Predictive, prognostic signatures for immuno-oncology using liquid biopsy

Abstract

Described here are methods to utilize genomic and epigenomic detection platforms to provide quantitative metrics, including those representative of molecular phenotypes, in a variety of iimmuno-oncology applications, including those related to the characterization of patient responsiveness, treatment efficacy, adverse events among otheres. In various embodiments, described herein is an example of generating prognostic and t outcomes of a cfDNA-derived promoter methylation score in predicting ICI benefit.

Description

PREDICTIVE, PROGNOSTIC SIGNATURES FOR IMMUNO-ONCOLOGY USING LIQUID BIOPSYCROSS-REFERENCE TO RELATED APPLICATION

[0001] The present application claims the benefit of U.S. Provisional Application No.63 / 643,735, filed May 7, 2024, U.S. Provisional Application No. 63 / 690,969, filed September 5,2024 and U.S. Provisional Application 63 / 791,032, filed April 18, 2025. All are incorporated by reference in their entirety for all purposes.BACKGROUND

[0002] Immune checkpoint inhibitors (ICIs) have revolutionized the treatment of non-small cell lung cancer (NSCLC). However, many patients experience limited benefit. Existing biomarkers, such as PD-L1 expression and tumor mutational burden (TMB), offer modest predictive value, highlighting the need for better tools to guide patient selection. Aberrant promoter methylation can regulate immune-related pathways and serve as a predictive biomarker for ICI response. Cell-free DNA (cfDNA)-based assays offer a noninvasive approach to capture this biology in real-world (RW) clinical settings. Described here are methods to utilize genomic and epigenomic detection platforms to provide quantitative metrics, including those representative of molecular phenotypes, in a variety of iimmuno- oncology applications, including those related to the characterization of patient responsiveness, treatment efficacy, adverse events among otheres. In various embodiments, described herein is an example of generating prognostic and t outcomes of a cfDNA-derived promoter methylation score in predicting ICI benefit.SUMMARY OF THE INVENTION

[0003] A computer-implemented method comprising: receiving at least one dataset in a computer system, wherein the dataset comprises a plurality of molecular phenotypes determined from a test sample of a patient and wherein the computer system comprises readable media comprises instructions that performs at least one classification of the test sample. In various embodiments,the molecular phenotypes comprise at least one of epigenetic marks, fragmentome profiles, TMB, human leukocyte antigen loss of heterozygosity (HLA LOH), sequence variants, 9p21.3 loss, tumor fraction (TF), medical imaging data, and histology data. In various embodiments, the epigenetic marks comprise at least one of promoter methylation for a plurality of genes, a plurality of methylation signatures, and a plurality of methylation states. In various embodiments, the plurality of genes comprises at least one gene selected from the consisting of: FAT1, STEAP1, CD276 (B7-H3), WT1, TAP2, PTGS2 (COX-2), MIF, CXCL12, CD44, LGALS1, TET2, TERT, VGLL4, KEAP1, CREB3L1,NT5E (CD73), HLA-A, CD47, TNFRSF9, AD0RA2B, TNFRSF14, CD40, TNFRSF12A, CLDN6, GRIN2A and CD8A. In various embodiments, the epigenetic marks comprise chromatin state, histone marks, histone acetylation, and / or chromatin conformation. The computer-implemented method of any preceding claim, wherein TMB comprises blood-derived TMB (bTMB). In various embodiments, the bTMB comprises a score or status. In various embodiments, the status comprises bTMB low or bTMB high. In various embodiments, the sequence variants comprise single nucleotide changes in at least one of STK11, KRAS, KEAP1, and EGFR. In various embodiments, the sequence variants in EGFR comprise exl9del and / or L858R. In various embodiments, the sequence variants in EGFR comprise at least one of Exon 19 deletion, EGFR Exon 19 insertion, EGFR G719X, EGFR Exon 20 insertion, EGFR T790M, EGFRL858R, and EGFR L861Q. In various embodiments, the sequence variants comprise small variants and / or structural changes in at least one of STK11, KRAS, KEAP1, and EGFR. In various embodiments, the imaging data comprises radiology data. In various embodiments, the radiology data comprises magnetic resonance imaging (MRI), computed tomography (CT), colonoscopy, mammography, and / or x-ray. In various embodiments, the radiology data comprises brain imaging, radiation oncology, abdominal and pelvic imaging, and / or thoracic imaging. In various embodiments, the histology data further comprises digital pathology data. In various embodiments, the histology data comprises tissue PD-L1 staining. In various embodiments, the histology data comprises immunohistochemistry, immunofluorescence, fish, hematoxylin and eosin staining (H&E). The computer-implemented method of any preceding claim, wherein the instructions further comprise artificial intelligence (Al) algorithms to analyze imaging data. In various embodiments, the Al algorithms comprise deep learning and / or machine learning. In various embodiments, the Al algorithms comprise principal component analysis (PCA), support vector machines (SVM), and / or convolutional neural networks (CNN). In various embodiments, the test sample is cell-free DNA. In variousembodiments, the subject is a cancer patient. In various embodiments, the cancer is Non-Small Cell Lung Cancer (NSCLC). In various embodiments, the cancer comprises breast cancer, colorectal cancer, colon cancer, prostate cancer, lung cancer, pancreatic cancer, ovarian cancer, melanoma, and / or liver cancer. In various embodiments, the classification if performed using a multivariate logistic regression model. In various embodiments, the classification is performed using Naive Bayes, decision trees, support vector machines (SVM), random forest classifier, k-nearest neighbors (KNN), or neural networks.

[0004] Described herein is a method, comprising: obtaining or having obtained a sample of a subject; determining one or more features in the sample; and determining a response, therapeutic efficacy, likelihood of adverse event, and cancer subtyping evolution, to an immune-oncology(IO) therapy for the subject. In various embodiments, the IO therapy is an immune checkpoint inhibitor(ICI). In various embodiments, the features comprise at least one of epigenetic marks, fragmentome profiles, TMB, human leukocyte antigen loss of heterozygosity (HLA LOH), sequence variants, 9p21.3 loss, tumor fraction (TF), medical imaging data, and histology data. In various embodiments, the features comprise at least one of promoter methylation for a plurality of genes, a plurality of methylation signatures, and a plurality of methylation states. In various embodiments, the plurality of genes comprises at least one gene selected from the consisting of FAT1, STEAP1, CD276 (B7-H3), WT1, TAP2, PTGS2 (COX-2), MIF, CXCL12, CD44, LGALS1, TET2, TERT, VGLL4, KEAP1, CREB3L1,NT5E (CD73), HLA-A, CD47, TNFRSF9, ADORA2B, TNFRSF14, CD40, TNFRSF12A, CLDN6, GRIN2A and CD8A. In various embodiments, the features comprise chromatin state, histone marks, histone acetylation, and / or chromatin conformation. In various embodiments, the TMB comprises blood-derived TMB (bTMB). The method of any preceding claim, wherein bTMB comprises a score or status. In various embodiments, the status comprises bTMB low or bTMB high. In various embodiments, the sequence variants comprise single nucleotide changes in at least one of STK11, KRAS, KEAP1, and EGFR. In various embodiments, the variants in EGFR comprise exl9del and / or L858R. In various embodiments, the sequence variants in EGFR comprise at least one of Exon 19 deletion, EGFR Exon 19 insertion, EGFR G719X, EGFR Exon 20 insertion, EGFR T790M, EGFR L858R, and EGFR L861Q. In various embodiments, the sequence variants comprise small variants and / or structural changes in at least one of STK11, KRAS, KEAP1, and EGFR. In various embodiments, the imaging data comprises radiology data. In various embodiments, the radiology data comprises magnetic resonance imaging (MRI), computed tomography (CT),colonoscopy, mammography, and / or x-rays. In various embodiments, the radiology data comprises brain imaging, radiation oncology, abdominal and pelvic imaging, and / or thoracic imaging. In various embodiments, the histology data further comprises digital pathology data. In various embodiments, the histology data comprises tissue PD-L1 staining. In various embodiments, the histology data comprises immunohistochemistry, immunofluorescence, fish, hematoxylin and eosin staining (H&E). The method of any preceding claim, wherein the response, therapeutic efficacy, likelihood of adverse event, and cancer subtyping evolution, is determined using a classification algorithm comprising multivariate logistic regression. In various embodiments, the response, therapeutic efficacy, likelihood of adverse event, and cancer subtyping evolution, is determined using a classification algorithm comprising at least one of Naive Bayes, decision tree, support vector machine (SVM), random forest classifier, k- nearest neighbor (KNN), and neural network.

[0005] Described herein is a computer-implemented method comprising: receiving at least one dataset in a computer system comprising a hardware processor and a computer-readable storage media, wherein the dataset comprises a plurality of molecular phenotypes obtained from a test sample of a patient and wherein the computer-readable media comprises instructions that, when executed by the processor, cause the hardware processor to perform at least one classification of the test sample comprising a first classification comprising an immune- oncology (IO) therapy response, therapeutic efficacy, likelihood of adverse event, and cancer subtyping evolution, or a response, therapeutic efficacy, likelihood of adverse event, and cancer subtyping evolution, to an immune checkpoint inhibitor(ICI) for the subject, wherein the molecular phenotypes comprise epigenetic data.

[0006] A system capable of performing any preceding claim.BRIEF DESCRIPTION OF FIGURES

[0007] Figure 1. Epigenomic methylation panel and caller trained using clinical samples. Methylation regions were identified by testing normal vs. cancer samples from large cohorts, including plasma from 3,000 cancer-free donors established regions where DNA does not have hypermethylation signal, plasma from 1,700+ CRC, breast, lung and bladder cancer patients was used to detect tumor methylation mutation molecules Can also call methylation in a pan-tumor manner. Methylation caller is compatible with an aggregate pan-cancer model.

[0008] Figure 2. Non-destructive platform that preferentially enriches for methylated tumor DNA. Epigenomic detection platform efficiently captures tumor signal

[0009] Figure 3. Differential methylated regions can predict drug response. Methylation panel and caller were developed with clinical samples to ensure capability to distinguish between normal and cancer samples. Comparison of normal vs. cancer samples, identified Differentially Methylation Regions (hypermethylated regions), which can detect tumor signal. Cancer samples from patients across CRC, breast, lung and bladder cancer to develop the caller. Further information is found, in Inti. PCT App. No. PCT / US2020 / 053610, PCT / US2020 / 016120, which are each incorporated by reference herein. This platform supports expanded Genotyping, TMB, MSI, promoter methylation (170 tumor suppressor genes + 24 HRR genes), sample-level tumor quantification, add-on modules Epigenomic detection assay has sensitive and validated promoter hypermethylation, analytical LoD of 0.89% VAF at 30ng and 1.1% at 5ng of input. Validated promoter hypermethylation positions Guardantlnfinity as a powerful assay for epigenetic-based classification applications that can be taken to LDT.

[0010] Figure 4. Multivariate logistic regression model for IO score bTMB. bTMB score or status; HLA LoH: Number of HLA LoH; Negative genomic variant +ve: STK11 / KRAS, KEAP1, EGFR exl9del or L858R mutated; 9p21.3 loss: Loss of 9p21.3 locus; Promoter hypermethylation (positive association): Promoter hypermethylation associated with positive response; Promoter hypermethylation (negative association): Promoter hypermethylation associated with negative response; Methylation signature: Methylation region differential between responder vs non-responder; TF: Can be used independently; a prior on whether the patient will respond to anything. A measure of patient risk.

[0011] Figure 5. TF as a prognostic marker. Data from NSCLC patients treated with various ICI regimens

[0012] Figure 6. TF as a prognostic marker. Baseline mTF is influenced by stage and response to prior treatment chi-squared for either (or both) chemo and stage regarding detection is similarly significant p-value = 0.004 and p-value = 0.001, respectively. Chemo and stage are significant predictors within logistic regression models predicting detection of a given sample in test splits of this dataset. Early diagnosis of the patient is partially overlapping in signal with observed stage within study.

[0013] Figure 7. TF as a prognostic marker. High Baseline TF group has significantly shorter OS: Split by median Patients with high methylation-based TF at baseline havesignificantly shorter OS. Grouping into high low near the median value for this training cohort. High methyl TF group has significantly shorter OS. Median methyl TF is 0.18.

[0014] Figure 8. TF as a prognostic marker. High Baseline TF group has significantly shorter OS: 25th vs 90th quantile Patients with high methylation-based TF at baseline have significantly shorter OS. Low baseline is defined by methylation TF less than or equal to the 25th quantile of methyl TF and are compared here to patients with High methyl TF, defined as greater than the 90th quantile for this training cohort. 90th quantile of baseline methyl TF is 4.9%

[0015] Figure 9. TF as a prognostic marker High mTF is associated with shorter rwOS. Adding additional covariates yields similar results with only gender and TF being significant.

[0016] Figure 10. TF as a prognostic marker. Incrementally higher baseline methylationbased TF is associated with worse outcomes

[0017] Figure 11. TF as a prognostic marker. Data from CRC cohort Methylation tumor fraction (epi-TF) high mCRC patients have significantly shorter real-world time to next treatment (rwTTNT). Significantly shorter rwTTNT for epi-TF high patients. Shorter one- year rwTTNT survival probability for epi-TF high patients. Included were 19 epi-TF high and 65 epi-TF low mCRC patients (n=84) included in this analysis. CPH results shown in forest plot included gender and age as covariates and line of therapy ( LI, L2, L3+ ) was stratified within in model. All patients were split into the two groups of high / low methylation-based TF. Each TF proxy was individually optimized for high / low threshold. Not shown: Upper quartile patients for epi-TF had significantly shorter rwTTNT relative to lower quartile (HR=8.98, p-value=0.02).

[0018] Figure 12. Methylation tumor fraction (epi-TF) high patients have significantly shorter real-world overall survival (rwOS). 19 epi-TF high and 65 epi-TF low mCRC patients (n=84) included in this analysis. CPH results shown in forest plot included gender and age as covariates. Line of therapy ( LI, L2, L3+ ) was stratified within in model.

[0019] All patients were split into the two groups of high / low methylation-based TF. Each TF proxy was individually optimized for high / low threshold.

[0020] Not shown: Upper quartile patients for epi-TF had significantly shorter rwOS relative to lower quartile (HR=11.95, p-value=0.045).

[0021] Figure 13. TF as a prognostic marker. Prospective Applications of Tumor Fraction

[0022] Figure 14. bTMB as a predictive marker. bTMB can be predictive of rwOS including when accounting for prognostic effect of shedding / TF (MVAF) *CPH model with covariates for known comorbidity, age, and smoking.

[0023] Figure 15. bTMB as a predictive marker. bTMB can be predictive of rwTTNT including when accounting for prognostic effect of MSAF

[0024] Figure 16. bTMB as a predictive marker ICI + chemo cohort. bTMB significantly predictive of rwOS or rwTTNT among evaluables when accounting for prognostic effect of MVAF. *CPH model with covariates for known comorbidity, age, and smoking. MSAF is used to stratify within the model not as a covariate here.

[0025] Figure 17. bTMB as a predictive marker. Chemo mono therapy cohort: bTMB not significantly predictive of rwOS or rwTTNT among evaluables when accounting for MVAF. *CPH model with covariates for known comorbidity, age, and smoking. MSAF is used to stratify within the model not as a covariate here.

[0026] Figure 18. bTMB as a predictive marker. Prognostic effect of MVAF can be combined with bTMB to identify high and low risk populations that can be visualized in univariate model for TTNT.

[0027] Figure 19. bTMB as a predictive marker. Prognostic effect of MSAF can be combined with bTMB to identify high and low risk populations that can be visualized in univariate model for OS.

[0028] Figure 20. bTMB as a predictive marker BTMB cutoffs. 19-24 muts / Mb significantly associated with rwOS among evaluables within ICI + chemo cohort. *CPH model with covariates for known comorbidity, age, and smoking. MSAF is used to stratify within the model not as a covariate here.

[0029] Figure 21. bTMB as a predictive marker BTMB cutoffs. 18-24 muts / Mb significantly associated with rwTTNT among evaluables within ICI + chemo cohort. *CPH model with covariates for known comorbidity, age, and smoking. MSAF is used to stratify within the model not as a covariate here.

[0030] Figure 22. Genomic variant uni-variate association with OS and TTNT. STK11 +KRAS mutant uni-variate association with TTNT and OS within ICI + chemo cohort STK11 by itself had similar but weaker association and was included in negative signature below, whereas KRAS by itself has less association with OS and TTNT Similar results for EGFR, ERBB2, KIT, STK11, or KEAPl.

[0031] Figure 23. Genomic variant uni-variate association with OS and TTNT Combination of all variants with negative association to outcome: TTNT and OS within chemo-only cohort OS significant consistent with these variants being associated with poorer response in the context of ICI treatment rather than any treatment. But also a higher overall hazard for death. Similar trends for the underlying variant groups.

[0032] Figure 24. Responder / Non-responder enrichment strategy. Individual Biomarkers using Epigenomic Methylation Detection. Meta-analysis of tumor and T cell-intrinsic mechanisms of sensitization to checkpoint inhibition: “gene expression values for CD274 (PD-L1), CD8A and CXCL9 were significant features for predicting ICI response” HR and p- value adjusted for age, gender, TF, and bTMB status. Results are underpowered due to CLDN6 high expression can lead to a worse overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI). Identified 7 DMRs corresponding to 7 differentially methylated genes (DMGs) including HOXB4, H0XA7, H0XD8, ITGA4, ZNF808, PTGER4, and B3GNTL1 that were highly associated with lung cancer. Calculate DMRs with significantly different normalized counts between responders and nonresponders.

[0033] Figure 25. Responder / Non-responder enrichment strategy. Blood Collection on Patients with Metastatic Urothelial Carcinoma (mUC) treated with Approved Therapies Patients who are receiving standard-of-care (SOC): Enfortumab Vedotin (n = 40), Sacituzumab Govitecan (n = 20), SOC Gem / Cis (neoadjuvant) (n = 20), SOC pembrolizumab (n=30), EV + pembrolizumab (n = 30, ongoing). For a SOC cohort patients treated with EV with C1D1, C2D1 and C3D1 time points collected. Similarly, ~40 patients treated with EV+P with C1D1, C2D1 and C3D1 time points collected and are actively collecting from additional patients.

[0034] Figure 26. Exemplary study for IO prediction dataset generation

[0035] Figure 27. Example study design

[0036] Figure 28. Patient counts by inmmune checkpoint inhibitor regimen.

[0037] Figure 29. Other biomarkers evaluable samples. Top panel shown is tumor mutation burden (tmb), bottom panel shown is microsatellite instability (MSI).

[0038] Figure 30. Proportion of unique samples by gene promoter methylation. General information on promoter methylation prior to final filtering.

[0039] Figure 31. Individual promoter methylation association with rwTTNT. Individual promoter methylation association with rwTTNT in NSCLC treated with IO in either first or second line using blood collection within 150 days prior to IO treatment.

[0040] Figure 32. KM curve visual representation of IO score. Visual of univariate association. IO score, defined as the presence of any positive feature and absence of any negative feature across 725 unique NSCLC patients treated with all IO combo regimens.

[0041] Figure 33. Tumor methylation score by IO score category. IO score categories have similar tumor fraction distributions

[0042] Figure 34. IO score categories are not correlated with known predictive biomarkers for immune checkpoint inhibitor response such as TMB. IO score positive or negative categories are not significantly correlated with blood tumor mutational burden (bTMB) category. IO score using promoter methylation from Guardantlnfinity assay thus brings additional information not already captured by TMB status that is useful for predicting whether a patient will respond to immune checkpoint inhibitors using a pretreatment blood draw.

[0043] Figure 35. IO score categories are not correlated with known predictive biomarkers for immune checkpoint inhibitor response such as MSI. Lack of correlation indicates that IO score using promoter methylation from Guardantlnfinity assay thus brings additional information not already captured by MSI status that is useful for predicting whether a patient will respond to immune checkpoint inhibitors using a pretreatment blood draw.DETAILED DESCRIPTION

[0044] While various embodiments of the disclosure have been shown and described herein, those skilled in the art will understand that such embodiments are provided by way of example only. Numerous variations, changes, and substitutions may occur to those skilled in the art without departing from the disclosure. It should be understood that various alternatives to the embodiments of the disclosure described herein may be employed.

[0045] The term “about” and its grammatical equivalents in relation to a reference numerical value can include a range of values up to plus or minus 10% from that value. For example, the amount “about 10 ” can include amounts from 9 to 11. The term “about” inrelation to a reference numerical value can include a range of values plus or minus 10%, 9%, 8%, 7%, 6%, 5%, 4%, 3%, 2%, or 1% from that value.

[0046] The term “at least” and its grammatical equivalents in relation to a reference numerical value can include the reference numerical value and greater than that value. For example, the amount “at least 10” can include the value 10 and any numerical value above 10, such as 11, 100, and 1,000.

[0047] The term “at most” and its grammatical equivalents in relation to a reference numerical value can include the reference numerical value and less than that value. For example, the amount “at most 10” can include the value 10 and any numerical value under 10, such as 9, 8, 5, 1, 0.5, and 0.1.

[0048] As used herein the singular forms “a”, “an”, and “the” can include plural referents unless the context clearly dictates otherwise. Thus, for example, reference to “a cell ” can include a plurality of such cells and reference to “the culture ” can include reference to one or more cultures and equivalents thereof known to those skilled in the art, and so forth. All technical and scientific terms used herein can have the same meaning as commonly understood to one of ordinary skill in the art to which this disclosure belongs unless clearly indicated otherwise.

[0049] Current approaches are to omit testing both genomic and epigenomic attributes of the patient sample or to perform multiple tests separately. Omitting genomic or epigenomic information can result in prescription of cancer therapies that could be known to be ineffective or withholding cancer therapies that could be known to be effective, had both genomic and epigenomic information been available. Cancer can be indicated by epigenetic variations, such as methylation. Examples of methylation changes in cancer include local gains of DNA methylation in the CpG islands at the transcription start site (TSS) of genes involved in normal growth control, DNA repair, cell cycle regulation, and / or cell differentiation. This hypermethylation can be associated with an aberrant loss of transcriptional capacity of involved genes and occurs at least as frequently as point mutations and deletions as a cause of altered gene expression. DNA methylation profiling can be used to detect regions with different extents of methylation (“differentially methylated regions” or “DMRs”) of the genome that are altered during development or that are perturbed by disease, for example, cancer or any cancer-associated disease. The genome of cancer cells harbor imbalance in the above DNA methylation patterns, and therefore in functional packaging of the DNA. The abnormalities of chromatin organization are therefore coupled withmethylation changes and may contribute to enhanced cancer profiling when analyzed jointly. Combining MBD-partitioning with fragmentomic data, such as fragment mapped starts and stops positions (correlated with nucleosome positions) , fragment length and associated nucleosome occupancy, can be used for chromatin structure analysis in hypermethylation studies with the aim to improve biomarker detection rate.

[0050] Methylation profiling can involve determining methylation patterns across different regions of the genome. For example, after partitioning molecules based on extent of methylation (e.g., relative number of methylated sites per molecule) and sequencing, the sequences of molecules in the different partitions can be mapped to a reference genome. This can show regions of the genome that, compared with other regions, are more highly methylated or are less highly methylated. In this way, genomic regions, in contrast to individual molecules, may differ in their extent of methylation.

[0051] A characteristic of nucleic acid molecules may be a modification, which may include various chemical or protein modifications (i.e. epigenetic modifications). Nonlimiting examples of chemical modification may include, but are not limited to, covalent DNA modifications, including DNA methylation. In some embodiments, DNA methylation includes addition of a methyl group to a cytosine at a CpG site (a cytosine followed by a guanine in a nucleic acid sequence). In some embodiments, DNA methylation includes addition of a methyl group to adenine, such as in N6-methyladenine. In some embodiments, DNA methylation is 5-methylation (modification of the 5th carbon of the 6 carbon ring of cytosine). In some embodiments, 5-methylation includes addition of a methyl group to the 5C position of the cytosine to create 5-methylcytosine (m5c). In some embodiments, methylation includes a derivative of m5c. Derivatives of m5c include, but are not limited to, 5- hydroxymethylcytosine (5-hmC), 5-formylcytosine (5-fC), and 5-caryboxylcytosine (5-caC). In some embodiments, DNA methylation is 3C methylation (modification of the 3rd carbon of the 6 carbon ring of cytosine). In some embodiments, 3C methylation includes addition of a methyl group to the 3C position of the cytosine to generate 3 -methylcytosine (3mC). Other examples include N6-methyladenine or glycosylation. DNA methylation includes addition of methyl groups to DNA (e.g. CpG) and can change the expression of methylated DNA region. Methylation can also occur at non CpG sites, for example, methylation can occur at a CpA, CpT, or CpC site. DNA methylation can change the activity of methylated DNA region. For example, when DNA in a promoter region is methylated, transcription of the gene may be repressed. DNA methylation is critical for normal development and abnormality inmethylation may disrupt epigenetic regulation. The disruption, e.g., repression, in epigenetic regulation may cause diseases, such as cancer. Promoter methylation in DNA may be indicative of cancer.

[0052] A CpG dyad is the dinucleotide CpG (cytosine-phosphate-guanine, i.e. a cytosine followed by a guanine in a 5’ - 3’ direction of the nucleic acid sequence) on the sense strand and its complementary CpG on the antisense strand of a double-stranded DNA molecule. CpG dyads can be either fully methylated or hemi-methylated (methylated on one strand only).

[0053] The CpG dinucleotide is underrepresented in the normal human genome, with the majority of CpG dinucleotide sequences being transcriptionally inert (e.g. DNA heterochromatic regions in pericentromeric parts of the chromosome and in repeat elements) and methylated. However, many CpG islands are protected from such methylation especially around transcription start sites (TSS).

