Cell-free DNA monitoring in combination panels

Abstract

Methods and compositions are disclosed for monitoring mutational load, cancer status, and vaccine efficacy using cell-free DNA sequencing after enrichment with combinatorial probe panels.

Description

[Technical Field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of U.S. Provisional Application No. 63 / 381,747, filed October 31, 2022, which is incorporated herein by reference in its entirety. [Background technology]

[0002] Background of the Invention Therapeutic vaccines based on tumor-specific antigens hold great promise as the next generation of personalized cancer immunotherapy. For example, cancers with a high mutational burden, such as non-small cell lung cancer (NSCLC) and melanoma, are particularly attractive targets for such therapies due to their relatively high potential for neoantigen generation. Early evidence indicates that neoantigen-based vaccination can induce T cell responses and that neoantigen-targeted cell therapy can induce tumor regression in certain patients under certain circumstances.

[0003] One of the questions regarding neoantigen vaccine design is which of the many coding mutations present in a tumor of interest could generate the "best" therapeutic neoantigen, e.g., an antigen capable of eliciting antitumor immunity and resulting in tumor regression. Targeting antigens that are common among cancer patients holds great promise as a vaccine strategy, including targeting both mutated neoantigens and non-mutated tumor antigens (e.g., inappropriately expressed tumor antigens).

[0004] Challenges in common antigen vaccine strategies include at least monitoring the cancer status and / or vaccine efficacy before or after administering a cancer vaccine to a subject. For example, many standard methods for disease monitoring, such as radiological evaluation (e.g., CT scan) or tumor biopsy, are invasive or burdensome. Furthermore, certain existing cell-free DNA monitoring methods only monitor a small portion (e.g., less than 50) of the mutations associated with the tumor exome, resulting in limited monitoring capabilities for the cancer status and burden, e.g., low monitoring sensitivity. Similarly, certain existing cell-free DNA monitoring methods (e.g., Wan et al.; Science Translational Medicine 17 Jun 2020: Vol. 12, Issue 548 (Non-Patent Document 1)) only monitor a larger number of mutations at low sequencing depth, resulting in low accuracy and reliability.

[0005] For cfDNA monitoring, assays are generally stratified into tumor-naive and tumor-informed. Tumor-naive monitoring utilizes fixed DNA target panel approaches, which tend to capture only a small number of variants across many patients. Tumor-informed approaches rely on sequencing biopsies to longitudinally track a set of defined, personalized variants over time, but generally monitor a smaller footprint. To overcome the smaller footprint of fixed gene sets and personalized panels, WES and whole-genome sequencing (WGS) of liquid biopsy samples offer expanded breadth for de novo variant discovery or detection without the need for tissue, allowing for use in either early detection or recurrence. However, increasing breadth of coverage generally increases costs, making these technologies impractical in clinical settings and / or requiring a lower overall sequencing depth to maintain cost and the use of various bioinformatics strategies.

[0006] Therefore, there is a need in the art for accurate, reliable, and minimally invasive cancer monitoring methods, such as cell-free DNA sequencing, that provide broad target coverage (e.g., at least 95% of the mutations present in the cancer exome) with high sequence read depth (e.g., at least 1000X). There is also a need for compositions and methods that can effectively monitor individual subject efficacy and enable more extensive monitoring, including monitoring tumor escape mutations. [Prior art documents] [Non-patent literature]

[0007] [Non-Patent Document 1] Wan et al.;Science Translational Medicine 17 Jun 2020:Vol.12,Issue 548 Summary of the Invention

[0008] Provided herein is a panel of polynucleotide probes for enriching cfDNA, the panel including: (A) one or more tumor-informative polynucleotide probes; and (B) one or more tumor-naive polynucleotide probes.

[0009] In some embodiments, the one or more tumor-informative polynucleotide probes are configured to capture a target sequence comprising an epitope sequence encoded by a cancer vaccine administered to a subject, the subject being determined to have a tumor expressing such epitope sequence. In some embodiments, the KRAS mutation is selected from the group consisting of a KRAS_G12C mutation, a KRAS_G12D mutation, a KRAS_G12V mutation, and a KRAS_Q61H mutation. In some aspects, the epitope sequence comprises a mutation selected from the group consisting of KRAS_G13D, KRAS_Q61K, TP53_R249M, CTNNB1_S45P, CTNNB1_S45F, ERBB2_Y772_A775dup, KRAS_G12D, KRAS_Q61R, CTNNB1_T41A, TP53_K132N, KRAS_G12A, KRAS_Q61L, TP53_R213L, BRAF_G466V, KRAS_G12V, KRAS_Q61H, CTNNB1_S37F, TP53_S127Y, TP53_K132E, and KRAS_G12C. In some aspects, the epitope sequence comprises an EGFR mutation. In some embodiments, the EGFR mutation comprises an EGFR_L858R mutation.

[0010] In some embodiments, the epitope sequence comprises one or more subject-specific epitopes, and the subject's tumor has been sequenced to determine the subject-specific epitopes encoded by the cancer vaccine. In some embodiments, the one or more subject-specific epitopes comprise at least two subject-specific epitopes, at least 10 subject-specific epitopes, at least 20 subject-specific epitopes, or between 2 and 20 subject-specific epitopes. In some embodiments, the one or more subject-specific epitopes comprise between 2 and 20 subject-specific epitopes.

[0011] In some embodiments, the panel further comprises additional tumor-informative polynucleotide probes capturing additional target sequences, wherein the tumor has been determined to express the additional target sequences, and the additional target sequences are not encoded by the cancer vaccine. In some embodiments, the additional target sequences include at least 10 target sequences, at least 20 target sequences, at least 30 target sequences, at least 100 target sequences, 10-500 target sequences, 30-500 target sequences, 100-500 target sequences, 10-100 target sequences, 30-100 target sequences, or 100-100 target sequences. In some embodiments, the additional target sequences are predicted to be presented by at least one HLA in the subject.

[0012] In some embodiments, the one or more tumor-naive polynucleotide probes are configured to capture a target sequence comprising a sequence of interest selected from the group consisting of cancer-associated genes, oncogenes, tumor suppressor genes, genes in the interferon-gamma signaling pathway, genes in the antigen processing pathway, and combinations thereof.

[0013] In some embodiments, the one or more tumor-naive polynucleotide probes are configured to capture a target sequence comprising a sequence of interest selected from each of a cancer-associated gene, an oncogene, a tumor suppressor gene, a gene in the interferon-gamma signaling pathway, and a gene in the antigen processing pathway.

[0014] In some aspects, the cancer-associated gene is selected from the group consisting of ABCA12, ACVR2A, AKAP9, BMPR2, COL12A1, CSMD3, DNAH5, DOCK3, FAT2, FAT3, FAT4, FGF10, FGF6, FLG, MAGI1, MDN1, MMAB, NBEA, OBSCN, PCBP1, PCLO, PLEKHA6, PROC, RAD54L, RELN, RPL22, RYR2, TCERG1, WRN, and ZDBF2. In some embodiments, the cancer-associated genes include each of ABCA12, ACVR2A, AKAP9, BMPR2, COL12A1, CSMD3, DNAH5, DOCK3, FAT2, FAT3, FAT4, FGF10, FGF6, FLG, MAGI1, MDN1, MMAB, NBEA, OBSCN, PCBP1, PCLO, PLEKHA6, PROC, RAD54L, RELN, RPL22, RYR2, TCERG1, WRN, and ZDBF2.

[0015] In some embodiments, the oncogene is selected from the group consisting of ABL1, AKT2, ALK, AR, BCL6, BCL9L, BRAF, BTK, CARD11, CCND1, CCND3, CTNNB1, DDR2, EGFR, ERBB2, ERBB3, FGFR1, FGFR3, FHOD3, FLT1, FLT3, GNAS, HRAS, KDR, KIT, KRAS, MAP2K1, MAP2K2, MECOM, MED12, MET, MTOR, NOTCH1, NOTCH2, NOTCH3, NRAS, NRG1, NTRK1, NTRK3, PDGFRA, PDGFRB, PIK3CA, PIK3CG, PSMB2, RET, ROS1, SF3B1, SMO, SYNE1, and ZBTB20. In some embodiments, the oncogenes include each of ABL1, AKT2, ALK, AR, BCL6, BCL9L, BRAF, BTK, CARD11, CCND1, CCND3, CTNNB1, DDR2, EGFR, ERBB2, ERBB3, FGFR1, FGFR3, FHOD3, FLT1, FLT3, GNAS, HRAS, KDR, KIT, KRAS, MAP2K1, MAP2K2, MECOM, MED12, MET, MTOR, NOTCH1, NOTCH2, NOTCH3, NRAS, NRG1, NTRK1, NTRK3, PDGFRA, PDGFRB, PIK3CA, PIK3CG, PSMB2, RET, ROS1, SF3B1, SMO, SYNE1, and ZBTB20.

[0016] In some aspects, the tumor suppressor gene is selected from the group consisting of TP53, PTEN, ARID1A, APC, AMER1, ASXL1, ATM, ATR, ATRX, AXIN2, BARD1, BRCA1, BRCA2, CASP8, CFH, CREBBP, DNMT3A, EP300, ERCC2, ERCC5, EXT1, FANCA, FANCD2, FANCI, FANCM, FAT1, FBXW7, HNF1A, MAX, MLH1, MSH3, MSH6, NF1, PIK3R1, PTCH1, PTPRT, RECQL4, RNF43, ROBO1, SLX4, SMAD2, SMAD3, SMAD4, SOX9, TCF7L2, TERT promoter, TET2, TGFBR2, TP53BP1, TSC1, TSC2, WNT16, XPC, ZFP36L2, and ZNRF3. In some embodiments, the tumor suppressor genes include each of TP53, PTEN, ARID1A, APC, AMER1, ASXL1, ATM, ATR, ATRX, AXIN2, BARD1, BRCA1, BRCA2, CASP8, CFH, CREBBP, DNMT3A, EP300, ERCC2, ERCC5, EXT1, FANCA, FANCD2, FANCI, FANCM, FAT1, FBXW7, HNF1A, MAX, MLH1, MSH3, MSH6, NF1, PIK3R1, PTCH1, PTPRT, RECQL4, RNF43, ROBO1, SLX4, SMAD2, SMAD3, SMAD4, SOX9, TCF7L2, TERT promoter, TET2, TGFBR2, TP53BP1, TSC1, TSC2, WNT16, XPC, ZFP36L2, and ZNRF3.

[0017] In some embodiments, the genes in the interferon-gamma signaling pathway are selected from the group consisting of IFNGR1, INFGR2, JAK1, JAK2, and STAT1. In some embodiments, the genes in the interferon-gamma signaling pathway include each of IFNGR1, INFGR2, JAK1, JAK2, and STAT1.

[0018] In some aspects, the antigen processing pathway genes are selected from the group consisting of B2M, HLA-A, HLA-B, HLA-C, HLA-E, TAP1, TAP2, NLRC5, CALR, CANX, PSMB2, and TAPBP. In some aspects, the antigen processing pathway genes include each of B2M, HLA-A, HLA-B, HLA-C, HLA-E, TAP1, TAP2, NLRC5, CALR, CANX, PSMB2, and TAPBP.

[0019] In some embodiments, the one or more tumor-naive polynucleotide probes are selected from the group consisting of ABCA12, ACVR2A, AKAP9, BMPR2, COL12A1, CSMD3, DNAH5, DOCK3, FAT2, FAT3, FAT4, FGF10, FGF6, FLG, MAGI1, MDN1, MMAB, NBEA, OBSCN, PCBP1, PCLO, PLEKHA6, PROC, RAD54L, RELN, RPL22, RYR2, TCERG1, WRN, ZDBF2, ABL1, AKT2, ALK, AR, BCL6, BCL9L, BRAF, BTK, CARD11, CCND1, CCND3, CTNNB1, DDR2, EGFR, ERBB2, ERBB3, FGFR1, FGFR3, FHOD3, FLT1, FLT3, GNAS, HRAS, KDR, KIT, KRAS, MAP2K1, MAP 2K2, MECOM, MED12, MET, MTOR, NOTCH1, NOTCH2, NOTCH3, NRAS, NRG1, NTRK1, NTRK3, PDGFRA, PDGFRB, PIK3CA, PIK3CG, RET, ROS1, SF3B1, SMO, SYNE 1, ZBTB20, TP53, PTEN, ARID1A, APC, AMER1, ASXL1, ATM, ATR, ATRX, AXIN2, BARD1, BRCA1, BRCA2, CASP8, CFH, CREBBP, DNMT3A, EP300, ERCC2, ERC C5, EXT1, FANCA, FANCD2, FANCI, FANCM, FAT1, FBXW7, HNF1A, MAX, MLH1, MSH3, MSH6, NF1, PIK3R1, PTCH1, PTPRT, RECQL4, RNF43, ROBO1, SLX4, SMA The antibody is configured to capture a target sequence containing a sequence of interest selected from the group consisting of D2, SMAD3, SMAD4, SOX9, TCF7L2, TERT promoter, TET2, TGFBR2, TP53BP1, TSC1, TSC2, WNT16, XPC, ZFP36L2, ZNRF3, IFNGR1, INFGR2, JAK1, JAK2, STAT1, B2M, HLA-A, HLA-B, HLA-C, HLA-E, TAP1, TAP2, NLRC5, CALR, CANX, PSMB2, TAPBP, and combinations thereof.

[0020] In some embodiments, the one or more tumor-naive polynucleotide probes are selected from the group consisting of ABCA12, ACVR2A, AKAP9, BMPR2, COL12A1, CSMD3, DNAH5, DOCK3, FAT2, FAT3, FAT4, FGF10, FGF6, FLG, MAGI1, MDN1, MMAB, NBEA, OBSCN, PCBP1, PCLO, PLEKHA6, PROC, RAD54L, RELN, RPL22, RYR2, TCERG1, WRN, ZDBF2, ABL1, AKT2, ALK, AR, BCL6, BCL9L , BRAF, BTK, CARD11, CCND1, CCND3, CTNNB1, DDR2, EGFR, ERBB2, ERBB3, FGFR1, FGFR3, FHOD3, FLT1, FLT3, GNAS, HRAS, KDR, KIT, KRAS, MAP2K1, M AP2K2, MECOM, MED12, MET, MTOR, NOTCH1, NOTCH2, NOTCH3, NRAS, NRG1, NTRK1, NTRK3, PDGFRA, PDGFRB, PIK3CA, PIK3CG, PSMB2, RET, ROS1, SF3B1 , SMO, SYNE1, ZBTB20, TP53, PTEN, ARID1A, APC, AMER1, ASXL1, ATM, ATR, ATRX, AXIN2, BARD1, BRCA1, BRCA2, CASP8, CFH, CREBBP, DNMT3A, EP300 , ERCC2, ERCC5, EXT1, FANCA, FANCD2, FANCI, FANCM, FAT1, FBXW7, HNF1A, MAX, MLH1, MSH3, MSH6, NF1, PIK3R1, PTCH1, PTPRT, RECQL4, RNF43, ROB The antibody is configured to capture a target sequence containing a sequence of interest selected from O1, SLX4, SMAD2, SMAD3, SMAD4, SOX9, TCF7L2, TERT promoter, TET2, TGFBR2, TP53BP1, TSC1, TSC2, WNT16, XPC, ZFP36L2, ZNRF3, IFNGR1, INFGR2, JAK1, JAK2, STAT1, B2M, HLA-A, HLA-B, HLA-C, HLA-E, TAP1, TAP2, NLRC5, CALR, CANX, PSMB2, and TAPBP.

[0021] In some embodiments, the one or more tumor-naive polynucleotide probes are selected from the group consisting of ABL1, AKT2, ALK, APC, AR, ATR, ATRX, BARD1, BCL6, BMPR1A, BRAF, BRCA1, BRCA2, BTK, CARD11, CCND1, CCND3, CDK12, CFH, CREBBP, CTNNB1, DDR2, DNMT3A, EGFR, EP300, ERBB2, ERBB3, ERCC2, ERCC5, EXT1, FANCA, FANCD2, FANCI, FANCM, FBXW7, FGF10, FGF6, FGFR1, FGFR3, FLI1, FLT1, FLT3, GNAS, HNF1A, HRAS, KDR, KIT, KRAS, MAGI1, MAP2K1, MAP2K2, MAX, MED12, MET, MLH1, MMAB, M The antibody is configured to capture a target sequence containing a sequence of interest selected from the group consisting of SH3, MSH6, MTOR, NF1, NFE2L2, NOTCH1, NOTCH2, NOTCH3, NRAS, NRG1, NTRK1, NTRK3, PDGFRA, PDGFRB, PIK3CA, PIK3CG, PIK3R1, PMS2, PPARG, PROC, PTCH1, RAD54L, RAF1, RECQL4, RET, ROS1, SF3B1, SF3B2, SLX4, SMO, TERT promoter, TET2, TP53BP1, TSC1, TSC2, WRN, XPA, XPC, ZNF395, B2M, HLA-A, HLA-B, HLA-C, TAP1, TAP2, NLRC5, IFNGR1, INFGR2, JAK1, JAK2, TP53, PTEN, and ARID1A.

[0022] In some embodiments, the one or more tumor-naive polynucleotide probes comprise two or more probes configured to capture the entire coding exon sequence of a given gene. In some embodiments, the one or more tumor-naive polynucleotide probes comprise two or more probes configured to capture a genomic region of interest associated with cancer.

[0023] In some embodiments, the tumor-informative polynucleotide probes and / or the tumor-naive polynucleotide probes include probes that include overlapping sequences.

[0024] In some embodiments, the panel comprises at least 20 probes, at least 30 probes, at least 40 probes, at least 50 probes, at least 60 probes, at least 70 probes, at least 80 probes, at least 90 probes, at least 100 probes, at least 200 probes, at least 300 probes, at least 400 probes, or at least 500 probes.

[0025] In some embodiments, the panel is configured to cover at least 100 kb, at least 300 kb, at least 300 kb, at least 400 kb, 100-400 kb, 200-400 kb, 300-400 kb, 100-500 kb, 200-500 kb, 300-500 kb, or 340-400 kb of the subject's genome.

[0026] In some embodiments, the one or more tumor-naive polynucleotide probes comprise polynucleotide probes configured to capture sequences associated with a given cancer that the subject is known to have or suspected to have, and optionally the cancer is CRC or NSCLC.

[0027] In some embodiments, the panel further comprises additional polynucleotide probes configured to capture sequences containing polymorphisms in the human population, which sequences containing polymorphisms can, in combination, individually distinguish subjects.

[0028] Also provided herein are methods for enriching cfDNA, the methods including: (a) providing a sample containing cfDNA; (b) providing a panel of polynucleotide probes comprising any one of the tumor-informative / tumor-naive combination panels provided herein; (c) contacting the sample containing cfDNA with the panel of polynucleotide probes under conditions sufficient for cfDNA containing target sequences of interest to hybridize with each respective polynucleotide probe; and (d) capturing hybridized cfDNA and polynucleotide probe pairs to enrich the cfDNA.

[0029] Also provided herein is a method for monitoring a cancer status in a subject who has, has had, or is suspected of having cancer, the method comprising: a. obtaining or having obtained sequencing data of cell-free DNA (cfDNA) from a sample from the subject, wherein the sequencing data comprises a target coverage of at least 50% of all polynucleotide regions of interest corresponding to mutations present in the cancer exome, the sequenced polynucleotide regions of interest comprise a read depth of at least 1000X, optionally the polynucleotide regions of interest comprise at least 50 mutations, optionally the average read depth is an average double-stranded read depth, and optionally the obtaining of the sequencing data comprises: obtaining or having obtained a sample from the subject; isolating or having isolated cfDNA; and b. obtaining or having obtained the exome, including concentrating or having concentrated the DNA and / or sequencing or having sequenced the cfDNA; and c. determining or having determined the frequency of mutations present in the exome to assess the cancer status, optionally wherein assessing the status includes assessing the presence and / or amount of cancer, and wherein the cfDNA is enriched prior to sequencing using (1) a panel of subject-specific polynucleotide probes, (2) a panel of tumor-naive polynucleotide probes, and / or (3) a combined panel of a panel of tumor-informative polynucleotide probes and a panel of tumor-naive polynucleotide probes, wherein the polynucleotide probes are configured to capture polynucleotide regions of interest.

[0030] Also provided herein is a method for monitoring a cancer status in a subject who has, has had, or is suspected of having cancer, the method comprising: a. obtaining or having obtained sequencing data of cell-free DNA (cfDNA) from a sample from the subject, wherein the sequencing data comprises a target coverage of at least 95% of all polynucleotide regions of interest corresponding to mutations present in the cancer exome, the polynucleotide regions of interest comprise at least 50 mutations, and the sequenced polynucleotide regions of interest comprise a double-stranded read depth of at least 1000X; and optionally, obtaining the sequencing data includes: obtaining or having obtained a sample from the subject; isolating or having isolated cfDNA; and concentrating or having concentrated cfDNA. and / or obtaining or having obtained cfDNA, including sequencing or having sequenced the cfDNA; and b. determining or having determined the frequency of at least 50 mutations present in the exome to assess the cancer status, optionally wherein assessing the status includes assessing the presence and / or amount of cancer, and wherein the cfDNA is enriched prior to sequencing using (1) a panel of tumor-informative polynucleotide probes, (2) a panel of tumor-naive polynucleotide probes, and / or (3) a combined panel of a panel of tumor-informative polynucleotide probes and a panel of tumor-naive polynucleotide probes, wherein the polynucleotide probes are configured to capture polynucleotide regions of interest.

[0031] Also provided herein are methods for assessing the effectiveness of a treatment in a subject who has, has had, or is suspected of having cancer, the method comprising: a. obtaining or having obtained sequencing data of cell-free DNA (cfDNA) from a pre-treatment sample from the subject, wherein the sequencing data comprises a target coverage of at least 50% of all polynucleotide regions of interest corresponding to mutations present in the exome of the cancer, the sequenced polynucleotide regions of interest comprise a read depth of at least 1000X, optionally the polynucleotide regions of interest comprise at least 50 mutations, and optionally the average read coverage is average double-stranded read coverage; and optionally, obtaining the sequencing data comprises, obtaining or having obtained a pre-treatment sample from the subject, isolating or having isolated pre-treatment cfDNA, enriching or having enriched the pre-treatment cfDNA, and / or sequencing or having sequenced the pre-treatment cfDNA. obtaining or having obtained sequencing data of cell-free DNA (cfDNA) from a post-treatment sample from the subject, optionally wherein the treatment includes a cancer vaccine comprising a neoantigen or an expression system encoding same, and wherein the sequencing data comprises target coverage of at least 50% of all polynucleotide regions of interest corresponding to mutations present in the cancer exome, and wherein the sequenced polynucleotide regions of interest comprise a read depth of at least 1000X, and optionally wherein the polynucleotide regions of interest comprise at least 50 mutations, and optionally wherein the average read coverage is average double-stranded read coverage, and optionally wherein obtaining the sequencing data comprises or has been obtained a post-treatment sample from the subject, isolating or having isolated the post-treatment cfDNA, enriching or having enriched the post-treatment cfDNA, and / or sequencing or having sequenced the post-treatment cfDNA; and c.determining or having determined the frequency of mutations present in the exome of pre-treatment cfDNA compared to post-treatment cfDNA to assess efficacy of the treatment, wherein optionally, an increase in the frequency of mutations in the post-treatment cfDNA compared to the pre-treatment cfDNA indicates an increased likelihood that the subject's tumor burden is increasing, and optionally, a decrease or maintenance of the frequency of mutations in the post-treatment cfDNA compared to the pre-treatment cfDNA indicates an increased likelihood that the subject's tumor burden is decreasing or stable, and wherein the cfDNA has been enriched prior to sequencing using (1) a panel of tumor-informative polynucleotide probes, (2) a panel of tumor-naive polynucleotide probes, and / or (3) a combined panel of a panel of tumor-informative polynucleotide probes and a panel of tumor-naive polynucleotide probes, wherein the polynucleotide probes are configured to capture polynucleotide regions of interest.

[0032] Also provided herein is a method for assessing the effectiveness of a treatment in a subject who has, has had, or is suspected of having cancer, the method comprising: a. obtaining or having obtained sequencing data of tumor-derived DNA from cancer-affected tissue from the subject, optionally wherein obtaining the sequencing data comprises collecting or having collected the cancer-affected tissue, isolating or having isolated the tumor-derived DNA, and sequencing or having sequenced the tumor-derived DNA; b. determining or having determined from the tumor-derived DNA sequencing data one or more tumor-associated mutations compared to the subject's wild-type germline nucleic acid sequence, optionally wherein one or more of the one or more tumor-associated mutations are associated with a peptide sequence encoded by the tumor-derived DNA; determining, or having determined, a neoantigen that comprises at least one alteration that renders it different from a corresponding peptide sequence encoded by the wild-type germline nucleic acid sequence of the subject; c. designing and / or selecting, or having designed and / or selected, (1) a panel of tumor-informative polynucleotide probes, (2) a panel of tumor-naive polynucleotide probes, and / or (3) a combined panel of a panel of tumor-informative polynucleotide probes and a panel of tumor-naive polynucleotide probes, wherein the polynucleotide probes are configured to capture at least tumor-associated mutations, and optionally the polynucleotide region of interest comprises at least 50 tumor-associated mutations; d.obtaining or having obtained sequencing data of cell-free DNA (cfDNA) from a pre-treatment sample from the subject, wherein the pre-treatment cfDNA is enriched using polynucleotide probes prior to sequencing, and the sequencing data comprises a target coverage of at least 50% of all polynucleotide regions of interest corresponding to tumor-associated mutations, and the sequenced polynucleotide regions of interest comprise a read depth of at least 1000X, and optionally, the average read coverage is average double-stranded read coverage, and optionally, obtaining the sequencing data comprises collecting or having collected a pre-treatment sample from the subject, isolating or having isolated pre-treatment cfDNA, enriching or having enriched the pre-treatment cfDNA, and / or sequencing or having sequenced the pre-treatment cfDNA; e. obtaining or having obtained cell-free DNA (cfDNA) from a post-treatment sample from the subject; and f. obtaining or having obtained sequencing data for the neoantigen (cfDNA), optionally wherein the treatment includes a cancer vaccine comprising a neoantigen or an expression system encoding same, and the post-treatment cfDNA is enriched using polynucleotide probes prior to sequencing, and the sequencing data comprises target coverage of at least 50% of all polynucleotide regions of interest corresponding to tumor-associated mutations, and the sequenced polynucleotide regions of interest comprise a read depth of at least 1000X, and optionally the average read coverage is average double-stranded read coverage, and optionally obtaining the sequencing data comprises collecting or having collected a post-treatment sample from the subject, isolating or having isolated the post-treatment cfDNA, enriching or having enriched the post-treatment cfDNA, and / or sequencing or having sequenced the post-treatment cfDNA; andThe method includes determining or having determined the frequency of tumor-associated mutations in pre-treatment cfDNA compared to post-treatment cfDNA to assess the efficacy of the treatment, wherein optionally at least one or more tumor-associated mutations associated with neoantigens are determined, and optionally, an increase in the frequency of mutations in post-treatment cfDNA compared to pre-treatment cfDNA indicates an increased likelihood that the subject's tumor burden is increasing, and optionally, a decrease or maintenance of the frequency of mutations in post-treatment cfDNA compared to pre-treatment cfDNA indicates an increased likelihood that the subject's tumor burden is decreasing or stable.

[0033] In some embodiments, the methods include designing and / or selecting or having designed and / or selected a combination panel of a panel of tumor-informant polynucleotide probes and a panel of tumor-naive polynucleotide probes. In some embodiments, the designed and / or selected combination panel includes any one of the tumor-informant / tumor-naive combination panels provided herein.

[0034] Also provided herein are methods for enriching cfDNA, including: (a) providing a sample containing cfDNA; (b) providing a panel of polynucleotide probes, the panel including (i) one or more tumor-informative polynucleotide probes and (ii) one or more tumor-naive polynucleotide probes; (c) contacting the sample containing cfDNA with the panel of polynucleotide probes under conditions sufficient to allow cfDNA containing target sequences of interest to hybridize with each respective polynucleotide probe; and (d) capturing hybridized cfDNA and polynucleotide probe pairs to enrich the cfDNA. In some embodiments, the panel includes any one of the tumor-informative / tumor-naive combination panels provided herein.

[0035] In some embodiments, the method includes one or more of the following steps: a. collecting or having collected a sample from the subject; b. isolating or having isolated cfDNA; c. concentrating or having concentrated cfDNA; or d. sequencing or having sequenced cfDNA.

[0036] In some aspects, the method includes each of the following steps: a. collecting or having collected a sample from a subject; b. isolating or having isolated cfDNA; c. concentrating or having concentrated cfDNA; and d. sequencing or having sequenced cfDNA.

[0037] In some embodiments, the average read depth comprises an average read coverage of at least 1500X, at least 2000X, at least 2500X, 3000X, at least 3500X, at least 4000X, at least 4500X, or at least 5000X. In some embodiments, the average read depth comprises an average read coverage in the range of 1000X to 5000X. In some embodiments, the average read depth comprises an average read coverage in the range of 1000X to 4000X, 1000X to 3000X, 1000X to 2000X, 2000X to 5000X, 2000X to 4000X, 2000X to 3000X, 3000X to 5000X, 3000X to 4000X, or 4000X to 5000X. In some embodiments, the average read depth comprises an average double-stranded read depth.

[0038] In some embodiments, each polynucleotide region of interest corresponding to mutations present in the exome comprises a read depth of at least 1000X. In some embodiments, each polynucleotide region of interest corresponding to mutations present in the exome comprises a read depth of at least 1000X, at least 1500X, at least 2000X, at least 2500X, 3000X, at least 3500X, at least 4000X, at least 4500X, or at least 5000X. In some embodiments, the target coverage comprises at least 60%, at least 70%, at least 80%, or at least 90% of the polynucleotide region of interest corresponding to mutations present in the cancer exome. In some embodiments, the target coverage comprises at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, at least 99.5%, at least 99.9%, or 100% of the polynucleotide region of interest corresponding to mutations present in the cancer exome.

[0039] In some embodiments, the target coverage includes at least 95% of the polynucleotide region of interest that corresponds to mutations present in the cancer exome, hi some embodiments, the polynucleotide region of interest includes at least 50, at least 60, at least 70, at least 80, or at least 90 mutations.

[0040] In some embodiments, the polynucleotide region of interest comprises at least 50 mutations, hi some embodiments, the polynucleotide region of interest comprises at least 100, at least 150, at least 200, at least 250, at least 300, at least 400, at least 500, at least 600, at least 700, at least 800, at least 900, or at least 1000 mutations.

[0041] In some aspects, the method includes the steps of: a. obtaining or having obtained sequencing data of tumor-derived DNA from cancer-affected tissue from the subject, optionally wherein obtaining the sequencing data comprises obtaining or having obtained the cancer-affected tissue, isolating or having isolated the tumor-derived DNA, and sequencing or having sequenced the tumor-derived DNA; b. determining or having determined from the tumor-derived DNA sequencing data one or more tumor-associated mutations relative to a wild-type germline nucleic acid sequence of the subject, optionally wherein one or more of the one or more tumor-associated mutations is a neoantigen that includes at least one change that causes a peptide sequence encoded by the tumor-derived DNA to differ from a corresponding peptide sequence encoded by a wild-type germline nucleic acid sequence of the subject; c. designing and / or selecting, or having designed and / or selected, (1) a panel of tumor-informed polynucleotide probes, (2) a panel of tumor-naive polynucleotide probes, and / or (3) a combined panel of a panel of tumor-informed polynucleotide probes and a panel of tumor-naive polynucleotide probes, wherein the polynucleotide probes are configured to capture polynucleotide regions of interest corresponding to tumor-associated mutations, and optionally the polynucleotide regions of interest include at least 50 tumor-associated mutations; and d. enriching, or having enriched, the cfDNA using the polynucleotide probes prior to sequencing.

[0042] In some aspects, the cancer is selected from the group consisting of lung cancer, melanoma, breast cancer, ovarian cancer, prostate cancer, kidney cancer, gastric cancer, colon cancer, testicular cancer, head and neck cancer, pancreatic cancer, brain cancer, B-cell lymphoma, acute myeloid leukemia, chronic myeloid leukemia, chronic lymphocytic leukemia, T-cell lymphocytic leukemia, non-small cell lung cancer, and small cell lung cancer.

[0043] In some embodiments, the subject has received a treatment. In some embodiments, the treatment includes a cancer vaccine. In some embodiments, the cancer vaccine includes a nucleic acid sequence encoding an epitope that encodes at least one mutation present in the cancer exome. In some embodiments, the cancer vaccine includes an alphavirus-based self-amplifying expression system. In some embodiments, the cancer vaccine includes a chimpanzee adenovirus (ChAdV)-based expression system.

[0044] In some embodiments, the method includes obtaining cfDNA sequencing data from two or more samples from a subject. In some embodiments, the two or more samples are collected at different time points. In some embodiments, the two or more samples are collected at different time points relative to administration of the treatment. In some embodiments, the pre-treatment sample is collected before administration of the treatment, and the post-treatment cfDNA is collected after administration of the treatment. In some embodiments, the determining step includes determining or having determined a mutation frequency in the pre-treatment cfDNA compared to the post-treatment cfDNA to assess the efficacy of the treatment, optionally determining at least one or more tumor-associated mutations associated with neoantigens, and optionally, an increase in the mutation frequency in the post-treatment cfDNA compared to the pre-treatment cfDNA indicates an increased likelihood that the subject's tumor burden is increasing, and optionally, a decrease or maintenance of the mutation frequency in the post-treatment cfDNA compared to the pre-treatment cfDNA indicates an increased likelihood that the subject's tumor burden is decreasing or stable.

[0045] In some embodiments, the frequency of one or more of the mutations in tumor naive panel in post-treatment cfDNA compared with pre-treatment cfDNA increases, indicating the possibility of immune escape mechanism tumor mutation.In some embodiments, the frequency of mutation in post-treatment cfDNA compared with pre-treatment cfDNA increases, indicating the possibility that the tumor burden of the subject is increasing.In some embodiments, the frequency of mutation in post-treatment cfDNA compared with pre-treatment cfDNA decreases or remains constant, indicating the possibility that the tumor burden of the subject is decreasing or stable.In some embodiments, the decrease comprises complete response (CR) or partial response (PR).

