Clonal hematopoiesis burden as a biomarker for immune checkpoint inhibitor response

Abstract

Described herein are techniques for determining a clonal hematopoiesis (CH) burden score. In some instances, sequence read data for a plurality of sequence reads derived from a sample of a subject may be received. At least one of a total number of clonal hematopoiesis (CH) alterations, a number of pathogenic CH alterations, a number of clonal CH alterations, or a maximum CH allele fraction in the sample based on the sequence read data may be determined. Furthermore, a CH burden score representing the CH burden for the subject may be determined based on at least one of the total number of CH alterations, the number of pathogenic CH alterations, the number of clonal CH alterations, or the maximum CH allele fraction in the sample, wherein the CH burden score provides a predictive measure of the subject's clinical response to a treatment for a disease.

Description

Docket No.: 197102014240 CLONAL HEMATOPOIESIS BURDEN AS A BIOMARKER FOR IMMUNE CHECKPOINT INHIBITOR RESPONSE CROSS-REFERENCE TO RELATED APPLICATION

[0001] This application claims the priority benefit of United States Provisional Patent Application Serial No. 63 / 467,097, filed May 17, 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 clonal hematopoiesis and its use as a biomarker for immune checkpoint inhibitor response. BACKGROUND

[0003] Clonal hematopoiesis (CH) is a phenomenon characterized by the clonal expansion of leukemogenic somatic mutations in hematopoietic stem cells. It is a common occurrence in humans that increases in prevalence with age, with an estimated 10-20% of individuals over the age of 70 having detectable CH. CH alterations can be found in individuals who do not have any detectable cancer, but who are associated with an increased risk of hematological malignancies as well as chronic inflammation. However, improved methods to detect and quantify CH alterations in patient samples, and to identify correlations between CH burden in patient samples and treatment responses when treating patients with, e.g., immune checkpoint inhibitors, are needed to translate these general observations into treatment selection guidelines that may improve healthcare outcomes. BRIEF SUMMARY OF THE INVENTION

[0004] Methods and systems for quantifying CH burden in a sample from a subject (e.g., a patient sample) and using the CH burden as a prognostic or predictive measure of clinical response when treating the subject (e.g., patient) for a disease (e.g., a cancer that may be susceptible to treatment with an immune checkpoint inhibitor) are described. The disclosed methods were developed based on the hypothesis that the presence and quantity of CH variants sf-5889500Docket No.: 197102014240 may drive upregulation of inflammatory signaling pathways that result in resistance to immune checkpoint inhibitors (ICI) in solid tumor malignancies.

[0005] Work is ongoing to optimize CH detection algorithms to better detect tumor-derived somatic alterations in different sample types. However, not much work has been done to quantify the CH burden for a given individual and investigate whether CH burden is associated with differential clinical treatment responses. The methods described herein thus enable the use of a quantitative determination of CH burden as a biomarker for patient outcomes.

[0006] Disclosed herein are methods comprising providing a plurality of nucleic acid molecules obtained from a sample from a subject; ligating one or more adapters onto one or more nucleic acid molecules from the plurality of nucleic acid molecules; amplifying the one or more ligated nucleic acid molecules from the plurality of nucleic acid molecules; capturing amplified nucleic acid molecules from the amplified nucleic acid molecules; sequencing, by a sequencer, the captured nucleic acid molecules to obtain a plurality of sequence reads that represent the captured nucleic acid molecules; receiving, at one or more processors, sequence read data for the plurality of sequence reads; determining, using the one or more processors, at least one of a total number of clonal hematopoiesis (CH) alterations, a number of pathogenic CH alterations, a number of clonal CH alterations, or a maximum CH allele fraction in the sample based on the sequence read data; and determining, using the one or more processors, a CH burden score representing the CH burden for the subject based on at least one of the total number of CH alterations, the number of pathogenic CH alterations, the number of clonal CH alterations, or the maximum CH allele fraction in the sample, wherein the CH burden score provides a predictive measure of the subject’s clinical response to a treatment for a disease.

