Methods and systems for determining methylation state categories

Abstract

Methods and systems for determining methylation state categories are described. The methods may comprise, for example, receiving a plurality of duplex consensus sequence reads obtained by sequencing single strand nucleic acid molecules of a double-stranded library; and classifying each duplex consensus sequence read of the plurality as belonging to one or more methylation state categories using a classification model. The methods may further comprise, for example, determining a methylation signature for the sample based on one or more methylation states and one or more methylation state categories.

Description

METHODS AND SYSTEMS FOR DETERMINING METHYLATION STATECATEGORIESCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the priority benefit of United States Provisional Patent Application Serial No. 63 / 608,743, filed December 11, 2023, the contents of which are incorporated herein by reference in their entirety.FIELD OF THE INVENTION

[0002] The present disclosure relates generally to methods and systems for analyzing genomic profiling data, and more specifically to methods and systems for determining methylation states and methylation state categories, using genomic profiling data.BACKGROUND

[0003] The methylation of particular genomic loci can indicate a disease status for a patient. For example, the genomes of certain cancers can be predisposed to methylation at specific loci.Moreover, the methylation of such loci may be distributed in a particular manner across both the sense and anti-sense strands of the cancer genome, and the particular patterning of methylation markers across both strands may indicate, for the patient, the cancer’s progression or status. The ability to resolve the pattern of methylation markers in a genome is of clinical and biomedical importance. Current sequencing methods for resolving methylation patterns, however, can be subject to laboratory artifacts. Methylation sequencing methods can be prone to systematic biases when detecting whether a given nucleotide is methylated, i.e., when determining a nucleotide’s methylation state. Errors in determining a nucleotide’s methylation state can alter the methylation signature of an entire sample, which in turn can affect the diagnostic and therapeutic strategies used for treatment of the patient. Improved methods are needed for determining methylation states at specific genomic loci and methylation signatures for patient samples. The present disclosure addresses these needs.BRIEF SUMMARY OF THE INVENTION

[0004] Disclosed herein are methods and systems that address shortcomings of current methylation sequencing (methyl- sequencing) techniques. Such shortcomings include a systematic negative bias when identifying methylated nucleotides at ends of DNA fragments. The sequencing library preparation steps involved in methyl-sequencing often fill the jagged ends of DNA fragments indiscriminately with unmethylated nucleotides. In addition, like other whole genome sequencing-based approaches, methyl-sequencing techniques are still subject to sporadic sequencing errors. The present disclosure addresses such errors by using a duplex methyl-sequencing technique to generate duplex consensus methyl sequence reads (based on sequencing of single- stranded DNA fragments from a methyl-sequencing library), and employing a computational method for classifying the duplex consensus methyl sequence reads based on the nucleotide methylation states of both strands of the duplex read. The duplex reads are classified into methylation state categories, where some categories are indicative of methylation state discrepancies resulting from a specific type of error. Duplex reads in categories associated with erroneous methylation states are then either corrected (e.g., in silico) or discarded. As a result, the methods herein enable improved accuracy in determining a sample’s methylation signature, by eliminating or reducing laboratory-bome errors. The laboratory-bome errors can be eliminated or reduced because the duplex reads provide a twofold increase in information relative to single- strand sequencing methods. The methods described herein articulate how the two-fold increase in information at the sequencing level can be used for identifying and correcting the laboratory-bome errors. This allows for more accurate detection of cancer-derived methylation patterns which can improve, for example, the results of assays for early cancer detection, minimal residual disease detection and tumor response monitoring.

[0005] In some aspects, disclosed herein is a method comprising: providing a plurality of genomic duplex nucleic acid molecules obtained from a sample from a subject; ligating tag sequence adapters onto one or more of the plurality of duplex nucleic acid molecules to generate tagged duplex nucleic acid molecules; subjecting the tagged duplex nucleic acid molecules to a conversion reaction, which converts an unmethylated cytosine to uracil, to generate convertedduplex nucleic acid molecules; amplifying the one or more converted duplex nucleic acid molecules to generate a double-stranded library; capturing the amplified tagged duplex nucleic acid molecules from the double- stranded library; separating the captured tagged duplex nucleic acid molecules into single strand nucleic acid molecules; sequencing, by a sequencer, the single strand nucleic acid molecules to obtain single strand consensus sequence reads to identify methylated CpG sites in the tagged duplex nucleic acid molecules; determining, by one or more processors, duplex consensus sequence reads from the single strand consensus sequence reads, and classifying, by the one or more processors, the duplex consensus sequence reads as belonging to one of two or more methylation state categories comprising at least one reliable methylation state category and at least one unreliable methylation state category, using a classification model.

[0006] In some aspects, the one or more methods disclosed herein can further comprise determining, using the one or more processors, the methylation signature for the sample based on methylation states at one or more CpG sites within duplex consensus sequence reads classified as belonging to at least one of the reliable methylation state categories.

[0007] In some embodiments, the duplex consensus sequence reads can comprise duplex methylated sequence reads, duplex hemi-methylated sequence reads, duplex unmethylated sequence reads, or any combination thereof. In any of the embodiments herein, based on the methylation signature, the sample can be determined to be hypermethylated in one or more genomic regions. In any of the embodiments herein, based on the methylation signature, the sample can be determined to be hypomethylated in one or more genomic regions.

[0008] In some embodiments, the classification model can be configured to identify discrepancies between methylation states at one or more CpG sites in a forward single strand consensus sequence (forward SSCS) corresponding to a given duplex consensus sequence read and in a reverse single strand consensus sequence (reverse SSCS) corresponding to the given duplex consensus sequence read and classify the duplex consensus sequence read based on a number of identified discrepancies.

[0009] In any of the embodiments herein, the classification model can be a deterministic model. In some embodiments, the deterministic model can be a decision tree model. In any of the embodiments herein, the subject can be suspected of having or is determined to have cancer.

[0010] In some embodiments, the cancer can be a B cell cancer (multiple myeloma), a melanoma, breast cancer, lung cancer, bronchus cancer, colorectal cancer, prostate cancer, pancreatic cancer, stomach cancer, ovarian cancer, urinary bladder cancer, brain cancer, central nervous system cancer, peripheral nervous system cancer, esophageal cancer, cervical cancer, uterine cancer, endometrial cancer, cancer of an oral cavity, cancer of a pharynx, liver cancer, kidney cancer, testicular cancer, biliary tract cancer, small bowel cancer, appendix cancer, salivary gland cancer, thyroid gland cancer, adrenal gland cancer, osteosarcoma, chondrosarcoma, a cancer of hematological tissue, an adenocarcinoma, an inflammatory myofibroblastic tumor, a gastrointestinal stromal tumor (GIST), colon cancer, multiple myeloma (MM), myelodysplastic syndrome (MDS), myeloproliferative disorder (MPD), acute lymphocytic leukemia (ALL), acute myelocytic leukemia (AML), chronic myelocytic leukemia (CML), chronic lymphocytic leukemia (CLL), polycythemia Vera, Hodgkin lymphoma, nonHodgkin lymphoma (NHL), soft-tissue sarcoma, fibrosarcoma, myxosarcoma, liposarcoma, osteogenic sarcoma, chordoma, angiosarcoma, endotheliosarcoma, lymphangiosarcoma, lymphangioendotheliosarcoma, synovioma, mesothelioma, Ewing's tumor, leiomyosarcoma, rhabdomyosarcoma, squamous cell carcinoma, basal cell carcinoma, adenocarcinoma, sweat gland carcinoma, sebaceous gland carcinoma, papillary carcinoma, papillary adenocarcinomas, medullary carcinoma, bronchogenic carcinoma, renal cell carcinoma, hepatoma, bile duct carcinoma, choriocarcinoma, seminoma, embryonal carcinoma, Wilms' tumor, bladder carcinoma, epithelial carcinoma, glioma, astrocytoma, medulloblastoma, craniopharyngioma, ependymoma, pinealoma, hemangioblastoma, acoustic neuroma, 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 cancer, essential thrombocythemia, agnogenic myeloid metaplasia, hypereosinophilic syndrome, systemic mastocytosis, familiar hypereosinophilia, chronic eosinophilic leukemia, neuroendocrine cancers, or a carcinoid tumor.

[0011] In some embodiments, the cancer can comprise acute lymphoblastic leukemia (Philadelphia chromosome positive), acute lymphoblastic leukemia (precursor B-cell), acute myeloid leukemia (FLT3+), acute myeloid leukemia (with an IDH2 mutation), anaplastic large cell lymphoma, basal cell carcinoma, B-cell chronic lymphocytic leukemia, bladder cancer, breast cancer (HER2 overexpressed / amplified), breast cancer (HER2+), breast cancer (HR+, HER2-), cervical cancer, cholangiocarcinoma, chronic lymphocytic leukemia, chronic lymphocytic leukemia (with 17p deletion), chronic myelogenous leukemia, chronic myelogenous leukemia (Philadelphia chromosome positive), classical Hodgkin lymphoma, colorectal cancer, colorectal cancer (dMMR / MSI-H), colorectal cancer (KRAS wild type), cryopyrin-associated periodic syndrome, a cutaneous T-cell lymphoma, dermatofibrosarcoma protuberans, a diffuse large B-cell lymphoma, fallopian tube cancer, a follicular B-cell nonHodgkin lymphoma, a follicular lymphoma, gastric cancer, gastric cancer (HER2+), gastroesophageal junction (GEJ) adenocarcinoma, a gastrointestinal stromal tumor, a gastrointestinal stromal tumor (KIT+), a giant cell tumor of the bone, a glioblastoma, granulomatosis with polyangiitis, a head and neck squamous cell carcinoma, a hepatocellular carcinoma, Hodgkin lymphoma, juvenile idiopathic arthritis, lupus erythematosus, a mantle cell lymphoma, medullary thyroid cancer, melanoma, a melanoma with a BRAF V600 mutation, a melanoma with a BRAF V600E or V600K mutation, Merkel cell carcinoma, multicentric Castleman's disease, multiple hematologic malignancies including Philadelphia chromosomepositive ALL and CML, multiple myeloma, myelofibrosis, a non-Hodgkin’s lymphoma, a nonresectable subependymal giant cell astrocytoma associated with tuberous sclerosis, a non- small cell lung cancer, a non-small cell lung cancer (ALK+), a non-small cell lung cancer (PD- L1+), a non-small cell lung cancer (with ALK fusion or ROS1 gene alteration), a non-small cell lung cancer (with BRAF V600E mutation), a non-small cell lung cancer (with an EGFR exon 19 deletion or exon 21 substitution (L858R) mutations), a non-small cell lung cancer (with an EGFR T790M mutation), ovarian cancer, ovarian cancer (with a BRCA mutation), pancreatic cancer, a pancreatic, gastrointestinal, or lung origin neuroendocrine tumor, a pediatric neuroblastoma, a peripheral T-cell lymphoma, peritoneal cancer, prostate cancer, a renal cell carcinoma, rheumatoid arthritis, a small lymphocytic lymphoma, a soft tissue sarcoma, a solid tumor (MSI-H / dMMR), a squamous cell cancer of the head and neck, a squamous non-small celllung cancer, thyroid cancer, a thyroid carcinoma, urothelial cancer, a urothelial carcinoma, or Waldenstrom's macroglobulinemia.

[0012] In some embodiments, the one or more methods disclosed herein can comprise treating the subject with an anti-cancer therapy. In some embodiments, the anti-cancer therapy can comprise a targeted anti-cancer therapy.

[0013] In some embodiments, the targeted anti-cancer therapy can comprise abemaciclib (Verzenio), abiraterone acetate (Zytiga), acalabrutinib (Calquence), ado-trastuzumab emtansine (Kadcyla), afatinib dimaleate (Gilotrif), aldesleukin (Proleukin), alectinib (Alecensa), alemtuzumab (Campath), alitretinoin (Panretin), alpelisib (Piqray), amivantamab-vmjw (Rybrevant), anastrozole (Arimidex), apalutamide (Erleada), asciminib hydrochloride (Scemblix), atezolizumab (Tecentriq), avapritinib (Ayvakit), avelumab (Bavencio), axicabtagene ciloleucel (Yescarta), axitinib (Inlyta), belantamab mafodotin-blmf (Blenrep), belimumab (Benlysta), belinostat (Beleodaq), belzutifan (Welireg), bevacizumab (Avastin), bexarotene (Targretin), binimetinib (Mektovi), blinatumomab (Blincyto), bortezomib (Velcade), bosutinib (Bosulif), brentuximab vedotin (Adcetris), brexucabtagene autoleucel (Tecartus), brigatinib (Alunbrig), cabazitaxel (Jevtana), cabozantinib (Cabometyx), cabozantinib (Cabometyx, Cometriq), canakinumab (Haris), capmatinib hydrochloride (Tabrecta), carfilzomib (Kyprolis), cemiplimab-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), denileukin diftitox (Ontak), denosumab (Xgeva), dinutuximab (Unituxin), dostarlimab-gxly (Jemperli), durvalumab (Imfinzi), duvelisib (Copiktra), elotuzumab (Empliciti), enasidenib 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 (Inrebic), fulvestrant (Faslodex), gefitinib (Iressa), gemtuzumab ozogamicin (Mylotarg), gilteritinib(Xospata), glasdegib maleate (Daurismo), hyaluronidase-zzxf (Phesgo), ibrutinib (Imbruvica), ibritumomab tiuxetan (Zevalin), idecabtagene vicleucel (Abecma), idelalisib (Zydelig), imatinibmesylate (Gleevec), infigratinib phosphate (Truseltiq), inotuzumab ozogamicin (Besponsa), iobenguane 1131 (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), lisocabtagene maraleucel (Breyanzi), loncastuximab tesirine-lpyl (Zynlonta), lorlatinib (Lorbrena), lutetium Lu 177-dotatate (Lutathera), margetuximab-cmkb (Margenza), midostaurin (Rydapt), mobocertinib succinate (Exkivity), mogamulizumab-kpkc (Poteligeo), moxetumomab pasudotox-tdfk (Lumoxiti), naxitamab-gqgk (Danyelza), necitumumab (Portrazza), neratinib maleate (Nerlynx), nilotinib (Tasigna), niraparib tosylate monohydrate (Zejula), nivolumab (Opdivo), obinutuzumab (Gazyva), ofatumumab (Arzerra), olaparib (Lynparza), olaratumab (Lartruvo), osimertinib (Tagrisso), palbociclib (Ibrance), panitumumab (Vectibix), panobinostat (Farydak), pazopanib (Votrient), pembrolizumab (Keytruda), pemigatinib (Pemazyre), pertuzumab (Perjeta), pexidartinib hydrochloride (Turalio), polatuzumab vedotin-piiq (Polivy), ponatinib hydrochloride (Iclusig), pralatrexate (Folotyn), pralsetinib (Gavreto), radium 223 dichloride (Xofigo), ramucirumab (Cyramza), regorafenib (Stivarga), ribociclib (Kisqali), ripretinib (Qinlock), rituximab (Rituxan), rituximab and hyaluronidase human (Rituxan Hycela), romidepsin (Istodax), rucaparib camsylate (Rubraca), ruxolitinib phosphate (Jakafi), sacituzumab govitecan-hziy (Trodelvy), seliciclib, selinexor (Xpovio), selpercatinib (Retevmo), selumetinib sulfate (Koselugo), siltuximab (Sylvant), sipuleucel-T (Provenge), sirolimus protein-bound particles (Fyarro), sonidegib (Odomzo), sorafenib (Nexavar), sotorasib (Lumakras), sunitinib (Sutent), tafasitamab-cxix (Monjuvi), tagraxofusp-erzs (Elzonris), talazoparib tosylate (Talzenna), tamoxifen (Nolvadex), tazemetostat hydrobromide (Tazverik), tebentafusp-tebn (Kimmtrak), temsirolimus (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 (Fareston), tucatinib (Tukysa), umbralisib tosylate (Ukoniq), vandetanib (Caprelsa), vemurafenib (Zelboraf), venetoclax (Venclexta), vismodegib (Erivedge), vorinostat (Zolinza), zanubrutinib (Brukinsa), ziv- aflibercept (Zaltrap), or any combination thereof.

[0014] In any of the embodiments herein, the one or more methods herein can further comprise obtaining the sample from the subject. In any of the embodiments herein, the sample can comprise a tissue biopsy sample, a liquid biopsy sample, or a normal control. In some embodiments, the sample can be a liquid biopsy sample, and can comprise blood, plasma, cerebrospinal fluid, sputum, stool, urine, or saliva. In some embodiments, the sample can be a liquid biopsy sample and can comprise circulating tumor cells (CTCs). In some embodiments, the sample can be a liquid biopsy sample and can comprise cell-free DNA (cfDNA), circulating tumor DNA (ctDNA), or any combination thereof. In some embodiments, the sample can be a liquid biopsy sample and can comprise cell-free DNA (cfDNA), circulating tumor DNA (ctDNA), or any combination thereof.

[0015] In any of the embodiments herein, the plurality of nucleic acid molecules can comprise a mixture of tumor nucleic acid molecules and non-tumor nucleic acid molecules. In some embodiments, the tumor nucleic acid molecules can be derived from a tumor portion of a heterogeneous tissue biopsy sample, and the non-tumor nucleic acid molecules are derived from a normal portion of the heterogeneous tissue biopsy sample. In some embodiments, the sample can comprise a liquid biopsy sample, and wherein the tumor nucleic acid molecules are derived from a circulating tumor DNA (ctDNA) fraction of the liquid biopsy sample, and the non-tumor nucleic acid molecules are derived from a non-tumor, cell-free DNA (cfDNA) fraction of the liquid biopsy sample. In any of the embodiments herein, the one or more adapters can comprise amplification primers, flow cell adaptor sequences, substrate adapter sequences, or sample index sequences. In any of the embodiments herein, the captured nucleic acid molecules can be captured from the amplified nucleic acid molecules by hybridization to one or more bait molecules. In some embodiments, the one or more bait molecules can comprise one or more nucleic acid molecules, each comprising a region that is complementary to a region of a captured nucleic acid molecule. In any of the embodiments herein, the amplifying nucleic acid molecules can comprise performing a polymerase chain reaction (PCR) amplification technique, a non-PCR amplification technique, or an isothermal amplification technique. In any of the embodiments herein, the sequencing can comprise use of a massively parallel sequencing (MPS) technique, whole genome sequencing (WGS), whole exome sequencing, targeted sequencing,direct sequencing, or Sanger sequencing technique. In some embodiments, the sequencing can comprise massively parallel sequencing, and the massively parallel sequencing technique comprises next generation sequencing (NGS). In any of the embodiments herein, the sequencer can comprise a next generation sequencer. In any of the embodiments herein, one or more of the plurality of sequencing reads can overlap one or more gene loci within one or more subgenomic intervals in the sample.

[0016] In some embodiments, the one or more gene loci comprises between 10 and 20 loci, between 10 and 40 loci, between 10 and 60 loci, between 10 and 80 loci, between 10 and 100 loci, between 10 and 150 loci, between 10 and 200 loci, between 10 and 250 loci, between 10 and 300 loci, between 10 and 350 loci, between 10 and 400 loci, between 10 and 450 loci, between 10 and 500 loci, between 20 and 40 loci, between 20 and 60 loci, between 20 and 80 loci, between 20 and 100 loci, between 20 and 150 loci, between 20 and 200 loci, between 20 and 250 loci, between 20 and 300 loci, between 20 and 350 loci, between 20 and 400 loci, between 20 and 500 loci, between 40 and 60 loci, between 40 and 80 loci, between 40 and 100 loci, between 40 and 150 loci, between 40 and 200 loci, between 40 and 250 loci, between 40 and 300 loci, between 40 and 350 loci, between 40 and 400 loci, between 40 and 500 loci, between 60 and 80 loci, between 60 and 100 loci, between 60 and 150 loci, between 60 and 200 loci, between 60 and 250 loci, between 60 and 300 loci, between 60 and 350 loci, between 60 and 400 loci, between 60 and 500 loci, between 80 and 100 loci, between 80 and 150 loci, between 80 and 200 loci, between 80 and 250 loci, between 80 and 300 loci, between 80 and 350 loci, between 80 and 400 loci, between 80 and 500 loci, between 100 and 150 loci, between 100 and 200 loci, between 100 and 250 loci, between 100 and 300 loci, between 100 and 350 loci, between 100 and 400 loci, between 100 and 500 loci, between 150 and 200 loci, between 150 and 250 loci, between 150 and 300 loci, between 150 and 350 loci, between 150 and 400 loci, between 150 and 500 loci, between 200 and 250 loci, between 200 and 300 loci, between 200 and 350 loci, between 200 and 400 loci, between 200 and 500 loci, between 250 and 300 loci, between 250 and 350 loci, between 250 and 400 loci, between 250 and 500 loci, between 300 and 350 loci, between 300 and 400 loci, between 300 and 500 loci, between 350 and 400 loci, between 350 and 500 loci, or between 400 and 500 loci.

[0017] In any of the embodiments herein, the one or more gene loci can comprise 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, CBFB, 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 (Cllorf30), EP300, EPHA3, EPHB1, EPHB4, ERBB2, ERBB3, ERBB4, ERCC4, ERG, ERRFI1, ESRI, 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, FLT3, FOXL2, FUBP1, GABRA6, GATA3, GATA4, GATA6, GID4 (C17orf39), GNA11, GNA13, GNAQ, GNAS, GRM3, GSK3B, H3F3A, HDAC1, HGF, HNF1A, HRAS, HSD3B1, ID3, 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, 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, PDGFRA, PDGFRB, PDK1, PIK3C2B, PIK3C2G, PIK3CA, PIK3CB, PIK3R1, PIM1, PMS2, POLDI, POLE, PPARG, PPP2R1A, PPP2R2A, PRDM1, PRKAR1A, PRKCI, PTCHI, PTEN, PTPN11, PTPRO, QKI, RAC1, RAD21, RAD51, RAD51B, RAD51C, RAD51D, RAD52, RAD54L, RAFI, RARA, RBI, RBM10, REL, RET, RICTOR, RNF43, ROS1, RPTOR, RSPO2, SDC4, SDHA, SDHB, SDHC, SDHD, SETD2, SF3B1, SGK1, SLC34A2, SMAD2, SMAD4, SMARCA4, SMARCB1, SMO, SNCAIP, S0CS1, SOX2, SOX9, SPEN, SPOP, SRC, STAG2, STAT3, STK11, SUFU, SYK, TBX3, TEK, TERC, TERT, TET2, TGFBR2, TIPARP, TMPRSS2, TNFAIP3,TNFRSF14, TP53, TSC1, TSC2, TYR03, U2AF1, VEGFA, VHL, WHSCI, WHSC1L1, WT1, XP01, XRCC2, ZNF217, ZNF703, or any combination thereof.

[0018] In any of the embodiments herein, the one or more gene loci comprise ABL, ALK, ALL, B4GALNT1, BALE, BCL2, BRAE, 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, IL-ip, IL-6, IL-6R, JAK1, JAK2, JAK3, KIT, KRAS, MEK, MET, MSI-H, mTOR, PARP, PD-1, PDGFR, PDGFRa, PDGFRp, PD-L1, PI3K5, PIGF, PTCH, RAF, RANKL, RET, ROS1, SLAMF7, VEGF, VEGFA, VEGFB, or any combination thereof.

[0019] In any of the embodiments herein, the one or more methods disclosed herein can further comprise generating, by the one or more processors, a report indicating the methylation signature. In some embodiments, the one or more methods disclosed herein can further comprise transmitting the report to a healthcare provider. In some embodiments, the report can be transmitted via a computer network or a peer-to-peer connection.In some aspects, disclosed herein is a method comprising: receiving, by one or more processors, a plurality of duplex consensus sequence reads determined from single strand consensus sequence reads obtained by sequencing single strand nucleic acid molecules derived from a double-stranded library subjected to a conversion reaction which converts an unmethylated cytosine to uracil; and classifying, by the one or more processors, duplex consensus sequence reads of the plurality as belonging to one of two or more methylation state categories comprising at least one reliable methylation state category and at least one unreliable methylation state category, using a classification model. In some aspects, the method disclosed herein can further comprise determining, by the one or more processors, a methylation signature for the sample based on methylation states at one or more CpG sites within duplex consensus sequence reads classified as belonging to at least one of the reliable methylation state categories.

[0020] In some embodiments, the duplex consensus sequence reads can comprise duplex methylated sequence reads, duplex hemi-methylated sequence reads, duplex unmethylated sequence reads, or any combination thereof. In any of the embodiments herein, based on themethylation signature, the sample can be determined to be hypermethylated in one or more genomic regions. In any of the embodiments herein, based on the methylation signature, the sample can be determined to be hypomethylated in one or more genomic regions.

[0021] In some embodiments, the classification model can be configured to identify discrepancies between methylation states at one or more CpG sites in a forward single strand consensus sequence (forward SSCS) corresponding to a given duplex consensus sequence read and in a reverse single strand consensus sequence (reverse SSCS) corresponding to the given duplex consensus sequence read and classify the duplex consensus sequence read based on a number of identified discrepancies.