[0046] In some embodiments, the method further comprises administering treatment to the subject after assessing the cancer state.In some embodiments, assessing the mutation frequency in cfDNA indicates that the subject has or still has the possibility of having cancer.

[0047] In some embodiments, the treatment comprises a cancer vaccine. In some embodiments, the cancer vaccine comprises a nucleic acid sequence encoding an epitope that encodes at least one of the mutations present in the exome. In some embodiments, the cancer vaccine comprises a self-amplifying expression system using an alphavirus. In some embodiments, the cancer vaccine comprises an expression system using a chimpanzee adenovirus (ChAdV).

[0048] In some aspects, the collecting step comprises taking a blood sample.

[0049] In some embodiments, the isolating step comprises centrifugation to separate the cfDNA from cells and / or cell debris. In some embodiments, the isolating step comprises isolating the cfDNA from whole blood. In some embodiments, isolating the cfDNA from whole blood comprises separating the plasma layer, the buffy coat, and red blood cells. In some embodiments, the cfDNA is isolated from the plasma layer.

[0050] In some embodiments, the sequencing step comprises next-generation sequencing (NGS) or Sanger sequencing. In some embodiments, NGS comprises duplex sequencing, whole-exome sequencing, whole-genome sequencing, de novo sequencing, stepwise sequencing, targeted amplicon sequencing, or shotgun sequencing.

[0051] In some embodiments, the enrichment step comprises enriching the cfDNA for polynucleotide regions of interest corresponding to mutations present in the exome prior to sequencing. In some embodiments, the enrichment comprises combining a panel of tumor-informative polynucleotide probes with a panel of tumor-naive polynucleotide probes. In some embodiments, separate samples are enriched separately for each of the panel of tumor-informative polynucleotide probes and the panel of tumor-naive polynucleotide probes.

[0052] In some embodiments, the tumor-informative polynucleotide probes comprise each of the polynucleotide regions of interest corresponding to mutations present in the exome. In some embodiments, the tumor-informative polynucleotide probes comprise at least 50%, at least 60%, at least 70%, at least 80%, at least 90%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, at least 99.5%, at least 99.9%, or 100% of the polynucleotide regions of interest corresponding to mutations present in the cancer exome. In some embodiments, the tumor-informative polynucleotide probes comprise at least 50, at least 60, at least 70, at least 80, at least 90 mutations, at least 100, at least 150, at least 200, at least 250, at least 300, at least 400, at least 500, at least 600, at least 700, at least 800, at least 900, or at least 1000 mutations, optionally including mutations present in the cancer exome.

[0053] In some embodiments, the enrichment step comprises hybridizing one or more polynucleotide probes to one or more polynucleotide regions of interest.

[0054] In some embodiments, the polynucleotide probe is 80 to 150 base pairs (bp) in length. In some embodiments, the polynucleotide probe is 50 to 100 bp, 50 to 150 bp, 80 to 140 bp, 80 to 130 bp, 80 to 120 bp, 80 to 110 bp, 80 to 100 bp, 80 to 90 bp, 90 to 150 bp, 90 to 140 bp, 90 to 130 bp, 90 to 120 bp, 90 to 110 bp, 90 to 100 bp, 100 to 150 bp, 100 to 140 bp, 100 to 130 bp, 100 to 1 They are 20bp, 100-110bp, 110-150bp, 110-140bp, 110-130bp, 110-120bp, 120-150bp, 120-140bp, 120-130bp, 130-150bp, 130-140bp, 140-150bp, 50bp, 60bp, 70bp, 80bp, 90bp, 100bp, 110bp, 120bp, 130bp, 140bp, or 150bp in length.

[0055] In some embodiments, one or more of the polynucleotide probes is biotinylated.

[0056] In some embodiments, tumor-informative polynucleotide probes are designed or selected after sequencing of a subject's tumor. In some embodiments, tumor-informative polynucleotide probes are designed or selected after exome sequencing of a subject's tumor. In some embodiments, tumor-informative polynucleotide probes are designed or selected to target all mutations in the sequenced tumor.

[0057] In some embodiments, the sequencing step comprises ligating a sequencing adapter to the cfDNA. In some embodiments, the sequencing adapter is configured for duplex sequencing.

[0058] In some embodiments, one or more of the mutations comprise point mutations, frameshift mutations, non-frameshift mutations, deletion mutations, insertion mutations, splice variants, genome rearrangements, splice antigens generated by proteasomes, or combinations thereof.In some embodiments, one or more of the mutations comprise at least one change that makes the peptide sequence encoded by cfDNA different from the corresponding peptide sequence encoded by the wild-type germline nucleic acid sequence of the subject.In some embodiments, one or more of the mutations comprise coding mutations, comprising at least one change that makes the peptide sequence encoded by cfDNA different from the corresponding peptide sequence encoded by the wild-type germline nucleic acid sequence of the subject. [Brief explanation of the drawings]

[0059] These and other features, aspects, and advantages of the present invention will become better understood with regard to the following description and accompanying drawings. [Figure 1] A detailed pipeline for the isolation and processing of ctDNA from patients is shown. Briefly, tumor-specific DNA variant alleles are identified from biopsied tumor tissue (point 1). Blood is collected from patients at specific points during their treatment schedule, and ctDNA is isolated and used to generate a UMI library (points 2 and 4). Baits designed based on the variants identified in the patient's tumor DNA (point 3) are used to purify ctDNA containing the identified variants (point 5). [Figure 2] Following the isolation and processing outlined in Figure 1, a detailed pipeline is shown after isolating and processing ctDNA from patients for analysis. Purified ctDNA is sequenced (point 6) to quantify the prevalence of specific identified variants. Repeated testing of ctDNA during treatment allows for monitoring tumor progression or response to treatment. [Figure 3A]FIG. 1 illustrates the isolation and sequencing of circulating tumor DNA (ctDNA) in two patients receiving GRANITE therapy, and is a graph showing the double-stranded read absolute coverage of DNA variants identified in ctDNA isolated from patient #1 (identified as pt0009). [Figure 3B] FIG. 1 illustrates the isolation and sequencing of circulating tumor DNA (ctDNA) in two patients receiving GRANITE therapy. Graph showing normalized double-stranded read coverage of DNA variants identified in ctDNA isolated from patient #1 (identified as pt0009). [Figure 3C] FIG. 1 illustrates the isolation and sequencing of circulating tumor DNA (ctDNA) in two patients receiving GRANITE therapy. FIG. 2 is a graph showing monitoring of tumor-specific DNA variant alleles in patient #1 during treatment, with TP52 R175H, APC T1556fs, and CDKN2A W110* highlighted. [Figure 3D] FIG. 1 illustrates the isolation and sequencing of circulating tumor DNA (ctDNA) in two patients receiving GRANITE therapy, and is a graph showing the double-stranded read absolute coverage of DNA variants identified in ctDNA isolated from patient #2 (identified as pt0005). [Figure 3E] FIG. 1 illustrates the isolation and sequencing of circulating tumor DNA (ctDNA) in two patients receiving GRANITE therapy. Normalized double-stranded read coverage of DNA variants identified in ctDNA isolated from patient #2 (identified as pt0005) is shown. [Figure 3F] FIG. 1 illustrates the isolation and sequencing of circulating tumor DNA (ctDNA) in two patients receiving GRANITE therapy. FIG. 1 is a graph showing monitoring of tumor-specific DNA variant alleles in patient #2 during treatment, including TRABD2B A385T, ADAR G751R, VILL L273fs, SURF2 P146L, TP53 P153fs, CSH2 A156V, and MAP2K2 E66K. [Figure 4A]FIG. 10 is a graph showing monitoring of variant allele frequency (VAF) in patient #1 (pt0009) during GRANITE therapy, showing the frequency of 11 identified tumor-specific variant alleles during treatment. [Figure 4B] Graph showing monitoring of variant allele frequency (VAF) in patient #1 (pt0009) during GRANITE therapy, showing trends in VAF for all variant alleles in isolated ctDNA during treatment. [Figure 4C] Graph showing monitoring of variant allele frequency (VAF) in patient #1 (pt0009) during GRANITE therapy, showing the mean % change in VAF between successive doses during treatment. [Figure 5A] 10A-10C are graphs illustrating ctDNA monitoring in additional examples of patients receiving GRANITE therapy. 10B shows ctDNA monitoring in non-small cell lung cancer (NSCLC) patients receiving GRANITE therapy. [Figure 5B] 10 is a graph illustrating ctDNA monitoring in an additional example of a patient receiving GRANITE therapy, showing tracking of ctDNA in a patient with microsatellite-stable colorectal cancer (MSS-CRC). [Figure 6A] FIG. 10 is a graph showing monitoring of ctDNA in a patient (identified as pt0101) receiving SLATE therapy, showing double-stranded read absolute coverage of specific KRAS allele variants in ctDNA isolated from the patient's plasma. [Figure 6B] Graph showing monitoring of ctDNA in a patient (identified as pt0101) receiving SLATE therapy, showing normalized double-stranded read coverage of specific KRAS allele variants in ctDNA isolated from the patient's plasma. [Figure 6C] FIG. 10 is a graph showing ctDNA monitoring in a patient (identified as pt0101) receiving SLATE therapy, showing KRAS variant allele duplex changes between successive doses. [Figure 7]1 is a graph showing monitoring of ctDNA associated with KRAS G12C mutation in NSCLC patients. [Figure 8] A detailed pipeline for the isolation and processing of ctDNA from patients for screening and manufacturing of patient-specific vaccines is shown. [Figure 9] A schematic diagram of the ctDNA monitoring assay is shown. Shotgun libraries from cfDNA, biopsy DNA, or gDNA from whole blood were prepared using double-stranded UMIs. Duplex sequencing requires variants to be observed on both strands of the double-stranded molecule, thereby reducing noise. Multiple patient-specific sets were combined to create supersets containing probes for 6–9 patients. A universal panel captured a set of common targets across all patient samples. [Figure 10] Figure 10A shows the number of potential variants covered by the indicated NGS panels, and Figure 10B shows the percentage of WES variants potentially covered by various NGS panels. [Figure 11] 1 shows the blood collection protocols for patients enrolled in SLATE (an "off-the-shelf" vaccine program) and GRANITE (a "personalized cancer vaccine" program). [Figure 12] Patient assays show that an average of approximately 140 variants per patient were monitored at high sequencing depth for variant calling at >1000x duplex consensus coverage. [Figure 13] Figure 13A shows cassette mutations observed in ctDNA and biopsies of the indicated patients. * indicates patients where biopsy was unavailable or tumor content was too low for the assay to detect the variant. Figure 13B shows that significant overlap was found when comparing variants in cfDNA with corresponding biopsies using the GRANITE assay, particularly in high-quality (RNALater or fresh-frozen) biopsies, where less frequent variants could be called. [Figure 14A]The presence of de novo variants and tumor histology (GEA, CRC, or NSCLC) in cfDNA samples from the indicated patients are shown. Many patients had additional variants present in their cfDNA that were not present in the original biopsy. De novo variants often arose when another patient had the targeted variant. [Figure 14B] We show that CHIP mutations were identified and excluded as somatic variants in tumors using matched normal gDNA from whole blood or PMBC. [Figure 14C] A representative patient, G08, is shown with two NLRC5 mutations (one of which tracked the average VAF of all variants) and two TAP1 mutations that emerged almost a year after receiving treatment. [Figure 14D] Figure 1 shows an overview of additional analyses of variants observed in cfDNA that were found outside of patient-specific variants. [Figure 14E] G09 cfDNA dynamics of novel variants, including multiple KRAS variants, are shown. [Figure 15] We show that all patient-specific variants captured in WES of biopsies were also captured using the patient-specific assay (100% concordance). [Figure 16] Figure 16A shows that variants in baseline biopsies were less frequent in archival biopsies. Figure 16B shows that on-treatment biopsy variants were more representative than variants present in archival biopsies. Despite all biopsies being from primary sites, only 12 of 135 targeted variants were shared between the three groups, indicating tumor heterogeneity. [Figure 17]Figure 17A shows variant dynamics in cfDNA over time in patient G01. Figure 17B shows targeted low-frequency variants in ctDNA for the indicated variants (SSH3, GRIA4, ZNF541, TMEM217, ZNF697, AHNAK2, SCHIP1, and CNR1). Figure 17C shows targeted variants in WES of ctDNA from patient G01 over time. Figure 17D shows brain met biopsy variants in WES of ctDNA from patient G01 over time. [Figure 18] Figures 18A-E show ctDNA monitoring of tumor variants in SLATE patients. Figure 18A shows the ctDNA VAF% of patient S2. Figure 18B shows the ctDNA VAF% of patient S5. Figure 18C shows the ctDNA VAF% of patient S10. Figure 18D shows the ctDNA VAF% of patient S13. Figure 18E shows a representative patient without MR, demonstrating loss of the start codon of B2M. SD = stable disease, PD = progressive disease, best overall response is indicated. [Figure 19] Figure 19A shows the fold change in HLA allele read percentage from molecular genetic responders (MR) and Figure 19B shows the fold change in HLA allele read percentage from molecular genetic non-responders (non-MR). [Figure 20] 1 shows a diagram outlining considerations for including subject-specific tumor-informative probes. [Figure 21] Figure 21A shows the percentage of CRC and NSCLC samples covered by the universal panel's target probes. Data are based on an analysis of 10,586 samples from cbioportal.org. Figure 21B shows a retrospective analysis of variants in patients from a previous study (GO-004) identified by universal panel version 1 (v1) or version 2 (v2). [Figure 22] A general strategy for monitoring chromosome 6 for loss of heterozygosity of HLA genes is presented. DETAILED DESCRIPTION OF THE INVENTION

[0060] Detailed Description definition In general, terms used in the claims and specification are intended to be interpreted as having their plain meaning as understood by a person of ordinary skill in the art. For further clarity, certain terms are defined below. If there is a conflict between the plain meaning and the written definition, the written definition shall control.

[0061] As used herein, the term "antigen" refers to a substance that induces an immune response. An antigen may be a neoantigen. An antigen may be a "common antigen," which is an antigen found among a particular population, for example, a particular population of cancer patients.

[0062] As used herein, the term "neoantigen" refers to an antigen that has at least one alteration that makes it different from the corresponding wild-type antigen, for example, through a mutation in a tumor cell or a tumor cell-specific post-translational modification. Neoantigens can include polypeptide or nucleotide sequences. Mutations can include frameshift or non-frameshift indels, missense or nonsense substitutions, splice site alterations, genomic rearrangements or gene fusions, or any genomic or expression alteration that results in a neo-ORF. Mutations can also include splice variants. Tumor cell-specific post-translational modifications can include aberrant phosphorylation. Tumor cell-specific post-translational modifications can also include splice antigens generated by the proteasome. See Liepe et al., A large fraction of HLA class I ligands are proteasome-generated spliced ​​peptides; Science. 2016 Oct 21;354(6310):354-358. Such common neoantigens are useful for inducing an immune response in subjects upon administration. Subjects can be identified for administration through the use of various diagnostic methods, such as the patient selection methods described in more detail below.

[0063] As used herein, the term "tumor antigen" is an antigen that is present in tumor cells or tissues of a subject but not in the corresponding normal cells or tissues of the subject, or an antigen derived from a polypeptide that is known or found to have altered expression in tumor cells or cancerous tissues compared to normal cells or tissues.

[0064] As used herein, the term "antigen-based vaccine" refers to a vaccine composition based on one or more antigens, e.g., multiple antigens. The vaccine may be nucleotide-based (e.g., virus-based, RNA-based, or DNA-based), protein-based (e.g., peptide-based), or a combination thereof.

[0065] As used herein, the term "candidate antigen" is a mutation or other abnormality that gives rise to a sequence that may represent an antigen.

[0066] As used herein, the term "coding region" is the portion or portions of a gene that encode a protein.

[0067] As used herein, the term "coding mutation" is a mutation that occurs in a coding region.

[0068] As used herein, the term "ORF" means open reading frame.

[0069] As used herein, the term "NEO-ORF" refers to a tumor-specific ORF that results from mutation or other abnormalities such as splicing.

[0070] As used herein, the term "missense mutation" is a mutation that results in the substitution of one amino acid for another.

[0071] As used herein, the term "nonsense mutation" is a mutation that results in the substitution of an amino acid with a stop codon or the removal of the standard start codon.

[0072] As used herein, the term "frameshift mutation" is a mutation that causes an alteration in the frame of a protein.

[0073] As used herein, the term "indel" is an insertion or deletion of one or more nucleic acids.

[0074] As used herein, the term percent "identity," in the context of two or more nucleic acid or polypeptide sequences, refers to two or more sequences or subsequences in which the specified percentage of nucleotides or amino acid residues are the same when compared and aligned for maximum correspondence, as measured using one of the sequence comparison algorithms described below (e.g., BLASTP and BLASTN or other algorithms available to those of skill in the art) or by visual inspection. Depending on the application, the percent "identity" may exist over a region of the sequences being compared, e.g., over a functional domain, or in other cases, over the entire length of the two sequences being compared.

[0075] In sequence comparison, one sequence typically serves as a reference sequence to which test sequences are compared. When using a sequence comparison algorithm, test and reference sequences are input into a computer, subsequence coordinates are designated, if necessary, and sequence algorithm program parameters are designated. The sequence comparison algorithm then calculates the percent sequence identity of the test sequence(s) relative to the reference sequence based on the designated program parameters. Alternatively, sequence similarity or dissimilarity can be established by the presence or absence of specific nucleotides, or, in the case of translated sequences, amino acids at selected sequence positions (e.g., sequence motifs).

[0076] Optimal alignment of sequences for comparison can be achieved, for example, by the local homology algorithm of Smith & Waterman, Adv. Appl. Math. 2:482 (1981), by the homology alignment algorithm of Needleman & Wunsch, J. Mol. Biol. 48:443 (1970), by the similarity search method of Pearson & Lipman, Proc. Nat'l. Acad. Sci. USA 85:2444 (1988), by computerized implementations of these algorithms (GAP, BESTFIT, FASTA, and TFASTA in the Wisconsin Genetics Software Package (Genetics Computer Group, 575 Science Dr., Madison, Wis.)), or by visual inspection (see generally Ausubel et al., infra). One example of a suitable algorithm for determining percent sequence identity and sequence similarity is the BLAST algorithm, which is described in Altschul et al., J. Mol. Biol. 215:403-410 (1990). Software for performing BLAST analyses is publicly available through the National Center for Biotechnology Information.

[0077] As used herein, the term "nonstop or readthrough" refers to a mutation that results in the removal of the natural stop codon.

[0078] As used herein, the term "epitope" is the specific portion of an antigen that is typically bound by an antibody or T-cell receptor.

[0079] As used herein, the term "immunogenic" is the ability to elicit an immune response, for example, via T cells, B cells, or both.

[0080] As used herein, the terms "HLA binding affinity" and "MHC binding affinity" refer to the affinity of binding between a particular antigen and a particular MHC allele.

[0081] As used herein, the term "bait" is a nucleic acid probe used to enrich for a specific sequence of DNA or RNA from a sample.

[0082] As used herein, the term "variant" refers to a difference between a nucleic acid of interest and a reference human genome used as a control.

[0083] As used herein, the term "variant calling" is the algorithmic determination, typically from sequencing, of the presence of a variant.

[0084] As used herein, the term "polymorphism" refers to a germline variant, i.e., a variant that is found in all DNA-bearing cells of an individual.

[0085] As used herein, the term "somatic variant" is a variant that occurs in the non-germline cells of an individual.

[0086] As used herein, the term "allele" is a version of a gene or a version of a gene sequence or a version of a protein.

[0087] As used herein, the term "HLA type" is complementary to HLA gene alleles.

[0088] As used herein, the term "nonsense-mediated decay" or "NMD" is the degradation of mRNA by a cell due to a premature stop codon.

[0089] As used herein, the term "truncal mutation" is a mutation that occurs early in tumor development and is present in the majority of cells of a tumor.

[0090] As used herein, the term "subclonal mutation" is a mutation that arises late in tumor development and is present in only a subset of tumor cells.

[0091] As used herein, the term "exome" is the subset of the genome that encodes proteins. The exome can be a collection of exons within the genome.

[0092] As used herein, the term "logistic regression" is a regression model for binary data from statistics that models the logit of the probability that the dependent variable is equal to 1 as a linear function of the dependent variable.

[0093] As used herein, the term "neural network" is a machine learning model for classification or regression that consists of multiple layers of linear transformations followed by element-wise nonlinearities that are typically trained by stochastic gradient descent and backpropagation.

[0094] As used herein, the term "proteome" is the set of all proteins expressed and / or translated by a cell, a group of cells, or an individual.

[0095] As used herein, the term "peptidome" refers to the set of all peptides presented by MHC-I or MHC-II on the cell surface. Peptidome can refer to the properties of a cell or a collection of cells (e.g., a tumor peptidome, which refers to the combination of the peptidomes of all cells that make up a tumor).

[0096] As used herein, the term "ELISpot" means enzyme-linked immunosorbent spot assay, which is a common method for monitoring immune responses in humans and animals.

[0097] As used herein, the term "tolerance or immune tolerance" is a state of immune non-responsiveness to one or more antigens, eg, self-antigens.

[0098] As used herein, the term "central tolerance" refers to tolerance that is affected in the thymus by either eliminating autoreactive T cell clones or promoting their differentiation into immunosuppressive regulatory T cells (Tregs).

[0099] As used herein, the term "peripheral tolerance" is tolerance that is affected in the periphery by downregulating or anergizing autoreactive T cells that survive central tolerance, or by promoting these T cells to differentiate into Tregs.

[0100] The term "sample" can include a single cell or multiple cells or cell fragments or an aliquot of bodily fluid obtained from a subject by means including venipuncture, excretion, ejaculation, massage, biopsy, needle aspiration, lavage sample, scraping, surgical incision, or other intervention or means known in the art.

[0101] The term "subject" encompasses a cell, tissue, or organism, human or non-human, whether male or female, in vivo, ex vivo, or in vitro. The term subject includes mammals, such as humans.

[0102] The term "mammal" encompasses both humans and non-humans, including, but not limited to, humans, non-human primates, canines, felines, murines, bovines, equines, and porcines.

[0103] The term "clinical factor" refers to a measure of a subject's condition, for example, disease activity or severity. "Clinical factor" includes all markers of a subject's health status, including non-sample markers, and / or other characteristics of the subject, such as, without limitation, age and sex. A clinical factor can be a score, value, or set of values ​​that can be obtained from evaluating a sample (or a collection of samples) from a subject or evaluating a subject under defined conditions. Clinical factors can also be predicted by markers and / or other parameters, for example, gene expression surrogates. Clinical factors can include tumor type, tumor subtype, and smoking history.

[0104] The term "alphavirus" refers to members of the Togaviridae family and are single-stranded, positive-sense RNA viruses. Alphaviruses are typically classified as either Old World, such as Sindbis virus, Ross River virus, Mayaro virus, Chikungunya virus, and Semliki Forest virus, or New World, such as Eastern equine encephalitis, Aura virus, Fort Morgan virus, or Venezuelan equine encephalitis virus and its derivative TC-83. Alphaviruses are typically self-replicating RNA viruses.

[0105] The term "alphavirus backbone" refers to the minimal sequence(s) of an alphavirus that allows for autonomous replication of the viral genome. The minimal sequence may include conserved sequences for nonstructural protein-mediated amplification, the nonstructural protein 1 (nsP1), nsP2, nsP3, nsP4 genes, and polyA sequences, as well as sequences for expression of viral subgenomic RNA, such as the 26S promoter element.

[0106] The term "sequence for nonstructural protein-mediated amplification" includes alphavirus conserved sequence elements (CSEs) well known to those of skill in the art, including, but not limited to, the alphavirus 5'UTR, a 51 nt CSE, a 24 nt CSE, or other 26S subgenomic promoter sequence, a 19 nt CSE, and the alphavirus 3'UTR.

[0107] The term "RNA polymerase" includes polymerases that catalyze the production of RNA polynucleotides from a DNA template, including, but not limited to, polymerases from bacteriophages such as T3, T7, and SP6.

[0108] The term "lipid" includes hydrophobic and / or amphipathic molecules. Lipids can be cationic, anionic, or neutral. Lipids can be synthetic or naturally derived, and in some cases, can be biodegradable. Lipids can include cholesterol, phospholipids, lipid conjugates, including, but not limited to, polyethylene glycol (PEG) conjugates (PEGylated lipids), waxes, oils, glycerides, fats, and fat-soluble vitamins. Lipids can also include dilinoleylmethyl-4-dimethylaminobutyrate (MC3) and MC3-like molecules.

[0109] The term "lipid nanoparticle" or "LNP" includes vesicle-like structures formed using a lipid-containing membrane surrounding an aqueous interior, also known as liposomes. Lipid nanoparticles include lipid-based compositions with a solid lipid core stabilized by surfactants. The core lipid can be fatty acids, acylglycerols, waxes, and mixtures of these surfactants. Biological membrane lipids, such as phospholipids, sphingomyelin, bile salts (sodium taurocholate), and sterols (cholesterol), can be used as stabilizers. Lipid nanoparticles can be formed using defined ratios of different lipid molecules, such as, but not limited to, defined ratios of one or more cationic, anionic, or neutral lipids. Lipid nanoparticles can encapsulate molecules within their outer membrane shell and then contact target cells to deliver the encapsulated molecules to the host cell cytosol. Lipid nanoparticles, including their surface, can be modified or functionalized with non-lipid molecules. Lipid nanoparticles can be unilamellar or multilamellar. Lipid nanoparticles can form complexes with nucleic acids. Unilamellar lipid nanoparticles can form complexes with nucleic acids, where the nucleic acid is in the aqueous interior. Multilamellar lipid nanoparticles can form complexes with nucleic acids, where the nucleic acid is in, forms, or is sandwiched between the aqueous interior.

[0110] The term "pharmaceutically effective amount" refers to an amount of vaccine components (such as peptides, engineered vectors, and / or adjuvants) that is effective, under a given route of administration, to provide sufficient levels of protein, protein expression, and / or cell signaling activity (e.g., adjuvant-mediated activation) to cells to confer vaccine benefit, i.e., some measurable level of immunity.

[0111] As used herein, terms such as "obtaining," "isolating," "enriching," "sequencing," "obtaining," "collecting," and "determining" refer to directly performing a step (e.g., directly performing a method) to obtain a result, e.g., directly obtaining a product, including, but not limited to, directly sequencing cfDNA to obtain cfDNA sequencing data, directly isolating cfDNA to obtain isolated cfDNA, directly enriching cfDNA to obtain an enriched cfDNA sample containing cfDNA, etc. As used herein, terms such as "obtaining," "isolating," "enriching," "sequencing," "obtaining," "collecting," and "determining" refer to receiving information or receiving a product indirectly without directly performing a step (e.g., without directly performing a method), for example, by receiving knowledge or product from another person or source (e.g., from a third-party laboratory that directly obtained cfDNA sequencing data, isolated cfDNA, enriched cfDNA, and / or collected a sample containing cfDNA, etc.). In some cases, another party or source is instructed to perform the steps directly (e.g., a third-party laboratory is instructed to obtain cfDNA sequencing data, isolate cfDNA, enrich cfDNA, and / or collect samples containing cfDNA, etc.). In some cases, the knowledge or product is purchased from another party or source that performed the steps directly (e.g., purchasing cfDNA sequencing data, isolated cfDNA, enriched cfDNA, and / or collected samples containing cfDNA, etc.).

[0112] Abbreviations: MHC: major histocompatibility complex; HLA: human leukocyte antigen or human MHC gene locus; NGS: next-generation sequencing; PPV: positive predictive value; TSNA: tumor-specific neoantigen; FFPE: formalin-fixed, paraffin-embedded; NMD: nonsense-mediated decay; NSCLC: non-small-cell lung cancer; DC: dendritic cell.

[0113] It should be noted that as used in this specification and the appended claims, the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise.

[0114] Unless specifically stated or otherwise clear from the context, the term "about" as used herein is understood to be within normal tolerances in the art, e.g., within two standard deviations of the mean. About may be understood to be within 10%, 9%, 8%, 7%, 6%, 5%, 4%, 3%, 2%, 1%, 0.5%, 0.1%, 0.05%, or 0.01% of the stated value. Unless otherwise clear from the context, all numerical values ​​provided herein are modified by the term about.

[0115] Any terms not directly defined herein should be understood to have the meaning commonly associated with those terms as understood within the technical field of the present invention. Certain terms are discussed herein to provide additional guidance to the practitioner in describing the compositions, devices, methods, etc. of embodiments of the present invention and how to make or use them. It will be recognized that the same thing may be said in more than one way. Thus, alternative language and synonyms may be used for any one or more of the terms discussed herein. Whether or not a term is detailed or discussed herein is immaterial. Several synonyms or substitute methods, materials, etc. are provided. Unless explicitly stated, the description of one or a few synonyms or equivalents does not exclude the use of other synonyms or equivalents. The use of examples, including examples of terms, is for illustrative purposes only and does not limit the scope and meaning of the embodiments of the present invention herein.

[0116] All references, issued patents, and patent applications cited within the body of this specification are hereby incorporated by reference in their entirety for all purposes.

[0117] Monitoring disease status and treatment effectiveness Provided herein are methods for monitoring disease status in a subject by analyzing cell-free DNA (cfDNA), particularly by monitoring mutation frequencies (e.g., tumor-associated mutations associated with cancer). For example, cfDNA can be used to monitor disease progression in patients undergoing treatment. The cfDNA analysis methods described herein provide a non-invasive way to assess and / or monitor disease, particularly compared to more invasive procedures such as tumor biopsy. The cfDNA analysis methods described herein are particularly useful for analyzing a large number of mutations, such as analyzing all or most of a tumor's exome. Generally, monitoring is performed by sequencing cfDNA with both broad target coverage (e.g., at least 50% of all polynucleotide regions of interest corresponding to mutations present in the subject's cancer exome) and high sequencing read depth ("deep sequencing," e.g., an average read depth of at least 1000X).

[0118] In one aspect, a method for monitoring the status of a cancer in a subject includes: a. obtaining or having obtained sequencing data of cfDNA from a sample from the subject, wherein the sequencing data includes a target coverage of at least 50% of all polynucleotide regions of interest corresponding to mutations present in the exome of the cancer, and wherein the sequenced polynucleotide regions of interest have an average read depth of at least 1000X; and b. determining or having determined the frequency of mutations present in the exome to assess the status of the cancer.

[0119] Two or more samples can be analyzed to assess the disease state of the subject.Therefore, in one embodiment, the method for monitoring the cancer state of the subject includes: a.obtaining or having obtained the sequencing data of the cfDNA from the first sample from the subject, wherein the sequencing data comprises at least 50% target coverage of all polynucleotide regions of interest corresponding to the mutations present in the exome of cancer, and the sequenced polynucleotide regions of interest have an average read depth of at least 1000X; b.obtaining or having obtained the sequencing data of the cfDNA from the second sample from the subject, wherein the sequencing data comprises at least 50% target coverage of all polynucleotide regions of interest corresponding to the mutations present in the exome of cancer, and the sequenced polynucleotide regions of interest have an average read depth of at least 1000X; And c.determining or having determined the frequency of the mutations present in the exome of the first cfDNA compared with the second cfDNA, to assess the cancer state.

[0120] Multiple samples containing cfDNA can be collected from a subject at different time points and used to monitor disease, for example, to monitor disease burden and / or response to treatment during treatment. Time points can be selected to monitor disease status at specific intervals. For example, time points can be selected based on the administration schedule of the treatment. Time points based on the administration schedule can include the same day as the administration of the treatment. Time points based on the administration schedule can include, but are not limited to, 1 day, 2 days, 3 days, 4 days, 5 days, and 6 days after administration. Time points based on the administration schedule can include, but are not limited to, 1 week, 2 weeks, 3 weeks, 4 weeks, 5 weeks, 6 weeks, 8 weeks, 10 weeks, and 12 weeks after administration. Time points based on the administration schedule can include, but are not limited to, 1 month, 2 months, 3 months, 6 months, and 12 months after administration.

[0121] The time points may be at regular time intervals, such as, but not limited to, daily, every 2 days, every 3 days, every 4 days, every 5 days, or every 6 days throughout the treatment period. Time points based on regular time intervals may include, but are not limited to, once a week, once every 2 weeks, once every 3 weeks, once every 4 weeks, once every 5 weeks, once every 6 weeks, once every 8 weeks, every 10 weeks, or once 12 weeks. Time points may also be selected based on regular time intervals, such as, but not limited to, once a month, once every 2 months, once every 3 months, once every 6 months, and once every 12 months. A combination of one or more of the above time intervals may also be used.

[0122] Analysis of cfDNA can be used to monitor the progression of disease in patients undergoing treatment.For example, longitudinal samples can be collected throughout treatment to monitor cancer status (for example, tumor burden over time).The increased frequency of mutations monitored over longitudinal samples can indicate that the tumor burden of the subject is likely to increase.The decreased or maintained frequency of mutations monitored over longitudinal samples can indicate that the tumor burden of the subject is likely to decrease or remain stable.

[0123] Analysis of cfDNA can be used to evaluate the effectiveness of treatment that is carried out on an object.Therefore, in one aspect, the method for evaluating the effectiveness of treatment for an object with cancer includes: a. obtaining or having obtained the sequencing data of the cfDNA from the pre-treatment sample from the object, wherein the sequencing data comprises at least 50% target coverage of all polynucleotide regions of interest corresponding to the mutations present in the exome of cancer, and the sequenced polynucleotide regions of interest have an average read depth of at least 1000X; b. obtaining or having obtained the sequencing data of the cfDNA from the post-treatment sample from the object, wherein the sequencing data comprises at least 50% target coverage of all polynucleotide regions of interest corresponding to the mutations present in the exome of cancer, and the sequenced polynucleotide regions of interest have an average read depth of at least 1000X; and c. determining or having determined the frequency of mutations present in the exome of the cfDNA before treatment compared with the cfDNA after treatment, in order to evaluate the effectiveness of treatment.