[0054] Protein modifications include binding to components of chromatin, particularly histones including modified forms thereof, and binding to other proteins, such as proteins involved in replication or transcription. The disclosure provides methods of processing and analyzing nucleic acids with different extents of modification, such that the nature of their original modification is correlated with a nucleic acid tag and can be decoded by sequencing the tag when nucleic acids are analyzed. Genetic variation of sample nucleic acid modifications can then be associated with the extent of modification (epigenetic variation) of that nucleic acid in the original sample, include single stranded (e.g., ssDNA or RNA) or double stranded molecules (e.g., dsDNA).

[0055] The loss of DNA can reduce the presence of one or more types of DNA such that the presence of the one or more types of DNA such as cfDNA, is difficult to detect. In one or more additional scenarios, existing methods to measure DNA methylation, such as enrichment or depletion methods, can have a relatively high level of resolution, such as about 100 base pairs (bp) to about 200 bp that can make accurately determining an amount of methylation of DNA difficult. The accuracy with which DNA methylation is determined can impact the accuracy of estimates of tumor fraction for samples. Since tumor fraction can be used to determine whether a sample is derived from a subject in which a tumor is present or not, the accuracy of determinations of tumor fraction estimates can impact diagnosis and / or treatment decisions for individuals.Samples

[0056] A sample can be any biological sample isolated from a subject. A sample can be a bodily sample. Samples can include body tissues, such as known or suspected solid tumors, whole blood, platelets, serum, plasma, stool, red blood cells, white blood cells or leucocytes, endothelial cells, tissue biopsies, cerebrospinal fluid synovial fluid, lymphatic fluid, ascites fluid, interstitial or extracellular fluid, the fluid in spaces between cells, including gingival crevicular fluid, bone marrow, pleural effusions, cerebrospinal fluid, saliva, mucous, sputum, semen, sweat, urine. Samples are preferably body fluids, particularly blood and fractions thereof, and urine. A sample can be in the form originally isolated from a subject or can have been subjected to further processing to remove or add components, such as cells, or enrich for one component relative to another. Thus, a preferred body fluid for analysis is plasma or serum containing cell-free nucleic acids. A sample can be isolated or obtained from a subject and transported to a site of sample analysis. The sample may be preserved and shipped at a desirable temperature, e.g., room temperature, 4°C, -20°C, and / or -80°C. A sample can be isolated or obtained from a subject at the site of the sample analysis. The subject can be a human, a mammal, an animal, a companion animal, a service animal, or a pet. The subject may have a cancer. The subject may not have cancer or a detectable cancer symptom. The subject may have been treated with one or more cancer therapy, e.g., any one or more of chemotherapies, antibodies, vaccines or biologies. The subject may be in remission. The subject may or may not be diagnosed as being susceptible to cancer or any cancer-associated genetic mutations / disorders.

[0057] The volume of plasma can depend on the desired read depth for sequenced regions. Exemplary volumes are 0.4-40 ml, 5-20 ml, 10-20 ml. For example, the volume can be 0.5 mL, 1 mL, 5 mL 10 mL, 20 mL, 30 mL, or 40 mL. The volume of sampled plasma may be 5 to 20 mL.

[0058] A sample can comprise various amounts of nucleic acid that contains genome equivalents. For example, a sample of about 30 ng DNA can contain about 10,000 (104) haploid human genome equivalents and, in the case of cfDNA, about 200 billion (2x1011) individual polynucleotide molecules. Similarly, a sample of about 100 ng of DNA can contain about 30,000 haploid human genome equivalents and, in the case of cfDNA, about 600 billion individual molecules.

[0059] A sample can comprise nucleic acids from different sources, e.g., from cells and cell-free of the same subject, from cells and cell-free of different subjects. A sample cancomprise nucleic acids carrying mutations. For example, a sample can comprise DNA carrying germline mutations and / or somatic mutations. Germline mutations refer to mutations existing in germline DNA of a subject. Somatic mutations refer to mutations originating in somatic cells of a subject, e.g., cancer cells. A sample can comprise DNA carrying cancer-associated mutations (e.g., cancer-associated somatic mutations). A sample can comprise an epigenetic variant (i.e. a chemical or protein modification), wherein the epigenetic variant is associated with the presence of a genetic variant such as a cancer- associated mutation. In some embodiments, the sample includes an epigenetic variant associated with the presence of a genetic variant, wherein the sample does not comprise the genetic variant.

[0060] Exemplary amounts of cell-free nucleic acids in a sample before amplification range from about 1 fg to about 1 pg, e.g., 1 pg to 200 ng, 1 ng to 100 ng, 10 ng to 1000 ng. For example, the amount can be up to about 600 ng, up to about 500 ng, up to about 400 ng, up to about 300 ng, up to about 200 ng, up to about 100 ng, up to about 50 ng, or up to about 20 ng of cell-free nucleic acid molecules. The amount can be at least 1 fg, at least 10 fg, at least 100 fg, at least 1 pg, at least 10 pg, at least 100 pg, at least 1 ng, at least 10 ng, at least 100 ng, at least 150 ng, or at least 200 ng of cell-free nucleic acid molecules. The amount can be up to 1 femtogram (fg), 10 fg, 100 fg, 1 picogram (pg), 10 pg, 100 pg, 1 ng, 10 ng, 100 ng, 150 ng, or 200 ng of cell-free nucleic acid molecules. The method can comprise obtaining 1 femtogram (fg) to 200 ng.

[0061] Cell-free nucleic acids are nucleic acids not contained within or otherwise bound to a cell or in other words nucleic acids remaining in a sample after removing intact cells. Cell-free nucleic acids include DNA, RNA, and hybrids thereof, including genomic DNA, mitochondrial DNA, siRNA, miRNA, circulating RNA (cRNA), tRNA, rRNA, small nucleolar RNA (snoRNA), Piwi-interacting RNA (piRNA), long non-coding RNA (long ncRNA), or fragments of any of these. Cell-free nucleic acids can be double-stranded, single-stranded, or a hybrid thereof. A cell-free nucleic acid can be released into bodily fluid through secretion or cell death processes, e.g., cellular necrosis and apoptosis. Some cell-free nucleic acids are released into bodily fluid from cancer cells e.g., circulating tumor DNA, (ctDNA). Others are released from healthy cells. In some embodiments, cfDNA is cell-free fetal DNA (cffDNA) In some embodiments, cell free nucleic acids are produced by tumor cells. In some embodiments, cell free nucleic acids are produced by a mixture of tumor cells and non-tumor cells.

[0062] Cell-free nucleic acids have an exemplary size distribution of about 100-500 nucleotides, with molecules of 110 to about 230 nucleotides representing about 90% of molecules, with a mode of about 168 nucleotides and a second minor peak in a range between 240 to 440 nucleotides. Cell-free nucleic acids can be isolated from bodily fluids through a fractionation or partitioning step in which cell-free nucleic acids, as found in solution, are separated from intact cells and other non-soluble components of the bodily fluid. Partitioning may include techniques such as centrifugation or filtration. Alternatively, cells in bodily fluids can be lysed and cell-free and cellular nucleic acids processed together. Generally, after addition of buffers and wash steps, nucleic acids can be precipitated with alcohol. Further clean up steps may be used such as silica based columns to remove contaminants or salts. Non-specific bulk carrier nucleic acids, such as Cot-1 DNA, DNA or protein for bisulfite sequencing, hybridization, and / or ligation, may be added throughout the reaction to optimize certain aspects of the procedure such as yield.

[0063] After such processing, samples can include various forms of nucleic acid including double stranded DNA, single stranded DNA and single stranded RNA. In some embodiments, single stranded DNA and RNA can be converted to double stranded forms so they are included in subsequent processing and analysis steps.Analytes

[0064] Analytes can include nucleic acid analytes, and non-nucleic acid analytes. The disclosure provides for detecting genetic variations in biological samples from a subject. Biological samples may include polynucleotides from cancer cells. Polynucleotides may be DNA (e.g., genomic DNA, cDNA), RNA (e.g., mRNA, small RNAs), or any combination thereof. Biological samples may include tumor tissue, e.g., from a biopsy. In some cases, biological samples may include blood or saliva. In particular cases, biological samples may comprise cell free DNA (“cfDNA”) or circulating tumor DNA (“ctDNA”). Cell free DNA can be present in, e.g., blood.

[0065] Examples of non-nucleic acid analytes include, but are not limited to, lipids, carbohydrates, peptides, proteins, glycoproteins (N-linked or O-linked), lipoproteins, phosphoproteins, specific phosphorylated or acetylated variants of proteins, amidation variants of proteins, hydroxylation variants of proteins, methylation variants of proteins, ubiquity lati on variants of proteins, sulfation variants of proteins, viral proteins (e.g., viral capsid, viral envelope, viral coat, viral accessory, viral glycoproteins, viral spike, etc.),extracellular and intracellular proteins, antibodies, and antigen binding fragments. This further includes receptor, an antigen, a surface protein, a transmembrane protein, a cluster of differentiation protein, a protein channel, a protein pump, a carrier protein, a phospholipid, a glycoprotein, a glycolipid, a cell-cell interaction protein complex, an antigen-presenting complex, a major histocompatibility complex, an engineered T-cell receptor, a T-cell receptor, a B-cell receptor, a chimeric antigen receptor, an extracellular matrix protein, a posttranslational modification (e.g., phosphorylation, glycosylation, ubiquitination, nitrosylation, methylation, acetylation or lipidation) state of a cell surface protein, a gap junction, and an adherens junction.

[0066] In general, the systems, apparatus, methods, and compositions can be used to analyze any number of analytes, further including both nucleic acid analytes and non-nucleic acid analytes. For example, the number of analytes that are analyzed can be at least about 2, at least about 3, at least about 4, at least about 5, at least about 6, at least about 7, at least about 8, at least about 9, at least about 10, at least about 11, at least about 12, at least about 13, at least about 14, at least about 15, at least about 20, at least about 25, at least about 30, at least about 40, at least about 50, at least about 100, at least about 1,000, at least about 10,000, at least about 100,000 or more different analytes present in a region of the sample or within an individual feature of the substrate. Methods for performing multiplexed assays to analyze two or more different analytes will be discussed in a subsequent section of this disclosure.

[0067] One or more nucleic acid analytes and / or non-nucleic acid analytes constitute a set of molecular interactions in a biological system under study (e.g., cells), which may be regarded as “interactome” - the molecular interactions that occur between molecules belonging to different biochemical families (proteins, nucleic acids, lipids, carbohydrates, etc.) and also within a given family. In various embodiments, an interactome is a protein- DNA interactome (network formed by transcription factors (and DNA or chromatin regulatory proteins) and their target genes. In other embodiments, interactome refers to protein-protein interaction network (PPI), or protein interaction network (PIN). The methods described herein allow for study and analysis of the interactome. Techniques such as proteogenomics (whole genome sequencing, whole exome sequencing and RNA-seq, and mass spectrometry as examples) can support study of the interactome.Analysis

[0068] The present methods can be used to diagnose presence of conditions, particularly cancer, in a subject, to characterize conditions (e.g., staging cancer or determining heterogeneity of a cancer), monitor response to treatment of a condition, effect prognosis risk of developing a condition or subsequent course of a condition. The present disclosure can also be useful in determining the efficacy of a particular treatment option. Successful treatment options may increase the amount of copy number variation or rare mutations detected in subject's blood if the treatment is successful as more cancers may die and shed DNA. In other examples, this may not occur. In another example, perhaps certain treatment options may be correlated with genetic profiles of cancers over time. This correlation may be useful in selecting a therapy. Additionally, if a cancer is observed to be in remission after treatment, the present methods can be used to monitor residual disease or recurrence of disease.

[0069] The types and number of cancers that may be detected may include blood cancers, brain cancers, lung cancers, skin cancers, nose cancers, throat cancers, liver cancers, bone cancers, lymphomas, pancreatic cancers, skin cancers, bowel cancers, rectal cancers, thyroid cancers, bladder cancers, kidney cancers, mouth cancers, stomach cancers, solid state tumors, heterogeneous tumors, homogenous tumors and the like. Type and / or stage of cancer can be detected from genetic variations including mutations, rare mutations, indels, copy number variations, transversions, translocations, inversion, deletions, aneuploidy, partial aneuploidy, polyploidy, chromosomal instability, chromosomal structure alterations, gene fusions, chromosome fusions, gene truncations, gene amplification, gene duplications, chromosomal lesions, DNA lesions, abnormal changes in nucleic acid chemical modifications, abnormal changes in epigenetic patterns, and abnormal changes in nucleic acid 5-methylcytosine.

[0070] Genetic and other analyte data can also be used for characterizing a specific form of cancer. Cancers are often heterogeneous in both composition and staging. Genetic profile data may allow characterization of specific sub-types of cancer that may be important in the diagnosis or treatment of that specific sub-type. This information may also provide a subject or practitioner clues regarding the prognosis of a specific type of cancer and allow either a subject or practitioner to adapt treatment options in accord with the progress of the disease. Some cancers can progress to become more aggressive and genetically unstable. Other cancers may remain benign, inactive or dormant. The system and methods of this disclosure may be useful in determining disease progression.

[0071] The present analyses are also useful in determining the efficacy of a particular treatment option. Successful treatment options may increase the amount of copy number variation or rare mutations detected in subject's blood if the treatment is successful as more cancers may die and shed DNA. In other examples, this may not occur. In another example, perhaps certain treatment options may be correlated with genetic profiles of cancers over time. This correlation may be useful in selecting a therapy. Additionally, if a cancer is observed to be in remission after treatment, the present methods can be used to monitor residual disease or recurrence of disease.

[0072] The present methods can also be used for detecting genetic variations in conditions other than cancer. Immune cells, such as B cells, may undergo rapid clonal expansion upon the presence of certain diseases. Clonal expansions may be monitored using copy number variation detection and certain immune states may be monitored. In this example, copy number variation analysis may be performed over time to produce a profile of how a particular disease may be progressing. Copy number variation or even rare mutation detection may be used to determine how a population of pathogens changes during the course of infection. This may be particularly important during chronic infections, such as HIV / AIDS or Hepatitis infections, whereby viruses may change life cycle state and / or mutate into more virulent forms during the course of infection. The present methods may be used to determine or profile rejection activities of the host body, as immune cells attempt to destroy transplanted tissue to monitor the status of transplanted tissue as well as altering the course of treatment or prevention of rejection.

[0073] Further, the methods of the disclosure may be used to characterize the heterogeneity of an abnormal condition in a subject. Such methods can include, e.g., generating a genetic profile of extracellular polynucleotides derived from the subject, wherein the genetic profile includes a plurality of data resulting from copy number variation and rare mutation analyses. In some embodiments, an abnormal condition is cancer. In some embodiments, the abnormal condition may be one resulting in a heterogeneous genomic population. In the example of cancer, some tumors are known to comprise tumor cells in different stages of the cancer. In other examples, heterogeneity may comprise multiple foci of disease. Again, in the example of cancer, there may be multiple tumor foci, perhaps where one or more foci are the result of metastases that have spread from a primary site.

[0074] The present methods can be used to generate or profile, fingerprint or set of data that is a summation of genetic information derived from different cells in a heterogeneousdisease. This set of data may comprise copy number variation and mutation analyses alone or in combination.

[0075] The present methods can be used to diagnose, prognose, monitor or observe cancers, or other diseases. In some embodiments, the methods herein do not involve the diagnosing, prognosing or monitoring a fetus and as such are not directed to non-invasive prenatal testing. In other embodiments, these methodologies may be employed in a pregnant subject to diagnose, prognose, monitor or observe cancers or other diseases in an unborn subject whose DNA and other polynucleotides may co-circulate with maternal molecules.Determination of 5-methylcytosine pattern of nucleic acids

[0076] Bisulfite-based sequencing and variants thereof provides a means of determining the methylation pattern of a nucleic acid. In some embodiments, determining the methylation pattern includes distinguishing 5-methylcytosine (5mC) from non-methylated cytosine. In some embodiments, determining methylation pattern includes distinguishing N6- methyladenine from non-methylated adenine. In some embodiments, determining the methylation pattern includes distinguishing 5-hydroxymethylcytosine (5hmC), 5- formylcytosine (5fC), and 5-carboxylcytosine (5caC) from non-methylated cytosine.Examples of bisulfite sequencing include, but are not limited to oxidative bisulfite sequencing (OX-BS-seq), Tet-assisted bisulfite sequencing (TAB-seq), and reduced bisulfite sequencing (redBS-seq).

[0077] Oxidative bisulfite sequencing (OX-BS-seq) is used to distinguish between 5mC and 5hmC, by first converting the 5hmC to 5fC, and then proceeding with bisulfite sequencing as previously described. Tet-assisted bisulfite sequencing (TAB-seq) can also be used to distinguish 5mc and 5hmC. In TAB-seq, 5hmC is protected by glucosylation. A Tet enzyme is then used to convert 5mC to 5caC before proceeding with bisulfite sequencing, as previously described. Reduced bisulfite sequencing is used to distinguish 5fC from modified cytosines.

[0078] Generally, in bisulfite sequencing, a nucleic acid sample is divided into two aliquots and one aliquot is treated with bisulfite. The bisulfite converts native cytosine and certain modified cytosine nucleotides (e.g. 5-formylcytosine or 5-carboxylcytosine) to uracil whereas other modified cytosines (e.g., 5- methylcytosine, 5-hydroxylmethylcystosine) are not converted. Comparison of nucleic acid sequences of molecules from the two aliquots indicates which cytosines were and were not converted to uracils. Consequently, cytosineswhich were and were not modified can be determined. The initial splitting of the sample into two aliquots is disadvantageous for samples containing only small amounts of nucleic acids, and / or composed of heterogeneous cell / tissue origins such as bodily fluids containing cell- free DNA.

[0079] The present disclosure provides methods allowing bisulfite sequencing and variants thereof. These methods work by linking nucleic acids in a population to a capture moiety, i.e., a label that can be captured or immobilized. Capture moieties include, without limitation, biotin, avidin, streptavidin, a nucleic acid including a particular nucleotide sequence, a hapten recognized by an antibody, and magnetically attractable particles. The extraction moiety can be a member of a binding pair, such as biotin / streptavidin or hapten / antibody. In some embodiments, a capture moiety that is attached to an analyte is captured by its binding pair which is attached to an isolatable moiety, such as a magnetically attractable particle or a large particle that can be sedimented through centrifugation. The capture moiety can be any type of molecule that allows affinity separation of nucleic acids bearing the capture moiety from nucleic acids lacking the capture moiety. Exemplary capture moieties are biotin which allows affinity separation by binding to streptavidin linked or linkable to a solid phase or an oligonucleotide, which allows affinity separation through binding to a complementary oligonucleotide linked or linkable to a solid phase. Following linking of capture moieties to sample nucleic acids, the sample nucleic acids serve as templates for amplification. Following amplification, the original templates remain linked to the capture moieties, but amplicons are not linked to capture moieties.

[0080] The capture moiety can be linked to sample nucleic acids as a component of an adapter, which may also provide amplification and / or sequencing primer binding sites. In some methods, sample nucleic acids are linked to adapters at both ends, with both adapters bearing a capture moiety. Preferably any cytosine residues in the adapters are modified, such as by 5methylcytosine, to protect against the action of bisulfite. In some instances, the capture moieties are linked to the original templates by a cleavable linkage (e.g., photocleavable desthiobiotin-TEG or uracil residues cleavable with USER™ enzyme, Chem. Commun. (Camb). 2015 Feb 21; 51(15): 3266-3269), in which case the capture moieties can, if desired, be removed.

[0081] The amplicons are denatured and contacted with an affinity reagent for the capture tag. Original templates bind to the affinity reagent whereas nucleic acid moleculesresulting from amplification do not. Thus, the original templates can be separated from nucleic acid molecules resulting from amplification.

[0082] Following separation or partition, the respective populations of nucleic acids (i.e., original templates and amplification products) can be subjected to bisulfite treatment with the original template population receiving bisulfite treatment and the amplification products not. Alternatively, the amplification products can be subjected to bisulfite treatment and the original template population is not. Following such treatment, the respective populations can be amplified (which in the case of the original template population converts uracils to thymines). The populations can also be subjected to biotin probe hybridization for enrichment. The respective populations are then analyzed and sequences compared to determine which cytosines were 5-methylated (or 5-hydroxylmethylated) in the original. Detection of a T nucleotide in the template population (corresponding to an unmethylated cytosine converted to uracil) and a C nucleotide at the corresponding position of the amplified population indicates an unmodified C. The presence of C's at corresponding positions of the original template and amplified populations indicates a modified C in the original sample.

[0083] In some embodiments, a method uses sequential DNA-seq and bisulfite-seq (BlS-seq) NGS library preparation of molecular tagged DNA libraries. This process is performed by labeling of adapters (e.g., biotin), DNA-seq amplification of whole library, parent molecule recovery (e.g. streptavidin bead pull down), bisulfite conversion and BIS- seq. In some embodiments, the method identifies 5-methylcytosine with single-base resolution, through sequential NGS-preparative amplification of parent library molecules with and without bisulfite treatment. This can be achieved by modifying the 5-methyl-ated NGS-adapters (directional adapters; Y-shaped / forked with 5-methylcytosine replacing) used in BlS-seq with a label (e.g., biotin) on one of the two adapter strands. Sample DNA molecules are adapter ligated, and amplified (e.g., by PCR). As only the parent molecules will have a labeled adapter end, they can be selectively recovered from their amplified progeny by label-specific capture methods (e.g., streptavidin-magnetic beads). As the parent molecules retain 5-methylation marks, bisulfite conversion on the captured library will yield single-base resolution 5-methylation status upon BlS-seq, retaining molecular information to corresponding DNA-seq. In some embodiments, the bisulfite treated library can be combined with a non-treated library prior to enrichment / NGS by addition of a sample tag DNA sequence in standard multiplexed NGS workflow. As with BlS-seq workflows,bioinformatics analysis can be carried out for genomic alignment and 5-methylated base identification. In sum, this method provides the ability to selectively recover the parent, ligated molecules, carrying 5-methylcytosine marks, after library amplification, thereby allowing for parallel processing for bisulfite converted DNA. This overcomes the destructive nature of bisulfite treatment on the quality / sensitivity of the DNA-seq information extracted from a workflow. With this method, the recovered ligated, parent DNA molecules (via labeled adapters) allow amplification of the complete DNA library and parallel application of treatments that elicit epigenetic DNA modifications. The present disclosure discusses the use of BlS-seq methods to identify cytosine5 -methylation (5-methylcytosine), but this is not limiting. Variants of BlS-seq have been developed to identify hydroxymethylated cytosines (5hmC; OX- BS-seq, TAB-seq), formylcytosine (5fC; redBS-seq) and carboxylcytosines. These methodologies can be implemented with the sequential / parallel library preparation described herein.Alternative Methods of Modified Nucleic Acid Analysis

[0084] The disclosure provides alternative methods for analyzing modified nucleic acids (e.g., methylated, linked to histones and other modifications discussed above). In some such methods, a population of nucleic acids bearing the modification to different extents (e.g., 0, 1, 2, 3, 4, 5 or more methyl groups per nucleic acid molecule) is contacted with adapters before fractionation of the population depending on the extent of the modification. Adapters attach to either one end or both ends of nucleic acid molecules in the population. Preferably, the adapters include different tags of sufficient numbers that the number of combinations of tags results in a low probability e.g., 95, 99 or 99.9% of two nucleic acids with the same start and stop points receiving the same combination of tags. Following attachment of adapters, the nucleic acids are amplified from primers binding to the primer binding sites within the adapters. Adapters, whether bearing the same or different tags, can include the same or different primer binding sites, but preferably adapters include the same primer binding site. Following amplification, the nucleic acids are contacted with an agent that preferably binds to nucleic acids bearing the modification (such as the previously described such agents). The nucleic acids are separated into at least two partitions differing in the extent to which the nucleic acids bear the modification from binding to the agents. For example, if the agent has affinity for nucleic acids bearing the modification, nucleic acids overrepresented in the modification (compared with median representation in the population)preferentially bind to the agent, whereas nucleic acids underrepresented for the modification do not bind or are more easily eluted from the agent. Following separation, the different partitions can then be subject to further processing steps, which typically include further amplification, and sequence analysis, in parallel but separately. Sequence data from the different partitions can then be compared.

[0085] Nucleic acids can be linked at both ends to Y-shaped adapters including primer binding sites and tags. The molecules are amplified. The amplified molecules are then fractionated by contact with an antibody preferentially binding to 5-methylcytosine to produce two partitions. One partition includes original molecules lacking methylation and amplification copies having lost methylation. The other partition includes original DNA molecules with methylation. The two partitions are then processed and sequenced separately with further amplification of the methylated partition. The sequence data of the two partitions can then be compared. In this example, tags are not used to distinguish between methylated and unmethylated DNA but rather to distinguish between different molecules within these partitions so that one can determine whether reads with the same start and stop points are based on the same or different molecules.

