Method and system for evaluating the circulating tumor DNA fraction in liquid biopsy samples

Abstract

A method for determining the circulating tumor DNA fraction in a liquid biopsy sample is described. The method may include, for example, receiving sequence read data for multiple sequence reads obtained for a sample from a subject; determining whether the sequence read data is sufficient to perform copy number variation (CNA) modeling; if the sequence read data is determined to be sufficient to perform CNA modeling, estimating the ctDNA fraction in the sample based on the copy number determined for at least one CNA using the CNA model; or, if the sequence read data is determined to be insufficient to perform CNA modeling, estimating the ctDNA fraction in the sample based on the identification of at least one somatic short variant in the sequence read data; and outputting the estimated ctDNA fraction in the sample.

Description

Technical Field

[0001] Cross - reference to Related Applications This application claims the benefit of priority of U.S. Provisional Patent Application No. 63 / 466,541, filed on May 15, 2023, the content of which is incorporated herein by reference in its entirety.

[0002] The present disclosure generally relates to methods and systems for analyzing genomic profiling data, and more specifically, to methods and systems for evaluating circulating tumor DNA (ctDNA) fractions in liquid biopsy samples based on genomic profiling data, and to its use for cancer detection, monitoring, and prognosis prediction.

Background Art

[0003] Liquid biopsy is a simple and non - invasive alternative to surgical biopsy that enables healthcare providers to determine various important characteristics of a cancer patient's tumor through the collection of a simple blood sample and subsequent genomic profiling of nucleic acid molecules extracted from the blood sample. Analysis of trace cancer cell DNA in the blood (generally referred to as circulating tumor DNA (ctDNA)) can provide clues as to which treatment is likely to be most effective for a given patient.

[0004] Typically, cell - free DNA samples isolated from liquid biopsies contain a mixture of DNA released from both cancer cells and normal cells. The ctDNA fraction is the fraction of DNA derived from cancer cells in the total cell - free DNA sample isolated from a liquid biopsy, but existing methods for estimating the ctDNA fraction lack the sensitivity and / or specificity required for accurate detection and determination of the ctDNA fraction in cell - free DNA samples.

Summary of the Invention

[0005] A method and system enabling more accurate determination of the ctDNA fraction based on sequence read data for cell-free DNA samples derived from liquid biopsy are disclosed herein. The disclosed method is based on a composite approach including: (i) estimation of the ctDNA fraction based on a copy number model that identifies the tumor fraction of the sample and the exact copy number at probed genomic locations that explain the observed sequence coverage and single nucleotide polymorphism (SNP) allele frequency data; and / or (ii) estimation of the ctDNA fraction from the observed allele frequencies of tumor somatic variants identified in the sample, based on a theoretical relationship between the tumor fraction and tumor somatic variant allele frequencies considering the copy number profile, the copy number profile being inferred from tumor somatic variant allele frequencies based on historical observations.

[0006] The more accurate determination of ctDNA fractions made possible by using the disclosed method can then be used to detect, monitor, and / or predict the treatment outcomes of cancer patients.

[0007] Disclosed herein is a method comprising: providing a plurality of nucleic acid molecules obtained from a sample from a subject; ligating one or more adapters onto one or more nucleic acid molecules from the plurality of nucleic acid molecules; amplifying one or more ligated nucleic acid molecules from the plurality of nucleic acid molecules; capturing amplified nucleic acid molecules from the amplified nucleic acid molecules; sequencing the captured nucleic acid molecules with a sequencer to obtain a plurality of sequence reads representing the captured nucleic acid molecules; receiving sequence read data for the plurality of sequence reads in one or more processors; and copying the sequence read data using one or more processors. The method includes determining whether the sequence read data is sufficient to perform number variation (CNA) modeling, and using one or more processors to (1) if the sequence read data is determined to be sufficient to perform CNA modeling, to use the model to estimate the ctDNA fraction in the sample based on at least the tumor purity and ploidy of the sample, or (2) if the sequence read data is determined to be insufficient to perform CNA modeling, to estimate the ctDNA fraction in the sample based on the identification of at least one tumor somatic cell short variant in the sequence read data, and to output the estimated ctDNA fraction in the sample using one or more processors.

[0008] In some embodiments, the method may further include comparing the estimated ctDNA fraction with at least one predetermined threshold and outputting a status call for the sample of at least high tumor fraction (TF-high) or low tumor fraction (TF-low) based on the comparison.

[0009] In some embodiments, determining whether sequence read data is sufficient to perform CNA modeling involves determining sequence coverage data, sequence coverage ratio data, allele fraction data, or any combination thereof for at least one genomic locus to which multiple sequence reads are mapped. In some embodiments, the at least one genomic locus includes at least one single nucleotide polymorphism (SNP) locus.

[0010] In some embodiments, performing CNA modeling involves using one or more processors to determine a copy number model that includes sample tumor purity, sample ploidy, and copy numbers of multiple genomic segments within ctDNA, which describes observed sequence coverage ratio data, allele fraction data, for at least one genomic locus within one or more subgenome segments to which multiple sequence reads are mapped.

[0011] In some embodiments, estimating the ctDNA fraction based on at least sample tumor purity and sample ploidy using a CNA model involves using an equation that describes the physical relationship between the ctDNA fraction and sample tumor purity and sample ploidy.

[0012] In some embodiments, estimating a ctDNA fraction based on at least one somatic short variant detected in sequence read data includes obtaining a list of short variants detected in sequence read data, applying a set of selection rules to the list of detected short variants to identify tumor somatic short variants, and estimating a ctDNA fraction based on the presence of at least one identified tumor somatic short variant. In some embodiments, the set of selection rules used to identify tumor somatic short variants in the list of detected short variants includes (i) removing short variants that appear on a blacklist of known germline variants, known indeterminate potential clonal hematopoietic (CHIP) variants, and known sequencing artifacts; (ii) retaining short variants that appear on a list of known tumor somatic short variants; (iii) retaining short variants that appear on a list of known genes that tend to show high amplification and have a higher allele frequency than other somatic short variants in the sample; (iv) retaining short variants that appear on a list of known rearrangements, or any combination thereof.

[0013] In some embodiments, estimating a ctDNA fraction based on at least one tumor somatic cell short variant includes: using one or more processors to determine the variant allele frequencies (VAFs) of one or more variants detected in a sample based on sequence read data; using one or more processors to generate an empirical distribution of ctDNA fraction values ​​corresponding to the determined VAFs of one or more variants based on historical data; using one or more processors to fit a model to the empirical distribution of ctDNA fraction values; and determining the ctDNA fraction of the sample based on the model.

[0014] In some embodiments, the subject is suspected of having cancer or has been determined to have cancer. In some embodiments, the cancer is B-cell carcinoma (multiple myeloma), melanoma, breast cancer, lung cancer, bronchial cancer, colorectal cancer, prostate cancer, pancreatic cancer, stomach cancer, ovarian cancer, bladder cancer, brain cancer, central nervous system cancer, peripheral nervous system cancer, esophageal cancer, uterine cancer, endometrial cancer, oral cancer, pharyngeal cancer, liver cancer, kidney cancer, testicular cancer, biliary tract cancer, small intestine cancer, appendiceal cancer, salivary gland cancer, thyroid cancer, adrenal cancer, osteosarcoma, chondrosarcoma, hematological cancer. Cancers, adenocarcinomas, inflammatory myofibroblastomas, gastrointestinal stromal tumors (GISTs), colon cancers, multiple myeloma (MM), myelodysplastic syndromes (MDS), myeloproliferative disorders (MPDs), acute lymphoblastic leukemia (ALL), acute myeloid leukemia (AML), chronic myeloid leukemia (CML), chronic lymphocytic leukemia (CLL), polycythemia vera, Hodgkin lymphoma, non-Hodgkin lymphoma (NHL), soft tissue sarcoma, fibrosarcoma, myxosarcoma, and fatty tissue. Tumors, osteosarcoma, chordoma, angiosarcoma, endosarcoma, lymphangiosarcoma, lymphangioendosarcoma, synoviomas, mesothelioma, Ewing's tumor, leiomyosarcoma, rhabdomyosarcoma, squamous cell carcinoma, basal cell carcinoma, adenocarcinoma, sweat gland carcinoma, sebaceous gland carcinoma, papillary carcinoma, papillary adenocarcinoma, medullary carcinoma, bronchogenic carcinoma, renal cell carcinoma, liver cancer, bile duct cancer, choriocarcinoma, seminoma, embryonic carcinoma, Wilms' tumor, bladder cancer, epithelial carcinoma, glioma, astrocytoma, medulloblastoma, craniopharyngioma, These include ependymoma, pineal cell tumor, hemangioblastoma, acoustic neuroblastoma, oligodendroglioma, meningioma, neuroblastoma, retinoblastoma, follicular lymphoma, diffuse large B-cell lymphoma, mantle cell lymphoma, hepatocellular carcinoma, thyroid cancer, gastric cancer, head and neck cancer, small cell carcinoma, essential thrombocythemia, aplastic myelogenesis, eosinophilic syndrome, systemic mastocytosis, familial eosinophilia, chronic eosinophilic leukemia, neuroendocrine carcinoma, or carcinoid tumors.

[0015] In some embodiments, cancers include acute lymphoblastic leukemia (Philadelphia chromosome positive), acute lymphoblastic leukemia (precursor B cell), acute myeloid leukemia (FLT3+), acute myeloid leukemia (with IDH2 mutation), anaplastic large cell lymphoma, basal cell carcinoma, B-cell chronic lymphocytic leukemia, bladder cancer, breast cancer (HER2 overexpression / amplification), breast cancer (HER2+), breast cancer (HR+, HER2-), cervical cancer, cholangiocarcinoma, chronic lymphocytic leukemia, chronic lymphocytic leukemia (with 17p deficiency), chronic myeloid leukemia, chronic myeloid leukemia (Philadelphia chromosome positive), classical Hodgkin lymphoma, and colon cancer. Rectal cancer, colorectal cancer (dMMR / MSI-H), colorectal cancer (KRAS wild-type), cryopyrin-associated periodic fever syndrome, cutaneous T-cell lymphoma, dermatofibrosarcoma protuberans, diffuse large B-cell lymphoma, fallopian tube cancer, follicular B-cell non-Hodgkin lymphoma, follicular lymphoma, gastric cancer, gastric cancer (HER2+), gastroesophageal junction (GEJ) adenocarcinoma, gastrointestinal stromal tumor, gastrointestinal stromal tumor (KIT+), giant cell tumor of bone, glioblastoma, granulomatosis with polyangiitis, head and neck squamous cell carcinoma, hepatocellular carcinoma, Hodgkin lymphoma, juvenile idiopathic arthritis, systemic lupus erythematosus, mantle cell lymphoma, medullary thyroid carcinoma, melanoma, BRAF Melanoma with V600 mutation, melanoma with BRAF V600E or V600K mutation, Merkel cell carcinoma, multicentric Castleman disease, multiple hematological malignancies including Philadelphia chromosome-positive ALL and CML, multiple myeloma, myelofibrosis, non-Hodgkin lymphoma, unresectable subependymal giant cell astrocytoma associated with tuberous sclerosis, non-small cell lung cancer, non-small cell lung cancer (ALK+), non-small cell lung cancer (PD-L1+), non-small cell lung cancer (with ALK fusion or ROS1 gene alteration), non-small cell lung cancer (with BRAF V600E mutation), non-small cell lung cancer (with EGFR exon 19 deletion or exon 21 substitution (L858R) mutation), non-small cell lung cancer (EGFRThis includes ovarian cancer (with T790M mutation), ovarian cancer (with BRCA mutation), pancreatic cancer, neuroendocrine tumors of pancreatic, gastrointestinal, or lung origin, pediatric neuroblastoma, peripheral T-cell lymphoma, peritoneal cancer, prostate cancer, renal cell carcinoma, rheumatoid arthritis, small lymphocytic lymphoma, soft tissue sarcoma, solid tumors (MSI-H / dMMR), squamous cell carcinoma of the head and neck, squamous non-small cell lung cancer, thyroid cancer, thyroid carcinoma, urothelial carcinoma, or primary gammaglobulinemia.

[0016] In some embodiments, the method further comprises treating the subject with anticancer therapy. In some embodiments, the anticancer therapy includes targeted anticancer therapy. In some embodiments, the targeted anticancer therapy includes abemaciclib (Verzenio), abiraterone acetate (Zytiga), acalabrutinib (Calquence), ado-trastuzumab emtansine (Kadcyla), afatinib dimaleate (Gilotrif), aldesleukin (Proleukin), alectinib (Alecensa), alemtuzumab (Campath), alitretinoin (Panretin), alpelisib (Piqray), and amivantamab-vmjw (Rybrevant), anastrozole (Arimidex), apalutamide (Erleada), aciminib hydrochloride (Scemblix), atezolizumab (Tecentriq), avapritinib (Ayvakit), avelumab (Bavencio), axicaptagensilolucel (Yescarta), axitinib (Inlyta), verantamab mahodotin-blmf (Blenrep), belimumab (Benlysta), belinostat (Beleodaq), verzutifan (W elireg), bevacizumab (Avastin), bexarotene (Targretin), binimetinib (Mektovi), blinatumomab (Blincyto), bortezomib (Velcade), bosutinib (Bosulif), brentuximab vedotin (Adcetris), brexkabutadiene oatlucel (Tecartus), brigutinib (Alunbrig), cabazitaxel (Jevtana), cabozantinib (Cabometyx), cabozantinib (Cabomety x, Cometriq), canakinumab (Ilaris), capmatinib hydrochloride (Tabrecta), carfilzomib (Kyprolis), semiprimab-rwlc (Libtayo), ceritinib (LDK378 / Zykadia), cetuximab (Erbitux), cobimetinib (Cotellic), copanlisib hydrochloride (Aliqopa), crizotinib (Xalkori), dabrafenib (Tafinlar), dacomitinib (Vizimpro), daratumumab (Darzalex),Daratumumab and hyaluronidase-fihj (Darzalex Faspro), darolutamide (Nubeqa), dasatinib (Sprycel), deniroikin difutitox (Ontak), denosumab (Xgeva), dinutuximab (Unituxin), dostallumab-gxly (Jemperli), durvalumab (Imfinzi), duvelisib (Copiktra), elotuzumab (Empliciti), enacidenib mesylate (Idhifa), encorafenib (Braftovi), enfortumab vedotin-ejfv (Padcev), entrectinib (Rozlytrek), enzalutamide (Xtandi), erdafitinib (Balversa), erlotinib (Tarceva), everolimus (Afinitor), exemestane (Aromasin), fam-trastuzumab deruxtecan-nxki (Enhertu), fedratinib hydrochloride (Inrebi) c) Fulvestrant (Faslodex), gefitinib (Iressa), gemtuzumab ozogamicin (Mylotarg), gilteritinib (Xospata), glassedegib maleate (Daurismo), hyaluronidase-zzxf (Phesgo), ibrutinib (Imbruvica), ibritumomab tiuxetan (Zevalin), idekabuta genbiculucel (Abecma), idelaritinib (Zydelig), imatinib mesylate (Gleevec), infiglatinib phosphate (Truseltiq), inotuzumab ozogamicin (Besponsa), iobenguan I131 (Azedra), ipilimumab (Yervoy), isatuximab-irfc (Sarclisa), ivosidenib (Tibsovo), ixazomib citrate (Ninlaro), lanreotide acetate (Somatuline) Depot), lapatinib (Tykerb), larotrectinib sulfate (Vitrakvi), lenvatinib mesylate (Lenvima), letrozole (Femara), lysocabategemmaralucel (Breyanzi), loncustuximab tesillin-lpyl (Zynlonta), lorlatinib (Lorbrena), lutetium Lu177-doteate (Lutathera), margetuximab-cmkb (Margenza),Midostaurin (Rydapt), Mobocertinib succinate (Exkivity), Mogamulizumab-kpkc (Poteligeo), Moxetumomab Pasdotox-tdfk (Lumoxiti), Naxitamab-gqgk (Danyelza), Necitumumab (Portrazza), Neratinib maleate (Nerlynx), Nilotinib (Tasigna), Niraparib tosylate monohydrate (Zejula), Nivolumab (Opdivo), Obinutuzumab (Gazyva), Ofatumumab (Arzerra), Olaparib (Lynparza), Oraratumumab (Lartruvo), Osimertinib (Tagrisso), Palbociclib (Ibrance), Panitumumab (Vecti bix), panobinostat (Farydak), pazopanib (Votrient), pembrolizumab (Keytruda), pemigatinib (Pemazyre), pertuzumab (Perjeta), pexidartinib hydrochloride (Turalio), polatuzumab vedotin-piiq (Polivy), ponatinib hydrochloride (Iclusig), pralatrexate (Folotyn), pralcetinib (Gavreto), radium-223 dichloride (Xofigo), ramucirumab (Cyramza), regorafenib (Stivarga), ribociclib (Kisqali), lipretinib (Qinlock), rituximab (Rituxan), rituximab and hyaluronidase (Rituxan) Hycela), Romidepsin (Istodax), Rucaparib cansylate (Rubraca), Ruxolitinib phosphate (Jakafi), Sacituzumab Govitecan-hziy (Trodelvy), Seliclib, Selinexol (Xpovio), Serpercatinib (Retevmo), Selumetinib sulfate (Koselugo), Siltuximab (Sylvant), Cipleucel-T Provenge) Sirolimus-binding protein particles (Fyarro), sonidecib (Odomzo), sorafenib (Nexavar), sotracib (Lumakras), sunitinib (Sutent), tafacitamab-cxix (Monjuvi), tagraxofusp-erzs (Elzonris), thalazoparib tosylate (Talzenna), tamoxifen (Nolvadex),Tazemetostat hydrobromide (Tazverik), teventafusp-tebn (Kimmtrak), temsirolimus (Torisel), tepotinib hydrochloride (Tepmetko), tisagenlecleucel (Kymriah), tisotumab vedotin-tftv (Tivdak), tocilizumab (Actemra), tofacitinib (Xeljanz), tositumomab (Bexxar), trametinib (Mekinist), trastuzumab (Herceptin), tretinoin (Ves) This includes anoids, tivozanib hydrochloride (Fotivda), toremifene (Fareston), tucatinib (Tukysa), umbralicib tosylate (Ukoniq), vandetanib (Caprelsa), vemurafenib (Zelboraf), venetoclax (Venclexta), bismodegib (Erivedge), vorinostat (Zolinza), zanubrutinib (Brukinsa), ziv-aflibercept (Zaltrap), or any combination thereof.

[0017] In some embodiments, the method further includes obtaining a sample from a subject. In some embodiments, the sample includes a tissue biopsy sample, a liquid biopsy sample, or a normal control. In some embodiments, the sample is a liquid biopsy sample and includes blood, plasma, cerebrospinal fluid, sputum, feces, urine, or saliva. In some embodiments, the sample is a liquid biopsy sample and includes circulating tumor cells (CTCs). In some embodiments, the sample is a liquid biopsy sample and includes cell-free DNA (cfDNA), circulating tumor DNA (ctDNA), or any combination thereof. In some embodiments, the plurality of nucleic acid molecules include a mixture of tumor nucleic acid molecules and non-tumor nucleic acid molecules. In some embodiments, the tumor nucleic acid molecules are derived from the tumor portion of a heterogeneous tissue biopsy sample, and the non-tumor nucleic acid molecules are derived from the normal portion of the heterogeneous tissue biopsy sample. In some embodiments, the sample includes a liquid biopsy sample, where the tumor nucleic acid molecules are derived from the circulating tumor DNA (ctDNA) fraction of the liquid biopsy sample, and the non-tumor nucleic acid molecules are derived from the non-tumor cell-free DNA (cfDNA) fraction of the liquid biopsy sample.

[0018] In some embodiments, one or more adapters include amplification primers, flow cell adapter sequences, substrate adapter sequences, or sample index sequences. In some embodiments, captured nucleic acid molecules are captured from amplified nucleic acid molecules by hybridization to one or more bait molecules. In some embodiments, one or more bait molecules include one or more nucleic acid molecules, each nucleic acid molecule including a region complementary to the region of the captured nucleic acid molecule. In some embodiments, amplification of nucleic acid molecules includes performing polymerase chain reaction (PCR) amplification techniques, non-PCR amplification techniques, or isothermal amplification techniques.

[0019] In some embodiments, sequencing includes the use of massively parallel sequencing (MPS) techniques, whole-genome sequencing (WGS), whole-exome sequencing, targeted sequencing, direct sequencing, or Sanger sequencing techniques. In some embodiments, sequencing includes massively parallel sequencing, and massively parallel sequencing techniques include next-generation sequencing (NGS). In some embodiments, sequencing includes next-generation sequencers.

[0020] In some embodiments, one or more of the sequence reads overlap with one or more loci in one or more subgenome segments in the sample. In some embodiments, one or more loci are 10-20 loci, 10-40 loci, 10-60 loci, 10-80 loci, 10-100 loci, 10-150 loci, 10-200 loci, 10-250 loci, 10-300 loci, 10-350 loci, 10-400 loci, 10-450 loci, 10-500 loci, 20-40 loci, 20-60 loci, 20-80 loci, 20-100 loci, 20-150 loci, 20-200 loci, 20- 250 loci, 20-300 loci, 20-350 loci, 20-400 loci, 20-500 loci, 40-60 loci, 40-80 loci, 40-100 loci, 40-150 loci, 40-200 loci, 40-250 loci, 40-300 loci, 40-350 loci, 40-400 loci, 40-500 loci, 60-80 loci, 60-100 loci, 60-150 loci, 60-200 loci, 60-250 loci, 60-300 loci, 60-3 50 loci, 60-400 loci, 60-500 loci, 80-100 loci, 80-150 loci, 80-200 loci, 80-250 loci, 80-300 loci, 80-350 loci, 80-400 loci, 80-500 loci, 100-150 loci, 100-200 loci, 100-250 loci, 100-300 loci, 100-350 loci, 100-400 loci, 100-500 loci, 150-200 loci, 150-250 loci, 150- Includes 300 loci, 150-350 loci, 150-400 loci, 150-500 loci, 200-250 loci, 200-300 loci, 200-350 loci, 200-400 loci, 200-500 loci, 250-300 loci, 250-350 loci, 250-400 loci, 250-500 loci, 300-350 loci, 300-400 loci, 300-500 loci, 350-400 loci, 350-500 loci, or 400-500 loci.

[0021] In some embodiments, one or more gene loci are ABL1, ACVR1B, AKT1, AKT2, AKT3, ALK, ALOX12B, AMER1, APC, AR, ARAF, ARFRP1, ARID1A, ASXL1, ATM, ATR, ATRX, AURKA, AURKB, AXIN1, AXL, BAP1, BARD1, BCL2, BCL2L1, BCL2L2, BCL6, BCOR, BCORL1, BCR, BRAF, BRCA1, BRCA2, BRD4, BRIP1, BTG1, BTG2, BTK, CALR, CARD11, CASP8, CB FB, CBL, CCND1, CCND2, CCND3, CCNE1, CD22, CD274, CD70, CD74, CD79A, CD79B, CDC73, CDH1, CDK12, CDK4, CDK6, CDK8, CDKN1A, CDKN1B, CDKN2A, CDKN2B, CDKN2C, CEBPA, CHEK1, CHEK2, CIC, CREBBP, CRKL, CSF1R, CSF3R, CTCF, CTNNA1, CTNNB1, CUL3, CUL4A, CXCR4, CYP17A1, DAXX, DDR1, DDR2, DIS3, DNMT3A, DOT1L, EED, EGFR, EMSY(C11orf30), ​​EP300, EPHA3, EPHB1, EPHB4, ERBB2, ERBB3, ERBB4, ERCC4, ERG, ERRFI1, ESR1, ETV4, ETV5, ETV6, EWSR1, EZH2, EZR, FAM46C, FANCA, FANCC, FANCG, FANCL, FAS, FBXW7, FGF10, FGF12, FGF14, FGF19, FGF23, FGF3, FGF4, FGF6, FGFR1, FGFR2, FGFR3, FGFR4, FH, FLCN, FLT1, F LT3, FOXL2, FUBP1, GABRA6, GATA3, GATA4, GATA6, GID4(C17orf39), GNA11, GNA13, GNAQ, GNAS, GRM3, GSK3B, H3F3A, HDAC1, HGF, HNF1A, HRAS, HSD3B1, I D3, IDH1, IDH2, IGF1R, IKBKE, IKZF1, INPP4B, IRF2, IRF4, IRS2, JAK1, JAK2, JAK3, JUN, KDM5A, KDM5C, KDM6A, KDR, KEAP1, KEL, KIT, KLHL6, KMT2A(MLL),KMT2D(MLL2), KRAS, LTK, LYN, MAF, MAP2K1, MAP2K2, MAP2K4, MAP3K1 MAP3K13, MAPK1, MCL1, MDM2, MDM4, MED12, MEF2B, MEN1, MERTK, MET, MI TF, MKNK1, MLH1, MPL, MRE11A, MSH2, MSH3, MSH6, MST1R, MTAP, MTOR, M UTYH, MYB, MYC, MYCL, MYCN, MYD88, NBN, NF1, NF2, NFE2L2, NFKBIA, NKX 2-1 NOTCH1, NOTCH2, NOTCH3, NPM1, NRAS, NT5C2, NTRK1, NTRK2, NTRK 3. NUTM1, P2RY8, PALB2, PARK2, PARP1, PARP2, PARP3, PAX5, PBRM1, PDC D1, PDCD1LG2, PDGFRA, PDGFRB, PDK1, PIK3C2B, PIK3C2G, PIK3CA, PIK 3CB, ​​PIK3R1, PIM1, PMS2, POLD1, POLE, PPARG, PPP2R1A, PPP2R2A, PRDM 1. PRKAR1A, PRKCI, PTCH1, PTEN, PTPN11, PTPRO, QKI, RAC1, RAD21, RA D51, RAD51B, RAD51C, RAD51D, RAD52, RAD54L, RAF1, RARA, RB1, RBM10 REL, RET, RICTOR, RNF43, ROS1, RPTOR, RSPO2, SDC4, SDHA, SDHB, SDHC SDHD, SETD2, SF3B1, SGK1, SLC34A2, SMAD2, SMAD4, SMARCA4, SMARCB1 SMO, SNCAIP, SOCS1, SOX2, SOX9, SPEN, SPOP, SRC, STAG2, STAT3, STK 11 SUFU, SYK, TBX3, TEK, TERC, TERT, TET2, TGFBR2, TIPARP, TMPRSS2 TNFAIP3, TNFRSF14, TP53, TSC1, TSC2, TYRO3, U2AF1, VEGFA, VHL, WHSC 1 WHSC1L1, WT1, XPO1, XRCC2, ZNF217, ZNF703.

[0022] In some embodiments, one or more gene loci are ABL, ALK, ALL, B4GALNT1, BAFF, BCL2, BRAF, BRCA, BTK, CD19, CD20, CD3, CD30, CD319, CD38, CD52, CDK4, CDK6, CML, CRACC, CS1, CTLA-4, dMMR, EGFR, ERBB1, ERBB2, FGFR1-3, FLT3, GD2, HDAC, HER1, H This includes ER2, HR, IDH2, IL-1β, IL-6, IL-6R, JAK1, JAK2, JAK3, KIT, KRAS, MEK, MET, MSI-H, mTOR, PARP, PD-1, PDGFR, PDGFRα, PDGFRβ, PD-L1, PI3Kδ, PIGF, PTCH, RAF, RANKL, RET, ROS1, SLAMF7, VEGF, VEGFA, VEGFB, or any combination thereof.

[0023] In some embodiments, the method further includes generating a report showing the estimated tumor fraction in a sample using one or more processors. In some embodiments, the method further includes sending the report to a healthcare provider. In some embodiments, the report is sent via a computer network or peer-to-peer connection.

[0024] Disclosed herein is a method for determining the circulating tumor DNA (ctDNA) fraction in a sample from a subject, the method comprising, with one or more processors, receiving sequence read data for a plurality of sequence reads obtained from a sample from the subject, using one or more processors to determine whether the sequence read data is sufficient to perform copy number alteration (CNA) modeling, and using one or more processors to: (1) if the sequence read data is determined to be sufficient to perform CNA modeling, estimating the ctDNA fraction in the sample based at least on tumor purity and ploidy of the sample using a model, or (2) if the sequence read data is determined to be insufficient to perform CNA modeling, estimating the ctDNA fraction in the sample based on identification of at least one tumor somatic short variant in the sequence read data, and using one or more processors to output the estimated ctDNA fraction in the sample.

[0025] In some embodiments, the method further comprises comparing the estimated ctDNA fraction to at least one predetermined threshold and outputting at least a high tumor fraction (TF-high) or low tumor fraction (TF-low) status call for the sample based on the comparison.