[0124] Multiple samples containing cfDNA can be collected at different time points relative to the administration of a treatment. A sample containing cfDNA can be collected before the administration of a treatment. A sample containing cfDNA can be collected after the administration of a treatment. A sample containing cfDNA can be collected simultaneously with the administration of a treatment. A sample containing cfDNA can be collected both before and after the administration of a treatment. For example, a first sample containing cfDNA can be collected before the administration of a treatment to a subject, and a second sample containing cfDNA can be collected after the administration of a treatment. A sample containing cfDNA can be collected both simultaneously with the administration of a treatment and after the administration of a treatment. For example, a first sample containing cfDNA can be collected simultaneously with the administration of a treatment to a subject, and a second sample containing cfDNA can be collected after the administration of a treatment. Multiple samples containing cfDNA (e.g., longitudinal samples) can be collected after the administration of a treatment.

[0125] Obtaining sequencing data may include one or more of the following steps: collecting or having collected a sample from a subject; isolating or having isolated cfDNA; concentrating or having concentrated cfDNA; and / or sequencing or having sequenced cfDNA. Obtaining sequencing data may include each of the following steps: collecting or having collected a sample from a subject; isolating or having isolated cfDNA; concentrating or having concentrated cfDNA; and / or sequencing or having sequenced cfDNA. Intermediates can be obtained to perform any of the above steps. For example, isolated cfDNA can be obtained from a third-party source and used to perform one or more of the remaining steps, such as enrichment and sequencing. Intermediates can be produced and instructed to perform any of the above steps. For example, enriched cfDNA can be produced and provided to a third-party source for performing one or more of the remaining steps, such as sequencing.

[0126] Cancer Monitoring The methods described herein can be used to monitor the state of a cancer, such as tumor burden.

[0127] The target disease may include cancer. Upon cell death, cancer cells may release their genomic DNA into the circulation, which is called circulating tumor DNA (ctDNA) or cfDNA from cancer cells. Various cancers can be monitored. For example, cancers that can be monitored include, but are not limited to, carcinoma, sarcoma, lymphoma or leukemia, germ cell tumor, blastoma, or other cancers. Carcinomas include epithelial neoplasms, squamous cell neoplasms, squamous cell carcinomas, basal cell neoplasms, and the like. Basal cell carcinoma, transitional cell papilloma and transitional cell carcinoma, adenoma and adenocarcinoma (gland), adenoma, adenocarcinoma, plastic gastritis insulinoma, glucagonoma, gastrinoma, vipoma, cholangiocarcinoma, hepatocellular carcinoma, adenoid carcinoma, appendix carcinoid tumor, prolactinoma, oncocytoma, Hurthle cell adenoma, renal cell carcinoma, Grawitz tumor, multiple endocrine adenomas, endometrioid adenoma, skin adnexal neoplasms, mucoepidermoid neoplasms, cystic, mucinous and serous neoplasms, cystadenoma, pseudomyxoma peritonei, ductal, lobular and These include, but are not limited to, medullary neoplasm, acinar cell neoplasm, composite epithelial neoplasm, Warthin's tumor, thymoma, specialized gonadal neoplasm, sex cord-stromal tumor, theca cell tumor, granulosa cell tumor, virilizing tumor, Sertoli-Leydig cell tumor, glomus tumor, paraganglioma, pheochromocytoma, glomus tumor, nevi and melanoma, pigmented nevi, malignant melanoma, melanoma, nodular melanoma, dysplastic nevi, lentigo maligna melanoma, superficial spreading melanoma, and malignant acral lentiginous melanoma. Sarcomas include, but are not limited to, Askin tumor, botryoid sarcoma, chondrosarcoma, Ewing's sarcoma, malignant hemangioendothelioma, malignant schwannoma, osteosarcoma, soft tissue sarcomas (alveolar soft part sarcoma, angiosarcoma, cystosarcoma phyllodes, dermatofibrosarcoma, desmoid tumor, desmoplastic small round cell tumor, epithelioid sarcoma, extraskeletal chondrosarcoma, extraskeletal osteosarcoma, fibrosarcoma, hemangiopericytoma, angiosarcoma, Kaposi's sarcoma, leiomyosarcoma, liposarcoma, lymphangiosarcoma, lymphosarcoma, malignant fibrous histiocytoma, neurofibrosarcoma, rhabdomyosarcoma, and synovial sarcoma).Lymphomas and leukemias include chronic lymphocytic leukemia / small lymphocytic lymphoma, B-cell prolymphocytic leukemia, lymphoplasmacytic lymphoma (Waldenstrom's macroglobulinemia, etc.), splenic marginal zone lymphoma, plasma cell myeloma, plasmacytoma, monoclonal immunoglobulin deposition disease, heavy chain disease, extranodal marginal zone B-cell lymphoma (also called malt lymphoma), nodal marginal zone B-cell lymphoma, follicular lymphoma, mantle cell lymphoma, diffuse large B-cell lymphoma, mediastinal (thymic) large B-cell lymphoma, intravascular large B-cell lymphoma, primary effusion lymphoma, Burkitt's lymphoma / leukemia, T-cell prolymphocytic leukemia, and large granular T-cell lymphoma. These include, but are not limited to, lymphocytic leukemia, aggressive NK-cell leukemia, adult T-cell leukemia / lymphoma, extranodal NK / T-cell lymphoma (nasal type), enteropathic T-cell lymphoma, hepatosplenic T-cell lymphoma, blastic NK-cell lymphoma, mycosis fungoides / Sezary syndrome, primary cutaneous CD30-positive T-cell lymphoproliferative dysplasia, primary cutaneous anaplastic large cell lymphoma, lymphomatoid papulosis, angioimmunoblastic T-cell lymphoma, peripheral T-cell lymphoma (not otherwise specified), anaplastic large cell lymphoma, classical Hodgkin lymphoma (nodular sclerosing, mixed cytology, lymphocyte-rich, lymphopenic, or non-lymphopenic), and nodular lymphocyte-predominant Hodgkin lymphoma. Germ cell tumors include, but are not limited to, germinoma, dysgerminoma, seminoma, non-germinoma germ cell tumor, embryonal carcinoma, endodermal sinus tumor, choriocarcinoma, teratoma, polyembryoma, and gonadoblastoma. Blastomas include, but are not limited to, nephroblastoma, medulloblastoma, and retinoblastoma. Other cancers include, but are not limited to, cancer of the labia, larynx, hypopharynx, tongue, salivary gland, stomach, adenocarcinoma, thyroid cancer (medullary and papillary thyroid), kidney, renal parenchymal, cervical, uterine, endometrial, choriocarcinoma, testicular, urinary tract, melanoma, brain tumors (such as glioblastoma, astrocytoma, meningioma, medulloblastoma, and peripheral neuroectodermal tumors), gallbladder, bronchial carcinoma, multiple myeloma, basal cell tumor, teratoma, retinoblastoma, choroidal melanoma, seminoma, rhabdomyosarcoma, craniopharyngioma, osteosarcoma, chondrosarcoma, myosarcoma, liposarcoma, fibrosarcoma, Ewing's sarcoma, and plasmacytoma.Cancers that may be monitored include, but are not limited to, lung cancer, melanoma, breast cancer, ovarian cancer, prostate cancer, kidney cancer, stomach cancer, colon cancer, testicular cancer, head and neck cancer, pancreatic cancer, brain cancer, B-cell lymphoma, acute myeloid leukemia, chronic myeloid leukemia, chronic lymphocytic leukemia, T-cell lymphocytic leukemia, non-small cell lung cancer, and small cell lung cancer.

[0128] Cancer monitoring can also include monitoring for cancer escape mutations (also called secondary mutations or escape mutants). For example, cancer monitoring can include monitoring for de novo mutations compared to a previous sequencing dataset, such as an initial biopsy, a longitudinal sample, a pre-treatment sample, or any other archived sample. Monitoring for cancer escape mutations can provide information on whether additional treatment is needed (e.g., whether treatment is effective against a cancer with a particular de novo mutation) and / or whether it will affect the effectiveness of current or proposed treatments. Escape mutations in cancer include genes targeted by the tumor-naive probe panels described herein, such as genes and mutations generally considered to be oncogenic (e.g., "driver" mutations that are thought to promote cancer and are generally considered to be gain-of-function mutations), tumor suppressor genes (e.g., genes generally considered to monitor and / or control tumor-associated traits, such as cell division, where mutations may interfere with the control of such traits and are generally considered to be loss-of-function mutations), genes in the interferon-gamma signaling pathway (including genes in the JAK / STAT signaling pathway), genes in antigen processing pathways (including monitoring HLA loss of heterozygosity), and other mutations commonly associated with cancer but not otherwise annotated (e.g., not yet annotated as oncogenes or tumor suppressor genes).

[0129] The tumor-informative / tumor-naive combination panel described herein allows for the simultaneous monitoring of cancer status, such as tumor burden, and cancer escape mutations using a single panel.

[0130] Tumor-specific mutations The methods described herein are applicable to tracking the presence of tumor-specific mutations associated with cancer cells present in cfDNA ("ctDNA").Tumor-specific mutations can include previously identified tumor-specific mutations, such as those found in the Catalogue of Somatic Mutations in Cancer (COSMIC) database.

[0131] Also disclosed herein are methods for identifying certain mutations (e.g., variants or alleles present in cancer cells). In particular, these mutations may be present in the genome, transcriptome, proteome, or exome of cancer cells in a subject with cancer, but not in normal tissue from such a subject. Specific methods for identifying tumor-specific neoantigens, including common neoantigens, are known to those skilled in the art, and are described in detail in, for example, International Patent Application Publication Nos. WO / 2017 / 106638, WO / 2018 / 195357, and WO / 2018 / 208856, each of which is incorporated herein by reference in its entirety for all purposes.

[0132] Genetic mutations in tumors can be considered useful for immunological targeting of tumors and / or monitoring tumor burden (e.g., disease status) if they result in changes in the amino acid sequence of a protein only within the tumor. Useful mutations include (1) nonsynonymous mutations that result in different amino acids in the protein, (2) read-through mutations that alter or delete a stop codon, resulting in translation of a longer protein with a novel tumor-specific sequence at the C-terminus, (3) splice site mutations that result in the inclusion of an intron in the mature mRNA, thereby resulting in a unique tumor-specific protein sequence, (4) chromosomal rearrangements (i.e., gene fusions) that result in a chimeric protein with a tumor-specific sequence at the junction of two proteins, and (5) frameshift mutations or deletions that result in a new open reading frame with a novel tumor-specific protein sequence. Mutations can also include one or more of the following: non-frameshift indels, missense or nonsense substitutions, splice site changes, genomic rearrangements or gene fusions, or any genomic or expression changes that result in neo-ORFs.

[0133] In tumor cells, peptides or mutant polypeptides containing mutations, for example, resulting from splice site, frameshift, readthrough, or gene fusion mutations, can be identified by sequencing the DNA, RNA, or protein of tumor cells versus normal cells.

[0134] Various methods are available for detecting the presence of specific mutations or alleles in an individual's DNA or RNA. Any of the sequencing methods described herein can be used to determine tumor-specific mutations. Advances in this field have led to accurate, easy, and inexpensive large-scale SNP genotyping. For example, several techniques have been reported, including dynamic allele-specific hybridization (DASH), microplate array diagonal gel electrophoresis (MADGE), pyrosequencing, oligonucleotide-specific ligation, the TaqMan system, and various DNA "chip" technologies such as the Affymetrix SNP chip. These methods utilize amplification of target gene regions, typically by PCR. Still other methods rely on invasive cleavage followed by mass spectrometry or the generation of small signal molecules by immobilized padlock probes and rolling circle amplification. Some of the methods known in the art for detecting specific mutations are summarized below.

[0135] PCR-based detection means can include simultaneous multiplex amplification of multiple markers.For example, it is well known in the art to select PCR primers to generate PCR products that do not overlap in size and can be analyzed simultaneously.Alternatively, differentially labeled primers can be used to amplify different markers, so that each can be differentially detected.Of course, hybridization-based detection means allow differential detection of multiple PCR products in a sample.Other techniques that allow multiplex analysis of multiple markers are known in the art.

[0136] Several methods have been developed to facilitate the analysis of single nucleotide polymorphisms in genomic DNA or cellular RNA. For example, single nucleotide polymorphisms can be detected by using specialized exonuclease-resistant nucleotides, as disclosed in Mundy, CR (U.S. Patent No. 4,656,127). According to this method, a primer complementary to the allele sequence 3' adjacent to the polymorphic site is hybridized to a target molecule obtained from a specific animal or human. If the polymorphic site on the target molecule contains a nucleotide complementary to a specific exonuclease-resistant nucleotide derivative present, the derivative will be incorporated into the end of the hybridized primer. This incorporation makes the primer resistant to exonucleases, thereby enabling its detection. Since the identity of the exonuclease-resistant derivative of the sample is known, the finding that the primer has become resistant to exonucleases reveals that the nucleotide(s) present at the polymorphic site of the target molecule are complementary to the nucleotide of the nucleotide derivative used in the reaction. This method has the advantage that it does not require the determination of large amounts of exogenous sequence data.

[0137] Solution-based methods can be used to determine the identity of the nucleotide at a polymorphic site. Cohen, D. et al. (French Patent No. 2,650,840, PCT Application No. WO91 / 02087). As in the method of Mundy in U.S. Patent No. 4,656,127, a primer complementary to the allelic sequence 3' adjacent to the polymorphic site is used. This method uses a labeled dideoxynucleotide derivative incorporated at the end of the primer when it is complementary to the nucleotide at the polymorphic site to determine the identity of the nucleotide at that site.

[0138] An alternative method known as Genetic Bit Analysis (GBA) has been reported by Goelet, P. et al. (PCT Application No. 92 / 15712). The Goelet, P. et al. method uses a mixture of labeled terminators and primers complementary to the sequence 3' to the polymorphic site. Thus, the incorporated labeled terminators are determined by and complementary to the nucleotides present at the polymorphic site of the target molecule being evaluated. In contrast to the method of Cohen et al. (French Patent No. 2,650,840; PCT Application No. WO91 / 02087), the Goelet, P. et al. method can be a heterogeneous phase assay, in which the primers or target molecules are immobilized on a solid phase.

[0139] Several other primer-directed nucleotide incorporation procedures for assaying polymorphic sites in DNA have been described (Komher, J.S. et al., Nucl. Acids Res. 17:7779-7784 (1989); Sokolov, B.P., Nucl. Acids Res. 18:3671 (1990); Syvanen, A.-C., et al., Genomics 8:684-692 (1990); Kuppuswamy, M.N. et al., Proc. Natl. Acad. Sci. (USA) 88:1143-1147 (1991); Prezant, T.R. et al., Hum. Mutat. 1:159-164 (1992); Ugozzoli, L. et al., GATA 9:107-112 (1992); Nyren, P. et al. al., Anal. Biochem. 208:171-175 (1993)). Unlike GBA, these methods utilize the incorporation of labeled deoxynucleotides to discriminate between bases at polymorphic sites. In such formats, signal is proportional to the number of incorporated deoxynucleotides, so polymorphisms occurring in runs of the same nucleotide can produce signals proportional to the length of the run (Syvanen, A.-C., et al., Amer. J. Hum. Genet. 52:46-59 (1993)).

[0140] Numerous initiatives are obtaining sequence information directly from millions of individual molecules of DNA or RNA in parallel. Real-time single-molecule sequencing during synthesis relies on the detection of fluorescent nucleotides, which are incorporated into nascent strands of DNA complementary to the template to be sequenced. In one method, 30-50 base-long oligonucleotides are covalently tethered at their 5' ends to a coverslip. These tethered strands serve two functions. First, they act as capture sites for target template strands when the template is configured with a capture tail complementary to the surface-bound oligonucleotide. They also act as primers for template-directed primer extension, which forms the basis for sequence readout. The capture primers serve as fixed sites for sequencing using multiple cycles of synthesis, detection, and chemical cleavage of the dye-linker to remove the dye. Each cycle involves the addition of a polymerase / labeled nucleotide mixture, rinsing, imaging, and dye cleavage. In an alternative method, the polymerase is modified with a fluorescent donor molecule and immobilized on a glass slide, and each nucleotide is color-coded with an acceptor fluorescent moiety attached to the gamma-phosphate. The system detects the interaction of the fluorescently tagged polymerase with the fluorescently modified nucleotide as it is incorporated into the new strand. Other decoding-by-synthesis techniques also exist.

[0141] Any suitable sequencing-by-synthesis platform can be used to identify mutations. As mentioned above, four major sequencing-by-synthesis platforms are currently available: Roche / 454 Life Science's Genome Sequencers, Illumina / Solexa's 1G Analyzer, Applied BioSystems' SOLiD system, and Helicos Biosciences' Heliscope system. Sequencing-by-synthesis platforms have also been reported by Pacific BioSciences and VisiGen Biotechnologies. In some embodiments, multiple nucleic acid molecules to be sequenced are attached to a support (e.g., a solid support). To immobilize the nucleic acid on the support, a capture sequence / universal priming site can be added to the 3' and / or 5' end of the template. The nucleic acid can be attached to the support by hybridizing the capture sequence to a complementary sequence covalently attached to the support. The capture sequence (also called a universal capture sequence) is a nucleic acid sequence complementary to the support-attached sequence, which can double as a universal primer.

[0142] As an alternative to capture sequences, a member of a binding pair (e.g., antibody / antigen, receptor / ligand, or avidin-biotin pairs, such as those described in U.S. Patent Application Publication No. 2006 / 0252077) can be linked to each fragment to be captured on a surface coated with the second member of the respective binding pair.

[0143] After capture, the sequence can be analyzed by single-molecule detection / sequencing, including, for example, template-dependent decoding-by-synthesis, as described, for example, in U.S. Patent No. 7,283,337. In decoding-by-synthesis, the surface-bound molecules are exposed to multiple labeled nucleotide triphosphates in the presence of a polymerase. The sequence of the template is determined by the order of labeled nucleotides incorporated onto the 3' end of the growing strand. This can be performed in real time or in a step-and-repeat fashion. For real-time analysis, each nucleotide can incorporate a different optical label, and multiple lasers can be used to stimulate the incorporated nucleotides.

[0144] Sequencing can also include other massively parallel sequencing or next-generation sequencing (NGS) technologies and platforms. Further examples of massively parallel sequencing technologies and platforms are Illumina HiSeq or MiSeq, Thermo PGM or Proton, Pac Bio RS II or Sequel, Qiagen Gene Reader, and Oxford Nanopore MinION. Additional similar current massively parallel sequencing technologies, and future generations of these technologies, can be used.

[0145] Any cell type or tissue can be used to isolate nucleic acid samples for use in the method for identifying tumor-specific mutations described herein.For example, DNA or RNA samples can be isolated from tumors or from body fluids (e.g., blood) or saliva collected by known techniques (e.g., venipuncture).Alternatively, nucleic acid testing can be performed on dried samples (e.g., hair or skin).In addition, one sample can be taken from tumors for sequencing, and another sample can be taken from normal tissues with the same tissue type as tumors for sequencing.One sample can be taken from tumors for sequencing, and another sample can be taken from normal tissues with different tissue types than tumors for sequencing. Tumors in which tumor-specific mutations can be identified include, but are not limited to, any of the tumors described herein, such as lung cancer, melanoma, breast cancer, ovarian cancer, prostate cancer, kidney cancer, stomach cancer, colon cancer, testicular cancer, head and neck cancer, pancreatic cancer, brain tumors, B-cell lymphoma, acute myeloid leukemia, chronic myeloid leukemia, chronic lymphocytic leukemia, and T-cell lymphocytic leukemia, non-small cell lung cancer, and small cell lung cancer. Alternatively, protein mass spectrometry can be used to identify or verify the presence of mutant peptides bound to MHC proteins on tumor cells. Peptides can be identified by acid elution from tumor cells or from HLA molecules immunoprecipitated from tumors, followed by mass spectrometry.

[0146] cfDNA processing Methods for processing cfDNA (for example, isolating and purifying cfDNA) are generally known to those skilled in the art.For example, the general method for isolating cfDNA is described in US-2020 / 0277667-A1, which is incorporated herein by reference for all purposes.See also, for example, the latest edition of Current Protocols in Molecular Biology.Exemplary methods for isolating cfDNA are also described in US Pat. No. 10,385,369-B2 and US Pat. No. 2020 / 0277667-A1, Cell-Free Plasma DNA as a Predictor of Outcome in Severe Sepsis and Septic Shock. Clin. Chem. 2008, v. 54, p. 1000-Diagnostics. Clin. Chem. 1007, Prediction of MYCN Amplification in Neuroblastoma Using Serum DNA and Real-Time Quantitative Polymerase Chain Reaction. JCO 2005, v. 23, p. 5205-5210, Circulating Nucleic Acids in Blood of Healthy Male and Female Donors. Clin. Chem. 2005, v. 51, p. 1317-1319, Use of Magnetic Beads for Plasma Cell-free DNA Extraction: Toward Automation of Plasma DNA Analysis for Molecular. 2003, v. 49, pp. 1953-1955; Chiu RWK, Poon LM, Lau TK, Leung TN, Wong EMC, Lo YM D. Effects of blood-processing protocols on fetal and total DNA quantification in maternal plasma. Clin Chem 2001; 47: 1607-1613; and Swinkels et al. Effects of Blood-Processing Protocols on Cell-free DNA Quantification in Plasma. Clinical Chemistry, 2003, vol. 49, no. 3, pp. 525-526, each of which is incorporated herein by reference for all purposes.

[0147] Commercially available kits for isolating and purifying cfDNA are known to those skilled in the art and include, but are not limited to, the QIAamp Circulating Nucleic Acid Kit and the Apostle MiniMax cfDNA Isolation Kit (Beckman Coulter; Indianapolis, IN).

[0148] A blood / plasma sample can be collected from a subject, and cfDNA can be isolated from the blood / plasma sample. Samples containing cfDNA other than blood can be collected for cfDNA isolation and purification (e.g., stool, mucus). Isolation of cfDNA can be performed, for example, by centrifugation to isolate cfDNA from cells or cell debris, or by isolating cfDNA from whole blood by separating the plasma layer, which may contain cfDNA, from the buffy coat and red blood cells. Whole blood can be collected into a cell-free DNA BCT tube and centrifuged at an appropriate speed to separate the plasma layer, buffy coat, and red blood cells. The plasma layer can then be removed and centrifuged again to remove residual cellular material. The supernatant can then be collected and stored at -80°C until extraction. As an illustrative, non-limiting example, whole blood can be collected into a 10 mL Streck cell-free DNA BCT tube (Streck; La Vista, NE, USA) and centrifuged at 1600xg for 10 minutes at ambient temperature to separate the plasma layer, buffy coat, and red blood cells. The plasma layer can then be removed and centrifuged again at 5000×g for 10 minutes to remove residual cellular material. The supernatant can then be collected and stored at −80° C. until extraction. Those skilled in the art will recognize that the above non-limiting exemplary protocol can be optimized based on particular experimental conditions.

[0149] To prepare a cfDNA library for sequencing, cfDNA is generally fragmented, for example, sheared, or enzymatically prepared (e.g., fragmented using NEBNext Ultra II FS DNA Module (NEB, Ipswich, MA)) to generate a library of the polynucleotide region of interest. Isolated nucleic acids (e.g., isolated cfDNA) can be fragmented or sheared using routine techniques. For example, DNA can be fragmented by physical shearing, enzymatic cleavage, chemical cleavage, and other methods well known to those skilled in the art. Those skilled in the art will recognize that the above non-limiting exemplary protocol can be optimized to generate a library of desired fragment length depending on the desired sequencing application, for example, for exome sequencing. For example, the time of enzymatic digestion can be optimized (e.g., 25 minutes using NEBNext Ultra II FS DNA Module as an illustrative example). The fragment length can be at least 100 bp, at least 150 bp, at least 200 bp, at least 250 bp, at least 300 bp, at least 400 bp, at least 500 bp, at least 600 bp, at least 700 bp, at least 800 bp, at least 900 bp, or at least 1000 bp. The fragment length can be 100-250 bp, 150-350 bp, 200-450 bp, 300-700 bp, or 500-1000 bp. The fragment length can be, on average, at least 100 bp, at least 150 bp, at least 200 bp, at least 250 bp, at least 300 bp, at least 400 bp, at least 500 bp, at least 600 bp, at least 700 bp, at least 800 bp, at least 900 bp, or at least 1000 bp. The fragment lengths can be on average 100-250 bp, 150-350 bp, 200-450 bp, 300-700 bp, or 500-1000 bp in length.

[0150] cfDNA enrichment cfDNA can be enriched to improve the detection and measurement of specific polynucleotide regions of interest. Typically, enrichment is performed on a library of fragmented cfDNA (e.g., a library of polynucleotide regions of interest). The region of interest can include polynucleotides known or suspected to encode one or more mutations. The region of interest can also include a gene translocation (e.g., a Bcr-Abl fusion). The region of interest can include polynucleotides encoding gene coding regions or fragments of gene coding regions, including tumor exome polynucleotides, such as tumor exome polynucleotides known or suspected to have subject- and / or tumor-specific mutations. In general, enrichment of polynucleotide regions of interest can improve targeted measurement of DNA regions of interest by subtracting noise from sequencing results (e.g., increasing sensitivity). The terms "enrich" and "enrichment" refer to the partial purification of analytes with a particular characteristic (e.g., nucleic acids known or suspected to have tumor-specific mutations) from analytes without that characteristic (e.g., nucleic acids that do not contain tumor-specific mutations). Enrichment typically increases the concentration of analytes with the characteristic (e.g., nucleic acids containing tumor-specific mutations) by at least 2-fold, at least 5-fold, or at least 10-fold compared to analytes without that characteristic. After enrichment, at least 10%, at least 20%, at least 50%, at least 80%, or at least 90% of the analytes in a sample may have the characteristic used for enrichment. For example, at least 10%, at least 20%, at least 50%, at least 80%, or at least 90% of the nucleic acid molecules in an enriched composition may contain strands with one or more tumor-specific mutations that have been modified to contain a capture tag.

[0151] Enriching cfDNA can include hybridizing one or more polynucleotide probes (also referred to herein as "baits") to one or more polynucleotide regions of interest. The bait sequences can be based on tumor-specific mutations derived from genome sequencing, such as sequencing the tumor exome of a biopsy. The baits can include a single polynucleotide sequence or a library of polynucleotide sequences derived from tumor sequencing. The bait sequences derived from tumor sequencing can be subject-specific. For example, a subject's tumor can be biopsied and sequenced to determine mutations associated with the subject's tumor; then, subject- and tumor-specific sequences can be used to design subject-specific baits for enriching regions of interest in the tumor exome, such as baits that can enrich all regions of interest with patient-specific tumor variants.

[0152] Baits can include panels that include a combination of tumor-informative and tumor-naive polynucleotide probes (also called "combined panels").

[0153] Tumor-informative polynucleotide probes include probes configured to capture target sequences (e.g., by hybridization and other modifications, such as biotinylation, as described elsewhere herein). The target sequence may include an epitope sequence encoded by a cancer vaccine to be administered to a subject, who has been determined to have a tumor that expresses such an epitope sequence. For example, a probe for such an epitope sequence may be considered tumor-informative if the cancer vaccine administered to the subject is obtained either: (a) when the vaccine is a personalized vaccine, and prior cancer / tumor sequencing informs the selection of epitopes for inclusion in the cancer vaccine itself; or (b) when the vaccine is an "off-the-shelf" vaccine, and the vaccine contains commonly occurring epitopes, but requires prior cancer / tumor sequencing to determine whether the subject meets the eligibility requirements for receiving the vaccine.

[0154] Exemplary epitope sequences that may be encoded by a cancer vaccine include epitopes with mutations such as, but not limited to, KRAS, G13D, KRAS_Q61K, TP53_R249M, CTNNB1_S45P, CTNNB1_S45F, ERBB2_Y772_A775dup, KRAS_G12D, KRAS_Q61R, CTNNB1_T41A, TP53_K132N, KRAS_G12A, KRAS_Q61L, TP53_R213L, BRAF_G466V, KRAS_G12V, KRAS_Q61H, CTNNB1_S37F, TP53_S127Y, TP53_K132E, and KRAS_G12C.

[0155] Exemplary epitope sequences that may be encoded by a cancer vaccine include KRAS mutations such as KRAS_G12C mutation, KRAS_G12D mutation, KRAS_G12V mutation, and KRAS_Q61H mutation. Exemplary epitope sequences that may be encoded by a cancer vaccine include KRAS_G12C mutation. Exemplary epitope sequences that may be encoded by a cancer vaccine include KRAS_G12D mutation. Exemplary epitope sequences that may be encoded by a cancer vaccine include KRAS_G12V mutation. Exemplary epitope sequences that may be encoded by a cancer vaccine include KRAS_Q61H mutation.

[0156] Exemplary epitope sequences that may be encoded by a cancer vaccine include EGFR mutations, such as the EGFR_L858R mutation. Exemplary epitope sequences that may be encoded by a cancer vaccine include the EGFR_L858R mutation.

[0157] The epitope sequence may include one or more subject-specific epitopes. The epitope sequence may include one or more subject-specific epitopes, and the subject's tumor has been sequenced to determine the subject-specific epitopes encoded by the cancer vaccine. The subject-specific epitopes may include at least two subject-specific epitopes, at least 10 subject-specific epitopes, at least 20 subject-specific epitopes, or 2-20 subject-specific epitopes. The subject-specific epitopes may include at least two subject-specific epitopes. The subject-specific epitopes may include at least 10 subject-specific epitopes. The subject-specific epitopes may include at least 20 subject-specific epitopes. The subject-specific epitopes may include 2-20 subject-specific epitopes.

[0158] The panel may further include additional tumor-informative polynucleotide probes that capture additional target sequences not encoded by the cancer vaccine, e.g., additional target sequences determined by cancer / tumor sequencing. For example, the panel may include additional target sequences predicted to be presented by the subject's HLA alleles but not selected for inclusion in the cancer vaccine. The panel may include additional target sequences determined by cancer / tumor sequencing that are known or suspected to be associated with cancer. The additional target sequences may include at least 10 target sequences, at least 20 target sequences, at least 30 target sequences, at least 100 target sequences, 10-500 target sequences, 30-500 target sequences, 100-500 target sequences, 10-100 target sequences, 30-100 target sequences, or 100-100 target sequences. The additional target sequences may include at least 10 target sequences. The additional target sequences may include at least 20 target sequences. The additional target sequences may include at least 30 target sequences. The additional target sequences may include at least 100 target sequences. The additional target sequences may include between 10 and 500 target sequences. The additional target sequences may include between 30 and 500 target sequences. The additional target sequences may include between 100 and 500 target sequences. The additional target sequences may include between 10 and 100 target sequences. The additional target sequences may include between 30 and 100 target sequences. The additional target sequences may include between 100 and 100 target sequences.

[0159] Tumor-naive polynucleotide probes can be configured to capture target sequences including, but not limited to, sequences of interest such as cancer-associated genes, oncogenes, tumor suppressor genes, genes in the interferon-gamma signaling pathway, genes in the antigen processing pathway, and combinations thereof.

[0160] Oncogenes (also called "driver" mutations) are genes that are generally considered or predicted to drive cancer and are typically considered gain-of-function mutations (e.g., KRAS mutations). Panels may include probes specific for cancer genes (including internal "hotspots") including, but not limited to, ABL1, AKT2, ALK, AR, BCL6, BCL9L, BRAF, BTK, CARD11, CCND1, CCND3, CTNNB1, DDR2, EGFR, ERBB2, ERBB3, FGFR1, FGFR3, FHOD3, FLT1, FLT3, GNAS, HRAS, KDR, KIT, KRAS, MAP2K1, MAP2K2, MECOM, MED12, MET, MTOR, NOTCH1, NOTCH2, NOTCH3, NRAS, NRG1, NTRK1, NTRK3, PDGFRA, PDGFRB, PIK3CA, PIK3CG, PSMB2, RET, ROS1, SF3B1, SMO, SYNE1, and ZBTB20. The panel may include probes specific for oncogenes (including "hotspots" within them) including each of the following: ABL1, AKT2, ALK, AR, BCL6, BCL9L, BRAF, BTK, CARD11, CCND1, CCND3, CTNNB1, DDR2, EGFR, ERBB2, ERBB3, FGFR1, FGFR3, FHOD3, FLT1, FLT3, GNAS, HRAS, KDR, KIT, KRAS, MAP2K1, MAP2K2, MECOM, MED12, MET, MTOR, NOTCH1, NOTCH2, NOTCH3, NRAS, NRG1, NTRK1, NTRK3, PDGFRA, PDGFRB, PIK3CA, PIK3CG, PSMB2, RET, ROS1, SF3B1, SMO, SYNE1, and ZBTB20.

[0161] Tumor suppressor genes are generally considered to monitor and / or regulate tumor-related traits, such as cell division, and mutations therein may interfere with the regulation of such traits and are typically considered loss-of-function mutations. The panel includes: TP53, PTEN, ARID1A, APC, AMER1, ASXL1, ATM, ATR, ATRX, AXIN2, BARD1, BRCA1, BRCA2, CASP8, CFH, CREBBP, DNMT3A, EP300, ERCC2, ERCC5, EXT1, FANCA, FANCD2, FANCI, FANCM, FAT1, FBXW7, HNF1A, MAX, MLH1, MSH3, MSH6, NF1, and PIK3R1. Probes may be included that are specific for tumor suppressor genes (including "hot spots" within them), including, but not limited to, PTCH1, PTPRT, RECQL4, RNF43, ROBO1, SLX4, SMAD2, SMAD3, SMAD4, SOX9, TCF7L2, TERT promoter, TET2, TGFBR2, TP53BP1, TSC1, TSC2, WNT16, XPC, ZFP36L2, and ZNRF3. The panel includes TP53, PTEN, ARID1A, APC, AMER1, ASXL1, ATM, ATR, ATRX, AXIN2, BARD1, BRCA1, BRCA2, CASP8, CFH, CREBBP, DNMT3A, EP300, ERCC2, ERCC5, EXT1, FANCA, FANCD2, FANCI, FANCM, FAT1, FBXW7, HNF1A, MAX, MLH1, MSH3, MSH6, NF1, PI Probes specific for tumor suppressor genes (including "hot spots" within them) may be included, including each of K3R1, PTCH1, PTPRT, RECQL4, RNF43, ROBO1, SLX4, SMAD2, SMAD3, SMAD4, SOX9, TCF7L2, TERT promoter, TET2, TGFBR2, TP53BP1, TSC1, TSC2, WNT16, XPC, ZFP36L2, and ZNRF3.