[0086] The disclosure provides further methods for analyzing a population of nucleic acid in which at least some of the nucleic acids include one or more modified cytosine residues, such as 5-methylcytosine and any of the other modifications described previously. In these methods, the population of nucleic acids is contacted with adapters including one or more cytosine residues modified at the 5C position, such as 5-methylcytosine. Preferably all cytosine residues in such adapters are also modified, or all such cytosines in a primer binding region of the adapters are modified. Adapters attach to both ends of nucleic acid molecules in the population. Preferably, the adapters include different tags of sufficient numbers that the number of combinations of tags results in a low probability e.g., 95, 99 or 99.9% of two nucleic acids with the same start and stop points receiving the same combination of tags. The primer binding sites in such adapters can be the same or different, but are preferably the same. After attachment of adapters, the nucleic acids are amplified from primers binding to the primer binding sites of the adapters. The amplified nucleic acids are split into first and second aliquots. The first aliquot is assayed for sequence data with or without further processing. The sequence data on molecules in the first aliquot is thus determined irrespective of the initial methylation state of the nucleic acid molecules. The nucleic acid molecules in the second aliquot are treated with bisulfite. This treatment convertsunmodified cytosines to uracils. The bisulfite treated nucleic acids are then subjected to amplification primed by primers to the original primer binding sites of the adapters linked to nucleic acid. Only the nucleic acid molecules originally linked to adapters (as distinct from amplification products thereof) are now amplifiable because these nucleic acids retain cytosines in the primer binding sites of the adapters, whereas amplification products have lost the methylation of these cytosine residues, which have undergone conversion to uracils in the bisulfite treatment. Thus, only original molecules in the populations, at least some of which are methylated, undergo amplification. After amplification, these nucleic acids are subject to sequence analysis. Comparison of sequences determined from the first and second aliquots can indicate among other things, which cytosines in the nucleic acid population were subject to methylation.Partitioning the Sample into a Plurality of Subsamples; Aspects of Samples; Analysis of Epigenetic Characteristics

[0087] In certain embodiments described herein, a population of different forms of nucleic acids (e.g., hypermethylated and hypomethylated DNA in a sample, such as a captured set of cfDNA as described herein) can be physically partitioned based on one or more characteristics of the nucleic acids prior to further analysis, e.g., differentially modifying or isolating a nucleobase, tagging, and / or sequencing. This approach can be used to determine, for example, whether certain sequences are hypermethylated or hypomethylated. In some embodiments, hypermethylation variable epigenetic target regions are analyzed to determine whether they show hypermethylation characteristic of tumor cells and / or hypomethylation variable epigenetic target regions are analyzed to determine whether they show hypomethylation characteristic of tumor cells. Additionally, by partitioning a heterogeneous nucleic acid population, one may increase rare signals, e.g., by enriching rare nucleic acid molecules that are more prevalent in one fraction (or partition) of the population. For example, a genetic variation present in hyper-methylated DNA but less (or not) in hypomethylated DNA can be more easily detected by partitioning a sample into hypermethylated and hypo-methylated nucleic acid molecules. By analyzing multiple fractions of a sample, a multi-dimensional analysis of a single locus of a genome or species of nucleic acid can be performed and hence, greater sensitivity can be achieved.

[0088] In some instances, a heterogeneous nucleic acid sample is partitioned into two or more partitions (e.g., at least 3, 4, 5, 6 or 7 partitions). In some embodiments, each partition isdifferentially tagged. Tagged partitions can then be pooled together for collective sample prep and / or sequencing. The partitioning-tagging-pooling steps can occur more than once, with each round of partitioning occurring based on a different characteristics (examples provided herein) and tagged using differential tags that are distinguished from other partitions and partitioning means.

[0089] Examples of characteristics that can be used for partitioning include sequence length, methylation level, nucleosome binding, sequence mismatch, immunoprecipitation, and / or proteins that bind to DNA. Resulting partitions can include one or more of the following nucleic acid forms: single-stranded DNA (ssDNA), double-stranded DNA (dsDNA), shorter DNA fragments and longer DNA fragments. In some embodiments, partitioning based on a cytosine modification (e.g., cytosine methylation) or methylation generally is performed and is optionally combined with at least one additional partitioning step, which may be based on any of the foregoing characteristics or forms of DNA. In some embodiments, a heterogeneous population of nucleic acids is partitioned into nucleic acids with one or more epigenetic modifications and without the one or more epigenetic modifications. Examples of epigenetic modifications include presence or absence of methylation; level of methylation; type of methylation (e.g., 5-methylcytosine versus other types of methylation, such as adenine methylation and / or cytosine hydroxymethylation); and association and level of association with one or more proteins, such as histones. Alternatively or additionally, a heterogeneous population of nucleic acids can be partitioned into nucleic acid molecules associated with nucleosomes and nucleic acid molecules devoid of nucleosomes. Alternatively or additionally, a heterogeneous population of nucleic acids may be partitioned into single-stranded DNA (ssDNA) and double-stranded DNA (dsDNA). Alternatively, or additionally, a heterogeneous population of nucleic acids may be partitioned based on nucleic acid length (e.g., molecules of up to 160 bp and molecules having a length of greater than 160 bp).

[0090] In some instances, each partition (representative of a different nucleic acid form) is differentially labelled, and the partitions are pooled together prior to sequencing. In other instances, the different forms are separately sequenced. In some embodiments, a population of different nucleic acids is partitioned into two or more different partitions. Each partition is representative of a different nucleic acid form, and a first partition (also referred to as a subsample) includes DNA with a cytosine modification in a greater proportion than a second subsample. Each partition is distinctly tagged. The first subsample is subjected to a procedurethat affects a first nucleobase in the DNA differently from a second nucleobase in the DNA of the first subsample, wherein the first nucleobase is a modified or unmodified nucleobase, the second nucleobase is a modified or unmodified nucleobase different from the first nucleobase, and the first nucleobase and the second nucleobase have the same base pairing specificity. The tagged nucleic acids are pooled together prior to sequencing. Sequence reads are obtained and analyzed, including to distinguish the first nucleobase from the second nucleobase in the DNA of the first subsample, in silico. Tags are used to sort reads from different partitions. Analysis to detect genetic variants can be performed on a partition-by- partition level, as well as whole nucleic acid population level. For example, analysis can include in silico analysis to determine genetic variants, such as CNV, SNV, indel, fusion in nucleic acids in each partition. In some instances, in silico analysis can include determining chromatin structure. For example, coverage of sequence reads can be used to determine nucleosome positioning in chromatin. Higher coverage can correlate with higher nucleosome occupancy in genomic region while lower coverage can correlate with lower nucleosome occupancy or nucleosome depleted region (NDR).

[0091] Samples can include nucleic acids varying in modifications including postreplication modifications to nucleotides and binding, usually noncovalently, to one or more proteins.

[0092] In an embodiment, the population of nucleic acids is one obtained from a serum, plasma or blood sample from a subject suspected of having neoplasia, a tumor, or cancer or previously diagnosed with neoplasia, a tumor, or cancer. The population of nucleic acids includes nucleic acids having varying levels of methylation. Methylation can occur from any one or more post-replication or transcriptional modifications. Post-replication modifications include modifications of the nucleotide cytosine, particularly at the 5 -position of the nucleobase, e.g., 5-methylcytosine, 5-hydroxymethylcytosine, 5-formylcytosine and 5- carboxylcytosine. The affinity agents can be antibodies with the desired specificity, natural binding partners or variants thereof (Bock et al., Nat Biotech 28: 1106-1114 (2010); Song et al., Nat Biotech 29: 68-72 (2011)), or artificial peptides selected e.g., by phage display to have specificity to a given target.

[0093] Examples of capture moieties contemplated herein include methyl binding domain (MBDs) and methyl binding proteins (MBPs) as described herein, including proteins such as MeCP2 and antibodies preferentially binding to 5-methylcytosine. Likewise, partitioning of different forms of nucleic acids can be performed using histone bindingproteins which can separate nucleic acids bound to histones from free or unbound nucleic acids. Examples of histone binding proteins that can be used in the methods disclosed herein include RBBP4, RbAp48 and SANT domain peptides. Although for some affinity agents and modifications, binding to the agent may occur in an essentially all or none manner depending on whether a nucleic acid bears a modification, the separation may be one of degree. In such instances, nucleic acids overrepresented in a modification bind to the agent at a greater extent that nucleic acids underrepresented in the modification. Alternatively, nucleic acids having modifications may bind in an all or nothing manner. But then, various levels of modifications may be sequentially eluted from the binding agent.

[0094] For example, in some embodiments, partitioning can be binary or based on degree / level of modifications. For example, all methylated fragments can be partitioned from unmethylated fragments using methyl-binding domain proteins (e.g., MethylMiner Methylated DNA Enrichment Kit (ThermoFisher Scientific)). Subsequently, additional partitioning may involve eluting fragments having different levels of methylation by adjusting the salt concentration in a solution with the methyl -binding domain and bound fragments. As salt concentration increases, fragments having greater methylation levels are eluted. In some instances, the final partitions are representative of nucleic acids having different extents of modifications (overrepresentative or underrepresentative of modifications). Overrepresentation and underrepresentation can be defined by the number of modifications born by a nucleic acid relative to the median number of modifications per strand in a population. For example, if the median number of 5-methylcytosine residues in nucleic acid in a sample is 2, a nucleic acid including more than two 5-methylcytosine residues is overrepresented in this modification and a nucleic acid with 1 or zero 5- methylcytosine residues is underrepresented. The effect of the affinity separation is to enrich for nucleic acids overrepresented in a modification in a bound phase and for nucleic acids underrepresented in a modification in an unbound phase (i.e. in solution). The nucleic acids in the bound phase can be eluted before subsequent processing.

[0095] When using MethylMiner Methylated DNA Enrichment Kit (ThermoFisher Scientific) various levels of methylation can be partitioned using sequential elutions. For example, a hypom ethylated partition (e.g., no methylation) can be separated from a methylated partition by contacting the nucleic acid population with the MBD from the kit, which is attached to magnetic beads. The beads are used to separate out the methylated nucleic acids from the non- methylated nucleic acids. Subsequently, one or more elution stepsare performed sequentially to elute nucleic acids having different levels of methylation. For example, a first set of methylated nucleic acids can be eluted at a salt concentration of 160 mM or higher, e.g., at least 150 mM, at least 200 mM, at least 300 mM, at least 400 mM, at least 500 mM, at least 600 mM, at least 700 mM, at least 800 mM, at least 900 mM, at least 1000 mM, or at least 2000 mM. After such methylated nucleic acids are eluted, magnetic separation is once again used to separate higher levels of methylated nucleic acids from those with lower level of methylation. The elution and magnetic separation steps can repeat themselves to create various partitions such as a hypomethylated partition (representative of no methylation), a methylated partition (representative of low level of methylation), and a hyper methylated partition (representative of high level of methylation).

[0096] In some methods, nucleic acids bound to an agent used for affinity separation are subjected to a wash step. The wash step washes off nucleic acids weakly bound to the affinity agent. Such nucleic acids can be enriched in nucleic acids having the modification to an extent close to the mean or median (i.e., intermediate between nucleic acids remaining bound to the solid phase and nucleic acids not binding to the solid phase on initial contacting of the sample with the agent). The affinity separation results in at least two, and sometimes three or more partitions of nucleic acids with different extents of a modification. While the partitions are still separate, the nucleic acids of at least one partition, and usually two or three (or more) partitions are linked to nucleic acid tags, usually provided as components of adapters, with the nucleic acids in different partitions receiving different tags that distinguish members of one partition from another. The tags linked to nucleic acid molecules of the same partition can be the same or different from one another. But if different from one another, the tags may have part of their code in common so as to identify the molecules to which they are attached as being of a particular partition. For further details regarding portioning nucleic acid samples based on characteristics such as methylation, see WO2018 / 119452, which is incorporated herein by reference. In some embodiments, the nucleic acid molecules can be fractionated into different partitions based on the nucleic acid molecules that are bound to a specific protein or a fragment thereof and those that are not bound to that specific protein or fragment thereof.

[0097] Nucleic acid molecules can be fractionated based on DNA-protein binding. Protein-DNA complexes can be fractionated based on a specific property of a protein. Examples of such properties include various epitopes, modifications (e.g., histone methylation or acetylation) or enzymatic activity. Examples of proteins which may bind toDNA and serve as a basis for fractionation may include, but are not limited to, protein A and protein G. Any suitable method can be used to fractionate the nucleic acid molecules based on protein bound regions. Examples of methods used to fractionate nucleic acid molecules based on protein bound regions include, but are not limited to, SDS-PAGE, chromatin- immuno-precipitation (ChIP), heparin chromatography, and asymmetrical field flow fractionation (AF4).

[0098] In some embodiments, partitioning of the nucleic acids is performed by contacting the nucleic acids with a methylation binding domain (“MBD”) of a methylation binding protein (“MBP”). MBD binds to 5 -methylcytosine (5mC). MBD is coupled to paramagnetic beads, such as Dynabeads® M-280 Streptavidin via a biotin linker. Partitioning into fractions with different extents of methylation can be performed by eluting fractions by increasing the NaCl concentration.

[0099] An exemplary method for molecular tag identification of MBD-bead partitioned libraries through NGS is as follows:

[0100] Physical partitioning of an extracted DNA sample (e.g., extracted blood plasma DNA from a human sample) using a methyl-binding domain protein-bead purification kit, saving all elutions from process for downstream processing.

[0101] Parallel application of differential molecular tags and NGS-enabling adapter sequences to each partition. For example, the hypermethylated, residual methylation ('wash'), and hypomethylated partitions are ligated with NGS-adapters with molecular tags.

[0102] Re-combining all molecular tagged partitions, and subsequent amplification using adapter-specific DNA primer sequences.

[0103] Enrichment / hybridization of re-combined and amplified total library, targeting genomic regions of interest (e.g., cancer-specific genetic variants and differentially methylated regions).

[0104] Re-amplification of the enriched total DNA library, appending a sample tag. Different samples are pooled and assayed in multiplex on an NGS instrument.

[0105] Bioinformatics analysis of NGS data, with the molecular tags being used to identify unique molecules, as well deconvolution of the sample into molecules that were differentially MBD-partitioned. This analysis can yield information on relative 5- methylcytosine for genomic regions, concurrent with standard genetic sequencing / variant detection.

[0106] Examples of MBPs contemplated herein include, but are not limited to:(a) MeCP2 is a protein preferentially binding to 5 -methyl -cytosine over unmodified cytosine.(b) RPL26, PRP8 and the DNA mismatch repair protein MHS6 preferentially bind to 5- hydroxymethyl-cytosine over unmodified cytosine.(c) FOXK1, FOXK2, FOXP1, FOXP4 and FOXI3 preferably bind to 5-formyl- cytosine over unmodified cytosine (lurlaro et al., Genome Biol. 14: R119 (2013)).(d) Antibodies specific to one or more methylated nucleotide bases.

[0107] In general, elution is a function of number of methylated sites per molecule, with molecules having more methylation eluting under increased salt concentrations. To elute the DNA into distinct populations based on the extent of methylation, one can use a series of elution buffers of increasing NaCl concentration. Salt concentration can range from about 100 nM to about 2500 mM NaCl. In one embodiment, the process results in three (3) partitions. Molecules are contacted with a solution at a first salt concentration and including a molecule including a methyl binding domain, which molecule can be attached to a capture moiety, such as streptavidin. At the first salt concentration a population of molecules will bind to the MBD and a population will remain unbound. The unbound population can be separated as a “hypomethylated” population. For example, a first partition representative of the hypomethylated form of DNA is that which remains unbound at a low salt concentration, e.g., 100 mM or 160 mM. A second partition representative of intermediate methylated DNA is eluted using an intermediate salt concentration, e.g., between 100 mM and 2000 mM concentration. This is also separated from the sample. A third partition representative of hypermethylated form of DNA is eluted using a high salt concentration, e.g., at least about 2000 mM.

[0108] The disclosure provides further methods for analyzing a population of nucleic acids in which at least some of the nucleic acids include one or more modified cytosine residues, such as 5 -methylcytosine and any of the other modifications described previously. In these methods, after partitioning, the subsamples of nucleic acids are contacted with adapters including one or more cytosine residues modified at the 5C position, such as 5- methylcytosine. Preferably all cytosine residues in such adapters are also modified, or all such cytosines in a primer binding region of the adapters are modified. Adapters attach to both ends of nucleic acid molecules in the population. Preferably, the adapters include different tags of sufficient numbers that the number of combinations of tags results in a low probability e.g., 95, 99 or 99.9% of two nucleic acids with the same start and stop pointsreceiving the same combination of tags. The primer binding sites in such adapters can be the same or different, but are preferably the same. After attachment of adapters, the nucleic acids are amplified from primers binding to the primer binding sites of the adapters. The amplified nucleic acids are split into first and second aliquots. The first aliquot is assayed for sequence data with or without further processing. The sequence data on molecules in the first aliquot is thus determined irrespective of the initial methylation state of the nucleic acid molecules. The nucleic acid molecules in the second aliquot are subjected to a procedure that affects a first nucleobase in the DNA differently from a second nucleobase in the DNA, wherein the first nucleobase includes a cytosine modified at the 5 position, and the second nucleobase includes unmodified cytosine. This procedure may be bisulfite treatment or another procedure that converts unmodified cytosines to uracils. The nucleic acids subjected to the procedure are then amplified with primers to the original primer binding sites of the adapters linked to nucleic acid. Only the nucleic acid molecules originally linked to adapters (as distinct from amplification products thereof) are now amplifiable because these nucleic acids retain cytosines in the primer binding sites of the adapters, whereas amplification products have lost the methylation of these cytosine residues, which have undergone conversion to uracils in the bisulfite treatment. Thus, only original molecules in the populations, at least some of which are methylated, undergo amplification. After amplification, these nucleic acids are subject to sequence analysis. Comparison of sequences determined from the first and second aliquots can indicate among other things, which cytosines in the nucleic acid population were subject to methylation.

[0109] Such an analysis can be performed using the following exemplary procedure. After partitioning, methylated DNA is linked to Y-shaped adapters at both ends including primer binding sites and tags. The cytosines in the adapters are modified at the 5 position (e.g., 5-methylated). The modification of the adapters serves to protect the primer binding sites in a subsequent conversion step (e.g., bisulfite treatment, TAP conversion, or any other conversion that does not affect the modified cytosine but affects unmodified cytosine). After attachment of adapters, the DNA molecules are amplified. The amplification product is split into two aliquots for sequencing with and without conversion. The aliquot not subjected to conversion can be subjected to sequence analysis with or without further processing. The other aliquot is subjected to a procedure that affects a first nucleobase in the DNA differently from a second nucleobase in the DNA, wherein the first nucleobase includes a cytosine modified at the 5 position, and the second nucleobase includes unmodified cytosine. Thisprocedure may be bisulfite treatment or another procedure that converts unmodified cytosines to uracils. Only primer binding sites protected by modification of cytosines can support amplification when contacted with primers specific for original primer binding sites. Thus, only original molecules and not copies from the first amplification are subjected to further amplification. The further amplified molecules are then subjected to sequence analysis. Sequences can then be compared from the two aliquots. As in the separation scheme discussed above, nucleic acid tags in adapters are not used to distinguish between methylated and unmethylated DNA but to distinguish nucleic acid molecules within the same partition.Subjecting the First Subsample to a Procedure that Affects a First Nucleobase in the DNA Differently from a Second Nucleobase in the DNA of the First Subsample

[0110] Methods disclosed herein comprise a step of subjecting the first subsample to a procedure that affects a first nucleobase in the DNA differently from a second nucleobase in the DNA of the first subsample, wherein the first nucleobase is a modified or unmodified nucleobase, the second nucleobase is a modified or unmodified nucleobase different from the first nucleobase, and the first nucleobase and the second nucleobase have the same base pairing specificity. In some embodiments, if the first nucleobase is a modified or unmodified adenine, then the second nucleobase is a modified or unmodified adenine; if the first nucleobase is a modified or unmodified cytosine, then the second nucleobase is a modified or unmodified cytosine; if the first nucleobase is a modified or unmodified guanine, then the second nucleobase is a modified or unmodified guanine; and if the first nucleobase is a modified or unmodified thymine, then the second nucleobase is a modified or unmodified thymine (where modified and unmodified uracil are encompassed within modified thymine for the purpose of this step).[OHl] In some embodiments, the first nucleobase is a modified or unmodified cytosine, then the second nucleobase is a modified or unmodified cytosine. For example, first nucleobase may comprise unmodified cytosine (C) and the second nucleobase may comprise one or more of 5-methylcytosine (mC) and 5-hydroxymethylcytosine (hmC). Alternatively, the second nucleobase may comprise C and the first nucleobase may comprise one or more of mC and hmC. Other combinations are also possible, as indicated, e.g., in the Summary above and the following discussion, such as where one of the first and second nucleobases includes mC and the other includes hmC.

[0112] In some embodiments, the procedure that affects a first nucleobase in the DNA differently from a second nucleobase in the DNA of the first subsample includes bisulfite conversion. Treatment with bisulfite converts unmodified cytosine and certain modified cytosine nucleotides (e.g. 5-formyl cytosine (fC) or 5 -carboxylcytosine (caC)) to uracil whereas other modified cytosines (e.g., 5-methylcytosine, 5-hydroxylmethylcystosine) are not converted. Thus, where bisulfite conversion is used, the first nucleobase includes one or more of unmodified cytosine, 5-formyl cytosine, 5-carboxylcytosine, or other cytosine forms affected by bisulfite, and the second nucleobase may comprise one or more of mC and hmC, such as mC and optionally hmC. Sequencing of bisulfite-treated DNA identifies positions that are read as cytosine as being mC or hmC positions. Meanwhile, positions that are read as T are identified as being T or a bisulfite-susceptible form of C, such as unmodified cytosine, 5-formyl cytosine, or 5-carboxylcytosine. Performing bisulfite conversion on a first subsample as described herein thus facilitates identifying positions containing mC or hmC using the sequence reads obtained from the first subsample. For an exemplary description of bisulfite conversion, see, e.g., Moss et al., Nat Commun. 2018; 9: 5068..

[0113] In some embodiments, the procedure that affects a first nucleobase in the DNA differently from a second nucleobase in the DNA of the first subsample includes oxidative bisulfite (Ox-BS) conversion. In some embodiments, the procedure that affects a first nucleobase in the DNA differently from a second nucleobase in the DNA of the first subsample includes Tet-assisted bisulfite (TAB) conversion. In some embodiments, the procedure that affects a first nucleobase in the DNA differently from a second nucleobase in the DNA of the first subsample includes Tet-assisted conversion with a substituted borane reducing agent, optionally wherein the substituted borane reducing agent is 2-picoline borane, borane pyridine, tert-butylamine borane, or ammonia borane. In some embodiments, the procedure that affects a first nucleobase in the DNA differently from a second nucleobase in the DNA of the first subsample includes chemical-assisted conversion with a substituted borane reducing agent, optionally wherein the substituted borane reducing agent is 2-picoline borane, borane pyridine, tert-butylamine borane, or ammonia borane. In some embodiments, the procedure that affects a first nucleobase in the DNA differently from a second nucleobase in the DNA of the first subsample includes APOBEC-coupled epigenetic (ACE) conversion.

[0114] In some embodiments, procedure that affects a first nucleobase in the DNA differently from a second nucleobase in the DNA of the first subsample includes enzymatic conversion of the first nucleobase, e.g., as in EM-Seq. See, e.g., Vaisvila R, et al. (2019) EM-seq: Detection of DNA methylation at single base resolution from picograms of DNA. bioRxiv; DOI: 10.1101 / 2019.12.20.884692, available at www.biorxiv.org / content / 10.1101 / 2019.12.20.884692vl. For example, TET2 and T4-PGT can be used to convert 5mC and 5hmC into substrates that cannot be deaminated by a deaminase (e.g., APOBEC3A), and then a deaminase (e.g., APOBEC3A) can be used to deaminate unmodified cytosines converting them to uracils.

[0115] In some embodiments, the procedure that affects a first nucleobase in the DNA differently from a second nucleobase in the DNA of the first subsample includes separating DNA originally including the first nucleobase from DNA not originally including the first nucleobase.

[0116] In some embodiments, the first nucleobase is a modified or unmodified adenine, and the second nucleobase is a modified or unmodified adenine. In some embodiments, the modified adenine is N6-methyladenine (mA). In some embodiments, the modified adenine is one or more of N6-methyladenine (mA), N6-hydroxymethyladenine (hmA), or N6- formyladenine (fA).

[0117] Techniques including methylated DNA immunoprecipitation (MeDIP) can be used to separate DNA containing modified bases such as mA from other DNA. See, e.g., Kumar et al., Frontiers Genet. 2018; 9: 640; Greer et al., Cell 2015; 161 : 868-878. An antibody specific for mA is described in Sun et al., Bioessays 2015; 37: 1155-62. Antibodies for various modified nucleobases, such as forms of thymine / uracil including halogenated forms such as 5-bromouracil, are commercially available. Various modified bases can also be detected based on alterations in their base-pairing specificity. For example, hypoxanthine is a modified form of adenine that can result from deamination and is read in sequencing as a G. See, e.g., US Patent 8,486,630; Brown, Genomes, 2nd Ed., John Wiley & Sons, Inc., New York, N.Y., 2002, chapter 14, “Mutation, Repair, and Recombination.”Enriching / Capturing Step, Amplification., Adaptors, Barcodes

[0118] In some embodiments, methods disclosed herein comprise a step of capturing one or more sets of target regions of DNA, such as cfDNA. Capture may be performed using any suitable approach known in the art. In some embodiments, capturing includes contacting the DNA to be captured with a set of target-specific probes. The set of target-specific probes may have any of the features described herein for sets of target-specific probes, including but not limited to in the embodiments set forth above and the sections relating to probes below.Capturing may be performed on one or more subsamples prepared during methods disclosed herein. In some embodiments, DNA is captured from at least the first subsample or the second subsample, e.g., at least the first subsample and the second subsample. Where the first subsample undergoes a separation step (e.g., separating DNA originally including the first nucleobase (e.g., hmC) from DNA not originally including the first nucleobase, such as hmC- seal), capturing may be performed on any, any two, or all of the DNA originally including the first nucleobase (e.g., hmC), the DNA not originally including the first nucleobase, and the second subsample. In some embodiments, the subsamples are differentially tagged (e.g., as described herein) and then pooled before undergoing capture.

[0119] The capturing step may be performed using conditions suitable for specific nucleic acid hybridization, which generally depend to some extent on features of the probes such as length, base composition, etc. Those skilled in the art will be familiar with appropriate conditions given general knowledge in the art regarding nucleic acid hybridization. In some embodiments, complexes of target-specific probes and DNA are formed.

[0120] In some embodiments, a method described herein includes capturing cfDNA obtained from a test subject for a plurality of sets of target regions. The target regions comprise epigenetic target regions, which may show differences in methylation levels and / or fragmentation patterns depending on whether they originated from a tumor or from healthy cells. The target regions also comprise sequence-variable target regions, which may show differences in sequence depending on whether they originated from a tumor or from healthy cells. The capturing step produces a captured set of cfDNA molecules, and the cfDNA molecules corresponding to the sequence-variable target region set are captured at a greater capture yield in the captured set of cfDNA molecules than cfDNA molecules corresponding to the epigenetic target region set. For additional discussion of capturing steps, capture yields, and related aspects, see W02020 / 160414, which is incorporated herein by reference for all purposes.