[0026] In some embodiments, determining whether the sequence read data is sufficient to perform CNA modeling comprises determining sequence coverage data, sequence coverage ratio data, allele fraction data, or any combination thereof for at least one genomic locus to which a plurality of sequence reads map. In some embodiments, the at least one genomic locus comprises at least one single nucleotide polymorphism (SNP) locus. In some embodiments, the determination of sequence coverage data, sequence coverage ratio data, allele fraction data, or any combination thereof for at least one genomic locus is based on preprocessing of the sequence read data.

[0027] In some embodiments, performing CNA modeling involves using one or more processors to determine a copy number model that includes observed sequence coverage ratio data, allele fraction data for at least one genomic locus within one or more sub-genomic intervals to which a plurality of sequence reads are mapped, for sample tumor purity, sample ploidy, and the copy number of a plurality of genomic segments that account for the allele fraction data. In some embodiments, the sequence coverage ratio data is determined by aligning a plurality of sequence reads that overlap at least one genomic locus within one or more sub-genomic intervals in the sample and in a control sample to a reference genome and determining the number of sequence reads that overlap at least one genomic locus within one or more sub-genomic intervals in the sample and in the control sample. In some embodiments, the control sample is a pair of normal samples, a process-matched control sample, or a panel of normal control samples. In some embodiments, the allele fraction data is determined by aligning a plurality of sequence reads that overlap at least one genomic locus within one or more sub-genomic intervals in the sample to the reference genome, detecting the number of alleles present at the at least one genomic locus, and determining the allele fraction for at least one of the alleles present at the at least one genomic locus.

[0028] In some embodiments, performing CNA modeling further includes generating segmentation data by aligning a plurality of sequence reads that overlap at least one genomic locus within one or more sub-genomic intervals in the sample to the reference genome and using a Pruned Exact Linear Time (PELT) method to process the aligned sequence read data, coverage ratio data, and allele fraction data to determine the number of segments required to account for the aligned sequence read data, where each segment has the same copy number. In some embodiments, the copy number model also outputs the segmentation data.

[0029] In some embodiments, the copy number model predicts the copy number of at least one genomic locus based on sequence coverage ratio data and allele fraction data. In some embodiments, the sequence coverage ratio data further includes sequence coverage ratio data for single nucleotide polymorphisms (SNPs) and introns associated with at least one genomic locus.

[0030] In some embodiments, the copy number model also predicts the tumor purity and ploidy of the sample. In some embodiments, the ploidy of the sample has a value in the range of 1 to 8.

[0031] In some embodiments, amplification is detected when the copy number of the corresponding segment is greater than or equal to the ploidy of the sample. In some embodiments, amplification is detected when the copy number for the corresponding segment is greater than or equal to the ploidy of the sample + a first predetermined value. In some embodiments, the first predetermined value is in the range of 2 to 500. In some embodiments, the first predetermined value is in the range of 2 to 10.

[0032] In some embodiments, amplification is detected when the copy number for the corresponding segment is greater than or equal to the ploidy of the sample + a second predetermined value, and the genomic locus is a member of a first predefined set of loci. In some embodiments, the second predetermined value is in the range of 0 to 500. In some embodiments, the second predetermined value is in the range of 2 to 10. In some embodiments, the first predefined set of loci includes one or more target loci, prognostic loci, oncolocos, or any combination thereof that could lead to the development of new drugs. In some embodiments, the first predefined set of loci includes the AR and ERBB2 loci.

[0033] In some embodiments, deletion detection involves identifying a homozygous deletion at at least one genomic locus in a corresponding segment. In some embodiments, a homozygous deletion is detected by determining the total copy number of a given genomic locus, which is equal to the sum of the copy numbers of a first allele and a second allele at that locus. In some embodiments, the first allele is the major allele, and the second allele is the minor allele. In some embodiments, a homozygous deletion is called when the total copy number for a given genomic locus is equal to a third predetermined value. In some embodiments, the third predetermined value is approximately zero.

[0034] In some embodiments, deletion detection includes identifying a heterozygous deletion at at least one genomic locus in the corresponding segment. In some embodiments, a heterozygous deletion is called when the copy number for a first allele at a given genomic locus is equal to a fourth predetermined value, and the copy number for a second allele at a given genomic locus is not equal to the fourth predetermined value. In some embodiments, the fourth predetermined value is approximately zero. In some embodiments, the first allele is the major allele, and the second allele is the minor allele.

[0035] In some embodiments, deletion detection involves identifying a partial deletion at at least one genomic locus within a corresponding segment. In some embodiments, a partial deletion is called for a given genomic locus if the log2 ratio (L2R) for adjacent genomic loci, single nucleotide polymorphisms (SNPs), and introns differs significantly from the log2 ratio for the genomic locus, and the log2 ratio for a given genomic locus differs significantly from the distribution of L2R ​​for non-adjacent genomic loci, single nucleotide polymorphisms (SNPs), and introns.

[0036] In some embodiments, estimating the ctDNA fraction based on at least sample tumor purity and sample ploidy using a CNA model involves using an equation that describes the physical relationship between the ctDNA fraction and sample tumor purity and sample ploidy. In some embodiments, the equation is given by:

number

[0037] In some embodiments, estimating a ctDNA fraction based on at least one somatic short variant detected in sequence read data includes obtaining a list of short variants detected in sequence read data, applying a set of selection rules to the list of detected short variants to identify somatic short variants of tumors, and estimating a ctDNA fraction based on the presence of at least one identified somatic short variant of tumors.

[0038] In some embodiments, estimating the ctDNA fraction based on at least one tumor somatic cell short variant includes: using one or more processors to determine the variant allele frequencies (VAFs) of one or more variants detected in sequence read data; using one or more processors to generate an empirical distribution of ctDNA fraction values ​​corresponding to the determined VAFs of one or more variants based on historical data; using one or more processors to fit a model to the empirical distribution of ctDNA fraction values; and determining the ctDNA fraction of a sample based on the model. In some embodiments, the method further includes determining a confidence interval for the ctDNA fraction based on the model. In some embodiments, the one or more variants include one or more short variants. In some embodiments, the one or more short variants include one or more somatic cell short variants. In some embodiments, the one or more somatic cell short variants are known not to be associated with uncertain latent clonal hematopoiesis (CHIP).

[0039] In some embodiments, generating an empirical distribution of ctDNA fraction values ​​involves calculating ctDNA fraction values ​​based on a known copy number for one or more tumor somatic cell short variants, and corresponding known sample ploidy for a plurality of historical samples having known VAFs for one or more tumor somatic cell short variants that are substantially the same as the VAFs determined for one or more tumor somatic cell short variants.

[0040] In some embodiments, generating an empirical distribution of ctDNA fraction values ​​includes pre-calculating ctDNA fraction values ​​based on the corresponding known sample ploidy of a plurality of historical samples having known copy numbers of one or more tumor somatic cell short variants and a range of VAF values ​​for one or more tumor somatic cell short variants, and selecting a subset of the pre-calculated ctDNA fraction values ​​corresponding to samples having known VAFs of one or more tumor somatic cell short variants that are substantially the same as the determined VAFs of one or more tumor somatic cell short variants.

[0041] In some embodiments, the ctDNA fraction value is calculated or selected for the tumor somatic cell variant exhibiting the highest VAF in the sample from the subject. In some embodiments, the ctDNA fraction value is calculated or selected for a ranked set of two or more tumor somatic cell short variants exhibiting the highest ranked VAF in the sample from the subject. In some embodiments, the ctDNA fraction value is calculated or selected for a predetermined set of two or more tumor somatic cell short variants detected in the sample from the subject. In some embodiments, the ctDNA fraction value is calculated or selected for a predetermined set of two or more tumor somatic cell short variants detected in the sample from the subject containing known driver mutations. In some embodiments, the ctDNA fraction value is calculated or selected for all tumor somatic cell short variants detected in the sample from the subject.

[0042] In some embodiments, the ctDNA fraction value is calculated or pre-calculated based on known VAFs for one or more tumor somatic cell short variants, known copy numbers for one or more tumor somatic cell short variants, and corresponding known sample ploidy for multiple historical samples. In some embodiments, the ctDNA fraction value is calculated or pre-calculated based on known VAFs for one or more tumor somatic cell short variants, known copy numbers for one or more tumor somatic cell short variants, and corresponding known sample ploidy for multiple historical samples by solving a set of equations that describe the relationship between (i) the ctDNA fraction, sample tumor purity, and sample ploidy, and the relationship between somatic cell VAF, sample tumor purity, copy numbers at genomic locations of one or more tumor somatic cell short variants, and the number of variant alleles for each of the one or more tumor somatic cell short variants, thereby excluding sample tumor purity and deriving the relationship for the ctDNA fraction as a function of somatic cell VAF, sample ploidy, copy numbers at genomic locations of one or more tumor somatic cell short variants, and the number of variant alleles for one or more tumor somatic cell short variants.

[0043] In some embodiments, the ctDNA fraction value is calculated using a first formula, where the product of sample tumor purity and sample ploidy is divided by the sum of the product of sample tumor purity and sample ploidy and twice the amount obtained by subtracting sample tumor purity from 1, based on the known VAF of one or more tumor somatic cell short variants, the known copy number of one or more tumor somatic cell short variants, and the corresponding known sample ploidy of multiple historical samples. The somatic cell VAF is calculated using a first formula, where the product of sample tumor purity and sample ploidy is divided by the sum of the product of sample tumor purity and sample ploidy and twice the amount obtained by subtracting sample tumor purity from 1. The ctDNA fraction is calculated or pre-calculated by solving a set of equations that include a second equation equals the product of the copy number at the genomic location of the tumor somatic cell short variant and the division by the sum of the product of 1 minus twice the amount obtained by subtracting the sample tumor purity. This eliminates the sample tumor purity and derives a relationship in which the ctDNA fraction is equal to the sample ploidy obtained by subtracting the copy number at the genomic location of one or more tumor somatic cell short variants from the sample ploidy and dividing by an amount equal to the sum of the ratio of the number of variant alleles of one or more tumor somatic cell short variants to the somatic VAF of each of the one or more tumor somatic cell short variants.

[0044] In some embodiments, the ctDNA fraction value is given by the following set of formulas, i.e.,

number

number

number

[0045] In some embodiments, the multiple historical samples include solid biopsy samples, liquid biopsy samples, or any combination thereof. In some embodiments, the multiple historical samples include cancer samples. In some embodiments, the multiple historical samples include samples of a single type of cancer. In some embodiments, the multiple historical samples include samples of multiple types of cancer.

[0046] In some embodiments, the multiple historical samples include bladder cancer samples, breast cancer samples, colorectal cancer samples, endometrial cancer samples, kidney cancer samples, leukemia samples, liver cancer samples, lung cancer samples, melanoma samples, non-Hodgkin lymphoma samples, pancreatic cancer samples, prostate cancer samples, thyroid cancer samples, or any combination thereof.

[0047] In some embodiments, the multiple historical samples include: acute lymphoblastic leukemia (Philadelphia chromosome positive) samples, acute lymphoblastic leukemia (precursor B cell) samples, acute myeloid leukemia (FLT3+) samples, acute myeloid leukemia (with IDH2 mutation) samples, anaplastic large cell lymphoma samples, basal cell carcinoma samples, B-cell chronic lymphocytic leukemia samples, bladder cancer samples, breast cancer (HER2 overexpression / amplification) samples, breast cancer (HER2+) samples, breast cancer (HR+, HER2) samples, cervical cancer samples, cholangiocarcinoma samples, chronic lymphocytic leukemia samples, chronic lymphocytic leukemia (with 17p deletion) samples, chronic myeloid leukemia samples, chronic myeloid leukemia (Philadelphia chromosome positive) samples, classical Hodgkin lymphoma samples, and colorectal cancer samples. Colorectal cancer (dMMR and MSI-H) samples, colorectal cancer (KRAS wild-type) samples, cryopyrin-associated periodic syndrome samples, cutaneous T-cell lymphoma samples, dermatofibrosarcoma protuberans samples, diffuse large B-cell lymphoma samples, fallopian tube cancer samples, follicular B-cell non-Hodgkin lymphoma samples, follicular lymphoma samples, gastric cancer samples, gastric cancer (HER2+) samples, gastroesophageal junction (GEJ) adenocarcinoma samples, gastrointestinal stromal tumor samples, gastrointestinal stromal tumor (KIT+) samples, giant cell tumors in bone samples, glioblastoma samples, granulomatosis with polyangiitis samples, head and neck squamous cell carcinoma samples, hepatocellular carcinoma samples, Hodgkin lymphoma samples, juvenile idiopathic arthritis samples, lupus erythematosus samples, mantle cell lymphoma samples, medullary thyroid carcinoma samples, melanoma samples, BRAF Melanoma samples with V600 mutations, melanoma samples with BRAF V600E or V600K mutations, Merkel cell carcinoma samples, multicentric Castleman disease samples, multiple hematological malignancies including Philadelphia chromosome-positive ALL and CML, multiple myeloma samples, myelofibrosis samples, non-Hodgkin lymphoma samples, unresectable subependymal giant cell astrocytoma samples associated with tuberous sclerosis, non-small cell lung cancer samples, non-small cell lung cancer (ALK+) samples, non-small cell lung cancer (PD-L1+) samples, non-small cell lung cancer (with ALK fusion or ROS1 gene alteration) samples, non-small cell lung cancer (with BRAF V600E mutation) samples, non-small cell lung cancer (with EGFR exon 19 deletion or exon 21 substitution (L858R) mutation), non-small cell lung cancer (EGFRThis includes samples with the T790M mutation, ovarian cancer samples, ovarian cancer samples (with BRCA mutations), pancreatic cancer samples, gastrointestinal cancer samples, neuroendocrine tumors of pulmonary origin, pediatric neuroblastoma samples, peripheral T-cell lymphoma samples, peritoneal cancer samples, prostate cancer samples, renal cell carcinoma samples, rheumatoid arthritis samples, small lymphocytic lymphoma samples, soft tissue sarcoma samples, solid tumor (MSI-H / dMMR) samples, squamous cell carcinoma samples of the head and neck, squamous non-small cell lung cancer samples, thyroid cancer samples, thyroid cancer samples, urothelial carcinoma samples, Waldenström macroglobulinemia samples, or any combination thereof.

[0048] In some embodiments, the model is a nonparametric probability density model.

[0049] In some embodiments, the ctDNA fraction determined for a sample is the most probable ctDNA fraction. In some embodiments, the ctDNA fraction determined for a sample is the mean, median, or mode of the dominant peak in the empirical distribution of ctDNA fraction values.

[0050] In some embodiments, the sample includes DNA extracted from a blood sample, plasma sample, cerebrospinal fluid sample, pleural fluid sample, sputum sample, stool sample, urine sample, or saliva sample.

[0051] In some embodiments, estimating the ctDNA fraction is based on determining the maximum somatic allele frequency (MSAF) of at least one tumor somatic cell short variant, detecting one or more genomic rearrangements, determining microsatellite instability, or any combination thereof.

[0052] In some embodiments, the set of selection rules used to identify tumor somatic cell short variants in a list of detected short variants includes (i) removing short variants that appear on a blacklist of known germline variants, known indeterminate potential clonal hematopoietic (CHIP) variants, and known sequencing artifacts; (ii) retaining short variants that appear on a list of known tumor somatic cell short variants; (iii) retaining short variants that appear on a list of known genes that tend to show high amplification and have a higher allele frequency than other somatic cell short variants in the sample; (iv) retaining short variants that appear on a list of known rearrangements; or any combination thereof.

[0053] In some embodiments, the blacklist of known germline variants, known indeterminate potential clonal hematopoietic (CHIP) variants, and known sequencing artifacts includes a list of germline variants, CHIP variants, and sequencing artifacts, or any combination thereof, observed in historical sequencing data.

[0054] In some embodiments, the list of known tumor somatic cell short variants includes somatic cell short variants that have been determined to have a high prevalence or high odds ratio between tumor cells and leukocytes.

[0055] In some embodiments, a list of known genes that tend to show high amplification includes KRAS, EGFR, CCND1, FGF19, FGF3, FGF4, MYC, AR, MDM2, CCNE1, ERBB2, WHSC1L1, FGFR1, ZNF703, or any combination thereof.

[0056] In some embodiments, the list of known rearrangements includes fusions between the following gene pairs, namely TMPRSS2-ERG, ALK-EML4, FGFR3-TACC3, RET-KIF5B, or any combination thereof.

[0057] In some embodiments, the set of selection rules used to identify tumor somatic cell short variants in a list of detected short variants further includes identifying short variants in which a fragment size shift between a reference allele and an alternative allele is detected in the sequence read data as tumor somatic cell short variants.

[0058] In some embodiments, the estimated ctDNA fraction of a sample is used to diagnose or confirm a disease in a subject. In some embodiments, the disease is cancer. In some embodiments, the method further includes selecting an anti-cancer therapy to administer to the subject based on the estimated ctDNA fraction of the sample. In some embodiments, the method further includes determining an effective dose of the anti-cancer therapy to administer to the subject based on the estimated ctDNA fraction of the sample. In some embodiments, the method further includes administering the anti-cancer therapy to the subject based on the estimated ctDNA fraction of the sample. In some embodiments, the anti-cancer therapy includes chemotherapy, radiotherapy, immunotherapy, targeted therapy, or surgery.

[0059] In some embodiments, the estimated ctDNA fraction is used as a prognostic biomarker to predict treatment outcomes in subjects with cancer. In some embodiments, the cancer is prostate cancer.

[0060] In some embodiments, the sample is a liquid biopsy sample and includes blood, plasma, cerebrospinal fluid, sputum, feces, urine, or saliva.

[0061] Disclosed herein is a method for predicting the treatment outcome of a subject with cancer, the method comprising: receiving sequence read data for a plurality of sequence reads obtained for a sample from the subject using one or more processors; determining, using one or more processors, whether the sequence read data is sufficient to perform copy number variation (CNA) modeling; estimating the ctDNA fraction in the sample based on at least sample tumor purity and sample ploidy derived from the CNA model using one or more processors, if the sequence read data is determined to be sufficient to perform CNA modeling, or estimating the ctDNA fraction in the sample based on the identification of at least one tumor somatic cell short variant detected in the sequence read data using one or more processors, if the sequence read data is determined to be insufficient to perform CNA modeling; outputting the estimated ctDNA fraction in the sample using one or more processors; and predicting the treatment outcome of the subject with a particular anti-cancer therapy based on a comparison of the estimated ctDNA fraction with a predetermined threshold. In some embodiments, the predetermined threshold is determined based on an analysis of ctDNA fraction and survival data for a cohort of patients with cancer. In some embodiments, a predetermined threshold is determined by adjusting an empirical threshold to maximize sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), or any combination thereof, for ctDNA fraction data for a cohort of patients with cancer. In some embodiments, the cancer is prostate cancer. In some embodiments, the anticancer therapy includes enzalutamide loading after abiraterone treatment.

[0062] Disclosed herein are methods for diagnosing a disease, the methods comprising diagnosing that a subject has a disease based on the determination of a ctDNA fraction from a sample from the subject, the ctDNA fraction being determined according to any of the methods described herein.

[0063] Disclosed herein is a method for selecting an anticancer therapy, the method comprising selecting an anticancer therapy for a subject in accordance with determining a ctDNA fraction for a sample from the subject, the ctDNA fraction being determined according to one of the methods described herein.

[0064] Disclosed herein is a method for treating cancer in a subject, the method comprising administering an effective dose of anticancer therapy to the subject in accordance with the determination of a ctDNA fraction of a sample from the subject, the ctDNA fraction being determined according to one of the methods described herein.

[0065] Disclosed herein are methods for monitoring the progression or recurrence of cancer in a subject, the method comprising: determining a first ctDNA fraction in a first sample obtained from the subject at a first time point according to any of the methods described herein; determining a second ctDNA fraction in a second sample obtained from the subject at a second time point; and comparing the first ctDNA fraction with the second ctDNA fraction to monitor the progression or recurrence of cancer. In some embodiments, the second ctDNA fraction of the second sample is determined according to any of the methods described herein. In some embodiments, the method further includes selecting an anticancer therapy for the subject in accordance with the progression of cancer. In some embodiments, the method further includes administering the anticancer therapy to the subject in accordance with the progression of cancer. In some embodiments, the method further includes adjusting the anticancer therapy for the subject in accordance with the progression of cancer. In some embodiments, the method further includes adjusting the dosage of the anticancer therapy or selecting a different anticancer therapy in accordance with the progression of cancer. In some embodiments, the method further includes administering the adjusted anticancer therapy to the subject. In some embodiments, the first time point is before the subject receives anticancer therapy, and the second time point is after the subject receives anticancer therapy. In some embodiments, the subject has cancer, is at risk of having cancer, is routinely screened for cancer, or is suspected of having cancer. In some embodiments, the cancer is a solid tumor. In some embodiments, the cancer is a blood cancer. In some embodiments, the anticancer therapy includes chemotherapy, radiotherapy, immunotherapy, targeted therapy, or surgery.

[0066] In some embodiments, the methods disclosed herein may further include determining, identifying, or applying the value of the ctDNA fraction of a sample as a diagnostic value related to the sample. In some embodiments, the methods may further include generating a genomic profile of the subject based on the determination of the ctDNA fraction. In some embodiments, the genomic profile of the subject further includes results from comprehensive genomic profiling (CGP) tests, gene expression profiling tests, cancer hotspot panel tests, DNA methylation tests, DNA fragmentation tests, RNA fragmentation tests, or any combination thereof. In some embodiments, the genomic profile of the subject further includes results from tests based on nucleic acid sequencing.

[0067] In some embodiments, the disclosed method may further include selecting, administering, or applying anticancer therapy to a subject based on the generated genomic profile.

[0068] In some embodiments, the determination of the ctDNA fraction for a sample is used when making a proposed treatment decision for the subject. In some embodiments, the determination of the ctDNA fraction for a sample is used when applying or administering a treatment to the subject.

[0069] Disclosed herein is a system comprising one or more processors and a memory communicatively coupled to one or more processors, which, when executed by one or more processors, causes the system to receive sequence read data for a plurality of sequence reads obtained for a sample from a subject, to determine whether the sequence read data is sufficient to perform copy number variation (CNA) modeling, and using one or more processors to (1) if the sequence read data is determined to be sufficient to perform CNA modeling, to use a model to estimate the ctDNA fraction in the sample based on at least the sample tumor purity and the ploidy of the sample, or (2) if the sequence read data is determined to be insufficient to perform CNA modeling, to estimate the ctDNA fraction in the sample based on the identification of at least one tumor somatic cell short variant in the sequence read data, and to output the estimated ctDNA fraction in the sample.

[0070] In some embodiments, the system further includes instructions, when executed by one or more processors, that cause the system to compare the estimated ctDNA fraction with at least one predetermined threshold and, based on the comparison, output a status call of at least high tumor fraction (TF-high) or low tumor fraction (TF-low) for the sample.

[0071] In some embodiments, determining whether sequence read data is sufficient to perform CNA modeling involves determining sequence coverage data, sequence coverage ratio data, allele fraction data, or any combination thereof for at least one genomic locus to which multiple sequence reads are mapped.

[0072] In some embodiments, performing CNA modeling involves using one or more processors to determine a copy number model that includes sample tumor purity, sample ploidy, and copy numbers of multiple genomic segments within ctDNA, which describes observed sequence coverage ratio data, allele fraction data, for at least one genomic locus within one or more subgenome segments to which multiple sequence reads are mapped.

[0073] In some embodiments, estimating the ctDNA fraction based on at least sample tumor purity and sample ploidy using a CNA model involves using an equation that describes the physical relationship between the ctDNA fraction and sample tumor purity and sample ploidy.

[0074] In some embodiments, estimating a ctDNA fraction based on at least one somatic short variant detected in sequence read data includes obtaining a list of short variants detected in sequence read data, applying a set of selection rules to the list of detected short variants to identify somatic short variants of tumors, and estimating a ctDNA fraction based on the presence of at least one identified somatic short variant of tumors.

[0075] In some embodiments, the set of selection rules used to identify tumor somatic cell short variants in a list of detected short variants includes (i) removing short variants that appear on a blacklist of known germline variants, known indeterminate potential clonal hematopoietic (CHIP) variants, and known sequencing artifacts; (ii) retaining short variants that appear on a list of known tumor somatic cell short variants; (iii) retaining short variants that appear on a list of known genes that tend to show high amplification and have a higher allele frequency than other somatic cell short variants in the sample; (iv) retaining short variants that appear on a list of known rearrangements; or any combination thereof.

[0076] In some embodiments, estimating a ctDNA fraction based on at least one tumor somatic cell short variant includes: using one or more processors to determine the variant allele frequencies (VAFs) of one or more variants detected in a sample based on sequence read data; using one or more processors to generate an empirical distribution of ctDNA fraction values ​​corresponding to the determined VAFs of one or more variants based on historical data; using one or more processors to fit a model to the empirical distribution of ctDNA fraction values; and determining the ctDNA fraction of the sample based on the model.

[0077] Disclosed herein is a non-temporary computer-readable storage medium for storing one or more programs, the one or more programs, when executed by one or more processors of the system, include instructions to cause the system to receive sequence read data for a plurality of sequence reads obtained for a sample from a subject, to determine whether the sequence read data is sufficient to perform copy number variation (CNA) modeling, and to use one or more processors to (1) if the sequence read data is determined to be sufficient to perform CNA modeling, to use the model to estimate the ctDNA fraction in the sample based on at least tumor purity and ploidy of the sample, or (2) if the sequence read data is determined to be insufficient to perform CNA modeling, to estimate the ctDNA fraction in the sample based on the identification of at least one tumor somatic cell short variant in the sequence read data, and to output the estimated ctDNA fraction in the sample.

[0078] In some embodiments, the non-temporary computer-readable storage medium, when executed by one or more processors of the system, further includes instructions that cause the system to compare the estimated ctDNA fraction with at least one predetermined threshold and, based on the comparison, output a status call of at least high tumor fraction (TF-high) or low tumor fraction (TF-low) for the sample.

[0079] In some embodiments, determining whether sequence read data is sufficient to perform CNA modeling involves determining sequence coverage data, sequence coverage ratio data, allele fraction data, or any combination thereof for at least one genomic locus to which multiple sequence reads are mapped.

[0080] In some embodiments, performing CNA modeling involves using one or more processors to determine a copy number model that includes sample tumor purity, sample ploidy, and copy numbers of multiple genomic segments within ctDNA, which describes observed sequence coverage ratio data, allele fraction data, for at least one genomic locus within one or more subgenome segments to which multiple sequence reads are mapped.

[0081] In some embodiments, estimating the ctDNA fraction based on at least sample tumor purity and sample ploidy using a CNA model involves using an equation that describes the physical relationship between the ctDNA fraction and sample tumor purity and sample ploidy.

[0082] In some embodiments, estimating a ctDNA fraction based on at least one somatic short variant detected in sequence read data includes obtaining a list of short variants detected in sequence read data, applying a set of selection rules to the list of detected short variants to identify somatic short variants of tumors, and estimating a ctDNA fraction based on the presence of at least one identified somatic short variant of tumors.

[0083] In some embodiments, the set of selection rules used to identify tumor somatic cell short variants in a list of detected short variants includes (i) removing short variants that appear on a blacklist of known germline variants, known indeterminate potential clonal hematopoietic (CHIP) variants, and known sequencing artifacts; (ii) retaining short variants that appear on a list of known tumor somatic cell short variants; (iii) retaining short variants that appear on a list of known genes that tend to show high amplification and have a higher allele frequency than other somatic cell short variants in the sample; (iv) retaining short variants that appear on a list of known rearrangements; or any combination thereof.

[0084] In some embodiments, estimating a ctDNA fraction based on at least one tumor somatic cell short variant includes: using one or more processors to determine the variant allele frequencies (VAFs) of one or more variants detected in a sample based on sequence read data; using one or more processors to generate an empirical distribution of ctDNA fraction values ​​corresponding to the determined VAFs of one or more variants based on historical data; using one or more processors to fit a model to the empirical distribution of ctDNA fraction values; and determining the ctDNA fraction of the sample based on the model.

[0085] It should be understood that all combinations of the concepts described above and any additional concepts described in more detail below (provided that such concepts are not mutually contradictory) are considered to be part of the subject matter of the invention disclosed herein. In particular, all combinations of the claimed subject matter appearing at the end of this disclosure are considered to be part of the subject matter of the invention disclosed herein. Embedding by reference

[0086] All publications, patents, and patent applications referenced herein are incorporated herein by reference in the same manner as each individual publication, patent, or patent application is specifically and individually indicated to be incorporated herein by reference in its entirety. In the event of any conflict between the terminology used herein and the terminology used in the incorporated references, the terminology used herein shall prevail. [Brief explanation of the drawing]

[0087] Various aspects of the disclosed methods, devices, and systems are described in detail in the appended claims. A better understanding of the features and advantages of the disclosed methods, devices, and systems will be obtained by referring to the following detailed description of exemplary embodiments and the appended drawings.