[0162] Interferon-γ signaling pathway genes are genes involved in interferon-γ signaling, such as genes in the JAK / STAT signaling pathway. A panel can include probes specific for genes in the interferon-γ signaling pathway (including "hot spots" within them), including, but not limited to, IFNGR1, INFGR2, JAK1, JAK2, and STAT1. A panel can include probes specific for genes in the interferon-γ signaling pathway (including "hot spots" within them), including each of IFNGR1, INFGR2, JAK1, JAK2, and STAT1.

[0163] Genes in the antigen processing pathway are genes involved in antigen processing and / or presentation (e.g., presentation by MHC), which may include monitoring for HLA heterozygosity defects. Panels may include probes specific for genes in the antigen processing pathway (including "hot spots" therein), including, but not limited to, B2M, HLA-A, HLA-B, HLA-C, HLA-E, TAP1, TAP2, NLRC5, CALR, CANX, PSMB2, and TAPBP. Panels may include probes specific for genes in the antigen processing pathway (including "hot spots" therein), including, but not limited to, B2M, HLA-A, HLA-B, HLA-C, HLA-E, TAP1, TAP2, NLRC5, CALR, CANX, PSMB2, and TAPBP.

[0164] Other mutations that are otherwise commonly associated with cancer may also be monitored, even if not otherwise annotated, for example, even if not already annotated as an oncogene or tumor suppressor gene. Panels may include probes specific for cancer-associated genes (including "hotspots" within them), including, but not limited to, ABCA12, ACVR2A, AKAP9, BMPR2, COL12A1, CSMD3, DNAH5, DOCK3, FAT2, FAT3, FAT4, FGF10, FGF6, FLG, MAGI1, MDN1, MMAB, NBEA, OBSCN, PCBP1, PCLO, PLEKHA6, PROC, RAD54L, RELN, RPL22, RYR2, TCERG1, WRN, and ZDBF2. The panel may include probes specific for cancer-associated genes (including "hot spots" within them) including each of the following: ABCA12, ACVR2A, AKAP9, BMPR2, COL12A1, CSMD3, DNAH5, DOCK3, FAT2, FAT3, FAT4, FGF10, FGF6, FLG, MAGI1, MDN1, MMAB, NBEA, OBSCN, PCBP1, PCLO, PLEKHA6, PROC, RAD54L, RELN, RPL22, RYR2, TCERG1, WRN, and ZDBF2.

[0165] Panels containing tumor-naive polynucleotide probes can be designed to capture each of the following: cancer-associated genes, oncogenes, tumor suppressor genes, genes in the interferon-gamma signaling pathway, and genes in the antigen processing pathway.

[0166] An exemplary, non-limiting set of tumor-naive polynucleotide probes includes ABCA12, ACVR2A, AKAP9, BMPR2, COL12A1, CSMD3, DNAH5, DOCK3, FAT2, FAT3, FAT4, FGF10, FGF6, FLG, MAGI1, MDN1, MMAB, NBEA, OBSCN, PCBP1, PCLO, PLEKHA6, PROC, RAD54L, RELN, RPL22, RYR2, TCERG1, WRN, ZDBF2, ABL1, AKT2, ALK, AR, BCL6, BCL9L, BRAF, BT K, CARD11, CCND1, CCND3, CTNNB1, DDR2, EGFR, ERBB2, ERBB3, FGFR1, FGFR3, FHOD3, FLT1, FLT3, GNAS, HRAS, KDR, KIT, KRAS, MAP2K1, MAP2K2, MECOM , MED12, MET, MTOR, NOTCH1, NOTCH2, NOTCH3, NRAS, NRG1, NTRK1, NTRK3, PDGFRA, PDGFRB, PIK3CA, PIK3CG, RET, ROS1, SF3B1, SMO, SYNE1, ZBTB20, TP 53, PTEN, ARID1A, APC, AMER1, ASXL1, ATM, ATR, ATRX, AXIN2, BARD1, BRCA1, BRCA2, CASP8, CFH, CREBBP, DNMT3A, EP300, ERCC2, ERCC5, EXT1, FANCA , FANCD2, FANCI, FANCM, FAT1, FBXW7, HNF1A, MAX, MLH1, MSH3, MSH6, NF1, PIK3R1, PTCH1, PTPRT, RECQL4, RNF43, ROBO1, SLX4, SMAD2, SMAD3, SMAD4, Included are probes specific for genes (including "hot spots" within them) including, but not limited to, SOX9, TCF7L2, TERT promoter, TET2, TGFBR2, TP53BP1, TSC1, TSC2, WNT16, XPC, ZFP36L2, ZNRF3, IFNGR1, INFGR2, JAK1, JAK2, STAT1, B2M, HLA-A, HLA-B, HLA-C, HLA-E, TAP1, TAP2, NLRC5, CALR, CANX, PSMB2, TAPBP, and combinations thereof.

[0167] An exemplary, non-limiting set of tumor-naive polynucleotide probes includes ABCA12, ACVR2A, AKAP9, BMPR2, COL12A1, CSMD3, DNAH5, DOCK3, FAT2, FAT3, FAT4, FGF10, FGF6, FLG, MAGI1, MDN1, MMAB, NBEA, OBSCN, PCBP1, PCLO, PLEKHA6, PROC, RAD54L, RELN, RPL22, RYR2, TCERG1, WRN, ZDBF2, ABL1, AKT2, ALK, AR, BCL6, B CL9L, BRAF, BTK, CARD11, CCND1, CCND3, CTNNB1, DDR2, EGFR, ERBB2, ERBB3, FGFR1, FGFR3, FHOD3, FLT1, FLT3, GNAS, HRAS, KDR, KIT, KRAS, M AP2K1, MAP2K2, MECOM, MED12, MET, MTOR, NOTCH1, NOTCH2, NOTCH3, NRAS, NRG1, NTRK1, NTRK3, PDGFRA, PDGFRB, PIK3CA, PIK3CG, PSMB2, RET, ROS1, SF3B1, SMO, SYNE1, ZBTB20, TP53, PTEN, ARID1A, APC, AMER1, ASXL1, ATM, ATR, ATRX, AXIN2, BARD1, BRCA1, BRCA2, CASP8, CFH, CREBBP , DNMT3A, EP300, ERCC2, ERCC5, EXT1, FANCA, FANCD2, FANCI, FANCM, FAT1, FBXW7, HNF1A, MAX, MLH1, MSH3, MSH6, NF1, PIK3R1, PTCH1, PTPRT, These probes include those specific for RECQL4, RNF43, ROBO1, SLX4, SMAD2, SMAD3, SMAD4, SOX9, TCF7L2, TERT promoter, TET2, TGFBR2, TP53BP1, TSC1, TSC2, WNT16, XPC, ZFP36L2, ZNRF3, IFNGR1, INFGR2, JAK1, JAK2, STAT1, B2M, HLA-A, HLA-B, HLA-C, HLA-E, TAP1, TAP2, NLRC5, CALR, CANX, and PSMB2.

[0168] An exemplary, non-limiting set of tumor-naive polynucleotide probes includes ABL1, AKT2, ALK, APC, AR, ATR, ATRX, BARD1, BCL6, BMPR1A, BRAF, BRCA1, BRCA2, BTK, CARD11, CCND1, CCND3, CDK12, CFH, CREBBP, CTNNB1, DDR2, DNMT3A, EGFR, EP300, ERBB2, ERBB3, ERCC2, ERCC5, EXT1, FANCA, FANCD2, FANCI, FANCM, FBXW7, FGF10, FGF6, FGFR1, FGFR3, FLI1, FLT1, FLT3, GNAS, HNF1A, HRAS, KDR, KIT, KRAS, MAGI1, MAP2K1, MAP2K2, MAX, MED12, MET, MLH1, Included are probes specific for MMAB, MSH3, MSH6, MTOR, NF1, NFE2L2, NOTCH1, NOTCH2, NOTCH3, NRAS, NRG1, NTRK1, NTRK3, PDGFRA, PDGFRB, PIK3CA, PIK3CG, PIK3R1, PMS2, PPARG, PROC, PTCH1, RAD54L, RAF1, RECQL4, RET, ROS1, SF3B1, SF3B2, SLX4, SMO, TERT promoter, TET2, TP53BP1, TSC1, TSC2, WRN, XPA, XPC, ZNF395, B2M, HLA-A, HLA-B, HLA-C, TAP1, TAP2, NLRC5, IFNGR1, INFGR2, JAK1, JAK2, TP53, PTEN, and ARID1A.

[0169] The probes in the panel can be designed to monitor the entire coding region of a selected gene, for example, through a series of overlapping probes spanning all exons of a particular gene. The probes in the panel can be designed to monitor specific regions or mutations ("hot spots") within a gene. The probes in the panel can be designed to include two or more probes configured to capture genomic regions of interest (e.g., entire coding regions or "hot spots") associated with cancer. The probes in the panel can be designed to include probes that include overlapping sequences with each other. As an illustrative, non-limiting example, overlapping probes can include a probe design in which two probes, each 90 nucleotides long, are offset by 20 bases from each other, allowing for 110 bp coverage for each target.

[0170] A probe panel may include at least 20 probes, at least 30 probes, at least 40 probes, at least 50 probes, at least 60 probes, at least 70 probes, at least 80 probes, at least 90 probes, at least 100 probes, at least 200 probes, at least 300 probes, at least 400 probes, or at least 500 probes. A probe panel may include at least 20 probes. A probe panel may include at least 30 probes. A probe panel may include at least 40 probes. A probe panel may include at least 50 probes. A probe panel may include at least 60 probes. A probe panel may include at least 70 probes. A probe panel may include at least 80 probes. A probe panel may include at least 90 probes. A probe panel may include at least 100 probes. A probe panel may include at least 200 probes. A probe panel may include at least 300 probes. The probe panel may include at least 400 probes. The probe panel may include at least 500 probes.

[0171] The probe panel may be configured to cover at least 100 kb, at least 300 kb, at least 300 kb, at least 400 kb, 100-400 kb, 200-400 kb, 300-400 kb, 100-500 kb, 200-500 kb, 300-500 kb, or 340-400 kb of the subject's genome. The probe panel may be configured to cover at least 100 kb of the subject's genome. The probe panel may be configured to cover at least 300 kb of the subject's genome. The probe panel may be configured to cover at least 400 kb of the subject's genome. The probe panel may be configured to cover 100-400 kb of the subject's genome. The probe panel may be configured to cover 200-400 kb of the subject's genome. The probe panel may be configured to cover 300-400 kb of the subject's genome. The probe panel may be configured to cover 100-500 kb of the subject's genome. The probe panel may be configured to cover 200-500 kb of the subject's genome. The probe panel may be configured to cover 300-500 kb of the subject's genome. The probe panel may be configured to cover 340-400 kb of the subject's genome.

[0172] Tumor-naive polynucleotide probes may include polynucleotide probes configured to capture sequences associated with a given cancer (such as CRC or NSCLC) that a subject is known to have or suspected to have. Tumor-naive polynucleotide probes may include polynucleotide probes configured to capture sequences associated with CRC. Tumor-naive polynucleotide probes may include polynucleotide probes configured to capture sequences associated with NSCLC. Tumor-naive polynucleotide probes may include polynucleotide probes configured to capture sequences associated with GEA.

[0173] The panel may further include additional polynucleotide probes configured to capture sequences containing polymorphisms (e.g., single nucleotide polymorphisms "SNPs") in a human population, which sequences, in combination, can individually identify ("fingerprint") subjects. Such sequences can be used, for example, when multiple subject samples are multiplexed for sequencing.

[0174] Hybridization typically refers to the process by which a nucleic acid strand binds to a complementary strand through base pairing, as known in the art. A nucleic acid is generally considered to selectively hybridize to a reference nucleic acid sequence if the two sequences specifically hybridize to each other under medium to high stringency hybridization and washing conditions. Medium and high stringency hybridization conditions are known (e.g., Ausubel, et al., Short Protocols in Molecular Biology, 3 rd(See, e.g., Wiley & Sons 1995 and Sambrook et al., Molecular Cloning: A Laboratory Manual, Third Edition, 2001, Cold Spring Harbor, NY). Hybridization protocols can be performed at about 42°C. Hybridization buffers include, but are not limited to, formamide, SSC, Denhardt's solution, SDS, and / or denatured carrier DNA. Hybridization protocols can include wash steps at 42°C in a buffer that may include SSC and SDS. An exemplary, non-limiting hybridization protocol involves hybridization at about 42°C in 50% formamide, 5xSSC, 5xDenhardt's solution, 0.5% SDS, and 100 μg / ml denatured carrier DNA, followed by two washes in 2xSSC and 0.5% SDS at room temperature, and two additional washes in 0.1xSSC and 0.5% SDS at 42°C. Another illustrative, non-limiting example of high stringency conditions includes overnight hybridization using a custom-designed xGen Lockdown probe and the xGen Hybridization and Wash Kit (IDT), involving hybridization in xGen Hybridization Buffer plus Hybridization Buffer Enhancer at 95°C for 30 seconds in a thermocycler, followed by 4-16 hours at 65°C, followed by one wash at room temperature in xGen Wash Buffer, followed by two washes at 65°C in xGen Stringent Wash Buffer, and finally three washes at room temperature in Wash Buffer 1, Wash Buffer 2, and Wash Buffer 3 (according to the manufacturer's instructions). One of skill in the art will recognize that the above non-limiting exemplary protocol can be optimized based on the particular hybridization reaction.

[0175] The baits are 80-150 base pairs (bp) in length, e.g., 80-140bp, 80-130bp, 80-120bp, 80-110bp, 80-100bp, 80-90bp, 90-150bp, 90-140bp, 90-130bp, 90-120bp, 90-110bp, 90-100bp, 100-150bp, 10 The bait may be 0 to 140 bp, 100 to 130 bp, 100 to 120 bp, 100 to 110 bp, 110 to 150 bp, 110 to 140 bp, 110 to 130 bp, 110 to 120 bp, 120 to 150 bp, 120 to 140 bp, 120 to 130 bp, 130 to 150 bp in length. The bait may be 80 to 150 bp in length. The bait may be 80 to 140 bp in length. The bait may be 80 to 130 bp in length. The bait may be 80 to 120 bp in length. The bait may be 80 to 110 bp in length. The bait may be 80 to 100 bp in length. The bait may be 80 to 90 bp in length. The bait may be 90 to 150 bp in length. The bait may be 90 to 140 bp in length. The bait may be 90 to 130 bp in length. The bait may be 90 to 120 bp in length. The bait may be 90 to 110 bp in length. The bait may be 90 to 100 bp in length. The bait may be 100 to 150 bp in length. The bait may be 100 to 140 bp in length. The bait may be 100 to 130 bp in length. The bait may be 100 to 120 bp in length. The bait may be 100 to 110 bp in length. The bait may be 110 to 150 bp in length. The bait may be 110 to 140 bp in length. The bait may be 110 to 130 bp in length. The bait may be 110 to 120 bp in length. The bait may be 120 to 150 bp in length. The bait may be 120 to 140 bp in length. The bait may be 120 to 130 bp in length. The bait may be 130 to 150 bp in length. The bait may be 130 to 140 bp in length. The bait may be 140 to 150 bp in length.

[0176] The polynucleotide probe may include an affinity tag. Affinity tags are typically molecules that can be covalently bound to a substrate molecule (e.g., a hybridization probe) and subsequently used for purification by binding the tag to another surface or material (e.g., a biotin tag that binds to streptavidin resin). Polynucleotide enrichment can be performed by affinity purification or any other suitable method based on the affinity tag used. In some embodiments, an affinity tag is added to the polynucleotide probe, the affinity tag is enriched for DNA molecules that hybridize with the tagged probe, and the enriched DNA molecules are sequenced.

[0177] The polynucleotide probe ("bait") can be biotinylated. Biotinylation refers to the covalent addition of a biotin moiety to the polynucleotide probe. The biotin moiety can include biotin or a biotin analog, such as desthiobiotin, oxybiotin, 2-iminobiotin, diaminobiotin, biotin sulfoxide, biocytin, etc. The biotin moiety typically binds to streptavidin with an affinity of at least 10 M. The enrichment step using biotinylated polynucleotide probes can be performed using magnetic streptavidin beads, although other supports, including but not limited to, microparticles, fibers, beads, and supports, can also be used.

[0178] In an illustrative, non-limiting example, enrichment can include (a) linking a biotin moiety to an oligonucleotide probe, (b) hybridizing cfDNA to the biotinylated probe, (c) enriching for biotinylated DNA molecules by binding to a support that binds biotin (e.g., streptavidin beads), (d) amplifying the enriched DNA using polymerase chain reaction, and (f) sequencing the amplified DNA to generate multiple sequence reads.

[0179] Based on the specific disease or treatment being monitored, multiple target polynucleotide regions can be selected and enriched.For example, in cancer patients, tumor genomic DNA sequencing can be used to identify tumor-specific mutations, which can be used to select target regions for disease monitoring.

[0180] The region of interest can be enriched from cfDNA before sequencing. The region of interest can also include a polynucleotide encoding a coding region, which can include tumor exome polynucleotides.

[0181] cfDNA sequencing The method for sequencing cfDNA is generally known to those skilled in the art.For example, the general method for sequencing cfDNA is described in US-2020 / 0277667-A1, which is incorporated herein by reference for all purposes.Generally, any of the sequencing methods described herein can be used.

[0182] Sequencing of isolated cfDNA can include next-generation sequencing (NGS) or Sanger sequencing. As used herein, the term "next-generation sequencing" or "high-throughput sequencing" refers to a so-called parallelized sequencing-by-synthesis or ligation platform. NGS methods can also include nanopore sequencing methods or methods based on electronic detection, and NGS can include duplex sequencing, whole-exome sequencing, whole-genome sequencing, de novo sequencing, stepwise sequencing, targeted amplicon sequencing, or shotgun sequencing. NGS can be performed on platforms such as NovaSeq using 2 x 151bp and 8bp index reads. Other NGS platforms include, but are not limited to, Illumina's HiSeq or MiSeq, Thermo's PGM or Proton, Pac Bio's RS II or Sequel, Qiagen's Gene Reader, and Oxford Nanopore's MinION, or any other suitable platform. Examples of such methods are those described by Margulies et al. (Nature 2005 437:376-80), Ronaghi et al. (Analytical Biochemistry 1996 242:84-9), Shendure (Science 2005 309:1728), Imelfort et al. (Brief Bioinform. 2009 10:609-18), Fox et al. (Methods Mol Biol. 2009;55379-108), Appleby et al. (Methods Mol Biol. 2009;513:19-39), English (PloS One. 2012 7:e47768), and Morozova (Genomics. 2008 92:255-64), which are incorporated by reference for a summary of the method and the specific steps of the method, including all starting products, reagents, and final products for each step.

[0183] NGS can generate at least 10,000, at least 50,000, at least 100,000, at least 500,000, at least 1M, at least 10M, at least 100M, or at least 1B sequence reads. NGS can generate at least 10,000 sequence reads. NGS can generate at least 50,000 sequence reads. NGS can generate at least 100,000 sequence reads. NGS can generate at least 500,000 sequence reads. NGS can generate at least 1M sequence reads. NGS can generate at least 10M sequence reads. NGS can generate at least 100M sequence reads. NGS can generate at least 1B sequence reads. Because the sequence reads can be analyzed by a computer, instructions for performing the steps can be written as programming, which can be recorded on a suitable physical computer-readable storage medium.

[0184] Whole library amplification can be performed on cfDNA, such as enriched cfDNA, using kits such as KAPA HiFi HotStart ReadyMix and NEBNext Multiple Oligos for Illumina.

[0185] As an illustrative, non-limiting example of the process described herein, whole blood can be collected from a given subject or from a cancer subject undergoing treatment, and cfDNA can be isolated from the whole blood. DNA sequencing from diseased tissue (e.g., cancer disease tissue, such as tissue from a tumor biopsy) can be used to identify subject-specific and / or tumor-specific mutations. Subject-specific and / or tumor-specific mutations can be used to design a library of biotinylated polynucleotide probes and / or guide the selection of biotinylated polynucleotide probes to enrich polynucleotide regions of interest from the cfDNA of a subject specific to the cancer / tumor of interest. Duplex sequencing adapters can be ligated to the cfDNA, which can then be analyzed by duplex sequencing to measure the frequency of all probed variant alleles.

[0186] Sequencing adapters and duplex sequencing Generally, in the case of methods involving next-generation sequencing, adapters are ligated to cfDNA to facilitate sequencing.The term "sequencing adapter" or "adapter" refers to the oligonucleotide that is ligated to the end of the polynucleotide from the library (for example, the fragmented cfDNA library of the target polynucleotide region) prepared before sequencing.Adapter ligation can be performed on fragmented and end-repaired DNA using a 5-base non-random unique molecular identifier (IDT, Coralville, Iowa).

[0187] Sequencing adapters can be configured for duplex sequencing. Generally, duplex sequencing allows independent tracking of both strands of an individual DNA molecule during sequencing. By comparing paired sequences, sequencing errors can be reduced by eliminating differences that do not occur in both DNA strands. Adapters configured for duplex sequencing can include xGen UMI adapters (IDT). An overview of sequencing adapters for duplex sequencing and their use is described in US 2017 / 0211140 A1, which is incorporated herein by reference for all purposes.

[0188] Lead Depth As used herein, sequencing read depth (referred to as X-fold (e.g., 1000X) read depth, sometimes referred to as sequencing read coverage) refers to the level of read coverage (e.g., number of unique reads) after detecting and removing duplicate reads (e.g., PCR duplicate reads). Generally, higher sequencing read depth correlates with higher confidence in variant detection. For example, reliable detection of variants, e.g., point mutations, occurring at frequencies greater than 5% and up to 10%, 15%, or 20% may typically require a sequencing depth of greater than 200X to ensure high detection confidence.

[0189] The sequence read depth can be the read depth of each individual mutation. The sequence read depth of each individual mutation can be at least 1000X. The sequence read depth of each individual mutation can be at least 1500X, at least 2000X, at least 2500X, at least 3000X, at least 3500X, at least 4000X, at least 4500X, or at least 5000X. The sequence read depth of each individual mutation can be at least 1500X. The sequence read depth of each individual mutation can be at least 2000X. The sequence read depth of each individual mutation can be at least 2500X. The sequence read depth of each individual mutation can be at least 3000X. The sequence read depth of each individual mutation can be at least 3500X. The sequence read depth of each individual mutation can be at least 4000X. The sequence read depth of each mutation may be at least 4500X. The sequence read depth of each mutation may be at least 5000X. The sequence read depth of each mutation may be in the range of 1000X to 5000X, for example, 1000X to 4000X, 1000X to 3000X, 1000X to 2000X, 2000X to 5000X, 2000X to 4000X, 2000X to 3000X, 3000X to 5000X, 3000X to 4000X, and 4000X to 5000X. The sequence read depth of each mutation may be in the range of 1000X to 5000X. The sequence read depth of each mutation may be in the range of 1000X to 4000X. The sequence read depth for each mutation may be in the range of 1000X to 3000X. The sequence read depth for each mutation may be in the range of 1000X to 2000X. The sequence read depth for each mutation may be in the range of 2000X to 5000X. The sequence read depth for each mutation may be in the range of 2000X to 4000X. The sequence read depth for each mutation may be in the range of 2000X to 3000X. The sequence read depth for each mutation may be in the range of 3000X to 5000X. The sequence read depth for each mutation may be in the range of 3000X to 4000X.The sequencing read depth for each mutation can be in the range of 4000X to 5000X. The sequencing read depth for each mutation can be in the range of at least 100X to 1000X.

[0190] The sequencing read depth can be a double-stranded read depth. The sequencing read depth can be a double-stranded read depth of each mutation. The double-stranded read depth of each mutation can be at least 1000X. The double-stranded read depth of each mutation can be at least 1500X, at least 2000X, at least 2500X, at least 3000X, at least 3500X, at least 4000X, at least 4500X, or at least 5000X. The double-stranded read depth of each mutation can be at least 1500X. The double-stranded read depth of each mutation can be at least 2000X. The double-stranded read depth of each mutation can be at least 2500X. The double-stranded read depth of each mutation can be at least 3000X. The double-stranded read depth of each mutation can be at least 3500X. The double-stranded read depth of each mutation may be at least 4000X. The double-stranded read depth of each mutation may be at least 4500X. The double-stranded read depth of each mutation may be at least 5000X. The double-stranded read depth of each mutation may be in the range of 1000X to 5000X, for example, 1000X to 4000X, 1000X to 3000X, 1000X to 2000X, 2000X to 5000X, 2000X to 4000X, 2000X to 3000X, 3000X to 5000X, 3000X to 4000X, and 4000X to 5000X. The double-stranded read depth of each mutation may be in the range of 1000X to 5000X. The double-stranded read depth for each mutation may be in the range of 1000X to 4000X. The double-stranded read depth for each mutation may be in the range of 1000X to 3000X. The double-stranded read depth for each mutation may be in the range of 1000X to 2000X. The double-stranded read depth for each mutation may be in the range of 2000X to 5000X. The double-stranded read depth for each mutation may be in the range of 2000X to 4000X. The double-stranded read depth for each mutation may be in the range of 2000X to 3000X. The double-stranded read depth for each mutation may be in the range of 3000X to 5000X. The double-stranded read depth for each mutation may be in the range of 3000X to 4000X. The double-stranded read depth for each mutation may be in the range of 4000X to 5000X.The double-stranded read depth for individual mutations can range from at least 100X to 1000X.

[0191] The sequencing read depth may be the average read depth. The average read depth refers to the average sequencing depth of multiple polynucleotide regions of interest (e.g., cancer exomes and / or regions of interest targeted for enrichment by any of the tumor-informed / tumor-naive combined panels described herein and / or by baiting for regions with subject-specific and tumor-specific variants). The average read depth may be the average read depth of the cancer exome. The average read depth may be the average read depth of regions of interest targeted for enrichment by any of the tumor-informed / tumor-naive combined panels described herein. The average read depth may be the average read depth of regions of interest with previously identified subject-specific and / or tumor-specific mutations. The average read depth may be the average read depth of enriched cfDNA. The average read depth may be the average read depth of cfDNA enriched by baiting for regions with subject-specific and tumor-specific variants.

[0192] The average depth of read may be at least 1000X. The average depth of read may be at least 1500X, at least 2000X, at least 2500X, at least 3000X, at least 3500X, at least 4000X, at least 4500X, or at least 5000X. The average depth of read may be at least 1500X. The average depth of read may be at least 2000X. The average depth of read may be at least 2500X. The average depth of read may be at least 3000X. The average depth of read may be at least 3500X. The average depth of read may be at least 4000X. The average depth of read may be at least 4500X. The average depth of read may be at least 5000X. The average read depth may be in the range of 1000X to 5000X, for example, 1000X to 4000X, 1000X to 3000X, 1000X to 2000X, 2000X to 5000X, 2000X to 4000X, 2000X to 3000X, 3000X to 5000X, 3000X to 4000X, and 4000X to 5000X. The average read depth may be in the range of 1000X to 5000X. The average read depth may be in the range of 1000X to 4000X. The average read depth may be in the range of 1000X to 3000X. The average read depth may be in the range of 1000X to 2000X. The average read depth may be in the range of 2000X to 5000X. The average read depth may be in the range of 2000X to 4000X. The average read depth may be in the range of 2000X to 3000X. The average read depth may be in the range of 3000X to 5000X. The average read depth may be in the range of 3000X to 4000X. The average read depth may be in the range of 4000X to 5000X. The average read depth may be in the range of at least 100X to 1000X.

[0193] The average read depth can be the average double-stranded read depth. The average double-stranded read depth can be at least 1000X. The average double-stranded read depth can be at least 1500X, at least 2000X, at least 2500X, at least 3000X, at least 3500X, at least 4000X, at least 4500X, or at least 5000X. The average double-stranded read depth can be at least 1500X. The average double-stranded read depth can be at least 2000X. The average double-stranded read depth can be at least 2500X. The average double-stranded read depth can be at least 3000X. The average double-stranded read depth can be at least 3500X. The average double-stranded read depth can be at least 4000X. The average double-stranded read depth can be at least 4500X. The average double-stranded read depth can be at least 5000X. The average double-stranded read depth can be in the range of 1000X to 5000X, for example, 1000X to 4000X, 1000X to 3000X, 1000X to 2000X, 2000X to 5000X, 2000X to 4000X, 2000X to 3000X, 3000X to 5000X, 3000X to 4000X, and 4000X to 5000X. The average double-stranded read depth can be in the range of 1000X to 5000X. The average double-stranded read depth can be in the range of 1000X to 4000X. The average double-stranded read depth can be in the range of 1000X to 3000X. The average double-stranded read depth can be in the range of 1000X to 2000X. The average double-stranded read depth may be in the range of 2000X to 5000X. The average double-stranded read depth may be in the range of 2000X to 4000X. The average double-stranded read depth may be in the range of 2000X to 3000X. The average double-stranded read depth may be in the range of 3000X to 5000X. The average double-stranded read depth may be in the range of 3000X to 4000X. The average double-stranded read depth may be in the range of 4000X to 5000X. The average double-stranded read depth may be in the range of at least 100X to 1000X.

[0194] Multiplexed Analysis The methods described herein include multiplex arrays capable of sequencing ("detecting") multiple polynucleotide regions of interest from a cfDNA sample. The cfDNA sample may include ctDNA containing one or more mutant alleles encoding genes within the tumor exome. One or more polynucleotide regions of interest can be selectively enriched by designing baits to target one or more polynucleotide regions of interest. One or more polynucleotide regions of interest can be selectively enriched by designing baits to target one or more polynucleotide regions of interest from the tumor exome. One or more polynucleotide regions of interest can be selectively enriched by designing baits to target one or more polynucleotide regions of interest from tumor exomes known to have or suspected to have subject- and tumor-specific mutations.

[0195] The one or more polynucleotide regions of interest may comprise 10 or more polynucleotide regions of interest. The one or more polynucleotide regions of interest may comprise 20 or more polynucleotide regions of interest. The one or more polynucleotide regions of interest may comprise 30 or more polynucleotide regions of interest. The one or more polynucleotide regions of interest may comprise 40 or more polynucleotide regions of interest. The one or more polynucleotide regions of interest may comprise 50 or more polynucleotide regions of interest. The one or more polynucleotide regions of interest may comprise 60 or more polynucleotide regions of interest. The one or more polynucleotide regions of interest may comprise 70 or more polynucleotide regions of interest. The one or more polynucleotide regions of interest may comprise 80 or more polynucleotide regions of interest. The one or more polynucleotide regions of interest may comprise 90 or more polynucleotide regions of interest. The one or more polynucleotide regions of interest may comprise 100 or more polynucleotide regions of interest. The one or more polynucleotide regions of interest may comprise 150 or more polynucleotide regions of interest. The one or more polynucleotide regions of interest may comprise 200 or more polynucleotide regions of interest. The one or more polynucleotide regions of interest may comprise 250 or more polynucleotide regions of interest. The one or more polynucleotide regions of interest may comprise 300 or more polynucleotide regions of interest. The one or more polynucleotide regions of interest may comprise 400 or more polynucleotide regions of interest. The one or more polynucleotide regions of interest may comprise 500 or more polynucleotide regions of interest. The one or more polynucleotide regions of interest may comprise 600 or more polynucleotide regions of interest. The one or more polynucleotide regions of interest may comprise 700 or more polynucleotide regions of interest. The one or more polynucleotide regions of interest may comprise 800 or more polynucleotide regions of interest. Or, the one or more polynucleotide regions of interest may comprise 900 or more polynucleotide regions of interest.

[0196] The one or more polynucleotide regions of interest can comprise at least 10% of the polynucleotide regions of interest corresponding to mutations present in the subject's tumor exome (in other words, at least 10% of all subject- and tumor-specific mutations associated with the tumor exome). The one or more polynucleotide regions of interest can comprise at least 20% of the polynucleotide regions of interest corresponding to mutations present in the subject's tumor exome. The one or more polynucleotide regions of interest can comprise at least 30% of the polynucleotide regions of interest corresponding to mutations present in the subject's tumor exome. The one or more polynucleotide regions of interest can comprise at least 40% of the polynucleotide regions of interest corresponding to mutations present in the subject's tumor exome. The one or more polynucleotide regions of interest can comprise at least 50% of the polynucleotide regions of interest corresponding to mutations present in the subject's tumor exome. The one or more polynucleotide regions of interest can comprise at least 60% of the polynucleotide regions of interest corresponding to mutations present in the subject's tumor exome. The one or more polynucleotide regions of interest can comprise at least 70% of the polynucleotide regions of interest corresponding to mutations present in the subject's tumor exome. The one or more polynucleotide regions of interest can comprise at least 80% of the polynucleotide regions of interest corresponding to mutations present in the subject's tumor exome. The one or more polynucleotide regions of interest may comprise at least 90% of the polynucleotide regions of interest corresponding to mutations present in the subject's tumor exome. The one or more polynucleotide regions of interest may comprise at least 95% of the polynucleotide regions of interest corresponding to mutations present in the subject's tumor exome. The one or more polynucleotide regions of interest may comprise at least 96% of the polynucleotide regions of interest corresponding to mutations present in the subject's tumor exome. The one or more polynucleotide regions of interest may comprise at least 97% of the polynucleotide regions of interest corresponding to mutations present in the subject's tumor exome. The one or more polynucleotide regions of interest may comprise at least 98% of the polynucleotide regions of interest corresponding to mutations present in the subject's tumor exome.The one or more polynucleotide regions of interest can comprise at least 99% of the polynucleotide regions of interest corresponding to mutations present in the subject's tumor exome. The one or more polynucleotide regions of interest can comprise at least 99.5% of the polynucleotide regions of interest corresponding to mutations present in the subject's tumor exome. The one or more polynucleotide regions of interest can comprise at least 99.9% of the polynucleotide regions of interest corresponding to mutations present in the subject's tumor exome. The one or more polynucleotide regions of interest can comprise 100% of the polynucleotide regions of interest corresponding to mutations present in the subject's tumor exome.