[0121] In some embodiments, a method described herein includes contacting cfDNA obtained from a test subject with a set of target-specific probes, wherein the set of targetspecific probes is configured to capture cfDNA corresponding to the sequence-variable target region set at a greater capture yield than cfDNA corresponding to the epigenetic target region set.

[0122] It can be beneficial to capture cfDNA corresponding to the sequence-variable target region set at a greater capture yield than cfDNA corresponding to the epigenetic target region set because a greater depth of sequencing may be necessary to analyze the sequencevariable target regions with sufficient confidence or accuracy than may be necessary to analyze the epigenetic target regions. The volume of data needed to determine fragmentation patterns (e.g., to test fsor perturbation of transcription start sites or CTCF binding sites) or fragment abundance (e.g., in hypermethylated and hypomethylated partitions) is generally less than the volume of data needed to determine the presence or absence of cancer-related sequence mutations. Capturing the target region sets at different yields can facilitate sequencing the target regions to different depths of sequencing in the same sequencing run (e.g., using a pooled mixture and / or in the same sequencing cell).

[0123] In various embodiments, the methods further comprise sequencing the captured cfDNA, e.g., to different degrees of sequencing depth for the epigenetic and sequencevariable target region sets, consistent with the discussion herein. In some embodiments, complexes of target-specific probes and DNA are separated from DNA not bound to targetspecific probes. For example, where target-specific probes are bound covalently or noncovalently to a solid support, a washing or aspiration step can be used to separate unbound material. Alternatively, where the complexes have chromatographic properties distinct from unbound material (e.g., where the probes comprise a ligand that binds a chromatographic resin), chromatography can be used.

[0124] As discussed in detail elsewhere herein, the set of target-specific probes may comprise a plurality of sets such as probes for a sequence-variable target region set and probes for an epigenetic target region set. In some such embodiments, the capturing step is performed with the probes for the sequence-variable target region set and the probes for the epigenetic target region set in the same vessel at the same time, e.g., the probes for the sequence-variable and epigenetic target region sets are in the same composition. This approach provides a relatively streamlined workflow. In some embodiments, the concentration of the probes for the sequence-variable target region set is greater that the concentration of the probes for the epigenetic target region set.

[0125] Alternatively, the capturing step is performed with the sequence-variable target region probe set in a first vessel and with the epigenetic target region probe set in a second vessel, or the contacting step is performed with the sequence-variable target region probe set at a first time and a first vessel and the epigenetic target region probe set at a second timebefore or after the first time. This approach allows for preparation of separate first and second compositions including captured DNA corresponding to the sequence-variable target region set and captured DNA corresponding to the epigenetic target region set. The compositions can be processed separately as desired (e.g., to fractionate based on methylation as described elsewhere herein) and recombined in appropriate proportions to provide material for further processing and analysis such as sequencing.

[0126] In some embodiments, the DNA is amplified. In some embodiments, amplification is performed before the capturing step. In some embodiments, amplification is performed after the capturing step.

[0127] In some embodiments, adapters are included in the DNA. This may be done concurrently with an amplification procedure, e.g., by providing the adapters in a 5’ portion of a primer, e.g., as described above. Alternatively, adapters can be added by other approaches, such as ligation.

[0128] In some embodiments, tags, which may be or include barcodes, are included in the DNA. Tags can facilitate identification of the origin of a nucleic acid. For example, barcodes can be used to allow the origin (e.g., subject) whence the DNA came to be identified following pooling of a plurality of samples for parallel sequencing. This may be done concurrently with an amplification procedure, e.g., by providing the barcodes in a 5’ portion of a primer, e.g., as described above. In some embodiments, adapters and tags / barcodes are provided by the same primer or primer set. For example, the barcode may be located 3’ of the adapter and 5’ of the target-hybridizing portion of the primer. Alternatively, barcodes can be added by other approaches, such as ligation, optionally together with adapters in the same ligation substrate.

[0129] Additional details regarding amplification, tags, and barcodes are discussed in the “General Features of the Methods” section below, which can be combined to the extent practicable with any of the foregoing embodiments and the embodiments set forth in the introduction and summary section.Captured Set

[0130] In some embodiments, a captured set of DNA (e.g., cfDNA) is provided. With respect to the disclosed methods, the captured set of DNA may be provided, e.g., by performing a capturing step after a partitioning step as described herein. The captured set may comprise DNA corresponding to a sequence-variable target region set, an epigenetictarget region set, or a combination thereof. In some embodiments the quantity of captured sequence-variable target region DNA is greater than the quantity of the captured epigenetic target region DNA, when normalized for the difference in the size of the targeted regions (footprint size).

[0131] Alternatively, first and second captured sets may be provided, including, respectively, DNA corresponding to a sequence-variable target region set and DNA corresponding to an epigenetic target region set. The first and second captured sets may be combined to provide a combined captured set.

[0132] In some embodiments in which a captured set including DNA corresponding to the sequence-variable target region set and the epigenetic target region set includes a combined captured set as discussed above, the DNA corresponding to the sequence-variable target region set may be present at a greater concentration than the DNA corresponding to the epigenetic target region set, e.g., a 1.1 to 1.2-fold greater concentration, a 1.2- to 1.4-fold greater concentration, a 1.4- to 1.6-fold greater concentration, a 1.6- to 1.8-fold greater concentration, a 1.8- to 2.0-fold greater concentration, a 2.0- to 2.2-fold greater concentration, a 2.2- to 2.4-fold greater concentration a 2.4- to 2.6-fold greater concentration, a 2.6- to 2.8-fold greater concentration, a 2.8- to 3.0-fold greater concentration, a 3.0- to 3.5- fold greater concentration, a 3.5- to 4.0, a 4.0- to 4.5-fold greater concentration, a 4.5- to 5.0- fold greater concentration, a 5.0- to 5.5-fold greater concentration, a 5.5- to 6.0-fold greater concentration, a 6.0- to 6.5-fold greater concentration, a 6.5- to 7.0-fold greater, a 7.0- to 7.5- fold greater concentration, a 7.5- to 8.0-fold greater concentration, an 8.0- to 8.5-fold greater concentration, an 8.5- to 9.0-fold greater concentration, a 9.0- to 9.5-fold greater concentration, 9.5- to 10.0-fold greater concentration, a 10- to 11-fold greater concentration, an 11- to 12-fold greater concentration a 12- to 13 -fold greater concentration, a 13- to 14-fold greater concentration, a 14- to 15-fold greater concentration, a 15- to 16-fold greater concentration, a 16- to 17-fold greater concentration, a 17- to 18-fold greater concentration, an 18- to 19-fold greater concentration, a 19- to 20-fold greater concentration, a 20- to 30- fold greater concentration, a 30- to 40-fold greater concentration, a 40- to 50-fold greater concentration, a 50- to 60-fold greater concentration, a 60- to 70-fold greater concentration, a 70- to 80-fold greater concentration, a 80- to 90-fold greater concentration, a 90- to 100-fold greater concentration, a 10- to 20-fold greater concentration, a 10- to 40-fold greater concentration, a 10- to 50-fold greater concentration, a 10- to 70-fold greater concentration, or a 10- to 100-fold greater concentration. The degree of difference in concentrationsaccounts for normalization for the footprint sizes of the target regions, as discussed in the definition section.Epigenetic Target Region Set

[0133] The epigenetic target region set may comprise one or more types of target regions likely to differentiate DNA from neoplastic (e.g., tumor or cancer) cells and from healthy cells, e.g., non-neoplastic circulating cells. Exemplary types of such regions are discussed in detail herein. The epigenetic target region set may also comprise one or more control regions, e.g., as described herein. In some embodiments, the epigenetic target region set has a footprint of at least 100 kb, e.g., at least 200 kb, at least 300 kb, or at least 400 kb. In some embodiments, the epigenetic target region set has a footprint in the range of 100-1000 kb, e.g., 100-200 kb, 200-300 kb, 300-400 kb, 400-500 kb, 500-600 kb, 600-700 kb, 700-800 kb, 800-900 kb, and 900-1,000 kb.HyDermethylation Variable Target Regions

[0134] In some embodiments, the epigenetic target region set includes one or more hypermethylation variable target regions. In general, hypermethylation variable target regions refer to regions where an increase in the level of observed methylation, e.g., in a cfDNA sample, indicates an increased likelihood that a sample (e.g., of cfDNA) contains DNA produced by neoplastic cells, such as tumor or cancer cells. For example, hypermethylation of promoters of tumor suppressor genes has been observed repeatedly. See, e.g., Kang et al., Genome Biol. 18:53 (2017) and references cited therein. In an example, hypermethylation variable target regions can include regions that do not necessarily differ in methylation in cancerous tissue relative to DNA from healthy tissue of the same type, but do differ in methylation (e.g., have more methylation) relative to cfDNA that is typical in healthy subjects. Where, for example, the presence of a cancer results in increased cell death such as apoptosis of cells of the tissue type corresponding to the cancer, such a cancer can be detected at least in part using such hypermethylation variable target regions. In some embodiments, hypermethylation variable target regions include one or more genomic regions, where the cfDNA molecules in those regions do not differ in methylation state in cancer subjects relative to cfDNA from healthy subjects, but the presence / increased quantity of hypermethylated cfDNA in those regions is indicative of a particular tissue type (e.g., cancer origin) and is presented as cfDNA with increased apoptosis (e.g. tumor shedding) into circulation.

[0135] Hypermethylation target regions may be obtained, e.g., from the Cancer Genome Atlas. Kang et al., Genome Biology 18:53 (2017), describe construction of a probabilistic method called CancerLocator using hypermethylation target regions from breast, colon, kidney, liver, and lung. In some embodiments, the hypermethylation target regions can be specific to one or more types of cancer. Accordingly, in some embodiments, the hypermethylation target regions include one, two, three, four, or five subsets of hypermethylation target regions that collectively show hypermethylation in one, two, three, four, or five of breast, colon, kidney, liver, and lung cancers.

[0136] In some embodiments, the probes for the epigenetic target region set comprise probes specific for one or more hypermethylation variable target regions. The hypermethylation variable target regions may be any of those set forth above. For example, in some embodiments, the probes specific for hypermethylation variable target regions comprise probes specific for a plurality of loci listed in Table 1, e.g., at least 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, or 100% of the loci listed in Table 1. In some embodiments, the probes specific for hypermethylation variable target regions comprise probes specific for a plurality of loci listed in Table 2, e.g., at least 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, or 100% of the loci listed in Table 2. In some embodiments, the probes specific for hypermethylation variable target regions comprise probes specific for a plurality of loci listed in Table 1 or Table 2, e.g., at least 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, or 100% of the loci listed in Table 1 or Table 2. In some embodiments, for each locus included as a target region, there may be one or more probes with a hybridization site that binds between the transcription start site and the stop codon (the last stop codon for genes that are alternatively spliced) of the gene. In some embodiments, the one or more probes bind within 300 bp of the listed position, e.g., within 200 or 100 bp. In some embodiments, a probe has a hybridization site overlapping the position listed above. In some embodiments, the probes specific for the hypermethylation target regions include probes specific for one, two, three, four, or five subsets of hypermethylation target regions that collectively show hypermethylation in one, two, three, four, or five of breast, colon, kidney, liver, and lung cancers.HvDomethylation Variable Target Regions

[0137] Global hypomethylation is a commonly observed phenomenon in various cancers. See, e.g., Hon et al., Genome Res. 22:246-258 (2012) (breast cancer); Ehrlich,Epigenomics 1 :239-259 (2009) (review article noting observations of hypomethylation in colon, ovarian, prostate, leukemia, hepatocellular, and cervical cancers). For example, regions such as repeated elements, e.g., LINE1 elements, Alu elements, centromeric tandem repeats, pericentromeric tandem repeats, and satellite DNA, and intergenic regions that are ordinarily methylated in healthy cells may show reduced methylation in tumor cells. Accordingly, in some embodiments, the epigenetic target region set includes hypomethylation variable target regions, where a decrease in the level of observed methylation indicates an increased likelihood that a sample (e.g., of cfDNA) contains DNA produced by neoplastic cells, such as tumor or cancer cells. In an example, hypomethylation variable target regions can include regions that do not necessarily differ in methylation state in cancerous tissue relative to DNA from healthy tissue of the same type, but do differ in methylation (e.g., are less methylated) relative to cfDNA that is typical in healthy subjects. Where, for example, the presence of a cancer results in increased cell death such as apoptosis of cells of the tissue type corresponding to the cancer, such a cancer can be detected at least in part using such hypomethylation variable target regions. In some embodiments, hypomethylation variable target regions include one or more genomic regions, where the cfDNA molecules in those regions do not differ in methylation state in cancer subjects relative to cfDNA from healthy subjects, but the presence / increased quantity of hypomethylated cfDNA in those regions is indicative of a particular tissue type (e.g., cancer origin) and is presented as cfDNA with increased apoptosis (e.g. tumor shedding) into circulation.

[0138] In some embodiments, hypomethylation variable target regions include repeated elements and / or intergenic regions. In some embodiments, repeated elements include one, two, three, four, or five of LINE1 elements, Alu elements, centromeric tandem repeats, pericentromeric tandem repeats, and / or satellite DNA.

[0139] Exemplary specific genomic regions that show cancer-associated hypomethylation include nucleotides 8403565-8953708 and 151104701-151106035 of human chromosome 1. In some embodiments, the hypomethylation variable target regions overlap or comprise one or both of these regions.

[0140] In some embodiments, the probes for the epigenetic target region set comprise probes specific for one or more hypomethylation variable target regions. The hypomethylation variable target regions may be any of those set forth above. For example, the probes specific for one or more hypomethylation variable target regions may includeprobes for regions such as repeated elements, e.g., LINE1 elements, Alu elements, centromeric tandem repeats, pericentromeric tandem repeats, and satellite DNA, and intergenic regions that are ordinarily methylated in healthy cells may show reduced methylation in tumor cells.

[0141] In some embodiments, probes specific for hypomethylation variable target regions include probes specific for repeated elements and / or intergenic regions. In some embodiments, probes specific for repeated elements include probes specific for one, two, three, four, or five of LINE1 elements, Alu elements, centromeric tandem repeats, pericentromeric tandem repeats, and / or satellite DNA.

[0142] Exemplary probes specific for genomic regions that show cancer-associated hypomethylation include probes specific for nucleotides 8403565-8953708 and / or 151104701-151106035 of human chromosome 1. In some embodiments, the probes specific for hypomethylation variable target regions include probes specific for regions overlapping or including nucleotides 8403565-8953708 and / or 151104701-151106035 of human chromosome

[0143] Probes for detecting the panel of regions can include those for detecting genomic regions of interest (hotspot regions) as well as nucleosome-aware probes (e.g., KRAS codons 12 and 13) and may be designed to optimize capture based on analysis of cfDNA coverage and fragment size variation impacted by nucleosome binding patterns and GC sequence composition. Regions used herein can also include non-hotspot regions optimized based on nucleosome positions and GC models. Subjects

[0144] In some embodiments, the DNA (e.g., cfDNA) is obtained from a subject having a cancer. In some embodiments, the DNA (e.g., cfDNA) is obtained from a subject suspected of having a cancer. In some embodiments, the DNA (e.g., cfDNA) is obtained from a subject having a tumor. In some embodiments, the DNA (e.g., cfDNA) is obtained from a subject suspected of having a tumor. In some embodiments, the DNA (e.g., cfDNA) is obtained from a subject having neoplasia. In some embodiments, the DNA (e.g., cfDNA) is obtained from a subject suspected of having neoplasia. In some embodiments, the DNA (e.g., cfDNA) is obtained from a subject in remission from a tumor, cancer, or neoplasia (e.g., following chemotherapy, surgical resection, radiation, or a combination thereof). In any of the foregoing embodiments, the cancer, tumor, or neoplasia or suspected cancer, tumor, or neoplasia may be of the lung, colon, rectum, kidney, breast, prostate, or liver. In some embodiments, the cancer, tumor, or neoplasia or suspected cancer, tumor, or neoplasia is ofthe lung. In some embodiments, the cancer, tumor, or neoplasia or suspected cancer, tumor, or neoplasia is of the colon or rectum. In some embodiments, the cancer, tumor, or neoplasia or suspected cancer, tumor, or neoplasia is of the breast. In some embodiments, the cancer, tumor, or neoplasia or suspected cancer, tumor, or neoplasia is of the prostate. In any of the foregoing embodiments, the subject may be a human subject.

[0145] In some embodiments, the sequence-variable target region probe set has a footprint of at least 0.5 kb, e.g., at least 1 kb, at least 2 kb, at least 5 kb, at least 10 kb, at least 20 kb, at least 30 kb, or at least 40 kb. In some embodiments, the epigenetic target region probe set has a footprint in the range of 0.5-100 kb, e.g., 0.5-2 kb, 2-10 kb, 10-20 kb, 20-30 kb, 30-40 kb, 40-50 kb, 50-60 kb, 60-70 kb, 70-80 kb, 80-90 kb, and 90-100 kb.

[0146] In some embodiments, the probes specific for the sequence-variable target region set comprise probes specific for target regions from at least 10, 20, 30, or 35 cancer- related genes, such as AKT1, ALK, BRAF, CCND1, CDK2A, CTNNB1, EGFR, ERBB2, ESRI, FGFR1, FGFR2, FGFR3, FOXL2, GATA3, GNA11, GNAQ, GNAS, HRAS, IDH1, IDH2, KIT, KRAS, MED12, MET, MYC, NFE2L2, NRAS, PDGFRA, PIK3CA, PPP2R1A, PTEN, RET, STK11, TP53, and U2AF1.Compositions Including Captured DNA

[0147] Provided herein is a combination including first and second populations of captured DNA. The first population may comprise or be derived from DNA with a cytosine modification in a greater proportion than the second population. The first population may comprise a form of a first nucleobase originally present in the DNA with altered base pairing specificity and a second nucleobase without altered base pairing specificity, wherein the form of the first nucleobase originally present in the DNA prior to alteration of base pairing specificity is a modified or unmodified nucleobase, the second nucleobase is a modified or unmodified nucleobase different from the first nucleobase, and the form of the first nucleobase originally present in the DNA prior to alteration of base pairing specificity and the second nucleobase have the same base pairing specificity. The second population does not comprise the form of the first nucleobase originally present in the DNA with altered base pairing specificity. In some embodiments, the cytosine modification is cytosine methylation. In some embodiments, the first nucleobase is a modified or unmodified cytosine and the second nucleobase is a modified or unmodified cytosine. The first and second nucleobase may be any of those discussed herein in the Summary or with respect to subjecting the firstsubsample to a procedure that affects a first nucleobase in the DNA differently from a second nucleobase in the DNA of the first subsample.

[0148] In some embodiments, the first population includes a sequence tag selected from a first set of one or more sequence tags and the second population includes a sequence tag selected from a second set of one or more sequence tags, and the second set of sequence tags is different from the first set of sequence tags. The sequence tags may comprise barcodes.

[0149] In some embodiments, the first population includes protected hmC, such as glucosylated hmC. In some embodiments, the first population was subjected to any of the conversion procedures discussed herein, such as bisulfite conversion, Ox-BS conversion, TAB conversion, ACE conversion, TAP conversion, TAPSP conversion, or CAP conversion. In some embodiments, the first population was subjected to protection of hmC followed by deamination of mC and / or C. In some embodiments of the combination, the first population includes or was derived from DNA with a cytosine modification in a greater proportion than the second population and the first population includes first and second subpopulations, and the first nucleobase is a modified or unmodified nucleobase, the second nucleobase is a modified or unmodified nucleobase different from the first nucleobase, and the first nucleobase and the second nucleobase have the same base pairing specificity. In some embodiments, the second population does not comprise the first nucleobase. In some embodiments, the first nucleobase is a modified or unmodified cytosine, and the second nucleobase is a modified or unmodified cytosine, optionally wherein the modified cytosine is mC or hmC. In some embodiments, the first nucleobase is a modified or unmodified adenine, and the second nucleobase is a modified or unmodified adenine, optionally wherein the modified adenine is mA.

[0150] In some embodiments, the first nucleobase (e.g., a modified cytosine) is biotinylated. In some embodiments, the first nucleobase (e.g., a modified cytosine) is a product of a Huisgen cycloaddition to P-6-azide-glucosyl-5-hydroxymethylcytosine that includes an affinity label (e.g., biotin).

[0151] In any of the combinations described herein, the captured DNA may comprise cfDNA. The captured DNA may have any of the features described herein concerning captured sets, including, e.g., a greater concentration of the DNA corresponding to the sequence-variable target region set (normalized for footprint size as discussed above) than of the DNA corresponding to the epigenetic target region set. In some embodiments, the DNA of the captured set includes sequence tags, which may be added to the DNA as describedherein. In general, the inclusion of sequence tags results in the DNA molecules differing from their naturally occurring, untagged form.

[0152] The combination may further comprise a probe set described herein or sequencing primers, each of which may differ from naturally occurring nucleic acid molecules. For example, a probe set described herein may comprise a capture moiety, and sequencing primers may comprise a non-naturally occurring label.Computer Systems, Processing of Real World Evidence (RWE)

[0153] Methods of the present disclosure can be implemented using, or with the aid of, computer systems. For example, such methods may comprise: partitioning the sample into a plurality of subsamples, including a first subsample and a second subsample, wherein the first subsample includes DNA with a cytosine modification in a greater proportion than the second subsample; subjecting the first subsample to a procedure that affects a first nucleobase in the DNA differently from a second nucleobase in the DNA of the first subsample, wherein the first nucleobase is a modified or unmodified nucleobase, the second nucleobase is a modified or unmodified nucleobase different from the first nucleobase, and the first nucleobase and the second nucleobase have the same base pairing specificity; and sequencing DNA in the first subsample and DNA in the second subsample in a manner that distinguishes the first nucleobase from the second nucleobase in the DNA of the first subsample.

[0154] In an aspect, the present disclosure provides a non-transitory computer-readable medium including computer-executable instructions which, when executed by at least one electronic processor, perform at least a portion of a method including: collecting cfDNA from a test subject; capturing a plurality of sets of target regions from the cfDNA, wherein the plurality of target region sets includes a sequence-variable target region set and an epigenetic target region set, whereby a captured set of cfDNA molecules is produced; sequencing the captured cfDNA molecules, wherein the captured cfDNA molecules of the sequence-variable target region set are sequenced to a greater depth of sequencing than the captured cfDNA molecules of the epigenetic target region set; obtaining a plurality of sequence reads generated by a nucleic acid sequencer from sequencing the captured cfDNA molecules; mapping the plurality of sequence reads to one or more reference sequences to generate mapped sequence reads; and processing the mapped sequence reads corresponding to the sequence-variable target region set and to the epigenetic target region set to determine the likelihood that the subject has cancer.

[0155] The code can be pre-compiled and configured for use with a machine with a processer adapted to execute the code or can be compiled during runtime. The code can be supplied in a programming language that can be selected to enable the code to execute in a pre-compiled or as-compiled fashion.

[0156] Additional details relating to computer systems and networks, databases, and computer program products are also provided in, for example, Peterson, Computer Networks: A Systems Approach, Morgan Kaufmann, 5th Ed. (2011), Kurose, Computer Networking: A Top-Down Approach, Pearson, 7th Ed. (2016), Elmasri, Fundamentals of Database Systems, Addison Wesley, 6th Ed. (2010), Coronel, Database Systems: Design, Implementation, & Management, Cengage Learning, 11th Ed. (2014), Tucker, Programming Languages, McGraw-Hill Science / Engineering / Math, 2nd Ed. (2006), and Rhoton, Cloud Computing Architected: Solution Design Handbook, Recursive Press (2011), each of which is hereby incorporated by reference in its entirety. Further information is found in PCT Pub. No. US2022032250 and U.S. App. No. 17832498.

[0157] Described herein is a method to generate an integrated data repository that includes multiple types of healthcare data, according to one or more implementations. The architecture may include a data integration and analysis system. The data integration and analysis system may obtain data from a number of data sources and integrate the data from the data sources into an integrated data repository. For example, the data integration and analysis system may obtain data from a electronic medical record, including for example, patient information, health insurrance claims, lifestyle data, repository. In various examples, the data integration and analysis system and the electronic medical record, including for example, patient information, health insurrance claims, lifestyle data, repository may be created and maintained by different entities. In one or more additional examples, the data integration and analysis system and the electronic medical record, including for example, patient information, health insurrance claims, lifestyle data, repository may be created and maintained by the same entity.

[0158] The data integration and analysis system may include a data analysis system. The data analysis system may receive integrated data repository requests from one or more computing devices, such as an example computing device. The one or more integrated data repository requests may cause data to be retrieved from the integrated data repository. In various examples, the one or more integrated data repository requests may cause data to be retrieved from one or more datasets generated by the data pipeline system. The integrated datarepository requests may specify the data to be retrieved from the integrated data repository and / or the one or more datasets generated by the data pipeline system. In one or more additional examples, the integrated data repository requests may include one or more prebuilt queries that correspond to computer-executable instructions that retrieve a specified set of data from the integrated data repository and / or one or more datasets generated by the data pipeline system.

[0159] In response to one or more integrated data repository requests, the data analysis system may analyze data retrieved from at least one of the integrated data repository or one or more datasets generated by the data pipeline system to generate data analysis results . The data analysis results may be sent to one or more computing devices, such as example computing devices. Although the illustrative example of hows that the one or more integrated data repository requests from one computing device and the data analysis results being sent to another computing device , in one or more additional implementations, the data analysis results may be received by a same computing device that sent the one or more integrated data repository requests . The data analysis results may be displayed by one or more user interfaces rendered by the computing device or the computing device.