[0022] In any of the embodiments herein, the classification model can be a deterministic model. In some embodiments, the deterministic model can be a decision tree model. In any of the embodiments herein, the classification model can be a probabilistic model. In some embodiments, the probabilistic model is a hidden Markov model, a Gaussian mixture model, or a Bayesian network model.

[0023] In any of the embodiments herein, the classification model can be configured to classify duplex consensus sequence reads into at least two categories comprising a concordant category in which no discrepancies between methylation states at the one or more CpG sites are identified, and a discordant category in which at least one discrepancy between methylation states at the one or more CpG sites is identified. In some embodiments, the methylation signature for the sample can be determined based only on the methylation states at the one or more CpG sites determined for duplex consensus sequence reads in the concordant category. In any of the embodiments herein, the classification model can be further configured to classify duplex consensus sequence reads for which at least one discrepancy between methylation states at the one or more CpG sites is identified into one of two or more discordant sub-categories.

[0024] In some embodiments, the two or more discordant sub-categories can comprise a first discordant sub-category for which the at least one discrepancy between methylation states at the one or more CpG sites can be explained by a nick repair and / or end repair error. In some embodiments, the first discordant sub-category can comprise duplex consensus sequences forwhich all CpG sites for which the methylation states can be discrepant are located at position(s) that are 3’ on a corresponding forward SSCS of all CpG sites for which the methylation states can be concordant.

[0025] In some embodiments, the methods disclosed herein can further comprise determining what portion of a given duplex consensus sequence read can comprise reliable methylation state data. In some embodiments, determining what portion of the given duplex consensus sequence read can comprise reliable methylation state data is based on an analysis of a location of the one or more CpG sites for which methylation states can be discordant relative to a location of one or more CpG sites for which methylation states can be concordant.

[0026] In some embodiments, the portion of the given duplex consensus sequence read that can comprise reliable methylation state data can include one or more CpG sites for which methylation states can be concordant and that are located 5’ relative to the one or more CpG sites for which methylation states can be discordant on the forward SSCS corresponding to the duplex consensus sequence read. In some embodiments, the methods disclosed herein can further comprise determining if the reliable portion of the given duplex consensus sequence read fully overlaps a methylation biomarker site.

[0027] In any of the embodiments herein, the methylation signature for the sample can be determined based on the methylation states at the one or more CpG sites determined for duplex consensus sequence reads in the concordant category, and on the methylation states at the one or more CpG sites in reliable portions of duplex consensus sequence reads for which at least one discrepancy between methylation states at the one or more CpG sites can be identified.

[0028] In any of the embodiments herein, the two or more discordant sub-categories can comprise a second discordant sub-category for which the at least one discrepancy between methylation states at the one or more CpG sites can be explained by a sequencing error and / or a single non-conversion event. In some embodiments, the second discordant sub-category can comprise duplex consensus sequences for which only one CpG site for which the methylation states can be discrepant can be identified.

[0029] In any of the embodiments herein, the two or more discordant sub-categories can comprise a third discordant sub-category for which the at least one discrepancy between methylation states at the one or more CpG sites can be explained by biological hemimethylation. In some embodiments, the third discordant sub-category can comprise duplex consensus sequences for which one corresponding SSCS can comprise CpG sites that can all be methylated and the other corresponding SSCS can comprise CpG sites that can all be unmethylated.

[0030] In any of the embodiments herein, the two or more discordant sub-categories can comprise a fourth discordant sub-category for which the at least one discrepancy between methylation states at the one or more CpG sites cannot be explained.

[0031] In any of the embodiments herein, the methods disclosed herein can further comprise processing sequence read data obtained by sequencing the double- stranded library to generate the plurality of duplex consensus sequence reads.

[0032] In any of the embodiments herein, the conversion reaction can comprise a bisulfite reaction to convert non-methylated cytosine to uracil. In any of the embodiments herein, the conversion reaction can comprise an enzymatic conversion reaction to convert non-methylated cytosine to uracil.

[0033] In some embodiments, the enzymatic conversion reaction can comprise the use of a tet methylcytosine dioxygenase 2 (TET2) enzyme to oxidize 5-methyl-cytosine (5mC) or 5- hydroxymethyl-cytosine (5hmC) to 5-carboxycytosine (5caC). In some embodiments, the enzymatic conversion reaction can further comprise the use of a combination of TET2 and T4 P- glucosyltransferase (T4-PGT) enzymes to convert 5-methyl-cytosine (5mC) or 5- hydroxymethyl-cytosine (5hmC) to 5-(D-glucosyl)oxymethyl-cytosine. In any of the embodiments herein, the enzymatic conversion reaction can comprise the use of an Apolipoprotein B mRNA Editing Catalytic Polypeptide-like (APOBEC) enzyme.

[0034] In any of the embodiments herein, the method can be used to correct for methylation bias in a methylation sequencing method.

[0035] In any of the embodiments herein, the sample can comprise a tissue biopsy sample, a liquid biopsy sample, or a normal control. In some embodiments, the sample can be a liquidbiopsy sample and can comprise blood, plasma, cerebrospinal fluid, sputum, stool, urine, or saliva. In some embodiments, the sample can be a liquid biopsy sample and can comprise circulating tumor cells (CTCs). In some embodiments, the sample can be a liquid biopsy sample and can comprise cell-free DNA (cfDNA), circulating tumor DNA (ctDNA), or any combination thereof. In any of the embodiments herein, the plurality of nucleic acid molecules can comprise a mixture of tumor nucleic acid molecules and non-tumor nucleic acid molecules. In some embodiments, the tumor nucleic acid molecules can be derived from a tumor portion of a heterogeneous tissue biopsy sample, and the non-tumor nucleic acid molecules can be derived from a normal portion of the heterogeneous tissue biopsy sample. In some embodiments, the sample can comprise a liquid biopsy sample, and the tumor nucleic acid molecules can be derived from a circulating tumor DNA (ctDNA) fraction of the liquid biopsy sample, and the non-tumor nucleic acid molecules can be derived from a non-tumor, cell-free DNA (cfDNA) fraction of the liquid biopsy sample.

[0036] In any of the embodiments herein, the methylation signature can be used to diagnose a disease. In any of the embodiments herein, the methylation signature can be used in making a treatment decision for a subject diagnosed with a disease. In any of the embodiments herein, the methylation signature can be used to track the treatment response for a subject who has been diagnosed with a disease and is currently undergoing treatment. In any of the embodiments herein, the methylation signature can be used to detect minimal residual disease. In any of the embodiments herein, the disease can be cancer.

[0037] In some aspects, disclosed herein is a system comprising: one or more processors; and a memory communicatively coupled to the one or more processors and configured to store instructions that, when executed by the one or more processors, can cause the system to: receive, by one or more processors, a plurality of duplex consensus sequence reads determined from single strand consensus sequence reads obtained by sequencing single strand nucleic acid molecules derived from a double-stranded library subjected to a conversion reaction which converts an unmethylated cytosine to uracil; and classify, by the one or more processors, duplex consensus sequence reads of the plurality as belonging to one of two or more methylation statecategories comprising at least one reliable methylation state category and at least one unreliable methylation state category, using a classification model.

[0038] In some aspects, the system can further comprise instructions that, when executed by the one or more processors, cause the system to determine a methylation signature for the sample based on methylation states at one or more CpG sites within duplex consensus sequence reads classified as belonging to at least one of the reliable methylation state categories. In some embodiments, the duplex consensus sequence reads can comprise duplex methylated sequence reads, duplex hemi-methylated sequence reads, duplex unmethylated sequence reads, or any combination thereof. In any of the embodiments herein, based on the methylation signature, the sample can be determined to be hypermethylated in one or more genomic regions. In any of the embodiments herein, based on the methylation signature, the sample can be determined to be hypomethylated in one or more genomic regions. In some embodiments, the classification model can be configured to identify discrepancies between methylation states at one or more CpG sites in a forward single strand consensus sequence (forward SSCS) corresponding to a given duplex consensus sequence read and in a reverse single strand consensus sequence (reverse SSCS) corresponding to the given duplex consensus sequence read and classify the duplex consensus sequence read based on a number of identified discrepancies. In any of the embodiments herein, the classification model can be a deterministic model. In some embodiments, the deterministic model can be a decision tree model.

[0039] In some aspects, disclosed herein is a non-transitory computer-readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by one or more processors of a system, can cause the system to: receive, by one or more processors, a plurality of duplex consensus sequence reads determined from single strand consensus sequence reads obtained by sequencing single strand nucleic acid molecules derived from a double-stranded library subjected to a conversion reaction which converts an unmethylated cytosine to uracil; and classify, by the one or more processors, duplex consensus sequence reads of the plurality as belonging to one of two or more methylation state categories comprising at least one reliable methylation state category and at least one unreliable methylation state category, using a classification model.

[0040] In some aspects, the non-transitory computer-readable storage medium can further comprise instructions that, when executed by the one or more processors, can cause the system to: determine a methylation signature for the sample based on methylation states at one or more CpG sites within duplex consensus sequence reads classified as belonging to at least one of the reliable methylation state categories. In some embodiments, the duplex consensus sequence reads can comprise duplex methylated sequence reads, duplex hemi-methylated sequence reads, duplex unmethylated sequence reads, or any combination thereof. In any of the embodiments herein, based on the methylation signature, the sample can be determined to be hypermethylated in one or more genomic regions. In any of the embodiments herein, based on the methylation signature, the sample can be determined to be hypomethylated in one or more genomic regions.INCORPORATION BY REFERENCE

[0041] All publications, patents, and patent applications mentioned in this specification are herein incorporated by reference in their entirety to the same extent as if each individual publication, patent, or patent application was specifically and individually indicated to be incorporated by reference in its entirety. In the event of a conflict between a term herein and a term in an incorporated reference, the term herein controls.BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Various aspects of the disclosed methods, devices, and systems are set forth with particularity in the appended claims. A better understanding of the features and advantages of the disclosed methods, devices, and systems will be obtained by reference to the following detailed description of illustrative embodiments and the accompanying drawings, of which:

[0043] FIG. 1 depicts a non-limiting exemplary method for determining methylation state categories, in accordance with some embodiments of the present disclosure.

[0044] FIG. 2 depicts a non-limiting example of a schematic illustrating a process by which duplex consensus sequence reads can be classified into a methylation state category.

[0045] FIG. 3 depicts an exemplary computing device or system, in accordance with one or more embodiments of the present disclosure.

[0046] FIG. 4 depicts an exemplary computer system or computer network, in accordance with some instances of the systems described herein.

[0047] FIGS. 5A-5E depict a non-limiting example of schematics illustrating different methylation state categories.

[0048] FIG. 5A depicts DNA fragments that are be fully methylated on both sense and antisense strands or fully unmethylated on both sense and antisense strands.

[0049] FIG. 5B depicts DNA fragments with discordant methylation sites (comprising unmethylated nucleotides on the forward / sense strand in this illustration) that are located 3’ of the nucleotide sites that are methylated on both the sense and antisense strands; such mismatched methylation sites can be considered end-repaired regions.

[0050] FIG. 5C depicts DNA fragments where only a single nucleotide site is discordant in its methylation status between the sense and antisense strands.

[0051] FIG. 5D depicts DNA fragments that can be considered hemi-methylated across all nucleotide sites.

[0052] FIG. 5E depicts DNA fragments that cannot be classified as belonging to one of the methylation states depicted in FIGS. 5A-5D.

[0053] FIG. 6A depicts a non-limiting example of data illustrating duplex consensus sequence reads, aligned to one another, for a healthy patient sample.

[0054] FIG. 6B depicts a non-limiting example of data illustrating duplex consensus sequence reads, aligned to one another, for a cancer patient sample.

[0055] FIG. 7A depicts a non-limiting example of a schematic illustrating a mechanism by which methylation sites on the 3’ ends of a DNA duplex may be the result of false positive unmethylated Cs.

[0056] FIG. 7B depicts a non-limiting example histogram of base pairs that can be trimmed from a sequencing read, in the absence of duplex sequencing information.

[0057] FIG. 8 depicts a non-limiting example of data comparing cancer mixture fractions obtained using a conventional sequencing method, versus a duplex sequencing-based method.

[0058] FIG. 9 provides a non-limiting schematic illustration of a method for defining CpG clusters for cfDNA analysis

[0059] FIG. 10 provides a non-limiting schematic illustration of a methylation quantitation metric for quantifying cfDNA methylation.

[0060] FIG. 11 provides a non-limiting example of data for the background distribution of CCMF in healthy samples.

[0061] FIG. 12 provides a non-limiting schematic illustration of a region selection methodology.

[0062] FIG. 13 provides a non-limiting of data illustrating the overlap between cancer types and signal enrichment.DETAILED DESCRIPTION

[0063] Determining an accurate methylation signature for a patient sample can be difficult. Laboratory artifacts introduced during methyl-sequencing can alter the methylation states of some CpG sites in the sample genome, which can result in an inaccurate depiction of the sample’s methylation signature. Disclosed herein is a method for determining a methylation signature for a patient sample. In accordance with the method, duplex consensus sequence reads are obtained by sequencing single strand nucleic acid molecules from a double stranded-library. The double stranded library can be prepared by extracting the nucleic acid molecules from the sample and subjecting the molecules to a conversion reaction. The conversion reaction can include ligating and / or hybridizing the nucleic acid molecules (e.g., double- stranded nucleic acid molecules) with unique molecular identifiers (UMIs), such that a nucleic acid molecule can be identified even after being denatured. These UMIs may be exogenously unique or exogenously non-unique. Exogenously unique UMIs may be either completely unique or substantially unique. For UMIs that are exogenously non-unique, the unique identity of the UMI is ascertained by a combination of the characteristics associated with a nucleic acid molecule and amolecular barcode (that is non-unique) attached to that nucleic acid molecule. The characteristics associated with the nucleic acid molecule may be, for example, a sequence identity of bases within the nucleic acid molecule sequence or a stop and start coordinate of the associated nucleic acid molecule.

[0064] Each duplex consensus sequence read can be classified as belonging to one or more methylation state categories — such as a reliable methylation state category and an unreliable methylation state category — using a classification model. The methylation signature for the patient sample can then be determined based on CpG methylation states within duplex consensus sequence reads classified as belonging to at least one of the reliable methylation state categories.

[0065] The classification model can be configured to be trained to identify mismatches between methylation states at one or more CpG sites in a forward single strand consensus sequence (forward SSCS) and a reverse single strand consensus sequence (reverse SSCS), for a given duplex consensus sequence read. The duplex consensus sequence read can be classified based on a number and positioning of identified discrepancies. The classification model can be a deterministic model, such as a decision tree model.

[0066] Current methods of resolving nucleotide methylation markers are prone to systematic laboratory artifacts. Namely, current methylation sequencing (methyl-sequencing) methods are often subject to negative bias, when determining whether a nucleotide from a given sequencing read was methylated in its native biological context. Many methylation sequencing methods nick the DNA fragments to be sequenced. As a result, the DNA fragments often possess at least one sticky or jagged end, i.e., a DNA strand at the end of the DNA fragment overhangs or extends beyond the length of the complementary DNA strand. Jagged ends, e.g., underhanging ends, are usually filled as a part of methyl-sequencing methods, either inadvertently by other enzymes necessary for the sequencing process, or intentionally by the experimenter. The filling of jagged ends, however, is typically done with unmethylated nucleotides. As a result, any native methylation markers that were present on the nucleotide ends prior to the DNA fragment’ s nicking are lost and incorrectly replaced by non-methylated nucleotides. The filling of the DNA fragments’ jagged ends results in the negative bias when detecting methylation markers viatraditional methyl-sequencing methods. Negative bias in this context refers to the notion that by indiscriminately filling in underhanging DNA strands with unmethylated nucleotides, the DNA fragments harbor more unmethylated nucleotides than they would otherwise. That is, the indiscriminate filling in of underhanging DNA strands with unmethylated nucleotides can result in an undercounting, i.e., negative bias, of unmethylated nucleotides among DNA fragments. The methods and systems disclosed herein rectify the negative bias during methyl-sequencing. In addition, methyl-sequencing methods are subject to conventional sequencing errors, typical of whole genome sequencing-based approaches.

[0067] The methods of the present disclosure rectify the aforementioned negative bias by utilizing a duplex-sequencing-based method that involves analyzing the entire nucleotide sequence of a duplex consensus sequencing read, one read at a time. By analyzing the read-level context of the read’s nucleotide states, the method described herein can predict whether one or more of the nucleotide methylation states derive from a laboratory artifact, such as a nick-filling process or a routine sequencing error, or if the observed methylation states are true to the methylation states of those of the sample’s native biological context. Mislabeled methylation states are corrected, and the duplex consensus sequencing read can be reclassified, if necessary, to the more likely methylation state category (e.g., concordant duplex or discordant duplex). By correcting methylation states that may have been affected by laboratory artifacts, the methods disclosed herein provide more accurate methylation state calls than if the described methods were not used. As a result of the more accurate methylation state calls, a more accurate methylation signature can be determined for the sample. By extension, the subject from which the sample was derived can receive more appropriate diagnostic and therapeutic actions.

[0068] Disclosed herein is a method of determining a methylation signature for a sample from a subject, comprising: receiving, by one or more processors, a plurality of duplex consensus sequence reads determined from single strand consensus sequence reads obtained by sequencing single strand nucleic acid molecules derived from a double- stranded library subjected to a conversion reaction which converts an unmethylated cytosine to uracil; and classifying, by the one or more processors, duplex consensus sequence reads of the plurality as belonging to one oftwo or more methylation state categories comprising at least one reliable methylation state category and at least one unreliable methylation state category, using a classification model.

[0069] In some instances, the classification model can be configured to identify discrepancies between methylation states at one or more CpG sites in a forward single strand consensus sequence (forward SSCS) corresponding to a given duplex consensus sequence read in a reverse single strand consensus sequence (reverse SSCS) corresponding to the given duplex consensus sequence read and classify the duplex consensus sequence read based on a number of identified discrepancies. In any of the embodiments herein, the classification model can be a deterministic model. In some embodiments, the deterministic model can be a decision tree model.Definitions

[0070] Unless otherwise defined, all of the technical terms used herein have the same meaning as commonly understood by one of ordinary skill in the art in the field to which this disclosure belongs.

[0071] As used in this specification and the appended claims, the singular forms “a”, “an”, and “the” include plural references unless the context clearly dictates otherwise. Any reference to “or” herein is intended to encompass “and / or” unless otherwise stated.

[0072] ‘ ‘About” and “approximately” shall generally mean an acceptable degree of error for the quantity measured given the nature or precision of the measurements. Exemplary degrees of error are within 20 percent (%), typically, within 10%, and more typically, within 5% of a given value or range of values.

[0073] As used herein, the terms “comprising” (and any form or variant of comprising, such as “comprise” and “comprises”), “having” (and any form or variant of having, such as “have” and “has”), “including” (and any form or variant of including, such as “includes” and “include”), or “containing” (and any form or variant of containing, such as “contains” and “contain”), are inclusive or open-ended and do not exclude additional, un-recited additives, components, integers, elements, or method steps.

[0074] As used herein, the terms “individual,” “patient,” or “subject” are used interchangeably and refer to any single animal, e.g., a mammal (including such non-human animals as, for example, dogs, cats, horses, rabbits, zoo animals, cows, pigs, sheep, and non-human primates) for which treatment is desired. In particular embodiments, the individual, patient, or subject herein is a human.

[0075] The terms “cancer” and “tumor” are used interchangeably herein. These terms refer to the presence of cells possessing characteristics typical of cancer-causing cells, such as uncontrolled proliferation, immortality, metastatic potential, rapid growth and proliferation rate, and certain characteristic morphological features. Cancer cells are often in the form of a tumor, but such cells can exist alone within an animal, or can be a non-tumorigenic cancer cell, such as a leukemia cell. These terms include a solid tumor, a soft tissue tumor, or a metastatic lesion. As used herein, the term “cancer” includes premalignant, as well as malignant cancers.

[0076] As used herein, “treatment” (and grammatical variations thereof such as “treat” or “treating”) refers to clinical intervention (e.g., administration of an anti-cancer agent or anticancer therapy) in an attempt to alter the natural course of the individual being treated, and can be performed either for prophylaxis or during the course of clinical pathology. Desirable effects of treatment include, but are not limited to, preventing occurrence or recurrence of disease, alleviation of symptoms, diminishment of any direct or indirect pathological consequences of the disease, preventing metastasis, decreasing the rate of disease progression, amelioration or palliation of the disease state, and remission or improved prognosis.

[0077] As used herein, the term “subgenomic interval” (or “subgenomic sequence interval”) refers to a portion of a genomic sequence.

[0078] As used herein, the term “subject interval” refers to a subgenomic interval or an expressed subgenomic interval (e.g., the transcribed sequence of a subgenomic interval).

[0079] As used herein, the terms “variant sequence” or “variant” are used interchangeably and refer to a modified nucleic acid sequence relative to a corresponding “normal” or “wild-type” sequence. In some instances, a variant sequence may be a “short variant sequence” (or “short variant”), i.e., a variant sequence of less than about 50 base pairs in length.

[0080] 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.

[0081] 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 for a genomic locus.

[0082] As used herein, the term “methylation state” refers to whether a given nucleotide, typically a CpG site, is methylated or not. The methylation state is binary, in that a nucleotide can be one of only two methylation states — methylated or not methylated, i.e., unmethylated.

[0083] As used herein, the term “methylation state category” refers to a category defined based on the distribution of methylation states across a sequencing read, such as a duplex consensus sequence read. The distribution of methylation states across the duplex consensus sequence read can refer to the positioning of methylated genomic loci, e.g., methylated CpG sites, across both the sense and antisense strands represented by the duplex consensus sequence read. The methylation state category can be a discrete category, such as a discordant state category (e.g., where there is a mismatch of methylation state at one or more corresponding sites, e.g., CpG sites, on the two strands of the duplex consensus sequence read) or a concordant state category (e.g., where there is a match of methylation state at the one or more corresponding sites, e.g., CpG sites, on the two strands of the duplex consensus sequence read). As an abbreviation, the duplex consensus sequencing read can be referred to as being, for example, “discordant” or “concordant”, and in such cases, the methylation state category of the sequence read is being referenced. A specific site within a duplex consensus sequence read may also be referred to as being fully methylated (duplex methylated) if corresponding sites, e.g., CpG sites, on both strands of the duplex are methylated, hemi-methylated if, e.g., a CpG site on one strand is methylated and the corresponding site on the other strand is not methylated, or fully unmethylated (duplex unmethylated) if corresponding sites, e.g., CpG sites, on both strands of the duplex are un methylated. Methylation state categories can also be associated with the degree of reliability of an observed duplex consensus sequence read, where the reliability of the read is with respect to whether or not the observed methylation states of the sites on the readderive, for example, from a laboratory artifact, versus a true biological phenomenon, as observed in the sample. In some cases, a discordant methylation state category may still be associated with a reliable determination of methylation state. For example, duplex consensus sequence reads for which all the CpG sites on one single strand consensus sequence (SSCS) are methylated, and all the CpG sites on the other SSCS are unmethylated can be indicative of biological hemi-methylation, and thus, the observed duplex consensus sequence read can be classified into a discordant methylation state category that is nonetheless considered a reliable determination of methylation state. In contrast, duplex consensus sequence reads for which only one internal CpG site (e.g., a CpG site not located near the ends of the read) on a single SSCS is unmethylated while the corresponding site on the other SSCS is methylated can be indicative of random sequencing error, and thus, the observed duplex consensus sequence read can be classified into a discordant methylation state category that is considered an unreliable determination of methylation state..

[0084] As used herein, the term “methylation signature” refers to the overall composition of methylation states across the sample, and more specifically, across the sample’s genomic data. The sample’s genomic data need not be uniform across the sample. For example, a cancer sample’s cells can often be subject to numerous and rapid mutations, which can result in genomic heterogeneity across the cells of the sample. The methylation signature can be comprised of diverse methylation states across numerous CpG loci in one or more genomic regions.

[0085] The section headings used herein are for organizational purposes only and are not to be construed as limiting the subject matter described.Methods for determining methylation state categories

[0086] Certain nucleotides can be methylated. Examples include, but are not limited to, nucleotides comprising a cytosine nucleobase that are followed by nucleotides comprising a guanine nucleobase (z.e., CpG sites) and, in some rare cases, nucleotides comprising a cytosine nucleobase that are followed by nucleotides comprising a non-guanine nucleobase (e.g., CpN sites). The composition and / or distribution of methylated nucleotides across a sample’s genomecan indicate the disease status of a patient. Determining such a methylation signature for a sample, however, can be challenging. Many methyl-sequencing methods are subject to laboratory artifacts, and in particular, are prone to underestimating the true number of methylated loci in a sample’s genome. During sequencing library preparation, the DNA fragments to be sequenced often possess jagged ends. The jagged ends are then indiscriminately filled with unmethylated nucleotides, regardless of whether the pre-filled nucleotides were methylated or not. The methods disclosed herein address the laboratory artifacts that arise during methyl-sequencing by identifying and rectifying methylation state errors present in duplex methylation sequence reads.

