed such as (for example) administering an increased dose of immunosuppressant agents and administering stronger immunosuppressant agents.

[0118] In some implementations, the biomedical condition is neurodegeneration. When assessing for neurodegeneration, in some implementations, the cfRNA sample is (or is derived from) cerebrospinal fluid. In some implementations, neurodegeneration is identified by gene signatures related to the neurodegenerative disorder, gene signatures related to neural tissue of origin, gene signatures related to neural cell types of origin, gene signatures related to inflammation, or a combination thereof. In some implementations, when neurodegeneration is identified, further diagnostic procedures are performed such as (for example) medical screening, assessments of motor activity or speech, and assessments of cognition. In some implementations, when neurodegeneration is identified, a treatment is performed such as (for example) medications for reducing neurodegenerative symptoms.

[0119] In some implementations, the biomedical condition is cancer. In some implementations, the screening method is performed as part of a cancer surveillance effort (e.g., before symptoms of cancer are present or are recognized). In some implementations, the screening method is performed during treatment to assess the treatment response. In some implementations, the screening method is performed after treatment to assess whether residual cancer (e.g., MRD) exists after a treatment, whichcan be performed periodically. In some implementations, the diagnostic method informs cancer subtype, cancer stage, and / or treatment strategy.

[0120] The screening can be performed for a number of neoplasm types, including (but not limited to) acute lymphoblastic leukemia (ALL), acute myeloid leukemia (AML), anal cancer, astrocytomas, basal cell carcinoma, bile duct cancer, bladder cancer, breast cancer, Burkitt’s lymphoma, cervical cancer, chronic lymphocytic leukemia (CLL) chronic myelogenous leukemia (CML), chronic myeloproliferative neoplasms, colorectal cancer, diffuse large B-cell lymphoma, endometrial cancer, ependymoma, esophageal cancer, esthesioneuroblastoma, Ewing sarcoma, fallopian tube cancer, follicular lymphoma, gallbladder cancer, gastric cancer, gastrointestinal carcinoid tumor, hairy cell leukemia, hepatocellular cancer, Hodgkin lymphoma, hypopharyngeal cancer, Kaposi sarcoma, Kidney cancer, Langerhans cell histiocytosis, laryngeal cancer, leukemia, liver cancer, lung cancer, lymphoma, melanoma, Merkel cell cancer, mesothelioma, mouth cancer, neuroblastoma, non-Hodgkin lymphoma, non-small cell lung cancer, osteosarcoma, ovarian cancer, pancreatic cancer, pancreatic neuroendocrine tumors, pharyngeal cancer, pituitary tumor, prostate cancer, rectal cancer, renal cell cancer, retinoblastoma, skin cancer, small cell lung cancer, small intestine cancer, squamous neck cancer, T-cell lymphoma, testicular cancer, thymoma, thyroid cancer, uterine cancer, upper tract urothelial cancer, vaginal cancer, and vascular tumors. In some implementations, when the cancer to be assessed is colorectal or gastric cancer, the cfRNA sample is (or is derived from) a stool sample. In some implementations, when the cancer to be assessed is bladder, kidney, prostate, or upper tract urothelial cancer, the cfRNA sample is (or is derived from) a urine sample.

[0121] In some implementations, when cancer is indicated, a number of follow-up clinical evaluations can be performed, including (but not limited to) physical exam, medical imaging, mammography, endoscopy, stool sampling, pap test, alpha-fetoprotein blood test, CA-125 test, prostate-specific antigen (PSA) test, biopsy extraction, bone marrow aspiration, and tumor marker detection tests. Medical imaging includes (but is not limited to) X-ray, magnetic resonance imaging (MRI), computed tomography (CT), ultrasound, and positron emission tomography (PET). Endoscopy includes (but is notlimited to) bronchoscopy, colonoscopy, colposcopy, cystoscopy, esophagoscopy, gastroscopy, laparoscopy, neuroendoscopy, proctoscopy, and sigmoidoscopy.

[0122] In some implementations, when cancer is indicated, a number of treatments can be performed, including (but not limited to) surgery, chemotherapy, radiation therapy, immunotherapy, targeted therapy, hormone therapy, stem cell transplant, and blood transfusion. In some implementations, an anti-cancer and / or chemotherapeutic agent is administered, including (but not limited to) alkylating agents, platinum agents, taxanes, vinca agents, anti-estrogen drugs, aromatase inhibitors, ovarian suppression agents, endocrine / hormonal agents, bisphophonate therapy agents and targeted biological therapy agents. Medications include (but are not limited to) cyclophosphamide, fluorouracil (or 5-fluorouracil or 5-FU), methotrexate, thiotepa, carboplatin, cisplatin, taxanes, paclitaxel, protein-bound paclitaxel, docetaxel, vinorelbine, tamoxifen, raloxifene, toremifene, fulvestrant, gemcitabine, irinotecan, ixabepilone, temozolmide, topotecan, vincristine, vinblastine, eribulin, mutamycin, capecitabine, capecitabine, anastrozole, exemestane, letrozole, leuprolide, abarelix, buserlin, goserelin, megestrol acetate, risedronate, pamidronate, ibandronate, alendronate, zoledronate, tykerb, daunorubicin, doxorubicin, epirubicin, idarubicin, valrubicin mitoxantrone, bevacizumab, cetuximab, ipilimumab, ado-trastuzumab emtansine, afatinib, aldesleukin, alectinib, alemtuzumab, atezolizumab, avelumab, axtinib, belimumab, belinostat, bevacizumab, blinatumomab, bortezomib, bosutinib, brentuximab vedoitn, briatinib, cabozantinib, canakinumab, carfilzomib, certinib, cetuximab, cobimetnib, crizotinib, dabrafenib, daratumumab, dasatinib, denosumab, dinutuximab, durvalumab, elotuzumab, enasidenib, erlotinib, everolimus, gefitinib, ibritumomab tiuxetan, ibrutnib, idelalisib, imatinib, ipilimumab, ixazomib, lapatinib, lenvatinib, midostaurin, nectiumumab, neratinib, nilotinib, niraparib, nivolumab, obinutuzumab, ofatumumab, olaparib, loaratumab, osimertinib, palbocicilib, panitumumab, panobinostat, pembrolizumab, pertuzumab, ponatinib, ramucirumab, reorafenib, ribociclib, rituximab, romidepsin, rucaparib, ruxolitinib, siltuximab, sipuleucel-T, sonidebib, sorafenib, temsirolimus, tocilizumab, tofacitinib, tositumomab, trametinib, trastuzumab, vandetanib, vemurafenib, venetoclax, vismodegib, vorinostat, and ziv-aflibercept. An individual may be treated, by a single medication or a combination of medications described herein. In some embodiments, thecombination of medications comprises cyclophosphamide, methotrexate, and 5-fluorouracil (CMF).

[0123] In some aspects disclose herein are methods comprising: (a) obtaining a sample comprising a plurality of cell-free nucleic acid molecules. In some embodiments, the plurality of cell-free nucleic acid molecules comprises a plurality of cell-free RNA molecules from a biological sample of an individual or a plurality of DNA molecules complementary to the plurality of cell-free RNA molecules. In some embodiments, the plurality of cell-free nucleic acid molecules comprises a plurality of cell-free RNA molecules from a biological sample of an individual. In some embodiments, the plurality of cell-free nucleic acid molecules comprises a plurality of DNA molecules complementary to the plurality of cell-free RNA molecules. In some embodiments, the method comprises (b) obtaining a panel of nucleic acid molecules that enriches for a subset of transcripts from a whole transcriptome (e.g., whole coding transcriptome, whole non-coding transcriptome, or both). In some embodiments, the method comprises (c) enriching the sample for the subset by contacting the sample with the panel of nucleic acid molecules. In some embodiments, enriching the sample for the subset by contacting the sample with the panel of nucleic acid molecules identifies a set of target genes from the enriched subset. In some embodiments, the method comprises (d) determining a presence or state of an injury to an organ associated with a therapeutic intervention for a biological condition. In some embodiments, the determining is based at least in part on the identified set of target genes. In some embodiments, the therapeutic intervention is selected from the group consisting of a radiation therapy, a cellular therapy, a non-cellular pharmacologic therapy, and a surgical procedure. In some embodiments, the therapeutic intervention comprises a radiation therapy. In some embodiments, the therapeutic intervention comprises a cellular therapy. In some embodiments, the therapeutic intervention comprises a non-cellular pharmacologic therapy. In some embodiments, the therapeutic intervention comprises and a surgical procedure. In some embodiments, a transcript may be a cell-free RNA molecule or a cell-free DNA molecules complementary to the cell-free RNA molecule.

[0124] In some aspects, the methods disclosed in the present disclosure can determine the presence or state of the injury to the organ, allowing for detection orprediction of treatment-related organ injury or injuries from the therapeutic intervention. In some embodiments, detection or prediction of treatment-related organ injury or injuries from the therapeutic intervention occurs prior to presentation of symptoms related to the organ injury or injuries. In some embodiments, detection or prediction of treatment-related organ injury or injuries by the methods disclosed in the present disclosure is not reliant on an associated traditional detection method or methods. Non-limiting examples of traditional detection methods for detecting organ injury or toxicity are listed in Table 4. In some embodiments, the organ is an organ listed in Table 4. In some embodiments, the organ is the lung. In some embodiments, the organ is the brain.

[0125] In some embodiments, the method comprises determining the presence of the injury to the organ following the therapeutic intervention. In some embodiments, the method comprises determining the state of the injury to the organ following the therapeutic intervention. In some embodiments, the state of the injury comprises progression of the injury to the organ.

[0126] In some embodiments, the method further comprises generating sequencing read data of the enriched subset. In some embodiments, the method further comprises aligning the sequencing read data to a reference database. In some embodiments, aligning the sequencing read data to a reference database identifies at least one member of the set of target genes. In some embodiments, the reference database comprises a reference genome, a reference transcriptome, or both. In some embodiments, the reference database comprises a reference genome. In some embodiments, the reference database comprises a reference transcriptome. In some embodiments, the method further comprises determining a gene expression profile of the at least one member of the set of target genes based on the aligning. In some embodiments, the method further comprises excluding a gene expression contribution of platelets from the gene expression profile. In some embodiments, the excluding is performed in silico.

[0127] In some embodiments, the method further comprises determining, based at least in part on the determined presence or state of the injury to the organ, the individual’s response to the therapeutic intervention for the biological condition. In some embodiments, the method further comprises modifying, based at least in part on the determined presence or state of the injury to the organ, the therapeutic intervention forthe biological condition of the individual. In some embodiments, the modifying comprises a modification in a dosage of the therapeutic intervention. In some embdiments, the modifying comprises a modification in delivery of the therapeutic intervention (e.g., intravenous administration, local administration, topical administration). In some embodiments, the modifying comprises a modification in exposure to the therapeutic intervention (e.g., modification in length of time or frequency ong delivery of the therapeutic intervention). In some embodiments, the modifying comprises a modification to the radiation therapy (e.g., a modification described in Table 3). In some embodiments, the modification to radiation therapy comprises treatment interruption (e.g., temporary suspension), treatment discontinuation (e.g., permanent stop to radiation therapy), dose reduction (e.g., reduction of total dose or dose per fraction), and / or field size reduction (e.g., reduction in radiation treatment volume). In some embodiments, the method further comprises subjecting the individual to the modified therapeutic intervention. In some embodiments, the method further comprises determining, based at least in part on the determined presence or state of the injury to the organ, a different therapeutic intervention for the biological condition of the individual. In some embodiments, the method further comprises subjecting the individual to the different therapeutic intervention.

[0128] In some aspects disclosed herein is a method comprising (a) identifying an individual comprising an injury to an organ following a therapeutic intervention for a biological condition. In some embodiments, the individual was identified as having the injury to the organ based at least in part on (i) obtaining a sample comprising a plurality of cell-free nucleic acid molecules, the plurality of cell-free nucleic acid molecules comprising a plurality of cell-free RNA molecules from a biological sample of the individual or a plurality of DNA molecules complementary to the plurality of cell-free RNA molecules; (ii) obtaining a panel of nucleic acid molecules that enriches for a subset of transcripts from a whole transcriptome (e.g., whole coding transcriptome, whole non-coding transcriptome, or both); and (iii) enriching the sample for the subset by contacting the sample with the panel of nucleic acid molecules, to identify a set of target genes from the enriched subset, wherein the identified set of target genes is indicative of a presence or state of the injury to the organ. In some embodiments, the method comprises (b) treating the individual with an alternative therapeutic intervention based at least in part on theidentified set of target genes. In some embodiments, a transcript may be a cell-free RNA molecule or a cell-free DNA molecules complementary to the cell-free RNA molecule.

[0129] In some aspects, the methods disclosed in the present disclosure can determine the presence or state of the injury to the organ, allowing for detection or prediction of treatment-related organ injury or injuries from the therapeutic intervention. In some embodiments, detection or prediction of treatment-related organ injury or injuries from the therapeutic intervention occurs prior to presentation of symptoms related to the organ injury or injuries. In some embodiments, detection or prediction of treatment-related organ injury or injuries by the methods disclosed in the present disclosure is not reliant on an associated traditional detection method or methods. Non-limiting examples of traditional detection methods for detecting organ injury or toxicity are listed in Table 4. In some embodiments, the organ is an organ listed in Table 4. In some embodiments, the organ is the lung. In some embodiments, the organ is the brain.

[0130] In some embodiments, the alternative therapeutic intervention comprises at least one regimen modification of the therapeutic intervention. In some embodiments, the at least one regimen modification of the therapeutic intervention comprises a modification in a dosage of the therapeutic intervention. In some embodiments, the at least one regimen modification of the therapeutic intervention comprises a modification in delivery of the therapeutic intervention (e.g., intravenous administration, local administration, topical administration). In some embodiments, the at least one regimen modification of the therapeutic intervention comprises a modification in exposure to the therapeutic intervention (e.g., modification in length of time or frequency ong delivery of the therapeutic intervention). In some embodiments, the at least one regimen modification of the therapeutic intervention comprises a modification to the radiation therapy (e.g., a modification described in Table 3). In some embodiments, the modification to radiation therapy comprises treatment interruption (e.g., temporary suspension), treatment discontinuation (e.g., permanent stop to radiation therapy), dose reduction (e.g., reduction of total dose or dose per fraction), and / or field size reduction (e.g., reduction in radiation treatment volume). In some embodiments, the alternative therapeutic intervention comprises a different therapeutic intervention for the biological condition of the individual.In some embodiments, the method comprises discontinuing the therapeutic intervention to the individual.

[0131] In some aspect disclosed herein is a method, comprising (a) obtaining a sample comprising a plurality of cell-free nucleic acid molecules. In some embodiments, the plurality of cell-free nucleic acid molecules comprising a plurality of cell-free RNA molecules from a biological sample of an individual or a plurality of DNA molecules complementary to the plurality of cell-free RNA molecules. In some embodiments, the method comprises (b) obtaining a panel of nucleic acid molecules that enriches for a subset of transcripts from a whole transcriptome (e.g., whole coding transcriptome, whole non-coding transcriptome, or both). In some embodiments, the method comprises (c) enriching the sample for the subset by contacting the sample with the panel of nucleic acid molecules, to identify a set of target genes from the enriched subset. In some embodiments, the method comprises (d) generating sequencing read data of the enriched subset. In some embodiments, the method comprises (e) aligning the sequencing read data to a reference database. In some embodiments, aligning the sequencing read data to a reference database identifies at least one member of the set of target genes. In some embodiments, the identified at least one member is indicative of a presence or state of an injury to an organ associated with a therapeutic intervention for a biological condition in the individual. In some embodiments, a transcript may be a cell-free RNA molecule or a cell-free DNA molecules complementary to the cell-free RNA molecule.

[0132] In some aspects, the methods disclosed in the present disclosure can determine the presence or state of the injury to the organ, allowing for detection or prediction of treatment-related organ injury or injuries from the therapeutic intervention. In some embodiments, detection or prediction of treatment-related organ injury or injuries from the therapeutic intervention occurs prior to presentation of symptoms related to the organ injury or injuries. In some embodiments, detection or prediction of treatment-related organ injury or injuries by the methods disclosed in the present disclosure is not reliant on an associated traditional detection method or methods. Non-limiting examples of traditional detection methods for detecting organ injury or toxicity are listed in Table 4. In some embodiments, the organ is an organ listed in Table 4. In some embodiments, the organ is the lung. In some embodiments, the organ is the brain.

[0133] In some embodiments, the reference database comprises a reference genome, a reference transcriptome, or both. In some embodiments, the reference database comprises a reference genome. In some embodiments, the reference database comprises a reference transcriptome.

[0134] In some embodiments, the method further comprises determining a gene expression profile of the at least one member of the set of target genes. In some embodiments, determining the gene expression profile of the at least one member of the set of target genes is based on the aligning. In some embodiments, the method further comprises excluding a gene expression contribution of platelets from the gene expression profile. In some embodiments, the excluding is performed in silico.

[0135] In some embodiments, therapeutic intervention comprises the radiation therapy. In some embodiments, the therapeutic intervention comprises the cellular therapy. In some embodiments, the therapeutic intervention comprises the non-cellular pharmacologic therapy. In some embodiments, the non-cellular pharmacologic therapy is selected from the group consisting of a protein therapy, an antibody therapy, a nucleic acid molecule therapy, and a small molecule therapy. In some embodiments, the therapeutic intervention comprises the surgical procedure. In some embodiments, the plurality of cell-free nucleic acid molecules comprises the plurality of cell-free RNA molecules. In some embodiments, the plurality of cell-free nucleic acid molecules comprises the plurality of DNA molecules produced by reverse transcription of the plurality of cell-free RNA molecules.