[0160] In one or more examples, the data analysis system may implement at least one of one or more machine learning techniques or one or more statistical techniques to analyze data retrieved in response to one or more integrated data repository requests. In one or more examples, the data analysis system may implement one or more artificial neural networks to analyze data retrieved in response to one or more integrated data repository requests. To illustrate, the data analysis system may implement at least one of one or more convolutional neural networks or one or more residual neural networks to analyze data retrieved from the integrated data repository in response to one or more integrated data repository requests. In at least some examples, the data analysis system may implement one or more random forests techniques, one or more support vector machines, or one or more Hidden Markov models to analyze data retrieved in response to one or more integrated data repository requests. One or more statistical models may also be implemented to analyze data retrieved in response to one or more integrated data repository requests to identify at least one of correlations or measures of significance between characteristics of individuals. For example, log rank tests may be applied to data retrieved in response to one or more integrated data repository requests. In addition, Cox proportional hazards models may be implemented with respect to date retrieved in response to one or more integrated data repository requests. Further, Wilcoxon signed ranktests may be applied to data retrieved in response to one or more integrated data repository requests. In still other examples, a z-score analysis may be performed with respect to data retrieved in response to one or more integrated data repository requests. In still additional examples, a Kaplan Meier analysis may be performed with respect to data retrieved in response to one or more integrated data repository requests. In at least some examples, one or more machine learning techniques may be implemented in combination with one or more statistical techniques to analyze data retrieved in response to one or more integrated data repository requests.

[0161] In one or more illustrative examples, the data analysis system may determine a rate of survival of individuals in which lung cancer is present in response to one or more treatments. In one or more additional illustrative examples, the data analysis system may determine a rate of survival of individuals having one or more genomic region mutations in which lung cancer is present in response to one or more treatments. In various examples, the data analysis system may generate the data analysis results in situations where the data retrieved from at least one of the integrated data repository or the one or more datasets generated by the data pipeline system satisfies one or more criteria. For example, the data analysis system may determine whether at least a portion of the data retrieved in response to one or more integrated data repository requests satisfies a threshold confidence level. In situations where the confidence level for at least a portion of the date retrieved in response to one or more integrated data repository requests is less than a threshold confidence level, the data analysis system may refrain from generating at least a portion of data analysis results. In scenarios where the confidence level for at least a portion of the data retrieved in response to one or more integrated data repository requests is at least a threshold confidence level, the data analysis system may generate at least a portion of the data analysis results. In various examples, the threshold confidence level may be related to the type of data analysis results being generated by the data analysis system.

[0162] In one or more illustrative examples, the data analysis system may receive an integrated data repository request to generate data analysis results that indicate a rate of survival of one or more individuals. In these instances, the data analysis system may determine whether the data stored by the integrated data repository and / or by one or more datasets generated by the data pipeline system satisfies a threshold confidence level, such as a Gold standard confidence level. In one or more additional examples, the data analysis system may receive an integrated data repository request to generate data analysis results thatindicate a treatment received by one or more individuals. In these implementations, the data analysis system may determine whether the data stored by the integrated data repository and / or by one or more datasets generated by the data pipeline system satisfies a lower threshold confidence level, such as a Bronze standard confidence level.

[0163] In one or more additional illustrative examples, the data analysis system may receive an integrated data repository request to determine individuals having one or more genomic mutations and that have received one or more treatments for a biological condition. Continuing with this example, the data analysis system can determine a survival rate of individuals with the one or more genomic mutations in relation to the one or more treatments received by the individuals. The data analysis system can then identify based on the survival rate of individuals and effectiveness of treatments for the individuals in relation to genomic mutations that may be present in the individuals. In this way, health outcomes of individuals may be improved by identifying prospective treatments that may be more effective for populations of individuals having one or more genomic mutations than current treatments being provided to the individuals.

[0164] Described herein is a machine in the form of a computer system within which a set of instructions may be executed for causing the machine to perform any one or more of the methodologies discussed herein, according to an example, according to an example implementation. For example, a machine in the example form of a computer system, within which instructions (e.g., software, a program, an application, an applet, an app, or other executable code) for causing the machine to perform any one or more of the methodologies discussed herein may be executed. For example, the instructions may cause the machine to implement the architectures and frameworks described previously, and to execute the methods described with respect to previously.

[0165] The instructions transform the general, non-programmed machine into a particular machine programmed to carry out the described and illustrated functions in the manner described. In alternative implementations, the machine operates as a standalone device or may be coupled (e.g., networked) to other machines. In a networked deployment, the machine may operate in the capacity of a server machine or a client machine in a server-client network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. The machine may comprise, but not be limited to, a server computer, a client computer, a personal computer (PC), a tablet computer, a laptop computer, a netbook, a set-top box (STB), a personal digital assistant (PDA), an entertainment media system, a cellulartelephone, a smart phone, a mobile device, a wearable device (e.g., a smart watch), a smart home device (e.g., a smart appliance), other smart devices, a web appliance, a network router, a network switch, a network bridge, or any machine capable of executing the instructions , sequentially or otherwise, that specify actions to be taken by the machine . Further, while only a single machine is illustrated, the term “machine” shall also be taken to include a collection of machines that individually or jointly execute the instructions to perform any one or more of the methodologies discussed herein.

[0166] Examples of computing devices may include logic, one or more components, circuits (e.g., modules), or mechanisms. Circuits are tangible entities configured to perform certain operations. In an example, circuits may be arranged (e.g., internally or with respect to external entities such as other circuits) in a specified manner. In an example, one or more computer systems (e.g., a standalone, client or server computer system) or one or more hardware processors (processors) may be configured by software (e.g., instructions, an application portion, or an application) as a circuit that operates to perform certain operations as described herein. In an example, the software may reside (1) on a non -transitory machine readable medium or (2) in a transmission signal. In an example, the software, when executed by the underlying hardware of the circuit, causes the circuit to perform the certain operations.

[0167] In an example, a circuit may be implemented mechanically or electronically. For example, a circuit may comprise dedicated circuitry or logic that is specifically configured to perform one or more techniques such as discussed above, such as including a special-purpose processor, a field programmable gate array (FPGA) or an application-specific integrated circuit (ASIC). In an example, a circuit may comprise programmable logic (e.g., circuitry, as encompassed within a general-purpose processor or other programmable processor) that may be temporarily configured (e.g., by software) to perform the certain operations. It will be appreciated that the decision to implement a circuit mechanically (e.g., in dedicated and permanently configured circuitry), or in temporarily configured circuitry (e.g., configured by software) may be driven by cost and time considerations.

[0168] Accordingly, the term “circuit” is understood to encompass a tangible entity, be that an entity that is physically constructed, permanently configured (e.g., hardwired), or temporarily (e.g., transitorily) configured (e.g., programmed) to operate in a specified manner or to perform specified operations. In an example, given a plurality of temporarily configured circuits, each of the circuits need not be configured or instantiated at any one instance in time. For example, where the circuits comprise, a general -purpose processorconfigured via software, the general -purpose processor may be configured as respective different circuits at different times. Software may accordingly configure a processor, for example, to constitute a particular circuit at one instance of time and to constitute a different circuit at a different instance of time.

[0169] In an example, circuits may provide information to, and receive information from, other circuits. In this example, the circuits may be regarded as being communicatively coupled to one or more other circuits. Where multiples of such circuits exist contemporaneously, communications may be achieved through signal transmission (e.g., over appropriate circuits and buses) that connect the circuits. In implementations in which multiple circuits are configured or instantiated at different times, communications between such circuits may be achieved, for example, through the storage and retrieval of information in memory structures to which the multiple circuits have access. For example, one circuit may perform an operation and store the output of that operation in a memory device to which it is communicatively coupled. A further circuit may then, at a later time, access the memory device to retrieve and process the stored output. In an example, circuits may be configured to initiate or receive communications with input or output devices and may operate on a resource (e.g., a collection of information).

[0170] The various operations of method examples described herein may be performed, at least partially, by one or more processors that are temporarily configured (e.g., by software) or permanently configured to perform the relevant operations. Whether temporarily or permanently configured, such processors may constitute processor-implemented circuits that operate to perform one or more operations or functions. In an example, the circuits referred to herein may comprise processor-implemented circuits.

[0171] Similarly, the methods described herein may be at least partially processor implemented. For example, at least some or all of the operations of a method may be performed by one or processors or processor-implemented circuits. The performance of certain of the operations may be distributed among the one or more processors, not only residing within a single machine, but deployed across a number of machines. In an example, the processor or processors may be located in a single location (e.g., within a home environment, an office environment or as a server farm), while in other examples the processors may be distributed across a number of locations.

[0172] The one or more processors may also operate to support performance of the relevant operations in a "cloud computing" environment or as a "software as a service.”

[0173] (SaaS). For example, at least some of the operations may be performed by a group of computers (as examples of machines including processors), with these operations being accessible via a network (e.g., the Internet) and via one or more appropriate interfaces (e.g., Application Program Interfaces (APIs).)

[0174] Example implementations (e.g., apparatus, systems, or methods) may be implemented in digital electronic circuitry, in computer hardware, in firmware, in software, or in any combination thereof. Example implementations may be implemented using a computer program product (e.g., a computer program, tangibly embodied in an information carrier or in a machine readable medium, for execution by, or to control the operation of, data processing apparatus such as a programmable processor, a computer, or multiple computers).

[0175] A computer program may be written in any form of programming language, including compiled or interpreted languages, and it may be deployed in any form, including as a standalone program or as a software module, subroutine, or other unit suitable for use in a computing environment. A computer program may be deployed to be executed on one computer or on multiple computers at one site or distributed across multiple sites and interconnected by a communication network.

[0176] In an example, operations may be performed by one or more programmable processors executing a computer program to perform functions by operating on input data and generating output. Examples of method operations may also be performed by, and example apparatus may be implemented as, special purpose logic circuitry (e.g., a field programmable gate array (FPGA) or an application-specific integrated circuit (ASIC)).

[0177] The computing system may include clients and servers. A client and server are generally remote from each other and generally interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship with each other. In implementations deploying a programmable computing system, it will be appreciated that both hardware and software architectures require consideration. Specifically, it will be appreciated that the choice of whether to implement certain functionality in permanently configured hardware (e.g., an ASIC), in temporarily configured hardware (e.g., a combination of software and a programmable processor), or a combination of permanently and temporarily configured hardware may be a design choice. Below are set out hardware (e.g., computing device) and software architectures that may be deployed in example implementations.

[0178] In an example, the computing device may operate as a standalone device or the computing device may be connected (e.g., networked) to other machines.

[0179] In a networked deployment, the computing device may operate in the capacity of either a server or a client machine in server-client network environments. In an example, computing device may act as a peer machine in peer-to-peer (or other distributed) network environments. The computing device may be a personal computer (PC), a tablet PC, a set-top box (STB), a mobile telephone, a web appliance, a network router, switch or bridge, or any machine capable of executing instructions (sequential or otherwise) specifying actions to be taken (e.g., performed) by the computing device . Further, while only a single computing device is illustrated, the term “computing device” shall also be taken to include any collection of machines that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein.Cancer and Other Diseases

[0180] The present methods can be used to diagnose presence of conditions, particularly cancer, in a subject, to characterize conditions (e.g., staging cancer or determining heterogeneity of a cancer), monitor response to treatment of a condition, effect prognosis risk of developing a condition or subsequent course of a condition. The present disclosure can also be useful in determining the efficacy of a particular treatment option. Successful treatment options may increase the amount of copy number variation or rare mutations detected in subject's blood if the treatment is successful as more cancers may die and shed DNA. In other examples, this may not occur. In another example, perhaps certain treatment options may be correlated with genetic profiles of cancers over time. This correlation may be useful in selecting a therapy.

[0181] Additionally, if a cancer is observed to be in remission after treatment, the present methods can be used to monitor residual disease or recurrence of disease.

[0182] In some embodiments, the methods and systems disclosed herein may be used to identify customized or targeted therapies to treat a given disease or condition in patients based on the classification of a nucleic acid variant as being of somatic or germline origin. Typically, the disease under consideration is a type of cancer. Non-limiting examples of such cancers include biliary tract cancer, bladder cancer, transitional cell carcinoma, urothelial carcinoma, brain cancer, gliomas, astrocytomas, breast carcinoma, metaplastic carcinoma, cervical cancer, cervical squamous cell carcinoma, rectal cancer, colorectal carcinoma, coloncancer, hereditary nonpolyposis colorectal cancer, colorectal adenocarcinomas, gastrointestinal stromal tumors (GISTs), endometrial carcinoma, endometrial stromal sarcomas, esophageal cancer, esophageal squamous cell carcinoma, esophageal adenocarcinoma, ocular melanoma, uveal melanoma, gallbladder carcinomas, gallbladder adenocarcinoma, renal cell carcinoma, clear cell renal cell carcinoma, transitional cell carcinoma, urothelial carcinomas, Wilms tumor, leukemia, acute lymphocytic leukemia (ALL), acute myeloid leukemia (AML), chronic lymphocytic leukemia (CLL), chronic myeloid leukemia (CML), chronic myelomonocytic leukemia (CMML), liver cancer, liver carcinoma, hepatoma, hepatocellular carcinoma, cholangiocarcinoma, hepatoblastoma, Lung cancer, non-small cell lung cancer (NSCLC), mesothelioma, B-cell lymphomas, nonHodgkin lymphoma, diffuse large B-cell lymphoma, Mantle cell lymphoma, T cell lymphomas, non-Hodgkin lymphoma, precursor T-lymphoblastic lymphoma / leukemia, peripheral T cell lymphomas, multiple myeloma, nasopharyngeal carcinoma (NPC), neuroblastoma, oropharyngeal cancer, oral cavity squamous cell carcinomas, osteosarcoma, ovarian carcinoma, pancreatic cancer, pancreatic ductal adenocarcinoma, pseudopapillary neoplasms, acinar cell carcinomas. Prostate cancer, prostate adenocarcinoma, skin cancer, melanoma, malignant melanoma, cutaneous melanoma, small intestine carcinomas, stomach cancer, gastric carcinoma, gastrointestinal stromal tumor (GIST), uterine cancer, or uterine sarcoma. Type and / or stage of cancer can be detected from genetic variations including mutations, rare mutations, indels, copy number variations, transversions, translocations, inversion, deletions, aneuploidy, partial aneuploidy, polyploidy, chromosomal instability, chromosomal structure alterations, gene fusions, chromosome fusions, gene truncations, gene amplification, gene duplications, chromosomal lesions, DNA lesions, abnormal changes in nucleic acid chemical modifications, abnormal changes in epigenetic patterns, and abnormal changes in nucleic acid 5 -methylcytosine.

[0183] Genetic data can also be used for characterizing a specific form of cancer. Cancers are often heterogeneous in both composition and staging. Genetic profile data may allow characterization of specific sub-types of cancer that may be important in the diagnosis or treatment of that specific sub-type. This information may also provide a subject or practitioner clues regarding the prognosis of a specific type of cancer and allow either a subject or practitioner to adapt treatment options in accord with the progress of the disease. Some cancers can progress to become more aggressive and genetically unstable. Othercancers may remain benign, inactive or dormant. The system and methods of this disclosure may be useful in determining disease progression.

[0184] Further, the methods of the disclosure may be used to characterize the heterogeneity of an abnormal condition in a subject. Such methods can include, e.g., generating a genetic profile of extracellular polynucleotides derived from the subject, wherein the genetic profile includes a plurality of data resulting from copy number variation and rare mutation analyses. In some embodiments, an abnormal condition is cancer. In some embodiments, the abnormal condition may be one resulting in a heterogeneous genomic population. In the example of cancer, some tumors are known to comprise tumor cells in different stages of the cancer. In other examples, heterogeneity may comprise multiple foci of disease. Again, in the example of cancer, there may be multiple tumor foci, perhaps where one or more foci are the result of metastases that have spread from a primary site.

[0185] The present methods can be used to generate our profile, fingerprint or set of data that is a summation of genetic information derived from different cells in a heterogeneous disease. This set of data may comprise copy number variation, epigenetic variation, and mutation analyses alone or in combination.

[0186] The present methods can be used to diagnose, prognose, monitor or observe cancers, or other diseases. In some embodiments, the methods herein do not involve the diagnosing, prognosing or monitoring a fetus and as such are not directed to non-invasive prenatal testing. In other embodiments, these methodologies may be employed in a pregnant subject to diagnose, prognose, monitor or observe cancers or other diseases in an unborn subject whose DNA and other polynucleotides may co-circulate with maternal molecules.

[0187] Non-limiting examples of other genetic-based diseases, disorders, or conditions that are optionally evaluated using the methods and systems disclosed herein include achondroplasia, alpha-1 antitrypsin deficiency, antiphospholipid syndrome, autism, autosomal dominant polycystic kidney disease, Charcot-Marie-Tooth (CMT), cri du chat, Crohn's disease, cystic fibrosis, Dercum disease, down syndrome, Duane syndrome, Duchenne muscular dystrophy, Factor V Leiden thrombophilia, familial hypercholesterolemia, familial Mediterranean fever, fragile X syndrome, Gaucher disease, hemochromatosis, hemophilia, holoprosencephaly, Huntington's disease, Klinefelter syndrome, Marfan syndrome, myotonic dystrophy, neurofibromatosis, Noonan syndrome, osteogenesis imperfecta, Parkinson's disease, phenylketonuria, Poland anomaly, porphyria, progeria, retinitis pigmentosa, severe combined immunodeficiency (SCID), sickle celldisease, spinal muscular atrophy, Tay-Sachs, thalassemia, trimethylaminuria, Turner syndrome, velocardiofacial syndrome, WAGR syndrome, Wilson disease, or the like.

[0188] In some embodiments, a method described herein includes detecting a presence or absence of DNA originating or derived from a tumor cell at a preselected timepoint following a previous cancer treatment of a subject previously diagnosed with cancer using a set of sequence information obtained as described herein. The method may further comprise determining a cancer recurrence score that is indicative of the presence or absence of the DNA originating or derived from the tumor cell for the test subject. Where a cancer recurrence score is determined, it may further be used to determine a cancer recurrence status. The cancer recurrence status may be at risk for cancer recurrence, e.g., when the cancer recurrence score is above a predetermined threshold. The cancer recurrence status may be at low or lower risk for cancer recurrence, e.g., when the cancer recurrence score is above a predetermined threshold. In particular embodiments, a cancer recurrence score equal to the predetermined threshold may result in a cancer recurrence status of either at risk for cancer recurrence or at low or lower risk for cancer recurrence.

[0189] In some embodiments, a cancer recurrence score is compared with a predetermined cancer recurrence threshold, and the test subject is classified as a candidate for a subsequent cancer treatment when the cancer recurrence score is above the cancer recurrence threshold or not a candidate for therapy when the cancer recurrence score is below the cancer recurrence threshold. In particular embodiments, a cancer recurrence score equal to the cancer recurrence threshold may result in classification as either a candidate for a subsequent cancer treatment or not a candidate for therapy.

[0190] The methods discussed above may further comprise any compatible feature or features set forth elsewhere herein, including in the section regarding methods of determining a risk of cancer recurrence in a test subject and / or classifying a test subject as being a candidate for a subsequent cancer treatment.Methods of Determining a Risk of Cancer Recurrence in a Test Subject and / or Classifying a Test Subject as Being a Candidate for a Subsequent Cancer Treatment.

[0191] In some embodiments, a method provided herein is a method of determining a risk of cancer recurrence in a test subject. In some embodiments, a method provided herein is a method of classifying a test subject as being a candidate for a subsequent cancer treatment.

[0192] Any of such methods may comprise collecting DNA (e.g., originating or derived from a tumor cell) from the test subject diagnosed with the cancer at one or more preselected timepoints following one or more previous cancer treatments to the test subject. The subject may be any of the subjects described herein. The DNA may be cfDNA. The DNA may be obtained from a tissue sample.

[0193] Any of such methods may comprise capturing a plurality of sets of target regions from DNA from the subject, wherein the plurality of target region sets includes a sequencevariable target region set and an epigenetic target region set, whereby a captured set of DNA molecules is produced. The capturing step may be performed according to any of the embodiments described elsewhere herein. In any of such methods, the previous cancer treatment may comprise surgery, administration of a therapeutic composition, and / or chemotherapy.

[0194] Any of such methods may comprise sequencing the captured DNA molecules, whereby a set of sequence information is produced. The captured DNA molecules of the sequence-variable target region set may be sequenced to a greater depth of sequencing than the captured DNA molecules of the epigenetic target region set.

[0195] Any of such methods may comprise detecting a presence or absence of DNA originating or derived from a tumor cell at a preselected timepoint using the set of sequence information. The detection of the presence or absence of DNA originating or derived from a tumor cell may be performed according to any of the embodiments thereof described elsewhere herein.

[0196] Methods of determining a risk of cancer recurrence in a test subject may comprise determining a cancer recurrence score that is indicative of the presence or absence, or amount, of the DNA originating or derived from the tumor cell for the test subject. The cancer recurrence score may further be used to determine a cancer recurrence status. The cancer recurrence status may be at risk for cancer recurrence, e.g., when the cancer recurrence score is above a predetermined threshold. The cancer recurrence status may be at low or lower risk for cancer recurrence, e.g., when the cancer recurrence score is above a predetermined threshold. In particular embodiments, a cancer recurrence score equal to the predetermined threshold may result in a cancer recurrence status of either at risk for cancer recurrence or at low or lower risk for cancer recurrence.

[0197] Methods of classifying a test subject as being a candidate for a subsequent cancer treatment may comprise comparing the cancer recurrence score of the test subject with apredetermined cancer recurrence threshold, thereby classifying the test subject as a candidate for the subsequent cancer treatment when the cancer recurrence score is above the cancer recurrence threshold or not a candidate for therapy when the cancer recurrence score is below the cancer recurrence threshold. In particular embodiments, a cancer recurrence score equal to the cancer recurrence threshold may result in classification as either a candidate for a subsequent cancer treatment or not a candidate for therapy. In some embodiments, the subsequent cancer treatment includes chemotherapy or administration of a therapeutic composition.

[0198] Any of such methods may comprise determining a disease-free survival (DFS) period for the test subject based on the cancer recurrence score; for example, the DFS period may be 1 year, 2 years, 3, years, 4 years, 5 years, or 10 years.

[0199] In some embodiments, the set of sequence information includes sequence-variable target region sequences, and determining the cancer recurrence score may comprise determining at least a first subscore indicative of the amount of SNVs, insertions / deletions, CNVs and / or fusions present in sequence-variable target region sequences.

[0200] In some embodiments, a number of mutations in the sequence-variable target regions chosen from 1, 2, 3, 4, or 5 is sufficient for the first subscore to result in a cancer recurrence score classified as positive for cancer recurrence. In some embodiments, the number of mutations is chosen from 1, 2, or 3.

[0201] In some embodiments, the set of sequence information includes epigenetic target region sequences, and determining the cancer recurrence score includes determining a second subscore indicative of the amount of molecules (obtained from the epigenetic target region sequences) that represent an epigenetic state different from DNA found in a corresponding sample from a healthy subject (e.g., cfDNA found in a blood sample from a healthy subject, or DNA found in a tissue sample from a healthy subject where the tissue sample is of the same type of tissue as was obtained from the test subject). These abnormal molecules (i.e., molecules with an epigenetic state different from DNA found in a corresponding sample from a healthy subject) may be consistent with epigenetic changes associated with cancer, e.g., methylation of hypermethylation variable target regions and / or perturbed fragmentation of fragmentation variable target regions, where “perturbed” means different from DNA found in a corresponding sample from a healthy subject.

[0202] In some embodiments, a proportion of molecules corresponding to the hypermethylation variable target region set and / or fragmentation variable target region setthat indicate hypermethylation in the hypermethylation variable target region set and / or abnormal fragmentation in the fragmentation variable target region set greater than or equal to a value in the range of 0.001%-10% is sufficient for the second subscore to be classified as positive for cancer recurrence. The range may be 0.001%-l%, 0.005%-l%, 0.01%-5%, 0.01%-2%, or 0.01%-l%.

[0203] In some embodiments, any of such methods may comprise determining a fraction of tumor DNA from the fraction of molecules in the set of sequence information that indicate one or more features indicative of origination from a tumor cell. This may be done for molecules corresponding to some or all of the epigenetic target regions, e.g., including one or both of hypermethylation variable target regions and fragmentation variable target regions (hypermethylation of a hypermethylation variable target region and / or abnormal fragmentation of a fragmentation variable target region may be considered indicative of origination from a tumor cell). This may be done for molecules corresponding to sequence variable target regions, e.g., molecules including alterations consistent with cancer, such as SNVs, indels, CNVs, and / or fusions. The fraction of tumor DNA may be determined based on a combination of molecules corresponding to epigenetic target regions and molecules corresponding to sequence variable target regions.

[0204] Determination of a cancer recurrence score may be based at least in part on the fraction of tumor DNA, wherein a fraction of tumor DNA greater than a threshold in the range of 10-11 to 1 or 10-10 to 1 is sufficient for the cancer recurrence score to be classified as positive for cancer recurrence. In some embodiments, a fraction of tumor DNA greater than or equal to a threshold in the range of 10-10 to 10-9, 10-9 to 10-8, 10-8 to 10-7, 10-7 to 10-6, 10-6 to 10-5, 10-5 to 10-4, 10-4 to 10-3, 10-3 to 10-2, or 10-2 to 10-1 is sufficient for the cancer recurrence score to be classified as positive for cancer recurrence. In some embodiments, the fraction of tumor DNA greater than a threshold of at least 10-7 is sufficient for the cancer recurrence score to be classified as positive for cancer recurrence. A determination that a fraction of tumor DNA is greater than a threshold, such as a threshold corresponding to any of the foregoing embodiments, may be made based on a cumulative probability. For example, the sample was considered positive if the cumulative probability that the tumor fraction was greater than a threshold in any of the foregoing ranges exceeds a probability threshold of at least 0.5, 0.75, 0.9, 0.95, 0.98, 0.99, 0.995, or 0.999. In some embodiments, the probability threshold is at least 0.95, such as 0.99.