[0087] FIG. 1 illustrates an exemplary schematic showing a general process 100 for determining methylation state categories. 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 the blocks of process 100 are divided up in any manner between the server and a client device. In other examples, the blocks of process 100 are divided up between the server and multiple client devices. Thus, while portions of process 100 are described herein as being performed by particular devices of a client-server system, it will be appreciated that process 100 is not so limited. In other examples, process 100 is performed using only a client device or only multiple client devices. In process 100, some blocks are, optionally, combined, the order of some blocks is, optionally, changed, and some blocks are, optionally, omitted. In some examples, additional steps may be performed in combination with the process 100. Accordingly, the operations as illustrated (and described in greater detail below) are exemplary by nature and, as such, should not be viewed as limiting.

[0088] FIG. 1 illustrates a method for determining a methylation signature for a sample from a subject. The sample can comprise a tissue biopsy sample, a liquid biopsy sample, or a normal control. The sample can be a liquid biopsy sample and can comprise blood, plasma, cerebrospinal fluid, sputum, stool, urine, or saliva. The sample can be a liquid biopsy sample and can comprise circulating tumor cells (CTCs). The sample can be a liquid biopsy sample and can comprise cell-free DNA (cfDNA), including circulating tumor DNA (ctDNA). The plurality of nucleic acid molecules can comprise a mixture of tumor nucleic acid molecules and non-tumor nucleic acid molecules. The tumor nucleic acid molecules can be derived from a tumor portion of a heterogeneous tissue biopsy sample, and the non-tumor nucleic acid molecules can be derived from a normal portion of the heterogeneous tissue biopsy sample. The sample can comprise a liquid biopsy sample, and the tumor nucleic acid molecules can be derived from a circulating tumor DNA (ctDNA) fraction of the liquid biopsy sample, and the non-tumor nucleic acid molecules can be derived from a non-tumor, cell-free DNA (cfDNA) fraction of the liquid biopsy sample.

[0089] At step 102 in FIG. 1, a plurality of duplex consensus sequence reads obtained by sequencing single strand nucleic acid molecules derived from a double- stranded library, where the double- stranded sequencing library is prepared using a plurality of duplex nucleic acid molecules extracted from the sample and is subjected to a conversion reaction, is received. The conversion reaction can comprise a bisulfite reaction to convert non-methylated cytosine to uracil. The conversion reaction can be preceded by the denaturation of the nucleic acid molecules into single strands, so that the bisulfite, which can derive from sodium bisulfite, e.g., via dissociation, can react with the single strands to convert the non-methylated cytosines. The bisulfite can then be removed from the reaction via a desalting process, followed by the desulfonation of sulfonyl uracil adducts at alkaline pH. The desulfonation solution can then be removed. The nucleic acid molecules subject to the bisulfite-based conversion reaction can then be subject to downstream processing for sequencing.

[0090] The conversion reaction can alternatively comprise an enzymatic conversion reaction to convert non-methylated cytosine to uracil. The enzymatic conversion reaction can comprise the use of a tet methylcytosine dioxygenase 2 (TET2) enzyme to oxidize 5-methyl-cytosine (5mC) or 5-hydroxymethyl-cytosine (5hmC) to 5-carboxycytosine (5caC). The enzymatic conversion reaction can further comprise the use of a combination of TET2 and T4 P-glucosyltransferase (T4-PGT) enzymes to convert 5-methyl-cytosine (5mC) or 5-hydroxymethyl-cytosine (5hmC) to 5-(D-glucosyl)oxymethyl-cytosine. The enzymatic conversion reaction can comprise the use of an Apolipoprotein B mRNA Editing Catalytic Polypeptide-like (APOBEC) enzyme. APOBEC enzymes are unable to deaminate TET2- and 5mC-derived products such as 5caC or 5-(D- glucosyljoxymethyl-cytosine. In contrast, APOBEC enzymes can readily convert unmethylatedcytosines into uracil. Thus, the oxidization or conversion of 5mC nucleotides protects the methylated cytosines, which results in the selective conversion of unmethylated cytosines into uracil. The nucleic acid molecules subject to the enzymatic conversion reaction can then be subject to downstream processing for sequencing.

[0091] The methods disclosed herein can further comprise processing sequence read data obtained by sequencing the double-stranded library to generate the plurality of duplex consensus sequence reads. Duplex consensus sequence reads can be derived using a duplex sequencing method, such as, but not limited to, the method described in Kennedy et al. (2014), “Detecting ultralow-frequency mutations by Duplex Sequencing”, Nature Protocols 9:2586-2606. Duplex consensus sequence reads are obtained by creating and amplifying a construct that comprises both strands of the original duplex to generate two types of PCR amplification product. One of the duplex amplicon products comprises, in its sense strand (positive strand), the sense strand sequence of the template molecule. The other duplex amplicon type comprises, in its antisense strand (negative strand), the antisense strand sequence of the template molecule. Sequencing the two amplicon product types (sometimes referred to in the art as the aP family and the Pa family) and determining their associated sequences can be used to determine the sequences of both the sense and antisense strands of the template DNA duplex molecule, referred to as the duplex consensus sequence read. The two amplicon product types can be distinguished based on the two types of tag sequence adapters ligated or hybridized to the template duplex molecule prior to amplification. One of the tag sequence adapters is ligated to, and used to amplify, the sense strand of the duplex molecule, and the other tag sequence adapter is ligated to, and used to amplify, the antisense strand of the duplex molecule. The duplex consensus sequence read is then determined on the basis of single strand consensus sequences determined from a plurality of sequence reads for each amplicon type. Additional adapter sequences, such as sequencing adapters or adapters for hybridizing to a flow cell for typical whole genome sequencing techniques can also be ligated onto the template molecule, or the template molecule’s derivatives (e.g., amplicons deriving from the template molecule). All of the necessary adapter molecules for the duplex sequencing workflow can be added at once, or at separate times.

[0092] In the case of duplex sequencing as applied to methyl- sequencing methods, the conversion reaction typical of methyl-sequencing methods, where the unmethylated cytosines are converted to uracil (e.g., via a bisulfite conversion reaction or an enzymatic conversion reaction), can be performed prior to the amplification of the template duplex nucleic acid molecule. The unmethylated cytosines that have been converted to uracil base pair with adenine, instead of guanine, and as a result, the amplicon products generated following the conversion reaction possess uracil- adenine base pairing or thymine-adenine base pairing, instead of cytosine-guanine base pairing. The resulting single or duplex consensus sequence reads can then be compared against a reference sequence to determine whether an observed thymine-adenine base pair is a true biological thymine-adenine base pair, or a thymine-adenine base pair indicative of an unmethylated cytosine in the sample genome. The duplex nucleic acid molecule can be cell free DNA (cfDNA).

[0093] At step 104 in FIG. 1, each duplex consensus sequence read of the plurality is classified as belonging to one of two or more methylation state categories, comprising at least one reliable methylation state category and at least one unreliable methylation state category, using a classification model. The duplex consensus sequence reads can comprise duplex methylated sequence reads, duplex hemi-methylated sequence reads, duplex unmethylated sequence reads, or any combination thereof. E. Based on the methylation signature, the sample can be determined to be hypermethylated in one or more genomic regions, and / or the sample can be determined to be hypomethylated in one or more genomic regions. The classification model can be configured to identify discrepancies between methylation states at one or more CpG sites in a forward single strand consensus sequence (forward SSCS) corresponding to a given duplex consensus sequence read and in a reverse single strand consensus sequence (reverse SSCS) corresponding to the given duplex consensus sequence read and classify the duplex consensus sequence read based on a number of identified discrepancies. The forward SSCS corresponds to one of the strands of a duplex nucleic acid molecule, typically the sense strand (but can instead be the antisense strand), and the reverse SSCS corresponds to the other strand of the duplex nucleic acid molecule, typically the antisense strand (but can be the sense strand, if the forward SSCS corresponds to the antisense strand). A discrepancy can refer to differing nucleotidemethylation states across complementary strands at a given locus, e.g., if at a given locus, one nucleotide is methylated, and its complementary nucleotide is unmethylated. The classification model can be a deterministic model, and the deterministic model can be a decision tree model. A deterministic model can refer to a model where a probability is not associated with the classification of a duplex consensus sequence read into a methylation state category. The decision tree model can refer to a series of branching conditional criteria that results in the classification of a duplex consensus sequence read into a methylation state category.

[0094] Alternatively, the classification model can be a probabilistic model, such as, but not limited to a hidden Markov model, a mixture model e.g., a Gaussian mixture model), or a Bayesian network model. A probabilistic model can refer to a model where a probability is associated with the classification of a duplex consensus sequence read into a methylation state category. A probability can also be associated with preceding classification problems within the model, that can affect the classification of, but not directly classify, the duplex consensus sequence read. The hidden Markov model can refer to a model where the system being modeled is assumed to be a Markov process with unobservable hidden states, and the system is being learned by observing an observable process. For example, the classification of duplex consensus sequence reads into methylation state categories can be an observable process, but the procedure or logic by which the duplex consensus sequence reads are categorized into methylation state categories can be the hidden Markov process. The mixture model can be a Bayesian or a non- Bayesian mixture model, e.g., a Gaussian Bayesian or Gaussian non-Bayesian mixture model. The mixture model can refer to a model where different subpopulations are assumed to exist within a single population, such as the existence of different methylation state categories. Each subpopulation in the mixture model can be described as a distinct distribution. In the case of a Gaussian mixture model, each subpopulation in the mixture model can be described as a distinct Gaussian distribution, e.g., the members of each methylation state category can be described as a distinct Gaussian distribution, and the Gaussian mixture model can be used to make statistical inferences about each methylation state category’s corresponding Gaussian distribution. A Bayesian network can refer to a directed graph, where each node in the graph is a Bayesian variable, and the edges connecting the nodes can denote a conditional and unidirectionalprobabilistic dependency. For example, a Bayesian network graph can articulate a series of probabilistic sequences that ultimately categorize a duplex consensus sequence into a methylation state category.

[0095] The classification model can be configured to classify duplex consensus sequence reads into at least two categories comprising a concordant category in which no discrepancies between methylation states at the one or more CpG sites can be identified, and a discordant category in which at least one discrepancy between methylation states at the one or more CpG sites can be identified. In the case where the duplex consensus sequence read can be classified in the concordant category, the read can be considered to be unmethylated if all the non-discrepancies comprise of unmethylated CpG sites. In the case where the duplex consensus sequence read can be classified in the concordant category, the read can be considered to be methylated, if all the non-discrepancies comprise of methylated CpG sites.

[0096] In the case that the duplex consensus sequence read can be classified in the discordant category, the classification model can explain the observed discordance can be further classified into one of two or more discordant categories. That is, the classification model can be further configured to classify duplex consensus sequence reads for which at least one discrepancy between methylation states at the one or more CpG sites is identified into one of two or more discordant sub-categories.

[0097] The two or more discordant sub-categories can comprise a first discordant sub-category for which the at least one discrepancy between methylation states at the one or more CpG sites can be explained by a nick repair and / or end repair event by unmethylated Cs. Accordingly, the first discordant sub-category can comprise duplex consensus sequences for which all CpG sites for which the methylation states can be discrepant are located at position(s) that are 3’ on a corresponding forward SSCS of all CpG sites for which the methylation states can be concordant. The first discordant sub-category can be considered to be an unreliable methylation state category, because the nick repair and / or end repair event by unmethylated Cs is typically the result of a sequencing library preparation step, i.e., laboratory artifact. In the case of the first discordant subcategory, further classification can be achieved, by classifying based on whether the portion of concordant CpG sites cover a known methylation biomarker site associated withcancer or non-cancer. In the case that the portion of concordant CpG sites cover a known methylation biomarker site associated with cancer, the portion of concordant CpG sites can be classified as being indicative of cancer. In the case that the portion of concordant CpG sites cover a known methylation biomarker site associated with non-cancer (e.g., a methylation biomarker site known to not be associated with cancer), the portion of concordant CpG sites can be classified as being indicative of non-cancer.

[0098] The two or more discordant sub-categories can comprise a second discordant subcategory for which the at least one discrepancy between methylation states at the one or more CpG sites can be explained by a sequencing error and / or a single non-conversion event. Accordingly, the second discordant sub-category can comprise duplex consensus sequences for which only one CpG site for which the methylation states can be discrepant can be identified. The second discordant sub-category can be considered to be an unreliable methylation state category, because the single methylation state discrepancy is typically the result of a sequencing error and / or single non-conversion event.

[0099] The two or more discordant sub-categories can comprise a third discordant sub-category for which the at least one discrepancy between methylation states at the one or more CpG sites can be explained by biological hemimethylation. Accordingly, the third discordant sub-category can comprise duplex consensus sequences for which one corresponding SSCS can comprise CpG sites that can all be methylated and the other corresponding SSCS can comprise CpG sites that can all be unmethylated. The third discordant sub-category can be considered to be a reliable methylation state category, because the observed methylation state pattern (one SSCS being completely methylated, and the corresponding SSCS being completely unmethylated) is typical of biological hemi-methylation.

[0100] The two or more discordant sub-categories can comprise a fourth discordant subcategory for which the at least one discrepancy between methylation states at the one or more CpG sites cannot be explained. Accordingly, the fourth discordant sub-category can comprise a pattern of methylation states that cannot be classified in the first discordant sub-category, the second discordant sub-category, or the third discordant sub-category. The fourth discordant sub-category can be considered to be an unreliable methylation state category, because a mechanism that can putatively explain the observed pattern of methylation states may not be known.

[0101] The methods disclosed herein can further comprise determining what portion of a given duplex consensus sequence read can comprise reliable methylation state data. Determining what portion of the given duplex consensus sequence read can comprise reliable methylation state data is based on an analysis of a location of the one or more CpG sites for which methylation states can be discordant relative to a location of one or more CpG sites for which methylation states can be concordant. The portion of the given duplex consensus sequence read that can comprise reliable methylation state data can include one or more CpG sites for which methylation states can be concordant and that are located 5’ relative to the one or more CpG sites for which methylation states can be discordant on the forward SSCS corresponding to the duplex consensus sequence read. The methods disclosed herein can further comprise determining if the reliable portion of the given duplex consensus sequence read fully overlaps a methylation biomarker site. If the portion of concordant CpG sites cover a set of known biomarker CpG sites (e.g., a methylation biomarker site associated with cancer), the portion of concordant CpG sites can be classified as being indicative of the known biomarker (e.g., a biomarker for cancer). If the portion of concordant CpG sites cover a set of known biomarker CpG sites (e.g., a methylation biomarker site known to not be associated with cancer), the portion of concordant CpG sites can be classified as being indicative of the known biomarker (e.g., a biomarker for non-cancer). In some instances, the methods disclosed herein can be used to correct for methylation bias in a methylation sequencing method and enable more accurate determination of methylation signatures. Methylation bias can be identified and corrected for by determining what portion of a given duplex consensus sequence read comprises reliable methylation state data and / or by classifying duplex consensus sequence reads into methylation state categories and basing a determination of, e.g., methylation signature only on the subset of duplex consensus sequence reads that have been classified into a reliable methylation state category. The identified methylation bias can be corrected by discarding the duplex consensus sequence reads for which methylation bias was observed. Alternatively, the identified methylation bias can be corrected by changing the nucleotides that are likely the result of, e.g., alibrary preparation artifact or sequencing error, in silico. For example, if unmethylated Cs are observed on the ends of a duplex consensus sequence read in a configuration that suggests that the unmethylated Cs are likely the result of a laboratory artifact-derived end repair or nickfilling event, then the consensus sequence associated with those unmethylated Cs can be modified in silico, such that they are recorded to be methylated Cs.

[0102] At step 106 in FIG. 1, the methylation signature for the sample based on methylation states at one or more CpG sites within duplex consensus sequence reads classified as belonging to at least one of the reliable methylation state categories, is classified. The methylation signature for the sample can be determined based only on the methylation states at the one or more CpG sites determined for duplex consensus sequence reads in the concordant category. The methylation signature for the sample can be determined based on the methylation states at the one or more CpG sites determined for duplex consensus sequence reads in the concordant category, and on the methylation states at the one or more CpG sites in reliable portions of duplex consensus sequence reads for which at least one discrepancy between methylation states at the one or more CpG sites is identified. The methylation signature can be used to diagnose a disease. A sample’s methylation signature can be indicative of a disease status (e.g., diagnosis or prognosis) for the subject or patient from which the sample derives. Accordingly, the methylation signature can be used in making a treatment decision for a subject diagnosed with a disease. Furthermore, the methylation signature can be used to track the treatment response for a subject who has been diagnosed with a disease and is currently undergoing treatment. The methylation signature can be used to also detect minimal residual disease, or cancer. The methylation signature can inform, in part, or in whole, a personalized diagnosis, prognosis, or treatment for the subject from which the sample derives.

[0103] In some instances, the gene panel may comprise 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, or more than 40 genes.

[0104] In some instances, the disclosed methods may be used to determine methylation state categories by assessing methylation state categories in 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, or more than 40 gene loci.

[0105] In some instances, the disclosed methods may be used to identify variants in the 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, CBFB, 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 (Cl lorf30), EP300, EPHA3, EPHB1, EPHB4, ERBB2, ERBB3, ERBB4, ERCC4, ERG, ERRFI1, ESRI, 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, FLT3, FOXL2, FUBP1, GABRA6, GATA3, GATA4, GATA6, GID4 (C17orf39), GNA11, GNA13, GNAQ, GNAS, GRM3, GSK3B, H3F3A, HDAC1, HGF, HNF1A, HRAS, HSD3B1, ID3, 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, 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, PDGFRA, PDGFRB, PDK1, PIK3C2B, PIK3C2G, PIK3CA, PIK3CB, PIK3R1, PIM1, PMS2, POLDI, POLE, PPARG, PPP2R1A, PPP2R2A, PRDM1, PRKAR1A, PRKCI, PTCHI, PTEN, PTPN11, PTPRO, QKI, RAC1, RAD21, RAD51, RAD51B, RAD51C, RAD51D, RAD52, RAD54L, RAFI, RARA, RBI, 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, STK11, SUFU, SYK, TBX3, TEK, TERC, TERT, TET2, TGFBR2, TIPARP, TMPRSS2, TNFAIP3, TNFRSF14, TP53, TSC1, TSC2, TYR03, U2AF1, VEGFA, VHL, WHSCI, WHSC1L1, WT1, XP01, XRCC2, ZNF217, or ZNF703 gene locus, or any combination thereof.

[0106] In some instances, the disclosed methods may be used to identify variants in the 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, IL-ip, IL-6, IL-6R, JAK1, JAK2, JAK3, KIT, KRAS, MEK, MET, MSI-H, mTOR, PARP, PD-1, PDGFR, PDGFRa, PDGFRP, PD-L1, PI3K5, PIGF, PTCH, RAF, RANKL, RET, ROS1, SLAMF7, VEGF, VEGFA, or VEGFB gene locus, or any combination thereof.

[0107] FIG. 2 depicts a schematic illustrating a logic 200 by which duplex consensus sequence reads can be classified into a methylation state category. The logic applies to each duplex consensus sequence read in a plurality. In this particular schematic, the duplex consensus sequence read is checked for whether all CpG sites in the read are concordant (conditional 202). If true, the read is considered to not have any discordances. Otherwise, each SSCS of the read is checked for whether all the discordant unmethylated CpG sites are 3’ of all the concordant CpG sites (conditional 204). If true, the discordances of the reads are considered to be explained by a nick or an end that has been repaired by unmethylated Cs. The read is then further interrogated and checked for whether the concordant CpG sites cover one or more known methylation biomarker sites, such as a methylation biomarker site known to be related to cancer or to noncancer (conditional 206). If true, the duplex consensus sequence read and / or the concordant CpG sites within the duplex consensus sequence read are classified as being related to cancer if the known methylation biomarker sites are known to be related to cancer, or are classified as being related to non-cancer if the known methylation biomarker sites are known to be related to noncancer. If false, the duplex consensus sequence read can be ignored as being, for example, too damaged for further classifying. If each SSCS of the read did not have all its discordant unmethylated CpG sites 3’ of all the concordant CpG sites, then the read is checked for whetheronly a single discordant CpG site exists (conditional 208). If true, the discordance is considered to be from a sequencing error or a single non-conversion event. Otherwise, the read is checked for whether the read comprises one fully methylated SSCS and another fully unmethylated SSCS (conditional 210). If true, the discordances are considered to be from biological hemimethylation. Otherwise, the discordances are considered to not be explained by a single derivative mechanism, and the next duplex consensus sequence read is analyzed by the logic 200.Methods of use

[0108] In some instances, the disclosed methods may further comprise one or more of the steps of: (i) obtaining the sample from the subject (e.g., a subject suspected of having 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 adaptor sequences, substrate adapter sequences, or sample index sequences), (iv) performing a methylation conversion reaction to convert, e.g., non-methylated cytosine to uracil, (v) amplifying the nucleic acid molecules (e.g., using a polymerase chain reaction (PCR) amplification technique, a non-PCR amplification technique, or an isothermal amplification technique), (vi) capturing nucleic acid molecules from the amplified nucleic acid molecules (e.g., by hybridization to one or more bait molecules, where the bait molecules each comprise one or more nucleic acid molecules that each comprising a region that is complementary to a region of a captured nucleic acid molecule), (vii) sequencing the nucleic acid molecules extracted from the sample (or library proxies derived therefrom) using, e.g., a next-generation (massively parallel) sequencing technique, a whole genome sequencing (WGS) technique, a whole exome sequencing technique, a targeted sequencing technique, a direct sequencing technique, or a Sanger sequencing technique) using, e.g., a next-generation (massively parallel) sequencer, (viii) combining the data from the sequenced nucleic acid molecules, including data regarding the methylation statuses of the sequenced nucleic acid molecules, with markers from other biological modalities, such as, but not limited to, proteomics-based markers (e.g., the detection of specific polypeptides, such as proteins) or fragmentomics-based markers (e.g., thedetection of certain attributes related to nucleic acid fragments, such as fragment size or the sequences of fragment ends), to determine, for example, the presence of ctDNA, and (ix) generating, displaying, transmitting, and / or delivering a report (e.g., an electronic, web-based, or paper report) to the subject (or patient), a caregiver, a healthcare provider, a physician, an oncologist, an electronic medical record system, a hospital, a clinic, a third-party payer, an insurance company, or a government office. In some instances, the report comprises output from the methods described herein. In some instances, all or a portion of the report may be displayed in the graphical user interface of an online or web-based healthcare portal. In some instances, the report is transmitted via a computer network or peer-to-peer connection.

[0109] The disclosed methods may be used with any of a variety of samples. For example, in some instances, the sample may comprise a tissue biopsy sample, a liquid biopsy sample, or a normal control. In some instances, the sample may be a liquid biopsy sample and may comprise blood, plasma, cerebrospinal fluid, sputum, stool, urine, or saliva. In some instances, the sample may be a liquid biopsy sample and may comprise circulating tumor cells (CTCs). In some instances, the sample may be a liquid biopsy sample and may comprise cell-free DNA (cfDNA), circulating tumor DNA (ctDNA), or any combination thereof.

[0110] In some instances, the nucleic acid molecules extracted from a sample may comprise a mixture of tumor nucleic acid molecules and non-tumor nucleic acid molecules. In some instances, the tumor nucleic acid molecules may be derived from a tumor portion of a heterogeneous tissue biopsy sample, and the non-tumor nucleic acid molecules may be derived from a normal portion of the heterogeneous tissue biopsy sample. In some instances, the sample may comprise a liquid biopsy sample, and the tumor nucleic acid molecules may be derived from a circulating tumor DNA (ctDNA) fraction of the liquid biopsy sample while the non- tumor nucleic acid molecules may be derived from a non-tumor, cell-free DNA (cfDNA) fraction of the liquid biopsy sample.

[0111] In some instances, the disclosed methods for determining methylation state categories may be used to diagnose (or as part of a diagnosis of) the presence of disease or other condition (e.g., cancer, genetic disorders (such as Down Syndrome and Fragile X), neurological disorders, or any other disease type where detection of variants, e.g., copy number alternations, arerelevant to diagnosing, treating, or predicting said disease) in a subject (e.g., a patient). In some instances, the disclosed methods may be applicable to diagnosis of any of a variety of cancers as described elsewhere herein.

[0112] In some instances, the disclosed methods for determining methylation state categories may be used to predict genetic disorders in fetal DNA. (e.g., for invasive or non-invasive prenatal testing). For example, sequence read data obtained by sequencing fetal DNA extracted from samples obtained using invasive amniocentesis, chorionic villus sampling (cVS), or fetal umbilical cord sampling techniques, or obtained using non-invasive sampling of cell-free DNA (cfDNA) samples (which comprises a mix of maternal cfDNA and fetal cfDNA), may be processed according to the disclosed methods to identify variants, e.g., copy number alterations, associated with, e.g., Down Syndrome (trisomy 21), trisomy 18, trisomy 13, and extra or missing copies of the X and Y chromosomes.