[0007] In some embodiments, the method further comprises determining, using the one or more processors, a composite tumor fraction (cTF) for the sample based on the sequence read data.

[0008] In some embodiments, the method further comprises predicting, based on the determined CH burden score and the determined cTF, at least one of: a survival prognosis for the subject, a clinical response of the subject to an immune checkpoint inhibitor, or a likelihood of an immune- related adverse event occurring for the subject.

[0009] In some embodiments, the method further comprises classifying the subject into: (i) a first clinical response class comprising first subjects, wherein the first subjects exhibit a CH 2 sf-5889500Docket No.: 197102014240 burden score greater than a predetermined CH burden threshold and a cTF greater than a predetermined cTF threshold; (ii) a second clinical response class comprising second subjects, wherein the second subjects exhibit a CH burden score greater than the predetermined CH burden threshold and a cTF less than the predetermined cTF threshold; (iii) a third clinical response class comprising third subjects, wherein the third subjects exhibit a CH burden score less than the predetermined CH burden threshold and a cTF greater than the predetermined cTF threshold; and (iv) a fourth clinical response class comprising fourth subjects, wherein the fourth subjects exhibit a CH burden score less than the predetermined CH burden threshold and a cTF less than the predetermined cTF threshold.

[0010] In some embodiments, the subject is suspected of having or is determined to have cancer. In some embodiments, 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, non-Hodgkin 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, sf-5889500Docket No.: 197102014240 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 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 non-Hodgkin 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 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 sf-5889500Docket No.: 197102014240 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 cell lung cancer, thyroid cancer, a thyroid carcinoma, urothelial cancer, a urothelial carcinoma, or Waldenstrom's macroglobulinemia.

[0012] In some embodiments, the method further comprises treating the subject with an anti- cancer therapy. In some embodiments, the anti-cancer therapy comprises a targeted anti-cancer therapy. In some embodiments, 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 (Ilaris), 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 sf-5889500Docket No.: 197102014240 mesylate (Gleevec), infigratinib phosphate (Truseltiq), inotuzumab ozogamicin (Besponsa), iobenguane I131 (Azedra), ipilimumab (Yervoy), isatuximab-irfc (Sarclisa), ivosidenib (Tibsovo), ixazomib citrate (Ninlaro), lanreotide acetate (Somatuline Depot), lapatinib (Tykerb), larotrectinib sulfate (Vitrakvi), lenvatinib mesylate (Lenvima), letrozole (Femara), 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.

[0013] In some embodiments, the method further comprises obtaining the sample from the subject. In some embodiments, the sample comprises a tissue biopsy sample, a liquid biopsy sf-5889500Docket No.: 197102014240 sample, or a normal control. In some embodiments, the sample is a liquid biopsy sample and comprises blood, plasma, cerebrospinal fluid, sputum, stool, urine, or saliva. In some embodiments, the sample is a liquid biopsy sample and comprises circulating tumor cells (CTCs). In some embodiments, the sample is a liquid biopsy sample and comprises cell-free DNA (cfDNA), circulating tumor DNA (ctDNA), or any combination thereof. In some embodiments, the plurality of nucleic acid molecules comprises a mixture of tumor nucleic acid molecules and non-tumor nucleic acid molecules. In some embodiments, 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. In some embodiments, 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.

[0014] In some embodiments, the one or more adapters comprise amplification primers, flow cell adaptor sequences, substrate adapter sequences, or sample index sequences. In some embodiments, the captured nucleic acid molecules are captured from the amplified nucleic acid molecules by hybridization to one or more bait molecules. In some embodiments, 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. In some embodiments, amplifying nucleic acid molecules comprises performing a polymerase chain reaction (PCR) amplification technique, a non-PCR amplification technique, or an isothermal amplification technique. In some embodiments, 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. In some embodiments, the sequencing comprises massively parallel sequencing, and the massively parallel sequencing technique comprises next generation sequencing (NGS). In some embodiments, the sequencer comprises a next generation sequencer. In some embodiments, the one or more of the plurality of sequencing reads overlap one or more gene loci within one or more subgenomic intervals in the sample. sf-5889500Docket No.: 197102014240

[0015] 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.