[0197] Mutations can include, but are not limited to, point mutations, frameshift mutations, non-frameshift mutations, deletion mutations, insertion mutations, splice variants, genome rearrangements, splice antigens generated by proteasomes, or combinations thereof. Mutations can include at least one change that makes the peptide sequence encoded by cfDNA different from the corresponding peptide sequence encoded by the subject's wild-type germline nucleic acid sequence. Mutations can consist of coding mutations that include at least one change that makes the peptide sequence encoded by cfDNA different from the corresponding peptide sequence encoded by the subject's wild-type germline nucleic acid sequence. One or more mutations can include 10 or more, 20 or more, 30 or more, 40 or more, 50 or more, 60 or more, 70 or more, 80 or more, or 90 or more mutations. The one or more mutations may include 100 or more, 150 or more, 200 or more, 250 or more, 300 or more, 400 or more, 500 or more, 600 or more, 700 or more, 800 or more, or 900 or more mutations. The mutations may be associated with the tumor exome. The one or more mutations may include at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, or at least 90% of the mutations present in the subject's tumor exome. The one or more mutations may include at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, at least 99.5%, at least 99.9%, or 100% of the mutations present in the subject's tumor exome.

[0198] Target Coverage As used herein, target coverage (typically expressed as a percentage) refers to the proportion of a polynucleotide region or regions (e.g., regions represented to at least some read depth in a sequencing dataset) that are sequenced. Generally, target coverage is described as the proportion of a desired region or regions (e.g., multiple polynucleotide regions of interest) that are covered. For example, target coverage can be the proportion of the whole genome, exome, cancer genome, cancer exome, and / or enriched region (e.g., cancer exome and / or region of interest that are targeted for enrichment by any of the tumor-informed / tumor-naive combined panels described herein, and / or by baiting for regions with target-specific and tumor-specific variants, etc.).

[0199] Target coverage can be the percentage of a subject's tumor and / or cancer exome that is sequenced. Target coverage can be at least 10% of the tumor and / or cancer exome. Target coverage can be at least 20% of the tumor and / or cancer exome. Target coverage can be at least 30% of the tumor and / or cancer exome. Target coverage can be at least 40% of the tumor and / or cancer exome. Target coverage can be at least 50% of the tumor and / or cancer exome. Target coverage can be at least 60% of the tumor and / or cancer exome. Target coverage can be at least 70% of the tumor and / or cancer exome. Target coverage can be at least 80% of the tumor and / or cancer exome. Target coverage can be at least 90% of the tumor and / or cancer exome. The target coverage may be at least 95% of the tumor and / or cancer exome. The target coverage may be at least 96% of the tumor and / or cancer exome. The target coverage may be at least 97% of the tumor and / or cancer exome. The target coverage may be at least 98% of the tumor and / or cancer exome. The target coverage may be at least 99% of the tumor and / or cancer exome. The target coverage may be at least 99.5% of the tumor and / or cancer exome. The target coverage may be at least 99.9% of the tumor and / or cancer exome. The target coverage may be 100% of the tumor and / or cancer exome.

[0200] Target coverage can be the percentage of the target polynucleotide region that has been sequenced. Target coverage can be at least 10% of the target polynucleotide region. Target coverage can be at least 20% of the target polynucleotide region. Target coverage can be at least 30% of the target polynucleotide region. Target coverage can be at least 40% of the target polynucleotide region. Target coverage can be at least 50% of the target polynucleotide region. Target coverage can be at least 60% of the target polynucleotide region. Target coverage can be at least 70% of the target polynucleotide region. Target coverage can be at least 80% of the target polynucleotide region. Target coverage can be at least 90% of the target polynucleotide region. Target coverage can be at least 95% of the target polynucleotide region. Target coverage can be at least 96% of the target polynucleotide region. Target coverage can be at least 97% of the target polynucleotide region. The target coverage can be at least 98% of the target polynucleotide region. The target coverage can be at least 99% of the target polynucleotide region. The target coverage can be at least 99.5% of the target polynucleotide region. The target coverage can be at least 99.9% of the target polynucleotide region. The target coverage can be 100% of the target polynucleotide region.

[0201] Target coverage can be the percentage of the sequenced polynucleotide region of interest targeted for enrichment (e.g., the cancer exome and / or region of interest targeted for enrichment by any of the tumor-informative / tumor-naive combined panels described herein and / or by baiting for regions with target-specific and tumor-specific variants, etc.). Target coverage can be at least 10% of the polynucleotide region of interest targeted for enrichment. Target coverage can be at least 20% of the polynucleotide region of interest targeted for enrichment. Target coverage can be at least 30% of the polynucleotide region of interest targeted for enrichment. Target coverage can be at least 40% of the polynucleotide region of interest targeted for enrichment. Target coverage can be at least 50% of the polynucleotide region of interest targeted for enrichment. Target coverage can be at least 60% of the polynucleotide region of interest targeted for enrichment. Target coverage can be at least 70% of the polynucleotide region of interest targeted for enrichment. Target coverage can be at least 80% of the polynucleotide region of interest targeted for enrichment. The target coverage may be at least 90% of the target polynucleotide region to be enriched. The target coverage may be at least 95% of the target polynucleotide region to be enriched. The target coverage may be at least 96% of the target polynucleotide region to be enriched. The target coverage may be at least 97% of the target polynucleotide region to be enriched. The target coverage may be at least 98% of the target polynucleotide region to be enriched. The target coverage may be at least 99% of the target polynucleotide region to be enriched. The target coverage may be at least 99.5% of the target polynucleotide region to be enriched. The target coverage may be at least 99.9% of the target polynucleotide region to be enriched. The target coverage may be 100% of the target polynucleotide region to be enriched.

[0202] Target coverage can be the percentage of polynucleotide regions targeted for enrichment by any of the tumor-informant / tumor-naive combination panels described herein.

[0203] Target coverage can be the percentage of sequenced polynucleotide regions of interest that correspond to mutations present in the exome of a subject's tumor and / or cancer. Target coverage can be at least 10% of all polynucleotide regions of interest that correspond to mutations present in the exome of a subject's tumor and / or cancer (e.g., coverage is at least 10% of all subject-specific and tumor-specific mutations associated with the exome of a tumor and / or cancer). Target coverage can be at least 20% of all polynucleotide regions of interest that correspond to mutations present in the exome of a subject's tumor and / or cancer. Target coverage can be at least 30% of all polynucleotide regions of interest that correspond to mutations present in the exome of a subject's tumor and / or cancer. Target coverage can be at least 40% of all polynucleotide regions of interest that correspond to mutations present in the exome of a subject's tumor and / or cancer. Target coverage can be at least 50% of all polynucleotide regions of interest that correspond to mutations present in the exome of a subject's tumor and / or cancer. Target coverage can be at least 60% of all polynucleotide regions of interest corresponding to mutations present in the exome of the subject's tumor and / or cancer. Target coverage can be at least 70% of all polynucleotide regions of interest corresponding to mutations present in the exome of the subject's tumor and / or cancer. Target coverage can be at least 80% of all polynucleotide regions of interest corresponding to mutations present in the exome of the subject's tumor and / or cancer. Target coverage can be at least 90% of all polynucleotide regions of interest corresponding to mutations present in the exome of the subject's tumor and / or cancer. Target coverage can be at least 95% of all polynucleotide regions of interest corresponding to mutations present in the exome of the subject's tumor and / or cancer. Target coverage can be at least 96% of all polynucleotide regions of interest corresponding to mutations present in the exome of the subject's tumor and / or cancer.Target coverage can be at least 97% of all polynucleotide regions of interest corresponding to mutations present in the exome of the subject's tumor and / or cancer. Target coverage can be at least 98% of all polynucleotide regions of interest corresponding to mutations present in the exome of the subject's tumor and / or cancer. Target coverage can be at least 99% of all polynucleotide regions of interest corresponding to mutations present in the exome of the subject's tumor and / or cancer. Target coverage can be at least 99.5% of all polynucleotide regions of interest corresponding to mutations present in the exome of the subject's tumor and / or cancer. Target coverage can be at least 99.9% of all polynucleotide regions of interest corresponding to mutations present in the exome of the subject's tumor and / or cancer. Target coverage can be 100% of all polynucleotide regions of interest corresponding to mutations present in the exome of the subject's tumor and / or cancer.

[0204] Target coverage can be the percentage of the subject tumor and / or cancer genome that is sequenced. Target coverage can be at least 10% of the tumor and / or cancer genome. Target coverage can be at least 20% of the tumor and / or cancer genome. Target coverage can be at least 30% of the tumor and / or cancer genome. Target coverage can be at least 40% of the tumor and / or cancer genome. Target coverage can be at least 50% of the tumor and / or cancer genome. Target coverage can be at least 60% of the tumor and / or cancer genome. Target coverage can be at least 70% of the tumor and / or cancer genome. Target coverage can be at least 80% of the tumor and / or cancer genome. Target coverage can be at least 90% of the tumor and / or cancer genome. Target coverage can be at least 95% of the tumor and / or cancer genome. The target coverage may be at least 96% of the tumor and / or cancer genome. The target coverage may be at least 97% of the tumor and / or cancer genome. The target coverage may be at least 98% of the tumor and / or cancer genome. The target coverage may be at least 99% of the tumor and / or cancer genome. The target coverage may be at least 99.5% of the tumor and / or cancer genome. The target coverage may be at least 99.9% of the tumor and / or cancer genome. The target coverage may be 100% of the tumor and / or cancer genome.

[0205] Target coverage can be the percentage of the region of interest that is sequenced, and the region of interest that is sequenced exceeds a certain read depth.Target coverage can be the percentage of the region of interest that is sequenced, and the region of interest that is sequenced has a read depth of at least 1000X.Target coverage can be the percentage of the region of interest that is sequenced, and the region of interest that is sequenced has a read depth of at least 1500X, at least 2000X, at least 2500X, at least 3000X, at least 3500X, at least 4000X, at least 4500X, or at least 5000X.Target coverage can be the percentage of the region of interest that is sequenced, and the region of interest that is sequenced has a read depth of at least 1500X. The target coverage can be a percentage of the region of interest that has been sequenced, the region of interest having a read depth of at least 2000X. The target coverage can be a percentage of the region of interest that has been sequenced, the region of interest having a read depth of at least 2500X. The target coverage can be a percentage of the region of interest that has been sequenced, the region of interest having a read depth of at least 3000X. The target coverage can be a percentage of the region of interest that has been sequenced, the region of interest having a read depth of at least 3500X. The target coverage can be a percentage of the region of interest that has been sequenced, the region of interest having a read depth of at least 4000X. The target coverage can be a percentage of the region of interest that has been sequenced, the region of interest having a read depth of at least 4500X. Target coverage can be the percentage of a region of interest that is sequenced, where the region of interest that is sequenced has a read depth of at least 5000X.

[0206] Target coverage can be the percentage of the region of interest that is sequenced, and the region of interest that is sequenced exceeds a certain average read depth.Target coverage can be the percentage of the region of interest that is sequenced, and the region of interest that is sequenced has an average read depth of at least 1000X.Target coverage can be the percentage of the region of interest that is sequenced, and the region of interest that is sequenced has an average read depth of at least 1500X, at least 2000X, at least 2500X, at least 3000X, at least 3500X, at least 4000X, at least 4500X, or at least 5000X.Target coverage can be the percentage of the region of interest that is sequenced, and the region of interest that is sequenced has an average read depth of at least 1500X. The target coverage can be a percentage of the region of interest that has been sequenced, the region of interest having an average read depth of at least 2000X. The target coverage can be a percentage of the region of interest that has been sequenced, the region of interest having an average read depth of at least 2500X. The target coverage can be a percentage of the region of interest that has been sequenced, the region of interest having an average read depth of at least 3000X. The target coverage can be a percentage of the region of interest that has been sequenced, the region of interest having an average read depth of at least 3500X. The target coverage can be a percentage of the region of interest that has been sequenced, the region of interest having an average read depth of at least 4000X. The target coverage can be a percentage of the region of interest that has been sequenced, the region of interest having an average read depth of at least 4500X. Target coverage can be the percentage of a region of interest that is sequenced, where the region of interest that is sequenced has an average read depth of at least 5000X.

[0207] Target coverage can be the percentage of the region of interest that is sequenced, and the region of interest that is sequenced exceeds a certain double-stranded read depth.Target coverage can be the percentage of the region of interest that is sequenced, and the region of interest that is sequenced has a double-stranded read depth of at least 1000X.Target coverage can be the percentage of the region of interest that is sequenced, and the region of interest that is sequenced has a double-stranded read depth of at least 1500X, at least 2000X, at least 2500X, at least 3000X, at least 3500X, at least 4000X, at least 4500X, or at least 5000X.Target coverage can be the percentage of the region of interest that is sequenced, and the region of interest that is sequenced has a double-stranded read depth of at least 1500X. The target coverage can be a percentage of the region of interest that has been sequenced, wherein the region of interest has a double-stranded read depth of at least 2000X. The target coverage can be a percentage of the region of interest that has been sequenced, wherein the region of interest has a double-stranded read depth of at least 2500X. The target coverage can be a percentage of the region of interest that has been sequenced, wherein the region of interest has a double-stranded read depth of at least 3000X. The target coverage can be a percentage of the region of interest that has been sequenced, wherein the region of interest has a double-stranded read depth of at least 3500X. The target coverage can be a percentage of the region of interest that has been sequenced, wherein the region of interest has a double-stranded read depth of at least 4000X. Target coverage can be the percentage of a region of interest that has been sequenced, where the region of interest has a double-stranded read depth of at least 4500X. Target coverage can be the percentage of a region of interest that has been sequenced, where the region of interest has a double-stranded read depth of at least 5000X.

[0208] Target coverage can be the percentage of the region of interest that is sequenced, and the region of interest that is sequenced exceeds a certain average double-stranded read depth.Target coverage can be the percentage of the region of interest that is sequenced, and the region of interest that is sequenced has an average double-stranded read depth of at least 1000X.Target coverage can be the percentage of the region of interest that is sequenced, and the region of interest that is sequenced has an average double-stranded read depth of at least 1500X, at least 2000X, at least 2500X, at least 3000X, at least 3500X, at least 4000X, at least 4500X, or at least 5000X.

[0209] The target coverage can be at least 10% of the region of interest, with the sequenced region of interest having a read depth or average read depth of at least 1000X, at least 1500X, at least 2000X, at least 2500X, at least 3000X, at least 3500X, at least 4000X, at least 4500X, or at least 5000X. The target coverage can be at least 20% of the region of interest, with the sequenced region of interest having a read depth or average read depth of at least 1000X, at least 1500X, at least 2000X, at least 2500X, at least 3000X, at least 3500X, at least 4000X, at least 4500X, or at least 5000X. The target coverage can be at least 30% of the region of interest, with the sequenced region of interest having a read depth or average read depth of at least 1000X, at least 1500X, at least 2000X, at least 2500X, at least 3000X, at least 3500X, at least 4000X, at least 4500X, or at least 5000X. The target coverage can be at least 40% of the region of interest, with the sequenced region of interest having a read depth or average read depth of at least 1000X, at least 1500X, at least 2000X, at least 2500X, at least 3000X, at least 3500X, at least 4000X, at least 4500X, or at least 5000X. Target coverage can be at least 50% of the region of interest, and the sequenced region of interest has a read depth or average read depth of at least 1000X, at least 1500X, at least 2000X, at least 2500X, at least 3000X, at least 3500X, at least 4000X, at least 4500X, or at least 5000X.The target coverage can be at least 60% of the region of interest, with the sequenced region of interest having a read depth or average read depth of at least 1000X, at least 1500X, at least 2000X, at least 2500X, at least 3000X, at least 3500X, at least 4000X, at least 4500X, or at least 5000X. The target coverage can be at least 70% of the region of interest, with the sequenced region of interest having a read depth or average read depth of at least 1000X, at least 1500X, at least 2000X, at least 2500X, at least 3000X, at least 3500X, at least 4000X, at least 4500X, or at least 5000X. Target coverage can be at least 80% of the region of interest, with the sequenced region of interest having a read depth or average read depth of at least 1000X, at least 1500X, at least 2000X, at least 2500X, at least 3000X, at least 3500X, at least 4000X, at least 4500X, or at least 5000X. Target coverage can be at least 90% of the region of interest, with the sequenced region of interest having a read depth or average read depth of at least 1000X, at least 1500X, at least 2000X, at least 2500X, at least 3000X, at least 3500X, at least 4000X, at least 4500X, or at least 5000X. Target coverage can be at least 95% of the region of interest, and the sequenced region of interest has a read depth or average read depth of at least 1000X, at least 1500X, at least 2000X, at least 2500X, at least 3000X, at least 3500X, at least 4000X, at least 4500X, or at least 5000X.The target coverage can be at least 96% of the region of interest, with the sequenced region of interest having a read depth or average read depth of at least 1000X, at least 1500X, at least 2000X, at least 2500X, at least 3000X, at least 3500X, at least 4000X, at least 4500X, or at least 5000X. The target coverage can be at least 97% of the region of interest, with the sequenced region of interest having a read depth or average read depth of at least 1000X, at least 1500X, at least 2000X, at least 2500X, at least 3000X, at least 3500X, at least 4000X, at least 4500X, or at least 5000X. The target coverage can be at least 98% of the region of interest, with the sequenced region of interest having a read depth or average read depth of at least 1000X, at least 1500X, at least 2000X, at least 2500X, at least 3000X, at least 3500X, at least 4000X, at least 4500X, or at least 5000X. The target coverage can be at least 99% of the region of interest, with the sequenced region of interest having a read depth or average read depth of at least 1000X, at least 1500X, at least 2000X, at least 2500X, at least 3000X, at least 3500X, at least 4000X, at least 4500X, or at least 5000X. Target coverage can be at least 99.5% of the region of interest, and the sequenced region of interest has a read depth or average read depth of at least 1000X, at least 1500X, at least 2000X, at least 2500X, at least 3000X, at least 3500X, at least 4000X, at least 4500X, or at least 5000X.Target coverage can be at least 99.9% of the region of interest, with the sequenced region of interest having a read depth or average read depth of at least 1000X, at least 1500X, at least 2000X, at least 2500X, at least 3000X, at least 3500X, at least 4000X, at least 4500X, or at least 5000X. Target coverage can be 100% of the region of interest, with the sequenced region of interest having a read depth or average read depth of at least 1000X, at least 1500X, at least 2000X, at least 2500X, at least 3000X, at least 3500X, at least 4000X, at least 4500X, or at least 5000X.

[0210] Target coverage can be at least 10% of the region of interest, with the sequenced region of interest having a double-stranded read depth or average double-stranded read depth of at least 1000X, at least 1500X, at least 2000X, at least 2500X, at least 3000X, at least 3500X, at least 4000X, at least 4500X, or at least 5000X. Target coverage can be at least 20% of the region of interest, with the sequenced region of interest having a double-stranded read depth or average double-stranded read depth of at least 1000X, at least 1500X, at least 2000X, at least 2500X, at least 3000X, at least 3500X, at least 4000X, at least 4500X, or at least 5000X. The target coverage can be at least 30% of the region of interest, with the sequenced region of interest having a double-stranded read depth or average double-stranded read depth of at least 1000X, at least 1500X, at least 2000X, at least 2500X, at least 3000X, at least 3500X, at least 4000X, at least 4500X, or at least 5000X. The target coverage can be at least 40% of the region of interest, with the sequenced region of interest having a double-stranded read depth or average double-stranded read depth of at least 1000X, at least 1500X, at least 2000X, at least 2500X, at least 3000X, at least 3500X, at least 4000X, at least 4500X, or at least 5000X. Target coverage can be at least 50% of the region of interest, and the sequenced region of interest has a double-stranded read depth or average double-stranded read depth of at least 1000X, at least 1500X, at least 2000X, at least 2500X, at least 3000X, at least 3500X, at least 4000X, at least 4500X, or at least 5000X.The target coverage can be at least 60% of the region of interest, with the sequenced region of interest having a double-stranded read depth or average double-stranded read depth of at least 1000X, at least 1500X, at least 2000X, at least 2500X, at least 3000X, at least 3500X, at least 4000X, at least 4500X, or at least 5000X. The target coverage can be at least 70% of the region of interest, with the sequenced region of interest having a double-stranded read depth or average double-stranded read depth of at least 1000X, at least 1500X, at least 2000X, at least 2500X, at least 3000X, at least 3500X, at least 4000X, at least 4500X, or at least 5000X. Target coverage can be at least 80% of the region of interest, with the sequenced region of interest having a double-stranded read depth or average double-stranded read depth of at least 1000X, at least 1500X, at least 2000X, at least 2500X, at least 3000X, at least 3500X, at least 4000X, at least 4500X, or at least 5000X. Target coverage can be at least 90% of the region of interest, with the sequenced region of interest having a double-stranded read depth or average double-stranded read depth of at least 1000X, at least 1500X, at least 2000X, at least 2500X, at least 3000X, at least 3500X, at least 4000X, at least 4500X, or at least 5000X. Target coverage can be at least 95% of the region of interest, and the sequenced region of interest has a double-stranded read depth or average double-stranded read depth of at least 1000X, at least 1500X, at least 2000X, at least 2500X, at least 3000X, at least 3500X, at least 4000X, at least 4500X, or at least 5000X.Target coverage can be at least 96% of the region of interest, with the sequenced region of interest having a double-stranded read depth or average double-stranded read depth of at least 1000X, at least 1500X, at least 2000X, at least 2500X, at least 3000X, at least 3500X, at least 4000X, at least 4500X, or at least 5000X. Target coverage can be at least 97% of the region of interest, with the sequenced region of interest having a double-stranded read depth or average double-stranded read depth of at least 1000X, at least 1500X, at least 2000X, at least 2500X, at least 3000X, at least 3500X, at least 4000X, at least 4500X, or at least 5000X. Target coverage can be at least 98% of the region of interest, with the sequenced region of interest having a double-stranded read depth or average double-stranded read depth of at least 1000X, at least 1500X, at least 2000X, at least 2500X, at least 3000X, at least 3500X, at least 4000X, at least 4500X, or at least 5000X. Target coverage can be at least 99% of the region of interest, with the sequenced region of interest having a double-stranded read depth or average double-stranded read depth of at least 1000X, at least 1500X, at least 2000X, at least 2500X, at least 3000X, at least 3500X, at least 4000X, at least 4500X, or at least 5000X. Target coverage can be at least 99.5% of the region of interest, and the sequenced region of interest has a double-stranded read depth or average double-stranded read depth of at least 1000X, at least 1500X, at least 2000X, at least 2500X, at least 3000X, at least 3500X, at least 4000X, at least 4500X, or at least 5000X.Target coverage can be at least 99.9% of the region of interest, with the sequenced region of interest having a double-stranded read depth or average double-stranded read depth of at least 1000X, at least 1500X, at least 2000X, at least 2500X, at least 3000X, at least 3500X, at least 4000X, at least 4500X, or at least 5000X. Target coverage can be 100% of the region of interest, with the sequenced region of interest having a double-stranded read depth or average double-stranded read depth of at least 1000X, at least 1500X, at least 2000X, at least 2500X, at least 3000X, at least 3500X, at least 4000X, at least 4500X, or at least 5000X.

[0211] The target coverage can be at least 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, or 99% of the region of interest, and the sequenced region of interest has a read depth or average read depth of at least 1000X. The target coverage can be at least 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, or 99% of the region of interest, and the sequenced region of interest has a read depth or average read depth of at least 1500X. The target coverage can be at least 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, or 99% of the region of interest, and the sequenced region of interest has a read depth or average read depth of at least 2000X. The target coverage can be at least 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, or 99% of the region of interest, and the sequenced region of interest has a read depth or average read depth of at least 2500X. The target coverage can be at least 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, or 99% of the region of interest, and the sequenced region of interest has a read depth or average read depth of at least 3000X. The target coverage can be at least 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, or 99% of the region of interest, and the sequenced region of interest has a read depth or average read depth of at least 3500X. Target coverage can be at least 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, or 99% of the region of interest, and the sequenced region of interest has a read depth or average read depth of at least 4000X.The target coverage can be at least 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, or 99% of the region of interest, and the sequenced region of interest has a read depth or average read depth of at least 4500X. The target coverage can be at least 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, or 99% of the region of interest, and the sequenced region of interest has a read depth or average read depth of at least 5000X.

[0212] The target coverage can be at least 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, or 99% of the region of interest, and the sequenced region of interest has a double-stranded read depth or average double-stranded read depth of at least 1000X. The target coverage can be at least 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, or 99% of the region of interest, and the sequenced region of interest has a double-stranded read depth or average double-stranded read depth of at least 1500X. The target coverage can be at least 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, or 99% of the region of interest, and the sequenced region of interest has a double-stranded read depth or an average double-stranded read depth of at least 2000X. The target coverage can be at least 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, or 99% of the region of interest, and the sequenced region of interest has a double-stranded read depth or an average double-stranded read depth of at least 2500X. The target coverage can be at least 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, or 99% of the region of interest, and the sequenced region of interest has a double-stranded read depth or an average double-stranded read depth of at least 3000X. The target coverage can be at least 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, or 99% of the region of interest, and the sequenced region of interest has a double-stranded read depth or an average double-stranded read depth of at least 3500X. Target coverage can be at least 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, or 99% of the region of interest, and the sequenced region of interest has a double-stranded read depth or average double-stranded read depth of at least 4000X.The target coverage can be at least 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, or 99% of the region of interest, and the sequenced region of interest has a double-stranded read depth or an average double-stranded read depth of at least 4500X. The target coverage can be at least 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, or 99% of the region of interest, and the sequenced region of interest has a double-stranded read depth or an average double-stranded read depth of at least 5000X.

[0213] evaluation After sequencing, sequence reads can be analyzed to quantitatively determine the frequency of variant alleles (also referred to as mutation allele frequency) in the subject's cfDNA.Methods for quantifying sequence reads and variant allele frequency (VAF) are known to those skilled in the art.Calculation programs for sequence analysis and VAF include, but are not limited to, BWA-MEM (Durbin et al., Bioinformatics, 2010), fgbio toolkit (Fulcrum Genomics), and freebayes (Marth et al., arXiv 2012), each of which is incorporated herein by reference for all purposes.Generally, the frequency of one or more mutations in a subject's cfDNA (e.g., VAF) is expressed as the percentage of mutation-specific sequence reads relative to the reads of the subject's wild-type germline nucleic acid sequence.For example, mutation frequency can be determined by counting the number of reads of a specific variant allele compared to the total number of cfDNA in a sample collected from a subject. Additionally, assessment of VAF can be combined with plasma cfDNA concentration (e.g., ng / ml) to estimate tumor genome concentration in plasma (see Bos, et al. Molecular Oncology (2020) doi:10.1002 / 1878-0261.12827 and Reinert et al., JAMA Oncol. 2019;5(8):1124-1131. Doi:10.1001 / jamaoncol.2019.0528, each of which is incorporated by reference for all purposes).

[0214] After determining the frequency (e.g., VAF) of one or more mutations in a subject's cfDNA (or alternatively, estimated tumor genome per ml of plasma), the mutation frequency or estimated tumor genome burden can then be assessed to characterize various disease or subject attributes, e.g., the subject's disease status, treatment efficacy, or a combination thereof.

[0215] Mutation frequency For example, assessment can be performed to assess the disease state of a subject, such as assessing the tumor burden of the subject. Assessment of tumor burden can be used in various applications, such as as part of disease diagnosis, disease prognosis, disease prediction, and / or disease progression monitoring. Assessment of disease progression can be performed by comparing mutation frequencies in samples taken from the subject at various time points. The change in mutation frequency can be relative to a fixed time point, for example, relative to a baseline mutation frequency, such as the mutation frequency determined on the first day of a treatment regimen.

[0216] An increase in mutation frequency from cfDNA mutation analysis of the first sample collected (e.g., the early longitudinal sample) compared to the mutation frequency from cfDNA mutation analysis of the second sample (e.g., the late longitudinal sample) can be evaluated as disease progression, refractory to treatment, and / or disease recurrence. A decrease in mutation frequency from cfDNA mutation analysis of the first sample collected (e.g., the early longitudinal sample) compared to the mutation frequency from cfDNA mutation analysis of the second sample (e.g., the late longitudinal sample) can be evaluated as a response. A response can be either a complete response (CR) or a partial response (PR). An increase in mutation frequency in post-treatment cfDNA compared to pre-treatment cfDNA can indicate an increased likelihood that the subject's tumor burden is increasing. A decrease or maintenance of mutation frequency in post-treatment cfDNA compared to pre-treatment cfDNA can indicate an increased likelihood that the subject's tumor burden is decreasing or stable.

[0217] An increase in mutation frequency (or alternatively, estimated tumor genome per ml of plasma) over time can be assessed as disease progression and / or recurrence. To be assessed as progression and / or recurrence, an increase in mutation frequency can be at least a 10%, at least a 20%, at least a 30%, at least a 40%, at least a 50%, at least a 60%, at least a 70%, at least a 80%, at least a 90%, or at least a 100% relative increase in mutation frequency between time points. To be assessed as progression and / or recurrence, an increase in mutation frequency can be at least a 2X, at least a 3X, at least a 4X, at least a 5X, at least a 6X, at least a 7X, at least a 8X, at least a 9X, or at least a 10X relative increase in mutation frequency between time points. An increase in mutation frequency can be a relative increase of at least 20X, at least 30X, at least 40X, at least 50X, at least 60X, at least 70X, at least 80X, at least 90X, or at least 100X in mutation frequency between time points to be assessed as progression and / or relapse.

[0218] A decrease in mutation frequency (or alternatively, estimated tumor genome per ml of plasma) over time can be assessed as disease remission. A decrease in mutation frequency can be assessed as a relative increase of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 90%, or at least 100% in mutation frequency between time points to be assessed as remission. A decrease in mutation frequency can be assessed as a relative increase of at least 2X, at least 3X, at least 4X, at least 5X, at least 6X, at least 7X, at least 8X, at least 9X, or at least 10X in mutation frequency between time points to be assessed as remission. A decrease in mutation frequency can be assessed as a relative increase of at least 20X, at least 30X, at least 40X, at least 50X, at least 60X, at least 70X, at least 80X, at least 90X, or at least 100X in mutation frequency between time points to be assessed as remission. A reduction in mutation frequency can be assessed as a remission, e.g., a reduction to an undetectable level of mutations in cfDNA, to be assessed as a complete remission.

[0219] Assessments can be performed to assess a subject's de novo mutational status, such as assessing whether the subject's cancer or tumor has developed tumor escape mutations (see, e.g., "Cancer Monitoring"). The increase in mutation frequency from cfDNA mutation analysis of a first sample collected (e.g., an early longitudinal sample) compared to the mutation frequency from cfDNA mutation analysis of a second sample (e.g., a late longitudinal sample) can be assessed based on the frequency of mutations (including de novo occurrence) associated with regions targeted by the tumor-naive probe panels described herein.

[0220] To evaluate the effect of treatment on disease, the mutation frequency in cfDNA (or alternatively, the estimated tumor genome per ml of plasma) can be compared between samples collected before and after treatment. An increase in mutation frequency from cfDNA mutation analysis of a sample collected before treatment compared to the mutation frequency from cfDNA mutation analysis of a sample collected after treatment can be evaluated as disease progression, refractory to treatment, and / or disease recurrence. A decrease in mutation frequency from cfDNA mutation analysis of a sample collected before treatment compared to the mutation frequency from cfDNA mutation analysis of a sample collected after treatment can be evaluated as a response. A response can be either a complete response (CR) or a partial response (PR). An increase in mutation frequency in post-treatment cfDNA compared to pre-treatment cfDNA can indicate an increased likelihood that the subject's tumor burden is increasing. A decrease or maintenance of mutation frequency in post-treatment cfDNA compared to pre-treatment cfDNA can indicate an increased likelihood that the subject's tumor burden is decreasing or stable.

[0221] After the evaluation step, the subject can be administered further treatment. For example, an initial measurement can be obtained from the patient before starting a multi-dose anti-cancer treatment regimen. Subsequent measurements can be obtained before the administration of each dose. Analysis of variant allele frequency in cfDNA at each stage allows for evaluation of the patient's response to each dose of the treatment regimen. The evaluation can further guide clinical decisions, including dosage, treatment options, etc. For example, clinical decisions (including introducing new treatments or discontinuing current treatments) can be informed by evaluating the tumor escape mutation status associated with the regions targeted by the tumor-naive probe panel described herein.

[0222] Therapeutic treatment The methods described herein can be performed after administering treatment to a patient. The treatment can include a cancer vaccine. The treatment can include targeted radiation therapy (e.g., external beam radiation therapy, brachytherapy). The treatment can include an immune checkpoint inhibitor, including, but not limited to, a PD-1 inhibitor (e.g., nivolumab, pembrolizumab), a PD-L1 inhibitor (e.g., avelumab, durvalumab), or a CTLA-4 inhibitor (e.g., ipilimumab). Treatment may include targeted therapy techniques, such as monoclonal antibody therapy (e.g., trastuzumab, bevacizumab), retinoids (e.g., ATRA, bexarotene), selective steroid hormone receptor modulators (e.g., tamoxifen, toremifene), or inhibitors of oncoproteins such as tyrosine kinases (TKs) (e.g., imatinib, erlotinib), mammalian target of rapamycin (mTOR) (e.g., everolimus, temsirolimus), or histone deacetylases (HDACs) (e.g., valproate, vorinostat). Treatment may include cytotoxic chemotherapy.Examples of cytotoxic chemotherapeutic agents include cisplatin, carboplatin, oxaliplatin, nedaplatin, azacitidine, capecitabine, carmofur, cladribine, clofarabine, cytarabine, decitabine, florouracil, floxuridine, fludaramine, mercaptopurine, nelarabine, pentostatin, tegafur, thioguanine, methotrexate, pemetrexed, raltitrexed, hydroxycarbamide, irinotecan, topotecan, danorubicin, doxorubicin, epirubicin, idarubicin, These include bicine, mitoxantrone, valrubicin, etoposide, teniposide, docetaxel, paclitaxel, vinblastine, vincristine, vindesine, vinflunine, vinorelbine, bendamustine, busulfan, carmustine, chlorambucil, chlormethine, dacarbazine, fotemustine, ifosfamide, lomustine, melphalan, streptozotocin, gemcitabine, cyclophosphamide, temozolomide, dacarbazine, altretamine, bleomycin, bortezomib, actinomycin D, estramustine, ixabepilone, mitomycin, and procarbazine.