[0136] In some embodiments, the biological condition is cancer. In some embodiments, the organ is selected from the group consisting of lung, liver, kidney, brain, spine, heart, bone marrow, and organs comprising the hematopoietic system. In some embodiments, the organ is a lung. In some embodiments, the organ is a brain. In some embodiments, the injury is pneumonitis. In some embodiments, the injury is radiation-induced pneumonitis. In some embodiments, the injury is non-cellular pharmacologic therapy-induced pneumonitis. In some embodiments, the non-cellular pharmacologic therapy-induced pneumonitis is induced by an immune checkpoint inhibitor (e.g., an immune checkpoint inhibitor targeting PD(L)-1 ). In some embodiments, the immune checkpoint inhibitor targeting PD(L)-1 is nivolumab, pembrolizumab, and / or durvalumab.In some embodiments, the injury is radiation necrosis. In some embodiments, the injury is radiation necrosis in the brain.

[0137] In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 5, Table 6, Table 8, Table 9, Table 21, Table 22, or a combination thereof. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 5, Table 6, Table 8, Table 9, Table 21, and Table 22. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in the one or more members selected from the group consisting of Table 5, Table 6, Table 8, Table 9, Table 21, Table 22, or a combination thereof. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in the one or more members selected from the group consisting of Table 5, Table 6, Table 8, Table 9, Table 21, and Table 22

[0138] In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 5. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 5. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 7.In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 7. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 6. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 6. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table23. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 23. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 24. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 24.

[0139] In some embodiments, the panel of nucleic acid molecules targets transcripts of one or more genes associated with one or more cell types selected from the group consisting of lung signaling alveolar epithelial type 2 cell, lung alveolar epithelial type 1 cell, lung alveolar epithelial type 2 cell, lung goblet cell, lung club cell, lung proliferating basal cell, lung platelet megakaryocyte cell, lung proliferating macrophage cell, lung TREM2+ dendritic cell, lung neutrophil cell, lung CD8+ memory effector T cell, lung EREG dendritic cell, lung NK cell, lung classical monocyte cell, lung CD8+ naive T cell, lung B cell, lung CD4+ memory effector T cell, and lung CD4+ naive T cell. In some embodiments, the one or more cell types are selected from the group consisting of lung signaling alveolar epithelial type 2 cell, lung signaling alveolar epithelial type 1 cell, lung alveolar epithelial type 2 cell, lung goblet cell, lung club cell, lung proliferating basal cell, and lung platelet megakaryocyte cell. In some embodiments, the one or more cell types are selected from the group consisting of lung signaling alveolar epithelial type 2 cell, lung signaling alveolar epithelial type 1 cell, lung alveolar epithelial type 2 cell, lung goblet cell, and lung proliferating basal cell.

[0140] In some embodiments, the identified set of target genes from the enriched subset is sufficient to determine radiation pneumonitis relative to a pre-radiation baseline. In some embodiments, the radiation pneumonitis is symptomatic radiation pneumonitis.

[0141] In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 8. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 8. In some embodiments, the panelof nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 10. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 10. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 9. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 9. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 25. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 25. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 26. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 26.

[0142] In some embodiments, the panel of nucleic acid molecules targets transcripts of one or more genes associated with one or more cell types selected from the group consisting of lung platelet megakaryocyte cell, lung capillary intermediate 1 cell, lung NK cell, lung NK T cell, lung CD8+ naive T cell, lung alveolar fibroblast cell, lung bronchial vessel 1 cell, lung basal cell, lung CD4+ naive T cell, lung adventitial fibroblast cell, lung plasmacytoid dendritic cell, and lung B cell. In some embodiments, the one or more cell types are selected from the group consisting of lung platelet megakaryocyte cell, lung capillary intermediate 1 cell, and lung capillary intermediate 1 cell. In some embodiments, the one or more cell types comprises lung platelet megakaryocyte cell.

[0143] In some embodiments, the identified set of target genes from the enriched subset is sufficient to determine radiation pneumonitis relative to a post-radiation baseline, optionally wherein the identified set of target genes from the enriched subset issufficient to determine symptomatic radiation pneumonitis relative to a post-radiation asymptomatic baseline.

[0144] In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 21. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 21. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 22. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 22.

[0145] In some embodiments, the panel of nucleic acid molecules targets transcripts of one or more genes associated with immune activation and lymphocyte signaling. In some embodiments, the one or more genes comprise CD79A / B, CD22, or CD3. In some embodiments, the panel of nucleic acid molecules targets transcripts of one or more genes associated with innate inflammation. In some embodiments, the one or more genes comprise CXCR1 or FPR1 / 2. In some embodiments, the panel of nucleic acid molecules targets transcripts of one or more genes associated with lung epithelial function. In some embodiments, the one or more genes comprise SFTPA1 / 2 or DUOXA1. In some embodiments, the panel of nucleic acid molecules targets transcripts of one or more genes associated with tissue remodeling. In some embodiments, the one or more genes comprise ITGA8, SLIT3, or COL14A1.

[0146] In some embodiments, the identified set of target genes from the enriched subset is sufficient to predict radiation pneumonitis prior to symptom onset.

[0147] In some embodiments, the determining in (d) exhibits a sensitivity that is greater than that of a control assay by at least about 20%. In some embodiments, the sensitivity is greater than that of the control assay by at least about 40%. In some embodiments, the control assay is based on a measure of lung volume upon exposure to a predetermined radiation dose.

[0148] In some embodiments, the predetermined radiation dose is about V5 Gy, about V10 Gy, about V20 Gy, about V25 Gy, about V30 Gy, about V35 Gy, about V40 Gy, about V45 Gy, about V50 Gy, about V55 Gy, or about V60 Gy. In some embodiments, the predetermined radiation dose is at least about V5 Gy, at least about V10 Gy, at least about V20 Gy, at least about V25 Gy, at least about V30 Gy, at least about V35 Gy, at least about V40 Gy, at least about V45 Gy, at least about V50 Gy, at least about V55 Gy, or at least about V60 Gy. In some embodiments, the predetermined radiation dose is at most about V5 Gy, at most about V10 Gy, at most about V20 Gy, at most about V25 Gy, at most about V30 Gy, at most about V35 Gy, at most about V40 Gy, at most about V45 Gy, at most about V50 Gy, at most about V55 Gy, or at most about V60 Gy. In some embodiments, the predetermined radiation dose is about V20 Gy. In some embodiments, the predetermined radiation dose is at least about V20 Gy. In some embodiments, the predetermined radiation dose is at most about V20 Gy. For example, a predetermined radiation dose of V20 Gy refers to the percentage volume of the organ receiving a dose of 20 Gy or higher.

[0149] In some embodiments, the injury is pneumonitis. In some embodiments, the injury is a non-cellular pharmacologic therapy-induced pneumonitis. In some embodiments, the non-cellular pharmacologic therapy-induced pneumonitis is induced by an immune checkpoint inhibitor (e.g., an immune checkpoint inhibitor targeting PD(L)-1). In some embodiments, the immune checkpoint inhibitor targeting PD(L)-1 is nivolumab, pembrolizumab, and / or durvalumab.

[0150] In some embodiments, the non-cellular pharmacologic therapy comprises administration of at least one antibody. In some embodiments, the at least one antibody comprises an immune checkpoint inhibitor (e.g., an immune checkpoint inhibitor targeting PD(L)-1). In some embodiments, the at least one antibody comprises nivolumab, pembrolizumab, and / or durvalumab.

[0151] In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 11. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 11. In some embodiments, thepanel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 27. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 27. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 28. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 28. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 29. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 29. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 30. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 30. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 31. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 31. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 32. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 32. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 33. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, atleast 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 33.

[0152] In some embodiments, the panel of nucleic acid molecules targets transcripts of one or more genes associated with inflammatory response, fatty acid, or MYC. In some embodiments, the one or more genes are associated with inflammatory response or fatty acid. In some embodiments, the panel of nucleic acid molecules targets transcripts of one or more genes associated with lung neutrophil cell, lung classical monocyte cell, lung vascular smooth muscle cell, lung OLR1 + classical monocyte cell, or lung capillary intermediate 2 cell. In some embodiments, the one or more genes are associated with lung neutrophil cell, lung classical monocyte cell, lung vascular smooth muscle cell, or lung OLR1 + classical monocyte cell. In some embodiments, the one or more genes are associated with lung neutrophil cell. In some embodiments, the panel of nucleic acid molecules targets transcripts of one or more genes associated with neutrophil, monocyte, T helper cell, Cajal-Retzius cell, Ductal cell, or hepatocyte. In some embodiments, the one or more genes are associated with neutrophil or monocyte.

[0153] In some embodiments, the identified set of target genes from the enriched subset is sufficient to determine non-cellular pharmacologic therapy-associated pneumonitis.

[0154] In some embodiments, the injury is necrosis. In some embodiments, the injury is radiation necrosis. In some embodiments, the injury is radiation necrosis to the brain.

[0155] In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 12, Table 34, Table 35, Table 15, Table 16, Table 18, Table 19, or a combination thereof. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 12, Table 34, Table 35, Table 15, Table 16, Table 18, and Table 19. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 12. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 12. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 14. In some embodiments, the panelof nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 14. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 34. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 34. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 36. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 36. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 37. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 37.

[0156] In some embodiments, the panel of nucleic acid molecules targets transcripts of one or more genes associated with one or more cell types selected from the group consisting of T cell, monocyte / macrophage, smooth muscle cell / pericyte, stromal cell, connective tissue cell, mast / basophil, type I pneumocyte, type II pneumocyte, intestinal cell, and hepatocyte. In some embodiments, the one or more cell types are selected from the group consisting of T cell, monocyte / macrophage, smooth muscle cell / pericyte, stromal cell, and connective tissue cell. In some embodiments, the one or more genes are associated with T cell.

[0157] In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 35.

[0158] In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 35. In some embodiments, the panel of nucleic acid molecules targetstranscripts of at least 5% of genes identified in Table 42. The method of claim 120, wherein the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 42.

[0159] In some embodiments, the panel of nucleic acid molecules targets transcripts of one or more genes associated with NOTCH, allograft rejection, MTORC1, heme metabolism, adipogenesis, oxidative phosphorylation (OxPhos), fatty acid, peroxisome, ROS, and MYC. In some embodiments, the one or more genes are associated with NOTCH or allograft.

[0160] In some embodiments, the identified set of target genes from the enriched subset is sufficient to determine radiation necrosis relative to a post-radiation baseline, optionally wherein the identified set of target genes from the enriched subset is sufficient to determine symptomatic radiation necrosis relative to a post-radiation asymptomatic baseline.

[0161] In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 15. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 15. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 17. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 17. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 16. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 16. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 38. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, atleast 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 38. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 39. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 39.

[0162] In some embodiments, the panel of nucleic acid molecules targets transcripts of one or more genes associated with one or more cell types selected from the group consisting of T cell, monocyte / macrophage, ciliated cell, monocyte / macrophage, B cell, ionocyte, hepatocyte, neutrophil / granulocyte, connective tissue cell, and type I pneumocyte. In some embodiments, the one or more cell types are selected from the group consisting of T cell, monocyte / macrophage, ciliated cell, monocyte / macrophage, and B cell. In some embodiments, the one or more cell types comprise T cell.

[0163] In some embodiments, the identified set of target genes from the enriched subset is sufficient to determine symptomatic radiation necrosis relative to a asymptomatic radiation necrosis baseline.

[0164] In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 18. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 18. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 20. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 20. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 19. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 19. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 5% of genes identifiedin Table 40. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 40. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 41. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 41.

[0165] In some embodiments, the panel of nucleic acid molecules targets transcripts of one or more genes associated with one or more cell types selected from the group consisting of T cell, B cell, smooth muscle cell / pericyte, ciliated cell, monocyte / macrophage, connective tissue cell, mast / basophil, type I pneumocyte, neutrophil / granulocyte, and hepatocyte. In some embodiments, the one or more cell types are selected from the group consisting of T cell, B cell, smooth muscle cell / pericyte, ciliated cell, and monocyte / macrophage. In some embodiments, the one or more cells comprise T cell.

[0166] In some embodiments, the identified set of target genes from the enriched subset is sufficient to determine symptomatic radiation necrosis relative to non-radiated brain metastasis baseline. In some embodiments, the therapeutic intervention comprises stereotactic radiosurgery.

[0167] In some embodiments, the organ comprises brain, bone, or lung. In some embodiments, the organ comprises brain. In some embodiments, the organ comprises bone. In some embodiments, the organ comprises lung.

[0168] In some embodiments, at least 5% of the nucleic acid molecules of the panel used for the enrichment of the sample enrich for the genes identified in one of: Table 5; Table 6; Table 8; Table 9; Table 21; Table 22; Table 27; Table 29; Table 32; Table 12; Table 34; Table 35; Table 15; Table 16; Table 18; or Table 19. In some embodiments, at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the nucleic acid molecules of the panel used for the enrichment of the sample enrich for the genes identified in one of: Table 5; Table 6; Table 8; Table 9; Table 21; Table 22; Table 27;Table 29; Table 32; Table 12; Table 34; Table 35; Table 15; Table 16; Table 18; or Table 19.

[0169] In some embodiments, the panel targets a subset of a pluralithy of exons of a target gene of the set. In some embodiments, an exon of the subset is identified as showing a higher concordance between an exon-level expression and a gene-level expression as compared to another exon of the plurality of exons. In some embodiments, the exon is identified as showing the highest concordance between the exon-level expression and the gene-level expression within the plurality of exons.

[0170] In some embodiments, the set of target genes comprises genes that are rarely abundant as cell-free RNA transcripts within a plurality of control liquid biopsies. In some embodiments, the plurality of control liquid biopsies is from a cohort of health subjects. In some embodiments, the panel of nucleic acid molecules targets transcripts of at least 5%, at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of a subset of genes identified in Table 1, wherein the subset consists of the genes indicated as “Yes” in the “RAG” column of Table 1.

[0171] In some aspect disclosed herein is a nucleic acid enrichment panel, comprising: a plurality of nucleic acid molecules that enriches a sample of a plurality of cell-free nucleic acid molecules for a subset of transcripts from a whole transcriptome (e.g., whole coding transcriptome, whole non-coding transcriptome, or both), the subset corresponding to at least 5% of the genes identified in: Table 5; Table 6; Table 8; Table 9; Table 21; Table 22; Table 27; Table 29; Table 32; Table 12; Table 34; Table 35; Table 15; Table 16; Table 18; or Table 19.

[0172] In some embodiments, the subset corresponds to at least 5% of the genes identified in Table 5. In some embodiments, the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 5. In some embodiments, the subset corresponds to at least 5% of the genes are identified in Table 7. In some embodiments, the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 7. In someembodiments, the subset corresponds to at least 5% of the genes are identified in Table 6. In some embodiments, the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 6. In some embodiments, the subset corresponds to at least 5% of the genes are identified in Table 23. In some embodiments, the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 23. In some embodiments, the subset corresponds to at least 5% of the genes are identified in Table 24. In some embodiments, the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 24. In some embodiments, the subset corresponds to at least 5% of the genes are identified in Table 8. In some embodiments, the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 8. In some embodiments, the subset corresponds to at least 5% of the genes are identified in Table 10. In some embodiments, the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 10. In some embodiments, the subset corresponds to at least 5% of the genes are identified in Table 9. In some embodiments, the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 9. In some embodiments, the subset corresponds to at least 5% of the genes are identified in Table 25. In some embodiments, the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 25. In some embodiments, the subset corresponds to at least 5% of the genes are identified in Table 26. In some embodiments, the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%,at least 90%, at least 95%, or 100% of the genes identified in Table 26. In some embodiments, the subset corresponds to at least 5% of the genes are identified in Table 21. In some embodiments, the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 21. In some embodiments, the subset corresponds to at least 5% of the genes are identified in Table 22. In some embodiments, the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 22. In some embodiments, the subset corresponds to at least 5% of the genes are identified in Table 27. In some embodiments, the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 27. In some embodiments, the subset corresponds to at least 5% of the genes are identified in Table 28. The method of claim 182, wherein the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 28. In some embodiments, the subset corresponds to at least 5% of the genes are identified in Table 29. In some embodiments, the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 29. In some embodiments, the subset corresponds to at least 5% of the genes are identified in Table 30. In some embodiments, the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 30. In some embodiments, the subset corresponds to at least 5% of the genes are identified in Table 31. In some embodiments, the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 31. In some embodiments, the subset corresponds to at least 5% of the genes are identified in Table 32. In some embodiments, the subset corresponds to atleast 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 32. In some embodiments, the subset corresponds to at least 5% of the genes are identified in Table 33. In some embodiments, the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 33. In some embodiments, the subset corresponds to at least 5% of the genes are identified in Table 12. In some embodiments, the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 12. In some embodiments, the subset corresponds to at least 5% of the genes are identified in Table 14. In some embodiments, the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 14. In some embodiments, the subset corresponds to at least 5% of the genes are identified in Table 34. In some embodiments, the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 34. In some embodiments, the subset corresponds to at least 5% of the genes are identified in Table 36. In some embodiments, the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 36. In some embodiments, the subset corresponds to at least 5% of the genes are identified in Table 37. In some embodiments, the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 37. In some embodiments, the subset corresponds to at least 5% of the genes are identified in Table 35. In some embodiments, the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 35. In some embodiments, the subset corresponds to at least 5% ofthe genes are identified in Table 42. In some embodiments, the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 42. In some embodiments, the subset corresponds to at least 5% of the genes are identified in Table 15. In some embodiments, the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 15. In some embodiments, the subset corresponds to at least 5% of the genes are identified in Table 17. In some embodiments, the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 17. In some embodiments, the subset corresponds to at least 5% of the genes are identified in Table 16. In some embodiments, the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 16. In some embodiments, the subset corresponds to at least 5% of the genes are identified in Table 38. In some embodiments, the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 38. In some embodiments, the subset corresponds to at least 5% of the genes are identified in Table 39. In some embodiments, the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 39. In some embodiments, the subset corresponds to at least 5% of the genes are identified in Table 18. In some embodiments, the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 18. In some embodiments, the subset corresponds to at least 5% of the genes are identified in Table 20. In some embodiments, the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genesidentified in Table 20. In some embodiments, the subset corresponds to at least 5% of the genes are identified in Table 19. In some embodiments, the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 19. In some embodiments, the subset corresponds to at least 5% of the genes are identified in Table 40. In some embodiments, the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 40. In some embodiments, the subset corresponds to at least 5% of the genes are identified in Table 41. In some embodiments, the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 41.