[0088] [Figure 1] This specification provides a non-limiting example of a process flowchart for estimating the ctDNA fraction according to one embodiment of the method disclosed herein. [Figure 2] This specification provides a non-limiting example of a process flowchart for estimating the ctDNA fraction according to another embodiment of the method disclosed herein. [Figure 3] An exemplary computing device or system according to one embodiment of the present disclosure is shown. [Figure 4] This specification shows an exemplary computer system or computer network that follows some of the examples of systems described herein. [Figure 5A-5B] This provides non-limiting examples of the application of the disclosed method for determining the tumor fraction in a liquid biopsy sample to predict the probability of progression-free survival and survival in prostate cancer patients stratified according to the tumor fraction (TF). Figure 5A: Plot of the probability of progression-free survival (PFS) as a function of time after initiation of enzalutamide treatment for prostate cancer patients previously treated with abiraterone. Figure 5B: Plot of the probability of overall survival (OS) as a function of time after initiation of enzalutamide treatment for prostate cancer patients previously treated with abiraterone. [Figure 6A-6B]This provides non-limiting examples of the use of prostate-specific antigen (PSA) as a prognostic biomarker for progression-free survival and overall survival in prostate cancer patients stratified according to PSA levels. Figure 6A: Plot of progression-free survival (PFS) probability as a function of time after initiation of enzalutamide treatment for prostate cancer patients previously treated with abiraterone. Figure 6B: Plot of overall survival (OS) probability as a function of time after initiation of enzalutamide treatment in prostate cancer patients previously treated with abiraterone. [Modes for carrying out the invention]

[0089] Methods and systems are described that enable more accurate determination of ctDNA fractions based on sequence read data for cell-free DNA samples derived from liquid biopsies. The disclosed methods are based on a combined approach including: (i) estimation of the ctDNA fraction based on a copy number model that identifies the tumor fraction of the sample and the exact copy number at probed genomic locations that explain the observed sequence coverage and single nucleotide polymorphism (SNP) allele frequency data; and / or (ii) estimation of the ctDNA fraction based on a theoretical relationship between the tumor fraction and somatic variant allele frequencies, taking into account the copy number profile.

[0090] The more accurate determination of ctDNA fractions made possible by using the disclosed method can then be used to detect, monitor, and / or predict the treatment outcomes of cancer patients.

[0091] Some examples describe a method for determining the circulating tumor DNA (ctDNA) fraction in a sample from a subject, comprising: receiving sequence read data for multiple sequence reads obtained for a sample from the subject; determining whether the sequence read data is sufficient to perform copy number variation (CNA) modeling; (1) if the sequence read data is determined to be sufficient to perform CNA modeling, estimating the ctDNA fraction in the sample based on at least the sample tumor purity and the ploidy of the sample; or (2) if the sequence read data is determined to be insufficient to perform CNA modeling, estimating the ctDNA fraction in the sample based on the identification of at least one tumor somatic cell short variant in the sequence read data; and outputting the estimated ctDNA fraction in the sample.

[0092] In some examples, the method may further include comparing the estimated ctDNA fraction with at least one predetermined threshold and outputting a status call for the sample of at least high tumor fraction (TF-high) or low tumor fraction (TF-low) based on the comparison.

[0093] definition Unless otherwise defined, all technical terms used herein have the same meaning as those generally understood by those skilled in the art of the field to which this disclosure pertains.

[0094] Where used herein and in the appended claims, the singular forms "a," "an," and "the" include plural references unless otherwise explicitly indicated by the context. Any reference to "or" herein is intended to include "and / or" unless otherwise specified.

[0095] "About" and "approximately" generally refer to an acceptable degree of error in the measured quantity, taking into account the nature or precision of the measurement. Exemplary degrees of error are within 20 percent (%) of a given value or range of values, typically within 10 percent, and more typically within 5 percent.

[0096] As used herein, the terms “comprising” (and any form or variation of “comprising,” such as “comprise” and “comprises”), “having” (and any form or variation of “having,” such as “have” and “has”), “including” (and any form or variation of “includes,” such as “include”), or “containing” (and any form or variation of “containing,” such as “contains” and “contain”) are inclusive or open-ended and do not preclude additional unlisted additives, components, integers, elements, or method steps.

[0097] As used herein, the terms “individual,” “patient,” or “subject” are interchangeable and refer to any single animal to which treatment is desired, e.g., a mammal (including non-human animals such as dogs, cats, horses, rabbits, zoo animals, cattle, pigs, sheep, and non-human primates). In certain embodiments, the individual, patient, or subject herein is a human.

[0098] The terms “cancer” and “tumor” are used interchangeably herein. These terms refer to the presence of cells that have characteristics typical of cancer-causing cells, such as uncontrolled growth, immortality, metastatic ability, rapid growth and proliferation rates, and certain characteristic morphological features. While cancer cells often take the form of tumors, such cells can exist alone in animals or may be non-tumor-forming cancer cells, such as leukemia cells. These terms include solid tumors, soft tissue tumors, or metastatic lesions. As used herein, the term “cancer” includes both precancerous and malignant cancers.

[0099] As used herein, “treatment” (and its grammatical variations such as “to treat” or “to treat”) refers to a clinical intervention (e.g., administering anticancer drugs or performing anticancer therapy) that seeks to alter the natural course of the individual being treated, and may be carried out for preventive purposes or in the course of a series of clinicopathological treatments. Desired effects of treatment include, but are not limited to, preventing the onset or recurrence of the disease, reducing symptoms, mitigating any direct or indirect pathological consequences of the disease, preventing metastasis, slowing the rate of disease progression, improving or alleviating the disease state, and achieving remission or an improved prognosis.

[0100] As used herein, the term “subgenome section” (or “subgenome sequence section”) refers to a portion of a genome sequence.

[0101] As used herein, the term “target section” refers to a subgenome section or an expression subgenome section (e.g., a transcription sequence of a subgenome section).

[0102] As used herein, the terms “variant sequence” or “variant” are used interchangeably and refer to nucleic acid sequences modified from the corresponding “normal” or “wild-type” sequences. In some examples, a variant sequence may be a “short variant sequence” (or “short variant”), i.e., a variant sequence less than approximately 50 base pairs in length.

[0103] The terms "allele frequency" and "allele fraction" are used interchangeably herein and refer to the fraction of sequence reads corresponding to a particular allele relative to the total number of sequence reads for a genomic locus.

[0104] The terms “variant allele frequency” and “variant allele fraction” are used interchangeably herein and refer to the fraction of sequence reads corresponding to a particular variant allele relative to the total number of sequence reads at a genomic locus.

[0105] As used herein, the term “tumor fraction” generally refers to the proportion of tumor-derived DNA molecules in the total DNA extracted from a sample. In the case of a liquid biopsy sample or a cell-free DNA (cfDNA) sample derived therefrom, the “circulating tumor DNA fraction” (or “ctDNA fraction”) refers to the proportion of cancer cell-derived (or tumor-derived) DNA in the total DNA present in the cfDNA sample.

[0106] Any headings used herein are for structural purposes only and should not be construed as limiting the subjects described. Method for determining the circulating tumor DNA fraction in liquid biopsy samples

[0107] The methods described herein may be used to estimate the fraction of circulating tumor DNA contained in cell-free DNA samples isolated from liquid biopsy specimens. These novel methods overcome the insufficient sensitivity and / or specificity problems often encountered with existing methods for detecting circulating tumor DNA and estimating the ctDNA fraction in cell-free DNA samples.

[0108] The more accurate determination of ctDNA fractions (e.g., tumor fractions of cell-free DNA (cfDNA) samples) made possible by the disclosed method allows for the determination of ctDNA fractions used for detecting, monitoring, and / or predicting the prognosis of cancer patients. For example, the data presented in Example 3 below demonstrate that ctDNA fractions may provide a better prognostic biomarker for treatment outcomes in prostate cancer patients than prostate-specific antigen (PSA) levels.

[0109] The disclosed method leverages the unique characteristics of a novel genome profiling assay that utilizes a targeted sequencing approach (see, for example, U.S. Patent No. 9,340,830, which is incorporated herein by reference in its entirety). Due to sequencing cost constraints, many commercial sequencing approaches employ either a “broad and shallow” approach (i.e., large regions of the genome (e.g., the entire genome or only exomes) are sequenced at shallow depths) or a “narrow and deep” approach (i.e., only small, selected portions of the genome are sequenced, but only to very deep depths). The “broad and shallow” approach allows for the detection of ploidy but cannot leverage short variant signals. The “narrow and deep” approach can detect short variants present in very small allele fractions but is necessarily limited to the detection of signals arising only from the narrow regions that are sequenced.

[0110] The novel genome profiling assays referenced above employ a method that involves contacting a sequencing library with at least two different bait sets designed to hybridize and select specific target genome regions, where the different bait sets exhibit differential efficiencies in selecting their respective target genome regions, resulting in different sequencing depths for the different target genome regions. Genomic regions expected to be hotspots for cancer-related somatic short variants are sequenced very deeply, thereby enabling the detection of somatic short variant signals in very low tumor fractions. Furthermore, a carefully selected set of additional small genome regions scattered throughout the genome are sequenced to a moderate depth, thereby enabling the construction of copy number models for detecting tumor fractions based on ploidy.

[0111] As described above, the methods described herein utilize this unique assay design to detect and estimate the ctDNA fraction using both ploidy and the presence of other types of alterations present in the patient's cancer genome, such as short variants, rearrangements, and microsatellite instability. The novel methods described herein also utilize a novel copy number modeling approach to leverage ploidy signals (see, for example, PCT International Patent Publication WO2023 / 060236, titled “Methods and Systems for Automated Calling of Copy Number Alterations,” which is incorporated herein by reference in its entirety), and a new set of heuristic rules for filtering variant data to identify tumor-derived variants in order to detect circulating tumor DNA and estimate the ctDNA fraction. The methods were validated against physical ground truth data of the ctDNA fraction obtained from the analysis of sequence read data of paired plasma / buffy coat (PBMC) samples.

[0112] Figure 1 provides a non-limiting example of a flowchart for process 100 for estimating the ctDNA fraction of a liquid biopsy sample (or a cell-free DNA sample derived therefrom). Process 100 can be performed, for example, using one or more electronic devices implementing a software platform. In some examples, process 100 is performed using a client-server system, and blocks of process 100 are divided in any way between the server and client devices. In other examples, blocks of process 100 are divided between the server and multiple client devices. Thus, while parts of process 100 are described herein as being performed by specific devices in a client-server system, it should be understood that process 100 is not limited in this way. In other examples, process 100 is performed using only one client device, or only multiple client devices. In process 100, some blocks are combined at will, the order of some blocks is changed at will, and some blocks are omitted at will. In some examples, additional steps may be performed in combination with process 100. Therefore, the actions illustrated (and described in more detail below) are essentially illustrative and should not be considered limiting.

[0113] In step 102 of Figure 1, sequence read data for multiple sequence reads obtained from a sample from a subject (e.g., a patient) is received (e.g., by one or more processors of a system configured to perform process 100). In some examples, the sequence read data is derived by sequencing cell-free DNA (cfDNA) extracted from a liquid biopsy sample. In some examples, the sequence read data may be received by the system as a binary alignment map (BAM) file.

[0114] In some cases, sequence read data for multiple sequence reads may be derived using targeted sequencing techniques, such as targeted exome sequencing. In some cases, sequence read data may be derived using whole-genome or whole-exome sequencing, as opposed to targeted exome sequencing, in order to increase the number of genomic features detected (e.g., the number of short variants, rearrangements, etc.). In some cases, the sequencing method may be based on synthetic sequencing (SBS) techniques, such as next-generation sequencing (NGS) techniques. In some cases, sequence read data may be generated using any of the following alternative sequencing methods (e.g., binding sequencing (SBS) or avidity sequencing (SBA) methods) and sequencing techniques (e.g., nanopore-based or microarray-based sequencing techniques).

[0115] In some cases, liquid biopsy samples may include blood, plasma, cerebrospinal fluid, sputum, stool, urine, or saliva.

[0116] Step 104 in Figure 1 evaluates the sequence read data to determine if it is of sufficient quality to perform copy number variation (CNA) modeling. The sequence read data may be evaluated, for example, by determining the noise level in the data and determining whether ploidy can be detected based on, for example, the deviation of the coverage log ratio (LR) and SNP frequency values ​​from what would be expected if ploidy were not present.

[0117] In step 106 of Figure 1, the estimate of the circulating tumor DNA (ctDNA) fraction in the sample is determined based on a copy number variation (CNA) model, provided that the sequence read data is deemed sufficient to perform CNA modeling. The ctDNA fraction for the sample is estimated based on the copy number profile (or ploidy signal) determined for the sequence read data based on the model, as described in more detail below with reference to Figure 2.

[0118] In step 108 of Figure 1, if the sequence read data is determined to be insufficient for CNA modeling, an estimate of the circulating tumor DNA (ctDNA) fraction in the sample is determined based on one or more somatic short variants (e.g., tumor-derived somatic short variants) detected in the sequence read data from the sample. The ctDNA fraction of the sample is estimated based on: (i) applying a set of heuristic rules to distinguish between tumor-derived somatic short variants (tumor somatic short variants) and non-tumor-derived (e.g., germline) somatic short variants; and (ii) using a model based on the empirical distribution of ctDNA fraction values ​​corresponding to VAFs determined for one or more short variants in the historical sequencing data, as will be explained in more detail below with reference to Figure 2.

[0119] Step 110 in Figure 1 outputs an estimated value for the ctDNA fraction of the sample. In some examples, the method may further include comparing the estimated ctDNA fraction to at least one predetermined threshold and, based on the comparison, outputting a status call for the sample, for example, high tumor fraction (TF-high) or low tumor fraction (TF-low).

[0120] In some cases, the estimated ctDNA fraction may be compared to at least two predetermined thresholds, and based on the comparison, a status call such as tumor fraction-high (TF-high), tumor fraction-medium (TF-medium), or tumor fraction-low (TF-low) may be output for the sample.

[0121] In some examples, a predetermined threshold (or TF threshold) used to stratify the samples (e.g., a first, second, or third predetermined threshold) may have values ​​such as 0.01, 0.02, 0.03, 0.04, 0.05, 0.1, 0.15, 0.2, or 0.25.

[0122] In some cases, the ctDNA fraction estimated and determined using the methods disclosed herein may be used as a prognostic biomarker to predict the treatment outcome of a subject with cancer. In some cases, for example, the cancer may be prostate cancer.

[0123] Figure 2 provides a non-limiting example of a flowchart for process 200 for estimating the ctDNA fraction of a liquid biopsy sample (or a cell-free DNA sample derived therefrom). Process 200 can be performed, for example, using one or more electronic devices implementing a software platform. In some examples, process 200 is performed using a client-server system, and blocks of process 200 are divided in any way between the server and client devices. In other examples, blocks of process 200 are divided between the server and multiple client devices. Thus, while parts of process 200 are described herein as being performed by specific devices in a client-server system, it should be understood that process 200 is not limited in this way. In other examples, process 200 is performed using only one client device, or only multiple client devices. In process 200, some blocks are combined at will, the order of some blocks is changed at will, and some blocks are omitted at will. In some examples, additional steps may be performed in combination with process 200. Therefore, the actions illustrated (and described in more detail below) are essentially illustrative and should not be considered limiting.

[0124] In step 202 of Figure 2, sequence read data for multiple sequence reads obtained from a sample from a subject (e.g., a patient) is received (e.g., by one or more processors of a system configured to perform process 200). In some examples, the sequence read data is derived by sequencing cell-free DNA (cfDNA) extracted from a liquid biopsy sample. In some examples, the sequence read data may be received by the system as a binary alignment map (BAM) file.

[0125] In some cases, sequence read data for multiple sequence reads may be derived using targeted sequencing techniques, such as targeted exome sequencing. In some cases, sequence read data may be derived using whole-genome or whole-exome sequencing, as opposed to targeted exome sequencing, in order to increase the number of genomic features detected (e.g., the number of short variants, rearrangements, etc.). In some cases, the sequencing method may be based on synthetic sequencing (SBS) techniques, such as next-generation sequencing (NGS) techniques. In some cases, sequence read data may be generated using any of the following alternative sequencing methods (e.g., binding sequencing (SBS) or avidity sequencing (SBA) methods) and sequencing techniques (e.g., nanopore-based or microarray-based sequencing techniques).

[0126] In some cases, liquid biopsy samples may include blood, plasma, cerebrospinal fluid, sputum, feces, urine, or saliva.

[0127] In some cases, the sample may include DNA (e.g., cell-free DNA) extracted from blood, plasma, cerebrospinal fluid, pleural fluid, sputum, stool, urine, or saliva samples.

[0128] Step 204 in Figure 2 evaluates the sequence read data to determine if it is of sufficient quality to perform copy number variation (CNA) modeling. As described above with reference to step 104 in Figure 1, the sequence read data may be evaluated by performing a quick check, for example, to determine the level of noise in the data and whether ploidy can be detected.

[0129] In some examples, determining whether sequence read data is sufficient to perform CNA modeling may involve determining sequence coverage data, sequence coverage ratio data, allele fraction data, or any combination thereof for at least one genomic locus to which multiple sequence reads are mapped. In some examples, for instance, at least one genomic locus may include at least one single nucleotide polymorphism (SNP) locus. In some examples, determining sequence coverage data, sequence coverage ratio data, allele fraction data, or any combination thereof for at least one genomic locus may be based on preprocessing the sequence read data.

[0130] In step 206 of Figure 2, if it is determined that the sequence read data is sufficient to perform CNA modeling, a copy number variation (CNA) model is generated. A method for modeling copy numbers and calling copy number variations is described, for example, in PCT International Patent Publication WO2023 / 060236, entitled "Methods and Systems for Automated Calling of Copy Number Alterations," which is incorporated herein by reference in its entirety.

[0131] In some examples, performing CNA modeling may involve generating a copy number model that determines and outputs the copy number for multiple genomic segments that describe the sample tumor purity, sample ploidy (or "ploidy"), and observed sequence coverage ratio data for at least one genomic locus within one or more subgenome segments where multiple sequence reads are mapped, as well as allele fraction data.

[0132] In some cases, sequence coverage ratio data can be determined by aligning multiple sequence reads that overlap at least one genomic locus (e.g., at least one of the genomic locuses, such as HG19 or HG38) within one or more subgenome segments (e.g., subgenome segments 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, or more than 100) in the sample and control samples to a reference genome (e.g., a human reference genome such as HG19 or HG38), and determining the number of sequence reads that overlap at at least one genomic locus within one or more subgenome segments in the sample and control samples. In some cases, the control samples are a pair of normal samples, process-fitted control samples, or a panel of normal control samples.

[0133] In some cases, sequence coverage ratio data may further include sequence coverage ratio data for single nucleotide polymorphisms (SNPs) and introns associated with at least one genomic locus.

[0134] In some cases, allele fraction data can be determined by aligning multiple sequence reads that overlap at at least one genomic locus within one or more subgenome segments in a sample to a reference genome (e.g., a human reference genome such as HG19 or HG38), detecting the number of alleles present at at least one genomic locus, and determining the allele fraction for at least one of the alleles present at at least one genomic locus.

[0135] In some examples, performing CNA modeling may further include generating segmented data by aligning multiple sequence reads that overlap at at least one genomic locus within one or more subgenome segments in a sample to a reference genome, and processing the aligned sequence read data, coverage ratio data, and allele fraction data using a pruned exact linear time (PELT) method to determine the number of segments required to consider the aligned sequence read data, such that each segment has the same copy number.

[0136] A copy number model can be used to predict and / or output the copy number of at least one genomic locus based on sequence coverage ratio data and allele fraction data. The copy number model can also be used to predict and / or output sample tumor purity (e.g., 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.9, or 1.0, if expressed as a fraction rather than a percentage) and ploidy (e.g., average copy number with values ​​ranging from 1 to 8) for a sample. In some examples, the copy number model can also output segmentation data.

[0137] As described above, details of exemplary copy number modeling approaches are described in PCT International Patent Publication WO2023 / 060236, entitled "Methods and Systems for Automated Calling of Copy Number Alterations," which is incorporated herein by reference in its entirety. In some examples, amplification may be detected when, for example, the copy number of a corresponding segment is greater than or equal to the ploidy of the sample. In some examples, amplification is detected when the copy number of a corresponding segment is greater than or equal to the ploidy of the sample + a first predetermined value (e.g., a first predetermined value in the range of 2 to 500; in some examples, the first predetermined value may be in the range of 2 to 10). In some cases, amplification may be detected when the copy number of the corresponding segment is greater than or equal to the ploidy of the sample plus a second predetermined value (e.g., in the range of 0 to 500, or in some cases, a second predetermined value in the range of 2 to 10), and the genomic locus is a member of a first predefined set of loci (e.g., a first predefined set of loci that includes one or more target loci, prognostic loci, oncoloci, or any combination thereof that could lead to the development of new drugs). In some cases, the first predefined set of loci may include the AR and ERBB2 loci.

[0138] In some cases, deletion detection may involve identifying homozygous deletions at at least one genomic locus within a corresponding segment. For example, in some cases, a homozygous deletion may be detected by determining the total copy number of a given genomic locus, which is equal to the sum of the copy numbers of the first and second alleles at that locus. In some cases, the first allele is the major allele and the second allele is the minor allele. In some cases, a homozygous deletion is called when the total copy number for a given genomic locus is equal to a third predetermined value (e.g., a third predetermined value approximately zero).

[0139] In some examples, deletion detection may involve identifying a heterozygous deletion at at least one genomic locus within a corresponding segment. In some examples, a heterozygous deletion may be called if the copy number of a first allele at a given genomic locus is equal to a fourth predetermined value, and the copy number of a second allele at a given genomic locus is not equal to the fourth predetermined value (e.g., the fourth predetermined value is approximately zero). In some examples, the first allele is the major allele and the second allele is the minor allele.

[0140] In some cases, deletion detection may involve identifying a partial deletion at at least one genomic locus within the corresponding segment. In some cases, for example, a partial deletion may be called for a given genomic locus if the log2 ratios (L2R) of adjacent genomic loci, single nucleotide polymorphisms (SNPs), and introns differ significantly from the log2 ratio of the genomic locus, and the log2 ratio of a given genomic locus differs significantly from the distribution of L2Rs of non-adjacent genomic loci, single nucleotide polymorphisms (SNPs), and introns.

[0141] In step 208 of Figure 2, the ctDNA fraction of the sample is calculated based at least on the tumor purity and ploidy of the sample predicted by the CNA model. In some examples, estimating the ctDNA fraction based at least on the sample tumor purity and sample ploidy predicted by the CAN model may involve using an equation that describes the physical relationship between the ctDNA fraction and the sample tumor purity and sample ploidy. For example, in some examples, the equation may be given by:

number

[0142] In some cases, the method may further include determining the confidence interval for the ctDNA fraction.

[0143] In step 210 of Figure 2, an estimated value of the ctDNA fraction of the sample is output. In some examples, as described above with reference to Figure 1, the method may further include comparing the estimated ctDNA fraction to at least one predetermined threshold and, based on the comparison, outputting a status call for the sample, for example, high tumor fraction (TF-high) or low tumor fraction (TF-low).

[0144] In step 212 of Figure 2, if the sequence read data is determined to be insufficient for performing CNA modeling, a preliminary list of variants (e.g., short variants) detected in the sequence read data is obtained. In some cases, the list of variants may include one or more short variants. In some cases, one or more short variants may include one or more somatic short variants. In some cases, one or more somatic short variants may include one or more somatic short variants that are known not to be associated with uncertain latent clonal hematopoiesis (CHIP) (e.g., they may be tumor somatic short variants).

[0145] In step 214 of Figure 2, a set of selection rules is applied to a list of variants (e.g., somatic short variants) to distinguish between tumor-derived somatic short variants and non-tumor-derived somatic short variants (e.g., germline somatic short variants).

[0146] In some examples, the set of selection rules used to identify tumor somatic cell short variants within the list of detected short variants may include: (i) removing short variants that appear on a blacklist of known germline variants, known indeterminate potential clonal hematopoietic (CHIP) variants, and known sequencing artifacts; (ii) retaining short variants that appear on a list of known tumor somatic cell short variants; (iii) retaining short variants that appear on a list of known genes that tend to show high amplification and have a higher allele frequency than other somatic cell short variants in the sample; (iv) retaining short variants that appear on a list of known rearrangements; or any combination thereof.

[0147] In some examples, blacklists of known germline variants, known indeterminate potential clonal hematopoietic (CHIP) variants, and known sequencing artifacts may include, for example, lists of germline variants, CHIP variants, and sequencing artifacts observed in historical sequencing data, or any combination thereof.

[0148] In some cases, the list of known tumor somatic short variants includes somatic short variants that have been determined to have a high prevalence odds ratio between tumor cells and leukocytes.

[0149] In some cases, a list of known genes that tend to exhibit high amplification may include, for example, KRAS, EGFR, CCND1, FGF19, FGF3, FGF4, MYC, AR, MDM2, CCNE1, ERBB2, WHSC1L1, FGFR1, ZNF703, or any combination thereof.

[0150] In some examples, the list of known rearrangements may include, for example, fusions between the following gene pairs: TMPRSS2-ERG, ALK-EML4, FGFR3-TACC3, RET-KIF5B, or any combination thereof.

[0151] In some examples, the set of selection rules used to identify tumor somatic cell short variants in a list of detected short variants may further include identifying short variants in which a fragment size shift between a reference allele and an alternative allele is detected in the sequence read data as tumor somatic cell short variants.

[0152] In step 216 of Figure 2, a determination is made as to whether at least one tumor somatic cell short variant has been identified / selected from the short variants in the preliminary list.

[0153] In step 218 of Figure 2, if the tumor somatic cell short variant is not identified in the preliminary list of short variants, the result "ctDNA not detected" is output.

[0154] In step 220 of Figure 2, if at least one tumor somatic cell short variant is detected in the sequence read data for the sample, the ctDNA fraction for the sample is determined based on the identified tumor somatic cell short variant.

[0155] In some examples, estimating the ctDNA fraction of a sample based on at least one somatic short variant detected in sequence read data may include obtaining a list of short variants detected in sequence read data, applying a set of selection rules to the list of detected short variants to identify somatic short variants of tumors, and estimating the ctDNA fraction based on the presence of at least one identified somatic short variant of tumors.

[0156] In some examples, estimating the ctDNA fraction of a sample based on at least one tumor somatic cell short variant detected in sequence read data may include: determining the variant allele frequencies (VAFs) of one or more variants detected in sequence read data; generating an empirical distribution of ctDNA fraction values ​​corresponding to the determined VAFs of one or more variants based on historical data; fitting a model to the empirical distribution of ctDNA fraction values; and determining the ctDNA fraction of the sample based on the model. In some examples, the model may be a nonparametric probability density model.

[0157] In some examples, the method may further include determining the confidence interval for the ctDNA fraction based on the model.

[0158] In some cases, generating an empirical distribution of ctDNA fraction values ​​involves calculating ctDNA fraction values ​​based on the known copy number of one or more tumor somatic cell short variants and the corresponding known sample ploidy of multiple historical samples having known VAFs of one or more tumor somatic cell short variants that are substantially the same as the determined VAFs of one or more tumor somatic cell short variants.

[0159] In some examples, generating an empirical distribution of ctDNA fraction values ​​involves pre-calculating ctDNA fraction values ​​based on the corresponding known sample ploidy of multiple historical samples having known copy numbers of one or more tumor somatic cell short variants and a range of VAF values ​​for one or more tumor somatic cell short variants, and selecting a subset of the pre-calculated ctDNA fraction values ​​corresponding to samples having known VAFs of one or more tumor somatic cell short variants that are substantially the same as the determined VAFs of one or more tumor somatic cell short variants.

[0160] In some examples, the ctDNA fraction value may be calculated or selected for the tumor somatic cell variant exhibiting the highest VAF in the sample from the subject. In some examples, the ctDNA fraction value may be calculated or selected for a ranked set of two or more tumor somatic cell short variants exhibiting the highest ranked VAF. In some examples, the ctDNA fraction value may be calculated or selected for a predetermined set of two or more tumor somatic cell short variants detected in the sample from the subject. In some examples, the ctDNA fraction value may be calculated or selected for a predetermined set of two or more tumor somatic cell short variants detected in the sample from the subject containing known driver mutations. In some examples, the ctDNA fraction value may be calculated or selected for all tumor somatic cell short variants detected in the sample from the subject.