[0113] In some instances, the disclosed methods for determining methylation state categories may be used to select a subject (e.g., a patient) for a clinical trial based on the methylation state determined for one or more gene loci. In some instances, patient selection for clinical trials based on, e.g., identification of methylation states at one or more gene loci, may accelerate the development of targeted therapies and improve the healthcare outcomes for treatment decisions.

[0114] In some instances, the disclosed methods for determining methylation state categories may be used to select an appropriate therapy or treatment (e.g., an anti-cancer therapy or anticancer treatment) for a subject. In some instances, for example, the anti-cancer therapy or treatment may comprise use of a poly (ADP-ribose) polymerase inhibitor (PARPi), a platinum compound, chemotherapy, radiation therapy, a targeted therapy (e.g., immunotherapy), surgery, or any combination thereof.

[0115] In some instances, the targeted therapy (or anti-cancer target therapy) may comprise abemaciclib (Verzenio), abiraterone acetate (Zytiga), acalabrutinib (Calquence), ado- trastuzumab emtansine (Kadcyla), afatinib dimaleate (Gilotrif), aldesleukin (Proleukin), alectinib (Alecensa), alemtuzumab (Campath), alitretinoin (Panretin), alpelisib (Piqray), amivantamab-vmjw (Rybrevant), anastrozole (Arimidex), apalutamide (Erleada), asciminibhydrochloride (Scemblix), atezolizumab (Tecentriq), avapritinib (Ayvakit), avelumab (Bavencio), axicabtagene ciloleucel (Yescarta), axitinib (Inlyta), belantamab mafodotin-blmf (Blenrep), belimumab (Benlysta), belinostat (Beleodaq), belzutifan (Welireg), bevacizumab (Avastin), bexarotene (Targretin), binimetinib (Mektovi), blinatumomab (Blincyto), bortezomib (Velcade), bosutinib (Bosulif), brentuximab vedotin (Adcetris), brexucabtagene autoleucel (Tecartus), brigatinib (Alunbrig), cabazitaxel (Jevtana), cabozantinib (Cabometyx), cabozantinib (Cabometyx, Cometriq), canakinumab (Haris), capmatinib hydrochloride (Tabrecta), carfilzomib (Kyprolis), cemiplimab-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), denileukin diftitox (Ontak), denosumab (Xgeva), dinutuximab (Unituxin), dostarlimab-gxly (Jemperli), durvalumab (Imfinzi), duvelisib (Copiktra), elotuzumab (Empliciti), enasidenib 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 (Inrebic), fulvestrant (Faslodex), gefitinib (Iressa), gemtuzumab ozogamicin (Mylotarg), gilteritinib (Xospata), glasdegib maleate (Daurismo), hyaluronidase-zzxf (Phesgo), ibrutinib (Imbruvica), ibritumomab tiuxetan (Zevalin), idecabtagene vicleucel (Abecma), idelalisib (Zydelig), imatinib mesylate (Gleevec), infigratinib phosphate (Truseltiq), inotuzumab ozogamicin (Besponsa), iobenguane 1131 (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), lisocabtagene maraleucel (Breyanzi), loncastuximab tesirine-lpyl (Zynlonta), lorlatinib (Lorbrena), lutetium Lu 177-dotatate (Lutathera), margetuximab-cmkb (Margenza), midostaurin (Rydapt), mobocertinib succinate (Exkivity), mogamulizumab-kpkc (Poteligeo), moxetumomab pasudotox-tdfk (Lumoxiti), naxitamab-gqgk (Danyelza), necitumumab (Portrazza), neratinib maleate (Nerlynx), nilotinib (Tasigna), niraparib tosylate monohydrate (Zejula), nivolumab (Opdivo), obinutuzumab (Gazyva), ofatumumab (Arzerra), olaparib (Lynparza), olaratumab (Lartruvo), osimertinib (Tagrisso), palbociclib (Ibrance), panitumumab (Vectibix), panobinostat (Farydak),pazopanib (Votrient), pembrolizumab (Keytruda), pemigatinib (Pemazyre), pertuzumab (Perjeta), pexidartinib hydrochloride (Turalio), polatuzumab vedotin-piiq (Polivy), ponatinib hydrochloride (Iclusig), pralatrexate (Folotyn), pralsetinib (Gavreto), radium 223 dichloride (Xofigo), ramucirumab (Cyramza), regorafenib (Stivarga), ribociclib (Kisqali), ripretinib (Qinlock), rituximab (Rituxan), rituximab and hyaluronidase human (Rituxan Hycela), romidepsin (Istodax), rucaparib camsylate (Rubraca), ruxolitinib phosphate (Jakafi), sacituzumab govitecan-hziy (Trodelvy), seliciclib, selinexor (Xpovio), selpercatinib (Retevmo), selumetinib sulfate (Koselugo), siltuximab (Sylvant), sipuleucel-T (Provenge), sirolimus protein-bound particles (Fyarro), sonidegib (Odomzo), sorafenib (Nexavar), sotorasib (Lumakras), sunitinib (Sutent), tafasitamab-cxix (Monjuvi), tagraxofusp-erzs (Elzonris), talazoparib tosylate (Talzenna), tamoxifen (Nolvadex), tazemetostat hydrobromide (Tazverik), tebentafusp-tebn (Kimmtrak), temsirolimus (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 (Fareston), tucatinib (Tukysa), umbralisib tosylate (Ukoniq), vandetanib (Caprelsa), vemurafenib (Zelboraf), venetoclax (Venclexta), vismodegib (Erivedge), vorinostat (Zolinza), zanubrutinib (Brukinsa), ziv- aflibercept (Zaltrap), or any combination thereof.

[0116] In some instances, the disclosed methods for determining methylation state categories may be used in treating a disease (e.g., a cancer) in a subject. For example, in response to determining methylation state categories using any of the methods disclosed herein, an effective amount of an anti-cancer therapy or anti-cancer treatment may be administered to the subject.

[0117] In some instances, the disclosed methods for determining methylation state categories may be used for monitoring disease progression or recurrence (e.g., cancer or tumor progression or recurrence) in a subject. For example, in some instances, the methods may be used to determine a methylation signature in a first sample obtained from the subject at a first time point, and used to determine a methylation signature in a second sample obtained from the subject at a second time point, where comparison of the first determination of a methylation signature and the second determination of a methylation signature allows one to monitor diseaseprogression or recurrence. In some instances, the first time point is chosen before the subject has been administered a therapy or treatment, and the second time point is chosen after the subject has been administered the therapy or treatment.

[0118] In some instances, the disclosed methods may be used for adjusting a therapy or treatment (e.g., an anti-cancer treatment or anti-cancer therapy) for a subject, e.g., by adjusting a treatment dose and / or selecting a different treatment in response to a change in the determination of a methylation signature.

[0119] In some instances, the methylation signature determined using the disclosed methods may be used as a prognostic or diagnostic indicator associated with the sample. For example, in some instances, the prognostic or diagnostic indicator may comprise an indicator of the presence of a disease (e.g., cancer) in the sample, an indicator of the probability that a disease (e.g., cancer) is present in the sample, an indicator of the probability that the subject from which the sample was derived will develop a disease (e.g., cancer) (i.e., a risk factor), or an indicator of the likelihood that the subject from which the sample was derived will respond to a particular therapy or treatment.

[0120] In some instances, the disclosed methods for determining methylation state categories may be implemented as part of a genomic profiling process that comprises identification of the presence of variant sequences at one or more gene loci in a sample derived from a subject as part of detecting, monitoring, predicting a risk factor, or selecting a treatment for a particular disease, e.g., cancer. In some instances, the variant panel selected for genomic profiling may comprise the detection of variant sequences at a selected set of gene loci. In some instances, the variant panel selected for genomic profiling may comprise detection of variant sequences at a number of gene loci through comprehensive genomic profiling (CGP), which is a next- generation sequencing (NGS) approach used to assess hundreds of genes (including relevant cancer biomarkers) in a single assay. Inclusion of the disclosed methods for determining methylation state categories as part of a genomic profiling process (or inclusion of the output from the disclosed methods for determining methylation state categories as part of the genomic profile of the subject) can improve the validity of, e.g., disease detection calls and treatment decisions,made on the basis of the genomic profile by, for example, independently confirming the presence of a methylation signature in a given patient sample.

[0121] In some instances, a genomic profile may comprise information on the presence of genes (or variant sequences thereof), copy number variations, epigenetic traits, proteins (or modifications thereof), and / or other biomarkers in an individual’s genome and / or proteome, as well as information on the individual’s corresponding phenotypic traits and the interaction between genetic or genomic traits, phenotypic traits, and environmental factors.

[0122] In some instances, a genomic profile for the subject may comprise results from a comprehensive genomic profiling (CGP) test, a nucleic acid sequencing-based test, a gene expression profiling test, a cancer hotspot panel test, a DNA methylation test, a DNA fragmentation test, an RNA fragmentation test, or any combination thereof.

[0123] In some instances, the method can further include administering or applying a treatment or therapy (e.g., an anti-cancer agent, anti-cancer treatment, or anti-cancer therapy) to the subject based on the generated genomic profile. An anti-cancer agent or anti-cancer treatment may refer to a compound that is effective in the treatment of cancer cells. Examples of anticancer agents or anti-cancer therapies include, but not limited to, alkylating agents, antimetabolites, natural products, hormones, chemotherapy, radiation therapy, immunotherapy, surgery, or a therapy configured to target a defect in a specific cell signaling pathway, e.g., a defect in a DNA mismatch repair (MMR) pathway.Samples

[0124] The disclosed methods and systems may be used with any of a variety of samples (also referred to herein as specimens) comprising nucleic acids (e.g., DNA or RNA) that are collected from a subject (e.g., a patient). Examples of a sample include, but are not limited to, a tumor sample, a tissue sample, a biopsy sample (e.g., a tissue biopsy, a liquid biopsy, or both), a blood sample (e.g., a peripheral whole blood sample), a blood plasma sample, a blood serum sample, a lymph sample, a saliva sample, a sputum sample, a urine sample, a gynecological fluid sample, a circulating tumor cell (CTC) sample, a cerebral spinal fluid (CSF) sample, a pericardial fluid sample, a pleural fluid sample, an ascites (peritoneal fluid) sample, a feces (or stool) sample, orother body fluid, secretion, and / or excretion sample (or cell sample derived therefrom). In certain instances, the sample may be frozen sample or a formalin-fixed paraffin-embedded (FFPE) sample.

[0125] In some instances, the sample may be collected by tissue resection (e.g., surgical resection), needle biopsy, bone marrow biopsy, bone marrow aspiration, skin biopsy, endoscopic biopsy, fine needle aspiration, oral swab, nasal swab, vaginal swab or a cytology smear, scrapings, washings or lavages (such as a ductal lavage or bronchoalveolar lavage), etc.

[0126] In some instances, the sample is a liquid biopsy sample, and may comprise, e.g., whole blood, blood plasma, blood serum, urine, stool, sputum, saliva, or cerebrospinal fluid. In some instances, the sample may be a liquid biopsy sample and may comprise circulating tumor cells (CTCs). In some instances, the sample may be a liquid biopsy sample and may comprise cell- free DNA (cfDNA), circulating tumor DNA (ctDNA), or any combination thereof.

[0127] In some instances, the sample may comprise one or more premalignant or malignant cells. Premalignant, as used herein, refers to a cell or tissue that is not yet malignant but is poised to become malignant. In certain instances, the sample may be acquired from a solid tumor, a soft tissue tumor, or a metastatic lesion. In certain instances, the sample may be acquired from a hematologic malignancy or pre-malignancy. In other instances, the sample may comprise a tissue or cells from a surgical margin. In certain instances, the sample may comprise tumor-infiltrating lymphocytes. In some instances, the sample may comprise one or more non- malignant cells. In some instances, the sample may be, or is part of, a primary tumor or a metastasis (e.g., a metastasis biopsy sample). In some instances, the sample may be obtained from a site (e.g., a tumor site) with the highest percentage of tumor (e.g., tumor cells) as compared to adjacent sites (e.g., sites adjacent to the tumor). In some instances, the sample may be obtained from a site (e.g., a tumor site) with the largest tumor focus (e.g., the largest number of tumor cells as visualized under a microscope) as compared to adjacent sites (e.g., sites adjacent to the tumor).

[0128] In some instances, the disclosed methods may further comprise analyzing a primary control (e.g., a normal tissue sample). In some instances, the disclosed methods may furthercomprise determining if a primary control is available and, if so, isolating a control nucleic acid (e.g., DNA) from said primary control. In some instances, the sample may comprise any normal control (e.g., a normal adjacent tissue (NAT)) if no primary control is available. In some instances, the sample may be or may comprise histologically normal tissue. In some instances, the method includes evaluating a sample, e.g., a histologically normal sample (e.g., from a surgical tissue margin) using the methods described herein. In some instances, the disclosed methods may further comprise acquiring a sub-sample enriched for non-tumor cells, e.g., by macro-dissecting non-tumor tissue from said NAT in a sample not accompanied by a primary control. In some instances, the disclosed methods may further comprise determining that no primary control and no NAT is available, and marking said sample for analysis without a matched control.

[0129] In some instances, samples obtained from histologically normal tissues (e.g., otherwise histologically normal surgical tissue margins) may still comprise a genetic alteration such as a variant sequence as described herein. The methods may thus further comprise re-classifying a sample based on the presence of the detected genetic alteration. In some instances, multiple samples (e.g., from different subjects) are processed simultaneously.

[0130] The disclosed methods and systems may be applied to the analysis of nucleic acids extracted from any of variety of tissue samples (or disease states thereof), e.g., 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 tissue, epithelial tissue, and blood. Tissue samples may be collected from any of the organs within an animal or human body. Examples of human organs include, but are not limited to, the brain, heart, lungs, liver, kidneys, pancreas, spleen, thyroid, mammary glands, uterus, prostate, large intestine, small intestine, bladder, bone, skin, etc.

[0131] In some instances, the nucleic acids extracted from the sample may comprise 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, tumor derived DNA or fragments thereof, mitochondrial DNA or fragments thereof, cell-free DNA (cfDNA), and circulating tumor DNA (ctDNA). Cell-free DNA (cfDNA) is comprised offragments of DNA that are released from normal and / or cancerous cells during apoptosis and necrosis, and circulate in the blood stream and / or accumulate in other bodily fluids. Circulating tumor DNA (ctDNA) is comprised of fragments of DNA that are released from cancerous cells and tumors that circulate in the blood stream and / or accumulate in other bodily fluids.

[0132] In some instances, DNA is extracted from nucleated cells from the sample. In some instances, a sample may have a low nucleated cellularity, e.g., when the sample is comprised mainly of erythrocytes, lesional cells that contain excessive cytoplasm, or tissue with fibrosis. In some instances, a sample with low nucleated cellularity may require more, e.g., greater, tissue volume for DNA extraction.

[0133] In some instances, the nucleic acids extracted from the sample may comprise ribonucleic acid (RNA) molecules. The analysis methods described herein can be adapted to assess and / or classify damaged RNA (or amplified cDNA derived therefrom). 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 certain abundant RNA sequences (e.g., ribosomal RNAs), cell-free RNA (cfRNA), messenger RNA (mRNA) or fragments thereof, the poly(A)-tailed mRNA fraction of the total RNA, ribosomal RNA (rRNA) or fragments thereof, transfer RNA (tRNA) or fragments thereof, and mitochondrial RNA or fragments thereof. In some instances, RNA may be extracted from the sample and converted to complementary DNA (cDNA) using, e.g., a reverse transcription reaction. In some instances, the cDNA is produced by random- primed cDNA synthesis methods. In other instances, the cDNA synthesis is initiated at the poly (A) tail of mature mRNAs by priming with oligo(dT)-containing oligonucleotides. Methods for depletion, poly(A) enrichment, and cDNA synthesis are well known to those of skill in the art.

[0134] In some instances, the sample may comprise a tumor content (e.g., comprising tumor cells or tumor cell nuclei), or a non-tumor content (e.g., immune cells, fibroblasts, and other non-tumor cells). In some instances, the tumor content of the sample may constitute a sample metric. In some instances, the sample may comprise a tumor content of at least 5-50%, 10-40%, 15-25%, or 20-30% tumor cell nuclei. In some instances, the sample may comprise a tumor content of at least 5%, at least 10%, at least 20%, at least 30%, at least 40%, or at least 50%tumor cell nuclei. In some instances, the percent tumor cell 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 within the sample that have nuclei. In some instances, for example when the sample is a liver sample comprising hepatocytes, a different tumor content calculation may be required due to the presence of hepatocytes having nuclei with twice, or more than twice, the DNA content of other, e.g., non-hepatocyte, somatic cell nuclei. In some instances, the sensitivity of detection of a genetic alteration, e.g., a variant sequence, or a determination of, e.g., micro satellite instability, may depend on the tumor content of the sample. For example, a sample having a lower tumor content can result in lower sensitivity of detection for a given size sample.

[0135] In some instances, as noted above, the sample comprises nucleic acid (e.g., DNA, RNA (or a cDNA derived from the RNA), or both), e.g., from a tumor or from normal tissue. In certain instances, the sample may further comprise a non-nucleic acid component, e.g., cells, protein, carbohydrate, or lipid, e.g., from the tumor or normal tissue.Subjects

[0136] In some instances, the sample is obtained (e.g., collected) from a subject (e.g., patient) with a condition or disease (e.g., a hyperproliferative disease or a non-cancer indication) or suspected of having the condition or disease. In some instances, the hyperproliferative disease is a cancer. In some instances, the cancer is a solid tumor or a metastatic form thereof. In some instances, the cancer is a hematological cancer, e.g., a leukemia or lymphoma.

[0137] In some instances, the subject has a cancer or is at risk of having a cancer or is unknown to have cancer (being screened for). For example, in some instances, the subject has a genetic predisposition to a cancer (e.g., having a genetic mutation that increases his or her baseline risk for developing a cancer). In some instances, the subject has been exposed to an environmental perturbation (e.g., radiation or a chemical) that increases his or her risk for developing a cancer. In some instances, the subject is in need of being monitored for development of a cancer. In some instances, the subject is in need of being monitored for cancer progression or regression, e.g., after being treated with an anti-cancer therapy (or anti-cancer treatment). In someinstances, the subject is in need of being monitored for relapse of cancer. In some instances, the subject is in need of being monitored for minimum residual disease (MRD). In some instances, the subject has been, or is being treated, for cancer. In some instances, the subject has not been treated with an anti-cancer therapy (or anti-cancer treatment).

[0138] In some instances, the subject (e.g., a patient) is being treated, or has been previously treated, with one or more targeted therapies. In some instances, e.g., for a patient who has been previously treated with a targeted therapy, a post-targeted therapy sample (e.g., specimen) is obtained (e.g., collected). In some instances, the post-targeted therapy sample is a sample obtained after the completion of the targeted therapy.

[0139] In some instances, the patient has not been previously treated with a targeted therapy. In some instances, e.g., for a patient who has not been previously treated with a targeted therapy, the sample comprises a resection, e.g., an original resection, or a resection following recurrence (e.g., following a disease recurrence post-therapy).Cancers

[0140] In some instances, the sample is acquired from a subject having a cancer. Exemplary cancers include, but are not limited to, B cell cancer (e.g., multiple myeloma), melanomas, breast cancer, lung cancer (such as non-small cell lung carcinoma or NSCLC), bronchus cancer, colorectal cancer, prostate cancer, pancreatic cancer, stomach cancer, ovarian cancer, urinary bladder cancer, brain or central nervous system cancer, peripheral nervous system cancer, esophageal cancer, cervical cancer, uterine or endometrial cancer, cancer of the oral cavity or pharynx, liver cancer, kidney cancer, testicular cancer, biliary tract cancer, small bowel or appendix cancer, salivary gland cancer, thyroid gland cancer, adrenal gland cancer, osteosarcoma, chondrosarcoma, cancer of hematological tissues, adenocarcinomas, inflammatory myofibroblastic tumors, gastrointestinal stromal tumor (GIST), colon cancer, multiple myeloma (MM), myelodysplastic syndrome (MDS), myeloproliferative disorder (MPD), acute lymphocytic leukemia (ALL), acute myelocytic leukemia (AML), chronic myelocytic leukemia (CML), chronic lymphocytic leukemia (CLL), polycythemia Vera, Hodgkin lymphoma, non-Hodgkin lymphoma (NHL), soft-tissue sarcoma, fibrosarcoma,myxosarcoma, liposarcoma, osteogenic sarcoma, chordoma, angiosarcoma, endothelio sarcoma, lymphangiosarcoma, lymphangioendotheliosarcoma, synovioma, mesothelioma, Ewing’s tumor, leiomyosarcoma, rhabdomyosarcoma, squamous cell carcinoma, basal cell carcinoma, adenocarcinoma, sweat gland carcinoma, sebaceous gland carcinoma, papillary carcinoma, papillary adenocarcinomas, medullary carcinoma, bronchogenic carcinoma, renal cell carcinoma, hepatoma, bile duct carcinoma, choriocarcinoma, seminoma, embryonal carcinoma, Wilms’ tumor, bladder carcinoma, epithelial carcinoma, glioma, astrocytoma, medulloblastoma, craniopharyngioma, ependymoma, pinealoma, hemangioblastoma, acoustic neuroma, 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 cancers, essential thrombocythemia, agnogenic myeloid metaplasia, hypereosinophilic syndrome, systemic mastocytosis, familiar hypereosinophilia, chronic eosinophilic leukemia, neuroendocrine cancers, carcinoid tumors, and the like.

[0141] In some instances, the cancer comprises acute lymphoblastic leukemia (Philadelphia chromosome positive), acute lymphoblastic leukemia (precursor B-cell), acute myeloid leukemia (FLT3+), acute myeloid leukemia (with an IDH2 mutation), anaplastic large cell lymphoma, basal cell carcinoma, B-cell chronic lymphocytic leukemia, bladder cancer, breast cancer (HER2 overexpressed / amplified), breast cancer (HER2+), breast cancer (HR+, HER2-), cervical cancer, cholangiocarcinoma, chronic lymphocytic leukemia, chronic lymphocytic leukemia (with 17p deletion), chronic myelogenous leukemia, chronic myelogenous leukemia (Philadelphia chromosome positive), classical Hodgkin lymphoma, colorectal cancer, colorectal cancer (dMMR and MSI-H), colorectal cancer (KRAS wild type), cryopyrin-associated periodic syndrome, a cutaneous T-cell lymphoma, dermatofibrosarcoma protuberans, a diffuse large B- cell lymphoma, fallopian tube cancer, a follicular B-cell non-Hodgkin lymphoma, a follicular lymphoma, gastric cancer, gastric cancer (HER2+), a gastroesophageal junction (GEJ) adenocarcinoma, a gastrointestinal stromal tumor, a gastrointestinal stromal tumor (KIT+), a giant cell tumor of the bone, a glioblastoma, granulomatosis with polyangiitis, a head and neck squamous cell carcinoma, a hepatocellular carcinoma, Hodgkin lymphoma, juvenile idiopathic arthritis, lupus erythematosus, a mantle cell lymphoma, medullary thyroid cancer, melanoma, amelanoma with a BRAF V600 mutation, a melanoma with a BRAF V600E or V600K mutation, Merkel cell carcinoma, multicentric Castleman’s disease, multiple hematologic malignancies including Philadelphia chromosome-positive ALL and CML, multiple myeloma, myelofibrosis, a non-Hodgkin’s lymphoma, a nonresectable subependymal giant cell astrocytoma associated with tuberous sclerosis, a non-small cell lung cancer, a non-small cell lung cancer (ALK+), a non-small cell lung cancer (PD-L1+), a non-small cell lung cancer (with ALK fusion or ROS1 gene alteration), a non-small cell lung cancer (with BRAF V600E mutation), a non-small cell lung cancer (with an EGFR exon 19 deletion or exon 21 substitution (L858R) mutations), a non- small cell lung cancer (with an EGFR T790M mutation), ovarian cancer, ovarian cancer (with a BRCA mutation), pancreatic cancer, a pancreatic, gastrointestinal, or lung origin neuroendocrine tumor, a pediatric neuroblastoma, a peripheral T-cell lymphoma, peritoneal cancer, prostate cancer, a renal cell carcinoma, rheumatoid arthritis, a small lymphocytic lymphoma, a soft tissue sarcoma, a solid tumor (MSLH / dMMR), a squamous cell cancer of the head and neck, a squamous non-small cell lung cancer, thyroid cancer, a thyroid carcinoma, urothelial cancer, a urothelial carcinoma, or Waldenstrom’s macroglobulinemia.