[0173] In some embodiments, the panel is for determining a presence or state of an injury to an organ associated with a therapeutic intervention for a biological condition.

[0174] In some embodiments, the plurality of cell-free nucleic acid molecules comprises a plurality of cell-free RNA molecules from a biological sample of an individual or a plurality of DNA molecules complementary to the plurality of cell-free RNA molecules. In some embodiments, the plurality of cell-free nucleic acid molecules comprises a plurality of cell-free RNA molecules from a biological sample of an individual. In some embodiments, the plurality of cell-free nucleic acid molecules comprises a plurality of DNA molecules complementary to the plurality of cell-free RNA molecules.

[0175] In some embodiments, the plurality of cell-free nucleic acid molecules comprises a plurality of cell-free DNA molecules from a biological sample of an individual or a plurality of DNA molecules complementary to the plurality of cell-free DNA molecules.

[0176] In some embodiments, at least 5% of the plurality of nucleic acid molecules used for the enrichment of the sample enrich for the genes identified in one of: Table 5; Table 6; Table 8; Table 9; Table 21; Table 22; Table 27; Table 29; Table 32; Table 12; Table 34; Table 35; Table 15; Table 16; Table 18; or Table 19 In some embodiments, at least 5% of the plurality of nucleic acid molecules used for the enrichment of the sample enrich for the genes identified in at least one of: Table 5; Table 6; Table 8; Table 9;Table 21; Table 22; Table 27; Table 29; Table 32; Table 12; Table 34; Table 35; Table 15; Table 16; Table 18; or Table 19. In some embodiments, at least 5% of the plurality of nucleic acid molecules used for the enrichment of the sample enrich for the genes identified in at least two of: Table 5; Table 6; Table 8; Table 9; Table 21; Table 22; Table 27; Table 29; Table 32; Table 12; Table 34; Table 35; Table 15; Table 16; Table 18; or Table 19. In some embodiments, at least 5% of the plurality of nucleic acid molecules used for the enrichment of the sample enrich for the genes identified in at least three of: Table 5; Table 6; Table 8; Table 9; Table 21; Table 22; Table 27; Table 29; Table 32; Table 12; Table 34; Table 35; Table 15; Table 16; Table 18; or Table 19. In some embodiments, at least 5% of the plurality of nucleic acid molecules used for the enrichment of the sample enrich for the genes identified in at least four of: Table 5; Table 6; Table 8; Table 9; Table 21; Table 22; Table 27; Table 29; Table 32; Table 12; Table 34; Table 35; Table 15; Table 16; Table 18; or Table 19. In some embodiments, at least 5% of the plurality of nucleic acid molecules used for the enrichment of the sample enrich for the genes identified in at least five of: Table 5; Table 6; Table 8; Table 9; Table 21; Table 22; Table 27; Table 29; Table 32; Table 12; Table 34; Table 35; Table 15; Table 16; Table 18; or Table 19. In some embodiments, at least 5% of the plurality of nucleic acid molecules used for the enrichment of the sample enrich for the genes identified in at least six of: Table 5; Table 6; Table 8; Table 9; Table 21; Table 22; Table 27; Table 29; Table 32; Table 12; Table 34; Table 35; Table 15; Table 16; Table 18; or Table 19 In some embodiments, at least 5% of the plurality of nucleic acid molecules used for the enrichment of the sample enrich for the genes identified in at least seven of: Table 5; Table 6; Table 8; Table 9; Table 21; Table 22; Table 27; Table 29; Table 32; Table 12; Table 34; Table 35; Table 15; Table 16; Table 18; or Table 19. In some embodiments, at least 5% of the plurality of nucleic acid molecules used for the enrichment of the sample enrich for the genes identified in at least eight of: Table 5; Table 6; Table 8; Table 9; Table 21; Table 22; Table 27; Table 29; Table 32; Table 12; Table 34; Table 35; Table 15; Table 16; Table 18; or Table 19. In some embodiments, at least 5% of the plurality of nucleic acid molecules used for the enrichment of the sample enrich for the genes identified in at least nine of: Table 5; Table 6; Table 8; Table 9; Table 21; Table 22; Table 27; Table 29; Table 32; Table 12; Table 34; Table 35; Table 15; Table 16; Table18; or Table 19. In some embodiments, at least 5% of the plurality of nucleic acid molecules used for the enrichment of the sample enrich for the genes identified in at least ten of: Table 5; Table 6; Table 8; Table 9; Table 21; Table 22; Table 27; Table 29; Table 32; Table 12; Table 34; Table 35; Table 15; Table 16; Table 18; or Table 19. In some embodiments, at least 5% of the plurality of nucleic acid molecules used for the enrichment of the sample enrich for the genes identified in at least eleven of: Table 5; Table 6; Table 8; Table 9; Table 21; Table 22; Table 27; Table 29; Table 32; Table 12; Table 34; Table 35; Table 15; Table 16; Table 18; or Table 19. In some embodiments, at least 5% of the plurality of nucleic acid molecules used for the enrichment of the sample enrich for the genes identified in at least twelve of: Table 5; Table 6; Table 8; Table 9; Table 21; Table 22; Table 27; Table 29; Table 32; Table 12; Table 34; Table 35; Table 15; Table 16; Table 18; or Table 19. In some embodiments, at least 5% of the plurality of nucleic acid molecules used for the enrichment of the sample enrich for the genes identified in at least thirteen of: Table 5; Table 6; Table 8; Table 9; Table 21; Table 22; Table 27; Table 29; Table 32; Table 12; Table 34; Table 35; Table 15; Table 16; Table 18; or Table 19. In some embodiments, at least 5% of the plurality of nucleic acid molecules used for the enrichment of the sample enrich for the genes identified in at least fourteen of: Table 5; Table 6; Table 8; Table 9; Table 21; Table 22; Table 27; Table 29; Table 32; Table 12; Table 34; Table 35; Table 15; Table 16; Table 18; or Table 19 In some embodiments, at least 5% of the plurality of nucleic acid molecules used for the enrichment of the sample enrich for the genes identified in at least fifteen of: Table 5; Table 6; Table 8; Table 9; Table 21; Table 22; Table 27; Table 29; Table 32; Table 12; Table 34; Table 35; Table 15; Table 16; Table 18; or Table 19. In some embodiments, at least 5% of the plurality of nucleic acid molecules used for the enrichment of the sample enrich for the genes identified in: Table 5; Table 6; Table 8; Table 9; Table 21; Table 22; Table 27; Table 29; Table 32; Table 12; Table 34; Table 35; Table 15; Table 16; Table 18; and Table 19.

[0177] In some embodiments, at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the plurality of nucleic acid molecules used for the enrichment of the sample enrich for the genes identified in one of: Table 5; Table 6; Table 8; Table 9;Table 21; Table 22; Table 27; Table 29; Table 32; Table 12; Table 34; Table 35; Table 15; Table 16; Table 18; or Table 19. In some embodiments, at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the plurality of nucleic acid molecules used for the enrichment of the sample enrich for the genes identified in at least one of: Table 5; Table 6; Table 8; Table 9; Table 21; Table 22; Table 27; Table 29; Table 32; Table 12; Table 34; Table 35; Table 15; Table 16; Table 18; or Table 19. In some embodiments, at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the plurality of nucleic acid molecules used for the enrichment of the sample enrich for the genes identified in at least two of: Table 5; Table 6; Table 8; Table 9; Table 21; Table 22; Table 27; Table 29; Table 32; Table 12; Table 34; Table 35; Table 15; Table 16; Table 18; or Table 19. In some embodiments, at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the plurality of nucleic acid molecules used for the enrichment of the sample enrich for the genes identified in at least three of: Table 5; Table 6; Table 8; Table 9; Table 21; Table 22; Table 27; Table 29; Table 32; Table 12; Table 34; Table 35; Table 15; Table 16; Table 18; or Table 19. In some embodiments, at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the plurality of nucleic acid molecules used for the enrichment of the sample enrich for the genes identified in at least four of: Table 5; Table 6; Table 8; Table 9; Table 21; Table 22; Table 27; Table 29; Table 32; Table 12; Table 34; Table 35; Table 15; Table 16; Table 18; or Table 19. In some embodiments, at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the plurality of nucleic acid molecules used for the enrichment of the sample enrich for the genes identified in at least five of: Table 5; Table 6; Table 8; Table 9; Table 21; Table 22; Table 27; Table 29; Table 32; Table 12; Table 34; Table 35; Table 15; Table 16; Table 18; or Table 19. In some embodiments, at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the plurality of nucleic acidmolecules used for the enrichment of the sample enrich for the genes identified in at least six of: Table 5; Table 6; Table 8; Table 9; Table 21; Table 22; Table 27; Table 29; Table 32; Table 12; Table 34; Table 35; Table 15; Table 16; Table 18; or Table 19. In some embodiments, at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the plurality of nucleic acid molecules used for the enrichment of the sample enrich for the genes identified in at least seven of: Table 5; Table 6; Table 8; Table 9; Table 21; Table 22; Table 27; Table 29; Table 32; Table 12; Table 34; Table 35; Table 15; Table 16; Table 18; or Table 19. In some embodiments, at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the plurality of nucleic acid molecules used for the enrichment of the sample enrich for the genes identified in at least eight of: Table 5; Table 6; Table 8; Table 9; Table 21; Table 22; Table 27; Table 29; Table 32; Table 12; Table 34; Table 35; Table 15; Table 16; Table 18; or Table 19. In some embodiments, at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the plurality of nucleic acid molecules used for the enrichment of the sample enrich for the genes identified in at least nine of: Table 5; Table 6; Table 8; Table 9; Table 21; Table 22; Table 27; Table 29; Table 32; Table 12; Table 34; Table 35; Table 15; Table 16; Table 18; or Table 19. In some embodiments, at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the plurality of nucleic acid molecules used for the enrichment of the sample enrich for the genes identified in at least ten of: Table 5; Table 6; Table 8; Table 9; Table 21; Table 22; Table 27; Table 29; Table 32; Table 12; Table 34; Table 35; Table 15; Table 16; Table 18; or Table 19. In some embodiments, at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the plurality of nucleic acid molecules used for the enrichment of the sample enrich for the genes identified in at least eleven of: Table 5; Table 6; Table 8; Table 9; Table 21; Table 22; Table 27; Table 29; Table 32; Table 12; Table 34; Table 35; Table 15; Table 16; Table 18; or Table 19 In some embodiments, at least 10%, at least 20%, at least 30%, at least 40%, at least50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the plurality of nucleic acid molecules used for the enrichment of the sample enrich for the genes identified in at least twelve of: Table 5; Table 6; Table 8; Table 9; Table 21; Table 22; Table 27; Table 29; Table 32; Table 12; Table 34; Table 35; Table 15; Table 16; Table 18; or Table 19. In some embodiments, at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the plurality of nucleic acid molecules used for the enrichment of the sample enrich for the genes identified in at least thirteen of:Table 5; Table 6; Table 8; Table 9; Table 21; Table 22; Table 27; Table 29; Table 32; Table 12; Table 34; Table 35; Table 15; Table 16; Table 18; or Table 19 In some embodiments, at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the plurality of nucleic acid molecules used for the enrichment of the sample enrich for the genes identified in at least fourteen of: Table 5; Table 6; Table 8; Table 9; Table 21; Table 22; Table 27; Table 29; Table 32; Table 12; Table 34; Table 35; Table 15; Table 16; Table 18; or Table 19. In some embodiments, at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the plurality of nucleic acid molecules used for the enrichment of the sample enrich for the genes identified in at least fifteen of: Table 5; Table 6; Table 8; Table 9; Table 21; Table 22; Table 27; Table 29; Table 32; Table 12; Table 34; Table 35; Table 15; Table 16; Table 18; or Table 19 In some embodiments, at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the plurality of nucleic acid molecules used for the enrichment of the sample enrich for the genes identified in: Table 5; Table 6; Table 8; Table 9; Table 21; Table 22; Table 27; Table 29; Table 32; Table 12; Table 34; Table 35; Table 15; Table 16; Table 18; and Table 19.

[0178] In some embodiments, the panel of nucleic acid molecules as provided herein targets at least or at most about 1 gene, at least or at most about 5 genes, at least or at most about 10 genes, at least or at most about 15 genes, at least or at most about 20 genes, at least or at most about 30 genes, at least or at most about 40 genes, at least orat most about 50 genes, at least or at most about 60 genes, at least or at most about 70 genes, at least or at most about 80 genes, at least or at most about 90 genes, at least or at most about 100 genes, at least or at most about 150 genes, at least or at most about 200 genes, at least or at most about 500 genes, at least or at most about 1,000 genes, at least or at most about 2,000 genes, at least or at most about 3,000 genes, at least or at most about 4,000 genes, at least or at most about 5,000 genes, at least or at most about 6,000 genes, at least or at most about 7,000 genes, at least or at most about 8,000 genes, at least or at most about 9,000 genes, at least or at most about 10,000 genes, at least or at most about 11,000 genes, at least or at most about 12,000 genes, at least or at most about 13,000 genes, at least or at most about 14,000 genes, at least or at most about 15,000 genes, at least or at most about 16,000 genes, at least or at most about 17,000 genes, at least or at most about 18,000 genes, at least or at most about 19,000 genes, or at least or at most about 20,000 genes.

[0179] In some embodiments, the nucleic acid enrichment panel targets a subset of a plurality of exons of a target gene. In some embodiments, an exon of the subset is identified as showing a higher concordance between an exon-level expression and a gene-level expression as compared to another exon of the plurality of exons. In some embodiments, the exon is identified as showing the highest concordance between the exon-level expression and the gene-level expression within the plurality of exons.

[0180] In some embodiments, the plurality of nucleic acid molecules in the panel comprises molecules that enrich for genes that are rarely abundant as cell-free RNA transcripts within a plurality of control liquid biopsies. In some embodiments, the plurality of control liquid biopsies is from a cohort of health subjects. In some embodiments, the plurality of nucleic acid molecules in the panel targets transcripts of at least 5%, at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of a subset of genes identified in Table 1, wherein the subset consists of the genes indicated as “Yes” in the “RAG” column of Table 1.EXAMPLES

[0181] Example 1: cfRNA Analysis

[0182] The systems and methods of the disclosure will be better understood with the several examples and supporting provided. Validation results show that the use of a targeted sequencing panel that is based on rare abundance genes improves detection of cancer using cfRNA samples. By using the RAG panel, detection of expression signatures was improved 50-fold over whole transcriptome sequencing. This method demonstrated improved detection of resistance to targeted treatments as compared to cfDNA methods. The method also allows for assessment for tissue of origin, which can be useful to identify the primary pathologic location. The results can be extrapolated to other cell-free diagnostic techniques, including diagnoses of a medical disorder, pregnancy, a fetal complication, a pregnancy complication, a neoplastic growth, cancer, a particular cancer type, a pathogen infection, immune activation, organ transplant rejection, neurodegeneration, tissue of origin, cell type of origin, and activation of a biochemical pathway.

[0183] An ultrasensitive method for detection of cell-free RNA

[0184] Disclosed herein is a method for cell-free RNA vsequencing termed RARE-Seq (Random priming & Affinity capture of cell-free RNA fragments for Enrichment analysis by Sequencing), a novel method optimized for cfRNA analysis. This approach improves the limit of detection for ctRNA over prior methods while maintaining high specificity for tumor-naive cancer detection. The utility of RARE-Seq in diverse clinical applications was also explored, such as non-invasive ctRNA genotyping, treatment resistance monitoring, tissue-of-origin analysis, and molecular characterization of non-malignant conditions.

[0185] Results

[0186] Pre-analytical factors impacting cfRNA

[0187] cfRNA was observed to be highly fragmented and did not contain detectable 18S and 28S rRNA peaks (FIG. 6A), indicating high levels of degradation. In blood samples from healthy donors with no history of cancer, the median concentration of cfRNA was 220 pg per mL plasma, representing the equivalent of approximately 20 cells (n=117; FIG. 6B). Factors that impacted cfRNA recovery and downstream expression analysis were evaluated. Multiple blood collection and RNA extraction protocols were identified that allowed similar RNA recovery from plasma (FIGs. 6C-6D) and factors such as hemolysis were found to not be associated with cfRNA yields (FIG. 6E). Wheninvestigating the effects of sample storage, a decrease of cfRNA concentration during the first month of freezer storage was observed (P=0.003; FIG. 6F) but no significant effect was observed after the first month for up to 10 years (P=0.74; FIG. 6G). Multiple steps in the assay workflow were optimized, including removal of contaminating DNA, complementary DNA (cDNA) synthesis, and end repair. These optimizations enhanced library preparation efficiency from low cfRNA inputs and minimized effects of contaminating cfDNA (FIGs. 7A-7H). It was confirmed that the optimizations resulted in reproducible cfRNA expression profiles in healthy controls (FIGs. 7I-7K). The optimized method disclosed here in is referred to as RARE-Seq (Random priming & Affinity capture of cell-free RNA fragments for Enrichment analysis by Sequencing) (FIG. 1A).