[0223] Also provided are methods of inducing a tumor-specific immune response in a subject, vaccinating against a tumor, and treating and / or alleviating symptoms of cancer in a subject by administering to the subject one or more antigens, e.g., multiple antigens identified using the methods disclosed herein.

[0224] In some embodiments, the subject has been diagnosed with cancer or is at risk of developing cancer. The subject may be a human, dog, cat, horse, or any animal in which a tumor-specific immune response is desired. The tumor may be any solid tumor, such as breast, ovarian, prostate, lung, kidney, stomach, colon, testicular, head and neck, pancreas, brain, melanoma, and other tumors of tissue organs, as well as hematological tumors, such as lymphomas and leukemias, for example, acute myeloid leukemia, chronic myeloid leukemia, chronic lymphocytic leukemia, T-cell lymphocytic leukemia, and B-cell lymphoma.

[0225] The antigen may be administered in an amount sufficient to induce a CTL response, a T cell response, or a B cell response.

[0226] The antigen can be administered alone or in combination with other therapeutic agents, such as chemotherapy treatments, immune checkpoint inhibitors, and / or other immunotherapies.

[0227] The optimal amount of each antigen to be included in the vaccine composition and the optimal administration regimen can be determined. For example, the antigen or its variants can be prepared for intravenous (iv), subcutaneous (sc), intradermal (id), intraperitoneal (ip), or intramuscular (im) injection. Methods of injection include sc, id, ip, im, and iv. Methods of DNA or RNA injection include id, im, sc, ip, and iv. Other methods of administering the vaccine composition are known to those skilled in the art.

[0228] Vaccines can be edited so that the selection, number, and / or amount of antigens present in the composition are tissue-, cancer-, and / or subject-specific. For example, the precise selection of peptides can be guided by the expression pattern of the parent protein in a given tissue, as well as by the patient's mutation or disease state. Selection can vary depending on the specific type of cancer, the disease state, the purpose of vaccination (e.g., preventative or targeting ongoing disease), prior treatment regimens, the patient's immune status, and, of course, the patient's HLA haplotype. Furthermore, vaccines can contain components that are personalized according to the individual needs of a particular patient. Examples include altering antigen selection according to the expression of antigens in a particular patient, or adjusting secondary treatments following a primary treatment or treatment scheme.

[0229] Patients can be identified for administration of antigen vaccines through various diagnostic methods, such as the patient selection methods described in detail below. Patient selection can involve identifying mutations in one or more genes or the expression pattern of one or more genes. In some cases, patient selection involves identifying the patient's haplotype. Various patient selection methods can be performed in parallel, for example, diagnosis by sequencing can identify both the patient's mutation and haplotype. Various patient selection methods can be performed sequentially, for example, one diagnostic test can identify a mutation and another diagnostic test can identify the patient's haplotype, where each test can be the same diagnostic method (e.g., both high-throughput sequencing) or different diagnostic methods (e.g., one high-throughput sequencing and the other Sanger sequencing).

[0230] For compositions used as vaccines against cancer, antigens with normal self-peptides similar to those highly expressed in normal tissues may be avoided or present in low amounts in the compositions described herein, whereas if a patient's tumor is known to express high amounts of a particular antigen, pharmaceutical compositions for the treatment of cancer may each be present in high amounts and / or may include multiple antigens specific for this particular antigen or pathway of this antigen.

[0231] Compositions containing antigens can be administered to individuals already suffering from cancer. In therapeutic applications, the compositions are administered to patients in an amount sufficient to induce a therapeutically effective response, for example, to stimulate an effective CTL response against tumor antigens and to cure or at least partially halt symptoms and / or complications. An amount appropriate to achieve this is defined as a "therapeutically effective dose." Amounts effective for this use will vary depending, for example, on the composition, the mode of administration, the stage and severity of the disease being treated, the patient's weight and general health, and the judgment of the prescribing physician. It should be noted that compositions are generally used in serious conditions, i.e., life-threatening or potentially life-threatening situations, particularly when cancer has metastasized. In such cases, the physician may feel that substantial over-administration of these compositions is possible and desirable, taking into account the minimization of exogenous substances and the relative non-toxicity of the antigen.

[0232] For therapeutic use, administration can begin at the time of tumor detection or surgical removal, followed by boosting doses thereafter until at least symptoms are substantially alleviated.

[0233] Pharmaceutical compositions for therapeutic treatment (e.g., vaccine compositions) are intended for parenteral, topical, nasal, oral, or local administration. Pharmaceutical compositions can be administered parenterally, for example, intravenously, subcutaneously, intradermally, or intramuscularly. Compositions can be administered at a surgical resection site to induce a local immune response against a tumor. Compositions can be administered to target specific diseased tissues and / or cells in a subject. Disclosed herein are compositions for parenteral administration, which comprise a solution of an antigen, or vaccine composition, dissolved or suspended in an acceptable carrier, e.g., an aqueous carrier. Various aqueous carriers can be used, e.g., water, buffered water, 0.9% saline, 0.3% glycine, hyaluronic acid, and the like. These compositions can be sterilized by conventional, well-known sterilization techniques or can be sterile filtered. The resulting aqueous solutions can be packaged for use as is or lyophilized, with the lyophilized preparation being combined with a sterile solution prior to administration. The compositions may contain pharmaceutically acceptable auxiliary substances required to approximate physiological conditions, such as pH adjusting and buffering agents, tonicity adjusting agents, wetting agents, etc., e.g., sodium acetate, sodium lactate, sodium chloride, potassium chloride, calcium chloride, sorbitan monolaurate, triethanolamine oleate, etc.

[0234] Antigens can also be administered via liposomes, which target them to specific cellular tissues, such as lymphoid tissues. Liposomes are also useful for extending half-life. Liposomes include emulsions, foams, micelles, insoluble monolayers, liquid crystals, phospholipid dispersions, lamellar layers, and the like. In these preparations, the antigen to be delivered is incorporated as part of the liposome, either alone or in combination with a molecule that binds to a receptor frequently found on lymphoid cells, such as a monoclonal antibody that binds to the CD45 antigen, or in combination with other therapeutic or immunogenic compositions. Liposomes loaded with the desired antigen can thus be directed to the site of lymphoid cells, where they deliver the selected therapeutic / immunogenic composition. Liposomes can be formed from standard vesicle-forming lipids, which generally include neutral and negatively charged phospholipids and sterols, such as cholesterol. The choice of lipid is generally guided by considerations, for example, of liposome size, acid lability, and liposome stability in the bloodstream. Various methods are available for preparing liposomes, as described, for example, in Szoka et al., Ann. Rev. Biophys. Bioeng. 9;467 (1980), U.S. Pat. Nos. 4,235,871, 4,501,728, 4,501,728, 4,837,028, and 5,019,369.

[0235] When directed to immune cells, the ligand incorporated within the liposome can include, for example, an antibody or fragment thereof specific for a cell surface determinant of the desired immune system cell. The liposome suspension can be administered intravenously, locally, topically, etc., at doses that vary depending, inter alia, on the mode of administration, the peptide being delivered, and the stage of disease being treated.

[0236] For therapeutic or immunization purposes, nucleic acids encoding peptides, optionally encoding one or more of the peptides described herein, can also be administered to patients. Numerous methods are conveniently used to deliver nucleic acids to patients. For example, nucleic acids can be delivered directly as "naked DNA." This technique is described, for example, in Wolff et al., Science 247:1465-1468 (1990) and U.S. Patent Nos. 5,580,859 and 5,589,466. Nucleic acids can also be administered using ballistic delivery, as described, for example, in U.S. Patent No. 5,204,253. Particles composed solely of DNA can be administered. Alternatively, DNA can be attached to particles such as gold particles. Techniques for delivering nucleic acid sequences can include viral vectors, mRNA vectors, and DNA vectors, with or without electroporation.

[0237] Nucleic acids can also be delivered in complexes with cationic compounds, such as cationic lipids. Lipid-mediated gene delivery methods are described, for example, in 9618372 WOAWO96 / 18372, 9324640 WOAWO93 / 24640, Mannino & Gould-Fogerite, BioTechniques 6(7):682-691 (1988), U.S. Patent No. 5,279,833 (Rose), U.S. Patent No. 5,279,833, 9106309 WOAWO91 / 06309, and Felgner et al., Proc. Natl. Acad. Sci. USA 84:7413-7414 (1987).

[0238] Antigens can also be used in viral vector-based vaccine platforms, such as vaccinia, fowlpox, self-replicating alphavirus, Maraba virus, adenovirus (see, e.g., Tatsis et al., Adenoviruses, Molecular Therapy (2004) 10, 616-629), or lentiviruses, including, but not limited to, second-generation, third-generation, or second / third-generation hybrid lentiviruses and recombinant lentiviruses of any generation designed to target specific cell types or receptors (see, e.g., Hu et al., Immunization Delivered by Lentiviral Vectors for Cancer and Infectious Diseases, Immunol Rev. (2011) 239(1):45-61; Sakuma et al., Lentiviral vectors: basic to translational, Biochem J. (2012) 443(3):603-18; Cooper et al., Rescue of splicing-mediated intron loss maximizes expression in lentiviral vectors). (See, e.g., Nucl. Acids Res. (2015) 43(1):682-690; Zufferey et al., Self-Inactivating Lentivirus Vector for Safe and Efficient In Vivo Gene Delivery, J. Virol. (1998) 72(12):9873-9880), etc. Depending on the packaging capacity of the viral vector-based vaccine platform, this approach can deliver one or more nucleotide sequences encoding one or more antigenic peptides.The sequence may be adjacent to non-mutated sequence, separated by linkers, or preceded by one or more sequences that target a subcellular compartment (see, e.g., Gros et al., Prospective identification of neoantigen-specific lymphocytes in the peripheral blood of melanoma patients, Nat Med. (2016) 22(4):433-8; Stronen et al., Targeting of cancer neoantigens with donor-derived T cell receptor repertoires, Science. (2016) 352(6291):1337-41; Lu et al., Efficient identification of mutated cancer antigens recognized by T cells associated with durable tumor regressions, Clin Cancer Res. (2014) 20(13):3401-10). Upon introduction into a host, vector-infected cells express the antigen, thereby eliciting a host immune (e.g., CTL) response against the peptide(s). Vaccinia vectors and methods useful in immunization protocols are described, for example, in U.S. Patent No. 4,722,848. Another vector is BCG (Bacille Calmette-Guerin). BCG vectors are described by Stover et al. (Nature 351:456-460 (1991)). A wide variety of other vaccine vectors useful for therapeutic administration of antigens or immunization, such as Salmonella typhi vectors, will be apparent to those skilled in the art from the description herein.

[0239] The vaccine may include an epitope-encoding nucleic acid whose sequence encodes one or more tumor- and / or subject-specific mutations, such as one or more of the mutations whose frequency in cfDNA has been determined. The vaccine system may include a self-replicating alphavirus-based expression system encoding an epitope-encoding nucleic acid whose sequence encodes one or more tumor- and / or subject-specific mutations. A self-replicating alphavirus-based expression system for use as a cancer vaccine is described in International Patent Application Publication No. WO / 2018 / 208856, which is incorporated herein by reference in its entirety for all purposes. The vaccine system may include a chimpanzee adenovirus (ChAdV)-based expression system encoding an epitope-encoding nucleic acid whose sequence encodes one or more tumor- and / or subject-specific mutations. A ChAdV-based expression system for use as a cancer vaccine is described in International Patent Application Publication No. WO / 2018 / 098362, which is incorporated herein by reference in its entirety for all purposes.

[0240] A means of administering nucleic acids uses minigene constructs encoding one or more epitopes. To generate DNA sequences encoding selected CTL epitopes (minigenes) for expression in human cells, the amino acid sequences of the epitopes are reverse-translated. A human codon usage table is used to guide codon selection for each amino acid. These epitope-encoding DNA sequences are directly adjacent to create a contiguous polypeptide sequence. Additional elements can be incorporated into the minigene design to optimize expression and / or immunogenicity. Examples of amino acid sequences that can be reverse-translated and included in the minigene sequence include helper T lymphocytes, epitopes, leader (signal) sequences, and endoplasmic reticulum retention signals. Furthermore, synthetic (e.g., polyalanine) or naturally occurring flanking sequences can be included adjacent to the CTL epitopes to improve CTL epitope presentation to the MHC. The minigene sequence is converted to DNA by assembling oligonucleotides encoding the plus and minus strands of the minigene. Overlapping oligonucleotides (30-100 bases long) are synthesized, phosphorylated, purified, and annealed under appropriate conditions using well-known techniques. The ends of the oligonucleotides are ligated using T4 DNA ligase. This synthetic minigene encoding the CTL epitope polypeptide can then be cloned into a desired expression vector.

[0241] Purified plasmid DNA can be prepared for injection using a variety of formulations. The most convenient of these is reconstitution of lyophilized DNA with sterile phosphate-buffered saline (PBS). Various methods have been reported, and new techniques may become available. As noted above, nucleic acids are conveniently formulated with cationic lipids. Additionally, glycolipids, fusogenic liposomes, peptides, and compounds collectively known as protective interacting non-condensing (PINC) compounds, can also be complexed with purified plasmid DNA to affect variables such as stability, intramuscular distribution, or transport to specific organs or cell types.

[0242] Also disclosed are methods of producing a vaccine comprising carrying out the steps of the methods disclosed herein and producing a vaccine comprising a plurality of antigens or a subset of a plurality of antigens.

[0243] The antigens disclosed herein can be produced using methods known in the art. For example, methods for producing the antigens or vectors disclosed herein (e.g., vectors containing at least one sequence encoding one or more antigens) can include culturing host cells containing at least one polynucleotide encoding the antigen or vector under conditions suitable for expression of the antigen or vector, and purifying the antigen or vector. Standard purification methods include chromatography, electrophoresis, immunological techniques, precipitation, dialysis, filtration, concentration, and chromatofocusing techniques.

[0244] The host cell may include Chinese hamster ovary (CHO) cells, NS0 cells, yeast, or HEK293 cells. The host cell may be transformed with one or more polynucleotides comprising at least one nucleic acid sequence encoding an antigen or vector disclosed herein, and optionally, the isolated polynucleotide further comprises a promoter sequence operably linked to the at least one nucleic acid sequence encoding the antigen or vector. In certain embodiments, the isolated polynucleotide may be a cDNA.

[0245] antigen Antigen can comprise nucleotide or polypeptide.For example, antigen can be the RNA sequence that encodes polypeptide sequence.Therefore, antigen that is useful for vaccine can comprise nucleotide sequence or polypeptide sequence.Antigen that can be used for cancer vaccine is described in International Patent Application Publication No. WO / 2019 / 226941, which is incorporated herein by reference in its entirety for all purposes.

[0246] Disclosed herein are isolated peptides comprising tumor-specific mutations identified by the methods disclosed herein, peptides comprising known tumor-specific mutations, and mutant polypeptides or fragments thereof identified by the methods disclosed herein. Neoantigen peptides can be described in relation to their coding sequences, where neoantigens include nucleotide sequences (e.g., DNA or RNA) that encode the relevant polypeptide sequence.

[0247] Also disclosed herein are peptides derived from any polypeptide known or found to be altered in expression in tumor cells or cancerous tissues compared to normal cells or tissues, for example, any polypeptide known or found to be abnormally expressed in tumor cells or cancerous tissues compared to normal cells or tissues.Suitable polypeptides from which antigen peptides can be derived can be found, for example, in the COSMIC database.COSMIC maintains comprehensive information on somatic mutations in human cancers.Peptides contain tumor-specific mutations.

[0248] The one or more polypeptides encoded by the antigen nucleotide sequence can comprise at least one of the following: a binding affinity to MHC with an IC50 value of less than 1000 nM; for MHC class I peptides, a length of 8-15, 8, 9, 10, 11, 12, 13, 14, or 15 amino acids; the presence of a sequence motif within or near the peptide that promotes proteasomal cleavage; and the presence of a sequence motif that promotes TAP transport; and for MHC class II peptides, a length of 6-30, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, or 30 amino acids; and the presence of a sequence motif within or near the peptide that promotes extracellular or lysosomal protease (e.g., cathepsin) cleavage or HLA-DM catalyzed HLA binding.

[0249] One or more antigens may be presented on the surface of the tumor.

[0250] The one or more antigens may be immunogenic in a tumor-bearing subject, for example, capable of eliciting a T cell or B cell response in the subject.

[0251] One or more antigens that induce an autoimmune response in a subject can be excluded from consideration in the context of generating a vaccine for a tumor-bearing subject.

[0252] The size of the at least one antigenic peptide molecule can include, but is not limited to, about 5, about 6, about 7, about 8, about 9, about 10, about 11, about 12, about 13, about 14, about 15, about 16, about 17, about 18, about 19, about 20, about 21, about 22, about 23, about 24, about 25, about 26, about 27, about 28, about 29, about 30, about 31, about 32, about 33, about 34, about 35, about 36, about 37, about 38, about 39, about 40, about 41, about 42, about 43, about 44, about 45, about 46, about 47, about 48, about 49, about 50, about 60, about 70, about 80, about 90, about 100, about 110, about 120 or more amino acid residues, and any range derivable therein. In a specific embodiment, the antigenic peptide molecule is 50 amino acids or less.

[0253] Antigen peptides and polypeptides may be 15 residues or less in length, usually about 8 to about 11 residues, particularly 9 or 10 residues, for MHC class I, and 6 to 30 residues for MHC class II.

[0254] If necessary, longer peptides can be designed in several ways. In some cases, when the potential for peptide presentation by HLA alleles is predicted or known, longer peptides can consist of either (1) individual presented peptides with extensions of 2 to 5 amino acids toward the N- and C-termini of each corresponding gene product, or (2) a concatenation of part or all of the presented peptide with the respective extended sequence. In other cases, when sequencing reveals long (more than 10 residues) neoepitope sequences present in tumors (e.g., due to frameshifts, readthrough, or intron inclusion resulting in novel peptide sequences), longer peptides can (3) consist of the entire region of novel tumor- or infection-specific amino acids, thus eliminating the need to select short peptides for the most potent HLA presentation, either computationally or based on in vitro testing. In either case, the use of longer peptides can allow for endogenous processing by patient cells, resulting in more effective antigen presentation and induction of T cell responses.

[0255] Antigenic peptides and polypeptides can be presented on HLA proteins. In some embodiments, the antigenic peptides and polypeptides are presented on HLA proteins and have higher affinity than wild-type peptides. In some embodiments, the antigenic peptide or polypeptide can have an IC50 of at least 5000 nM, at least 1000 nM, at least 500 nM, at least 250 nM, at least 200 nM, at least 150 nM, at least 100 nM, at least 50 nM, or less.

[0256] In some embodiments, the antigenic peptides and polypeptides do not induce an autoimmune response and / or do not induce immune tolerance when administered to a subject.

[0257] Compositions containing at least two or more antigenic peptides are also provided. In some embodiments, the composition contains at least two distinct peptides. The at least two distinct peptides may be derived from the same polypeptide. Distinct polypeptides mean that the peptides differ in length, amino acid sequence, or both. The peptides may be derived from any polypeptide known or found to contain tumor-specific mutations, or from any polypeptide known or found to have altered expression in tumor cells or cancerous tissues compared to normal cells or tissues, for example, any polypeptide known or found to be aberrantly expressed in tumor cells or cancerous tissues compared to normal cells or tissues. Suitable polypeptides from which antigenic peptides can be derived can be found, for example, in the COSMIC database or the AACR Genomics Evidence Neoplasia Information Exchange (GENIE) database. COSMIC maintains comprehensive information on somatic mutations in human cancers. AACR GENIE aggregates clinical-grade cancer genomic data and correlates them with clinical outcomes from tens of thousands of cancer patients. The peptides contain tumor-specific mutations. In some embodiments, the tumor-specific mutation is a driver mutation of a particular cancer type.

[0258] Antigenic peptides and polypeptides with desired activities or properties can be modified to confer certain desired attributes, e.g., improved pharmacological properties, while increasing or at least substantially retaining the biological activity of the unmodified peptide, i.e., binding to desired MHC molecules and activating appropriate T cells. For example, antigenic peptides and polypeptides can undergo various alterations, such as conservative or non-conservative substitutions, which may provide certain advantages in their use, such as improved MHC binding, stability, or presentation. Conservative substitutions refer to the replacement of one amino acid residue with another that is biologically and / or chemically similar, e.g., the replacement of one hydrophobic residue with another, or one polar residue with another. Substitutions include Gly, Ala; Val, Ile, Leu, Met; Asp, Glu; Asn, Gln; Ser, Thr; Lys, Arg; and combinations such as Phe and Tyr. The effects of single amino acid substitutions can also be probed using D-amino acids. Such modifications can be carried out using well-known peptide synthesis procedures, for example, as described in Merrifield, Science 232:341-347 (1986), Barany & Merrifield, The Peptides, Gross & Meienhofer, eds. (NY, Academic Press), pp. 1-284 (1979), and Stewart & Young, Solid Phase Peptide Synthesis, (Rockford, Ill., Pierce), 2nd Ed. (1984).

[0259] Modification of peptides and polypeptides with various amino acid mimetics or unnatural amino acids can be particularly useful in increasing peptide and polypeptide stability in vivo. Stability can be assayed in a number of ways. For example, peptidases and various biological media, such as human plasma and serum, have been used to test stability. See, for example, Verhoef et al., Eur. J. Drug Metab Pharmacokin. 11:291-302 (1986). Peptide half-life can be conveniently determined using a 25% human serum (v / v) assay. The protocol is generally as follows: Pooled human serum (type AB, non-heat-inactivated) is defatted by centrifugation before use. The serum is then diluted to 25% with RPMI tissue culture medium and used to test peptide stability. At predetermined time intervals, a small amount of the reaction solution is removed and added to an aqueous solution of 6% trichloroacetic acid or ethanol. The turbid reaction sample is cooled (4°C) for 15 minutes, after which precipitated serum proteins are pelleted by centrifugation. The presence of the peptide is then determined by reverse phase HPLC using stability specific chromatographic conditions.

[0260] Peptides and polypeptides can be modified to achieve desired attributes other than improved serum half-life. For example, the ability of a peptide to induce CTL activity can be enhanced by conjugation to a sequence containing at least one epitope capable of inducing a T helper cell response. The immunogenic peptide / T helper conjugate can be linked by a spacer molecule. The spacer is typically composed of relatively small, neutral molecules, such as amino acids or amino acid mimetics, that are substantially uncharged under physiological conditions. The spacer is typically selected from, for example, Ala, Gly, or neutral spacers of nonpolar or neutral polar amino acids. It will be understood that the optional spacer need not be composed of the same residues and can therefore be a hetero- or homo-oligomer. If present, the spacer is generally at least one or two residues, more typically three to six residues. Alternatively, the peptide can be linked to the T helper peptide without a spacer.

[0261] The antigenic peptide can be linked directly to the T helper peptide, or via a spacer at either the amino or carboxy terminus of the peptide. The amino terminus of either the antigenic peptide or the T helper peptide can be acylated. Exemplary T helper peptides include tetanus toxoid 830-843, influenza 307-319, malaria circumsporozoite 382-398, and 378-389.

[0262] Proteins or peptides can be produced by any technique known to those of skill in the art, such as expressing the protein, polypeptide, or peptide by standard molecular biology techniques, isolating the protein or peptide from a natural source, or chemically synthesizing the protein or peptide. Nucleotide and protein, polypeptide, and peptide sequences corresponding to various genes have been previously disclosed and can be found in computerized databases known to those of skill in the art. One such database is the Genbank and GenPept databases of the National Center for Biotechnology Information at the National Institutes of Health website. The coding regions of known genes can be amplified and / or expressed using techniques disclosed herein or that would be known to those of skill in the art. Alternatively, various commercially available preparations of proteins, polypeptides, and peptides are known to those of skill in the art.

[0263] In a further aspect, the antigen includes a nucleic acid (e.g., a polynucleotide) encoding an antigenic peptide or a portion thereof. The polynucleotide can be, for example, DNA, cDNA, PNA, CNA, or RNA (e.g., mRNA), and can be single-stranded and / or double-stranded, or a naturally occurring polynucleotide or a stabilized form, such as a polynucleotide having a phosphorothioate backbone, or a combination thereof, with or without introns. In yet another aspect, an expression vector capable of expressing a polypeptide or a portion thereof is provided. Expression vectors for various cell types are well known in the art and can be selected without undue experimentation. Generally, the DNA is inserted into an expression vector, such as a plasmid, in the appropriate orientation and correct reading frame for expression. If necessary, the DNA can be ligated to appropriate transcriptional and translational regulatory nucleotide sequences recognized by the desired host; such controls are generally available on the expression vector. The vector is then introduced into the host using standard techniques. Guidance can be found, for example, in Sambrook et al. (1989) Molecular Cloning, A Laboratory Manual, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY. [Example]

[0264] Below are examples of specific embodiments for carrying out the present invention. The examples are provided for illustrative purposes only and are not intended to limit the scope of the present disclosure in any way. While attempts have been made to ensure accuracy with respect to numbers used (e.g., amounts, temperatures, etc.), some experimental error and deviation should, of course, be allowed for.

[0265] The practice of the present invention will employ, unless otherwise indicated, conventional methods of protein chemistry, biochemistry, recombinant DNA technology, and pharmacology, which are within the skill of the art. Such techniques are fully explained in the literature, see, for example, T.E. Creighton, Proteins: Structures and Molecular Properties (W.H. Freeman and Company, 1993); A.L. Lehninger, Biochemistry (Worth Publishers, Inc., current addition); Sambrook, et al., Molecular Cloning: A Laboratory Manual (2nd Edition, 1989); Methods In Enzymology (S. Colowick and N. Kaplan eds., Academic Press, Inc.); Reming's Pharmaceutical Sciences, 18th Edition (Easton, Pennsylvania: Mack Publishing Company, 1990); Carey and Sundberg Advanced Organic Chemistry 3 rd Ed. (Plenum Press) Vols A and B (1992).

[0266] In the examples, we outline the provision of a cell-free DNA (cfDNA) assay used to monitor mutation frequency. Furthermore, we present data from treatment monitoring of mutation frequency in cfDNA from patient plasma, processed and analyzed using the provided protocol (see below). Notably, in the case of GRANITE patients, over 200 mutations were monitored, representing all or the majority of high-quality mutation calls associated with each patient's tumor exome. The results demonstrate that the described method provides a robust method for monitoring mutation frequency.

[0267] Example 1 – Cell-free DNA monitoring method Below is a protocol describing the cell-free DNA monitoring assay.

[0268] Plasma sample collection Whole blood was collected from patients at regularly scheduled visits (approximately monthly intervals) consistent with treatment. Whole blood was collected into 10 mL Streck cell-free DNA BCT tubes (Streck; La Vista, NE, USA) and centrifuged at 1600 × g for 10 minutes at ambient temperature to separate the plasma layer, buffy coat, and red blood cells. The plasma layer was removed and centrifuged again at 5000 × g for 10 minutes to remove residual cellular material. The supernatant was collected and stored at −80°C until extraction.

[0269] cfDNA extraction and quantification Upon thawing the isolated plasma at ambient temperature, the plasma was centrifuged at 5,000×g for 5 minutes to remove cryoprecipitate formed during storage. cfDNA was extracted using the Apostle MiniMax cfDNA Isolation Kit (Beckman Coulter; Indianapolis, IN). Extracted cfDNA was quantified using the Qubit 1× High Sensitivity dsDNA Assay on a Qubit Fluorometer 4.0 (Thermo Fisher Scientific). For selected samples, 1 μL was used for sample visualization on an Agilent TapeStation using the HSD1000 kit.

[0270] gDNA isolation Genomic DNA from each sample was extracted using the Qiagen Tissue AllPrep Kit, while 50,000 PMBCs were isolated. For RNAlater samples, genomic DNA was isolated from tissues stored in RNAlater using the Qiagen DNA / RNA Mini AllPrep Kit.

[0271] Library preparation and hybrid capture of double-stranded libraries Libraries were prepared using up to 20 ng of cfDNA using the KAPA Hyper Prep Kit (KAPA Biosystems; Wilmington, MA) according to the manufacturer's instructions. For libraries derived from gDNA, 30 ng of gDNA was first fragmented using the NEBNext Ultra II FS DNA Module (NEB, Ipswich, MA) at 37°C for 25 minutes, followed by 65°C for 30 minutes. After end repair, adapter ligation was performed for 30 minutes using a pool of double-stranded adapters containing 5-base nonrandom unique molecular identifiers (IDT, Coralville, Iowa). All library amplifications were performed using KAPA HiFi HotStart ReadyMix and NEBNext Multiple Oligos for Illumina (96 Unique Dual Index Primer Pairs).

[0272] After double-stranded library preparation, selected regions of interest were hybridized overnight to 750 ng of double-stranded library using custom-designed xGen Lockdown probes and the xGen Hybridization and Wash kit according to the manufacturer's instructions (IDT). The final library was quantified and normalized using the Qubit 1x High Sensitivity dsDNA Assay.

[0273] Sequencing and analysis Normalized samples were pooled in equimolar amounts and sequenced on a NovaSeq using 2 × 151 bp and 8 bp index reads.

[0274] The diagrams in Figures 1 and 2 and Table 1 show the specifications of the steps used to isolate and monitor mutant alleles in individual patient ctDNA.

[0275] Table 1. Assay specifications for ctDNA monitoring TIFF2026502789000002.tif35165

[0276] Tumor-specific DNA variant alleles were identified in patients from biopsied tumor tissue and used to generate baits to isolate tumor-specific DNA from total circulating cell-free DNA (cfDNA) in the patient's blood sample. The isolated ctDNA was subjected to duplex sequencing and analyzed for duplex consensus sequences. Sequencing multiple blood draws during treatment allowed for minimally invasive monitoring of patient response.

[0277] After sequencing and generating FASTQ files, UMIs were extracted and assigned to tags for each read before alignment with BWA-MEM (Durbin et al., Bioinformatics, 2010). The fgbio toolkit (Fulcrum Genomics) was used to group reads by UMI in the aligned bam files and call duplex consensus reads. Prior to data analysis, unaligned bam files from fgbio were aligned using BWA-MEM, and then variant allele frequencies (VAFs) for each somatic variant of interest were obtained using freebayes (Marth et al., arXiv, 2012).

[0278] Cancer vaccine administration An open-label, multicenter, multiple-dose Phase 1 / 2 study was conducted to evaluate the dose, safety and tolerability, immunogenicity, and early clinical activity of a heterologous prime / boost vaccination strategy. Two vaccine programs were evaluated: GRANITE and SLATE. The clinical trial design is described in International Patent Application Publication No. WO / 2019 / 226941, which is incorporated herein by reference in its entirety for all purposes.

[0279] A personalized neoantigen cancer vaccine ("GRANITE") was administered in combination with an immune checkpoint inhibitor to patients with advanced cancer. The GRANITE heterologous prime / boost vaccine regimen included (1) a ChAdV [GRT-C901] used as the prime vaccination and (2) a SAM [GRT-R902] formulated in LNPs used in the boost vaccination following GRT-C901. The ChAdV vector is based on a modified ChAdV68 sequence. The SAM vector is based on an RNA alphavirus backbone. Both GRT-C901 and GRT-R902 expressed the same 20 personalized neoantigens and two universal CD4 T cell epitopes (PADRE and tetanus toxoid). Tumors were used for whole exome and transcriptome sequencing to detect somatic mutations, and blood was used for HLA typing and germline exome variant detection / subtraction to generate personalized neoantigen cassettes using the EDGE algorithm for 10 subjects (patients 1-10, herein referred to as patients G1-G10).

[0280] The common neoantigen cancer vaccine (SLATE) was administered in combination with an immune checkpoint inhibitor to patients with advanced cancer. The SLATE heterologous prime / boost vaccine regimen included (1) ChAdV [GRT-C903] used as the prime vaccination and (2) LNP-formulated SAM [GRT-R904] used in the boost vaccination following GRT-C903. Both GRT-C903 and GRT-R904 express the same 20 common neoantigens derived from a specific list of oncogenic mutations and two universal CD4 T cell epitopes (PADRE and tetanus toxoid). For subject enrollment, tumors were used for whole-exome and transcriptome sequencing to detect somatic mutations, and blood was used for HLA typing. Enrolled SLATE subjects were determined to have the KRAS mutation G12C, predicted to be presented by HLA A02:01 (patients S1, S2, and S3), the KRAS mutation Q61H, predicted to be presented by HLA A01:01 (patients S4 and S7), or the KRAS mutation G12V, predicted to be presented by HLA A03:01 or A11:01 (patient S9: A03:01, patients S11 and S15: A11:01).

[0281] In both dosing studies (i.e., the GRANITE and SLATE vaccine regimens), the vaccine was administered bilaterally (e.g., into each deltoid muscle) via IM injection in combination with immune checkpoint inhibitors, specifically SC ipilimumab and IV nivolumab. The studies proceeded in two sequential phases.

[0282] GRT-C901 and GRT-C903 are replication-deficient E1- and E3-deleted adenoviral vectors based on chimpanzee adenovirus 68. The vectors contained expression cassettes encoding 20 neoantigens and two universal CD4 T cell epitopes (PADRE and tetanus toxoid). GRT-C901 and GRT-C903 were injected at 5 × 1011 The vaccine was formulated into a solution at vp / mL and 1.0 mL was injected IM into each of two bilateral vaccine injection sites in opposing deltoid muscles. The GRT-C901 and GRT-C903 vectors differ only in the neoantigen encoded within the cassette.