[0161] In some cases, ctDNA fraction values ​​may be calculated or pre-calculated based on the known VAF of one or more tumor somatic cell short variants, the known copy number of one or more tumor somatic cell short variants, and the corresponding known sample ploidy of multiple historical samples.

[0162] In some cases, ctDNA fraction values ​​may be calculated or pre-calculated based on known VAFs for one or more tumor somatic cell short variants, known copy numbers for one or more tumor somatic cell short variants, and corresponding known sample ploidy for multiple historical samples by solving a set of equations that describe the relationship between (i) the ctDNA fraction, sample tumor purity, and sample ploidy, and the relationship between somatic cell VAFs, sample tumor purity, copy numbers at genomic locations of one or more tumor somatic cell short variants, and the number of variant alleles for each of the one or more tumor somatic cell short variants, thereby excluding sample tumor purity and deriving the relationship for ctDNA fraction as a function of somatic cell VAFs, sample ploidy, copy numbers at genomic locations of one or more tumor somatic cell short variants, and the number of variant alleles for one or more tumor somatic cell short variants.

[0163] In some cases, the ctDNA fraction value is based on the known VAF of one or more tumor somatic cell short variants, the known copy number of one or more tumor somatic cell short variants, and the corresponding known sample ploidy of multiple historical samples. i) The first formula defines the DNA fraction as equal to the product of sample tumor purity and ploidy divided by the sum of the product of sample tumor purity and ploidy and twice the amount obtained by subtracting sample tumor purity from 1, ii) Somatic VAF may be calculated or pre-calculated by solving a set of equations including a second equation that equals the product of the sample tumor purity and the number of variant alleles for each of one or more tumor somatic cell short variants by dividing by the sum of the product of the product of the sample tumor purity and the copy number at the genomic location of one or more tumor somatic cell short variants and twice the amount obtained by subtracting the sample tumor purity from 1. After removing the tumor purity from the sample, a relationship is derived in which the ctDNA fraction is equal to the sample ploidy obtained by subtracting the copy number at the genomic location of one or more tumor somatic cell short variants from the sample ploidy and dividing by an amount equal to the sum of the ratio of the number of variant alleles of one or more tumor somatic cell short variants to the somatic VAF of each of the one or more tumor somatic cell short variants.

[0164] In some cases, the ctDNA fraction value is a set of the following formulas, i.e.,

number

number

number

[0165] In some examples, multiple historical samples may include solid biopsy samples, liquid biopsy samples, or any combination thereof. In some examples, multiple historical samples may include cancer samples. In some examples, multiple historical samples may include samples of a single type of cancer. In some examples, multiple historical samples may include samples of multiple types of cancer.

[0166] In some examples, multiple historical samples may include bladder cancer samples, breast cancer samples, colorectal cancer samples, endometrial cancer samples, kidney cancer samples, leukemia samples, liver cancer samples, lung cancer samples, melanoma samples, non-Hodgkin lymphoma samples, pancreatic cancer samples, prostate cancer samples, thyroid cancer samples, or any combination thereof.

[0167] In some cases, multiple historical samples were included: acute lymphoblastic leukemia (Philadelphia chromosome positive) sample, acute lymphoblastic leukemia (precursor B cell) sample, acute myeloid leukemia (FLT3+) sample, acute myeloid leukemia (with IDH2 mutation) sample, anaplastic large cell lymphoma sample, basal cell carcinoma sample, B-cell chronic lymphocytic leukemia sample, bladder cancer sample, breast cancer (HER2 overexpression / amplification) sample, breast cancer (HER2+) sample, breast cancer (HR+, HER2) sample, cervical cancer sample, cholangiocarcinoma sample, chronic lymphocytic leukemia sample, chronic lymphocytic leukemia (with 17p deletion) sample, chronic myeloid leukemia sample, chronic myeloid leukemia (Philadelphia chromosome positive) sample, classical Hodgkin lymphoma sample, colorectal cancer sample, etc. Colorectal cancer (dMMR and MSI-H) samples, colorectal cancer (KRAS wild-type) samples, cryopyrin-associated periodic syndrome samples, cutaneous T-cell lymphoma samples, dermatofibrosarcoma protuberans samples, diffuse large B-cell lymphoma samples, fallopian tube cancer samples, follicular B-cell non-Hodgkin lymphoma samples, follicular lymphoma samples, gastric cancer samples, gastric cancer (HER2+) samples, gastroesophageal junction (GEJ) adenocarcinoma samples, gastrointestinal stromal tumor samples, gastrointestinal stromal tumor (KIT+) samples, giant cell tumors in bone samples, glioblastoma samples, granulomatosis with polyangiitis samples, head and neck squamous cell carcinoma samples, hepatocellular carcinoma samples, Hodgkin lymphoma samples, juvenile idiopathic arthritis samples, lupus erythematosus samples, mantle cell lymphoma samples, medullary thyroid carcinoma samples, melanoma samples, BRAF Melanoma samples with V600 mutations, melanoma samples with BRAF V600E or V600K mutations, Merkel cell carcinoma samples, multicentric Castleman disease samples, multiple hematological malignancies including Philadelphia chromosome-positive ALL and CML, multiple myeloma samples, myelofibrosis samples, non-Hodgkin lymphoma samples, unresectable subependymal giant cell astrocytoma samples associated with tuberous sclerosis, non-small cell lung cancer samples, non-small cell lung cancer (ALK+) samples, non-small cell lung cancer (PD-L1+) samples, non-small cell lung cancer (with ALK fusion or ROS1 gene alteration) samples, non-small cell lung cancer (with BRAF V600E mutation) samples, non-small cell lung cancer (with EGFR exon 19 deletion or exon 21 substitution (L858R) mutation), non-small cell lung cancer (EGFRThis may include samples with the T790M mutation, ovarian cancer samples, ovarian cancer samples (with BRCA mutations), pancreatic cancer samples, gastrointestinal cancer samples, neuroendocrine tumors of pulmonary origin, pediatric neuroblastoma samples, peripheral T-cell lymphoma samples, peritoneal cancer samples, prostate cancer samples, renal cell carcinoma samples, rheumatoid arthritis samples, small lymphocytic lymphoma samples, soft tissue sarcoma samples, solid tumor (MSI-H / dMMR) samples, squamous cell carcinoma samples of the head and neck, squamous non-small cell lung cancer samples, thyroid cancer samples, thyroid cancer samples, urothelial carcinoma samples, urothelial carcinoma samples, Waldenström macroglobulinemia samples, or any combination thereof.

[0168] In some cases, the ctDNA fraction determined for a sample may be the most likely ctDNA fraction. In some cases, the ctDNA fraction determined for a sample may be the mean, median, or mode of the dominant peak in the empirical distribution of ctDNA fraction values.

[0169] In some cases, the ctDNA fraction determined for a sample may be based on determining the maximum somatic allele frequency (MSAF) for at least one tumor somatic cell short variant, detecting one or more genomic rearrangements, determining microsatellite instability, or any combination thereof.

[0170] Next, the estimated ctDNA fraction for the sample is output in step 210 of Figure 2. Here again, as described above with reference to Figure 1, the method may further include comparing the estimated ctDNA fraction to at least one predetermined threshold and, based on the comparison, outputting a status call for the sample, for example, high tumor fraction (TF-high) or low tumor fraction (TF-low). How to use

[0171] In some examples, the disclosed method involves (i) obtaining a sample from a subject (e.g., a subject suspected of having cancer or determined to have cancer), (ii) extracting nucleic acid molecules (e.g., a mixture of tumor nucleic acid molecules and non-tumor nucleic acid molecules) from the sample, (iii) ligating one or more adapters to the nucleic acid molecules extracted from the sample (e.g., one or more amplification primers, flow cell adapter sequences, substrate adapter sequences, or sample index sequences), and (iv) carrying out a methylation conversion reaction to obtain, for example, unmethylated cytosine. (v) a step of converting to uracil, (v) a step of amplifying the nucleic acid molecule (e.g., using polymerase chain reaction (PCR) amplification techniques, non-PCR amplification techniques, or isothermal amplification techniques), (vi) a step of capturing the nucleic acid molecule from the amplified nucleic acid molecule (e.g., by hybridization to one or more bait molecules, each of which contains one or more nucleic acid molecules with a region complementary to the region of the captured nucleic acid molecule), (vii) a step of using, for example, next-generation (large-scale parallel) sequencing techniques, whole-genome sequencing (WGS) techniques, whole-exome sequencing techniques (viii) the steps of (viii) sequencing nucleic acid molecules (or library proxies derived therefrom) extracted from a sample using, for example, a next-generation (large-scale parallel) sequencer, using targeted sequencing technology, direct sequencing technology, or Sanger sequencing technology; (viii) combining nucleic acid sequence data (including, for example, variant data, copy number data, methylation status data, etc., of the sequenced nucleic acid molecules) with other biomarker data modalities, including but not limited to proteomics-based biomarker data (e.g., detection of specific polypeptides such as proteins) or fragment-mixing-based biomarker data (e.g., detection of specific attributes related to nucleic acid fragments such as fragment size or fragment end sequence), for example, determining the presence of ctDNA in the sample and / or determining the diagnosis, prognosis, and / or prediction of treatment response of the subject; and (ix) generating, displaying, transmitting, and / or delivering reports (e.g., electronic, web-based, or paper reports) to the subject (or patient), caregivers, healthcare providers, physicians, oncologists, electronic medical record systems, hospitals, clinics, third-party payers, insurance companies, or government agencies.It may further include one or more of the following. In some examples, the report includes output from the methods described herein. In some examples, all or part of the report may be displayed in a graphical user interface of an online or web-based healthcare portal. In some examples, the report is transmitted over a computer network or peer-to-peer connection.

[0172] The disclosed method may be used with any of a variety of samples. For example, in some cases the sample may include a tissue biopsy sample, a liquid biopsy sample, or a normal control. In some cases the sample may be a liquid biopsy sample and may include blood, plasma, cerebrospinal fluid, sputum, feces, urine, or saliva. In some cases the sample may be a liquid biopsy sample and may include circulating tumor cells (CTCs). In some cases the sample may be a liquid biopsy sample and may include cell-free DNA (cfDNA). In some cases cell-free DNA (cfDNA) or a portion thereof may include circulating tumor DNA (ctDNA). In some cases the liquid biopsy sample may include a combination of cell-free DNA (cfDNA) and circulating tumor DNA (ctDNA).

[0173] In some cases, the nucleic acid molecules extracted from the sample may consist of a mixture of tumor nucleic acid molecules and non-tumor nucleic acid molecules. In some cases, the tumor nucleic acid molecules may originate from the tumor portion of the xenotissue biopsy sample, and the non-tumor nucleic acid molecules may originate from the normal portion of the xenotissue biopsy sample. In some cases, the sample may consist of a liquid biopsy sample, and the tumor nucleic acid molecules may originate from the circulating tumor DNA (ctDNA) fraction of the liquid biopsy sample, and the non-tumor nucleic acid molecules may originate from the non-tumor cell-free DNA (cfDNA) fraction of the liquid biopsy sample.

[0174] In some examples, the disclosed methods for determining ctDNA fractions may be used to diagnose (or as part of a diagnosis) the presence of a disease or other condition in a subject (e.g., a patient) (e.g., cancer, genetic disorders (such as Down syndrome and fragile X), neurological disorders, or any other disease type in which the detection of a variant, e.g., copy number change, is relevant to the diagnosis, treatment, or prediction of the disease). In some examples, the disclosed methods may be applicable to the diagnosis of any of the various cancers, as described elsewhere in this Spec.

[0175] In some cases, the disclosed methods for determining ctDNA fractions may be used to select subjects (e.g., patients) for clinical trials based on the ctDNA fraction values ​​determined for samples from subjects. In some cases, for example, patient selection for clinical trials based on ctDNA fractions may accelerate the development of targeted therapies and improve health management outcomes for treatment decisions.

[0176] In some cases, the disclosed methods for determining the ctDNA fraction may be used to select an appropriate therapy or treatment (e.g., anti-cancer therapy or anti-cancer treatment) for a subject. In some cases, for example, anti-cancer therapy or treatment may include the use of poly(ADP-ribose) polymerase inhibitors (PARPi), platinum compounds, chemotherapy, radiotherapy, targeted therapy, immunotherapy, neoantigen-based therapy, surgery, or any combination thereof.

[0177] In some cases, anticancer therapy or treatment may include targeted anticancer therapy or treatment (e.g., monoclonal antibody-based therapy, enzyme inhibitor-based therapy, antibody-drug conjugate therapy, hormone therapy, and / or targeted radiotherapy) that targets specific molecules necessary for the growth, division, and expansion of cancer cells. In some cases, targeted anticancer therapy may include abemaciclib (Verzenio), abiraterone acetate (Zytiga), acalabrutinib (Calquence), ado-trastuzumab emtansine (Kadcyla), afatinib dimaleate (Gilotrif), alectinib (Alecensa), alemtuzumab (Campath), alitretinoin (Panretin), alpelisib (Piqray), and amivantamab-vmjw (Rybreva). nt), anastrozole (Arimidex), apalutamide (Erleada), aciminib hydrochloride (Scemblix), atezolizumab (Tecentriq), avapritinib (Ayvakit), avelumab (Bavencio), axicaptagensilolucel (Yescarta), axitinib (Inlyta), verantamab mahodotin-blmf (Blenrep), belimumab (Benlysta), belinostat (Beleodaq), Belz Tifan (Welireg), bevacizumab (Avastin), bexarotene (Targretin), binimetinib (Mektovi), blinatumomab (Blincyto), bortezomib (Velcade), bosutinib (Bosulif), brentuximab vedotin (Adcetris), brexkabutadiene oatlucel (Tecartus), brigutinib (Alunbrig), cabazitaxel (Jevtana), cabozantinib (Cabome Cabozantinib (Cabometyx, Cometriq), canakinumab (Ilaris), capmatinib hydrochloride (Tabrecta), carfilzomib (Kyprolis), semiprimab-rwlc (Libtayo), ceritinib (LDK378 / Zykadia), cetuximab (Erbitux), cobimetinib (Cotellic), crizotinib (Xalkori), dabrafenib (Tafinlar), dacomitinib (Vizimpro),Daratumumab (Darzalex), Daratumumab and hyaluronidase-fihj (Darzalex Faspro), Darorutamide (Nubeqa), Dasatinib (Sprycel), Deniroikin difutitox (Ontak), Denosumab (Xgeva), Dinutuximab (Unituxin), Dostallumab-gxly (Jemperli), Durvalumab (Imfinzi), Dubellisib (Copiktra), Elotuzumab (Empliciti), Enasidenib mesylate (Idhifa), Enco Rafenib (Braftovi), enfortumab vedotin-ejfv (Padcev), entrectinib (Rozlytrek), enzalutamide (Xtandi), erdafitinib (Balversa), erlotinib (Tarceva), everolimus (Afinitor), exemestane (Aromasin), fam-trastuzumab deruxtecan-nxki (Enhertu), fedra hydrochloride Tinib (Inrebic), fulvestrant (Faslodex), gefitinib (Iressa), gemtuzumab ozogamicin (Mylotarg), gilteritinib (Xospata), glass-degib maleate (Daurismo), hyaluronidase-zzxf (Phesgo), ibrutinib (Imbruvica), ibritumomab tiuxetan (Zevalin), idekabuta genbiculose Abecma, Idelalisib (Zydelig), Imatinib mesylate (Gleevec), Infiglatinib phosphate (Truseltiq), Inotuzumab ozogamicin (Besponsa), Ipilimumab (Yervoy), Isatuximab-irfc (Sarclisa), Ivosidenib (Tibsovo), Ixazomib citrate (Ninlaro), Lanreotide acetate (Somatuline) Depot), lapatinib (Tykerb), larotrectinib sulfate (Vitrakvi), lenvatinib mesylate (Lenvima), letrozole (Femara), lysocabategemmaralucel (Breyanzi), loncustuximab tesillin-lpyl (Zynlonta), lorlatinib (Lorbrena), lutetium Lu177-doteate (Lutathera), margetuximab-cmkb (Margenza),Midostaurin (Rydapt), Mobocertinib succinate (Exkivity), Mogamulizumab-kpkc (Poteligeo), Moxetumomab Pasdotox-tdfk (Lumoxiti), Naxitamab-gqgk (Danyelza), Necitumumab (Portrazza), Neratinib maleate (Nerlynx), Nilotinib (Tasigna), Niraparib tosylate monohydrate (Zejula), Nivolumab (Opdivo), Obinutuzumab (Gazyva), Ofatumumab (Arzerra), Olaparib (Lynparza), Oraratumab (Lartruvo), Osimertinib (Tagrisso), Palbociclib (Ibrance), Panit Mumab (Vectibix), pazopanib (Votrient), pembrolizumab (Keytruda), pemigatinib (Pemazyre), pertuzumab (Perjeta), pexidartinib hydrochloride (Turalio), polatuzumab vedotin-piiq (Polivy), ponatinib hydrochloride (Iclusig), pralatrexate (Folotyn), pralcetinib (Gavreto), radium-223 chloride (Xofigo), ramucirumab (Cyramza), regorafenib (Stivarga), ribociclib (Kisqali), lipretinib (Qinlock), rituximab (Rituxan), rituximab and hyaluronidase (Rituxan) Hycela), Romidepsin (Istodax), Rucaparib cansylate (Rubraca), Ruxolitinib phosphate (Jakafi), Sacituzumab govitecan-hziy (Trodelvy), Celiclib, Selinexol (Xpovio), Serpercatinib (Retevmo), Selumetinib sulfate (Koselugo), Siltuximab (Sylvant), Sirolimus-binding protein particles (Fyarro), So Nidezib (Odomzo), sorafenib (Nexavar), sotracib (Lumakras), sunitinib (Sutent), tafacitamab-cxix (Monjuvi), tagraxofusp-erzs (Elzonris), thalazoparib tosylate (Talzenna), tamoxifen (Nolvadex), tazemetostat hydrobromide (Tazverik), teventafusp-tebn (Kimmtrak),Temsirolimus (Torisel), tepotinib hydrochloride (Tepmetko), tisagenlecleucel (Kymriah), tisotumab vedotin-tftv (Tivdak), tocilizumab (Actemra), tofacitinib (Xeljanz), tocitumomab (Bexxar), trametinib (Mekinist), trastuzumab (Herceptin), tretinoin (Vesanoid), tivozanib hydrochloride (Fotivda), This may include remifene (Fareston), tucatinib (Tukysa), umbralicib tosylate (Ukoniq), vandetanib (Caprelsa), vemurafenib (Zelboraf), venetoclax (Venclexta), bismodegib (Erivedge), vorinostat (Zolinza), zanubrutinib (Brukinsa), ziv-aflibercept (Zaltrap), or any combination thereof.

[0178] In some cases, anti-cancer therapy or treatment may include immunotherapy (e.g., cancer treatment that acts by stimulating the immune system to fight cancer). In some cases, immunotherapy may include, for example, immune system modulators (e.g., cytokines, e.g., interferon or interleukin), immune checkpoint inhibitors (e.g., anti-PD-1 or anti-PD-L1 antibodies), T cell transfer therapy (e.g., tumor-infiltrating lymphocyte (TIL) therapy in which lymphocytes extracted from the patient's tumor are selected for their ability to recognize tumor cells and proliferate before reintroduction into the patient, or CAR T cell therapy in which the patient's T cells are modified to express CAR proteins before reintroduction into the patient), monoclonal antibody-based therapy (e.g., monoclonal antibodies that bind to cell surface markers on cancer cells to facilitate recognition by the immune system), or cancer treatment vaccines (e.g., vaccines based on tumor cells, tumor-associated neoantigens, or dendritic cells, etc., that stimulate the immune system to fight cancer).

[0179] In some cases, anticancer therapy or treatment may include neoantigen-based therapies. Non-exclusive examples of neoantigen-based therapies include T-cell receptor (TCR) engineered T-cell (TCR-T) therapy, chimeric antigen receptor T-cell (CAR-T) therapy, TCR bispecific antibody therapy, and cancer vaccines. TCR-T therapy is produced by genetically engineering a patient's T cells to express T cells specific to the neoantigen of interest, and then injecting them back into the patient. CAR-T therapy is produced by genetically engineering a patient's T cells to express a chimeric antigen receptor molecule containing intracellular signaling and co-signaling domains and an extracellular antigen-binding domain. CAR-T therapy does not always rely on neoantigen presentation, but can be designed to be neoantigen-directed. TCR bispecific antibody therapy is a small, engineered antibody molecule containing a neoantigen-specific TCR at one end and a CD3-directed single-chain variable fragment at the other end. Cancer vaccines may contain RNA molecules, DNA molecules, peptides, or combinations thereof, designed to enhance the immune system's ability to find and destroy neoantigen-presenting cells.

[0180] In some cases, the disclosed methods for determining the ctDNA fraction may be used in treating a disease (e.g., cancer) in a subject. For example, an effective dose of anticancer therapy or anticancer treatment may be administered to the subject depending on the determination of the ctDNA fraction in a sample from the subject using one of the methods disclosed herein.

[0181] In some examples, the disclosed method for determining the ctDNA fraction may be used to monitor disease progression or recurrence (e.g., progression or recurrence of cancer or tumor) in a subject. For example, in some examples, the method may be used to determine the ctDNA fraction in a first sample obtained from the subject at a first time point, and may be used to determine the ctDNA fraction in a second sample obtained from the subject at a second time point, where comparison of the first determination of the ctDNA fraction with the second determination of the ctDNA fraction allows for monitoring of disease progression or recurrence. In some examples, the first time point is selected before the subject receives therapy or treatment, and the second time point is selected after the subject receives therapy or treatment.

[0182] In some examples, the disclosed method may be used to adjust a therapy or treatment for a subject (e.g., anti-cancer therapy or anti-cancer treatment) by adjusting the therapeutic dose and / or selecting a different therapy in response to changes in the determination of the ctDNA fraction.

[0183] In some cases, the values ​​of the ctDNA fraction determined using the disclosed method may be used as prognostic or diagnostic indicators related to the sample. For example, in some cases, the prognostic or diagnostic indicators may include indicators of the presence of a disease (e.g., cancer) in the sample, indicators of the probability that a disease (e.g., cancer) is present in the sample, indicators of the probability that the subject from which the sample originates will develop a disease (e.g., cancer) (i.e., risk factors), or indicators of the likelihood that the subject from which the sample originates will respond to a particular therapy or treatment.

[0184] In some examples, the disclosed method for determining ctDNA fractions may be implemented as part of a genomic profiling process that involves identifying the presence of variant sequences at one or more loci in a sample derived from a subject, as part of the detection, monitoring, prediction of risk factors, or selection of treatment for a specific disease, e.g., cancer. In some examples, the variant panel selected for genomic profiling may include the detection of variant sequences at a selected set of loci. In some examples, the variant panel selected for genomic profiling may include the detection of variant sequences at several loci via comprehensive genomic profiling (CGP) (a next-generation sequencing (NGS) approach used to evaluate hundreds of genes (including associated cancer biomarkers) in a single assay). Including the disclosed method for determining ctDNA fractions as part of a genomic profiling process (or including the output from the disclosed method for determining ctDNA fractions as part of a subject's genomic profile) can improve the validity of, for example, disease detection calls and treatment decisions made based on the genomic profile, by independently confirming the presence of cancer in a given patient sample.

[0185] In some examples, a genomic profile may include information about the presence of genes (or their variant sequences), copy number variations, epigenetic traits, proteins (or their modifications), and / or other biomarkers in an individual's genome and / or proteome, as well as information about the individual's corresponding phenotypic traits and the interactions between genetic or genomic traits, phenotypic traits, and environmental factors.

[0186] In some cases, the genome profile in question may include results from comprehensive genome profiling (CGP) studies, nucleic acid sequencing-based studies, gene expression profiling studies, cancer hotspot panel studies, DNA methylation studies, DNA fragmentation studies, RNA fragmentation studies, or any combination thereof.

[0187] In some examples, the method may further include administering or applying a treatment or therapy (e.g., an anticancer agent, anticancer treatment, or anticancer therapy) based on the generated genomic profile. The anticancer agent or anticancer treatment may refer to a compound that is effective in treating cancer cells. Examples of anticancer agents or anticancer therapies include, but are not limited to, alkylating agents, antimetabolites, natural products, hormones, chemotherapy, radiotherapy, immunotherapy, surgery, or therapies configured to target defects in specific cellular signaling pathways, such as defects in the DNA mismatch repair (MMR) pathway. sample

[0188] The disclosed methods and systems may be used with any of a variety of samples (also referred to herein as specimens) containing nucleic acids (e.g., DNA or RNA) collected from a subject (e.g., a patient). Examples of samples include, but are not limited to, tumor samples, tissue samples, biopsy samples (e.g., tissue biopsy, fluid biopsy, or both), blood samples (e.g., peripheral whole blood samples), plasma samples, serum samples, lymph samples, saliva samples, sputum samples, urine samples, gynecological fluid samples, circulating tumor cell (CTC) samples, cerebrospinal fluid (CSF) samples, pericardial fluid samples, pleural fluid samples, ascites (peritoneal fluid) samples, fecal (or stool) samples, or other bodily fluid, secretion, and / or excretory samples (or cell samples derived therefrom). In certain examples, the sample may be a frozen sample or a formalin-fixed paraffin-embedded (FFPE) sample.

[0189] In some cases, specimens may be collected by tissue excision (e.g., surgical excision), needle biopsy, bone marrow biopsy, bone marrow aspiration, skin biopsy, endoscopic biopsy, fine-needle aspiration, oral swab, nasal swab, vaginal swab, or cytological smear, abrasion, lavage, or lavage fluid (such as tubular lavage fluid or bronchoalveolar lavage fluid).

[0190] In some cases, the sample is a liquid biopsy sample and may include, for example, whole blood, plasma, serum, urine, stool, sputum, saliva, or cerebrospinal fluid. In some cases, the sample may be a liquid biopsy sample and may contain circulating tumor cells (CTCs). In some cases, the sample may be a liquid biopsy sample and may contain cell-free DNA (cfDNA), circulating tumor DNA (ctDNA), or any combination thereof.

[0191] In some examples, the sample may contain one or more pre-malignant or malignant cells. As used herein, pre-malignant tumor refers to cells or tissue that are not yet malignant but are ready to become malignant. In certain examples, the sample may be obtained from a solid tumor, a soft tissue tumor, or a metastatic lesion. In certain examples, the sample may be obtained from a hematological malignancy or a pre-malignant tumor. In other examples, the sample may contain tissue or cells from a surgical margin. In certain examples, the sample may contain tumor-infiltrating lymphocytes. In some examples, the sample may contain one or more non-malignant cells. In some examples, the sample may be a primary tumor or a metastasis (e.g., a metastatic biopsy sample), or a portion thereof. In some examples, the sample may be obtained from the site with the highest percentage of tumor cells (e.g., tumor site) compared to adjacent sites (e.g., sites adjacent to the tumor). In some cases, the sample may be obtained from the site containing the largest tumor lesion (e.g., the site with the largest number of tumor cells as viewed under a microscope) compared to adjacent sites (e.g., sites adjacent to the tumor).

[0192] In some examples, the disclosed method may further include analyzing a primary control (e.g., a normal tissue sample). In some examples, the disclosed method may further include determining whether a primary control is available and, if available, isolating a control nucleic acid (e.g., DNA) from the primary control. In some examples, the sample may include any normal control (e.g., normal adjacent tissue (NAT)) if a primary control is not available. In some examples, the sample may be or may include histologically normal tissue. In some examples, the method includes evaluating a sample, e.g., a histologically normal sample (e.g., from a surgical tissue margin) using the method described herein. In some examples, the disclosed method may further include obtaining a partial sample enriched with non-tumor cells by, for example, macro-dissecting non-tumor tissue from the NAT in the sample without a primary control. In some examples, the disclosed method may further include determining that a primary control and NAT are not available and marking the sample for analysis without a matched control.

[0193] In some cases, samples obtained from histologically normal tissue (e.g., otherwise histologically normal tissue margins) may still contain genetic alterations, such as variant sequences, as described herein. Therefore, the method may further include reclassifying the samples based on the presence of detected genetic alterations. In some cases, multiple samples (e.g., from different subjects) are processed simultaneously.

[0194] The disclosed methods and systems may be applied to the analysis of nucleic acids extracted from various tissue samples (or disease states thereof), such as solid tissue samples, soft tissue samples, metastatic lesions, or liquid biopsy samples. Examples of tissues include, but are not limited to, connective tissue, muscle tissue, nervous system tissue, epithelial tissue, and blood. Tissue samples may be collected from any organ in the animal or human body. Examples of human organs include, but are not limited to, the brain, heart, lungs, liver, kidneys, pancreas, spleen, thyroid gland, mammary glands, uterus, prostate, large intestine, small intestine, bladder, bone, and skin.