[0188] Platelet genes are enriched in cfRNA

[0189] Expression differences between cellular and cell-free RNA were characterized by applying RARE-Seq to cfRNA and matched leukocyte RNA from 10 healthy adults. Differential expression analysis identified 8,336 significant genes, including 5,271 genes over-represented in cfRNA and 3,065 genes over-represented in leukocyte RNA (FIG.1B). Using gene set enrichment analysis (GSEA) and cell type-specific gene sets from a single cell RNA-sequencing atlas (FIG. 1 C), it was observed that cfRNA was significantly enriched for non-hematopoietic cell types, including endothelial cells, hepatocytes, and fibroblasts (n=71 total). In contrast, leukocyte RNA was significantly enriched for 12 major hematopoietic cell types of myeloid and lymphoid lineages, including T cells, NK cells, neutrophils, and monocytes. Two hematopoietic cell types were enriched in cfRNA -erythroid precursors and platelets, suggesting their contributions to the cfRNA pool during erythropoiesis and megakaryocytopoiesis.

[0190] The significant enrichment of platelet transcripts in cfRNA (FIG. 1C)suggests that residual platelets not removed during plasma isolation may confound cfRNA analysis. To test this, cfRNA concentrations using three different centrifugation speeds to isolate plasma were compared (n=32). The median concentration was ~7.5-fold higher at 1200xG than 2500xG (1.72 ng / mL vs 0.23 ng / mL; P= 0.000052; FIG. 1D). Furthermore, average expression of platelet-specific genes (n=730) was significantly associated with centrifugation speed (FIG. 1 E). Principal component analysis (PCA) revealed that the first principal component (PC1) captured 70% of expression variation and was stronglyassociated with platelet expression (R=-0.99, P≤ 2.2e-16) (FIGs. 1F-1G). Similar results were found even among samples processed with a fixed centrifugation speed, suggesting that differences in platelet counts may also contribute to variation of platelet transcripts in cfRNA (R=0.98, P≤ 1.3e-11 ) (FIG. 1H). Collectively, these results suggest that platelets represent the largest source of expression variation in cfRNA and that platelet contamination is a critical pre-analytical variable to control for in cfRNA analyses.

[0191] Although individual platelets contain less RNA content than leukocytes, platelets outnumber leukocytes by approximately two orders of magnitude in blood. Additionally, physiological variation in platelet counts span an approximately three-fold range in healthy adults with further variability observed in the context of cancer. Therefore, even optimized methods to minimize plasma contamination by platelets may fail to completely remove platelet-derived RNA from cfRNA samples. Separately, from a practical standpoint, it is may not be feasible to standardize blood banking protocols, especially when profiling previously collected samples. Therefore, investigations into whether expression contributions from platelet contamination could be algorithmically eliminated were performed. To do so, an in silico approach to remove unwanted gene expression driven by varying platelet contamination levels was developed (FIG. 11). This approach aims to separate wanted variation (e.g., cancer-associated gene expression differences) from unwanted variation (e.g., technical or biological noise) by performing factor analysis of the expression of negative control genes for which only unwanted variation is expected to be present. This approach successfully removed the unwanted variation contributed by platelets, as shown by the lack of the correlation between platelet expression and PC1 after correction (R=0.07, P=0.69) (FIG. 1J). Hierarchical clustering further confirmed this effect, as samples clustered according to platelet gene expression before correction (FIG. 1K) but according to inter-individual expression differences after correction (FIG. 1L). Therefore, addressing platelet contamination in cfRNA analyses can be useful for reliably uncovering gene expression contributions from non-platelet sources.

[0192] Enhancing sensitivity of ctRNA detection

[0193] While cfRNA is highly enriched for expression of genes from non-hematopoietic sources, the absolute expression levels of these genes are relatively low compared to hematopoietic-derived transcripts (e.g. globins) and highly expressed housekeepinggenes (e.g. beta actin). Selectively capturing genes absent from or lowly expressed in healthy control cfRNA may enhance detection of non-hematopoietic tissue or disease signatures and expand the utility of RARE-Seq to a wide range of clinical applications. To test this, ‘rare abundance genes’ (RAGs) were identified using whole coding transcriptome RARE-seq data from healthy cfRNA samples (n=50) and RNA-seq data from healthy whole blood samples (n=307) from the GTEx project (FIG. 2A). RAGs were defined based on consistent low or absent expression in cfRNA and whole blood (n=5,632 genes; FIG. 8A; Table 1). Strikingly, as compared to all protein coding transcripts, RAGs were substantially enriched both for non-hematopoietic tissue-specific genes and cancerspecific genes from diverse cancers (FIGs. 2B-2C).

[0194] The enrichment of genes specific for multiple tissue and cancer types among RAGs suggests that a RAG-targeted sequencing panel may increase sensitivity for detection of cancer or other conditions of interest without use of a bespoke panel design for each application. Therefore, a capture panel targeting 4,737 RAGs and 50 housekeeping genes was designed, prioritizing genes with relatively uniform expression across healthy cfRNA and whole blood samples while covering a broad dynamic range of expression (FIG. 8B). While these modules comprise the core of the RAG-focused capture panel design and may be used by themselves to explore diverse pathophysiologic conditions without further customization, additional targets may be optionally added to the capture panel if desired (FIG. 8C). For example, in this study, genes known to be recurrently mutated in lung cancers (n=122 genes) were added, including genes such as TP53, KRAS, and RET (FIG. 8D). The full collection of captured genes is listed in Table 1.

[0195] Next, five healthy cfRNA samples were profiled using both whole coding transcriptome and the RAG capture panels using RARE-Seq. Gene expression was highly correlated between matched libraries (R=0.98, P≤ 2.2e-16) (FIG. 8E), including for the housekeeping genes (R=0.97, P≤ 2.2e-16) (FIG. 8F). Within the subset of genes shared across both capture panels, RAG capture resulted in higher unique sequencing depth (FIG. 2D), more genes detected (FIG. 2E), and higher unique sequencing depth per gene detected (FIG. 2F). Thus, targeted capture of RAGs effectively increases sensitivity for detection of genes that are lowly expressed in healthy cfRNA.

[0196] Next, the limit of 95% detection (LOD95) for transcriptional signatures of interest was determined using either whole coding transcriptome or RAG capture panels. To score the presence of a gene signature of interest in cfRNA samples, an analytical framework was developed to compute a cfRNA gene signature enrichment score (ES) by comparing the expression of pre-defined signature genes in a sample of interest to a “meta-reference” consisting of cfRNA samples from healthy controls and using bootstrapping to estimate its significance (FIG. 2G). An advantage of this approach is that unlike machine learning-based methods, it does not require a large training cohort of patient cfRNA samples, as it leverages existing gene expression datasets to define gene signatures and associated gene-wise weights based on their expression levels in the tissue or condition of interest.

[0197] To establish the LOD95 of RARE-Seq, in silico serial dilutions of RNA from the NCI-H1975 NSCLC cell line into healthy control cfRNA at various cancer fractions were generated. To enable using our cfRNA ES framework, an NCI-H1975-specific gene signature comprising the top differentially expressed genes (DEGs) distinguishing NCI-H1975 from a meta-reference healthy cfRNA profile (H1975 Sig; n=122 genes) was generated. Using this signature, the LOD95 for RAG RARE-Seq was 0.05% (FIG. 2H), >50-fold more sensitive than for whole coding transcriptome RARE-Seq (2.8%; FIG. 2I), demonstrating the value of targeting RAGs. To experimentally confirm this result, in vitro dilution series were created by spiking NCI-H1975 RNA into healthy donor cfRNA at defined concentrations. These in vitro spikes demonstrated strong agreement with our in silico results (FIGs. 2H-2I). Next, the effect of sequencing coverage depth (range 10-50 million reads) on the LOD95 of RAG-targeted RARE-Seq was evaluated and it was found that higher sequencing depth improved the LOD95 (FIG. 8I). Accordingly, 50 million reads were targeted for subsequent experiments using RARE-Seq with the RAG panel.

[0198] Detection of non-small lung cancer

[0199] Having developed and optimized RARE-Seq, data for cfRNA analysis across several clinical applications was generated using the methods disclosed elsewhere herein (e.g., RARE-Seq). Given the higher analytic sensitivity observed by capturing RAGs, RAG-focused RARE-Seq was employed for these experiments. First, performance for tumor-naTve detection of lung adenocarcinomas (LUAD), the most common type ofNSCLC, was investigated. cfRNA from 139 LUAD patients (stage I: n=20, II: n=8, III: n=15, IV: n=96 patients) and 50 non-cancer controls were analyzed. cfRNA samples included 26 risk-matched controls with history of substantial tobacco exposure collected at the time of low-dose computed tomography (LDCT) screening for lung cancer. Concentrations of cfRNA and sequencing depth were similar between groups (FIGs. 9A-9B). Platelet-adjusted differential expression analysis between LUAD and controls identified 96 genes over-expressed in LUAD, including key genes known to be highly expressed in LUADs such as SLC34A2, SFTA2, and NKX2-1 (also known as TTF1; FIG.3A). LUAD DEGs were confirmed to be highly expressed in LUAD tumors from TCGA while being depleted in m eta-reference cfRNA controls (FIG. 3B). Additionally, using cell type-specific gene signatures, GSEA revealed LUAD cfRNA to be most enriched for epithelial cells including pulmonary alveolar type II cells, and most depleted for naive and memory B lymphocytes (FIG. 3C). Thus, cfRNA from LUAD patients is enriched for transcripts that are highly expressed in LUAD tumors.

[0200] To further quantify the enrichment of lung adenocarcinoma derived transcripts, the cfRNA ES framework described elsewhere herein was applied using an LUAD-specific gene signature (LUAD Sig; n=72 genes) identified using RNA-seq data from LUAD tumors within TCGA, LUAD cell lines from the CCLE, whole blood (GTEx), and m eta-reference cfRNA controls. The LUAD Sig was detected in 101 out of 139 LUAD subjects (73% sensitivity at 95% specificity in non-cancer controls; FIG. 3D) and was significantly associated with stage (sensitivity by stage: l=30%, ll=62%, lll=67%, IV=83%; P=9.1e-16; FIG. 3E). In addition, in a subset of plasma samples that were also interrogated using tumor-naive ctDNA analysis (n=38), the LUAD cfRNA ES was significantly correlated with ctDNA variant allele frequency (VAF) (R=0.43, P=0.009) (FIG.3F). Strikingly, 34% of these samples were detected by only RARE-Seq and none were detected by only ctDNA analysis (FIG. 3G), indicating that tumor-naive cfRNA analysis is more sensitive than tumor-naive ctDNA analysis. Importantly, technical variables that varied across groups such as library input and unique sequencing depth, extraction method, and capture panel were not associated with sensitivity (FIGs. 9E-9H), indicating that these variables did not drive detection. In addition, detection was not significantly associated with biological factors such as patient smoking status or the presence ofoncogenic driver mutations (Fig. 9I-9K). Finally, it was observed that platelet correction significantly improved sensitivity (P=0.03; FIG. 9L).

[0201] Genotyping of somatic mutations in ctRNA

[0202] The ability to genotype cancer-derived somatic mutations using RARE-Seq was explored. ctRNA-based variant detection could be a useful addition to cfRNA analysis. Therefore, a custom ctRNA genotyping approach to detect clinically actionable variants was developed and clinically actionable variants included single nucleotide variants (SNVs), insertions / deletions (indels), splice variants, and gene fusions, in recurrent NSCLC oncogenes. To improve specificity, putative variants were censored if their VAF did not significantly exceed the background error rate observed in a subset of withheld controls (n=50; FIG. 10A). Performance was assessed in 136 stage IV LUAD patients with known variants from prior tumor- or ctDNA-genotyping, as well as 97 healthy and LDCT controls. One or more somatic driver mutations were detected in 28% of LUAD patients and 1% of controls, including key NSCLC oncogenic drivers such as EGFR L858R and exon 19 deletions, KRAS G12C, ROS1 and RET fusions, and MET exon skipping events (FIGs. 3H-3I; FIGs. 10B-10D). Of the 162 variants identified by tumor tissue genomic DNA or ctDNA genotyping in the same patients, 35 (22%) were also detectable in ctRNA, and detection rates were similar across variant types (FIG. 3J). Of the 34 variants identified by tumor tissue genomic DNA in patients with matched cfRNA and cfDNA data available, 11 variants were detectable in ctDNA compared to 3 variants detectable in ctRNA, indicating that ctRNA genotyping may be useful to supplement expression-based analyses (FIG. 10E). A significant relationship was observed between variant detection rates and corresponding expression levels; for instance, EGFR variants were more frequently detected in cfRNA samples with high EGFR expression (P=0.0001;FIG. 3K). Thus, RARE-Seq analysis allows simultaneous identification of somatic mutations in cancer patients.

[0203] Given that ctRNA expression signatures and ctRNA somatic variation can individually distinguish between cancer patients and non-cancer controls, a machine learning approach was tested to combine these features into a single detection algorithm. A weighted elastic net (EN) classifier was trained to distinguish LUAD and control cfRNA using 10-fold nested cross validation (CV) in a training cohort of 96 stage IV cases and47 controls. The model demonstrated high classification performance (AUROC=0.94;FIGs. 3L-3M) and selected 23 features including gene expression features (e.g., key LUAD markers such as SLC34A2) and mutation-based features (i.e. presence of EGFR L858R or KRAS mutation) (FIG. 10F). To test whether the cohort was sufficiently large, classifier training and cross-validation were repeated using sub-sampling and stable performance was observed when using at least 100 samples for model training (FIG.10G). The EN model performed similarly well in a withheld validation cohort collected at an independent institution (AUROC=0.95) (FIGs. 3L-3M). Performance of the EN classifier was statistically similar to the ES framework in the training cohort (P=0.27), but improved performance in the validation cohort (P=0.001). Therefore, when sufficient samples are available, machine learning-based approaches can also be used to train robust classifiers for RARE-Seq.

[0204] Identifying resistance to EGFR therapies

[0205] In addition to identifying emergent somatic mutations mediating resistance, ctRNA analysis (e.g., RARE-Seq) may allow detection of non-genetic therapeutic resistance mechanisms, such as histologic transformation. To explore this question, EGFR-mutant LUAD patients treated with EGFR tyrosine kinase inhibitors (TKIs) were investigated. Acquired resistance to EGFR TKIs is caused by heterogenous mechanisms, including both EGFR-dependent mechanisms (such as secondary point mutations in the EGFR gene) and EGFR-independent mechanisms (such as bypass pathway activation or histologic transformation). Plasma samples were profiled from 10 patients with stage IV EGFR-mutant LUAD who had received osimertinib after progressing on at least one prior EGFR TKI and who then developed resistance to osimertinib. The mechanism of resistance was determined by tissue biopsy at the time of progression, revealing small cell histological transformation in 4 patients, MET gene amplifications in 3 patients, and emergent EGFR C797S point mutations in another 3 patients (FIG.4A). For each patient, blood was collected prior to start of osimertinib and at one or more timepoints after progression (n=24 timepoints total).

[0206] Histological transformation to small cell lung cancer (SCLC) was evaluated using a SCLC signature (SCLC Sig) defined using public tumor gene expression data. The signature comprised 73 genes, including canonical SCLC markers such as ASCL1,NEUROD1, and INSM1. At time of radiologic progression, the SCLC Sig was detected in three of four (75%) patients with biopsy-proven histological transformation (FIG. 4B). For one of these patients, a plasma sample collected 274 days after the initiation of SCLC-targeted chemotherapy was analyzed (e.g., carboplatin / etoposide) and decreased SCLC cfRNA ES was observed, suggesting treatment response of the small cell component (FIG. 4C)

[0207] Next, performance of RARE-Seq for identifying genetic mechanisms of resistance was assessed. To detect MET pathway activation caused by MET amplification using ctRNA a MET amplification signature (METamp Sig) was developed and comprised nine genes differentially expressed in three EGFR-mutant NSCLC cell lines with acquired resistance due to MET amplification. The METamp Sig was detected in two of three (67%) patients with biopsy-proven MET amplification (FIG. 4D). Notably, decreased METamp cfRNA ES was observed in one patient after the MET inhibitor savolitinib was added to osimertinib, coincident with a partial response by imaging (FIG.4E). EGFR C797S mutant reads in two of three (67%) patients known to have acquired this mutation based on tumor DNA or cfDNA analysis were identified (FIGs. 4F-4G).These resistance mechanisms were not detected in any pre-resistance timepoint, confirming the specificity of the methods described elsewhere herein (e.g., RARE-Seq). Together, these results demonstrate that RARE-Seq can identify both non-genetic and genetic mechanisms underlying EGFRTKI resistance, including histologic transformation which is not detectable by mutation-based ctDNA approaches.

[0208] Tissue of origin determination in ctRNA

[0209] Use of the RARE-Seq assay was also tested for detecting other cancer types. Plasma samples were analyzed from patients with three types of other carcinomas: pancreatic adenocarcinoma (PAAD; stage IV: n=10), prostate adenocarcinoma (PRAD; stage III: n=1, IV: n=8) and hepatocellular carcinoma (LIHC; stage I: n=3, II: n=5, IV: n=2). Cancer-specific gene signatures were generated for each cancer type and evaluated using the cfRNA ES framework. Sensitivity of cancer-specific cfRNA ES detection was 80%, 90%, and 100% for LIHC, PAAD, and PRAD, respectively, after adjusting for multiple hypothesis testing (FIG. 5A). Given that cancer-specific signatures were detectable in multiple cancer types, ctRNA analysis was applied to distinguish betweencancer types. The ability to identify a malignancy’s tissue of origin (TOO) non-invasively can be helpful for patients with advanced cancers of unknown primary (CUP) who have no clinically detected primary tumor. Thus, additional cancer signatures were generated, capturing genes uniquely expressed in each cancer compared to all other cancers, as well as to cfRNA from patients without cancer (cancer-TOO signatures). TOO was assessed for any advanced stage cancer sample detected by at least one cancer-specific signature. The top predicted TOO was correct 89% of the time and the top two predicted TOOs identified the correct tumor type 96% of the time (FIGs. 5B-5C). Thus, cfRNA analysis may enable non-invasive TOO identification.