[0283] GRT-R902 and GRT-R904 are alphavirus-derived SAM vectors. The GRT-R902 and GRT-R904 vectors encoded viral proteins and 5' and 3' RNA sequences required for RNA amplification, but not structural proteins. The SAM vectors were formulated into LNPs containing four lipids: ionizable amino lipid, phosphatidylcholine, cholesterol, and a PEG-based coating lipid, to encapsulate the SAM and form the LNP. The GRT-R902 vector contained the same neoantigen expression cassette used in GRT-C901 and GRT-R904, respectively. The GRT-R904 vector contained the same neoantigen expression cassette used in GRT-C903. GRT-R902 and GRT-R904 were formulated into a solution at 1 mg / mL and injected IM into each of two bilateral vaccine injection sites in opposing deltoid muscles (the deltoid on either side is preferred, but the gluteal [dorsal or ventral] or rectus femoris muscles may also be used). The boost vaccination site was as close as possible to the prime vaccination site. Injection volume was based on the administered dose. The dose level amounts refer explicitly to the amount of SAM vector, i.e., not other components such as LNP. The LNP:SAM ratio was approximately 24:1. Thus, the LNP doses were 720 μg, 2400 μg, and 7200 μg for each dose level of GRT-R902 / GRT-R904, respectively (see below).

[0284] Ipilimumab is a human monoclonal IgG1 antibody that binds to cytotoxic T-lymphocyte antigen 4 (CTLA-4). Ipilimumab was formulated into a solution at 5 mg / mL and injected SC proximal (within approximately 2 cm) of each of the two vaccination sites. Four 1.5 mL (7.5 mg) injections of 30 mg of antibody were administered proximal to the vaccine-draining LNs of each of the two vaccination sites (i.e., 1.5 mL below the vaccination site and 1.5 mL above the vaccination site in each of the deltoid, ventral gluteal, dorsal gluteal, or rectus femoris muscles [deltoid preferred, but variable depending on clinical site and patient preference]).

[0285] Nivolumab is a human monoclonal IgG4 antibody that blocks the interaction of PD-1 with its ligands, PD-L1 and PD-L2. Nivolumab was formulated into a solution at 10 mg / mL and administered as an IV infusion (480 mg) at the protocol-specified dose through a low-protein-binding in-line filter with a pore size of 0.2 to 1.2 microns. It was not administered as an IV push or bolus injection. Nivolumab infusion was immediately followed by a diluent flush to clear the line. Nivolumab was administered on the same day following each vaccination with or without ipilimumab (i.e., GRT-C901, GRT-R902, GRT-C903, or GRT-R904, respectively). The dose and route of nivolumab were based on the Food and Drug Administration-approved dose and route.

[0286] result Monitoring ctDNA in cfDNA-containing samples was used to track patient response to treatment. Specifically, patients receiving tumor neoantigen-based vaccine therapies (GRANITE and SLATE) were monitored throughout treatment. Sequencing of cancer exome-associated mutations was performed with both high target coverage and high read depth.

[0287] Two separate patients (G1 and G2) receiving GRANITE therapy were monitored for ctDNA response. Details of all ctDNA isolations from each patient are listed in Table 2.

[0288] Table 2. Details of ctDNA isolation from patients receiving GRANITE therapy TIFF2026502789000003.tif154165

[0289] Double-stranded read coverage during the treatment period for patient G1 is shown in Figures 3A and 3B. The mean sequencing read depth (mean double-stranded read target coverage [×]) of targets in the cfDNA samples ranged from 2817× to 5017×, with over 87% of targets (over 330 variants monitored) having double-stranded reads of 2000× or greater and over 68% of targets having double-stranded reads of 4000× or greater (excluding D5D1 and D6D1). Sequencing profiles demonstrated high target coverage at high read depth.

[0290] We monitored the mutant allele frequency in cfDNA of GRANITE patient G1 during treatment. As shown in Figure 3C and Table 3, 117 mutant alleles out of over 330 subject- and tumor-specific variants were monitored in G1's ctDNA. Figures 4A-C also show the mutant allele frequency in ctDNA isolated from G1 throughout the course of disease. Figure 4A shows the mutant allele frequency for 11 of the 20 mutations detected at baseline. Figure 4B shows the mean mutant allele frequency. Figure 4C shows the percent change in mean mutant allele frequency. There was an initial spike in tumor-specific variant allele frequency (VAF) (also referred to as mutant allele frequency (MAF)) after doses 1 and 2, followed by a decrease after dose 3 suggesting a response to treatment, followed by a gradual increase over the first 168 days, correlating with stable disease. Thereafter, there was a significant increase in mutant allele frequency after day 168 (week 24), correlating with disease progression. Therefore, monitoring mutant allele frequencies in cfDNA served as an effective noninvasive surrogate for monitoring disease status, including assessing disease progression and the efficacy of treatment regimens.

[0291] Table 3. VAF values ​​from ctDNA of patient G1 TIFF2026502789000004.tif229141TIFF2026502789000005.tif233165TIFF20265027890 00006.tif231165TIFF2026502789000007.tif234165TIFF2026502789000008.tif231165

[0292] Double-stranded read coverage during the treatment period for patient G2 is shown in Figures 3D and 3E. The mean post-consensus target read coverage in the cfDNA samples ranged from 3877× to 4534×, with over 93% of targets (over 240 variants monitored) having double-stranded reads of 2000× or greater and over 76% of targets having double-stranded reads of 3000× or greater. Sequencing profiles demonstrated high target coverage at high read depth.

[0293] We monitored mutant allele frequency in cfDNA during the treatment period of GRANITE patient G2. As shown in Figure 3F, ctDNA was undetectable above the minimum call threshold during patient G2's treatment regimen, which correlated with an extended disease-free interval (no evidence of disease at any time point during postoperative testing). Thus, monitoring mutant allele frequency in cfDNA served as a valid noninvasive surrogate for disease monitoring, including assessment of disease presence and disease burden.

[0294] Table 4. VAF values ​​from ctDNA of patient G2 TIFF2026502789000009.tif112165

[0295] We monitored the mutant allele frequency in cfDNA of GRANITE patients G3 and G8 during treatment. Figures 5A and 5B show the tracking of multiple variant alleles in each patient's ctDNA. Both patients showed a steady decrease in VAF after an initial spike approximately 1 month after first treatment. This decrease was associated with an overall decrease in tumor volume. Patient G3 demonstrated a maximum 6-fold decrease in VAF (mean VAF 0.69% at week 4 vs. 0.12% at week 20), with all 20 monitored variants detected. Patient G3's cfDNA profile correlated with disease progression at week 8, subsequent stabilization by week 16, and minimal progression (T-cell decline) at week 24. Patient G8 demonstrated a continued decrease in mutant allele frequency, including loss of some variant detection (16 of the 20 monitored variants were detected), correlating with stable disease. Therefore, monitoring mutant allele frequencies in cfDNA served as a valid noninvasive surrogate for monitoring disease status, including assessing disease progression.

[0296] The mutant allele frequency in cfDNA was also monitored during the treatment period of patient S1 with SLATE. Details of all ctDNA isolations are detailed in Table 5.

[0297] Table 5. Details of ctDNA isolation from patient S1 receiving SLATE therapy TIFF2026502789000010.tif39165

[0298] Double-stranded read coverage during treatment for patient S1 is shown in Figures 6A and 6B. The mean read coverage of consensus targets in cfDNA samples ranged from 2728× to 3660×, with over 98% of targets having double-stranded reads of 1000× or greater and over 78% of targets having double-stranded reads of 2000× or greater.

[0299] The mutant allele frequency in cfDNA was monitored during the treatment period. As shown in Figure 6C, a steady increase in ctDNA tumor content was observed, indicating a progressive tumor. The results of the analysis of all ctDNA from patient S1 are shown in Table 4. Therefore, monitoring the mutant allele frequency in cfDNA served as an effective noninvasive surrogate for monitoring disease status, including assessing disease progression.

[0300] The tumor of SLATE patient S2 was determined to harbor a KRAS G12C mutation, and variant-specific tracking of the KRAS G12C mutation was used for monitoring. As shown in Figure 7, an overall decrease in the VAF of KRAS mutations was observed, which correlated with a 20% reduction in tumor volume by week 8. Therefore, monitoring mutant allele frequency in cfDNA served as an effective noninvasive surrogate for monitoring disease status, including assessing disease progression.

[0301] The results demonstrate that mutant allele frequencies in cfDNA can be monitored throughout treatment for a number of tumor- and subject-specific mutations. The results also demonstrate that monitoring mutant allele frequencies in cfDNA served as a valid noninvasive surrogate for monitoring disease status, including assessing disease progression, the presence and burden of disease, and the efficacy of treatment regimens.

[0302] Table 6. Results of ctDNA analysis from patients receiving SLATE therapy TIFF2026502789000011.tif144165

[0303] Example 2 – Cell-free DNA monitoring using a combined panel in the GRANITE and SLATE vaccine programs Circulating tumor DNA (ctDNA) is a novel, minimally invasive diagnostic and prognostic biomarker for patients undergoing immunotherapy. Example 2 describes a combined approach using tumor-informative and tumor-naive ctDNA monitoring assays to assess ctDNA dynamics and tumor evolution over time.

[0304] The method describing the cell-free DNA monitoring assay is outlined in Example 1 and, briefly, is as follows: Patient biopsies were taken to screen for GRANITE vaccine production. For patients with sufficient neoantigens, personalized vaccines were produced. After patients were enrolled in the study, a baseline biopsy was taken. Patients were then administered the vaccine for treatment ("GRANITE" program) (Figure 8). Certain subjects were instead administered an "off-the-shelf" common neoantigen vaccine ("SLATE" program).

[0305] We designed a combined probe panel for target enrichment, including both tumor-informed and tumor-naive probes. Neoantigens were predicted from whole-exome sequencing (WES) of tumor DNA and whole-transcriptome sequencing of tumor RNA. We designed a tumor-informed, patient-specific panel for all coding mutations detected by whole-exome sequencing (WES) of archival tissues (median: 123; range: 67-402), including patients selected for the Personalized Neoantigen Vaccine Program (GRANITE). A tumor-naive panel (also called a "universal" panel) was designed and included in the patient panel to monitor frequently mutated tumor hotspots and genes implicated in immunotherapy resistance. Specifically, the panel monitored genes and mutations commonly considered to be oncogenic (e.g., "driver" mutations thought to promote cancer and generally considered gain-of-function mutations), tumor suppressor genes (e.g., genes generally considered to monitor and / or regulate tumor-associated traits, such as cell division, where mutations may interfere with the regulation of such traits and generally considered loss-of-function mutations), genes in the interferon-gamma signaling pathway (including genes in the JAK / STAT signaling pathway), genes in antigen processing pathways (including monitoring HLA loss of heterozygosity), and mutations commonly associated with cancer but not otherwise annotated (e.g., not yet annotated as oncogenes or tumor suppressor genes). The probes in the panel were designed to monitor the entire exon-coding region of the selected genes or to monitor specific regions or mutations ("hotspots") within the genes.

[0306] Cell-free DNA (cfDNA) samples were collected monthly during treatment (mean 7; range: 1–18). Using double-stranded unique molecular identifiers (UMIs), libraries were prepared from cfDNA, matched normal DNA, and biopsy DNA and captured using a combination of personalized panels, universal panels, or WES (Figure 9). Shotgun libraries from cfDNA, biopsy DNA, or gDNA from whole blood or PBMCs were prepared using double-stranded UMIs. Duplex sequencing required variants to be observed on both strands of a double-stranded molecule, thereby reducing noise. Prior to consensus deduplication, enriched double-stranded libraries were sequenced to an average target depth of >65,000x.

[0307] Patient-specific variants and universal regions were enriched and subjected to de novo variant calling. Patient-specific regions were tumor-specific, while the universal panel was tumor-naive. Multiple patient-specific sets were combined to create supersets containing probes from 6–9 patients. The universal panel captured a set of common targets across all patient samples (see Table 7 for an example). The panel combination was not only patient-specific but also offered the flexibility to observe variants not present in the original tumor.

[0308] An overview of the GRANITE cfDNA monitoring assay is also provided in Table 6.

[0309] Table 6. GRANITE cfDNA Monitoring Assay TIFF2026502789000012.tif58165

[0310] Table 7 provides an overview of the cancer-associated genes and hotspots monitored in the universal tumor-naive panel. The panel also included approximately 290 probes designed to capture positions with common polymorphisms (SNPs) in the human population, which are used for fingerprinting to identify individual subjects (e.g., when multiplexing sequences from multiple subjects).

[0311] Table 7. Exemplary Genes of the Universal Panel TIFF2026502789000013.tif125165

[0312] Figures 10A and 10B show variant coverage of manufacturing-like variants in personalized GRANITE panels. Figure 10A shows the number of potential variants covered by various commercially available NGS panels, including both tumor-naive and tumor-informed panels separately, compared with coverage by the designed GRANITE panel and whole-exome sequencing. Figure 10B shows the percentage of potential WES variants covered by various NGS panels. Neoantigen coverage was determined by variants present in manufacturing biopsies of treated GRANITE patients. In the absence of archival biopsies, SLATE patients are intentionally monitored for only one mutation (i.e., the tumor-informed mutation determined by sequencing that indicated the SLATE patient to receive the SLATE vaccine). With the use of static panels, an average of 5–10 variants are monitored in GRANITE patients. However, with the use of tumor-informed panels, that range increases to 16–50 variants. Regardless of panel type, currently available assays do not overlap with the majority of tumor-specific neoantigens in the GRANITE vaccine.

[0313] Figure 11 shows the blood collection protocol for patients receiving the SLATE and GRANITE vaccines. Whole blood is collected at the time of vaccine administration and centrifuged to collect plasma, serum, and the buffy coat layer. Most cfDNA results from white blood cell turnover. The collection protocol includes collection of buffy coat and / or whole blood for sequencing of matched normal cfDNA from the patient. Buffy coat collection allows for the exclusion of matched normal cfDNA from clonal hematopoiesis via CHIP. High cfDNA concentrations are observed in patients with advanced disease. Patient samples yield a median yield of 15 ng per ml of plasma from whole blood. Using this, hGE was calculated to be 3,000 he per 10 ng. Sensitivity is limited by the number of molecules. Figure 11 shows the cfDNA yield (ng / ml) per ml of plasma collected from GEA, CRC, NSCLC, or other tumor tissues, or healthy donors from patients with GRANITE or SLATE.

[0314] Figure 12 shows that the GRANITE patient assay monitored an average of approximately 140 variants per patient at high sequencing depth for variant calling at duplex consensus coverage of over 1000X. Patient samples were sequenced to achieve an average target depth of approximately 100,000X (paired-end), which decreased to approximately 3900X depth after duplex consensus.

[0315] Table 8 provides an overview of patient samples, including tissue type, number of cfDNA samples, biopsies, number of targeted variants, and range of input cfDNA amounts (ng) and mean VAF for longitudinal samples.

[0316] (Table 8) TIFF2026502789000014.tif223165TIFF2026502789000015.tif174165

[0317] Figures 13A and 13B show that the majority of neoantigens were found in cfDNA and patient biopsies using the GRANITE assay. Following multiple therapies, the majority of neoantigens were found in patient cfDNA or tumor biopsies, indicating that many of the neoantigens were truncal variants suitable for personalized neoantigen vaccines. Figure 13A shows the cassette mutations ( * indicates patients for whom a biopsy was unavailable or whose tumor load was too low for the assay to detect the variant. Significant overlap was found when using the GRANITE assay to compare variants in cfDNA with corresponding biopsies, particularly in high-quality (RNALater or fresh-frozen) biopsies, allowing for calling of less frequent variants (Figure 13B).

[0318] A universal panel based on tumor-naive regions captured de novo variants following GRANITE vaccine treatment. Figure 14A shows the presence of de novo variants and tumor histology (GEA, CRC, or NSCLC) in cfDNA samples from the indicated patients. Importantly, many patients had additional variants present in their cfDNA that were not present in the original biopsy. De novo variants often arose when another patient had the targeted variant. CHIP mutations were identified using matched normal gDNA from whole blood or PMBC and excluded as somatic variants in the tumor (Figure 14B). For example, patient G08 had two NLRC5 mutations (one of which tracked the average VAF of all variants) and two TAP1 mutations that emerged almost a year after treatment (Figure 14C). Figure 14D summarizes additional analysis of variants observed in cfDNA outside of patient-specific variants, demonstrating that 75% of evaluated patients had newly detected variants in cfDNA, including driver variants (KRAS and BRAF) and resistance variants (TAP1). Figure 14E shows that multiple complex KRAS variants were detected in patient G09, which were not detected in either archival or follow-up biopsies. The KRAS Q61H variant followed the same trajectory as archival tumor variants, whereas the three KRAS G12 variants developed with different dynamics at low VAF, demonstrating how multiple KRAS G12 hotspot variants can be captured using cfDNA to capture metastatic disease. Thus, the tumor-naive panel effectively monitored for additional mutations, including those potentially involved in immune evasion.

[0319] Duplex sequencing enabled better biopsy sensitivity. Targeted variants also provided insight into tumor heterogeneity. All patient-specific variants captured in WES of biopsies were also captured using the patient-specific assay (100% concordance), see Figure 15 and Table 9. A select number of de novo variants were observed in both assays, including novel variants not captured by WES. Without unbiased WES, most de novo variants would not be captured. Despite these differences, the GRANITE monitoring assay captured 219 / 353 (62%) of the variant collection observed between the two methods.

[0320] Table 9. Comparison of patient-specific variants captured in patient-specific cfDNA assays and WES TIFF2026502789000016.tif41165

[0321] When monitoring assays were used on patient biopsies, variants in baseline biopsies were less frequent in archival biopsies (Figure 16A). On-treatment biopsy variants were more representative than variants present in archival biopsies. Despite all biopsies being from primary sites, only 12 of 135 targeted variants were shared between the three groups, indicating tumor heterogeneity (Figure 16B). In cfDNA, 115 of 135 variants were observed. Of the unobserved variants, all but one were restricted to the baseline biopsy (Figure 16B).

[0322] Exploratory analysis identified variants from brain metastases in longitudinal cfDNA samples. A subset of variants was found to emerge only near the end of treatment in personalized assays, some of which were only found in the final biopsy (brain met). Figure 17A shows the variant dynamics of cfDNA over time in patient G01. Figure 17B shows targeted low-frequency variants in ctDNA for the indicated variants (SSH3, GRIA4, ZNF541, TMEM217, ZNF697, AHNAK2, SCHIP1, and CNR1). Figure 17C shows targeted variants in WES of ctDNA from patient G01 over time. Figure 17D shows brain met biopsy variants in WES of ctDNA from patient G01 over time. The brain met biopsies contained variants not found in biopsies taken early in treatment. Using WES of cfDNA, we were able to track the dynamics of 32 variants. The dashed lines in Figure 17D indicate variants that were also found in the patient-specific assays.

[0323] Thus, in 24 patients, a median of 92.5% of neoantigens (range: 45–100%) and a median of 84% of all targeted variants (range: 24–99%) were found in cfDNA. Signs of heterogeneity were observed in both cfDNA and biopsies, and duplex sequencing improved detection of targeted variants in biopsies with low tumor content. Combining tumor-informed and tumor-naive panels, de novo variants were found in cfDNA from 19 patients. De novo variants were found in regions targeted by the tumor-naive panel or in locations where other patients harbored targeted variants. For example, evidence of acquired immune escape was observed in a patient with colorectal cancer due to a loss-of-function biallelic mutation in TAP1. Longitudinal cfDNA WES in patients with gastroesophageal adenocarcinoma identified copy number changes, including HLA heterozygous losses, and novel subclonal variants between treatment biopsies and cfDNA.

[0324] Based on the results of Example 2, longitudinal cfDNA monitoring in patients treated with neoantigen vaccines provided early insight into patients' response to treatment. Using a combined tumor-informed and tumor-naive monitoring panel approach, ctDNA dynamics demonstrated by targeting multiple mutations also tracked tumor burden, evolution, and emerging resistance. Specifically, (1) comprehensive tumor-informed ctDNA monitoring improved coverage and maintained sensitivity for monitoring the longitudinal dynamics of tumor burden and vaccine-delivered neoantigens in patients treated with a personalized cancer vaccine regimen (GRANITE); significant concordance was observed between plasma and tumor biopsy samples, demonstrating the ability of the ctDNA monitoring assay to detect and monitor variants at metastatic sites; and (3) the combined use of a rationally designed tumor-naive panel detected and monitored key tumor-specific immune evasion events, providing insight into the mechanism of action of personalized neoantigen immunotherapy.

[0325] Example 3: Cell-free DNA monitoring using a combination panel in SLATE vaccine therapy Methods for sequencing and analyzing circulating tumor (ct) DNA We designed a universal set of capture probes to capture mutations, oncogenic hotspots, and fingerprinting SNPs targeted by the "off-the-shelf" consensus neoantigen vaccine cassette ("SLATE"). Vaccine cassette design and production were performed as previously reported (Palmer et al., "Individualized, heterologous chimpanzee adenovirus and self-amplifying mRNA neoantigen vaccine for advanced metastatic solid tumors: phase 1 trial interim results," Nat Med. Aug 15, 2022, incorporated herein by reference for all purposes). Probes designed to capture entire coding regions (such as TP53, PTEN, ARID1A, and genes involved in antigen presentation (B2M, TAP1 / 2, and HLA-A, B, and C)) were also designed for panel capture. Figure 22 shows a general strategy for monitoring chromosome 6 for loss of heterozygosity of HLA genes.

[0326] Probes were designed and synthesized by Integrated DNA Technologies (IDT). Genomic DNA from whole blood or PMBCs from the same patient was fragmented prior to library preparation using the NEB FS module (NEB, Ipswich, MA). Shotgun libraries of cfDNA (up to 30 ng) and fragmented genomic DNA from the same patient (20–30 ng) were prepared using a KAPA HyperPrep (KAPA Biosystems, Wilmington, MA) kit with a customized pool of double-stranded adapters containing unique molecular identifiers (UMIs) for duplex sequencing (IDT). Shotgun libraries were captured overnight using the IDT xGen Hybridization and Wash kit. Enriched libraries were sequenced on an Illumina NovaSeq to a minimum mean raw depth of 65,000x. Briefly, UMIs were clipped from raw sequencing reads prior to alignment to hg38 using BWA-MEM. Aligned reads were grouped by position and duplex identifier using fgbio. Consensus reads were generated using 3x (3 supporting reads from each strand) duplexes and realigned to hg38. Variant calling was performed using FreeBayes and VarDictJava. Percent change in ctDNA was calculated as the change in VAF of SLATE variants at enrollment from the baseline sample.

[0327] Effective control of tumor growth observed in a subset of patients treated with SLATEv1 The clinical activity of the SLATE vaccine regimen was evaluated by the secondary endpoints of the phase 1 trial: ORR and PFS using RECIST v1.1 criteria, as well as OS. Eight of 19 patients (42%) had a best overall response (BOR) of stable disease (SD), including four patients with NSCLC, two with PDA, one with CRC, and one with pancreatobiliary adenocarcinoma. All other patients had a BOR of PD (Supplementary Table 2). The median PFS for all patients in phase 1 was 1.9 months (95% confidence interval (CI) = [1.7, 3.9 months]). The median OS across all tumor types was 7.9 months (95% CI = [4.7, 10.9 months]) (data not shown). Seventy-nine percent (15 of 19) of patients treated in phase 1 showed OR with progressive disease, and all progressed early during treatment while neoantigen-specific T cell responses were still being generated (11 of 15 within 2 months and 4 of 15 within 4 months after first vaccination). Despite rapid progression in many patients, 2 of 4 patients with NSCLC and SD who had previously progressed on ICB had reductions in target lesions, indicating tumor cell lysis by vaccine-induced T cells.

[0328] While CT scans can provide insight into the antitumor efficacy of cytotoxic therapy, immunotherapy responses that induce strong T cell responses can be misclassified due to T cell infiltration into tumors and subsequent antigen-induced proliferation (which can lead to increased lesion size). Monitoring circulating ctDNA has been shown to correlate with clinical outcomes, such as progression-free survival (PFS) and overall survival (OS)17-19, providing an alternative to CT scans for longitudinal assessment of antitumor efficacy in patients treated with immunotherapy. In fact, recent evidence suggests that a reduction in ctDNA may be a sensitive marker of early immunotherapy response and correlates favorably with improved survival outcomes compared with imaging studies. Therefore, we assessed the levels of ctDNA corresponding to the target neoantigens in the vaccine cassette as an exploratory endpoint using tumor-informative probes, e.g., to monitor the neoepitopes encoded by the SLATE cassette, which qualified subjects for clinical trials. Molecular response (MR), characterized by a ≥30% reduction in neoantigen-specific ctDNA compared with baseline levels, was observed in 23% (4 of 17) of MR-evaluable patients, two of whom also showed SD despite having progressed on previous ICB, suggesting effective immune control of tumor growth induced by vaccine-elicited T cells in a subset of patients (data not shown).

[0329] In addition to the reduction in ctDNA levels of vaccine-targeted neoantigens, a reduction in ctDNA of additional variants not encoded by the vaccine, such as mutations in epitopes predicted to be presented by the target HLA and / or monitored by additional tumor-informative probes for additional mutations commonly associated with cancer (e.g., driver mutations), was observed in all four patients with molecular response (MR) (Figures 18A-E), providing further evidence for effective tumor tropism of T cells induced by vaccines specific for one of the tumor neoantigens. Figures 18A-E show ctDNA monitoring of tumor variants in SLATE patients. The ctDNA levels over time after each patient's prime vaccination are shown for the vaccine-encoded tumor mutation (solid line) and additional detected somatic mutations (dashed line). ctDNA levels are reported as variant allele frequency (VAF) as a percentage of total reads. Figure 18A shows the ctDNA VAF% for patient S2. Figure 18B shows the ctDNA VAF% of patient S5. Figure 18C shows the ctDNA VAF% of patient S10. Figure 18D shows the ctDNA VAF% of patient S13. Figure 18E shows a representative patient without MR, demonstrating loss of the start codon for B2M. SD = stable disease, PD = progressive disease, best overall response is indicated.

[0330] Although the immune system can control tumor growth, tumors can escape immune control from targeted immune responses. One mechanism by which tumors have been shown to evade immune responses is through disruption of antigen presentation pathways. A universal tumor-naive panel containing probes for monitoring antigen presentation genes was included in the ctDNA panel to assess whether patients treated with SLATEv1 exhibit impaired antigen presentation at progression. Two patients (one molecular responder and one molecular non-responder) showed progressive loss of heterozygosity (LOH) of relevant neoantigen-matched HLA alleles during treatment (Figure 19, A and B), suggesting that vaccine-induced T cells exerted immune pressure against the tumor, triggering this immune escape mechanism. Figure 19, A, shows the fold change in HLA allele read percentage from a molecular responder (MR). Figure 19, B, shows the fold change in HLA allele read percentage from a molecular non-responder (non-MR). One of the molecular responders, S13, showed some evidence of HLA LOH in baseline samples (p=0.015), but this was not evident in subsequent post-treatment samples corresponding to the decline in ctDNA. Evidence of HLA LOH was observed at all subsequent time points (p<0.01), which was associated with PD, suggesting the proliferation of tumor cells resistant to T cell control. Another patient, S4, had a variant resulting in the loss of the B2M start codon, which has been shown to reduce antigen presentation. This variant was detected at low frequency at baseline and increased in frequency during treatment with the vaccine regimen (Figure 18E). HLA LOH was also observed in HLA-A (neoantigen-matched HLA allele) and HLA-C in samples collected 9 weeks after the first vaccination (final collection). The B2M mutation and complete LOH may explain the lack of clinical activity observed in this patient. Taken together, these data demonstrate preliminary indications of clinical activity in a subset of patients with advanced / metastatic solid tumors treated with the SLATEv1 neoantigen vaccine.

[0331] Example 4: Selection of Panel Probe Targets FIG. 20 shows a diagram outlining considerations for including subject-specific tumor-informative probes.

[0332] Additionally, the Version 1 Universal Panel targets were further updated to target specific histologies (e.g., CRC and NSCLC), resulting in Universal Panel Version 2. The Universal Panel was updated by reviewing the literature, TCGA, COSMIC, and MyCancerGenome datasets for hotspots, which included monitoring genes and mutations commonly considered to be oncogenic (e.g., "driver" mutations that are thought to promote cancer and are generally considered gain-of-function mutations), tumor suppressor genes (e.g., genes generally considered to monitor and / or regulate tumor-associated traits, such as cell division, where mutations may interfere with the regulation of such traits and are generally considered loss-of-function mutations), genes in the interferon-γ signaling pathway, genes in the JAK / STAT signaling pathway, genes in the antigen processing pathway (including monitoring HLA loss of heterozygosity), and mutations commonly associated with cancer but not otherwise annotated (e.g., not yet annotated as oncogenes or tumor suppressor genes). Initial results were prioritized based on prevalence, proximity, and oncogene / suppressor status, resulting in 133 priority regions. Examples of targets in Universal Panel Version 1 are shown in Table 10.

[0333] (Table 10) Universal Panel Version 1 TIFF2026502789000017.tif128165TIFF2026502789000018.tif97165

[0334] The updated Universal Panel Version 2 is shown in Table 11.

[0335] Table 11. Universal Panel Version 2 (Focused on CRC / NSCLC) TIFF2026502789000019.tif211165TIFF2026502789000020.tif179165

[0336] Figure 21A shows the percentage of CRC and NSCLC samples covered by the universal panel's target probes. Up to 86.4% of all samples with one or more mutations are covered by the universal panel. Up to 49.8% of all samples with two or more mutations are covered by the universal panel. Data are based on an analysis of 10,586 samples from cbioportal.org. Figure 21B shows a retrospective analysis of variants in patients from a previous study (GO-004) identified by universal panel version 1 (v1) or version 2 (v2). A summary of the data is shown in Table 12.

[0337] Table 12: Variants covered by universal panels v1 and v2 TIFF2026502789000021.tif27165

[0338] While the present invention has been particularly shown and described with reference to preferred and various alternative embodiments, it will be understood by those skilled in the relevant art that various changes in form and detail may be made therein without departing from the spirit and scope of the invention.

[0339] All references, issued patents, and patent applications cited within the body of this specification are hereby incorporated by reference in their entirety for all purposes.

Claims

1. A panel of polynucleotide probes for enriching cfDNA, comprising: (A) one or more tumor-informed polynucleotide probes; (B) one or more tumor-naive polynucleotide probes; The panel.

2. 2. The panel of claim 1, wherein the one or more tumor-informative polynucleotide probes are configured to capture a target sequence comprising an epitope sequence encoded by a cancer vaccine administered to a subject, the subject being determined to have a tumor expressing the epitope sequence.

3. The panel of claim 2 , wherein the epitope sequence comprises a KRAS mutation.

4. 4. The panel of claim 3, wherein the KRAS mutation is selected from the group consisting of a KRAS_G12C mutation, a KRAS_G12D mutation, a KRAS_G12V mutation, and a KRAS_Q61H mutation.

5. 3. The panel of claim 2, wherein the epitope sequence comprises a mutation selected from the group consisting of KRAS_G13D, KRAS_Q61K, TP53_R249M, CTNNB1_S45P, CTNNB1_S45F, ERBB2_Y772_A775dup, KRAS_G12D, KRAS_Q61R, CTNNB1_T41A, TP53_K132N, KRAS_G12A, KRAS_Q61L, TP53_R213L, BRAF_G466V, KRAS_G12V, KRAS_Q61H, CTNNB1_S37F, TP53_S127Y, TP53_K132E, and KRAS_G12C.

6. The panel of claim 2 , wherein the epitope sequence comprises an EGFR mutation.

7. The panel of claim 6 , wherein the EGFR mutation comprises an EGFR_L858R mutation.

8. 3. The panel of claim 2, wherein the epitope sequence comprises one or more subject-specific epitopes, and the tumor of the subject has been sequenced to determine the subject-specific epitopes encoded by the cancer vaccine.

9. 9. The panel of claim 8, wherein the one or more subject-specific epitopes comprise at least two subject-specific epitopes, at least 10 subject-specific epitopes, at least 20 subject-specific epitopes, or between 2 and 20 subject-specific epitopes.

10. 9. The panel of claim 8, wherein the one or more subject-specific epitopes comprise between 2 and 20 subject-specific epitopes.

11. 11. The panel of any one of claims 2-10, wherein the panel further comprises an additional tumor-informative polynucleotide probe that captures an additional target sequence, wherein the tumor has been determined to express the additional target sequence, and wherein the additional target sequence is not encoded by the cancer vaccine.

12. 12. The panel of claim 11, wherein the additional target sequences comprise at least 10 target sequences, at least 20 target sequences, at least 30 target sequences, at least 100 target sequences, between 10 and 500 target sequences, between 30 and 500 target sequences, between 100 and 500 target sequences, between 10 and 100 target sequences, between 30 and 100 target sequences, or between 100 and 100 target sequences.

13. 13. The panel of claim 11 or 12, wherein the additional target sequence is predicted to be presented by at least one HLA of the subject.

14. 14. The panel of any one of claims 1 to 13, wherein the one or more tumor-naive polynucleotide probes are configured to capture a target sequence comprising a sequence of interest selected from the group consisting of cancer-associated genes, oncogenes, tumor suppressor genes, genes in the interferon-gamma signaling pathway, genes in the antigen processing pathway, and combinations thereof.

15. The panel of any one of claims 1 to 13, wherein the one or more tumor-naive polynucleotide probes are configured to capture a target sequence comprising a sequence of interest selected from each of cancer-associated genes, oncogenes, tumor suppressor genes, genes in the interferon-gamma signaling pathway, and genes in the antigen processing pathway.

16. 16. The panel of claim 14 or 15, wherein the cancer-associated genes are selected from the group consisting of ABCA12, ACVR2A, AKAP9, BMPR2, COL12A1, CSMD3, DNAH5, DOCK3, FAT2, FAT3, FAT4, FGF10, FGF6, FLG, MAGI1, MDN1, MMAB, NBEA, OBSCN, PCBP1, PCLO, PLEKHA6, PROC, RAD54L, RELN, RPL22, RYR2, TCERG1, WRN, and ZDBF2.

17. 16. The panel of claim 14 or 15, wherein the cancer-associated genes include each of ABCA12, ACVR2A, AKAP9, BMPR2, COL12A1, CSMD3, DNAH5, DOCK3, FAT2, FAT3, FAT4, FGF10, FGF6, FLG, MAGI1, MDN1, MMAB, NBEA, OBSCN, PCBP1, PCLO, PLEKHA6, PROC, RAD54L, RELN, RPL22, RYR2, TCERG1, WRN, and ZDBF2.