[0142] In some instances, the cancer is a hematologic malignancy (or premaligancy). As used herein, a hematologic malignancy refers to a tumor of the hematopoietic or lymphoid tissues, e.g., a tumor that affects blood, bone marrow, or lymph nodes. Exemplary hematologic malignancies include, but are not limited to, leukemia (e.g., acute lymphoblastic leukemia (ALL), acute myeloid leukemia (AML), chronic lymphocytic leukemia (CLL), chronic myelogenous leukemia (CML), hairy cell leukemia, acute monocytic leukemia (AmoL), chronic myelomonocytic leukemia (CMML), juvenile myelomonocytic leukemia (JMML), or large granular lymphocytic leukemia), lymphoma (e.g., AIDS-related lymphoma, cutaneous T-cell lymphoma, Hodgkin lymphoma (e.g., classical Hodgkin lymphoma or nodular lymphocyte- predominant Hodgkin lymphoma), mycosis fungoides, non-Hodgkin lymphoma (e.g., B-cell non-Hodgkin lymphoma (e.g., Burkitt lymphoma, small lymphocytic lymphoma (CLL / SLL), diffuse large B-cell lymphoma, follicular lymphoma, immunoblastic large cell lymphoma, precursor B-lymphoblastic lymphoma, or mantle cell lymphoma) or T-cell non-Hodgkin lymphoma (mycosis fungoides, anaplastic large cell lymphoma, or precursor T-lymphoblasticlymphoma)), primary central nervous system lymphoma, Sezary syndrome, Waldenstrom macroglobulinemia), chronic myeloproliferative neoplasm, Langerhans cell histiocytosis, multiple myeloma / plasma cell neoplasm, myelodysplastic syndrome, or myelodysplastic / myeloproliferative neoplasm.Nucleic acid extraction and processing

[0143] DNA, RNA or both may be extracted from tissue samples, biopsy samples, blood samples, or other bodily fluid samples using any of a variety of techniques known to those of skill in the art (see, e.g., Example 1 of International Patent Application Publication No. WO 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)). Protocols for RNA isolation are disclosed in, e.g., the Maxwell® 16 Total RNA Purification Kit Technical Bulletin (Promega Literature #TB351, August 2009, Promega Corporation, Madison, WI).

[0144] A typical DNA extraction procedure, for example, comprises (i) collection of the fluid sample, cell sample, or tissue sample from which DNA is to be extracted, (ii) disruption of cell membranes (z.e., cell lysis), if necessary, to release DNA and other cytoplasmic components, (iii) treatment of the fluid sample or lysed sample with a concentrated salt solution to precipitate proteins, lipids, and RNA, followed by centrifugation to separate out the precipitated proteins, lipids, and RNA, and (iv) purification of DNA from the supernatant to remove detergents, proteins, salts, or other reagents used during the cell membrane lysis step.

[0145] Disruption of cell membranes may be performed using a variety of mechanical shear e.g., by passing through a French press or fine needle) or ultrasonic disruption techniques. The cell lysis step often comprises the use of detergents and surfactants to solubilize lipids the cellular and nuclear membranes. In some instances, the lysis step may further comprise use of proteases to break down protein, and / or the use of an Rnase for digestion of RNA in the sample.

[0146] Examples of suitable techniques for DNA purification include, but are not limited to, (i) precipitation in ice-cold ethanol or isopropanol, followed by centrifugation (precipitation of DNA may be enhanced by increasing ionic strength, e.g., by addition of sodium acetate), (ii) phenol-chloroform extraction, followed by centrifugation to separate the aqueous phase containing the nucleic acid from the organic phase containing denatured protein, and (iii) solid phase chromatography where the nucleic acids adsorb to the solid phase (e.g., silica or other) depending on the pH and salt concentration of the buffer.

[0147] In some instances, cellular and histone proteins bound to the DNA may be removed either by adding a protease or by having precipitated the proteins with sodium or ammonium acetate, or through extraction with a phenol-chloroform mixture prior to a DNA precipitation step.

[0148] In some instances, DNA may be extracted using any of a variety of suitable commercial DNA extraction and purification kits. Examples include, but are not limited to, the QIAamp (for isolation of tumor derived 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 of kits from Promega (Madison, WI).

[0149] As noted above, in some instances the sample may comprise a formalin-fixed (also known as formaldehyde-fixed, or paraformaldehyde-fixed), paraffin-embedded (FFPE) tissue preparation. For example, the FFPE sample may be a tissue sample embedded in a matrix, e.g., an FFPE block. Methods to isolate nucleic acids (e.g., DNA) from formaldehyde- or paraformaldehyde-fixed, paraffin-embedded (FFPE) tissues are disclosed in, e.g., Cronin, et al., (2004) Am J Pathol. 164(l):35-42; Masuda, et al., (1999) Nucleic Acids Res. 27(22):4436- 4443; Specht, et al., (2001) Am J Pathol. 158(2):419-429; the Ambion RecoverAll™ Total Nucleic Acid Isolation Protocol (Ambion, Cat. No. AM1975, September 2008); the Maxwell® 16 FFPE Plus EEV DNA Purification Kit Technical Manual (Promega Eiterature #TM349, February 2011); the E.Z.N.A.® FFPE DNA Kit 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, theRecoverAll™ Total Nucleic Acid Isolation Kit uses xylene at elevated temperatures to solubilizeparaffin-embedded samples and a glass-fiber filter to capture nucleic acids. The Maxwell® 16 FFPE Plus LEV DNA Purification Kit is used with the Maxwell® 16 Instrument for purification of DNA from 1 to 10 pm sections of FFPE tissue. DNA is purified using silica-clad paramagnetic particles (PMPs), and eluted in low elution volume. The E.Z.N.A.® FFPE DNA Kit uses a spin column and buffer system for isolation of DNA. QIAamp® DNA FFPE Tissue Kit uses QIAamp® DNA Micro technology for purification of genomic and mitochondrial DNA.

[0150] In some instances, the disclosed methods may further comprise determining or acquiring a yield value for the nucleic acid extracted from the sample and comparing the determined value to a reference value. For example, if the determined or acquired value is less than the reference value, the nucleic acids may be amplified prior to proceeding with library construction. In some instances, the disclosed methods may further comprise determining or acquiring a value for the size (or average size) of nucleic acid fragments in the sample, and comparing the determined or acquired value to a reference value, e.g., a size (or average size) of at least 100, 200, 300, 400, 500, 600, 700, 800, 900, or 1000 base pairs (bps). In some instances, one or more parameters described herein may be adjusted or selected in response to this determination.

[0151] After isolation, the nucleic acids are typically dissolved in a slightly alkaline buffer, e.g., Tris-EDTA (TE) buffer, or in ultra-pure water. In some instances, the isolated nucleic acids (e.g., DNA) may be fragmented or sheared by using any of a variety of techniques known to those of skill in the art. For example, DNA can be fragmented by physical shearing methods, enzymatic cleavage methods, chemical cleavage methods, and other methods known to those of skill in the art. Methods for DNA shearing are described in Example 4 in International Patent Application Publication No. WO 2012 / 092426. In some instances, alternatives to DNA shearing methods can be used to avoid a ligation step during library preparation.Library preparation

[0152] In some instances, the nucleic acids isolated from the sample may be used to construct a library (e.g., a nucleic acid library as described herein). In some instances, the nucleic acids are fragmented using any of the methods described above, optionally subjected to repair of chainend damage, and optionally ligated to synthetic adapters, primers, and / or barcodes (e.g., amplification primers, sequencing adapters, flow cell adapters, substrate adapters, sample barcodes or indexes, and / or unique molecular identifier sequences), size-selected (e.g., by preparative gel electrophoresis or bead cleanup), and / or amplified (e.g., using PCR, a non-PCR amplification technique, or an isothermal amplification technique). In some instances, the fragmented and adapter-ligated group of nucleic acids is used without explicit size selection or amplification prior to hybridization-based selection of target sequences. In some instances, the nucleic acid is amplified by any of a variety of specific or non-specific nucleic acid amplification methods known to those of skill in the art. In some instances, the nucleic acids are amplified, e.g., by a whole-genome amplification method such as random-primed stranddisplacement amplification. Examples of nucleic acid library preparation techniques for nextgeneration sequencing are described in, e.g., van Dijk, et al. (2014), Exp. Cell Research 322:12 - 20, and Illumina’s DNA sample preparation kit.

[0153] In some instances, the resulting nucleic acid library may contain all or substantially all of the complexity of the genome. The term “substantially all” in this context refers to the possibility that there can in practice be some unwanted loss of genome complexity during the initial steps of the procedure. The methods described herein also are useful in cases where the nucleic acid library comprises a portion of the genome, e.g., where the complexity of the genome is reduced by design. In some instances, any selected portion of the genome can be used with a method described herein. For example, in certain embodiments, the entire exome or a subset thereof is isolated. In some instances, the library may include at least 95%, 90%, 80%, 70%, 60%, 50%, 40%, 30%, 20%, 10%, or 5% of the DNA. In some instances, the library may consist of cDNA copies of DNA that includes copies of at least 95%, 90%, 80%, 70%, 60%, 50%, 40%, 30%, 20%, 10%, or 5% of the DNA. In certain instances, the amount of nucleic acid used to generate the 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.

[0154] In some instances, a library (e.g., a nucleic acid library) includes a collection of nucleic acid molecules. As described herein, the nucleic acid molecules of the library can include atarget nucleic acid molecule (e.g., a tumor nucleic acid molecule, a reference nucleic acid molecule and / or a control nucleic acid molecule; also referred to herein as a first, second and / or third nucleic acid molecule, respectively). The nucleic acid molecules of the library can be from a single subject or individual. In some instances, a library can comprise nucleic acid molecules derived from more than one subject (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30 or more subjects). For example, two or more libraries from different subjects can be combined to form a library having nucleic acid molecules from more than one subject (where the nucleic acid molecules derived from each subject are optionally ligated to a unique sample barcode corresponding to a specific subject). In some instances, the subject is a human having, or at risk of having, a cancer or tumor.

[0155] In some instances, the library (or a portion thereof) may comprise one or more subgenomic intervals. In some instances, a subgenomic interval can be a single nucleotide position, e.g., a nucleotide position for which a variant at the position is associated (positively or negatively) with a tumor phenotype. In some instances, a subgenomic interval comprises more than one nucleotide position. Such instances include sequences of at least 2, 5, 10, 50, 100, 150, 250, or more than 250 nucleotide positions in length. Subgenomic intervals can comprise, e.g., one or more entire genes (or portions thereof), one or more exons or coding sequences (or portions thereof), one or more introns (or portion thereof), one or more microsatellite region (or portions thereof), or any combination thereof. A subgenomic interval can comprise all or a part of a fragment of a naturally occurring nucleic acid molecule, e.g., a DNA molecule. For example, a subgenomic interval can correspond to a fragment of DNA which is subjected to a sequencing reaction. In some instances, a subgenomic interval is a continuous sequence from a genomic source. In some instances, a subgenomic interval includes sequences that are not contiguous in the genome. In some instances, the subgenomic interval comprises a tumor nucleic acid molecule. In some instances, the subgenomic interval comprises a non-tumor nucleic acid molecule.Targeting gene loci for analysis

[0156] The methods described herein can be used in combination with, or as part of, a method for evaluating a plurality or set of subject intervals (e.g., target sequences), e.g., from a set of genomic loci (e.g., gene loci or fragments thereof), as described herein.

[0157] In some instances, the set of genomic loci evaluated by the disclosed methods comprises a plurality of, e.g., genes, which in mutant form, are associated with an effect on cell division, growth or survival, or are associated with a cancer, e.g., a cancer described herein. In some instances, the set of genomic loci evaluated by the disclosed methods comprises a plurality of non-coding sequences, such as promoters, enhancers, LI sequences, and / or CpG sites. In mutant and / or methylated form, the non-coding sequences can be associated with an effect on gene expression (e.g., gene expression levels), cell division, growth or survival, or are associated with a cancer, e.g., a cancer described herein.

[0158] In some instances, the set of gene loci evaluated by the disclosed methods comprises 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 gene loci.

[0159] In some instances, the selected gene loci (also referred to herein as target gene loci or target sequences), or fragments thereof, may include subject intervals comprising non-coding sequences, coding sequences, intragenic regions, or intergenic regions of the subject genome. For example, the subject intervals can include a non-coding sequence or fragment thereof (e.g., a promoter sequence, enhancer sequence, 5’ untranslated region (5’ UTR), 3’ untranslated region (3’ UTR), or a fragment thereof), a coding sequence of fragment thereof, an exon sequence or fragment thereof, an intron sequence or a fragment thereof.Target and comprehensive capture reagents

[0160] The methods described herein may comprise contacting a nucleic acid library with a plurality of target capture reagents in order to select and capture a plurality of specific target sequences (e.g., gene sequences or fragments thereof) for analysis. In some instances, a target capture reagent (i.e., a molecule which can bind to and thereby allow capture of a targetmolecule) is used to select the subject intervals to be analyzed. For example, a target capture reagent can be a bait molecule, e.g., a nucleic acid molecule (e.g., a DNA molecule or RNA molecule) which can hybridize to (z.e., is complementary to) a target molecule, and thereby allows capture of the target nucleic acid. In some instances, the target capture reagent, e.g., a bait molecule (or bait sequence), is a capture oligonucleotide (or capture probe). In some instances, the target nucleic acid is a DNA molecule, an RNA molecule, a cDNA molecule derived from an RNA molecule, a micro satellite DNA sequence, and the like. In some instances, the target capture reagent is suitable for solution-phase hybridization to the target. In some instances, the target capture reagent is suitable for solid-phase hybridization to the target. In some instances, the target capture reagent is suitable for both solution-phase and solid-phase hybridization to the target. The design and construction of target capture reagents is described in more detail in, e.g., International Patent Application Publication No. WO 2020 / 236941, the entire content of which is incorporated herein by reference.

[0161] The methods described herein can comprise contacting a nucleic acid library using a comprehensive sequencing approach, such as, but not limited to, whole genome sequencing (WGS), whole exome sequencing (WES), and / or RNA sequencing (RNAseq), wherein the latter can comprise capturing non-coding transcripts, nascent transcripts, mRNA transcripts, and / or any combination thereof.

[0162] The methods described herein provide for optimized sequencing of a large number of genomic loci (e.g., genes or gene products (e.g., mRNA), micro satellite loci, etc.) from samples (e.g., cancerous tissue specimens, liquid biopsy samples, and the like) from one or more subjects by the appropriate selection of target capture reagents to select the target nucleic acid molecules to be sequenced. In some instances, a target capture reagent may hybridize to a specific target locus, e.g., a specific target gene locus or fragment thereof. In some instances, a target capture reagent may hybridize to a specific group of target loci, e.g., a specific group of gene loci or fragments thereof. In some instances, a plurality of target capture reagents comprising a mix of target- specific and / or group- specific target capture reagents may be used.

[0163] In some instances, the number of target capture reagents (e.g., bait molecules) in the plurality of target capture reagents (e.g., a bait set) contacted with a nucleic acid library tocapture a plurality of 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.

[0164] In some instances, the overall length of the target capture reagent sequence can be between about 70 nucleotides and 1000 nucleotides. In one instance, the target capture reagent length is between about 100 and 300 nucleotides, 110 and 200 nucleotides, or 120 and 170 nucleotides, in length. In addition to those mentioned above, intermediate oligonucleotide lengths of about 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 in length can be used in the methods described herein. In some embodiments, oligonucleotides of about 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, or 230 bases can be used.

[0165] In some instances, each target capture reagent sequence can include: (i) a target- specific capture sequence (e.g., a gene locus or micro satellite locus- specific complementary sequence), (ii) an adapter, primer, barcode, and / or unique molecular identifier sequence, and (iii) universal tails on one or both ends. As used herein, the term “target capture reagent” can refer to the target- specific target capture sequence or to the entire target capture reagent oligonucleotide including the target- specific target capture sequence.

[0166] In some instances, the target- specific capture sequences in the target capture reagents are between about 40 nucleotides and 1000 nucleotides in length. In some instances, the targetspecific capture sequence is between about 70 nucleotides and 300 nucleotides in length. In some instances, the target- specific sequence is between about 100 nucleotides and 200 nucleotides in length. In yet other instances, the target- specific sequence is between about 120 nucleotides and 170 nucleotides in length, typically 120 nucleotides in length. Intermediate lengths in addition to those mentioned above also can be used in the methods described herein, such as target- specific sequences of about 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 900nucleotides in length, as well as target- specific sequences of lengths between the above- mentioned lengths.

[0167] In some instances, the target capture reagent may be designed to select a subject interval containing one or more rearrangements, e.g., an intron containing a genomic rearrangement. In such instances, the target capture reagent is designed such that repetitive sequences are masked to increase the selection efficiency. In those instances where the rearrangement has a known juncture sequence, complementary target capture reagents can be designed to recognize the juncture sequence to increase the selection efficiency.

[0168] In some instances, the disclosed methods may comprise 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 instances, the hybridization-based capture methods and target capture reagent compositions disclosed herein may provide for the capture and homogeneous coverage of a set of target sequences, while minimizing coverage of genomic sequences outside of the targeted set of sequences. In some instances, the target sequences may include the entire exome or a selected subset thereof. In some instances, the target sequences may include, e.g., a large chromosomal region (e.g., a whole chromosome arm). The methods and compositions disclosed herein provide different target capture reagents for achieving different sequencing depths and patterns of coverage for complex sets of target nucleic acid sequences.

[0169] Typically, DNA molecules are used as target capture reagent sequences, although RNA molecules can also be used. In some instances, a DNA molecule target capture reagent can be single stranded DNA (ssDNA) or double- stranded DNA (dsDNA). In some instances, an RNA- DNA duplex is more stable than a DNA-DNA duplex and therefore provides for potentially better capture of nucleic acids.

[0170] In some instances, the disclosed methods comprise 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 comprise: providing one or a plurality of nucleic acid libraries, each comprising a plurality of nucleic acid molecules (e.g., a plurality of target nucleic acidmolecules and / or reference nucleic acid molecules) extracted from one or more samples from one or more subjects; contacting the one or a plurality of libraries (e.g., in a solution-based hybridization reaction) with one, two, three, four, five, or more than five pluralities of target capture reagents (e.g., oligonucleotide target capture reagents) to form a hybridization mixture comprising a plurality of target capture reagent / nucleic acid molecule hybrids; separating the plurality of target capture reagent / nucleic acid molecule hybrids from said hybridization mixture, e.g., by contacting said hybridization mixture with a binding entity that allows for separation of said plurality of target capture reagent / nucleic acid molecule hybrids from the hybridization mixture, thereby providing a library catch (e.g., a selected or enriched subgroup of nucleic acid molecules from the one or a plurality of libraries).

[0171] In some instances, the disclosed methods may further comprise amplifying the library catch (e.g., by performing PCR). In other instances, the library catch is not amplified.

[0172] In some instances, the target capture reagents can be part of a kit which can optionally comprise instructions, standards, buffers or enzymes or other reagents.Hybridization conditions

[0173] As noted above, the methods disclosed herein may include the step of contacting the library (e.g., the nucleic acid library) with a plurality of target capture reagents to provide a selected library target nucleic acid sequences (i.e., the library catch). The contacting step can be effected in, e.g., solution-based hybridization. In some instances, the method includes repeating the hybridization step for one or more additional rounds of solution-based hybridization. In some instances, the method further includes subjecting the library catch to one or more additional rounds of solution-based hybridization with the same or a different collection of target capture reagents.

[0174] In some instances, the contacting step is effected using a solid support, e.g., an array. Suitable solid supports for hybridization are described in, e.g., Albert, T.J. et al. (2007) Nat. Methods 4(11):903-5; Hodges, E. et al. (2007) Nat. Genet. 39(12): 1522-7; and Okou, D.T. et al. (2007) Nat. Methods 4(11 ):907-9, the contents of which are incorporated herein by reference in their entireties.

[0175] Hybridization methods that can be adapted for use in the methods herein are described in the art, e.g., as described in International Patent Application Publication No. WO 2012 / 092426. Methods for hybridizing target capture reagents to a plurality of target nucleic acids are described in more detail in, e.g., International Patent Application Publication No. WO 2020 / 236941, the entire content of which is incorporated herein by reference.Sequencing methods

[0176] The methods and systems disclosed herein can be used in combination with, or as part of, a method or system for sequencing nucleic acids (e.g., a next-generation sequencing system) to generate a plurality of sequence reads that overlap one or more gene loci within a subgenomic interval in the sample and thereby determine, e.g., gene allele sequences at a plurality of gene loci. “Next-generation sequencing” (or “NGS”) as used herein may also be referred to as “massively parallel sequencing” (or “MPS”), and refers to any sequencing method that determines the nucleotide sequence of either individual nucleic acid molecules (e.g., as in single molecule sequencing) or clonally expanded proxies for individual nucleic acid molecules in a high throughput fashion (e.g., wherein greater than 103, 104, 105or more than 105molecules are sequenced simultaneously).

[0177] Next-generation sequencing methods are known in the art, and are described in, e.g., 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 in, e.g., International Patent Application Publication No. WO 2012 / 092426. In some instances, the sequencing may comprise, for example, whole genome sequencing (WGS), whole exome sequencing, targeted sequencing, or direct sequencing. In some instances, sequencing may be performed using, e.g., Sanger sequencing. In some instances, the sequencing may comprise a paired-end sequencing technique that allows both ends of a fragment to be sequenced and generates high-quality, alignable sequence data for detection of, e.g., genomic rearrangements, repetitive sequence elements, gene fusions, and novel transcripts.

[0178] The disclosed methods and systems may be implemented using sequencing platforms such as the Roche 454, Illumina Solexa, ABI-SOLiD, ION Torrent, Complete Genomics, Pacific Bioscience, Helicos, and / or the Polonator platform. In some instances, sequencing may comprise Illumina MiSeq sequencing. In some instances, sequencing may comprise Illumina HiSeq sequencing. In some instances, sequencing may comprise Illumina NovaSeq sequencing. Optimized methods for sequencing a large number of target genomic loci in nucleic acids extracted from a sample are described in more detail in, e.g., International Patent Application Publication No. WO 2020 / 236941, the entire content of which is incorporated herein by reference.

[0179] In certain instances, the disclosed methods comprise one or more of the steps of: (a) acquiring a library comprising a plurality of normal and / or tumor nucleic acid molecules from a sample; (b) simultaneously or sequentially contacting the library with one, two, three, four, five, or more than five pluralities of target capture reagents under conditions that allow hybridization of the target capture reagents to the target nucleic acid molecules, thereby providing a selected set of captured normal and / or tumor nucleic acid molecules (z.e., a library catch); (c) separating the selected subset of the nucleic acid molecules (e.g., the library catch) from the hybridization mixture, e.g., by contacting the hybridization mixture with a binding entity that allows for separation of the target capture reagent / nucleic acid molecule hybrids from the hybridization mixture, (d) sequencing the library catch to acquiring a plurality of reads (e.g., sequence reads) that overlap one or more subject intervals (e.g., one or more target sequences) from said library catch that may comprise a mutation (or alteration), e.g., a variant sequence comprising a somatic mutation or germline mutation; (e) aligning said sequence reads using an alignment method as described elsewhere herein; and / or (f) assigning a nucleotide value for a nucleotide position in the subject interval (e.g., calling a mutation using, e.g., a Bayesian method or other method described herein) from one or more sequence reads of the plurality.

[0180] In some instances, acquiring sequence reads for one or more subject intervals may comprise 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 least800, 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, e.g., genomic loci, gene loci, microsatellite loci, etc. In some instances, acquiring a sequence read for one or more subject intervals may comprise sequencing a subject interval for any number of loci within the range described in this paragraph, e.g., for at least 2,850 gene loci.

[0181] In some instances, acquiring a sequence read for one or more subject intervals comprises sequencing a subject interval with a sequencing method that provides a sequence read length (or average sequence read length) of at least 20 bases, at least 30 bases, at least 40 bases, at least 50 bases, at least 60 bases, at least 70 bases, at least 80 bases, at least 90 bases, at least 100 bases, at least 120 bases, at least 140 bases, at least 160 bases, at least 180 bases, at least 200 bases, at least 220 bases, at least 240 bases, at least 260 bases, at least 280 bases, at least 300 bases, at least 320 bases, at least 340 bases, at least 360 bases, at least 380 bases, or at least 400 bases. In some instances, acquiring a sequence read for the one or more subject intervals may comprise sequencing a subject interval with a sequencing method that provides a sequence read length (or average sequence read length) of any number of bases within the range described in this paragraph, e.g., a sequence read length (or average sequence read length) of 56 bases.

[0182] In some instances, acquiring a sequence read for one or more subject intervals may comprise sequencing with at least lOOx or more coverage (or depth) on average. In some instances, acquiring a sequence read for one or more subject intervals may comprise sequencing with at least lOOx, at least 150x, at least 200x, at least 250x, at least 500x, at least 750x, at least l,000x, at least 1,500 x, 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 or more coverage (or depth) on average. In some instances, acquiring a sequence read for one or more subject intervals may comprise sequencing with an average coverage (or depth) having any value within the range of values described in this paragraph, e.g., at least 160x.