[0195] In some cases, nucleic acids extracted from a sample may contain deoxyribonucleic acid (DNA) molecules. Examples of DNA that may be suitable for analysis by the disclosed methods include, but are not limited to, genomic DNA or fragments thereof, mitochondrial DNA or fragments thereof, cell-free DNA (cfDNA), and circulating tumor DNA (ctDNA). Cell-free DNA (cfDNA) consists of DNA fragments released from normal and / or cancer cells during apoptosis and necrosis, circulating in the bloodstream and / or accumulating in other bodily fluids. Circulating tumor DNA (ctDNA) consists of DNA fragments released from cancer cells and tumors that circulate in the bloodstream and / or accumulate in other bodily fluids.

[0196] In some cases, DNA is extracted from nucleated cells in a sample. In some cases, the sample may have low nucleated cell solidity, for example, if the sample consists mainly of red blood cells, diseased cells containing excess cytoplasm, or tissue with fibrosis. In some cases, samples with low nucleated cell solidity may require more, for example, a larger tissue volume, for DNA extraction.

[0197] In some examples, nucleic acids extracted from a sample may include ribonucleic acid (RNA) molecules. Examples of RNA that may be suitable for analysis by the disclosed methods include, but are not limited to, total cellular RNA, total cellular RNA after depletion of a specific amount of RNA sequence (e.g., ribosomal RNA), cell-free RNA (cfRNA), messenger RNA (mRNA) or fragments thereof, poly(A) tail mRNA fraction of total RNA, ribosomal RNA (rRNA) or fragments thereof, transfer RNA (tRNA) or fragments thereof, and mitochondrial RNA or fragments thereof. In some examples, RNA may be extracted from a sample and converted to complementary DNA, for example, using a reverse transcription reaction. In some examples, cDNA is produced by a random-primed cDNA synthesis method. In other examples, cDNA synthesis is initiated at the poly(A) tail of mature mRNA by priming with oligo(dT)-containing oligonucleotides. Methods for depletion, poly(A) enrichment, and cDNA synthesis are well known to those skilled in the art.

[0198] In some cases, the sample may contain tumor content (e.g., tumor cells or tumor cell nuclei) or non-tumor content (e.g., immune cells, fibroblasts, and other non-tumor cells). In some cases, the tumor content of the sample may constitute a sample index. In some cases, the sample may contain tumor content with at least 5-50%, 10-40%, 15-25%, or 20-30% tumor cell nuclei. In some cases, the sample may contain tumor content with at least 5%, at least 10%, at least 20%, at least 30%, at least 40%, or at least 50% tumor cell nuclei. In some cases, the percentage of tumor nuclei (e.g., sample fraction) is determined (e.g., calculated) by dividing the number of tumor cells in the sample by the total number of all cells in the sample that have nuclei. In some cases, for example, when the sample is a liver sample containing hepatocytes, different tumor content calculations may be required due to the presence of two or more times the number of hepatocytes with nuclei, other DNA content, e.g., non-hepatocytes, somatic cell nuclei. In some cases, the sensitivity to detecting genetic alterations, such as variant sequences, or the sensitivity to determining microsatellite instability, for example, may depend on the tumor content of the sample. For instance, a sample with a lower tumor content may result in lower sensitivity to detection for a sample of a given size.

[0199] In some examples, as described above, the sample includes nucleic acids (e.g., DNA, RNA (or cDNA derived from RNA), or both) from, for example, a tumor or normal tissue. In certain examples, the sample may further include non-nucleic acid components derived from, for example, a tumor or normal tissue, such as cells, proteins, carbohydrates, or lipids. subject

[0200] In some cases, the sample is obtained (e.g., collected) from a subject (e.g., a patient) who has a specific condition or disease (e.g., a hyperproliferative disorder or a non-cancerous indicator) or is suspected of having a certain condition or disease. In some cases, the hyperproliferative disorder is cancer. In some cases, cancer is a solid tumor or its metastatic form. In some cases, cancer is a blood cancer, e.g., leukemia or lymphoma.

[0201] In some cases, the subject has cancer or is at risk of developing cancer. For example, in some cases, the subject has a genetic predisposition to cancer (e.g., having a gene mutation that increases the baseline risk of developing cancer). In some cases, the subject is exposed to environmental changes (e.g., radiation or chemicals) that increase the risk of developing cancer. In some cases, the subject needs to be monitored for the development of cancer. In some cases, the subject needs to be monitored for cancer progression or regression after treatment with anti-cancer therapy (or anti-cancer treatment), for example. In some cases, the subject needs to be monitored for cancer recurrence. In some cases, the subject needs to be monitored for minimal residual disease (MRD). In some cases, the subject has been treated for cancer or is being treated. In some cases, the subject has not been treated with anti-cancer therapy (or anti-cancer treatment).

[0202] In some cases, the subject (e.g., a patient) is being treated with or has been treated with one or more targeted therapies. In some cases, for example, a post-targeted therapy sample (e.g., a specimen) is obtained (e.g., collected) from a patient who has been previously treated with a targeted therapy. In some cases, the post-targeted therapy sample is a sample obtained after the completion of the targeted therapy.

[0203] In some cases, the patients have not been previously treated with targeted therapy. In some cases, for example, in patients who have not been previously treated with targeted therapy, the samples include excisions, e.g., original excisions, or excisions after recurrence (e.g., after disease recurrence following therapy). cancer

[0204] In some cases, samples are obtained from subjects with cancer. Typical cancers include B-cell carcinoma (e.g., multiple myeloma), melanoma, breast cancer, lung cancer (such as non-small cell lung cancer or NSCLC), bronchial cancer, colorectal cancer, prostate cancer, pancreatic cancer, stomach cancer, ovarian cancer, bladder cancer, brain or central nervous system cancer, peripheral nervous system cancer, esophageal cancer, cervical cancer, uterine or endometrial cancer, oral or pharyngeal cancer, liver cancer, kidney cancer, testicular cancer, biliary tract cancer, small intestine or appendiceal cancer, salivary gland cancer, thyroid cancer, adrenal cancer, and bone and flesh cancer. Tumors, chondrosarcomas, hematological cancers, adenocarcinomas, inflammatory myofibroblastic tumors, gastrointestinal stromal tumors (GIST), colon cancer, multiple myeloma (MM), myelodysplastic syndromes (MDS), myeloproliferative disorders (MPD), acute lymphoblastic leukemia (ALL), chronic myeloid leukemia (AML), chronic myeloid leukemia (CML), chronic lymphocytic leukemia (CLL), polycythemia vera, Hodgkin lymphoma, non-Hodgkin lymphoma (NHL), soft tissue sarcomas, fibrosarcomas, myxosarcomas, Liposarcoma, osteosarcoma, chordoma, angiosarcoma, endosarcoma, lymphangiosarcoma, lymphangiosarcoma, synoviomas, mesothelioma, Ewing's tumor, leiomyosarcoma, rhabdomyosarcoma, squamous cell carcinoma, basal cell carcinoma, adenocarcinoma, sweat gland carcinoma, sebaceous gland carcinoma, papillary carcinoma, papillary adenocarcinoma, medullary carcinoma, bronchogenic carcinoma, renal cell carcinoma, liver cancer, cholangiocarcinoma, choriocarcinoma, seminoma, embryonic carcinoma, Wilms' tumor, bladder cancer, epithelial carcinoma, glioma, astrocytoma, medulloblastoma, craniopharyngioma, ependymoma, pineal carcinoma Examples of such tumors include, but are not limited to, somatic cell tumors, hemangioblastomas, acoustic neuroblastomas, oligodendrogliomas, meningiomas, neuroblastomas, retinoblastomas, follicular lymphomas, diffuse large B-cell lymphomas, mantle cell lymphomas, hepatocellular carcinomas, thyroid cancers, gastric cancers, head and neck cancers, small cell carcinomas, essential thrombocythemia, aplastic myelogenesis, eosinophilia syndrome, systemic mastocytosis, familial hypereosinophilia, chronic eosinophilia, neuroendocrine carcinomas, and carcinoid tumors.

[0205] In some cases, cancers include acute lymphoblastic leukemia (Philadelphia chromosome positive), acute lymphoblastic leukemia (precursor B cell), acute myeloid leukemia (FLT3+), acute myeloid leukemia (with IDH2 mutation), anaplastic large cell lymphoma, basal cell carcinoma, B-cell chronic lymphocytic leukemia, bladder cancer, breast cancer (HER2 overexpression / amplification), breast cancer (HER2+), breast cancer (HR+, HER2-), cervical cancer, cholangiocarcinoma, chronic lymphocytic leukemia, chronic lymphocytic leukemia (with 17p deficiency), chronic myeloid leukemia, chronic myeloid leukemia (Philadelphia chromosome positive), classical Hodgkin lymphoma, and colorectal cancer. Cancer, colorectal cancer (dMMR and MSI-H), colorectal cancer (KRAS wild-type), cryopyrin-associated periodic fever syndrome, cutaneous T-cell lymphoma, dermatofibrosarcoma protuberans, diffuse large B-cell lymphoma, fallopian tube cancer, follicular B-cell non-Hodgkin lymphoma, follicular lymphoma, gastric cancer, gastric cancer (HER2+), gastroesophageal junction (GEJ) adenocarcinoma, gastrointestinal stromal tumor, gastrointestinal stromal tumor (KIT+), giant cell tumor of bone, glioblastoma, granulomatosis with polyangiitis, head and neck squamous cell carcinoma, hepatocellular carcinoma, Hodgkin lymphoma, juvenile idiopathic arthritis, systemic lupus erythematosus, mantle cell lymphoma, medullary thyroid carcinoma, melanoma, BRAF Melanoma with V600 mutation, melanoma with BRAF V600E or V600K mutation, Merkel cell carcinoma, multicentric Castleman disease, multiple hematological malignancies including Philadelphia chromosome-positive ALL and CML, multiple myeloma, myelofibrosis, non-Hodgkin lymphoma, unresectable subependymal giant cell astrocytoma associated with tuberous sclerosis, non-small cell lung cancer, non-small cell lung cancer (ALK+), non-small cell lung cancer (PD-L1+), non-small cell lung cancer (with ALK fusion or ROS1 gene alteration), non-small cell lung cancer (with BRAF V600E mutation), non-small cell lung cancer (with EGFR exon 19 deletion or exon 21 substitution (L858R) mutation), non-small cell lung cancer (EGFRThis includes ovarian cancer (with T790M mutation), ovarian cancer (with BRCA mutation), pancreatic cancer, neuroendocrine tumors of pancreatic, gastrointestinal, or lung origin, pediatric neuroblastoma, peripheral T-cell lymphoma, peritoneal cancer, prostate cancer, renal cell carcinoma, rheumatoid arthritis, small lymphocytic lymphoma, soft tissue sarcoma, solid tumors (MSI-H / dMMR), squamous cell carcinoma of the head and neck, squamous non-small cell lung cancer, thyroid cancer, thyroid carcinoma, urothelial carcinoma, or primary gammaglobulinemia.

[0206] In some cases, cancer is a hematological malignancy (or pre-malignancy). As used herein, hematological malignancy refers to tumors of hematopoietic or lymphoid tissue, such as tumors affecting the blood, bone marrow, or lymph nodes. Exemplary hematological malignancies include leukemia (e.g., acute lymphoblastic leukemia (ALL), acute myeloid leukemia (AML), chronic lymphocytic leukemia (CLL), chronic myeloid leukemia (CML), hairy cell leukemia, acute monocytic leukemia (AMoL), chronic myelomonocytic leukemia (CMML), juvenile myelomonocytic leukemia (JMML), or macrogranular lymphocytic leukemia), lymphoma (e.g., AIDS-associated lymphoma, cutaneous T-cell lymphoma, Hodgkin lymphoma (e.g., classical Hodgkin lymphoma or nodular lymphocyte-predominant lymphoma) This includes, but is not limited to, primary central nervous system lymphomas (Hodgkin lymphoma type 1), mycosis fungoides, non-Hodgkin lymphomas (e.g., B-cell non-Hodgkin lymphomas (e.g., Burkitt lymphoma, small lymphocytic lymphoma / small cell lymphoma (CLL / SLL), diffuse large B-cell lymphoma, follicular lymphoma, immunoblastic large cell lymphoma, progenitor B-lymphoblastic lymphoma, or mantle cell lymphoma) or T-cell non-Hodgkin lymphomas (mycosis fungoides, anaplastic large cell lymphoma, or progenitor T-lymphoblastic lymphoma)), and primary central nervous system lymphomas. Nucleic acid extraction and processing

[0207] DNA or RNA can be extracted from tissue samples, biopsy samples, blood samples, or other bodily fluid samples using any of the various techniques known to those skilled in the art (see, for example, Example 1 of International Patent Application Publication No. 2012 / 092426, Tan, et al. (2009), “DNA, RNA, and Protein Extraction: The Past and The Present”, J. Biomed. Biotech. 2009:574398, the technical literature for the Maxwell® 16 LEV Blood DNA Kit (Promega Corporation, Madison, WI), and the Maxwell 16 Buccal Swab LEV DNA Purification Kit Technical Manual (Promega Literature #TM333, January 1, 2011, Promega Corporation, Madison, WI)). A protocol for RNA isolation is disclosed, for example, in Maxwell® 16 Total RNA Purification Kit Technical Bulletin (Promega Literature #TB351, August 2009, Promega Corporation, Madison, WI).

[0208] A typical DNA extraction procedure includes, for example, (i) collecting a fluid, cell, or tissue sample from which DNA will be extracted; (ii) disrupting the cell membrane (i.e., cell lysis), if necessary, to release DNA and other cytoplasmic components; (iii) treating the fluid or lysed sample with a concentrated salt solution to precipitate proteins, lipids, and RNA, followed by centrifugation to separate the precipitated proteins, lipids, and RNA; and (iv) purifying the DNA from the supernatant to remove any detergents, proteins, salts, or other reagents used during the cell membrane lysis step.

[0209] Cell membrane disruption can be carried out using various mechanical shearing techniques (e.g., French press or fine needle) or ultrasonic disruption techniques. The cell lysis step often involves the use of detergents and surfactants to dissolve lipids, cells, and nuclear membranes. In some examples, the lysis step may further include the use of proteases to disrupt proteins and / or RNases for digestion of RNA in the sample.

[0210] Examples of suitable techniques for DNA purification include, but are not limited to, (i) precipitation in ice-cold ethanol or isopropanol, followed by centrifugation (e.g., DNA precipitation which may be enhanced by increasing ionic strength, such as by adding sodium acetate); (ii) phenol-chloroform extraction, followed by centrifugation to separate the aqueous phase containing nucleic acids from the organic phase containing denatured proteins; and (iii) solid-phase chromatography in which nucleic acids are adsorbed onto a solid phase (e.g., silica or others) depending on the pH and salt concentration of the buffer.

[0211] In some cases, DNA-bound cell and histone proteins can be removed by adding proteases, by precipitating the proteins with sodium acetate or ammonium acetate, or by extraction with a phenol-chloroform mixture prior to the DNA precipitation step.

[0212] In some cases, DNA can be extracted using one of a variety of suitable commercially available DNA extraction and purification kits. Examples include, but are not limited to, the QIAamp (for isolation of genomic DNA from human samples) and DNAeasy (for isolation of genomic DNA from animal or plant samples) kits from Qiagen (Germantown, MD), or the Maxwell® and ReliaPrep® series from Promega (Madison, WI).

[0213] As described above, in some examples, the sample may involve formalin fixation (formaldehyde fixation or paraformaldehyde fixation) and paraffin-embedded (FFPE) tissue preparation. For example, an FFPE sample may be a tissue sample embedded in a substrate, such as an FFPE block. Methods for isolating nucleic acids (e.g., DNA) from formaldehyde-fixed or paraformaldehyde-fixed, paraffin-embedded (FFPE) tissues include, for example, Cronin, et al., (2004) Am J Pathol. 164(1):35-42, Masuda, et al., (1999) Nucleic Acids Res. 27(22):4436-4443, Specht, et al., (2001) Am J Pathol. 158(2):419-429, Ambion RecoverAll (trademark) Total Nucleic Acid Isolation Protocol (Ambion, Cat. No. AM1975, September 2008), Maxwell (registered trademark) 16 FFPE Plus LEV DNA Purification Kit Technical Manual (Promega Literature #TM349, February 2011), and EZNA (registered trademark) FFPE DNA Kit This is disclosed in the Handbook (OMEGA bio-tek, Norcross, GA, product numbers D3399-00, D3399-01, and D3399-02, June 2009) and the QIAamp® DNA FFPE Tissue Handbook (Qiagen, Cat. No. 37625, October 2007). For example, the RecoverAll® Total Nucleic Acid Isolation Kit solubilizes paraffin-embedded samples using xylene at high temperatures and captures nucleic acids by passing them through a glass fiber filter. The Maxwell® 16 FFPE Plus LEV DNA Purification Kit is used with the Maxwell® 16 Instrument to purify genomic DNA from 1-10 μm sections of FFPE tissue.DNA is purified using silica-clad paramagnetic particles (PMPs) and eluted at low elution volumes. The EZNA® FFPE DNA Kit uses a spin column and buffer system for genomic DNA isolation. The QIAamp® DNA FFPE Tissue Kit uses QIAamp® DNA Micro technology for the purification of genomic and mitochondrial DNA.

[0214] In some examples, the disclosed method may further include determining or obtaining a yield value of nucleic acids extracted from a sample and comparing the determined value to a reference value. For example, if the determined or obtained value is less than the reference value, the nucleic acids may be amplified before proceeding with library construction. In some examples, the disclosed method may further include determining or obtaining a value for the size (or average size) of nucleic acid fragments in the sample and comparing the determined or obtained value to a reference value, for example, the size (or average size) of at least 100, 200, 300, 400, 500, 600, 700, 800, 900, or 1000 base pairs (bps). In some examples, one or more parameters described herein may be adjusted or selected in accordance with this determination.

[0215] After isolation, nucleic acids are typically dissolved in a slightly alkaline buffer, such as Tris-EDTA(TE) buffer or ultrapure water. In some cases, isolated nucleic acids (e.g., genomic DNA) can be fragmented or sheared by using any of the various techniques known to those skilled in the art. For example, genomic DNA can be fragmented by physical shearing, enzymatic cleavage, chemical cleavage, and other methods well known to those skilled in the art. A method for DNA shearing is described, for example, in Example 4 of International Patent Application Publication No. 2012 / 092426. In some cases, alternative methods to DNA shearing can be used to avoid the ligation step during library preparation. Library preparation

[0216] In some cases, nucleic acids isolated from a sample may be used to construct a library (e.g., the nucleic acid library described herein). In some cases, nucleic acids are fragmented using one of the methods described above, optionally subjected to repair of strand-end damage, optionally ligated to synthesize adapters, primers, and / or barcodes (e.g., amplification primers, sequencing adapters, flow cell adapters, substrate adapters, sample barcodes or indices, and / or unique molecular identifier sequences), sizing selected (e.g., by preparative gel electrophoresis), and / or amplified (e.g., using PCR, non-PCR amplification techniques, or isothermal amplification techniques). In some cases, the fragmented and adapter-ligated nucleic acid group is used without explicit sizing or amplification prior to hybridization-based selection of target sequences. In some cases, nucleic acids are amplified by any of the various specific or nonspecific nucleic acid amplification methods known to those skilled in the art. In some cases, nucleic acids are amplified by whole-genome amplification methods, such as random prime strand substitution amplification. Examples of nucleic acid library preparation techniques for next-generation sequencing are described, for example, in van Dijk, et al. (2014), Exp. Cell Research 322:12-20, and in Illumina's genomic DNA sample preparation kit.

[0217] In some examples, the resulting nucleic acid library may contain all or substantially all of the complexity of the genome. In this context, the term “substantially all” actually refers to the possibility that there may be some undesirable loss of genomic complexity during the initial steps of the procedure. The methods described herein are also useful when the nucleic acid library is part of a genome, for example, when the complexity of the genome is reduced by design. In some examples, any selected portion of the genome can be used with the methods described herein. For example, in certain embodiments, the entire exome or a subset thereof is isolated. In some examples, the library may contain at least 95%, 90%, 80%, 70%, 60%, 50%, 40%, 30%, 20%, 10%, or 5% of genomic DNA. In some examples, the library may consist of cDNA copies of genomic DNA containing at least 95%, 90%, 80%, 70%, 60%, 50%, 40%, 30%, 20%, 10%, or 5% of genomic DNA. In certain cases, the amount of nucleic acid used to generate a nucleic acid library may be less than 5 micrograms, less than 1 microgram, less than 500 ng, less than 200 ng, less than 100 ng, less than 50 ng, less than 10 ng, less than 5 ng, or less than 1 ng.

[0218] In some examples, a library (e.g., a nucleic acid library) contains a collection of nucleic acid molecules. As described herein, the nucleic acid molecules in a library may include target nucleic acid molecules (e.g., tumor nucleic acid molecules, reference nucleic acid molecules and / or control nucleic acid molecules, also referred herein to as the first, second and / or third nucleic acid molecules, respectively). The nucleic acid molecules in a library may originate from a single subject or individual. In some examples, a library may include nucleic acid molecules originating from two or more subjects (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30 or more subjects). For example, two or more libraries originating from different subjects may be combined to form a library having nucleic acid molecules from two or more subjects (the nucleic acid molecules originating from each subject are optionally ligated to a unique sample barcode corresponding to a particular subject). In some examples, the subjects are humans who have or are at risk of having cancer or tumors.

[0219] In some cases, a library (or a portion thereof) may contain one or more subgenome segments. In some cases, a subgenome segment may be a single nucleotide location, for example, a nucleotide location where a variant is associated (positive or negative) with a tumor phenotype. In some cases, a subgenome segment may contain two or more nucleotide locations. Such examples include sequences of nucleotide locations with lengths of at least 2, 5, 10, 50, 100, 150, 250, or more than 250. A subgenome segment may include, for example, one or more whole genes (or portions thereof), one or more exons or coding sequences (or portions thereof), one or more introns (or portions thereof), one or more microsatellite regions (or portions thereof), or any combination thereof. A subgenome segment may include all or part of a naturally occurring nucleic acid molecule, for example, a fragment of a genomic DNA molecule. For example, a subgenome segment may correspond to a fragment of genomic DNA subjected to a sequencing reaction. In some cases, a subgenome segment is a continuous sequence from a genomic source. In some cases, subgenome segments contain non-contiguous sequences within the genome; for example, subgenome segments in cDNA may include exon-exon junctions formed as a result of splicing. In some cases, subgenome segments contain tumor nucleic acid molecules. In some cases, subgenome segments contain non-tumor nucleic acid molecules. Targeting of gene loci for analysis

[0220] The methods described herein can be used, for example, in combination with, or as part of, a method for evaluating a set of target intervals (e.g., target sequences) from a set of genomic loci (e.g., loci or fragments thereof), as described herein.

[0221] In some examples, the set of genomic loci evaluated by the disclosed method includes a plurality of genes, e.g., that are associated in variant forms with effects on cell division, growth, or survival, or with cancer, e.g., cancer as described herein.

[0222] In some examples, the set of loci evaluated by the disclosed method includes at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 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, at least 100, or more than 100 loci.

[0223] In some examples, a selected locus (also referred to herein as a target locus or target sequence) or fragment thereof may include a target interval comprising a non-coding sequence, coding sequence, intra-gene region, or inter-gene region of the target genome. For example, a target interval may include a non-coding sequence or fragment thereof (e.g., a promoter sequence, an enhancer sequence, a 5' untranslated region (5'UTR), a 3' untranslated region (3'UTR), or fragments thereof), a coding sequence of that fragment, an exon sequence or fragment thereof, or an intron sequence or fragment thereof. Target capture reagent

[0224] Methods described herein may involve contacting a nucleic acid library with multiple target capture reagents to select and capture multiple specific target sequences (e.g., gene sequences or fragments thereof) for analysis. In some examples, the target capture reagent (i.e., a molecule that binds to a target molecule and thereby enables the capture of the target molecule) is used to select the target segment to be analyzed. For example, the target capture reagent may be a bait molecule, e.g., a nucleic acid molecule (e.g., a DNA molecule or RNA molecule), that hybridizes to (i.e., is complementary to) the target molecule and thereby enables the capture of the target nucleic acid. In some examples, the target capture reagent, e.g., the bait molecule (or bait sequence), is a capture oligonucleotide (or capture probe). In some examples, the target nucleic acid may be a genomic DNA molecule, an RNA molecule, a cDNA molecule derived from an RNA molecule, a microsatellite DNA sequence, etc. In some examples, the target capture reagent is suitable for solution-phase hybridization of the target. In some examples, the target capture reagent is suitable for solid-phase hybridization of the target. In some examples, target capture reagents are suitable for both solution-phase and solid-phase hybridization of targets. The design and construction of target capture reagents are described in detail, for example, in International Patent Application Publication No. 2020 / 236941, the entire contents of which are incorporated herein by reference.

[0225] The methods described herein provide optimized sequencing of numerous genomic loci (e.g., genes or gene products (e.g., mRNA), microsatellite loci, etc.) from samples from one or more subjects (e.g., cancer tissue samples, liquid biopsy samples, etc.) by appropriate selection of target capture reagents for selecting target nucleic acid molecules to be sequenced. In some examples, the target capture reagent may hybridize to a specific target locus, e.g., a specific target locus or fragment thereof. In some examples, the target capture reagent may hybridize to a specific group of target loci, e.g., a specific group of loci or fragment thereof. In some examples, multiple target capture reagents may be used, including a mixture of target-specific and / or group-specific target capture reagents.

[0226] In some examples, the number of target capture reagents (e.g., bait molecules) in multiple target capture reagents (e.g., bait sets) that come into contact with a nucleic acid library to capture multiple target sequences for nucleic acid sequencing is greater than 10, greater than 50, greater than 100, greater than 200, greater than 300, greater than 400, greater than 500, greater than 600, greater than 700, greater than 800, greater than 900, greater than 1,000, greater than 1,250, greater than 1,500, greater than 1,750, greater than 2,000, greater than 3,000, greater than 4,000, greater than 5,000, greater than 10,000, greater than 25,000, or greater than 50,000.

[0227] In some examples, the total length of the target capture reagent sequence can be approximately 70 to 1000 nucleotides. In one example, the length of the target capture reagent is approximately 100 to 300 nucleotides, 110 to 200 nucleotides, or 120 to 170 nucleotides. In addition to the above, intermediate oligonucleotide lengths of approximately 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 300, 400, 500, 600, 700, 800, and 900 nucleotides can be used in the method described herein. In some embodiments, oligonucleotides with approximately 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, or 230 bases can be used.

[0228] In some examples, each target capture reagent sequence may include (i) a target-specific capture sequence (e.g., a locus- or microsatellite locus-specific complementary sequence), (ii) an adapter, primer, barcode, and / or a unique molecular identifier sequence, and (iii) a universal tail at one or both ends. As used herein, the term “target capture reagent” may refer to a target-specific target capture sequence or the entire target capture reagent oligonucleotide containing the target-specific target capture sequence.

[0229] In some examples, the target-specific capture sequence in the target capture reagent is approximately 40 to 1000 nucleotides long. In some examples, the target-specific capture sequence is approximately 70 to 300 nucleotides long. In some examples, the target-specific sequence is approximately 100 to 200 nucleotides long. In yet another example, the target-specific sequence is approximately 120 to 170 nucleotides long, typically 120 nucleotides long. In addition to the above, target-specific sequences of intermediate lengths, for example, approximately 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 300, 400, 500, 600, 700, 800, and 900 nucleotides long, as well as target-specific sequences of lengths between the above lengths, can also be used in the methods described herein.

[0230] In some cases, target capture reagents may be designed to select a target interval containing one or more rearrangements, such as an intron containing a genomic rearrangement. In such cases, the target capture reagent is designed so that repetitive sequences are masked to enhance selection efficiency. In these cases, where the rearrangement has a known ligature sequence, a complementary target capture reagent can be designed for the ligature sequence to enhance selection efficiency.

[0231] In some examples, the disclosed methods may involve the use of target capture reagents designed to capture two or more different target categories, each category having a different target capture reagent design strategy. In some examples, the hybridization-based capture methods and target capture reagent compositions disclosed herein provide capture and homogeneous coverage of a target sequence set, while minimizing coverage of genomic sequences outside the targeted sequence set. In some examples, the target sequences may include an entire exome of genomic DNA or a selected subset thereof. In some examples, the target sequences may include, for example, a large chromosomal region (e.g., an entire chromosome arm). The methods and compositions disclosed herein provide different target capture reagents for achieving different sequencing depth and coverage patterns for composite target nucleic acid sequence sets.