[0210] Application to non-malignant conditions

[0211] Several potential applications of RARE-Seq beyond oncologic settings were explored by first testing whether cfRNA expression might reveal lung injury caused by benign pulmonary conditions. cfRNA from three patients with chronic obstructive pulmonary disorder (COPD), seven patients with active COVID-19 infections (n=13 timepoints), and 19 patients admitted to the intensive care unit (ICU) with evidence of pulmonary injury (including 9 patients with acute respiratory distress syndrome, or ARDS) were profiled. Across these diverse pulmonary conditions, lung derived cfRNA was detectable in 49% of patients using a normal lung gene expression signature derived from GTEx (FIG. 5D). In contrast, among adult subjects without known pulmonary conditions (n=42), lung cfRNA was detected in 14%, with detection being significantly more frequent in current smokers than in former- or never-smokers (45.5% vs 3.6%, P=0.004; FIG. 5E).This result suggests that lung cfRNA is induced by ongoing injury to pulmonary epithelium caused by recent exposure to tobacco smoke. Conversely, in patients with known pulmonary conditions, detection was significantly greater in those who were on a ventilator at the time of blood collection (FIG. 5F), likely reflecting the severity of the pulmonary condition as well as insult to pulmonary epithelium from mechanical ventilation. Thus, cfRNA analysis may have utility for assessment of benign conditions involving acute and chronic patterns of tissue injury.

[0212] Separately, given the rapidly growing interest in RNA-based vaccines and therapeutics, the utility of RARE-Seq for simultaneously tracking RNA-based treatments and their effects on the host was explored. To do so, plasma from five individualsreceiving mRNA-based COVID-19 vaccinations (n=20 timepoints) were longitudinally profiled. RARE-Seq was performed on these samples using the RAG capture panel supplemented with baits targeting the sequence of the mRNA vaccine (FIGs. 8C).Vaccine RNA was detectable immediately after vaccination and persisted for up to six weeks after injection (FIG. 5G). Intriguingly, host genes that were differentially expressed after vaccination were enriched for inflammatory responses, interferon gamma responses, and cytokine signaling, reflecting immune activation in response to vaccination (FIG. 5H). Vaccine RNA and host immune response genes were both highest in the first week post-vaccination (FIG. 5I). These results highlight the use of RARE-Seq for simultaneously measuring pharmacokinetic and pharmacodynamic aspects of RNA-based therapies.

[0213] Conclusions

[0214] Described herein is a novel method for cfRNA profiling. Examples using the method disclosed elsewhere herein demonstrated the method’s performance in NSCLC and results for a variety of additional clinical applications. After end-to-end optimization, the method (e.g., RARE-Seq) was shown to achieve a limit of detection below that of tumor-naive ctDNA-based methods, resulting in a higher sensitivity for detection of NSCLC. In some embodiments, an aspect of the method is the specific capture of transcripts that are absent or very lowly expressed in plasma of healthy individuals, facilitating improved sensitivity for transcripts stemming from non-hematologic tissues. These transcripts are highly enriched for cancer- and solid organ-specific genes and therefore useful for detection of pathophysiology occurring outside of the blood. RAG-focused RARE-Seq can be applied to diverse pathophysiologic conditions without the use for bespoke capture panels.

[0215] One observation was the high proportion of platelet-derived transcripts present in cfRNA. These transcripts largely stem from intact platelets that partition with plasma during blood sample processing and represent a major confounder for cfRNA analyses. Expression differences in cfRNA of cancer patients versus controls may largely stem from differences in platelet-derived transcripts. Therefore, a computational approach was developed for eliminating confounding effects caused by platelet RNA and it was found that this significantly improved sensitivity for detection of lung cancer. Whileplatelet contamination can also be reduced using preanalytical approaches such as increasing centrifugation speed, even platelet poor plasma preparations may not be entirely platelet free. Additionally, such procedures may not be feasible when analyzing previously banked samples. Therefore, the approach disclosed elsewhere herein for removing platelet contributions to cfRNA gene expression profiles can be broadly useful for cfRNA studies.

[0216] Exploration of potential applications using the method disclosed elsewhere herein suggests that ultrasensitive cfRNA analysis may be useful in diverse clinical settings. For example, an application is non-invasive identification of cancer types. Such an approach can be useful for non-invasive identification of TOO in patients with cancers of unknown primary or distinguishing between cancer subtypes. Since the approach enables tumor naive detection of ctRNA, it can be used for cancer screening, either alone or in combination with other diagnostic modalities.

[0217] When considering EGFR-mutant NSCLC patients treated with EGFR TKIs, the ability of RARE-Seq to simultaneously query for the presence of somatic mutations (e.g., EGFR C797S) and the transcriptional effects of pathway activation via somatic alterations (e.g., MET amplification) or non-genetic mechanisms (e.g., histologic transformation) allows more comprehensive non-invasive profiling of resistance mechanisms than previously possible. Since histologic transformation can be a mechanism of resistance that occurs in diverse cancer types and because its diagnosis currently requires invasive tissue biopsies, its detection using cfRNA and the method disclosed elsewhere herein can be useful. Additionally, RARE-Seq can also be combined with other approaches such as methylation- and fragmentomics-based liquid biopsy approaches.

[0218] Finally, RARE-Seq can be useful in detecting non-cancer derived cfRNA signatures in patients with both malignant and benign conditions. For example, data collected from the method disclosed elsewhere herein demonstrating the presence of normal lung RNA in the plasma of patients with acute lung injury due to conditions such as COVID-19 infection or ARDS suggests cfRNA analysis also provides blood-based monitoring of tissue injury. Separately, as demonstrated in the analysis of cfRNA in plasma samples after COVID-19 mRNA vaccination, RARE-Seq enables simultaneous tracking of mRNA vaccines or RNA therapeutics and measurement of host responses.

[0219] In summary, a versatile method for cfRNA analysis that has potential utility in diverse clinical settings was developed and described elsewhere herein. The results from the method also include several important biological and technical insights that enable more sensitive cfRNA analysis that is broadly useful for cfRNA-based liquid biopsy applications. The methods and techniques described herein enable novel non-invasive diagnostic applications, with the promise of advancing precision medicine and improving patient care.

[0220] Methods

[0221] Human participants and cohorts

[0222] All samples analyzed in this study were collected with informed consent using protocols approved by Institutional Review Boards at their respective centers. Collection centers included Stanford University, Memorial Sloan Kettering Cancer Center (MSKCC), Massachusetts General Hospital (MGH), and University Hospital Zurich, as detailed below. In total, 459 blood samples were collected from 375 individuals (FIG. 11). The clinical and demographic characteristics are presented in Table 43 for non-cancer cancer cohorts and in Table 44 for cancer cohorts.

[0223] Cancer cohorts. Blood samples were collected from non-small cell lung cancer (NSCLC) patients enrolled at Stanford University (n=99), MSKCC (n=40), and MGH (n=52). Of these, 138 (99%) were diagnosed with lung adenocarcinoma (LUAD) and 2 patients (1%) had mixed adenosquamous histology. Stage information was determined using the American Joint Committee on Cancer (AJCC) 8thedition. For 10 EGFR-mutant NSCLC patients enrolled at MGH, blood was collected at multiple timepoints prior to treatment with and after progression on the EGFR tyrosine kinase inhibitor (TKI) osimertinib, where available (n=24 plasma samples). Tissue biopsies were collected at the time of progression to identify the mechanism of resistance. For additional exploratory analyses, blood samples were collected at Stanford University from patients diagnosed with advanced stage pancreatic adenocarcinoma (PAAD, n=10) and advanced stage prostate adenocarcinoma (PRAD, n=9) and at University Hospital Zurich from patients with hepatocellular carcinoma (LIHC, n=10).

[0224] Non-cancer cohorts. Blood from individuals with no known cancer or benign pulmonary conditions was collected at Stanford University for technical experiments andfor method development controls (n=45). A subset of these samples comprised the ‘metareference’ set that was used to define expected expression in healthy cfRNA for various downstream analyses (n=15). Blood samples were collected from individuals at the time of low-dose computed tomography (LDCT) screening at Stanford University (n=27) and at MGH (n=50), who qualified for such screening based on smoking history (> 30 pack years) and age (55-80 years). Three individuals at the time of LDCT screening were noted to have chronic obstructive pulmonary disease (COPD). In addition, blood samples were collected from critically ill patients admitted to the intensive care unit (ICU) at Stanford Hospital (n=20). A subset of patients met rigorous definition of acute respiratory distress syndrome (ARDS) at the time of blood draw (n=9). Finally, blood samples were collected from individuals at Stanford University with coronavirus disease 2019 (COVID-19; n=13 timepoints from n=7 individuals) as well as from individuals after receiving COVID-19 mRNA vaccination (Pfizer-BioNTech or Moderna; n=20 timepoints from n=5 individuals).

[0225] Blood collection and plasma processing

[0226] Peripheral blood samples were collected and processed according to protocols at their respective centers. For technical experiments conducted at Stanford, whole blood was collected in EDTA tubes and plasma isolated using centrifugation at 2,500G for 10 minutes at 4°C. Optical density at 414 nm was measured using a Nanodrop spectrophotometer instrument to quantify levels of hemolysis in plasma. After centrifugation, all plasma was stored at -80°C until cell-free nucleic acid isolation.

[0227] cfRNA extraction

[0228] Cell-free nucleic acids were extracted from plasma using the miRNA protocol from the QIAamp Circulating Nucleic Acid kit (Qiagen (range 0.2-8.0 mL). Extraction was performed according to manufacturer’s instructions with minor modifications. The resulting eluate was incubated with 14 U DNase I (RNase-Free DNase Set, Qiagen) for 30 minutes at room temperature to digest DNA. The digested eluate was purified using the Zymo RNA Clean& Concentrator kit and stored at -80°C.

[0229] Plasma processed before February 2021 was extracted using phenol / chloroform phase separation and purified using QIAamp Viral RNA kit (Qiagen). In brief, plasma was first incubated with 3 volumes of TRI Reagent LS (Molecular Research Center), and then incubated with 0.4 volumes of chloroform (relative to plasmainput). Phase separation was performed using Maxtract 50mL conical tubes (Qiagen) spun at 1,500G for 5 minutes at room temperature. The RNA-containing aqueous phase was carefully removed, and RNA extracted according to the QIAamp Viral RNA kit protocol. DNA digestion and clean-up was performed as described above. For the subset of samples digested using the “on column” protocol, 28 U DNase I was added directly to the sample bound to the silica column and incubated for 15 minutes, according to the manufacturer recommendations. Careful analysis was done to compare cfRNA yield (FIG.6D), gene expression distribution (FIG. 7B), and LUAD Sig detection rates (FIG. 9G) between different extraction methods. As no significant differences were found, cfRNA samples extracted using both methods were combined for the analyses presented in this manuscript.

[0230] cfRNA quantification

[0231] Quantitative real-time polymerase chain reaction (qRT-PCR) was used for quantification of cfRNA. An RNA-specific primer was designed to cover a 97-bp amplicon spanning 2 exon boundaries in the housekeeping gene GAPDH. A DNA-specific primer was designed to cover a 78bp transcriptionally silent region of chromosome 12. Reverse transcription was first performed using the High-Capacity cDNA Reverse Transcription kit (Applied Biosystems). Quantitative PCR (qPCR) was then run using 2X Power SYBR Green PCR Master Mix (Thermo Fisher Scientific) on an Applied Biosystems 7500 Fast Real-Time PCR or QuantStudio 7 Pro instruments. Universal Human Reference RNA (Thermo Fisher Scientific) was run in parallel to generate a standard curve, and cfRNA concentrations were calculated by comparing the sample’s RNA-specific Ct value to the standard curve. If DNA was detected using the DNA-specific primer, DNA digestion, clean-up, and quantification was repeated. The cfRNA size distribution was assessed using the Agilent Bioanalyzer RNA 6000 Pico chip.

[0232] cfRNA library preparation and sequencing

[0233] RARE-Seq. An input mass of 424 pg cfRNA was targeted, which represents the 25thpercentile of cfRNA yield for 4 mL of plasma in healthy controls (FIG. 6B). For samples with less than 424 pg, all extracted cfRNA was used for library preparation (range 2-424 pg). Double-stranded complementary DNA (cDNA) was synthesized from cfRNA using the NEBNext Ultra™ II RNA First-Strand Synthesis Module and Non-DirectionalSecond Strand Synthesis Module (New England Biolabs). Double-stranded cDNA was treated with 100 U S1 nuclease (Thermo Fisher) for 30 minutes at room temperature to hydrolyze incomplete (e.g., single-stranded) regions. The KAPA Hyper Prep kit (Kapa Biosystems) was used to prepare libraries for sequencing, following manufacturer’s instructions with slight modifications as previously described36. Whole coding transcriptome capture was performed using the Roche Nimblegen SeqCap EZ MedExome Target Enrichment Kit and or the Twist Biosciences Comprehensive Exome Hybridization kit, following respective manufacturer’s instructions. Single-plex capture with a custom gene panel targeting rare abundance genes (see 'RAG capture panel design’ below) was performed using the Twist Biosciences Hybridization kit. Captured libraries were sequenced using 2x150-bp paired-end reads on Illumina HiSeq4000 or NovaSeq6000 instruments.

[0234] SMART-Seq. cfRNA from 3 controls was input into library preparation using the SMART-Seq Stranded kit (TaKaRa) per manufacturer’s instructions and without additional fragmentation. Libraries were amplified and sequenced using 150-bp paired-end runs on an Illumina NovaSeq6000.

[0235] Leukocyte RNA processing

[0236] After plasma separation, leukocytes were isolated from plasma-depleted whole blood samples using SepMate PBMC isolation tubes (Stem Cell Technologies), according to manufacturer instructions. Leukocyte cell pellets were stored at -80°C. RNA was extracted from leukocyte cell pellets by mixing with 800ul TRIzol (Invitrogen) and 200ul chloroform, followed by centrifugation at 13,000G for 15 minutes at 4°C. RNA was then purified from the aqueous supernatant using Qiagen RNeasy kit, per manufacturer’s instructions. Total RNA was quantified with the NanoDrop instrument and Agilent Bioanalyzer RNA 6000 Pico chip. 5-10 ng RNA was used for RARE-Seq library preparation and whole coding transcriptome capture as previously described. Fragmentation was performed before first strand synthesis as recommended by manufacturer.

[0237] NCI-H1975 cell line RNA processing

[0238] NCI-H1975 lung adenocarcinoma cells were acquired from American Type Culture Collection (ATCC CRL-5908). Cellular RNA was extracted from cell pellets andquantified as described for leukocyte RNA. To create in vitro NCI-H1975 cell line spikes, NCI-H1975 RNA was serially diluted into cfRNA from a single healthy individual to create samples with 10%, 1%, 0.1%, 0.01%, and 0.001% cancer fraction by mass. Triplicate RARE-seq libraries were generated from each mixture as well as NCI-H1975 RNA (100% cancer fraction) and cfRNA alone (0% cancer fraction) and captured with whole coding transcriptome panel. Additional mixtures using cfRNA from a different healthy individual were created for capture with the RAG panel (see ‘RAG capture panel design’ below). Cell line authentication was performed by confirming presence of validated EGFR, TP53, PIK3CA, and CDKN2A variants in RNA sequencing data, and tissue culture facilities were regularly tested for mycoplasma contamination.

[0239] cfDNA library preparation and sequencing

[0240] Cell-free DNA (cfDNA) was extracted from plasma using the standard cfDNA protocol from the QIAamp Circulating Nucleic Acid kit (Qiagen). After extraction, cfDNA was quantified with Qubit double-stranded DNA High Sensitivity kit (Thermo Fisher Scientific) and High Sensitivity NGS Fragment Analyzer (Agilent). CAncer Personalized Profiling by deep Sequencing (CAPP-Seq) was used to create sequencing libraries from 32 ng cfDNA. Thereafter, hybridization-based capture (Roche NimbleGen) was used to target genes recurrently mutated in lung cancer. Libraries were sequenced using 2x150bp paired-end reads on Illumina HiSeq4000 instruments.

[0241] Mapping, deduplication, and quality control for RARE-Seq

[0242] FASTQ files were first demultiplexed using a custom pipeline, as previously described. Fastp (v0.20.0) trimmed the first 10 bases from the 5’ end of Read 1 and the 3’ end of Read 2 as well as removed low quality reads (e.g., reads with more than 20% of bases with Phred score <30) or fragments shorter than 35 bp from each sample. Remaining high quality reads were aligned to the reference transcriptome (GENCODE v27) and to the human genome (hg19) using STAR 2-pass (v2.7.0). PCR duplicates were removed from both transcriptome-aligned and genome-aligned files using a custom barcoding approach42(samtools v1.11). Deduplicated reads were used for gene-level expression estimation using RSEM (v1.2.28).