[0183] In some instances, acquiring a read for the one or more subject intervals comprises sequencing with an average sequencing depth having any value ranging from at least lOOx to at least 6,000x for greater than about 90%, 92%, 94%, 95%, 96%, 97%, 98%, or 99% of the geneloci sequenced. For example, in some instances acquiring a read for the subject interval comprises sequencing with an average sequencing depth of at least 125x for at least 99% of the gene loci sequenced. As another example, in some instances acquiring a read for the subject interval comprises sequencing with an average sequencing depth of at least 4,100x for at least 95% of the gene loci sequenced.

[0184] In some instances, the relative abundance of a nucleic acid species in the library can be estimated by counting the relative number of occurrences of their cognate sequences (e.g., the number of sequence reads for a given cognate sequence) in the data generated by the sequencing experiment.

[0185] In some instances, the disclosed methods and systems provide nucleotide sequences for a set of subject intervals (e.g., gene loci), as described herein. In certain instances, the sequences are provided without using a method that includes a matched normal control (e.g., a wild-type control) and / or a matched tumor control (e.g., primary versus metastatic).

[0186] In some instances, the level of sequencing depth as used herein (e.g., an X-fold level of sequencing depth) refers to the number of reads (e.g., unique reads) obtained after detection and removal of duplicate reads (e.g., PCR duplicate reads). In other instances, duplicate reads are evaluated, e.g., to support detection of copy number alteration (CNAs).Alignment

[0187] Alignment is the process of matching a read with a location, e.g., a genomic location or locus. In some instances, NGS reads may be aligned to a known reference sequence (e.g., a wild-type sequence). In some instances, NGS reads may be assembled de novo. Methods of sequence alignment for NGS reads are described in, e.g., Trapnell, C. and Salzberg, S.L. Nature Biotech., 2009, 27:455-457. Examples of de novo sequence assemblies are described in, e.g., Warren R., et al., Bioinformatics, 2007, 23:500-501; Butler, J. et al., Genome Res., 2008, 18:810-820; and Zerbino, D.R. and Birney, E., Genome Res., 2008, 18:821-829. Optimization of sequence alignment is described in the art, e.g., as set out in International Patent Application Publication No. WO 2012 / 092426. Additional description of sequence alignment methods isprovided in, e.g., International Patent Application Publication No. WO 2020 / 236941, the entire content of which is incorporated herein by reference.

[0188] Misalignment (e.g., the placement of base-pairs from a short read at incorrect locations in the genome), e.g., misalignment of reads due to sequence context (e.g., the presence of repetitive sequence) around an actual cancer mutation can lead to reduction in sensitivity of mutation detection, can lead to a reduction in sensitivity of mutation detection, as reads for the alternate allele may be shifted off the histogram peak of alternate allele reads. Other examples of sequence context that may cause misalignment include short-tandem repeats, interspersed repeats, low complexity regions, insertions - deletions (indels), and paralogs. If the problematic sequence context occurs where no actual mutation is present, misalignment may introduce artifactual reads of “mutated” alleles by placing reads of actual reference genome base sequences at the wrong location. Because mutation-calling algorithms for multigene analysis should be sensitive to even low-abundance mutations, sequence misalignments may increase false positive discovery rates and / or reduce specificity.

[0189] In some instances, the methods and systems disclosed herein may integrate the use of multiple, individually-tuned, alignment methods or algorithms to optimize base-calling performance in sequencing methods, particularly in methods that rely on massively parallel sequencing (MPS) of a large number of diverse genetic events at a large number of diverse genomic loci. In some instances, the disclosed methods and systems may comprise the use of one or more global alignment algorithms. In some instances, the disclosed methods and systems may comprise 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), the Striped Smith-Waterman algorithm (see, e.g., Farrar (2007), “Striped Smith-Waterman SpeedsDatabase Searches Six Times Over Other SIMD Implementations”, 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.

[0190] In some instances, the methods and systems disclosed herein may also comprise the use of a sequence assembly algorithm, e.g., the Arachne sequence assembly algorithm (see, e.g., Batzoglou, et al. (2002), “ARACHNE: A Whole-Genome Shotgun Assembler”, Genome Res. 12:177-189).

[0191] In some instances, the alignment method used to analyze sequence reads is not individually customized or tuned for detection of different variants (e.g., point mutations, insertions, deletions, and the like) at different genomic loci. In some instances, different alignment methods are used to analyze reads that are individually customized or tuned for detection of at least a subset of the different variants detected at different genomic loci. In some instances, different alignment methods are used to analyze reads that are individually customized or tuned to detect each different variant at different genomic loci. In some instances, tuning can be a function of one or more of: (i) the genetic locus (e.g., gene loci, microsatellite locus, or other subject interval) being sequenced, (ii) the tumor type associated with the sample, (iii) the variant being sequenced, or (iv) a characteristic of the sample or the subject. The selection or use of alignment conditions that are individually tuned to a number of specific subject intervals to be sequenced allows optimization of speed, sensitivity, and specificity. The method is particularly effective when the alignment of reads for a relatively large number of diverse subject intervals are optimized.

[0192] In some instances, the method includes the use of an alignment method optimized for rearrangements in combination with other alignment methods optimized for subject intervals not associated with rearrangements.

[0193] In some instances, the methods disclosed herein further comprise selecting or using an alignment method for analyzing, e.g., aligning, a sequence read, wherein said alignment method is a function of, is selected responsive to, or is optimized for, one or more of: (i) tumor type, e.g., the tumor type in the sample; (ii) the location (e.g., a gene locus) of the subject intervalbeing sequenced; (iii) the type of variant (e.g., a point mutation, insertion, deletion, substitution, copy number variation (CNV), rearrangement, or fusion) in the subject interval being sequenced; (iv) the site (e.g., nucleotide position) being analyzed; (v) the type of sample (e.g., a sample described herein); and / or (vi) adjacent sequence(s) in or near the subject interval being evaluated (e.g., according to the expected propensity thereof for misalignment of the subject interval due to, e.g., the presence of repeated sequences in or near the subject interval).

[0194] In some instances, the methods disclosed herein allow for the rapid and efficient alignment of troublesome reads, e.g., a read having a rearrangement. Thus, in some instances where a read for a subject interval comprises a nucleotide position with a rearrangement, e.g., a translocation, the method can comprise using an alignment method that is appropriately tuned and that includes: (i) selecting a rearrangement reference sequence for alignment with a read, wherein said rearrangement reference sequence aligns with a rearrangement (in some instances, the reference sequence is not identical to the genomic rearrangement); and (ii) comparing, e.g., aligning, a read with said rearrangement reference sequence.

[0195] In some instances, alternative methods may be used to align troublesome reads. These methods are particularly effective when the alignment of reads for a relatively large number of diverse subject intervals is optimized. By way of example, a method of analyzing a sample can comprise: (i) performing a comparison (e.g., an alignment comparison) of a read using a first set of parameters (e.g., using a first mapping algorithm, or by comparison with a first reference sequence), and determining if said read meets a first alignment criterion (e.g., the read can be aligned with said first reference sequence, e.g., with less than a specific number of mismatches); (ii) if said read fails to meet the first alignment criterion, performing a second alignment comparison using a second set of parameters, (e.g., using a second mapping algorithm, or by comparison with a second reference sequence); and (iii) optionally, determining if said read meets said second criterion (e.g., the read can be aligned with said second reference sequence, e.g., with less than a specific number of mismatches), wherein said second set of parameters comprises use of, e.g., said second reference sequence, which, compared with said first set of parameters, is more likely to result in an alignment with a read for a variant (e.g., a rearrangement, insertion, deletion, or translocation).

[0196] In some instances, the alignment of sequence reads in the disclosed methods may be combined with a mutation calling method as described elsewhere herein. As discussed herein, reduced sensitivity for detecting actual mutations may be addressed by evaluating the quality of alignments (manually or in an automated fashion) around expected mutation sites in the genes or genomic loci (e.g., gene loci) being analyzed. In some instances, the sites to be evaluated can be obtained from databases of the human genome (e.g., the HG19 human reference genome) or cancer mutations (e.g., COSMIC). Regions that are identified as problematic can be remedied with the use of an algorithm selected to give better performance in the relevant sequence context, e.g., by alignment optimization (or re-alignment) using slower, but more accurate alignment algorithms such as Smith-Waterman alignment. In cases where general alignment algorithms cannot remedy the problem, customized alignment approaches may be created by, e.g., adjustment of maximum difference mismatch penalty parameters for genes with a high likelihood of containing substitutions; adjusting specific mismatch penalty parameters based on specific mutation types that are common in certain tumor types (e.g. C~^T in melanoma); or adjusting specific mismatch penalty parameters based on specific mutation types that are common in certain sample types (e.g. substitutions that are common in FFPE).

[0197] Reduced specificity (increased false positive rate) in the evaluated subject intervals due to misalignment can be assessed by manual or automated examination of all mutation calls in the sequencing data. Those regions found to be prone to spurious mutation calls due to misalignment can be subjected to alignment remedies as discussed above. In cases where no algorithmic remedy is found possible, “mutations” from the problem regions can be classified or screened out from the panel of targeted loci.Alignment of methyl-seq sequence reads

[0198] In some instances, the methods may include the use of an alignment method optimized for aligning sequence reads for DNA that has been converted using, e.g., a bisulfite reaction, to convert unmethylated cytosine residues to uracil (which is interpreted as a thymine in sequencing results). In some instances, sequence reads may be aligned to two genomes in silico. e.g., converted and unconverted versions of the reference genome, using such alignment tools.Methylation occurs primarily at CpG sites, but may also occur less frequently at non-CpG sites (e.g., CHG or CHH sites).

[0199] In some instances, the sequence read data may be obtained using a nucleic acid sequencing method comprising the use of a bisulfite- or enzymatic-conversion reaction (e.g., during library preparation) to convert non-methylated cytosine to uracil (see, e.g., Li, et al. (2011), “DNA Methylation Detection: Bisulfite Genomic Sequencing Analysis”, Methods Mol. Biol. 791:11-21).

[0200] In some instances, the sequence read data may be obtained using a nucleic acid sequencing method comprising the use of alternative chemical and / or enzymatic reactions (e.g., during library preparation) to convert non-methylated cytosine to uracil (or to convert methylated cytosine to dihydrouracil). For example, enzymatic deamination of non-methylated cytosine using APOBEC to form uracil can be performed using, e.g., the Enzymatic Methyl-seq Kit from New England BioLabs (Ipswich, MA) which uses prior treatment with ten-eleven translocation methylcytosine dioxygenase 2 (TET2) to oxidize 5-mC and 5-hmC, thereby providing greater protection of the methylated cytosine from deamination by APOBEC). Liu, et al. (2019) recently described a bisulfite-free and base-level-resolution sequencing-based method, TET-Assisted Pyridine borane Sequencing (TAPS), for detection of 5mC and 5hmC. The method combines ten-eleven translocation methylcytosine dioxygenase (TET)-mediated oxidation of 5mC and 5hmC to 5-carboxylcytosine (5caC) with pyridine borane reduction of 5caC to dihydrouracil (DHU). Subsequent PCR amplification converts DHU to thymine, thereby enabling conversion of methylated cytosines to thymine (Liu, et al. (2019), “Bisulfite-Free Direct Detection of 5 -Methylcytosine and 5-Hydroxymethylcytosine at Base Resolution”, Nature Biotechnology, vol. 37, pp. 424-429).

[0201] In some instances, the sequence read data may be obtained using a nucleic acid sequencing method comprising the use of Methylated DNA Immunoprecipitation (MeDIP).

[0202] Examples of alignment tools optimized for aligning sequence reads for converted DNA include, but are not limited to, NovoAlign (Novocraft Technologies, Selangor, Malaysia), andthe Bismark tool (Krueger, et al. (2011), “Bismark: A Flexible Aligner and Methylation Caller for Bisulfite-Seq Applications”, Bioinformatics 27(11): 1571- 1572).Methylation status calling

[0203] In some instances, the methods described herein may comprise the use of a methylation status calling method, e.g., to call the methylation status of the CpG sites based on the sequence reads and fragments (complementary pairs of forward and reverse sequence reads) derived from DNA that has been subjected to a chemical or enzymatic conversion reaction, e.g., to convert unmethylated cytosine residues to uracil (which is interpreted as a thymine in sequencing results). Examples of such methylation status calling tools include, but are not limited to, the Bismark tool (Krueger, et al. (2011), “Bismark: A Flexible Aligner and Methylation Caller for Bisulfite-Seq Applications”, Bioinformatics 27(11): 1571-1572), TARGOMICS (Garinet, et al. (2017), “Calling Chromosome Alterations, DNA Methylation Statuses, and Mutations in Tumors by Simple Targeted Next-Generation Sequencing - A Solution for Transferring Integrated Pangenomic Studies into Routine Practice?”, J. Molecular Diagnostics 19(5):776- 787), Bicycle (Grana, et al. (2018) “Bicycle: A Bioinformatics Pipeline to Analyze Bisulfite Sequencing Data”, Bioinformatics 34(8): 1414-5), SMAP (Gao, et al. (2015), “SMAP: A Streamlined Methylation Analysis Pipeline for Bisulfite Sequencing”, Gigascience 4:29), and MeDUSA (Wilson, et al. (2016), “Computational Analysis and Integration of MeDIP-Seq Methylome Data”, in: Kulski JK, editor, Next Generation Sequencing: Advances, Applications and Challenges. Rijeka: InTech, p. 153-69). See also, Rauluseviciute, et al. (2019), “DNA Methylation Data by Sequencing: Experimental Approaches and Recommendations for Tools and Pipelines for Data Analysis”, Clinical Epigenetics 11:193.Systems

[0204] Also disclosed herein are systems designed to implement any of the disclosed methods for determining methylation state categories in a sample from a subject. The systems may comprise, e.g., one or more processors, and a memory unit communicatively coupled to the one or more processors and configured to store instructions that, when executed by the one or more processors, cause the system to: receive, by one or more processors, a plurality of duplexconsensus sequence reads determined from single strand consensus sequence reads obtained by sequencing single strand nucleic acid molecules derived from a double- stranded library subjected to a conversion reaction which converts an unmethylated cytosine to uracil; and classify, by the one or more processors, duplex consensus sequence reads of the plurality as belonging to one of two or more methylation state categories comprising at least one reliable methylation state category and at least one unreliable methylation state category, using a classification model.

[0205] In some instances, the disclosed systems may further comprise a sequencer, e.g., a next generation sequencer (also referred to 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.

[0206] In some instances, the disclosed systems may be used for determining a methylation signature in any of a variety of samples as described herein e.g., a tissue sample, biopsy sample, hematological sample, or liquid biopsy sample derived from the subject).

[0207] In some instances, the plurality of gene loci for which sequencing data is processed to determine a methylation signature may comprise at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more than 10 gene loci.

[0208] In some instance, the nucleic acid sequence data is acquired using a next generation sequencing technique (also referred to as a massively parallel sequencing technique) having a read-length of less than 400 bases, less than 300 bases, less than 200 bases, less than 150 bases, less than 100 bases, less than 90 bases, less than 80 bases, less than 70 bases, less than 60 bases, less than 50 bases, less than 40 bases, or less than 30 bases.

[0209] In some instances, the determination of a methylation signature is used to select, initiate, adjust, or terminate a treatment for cancer in the subject (e.g., a patient) from which the sample was derived, as described elsewhere herein.

[0210] In some instances, the disclosed systems may further comprise sample processing and library preparation workstations, microplate-handling robotics, fluid dispensing systems, temperature control modules, environmental control chambers, additional data storage modules, data communication modules (e.g., Bluetooth®, WiFi, intranet, or internet communication hardware and associated software), display modules, one or more local and / or cloud-based software packages (e.g., instrument / system control software packages, sequencing data analysis software packages), etc., or any combination thereof. In some instances, the systems may comprise, or be part of, a computer system or computer network as described elsewhere herein.Computer systems and networks

[0211] FIG. 3 illustrates an example of a computing device or system in accordance with one embodiment. Device 300 can be a host computer connected to a network. Device 300 can be a client computer or a server. As shown in FIG. 3, device 300 can be any suitable type of microprocessor-based device, such as a personal computer, workstation, server or handheld computing device (portable electronic device) such as a phone or tablet. The device can include, for example, one or more processor(s) 310, input devices 320, output devices 330, memory or storage devices 340, communication devices 360, and nucleic acid sequencers 370. Software module 350 residing in memory or storage device 340 may comprise, e.g., an operating system as well as software for executing the methods described herein. Input device 320 and output device 330 can generally correspond to those described herein, and can either be connectable or integrated with the computer.

[0212] Input device 320 can be any suitable device that provides input, such as a touch screen, keyboard or keypad, mouse, or voice-recognition device. Output device 330 can be any suitable device that provides output, such as a touch screen, haptics device, or speaker.

[0213] Storage 340 can be any suitable device that provides storage (e.g., an electrical, magnetic or optical memory including a RAM (volatile and non-volatile), cache, hard drive, or removablestorage disk). Communication device 360 can include any suitable device capable of transmitting and receiving signals over a network, such as a network interface chip or device. The components of the computer can be connected in any suitable manner, such as via a wired media (e.g., a physical system bus 380, Ethernet connection, or any other wire transfer technology) or wirelessly (e.g., Bluetooth®, Wi-Fi®, or any other wireless technology).

[0214] Software module 350, which can be stored as executable instructions in storage 340 and executed by processor(s) 310, can include, for example, an operating system and / or the processes that embody the functionality of the methods of the present disclosure (e.g., as embodied in the devices as described herein).

[0215] Software module 350 can also be stored and / or transported within any non-transitory computer-readable storage medium for use by or in connection with an instruction execution system, apparatus, or device, such as those described herein, that can fetch instructions associated with the software from the instruction execution system, apparatus, or device and execute the instructions. In the context of this disclosure, a computer-readable storage medium can be any medium, such as storage 340, that can contain or store processes for use by or in connection with an instruction execution system, apparatus, or device. Examples of computer- readable storage media may include memory units like hard drives, flash drives and distribute modules that operate as a single functional unit. Also, various processes described herein may be embodied as modules configured to operate in accordance with the embodiments and techniques described above. Further, while processes may be shown and / or described separately, those skilled in the art will appreciate that the above processes may be routines or modules within other processes.

[0216] Software module 350 can also be propagated within any transport medium for use by or in connection with an instruction execution system, apparatus, or device, such as those described above, that can fetch instructions associated with the software from the instruction execution system, apparatus, or device and execute the instructions. In the context of this disclosure, a transport medium can be any medium that can communicate, propagate or transport programming for use by or in connection with an instruction execution system, apparatus, ordevice. The transport readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic or infrared wired or wireless propagation medium.

[0217] Device 300 may be connected to a network (e.g., network 404, as shown in FIG. 4 and / or described below), which can be any suitable type of interconnected communication system. The network can implement any suitable communications protocol and can be secured by any suitable security protocol. The network can comprise network links of any suitable arrangement 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.

[0218] Device 300 can be implemented using any operating system, e.g., an operating system suitable for operating on the 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 the present disclosure can be deployed in different configurations, such as in a client / server arrangement or through a Web browser as a Web-based application or Web service, for example. In some embodiments, the operating system is executed by one or more processors, e.g., processor(s) 310.

[0219] Device 300 can further include a nucleic acid sequencer 370, which can be any suitable nucleic acid sequencing instrument.

[0220] FIG. 4 illustrates an example of a computing system in accordance with 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 sequencers can include, without limitation, 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, or Pacific Biosciences’ PacBio® RS system.

[0221] Devices 300 and 406 may communicate, e.g., using suitable communication interfaces via network 404, such as a Local Area Network (LAN), Virtual Private Network (VPN), or theInternet. In some embodiments, network 404 can 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, in part or in whole, via wireless or hardwired communications, such as Ethernet, IEEE 802.1 lb wireless, or the like. Additionally, devices 300 and 406 may communicate, e.g., using suitable communication interfaces, via a second network, such as a mobile / cellular network. Communication between devices 300 and 406 may further include or communicate with various servers such as a mail server, mobile server, media server, telephone server, and the like. In some embodiments, Devices 300 and 406 can communicate directly (instead of, or in addition to, communicating via network 404), e.g., via wireless or hardwired communications, such as Ethernet, IEEE 802.11b wireless, or the like. In some embodiments, devices 300 and 406 communicate via communications 408, which can be a direct connection or can occur via a network (e.g., network 404).

[0222] One or all of devices 300 and 406 generally include logic (e.g., http web server logic) or are programmed to format data, accessed from local or remote databases or other sources of data and content, for providing and / or receiving information via network 404 according to various examples described herein.EXAMPLESExample 1 - Methylation state classifications for duplex consensus sequence reads

[0223] FIGS. 5A-5E depict schematics exemplifying possible methylation states for duplex consensus sequence reads, as classified by a deterministic model, such as a decision tree model. The schematics represent methylated CpG sites with filled circles and non-methylated CpG sites with blank circles. The schematic FIG. 5A (502) illustrates a methylation state wherein all the CpG sites are concordant between the forward single strand consensus sequence (SSCS) and the reverse SSCS. In such a state, all the CpG sites can be methylated, or all the CpG sites can be unmethylated, and no discordances need be explained, because no discordances are observed. For example, sense strand 504 and antisense strand 506 both possess all methylated CpG sites.

[0224] The schematic FIG. 5B (508) illustrates a methylation state wherein on each SSCS for a duplex consensus sequence read, all the discordant unmethylated CpG sites are 3’ on theforward consensus sequence of all the concordant CpG sites between the forward and reverse SSCSs. When such a state is observed, the discordances are explained by either a nick or an endrepair event wherein unmethylated Cs were used to fill the underhanging nucleotides. The portion of the duplex consensus sequence for which concordance of methylation states between the forward and reverse SSCSs is observed thus represent reliable data. For example, sense strand 510 possesses unmethylated CpG sites at the 3’ end, and those unmethylated CpG sites are complementary to methylated CpG sites on the antisense strand 512. Further analyses then checks for whether the concordant CpG sites cover a known methylation biomarker site, in which case the data for the reliable portion of the duplex consensus sequence may be included in determining methylation signatures.

[0225] The schematic FIG. 5C (514) illustrates a methylation state wherein the duplex consensus sequence read possesses only a single discordant CpG site. When such a state is observed, the discordance is likely derived from a sequencing error or a single non-conversion event, where the conversion refers to, e.g., an enzymatic conversion reaction used to convert non-methylated CpG sites into uracil. For example, between sense strand 516 and antisense strand 518, only one unmethylated CpG site is shown.

[0226] The schematic FIG. 5D (520) illustrates a methylation state wherein only one SSCS (either the forward or the reverse) is fully methylated, whereas the other SSCS (either the reverse or the forward) is fully unmethylated, for a duplex consensus sequence read. When such a state is observed, the discordances are considered to be true biological cases of hemimethylation, rather than an artifact from methyl-sequencing library preparation. For example, all the CpG sites on sense strand 522 are unmethylated, whereas all the CpG sites on antisense strand 524 are methylated.

[0227] The schematic FIG. 5E (526) illustrates a methylation state that cannot be classified as one of the methylation states depicted in FIGS. 5A-5D. When such a state is observed, the discordances fail to be explained by any single derivative mechanism and can be discarded from further analyses. A different duplex consensus sequence read overlapping the same genomic locus may be analyzed for methylation state classification. For example, between sense strand528 and antisense strand 530, a pattern of methylation statuses indicative of a derivative mechanism fail to be shown.

[0228] FIG. 6A and FIG. 6B depict example data of duplex consensus sequence reads for various DNA fragments, aligned to one another. The CpG sites and their relevant duplex consensus sequence read statuses are labelled as indicated in the legend. In FIG. 6A, the methylated sites are represented as filled-in circles, the unmethylated sites are represented as blank circles, sites where both the forward and reverse SSCSs agree are represented as a box filled in with horizontal lines, sites where a nicking event resulted in the observed discordance between SSCSs across methylation sites are represented as a box filled with a cross-hatch, and sites where a nicking event did not result in the observed SSCSs are represented as a box filled in with a vertical line. The sample from which the reads depicted in FIG. 6A derives is healthy and is from the genomic loci chrl:918669-918761. The vertical box in the center of the plot that spans across the reads highlights some of the sites for which methylation state differs when compared to that for a sample with a high tumor fraction (TF), which is depicted in FIG. 6B.