[0232] Typically, DNA molecules are used as target capture reagent sequences, but RNA molecules can also be used. In some examples, the DNA molecular target capture reagent can be single-stranded DNA (ssDNA) or double-stranded DNA (dsDNA). In some examples, RNA-DNA double helix is ​​more stable than DNA-DNA double helix and therefore potentially provides better nucleic acid capture.

[0233] In some examples, the disclosed methods include providing a selected set of nucleic acid molecules (e.g., a library catch) captured from one or more nucleic acid libraries. For example, the method may include providing one or more nucleic acid libraries, each containing multiple nucleic acid molecules (e.g., multiple target nucleic acid molecules and / or reference nucleic acid molecules) extracted from one or more samples from one or more subjects; contacting one or more libraries (e.g., in a solution-based hybridization reaction) with one, two, three, four, five, or more than five target capture reagents (e.g., oligonucleotide target capture reagents) to form a hybridization mixture containing multiple target capture reagent / nucleic acid molecule hybrids; and separating the multiple target capture reagent / nucleic acid molecule hybrids from the hybridization mixture by contacting the hybridization mixture with a binding entity that enables the separation of the multiple target capture reagent / nucleic acid molecule hybrids from the hybridization mixture, thereby providing a library catch (e.g., a subgroup of selected or concentrated nucleic acid molecules from one or more libraries).

[0234] In some examples, the disclosed method may further include amplifying the library catch (e.g., by performing PCR). In other examples, the library catch is not amplified.

[0235] In some cases, the target capture reagent may be part of a kit that optionally includes instructions, standards, buffers, enzymes, or other reagents. Hybridization conditions

[0236] As described above, the methods disclosed herein may include the step of contacting a library (e.g., a nucleic acid library) with a selection of target capture reagents to contact a library target nucleic acid sequence (i.e., library catch). The contact step can be carried out, for example, by solution-based hybridization. In some examples, the method includes repeating the hybridization step with respect to one or more additional solution-based hybridizations. In some examples, the method further includes subjecting the library catch to one or more additional solution-based hybridizations with the same or different sets of target capture reagents.

[0237] In some examples, the contact step is carried out using a solid support, such as an array. Suitable solid supports for hybridization are described, for example, in Albert, T.Jet al. (2007) Nat. Methods 4(11):903-5, Hodges, E. et al. (2007) Nat. Genet. 39(12):1522-7, and Okou, D. et al. (2007) Nat. Methods 4(11):907-9, the contents of which are incorporated herein by reference in their entirety.

[0238] Hybridization methods that can be adapted for use in the methods described herein are described in the art, for example, in International Patent Application Publication No. 2012 / 092426. Methods for hybridizing a target capture reagent to multiple target nucleic acids are described in detail, for example, in International Patent Application Publication No. 2020 / 236941, the entirety of which is incorporated herein by reference. Sequence determination method

[0239] The methods and systems disclosed herein, when used in combination with or as part of a method or system for sequencing nucleic acids (e.g., a next-generation sequencing system), can generate multiple sequence reads that overlap with one or more loci within a subgenome section in a sample, thereby enabling the determination of allelic sequences at multiple loci, for example. “Next-generation sequencing” (or “NGS”) as used herein may also be referred to as “ultra-parallel sequencing” (or “MPS”), which sequences the nucleotide sequences of individual nucleic acid molecules (e.g., in single-molecule sequencing) or cloned proxies of individual nucleic acid molecules in a high-throughput manner (e.g., 10¹⁶). 3 , 10 4 , 10 5 , or 10 5 This refers to any sequencing method that determines the sequence of molecules (where more than a certain number of molecules are sequenced simultaneously).

[0240] Next-generation sequencing methods are known in the art and are described, for example, in Metzker, M. (2010) Nature Biotechnology Reviews 11:31-46, which is incorporated herein by reference. Other examples of sequencing methods suitable for use when implementing the methods and systems disclosed herein are described, for example, in International Patent Application Publication No. 2012 / 092426. In some examples, sequencing may include, for example, whole-genome sequencing (WGS), whole-exome sequencing, targeted sequencing, or direct sequencing. In some examples, sequencing may be performed using, for example, Sanger sequencing. In some examples, sequencing may include paired-end sequencing techniques that enable sequencing of both ends of a fragment and generate high-quality alignable sequence data for, for example, the detection of genome rearrangements, repetitive sequence elements, gene fusions, and novel transcripts.

[0241] The disclosed methods and systems may be implemented using sequencing platforms such as the Roche / 454 Genome Sequencer (GS) FLX System, Illumina / Solexa Genome Analyzer (GA), Illumina's HiSeq® 2500, HiSeq® 3000, HiSeq® 4000 and NovaSeq® 6000 Sequencing Systems, Life / APG's Support Oligonucleotide Ligation Detection (SOLiD) system, Polonator's G.007 system, Helicos BioSciences' HeliScope Gene Sequencing system, or Pacific Biosciences' PacBio® RS platform. In some examples, sequencing may include Illumina MiSeq® sequencing. In some examples, sequencing may include Illumina HiSeq® sequencing. In some examples, sequencing may include Illumina NovaSeq® sequencing. An optimized method for sequencing multiple target genomic loci in nucleic acids extracted from a sample is described in detail, for example, in International Patent Application Publication No. 2020 / 236941, the entire contents of which are incorporated herein by reference.

[0242] In a particular example, the disclosed method is to (a) obtain a library containing multiple normal and / or tumor nucleic acid molecules from a sample; (b) contact the library with one, two, three, four, five, or six or more target capture reagents simultaneously or sequentially under conditions that allow hybridization of the target capture reagents to target nucleic acid molecules, thereby providing a selected set of captured normal and / or tumor nucleic acid molecules (i.e., library catch); and (c) hybridize a selected subset of nucleic acid molecules (e.g., library catch) by contacting the hybridization mixture with a binding entity that allows separation of the target capture reagent / nucleic acid molecule hybrid from the hybridization mixture. The process includes one or more of the following steps: (d) separating from the synthesis mixture; (e) sequencing the library catch to obtain multiple reads (e.g., sequence reads) from the library catch, which may contain variant sequences including mutations (or modifications), such as somatic mutations or germline mutations, that overlap with one or more target segments (e.g., one or more target sequences); (e) aligning the sequence reads using an alignment method described elsewhere in this Spec; and / or (f) assigning nucleotide values ​​to nucleotide positions within a target segment (e.g., calling mutations using Bayesian methods, e.g., or other methods described herein).

[0243] In some examples, obtaining sequence reads for one or more target segments may involve sequencing at least 1, at least 5, at least 10, at least 20, at least 30, at least 40, at least 50, at least 100, at least 150, at least 200, at least 250, at least 300, at least 350, at least 400, at least 450, at least 500, at least 550, at least 600, at least 650, at least 700, at least 750, at least 800, at least 850, at least 900, at least 950, at least 1,000, at least 1,250, at least 1,500, at least 1,750, at least 2,000, at least 2,250, at least 2,500, at least 2,750, at least 3,000, at least 3,500, at least 4,000, at least 4,500, or at least 5,000 loci, such as genomic loci, genomic loci, microsatellite loci, etc. In some examples, obtaining sequence reads for one or more target intervals may involve sequencing target intervals for any number of loci within the range described in this paragraph, e.g., at least 2,850 loci.

[0244] In some examples, obtaining sequence reads for one or more target segments involves sequencing the target segments using a sequencing method that provides sequence read lengths (or average sequence read lengths) of at least 20, 30, 40, 50, 60, 70, 80, 90, 100, 120, 140, 160, 180, 200, 220, 240, 260, 280, 300, 320, 340, 360, 380, or 400 bases. In some examples, obtaining sequence reads for one or more target segments may involve sequencing the target segments using a sequencing method that provides sequence read lengths (or average sequence read lengths) of any number of bases within the range described in this paragraph, for example, a sequence read length (or average sequence read length) of 56 bases.

[0245] In some examples, obtaining sequence reads for one or more target intervals may include sequencing with an average coverage (or depth) of at least 100x. In some examples, obtaining sequence reads for one or more target intervals may include sequencing with an average coverage (or depth) of at least 100x, at least 150x, at least 200x, at least 250x, at least 500x, at least 750x, at least 1,000x, at least 1,500x, at least 2,000x, at least 2,500x, at least 3,000x, at least 3,500x, at least 4,000x, at least 4,500x, at least 5,000x, at least 5,500x, or at least 6,000x. In some examples, obtaining sequence reads for one or more target intervals may include sequencing with an average coverage (or depth) of at least 160x, for example, having any value within the range of values ​​described in this paragraph.

[0246] In some examples, obtaining sequence reads for one or more target intervals involves sequencing approximately 90%, 92%, 94%, 95%, 96%, 97%, 98%, or more than 99% of sequenced loci at an average sequencing depth of at least 100× to at least 6,000×. For example, in some examples, obtaining reads for a target interval involves sequencing at least 125× at an average sequencing depth for at least 99% of sequenced loci. In another example, in some examples, obtaining reads for a target interval involves sequencing at least 4,100× at an average sequencing depth for at least 95% of sequenced loci.

[0247] In some cases, the relative abundance of nucleic acid species in a library can be estimated by counting the relative number of occurrences of those congeneral sequences in the data generated by sequencing experiments (e.g., the number of sequence reads for a given congeneral sequence).

[0248] In some examples, the disclosed methods and systems provide nucleotide sequences for a set of target intervals (e.g., loci) as described herein. In certain specific cases, the sequences are provided without using methods that include matched normal controls (e.g., wild-type controls) and / or matched tumor controls (e.g., primary versus metastatic).

[0249] In some examples, as used herein, the level of sequencing depth (e.g., X-fold level of sequencing depth) refers to the number of reads (e.g., unique reads) obtained after the detection and removal of duplicate reads (e.g., PCR duplicate reads). In other examples, duplicate reads are evaluated, for example, to aid in the detection of copy number variation (CNA). alignment

[0250] Alignment is the process of matching a read to a specific location, such as a genomic location or locus. In some cases, NGS reads may be aligned to a known reference sequence (e.g., a wild-type sequence). In some cases, NGS reads may be de novo assembled. Methods for sequence alignment of NGS reads are described, for example, in Trapnell, C. and Salzberg, SLNature Biotech., 2009, 27:455-457. Examples of de novo sequence assembly are described, for example, in Warren R. et al., Bioinformatics, 2007, 23:500-501, Butler J. et al., Genome Res., 2008, 18:810-820, and Zerbino DR and Birney E., Genome Res., 2008, 18:821-829. The optimization of sequence alignment has been described in the Art, for example, in International Patent Application Publication No. 2012 / 092426. Further descriptions of sequence alignment methods are described in detail, for example, in International Patent Application Publication No. 2020 / 236941, the entirety of which is incorporated herein by reference.

[0251] Misalignment (e.g., placement of base pairs from short reads in an inaccurate location within the genome), such as a shift in the histogram peak of an alternative allele read, can lead to decreased sensitivity in mutation detection due to the sequence context surrounding the actual cancer mutation (e.g., the presence of repetitive sequences). Other examples of sequence contexts that can cause misalignment include short tandem repeats, scattered repetitive sequences, low-complexity regions, insertions-deletions (indels), and paralogs. In cases where a problematic sequence situation arises when no actual mutation is present, misalignment may introduce artifact reads of a “mutant” allele by placing reads of the actual reference genome sequence in the wrong location. Since mutation calling algorithms for multiplex analysis must be sensitive even to low-abundance mutations, sequence misalignment can increase the false-positive detection rate and / or decrease specificity.

[0252] In some examples, the methods and systems disclosed herein may integrate the use of multiple individually tailored alignment methods or algorithms to optimize base call performance in sequencing methods, particularly those that rely on massively parallel sequencing (MPS) of numerous diverse genetic events at numerous diverse genomic loci. In some examples, the disclosed methods and systems may include the use of one or more global alignment algorithms. In some examples, the disclosed methods and systems may include the use of one or more local alignment algorithms.Examples of alignment algorithms that may be used include, but are not limited to, the Burrows-Wheeler Alignment (BWA) software bundle (see, e.g., Li, et al. (2009), “Fast and Accurate Short Read Alignment with Burrows-Wheeler Transform”, Bioinformatics 25:1754-60, Li, et al. (2010), “Fast and Accurate Long-Read Alignment with Burrows-Wheeler Transform”, Bioinformatics epub.PMID:20080505), the Smith-Waterman algorithm (see, e.g., Smith, et al. (1981), “Identification of Common Molecular Subsequences”, J. Molecular Biology 147(1):195-197), and the Striped Smith-Waterman algorithm (see, e.g., Farrar (2007), “Striped Smith-Waterman Speeds Database Searches Six Times Over Other Examples include the SIMD Implementations (see Bioinformatics 23(2):156-161), the Needleman-Wunsch algorithm (Needleman, et al. (1970) “A General Method Applicable to the Search for Similarities in the Amino Acid Sequence of Two Proteins”, J. Molecular Biology 48(3):443-53), or any combination thereof.

[0253] In some examples, the methods and systems disclosed herein may also include the use of sequence assembly algorithms, such as the Arachne sequence determination assembly algorithm (see, for example, Batzoglou, et al. (2002), “ARACHNE: A Whole-Genome Shotgun Assembler”, Genome Res. 12: 177-189).

[0254] In some cases, the alignment method used to analyze sequence reads is not individually customized or adjusted for the detection of different variants (e.g., point mutations, insertions, deletions, etc.) at different genomic loci. In some cases, a different alignment method is used to analyze reads that is individually customized or adjusted for the detection of at least a subset of different variants detected at different genomic loci. In some cases, a different alignment method is used to analyze reads that is individually customized or adjusted for the detection of each different variant at different genomic loci. In some cases, the adjustment can be a function of one or more of the following: (i) the sequenced locus (e.g., a locus, microsatellite locus, or other target interval), (ii) the tumor type associated with the sample, (iii) the sequenced variant, or (iv) the characteristics of the sample or target. The selection or use of alignment conditions individually adjusted for several specific target intervals to be sequenced allows for optimization of speed, sensitivity, and specificity. This method is particularly effective when read alignment is optimized for a relatively large number of diverse target intervals.

[0255] In some examples, the method includes a combination of an alignment method optimized for reorganization and other alignment methods optimized for target intervals not associated with reorganization.

[0256] In some examples, the methods disclosed herein further include selecting or using an alignment method for analyzing, e.g., aligning, sequence reads, the alignment method being a function of, or selected accordingly, or optimized for, one or more of the following: (i) tumor type, e.g., tumor type in a sample; (ii) location of the segment to be sequenced (e.g., locus); (iii) type of variant within the segment to be sequenced (e.g., point mutation, insertion, deletion, substitution, copy number mutation (CNV), rearrangement, or fusion); (iv) site to be analyzed (e.g., nucleotide position); (v) type of sample (e.g., a sample as described herein); and / or (vi) adjacent sequences within or near the segment to be evaluated (e.g., according to its expected tendency toward misalignment of the segment due to the presence of repeat sequences within or near the segment).

[0257] In some cases, the methods disclosed herein enable the rapid and efficient alignment of cumbersome reads, e.g., reads having rearrangements. Thus, in some cases where the reads for the target interval include rearrangements, e.g., nucleotide positions with translocations, the methods may be appropriately modified and may include using an alignment method comprising: (i) selecting a rearrangement reference sequence for alignment with the read, such that the rearrangement reference sequence aligns with the rearrangement (in some cases, the reference sequence is not identical to the genomic rearrangement); and (ii) comparing, e.g., aligning the read with the rearrangement reference sequence.

[0258] In some cases, alternative methods may be used to align problematic reads. These methods are particularly effective when the alignment of reads is optimized for a relatively large number of diverse target intervals. For example, a method for analyzing a sample may involve (i) performing a read comparison (e.g., alignment comparison) using a first set of parameters (e.g., by using a first mapping algorithm or by comparison with a first reference sequence) to determine whether the read satisfies a first alignment criterion (e.g., the read can be aligned with the first reference sequence, e.g., with fewer than a certain number of mismatches), and (ii) if the read does not satisfy the first alignment criterion, performing a second alignment comparison using a second set of parameters (e.g., a second mapping algorithm). (iii) optionally, determining whether the read satisfies a second criterion (e.g., whether the read can be aligned with the second reference sequence, e.g., with fewer than a certain number of mismatches), which may include determining whether the second set of parameters is more likely to result in alignment with the read for the variant (e.g., rearrangement, insertion, deletion, or translocation) compared to the first set of parameters.

[0259] In some cases, the alignment of sequence reads in the disclosed method may be combined with mutation calling methods described elsewhere in this Spec. As discussed herein, any decrease in sensitivity for detecting actual mutations may be addressed by evaluating (in a manual or automated manner) the quality of the alignment around the expected mutation site of the gene or genomic locus (e.g., locus) being analyzed. In some cases, the sites to be evaluated may be obtained from a database of the human genome (e.g., the HG19 human reference genome) or cancer mutations (e.g., COSMIC). Regions identified as problematic may be repaired by alignment optimization (or realignment) using a slower but more accurate alignment algorithm, such as the Smith-Waterman alignment, using an algorithm selected to give better performance in the relevant sequence context. If general alignment algorithms fail to improve the situation, a customized alignment approach may be created, for example, by adjusting the largest and most different mismatch penalty parameters for high-likelihood genes, including substitutions; by adjusting specific mismatch penalty parameters based on specific mutation types common to a particular tumor type (e.g., C→T in melanoma); or by adjusting specific mismatch penalty parameters based on specific mutation types common to a particular sample type (e.g., substitutions common in FFPE).

[0260] The decrease in specificity of the evaluated target interval (increase in false positive rate) due to misalignment can be assessed by manually or automatically checking all mutation calls in the sequencing data. Regions found to be prone to false mutation calls due to misalignment can be subjected to the alignment improvements discussed above. If algorithmic improvements are not possible, "mutations" from the problem region can be classified or screened from a panel of target loci. Mutation Invocation

[0261] A base call refers to the raw output of a sequencing device, for example, the determined sequence of nucleotides in an oligonucleotide molecule. A mutation call refers to the process of selecting a nucleotide value, for example, A, G, T, or C, for a given nucleotide position being sequenced. Typically, a sequence read (or base call) for a position will provide two or more values, for example, some reads will indicate T and some will indicate G. A mutation call is the process of assigning the correct nucleotide value, for example, one of those values, to the sequence. Although called a "mutation" call, it can be applied to assign a nucleotide value to any nucleotide position, for example, a position corresponding to a mutant allele, a wild-type allele, an allele not characterized as mutant or wild-type, or a position not characterized by variability.

[0262] In some examples, the disclosed methods may include the use of customized or tuned variant calling algorithms or their parameters to optimize performance when applied to sequencing data, particularly in methods that rely on massively parallel sequencing (MPS) of numerous diverse genetic events at numerous diverse genomic loci (e.g., loci, microsatellite regions, etc.) in a sample, e.g., a subject with cancer. Optimization of variant calling is described in the Art, for example, in International Patent Application Publication No. 2012 / 092426.

[0263] Methods for mutation calling may include one or more of the following: making independent calls based on information at each position in the reference sequence (e.g., examining sequence reads; examining base calls and quality scores; calculating the probabilities of observed bases and quality scores given potential genotypes; and assigning genotypes (e.g., using Bayes' rules)); removing false positives (e.g., using depth thresholds to reject SNPs with read depths much lower or higher than expected; local readjustment to remove false positives due to small indels); and refining calls by performing analysis based on linkage disequilibrium (LD) / complementation.

[0264] Formulas used to calculate genotype likelihoods associated with specific genotypes and locations are described, for example, in Li H. and Durbin R. Bioinformatics, 2010;26(5):589-95. Prior predictions for specific mutations in a particular cancer type can be used when evaluating samples from that cancer type. Such likelihoods can be obtained from publicly available cancer mutation databases, such as the Catalogue of Somatic Mutation in Cancer (COSMIC), HGMD (Human Gene Mutation Database), The SNP Consortium, Breast Cancer Mutation Data Base (BIC), and Breast Cancer Gene Database (BCGD).

[0265] Examples of LD / complementary analysis are described, for example, in Browning, BLand Yu, Z. Am. J. Hum. Genet. 2009, 85(6):847-61. Examples of low-coverage SNP calling methods are described, for example, in Li, Y., et al., Annu. Rev. Genomics Hum. Genet. 2009, 10:387-406.

[0266] After alignment, substitution detection can be performed using a calling method (e.g., a Bayesian mutation calling method), which is applied to each base in the target interval, e.g., to the exons of the gene or other loci being evaluated, and the presence of alternative alleles is observed. This method compares the probability of observing read data in the presence of a mutation with the probability of observing read data in the presence of base call errors only. If this comparison strongly supports the presence of a mutation, the mutation can be called.

[0267] The advantage of the Bayesian mutation detection approach is that the comparison between the probability of mutation presence and the probability of base calling error alone can be weighted by prior predictions of the presence of the mutation at that site. If several reads of an alternative allele are observed at a frequently mutated site for a given cancer type, the presence of the mutation may be reliably called even if the amount of evidence for the mutation does not meet the usual threshold. This flexibility can then be used to increase the sensitivity of detecting rarer mutations / lower purity samples or to make the test more robust against reduced read coverage. The likelihood of a random base pair in the genome being mutated in cancer is approximately 1e-6. For example, the likelihood of specific mutations occurring at many sites in a typical polygenic cancer genome panel can be orders of magnitude higher. These likelihoods may originate from publicly available databases of cancer mutations (e.g., COSMIC).

[0268] Indel calling is the process of finding bases in sequencing data that differ from a reference sequence due to insertions or deletions, typically including associated confidence scores or statistical evidence measures. Indel calling methods may include steps of identifying candidate indels, calculating genotype likelihood by local realignment, and performing LD-based genotype inference and calling. Typically, Bayesian methods are used to obtain potential indel candidates, which are then tested against reference sequences within a Bayesian framework.

[0269] Algorithms for generating candidate indels are described, for example, in McKenna, A., et al., Genome Res. 2010; 20(9): 1297-303, Ye, K., et al., Bioinformatics, 2009; 25(21): 2865-71, Lunter, G., and Goodson, M., Genome Res. 2011; 21(6): 936-9, and Li, H., et al. (2009), Bioinformatics 25(16): 2078-9.

[0270] One method for generating indel calls and individual-level genotype likelihoods is the Dindel algorithm (Albers CA et al., Genome Res. 2011;21(6):961-73). For example, a Bayesian EM algorithm can be used to analyze reads, perform initial indel calls, generate genotype likelihoods for each candidate indel, and then perform genotype completion using, for example, QCALL (Le SQ and Durbin R. Genome Res. 2011;21(6):952-60). Parameters such as pre-observation predictions of indels can be adjusted based on the size or location of the indels (e.g., increased or decreased).

[0271] Methods have been developed to address limited deviations from 50% or 100% allele frequencies for the analysis of cancer DNA. (See, e.g., SNVMix - Bioinformatics. 2010 March 15;26(6):730-736.) However, the methods disclosed herein allow for consideration of frequencies (or allele fractions) in the range of 1% to 100% (i.e., allele fractions in the range of 0.01 to 1.0), and in particular, the possibility of the presence of mutant alleles at levels less than 50%. This approach is particularly important, for example, for detecting mutations in low-purity FFPE samples of natural (multiclonal) tumor DNA.

[0272] In some examples, the variant calling methods used to analyze array reads are not individually customized or adjusted for the detection of different variants at different genomic loci. In some examples, different variant calling methods that are individually customized or fine-tuned for at least a subset of the different variants detected at different genomic loci are used. In some examples, different variant calling methods that are individually customized or fine-tuned for each different variant detected at each different genomic locus are used. The customization or adjustment can be based on one or more of the factors described herein, such as the type of cancer in the sample, the gene or locus where the sequenced target interval is located, or the variant being sequenced. This selection or use of variant calling methods that are individually customized or fine-tuned for the number of sequenced target intervals enables optimization of the speed, sensitivity, and specificity of variant calling.

[0273] In some examples, nucleotide values are assigned to the nucleotide positions of each of X unique target intervals using a unique variant calling method, where X is at least 2, at least 3, at least 4, at least 5, at least 10, at least 15, at least 20, at least 30, at least 40, at least 50, at least 60, at least 70, at least 80, at least 90, at least 100, at least 200, at least 300, at least 400, at least 500, at least 1000, at least 1500, at least 2000, at least 2500, at least 3000, at least 3500, at least 4000, at least 4500, at least 5000, or more. The calling methods are different and can be made unique, for example, by depending on different Bayesian prior values.

[0274] In some examples, assigning the nucleotide value is a function of an expected value or a value representing an expected value in a previous (e.g., in the literature) of observing a variant, e.g., a read indicating a mutation, at that nucleotide position in that type of tumor.

[0275] In some examples, the method includes assigning nucleotide values (e.g., variant calls) for at least 10, 20, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, or 1,000 nucleotide positions, each assignment being a function of an expected value from before (e.g., in the literature) or a unique value (contrasted with the values of other assignments) representing a variant, e.g., a variant at that nucleotide position in a tumor of that type, prior to observing a read indicating the variant.

[0276] In some examples, assigning the nucleotide values is a function of a set of values representing the probability of observing a read indicating the variant at that nucleotide position when the variant is present in the sample at a particular frequency (e.g., 1%, 5%, 10%, etc.) and / or when the variant is not present (e.g., observed in the read due to only base calling error).

[0277] In some examples, the variant calling method described herein includes: (a) for each of the X target intervals, for each nucleotide position therein, obtaining (i) a first value that is an expected value from before (e.g., in the literature) or a value representing the same for a variant, e.g., a variant at that nucleotide position in a tumor of type X, prior to observing a read indicating the variant, and (ii) a set of second values representing the likelihood of observing a read indicating the variant at that nucleotide position when the variant is present in the sample at a certain frequency (e.g., 1%, 5%, 10%, etc.) and / or when the variant is not present (e.g., observed in the read due to only base calling error); and (b) in accordance with the values, for example, by weighting a comparison between values in a second set that uses the first value, e.g., by the Bayesian method described herein, assigning a nucleotide value (e.g., calling a variant) from the read to each of the nucleotide positions, thereby analyzing the sample.

[0278] Additional descriptions of exemplary nucleic acid sequencing methods, mutation calling methods, and methods for the analysis of genetic variants are provided, for example, in U.S. Patent Nos. 9,340,830, 9,792,403, 11,136,619, 11,118,213, and International Patent Application Publication No. 2020 / 236941, the entire contents of each of which are incorporated herein by reference. system

[0279] Also disclosed herein is a system designed to implement any of the disclosed methods for determining the ctDNA fraction in a liquid biopsy sample from a subject. The system may include, for example, one or more processors and a memory unit configured to communicate with one or more processors, and when executed by one or more processors, to cause the system to receive sequence read data for a plurality of sequence reads obtained for a sample from a subject, to determine whether the sequence read data is sufficient to perform copy number variation (CNA) modeling, (1) if the sequence read data is determined to be sufficient to perform CNA modeling, to estimate the ctDNA fraction in the sample based on at least the sample tumor purity and the ploidy of the sample, or (2) if the sequence read data is determined to be insufficient to perform CNA modeling, to estimate the ctDNA fraction in the sample based on the identification of at least one tumor somatic cell short variant in the sequence read data, and to output the estimated ctDNA fraction in the sample.

[0280] In some examples, the system's memory unit, when executed by one or more processors, may further include instructions that cause the system to compare the estimated ctDNA fraction to at least one predetermined threshold and, based on the comparison, output a status call for the sample of at least high tumor fraction (TF-high) or low tumor fraction (TF-low).

[0281] In some examples, the disclosed systems may further include a sequencer, for example, a next-generation sequencer (also known as a massively parallel sequencer). Examples of next-generation (or massively parallel) sequencing platforms include, but are not limited to, Roche / 454's Genome Sequencer (GS) FLX system, Illumina / Solexa's Genome Analyzer (GA), Illumina's HiSeq® 2500, HiSeq® 3000, HiSeq® 4000 and NovaSeq® 6000 sequencing systems, Life / APG's Support Oligonucleotide Ligation Detection (SOLiD) system, Polonator's G.007 system, Helicos BioSciences' HeliScope Gene Sequencing system, ThermoFisher Scientific's Ion Torrent Genexus system, or Pacific Biosciences' PacBio® RS system.