18. The oncogenes are selected from the group consisting of ABL1, AKT2, ALK, AR, BCL6, BCL9L, BRAF, BTK, CARD11, CCND1, CCND3, CTNNB1, DDR2, EGFR, ERBB2, ERBB3, FGFR1, FGFR3, FHOD3, FLT1, FLT3, GNAS, HRAS, KDR, KIT, KRAS, MAP2K1, MAP2K2, MECO 18. The panel of any one of claims 14 to 17, wherein the target gene is selected from the group consisting of M, MED12, MET, MTOR, NOTCH1, NOTCH2, NOTCH3, NRAS, NRG1, NTRK1, NTRK3, PDGFRA, PDGFRB, PIK3CA, PIK3CG, PSMB2, RET, ROS1, SF3B1, SMO, SYNE1, and ZBTB20.

19. The oncogenes are selected from the group consisting of ABL1, AKT2, ALK, AR, BCL6, BCL9L, BRAF, BTK, CARD11, CCND1, CCND3, CTNNB1, DDR2, EGFR, ERBB2, ERBB3, FGFR1, FGFR3, FHOD3, FLT1, FLT3, GNAS, HRAS, KDR, KIT, KRAS, MAP2K1, MAP2K2, M 18. The panel of any one of claims 14 to 17, comprising each of ECOM, MED12, MET, MTOR, NOTCH1, NOTCH2, NOTCH3, NRAS, NRG1, NTRK1, NTRK3, PDGFRA, PDGFRB, PIK3CA, PIK3CG, PSMB2, RET, ROS1, SF3B1, SMO, SYNE1, and ZBTB20.

20. The tumor suppressor gene is selected from the group consisting of TP53, PTEN, ARID1A, APC, AMER1, ASXL1, ATM, ATR, ATRX, AXIN2, BARD1, BRCA1, BRCA2, CASP8, CFH, CREBBP, DNMT3A, EP300, ERCC2, ERCC5, EXT1, FANCA, FANCD2, FANCI, FANCM, FAT1, FBXW7, HNF1A, MAX, MLH1, MSH3, 20. The panel of any one of claims 14 to 19, wherein the target gene is selected from the group consisting of MSH6, NF1, PIK3R1, PTCH1, PTPRT, RECQL4, RNF43, ROBO1, SLX4, SMAD2, SMAD3, SMAD4, SOX9, TCF7L2, TERT promoter, TET2, TGFBR2, TP53BP1, TSC1, TSC2, WNT16, XPC, ZFP36L2, and ZNRF3.

21. The tumor suppressor gene is selected from the group consisting of TP53, PTEN, ARID1A, APC, AMER1, ASXL1, ATM, ATR, ATRX, AXIN2, BARD1, BRCA1, BRCA2, CASP8, CFH, CREBBP, DNMT3A, EP300, ERCC2, ERCC5, EXT1, FANCA, FANCD2, FANCI, FANCM, FAT1, FBXW7, HNF1A, MAX, MLH1, MS 20. The panel of any one of claims 14 to 19, comprising each of H3, MSH6, NF1, PIK3R1, PTCH1, PTPRT, RECQL4, RNF43, ROBO1, SLX4, SMAD2, SMAD3, SMAD4, SOX9, TCF7L2, TERT promoter, TET2, TGFBR2, TP53BP1, TSC1, TSC2, WNT16, XPC, ZFP36L2, and ZNRF3.

22. The panel of any one of claims 14 to 21, wherein the genes of the interferon-gamma signaling pathway are selected from the group consisting of IFNGR1, INFGR2, JAK1, JAK2, and STAT1.

23. The panel of any one of claims 14 to 21, wherein the genes of the interferon-gamma signaling pathway include each of IFNGR1, INFGR2, JAK1, JAK2, and STAT1.

24. 24. The panel of any one of claims 14 to 23, wherein the antigen processing pathway genes are selected from the group consisting of B2M, HLA-A, HLA-B, HLA-C, HLA-E, TAP1, TAP2, NLRC5, CALR, CANX, PSMB2, and TAPBP.

25. 24. The panel of any one of claims 14 to 23, wherein the antigen processing pathway genes include each of B2M, HLA-A, HLA-B, HLA-C, HLA-E, TAP1, TAP2, NLRC5, CALR, CANX, PSMB2, and TAPBP.

26. The one or more tumor-naive polynucleotide probes are selected from the group consisting of ABCA12, ACVR2A, AKAP9, BMPR2, COL12A1, CSMD3, DNAH5, DOCK3, FAT2, FAT3, FAT4, FGF10, FGF6, FLG, MAGI1, MDN1, MMAB, NBEA, OBSCN, PCBP1, PCLO, PLEKHA6, PROC, RAD54L, RELN, RPL22, RYR2, TCERG1, WRN, ZDBF2, ABL1, AKT2, ALK, AR, BCL6, BCL9L, BRAF, BTK, CAR D11, CCND1, CCND3, CTNNB1, DDR2, EGFR, ERBB2, ERBB3, FGFR1, FGFR3, FHO D3, FLT1, FLT3, GNAS, HRAS, KDR, KIT, KRAS, MAP2K1, MAP2K2, MECOM, MED1 2, MET, MTOR, NOTCH1, NOTCH2, NOTCH3, NRAS, NRG1, NTRK1, NTRK3, PDGFRA , PDGFRB, PIK3CA, PIK3CG, RET, ROS1, SF3B1, SMO, SYNE1, ZBTB20, TP53, PT EN, ARID1A, APC, AMER1, ASXL1, ATM, ATR, ATRX, AXIN2, BARD1, BRCA1, BRC A2, CASP8, CFH, CREBBP, DNMT3A, EP300, ERCC2, ERCC5, EXT1, FANCA, FANC D2, FANCI, FANCM, FAT1, FBXW7, HNF1A, MAX, MLH1, MSH3, MSH6, NF1, PIK3R 1, PTCH1, PTPRT, RECQL4, RNF43, ROBO1, SLX4, SMAD2, SMAD3, SMAD4, SOX9, The panel of any one of claims 1 to 13, which is configured to capture a target sequence comprising a sequence of interest selected from the group consisting of TCF7L2, TERT promoter, TET2, TGFBR2, TP53BP1, TSC1, TSC2, WNT16, XPC, ZFP36L2, ZNRF3, IFNGR1, INFGR2, JAK1, JAK2, STAT1, B2M, HLA-A, HLA-B, HLA-C, HLA-E, TAP1, TAP2, NLRC5, CALR, CANX, PSMB2, TAPBP, and combinations thereof.

27. The one or more tumor-naive polynucleotide probes may be selected from the group consisting of ABCA12, ACVR2A, AKAP9, BMPR2, COL12A1, CSMD3, DNAH5, DOCK3, FAT2, FAT3, FAT4, FGF10, FGF6, FLG, MAGI1, MDN1, MMAB, NBEA, OBSCN, PCBP1, PCLO, PLEKHA6, PROC, RAD54L, RELN, RPL22, RYR2, TCERG1, WRN, ZDBF2, ABL1, AKT2, ALK, AR, BCL6, BCL9L, BRAF, BTK, CA RD11, CCND1, CCND3, CTNNB1, DDR2, EGFR, ERBB2, ERBB3, FGFR1, FGFR3, F HOD3, FLT1, FLT3, GNAS, HRAS, KDR, KIT, KRAS, MAP2K1, MAP2K2, MECOM, ME D12, MET, MTOR, NOTCH1, NOTCH2, NOTCH3, NRAS, NRG1, NTRK1, NTRK3, PDGF RA, PDGFRB, PIK3CA, PIK3CG, PSMB2, RET, ROS1, SF3B1, SMO, SYNE1, ZBTB2 0, TP53, PTEN, ARID1A, APC, AMER1, ASXL1, ATM, ATR, ATRX, AXIN2, BARD1 , BRCA1, BRCA2, CASP8, CFH, CREBBP, DNMT3A, EP300, ERCC2, ERCC5, EXT1, FANCA, FANCD2, FANCI, FANCM, FAT1, FBXW7, HNF1A, MAX, MLH1, MSH3, MSH6 , NF1, PIK3R1, PTCH1, PTPRT, RECQL4, RNF43, ROBO1, SLX4, SMAD2, SMAD3, The panel of any one of claims 1 to 13, which is configured to capture a target sequence comprising a sequence of interest selected from each of SMAD4, SOX9, TCF7L2, TERT promoter, TET2, TGFBR2, TP53BP1, TSC1, TSC2, WNT16, XPC, ZFP36L2, ZNRF3, IFNGR1, INFGR2, JAK1, JAK2, STAT1, B2M, HLA-A, HLA-B, HLA-C, HLA-E, TAP1, TAP2, NLRC5, CALR, CANX, PSMB2, and TAPBP.

28. The one or more tumor-naive polynucleotide probes may be selected from the group consisting of ABL1, AKT2, ALK, APC, AR, ATR, ATRX, BARD1, BCL6, BMPR1A, BRAF, BRCA1, BRCA2, BTK, CARD11, CCND1, CCND3, CDK12, CFH, CREBBP, CTNNB1, DDR2, DNMT3A, EGFR, EP300, ERBB2, ERBB3, E RCC2, ERCC5, EXT1, FANCA, FANCD2, FANCI, FANCM, FBXW7, FGF10, FGF6, FGFR1, FGFR3, FLI1, FLT1, FLT3, GN AS, HNF1A, HRAS, KDR, KIT, KRAS, MAGI1, MAP2K1, MAP2K2, MAX, MED12, MET, MLH1, MMAB, MSH3, MSH6, MTOR, NF 1, NFE2L2, NOTCH1, NOTCH2, NOTCH3, NRAS, NRG1, NTRK1, NTRK3, PDGFRA, PDGFRB, PIK3CA, PIK3CG, PIK3R1, PMS2, PPARG, PROC, PTCH1, RAD54L, RAF1, RECQL4, RET, ROS1, SF3B1, SF3B2, SLX4, SMO, TERT promoter, TET2, T The panel according to any one of claims 1 to 13, which is configured to capture a target sequence comprising a sequence of interest selected from each of P53BP1, TSC1, TSC2, WRN, XPA, XPC, ZNF395, B2M, HLA-A, HLA-B, HLA-C, TAP1, TAP2, NLRC5, IFNGR1, INFGR2, JAK1, JAK2, TP53, PTEN, and ARID1A.

29. 29. The panel of any one of claims 1-28, wherein the one or more tumor-naive polynucleotide probes comprise two or more probes configured to capture the entire coding exon sequence of a given gene.

30. The panel of any one of claims 1 to 29, wherein the one or more tumor-naive polynucleotide probes comprise two or more probes configured to capture genomic regions of interest associated with cancer.

31. The panel of any one of claims 1 to 30, wherein the tumor-informative polynucleotide probes and / or the tumor-naive polynucleotide probes comprise probes that comprise overlapping sequences.

32. 32. The panel of any one of claims 1 to 31, comprising at least 20 probes, at least 30 probes, at least 40 probes, at least 50 probes, at least 60 probes, at least 70 probes, at least 80 probes, at least 90 probes, at least 100 probes, at least 200 probes, at least 300 probes, at least 400 probes, or at least 500 probes.

33. 33. The panel of any one of claims 1 to 32, configured to cover at least 100 kb, at least 300 kb, at least 300 kb, at least 400 kb, 100-400 kb, 200-400 kb, 300-400 kb, 100-500 kb, 200-500 kb, 300-500 kb, or 340-400 kb of the subject's genome.

34. 34. The panel of any one of claims 1-33, wherein the one or more tumor-naive polynucleotide probes comprise polynucleotide probes configured to capture sequences associated with a given cancer that the subject is known to have or suspected to have, and optionally the cancer is CRC or NSCLC.

35. 35. The panel of any one of claims 1 to 34, wherein the panel further comprises additional polynucleotide probes configured to capture sequences comprising polymorphisms in a human population, wherein the sequences comprising the polymorphisms, in combination, are capable of individually distinguishing the subject.

36. 1. A method for concentrating cfDNA, comprising: (a) providing a sample comprising cfDNA; (b) providing a panel of polynucleotide probes comprising any one of the panels of any one of claims 1 to 35; (c) contacting a sample containing cfDNA with the panel of polynucleotide probes under conditions sufficient for cfDNA containing target sequences of interest to hybridize to each respective polynucleotide probe; (d) capturing the hybridized cfDNA and polynucleotide probe pairs to enrich the cfDNA; The method comprising:

37. 1. A method for monitoring cancer status in a subject who has, has had, or is suspected of having cancer, comprising: obtaining or having obtained sequencing data of cell-free DNA (cfDNA) from a sample from the subject, wherein the sequencing data comprises a target coverage of at least 50% of all polynucleotide regions of interest corresponding to mutations present in the exome of the cancer, wherein the sequenced polynucleotide regions of interest comprise a read depth of at least 1000X, optionally wherein the polynucleotide regions of interest comprise at least 50 mutations, and optionally wherein the average read depth is an average double-stranded read depth, and optionally wherein obtaining the sequencing data comprises or has been obtaining the sample from the subject, isolating or having isolated the cfDNA, enriching or having enriched the cfDNA, and / or sequencing or having sequenced the cfDNA; b. determining or having determined the frequency of said mutations present in said exome to assess said state of said cancer, optionally wherein said assessing state comprises assessing the presence and / or amount of cancer; Including, the cfDNA is enriched prior to sequencing using (1) a panel of subject-specific polynucleotide probes, (2) a panel of tumor-naive polynucleotide probes, and / or (3) a combined panel of a panel of tumor-informative polynucleotide probes and a panel of tumor-naive polynucleotide probes, wherein the polynucleotide probes are configured to capture the polynucleotide region of interest; The method.

38. 1. A method for monitoring cancer status in a subject who has, has had, or is suspected of having cancer, comprising: obtaining or having obtained sequencing data of cell-free DNA (cfDNA) from a sample from the subject, wherein the sequencing data comprises a target coverage of at least 95% of all polynucleotide regions of interest corresponding to mutations present in the exome of the cancer, the polynucleotide regions of interest comprise at least 50 mutations, and the sequenced polynucleotide regions of interest comprise a double-stranded read depth of at least 1000X, and optionally, obtaining the sequencing data comprises or has obtained the sample from the subject, isolating or has isolated the cfDNA, concentrating or has concentrated the cfDNA, and / or sequencing or has sequenced the cfDNA; b. determining or having determined the frequency of said at least 50 mutations present in said exome to assess said state of said cancer, optionally wherein said assessing state comprises assessing the presence and / or amount of cancer; Including, the cfDNA is enriched prior to sequencing using (1) a panel of tumor-informative polynucleotide probes, (2) a panel of tumor-naive polynucleotide probes, and / or (3) a combined panel of a panel of tumor-informative polynucleotide probes and a panel of tumor-naive polynucleotide probes, wherein the polynucleotide probes are configured to capture the polynucleotide region of interest; The method.

39. 1. A method for assessing the effectiveness of a treatment in a subject who has, has had, or is suspected of having cancer, comprising: obtaining or having obtained sequencing data of cell-free DNA (cfDNA) from a pre-treatment sample from the subject, wherein the sequencing data comprises a target coverage of at least 50% of all polynucleotide regions of interest corresponding to mutations present in the exome of the cancer, wherein the sequenced polynucleotide regions of interest comprise a read depth of at least 1000X, optionally wherein the polynucleotide regions of interest comprise at least 50 mutations, and optionally wherein the average read coverage is average double-stranded read coverage, and optionally wherein obtaining the sequencing data comprises or has comprised obtaining a pre-treatment sample from the subject, isolating or having isolated pre-treatment cfDNA, enriching or having enriched the pre-treatment cfDNA, and / or sequencing or having sequenced the pre-treatment cfDNA; b. Obtaining or having obtained sequencing data of cell-free DNA (cfDNA) from a post-treatment sample from the subject, optionally wherein the treatment includes a cancer vaccine comprising a neoantigen or an expression system encoding the same, the sequencing data comprises a target coverage of at least 50% of all polynucleotide regions of interest corresponding to mutations present in the exome of the cancer, the sequenced polynucleotide regions of interest comprise a read depth of at least 1000X, optionally wherein the polynucleotide regions of interest comprise at least 50 mutations, optionally wherein the average read coverage is average double-stranded read coverage, and optionally wherein obtaining the sequencing data comprises or has been obtained the post-treatment sample from the subject, isolating or having been isolated post-treatment cfDNA, enriching or having been enriched post-treatment cfDNA, and / or sequencing or having been sequenced post-treatment cfDNA; c) determining or having determined the frequency at which the mutation is present in the exome of the pre-treatment cfDNA compared to the post-treatment cfDNA to assess the efficacy of the treatment, wherein optionally, an increase in the frequency of the mutation in the post-treatment cfDNA compared to the pre-treatment cfDNA indicates an increased likelihood that the subject's tumor burden is increasing, and optionally, a decrease or maintenance of the frequency of the mutation in the post-treatment cfDNA compared to the pre-treatment cfDNA indicates an increased likelihood that the subject's tumor burden is decreasing or stable; Including, the cfDNA is enriched prior to sequencing using (1) a panel of tumor-informative polynucleotide probes, (2) a panel of tumor-naive polynucleotide probes, and / or (3) a combined panel of a panel of tumor-informative polynucleotide probes and a panel of tumor-naive polynucleotide probes, wherein the polynucleotide probes are configured to capture the polynucleotide region of interest; The method.

40. 1. A method for assessing the effectiveness of a treatment in a subject who has, has had, or is suspected of having cancer, comprising: a) obtaining or having obtained sequencing data of tumor-derived DNA from cancer-affected tissue from the subject, optionally wherein obtaining the sequencing data comprises harvesting or having harvested the cancer-affected tissue, isolating or having isolated the tumor-derived DNA, and sequencing or having sequenced the tumor-derived DNA; b. determining or having determined from the tumor-derived DNA sequencing data one or more tumor-associated mutations relative to the subject's wild-type germline nucleic acid sequence, optionally wherein one or more of the one or more tumor-associated mutations is associated with a neoantigen that includes at least one change that causes a peptide sequence encoded by the tumor-derived DNA to differ from a corresponding peptide sequence encoded by the subject's wild-type germline nucleic acid sequence; c. designing and / or selecting, or having selected, (1) a panel of tumor-informative polynucleotide probes, (2) a panel of tumor-naive polynucleotide probes, and / or (3) a combined panel of a panel of tumor-informative polynucleotide probes and a panel of tumor-naive polynucleotide probes, wherein the polynucleotide probes are configured to capture at least the tumor-associated mutations, and optionally, the polynucleotide region of interest comprises at least 50 tumor-associated mutations; d. Obtaining or having obtained sequencing data of cell-free DNA (cfDNA) from a pre-treatment sample from the subject, wherein the pre-treatment cfDNA is enriched using the polynucleotide probes prior to sequencing, the sequencing data comprising a target coverage of at least 50% of all polynucleotide regions of interest corresponding to the tumor-associated mutations, the sequenced polynucleotide regions of interest comprising a read depth of at least 1000X, optionally with an average read coverage of average double-stranded read coverage, and optionally, obtaining the sequencing data comprising: collecting or having collected a pre-treatment sample from the subject; isolating or having isolated the pre-treatment cfDNA; enriching or having enriched the pre-treatment cfDNA; and / or sequencing or having sequenced the pre-treatment cfDNA; e. Obtaining or having obtained sequencing data of cell-free DNA (cfDNA) from a post-treatment sample from the subject, optionally wherein the treatment includes a cancer vaccine comprising the neoantigen or an expression system encoding it, and the post-treatment cfDNA is enriched using the polynucleotide probes prior to sequencing, and the sequencing data comprises a target coverage of at least 50% of all polynucleotide regions of interest corresponding to the tumor-associated mutations, and the sequenced polynucleotide regions of interest comprise a read depth of at least 1000X, and optionally the average read coverage is average double-stranded read coverage, and optionally obtaining the sequencing data comprises collecting or having collected the post-treatment sample from the subject, isolating or having isolated the post-treatment cfDNA, enriching or having enriched the post-treatment cfDNA, and / or sequencing or having sequenced the post-treatment cfDNA; f. determining or having determined the frequency of the tumor-associated mutations in the pre-treatment cfDNA compared to the post-treatment cfDNA to assess the efficacy of the treatment, optionally wherein at least the one or more tumor-associated mutations associated with the neoantigen are determined, and optionally wherein an increase in the frequency of the mutations in the post-treatment cfDNA compared to the pre-treatment cfDNA indicates an increased likelihood that the subject's tumor burden is increasing, and optionally wherein a decrease or maintenance of the frequency of the mutations in the post-treatment cfDNA compared to the pre-treatment cfDNA indicates an increased likelihood that the subject's tumor burden is decreasing or stable; Including, The method.

41. A method according to any one of the preceding method claims, comprising designing and / or selecting, or having designed and / or selected, a combined panel of a panel of tumor-informative polynucleotide probes and a panel of tumor-naive polynucleotide probes.

42. 42. The method of claim 41, wherein the combination panel designed and / or selected comprises any one of the panels of any one of claims 1 to 35.

43. 1. A method for concentrating cfDNA, comprising: (a) providing a sample comprising cfDNA; (b) providing a panel of polynucleotide probes, said panel comprising: (i) one or more tumor-informative polynucleotide probes; and (ii) one or more tumor-naive polynucleotide probes providing, (c) contacting a sample containing cfDNA with the panel of polynucleotide probes under conditions sufficient for cfDNA containing target sequences of interest to hybridize to each respective polynucleotide probe; (d) capturing the hybridized cfDNA and polynucleotide probe pairs to enrich the cfDNA; The method comprising:

44. The method of claim 43, wherein the panel comprises any one of the panels of any one of claims 1 to 35.

45. a. collecting or having collected said sample from said subject; b. isolating or having isolated the cfDNA; c. concentrating or allowing the cfDNA to be concentrated; or d. Sequencing or having sequenced the cfDNA 10. A method according to any one of the preceding claims, comprising one or more of:

46. a. collecting or having collected said sample from said subject; b. isolating or keeping the cfDNA isolated; c. concentrating or allowing the cfDNA to be concentrated; and d. Sequencing or having sequenced the cfDNA 10. A method according to any one of the preceding claims, comprising each of:

47. 10. The method of any one of the preceding claims, wherein the average read depth comprises an average read coverage of at least 1500X, at least 2000X, at least 2500X, 3000X, at least 3500X, at least 4000X, at least 4500X, or at least 5000X.

48. 10. The method of any one of the preceding claims, wherein the average read depth comprises an average read coverage in the range of 1000X to 5000X.

49. 10. The method of any one of the preceding claims, wherein the average read depth comprises an average read coverage in the range of 1000X to 4000X, 1000X to 3000X, 1000X to 2000X, 2000X to 5000X, 2000X to 4000X, 2000X to 3000X, 3000X to 5000X, 3000X to 4000X, or 4000X to 5000X.

50. 10. The method of any one of the preceding claims, wherein the average read depth comprises an average double-stranded read depth.

51. 10. The method of any one of the preceding claims, wherein each of the polynucleotide regions of interest corresponding to the mutations present in the exome comprises a read depth of at least 1000X.

52. 10. The method of any one of the preceding claims, wherein each of the polynucleotide regions of interest corresponding to the mutations present in the exome comprises a read depth of at least 1000X, at least 1500X, at least 2000X, at least 2500X, 3000X, at least 3500X, at least 4000X, at least 4500X, or at least 5000X.

53. 10. The method of any one of the preceding claims, wherein the target coverage comprises at least 60%, at least 70%, at least 80%, or at least 90% of the polynucleotide regions of interest that correspond to the mutations present in the exome of the cancer.

54. 10. The method of any one of the preceding claims, wherein the target coverage comprises at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, at least 99.5%, at least 99.9%, or 100% of the polynucleotide regions of interest that correspond to the mutations present in the exome of the cancer.

55. 10. The method of any one of the preceding claims, wherein the target coverage comprises at least 95% of the polynucleotide regions of interest that correspond to the mutations present in the exome of the cancer.

56. 10. The method of any one of the preceding claims, wherein the polynucleotide region of interest comprises at least 50, at least 60, at least 70, at least 80, or at least 90 mutations.

57. 10. The method of any one of the preceding claims, wherein the polynucleotide region of interest comprises at least 50 mutations.

58. 10. The method of any one of the preceding claims, wherein the polynucleotide region of interest comprises at least 100, at least 150, at least 200, at least 250, at least 300, at least 400, at least 500, at least 600, at least 700, at least 800, at least 900, or at least 1000 mutations.

59. a) obtaining or having obtained sequencing data of tumor-derived DNA from cancer-affected tissue from the subject, optionally wherein obtaining the sequencing data comprises harvesting or having harvested the cancer-affected tissue, isolating or having isolated the tumor-derived DNA, and sequencing or having sequenced the tumor-derived DNA; b. determining or having determined from the tumor-derived DNA sequencing data one or more tumor-associated mutations relative to the subject's wild-type germline nucleic acid sequence, optionally wherein one or more of the one or more tumor-associated mutations is associated with a neoantigen that includes at least one change that causes a peptide sequence encoded by the tumor-derived DNA to differ from a corresponding peptide sequence encoded by the subject's wild-type germline nucleic acid sequence; c. designing and / or selecting, or having designed and / or selected, (1) a panel of tumor-informative polynucleotide probes, (2) a panel of tumor-naive polynucleotide probes, and / or (3) a combined panel of a panel of tumor-informative polynucleotide probes and a panel of tumor-naive polynucleotide probes, wherein the polynucleotide probes are configured to capture the polynucleotide region of interest corresponding to the tumor-associated mutations, and optionally, the polynucleotide region of interest comprises at least 50 tumor-associated mutations; d. using said polynucleotide probes to enrich or have enriched said cfDNA prior to sequencing; 10. A method according to any one of the preceding claims, comprising:

60. 10. The method of any one of the preceding claims, wherein the cancer is selected from the group consisting of lung cancer, melanoma, breast cancer, ovarian cancer, prostate cancer, kidney cancer, gastric cancer, colon cancer, testicular cancer, head and neck cancer, pancreatic cancer, brain cancer, B-cell lymphoma, acute myeloid leukemia, chronic myeloid leukemia, chronic lymphocytic leukemia, T-cell lymphocytic leukemia, non-small cell lung cancer, and small cell lung cancer.

61. The method of any one of claims 1 to 60, wherein the subject has undergone treatment.

62. 62. The method of claim 61, wherein the treatment comprises a cancer vaccine.

63. The cancer vaccine, a nucleic acid sequence encoding an epitope that encodes at least one of the mutations present in the exome of the cancer; 63. The method of claim 62, comprising:

64. 64. The method of claim 62 or 63, wherein the cancer vaccine comprises an alphavirus-based self-amplifying expression system.

65. 64. The method of claim 62 or 63, wherein the cancer vaccine comprises an expression system using chimpanzee adenovirus (ChAdV).

66. 10. The method of any one of the preceding claims, comprising obtaining cfDNA sequencing data from two or more samples from the subject.

67. 67. The method of claim 66, wherein the two or more samples are taken at different time points.

68. 68. The method of claim 67, wherein the two or more samples are taken at different times relative to administration of treatment.

69. 69. The method of claim 68, wherein the pre-treatment sample is collected before administration of the treatment and the post-treatment cfDNA is collected after administration of the treatment.

70. 70. The method of claim 69, wherein said determining step comprises determining or having determined the frequency of said mutations in the pre-treatment cfDNA compared to the post-treatment cfDNA to assess the effectiveness of the treatment, optionally wherein at least the one or more tumor-associated mutations associated with said neoantigens are determined, optionally wherein an increase in the frequency of said mutations in the post-treatment cfDNA compared to the pre-treatment cfDNA indicates an increased likelihood that the subject's tumor burden is increasing, and optionally wherein a decrease or maintenance of the frequency of said mutations in the post-treatment cfDNA compared to the pre-treatment cfDNA indicates an increased likelihood that the subject's tumor burden is decreasing or stable.

71. 71. The method of claim 70, wherein said increased frequency of one or more of said mutations in said tumor naive panel in said post-treatment cfDNA compared to said pre-treatment cfDNA indicates a likely tumor mutation of an immune evasion mechanism.

72. 71. The method of claim 70, wherein said increased frequency of said mutation in said post-treatment cfDNA compared to said pre-treatment cfDNA indicates an increased likelihood that the subject has increasing tumor burden.

73. 71. The method of claim 70, wherein said decrease or maintenance of the frequency of said mutation in said post-treatment cfDNA compared to said pre-treatment cfDNA indicates an increased likelihood that the subject's tumor burden has decreased or stabilized.

74. 72. The method of claim 71, wherein the reduction comprises a complete response (CR) or a partial response (PR).

75. 10. The method of any one of the preceding claims, further comprising administering a treatment to the subject after assessing the status of the cancer.

76. 76. The method of claim 75, wherein assessing the frequency of the mutations in the cfDNA indicates the likelihood that the subject has or still has cancer.

77. 77. The method of claim 75 or 76, wherein the treatment comprises a cancer vaccine.

78. The cancer vaccine, a nucleic acid sequence encoding an epitope that encodes at least one of said mutations present in said exome; 78. The method of claim 77, comprising:

79. 79. The method of claim 77 or 78, wherein the cancer vaccine comprises an alphavirus-based self-amplifying expression system.

80. 79. The method of claim 77 or 78, wherein the cancer vaccine comprises an expression system using chimpanzee adenovirus (ChAdV).

81. 10. The method of any one of the preceding claims, wherein the collecting step comprises collecting a blood sample.

82. 10. The method of any one of the preceding claims, wherein the isolating step comprises centrifugation to separate cfDNA from cells and / or cell debris.

83. 10. The method of any one of the preceding claims, wherein the isolating step comprises isolating cfDNA from whole blood.

84. 84. The method of claim 83, wherein isolating cfDNA from whole blood comprises separating the plasma layer, the buffy coat, and the red blood cells.

85. 85. The method of claim 84, wherein the cfDNA is isolated from the plasma layer.

86. 10. The method of any one of the preceding claims, wherein the sequencing step comprises next generation sequencing (NGS) or Sanger sequencing.

87. 87. The method of claim 86, wherein the NGS comprises duplex sequencing, whole exome sequencing, whole genome sequencing, de novo sequencing, staged sequencing, targeted amplicon sequencing, or shotgun sequencing.

88. 10. The method of any one of the preceding claims, wherein the enrichment step comprises enriching the cfDNA for the polynucleotide regions of interest that correspond to the mutations present in the exome prior to sequencing.

89. 89. The method of claim 88, wherein the enrichment comprises a combination of the panel of tumor-informative polynucleotide probes and the panel of tumor-naive polynucleotide probes, and optionally, separate samples are enriched separately for each of the panel of tumor-informative polynucleotide probes and the panel of tumor-naive polynucleotide probes.

90. 90. The method of Claim 89, wherein the tumor informative polynucleotide probes comprise each of the polynucleotide regions of interest corresponding to the mutations present in the exome.

91. 91. The method of any one of claims 88-90, wherein the tumor informative polynucleotide probes comprise at least 50%, at least 60%, at least 70%, at least 80%, at least 90%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, at least 99.5%, at least 99.9%, or 100% of the polynucleotide regions of interest that correspond to the mutations present in the exome of the cancer.

92. 91. The method of any one of claims 88-90, wherein the tumor informative polynucleotide probe comprises at least 50, at least 60, at least 70, at least 80, at least 90 mutations, at least 100, at least 150, at least 200, at least 250, at least 300, at least 400, at least 500, at least 600, at least 700, at least 800, at least 900, or at least 1000 mutations, optionally comprising said mutations present in the exome of the cancer.

93. 10. The method of any one of the preceding claims, wherein the enrichment step comprises hybridizing one or more polynucleotide probes to one or more of the polynucleotide regions of interest.

94. 10. Any one of the preceding claims, wherein the polynucleotide probe is 80 to 150 base pairs (bp) in length.

95. The polynucleotide probes may be 50 to 100 bp, 50 to 150 bp, 80 to 140 bp, 80 to 130 bp, 80 to 120 bp, 80 to 110 bp, 80 to 100 bp, 80 to 90 bp, 90 to 150 bp, 90 to 140 bp, 90 to 130 bp, 90 to 120 bp, 90 to 110 bp, 90 to 100 bp, 100 to 150 bp, 100 to 140 bp, 100 to 130 bp, 100 to 120 bp, 100 to 110 bp 95. The panel or method of claim 94, wherein the nucleic acid sequence is 10-150 bp, 110-140 bp, 110-130 bp, 110-120 bp, 120-150 bp, 120-140 bp, 120-130 bp, 130-150 bp, 130-140 bp, 140-150 bp, 50 bp, 60 bp, 70 bp, 80 bp, 90 bp, 100 bp, 110 bp, 120 bp, 130 bp, 140 bp, or 150 bp in length.

96. 96. The panel or method of any one of claims 88 to 95, wherein the one or more polynucleotide probes are biotinylated.

97. 10. Any one of the preceding claims, wherein the tumor-informative polynucleotide probes are designed or selected after sequencing of the subject's tumor.

98. 98. The panel or method of claim 97, wherein said tumor-informative polynucleotide probes are designed or selected after exome sequencing of said tumor of said subject.

99. 99. The panel or method of claim 98, wherein said tumor informative polynucleotide probes are designed or selected to target all mutations in said sequenced tumor.

100. 10. The method of any one of the preceding claims, wherein the sequencing step comprises ligating sequencing adapters to the cfDNA.

101. 101. The method of claim 100, wherein the sequencing adapter is configured for duplex sequencing.

102. 10. The panel or method of any one of the preceding claims, wherein one or more of the mutations comprise a point mutation, a frameshift mutation, a non-frameshift mutation, a deletion mutation, an insertion mutation, a splice variant, a genomic rearrangement, a proteasome-generated splice antigen, or a combination thereof.

103. 10. The panel or method of any one of the preceding claims, wherein one or more of the mutations comprises at least one change that causes the peptide sequence encoded by the cfDNA to differ from the corresponding peptide sequence encoded by the wild-type germline nucleic acid sequence of the subject.

104. 10. The panel or method of any one of the preceding claims, wherein the one or more mutations consist of a coding mutation comprising at least one change that causes the peptide sequence encoded by the cfDNA to differ from the corresponding peptide sequence encoded by the wild-type germline nucleic acid sequence of the subject.

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