[0229] In FIG. 6B, data for a different sample from the same genomic location as that for FIG. 6A is depicted, wherein the sample in this case possesses a high TF, and the plot depicts reads aligned to the genomic loci chrl:918669-918761. The vertical box in the center of the plot spans across the reads and highlights a region of relatively hypomethylated CpG status in the high TF sample, relative to corresponding sites indicated in the healthy sample depicted in FIG. 6A. In FIG. 6B, the methylated sites are represented as filled-in circles, the unmethylated sites are represented as blank circles, sites where both the forward and reverse SSCSs agree are represented as a box filled in with horizontal lines, sites where a nicking event resulted in the observed discordance between SSCSs across methylation sites are represented as a box filled with a cross-hatch, sites where a nicking event did not result in the observed SSCSs are represented as a box filled in with a vertical line, and sites where a non-conversion event resulted in the observed discordance between SSCSs across methylation sites are represented as a box filled with parallel diagonal lines.Example 2 - Information loss in the absence of duplex sequencing methods

[0230] FIG. 7A depicts a schematic exemplifying a mechanism by which methylation sites on the 3’ ends of a DNA duplex may be the result of false positive unmethylated Cs. The filled-in circles denote methylated bases, whereas the non-filled-in circles denote unmethylated bases. The lightning symbols denote potential sites of nicking on the DNA duplex. The schematic illustrates that, 3’ of the bases where the nicking event occurs, conventional methyl- sequencing library preparation methods fill the underhanging bases with unmethylated Cs, regardless of the methylation status of the nucleotides that were present before they were removed via nicking.

[0231] FIG. 7B depicts an example histogram of sequence read lengths, in base pairs, and a threshold that indicates how many bases can be trimmed from the 3’ end of a sequence read to avoid including artifacts arising from false positive unmethylated Cs for downstream methylation state analyses. The dashed vertical line on the histogram indicates an optimal threshold size for trimming sequence reads, where only trim sizes less than the size indicated by the dashed vertical line (z.e., to the left of the dashed vertical line) are used. Determining the optimal threshold size for trimming sequence reads can be relevant for duplex-based sequencing to account for the mix of duplex and non-duplex fragments present in every sequencing library. Use of an optimal threshold size provides a statistical basis for determining how to handle the non-duplex fragments. The optimal threshold informs the degree to which the sequence reads are trimmed when the fragments lacks duplex information.Example 3 - Signal to noise improvements from duplex sequencing methods

[0232] FIG. 8 depicts a comparison between consensus cluster unmethylated fraction (CCUF) data obtained using a conventional sequencing method wherein the consensus sequence reads are not sorted into duplex consensus sequence reads, as shown on the left side of the figure, versus a duplex sequencing-based method, wherein the consensus sequence reads are sorted into duplex consensus sequence reads. FIG. 8 depicts CCUF data from pooled healthy samples, i.e., cfDNA pooled from multiple individuals with no cancer; mixture sample from A, i.e., mixtures of cancer DNA samples from subject A with the healthy pool at input DNA fractions indicated on the x-axis; neat level from subject A, i.e., cfDNA from subject A with advanced non-smallcell lung cancer (NSCLC); mixture sample from B, i.e., ; i.e., mixtures of cancer DNA samples from subject B with the healthy pool at input DNA fractions indicated on the x-axis; and neat level from B, i.e., cfDNA from subject B with advanced NSCLC. In the case of the conventional method, as depicted in the figure on the left, the non-duplex consensus cluster unmethylated fraction (CCUF) determined for samples comprising different fractions of cancer-derived DNA remained at a constant level, even as the cancer mixture fraction was increased, whereas for the figure on the right, the duplex consensus cluster methylated fraction (CCMF) determined for samples comprising different fractions of cancer-derived DNA increased as the cancer mixture fraction increased. The results indicated that the duplex sequencing-based method is able to identify the increasing levels of cancer mixture fraction, whereas the traditional non-duplex sequencing-based method is not.Example 4 - Selection of differentially methylated regions for tumor response monitoring by liquid biopsy in multiple tumor types

[0233] Patients undergoing systemic anti-cancer therapy suffer from side effects and financial toxicity. Tumor response monitoring (TRM) is critical to monitor the tumor’s progression to determine if the patient is truly benefiting from the treatment. Liquid biopsy-based monitoring of tumor response via circulating tumor DNA (ctDNA) promises many improvements over existing imaging technologies. Tracking ctDNA levels based on sequence mutations is difficult when ctDNA levels are low or there are few mutations. This is not a problem when tracking methylation based on differentially methylated regions (DMRs), because tumors have widespread aberrant methylation. While existing DMR algorithms find differential methylation between healthy and cancerous tissue, TRM requires detecting ctDNA at low levels relative to cell-free DNA (cfDNA) from healthy origin. We developed a computational method for selecting DMRs from cfDNA whole genome bisulfite sequencing (WGBS) data based on the principles of maximizing signal-to-noise ratio, handling uncertainty from a limited number of training samples. We applied this method to WGBS data from a set of patients with lung, breast, colorectal, or prostate cancer as well as healthy controls and show concordance with sequencemutation estimates of ctDNA. While there is overlap in the DMRs between tumor types, sensitivity performance is maximized when using a DMR set specific to the patient’s tumortype. This method tells which regions of the epigenome are most potentially informative, given the diversity of methylation patterns both in the healthy population’s cfDNA and in ctDNA in patients with the same tumor type.

[0234] For identification of high confidence DMRs in cfDNA, a large cohort was procured consisting of plasma samples from healthy participants and cancer patients with tumors in each of four tissues of interest: lung, breast, prostate, and colorectal tissue (see Table 1). Since there are known associations between age and methylation, care was taken to include healthy participants with comparable age to the disease population. Since in most patients the majority of cfDNA is derived from healthy tissue and only a small minority is ctDNA, additional care was taken to include advanced stage patients with very high tumor fractions (TF). These samples are likely to be more informative since a larger fraction of their content is signal. The procured samples were subjected to whole genome methylation sequencing, either WGBS or enzymatic methylation Seq (EM-Seq) and analyzed via a custom methylation pipeline.Table 1. Procured cohort summary.

[0235] To identify genome wide DMRs for cfDNA analysis, the genome was broken down into a set of overlapping candidate regions for assessment (see FIG. 9). Since methylation within a strand of DNA tends to be autocorrelated, that is adjacent CpGs tend to have the same methylation state as each other, the genome was divided into clusters containing multiple CpGs (min 3) that might be regulated or dysregulated in concert. These were sized to be up to 80bpwide so that cfDNA fragments which are 166bp long on average, might span them completely and therefore provide information on whether the contained CpGs are all turned on or off together (described more below). Overlapping clusters of CpGs were included in the candidate set so that cfDNA fragments that fall between two adjacent non-overlapping CpG clusters might still be counted for a redundant CpG cluster in between and overlapping (see Figure). This way all cfDNA fragments sufficiently long to cover a minimum of CpGs could be used to evaluate a cluster, which is more efficient and allows for more optimal selection of DMR clusters.

[0236] Given the known propensity for methylation states to be correlated between CpGs in close proximity, a methylation metric for a cluster was defined: cluster consensus methylation fraction (CCMF) as well as several variations (see FIG. 10). In its simplest form this is defined as illustrated in the figure: the fraction of read fragments spanning a set of CpGs that are all methylated (z.e., at most one CpG unmethylated). The variants of the definition which were also explored for DMR selection are, assessing the fraction of fragments that are almost fully unmethylated (z.e. the symmetric opposite definition) or CCUF, a stricter definition requiring all CpGs to be methylated and a less strict definition of only requiring the majority of CpGs to be methylated to be counted in the numerator. These methylation metrics together have the advantage of quantifying biological methylation differences in methylation, rather than technical differences resulting, for example, from a sequencing error.

[0237] FIG. 11 shows the low end of the general background distribution of CCMF using the majority methylated definition in aggregated healthy data, meaning all the reads from all the healthy samples piled on top of each other within the candidate DMR clusters in a representative subset of the whole genome. The CCMF distribution of aggregate healthy cfDNA data across these sites shows CCMF peak at 0. This means that for read fragments at 22,000x depth derived from hundreds of healthy participants from the broad mixture of different cell types contributing to the cfDNA in the plasma, there are no fragments showing majority methylation across the sites in these clusters. These clusters therefore show a clean background for detection if they have foreground cancer methylation signal as assessed next.

[0238] FIG. 12 shows the overall set of candidate DMRs going through a series of filters to identify DMRs which are hypermethylated in the four cancer organs of origin and a “pan” setrepresenting the union of the data from these four cancer areas. A comparable method could be applied to identify DMRs which are hypomethylated in cancer but is not shown here. There are a set of filters representing inherent features of the genome at each cluster followed by three sets of filters based on our training data. The overall structure of the selection is that first genome inherent filters are applied, such as GC content, second coverage filters are applied to ensure confidence in the selected sites, third background filters and fourth foreground (z.e., cancer ctDNA) filters are applied. The final, foreground selection step is done separately for each of the four cancer organs of origin and for the union of these to get five total DMR sets.

[0239] In FIG. 13, the sizes in bp and overlap of the identified DMR sets between the pancancer set and each of the four chosen diseases are shown (A), and for lung, breast, and prostate against each other (B). While there is a core set of regions that were often identified as being differentially methylated between different cancers and the plasma cell types, this represented a minority of the DMRs, and most were disease specific. In part C we show CCMF data (pan cancer DMR set) for healthy samples and a set of contrived samples which were diluted down to a tumor fraction of 1% from some of the procured samples which had sufficiently high tumor fraction as measured independently to allow this. Overall, there was a strong separation between the 1% TF samples and the healthy. A trend was observed between the different tissue types at matched TF with lung showing the lowest and prostate showing the highest degree of aberrant methylation in the selected pan cancer DMRs.EXEMPLARY IMPLEMENTATIONS