[0243] Quality control was assessed using metrics calculated by the RNASeQC package (v2.3.5) such as read mapping quality and mapping rates, including exonic,intronic, and intergenic rates and ribosomal RNA rates. In addition, DNA contamination was estimated by calculating the percent of reads that map to intronic sequences out of the total number of reads that span an exon boundary. Samples were removed from downstream analysis if they had fewer than 20 million reads or if they had greater than 20% estimated DNA contamination. Together, these QC thresholds removed four samples.

[0244] Gene expression normalization and platelet correction

[0245] RSEM expected counts for captured genes were used for expression analyses (tximport R package v1.22). Counts were first normalized using the Trimmed Mean of M-values (TMM) method, which accounts for sample-to-sample variation in library size and transcriptome complexity (edgeR R package v3.36). Log-transformed and normalized expression values are referred to as 1og2NX’ in this manuscript. A modified version of the Remove Unwanted Variation (RUV) approach was then used to correct for expression variation caused by platelets (RUVseq R package v1.28). In brief, this method estimates factors of unwanted variation in gene expression using negative control genes (RUVg) or negative control samples (RUVs) that are impacted by unwanted technical or biological noise, but not the biological covariates of interest (e.g. cancer). For whole coding transcriptome libraries, the factor of unwanted platelet variation was estimated by RUVg using 130 platelet cell-type marker genes from PanglaoDB as the set of negative control genes (FIG. 11). For RAG captured libraries, since most platelet genes were intentionally excluded from panel (see ‘RAG capture panel design’ below), factors of unwanted variation were estimated using the healthy control meta-reference cfRNA (n=15) as negative control samples within RUVs. The factor of unwanted platelet variation was selected to be the factor that most correlated with platelet expression, defined as average log2NX across 21 captured platelet cell-type marker genes (e.g., PPBP, F5, F7, etc.) (FIG. 8G). In both cases, platelet-corrected gene expression was then determined using the residuals from ordinary least squares regression on the factor of unwanted platelet variation. RUVg and RUVs performed similarly in whole coding transcriptome samples (FIG. 1 J, FIG. 8H). After normalization and correction, quality control was performed by calculating the Pearson correlation between each sample’s gene expression profile and the average of all other samples from the same sample group (i.e., ‘within-groupcorrelation’). For controls with more than one biological replicate, the replicate with the highest within-group correlation was selected for downstream analyses.

[0246] Differential gene expression analysis

[0247] Differential gene expression analysis was performed using DESeq2 (DESeq2 R package v1.34). For analysis of cfRNA and cellular RNA differential expression, a generalized linear model (GLM) was built using sample type as the covariate. For analysis of cancer versus control differential expression, the GLM used condition (e.g., cancer or control) and the factor of unwanted platelet variation determined by RUV as covariates. Wald test was used to determine significance of log fold change estimates followed by Benjamini-Hochberg (BH) multiple hypothesis correction. For the COVID-19 vaccination time series analysis, significance of differential expression was evaluated using a likelihood ratio test (LRT) comparing the full GLM including vaccine timepoint and factor of unwanted variation compared to a reduced model with vaccine terms removed. For all analyses, significantly differentially expressed genes were defined by absolute log fold change greater than one and adjusted p-value < 0.05. Functional analysis of differentially expressed genes used gene set enrichment analysis (GSEA) of pre-ranked log fold change estimates (fgsea R package v1.20). Significance was determined using an adaptive multi-level split Monte-Carlo scheme and adjusted using BH multiple hypothesis correction. GSEA was performed using either cell marker genes from the PanglaoDB single-cell RNA sequencing database and / or the molecular signatures database (MSigDb v7.1).

[0248] RAG-focused capture panel design

[0249] To identify rare-abundance genes (RAGs) in cfRNA, gene expression data were analyzed from controls generated with the whole coding transcriptome RARE-Seq method (n=50 biological replicates from n=28 individuals). Expression was normalized using TMM followed by platelet correction, and genes were categorized as expressed or unexpressed using k-means clustering (k=2) within each sample. RAGs were defined as genes that were expressed in less than 5 percent of cfRNA samples and for which average log2NX was in the bottom 30 percent of all genes. To further refine the RAG list, gene expression data from whole blood samples generated by The Genotype-Tissue Expression (GTEx) project were accessed through the UCSC Xena repository (n=307).After TMM normalization and k-means clustering, RAGs were additionally defined as genes expressed in less than 40 percent of whole blood samples. RAGs are listed in Table 1. Next, expression uniformity was calculated in cfRNA using Gini coefficient, and endogenous control genes were selected from housekeeping genes with a Gini < 0.2 in circulation. In addition, lung cancer-associated genes were manually curated for inclusion in the panel, including genes that are recurrently mutated or rearranged in lung cancer or that are aberrantly expressed in lung cancer histological types. In total, the RAG-focused capture panel included 80,866 probes targeting 7,766,820 bp and 5,546 unique genes, including 4,737 RAGs (Table 1). Probes were also designed to target the sequence of the Pfizer-BioNTech COVID-19 mRNA vaccine and were used to supplement the RAG capture panel for samples collected for vaccine time course analysis.

[0250] A subset of cfRNA libraries were captured using a second version (V2) of the RAG-focused capture panel. In total, the V2 RAG-focused capture panel included 96,360 probes targeting 9,256,091 bp and 6,847 unique genes (Table 1). 5,079 (91.6%) genes and 4,594 (97.0%) RAGs were shared between panels. No significant differences in detection rate were observed between panels (FIG. 9H), and so cfRNA samples captured using both panels were combined for the analyses presented in this manuscript. Gene expression normalization and enrichment score detection was performed separately, using meta-reference controls captured with the respective panel. Differential expression analysis was performed using the full cohort but was restricted to the subset of shared genes between panels (n=5,079 genes).

[0251] Cell type, tissue, and cancer gene signatures

[0252] For cell type-specific signatures, 7,481 marker genes from 170 human cell types were downloaded from the PanglaoDB single-cell RNA sequencing database.

[0253] Tissue- and cancer-enriched signatures were identified using gene expression data from the Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) projects was analyzed. Gene-level read counts generated by the UCSC Toil RNA sequencing bioinformatic pipeline were accessed in the UCSC Xena repository. Gene expression was TMM-normalized and outlier samples were removed using the previously described within-group correlation metric. The TCGA cohort was further filtered to exclude tumors with less than 60 percent tumor purity using previously reported consensus purityestimates (CPE). The normal tissue types that were evaluated from GTEx included bladder (n=9), brain (n=1,107), breast (n=168), colon (n=261), esophagus (n=598), kidney (n=24), liver (n=97), lung (n=241), ovary (n=79), pancreas (n=145), prostate (n=86), skin (n=497), stomach (n=167), and whole blood (n=290). Genes were defined as tissue-enriched if expression was 5-fold higher in each tissue compared to all other tissues evaluated, and if average tissue log2NX was greater than zero. Similarly, the cancer tissue types that were evaluated from TCGA included bladder (BLCA, n=344), brain (GBM, n=144; LGG, n=183), breast (BRCA, n=914), colon (COAD, n=270), esophagus (ESCA, n=181), kidney (KICH, n=65; KIRC, n=350; KIRP, n=262), liver (LIHC, n=164), lung (LUAD, n=309; LUSC, n=365), ovary (OV, n=412), pancreas (PAAD, n=177), prostate (PRAD, n=473), melanoma (SKCM, n=94), and stomach (STAD, n=413). Genes were defined as cancer-enriched if expression was 5-fold higher in each cancer tissue type compared to all other cancer types evaluated, and if average cancer tissue log2NX was greater than zero.

[0254] Cancer-specific gene signatures were created to include the genes most differentially expressed in each cancer tissue compared to whole blood and metareference cfRNA, and therefore most likely to be detected in cfRNA mixtures with high hematopoietic background. For this purpose, the TCGA cancer cohort was supplemented with gene expression data from the Cancer Cell Line Encyclopedia (CCLE) accessed through Xena (LIHC, n=25; LUAD, n=76; PAAD, n=40, PRAD, n=7, SCLC, n=50) and an additional SCLC study (n=79). DESeq2 differential gene expression analysis quantified the gene-wise fold change between cancer tissue expression and whole blood and metareference cfRNA expression. Each gene was also categorized as...

Claims

WHAT IS CLAIMED IS:

1. A method comprising:(a) obtaining a sample comprising a plurality of cell-free nucleic acid molecules, the plurality of cell-free nucleic acid molecules comprising a plurality of cell-free RNA molecules from a biological sample of an individual or a plurality of DNA molecules complementary to the plurality of cell-free RNA molecules;(b) obtaining a panel of nucleic acid molecules that enriches for a subset of transcripts from a whole transcriptome;(c) enriching the sample for the subset by contacting the sample with the panel of nucleic acid molecules, to identify a set of target genes from the enriched subset; and (d) determining, based at least in part on the identified set of target genes, a presence or state of an injury to an organ associated with a therapeutic intervention for a biological condition, wherein the therapeutic intervention is selected from the group consisting of a radiation therapy, a cellular therapy, a non-cellular pharmacologic therapy, and a surgical procedure.

2. The method of claim 1, comprising determining the presence of the injury to the organ following the therapeutic intervention.

3. The method of claim 1, comprising determining the state of the injury to the organ following the therapeutic intervention.

4. The method of claim 2, wherein the state of the injury comprises progression of the injury to the organ.

5. The method of any one of the preceding claims, further comprising:generating sequencing read data of the enriched subset; andaligning the sequencing read data to a reference database to identify at least one member of the set of target genes.

6. The method of claim 5, wherein the reference database comprises a reference genome, a reference transcriptome, or both.

7. The method of claim 5 or 6, further comprising determining a gene expression profile of the at least one member of the set of target genes based on the aligning.

8. The method of claim 7, further comprising excluding, in silico, a gene expression contribution of platelets from the gene expression profile.

9. The method of any one of the preceding claims, further comprising determining, based at least in part on the determined presence or state of the injury to the organ, the individual’s response to the therapeutic intervention for the biological condition.

10. The method of any one of the preceding claims, further comprising modifying, based at least in part on the determined presence or state of the injury to the organ, the therapeutic intervention for the biological condition of the individual.

11. The method of claim 10, further comprising subjecting the individual to the modified therapeutic intervention.

12. The method of any one of the preceding claims, further comprising determining, based at least in part on the determined presence or state of the injury to the organ, a different therapeutic intervention for the biological condition of the individual.

13. The method of claim 12, further comprising subjecting the individual to the different therapeutic intervention.

14. A method, comprising:(a) identifying an individual comprising an injury to an organ following a therapeutic intervention fora biological condition, wherein the individual was identified as having the injury to the organ based at least in part on:(i) obtaining a sample comprising a plurality of cell-free nucleic acid molecules, the plurality of cell-free nucleic acid molecules comprising a plurality of cell-free RNA molecules from a biological sample of the individual or a plurality of DNA molecules complementary to the plurality of cell-free RNA molecules;(ii) obtaining a panel of nucleic acid molecules that enriches for a subset of transcripts from a whole coding transcriptome; and(iii) enriching the sample for the subset by contacting the sample with the panel of nucleic acid molecules, to identify a set of target genes from the enriched subset, wherein the identified set of target genes is indicative of a presence or state of the injury to the organ; and(b) treating the individual with an alternative therapeutic intervention based at least in part on the identified set of target genes.

15. The method of claim 14, wherein the alternative therapeutic intervention comprises at least one regimen modification of the therapeutic intervention.

16. The method of claim 14, wherein the alternative therapeutic intervention comprises a different therapeutic intervention for the biological condition of the individual.

17. The method of claim 16, further comprising discontinuing the therapeutic intervention to the individual.

18. A method, comprising:(a) obtaining a sample comprising a plurality of cell-free nucleic acid molecules, the plurality of cell-free nucleic acid molecules comprising a plurality of cell-free RNA molecules from a biological sample of an individual or a plurality of DNA molecules complementary to the plurality of cell-free RNA molecules;(b) obtaining a panel of nucleic acid molecules that enriches for a subset of transcripts from a whole transcriptome; and(c) enriching the sample for the subset by contacting the sample with the panel of nucleic acid molecules, to identify a set of target genes from the enriched subset;(d) generating sequencing read data of the enriched subset; and(e) aligning the sequencing read data to a reference database to identify at least one member of the set of target genes, wherein the identified at least one member is indicative of a presence or state of an injury to an organ associated with a therapeutic intervention for a biological condition in the individual.

19. The method of claim 18, wherein the aligning the sequencing read data to the reference database comprises aligning reads from greater than 100, greater than 500, or greater than 1,000 different cfRNA molecules or cDNA molecules complementary to the plurality of cell-free RNA molecules to the reference database.

20. The method of claim 19, wherein the reference database comprises greater than 5 kilobases (kb), greater than 100 kb, greater than 1 megabases (Mb), greater than 10 Mb, greater than 100 Mb, greater than 1 gigabase (Gb), or greater than 3 Gb.

21. The method of any one of claims 18-20, wherein the reference database comprises a reference genome, a reference transcriptome, or both.

22. The method of any one of claims 18-21, further comprising determining a gene expression profile of the at least one member of the set of target genes based on the aligning.

23. The method of claim 22, further comprising excluding, in silico, a gene expression contribution of platelets from the gene expression profile.

24. The method of any one of claims 1 -23, wherein the panel of nucleic acid molecules targets transcripts of at least 5% of a subset of genes identified in Table 1, wherein the subset consists of the genes indicated as “Yes” in the “RAG Capture Panel V2” column of Table 1.

25. The method of claim 24, wherein the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the subset of genes.

26. The method of claim 24 or 25, wherein at least 5% of the nucleic acid molecules of the panel used for the enrichment of the sample enrich for the subset of genes.

27. The method of claim 26, wherein at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the nucleic acid molecules of the panel used for the enrichment of the sample enrich for the subset of genes.

28. The method of any one of claims 24-27, wherein the at least 5% of the subset of genes comprises at least one gene indicated in Table 45.

29. The method of claim 28, wherein the at least one gene comprises at least 5%, at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes indicated in Table 45.

30. The method of any one of claims 24-27, wherein the at least 5% of the subset of genes comprises at least one gene indicated in Table 1 as “Yes” in the “RAG Capture Panel V2” column of and as in the “RAG Capture Panel V1” column.

31. The method of claim 30, wherein the at least one gene comprises at least 5%, at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes indicated in Table 1 as “Yes” in the “RAG Capture Panel V2” column of and as in the “RAG Capture Panel V1” column.

32. The method of any one of claims 1 -23, wherein the panel of nucleic acid molecules targets transcripts of at least 5% of a subset of genes identified in Table 1, wherein the subset consists of the genes indicated as “Yes” in the “RAG Capture Panel V1” column of Table 1.

33. The method of claim 32, wherein the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the subset of genes.

34. The method of claim 32 or 33, wherein at least 5% of the nucleic acid molecules of the panel used for the enrichment of the sample enrich for the subset of genes.

35. The method of claim 34, wherein at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the nucleic acid molecules of the panel used for the enrichment of the sample enrich for the subset of genes.

36. The method of any one of claims 1 -35, wherein the panel of nucleic acid molecules that enriches for the subset of transcripts comprises nucleic acids appended to a pulldown tag.

37. The method of claim 36, wherein the pull-down tag comprises biotin.

38. The method of any one of claims 1-37, wherein the therapeutic intervention comprises the radiation therapy.

39. The method of any one of claims 1-37, wherein the therapeutic intervention comprises the cellular therapy.

40. The method of any one of claims 1-37, wherein the therapeutic intervention comprises the non-cellular pharmacologic therapy.

41. The method of claim 40, wherein the non-cellular pharmacologic therapy is selected from the group consisting of a protein therapy, an antibody therapy, a nucleic acid molecule therapy, and a small molecule therapy.

42. The method of any one of claims 1-37, wherein the therapeutic intervention comprises the surgical procedure.

43. The method of any one of claims 1 -42, wherein the plurality of cell-free nucleic acid molecules comprises the plurality of cell-free RNA molecules.

44. The method of any one of claims 1 -42, wherein the plurality of cell-free nucleic acid molecules comprises the plurality of DNA molecules produced by reverse transcription of the plurality of cell-free RNA molecules.

45. The method of any one of the preceding claims, wherein the biological condition is cancer.

46. The method of any one of the preceding claims, wherein the organ is selected from the group consisting of lung, liver, kidney, brain, spine, heart, bone marrow, and organs comprising the hematopoietic system.

47. The method of any one of the preceding claims, wherein the injury is pneumonitis, optionally wherein the injury is radiation-induced pneumonitis.

48. The method of claim 47, wherein the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 5, Table 6, Table 8, Table 9, Table 21, and Table 22.

49. The method of claim 48, wherein the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in the one or more members selected from the group consisting of Table 5, Table 6, Table 8, Table 9, Table 21, and Table 22.

50. The method of claim 47, wherein the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 5.

51. The method of claim 50, wherein the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 5.

52. The method of claim 50, wherein the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 7.

53. The method of claim 52, wherein the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 7.

54. The method of claim 47, wherein the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 6.

55. The method of claim 54, wherein the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 6.

56. The method of claim 54, wherein the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 23.

57. The method of claim 56, wherein the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 23.

58. The method of claim 54, wherein the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 24.

59. The method of claim 58, wherein the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 24.

60. The method of claim 47, wherein the panel of nucleic acid molecules targets transcripts of one or more genes associated with one or more cell types selected from the group consisting of lung signaling alveolar epithelial type 2 cell, lung alveolar epithelial type 1 cell, lung alveolar epithelial type 2 cell, lung goblet cell, lung club cell, lung proliferating basal cell, lung platelet megakaryocyte cell, lung proliferating macrophage cell, lung TREM2+ dendritic cell, lung neutrophil cell, lung CD8+ memory effector T cell, lung EREG dendritic cell, lung NK cell, lung classical monocyte cell, lung CD8+ naive T cell, lung B cell, lung CD4+ memory effector T cell, and lung CD4+ naive T cell.