[0205] In some embodiments, the set of sequence information includes sequence-variable target region sequences and epigenetic target region sequences, and determining the cancer recurrence score includes determining a first subscore indicative of the amount of SNVs, insertions / deletions, CNVs and / or fusions present in sequence-variable target region sequences and a second subscore indicative of the amount of abnormal molecules in epigenetic target region sequences, and combining the first and second subscores to provide the cancer recurrence score. Where the first and second subscores are combined, they may be combined by applying a threshold to each subscore independently (e.g., greater than a predetermined number of mutations (e.g., > 1) in sequence-variable target regions, and greater than a predetermined fraction of abnormal molecules (i.e., molecules with an epigenetic state different from the DNA found in a corresponding sample from a healthy subject; e.g., tumor) in epigenetic target regions), or training a machine learning classifier to determine status based on a plurality of positive and negative training samples.

[0206] In some embodiments, a value for the combined score in the range of -4 to 2 or -3 to 1 is sufficient for the cancer recurrence score to be classified as positive for cancer recurrence.

[0207] In any embodiment where a cancer recurrence score is classified as positive for cancer recurrence, the cancer recurrence status of the subject may be at risk for cancer recurrence and / or the subject may be classified as a candidate for a subsequent cancer treatment.

[0208] In some embodiments, the cancer is any one of the types of cancer described elsewhere herein, e.g., colorectal cancer.Therapies and Related Administration

[0209] In certain embodiments, the methods disclosed herein relate to identifying and administering customized therapies to patients given the status of a nucleic acid variant as being of somatic or germline origin. In some embodiments, essentially any cancer therapy (e.g., surgical therapy, radiation therapy, chemotherapy, and / or the like) may be included as part of these methods. Typically, customized therapies include at least one immunotherapy (or an immunotherapeutic agent). Immunotherapy refers generally to methods of enhancing an immune response against a given cancer type. In certain embodiments, immunotherapy refers to methods of enhancing a T cell response against a tumor or cancer.

[0210] In certain embodiments, the status of a nucleic acid variant from a sample from a subject as being of somatic or germline origin may be compared with a database of comparator results from a reference population to identify customized or targeted therapiesfor that subject. Typically, the reference population includes patients with the same cancer or disease type as the test subject and / or patients who are receiving, or who have received, the same therapy as the test subject. A customized or targeted therapy (or therapies) may be identified when the nucleic variant and the comparator results satisfy certain classification criteria (e.g., are a substantial or an approximate match).

[0211] In certain embodiments, the customized therapies described herein are typically administered parenterally (e.g., intravenously or subcutaneously). Pharmaceutical compositions containing an immunotherapeutic agent are typically administered intravenously. Certain therapeutic agents are administered orally. However, customized therapies (e.g., immunotherapeutic agents, etc.) may also be administered by methods such as, for example, buccal, sublingual, rectal, vaginal, intraurethral, topical, intraocular, intranasal, and / or intraauricular, which administration may include tablets, capsules, granules, aqueous suspensions, gels, sprays, suppositories, salves, ointments, or the like.

[0212] While preferred embodiments of the present invention have been shown and described herein, it will be obvious to those skilled in the art that such embodiments are provided by way of example only. It is not intended that the invention be limited by the specific examples provided within the specification. While the invention has been described with reference to the aforementioned specification, the descriptions and illustrations of the embodiments herein are not meant to be construed in a limiting sense. Numerous variations, changes, and substitutions will now occur to those skilled in the art without departing from the invention. Furthermore, it should be understood that all aspects of the invention are not limited to the specific depictions, configurations or relative proportions set forth herein which depend upon a variety of conditions and variables. It should be understood that various alternatives to the embodiments of the disclosure described herein may be employed in practicing the invention. It is therefore contemplated that the disclosure shall also cover any such alternatives, modifications, variations or equivalents. It is intended that the following claims define the scope of the invention and that methods and structures within the scope of these claims and their equivalents be covered thereby.

[0213] While the foregoing disclosure has been described in some detail by way of illustration and example for purposes of clarity and understanding, it will be clear to one of ordinary skill in the art from a reading of this disclosure that various changes in form and detail can be made without departing from the true scope of the disclosure and may be practiced within the scope of the appended claims. For example, all the methods, systems,computer readable media, and / or component features, steps, elements, or other aspects thereof can be used in various combinations.Biomarkers

[0214] The disclosure provides methods of using biomarkers for the diagnosis, prognosis, and therapy selection of a subject suffering from e.g., cancer. A biomarker may be any gene or variant of a gene whose presence, mutation, deletion, substitution, copy number, or translation (i.e., to a protein) is an indicator of a disease state. Biomarkers of the present disclosure may include the presence, mutation, deletion, substitution, copy number, or translation. Examples include the aforementioned in any one or more of EGFR, KRAS, MET, BRAF, MYC, NRAS, ERBB2, ALK, Notch, PIK3CA, APC, and SMO. Additional examples include one or more of: FAT1, STEAP1, CD276 (B7-H3), WT1, TAP2, PTGS2 (COX-2), MIF, CXCL12, CD44, LGALS1, TET2, TERT, VGLL4, KEAP1, CREB3L1,NT5E (CD73), HLA-A, CD47, TNFRSF9, ADORA2B, TNFRSF14, CD40, TNFRSF12A, CLDN6, GRIN2A and CD8A.

[0215] A biomarker is a genetic variant associated with one or more cancers. Biomarkers may be determined using any of several resources or methods. A biomarker may have been previously discovered or may be discovered de novo using experimental or epidemiological techniques. Detection of a biomarker may be indicative of cancer when the biomarker is highly correlated to cancer. Detection of a biomarker may be indicative of cancer when a biomarker in a region or gene occur with a frequency that is greater than a frequency for a given background population or dataset.

[0216] In various embodiments, biomarkers, including the aforementioned, may be described as 1) genes encoding transcription factors or signaling factors and 2) the cis regulatory elements that in a positive or negative direction control the expression of those genes. Each of the cis regulatory elements receives multiple inputs from other genes in the network, the inputs being transcription factors which bind to a specific element that contains a specific cis nucleic acid sequence target sites. Functional linkages of which the network is composed are those between the outputs of regulatory genes and the sets of genomic target sites to which their products bind. These functional linkages which orchestrate in both a spatial and temporal fashion the differentiation fate and development plan of a cell or organism establish a network topology that can be analogized to electronic circuitry, its associated switches, capacitors and resistors. A variety of examples are depicted in U.S. Pat. Pub. No.2014 / 0234974, which is fully incorporated by reference herein. In this aspect, various transcription factors or signaling factors are nodes in a network topology organized by these functional linkages as organized by the inputs and outputs of regulatory genes.

[0217] Publicly available resources such as scientific literature and databases may describe in detail genetic variants found to be associated with cancer. Scientific literature may describe experiments or genome-wide association studies (GWAS) associating one or more genetic variants with cancer. Databases may aggregate information gleaned from sources such as scientific literature to provide a more comprehensive resource for determining one or more biomarkers. Non-limiting examples of databases include FANTOM, GT ex, GEO, Body Atlas, INSiGHT, OMIM (Online Mendelian Inheritance in Man, omim.org), cBioPortal (cbioportal.org), CIViC (Clinical Interpretations of Variants in Cancer, civic.genome.wustl.edu), DOCM (Database of Curated Mutations, docm.genome.wustl.edu), and ICGC Data Portal (dcc.icgc.org). In a further example, the COSMIC (Catalogue of Somatic Mutations in Cancer) database allows for searching of biomarkers by cancer, gene, or mutation type. Biomarkers may also be determined de novo by conducting experiments such as case control or association (e.g, genome-wide association studies) studies.

[0218] One or more biomarkers may be detected in the sequencing panel. A biomarker may be one or more genetic variants associated with cancer. Biomarkers can be selected from single nucleotide variants (SNVs), copy number variants (CNVs), insertions or deletions (e.g., indels), gene fusions and inversions. Biomarkers may affect the level of a protein. Biomarkers may be in a promoter or enhancer, and may alter the transcription of a gene. The biomarkers may affect the transcription and / or translation efficacy of a gene. The biomarkers may affect the stability of a transcribed mRNA. The biomarker may result in a change to the amino acid sequence of a translated protein. The biomarker may affect splicing, may change the amino acid coded by a particular codon, may result in a frameshift, or may result in a premature stop codon. The biomarker may result in a conservative substitution of an amino acid. One or more biomarkers may result in a conservative substitution of an amino acid. One or more biomarkers may result in a nonconservative substitution of an amino acid.

[0219] One or more of the biomarkers may be a driver mutation. A driver mutation is a mutation that gives a selective advantage to a tumor cell in its microenvironment, through either increasing its survival or reproduction. None of the biomarkers may be a driver mutation. One or more of the biomarkers may be a passenger mutation. A passenger mutationis a mutation that has no effect on the fitness of a tumor cell but may be associated with a clonal expansion because it occurs in the same genome with a driver mutation.

[0220] The frequency of a biomarker may be as low as 0.001%. The frequency of a biomarker may be as low as 0.005%. The frequency of a biomarker may be as low as 0.01%. The frequency of a biomarker may be as low as 0.02%. The frequency of a biomarker may be as low as 0.03%. The frequency of a biomarker may be as low as 0.05%. The frequency of a biomarker may be as low as 0.1%. The frequency of a biomarker may be as low as 1%.

[0221] No single biomarker may be present in more than 50%, of subjects having the cancer. No single biomarker may be present in more than 40%, of subjects having the cancer. No single biomarker may be present in more than 30%, of subjects having the cancer. No single biomarker may be present in more than 20%, of subjects having the cancer. No single biomarker may be present in more than 10%, of subjects having the cancer. No single biomarker may be present in more than 5%, of subjects having the cancer. A single biomarker may be present in 0.001% to 50% of subjects having cancer. A single biomarker may be present in 0.01% to 50% of subjects having cancer. A single biomarker may be present in 0.01% to 30% of subjects having cancer. A single biomarker may be present in 0.01% to 20% of subjects having cancer. A single biomarker may be present in 0.01% to 10% of subjects having cancer. A single biomarker may be present in 0.1% to 10% of subjects having cancer. A single biomarker may be present in 0.1% to 5% of subjects having cancer.

[0222] Detection of a biomarker may indicate the presence of one or more cancers. Detection may indicate presence of a cancer selected from the group including ovarian cancer, pancreatic cancer, breast cancer, colorectal cancer, non-small cell lung carcinoma (e.g., squamous cell carcinoma, or adenocarcinoma) or any other cancer. Detection may indicate the presence of any cancer selected from the group including ovarian cancer, pancreatic cancer, breast cancer, colorectal cancer, non-small cell lung carcinoma (squamous cell or adenocarcinoma) or any other cancer. Detection may indicate the presence of any of a plurality of cancers selected from the group including ovarian cancer, pancreatic cancer, breast cancer, colorectal cancer and non-small cell lung carcinoma (squamous cell or adenocarcinoma), or any other cancer. Detection may indicate presence of one or more of any of the cancers mentioned in this application.

[0223] One or more cancers may exhibit a biomarker in at least one exon in the panel. One or more cancers selected from the group including ovarian cancer, pancreatic cancer, breast cancer, colorectal cancer, non-small cell lung carcinoma (squamous cell or adenocarcinoma),or any other cancer, each exhibit a biomarker in at least one exon in the panel. Each of at least 3 of the cancers may exhibit a biomarker in at least one exon in the panel. Each of at least 4 of the cancers may exhibit a biomarker in at least one exon in the panel. Each of at least 5 of the cancers may exhibit a biomarker in at least one exon in the panel. Each of at least 8 of the cancers may exhibit a biomarker in at least one exon in the panel. Each of at least 10 of the cancers may exhibit a biomarker in at least one exon in the panel. All of the cancers may exhibit a biomarker in at least one exon in the panel.

[0224] If a subject has a cancer, the subject may exhibit a biomarker in at least one exon or gene in the panel. At least 85% of subjects having a cancer may exhibit a biomarker in at least one exon or gene in the panel. At least 90%, of subjects having a cancer may exhibit a biomarker in at least one exon or gene in the panel. At least 92% of subjects having a cancer may exhibit a biomarker in at least one exon or gene in the panel. At least 95% of subjects having a cancer may exhibit a biomarker in at least one exon or gene in the panel. At least 96% of subjects having a cancer may exhibit a biomarker in at least one exon or gene in the panel. At least 97% of subjects having a cancer may exhibit a biomarker in at least one exon or gene in the panel. At least 98% of subjects having a cancer may exhibit a biomarker in at least one exon or gene in the panel. At least 99% of subjects having a cancer may exhibit a biomarker in at least one exon or gene in the panel. At least 99.5% of subjects having a cancer may exhibit a biomarker in at least one exon or gene in the panel.

[0225] If a subject has a cancer, the subject may exhibit a biomarker in at least one region in the panel. At least 85% of subjects having a cancer may exhibit a biomarker in at least one region in the panel. At least 90%, of subjects having a cancer may exhibit a biomarker in at least one region in the panel. At least 92% of subjects having a cancer may exhibit a biomarker in at least one region in the panel. At least 95% of subjects having a cancer may exhibit a biomarker in at least one region in the panel. At least 96% of subjects having a cancer may exhibit a biomarker in at least one region in the panel. At least 97% of subjects having a cancer may exhibit a biomarker in at least one region in the panel. At least 98% of subjects having a cancer may exhibit a biomarker in at least one region in the panel. At least 99% of subjects having a cancer may exhibit a biomarker in at least one region in the panel. At least 99.5% of subjects having a cancer may exhibit a biomarker in at least one region in the panel.

[0226] Detection may be performed with a high sensitivity and / or a high specificity. Sensitivity can refer to a measure of the proportion of positives that are correctly identified assuch. In some cases, sensitivity refers to the percentage of all existing biomarkers that are detected. In some cases, sensitivity refers to the percentage of sick people who are correctly identified as having certain disease. Specificity can refer to a measure of the proportion of negatives that are correctly identified as such. In some cases, specificity refers to the proportion of unaltered bases which are correctly identified. In some cases, specificity refers to the percentage of healthy people who are correctly identified as not having certain disease. The non-unique tagging method described previously significantly increases specificity of detection by reducing noise generated by amplification and sequencing errors, which reduces frequency of false positives. Detection may be performed with a sensitivity of at least 95%, 97%, 98%, 99%, 99.5%, or 99.9% and / or a specificity of at least 80%, 90%, 95%, 97%, 98% or 99%. Detection may be performed with a sensitivity of at least 90%, 95%, 97%, 98%, 99%, 99.5%, 99.6%, 99.98%, 99.9% or 99.95%. Detection may be performed with a specificity of at least 90%, 95%, 97%, 98%, 99%, 99.5%, 99.6%, 99.98%, 99.9% or 99.95%. Detection may be performed with a specificity of at least 70% and a sensitivity of at least 70%, a specificity of at least 75% and a sensitivity of at least 75%, a specificity of at least 80% and a sensitivity of at least 80%, a specificity of at least 85% and a sensitivity of at least 85%, a specificity of at least 90% and a sensitivity of at least 90%, a specificity of at least 95% and a sensitivity of at least 95%, a specificity of at least 96% and a sensitivity of at least 96%, a specificity of at least 97% and a sensitivity of at least 97%, a specificity of at least 98% and a sensitivity of at least 98%, a specificity of at least 99% and a sensitivity of at least 99%, or a specificity of 100% a sensitivity of 100%. In some cases, the methods can detect a biomarker at a sensitivity of sensitivity of about 80% or greater. In some cases, the methods can detect a biomarker at a sensitivity of sensitivity of about 95% or greater. In some cases, the methods can detect a biomarker at a sensitivity of sensitivity of about 80% or greater, and a sensitivity of sensitivity of about 95% or greater.

[0227] Detection may be highly accurate. Accuracy may apply to the identification of biomarkers in cell free DNA, and / or to the diagnosis of cancer. Statistical tools, such as covariate analysis described above, may be used to increase and / or measure accuracy. The methods can detect a biomarker at an accuracy of at least 80%, 90%, 95%, 97%, 98% or 99%, 99.5%, 99.6%, 99.98%, 99.9%, or 99.95%. In some cases, the methods can detect a biomarker at an accuracy of at least 95% or greater.Cancer Treatments, Therapies

[0228] In some cases, the cancer treatment includes, without limitation, imatinib, gefatinib, afatinib, dacomitinib, sunitinib, sorafenib, vandetanib, brivanib, cabozantib, neratinib, tivantinib, bevacizumab, cixutumumab, dalotuzumab, figitumumab, rilotumumab, onartuzumab, ganitumab, ramucirumab, ridaforolimus, tensirolimus, everolimus, BMS- 690514, BMS-754807, EMD 525797, GDC-0973, GDC-0941, MK-2206, AZD6244, GSK1120212, PX-866, XL821, IMC-A12, MM-121, PF-02341066, RG7160, and Sym004. Antibodies suitable for use as anti-EGFR therapy include cetuximab (Trade Name: Erbitux) and panitumumab (Trade Name: Vectibex). In some cases. In some cases, the cancer treatment includes EGFR tyrosine kinase inhibitors such as gefitinib (Trade Name: Iressa), erlotinib (Trade Name: Tarceva), lapatinib, canertinib, and cetuximab.

[0229] In some instances, therapties may be used in combination, such as an anti-EGFR therapy and an anti-EGFR therapy. Anti-EGFR therapy may be used in combination with any combination of chemotherapeutic agents or chemotherapeutic regimens, for example, FOLFOX (fluorouracil [5-FU] / leucovorin / oxaliplatin), FOLFIRI (5- FU / leucovorin / irinotecan), and the like.

[0230] In some embodiments, the therapy includes an epigenetic regulator, including HAT, HDAC inhibitors as examples. Other examples including, EP015666, LLY-283, JNJ- 64619178, BRD0639, AMG193, TNG908, SCR— 6920, PRT543, PRT811, MRTX1719, cycloleucine, aminobicycle-hexane-carboxcyclic acid, FIDAS agents, PF-9366, AGR-25696, AG-270, Compound 28, IDE397. Other examples include agents described in Bray et al., Front. Onco. 2023, which is fully incorporated by reference herein.

[0231] In some embodiments, the therapy includes paclitaxel (chemotherapeutic drug), ipatasertib (AKT inhibitor), PI3K-Beta inhibitor, AZD8186, docetaxel, tyrosine kinase inhibitor, pazopanib, mTOR inhibitor, everolimus (NCT01430572), PI3K-Beta inhibitor GSK2636771, and immunotherapy, pembrolizumab (NCT03131908), tastuzumab. Other examples include agents described in Ertay et al., Genes and Diseases. 2023, and Dillon and Miller Curr Drug Targets 2015, each of which is fully incorporated by reference herein.

[0232] In some aspects, a cancer treatment is administered to a subject. In some cases, the cancer treatment is administered in combination another therapy, such as a non-anti-EGFR therapy with anti-EGFR therapy.Sequencing panel

[0233] To improve the likelihood of detecting tumor indicating mutations, the region of DNA sequenced may comprise a panel of genes or genomic regions. Selection of a limited region for sequencing (e.g., a limited panel) can reduce the total sequencing needed (e.g., a total amount of nucleotides sequenced. A sequencing panel can target a plurality of different genes or regions to detect a single cancer, a set of cancers, or all cancers.

[0234] In some aspects, a panel targets a plurality of different genes or genomic regions is selected such that a determined proportion of subjects having a cancer exhibits a genetic variant or biomarker in one or more different genes or genomic regions in the panel. The panel may be selected to limit a region for sequencing to a fixed number of base pairs. The panel may be selected to sequence a desired amount of DNA. The panel may be further selected to achieve a desired sequence read depth. The panel may be selected to achieve a desired sequence read depth or sequence read coverage for an amount of sequenced base pairs. The panel may be selected to achieve a theoretical sensitivity, a theoretical specificity and / or a theoretical accuracy for detecting one or more genetic variants in a sample.

[0235] Probes for detecting the panel of regions can include those for detecting hotspots regions as well as nucleosome-aware probes (e.g., KRAS codons 12 and 13) and may be designed to optimize capture based on analysis of cfDNA coverage and fragment size variation impacted by nucleosome binding patterns and GC sequence composition. Regions used herein can also include non-hotspot regions optimized based on nucleosome positions and GC models. The panel can comprise a plurality of subpanels, including subpanels for identifying tissue of origin (e.g., use of published literature to define 50-100 baits representing genes with most diverse transcription profile across tissues (not necessarily promoters)), whole genome scaffold (e.g., for identifying ultra-conservative genomic content and tiling sparsely across chromosomes with handful of probes for copy number base lining purposes), transcription start site (TSS) / CpG islands (e.g., for capturing differential methylated regions (e.g., Differentially Methylated Regions (DMRs)) in for example in promoters of tumor suppressor genes (e.g., SEPT9 / VIM in colorectal cancer)). In some embodiments, markers for a tissue of origin are tissue-specific epigenetic markers.

[0236] The one or more regions in the panel can comprise one or more loci from one or a plurality of genes. The plurality of genes may be selected for sequencing and biomarker detection. Genes included in the region to be sequenced may be selected from genes known to be involved in cancer, or from genes not involved in cancer. For example theplurality of genes in the panel may be oncogenes, tumor suppressors, growth factors, DNA repair genes, signaling genes, transcription factors, receptors or metabolic genes. Examples include one or more of: FAT1, STEAP1, CD276 (B7-H3), WT1, TAP2, PTGS2 (COX-2), MIF, CXCL12, CD44, LGALS1, TET2, TERT, VGLL4, KEAP1, CREB3L1,NT5E (CD73), HLA-A, CD47, TNFRSF9, AD0RA2B, TNFRSF14, CD40, TNFRSF12A, CLDN6, GRIN2A and CD8A.

[0237] In some cases, the one or more regions in a panel for colorectal cancer can comprise one or more loci from one or a plurality of genes, including one of, two of, three of, four of, or five of TP53, APC, BRAF, KRAS, and NRAS. In some cases, the one or more regions in a panel for ovarian cancer can comprise one or more loci from one or a plurality of genes, including TP53. In some cases, the one or more regions in a panel for pancreatic cancer can comprise one or more loci from one or a plurality of genes, including one or both of TP53 and KRAS. In some cases, the one or more regions in a panel for lung adenocarcinoma can comprise one or more loci from one or a plurality of genes, including one of, two of, three of, four of, five of, six of, seven of, or eight of TP53, BRAF, KRAS, EGFR, ERBB2, MET, STK11, and ALK. In some cases, the one or more regions in a panel for lung squamous cell carcinoma can comprise one or more loci from one or a plurality of genes, including one of, two of, three of, four of, or five of TP53, BRAF, KRAS, MET, and ALK. In some cases, the one or more regions in a panel for breast cancer can comprise one or more loci from one or a plurality of genes, including one of, two of, three of, or four of TP53, GAT A3, PIK3CA, and ESRI. In some cases, one or more regions in a panel can comprise one or more loci from a combination of any of the above genes, for example, to detect a combination of cancer types. In some cases, one or more regions in a panel can comprise one or more loci from each of the preceding genes, for example, in a pan-cancer panel. In some cases, the one or more regions in a panel for lung cancer can comprise one or more loci from a plurality of genes, including one of, two of, three of, four of, five of, six of, seven of, eight of, nine of, 10 of, 11 of, 12 of, 13 of, 14 of, 15 of, 16 of, 17 of, 18 of, 19 of, or 20 of EGFR, KRAS, TP53, CDKN2A, STK11, BRAF, PIK3CA, RBI, ERBB2, PTEN, NFE2L2, MET, CTNNB1, NRAS, MUC16, NF1, BAB, SMARCA4, ATM, NTRK3, ERBB4, FAT1, STEAP1, CD276 (B7-H3), WT1, TAP2, PTGS2 (COX-2), MIF, CXCL12, CD44, LGALS1,TET2, TERT, VGLL4, KEAP1, CREB3L1,NT5E (CD73), HLA-A, CD47, TNFRSF9, AD0RA2B, TNFRSF14, CD40, TNFRSF12A, CLDN6, GRIN2A and CD8A.

[0238] Such a panel also may include, or have substituted for any or all of the above, any or all of an EGFR Exon 19 deletion, EGFR L858R, EGFR C797S, EGFR T790M, EGFR S645C, ARAF S214C and S214F, ERBB2 S418T, MET exon 14 skipping, SNVs and indels. Many of these genes may be clinically actionable, such that an observed anomaly in MAF (e.g., significantly higher or lower than in normal control subjects) may be indicative of a clinical state relevant to lung cancer, such as diagnosis, prognosis, risk stratification, treatment selection, tumor resistance to treatment, tumor burden, etc. Such a lung cancer targeted panel may comprise a relatively small number of these lung cancer associated genes.

[0239] In some cases, the one or more regions in a panel for breast cancer can comprise one or more loci from a plurality of genes, including any one of, or any combination of, FAT1, STEAP1, CD276 (B7-H3), WT1, TAP2, PTGS2 (COX-2), MIF, CXCL12, CD44, LGALS1, TET2, TERT, VGLL4, KEAP1, CREB3L1,NT5E (CD73), HLA-A, CD47, TNFRSF9, ADORA2B, TNFRSF14, CD40, TNFRSF12A, CLDN6, GRIN2A and CD8A. Many of these genes may be clinically actionable, such that an observed anomaly in MAF (e.g., significantly higher or lower than in normal control subjects) may be indicative of a clinical state relevant to breast cancer, such as diagnosis, prognosis, risk stratification, treatment selection, tumor resistance to treatment, tumor burden, etc. Such a breast cancer targeted panel may comprise a relatively small number of these breast cancer associated genes.

[0240] In some cases, the one or more regions in a panel for colorectal cancer can comprise one or more loci from a plurality of genes, including one of, two of, three of, four of, five of, or six of TP53, BRAF, KRAS, APC, TGFBR, and PIK3CA. Many of these genes may be clinically actionable, such that an observed anomaly in MAF (e.g., significantly higher or lower than in normal control subjects) may be indicative of a clinical state relevant to colorectal cancer, such as diagnosis, prognosis, risk stratification, treatment selection, tumor resistance to treatment, tumor burden, etc. Such a colorectal cancer targeted panel may comprise a relatively small number of these colorectal cancer associated genes.