[0240] Exemplary implementations of the methods and systems described herein include:1. A method comprising: providing a plurality of duplex nucleic acid molecules obtained from a sample from a subject; ligating tag sequence adapters onto one or more of the plurality of duplex nucleic acid molecules to generate tagged duplex nucleic acid molecules;subjecting the tagged duplex nucleic acid molecules to a conversion reaction, which converts an unmethylated cytosine to uracil, to generate converted duplex nucleic acid molecules; amplifying the one or more converted duplex nucleic acid molecules to generate a double-stranded library; capturing the amplified tagged duplex nucleic acid molecules from the double- stranded library; separating the captured tagged duplex nucleic acid molecules into single strand nucleic acid molecules; sequencing, by a sequencer, the single strand nucleic acid molecules to obtain single strand consensus sequence reads to identify methylated CpG sites in the tagged duplex nucleic acid molecules; determining, by one or more processors, duplex consensus sequence reads from the single strand consensus sequence reads, and classifying, by the one or more processors, the duplex consensus sequence reads as belonging to one of two or more methylation state categories comprising at least one reliable methylation state category and at least one unreliable methylation state category, using a classification model.2. The method of clause 1, further comprising determining, by the one or more processors, a methylation signature for the sample based on methylation states at one or more CpG sites within duplex consensus sequence reads classified as belonging to at least one of the reliable methylation state categories.3. The method of clause 1 or clause 2, wherein the duplex consensus sequence reads comprise duplex methylated sequence reads, duplex hemi-methylated sequence reads, duplex unmethylated sequence reads, or any combination thereof.4. The method of any one of clauses 1 to 3, wherein based on the methylation signature, the sample is determined to be hypermethylated in one or more genomic regions.5. The method of any one of clauses 2 to 4, wherein based on the methylation signature, the sample is determined to be hypomethylated in one or more genomic regions.6. The method of any one of clauses 1 to 5, wherein the classification model is configured to identify discrepancies between methylation states at one or more CpG sites in a forward single strand consensus sequence (forward SSCS) corresponding to a given duplex consensus sequence read and in a reverse single strand consensus sequence (reverse SSCS) corresponding to the given duplex consensus sequence read and classify the duplex consensus sequence read based on a number of identified discrepancies.7. The method of any one of clauses 1 to 6, wherein the classification model is a deterministic model.8. The method of clause 7, wherein the deterministic model is a decision tree model.9. The method of any one of clauses 1 to 8, wherein the subject is suspected of having or is determined to have cancer.10. The method of clause 9, wherein the cancer is a B cell cancer (multiple myeloma), a melanoma, breast cancer, lung cancer, bronchus cancer, colorectal cancer, prostate cancer, pancreatic cancer, stomach cancer, ovarian cancer, urinary bladder cancer, brain cancer, central nervous system cancer, peripheral nervous system cancer, esophageal cancer, cervical cancer, uterine cancer, endometrial cancer, cancer of an oral cavity, cancer of a pharynx, liver cancer, kidney cancer, testicular cancer, biliary tract cancer, small bowel cancer, appendix cancer, salivary gland cancer, thyroid gland cancer, adrenal gland cancer, osteosarcoma, chondrosarcoma, a cancer of hematological tissue, an adenocarcinoma, an inflammatory myofibroblastic tumor, a gastrointestinal stromal tumor (GIST), colon cancer, multiple myeloma (MM), myelodysplastic syndrome (MDS), myeloproliferative disorder (MPD), acute lymphocytic leukemia (ALL), acute myelocytic leukemia (AML), chronic myelocytic leukemia (CML), chronic lymphocytic leukemia (CLL), polycythemia Vera, Hodgkin lymphoma, nonHodgkin lymphoma (NHL), soft-tissue sarcoma, fibrosarcoma, myxosarcoma, liposarcoma, osteogenic sarcoma, chordoma, angiosarcoma, endotheliosarcoma, lymphangiosarcoma, lymphangioendotheliosarcoma, synovioma, mesothelioma, Ewing’s tumor, leiomyosarcoma,rhabdomyosarcoma, squamous cell carcinoma, basal cell carcinoma, adenocarcinoma, sweat gland carcinoma, sebaceous gland carcinoma, papillary carcinoma, papillary adenocarcinomas, medullary carcinoma, bronchogenic carcinoma, renal cell carcinoma, hepatoma, bile duct carcinoma, choriocarcinoma, seminoma, embryonal carcinoma, Wilms’ tumor, bladder carcinoma, epithelial carcinoma, glioma, astrocytoma, medulloblastoma, craniopharyngioma, ependymoma, pinealoma, hemangioblastoma, acoustic neuroma, 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 cancer, essential thrombocythemia, agnogenic myeloid metaplasia, hypereosinophilic syndrome, systemic mastocytosis, familiar hypereosinophilia, chronic eosinophilic leukemia, neuroendocrine cancers, or a carcinoid tumor.11. The method of clause 9, wherein the cancer comprises acute lymphoblastic leukemia (Philadelphia chromosome positive), acute lymphoblastic leukemia (precursor B-cell), acute myeloid leukemia (FLT3+), acute myeloid leukemia (with an IDH2 mutation), anaplastic large cell lymphoma, basal cell carcinoma, B-cell chronic lymphocytic leukemia, bladder cancer, breast cancer (HER2 overexpressed / amplified), breast cancer (HER2+), breast cancer (HR+, HER2-), cervical cancer, cholangiocarcinoma, chronic lymphocytic leukemia, chronic lymphocytic leukemia (with 17p deletion), chronic myelogenous leukemia, chronic myelogenous leukemia (Philadelphia chromosome positive), classical Hodgkin lymphoma, colorectal cancer, colorectal cancer (dMMR / MSI-H), colorectal cancer (KRAS wild type), cryopyrin-associated periodic syndrome, a cutaneous T-cell lymphoma, dermatofibrosarcoma protuberans, a diffuse large B-cell lymphoma, fallopian tube cancer, a follicular B-cell nonHodgkin lymphoma, a follicular lymphoma, gastric cancer, gastric cancer (HER2+), gastroesophageal junction (GEJ) adenocarcinoma, a gastrointestinal stromal tumor, a gastrointestinal stromal tumor (KIT+), a giant cell tumor of the bone, a glioblastoma, granulomatosis with polyangiitis, a head and neck squamous cell carcinoma, a hepatocellular carcinoma, Hodgkin lymphoma, juvenile idiopathic arthritis, lupus erythematosus, a mantle cell lymphoma, medullary thyroid cancer, melanoma, a melanoma with a BRAF V600 mutation, a melanoma with a BRAF V600E or V600K mutation, Merkel cell carcinoma, multicentricCastleman’s disease, multiple hematologic malignancies including Philadelphia chromosomepositive ALL and CML, multiple myeloma, myelofibrosis, a non-Hodgkin’s lymphoma, a nonresectable subependymal giant cell astrocytoma associated with tuberous sclerosis, a nonsmall cell lung cancer, a non-small cell lung cancer (ALK+), a non-small cell lung cancer (PD- L1+), a non-small cell lung cancer (with ALK fusion or ROS1 gene alteration), a non-small cell lung cancer (with BRAF V600E mutation), a non-small cell lung cancer (with an EGFR exon 19 deletion or exon 21 substitution (L858R) mutations), a non-small cell lung cancer (with an EGFR T790M mutation), ovarian cancer, ovarian cancer (with a BRCA mutation), pancreatic cancer, a pancreatic, gastrointestinal, or lung origin neuroendocrine tumor, a pediatric neuroblastoma, a peripheral T-cell lymphoma, peritoneal cancer, prostate cancer, a renal cell carcinoma, rheumatoid arthritis, a small lymphocytic lymphoma, a soft tissue sarcoma, a solid tumor (MSLH / dMMR), a squamous cell cancer of the head and neck, a squamous non-small cell lung cancer, thyroid cancer, a thyroid carcinoma, urothelial cancer, a urothelial carcinoma, or Waldenstrom’s macroglobulinemia.12. The method of clause 11, further comprising treating the subject with an anti-cancer therapy.13. The method of clause 12, wherein the anti-cancer therapy comprises a targeted anti-cancer therapy.14. The method of clause 13, wherein the targeted anti-cancer therapy comprises abemaciclib (Verzenio), abiraterone acetate (Zytiga), acalabrutinib (Calquence), ado-trastuzumab emtansine (Kadcyla), afatinib dimaleate (Gilotrif), aldesleukin (Proleukin), alectinib (Alecensa), alemtuzumab (Campath), alitretinoin (Panretin), alpelisib (Piqray), amivantamab-vmjw (Rybrevant), anastrozole (Arimidex), apalutamide (Erleada), asciminib hydrochloride (Scemblix), atezolizumab (Tecentriq), avapritinib (Ayvakit), avelumab (Bavencio), axicabtagene ciloleucel (Yescarta), axitinib (Inlyta), belantamab mafodotin-blmf (Blenrep), belimumab (Benlysta), belinostat (Beleodaq), belzutifan (Welireg), bevacizumab (Avastin), bexarotene (Targretin), binimetinib (Mektovi), blinatumomab (Blincyto), bortezomib (Velcade), bosutinib (Bosulif), brentuximab vedotin (Adcetris), brexucabtagene autoleucel (Tecartus), brigatinib (Alunbrig), cabazitaxel (Jevtana), cabozantinib (Cabometyx), cabozantinib (Cabometyx,Cometriq), canakinumab (Haris), capmatinib hydrochloride (Tabrecta), carfilzomib (Kyprolis),cemiplimab-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), denileukin diftitox (Ontak), denosumab (Xgeva), dinutuximab (Unituxin), dostarlimab-gxly (Jemperli), durvalumab (Imfinzi), duvelisib (Copiktra), elotuzumab (Empliciti), enasidenib 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 (Inrebic), fulvestrant (Faslodex), gefitinib (Iressa), gemtuzumab ozogamicin (Mylotarg), gilteritinib (Xospata), glasdegib maleate (Daurismo), hyaluronidase-zzxf (Phesgo), ibrutinib (Imbruvica), ibritumomab tiuxetan (Zevalin), idecabtagene vicleucel (Abecma), idelalisib (Zydelig), imatinib mesylate (Gleevec), infigratinib phosphate (Truseltiq), inotuzumab ozogamicin (Besponsa), iobenguane 1131 (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), lisocabtagene maraleucel (Breyanzi), loncastuximab tesirine-lpyl (Zynlonta), lorlatinib (Lorbrena), lutetium Lu 177-dotatate (Lutathera), margetuximab-cmkb (Margenza), midostaurin (Rydapt), mobocertinib succinate (Exkivity), mogamulizumab-kpkc (Poteligeo), moxetumomab pasudotox-tdfk (Lumoxiti), naxitamab-gqgk (Danyelza), necitumumab (Portrazza), neratinib maleate (Nerlynx), nilotinib (Tasigna), niraparib tosylate monohydrate (Zejula), nivolumab (Opdivo), obinutuzumab (Gazyva), ofatumumab (Arzerra), olaparib (Lynparza), olaratumab (Lartruvo), osimertinib (Tagrisso), palbociclib (Ibrance), panitumumab (Vectibix), panobinostat (Farydak), pazopanib (Votrient), pembrolizumab (Keytruda), pemigatinib (Pemazyre), pertuzumab (Perjeta), pexidartinib hydrochloride (Turalio), polatuzumab vedotin-piiq (Polivy), ponatinib hydrochloride (Iclusig), pralatrexate (Folotyn), pralsetinib (Gavreto), radium 223 dichloride (Xofigo), ramucirumab (Cyramza), regorafenib (Stivarga), ribociclib (Kisqali), ripretinib (Qinlock), rituximab (Rituxan), rituximab and hyaluronidase human (Rituxan Hycela), romidepsin (Istodax), rucaparib camsylate (Rubraca), ruxolitinib phosphate (Jakafi), sacituzumab govitecan-hziy (Trodelvy), seliciclib, selinexor (Xpovio), selpercatinib (Retevmo),selumetinib sulfate (Koselugo), siltuximab (Sylvant), sipuleucel-T (Provenge), sirolimus protein-bound particles (Fyarro), sonidegib (Odomzo), sorafenib (Nexavar), sotorasib (Lumakras), sunitinib (Sutent), tafasitamab-cxix (Monjuvi), tagraxofusp-erzs (Elzonris), talazoparib tosylate (Talzenna), tamoxifen (Nolvadex), tazemetostat hydrobromide (Tazverik), tebentafusp-tebn (Kimmtrak), temsirolimus (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 (Fareston), tucatinib (Tukysa), umbralisib tosylate (Ukoniq), vandetanib (Caprelsa), vemurafenib (Zelboraf), venetoclax (Venclexta), vismodegib (Erivedge), vorinostat (Zolinza), zanubrutinib (Brukinsa), ziv- aflibercept (Zaltrap), or any combination thereof.15. The method of any one of clauses 1 to 14, further comprising obtaining the sample from the subject.16. The method of any one of clauses 1 to 15, wherein the sample comprises a tissue biopsy sample, a liquid biopsy sample, or a normal control.17. The method of clause 16, wherein the sample is a liquid biopsy sample and comprises blood, plasma, cerebrospinal fluid, sputum, stool, urine, or saliva.18. The method of clause 16, wherein the sample is a liquid biopsy sample and comprises circulating tumor cells (CTCs).19. The method of clause 16, wherein the sample is a liquid biopsy sample and comprises cell- free DNA (cfDNA), circulating tumor DNA (ctDNA), or any combination thereof.20. The method of any one of clauses 1 to 19, wherein the plurality of nucleic acid molecules comprises a mixture of tumor nucleic acid molecules and non-tumor nucleic acid molecules.21. The method of clause 20, wherein the tumor nucleic acid molecules are derived from a tumor portion of a heterogeneous tissue biopsy sample, and the non-tumor nucleic acid molecules are derived from a normal portion of the heterogeneous tissue biopsy sample.22. The method of clause 20, wherein the sample comprises a liquid biopsy sample, and wherein the tumor nucleic acid molecules are derived from a circulating tumor DNA (ctDNA) fraction of the liquid biopsy sample, and the non-tumor nucleic acid molecules are derived from a nontumor, cell-free DNA (cfDNA) fraction of the liquid biopsy sample.23. The method of any one of clauses 1 to 22, wherein the one or more adapters comprise amplification primers, flow cell adaptor sequences, substrate adapter sequences, or sample index sequences.24. The method of any one of clauses 1 to 23, wherein the captured nucleic acid molecules are captured from the amplified nucleic acid molecules by hybridization to one or more bait molecules.25. The method of clause 24, wherein the one or more bait molecules comprise one or more nucleic acid molecules, each comprising a region that is complementary to a region of a captured nucleic acid molecule.26. The method of any one of clauses 1 to 25, wherein amplifying nucleic acid molecules comprises performing a polymerase chain reaction (PCR) amplification technique, a non-PCR amplification technique, or an isothermal amplification technique.27. The method of any one of clauses 1 to 26, wherein the sequencing comprises use of a massively parallel sequencing (MPS) technique, whole genome sequencing (WGS), whole exome sequencing, targeted sequencing, direct sequencing, or Sanger sequencing technique.28. The method of clause 27, wherein the sequencing comprises massively parallel sequencing, and the massively parallel sequencing technique comprises next generation sequencing (NGS).29. The method of any one of clauses 1 to 28, wherein the sequencer comprises a next generation sequencer.30. The method of any one of clauses 1 to 29, wherein one or more of the plurality of sequencing reads overlap one or more gene loci within one or more subgenomic intervals in the sample.31. The method of clause 30, wherein the one or more gene loci comprises between 10 and 20 loci, between 10 and 40 loci, between 10 and 60 loci, between 10 and 80 loci, between 10 and 100 loci, between 10 and 150 loci, between 10 and 200 loci, between 10 and 250 loci, between 10 and 300 loci, between 10 and 350 loci, between 10 and 400 loci, between 10 and 450 loci, between 10 and 500 loci, between 20 and 40 loci, between 20 and 60 loci, between 20 and 80 loci, between 20 and 100 loci, between 20 and 150 loci, between 20 and 200 loci, between 20 and 250 loci, between 20 and 300 loci, between 20 and 350 loci, between 20 and 400 loci, between 20 and 500 loci, between 40 and 60 loci, between 40 and 80 loci, between 40 and 100 loci, between 40 and 150 loci, between 40 and 200 loci, between 40 and 250 loci, between 40 and 300 loci, between 40 and 350 loci, between 40 and 400 loci, between 40 and 500 loci, between 60 and 80 loci, between 60 and 100 loci, between 60 and 150 loci, between 60 and 200 loci, between 60 and 250 loci, between 60 and 300 loci, between 60 and 350 loci, between 60 and 400 loci, between 60 and 500 loci, between 80 and 100 loci, between 80 and 150 loci, between 80 and 200 loci, between 80 and 250 loci, between 80 and 300 loci, between 80 and 350 loci, between 80 and 400 loci, between 80 and 500 loci, between 100 and 150 loci, between 100 and 200 loci, between 100 and 250 loci, between 100 and 300 loci, between 100 and 350 loci, between 100 and 400 loci, between 100 and 500 loci, between 150 and 200 loci, between 150 and 250 loci, between 150 and 300 loci, between 150 and 350 loci, between 150 and 400 loci, between 150 and 500 loci, between 200 and 250 loci, between 200 and 300 loci, between 200 and 350 loci, between 200 and 400 loci, between 200 and 500 loci, between 250 and 300 loci, between 250 and 350 loci, between 250 and 400 loci, between 250 and 500 loci, between 300 and 350 loci, between 300 and 400 loci, between 300 and 500 loci, between 350 and 400 loci, between 350 and 500 loci, or between 400 and 500 loci.32. The method of clause 30 or clause 31, wherein the one or more gene loci comprise 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, CBFB, 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, D0T1L, EED, EGFR, EMSY (Cllorf30), EP300, EPHA3, EPHB1, EPHB4, ERBB2, ERBB3, ERBB4, ERCC4, ERG, ERRFI1, ESRI, 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, FECN, FET1, FET3, FOXE2, FUBP1, GABRA6, GATA3, GATA4, GATA6, GID4 (C17orf39), GNA11, GNA13, GNAQ, GNAS, GRM3, GSK3B, H3F3A, HDAC1, HGF, HNF1A, HRAS, HSD3B1, ID3, IDH1, IDH2, IGF1R, IKBKE, IKZF1, INPP4B, IRF2, IRF4, IRS2, JAK1, JAK2, JAK3, JUN, KDM5A, KDM5C, KDM6A, KDR, KEAP1, KEF, KIT, KEHE6, KMT2A (MEE), 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, PDGFRA, PDGFRB, PDK1, PIK3C2B, PIK3C2G, PIK3CA, PIK3CB, PIK3R1, PIM1, PMS2, POLDI, POLE, PPARG, PPP2R1A, PPP2R2A, PRDM1, PRKAR1A, PRKCI, PTCHI, PTEN, PTPN11, PTPRO, QKI, RAC1, RAD21, RAD51, RAD51B, RAD51C, RAD51D, RAD52, RAD54L, RAFI, RARA, RBI, 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, STK11, SUFU, SYK, TBX3, TEK, TERC, TERT, TET2, TGFBR2, TIPARP, TMPRSS2, TNFAIP3, TNFRSF14, TP53, TSC1, TSC2, TYRO3, U2AF1, VEGFA, VHL, WHSCI, WHSC1L1, WT1, XPO1, XRCC2, ZNF217, ZNF703, or any combination thereof.33. The method of clause 31 or clause 32, wherein the one or more gene loci comprise 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, FFT3, GD2, HDAC, HER1, HER2, HR, IDH2, IL-ip, IE-6, IL-6R, JAK1, JAK2, JAK3, KIT, KRAS, MEK, MET, MSI-H, mTOR, PARP, PD-1, PDGFR, PDGFRa, PDGFRP, PD-L1, PI3K5, PIGF, PTCH, RAF, RANKL, RET, ROS1, SLAMF7, VEGF, VEGFA, VEGFB, or any combination thereof.34. The method of any one of clauses 1 to 33, further comprising generating, by the one or more processors, a report indicating the methylation signature.35. The method of clause 34, further comprising transmitting the report to a healthcare provider.36. The method of clause 35, wherein the report is transmitted via a computer network or a peer- to-peer connection.37. A method comprising: receiving, by one or more processors, a plurality of duplex consensus sequence reads determined from single strand consensus sequence reads obtained by sequencing single strand nucleic acid molecules derived from a double- stranded library subjected to a conversion reaction which converts an unmethylated cytosine to uracil; and classifying, by the one or more processors, duplex consensus sequence reads of the plurality as belonging to one of two or more methylation state categories comprising at least one reliable methylation state category and at least one unreliable methylation state category, using a classification model.38. The method of clause 37, further comprising determining, by the one or more processors, a methylation signature for the sample based on methylation states at one or more CpG sites within duplex consensus sequence reads classified as belonging to at least one of the reliable methylation state categories.39. The method of clause 37 or clause 38, wherein the duplex consensus sequence reads comprise duplex methylated sequence reads, duplex hemi-methylated sequence reads, duplex unmethylated sequence reads, or any combination thereof.40. The method of cany one of clauses 37 to 39, wherein based on the methylation signature, the sample is determined to be hypermethylated in one or more genomic regions.41. The method of any one of clauses 37 to 40, wherein based on the methylation signature, the sample is determined to be hypomethylated in one or more genomic regions.42. The method of clause 41, wherein the classification model is configured to identify discrepancies between methylation states at one or more CpG sites in a forward single strand consensus sequence (forward SSCS) corresponding to a given duplex consensus sequence read and in a reverse single strand consensus sequence (reverse SSCS) corresponding to the given duplex consensus sequence read and classify the duplex consensus sequence read based on a number of identified discrepancies.43. The method of any one of clauses 37 to 42, wherein the classification model is a deterministic model.44. The method of clause 43, wherein the deterministic model is a decision tree model.45. The method of any one of clauses 37 to 44, wherein the classification model is a probabilistic model.46. The method of clause 45, wherein the probabilistic model is a hidden Markov model, a Gaussian mixture model, or a Bayesian network model.47. The method of any one of clauses 37 to 46, wherein the classification model is configured to classify duplex consensus sequence reads into at least two categories comprising a concordant category in which no discrepancies between methylation states at the one or more CpG sites are identified, and a discordant category in which at least one discrepancy between methylation states at the one or more CpG sites is identified.48. The method of clause 47, wherein the methylation signature for the sample is determined based only on the methylation states at the one or more CpG sites determined for duplex consensus sequence reads in the concordant category.49. The method of any one of clauses 37 to 48, wherein the classification model is further configured to classify duplex consensus sequence reads for which at least one discrepancy between methylation states at the one or more CpG sites is identified into one of two or more discordant sub-categories.50. The method of clause 49, wherein the two or more discordant sub-categories comprise a first discordant sub-category for which the at least one discrepancy between methylation states at the one or more CpG sites can be explained by a nick repair and / or end repair error.51. The method of clause 50, wherein the first discordant sub-category comprises duplex consensus sequences for which all CpG sites for which the methylation states are discrepant are located at position(s) that are 3’ on a corresponding forward SSCS of all CpG sites for which the methylation states are concordant.52. The method of clause 51, further comprising determining what portion of a given duplex consensus sequence read comprises reliable methylation state data.53. The method of clause 52, wherein determining what portion of the given duplex consensus sequence read comprises reliable methylation state data is based on an analysis of a location of the one or more CpG sites for which methylation states are discordant relative to a location of one or more CpG sites for which methylation states are concordant.54. The method of clause 53, wherein the portion of the given duplex consensus sequence read that comprises reliable methylation state data includes one or more CpG sites for which methylation states are concordant and that are located 5’ relative to the one or more CpG sites for which methylation states are discordant on the forward SSCS corresponding to the duplex consensus sequence read.55. The method of clause 54, further comprising determining if the reliable portion of the given duplex consensus sequence read fully overlaps a methylation biomarker site.56. The method of any one of clauses 52 to 55, wherein the methylation signature for the sample is determined based on the methylation states at the one or more CpG sites determined for duplex consensus sequence reads in the concordant category, and on the methylation states at the one or more CpG sites in reliable portions of duplex consensus sequence reads for which at least one discrepancy between methylation states at the one or more CpG sites is identified.57. The method of any one of clauses 49 to 56, wherein the two or more discordant subcategories comprise a second discordant sub-category for which the at least one discrepancybetween methylation states at the one or more CpG sites can be explained by a sequencing error and / or a single non-conversion event.58. The method of clause 57, wherein the second discordant sub-category comprises duplex consensus sequences for which only one CpG site for which the methylation states are discrepant is identified.59. The method of any one of clauses 49 to 58, wherein the two or more discordant subcategories comprise a third discordant sub-category for which the at least one discrepancy between methylation states at the one or more CpG sites can be explained by biological hemimethylation .60. The method of clause 59, wherein the third discordant sub-category comprises duplex consensus sequences for which one corresponding SSCS comprises CpG sites that are all methylated and the other corresponding SSCS comprises CpG sites that are all unmethylated.61. The method of any one of clauses 49 to 60, wherein the two or more discordant subcategories comprise a fourth discordant sub-category for which the at least one discrepancy between methylation states at the one or more CpG sites cannot be explained.62. The method of any one of clauses 37 to 61, further comprising processing sequence read data obtained by sequencing the double-stranded library to generate the plurality of duplex consensus sequence reads.63. The method of any one of clauses 37 to 62, wherein the conversion reaction comprises a bisulfite reaction to convert non-methylated cytosine to uracil.64. The method of any one of clauses 37 to 63, wherein the conversion reaction comprises an enzymatic conversion reaction to convert non-methylated cytosine to uracil.65. The method of clause 64, wherein the enzymatic conversion reaction comprises the use of a tet methylcytosine dioxygenase 2 (TET2) enzyme to oxidize 5-methyl-cytosine (5mC) or 5- hydroxymethyl-cytosine (5hmC) to 5-carboxycytosine (5caC).66. The method of clause 65, wherein the enzymatic conversion reaction further comprises the use of a combination of TET2 and T4 P-glucosyltransferase (T4-PGT) enzymes to convert 5-methyl-cytosine (5mC) or 5-hydroxymethyl-cytosine (5hmC) to 5-(D-glucosyl)oxymethyl- cy to sine.67. The method of any one of clauses 64 to 66, wherein the enzymatic conversion reaction comprises the use of an Apolipoprotein B mRNA Editing Catalytic Polypeptide-like (APOBEC) enzyme.68. The method of any one of clauses 37 to 67, wherein the method is used to correct for methylation bias in a methylation sequencing method.69. The method of any one of clauses 37 to 68, wherein the sample comprises a tissue biopsy sample, a liquid biopsy sample, or a normal control.70. The method of clause 69, wherein the sample is a liquid biopsy sample and comprises blood, plasma, cerebrospinal fluid, sputum, stool, urine, or saliva.71. The method of clause 69, wherein the sample is a liquid biopsy sample and comprises circulating tumor cells (CTCs).72. The method of clause 71, wherein the sample is a liquid biopsy sample and comprises cell- free DNA (cfDNA), circulating tumor DNA (ctDNA), or any combination thereof.73. The method of any one of clauses 37 to 72, wherein the plurality of nucleic acid molecules comprises a mixture of tumor nucleic acid molecules and non-tumor nucleic acid molecules.74. The method of clause 73, wherein the tumor nucleic acid molecules are derived from a tumor portion of a heterogeneous tissue biopsy sample, and the non-tumor nucleic acid molecules are derived from a normal portion of the heterogeneous tissue biopsy sample.75. The method of clause 74, wherein the sample comprises a liquid biopsy sample, and wherein the tumor nucleic acid molecules are derived from a circulating tumor DNA (ctDNA) fraction of the liquid biopsy sample, and the non-tumor nucleic acid molecules are derived from a non- tumor, cell-free DNA (cfDNA) fraction of the liquid biopsy sample.76. The method of any one of clauses 37 to 75, wherein the methylation signature is used to diagnose a disease.77. The method of any one of clauses 37 to 76, wherein the methylation signature is used in making a treatment decision for a subject diagnosed with a disease.78. The method of any one of clauses 37 to 77, wherein the methylation signature is used to track the treatment response for a subject who has been diagnosed with a disease and is currently undergoing treatment.79. The method of any one of clauses 37 to 77, wherein the methylation signature is used to detect minimal residual disease.80. The method of clauses 76 to 79, wherein the disease is cancer.81. A method for diagnosing a disease, the method comprising diagnosing that a subject has the disease based on a determination of the methylation signature for the sample from the subject, wherein the methylation signature is determined according to the method of any one of clauses 1 to 80.82. A method of selecting an anti-cancer therapy, the method comprising: responsive to determining the methylation signature for the sample from the subject, selecting an anti-cancer therapy for the subject, wherein the methylation signature is determined according to the method of any one of clauses 1 to 81.83. A method of treating a cancer in a subject, comprising: responsive to determining the methylation signature for the sample from the subject, administering an effective amount of an anti-cancer therapy to the subject, wherein the methylation signature is determined according to the method of any one of clauses 1 to 82.84. A method for monitoring cancer progression or recurrence in a subject, the method comprising: determining a first methylation signature in a first sample obtained from the subject at a first time point according to the method of any one of clauses 1 to 83; and determining a second methylation signature in a second sample obtained from the subject at a second time point; and comparing the first methylation signature to the second methylation signature, thereby monitoring the cancer progression or recurrence.85. The method of clause 84, wherein the second methylation signature for the second sample is determined according to the method of any one of clauses 1 to 84.86. The method of clause 83 or clause 84, further comprising selecting an anti-cancer therapy for the subject in response to the cancer progression.87. The method of clause 83 or clause 84, further comprising administering an anti-cancer therapy to the subject in response to the cancer progression.88. The method of clause 83 or clause 84, further comprising adjusting an anti-cancer therapy for the subject in response to the cancer progression.89. The method of any one of clauses 86 to 88, further comprising adjusting a dosage of the anticancer therapy or selecting a different anti-cancer therapy in response to the cancer progression.90. The method of clause 89, further comprising administering the adjusted anti-cancer therapy to the subject.91. The method of any one of clauses 84 to 90, wherein the first time point is before the subject has been administered an anti-cancer therapy, and wherein the second time point is after the subject has been administered the anti-cancer therapy.92. The method of any one of clauses 84 to 91, wherein the subject has a cancer, is at risk of having a cancer, is being routine tested for cancer, or is suspected of having a cancer.93. The method of any one of clauses 84 to 92, wherein the cancer is a solid tumor.94. The method of any one of clauses 84 to 92, wherein the cancer is a hematological cancer.95. The method of any one of clauses 86 to 94, wherein the anti-cancer therapy comprises chemotherapy, radiation therapy, immunotherapy, a targeted therapy, or surgery.96. The method of any one of clauses 1 to 95, further comprising determining, identifying, or applying the methylation signature for the sample as a diagnostic indicator associated with the sample.97. The method of any one of clauses 1 to 96, further comprising generating a genomic profile for the subject based on the determination of the methylation signature.98. The method of clause 97, wherein the genomic profile for the subject further comprises results from a comprehensive genomic profiling (CGP) test, a gene expression profiling test, a cancer hotspot panel test, a DNA methylation test, a DNA fragmentation test, an RNA fragmentation test, or any combination thereof.99. The method of clause 97 or clause 98, wherein the genomic profile for the subject further comprises results from a nucleic acid sequencing-based test.100. The method of any one of clauses 97 to 99, further comprising selecting an anti-cancer therapy, administering an anti-cancer therapy, or applying an anti-cancer therapy to the subject based on the generated genomic profile.101. The method of any one of clauses 1 to 100, wherein the determination of the methylation signature for the sample is used in making suggested treatment decisions for the subject.102. The method of any one of clauses 1 to 101, wherein the determination of the methylation signature for the sample is used in applying or administering a treatment to the subject.103. A system comprising: one or more processors; and a memory communicatively coupled to the one or more processors and configured to store instructions that, when executed by the one or more processors, cause the system to: receive, by one or more processors, a plurality of duplex consensus sequence reads determined from single strand consensus sequence reads obtained by sequencing single strand nucleic acid molecules derived from a double- stranded library subjected to a conversion reaction which converts an unmethylated cytosine to uracil; and classify, by the one or more processors, duplex consensus sequence reads of the plurality as belonging to one of two or more methylation state categories comprising at least one reliable methylation state category and at least one unreliable methylation state category, using a classification model.104. The system of clause 103, further comprising instructions that, when executed by the one or more processors, cause the system to determine a methylation signature for the sample based on methylation states at one or more CpG sites within duplex consensus sequence reads classified as belonging to at least one of the reliable methylation state categories.105. The system of clause 103 or clause 104, wherein the duplex consensus sequence reads comprise duplex methylated sequence reads, duplex hemi-methylated sequence reads, duplex unmethylated sequence reads, or any combination thereof.106. The system of any one of clauses 103 to 105, wherein based on the methylation signature, the sample is determined to be hypermethylated in one or more genomic regions.107. The system of any one of clauses 103 to 106, wherein based on the methylation signature, the sample is determined to be hypomethylated in one or more genomic regions.108. The system of any one of clauses 103 to 107, wherein the classification model is configured to identify discrepancies between methylation states at one or more CpG sites in a forward single strand consensus sequence (forward SSCS) corresponding to a given duplex consensus sequence read and in a reverse single strand consensus sequence (reverse SSCS) corresponding to the given duplex consensus sequence read and classify the duplex consensus sequence read based on a number of identified discrepancies.109. The system of any one of clauses 103 to 108, wherein the classification model is a deterministic model.110. The system of clause 109, wherein the deterministic model is a decision tree model.111. A non-transitory computer-readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by one or more processors of a system, cause the system to: receive, by one or more processors, a plurality of duplex consensus sequence reads determined from single strand consensus sequence reads obtained by sequencing single strand nucleic acid molecules derived from a double- stranded library subjected to a conversion reaction which converts an unmethylated cytosine to uracil; andclassify, by the one or more processors, duplex consensus sequence reads of the plurality as belonging to one of two or more methylation state categories comprising at least one reliable methylation state category and at least one unreliable methylation state category, using a classification model.112. The non-transitory computer-readable storage medium of clause 111, further comprising instructions that, when executed by the one or more processors, cause the system to: determine a methylation signature for the sample based on methylation states at one or more CpG sites within duplex consensus sequence reads classified as belonging to at least one of the reliable methylation state categories.113. The non-transitory computer-readable storage medium of clause 111 or clause 112, wherein the duplex consensus sequence reads comprise duplex methylated sequence reads, duplex hemimethylated sequence reads, duplex unmethylated sequence reads, or any combination thereof.114. The non-transitory computer-readable storage medium of any one of clauses 111 to 113, wherein based on the methylation signature, the sample is determined to be hypermethylated in one or more genomic regions.115. The non-transitory computer-readable storage medium of any one of clauses 111 to 114, wherein based on the methylation signature, the sample is determined to be hypomethylated in one or more genomic regions.116. The non-transitory computer-readable storage medium of any one of clauses 111 to 115, wherein the classification model is configured to identify discrepancies between methylation states at one or more CpG sites in a forward single strand consensus sequence (forward SSCS) corresponding to a given duplex consensus sequence read and in a reverse single strand consensus sequence (reverse SSCS) corresponding to the given duplex consensus sequence read and classify the duplex consensus sequence read based on a number of identified discrepancies.117. The non-transitory computer-readable storage medium of any one of clauses 111 to 116, wherein the classification model is a deterministic model.118. The non-transitory computer-readable storage medium of clause 117, wherein the deterministic model is a decision tree model.

[0241] It should be understood from the foregoing that, while particular implementations of the disclosed methods and systems have been illustrated and described, various modifications can be made thereto and are contemplated herein. It is also not intended that the invention be limited by the specific examples provided within the specification. While the invention has been described with reference to the aforementioned specification, the descriptions and illustrations of the preferable embodiments herein are not meant to be construed in a limiting sense. Furthermore, it shall be understood that all aspects of the invention are not limited to the specific depictions, configurations or relative proportions set forth herein which depend upon a variety of conditions and variables. Various modifications in form and detail of the embodiments of the invention will be apparent to a person skilled in the art. It is therefore contemplated that the invention shall also cover any such modifications, variations and equivalents.

Claims

CLAIMSWhat is claimed is:

1. A method comprising: receiving, by one or more processors, a plurality of duplex consensus sequence reads determined from single strand consensus sequence reads obtained by sequencing single strand nucleic acid molecules derived from a double- stranded library subjected to a conversion reaction which converts an unmethylated cytosine to uracil; and classifying, by the one or more processors, duplex consensus sequence reads of the plurality as belonging to one of two or more methylation state categories comprising at least one reliable methylation state category and at least one unreliable methylation state category, using a classification model.

2. The method of claim 1, further comprising determining, by the one or more processors, a methylation signature for the sample based on methylation states at one or more CpG sites within duplex consensus sequence reads classified as belonging to at least one of the reliable methylation state categories.

3. The method of claim 1, wherein the duplex consensus sequence reads comprise duplex methylated sequence reads, duplex hemi-methylated sequence reads, duplex unmethylated sequence reads, or any combination thereof.

4. The method of claim 1, wherein based on the methylation signature, the sample is determined to be hypermethylated in one or more genomic regions.

5. The method of claim 1, wherein based on the methylation signature, the sample is determined to be hypomethylated in one or more genomic regions.

6. The method of claim 5, wherein the classification model is configured to identify discrepancies between methylation states at one or more CpG sites in a forward single strand consensus sequence (forward SSCS) corresponding to a given duplex consensus sequence readand in a reverse single strand consensus sequence (reverse SSCS) corresponding to the given duplex consensus sequence read and classify the duplex consensus sequence read based on a number of identified discrepancies.

7. The method of claim 1, wherein the classification model is a deterministic model or a probabilistic model.

8. The method of claim 7, wherein the classification model is a deterministic model, and wherein the deterministic model is a decision tree model.

9. The method of claim 7, wherein the classification model is a probabilistic model, and wherein the probabilistic model is a hidden Markov model, a mixture model, or a Bayesian network model.

10. The method of claim 1, wherein the classification model is configured to classify duplex consensus sequence reads into at least two categories comprising a concordant category in which no discrepancies between methylation states at the one or more CpG sites are identified, and a discordant category in which at least one discrepancy between methylation states at the one or more CpG sites is identified.

11. The method of claim 1, wherein the classification model is further configured to classify duplex consensus sequence reads for which at least one discrepancy between methylation states at the one or more CpG sites is identified into one of two or more discordant sub-categories.

12. The method of claim 1, further comprising processing sequence read data obtained by sequencing the double-stranded library to generate the plurality of duplex consensus sequence reads.

13. The method of claim 1, wherein the conversion reaction comprises a bisulfite reaction to convert non-methylated cytosine to uracil.

14. The method of claim 1, wherein the conversion reaction comprises an enzymatic conversion reaction to convert non-methylated cytosine to uracil.

15. The method of claim 1, wherein the method is used to correct for methylation bias in a methylation sequencing method.

16. The method of claim 1, wherein the sample comprises a tissue biopsy sample, a liquid biopsy sample, or a normal control.

17. The method of claim 1, wherein the plurality of nucleic acid molecules comprises a mixture of tumor nucleic acid molecules and non-tumor nucleic acid molecules.

18. The method of claim 1, wherein the methylation signature is used to diagnose a disease, to make a treatment decision for a subject diagnosed with a disease, to track a treatment response for a subject who has been diagnosed with a disease and is currently undergoing treatment, or to detect minimal residual disease.

19. A system comprising: one or more processors; and a memory communicatively coupled to the one or more processors and configured to store instructions that, when executed by the one or more processors, cause the system to: receive, by one or more processors, a plurality of duplex consensus sequence reads determined from single strand consensus sequence reads obtained by sequencing single strand nucleic acid molecules derived from a double- stranded library subjected to a conversion reaction which converts an unmethylated cytosine to uracil; and classify, by the one or more processors, duplex consensus sequence reads of the plurality as belonging to one of two or more methylation state categories comprising at least one reliable methylation state category and at least one unreliable methylation state category, using a classification model.

20. A non-transitory computer-readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by one or more processors of a system, cause the system to:receive, by one or more processors, a plurality of duplex consensus sequence reads determined from single strand consensus sequence reads obtained by sequencing single strand nucleic acid molecules derived from a double- stranded library subjected to a conversion reaction which converts an unmethylated cytosine to uracil; and classify, by the one or more processors, duplex consensus sequence reads of the plurality as belonging to one of two or more methylation state categories comprising at least one reliable methylation state category and at least one unreliable methylation state category, using a classification model.

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