61. The method of claim 60, wherein the one or more cell types are selected from the group consisting of lung signaling alveolar epithelial type 2 cell, lung signaling alveolar epithelial type 1 cell, lung alveolar epithelial type 2 cell, lung goblet cell, lung club cell, lung proliferating basal cell, and lung platelet megakaryocyte cell.

62. The method of claim 60, wherein the one or more cell types are selected from the group consisting of lung signaling alveolar epithelial type 2 cell, lung signaling alveolar epithelial type 1 cell, lung alveolar epithelial type 2 cell, lung goblet cell, and lung proliferating basal cell.

63. The method of any one of the preceding claims, wherein the identified set of target genes from the enriched subset is sufficient to determine radiation pneumonitis relative to a pre-radiation baseline, optionally wherein the radiation pneumonitis is symptomatic radiation pneumonitis.

64. The method of claim 47, wherein the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 8.

65. The method of claim 64, wherein the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 8.

66. The method of claim 64, wherein the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 10.

67. The method of claim 66, wherein the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 10.

68. The method of claim 47, wherein the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 9.

69. The method of claim 68, wherein the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 9.

70. The method of claim 68, wherein the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 25.

71. The method of claim 70, wherein the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 25.

72. The method of claim 68, wherein the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 26.

73. The method of claim 72, wherein the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 26.

74. The method of claim 47, wherein the panel of nucleic acid molecules targets transcripts of one or more genes associated with one or more cell types selected from the group consisting of lung platelet megakaryocyte cell, lung capillary intermediate 1 cell, lung NK cell, lung NK T cell, lung CD8+ naive T cell, lung alveolar fibroblast cell, lung bronchial vessel 1 cell, lung basal cell, lung CD4+ naive T cell, lung adventitial fibroblast cell, lung plasmacytoid dendritic cell, and lung B cell.

75. The method of claim 74, wherein the one or more cell types are selected from the group consisting of lung platelet megakaryocyte cell, lung capillary intermediate 1 cell, and lung capillary intermediate 1 cell.

76. The method of claim 74, wherein the one or more cell types comprises lung platelet megakaryocyte cell.

77. The method of any one of the preceding claims, wherein the identified set of target genes from the enriched subset is sufficient to determine radiation pneumonitis relative to a post-radiation baseline, optionally wherein the identified set of target genes from the enriched subset is sufficient to determine symptomatic radiation pneumonitis relative to a post-radiation asymptomatic baseline.

78. The method of claim 47, wherein the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 21.

79. The method of claim 78, wherein the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 21.

80. The method of claim 78, wherein the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 22.

81. The method of claim 80, wherein the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 22.

82. The method of claim 47, wherein the panel of nucleic acid molecules targets transcripts of one or more genes associated with immune activation and lymphocyte signaling.

83. The method of claim 82, wherein the one or more genes comprise CD79A / B, CD22, or CD3.

84. The method of claim 47, wherein the panel of nucleic acid molecules targets transcripts of one or more genes associated with innate inflammation.

85. The method of claim 84, wherein the one or more genes comprise CXCR1 or FPR1 / 2.

86. The method of claim 47, wherein the panel of nucleic acid molecules targets transcripts of one or more genes associated with lung epithelial function.

87. The method of claim 86, wherein the one or more genes comprise SFTPA1 / 2 or DUOXA1.

88. The method of claim 47, wherein the panel of nucleic acid molecules targets transcripts of one or more genes associated with tissue remodeling.

89. The method of claim 88, wherein the one or more genes comprise ITGA8, SLIT3, or COL14A1.

90. The method of any one of the preceding claims, wherein the identified set of target genes from the enriched subset is sufficient to predict radiation pneumonitis prior to symptom onset.

91. The method of any one of the preceding claims, wherein the determining in (d) exhibits a sensitivity that is greater than that of a control assay by at least about 20%.

92. The method of claim 91, wherein the sensitivity is greater than that of the control assay by at least about 40%.

93. The method of claim 91 or 92, wherein the control assay is based on a measure of lung volume upon exposure to a predetermined radiation dose.

94. The method of any one of the preceding claims, wherein the injury is pneumonitis, optionally wherein the injury is a non-cellular pharmacologic therapy-induced pneumonitis.

95. The method of claim 94, wherein the non-cellular pharmacologic therapy comprises administration of at least one antibody.

96. The method of claim 94, wherein the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 11.

97. The method of claim 96, wherein the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 11.

98. The method of claim 94, wherein the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 27.

99. The method of claim 98, wherein the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 27.

100. The method of claim 98, wherein the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 28.

101. The method of claim 100, wherein the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 28.

102. The method of claim 94, wherein the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 29.

103. The method of claim 102, wherein the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 29.

104. The method of claim 102, wherein the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 30.

105. The method of claim 104, wherein the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 30.

106. The method of claim 102, wherein the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 31.

107. The method of claim 106, wherein the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 31.

108. The method of claim 94, wherein the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 32.

109. The method of claim 108, wherein the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 32.

110. The method of claim 108, wherein the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 33.

111. The method of claim 110, wherein the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 33.

112. The method of claim 94, wherein the panel of nucleic acid molecules targets transcripts of one or more genes associated with inflammatory response, fatty acid, or MYC.

113. The method of claim 112, wherein the one or more genes are associated with inflammatory response or fatty acid.

114. The method of claim 94, wherein the panel of nucleic acid molecules targets transcripts of one or more genes associated with lung neutrophil cell, lung classicalmonocyte cell, lung vascular smooth muscle cell, lung OLR1 + classical monocyte cell, or lung capillary intermediate 2 cell.

115. The method of claim 114, wherein the one or more genes are associated with lung neutrophil cell, lung classical monocyte cell, lung vascular smooth muscle cell, or lung OLR1 + classical monocyte cell.

116. The method of claim 114, wherein the one or more genes are associated with lung neutrophil cell.

117. The method of claim 94, wherein the panel of nucleic acid molecules targets transcripts of one or more genes associated with neutrophil, monocyte, T helper cell, Cajal-Retzius cell, Ductal cell, or hepatocyte.

118. The method of claim 117, wherein the one or more genes are associated with neutrophil or monocyte.

119. The method of any one of the preceding claims, wherein the identified set of target genes from the enriched subset is sufficient to determine non-cellular pharmacologic therapy-associated pneumonitis.

120. The method of any one of the preceding claims, wherein the injury is necrosis, optionally wherein the injury is radiation necrosis.

121. The method of claim 120, wherein the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 12, Table 34, Table 35, Table 15, Table 16, Table 18, and Table 19.

122. The method of claim 120, wherein the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 12.

123. The method of claim 122, wherein the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 12.

124. The method of claim 122, wherein the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 14.

125. The method of claim 124, wherein the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 14.

126. The method of claim 120, wherein the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 34.

127. The method of claim 126, wherein the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 34.

128. The method of claim 126, wherein the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 36.

129. The method of claim 128, wherein the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 36.

130. The method of claim 126, wherein the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 37.

131. The method of claim 130, wherein the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 37.

132. The method of claim 120, wherein the panel of nucleic acid molecules targets transcripts of one or more genes associated with one or more cell types selected from the group consisting of T cell, monocyte / macrophage, smooth muscle cell / pericyte, stromal cell, connective tissue cell, mast / basophil, type I pneumocyte, type II pneumocyte, intestinal cell, and hepatocyte.

133. The method of claim 132, wherein the one or more cell types are selected from the group consisting of T cell, monocyte / macrophage, smooth muscle cell / pericyte, stromal cell, and connective tissue cell.

134. The method of claim 132, wherein the one or more genes are associated with T cell.

135. The method of claim 120, wherein the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 35.

136. The method of claim 135, wherein the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 35.

137. The method of claim 135, wherein the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 42.

138. The method of claim 137, wherein the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 42.

139. The method of claim 120, wherein the panel of nucleic acid molecules targets transcripts of one or more genes associated with NOTCH, allograft rejection, MTORC1, heme metabolism, adipogenesis, oxidative phosphorylation (OxPhos), fatty acid, peroxisome, ROS, and MYC.

140. The method of claim 139, wherein the one or more genes are associated with NOTCH or allograft.

141. The method of any one of the preceding claims, wherein the identified set of target genes from the enriched subset is sufficient to determine radiation necrosis relative to a post-radiation baseline, optionally wherein the identified set of target genes from the enriched subset is sufficient to determine symptomatic radiation necrosis relative to a post-radiation asymptomatic baseline.

142. The method of claim 120, wherein the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 15.

143. The method of claim 142, wherein the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 15.

144. The method of claim 142, wherein the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 17.

145. The method of claim 144, wherein the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 17.

146. The method of claim 120, wherein the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 16.

147. The method of claim 146, wherein the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 16.

148. The method of claim 146, wherein the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 38.

149. The method of claim 148, wherein the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 38.

150. The method of claim 146, wherein the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 39.

151. The method of claim 150, wherein the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 39.

152. The method of claim 120, wherein the panel of nucleic acid molecules targets transcripts of one or more genes associated with one or more cell types selected from the group consisting of T cell, monocyte / macrophage, ciliated cell, monocyte / macrophage, B cell, ionocyte, hepatocyte, neutrophil / granulocyte, connective tissue cell, and type I pneumocyte.

153. The method of claim 152, wherein the one or more cell types are selected from the group consisting of T cell, monocyte / macrophage, ciliated cell, monocyte / macrophage, and B cell.

154. The method of claim 153, wherein the one or more cell types comprise T cell.

155. The method of any one of the preceding claims, wherein the identified set of target genes from the enriched subset is sufficient to determine symptomatic radiation necrosis relative to a asymptomatic radiation necrosis baseline.

156. The method of claim 120, wherein the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 18.

157. The method of claim 156, wherein the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 18.

158. The method of claim 156, wherein the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 20.

159. The method of claim 158, wherein the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 20.

160. The method of claim 120, wherein the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 19.

161. The method of claim 160, wherein the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 19.

162. The method of claim 160, wherein the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 40.

163. The method of claim 162, wherein the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 40.

164. The method of claim 160, wherein the panel of nucleic acid molecules targets transcripts of at least 5% of genes identified in Table 41.

165. The method of claim 164, wherein the panel of nucleic acid molecules targets transcripts of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 41.

166. The method of claim 120, wherein the panel of nucleic acid molecules targets transcripts of one or more genes associated with one or more cell types selected from the group consisting of T cell, B cell, smooth muscle cell / pericyte, ciliated cell, monocyte / macrophage, connective tissue cell, mast / basophil, type I pneumocyte, neutrophil / granulocyte, and hepatocyte.

167. The method of claim 166, wherein the one or more cell types are selected from the group consisting of T cell, B cell, smooth muscle cell / pericyte, ciliated cell, and monocyte / macrophage.

168. The method of claim 166, wherein the one or more cells comprise T cell.

169. The method of any one of the preceding claims, wherein the identified set of target genes from the enriched subset is sufficient to determine symptomatic radiation necrosis relative to non-radiated brain metastasis baseline.

170. The method of any one of claims 120-169, wherein the therapeutic intervention comprises stereotactic radiosurgery.

171. The method of any one of claims 120-170, wherein the organ comprises brain, bone, or lung.

172. The method of claim 1, wherein at least 5% of the nucleic acid molecules of the panel used for the enrichment of the sample enrich for the genes identified in one of: Table 5; Table 6; Table 8; Table 9; Table 21; Table 22; Table 27; Table 29; Table 32; Table 12; Table 34; Table 35; Table 15; Table 16; Table 18; or Table 19.

173. The method of claim 172, wherein at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the nucleic acid molecules of the panel used for the enrichment of the sample enrich for the genes identified in one of: Table 5; Table 6; Table 8; Table 9; Table 21; Table 22; Table 27; Table 29; Table 32; Table 12; Table 34; Table 35; Table 15; Table 16; Table 18; or Table 19.

174. The method of any one of the preceding claims, wherein the panel targets a subset of a pluralithy of exons of a target gene of the set, wherein an exon of the subset isidentified as showing a higher concordance between an exon-level expression and a gene-level expression as compared to another exon of the plurality of exons.

175. The method of claim 174, wherein the exon is identified as showing the highest concordance between the exon-level expression and the gene-level expression within the plurality of exons.

176. A nucleic acid enrichment panel, comprising:a plurality of nucleic acid molecules that enriches a sample of a plurality of cell-free nucleic acid molecules for a subset of transcripts from a whole transcriptome, the subset corresponding to at least 5% of the genes identified in: Table 5; Table 6; Table 8; Table 9; Table 21; Table 22; Table 27; Table 29; Table 32; Table 12; Table 34; Table 35; Table 15; Table 16; Table 18; or Table 19.

177. The nucleic acid enrichment panel of claim 176, wherein the subset of transcripts corresponds to at least 5% of a subset of genes identified in Table 1, wherein the subset of genes consists of the genes indicated as “Yes” in the “RAG Capture Panel V2” column of Table 1.

178. The nucleic acid enrichment panel of claim 177, wherein the subset of transcripts corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the subset of genes.

179. The nucleic acid enrichment panel of claim 176 or 177, wherein at least 5% of the plurality of nucleic acid molecules of the panel used for the enrichment of the sample enrich for the subset of genes.

180. The nucleic acid enrichment panel of claim 179, wherein at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the plurality of nucleic acid molecules of the panel used for the enrichment of the sample enrich for the subset of genes.

181. The nucleic acid enrichment panel of any one of claims 176-180, wherein the at least 5% of the subset of genes comprises at least one gene indicated in Table 45.

182. The nucleic acid enrichment panel of claim 181, wherein the at least one gene comprises at least 5%, at least 10%, at least 20%, at least 30%, at least 40%, at least50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes indicated in Table 45.

183. The nucleic acid enrichment panel of any one of claims 176-180, wherein the at least 5% of the subset of genes comprises at least one gene indicated in Table 1 as “Yes” in the “RAG Capture Panel V2” column of and as in the “RAG Capture Panel V1” column.

184. The nucleic acid enrichment panel of claim 183, wherein the at least one gene comprises at least 5%, at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes indicated in Table 1 as “Yes” in the “RAG Capture Panel V2” column of and as in the “RAG Capture Panel V1” column.

185. The nucleic acid enrichment panel of claim 176, wherein the subset of transcripts corresponds to at least 5% of a subset of genes identified in Table 1, wherein the subset of genes consists of the genes indicated as “Yes” in the “RAG Capture Panel V1” column of Table 1.

186. The nucleic acid enrichment panel of claim 185, wherein the subset of transcripts corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the subset of genes.

187. The nucleic acid enrichment panel of claim 185 or 186, wherein at least 5% of the plurality of nucleic acid molecules of the panel used for the enrichment of the sample enrich for the subset of genes.

188. The nucleic acid enrichment panel of claim 187, wherein at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the plurality of nucleic acid molecules of the panel used for the enrichment of the sample enrich for the subset of genes.

189. The nucleic acid enrichment panel of claim 176, wherein the subset corresponds to at least 5% of the genes identified in Table 5.

190. The nucleic acid enrichment panel of claim 189, wherein the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, atleast 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 5.

191. The nucleic acid enrichment panel of claim 176, wherein the subset corresponds to at least 5% of the genes are identified in Table 7.

192. The nucleic acid enrichment panel of claim 191, wherein the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 7.

193. The nucleic acid enrichment panel of claim 176, wherein the subset corresponds to at least 5% of the genes are identified in Table 6.

194. The nucleic acid enrichment panel of claim 193, wherein the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 6.

195. The nucleic acid enrichment panel of claim 176, wherein the subset corresponds to at least 5% of the genes are identified in Table 23.

196. The nucleic acid enrichment panel of claim 195, wherein the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 23.

197. The nucleic acid enrichment panel of claim 176, wherein the subset corresponds to at least 5% of the genes are identified in Table 24.

198. The nucleic acid enrichment panel of claim 197, wherein the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 24.

199. The nucleic acid enrichment panel of claim 176, wherein the subset corresponds to at least 5% of the genes are identified in Table 8.

200. The nucleic acid enrichment panel of claim 199, wherein the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, atleast 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 8.

201. The nucleic acid enrichment panel of claim 176, wherein the subset corresponds to at least 5% of the genes are identified in Table 10.

202. The nucleic acid enrichment panel of claim 201, wherein the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 10.

203. The nucleic acid enrichment panel of claim 176, wherein the subset corresponds to at least 5% of the genes are identified in Table 9.

204. The nucleic acid enrichment panel of claim 203, wherein the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 9.

205. The nucleic acid enrichment panel of claim 176, wherein the subset corresponds to at least 5% of the genes are identified in Table 25.

206. The nucleic acid enrichment panel of claim 205, wherein the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 25.

207. The nucleic acid enrichment panel of claim 176, wherein the subset corresponds to at least 5% of the genes are identified in Table 26.

208. The nucleic acid enrichment panel of claim 207, wherein the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 26.

209. The nucleic acid enrichment panel of claim 176, wherein the subset corresponds to at least 5% of the genes are identified in Table 21.

210. The nucleic acid enrichment panel of claim 209, wherein the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, atleast 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 21.

211. The nucleic acid enrichment panel of claim 176, wherein the subset corresponds to at least 5% of the genes are identified in Table 22.

212. The nucleic acid enrichment panel of claim 211, wherein the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 22.

213. The nucleic acid enrichment panel of claim 176, wherein the subset corresponds to at least 5% of the genes are identified in Table 27.

214. The nucleic acid enrichment panel of claim 213, wherein the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 27.

215. The nucleic acid enrichment panel of claim 176, wherein the subset corresponds to at least 5% of the genes are identified in Table 28.