[0282] In some cases, nucleic acid sequence data are obtained using next-generation sequencing technologies (also known as ultra-parallel sequencing technologies) with read lengths of less than 400, 300, 200, 150, 100, 90, 80, 70, 60, 50, 40, or 30 bases.

[0283] In some cases, the determination of the ctDNA fraction of a liquid biopsy sample may be used to select, initiate, adjust, or terminate cancer treatment in the subject from which the sample originates (e.g., a patient), as described elsewhere in this specification.

[0284] In some examples, the disclosed system may further include a sample handling and library preparation workstation, a microplate handling robot, a fluid dispensing system, a temperature control module, an environmental control chamber, an additional data storage module, a data communication module (e.g., Bluetooth®, WiFi, intranet, or internet communication hardware and associated software), a display module, one or more local and / or cloud-based software packages (e.g., an instrument / system control software package, a sequencing data analysis software package), or any combination thereof. In some examples, the system may include, or be part of, a computer system or computer network as described elsewhere in this Spec. Computer systems and networks

[0285] Figure 3 illustrates an example of a computing device or system according to one embodiment. Device 300 may be a host computer connected to a network. Device 300 may be a client computer or a server. As shown in Figure 3, device 300 may be any suitable type of microprocessor-based device, such as a personal computer, workstation, server, or handheld computing device (portable electronic device, e.g., telephone or tablet). The device may include, for example, one or more processors 310, an input device 320, an output device 930, a memory or storage device 340, a communication device 360, and a nucleic acid sequencer 370. Software 350 residing in the memory or storage device 340 may include, for example, an operating system and software for carrying out the methods described herein. The input device 320 and the output device 330 may generally correspond to those described herein, may be connectable to a computer, or may be integrated with a computer.

[0286] The input device 320 can be any suitable device that provides input, such as a touchscreen, keyboard or keypad, mouse, or voice recognition device. The output device 330 can be any suitable device that provides output, such as a touchscreen, haptic device, or speaker.

[0287] Storage 340 can be any suitable device that provides storage (e.g., electrical, magnetic, or optical memory, including RAM (volatile and non-volatile), cache, hard drive, or removable storage disk). Communication device 360 ​​may include any suitable device capable of sending and receiving signals over a network, such as a network interface chip or device. Computer components can be connected in any suitable manner, for example, via wired media (e.g., physical system bus 380, Ethernet connection, or any other wired transfer technology) or wirelessly (e.g., Bluetooth®, Wi-Fi®, or any other wireless technology).

[0288] The software module 350 is stored in the storage 340 as executable instructions and can be executed by the processor 310, and may include, for example, a process that embodies the functions of an operating system and / or a method of the disclosure (for example, embodied in the above device).

[0289] The software module 350 may also be stored and / or transferred to any non-temporary computer-readable storage medium for use by or in connection with an instruction execution system, apparatus, or device (e.g., those described herein), and may fetch and execute instructions associated with the software from the instruction execution system, apparatus, or device. In the context of this disclosure, the computer-readable storage medium may be any medium, such as storage 340, and may contain or store processes for use by or in connection with an instruction execution system, apparatus, or device. Examples of computer-readable storage media include hard drives, flash drives, and memory units such as distribution modules, which operate as single functional units. Furthermore, the various processes described herein may be embodied as modules configured to operate according to the embodiments and techniques described above. Further, although processes may be shown and / or described separately, those skilled in the art will understand that the above processes may be routines or modules within other processes.

[0290] The software module 350 can also be propagated by an instruction execution system, apparatus, or any other device, or in any transmission medium for use in connection with them, to fetch and execute instructions associated with the software from the instruction execution system, apparatus, or device. In the context of this disclosure, the transmission medium can be any medium that can communicate, propagate, or transmit transmission programming by or for use in connection with the instruction execution system, apparatus, or device. The transmission-readable medium may include, but is not limited to, wired or wireless transmission media of electronic, magnetic, optical, electromagnetic, or infrared.

[0291] Device 300 may be connected to a network (e.g., network 404, shown in Figure 4 and / or described below), which may be any preferred type of interconnected communication system. The network may implement any preferred communication protocol and may be protected by any preferred security protocol. The network may include network links in any preferred configuration that can implement the transmission and reception of network signals, such as wireless network connections (T1 or T3 lines), cable networks, DSL, or telephone lines.

[0292] Device 300 can be implemented using any operating system, for example, an operating system suitable for running over a network. Software module 350 can be written in any suitable programming language, such as C, C++, Java, or Python. In various embodiments, application software embodying the functionality of this disclosure can be deployed in different configurations (for example, in a client / server deployment, or via a web browser as a web-based application or web service). In some embodiments, the operating system is run by one or more processors, for example, processor 310.

[0293] Device 300 may further include a sequencer 370, which can be any suitable nucleic acid sequencing instrument.

[0294] FIG. 4 illustrates an example of a computing system according to one embodiment. In system 400, device 300 (e.g., as described above and illustrated in FIG. 3) is connected to network 404, which is also connected to device 406. In some embodiments, device 406 is a sequencer. Exemplary sequencing devices can include, but are not limited to, the Roche / 454 Genome Sequencer (GS) FLX System, the Illumina / Solexa Genome Analyzer (GA), the Illumina HiSeq® 2500, HiSeq® 3000, HiSeq® 4000, and NovaSeq® 6000 sequencing systems, the Life / APG Support Oligonucleotide Ligation Detection (SOLiD) system, the Polonator G.007 system, the Helicos BioSciences HeliScope Gene sequencing system, or the Pacific Biosciences PacBio® RS system.

[0295] Devices 300 and 406 may communicate using an appropriate communication interface over a network 404, such as a local area network (LAN), a virtual private network (VPN), or the internet. In some embodiments, network 404 may be, for example, the internet, an intranet, a virtual private network, a cloud network, a wired network, or a wireless network. Devices 300 and 406 may communicate partially or entirely over wireless or wired communication, such as Ethernet or IEEE 802.11b wireless. Additionally, devices 300 and 406 may communicate over a second network, such as a mobile / cellular network, using a suitable communication interface. Communication between devices 300 and 406 may further include, or communicate with, various servers, such as mail servers, mobile servers, media servers, and telephone servers. In some embodiments, devices 300 and 406 may communicate directly (instead of, or in addition to, communication over network 404) over wireless or wired communication, such as Ethernet or IEEE 802.11b wireless. In some embodiments, devices 300 and 406 communicate either through a direct connection or through communication 408 that can occur over a network (e.g., network 404).

[0296] One or all of devices 300 and 406 are generally programmed to include logic (e.g., HTTP web server logic) accessed from local or remote databases or other sources of data and content, or to format data, in order to provide and / or receive information over network 404 in accordance with the various examples described herein. [Examples]

[0297] The following examples are provided for illustrative purposes only and are not intended to limit the scope of this disclosure. Example 1 - Verification of ctDNA tumor fraction determination (heuristic rule-based filtering)

[0298] The disclosed method for determining the ctDNA tumor fraction was validated using sequence read data from 530 matched plasma and buffy coat samples from various cancer types, including non-small cell lung cancer (NSCLC), prostate cancer, breast cancer, colorectal cancer (CRC), pancreatic cancer, ovarian cancer, esophageal cancer, cholangiocarcinoma, and cancer of unknown primary origin (CUP). Sequence read data for buffy coat samples were used to confirm the identity of non-tumor somatic variants, thereby allowing them to be excluded from further analysis.

[0299] Somatic short variants were identified in plasma samples based on extensive filtering of a list of short variants detected in sequence read data derived from the sample according to a series of heuristic rules, thereby removing known "blacklist" variants, including, but not limited to, potentially uncertain clonal hematopoietic (CHIP) variants, germline variants, and artifacts from the sequencing and / or variant calling methods used. Determining tumor fraction (TF) positivity for a given sample (or determining the TF value for a sample) required the identification of multiple potential tumor-derived somatic short variants to improve the reliability of the TF-positive call. If a specific known non-CHIP somatic variant ("whitelist" variants (e.g., KRAS variant, G12 variant, or EGFR exon 19 deletion) or a variant in commonly amplified genes (e.g., KRAS, EGFR, CCND1, FGF19, FGF3, FGF4, MYC, AR, MDM2, CCNE1, ERBB2, WHSC1L1, FGFR1, ZNF703, or) is detected and found to have a higher VAF compared to other variants detected in the sample, the requirement for identifying multiple tumor-derived short somatic variants may be invalidated. Other possible filters that may be used to distinguish CHIP from germline and somatic variants include the removal of specific rearrangements known to be somatic (e.g., TMPRSS2-ERG, ALK-EML4, FGFR3-TACC3, RET-KIF5B) and / or CHIP variants identified using a fragment mix-based (fragment size-based) approach in combination with detected somatic short variants.

[0300] Table 1 provides an overview of validation data for determining whether a sample is TF-positive or TF-negative based on filtered variant data from 530 matched plasma and buffy coat samples. In this embodiment, a TF-positive sample was defined as one in which at least one true somatic variant was detected with a VAF ≥ 0.01. A true somatic variant was defined as a variant having coverage greater than 200 × in the plasma sample and having a VAF that was statistically significant compared to that in the buffy coat sample. [Table 1]

[0301] The sensitivity for determining TF positivity was 262 / 283 = 92.6%. The specificity for determining TF positivity was 234 / 247 = 94.7%. The positive predictive value (PPV) was 262 / 275 = 95.3%. The negative predictive value (NPV) was 234 / 255 = 91.8%. Example 2 - Verification of ctDNA tumor fraction determination (fragment mix and short variants)

[0302] In this embodiment, an alternative approach based on a combination of fragment mix and short variant analysis was used to identify CHIPs, germline variants, and tumor-derived somatic cell variants. Here again, validation data were based on sequence read data from 530 matched plasma and buffy coat samples. Using sequence read data for buffy coat samples, the identity of non-tumor somatic cell variants could be confirmed, and as a result, they could be excluded from further analysis.

[0303] Fragment size shift is a fairly consistent genome-wide parameter observed for cell-free DNA from a given sample. However, some variability exists in fragment size shift at some genomic loci, likely due to location-specific biological differences. Therefore, even if the majority of DNA fragments in the entire sample shift, some somatic variants do not show fragment size shift, making it difficult to use fragment size-based (fragment mix) analysis to predict CHIP variants. Furthermore, DNA fragments from small fractions of the sample do not show shift, in which case not all somatic variants are identified as such.

[0304] In contrast, fragment size shift in short variants is a strong indicator of somatic status. Based on analysis of sequence read data from matched plasma-buffy coat samples, short variants exhibiting a strong fragment size shift are almost always somatic (for variants with a significant fragment size shift, defined as having a Kolmogorov-Smirnov p-value < 0.001 between the reference and alternative alleles, >99%). Non-limiting examples of fragment size shift data are provided in Table 2. [Table 2]

[0305] Table 3 provides an overview of validation data for determining the TF elevation, TF detection, or TF non-detection status of samples based on variant data from 530 matched plasma and peripheral blood mononuclear cell (PBMC) samples. Plasma variants were evaluated in the sequence read data of matched PBMC samples using a production variant calling method. Variants with PBMC VAF < (plasma VAF) / 10 were assigned as somatic cell variants. Variants with significant coverage dropout in PBMC samples were excluded from analysis. [Table 3]

[0306] The overall TF call rate increased by 47%, and 16% were detected. The results demonstrate the very high predictiveness of this method (>99% of TF elevation calls were confirmed to be elevation based on the corresponding buffy coat sample analysis). 52% of the samples had a maximum sVAF greater than 1% (88% were called TF elevation, and 93% were called TF elevation or TF detection), where maximum sVAF was defined as the VAF of a named plasma variant present in the buffy coat sample at less than one-tenth of the plasma VAF. Variants with very low coverage in the buffy coat sample (e.g., <100× or <500× and relative buffy coat coverage <0.2) were also excluded from the analysis.

[0307] The specificity was >95% (no false positives were detected in TF-elevated samples). Three whitelisted somatic short variants and two fragment size-shift false positives were identified (with VAFS of 0.14%, 0.16%, 0.19%, 0.21%, and 0.27%, respectively). Example 3 - Application to predicting the prognosis of prostate cancer

[0308] Figures 5A–5B provide non-limiting examples of the application of the disclosed method for determining the tumor fraction in a liquid biopsy sample to predict the probability of progression-free survival and survival in patients with prostate cancer. Figures 6A–6B provide non-limiting examples of the use of prostate-specific antigen (PSA) as a prognostic biomarker for the probability of progression-free survival and survival in patients with prostate cancer.

[0309] For the data plotted in Figures 5A and 5B, tumor fractions were determined based on the analysis of sequence read data for plasma samples using the methods disclosed herein. A 2% TF threshold was used to stratify prostate cancer patients treated with enzalutamide (Enza) challenge after abiraterone (Abi) treatment. 26% of patients in the cohort (494 patients in total) had a TF value less than 2%. Figure 5A provides plots of the probability of progression-free survival (PFS) as a function of time after initiation of Enza treatment for patients with TF < 2% and patients with TF ≥ 2%. Figure 5B provides a similar plot of the probability of overall survival (OS) as a function of time after initiation of Enza treatment. Insets in each figure provide a summary of the observed median of progression-free survival or overall survival, along with the observed hazard ratio (HR), 95% confidence interval (CI), and p-value for each plot. The table below each plot provides the actual number of patients at risk as a function of time.

[0310] Regarding the data plotted in Figures 6A and 6B, "low" PSA levels represent 26% of patients in the cohort. th PSA levels below the percentile were defined. Figure 6A provides plots of the probability of progression-free survival (PFS) as a function of time after initiation of Enza treatment for patients with low PSA and patients with high PSA. Figure 6B provides a similar plot of the probability of overall survival (OS) as a function of time after initiation of Enza treatment. Insets in each figure provide a summary of the observed median of progression-free survival or overall survival, respectively, along with the observed hazard ratio (HR), 95% confidence interval (CI), and p-value for each plot. The table below each plot provides the actual number of patients at risk as a function of time.

[0311] A comparison of the data in Figures 5A-5B and 6A-6B shows that TF is a much better prognostic biomarker for identifying men who are likely to have a clinical benefit from treatment with enzalutamide (Enza) challenge after abiraterone (Abi) treatment. Example Implementation