[0241] In some embodiments, the one or more regions in the panel comprise one or more loci from one or a plurality of genes for detecting residual cancer after surgery. This detection can be earlier than is possible for existing methods of cancer detection. In some embodiments, the one or more regions in the panel comprise one or more loci from one or a plurality of genes for detecting cancer in a high-risk patient population. For example,smokers have much higher rates of lung cancer than the general population. Moreover, smokers can develop other lung conditions that make cancer detection more difficult, such as the development of irregular nodules in the lungs. In some embodiments, the methods described herein detect cancer in high risk patients earlier than is possible for existing methods of cancer detection.

[0242] A region may be selected for inclusion in a sequencing panel based on a number of subjects with a cancer that have a biomarker in that gene or region. A region may be selected for inclusion in a sequencing panel based on prevalence of subjects with a cancer and a biomarker present in that gene. Presence of a biomarker in a region may be indicative of a subject having cancer.

[0243] In some instances, the panel may be selected using information from one or more databases. The information regarding a cancer may be derived from cancer tumor biopsies or cfDNA assays. A database may comprise information describing a population of sequenced tumor samples. A database may comprise information about mRNA expression in tumor samples. A databased may comprise information about regulatory elements in tumor samples. The information relating to the sequenced tumor samples may include the frequency various genetic variants and describe the genes or regions in which the genetic variants occur. The genetic variants may be biomarkers. A non-limiting example of such a database is COSMIC. COSMIC is a catalogue of somatic mutations found in various cancers. For a particular cancer, COSMIC ranks genes based on frequency of mutation. A gene may be selected for inclusion in a panel by having a high frequency of mutation within a given gene. For instance, COSMIC indicates that 33% of a population of sequenced breast cancer samples have a mutation in TP53 and 22% of a population of sampled breast cancers have a mutation in KRAS. Other ranked genes, including APC, have mutations found only in about 4% of a population of sequenced breast cancer samples. TP53 and KRAS may be included in a sequencing panel based on having relatively high frequency among sampled breast cancers (compared to APC, for example, which occurs at a frequency of about 4%). COSMIC is provided as a non-limiting example, however, any database or set of information may be used that associates a cancer with biomarker located in a gene or genetic region. In another example, as provided by COSMIC, of 1156 biliary tract cancer samples, 380 samples (33%) carried mutations in TP53. Several other genes, such as APC, have mutations in 4-8% of all samples. Thus, TP53 may be selected for inclusion in the panel based on a relatively high frequency in a population of biliary tract cancer samples.

[0244] A gene or region may be selected for a panel where the frequency of a biomarker is significantly greater in sampled tumor tissue or circulating tumor DNA than found in a given background population. A combination of regions may be selected for inclusion of a panel such that at least a majority of subjects having a cancer will have a biomarker present in at least one of the regions or genes in the panel. The combination of regions may be selected based on data indicating that, for a particular cancer or set of cancers, a majority of subjects have one or more biomarkers in one or more of the selected regions. For example, to detect cancer 1, a panel including regions A, B, C, and / or D may be selected based on data indicating that 90% of subjects with cancer 1 have a biomarker in regions A, B, C, and / or D of the panel. Alternately, biomarkers may be shown to occur independently in two or more regions in subjects having a cancer such that, combined, a biomarker in the two or more regions is present in a majority of a population of subjects having a cancer. For example, to detect cancer 2, a panel including regions X, Y, and Z may be selected based on data indicating that 90% of subjects have a biomarker in one or more regions, and in 30% of such subjects a biomarker is detected only in region X, while biomarkers are detected only in regions Y and / or Z for the remainder of the subjects for whom a biomarker was detected. Biomarkers present in one or more regions previously shown to be associated with one or more cancers may be indicative of or predictive of a subject having cancer if a biomarker is detected in one or more of those regions 50% or more of the time. Computational approaches such as models employing conditional probabilities of detecting cancer given a known cancer frequency for a set of biomarkers within one or more regions may be used to predict which regions, alone or in combination, may be predictive of cancer. Other approaches for panel selection involve the use of databases describing information from studies employing comprehensive genomic profiling of tumors with large panels and / or whole genome sequencing (WGS, RNA-seq, Chip-seq, bisulfate sequencing, ATAC-seq, and others). Information gleaned from literature may also describe pathways commonly affected and mutated in certain cancers. Panel selection may be further informed by the use of ontologies describing genetic information.

[0245] Genes included in the panel for sequencing can include the fully transcribed region, the promoter region, enhancer regions, regulatory elements, and / or downstream sequence. To further increase the likelihood of detecting tumor indicating mutations only exons may be included in the panel. The panel can comprise all exons of a selected gene, or only one or more of the exons of a selected gene. The panel may comprise of exons from each of aplurality of different genes. The panel may comprise at least one exon from each of the plurality of different genes.

[0246] In some aspects, a panel of exons from each of a plurality of different genes is selected such that a determined proportion of subjects having a cancer exhibit a genetic variant in at least one exon in the panel of exons.

[0247] At least one full exon from each different gene in a panel of genes may be sequenced. The sequenced panel may comprise exons from a plurality of genes. The panel may comprise exons from 2 to 100 different genes, from 2 to 70 genes, from 2 to 50 genes, from 2 to 30 genes, from 2 to 15 genes, or from 2 to 10 genes.

[0248] A selected panel may comprise a varying number of exons. The panel may comprise from 2 to 3000 exons. The panel may comprise from 2 to 1000 exons. The panel may comprise from 2 to 500 exons. The panel may comprise from 2 to 100 exons. The panel may comprise from 2 to 50 exons. The panel may comprise no more than 300 exons. The panel may comprise no more than 200 exons. The panel may comprise no more than 100 exons. The panel may comprise no more than 50 exons. The panel may comprise no more than 40 exons. The panel may comprise no more than 30 exons. The panel may comprise no more than 25 exons. The panel may comprise no more than 20 exons. The panel may comprise no more than 15 exons. The panel may comprise no more than 10 exons. The panel may comprise no more than 9 exons. The panel may comprise no more than 8 exons. The panel may comprise no more than 7 exons.

[0249] The panel may comprise one or more exons from a plurality of different genes. The panel may comprise one or more exons from each of a proportion of the plurality of different genes. The panel may comprise at least two exons from each of at least 25%, 50%, 75% or 90% of the different genes. The panel may comprise at least three exons from each of at least 25%, 50%, 75% or 90% of the different genes. The panel may comprise at least four exons from each of at least 25%, 50%, 75% or 90% of the different genes.

[0250] The sizes of the sequencing panel may vary. A sequencing panel may be made larger or smaller (in terms of nucleotide size) depending on several factors including, for example, the total amount of nucleotides sequenced or a number of unique molecules sequenced for a particular region in the panel. The sequencing panel can be sized 5 kb to 50 kb. The sequencing panel can be 10 kb to 30 kb in size. The sequencing panel can be 12 kb to 20 kb in size. The sequencing panel can be 12 kb to 60 kb in size. The sequencing panel can be at least lOkb, 12 kb, 15 kb, 20 kb, 25 kb, 30 kb, 35 kb, 40 kb, 45 kb, 50 kb, 60 kb, 70 kb, 80 kb,90 kb, 100 kb , 110 kb, 120 kb, 130 kb, 140 kb, or 150 kb in size. The sequencing panel may be less than 100 kb, 90 kb, 80 kb, 70 kb, 60 kb, or 50 kb in size.

[0251] The panel selected for sequencing can comprise at least 1, 5, 10, 15, 20, 25, 30, 40, 50, 60, 80, or 100 regions. In some cases, the regions in the panel are selected that the size of the regions are relatively small. In some cases, the regions in the panel have a size of about 10 kb or less, about 8 kb or less, about 6 kb or less, about 5 kb or less, about 4 kb or less, about 3 kb or less, about 2.5 kb or less, about 2 kb or less, about 1.5 kb or less, or about 1 kb or less or less. In some cases, the regions in the panel have a size from about 0.5 kb to about 10 kb, from about 0.5 kb to about 6 kb, from about 1 kb to about 11 kb, from about 1 kb to about 15 kb, from about 1 kb to about 20 kb, from about 0.1 kb to about 10 kb, or from about 0.2 kb to about 1 kb. For example, the regions in the panel can have a size from about 0.1 kb to about 5 kb.

[0252] The panel selected herein can allow for deep sequencing that is sufficient to detect low-frequency genetic variants (e.g., in cell-free nucleic acid molecules obtained from a sample). An amount of genetic variants in a sample may be referred to in terms of the minor allele frequency for a given genetic variant. The minor allele frequency may refer to the frequency at which minor alleles (e.g., not the most common allele) occurs in a given population of nucleic acids, such as a sample. Genetic variants at a low minor allele frequency may have a relatively low frequency of presence in a sample. In some cases, the panel allows for detection of genetic variants at a minor allele frequency of at least 0.0001%, 0.001%, 0.005%, 0.01%, 0.05%, 0.1%, or 0.5%. The panel can allow for detection of genetic variants at a minor allele frequency of 0.001% or greater. The panel can allow for detection of genetic variants at a minor allele frequency of 0.01% or greater. The panel can allow for detection of genetic variant present in a sample at a frequency of as low as 0.0001%, 0.001%, 0.005%, 0.01%, 0.025%, 0.05%, 0.075%, 0.1%, 0.25%, 0.5%, 0.75%, or 1.0%. The panel can allow for detection of biomarkers present in a sample at a frequency of at least 0.0001%, 0.001%, 0.005%, 0.01%, 0.025%, 0.05%, 0.075%, 0.1%, 0.25%, 0.5%, 0.75%, or 1.0%. The panel can allow for detection of biomarkers at a frequency in a sample as low as 1.0%. The panel can allow for detection of biomarkers at a frequency in a sample as low as 0.75%. The panel can allow for detection of biomarkers at a frequency in a sample as low as 0.5%. The panel can allow for detection of biomarkers at a frequency in a sample as low as 0.25%. The panel can allow for detection of biomarkers at a frequency in a sample as low as 0.1%. The panel can allow for detection of biomarkers at a frequency in a sample as low as 0.075%.The panel can allow for detection of biomarkers at a frequency in a sample as low as 0.05%.The panel can allow for detection of biomarkers at a frequency in a sample as low as 0.025%.The panel can allow for detection of biomarkers at a frequency in a sample as low as 0.01%.The panel can allow for detection of biomarkers at a frequency in a sample as low as 0.005%.The panel can allow for detection of biomarkers at a frequency in a sample as low as 0.001%.The panel can allow for detection of biomarkers at a frequency in a sample as low as0.0001%. The panel can allow for detection of biomarkers in sequenced cfDNA at a frequency in a sample as low as 1.0% to 0.0001%. The panel can allow for detection of biomarkers in sequenced cfDNA at a frequency in a sample as low as 0.01% to 0.0001%.

[0253] A genetic variant can be exhibited in a percentage of a population of subjects who have a disease (e.g., cancer). In some cases, at least 1%, 2%, 3%, 5%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95%, or 99% of a population having the cancer exhibit one or more genetic variants in at least one of the regions in the panel. For example, at least 80% of a population having the cancer may exhibit one or more genetic variants in at least one of the regions in the panel.

[0254] The panel can comprise one or more regions from each of one or more genes. In some cases, the panel can comprise one or more regions from each of at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 40, 50, or 80 genes. In some cases, the panel can comprise one or more regions from each of at most 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 40, 50, or 80 genes. In some cases, the panel can comprise one or more regions from each of from about 1 to about 80, from 1 to about 50, from about 3 to about 40, from 5 to about 30, from 10 to about 20 different genes.

[0255] The regions in the panel can be selected so that one or more epigenetically modified regions are detected. The one or more epigenetically modified regions can be acetylated, methylated, ubiquitylated, phosphorylated, sumoylated, ribosylated, and / or citrullinated. For example, the regions in the panel can be selected so that one or more methylated regions are detected.

[0256] The regions in the panel can be selected so that they comprise sequences differentially transcribed across one or more tissues. In some cases, the regions can comprise sequences transcribed in certain tissues at a higher level compared to other tissues. For example, the regions can comprise sequences transcribed in certain tissues but not in other tissues.

[0257] The regions in the panel can comprise coding and / or non-coding sequences. For example, the regions in the panel can comprise one or more sequences in exons, introns,promoters, 3’ untranslated regions, 5’ untranslated regions, regulatory elements, transcription start sites, and / or splice sites. In some cases, the regions in the panel can comprise other noncoding sequences, including pseudogenes, repeat sequences, transposons, viral elements, and telomeres. In some cases, the regions in the panel can comprise sequences in non-coding RNA, e.g., ribosomal RNA, transfer RNA, Piwi-interacting RNA, and microRNA.

[0258] The regions in the panel can be selected to detect (diagnose) a cancer with a desired level of sensitivity (e.g., through the detection of one or more genetic variants). For example, the regions in the panel can be selected to detect the cancer (e.g., through the detection of one or more genetic variants) with a sensitivity of at least 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5%, or 99.9%. The regions in the panel can be selected to detect the cancer with a sensitivity of 100%.

[0259] The regions in the panel can be selected to detect (diagnose) a cancer with a desired level of specificity (e.g., through the detection of one or more genetic variants). For example, the regions in the panel can be selected to detect cancer (e.g., through the detection of one or more genetic variants) with a specificity of at least 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5%, or 99.9%. The regions in the panel can be selected to detect the one or more genetic variant with a specificity of 100%.

[0260] The regions in the panel can be selected to detect (diagnose) a cancer with a desired positive predictive value. Positive predictive value can be increased by increasing sensitivity (e.g., chance of an actual positive being detected) and / or specificity (e.g., chance of not mistaking an actual negative for a positive). As a non-limiting example, regions in the panel can be selected to detect the one or more genetic variant with a positive predictive value of at least 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5%, or 99.9%. The regions in the panel can be selected to detect the one or more genetic variant with a positive predictive value of 100%.

[0261] The regions in the panel can be selected to detect (diagnose) a cancer with a desired accuracy. As used herein, the term “accuracy” may refer to the ability of a test to discriminate between a disease condition (e.g., cancer) and health. Accuracy may be can be quantified using measures such as sensitivity and specificity, predictive values, likelihood ratios, the area under the ROC curve, Youden’s index and / or diagnostic odds ratio.

[0262] Accuracy may presented as a percentage, which refers to a ratio between the number of tests giving a correct result and the total number of tests performed. The regions in the panel can be selected to detect cancer with an accuracy of at least 50%, 55%, 60%, 65%,70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5%, or 99.9%. The regions in the panel can be selected to detect cancer with an accuracy of 100%.

[0263] A panel may be selected such that when one or more regions or genes in the panel are removed, specificity is appreciably decreased. Removal of one region from the panel may result in a decrease in specificity of at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, or more.

[0264] A panel may be selected such that the addition of one or more regions or genes to the panel does not appreciably increase the specificity of the panel, e.g., does not increase the specificity by more than 1%, 2%, 5%, 10%, 15%, or 20%.

[0265] A panel may be of a size such that when one or more regions or genes in the panel are removed, this appreciably decreases sensitivity, e.g., sensitivity is decreased by at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, or more.

[0266] A panel may be selected such that the addition of one or more regions or genes to the panel does not appreciably increase the sensitivity of the panel, e.g., does not increase the sensitivity by more than 1%, 2%, 5%, 10%, 15%, or 20%.

[0267] A panel may be of a size such that when one or more regions or genes in the panel are removed, accuracy is appreciably decreased, e.g., accuracy is decreased by at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, or more.

[0268] A panel may be selected such that the addition of one or more regions or genes to the panel does not appreciably increase the accuracy of the panel, e.g., does not increase the accuracy by more than 1%, 2%, 5%, 10%, 15%, or 20%.

[0269] A panel may be of a size such that when one or more regions or genes the panel are removed, positive predictive value is appreciably decreased, e.g., positive predictive value is decreased by at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, or more.

[0270] A panel may be selected such that the addition of one or more regions or genes to the panel does not appreciably increase the positive predictive value of the panel, e.g., does not increase the positive predictive value by more than 1%, 2%, 5%, 10%, 15%, or 20%

[0271] A panel may be selected to be highly sensitive and detect low frequency genetic variants. For instance, a panel may be selected such that a genetic variant or biomarker present in a sample at a frequency as low as 0.01%, 0.05%, or 0.001% may be detected at a sensitivity of at least 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5%, or 99.9%. Regions in a panel may be selected to detect a biomarker present at a frequency of 1% or less in a sample with a sensitivity of 70% or greater. A panelmay be selected to detect a biomarker at a frequency in a sample as low as 0.1% with a sensitivity of at least 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5%, or 99.9%. A panel may be selected to detect a biomarker at a frequency in a sample as low as 0.01% with a sensitivity of at least 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5%, or 99.9%. A panel may be selected to detect a biomarker at a frequency in a sample as low as 0.001% with a sensitivity of at least 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5%, or 99.9%.

[0272] A panel may be selected to be highly specific and detect low frequency genetic variants. For instance, a panel may be selected such that a genetic variant or biomarker present in a sample at a frequency as low as 0.01%, 0.05%, or 0.001% may be detected at a specificity of at least 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5%, or 99.9%. Regions in a panel may be selected to detect a biomarker present at a frequency of 1% or less in a sample with a specificity of 70% or greater. A panel may be selected to detect a biomarker at a frequency in a sample as low as 0.1% with a specificity of at least 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5%, or 99.9%. A panel may be selected to detect a biomarker at a frequency in a sample as low as 0.01% with a specificity of at least 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5%, or 99.9%. A panel may be selected to detect a biomarker at a frequency in a sample as low as 0.001% with a specificity of at least 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5%, or 99.9%.

[0273] A panel may be selected to be highly accurate and detect low frequency genetic variants. A panel may be selected such that a genetic variant or biomarker present in a sample at a frequency as low as 0.01%, 0.05%, or 0.001% may be detected at an accuracy of at least 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5%, or 99.9%. Regions in a panel may be selected to detect a biomarker present at a frequency of 1% or less in a sample with an accuracy of 70% or greater. A panel may be selected to detect a biomarker at a frequency in a sample as low as 0.1% with an accuracy of at least 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5%, or 99.9%. A panel may be selected to detect a biomarker at a frequency in a sample as low as 0.01% with an accuracy of at least 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5%, or 99.9%. A panel may be selected to detect a biomarker at a frequency in a sample as low as 0.001% with an accuracy of at least 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5%, or 99.9%.

[0274] A panel may be selected to be highly predictive and detect low frequency genetic variants. A panel may be selected such that a genetic variant or biomarker present in a sample at a frequency as low as 0.01%, 0.05%, or 0.001% may have a positive predictive value of at least 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5%, or 99.9%.

[0275] The concentration of probes or baits used in the panel may be increased (2 to 6 ng / pL) to capture more nucleic acid molecule within a sample. The concentration of probes or baits used in the panel may be at least 2 ng / pL, 3 ng / pL, 4 ng / pL, 5 ng / pL, 6 ng / pL, or greater. The concentration of probes may be about 2 ng / pL to about 3 ng / pL, about 2 ng / pL to about 4 ng / pL, about 2 ng / pL to about 5 ng / pL, about 2 ng / pL to about 6 ng / pL. The concentration of probes or baits used in the panel may be 2 ng / pL or more to 6 ng / pL or less. In some instances this may allow for more molecules within a biological to be analyzed thereby enabling lower frequency alleles to be detected.Genetic Analysis

[0276] Genetic analysis includes detection of nucleotide sequence variants and copy number variations. Genetic variants can be determined by sequencing. The sequencing method can be massively parallel sequencing, that is, simultaneously (or in rapid succession) sequencing any of at least 100,000, 1 million, 10 million, 100 million, or 1 billion polynucleotide molecules. Sequencing methods may include, but are not limited to: high-throughput sequencing, pyrosequencing, sequencing-by-synthesis, single-molecule sequencing, nanopore sequencing, semiconductor sequencing, sequencing-by-ligation, sequencing-by-hybridization, RNA-Seq (Illumina), Digital Gene Expression (Helicos), Next-generation sequencing, Single Molecule Sequencing by Synthesis (SMSS)(Helicos), massively-parallel sequencing, Clonal Single Molecule Array (Solexa), shotgun sequencing, Maxam-Gilbert or Sanger sequencing, primer walking, sequencing using PacBio, SOLiD, Ion Torrent, or Nanopore platforms and any other sequencing methods known in the art.

[0277] Sequencing can be made more efficient by performing sequence capture, that is, the enrichment of a sample for target sequences of interest, e.g., sequences including the KRAS and / or EGFR genes or portions of them containing sequence variant biomarkers. Sequence capture can be performed using immobilized probes that hybridize to the targets of interest.

[0278] Cell free DNA can include small amounts of tumor DNA mixed with germline DNA. Sequencing methods that increase sensitivity and specificity of detecting tumor DNA, and, in particular, genetic sequence variants and copy number variation, can be useful in the methodsof this invention. Such methods are described in, for example, in WO 2014 / 039556. These methods not only can detect molecules with a sensitivity of up to or greater than 0.1%, but also can distinguish these signals from noise typical in current sequencing methods. Increases in sensitivity and specificity from blood-based samples of cfDNA can be achieved using various methods. One method includes high efficiency tagging of DNA molecules in the sample, e.g., tagging at least any of 50%, 75% or 90% of the polynucleotides in a sample. This increases the likelihood that a low-abundance target molecule in a sample will be tagged and subsequently sequenced, and significantly increases sensitivity of detection of target molecules.

[0279] Another method involves molecular tracking, which identifies sequence reads that have been redundantly generated from an original parent molecule, and assigns the most likely identity of a base at each locus or position in the parent molecule. This significantly increases specificity of detection by reducing noise generated by amplification and sequencing errors, which reduces frequency of false positives.

[0280] Methods of the present disclosure can be used to detect genetic variation in non- uniquely tagged initial starting genetic material (e.g., rare DNA) at a concentration that is less than 5%, 1%, 0.5%, 0.1%, 0.05%, or 0.01%, at a specificity of at least 99%, 99.9%, 99.99%, 99.999%, 99.9999%, or 99.99999%. Sequence reads of tagged polynucleotides can be subsequently tracked to generate consensus sequences for polynucleotides with an error rate of no more than 2%, 1%, 0.1%, or 0.01%.

[0281] Copy number variation determination can involve determining a quantitative measure of polynucleotides in a sample mapping to a genetic locus, such as the EGFR gene or KRAS gene. The quantitative measure can be a number. Once the total number of polynucleotides mapping to a locus is determined, this number can be used in standard methods of determining Copy Number Variation at the locus. A quantitative measure can be normalized against a standard. In one method, a quantitative measure at a test locus can be standardized against a quantitative measure of polynucleotides mapping to a control locus in the genome, such as gene of known copy number. In another method, the quantitative measure can be compared against the amount of nucleic acid in the original sample. For example, the quantitative measure can be compared against an expected measure for diploidy. In another method, the quantitative measure can be normalized against a measure from a control sample, and normalized measures at different loci can be compared. In another method, quantifying involves quantifying parent or original molecules in a sample mapping to a locus, rather thannumber of sequence reads. A copy number variation may be an amplification or a deletion or truncation of a gene. An amplification may be 3, 4, 5, 6, 7, 8, 9, 10, or 10 or more copies of a gene. A deletion or truncation may be 0 or 1 copies of a gene.

[0282] An example of a method for detecting copy number variation may include an array. The array may comprise a plurality of capture probes. The capture probes can be oligonucleotides that are bound to the surface of the array The capture probes may bind to at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12 genes as set forth in Table 1. DNA derived from the subject may be labeled (e.g., with a fluorophore) prior to hybidization for detection.

[0283] In other examples, a gene of interest may be amplified using primers that recognize the gene of interest. The primers may hybridize to a gene upstream and / or downstream of a particular region of interest (e.g., upstream of a mutation site). A detection probe may be hybridized to the amplification product. Detection probes may specifically hybridize to a wild-type sequence or to a mutated / variant sequence. Detection probes may be labeled with a detectable label (e.g., with a fluorophore). Detection of a wild-type or mutant sequence may be performed by detecting the detectable label (e.g., fluorescence imaging). In examples of copy number variation, a gene of interest may be compared with a reference gene.Differences in copy number between the gene of interest and the reference gene may indicate amplification or deletion / truncation of a gene. Examples of platforms suitable to perform the methods described herein include digital PCR platforms such as e.g., Fluidigm Digital Array.EXAMPLESDifferential methylated regions can predict drug response

[0284] Methylation regions were identified by testing normal vs. cancer samples from large cohorts.

[0285] Plasma from 3,000 cancer-free donors established regions where DNA does not have hypermethylation signal. Plasma from 1,700+ CRC, breast, lung and bladder cancer patients was used to detect tumor methylation mutation molecules. Can also call methylation in a pantumor manner. Methylation caller is compatible with an aggregate pan-cancer model.

[0286] Methylation panel and caller were developed with clinical samples to ensure capability to distinguish between normal and cancer samples. Comparison of normal vs. cancer samples, identified Differentially Methylation Regions (hypermethylated regions),which can detect tumor signal. Cancer samples from patients across CRC, breast, lung and bladder cancer to develop the caller. Further information is found, in Inti. PCT App. No. PCT / US2020 / 053610, PCT / US2020 / 016120, which are each incorporated by reference herein.

[0287] This platform supports expanded Genotyping, TMB, MSI, promoter methylation (170 tumor suppressor genes + 24 HRR genes), sample-level tumor quantification. Epigenomic detection assay has sensitive and validated promoter hypermethylation, analytical LoD of 0.89% VAF at 30ng and 1.1% at 5ng of input. Validated promoter hypermethylation positions Guardantlnfinity as a powerful assay for epigenetic-based classification applications that can be taken to LDT.Prognostic and Predicative Signatures Explained

[0288] Prognostic: How is the patient going to perform? Not tied to whether a patient will have a better response on one therapy versus another. Can be used to escalate or deescalate treatment. In an example, patients with high baseline Tumor Fraction (TF) have worse prognosis than patients with low baseline TF across solid tumor types.