216. The nucleic acid enrichment panel of claim 215, wherein the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 28.

217. The nucleic acid enrichment panel of claim 176, wherein the subset corresponds to at least 5% of the genes are identified in Table 29.

218. The nucleic acid enrichment panel of claim 217, wherein the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 29.

219. The nucleic acid enrichment panel of claim 176, wherein the subset corresponds to at least 5% of the genes are identified in Table 30.

220. The nucleic acid enrichment panel of claim 219, wherein the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, atleast 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 30.

221. The nucleic acid enrichment panel of claim 176, wherein the subset corresponds to at least 5% of the genes are identified in Table 31.

222. The nucleic acid enrichment panel of claim 221, wherein the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 31.

223. The nucleic acid enrichment panel of claim 176, wherein the subset corresponds to at least 5% of the genes are identified in Table 32.

224. The nucleic acid enrichment panel of claim 223, wherein the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 32.

225. The nucleic acid enrichment panel of claim 176, wherein the subset corresponds to at least 5% of the genes are identified in Table 33.

226. The nucleic acid enrichment panel of claim 225, wherein the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 33.

227. The nucleic acid enrichment panel of claim 176, wherein the subset corresponds to at least 5% of the genes are identified in Table 12.

228. The nucleic acid enrichment panel of claim 227, wherein the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 12.

229. The nucleic acid enrichment panel of claim 176, wherein the subset corresponds to at least 5% of the genes are identified in Table 14.

230. The nucleic acid enrichment panel of claim 229, wherein the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, atleast 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 14.

231. The nucleic acid enrichment panel of claim 176, wherein the subset corresponds to at least 5% of the genes are identified in Table 34.

232. The nucleic acid enrichment panel of claim 231, wherein the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 34.

233. The nucleic acid enrichment panel of claim 176, wherein the subset corresponds to at least 5% of the genes are identified in Table 36.

234. The nucleic acid enrichment panel of claim 233, wherein the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 36.

235. The nucleic acid enrichment panel of claim 176, wherein the subset corresponds to at least 5% of the genes are identified in Table 37.

236. The nucleic acid enrichment panel of claim 235, wherein the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 37.

237. The nucleic acid enrichment panel of claim 176, wherein the subset corresponds to at least 5% of the genes are identified in Table 35.

238. The nucleic acid enrichment panel of claim 237, wherein the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 35.

239. The nucleic acid enrichment panel of claim 176, wherein the subset corresponds to at least 5% of the genes are identified in Table 42.

240. The nucleic acid enrichment panel of claim 239, wherein the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, atleast 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 42.

241. The nucleic acid enrichment panel of claim 176, wherein the subset corresponds to at least 5% of the genes are identified in Table 15.

242. The nucleic acid enrichment panel of claim 241, wherein the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 15.

243. The nucleic acid enrichment panel of claim 176, wherein the subset corresponds to at least 5% of the genes are identified in Table 17.

244. The nucleic acid enrichment panel of claim 243, wherein the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 17.

245. The nucleic acid enrichment panel of claim 176, wherein the subset corresponds to at least 5% of the genes are identified in Table 16.

246. The nucleic acid enrichment panel of claim 245, wherein the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 16.

247. The nucleic acid enrichment panel of claim 176, wherein the subset corresponds to at least 5% of the genes are identified in Table 38.

248. The nucleic acid enrichment panel of claim 247, wherein the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 38.

249. The nucleic acid enrichment panel of claim 176, wherein the subset corresponds to at least 5% of the genes are identified in Table 39.

250. The nucleic acid enrichment panel of claim 249, wherein the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, atleast 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 39.

251. The nucleic acid enrichment panel of claim 176, wherein the subset corresponds to at least 5% of the genes are identified in Table 18.

252. The nucleic acid enrichment panel of claim 251, wherein the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 18.

253. The nucleic acid enrichment panel of claim 176, wherein the subset corresponds to at least 5% of the genes are identified in Table 20.

254. The nucleic acid enrichment panel of claim 253, wherein the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 20.

255. The nucleic acid enrichment panel of claim 176, wherein the subset corresponds to at least 5% of the genes are identified in Table 19.

256. The nucleic acid enrichment panel of claim 255, wherein the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 19.

257. The nucleic acid enrichment panel of claim 176, wherein the subset corresponds to at least 5% of the genes are identified in Table 40.

258. The nucleic acid enrichment panel of claim 257, wherein the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 40.

259. The nucleic acid enrichment panel of claim 176, wherein the subset corresponds to at least 5% of the genes are identified in Table 41.

260. The nucleic acid enrichment panel of claim 259, wherein the subset corresponds to at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, atleast 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the genes identified in Table 41.

261. The nucleic acid enrichment panel of any one of the preceding claims, wherein the panel is for determining a presence or state of an injury to an organ associated with a therapeutic intervention for a biological condition.

262. The nucleic acid enrichment panel of any one of the preceding claims, wherein the plurality of cell-free nucleic acid molecules comprises a plurality of cell-free RNA molecules from a biological sample of an individual or a plurality of DNA molecules complementary to the plurality of cell-free RNA molecules.

263. The nucleic acid enrichment panel of claim 176, wherein at least 5% of the plurality of nucleic acid molecules used for the enrichment of the sample enrich for the genes identified in one of: Table 5; Table 6; Table 8; Table 9; Table 21; Table 22; Table 27; Table 29; Table 32; Table 12; Table 34; Table 35; Table 15; Table 16; Table 18; or Table 19.

264. The nucleic acid enrichment panel of claim 263, wherein at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% of the plurality of nucleic acid molecules used for the enrichment of the sample enrich for the genes identified in one of: Table 5; Table 6; Table 8; Table 9; Table 21; Table 22; Table 27; Table 29; Table 32; Table 12; Table 34; Table 35; Table 15; Table 16; Table 18; or Table 19.

265. The nucleic acid enrichment panel of any one of the preceding claims, wherein the nucleic acid enrichment panel targets a subset of a plurality of exons of a target gene, wherein an exon of the subset is identified as showing a higher concordance between an exon-level expression and a gene-level expression as compared to another exon of the plurality of exons.

266. The nucleic acid enrichment panel of claim 265, wherein the exon is identified as showing the highest concordance between the exon-level expression and the gene-level expression within the plurality of exons.

267. The nucleic acid enrichment panel of any one of the preceding claims, wherein the panel of nucleic acid molecules that enriches for the subset of transcripts comprises nucleic acids appended to a pull-down tag.

268. The nucleic acid enrichment panel of claim 267, wherein the pull-down tag comprises biotin.

269. A mixture comprising (1) the nucleic acid enrichment panel of any of claims 176 to 266 and (2) a plurality of cell-free nucleic acid molecules from a biological sample of an individual or a plurality of DNA molecules complementary to the plurality of cell-free RNA molecules.

270. A method of preparing for sequencing of cell-free RNA, comprising:providing a sample comprising nucleic acids for sequencing, wherein the nucleic acids for sequencing are cell-free RNA or nucleic acids derived from and representative of cell-free RNA; andcontacting the sample with a panel of nucleic acid molecules that comprises molecules having sequences from or complement to gene transcripts that are rarely abundant as cell-free RNA molecules within control liquid biopsies such that the panel of nucleic acid molecules anneals with a subset of the nucleic acids for sequencing.

271. The method of claim 270, wherein the transcripts that are rarely are defined as transcripts that are expressed within cell-free RNA in less than 30% liquid biopsies of a cohort of control liquid biopsies.

272. The method of claim 271, wherein the transcripts that are rarely abundant are defined as transcripts that are expressed within cell-free RNA in less than 5% liquid biopsies of the cohort of control liquid biopsies.

273. The method of any one of claims 270-272, wherein the transcripts that are rarely abundant are defined as transcripts that are in the bottom 60% of genes with respect to normalized expression across with cell-free RNA of a cohort of control liquid biopsies.

274. The method of any one of claims 271-273, wherein the cohort of control liquid biopsies comprises at least 5 liquid biopsies.

275. The method of claim 274, wherein the cohort of control liquid biopsies comprises at least 50 liquid biopsies.

276. The method of any one of claims 270-275, wherein the control liquid biopsies are collected from individuals not having one or more the following when the biopsy is collected: an observed pathogenic infection, a diagnosed cancer, a diagnosedmetabolic disorder, a diagnosed neurological disorder, a diagnosed immunodeficiency disorder, a diagnosed autoimmune disorder, a diagnosed inflammatory disorder, a diagnosed cardiovascular disorder, a diagnosed renal disorder, a diagnosed hepatic disorder, active pregnancy, a diagnosed pregnancy complication, a diagnosed fetal complication, an organ transplant, active rejection of an organ transplant, obesity, malnourishment, cachexia, and an abnormality on a clinical test.

277. The method of any one of claims 270-276, wherein the transcripts that are rarely abundant comprise at least of 50% of transcripts within Table 1.

278. The method of claim 277, wherein the transcripts that are rarely abundant comprise at least of 90% of transcripts within Table 1.

279. The method of claim 278, wherein the transcripts that are rarely abundant comprise at least of 100% of transcripts within Table 1.

280. The method of any one of claims 270-279, wherein the panel of nucleic acid molecules excludes at least 50% of whole-exome gene transcripts that are not transcripts that are rarely abundant as cell-free RNA molecules.

281. The method of claim 280, wherein the panel of nucleic acid molecules excludes at least 90% of whole-exome gene transcripts that are not transcripts that are rarely abundant as cell-free RNA molecules.

282. The method of any one of claims 270-281, wherein the panel of nucleic acid molecules consists of 5000 or fewer gene transcripts in addition to transcripts that are rarely abundant as cell-free RNA molecules.

283. The method of claim 282, wherein the panel of nucleic acid molecules consists of 500 or fewer gene transcripts in addition to transcripts that are rarely abundant as cell- free RNA molecules.

284. The method of any one of claims 270-283, wherein the panel of nucleic acid molecules further comprises one or more of: tissue-specific transcripts, cell-type- specific transcripts, clinically relevant transcripts, B-cell receptor and T-cell receptor transcripts, biomarkers, commonly mutagenized transcripts and a set of control transcripts for normalization between samples.

285. The method of claim 284, wherein the biomarkers are associated with one of the following biological characteristics: a medical disorder, pregnancy, a fetalcomplication, a pregnancy complication, a neoplastic growth, cancer, a particular cancer type, a pathogen infection, immune activation, organ transplant rejection, neurodegeneration, tissue of origin, cell type of origin, or activation of a biochemical pathway.

286. The method of any one of claims 270-285, wherein the panel of nucleic acid molecules are a set of probes for targeted capture hybridization.

287. The method of any one of claims 270-285, wherein the panel of nucleic acid molecules are a set of primers for targeted amplification.

288. The method of any one of claims 270-287, wherein the sample of cfRNA is derived from blood, plasma, lymph, mucus, sputum, cerebrospinal fluid, amniotic fluid, urine, or stool.

289. The method of claim 270 further comprising:generating a sequencing library derived from the sample; andperforming targeted sequencing of the sequencing library to yield a sequencing result of the cell-free RNA, wherein the sequencing is targeted towards the panel of nucleic acid molecules.

290. The method of claim 289, further comprising:removing platelet expression from the sequencing result in silico.

291. A panel of nucleic acids for targeting transcripts that are rarely abundant as cell- free RNA molecules, the panel comprising:nucleic acid molecules having sequences from or complement to gene transcripts that are rarely abundant as cell-free RNA molecules within control liquid biopsies.

292. The panel of nucleic acids of claim 291, wherein the transcripts that are rarely are defined as transcripts that are expressed within cell-free RNA in less than 30% liquid biopsies of a cohort of control liquid biopsies.

293. The panel of nucleic acids of claim 292, wherein the transcripts that are rarely abundant are defined as transcripts that are expressed within cell-free RNA in less than 5% liquid biopsies of the cohort of control liquid biopsies.

294. The panel of nucleic acids of any one of claims 291-293, wherein the transcripts that are rarely abundant are defined as transcripts that are in the bottom 60% of geneswith respect to normalized expression across with cell-free RNA of a cohort of control liquid biopsies.

295. The panel of nucleic acids of claim 294, wherein the transcripts that are rarely abundant are defined as transcripts that are in the bottom 30% of genes with respect to normalized expression across with cell-free RNA of a cohort of control liquid biopsies.

296. The panel of nucleic acids of any one of claims 291-295, wherein the transcripts that are rarely abundant as cell-free RNA molecules within control liquid biopsies are defined as transcripts having log transformed and normalized expression values less than zero across a population of control liquid biopsies.

297. The panel of nucleic acids of any one of claims 292-296, wherein the cohort of control liquid biopsies comprises at least 5 liquid biopsies.

298. The panel of nucleic acids of claim 297, wherein the cohort of control liquid biopsies comprises at least 50 liquid biopsies.

299. The panel of nucleic acids of any one of claims 291 -298, wherein the control liquid biopsies are collected from individuals not having one or more the following when the biopsy is collected: an observed pathogenic infection, a diagnosed cancer, a diagnosed metabolic disorder, a diagnosed neurological disorder, a diagnosed immunodeficiency disorder, a diagnosed autoimmune disorder, a diagnosed inflammatory disorder, a diagnosed cardiovascular disorder, a diagnosed renal disorder, a diagnosed hepatic disorder, active pregnancy, a diagnosed pregnancy complication, a diagnosed fetal complication, an organ transplant, active rejection of an organ transplant, obesity, malnourishment, cachexia, and an abnormality on a clinical test.

300. The panel of nucleic acids of any one of claims 291-299, wherein the transcripts that are rarely abundant comprise at least of 50% of transcripts within Table 1.

301. The panel of nucleic acids of claim 300, wherein the transcripts that are rarely abundant comprise at least of 90% of transcripts within Table 1.

302. The panel of nucleic acids of claim 301, wherein the transcripts that are rarely abundant comprise at least of 100% of transcripts within Table 1.

303. The panel of nucleic acids of any one of claims 291-302, wherein the panel of nucleic acid molecules excludes at least 50% of whole-exome gene transcripts that are not transcripts that are rarely abundant as cell-free RNA molecules.

304. The panel of nucleic acids of claim 303, wherein the panel of nucleic acid molecules excludes at least 90% of whole-exome gene transcripts that are not transcripts that are rarely abundant as cell-free RNA molecules.

305. The panel of nucleic acids of any one of claims 291-304, wherein the panel of nucleic acid molecules consists of 5000 or fewer gene transcripts in addition to transcripts that are rarely abundant as cell-free RNA molecules.

306. The panel of nucleic acids of claim 305, wherein the panel of nucleic acid molecules consists of 500 or fewer gene transcripts in addition to transcripts that are rarely abundant as cell-free RNA molecules.

307. The panel of nucleic acids of any one of claims 291-306, wherein the panel of nucleic acid molecules further comprises one or more of: tissue-specific transcripts, cell-type-specific transcripts, clinically relevant transcripts, B-cell receptor and T-cell receptor transcripts, biomarkers, commonly mutagenized transcripts and a set of control transcripts for normalization between samples.

308. The panel of nucleic acids of claim 307, wherein the biomarkers are associated with one of the following biological characteristics: a medical disorder, pregnancy, a fetal complication, a pregnancy complication, a neoplastic growth, cancer, a particular cancer type, a pathogen infection, immune activation, organ transplant rejection, neurodegeneration, tissue of origin, cell type of origin, or activation of a biochemical pathway.

309. The panel of nucleic acids of any one of claims 291-308, wherein the panel of nucleic acid molecules further comprises a set of control transcripts for normalization between samples.

310. The panel of nucleic acids of any one of claims 291-309, wherein the panel of nucleic acid molecules are a set of probes for targeted capture hybridization.

311. The panel of nucleic acids of any one of claims 291-310, wherein the panel of nucleic acid molecules are a set of primers for targeted amplification.

312. A computational method of classifying a lung cancer, comprising:receiving, using a computational processing system, a sequencing result of cell-free RNA sample;generating, using the computational processing system, two or more features as input for a computational machine-learning model;classifying or predicting the likelihood, using the computational processing system, whether the cell-free RNA sample includes RNA from a lung cancer by utilizing the two or more features within the computational machine-learning model.

313. The method of claim 312, wherein the two or more features comprise one or more transcript expression features and one or more mutation-based features.

314. The method of claim 313, wherein the one or more transcript expression features comprises expression of one or more of the following genes: SLC34A2, FOLR1, TUBB, SCEL,CXCL13, CEACAM5, CLDN1, FGG, CCND1, GPR37, PLEKHS1, SLC16A9, FLRT3, AGR2, SEMA7A, VSTM2L, VSTM4, ACTA2, MAL2, or FAM83A.

315. The method of claim 313, wherein the one or more mutation-based features comprises one or more of the following mutations: any KRAS mutation or EGFR L858R mutation.

316. A method comprising:(a) obtaining a sample comprising a plurality of cell-free nucleic acid molecules, the plurality of cell-free nucleic acid molecules comprising a plurality of cell-free RNA molecules from a biological sample of an individual or a plurality of DNA molecules complementary to the plurality of cell-free RNA molecules;(b) obtaining a panel of nucleic acid molecules that enriches for a subset of transcripts from a whole transcriptome;(c) enriching the sample for the subset by contacting the sample with the panel of nucleic acid molecules, to identify a set of target genes from the enriched subset, wherein the panel targets a subset of a plurality of exons of a target gene of the set, wherein an exon of the subset is identified as showing a higher concordancebetween an exon-level expression and a gene-level expression as compared to another exon of the plurality of exons.

317. The method of claim 316, wherein the exon is identified as showing the highest concordance between the exon-level expression and the gene-level expression within the plurality of exons.

Images