[0312] Exemplary implementations of the methods and systems described herein include the following: 1. A method, To provide multiple nucleic acid molecules obtained from samples from the target, Ligating one or more adapters onto one or more nucleic acid molecules from multiple nucleic acid molecules, Amplifying one or more ligated nucleic acid molecules from multiple nucleic acid molecules, The process involves capturing amplified nucleic acid molecules from amplified nucleic acid molecules, The sequencer is used to sequence the captured nucleic acid molecule and obtain multiple sequence reads representing the captured nucleic acid molecule. In one or more processors, receiving array read data for multiple array reads, Using one or more processors, determine whether the sequence read data is sufficient to perform copy number variation (CNA) modeling, Using one or more processors, (1) If the sequence read data is determined to be sufficient to perform CNA modeling, use the model to estimate the ctDNA fraction in the sample based at least on the tumor purity and ploidy of the sample, or (2) If the sequence read data is deemed insufficient for CNA modeling, the ctDNA fraction in the sample will be estimated based on the identification of at least one tumor somatic cell short variant in the sequence read data, A method comprising using one or more processors to output an estimated ctDNA fraction in a sample. 2. The method according to Clause 1, further comprising comparing the estimated ctDNA fraction with at least one predetermined threshold, and outputting a status call of at least high tumor fraction (TF-high) or low tumor fraction (TF-low) for the sample based on the comparison. 3. The method according to Clause 1 or Clause 2, wherein determining whether sequence read data is sufficient to perform CNA modeling includes determining sequence coverage data, sequence coverage ratio data, allele fraction data, or any combination thereof for at least one genomic locus to which multiple sequence reads are mapped. 4. The method according to Clause 3, wherein at least one genomic locus includes at least one single nucleotide polymorphism (SNP) locus. 5. Performing CNA modeling is The method according to any one of Clauses 1 to 4, comprising using one or more processors to determine a copy number model including sample tumor purity, sample ploidy, and copy numbers of multiple genomic segments in ctDNA, which describes observed sequence coverage ratio data, allele fraction data, for at least one genomic locus within one or more subgenome segments to which multiple sequence reads are mapped. 6. Estimating the ctDNA fraction based on at least the sample tumor purity and sample ploidy using a CNA model, the method according to any one of Clauses 1 to 5, which includes using an equation that describes the physical relationship between the ctDNA fraction and the sample tumor purity and sample ploidy. 7. Estimating the ctDNA fraction based on at least one somatic short variant detected in sequence read data is possible. To obtain a list of short variants detected in the sequence read data, Identifying tumor somatic cell short variants by applying a set of selection rules to the list of detected short variants, The method according to any one of Clauses 1 to 6, comprising estimating a ctDNA fraction based on the presence of at least one identified tumor somatic cell short variant. 8. The method according to Clause 7, wherein the set of selection rules used to identify tumor somatic cell short variants in the list of detected short variants includes (i) removing short variants that appear on a blacklist of known germline variants, known indeterminate potential clonal hematopoietic (CHIP) variants, and known sequencing artifacts; (ii) retaining short variants that appear on a list of known tumor somatic cell short variants; (iii) retaining short variants that appear on a list of known genes that tend to show high amplification and have a higher allele frequency than other somatic cell short variants in the sample; (iv) retaining short variants that appear on a list of known rearrangements, or any combination thereof. 9. Estimating the ctDNA fraction based on at least one tumor somatic cell short variant is Using one or more processors, determine the variant allele frequency (VAF) for one or more variants detected in the sample based on sequence read data, Using one or more processors, generate an empirical distribution of ctDNA fraction values ​​corresponding to determined VAFs for one or more variants based on historical data, Using one or more processors, the model is fitted to the empirical distribution of ctDNA fraction values, The method according to any one of the clauses 1 to 8, comprising determining the ctDNA fraction of a sample based on a model. 10. The subjects are those suspected of having cancer or determined to have cancer, as described in any one of the provisions 1 to 9. 11. Cancers include B-cell carcinoma (multiple myeloma), melanoma, breast cancer, lung cancer, bronchial cancer, colorectal cancer, prostate cancer, pancreatic cancer, stomach cancer, ovarian cancer, bladder cancer, brain cancer, central nervous system cancer, peripheral nervous system cancer, esophageal cancer, uterine cancer, endometrial cancer, oral cancer, pharyngeal cancer, liver cancer, kidney cancer, testicular cancer, biliary tract cancer, small intestine cancer, appendiceal cancer, salivary gland cancer, thyroid cancer, adrenal gland cancer, osteosarcoma, chondrosarcoma, blood tissue cancer, adenocarcinoma, and inflammation. Myofibroblastoma, gastrointestinal stromal tumor (GIST), colon cancer, multiple myeloma (MM), myelodysplastic syndrome (MDS), myeloproliferative disorder (MPD), acute lymphoblastic leukemia (ALL), acute myeloid leukemia (AML), chronic myeloid leukemia (CML), chronic lymphocytic leukemia (CLL), polycythemia vera, Hodgkin lymphoma, non-Hodgkin lymphoma (NHL), soft tissue sarcoma, fibrosarcoma, myxosarcoma, liposarcoma, osteosarcoma, chordoma, Angiosarcoma, endosarcoma, lymphangiosarcoma, lymphangioendosarcoma, synoviomyoma, mesothelioma, Ewing's tumor, leiomyosarcoma, rhabdomyosarcoma, squamous cell carcinoma, basal cell carcinoma, adenocarcinoma, sweat gland carcinoma, sebaceous gland carcinoma, papillary carcinoma, papillary adenocarcinoma, medullary carcinoma, bronchogenic carcinoma, renal cell carcinoma, liver cancer, cholangiocarcinoma, choriocarcinoma, seminoma, embryonic carcinoma, Wilms' tumor, bladder cancer, epithelial carcinoma, glioma, astrocytoma, medulloblastoma, craniopharyngioma, ependymoma, pineal cell carcinoma The method according to Clause 10, which is hemangioblastoma, acoustic neuroblastoma, oligodendroglioma, meningioma, neuroblastoma, retinoblastoma, follicular lymphoma, diffuse large B-cell lymphoma, mantle cell lymphoma, hepatocellular carcinoma, thyroid cancer, gastric cancer, head and neck cancer, small cell carcinoma, essential thrombocythemia, aplastic myelogenesis, eosinophilic syndrome, systemic mastocytosis, familial eosinophilia, chronic eosinophilic leukemia, neuroendocrine carcinoma, or carcinoid tumor. 12. Cancers include acute lymphoblastic leukemia (Philadelphia chromosome positive), acute lymphoblastic leukemia (precursor B cell), acute myeloid leukemia (FLT3+), acute myeloid leukemia (with IDH2 mutation), anaplastic large cell lymphoma, basal cell carcinoma, B-cell chronic lymphocytic leukemia, bladder cancer, breast cancer (HER2 overexpression / amplification), breast cancer (HER2+), breast cancer (HR+, HER2-), cervical cancer, cholangiocarcinoma, chronic lymphocytic leukemia, chronic lymphocytic leukemia (with 17p deficiency), chronic myeloid leukemia, chronic myeloid leukemia (Philadelphia chromosome positive), classical Hodgkin lymphoma, colorectal cancer, Colorectal cancer (dMMR / MSI-H), colorectal cancer (KRAS wild-type), cryopyrin-associated periodic fever syndrome, cutaneous T-cell lymphoma, dermatofibrosarcoma protuberans, diffuse large B-cell lymphoma, fallopian tube cancer, follicular B-cell non-Hodgkin lymphoma, follicular lymphoma, gastric cancer, gastric cancer (HER2+), gastroesophageal junction (GEJ) adenocarcinoma, gastrointestinal stromal tumor, gastrointestinal stromal tumor (KIT+), giant cell tumor of bone, glioblastoma, granulomatosis with polyangiitis, head and neck squamous cell carcinoma, hepatocellular carcinoma, Hodgkin lymphoma, juvenile idiopathic arthritis, systemic lupus erythematosus, mantle cell lymphoma, medullary thyroid carcinoma, melanoma, BRAF Melanoma with V600 mutation, melanoma with BRAF V600E or V600K mutation, Merkel cell carcinoma, multicentric Castleman disease, multiple hematological malignancies including Philadelphia chromosome-positive ALL and CML, multiple myeloma, myelofibrosis, non-Hodgkin lymphoma, unresectable subependymal giant cell astrocytoma associated with tuberous sclerosis, non-small cell lung cancer, non-small cell lung cancer (ALK+), non-small cell lung cancer (PD-L1+), non-small cell lung cancer (with ALK fusion or ROS1 gene alteration), non-small cell lung cancer (with BRAF V600E mutation), non-small cell lung cancer (with EGFR exon 19 deletion or exon 21 substitution (L858R) mutation), non-small cell lung cancer (EGFRThe method according to Clause 10, including ovarian cancer (with T790M mutation), ovarian cancer (with BRCA mutation), pancreatic cancer, neuroendocrine tumors of pancreatic, gastrointestinal, or lung origin, pediatric neuroblastoma, peripheral T-cell lymphoma, peritoneal cancer, prostate cancer, renal cell carcinoma, rheumatoid arthritis, small lymphocytic lymphoma, soft tissue sarcoma, solid tumor (MSI-H / dMMR), squamous cell carcinoma of the head and neck, squamous non-small cell lung cancer, thyroid cancer, thyroid carcinoma, urothelial carcinoma, or primary gammaglobulinemia. 13. The method described in any one of the clauses 10 to 12, further comprising treating the subject with anticancer therapy. 14. Anti-cancer therapy, including targeted anti-cancer therapy, as described in Clause 13. 15. In some embodiments, targeted anticancer therapy includes abemaciclib (Verzenio), abiraterone acetate (Zytiga), acalabrutinib (Calquence), ado-trastuzumab emtansine (Kadcyla), afatinib dimaleate (Gilotrif), aldesleukin (Proleukin), alectinib (Alecensa), alemtuzumab (Campath), alitretinoin (Panretin), alpelisib (Piqray), and amivantamab-vmjw (Rybr). evant), anastrozole (Arimidex), apalutamide (Erleada), aciminib hydrochloride (Scemblix), atezolizumab (Tecentriq), avapritinib (Ayvakit), avelumab (Bavencio), axicaptagensilolucel (Yescarta), axitinib (Inlyta), verantamab mahodotin-blmf (Blenrep), belimumab (Benlysta), belinostat (Beleodaq), verzutifan (Welireg), bevacizumab (Avastin), Bexarotene (Targretin), Binimetinib (Mektovi), Blinatumomab (Blincyto), Bortezomib (Velcade), Bosutinib (Bosulif), Brentuximab Vedotin (Adcetris), Brexcabutadiene Autolucel (Tecartus), Brigutinib (Alunbrig), Cabazitaxel (Jevtana), Cabozantinib (Cabometyx), Cabozantinib (Cabometyx, Cometriq), Canakinumab (Ila ris), capmatinib hydrochloride (Tabrecta), carfilzomib (Kyprolis), semiprimab-rwlc (Libtayo), ceritinib (LDK378 / Zykadia), cetuximab (Erbitux), cobimetinib (Cotellic), copanlisib hydrochloride (Aliqopa), crizotinib (Xalkori), dabrafenib (Tafinlar), dacomitinib (Vizimpro), daratumumab (Darzalex), daratumumab and hyaluronidase-fihj (Darzalex)Faspro, darolutamide (Nubeqa), dasatinib (Sprycel), deniroikin difutitox (Ontak), denosumab (Xgeva), dinutuximab (Unituxin), dostallumab-gxly (Jemperli), durvalumab (Imfinzi), duvelisib (Copiktra), elotuzumab (Empliciti), enacidenib mesylate (Idhifa), encorafenib (Braftovi), enfortumab vedotin-ejfv (Padcev), entrectinib (Rozlytrek), enzalutamide (Xtandi), erdafitinib (Balversa), erlotinib (Tarceva), everolimus (Afinitor), exemestane (Aromasin), fam-trastuzumab deruxtecan-nxki (Enhertu), fedratinib hydrochloride (Inrebi) c) Fulvestrant (Faslodex), gefitinib (Iressa), gemtuzumab ozogamicin (Mylotarg), gilteritinib (Xospata), glassedegib maleate (Daurismo), hyaluronidase-zzxf (Phesgo), ibrutinib (Imbruvica), ibritumomab tiuxetan (Zevalin), idekabuta genbiculucel (Abecma), idelaritinib (Zydelig), imatinib mesylate (Gleevec), infiglatinib phosphate (Truseltiq), inotuzumab ozogamicin (Besponsa), iobenguan I131 (Azedra), ipilimumab (Yervoy), isatuximab-irfc (Sarclisa), ivosidenib (Tibsovo), ixazomib citrate (Ninlaro), lanreotide acetate (Somatuline)Depot), lapatinib (Tykerb), larotrectinib sulfate (Vitrakvi), lenvatinib mesylate (Lenvima), letrozole (Femara), lysocabategemmaralucel (Breyanzi), loncustuximab tesillin-lpyl (Zynlonta), lorlatinib (Lorbrena), lutetium Lu177-doteate (Lutathera), margetuximab-cmkb (Margenza), midostauri Rydapt, mobocertinib succinate (Exkivity), mogamulizumab-kpkc (Poteligeo), moxetumomab pasdotox-tdfk (Lumoxiti), naxitamab-gqgk (Danyelza), necitumumab (Portrazza), neratinib maleate (Nerlynx), nilotinib (Tasigna), niraparib tosylate monohydrate (Zejula), nivolumab (Opdivo), obinutuzumab (Gazy va), ofatumumab (Arzerra), olaparib (Lynparza), olalatumab (Lartruvo), osimertinib (Tagrisso), palbociclib (Ibrance), panitumumab (Vectibix), panobinostat (Farydak), pazopanib (Votrient), pembrolizumab (Keytruda), pemigatinib (Pemazyre), pertuzumab (Perjeta), pexidartinib hydrochloride (Turali) o) Polatuzumab vedotin-piiq (Polivy), ponatinib hydrochloride (Iclusig), pralatrexate (Folotyn), pralcetinib (Gavreto), radium-223 dichloride (Xofigo), ramucirumab (Cyramza), regorafenib (Stivarga), ribociclib (Kisqali), lipretinib (Qinlock), rituximab (Rituxan), rituximab and hyaluronidase (Rituxan)Hycela), Romidepsin (Istodax), Rucaparib cansylate (Rubraca), Ruxolitinib phosphate (Jakafi), Sacituzumab Govitecan-hziy (Trodelvy), Celiclib, Selinexol (Xpovio), Serpercatinib (Retevmo), Selumetinib sulfate (Koselugo), Siltuximab (Sylvant), Cypleucel-T (Provenge) Sirolimus-binding protein particles (F Yarro), sonidecib (Odomzo), sorafenib (Nexavar), sotracib (Lumakras), sunitinib (Sutent), tafacitamab-cxix (Monjuvi), tagraxofussp-erzs (Elzonris), thalazoparib tosylate (Talzenna), tamoxifen (Nolvadex), tazemetostat hydrobromide (Tazverik), teventafussp-tebn (Kimmtrak), temb Sirolimus (Torisel), Tepotinib hydrochloride (Tepmetko), Tisagenlecleucel (Kymriah), Tisotumab vedotin-tftv (Tivdak), Tocilizumab (Actemra), Tofacitinib (Xeljanz), Tositumomab (Bexxar), Trametinib (Mekinist), Trastuzumab (Herceptin), Tretinoin (Vesanoid), Tivozanib hydrochloride (Fotivda), Toremifene The method according to Clause 14, including (Fareston), tucatinib (Tukysa), umbralicib tosylate (Ukoniq), vandetanib (Caprelsa), vemurafenib (Zelboraf), venetoclax (Venclexta), bismodegib (Erivedge), vorinostat (Zolinza), zanubrutinib (Brukinsa), ziv-aflibercept (Zaltrap), or any combination thereof. 16. The method described in any one of the clauses 1 to 15, further comprising obtaining a sample from the subject. 17. The specimen is a tissue biopsy specimen, a liquid biopsy specimen, or a normal control, as described in any one of the provisions 1 to 16. 18. The sample is a liquid biopsy sample, comprising blood, plasma, cerebrospinal fluid, sputum, feces, urine, or saliva, as described in Clause 17. 19. The specimen is a liquid biopsy specimen containing circulating tumor cells (CTCs) as described in Clause 17. 20. The method according to Clause 17, wherein the sample is a liquid biopsy sample and comprises cell-free DNA (cfDNA), circulating tumor DNA (ctDNA), or any combination thereof. 21. The method according to any one of the claims 1 to 20, wherein the plurality of nucleic acid molecules include a mixture of tumor nucleic acid molecules and non-tumor nucleic acid molecules. 22. The method according to Clause 21, wherein tumor nucleic acid molecules are derived from the tumor portion of the heterogeneous tissue biopsy sample, and non-tumor nucleic acid molecules are derived from the normal portion of the heterogeneous tissue biopsy sample. 23. The method according to Clause 21, wherein the sample comprises a liquid biopsy sample, the tumor nucleic acid molecules are derived from the circulating tumor DNA (ctDNA) fraction of the liquid biopsy sample, and the non-tumor nucleic acid molecules are derived from the non-tumor cell-free DNA (cfDNA) fraction of the liquid biopsy sample. 24. One or more adapters comprising an amplification primer, a flow cell adapter sequence, a substrate adapter sequence, or a sample index sequence, as described in any one of the methods in Clauses 1 to 23. 25. The method according to any one of the claims 1 to 24, wherein the captured nucleic acid molecule is captured from the nucleic acid molecule amplified by hybridization to one or more bait molecules. 26. The method according to Clause 25, wherein one or more bait molecules comprise one or more nucleic acid molecules, and each nucleic acid molecule comprises a region complementary to the region of the captured nucleic acid molecule. 27. Amplification of nucleic acid molecules by any one of the methods described in Clauses 1 to 26, including performing polymerase chain reaction (PCR) amplification techniques, non-PCR amplification techniques, or isothermal amplification techniques. 28. Sequencing as described in any one of Clauses 1 to 27, including the use of massively parallel sequencing (MPS) technology, whole-genome sequencing (WGS), whole-exome sequencing, targeted sequencing, direct sequencing, or Sanger sequencing technology. 29. Sequencing includes massively parallel sequencing, and massively parallel sequencing techniques include next-generation sequencing (NGS), as described in Clause 28. 30. A sequencer, including a next-generation sequencer, as described in any one of Clauses 1 to 29. 31. The method according to any one of the multiple sequencing reads, wherein one or more of the multiple sequencing reads overlap with one or more gene loci in one or more subgenome segments in the sample. 32.1 or more gene loci are: 10-20 loci, 10-40 loci, 10-60 loci, 10-80 loci, 10-100 loci, 10-150 loci, 10-200 loci, 10-250 loci, 10-300 loci, 10-350 loci, 10-400 loci, 10-450 loci, 10-500 loci, 20-40 loci, 20-60 loci, 20-80 loci, 20-100 loci, 20-150 loci, 20-200 loci, 20-250 loci, 20 ~300 gene loci, 20~350 gene loci, 20~400 gene loci, 20~500 gene loci, 40~60 gene loci, 40~80 gene loci, 40~100 gene loci, 40~150 gene loci, 40~200 gene loci, 40~250 gene loci, 40~300 gene loci, 40~350 gene loci, 40~400 gene loci, 40~500 gene loci, 60~80 gene loci, 60~100 gene loci, 60~150 gene loci, 60~200 gene loci, 60~250 gene loci, 60~300 gene loci, 60~350 gene loci, 60~ 400 loci, 60-500 loci, 80-100 loci, 80-150 loci, 80-200 loci, 80-250 loci, 80-300 loci, 80-350 loci, 80-400 loci, 80-500 loci, 100-150 loci, 100-200 loci, 100-250 loci, 100-300 loci, 100-350 loci, 100-400 loci, 100-500 loci, 150-200 loci, 150-250 loci, 150-300 loci, 15 The method according to Clause 31, including 0-350 loci, 150-400 loci, 150-500 loci, 200-250 loci, 200-300 loci, 200-350 loci, 200-400 loci, 200-500 loci, 250-300 loci, 250-350 loci, 250-400 loci, 250-500 loci, 300-350 loci, 300-400 loci, 300-500 loci, 350-400 loci, 350-500 loci, or 400-500 loci. 33.1 and some other genes are ABL1, ACVR1B, AKT1, AKT2, AKT3, ALK, ALOX12B, AMER1, and APC AR, ARAF, ARFRP1, ARID1A, ASXL1, ATM, ATR, ATRX, AURKA, AURKB, AXIN1, AXL BAP1, BARD1, BCL2, BCL2L1, BCL2L2, BCL6, BCR, BCORL1, BCR, BRAF, BRCA1 BRCA2, BRD4, BRIP1, BTG1, BTG2, BTK, CALR, CARD11, CASP8, CBFB, CBL, CCND 1, CCND2, CCND3, CCNE1, CD22, CD274, CD70, CD74, CD79A, CD79B, CDC73, CDH 1, CDK12, CDK4, CDK6, CDK8, CDKN1A, CDKN1B, CDKN2A, CDKN2B, CDKN2C, CEBPA CHEK1, CHEK2, CIC, CREBBP, CRKL, CSF1R, CSF3R, CTCF, CTNNA1, CTNNB1, CU L3, CUL4A, CXCR4, CYP17A1, DAXX, DDR1, DDR2, DIS3, DNMT3A, DOT1L, EED, EGF R, EMSY(C11orf30), ​​EP300, EPHA3, EPHB1, EPHB4, ERBB2, ERBB3, ERBB4, ERC C4, ERG, ERRFI1, ESR1, ETV4, ETV5, ETV6, EWSR1, EZH2, EZR, FAM46C, FANCA, F ANCC, FANCG, FANCL, FAS, FBXW7, FGF10, FGF12, FGF14, FGF19, FGF23, FGF3 FGF4, FGF6, FGFR1, FGFR2, FGFR3, FGFR4, FH, FLCN, FLT1, FLT3, FOXL2, FUBP1 GABRA6, GATA3, GATA4, GATA6, GID4(C17orf39), GNA11, GNA13, GNAQ, GNAS GRM3, GSK3B, H3F3A, HDAC1, HGF, HNF1A, HRAS, HSD3B1, ID3, IDH1, IDH2, IGF 1R, IKBKE, IKZF1, INPP4B, IRF2, IRF4, IRS2, JAK1, JAK2, JAK3, JUN, KDM5A, K DM5C, KDM6A, KDR, KEAP1, KEL, KIT, KLHL6, KMT2A(MLL), KMT2D(MLL2), KRAS.LTK, LYN, MAF, MAP2K1, MAP2K2, MAP2K4, MAP3K1, MAP3K13, MAPK1, MCL1, MDM2, MDM4, MED12, MEF2B, MEN1, MERTK, MET, MITF, MKNK1, MLH1, MPL , MRE11A, MSH2, MSH3, MSH6, MST1R, MTAP, MTOR, MUTYH, MYB, MYC, MYCL, MYCN, MYD88, NBN, NF1, NF2, NFE2L2, NFKBIA, NKX2-1, NOTCH1, NOTCH2 , NOTCH3, NPM1, NRAS, NT5C2, NTRK1, NTRK2, NTRK3, NUTM1, P2RY8, PALB2, PARK2, PARP1, PARP2, PARP3, PAX5, PBRM1, PDCD1, PDCD1LG2, PDGFR A, PDGFRB, PDK1, PIK3C2B, PIK3C2G, PIK3CA, PIK3CB, PIK3R1, PIM1, PMS2, POLD1, POLE, PPARG, PPP2R1A, PPP2R2A, PRDM1, PRKAR1A, PRKCI, P TCH1, PTEN, PTPN11, PTPRO, QKI, RAC1, RAD21, RAD51, RAD51B, RAD51C, RAD51D, RAD52, RAD54L, RAF1, RARA, RB1, RBM10, REL, RET, RICTOR, RN F43, ROS1, RPTOR, RSPO2, SDC4, SDHA, SDHB, SDHC, SDHD, SETD2, SF3B1, SGK1, SLC34A2, SMAD2, SMAD4, SMARCA4, SMARCB1, SMO, SNCAIP, SOCS1 The method described in Clause 31 or Clause 32, including SOX2, SOX9, SPEN, SPOP, SRC, STAG2, STAT3, STK11, SUFU, SYK, TBX3, TEK, TERC, TERT, TET2, TGFBR2, TIPARP, TMPRSS2, TNFAIP3, TNFRSF14, TP53, TSC1, TSC2, TYRO3, U2AF1, VEGFA, VHL, WHSC1, WHSC1L1, WT1, XPO1, XRCC2, ZNF217, ZNF703, or any combination thereof. 34. One or more gene loci are ABL, ALK, ALL, B4GALNT1, BAFF, BCL2, BRAF, BRCA, BTK, CD19, CD20, CD3, CD30, CD319, CD38, CD52, CDK4, CDK6, CML, CRACC, CS1, CTLA-4, dMMR, EGFR, ERBB1, ERBB2, FGFR1-3, FLT3, GD2, HDAC, HER1, HER2, HR, IDH2, I The method according to Clause 31 or Clause 32, including L-1β, IL-6, IL-6R, JAK1, JAK2, JAK3, KIT, KRAS, MEK, MET, MSI-H, mTOR, PARP, PD-1, PDGFR, PDGFRα, PDGFRβ, PD-L1, PI3Kδ, PIGF, PTCH, RAF, RANKL, RET, ROS1, SLAMF7, VEGF, VEGFA, VEGFB, or any combination thereof. 35. The method according to any one of the clauses 1 to 34, further comprising generating a report showing the estimated tumor fraction in a sample using one or more processors. 36. The method described in Clause 35, further including sending a report to a healthcare provider. 37. The report is transmitted via a computer network or peer-to-peer connection as described in Clause 36. 38. A method for determining the circulating tumor DNA (ctDNA) fraction in a sample from a subject, wherein the method is: One or more processors receive sequence read data for multiple sequence reads obtained from a sample of the target, Using one or more processors, determine whether the sequence read data is sufficient to perform copy number variation (CNA) modeling, Using one or more processors, (1) If the sequence read data is determined to be sufficient to perform CNA modeling, use the model to estimate the ctDNA fraction in the sample based at least on the tumor purity and ploidy of the sample, or (2) If the sequence read data is deemed insufficient for CNA modeling, the ctDNA fraction in the sample will be estimated based on the identification of at least one tumor somatic cell short variant in the sequence read data, A method comprising using one or more processors to output an estimated ctDNA fraction in a sample. 39. The method according to clause 38, further comprising comparing the estimated ctDNA fraction with at least one predetermined threshold, and outputting a status call of at least high tumor fraction (TF-high) or low tumor fraction (TF-low) for the sample based on the comparison. 40. The method according to Clause 38 or 39, wherein determining whether sequence read data is sufficient to perform CNA modeling includes determining sequence coverage data, sequence coverage ratio data, allele fraction data, or any combination thereof for at least one genomic locus to which multiple sequence reads are mapped. 41. The method according to clause 40, wherein at least one genomic locus comprises at least one single nucleotide polymorphism (SNP) locus. 42. Determination of sequence coverage data, sequence coverage ratio data, allele fraction data, or any combination thereof for at least one genomic locus, based on preprocessing of sequence read data, as described in Clause 40 or Clause 41. 43. Performing CNA modeling is The method according to any one of the claims 38 to 42, comprising using one or more processors to determine a copy number model that includes observed sequence coverage ratio data for at least one genomic locus within one or more subgenome intervals where multiple sequence reads are mapped, allele fraction data, and copy number data for multiple genomic segments. 44. The method of Clause 42 or 43, wherein sequence coverage ratio data is determined by aligning multiple sequence reads that overlap with at least one genomic locus in one or more subgenome sections in the sample and control sample to a reference genome, and by determining the number of sequence reads that overlap with at least one genomic locus in one or more subgenome sections in the sample and control sample. 45. The control sample is a pair of normal samples, process-matched control samples, or a panel of normal control samples, as described in Clause 44. 46. ​​The method according to any one of the clauses 43 to 45, wherein allele fraction data is determined by aligning multiple sequence reads that overlap with at least one genomic locus in one or more subgenome sections in the sample with a reference genome, detecting the number of alleles present at at least one genomic locus, and determining the allele fraction for at least one of the alleles present at at least one genomic locus. 47. Performing CNA modeling is Aligning multiple sequence reads that overlap with at least one genomic locus within one or more subgenome segments in the sample with the reference genome, The method according to any one of the claims 43 to 46, further comprising processing aligned sequence read data, coverage ratio data, and allele fraction data using a pruned exact linear time (PELT) method to determine the number of segments that need to be considered in the aligned sequence read data, wherein each segment has the same copy number, and generating segmentation data by determining this. 48. A copy number model according to any one of the provisions 43 to 47, which predicts the copy number of at least one genomic locus based on sequence coverage ratio data and allele fraction data. 49. The method according to 48, further comprising sequence coverage ratio data for single nucleotide polymorphisms (SNPs) and introns associated with at least one genomic locus. 50. The copy number model according to the method of Clause 48 or Clause 49, which also predicts the tumor purity and ploidy of the sample. 51. The method described in any one of the clauses 47-50, wherein the copy number model also outputs segmented data. 52. The ploidy for the sample is as described in any one of the methods in clauses 43 to 51, having a value in the range of 1 to 8. 53. The method according to any one of the clauses 43 to 52, wherein amplification is detected when the copy number for the corresponding segment is greater than or equal to the ploidy of the sample. 54. The method according to clause 53, wherein amplification is detected when the copy number for the corresponding segment is greater than or equal to the ploidy of the sample + a first predetermined value. 55. The method described in Clause 54, wherein the first specified value is a value in the range of 2 to 500. 56. The method described in Clause 54 or Clause 55, wherein the first specified value is a value in the range of 2 to 10. 57. The method according to any one of the clauses 54 to 56, wherein amplification is detected when the copy number for the corresponding segment is greater than or equal to the ploidy of the sample + a second predetermined value, and the genomic locus is a member of the first predefined set of loci. 58. The method according to Clause 57, wherein the second specified value is a value in the range of 0 to 500. 59. The method described in Clause 57 or Clause 58, wherein the second specified value is a value in the range of 2 to 10. 60. The method described in any one of the clauses 57 to 59, wherein the first defined set of gene loci includes one or more gene target loci, prognostic gene loci, oncogene loci, or any combination thereof, which could lead to the development of a new drug. 61. The method according to Clause 60, wherein the first predefined set of loci includes the AR and ERBB2 loci. 62. The method according to any one of the clauses 43 to 61, wherein the detection of a deletion includes identifying a homozygous deletion of at least one genomic locus in the corresponding segment. 63. Homozygous deletions are detected by determining the total copy number of a given genomic locus, which is equal to the sum of the copy numbers of the first and second alleles at that locus, according to the method of Clause 62. 64. The method according to Clause 63, wherein the first allele is the major allele and the second allele is the minor allele. 65. The method according to Clause 63 or Clause 64, wherein a homozygous deletion is called if the total copy number of a given genomic locus is equal to a third predetermined value. 66. The method described in Clause 65, wherein the third specified value is approximately zero. 67. The method according to any one of the clauses 43 to 66, wherein the detection of a deletion includes identifying a heterozygous deletion of at least one genomic locus in the corresponding segment. 68. The method according to clause 67, wherein a heterozygous deletion is called if the copy number of a first allele at a given genomic locus is equal to a fourth predetermined value and the copy number of a second allele at a given genomic locus is not equal to a fourth predetermined value. 69. The method described in Clause 68, wherein the fourth specified value is approximately zero. 70. The method according to Clause 68 or Clause 69, wherein the first allele is the major allele and the second allele is the minor allele. 71. The method according to any one of the clauses 43 to 70, wherein the detection of a deletion comprises identifying a partial deletion of at least one genomic locus in the corresponding segment. 72. The method according to clause 71, wherein a partial deletion is called for a given genomic locus if the log2 ratio (L2R) of adjacent genomic loci, single nucleotide polymorphisms (SNPs), and introns differs significantly from the log2 ratio of a given genomic locus, and the log2 ratio of a given genomic locus differs significantly from the distribution of L2R ​​of non-adjacent genomic loci, single nucleotide polymorphisms (SNPs), and introns. 73. Estimating the ctDNA fraction based on at least the sample tumor purity and sample ploidy using a CNA model, the method according to any one of Clauses 38 to 72, comprising using an equation that describes the physical relationship between the ctDNA fraction and the sample tumor purity and sample ploidy. Equation 74 is given by the following:

number

number

number

[0313] From the above, specific implementations of the disclosed methods and systems have been illustrated and described, but it should be understood that various modifications can be made thereto and are intended herein. The invention is not intended to be limited by the specific examples provided herein. While the invention has been described with reference to the above specification, the descriptions and illustrations of preferred embodiments herein are not meant to be construed as limiting. Furthermore, it should be understood that all aspects of the invention are not limited to the specific descriptions, configurations, or relative proportions described herein, which depend on various conditions and variables. Various modifications in the forms and details of embodiments of the invention will be apparent to those skilled in the art. Therefore, the invention is also intended to encompass any such modifications, variations, and equivalents.

Claims

1. A method for determining the circulating tumor DNA (ctDNA) fraction in a sample from a subject, wherein the method is: One or more processors receive sequence read data for multiple sequence reads obtained for the sample from the target, Using one or more of the aforementioned processors, determine whether the sequence read data is sufficient to perform copy number variation (CNA) modeling. Using one or more of the above processors, (1) If the sequence read data is determined to be sufficient for performing CNA modeling, the model is used to estimate the ctDNA fraction in the sample based at least on the tumor purity and ploidy of the sample, or (2) If the sequence read data is determined to be insufficient for performing CNA modeling, the ctDNA fraction in the sample is estimated based on the identification of at least one tumor somatic cell short variant in the sequence read data, A method comprising using one or more processors to output the estimated ctDNA fraction in the sample.

2. The method according to claim 1, further comprising comparing the estimated ctDNA fraction with at least one predetermined threshold, and outputting a status call for the sample of at least high tumor fraction (TF-high) or low tumor fraction (TF-low) based on the comparison.

3. The method according to claim 1, wherein determining whether the sequence read data is sufficient to perform CNA modeling includes determining sequence coverage data, sequence coverage ratio data, allele fraction data, or any combination thereof for at least one genomic locus to which the plurality of sequence reads are mapped.

4. Performing CNA modeling is The method according to claim 1, comprising using one or more processors to determine a copy number model that includes observed sequence coverage ratio data for at least one genomic locus within one or more subgenome intervals to which the plurality of sequence reads are mapped, and copy number data for a plurality of genomic segments describing the sample tumor purity, sample ploidy, and allele fraction data.

5. The method according to claim 4, wherein the allele fraction data is determined by aligning the plurality of sequence reads that overlap with at least one genomic locus in one or more subgenome sections in the sample with a reference genome, detecting the number of alleles present at the at least one genomic locus, and determining the allele fraction for at least one of the alleles present at the at least one genomic locus.

6. Performing CNA modeling is Aligning multiple sequence reads that overlap with at least one genomic locus within one or more subgenome sections in the sample with a reference genome, The method according to claim 4, further comprising: processing aligned sequence read data, coverage ratio data, and allele fraction data using a pruned exact linear time (PELT) method to determine the number of segments that need to be considered in the aligned sequence read data, wherein each segment has the same copy number; and generating segmentation data by determining this.

7. The method according to claim 4, wherein amplification is detected when the number of copies for the corresponding segment is greater than or equal to the plicativity of the sample.

8. The method according to claim 4, wherein the detection of the deletion includes identifying a homozygous deletion of at least one genomic locus in the corresponding segment.

9. The method according to claim 4, wherein the detection of the deletion includes identifying a heterozygous deletion of at least one genomic locus in the corresponding segment.

10. The method according to claim 4, wherein the detection of the deletion includes identifying a partial deletion of at least one genomic locus in the corresponding segment.

11. The method according to claim 1, wherein estimating the ctDNA fraction based at least on the sample tumor purity and sample ploidy using the CNA model includes using an equation that describes the physical relationship between the ctDNA fraction, the sample tumor purity and sample ploidy.

12. The above formula is given by the following: [Math 1] The method according to claim 11, wherein ρ is the purity of the sample tumor and ψ is the ploidy.

13. Estimating the ctDNA fraction based on at least one somatic cell short variant detected in the sequence read data is: To obtain a list of short variants detected in the sequence read data, To identify tumor somatic cell short variants by applying a set of selection rules to the list of detected short variants, The method according to claim 1, comprising estimating the ctDNA fraction based on the presence of at least one identified tumor somatic cell short variant.

14. Estimating the ctDNA fraction based on the at least one tumor somatic cell short variant is, Using one or more of the aforementioned processors, the variant allele frequency (VAF) for one or more variants detected in the sequence read data is determined. Using the one or more processors, generate an empirical distribution of ctDNA fraction values ​​corresponding to the determined VAF for the one or more variants based on historical data, Using one or more of the aforementioned processors, the model is fitted to the empirical distribution of ctDNA fraction values. The method according to claim 1, comprising determining the ctDNA fraction of the sample based on the model.

15. The method according to claim 14, further comprising determining a confidence interval for the ctDNA fraction based on the model.

16. The method according to claim 14, wherein generating the empirical distribution of ctDNA fraction values ​​involves calculating ctDNA fraction values ​​based on a known copy number for one or more tumor somatic cell short variants and corresponding known sample ploidy for a plurality of historical samples having a known VAF for one or more tumor somatic cell short variants that is substantially the same as the determined VAF for the one or more tumor somatic cell short variants.

17. The method according to claim 14, wherein generating the empirical distribution of ctDNA fraction values ​​comprises pre-calculating ctDNA fraction values ​​based on the known copy numbers of one or more tumor somatic cell short variants and the corresponding known sample ploidy of a plurality of historical samples having a range of VAF values ​​for the one or more tumor somatic cell short variants, and selecting a subset of the pre-calculated ctDNA fraction values ​​corresponding to samples having known VAFs for one or more tumor somatic cell short variants that are substantially the same as the determined VAFs for the one or more tumor somatic cell short variants.

18. The method according to claim 16, wherein the ctDNA fraction value is calculated or pre-calculated based on the known VAF for one or more tumor somatic cell short variants, the known copy number for one or more tumor somatic cell short variants, and the corresponding known sample ploidy for the plurality of historical target samples.

19. The method according to claim 16, wherein the ctDNA fraction value is calculated or pre-calculated based on the known VAF for the one or more tumor somatic cell short variants, the known copy number for the one or more tumor somatic cell short variants, and the corresponding known sample ploidy for the plurality of historical target samples by solving a set of equations that describe the relationship between (i) the ctDNA fraction, the sample tumor purity, and the sample ploidy, and (ii) the relationship between the somatic cell VAF, the sample tumor purity, the copy number of the one or more tumor somatic cell short variants at the genomic location, and the number of variant alleles for each of the one or more tumor somatic cell short variants, thereby excluding the sample tumor purity and deriving a relationship for the ctDNA fraction as a function of the somatic cell VAF, the sample ploidy, the copy number of the one or more tumor somatic cell short variants at the genomic location, and the number of variant alleles for the one or more tumor somatic cell short variants.

20. The ctDNA fraction value is a set of the following formulas, i.e., [Math 2] [Math 3] By solving the equation, the known VAF for one or more tumor somatic cell short variants, the known copy number for one or more tumor somatic cell short variants, and the corresponding known sample ploidy for the multiple historical target samples are calculated or precalculated, and ρ is eliminated. [Math 4] The method according to claim 16, wherein the relationship between the ctDNA fraction described by and the somatic cell VAF is obtained, in the formula where p is the sample tumor purity, ψ is the sample ploidy, C is the copy number at the genomic location of the one or more tumor somatic cell short variants, and V is the number of a variant alleles for each of the one or more tumor somatic cell short variants.

21. The method according to claim 14, wherein the model is a nonparametric probability density model.

22. The method according to claim 1, wherein the estimation of the ctDNA fraction is based on the determination of the maximum somatic allele frequency (MSAF) of at least one tumor somatic cell short variant, the detection of one or more genomic rearrangements, the determination of microsatellite instability, or any combination thereof.

23. The method of claim 13, wherein the set of selection rules used to identify tumor somatic cell short variants in the list of detected short variants includes (i) removing short variants that appear on a blacklist of known germline variants, known indeterminate potential clonal hematopoietic (CHIP) variants, and known sequencing artifacts; (ii) retaining short variants that appear on a list of known tumor somatic cell short variants; (iii) retaining short variants that appear on a list of known genes that tend to show high amplification and have a higher allele frequency than other somatic cell short variants in the sample; (iv) retaining short variants that appear on a list of known rearrangements, or any combination thereof.

24. The method of claim 13, wherein the set of selection rules used to identify tumor somatic cell short variants in the list of detected short variants further includes identifying short variants in which a fragment size shift between the reference allele and the alternative allele is detected in sequence read data as tumor somatic cell short variants.

25. The method according to claim 1, wherein the estimated ctDNA fraction of the sample is used to diagnose or confirm cancer in the subject.

26. The method according to claim 25, further comprising selecting an anticancer therapy to be administered to the subject based on the estimated ctDNA fraction of the sample, determining an effective dose of the anticancer therapy to be administered to the subject, or administering the anticancer therapy to the subject.

27. The method according to claim 26, wherein the anti-cancer treatment includes chemotherapy, radiation therapy, immunotherapy, targeted therapy, or surgery.

28. The method according to claim 1, wherein the estimated ctDNA fraction is used as a prognostic biomarker for predicting treatment outcomes in subjects with cancer.

29. The method according to claim 28, wherein the cancer is prostate cancer.

30. The method according to claim 1, wherein the sample is a liquid biopsy sample and includes blood, plasma, cerebrospinal fluid, sputum, feces, urine, or saliva.

31. A method for predicting the treatment outcome of a subject with cancer, wherein the method is One or more processors receive sequence read data for multiple sequence reads obtained for the sample from the target, Using one or more of the aforementioned processors, determine whether the sequence read data is sufficient to perform copy number variation (CNA) modeling. If it is determined that the sequence read data is sufficient to perform CNA modeling using one or more of the above processors, the ctDNA fraction in the sample is estimated based on at least the sample tumor purity and sample ploidy derived from the CNA model, or If the sequence read data is determined to be insufficient for performing CNA modeling, one or more processors are used to estimate the ctDNA fraction in the sample based on the identification of at least one tumor somatic cell short variant detected in the sequence read data. Using one or more of the above processors, output the estimated ctDNA fraction in the sample, A method comprising predicting the treatment outcome of a subject with a specific anti-cancer therapy based on a comparison of the estimated ctDNA fraction with a predetermined threshold.

32. The method according to claim 31, wherein the predetermined threshold is determined based on an analysis of ctDNA fractions and survival data for a cohort of patients having the cancer.

33. The method according to claim 32, wherein the predetermined threshold is determined by adjusting an empirical threshold to maximize sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), or any combination thereof, for the ctDNA fraction data for the cohort of patients having the cancer.

34. The method according to claim 31, wherein the cancer is prostate cancer.

35. The method according to claim 31, wherein the anticancer therapy includes enzalutamide loading after abiraterone treatment.

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