[0289] Predictive: How is the patient going to perform on a specific therapy. In an example, bTMB is predictive of patients that will have better response to ICI-based therapy and is not predictive of how they will respond to Chemotherapy.Exemplary prognostic biomarkers:

[0290] In various examples of prognostic marked include, baseline TF, hypermethylation of COL4A5 in Gastric Cancer (better prognosis), promoter hypermethylation of CLEC14A in LU AD (worse prognosis). Other biomarkers for use in immuno oncology (IO) response prediction include molecular response (e.g., immune checkopint (ICI) treatment), tumor mutation burder (TMB), PD-L1 staining, HLA loss of heterohygosity (LoH), 9p21.3 loss, STK11 / KRAS, KEAP1, EGFR exl9del or L858R mutated patients have worse outcomes on ICI (potentially other therapies).

[0291] Other examples include for primary tumours and circulating DNA, epigenetic alterations in chromatin confirmations (e.g., loci encoding for DAPK1, HSD3B2, SRD5A3, MMP1, and miRNA98).Multivariate logistic regression model for IO score

[0292] A multivariate logistic regression model can be utilized to incorporate various quantitative metrics. This includes, bTMB: bTMB score or status, HLA LoH: Number of HLA LoH, Negative genomic variant +ve: STK11 / KRAS, KEAP1, EGFR exl9del or L858R mutated

[0293] 9p21.3 loss: Loss of 9p21.3 locus, Promoter hypermethylation (positive association): Promoter hypermethylation associated with positive response. Promoter hypermethylation (negative association): Promoter hypermethylation associated with negative response. Methylation signature: Methylation region differential between responder vs non-responder. TF : Can be used independently; a prior on whether the patient will respond to anything. A measure of patient risk.Response Adapted Immunotherapy with Nivolumab and Salvage Ipilimumab in Patients with mUC

[0294] In an exemplary study design suitable for IO prediction dataset generation, the Inventors evaluated a study including patients treated with nivolumab monotherapy (q2week cycles); confirmed responses achieved in 23%, including a CR in 9%; the median DOR was 25 months (range: 6 to 47+ months). Other patients received the combination of nivolumab plus ipilimumab (4 q3weeks cycles then nivolumab maintenance); confirmed responses were achieved in 26%, including a CR in 9 %; the DOR was 6 months (range: 3 to 24+ months)

[0295] Research blood specimens were obtained on cycle 1 day 1, cycle 2 day 1, cycle 3 day 1, cycle 4 day 1, cycle 5 day 1, cycle 6 day 1, cycle 7 day 1 and end of treatment for nivolumab monotherapy. Research blood specimens are obtained on cycle 1 day 1, cycle 2 day 1, cycle 3 day 1, cycle 4 day 1, end of treatment for ipilimumab and nivolumab salvage therapy

[0296] For an ipi / nivo study: the nivo monotherapy cohort had samples collected DI of cycles 1-7 every ~2 weeks through week 12 (C2D1 = 2 weeks post initiation of nivo, C3D1 = 4 weeks post initiation of nivo); for the ipi / nivo cohort patients had samples collected cycles 1-4 every ~3 weeks up through week 12. As before, calculate DMRs with significantly different normalized counts between responders and non-responders for IO signature componentsAtezolizumab with Gemcitabine and Cisplatin at First Line Treatment for mUC: A Randomized Multicenter Study of Two Dosing Schedules

[0297] In an exemplary study design suitable for IO prediction dataset generation, the Inventors evaluated a study including randomized study of two dosing schedules - 2 parallel Simon 2 stage designs, RECIST 1.1 confirmed response (PR / CR) was primary endpoint, Null 40% and Alt 65%. Enrolled prior to redesign included Schedule 1 (A x2cycles then+GC), Schedule 2 (GC x2cycles then +A). Enrolled after redesign included Schedule 2 (GC x2cycles then +A), Schedule 3 (A xlcycle then +GC). Research blood specimens were obtained on cycle 1 day 1, cycle 2 day 1, cycle 3 day 1, and cycle 4 day 1Real world evidence clinical + multi-omics database identifies biomarkers to predict response to therapy

[0298] In accordance with the above study design examples, using a real world evidence database, the Inventors identified a methylation signature for predicting response to immune checkpoint inhibitor (ICI) Further information is found, in Inti. PCT App. No. PCT / US2022 / 038941, PCT / US2022 / 042262, which are each incorporated by reference herein.

[0299] The study included 110 NSCLC patients within epigenomic test taken prior to start of ICI therapy and a tumor fraction >0.05% were included in the study. 163 promoter loci reported within HRD / IO modules and observed as hypermethylated in more than 5 patients were evaluated for association with real world time to next treatment (rwTTNT) via a multivariate analysis including gender and amount of ctDNA shedding as covariates.

[0300] Hypermethylation of eight promoters were found to be significantly associated with rwTTNT, including 8 promoters that are methylated prior to treatment lead to poorer response to immune checkpoint inhibitors. The genes are: TNFRSF9, ADORA2B, TNFRSF14, CD40, TNFRSF12A, CLDN6, GRIN2A and CD8A.

[0301] The eight promoters identified can subsequently be combined into a predictive model for ICI response. The predictive model will be validated on independent cohorts.Methylation signature can be developed using sensitive and validated promoter hypermethylation on epigenomic detection assay for predicting patient response to ICI.TNFRSF9 promoter methylation

[0302] The most significant negative association was for TNFRSF9 promoter. TNFRSF9 promoter methylation, may be a notable predictive biomarker for lack of response to immunotherapy

[0303] In patients receiving anti-PD-1 immunotherapy (mRNA ICB cohort), we found that TNFRSF9 hypermethylation and reduced mRNA expression correlated with poor PFS and response. Frohlich et al. EBioMedicine. 2020.

[0304] The results described herein are consistent with the notion that TNFRSF9 mRNA expression is regulated via DNA methylation. The observed correlations between TNFRSF9 DNA methylation or mRNA expression with known features of response to immune checkpoint blockage suggest TNFRSF9 methylation could serve as a biomarker in the context of immunotherapies.

[0305] Here, the Inventors identified TNFRSF9 DNA methylation and others, including ADORA2B, TNFRSF14, CD40, TNFRSF12A, CLDN6, GRIN2A and CD8A, any one or more aforementioned genes being capable of predicting patient response, such as disease progression in patients under immunotherapy.Genomic And Methylation Biomarkers For Determining Adverse Event Risk Associated With Therapy

[0306] A variety of adverse events (AEs) are associated with certain anticancer therapies, including pneumonitis, cardiotoxicity, hepatotoxicity, neurotoxicity, among others. Treatment necessitates recognition of associated AEs and a need to develop and refine monitoring and management strategies for these AEs. It is important to identify AEs when asymptomatic because effective therapy can be continued in those patients, which may improve treatment outcomes. However, no such methods presently exist.

[0307] Described herein are methods and compositions to assess the validity and utility of detection and monitoring of ADC and immune-onco (lO)-related toxicities in common cancers. This includes generation of de novo model incorporating DMRs associated with AEs, including identification 19 lung, heart, etc. specific DMRs.ADCs including Toxicity, Generally

[0308] ADCs recognize and binding to target antigens on the surface of cancer cells. Thereafter, ADCs are internalised through endocytosis with formation of early endosomes. The early endosomes mature into late endosomes, which then fuse with lysosomes.

[0309] What happens next depends on whether the ADC is designed with a cleavable linker or non-cleavable link. ADCs with cleavable linkers release their payload depending on the localisation of enzymes and other factors. ADCs with non-cleavable linkers release their payloads when antibodies are degraded in lysosomes. Payloads that are released into the cytosol induce cell death via DNA damage or microtubule inhibition. Intracellular release may lead to passage through the cell membrane and exert an antitumor effect on surrounding cancer cells. This bystander effect helps account for heterogeneous populations of cancer cells and that may otherwise lack target antigens not expressed by all cancer cells.

[0310] Without being bound by any particular theory, the use of epigenetic regulators within ADCs, or in combination with ADCs indicates a relationship between methylation, including the epigenetic profile of an individual and its consequent modulation of the use of ADCs, including efficacy, responsiveness, toxicity, among others. In this aspect, the methylation profiling of individual subjects, including DMRs, is likely to prove highly informative and guide treatment decisions, including minimization of adverse event risk.IO Toxicity, Generally

[0311] Immunotherapy, including immune checkpoint inhibitors (PD-1 / PD-L1 inhibitors), function based immune cell inhibitory signals arising from activation of immune checkpoint molecules, which block their activity and proliferation. Exploitation by cancer cells of immune checkpoints leads to impaired immunosurveillance. Therapeutic blocking antibodies against PD-1, PD-L1, and cytotoxic T cell antigen 4 (CTLA-4) are common immune checkpoints.

[0312] Immunotherapy agents, including PD-1 / PD-L1 inhibitors, also are associated with a poorly understood spectrum of toxicities. In particular, the novelty of these therapeutic strategies present challenges that are different from those associated with traditional chemotherapy agents or monoclonal antibodies.

[0313] Without being bound by any particular theory, PD-1 / PD-L1 inhibitors are special in that patients’ susceptibility to their toxicities is not only dependent on the specific agents used in cancer treatments, but also likely results from individual patient characteristics. Toxicities can affect almost any organ, with varying scales, severities, and frequencies. In this aspect, the methylation profiling of individual subjects, including DMRs, is likely to prove highly informative and guide treatment decisions, including minimization of adverse event risk.Examples of ADC, IO Toxicity

[0314] ILD / Pneumoniti sDato-DxD (BR), Pembro (BR, LU), TDxD (BR), IpiNivo (MEL, LU), Sac-Gov (BR), Cholangio

[0315] Cardiotoxicity

[0316] TDxD (dec. EF); Anti-HER2 (BR, CRC, cholangio); Sac-Gov (BR), many chemos (?)

[0317] HepatotoxicityDato-DxD (BR), Pembro (BR, LU), TDxD (BR), IpiNivo (MEL, LU), Sac-Gov (BR)IQ

[0318] Neuropathy / Neurotoxicity )CIPN (CRC), EB V(BLA), CLEOPATRA (BR)Predicting patient response to IO using genomic and epigenomic promoter methylation signatureIn accordance with the above study design examples, the Inventors utilized data from NSCLC patients with tumor fraction (TF) >0.01% who underwent GuardantINFINITY cfDNA testing and subsequently received ICI-based therapy were identified from the INFORM database. A promoter methylation score was computed from 18 binary features (9 positive, 9 negative). Patients were classified as having a “positive” score (>1 positive feature) or “negative” score (>1 negative feature). Cox proportional hazards (CPH) modeling evaluated real -world time to next treatment (rwTTNT), adjusting for age, gender, and TF.Among 725 eligible NSCLC patients, 290 (40%) had a positive methylation score and 435 (60%) had a negative score. All patients exhibited at least one feature. Median TF was comparable between groups (positive: 4.7%, negative: 4.2%). Patients with a positive score had significantly longer rwTTNT (HR=0.58; 95% CI: 0.41-0.83; p=0.003) compared to those with a negative score, after covariate adjustment in the CPH model.

[0319] Preliminary results from this study demonstrates the clinical feasibility of using cfDNA-based promoter methylation profiles to stratify NSCLC patients by likelihood of ICI benefit. A positive methylation score, as defined by GuardantINFINITY, is associated withimproved rwTTNT, independent of baseline TF, age, or gender. These results support further validation of promoter methylation as a predictive biomarker in immunotherapy.Individual promoter methylation association with rwTTNT

[0320] The above served to investigate individual promoter methylation association with rwTTNT in NSCLC treated with IO in either first or second line using blood collection within 150 days prior to IO treatment. Promoter hypermethylation (positive association): Promoter hypermethylation associated with positive response. Promoter hypermethylation (negative association): Promoter hypermethylation associated with negative response.Promoter Methylation and Immune Checkpoint Inhibitor Response in Cancer for genes in negative and positive promoter methylation features

[0321] Promoter methylation, an epigenetic modification influencing gene expression, has emerged as a pivotal factor in modulating tumor-immune interactions and responsiveness to immune checkpoint inhibitors (ICIs). Recent studies have elucidated the roles of specific genes, categorized here as positive or negative features, in shaping the tumor microenvironment and affecting ICI efficacy.Positive Methylation Features:

[0322] FAT1 : Hypermethylation of the FAT1 promoter correlates with decreased expression, which is associated with enhanced immune infiltration and improved responses to ICIs in various cancers, including triple-negative breast cancer.

[0323] STEAP1 : STEAP1 overexpression, often due to promoter hypomethylation, contributes to tumor progression. Targeting STEAP1 has shown promise in enhancing the efficacy of ICIs, particularly in prostate cancer models.

[0324] CD276 (B7-H3): CD276 acts as an immune checkpoint molecule. Its overexpression, potentially driven by promoter hypomethylation, is linked to immune evasion and poor prognosis. Inhibiting CD276 can potentiate anti-tumor immune responses.

[0325] WT1 : Promoter hypomethylation leads to increased WT1 expression, which has been associated with resistance to ICIs. Combining WT1 -targeted therapies with ICIs may overcome this resistance.

[0326] TAP2: Hypermethylation of the TAP2 promoter results in reduced antigen presentation, facilitating immune escape. Restoring TAP2 expression can enhance tumor immunogenicity and ICI response.

[0327] PTGS2 (COX-2): PTGS2 promoter hypomethylation leads to elevated COX-2 expression, promoting an immunosuppressive environment. Inhibiting COX-2 can reverse this effect, improving ICI efficacy.

[0328] MIF: Promoter hypomethylation of MIF increases its expression, contributing to tumor immune evasion. Targeting MIF may enhance the effectiveness of ICIs.

[0329] CXCL12: Hypermethylation of the CXCL12 promoter diminishes its expression, which can disrupt the recruitment of immunosuppressive cells and improve ICI responses.

[0330] CD44: CD44 expression, influenced by promoter methylation, plays a role in tumor progression and immune evasion. Modulating its expression may impact ICI outcomes.

[0331] Negative Methylation Features:

[0332] LGALS1 : Promoter hypomethylation leads to increased LGALS1 expression, which is associated with T-cell apoptosis and ICI resistance. Targeting LGALS1 may enhance antitumor immunity.

[0333] TET2: Loss-of-function mutations and promoter hypermethylation of TET2 impair DNA demethylation processes, leading to reduced immune surveillance and diminished ICI responses.

[0334] TERT : TERT promoter mutations and hypomethylation are linked to increased expression, contributing to tumor immortality and potential resistance to ICIs.

[0335] VGLL4: Hypermethylation of the VGLL4 promoter reduces its expression, which is associated with increased PD-L1 levels and immune evasion. Restoring VGLL4 expression may improve ICI efficacy.

[0336] KEAP1 : Promoter hypermethylation or mutations in KEAP1 lead to NRF2 pathway activation, promoting an immunosuppressive tumor microenvironment and ICI resistance.

[0337] CREB3L1 : Hypermethylation-induced silencing of CREB3L1 is linked to enhanced tumor proliferation and reduced immune infiltration, negatively impacting ICI responses.

[0338] NT5E (CD73): Promoter hypomethylation increases NT5E expression, leading to adenosine accumulation and immunosuppression, thereby reducing ICI effectiveness.

[0339] HLA-A: Hypermethylation of the HLA-A promoter diminishes antigen presentation, facilitating immune escape and reducing ICI efficacy.

[0340] CD47: Promoter hypomethylation elevates CD47 expression, delivering a "don't eat me" signal to macrophages and impairing anti-tumor immunity, thus hindering ICI responses.

[0341] Collectively, these findings underscore the significance of promoter methylation in regulating genes that influence the tumor-immune interface. Understanding these epigenetic modifications provides valuable insights into patient stratification and the development of combination therapies to enhance the efficacy of immune checkpoint inhibitors.IO score categories have similar tumor fraction distributions

[0342] Baseline tumor fraction is a known biomarker that is associated with disease burden of cancer patients and thus correlated with treatment response such as real-world time to next treatment (rwTTNT). rwTTNT is defined as the duration of time from the start of immune checkpoint inhibitor treatment until the start of a new treatment or death. Patients benefiting from immune checkpoint inhibitor treatment stay on treatment and thus longer rwTTNT is a strong metric for patients maintaining benefit from treatment. The similar distributions of tumor fraction among IO score categories demonstrates that longer rwTTNT in IO score positive patients is not due to an indirect correlation with tumor fraction. Tumor fraction is not significantly different between IO score positive or negative groups.IO score categories are not correlated with known predictive biomarkers for immune checkpoint inhibitor response such as TMB or MSI

[0343] IO score positive or negative categories are not significantly correlated with blood tumor mutational burden (bTMB) category. TMB is a known biomarker for predicting response to immune checkpoint inhibitors. IO score using promoter methylation from Guardantlnfinity assay thus brings additional information not already captured by TMB status that is useful for predicting whether a patient will respond to immune checkpoint inhibitors using a pretreatment blood draw.

[0344] IO score positive or negative categories are not significantly correlated with blood microsatellite unstable (MSI) categories. MSI is also used to predict response of patients to immune checkpoint inhibitors. Lack of correlation indicates that IO score using promoter methylation from Guardantlnfinity assay thus brings additional information not already captured by MSI status that is useful for predicting whether a patient will respond to immune checkpoint inhibitors using a pretreatment blood draw.10 score positive status is significantly associated with longer rwTTNT even when accounting for TMB status as a covariate

[0345] Hazard ratio (HR) of 0.79 is directionally as expected (expect better response of TMB High patients relative to TMB low patients. However, the p-value for TMB category High association with rwTTNT is not significant in this cohort. Also as expected, tumor fraction high (tf group high in the table), defined as patients with TF slightly greater than the median TF (6% threshold; median was 4.5%) value in their Guardantlnfinity test, had shorter rwTTNT than patients with below 6% TF. Nonetheless, even after accounting for TMB, TF and other Covariates, IO score positivity among patients is significantly associated with longer rwTTNT on immune checkpoint inhibitors.Table 1. Main clinical association results of the developed IO scoreTable 2. IO score positive status is significantly associated with longer rwTTNT even when accounting for TMB status as a covariate

Claims

CLAIMS1. A computer-implemented method comprising: receiving at least one dataset in a computer system, wherein the dataset comprises a plurality of molecular phenotypes determined from a test sample of a patient and wherein the computer system comprises readable media comprises instructions that performs at least one classification of the test sample. The computer-implemented method of any preceding claim, wherein the molecular phenotypes comprise at least one of epigenetic marks, fragmentome profiles, TMB, human leukocyte antigen loss of heterozygosity (HLA LOH), sequence variants, 9p21.3 loss, tumor fraction (TF), medical imaging data, and histology data.3 The computer-implemented method of any preceding claim, wherein the epigenetic marks comprise at least one of promoter methylation for a plurality of genes, a plurality of methylation signatures, and a plurality of methylation states. The method of claim 3, wherein the plurality of genes comprises at least one gene selected from the consisting of: FAT1, STEAP1, CD276 (B7-H3), WT1, TAP2, PTGS2 (COX-2), MIF, CXCL12, CD44, LGALS1, TET2, TERT, VGLL4, KEAP1, CREB3L1,NT5E (CD73), HL A- A, CD47, TNFRSF9, AD0RA2B, TNFRSF14, CD40, TNFRSF12A, CLDN6, GRIN2A and CD8A.5 The computer-implemented method of any preceding claim, wherein the epigenetic marks comprise chromatin state, histone marks, histone acetylation, and / or chromatin conformation.6 The computer-implemented method of any preceding claim, wherein TMB comprises blood- derived TMB (bTMB).7 The computer-implemented method of any preceding claim, wherein bTMB comprises a score or status.8 The computer-implemented method of any preceding claim, wherein status comprises bTMB low or bTMB high.9 The computer-implemented method of any preceding claim, wherein sequence variants comprise single nucleotide changes in at least one of STK11, KRAS, KEAP1, and EGFR.10 The computer-implemented method of claim 8, wherein the sequence variants in EGFR comprise exl9del and / or L858R.11 The computer-implemented method of any preceding claim, wherein the sequence variants in EGFR comprise at least one of Exon 19 deletion, EGFR Exon 19 insertion, EGFR G719X, EGFR Exon 20 insertion, EGFR T790M, EGFR L858R, and EGFR L861Q.

12. The computer-implemented method of any preceding claim, wherein sequence variants comprise small variants and / or structural changes in at least one of STK11, KRAS, KEAP1, and EGFR.

13. The computer-implemented method of any preceding claim, wherein imaging data comprises radiology data.

14. The computer-implemented method of any preceding claim, wherein the radiology data comprises magnetic resonance imaging (MRI), computed tomography (CT), colonoscopy, mammography, and / or x-ray.

15. The computer-implemented method of any preceding claim, wherein the radiology data comprises brain imaging, radiation oncology, abdominal and pelvic imaging, and / or thoracic imaging.

16. The computer-implemented method of any preceding claim, wherein histology data further comprises digital pathology data.

17. The computer-implemented method of any preceding claim, wherein the histology data comprises tissue PD-L1 staining.

18. The computer-implemented method of any preceding claim, wherein histology data comprises immunohistochemistry, immunofluorescence, fish, hematoxylin and eosin staining (H&E).

19. The computer-implemented method of any preceding claim, wherein the instructions further comprise artificial intelligence (Al) algorithms to analyze imaging data.

20. The computer-implemented method of any preceding claim, wherein the Al algorithms comprise deep learning and / or machine learning.

21. The computer-implemented method of any preceding claim, wherein the Al algorithms comprise principal component analysis (PCA), support vector machines (SVM), and / or convolutional neural networks (CNN).

22. The computer-implemented method of any preceding claim, wherein the test sample is cell- free DNA.

23. The computer-implemented method of any preceding claim, wherein the subject is a cancer patient.

24. The computer-implemented method of any preceding claim, wherein the cancer is Non-Small Cell Lung Cancer (NSCLC).

25. The computer-implemented method of any preceding claim, wherein the cancer comprises breast cancer, colorectal cancer, colon cancer, prostate cancer, lung cancer, pancreatic cancer, ovarian cancer, melanoma, and / or liver cancer.

26. The computer-implemented method of any preceding claim, wherein the classification if performed using a multivariate logistic regression model.

27. The computer-implemented method of any preceding claim, wherein the classification is performed using Naive Bayes, decision trees, support vector machines (SVM), random forest classifier, k-nearest neighbors (KNN), or neural networks.

28. A method, comprising: obtaining or having obtained a sample of a subject; determining one or more features in the sample; and determining a response to an immune-oncology(IO) therapy for the subject.

29. The method of any preceding claim,, wherein the IO therapy is an immune checkpoint inhibitor(ICI).

30. The method of any preceding claim,, wherein the features comprise at least one of epigenetic marks, fragmentome profiles, TMB, human leukocyte antigen loss of heterozygosity (HLA LOH), sequence variants, 9p21.3 loss, tumor fraction (TF), medical imaging data, and histology data.

31. The method of any preceding claim,, wherein the features comprise at least one of promoter methylation for a plurality of genes, a plurality of methylation signatures, and a plurality of methylation states.

32. The method of any preceding claim,, wherein the plurality of genes comprises at least one gene selected from the consisting of: FAT1, STEAP1, CD276 (B7-H3), WT1, TAP2, PTGS2 (COX-2), MIF, CXCL12, CD44, LGALS1, TET2, TERT, VGLL4, KEAP1, CREB3L1,NT5E (CD73), HLA-A, CD47, TNFRSF9, ADORA2B, TNFRSF14, CD40, TNFRSF12A, CLDN6, GRIN2A and CD8A.

33. The method of any preceding claim, wherein the features comprise chromatin state, histone marks, histone acetylation, and / or chromatin conformation.

34. The method of any preceding claim, wherein TMB comprises blood-derived TMB (bTMB).

35. The method of any preceding claim, wherein bTMB comprises a score or status.

36. The method of any preceding claim, wherein status comprises bTMB low or bTMB high.

37. The method of any preceding claim, wherein sequence variants comprise single nucleotide changes in at least one of STK11, KRAS, KEAP1, and EGFR.

38. The method of any preceding claim, wherein the sequence variants in EGFR comprise exl9del and / or L858R.

39. The method of any preceding claim, wherein the sequence variants in EGFR comprise at least one of Exon 19 deletion, EGFR Exon 19 insertion, EGFR G719X, EGFR Exon 20 insertion, EGFR T790M, EGFR L858R, and EGFR L861Q.

40. The method of any preceding claim, wherein sequence variants comprise small variants and / or structural changes in at least one of STK11, KRAS, KEAP1, and EGFR.

41. The method of any preceding claim, wherein imaging data comprises radiology data.

42. The method of any preceding claim, wherein the radiology data comprises magnetic resonance imaging (MRI), computed tomography (CT), colonoscopy, mammography, and / or x-rays.

43. The method of any preceding claim, wherein the radiology data comprises brain imaging, radiation oncology, abdominal and pelvic imaging, and / or thoracic imaging.

44. The method of any preceding claim, wherein histology data further comprises digital pathology data.

45. The method of any preceding claim, wherein the histology data comprises tissue PD-L1 staining.

46. The method of any preceding claim, wherein histology data comprises immunohistochemistry, immunofluorescence, fish, hematoxylin and eosin staining (H&E).

47. The method of any preceding claim, wherein the response is determined using a classification algorithm comprising multivariate logistic regression.

48. The method of any preceding claim, wherein the response is determined using a classification algorithm comprising at least one of Naive Bayes, decision tree, support vector machine (SVM), random forest classifier, k-nearest neighbor (KNN), and neural network.

49. A computer-implemented method comprising: receiving at least one dataset in a computer system comprising a hardware processor and a computer-readable storage media, wherein the dataset comprises a plurality of molecular phenotypes obtained from a test sample of a patient and wherein the computer-readable media comprises instructions that, when executed by the processor, cause the hardware processor to perform at least one classification of the test sample comprising a first classification comprising an immune-oncology (IO) therapy response or a response to an immune checkpoint inhibitor(ICI) for the subject, wherein the molecular phenotypes comprise epigenetic data.

50. A system capable of performing any preceding claim.

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