[0016] In some embodiments, 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, sf-5889500Docket No.: 197102014240 CYP17A1, DAXX, DDR1, DDR2, DIS3, DNMT3A, DOT1L, EED, EGFR, EMSY (C11orf30), EP300, EPHA3, EPHB1, EPHB4, ERBB2, ERBB3, ERBB4, ERCC4, ERG, ERRFI1, ESR1, ETV4, ETV5, ETV6, EWSR1, EZH2, EZR, FAM46C, FANCA, FANCC, FANCG, FANCL, FAS, FBXW7, FGF10, FGF12, FGF14, FGF19, FGF23, FGF3, FGF4, FGF6, FGFR1, FGFR2, FGFR3, FGFR4, FH, FLCN, FLT1, 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, POLD1, POLE, PPARG, PPP2R1A, PPP2R2A, PRDM1, PRKAR1A, PRKCI, PTCH1, PTEN, PTPN11, PTPRO, QKI, RAC1, RAD21, RAD51, RAD51B, RAD51C, RAD51D, RAD52, RAD54L, RAF1, RARA, RB1, RBM10, REL, RET, RICTOR, RNF43, ROS1, RPTOR, RSPO2, SDC4, SDHA, SDHB, SDHC, SDHD, SETD2, SF3B1, SGK1, SLC34A2, SMAD2, SMAD4, SMARCA4, SMARCB1, SMO, SNCAIP, SOCS1, SOX2, SOX9, SPEN, SPOP, SRC, STAG2, STAT3, STK11, SUFU, SYK, TBX3, TEK, TERC, TERT, TET2, TGFBR2, TIPARP, TMPRSS2, TNFAIP3, TNFRSF14, TP53, TSC1, TSC2, TYRO3, U2AF1, VEGFA, VHL, WHSC1, WHSC1L1, WT1, XPO1, XRCC2, ZNF217, ZNF703, or any combination thereof.

[0017] In some embodiments, 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, FLT3, GD2, HDAC, HER1, HER2, HR, IDH2, IL-1β, IL-6, IL-6R, JAK1, JAK2, JAK3, KIT, KRAS, MEK, MET, MSI-H, mTOR, PARP, PD-1, PDGFR, PDGFRα, PDGFRβ, PD-L1, PI3Kδ, PIGF, PTCH, RAF, RANKL, RET, ROS1, SLAMF7, VEGF, VEGFA, VEGFB, or any combination thereof. sf-5889500Docket No.: 197102014240

[0018] In some embodiments, the method further comprises generating, by the one or more processors, a report indicating that a genetic variant is present or absent. In some embodiments, the method further comprises transmitting the report to a healthcare provider. In some embodiments, the report is transmitted via a computer network or a peer-to-peer connection.

[0019] Disclosed herein are methods for determining a clonal hematopoiesis (CH) burden score, comprising receiving, at one or more processors, sequence read data for a plurality of sequence reads derived from a sample from a subject; determining, using the one or more processors, at least one of a total number of clonal hematopoiesis (CH) alterations, a number of pathogenic CH alterations, a number of clonal CH alterations, or a maximum CH allele fraction in the sample based on the sequence read data; and determining, using the one or more processors, a CH burden score representing the CH burden for the subject based on at least one of the total number of CH alterations, the number of pathogenic CH alterations, the number of clonal CH alterations, or the maximum CH allele fraction in the sample, wherein the CH burden score provides a predictive measure of the subject’s clinical response to a treatment for a disease.

[0020] In some embodiments, the method further comprises determining, using the one or more processors, a composite tumor fraction (cTF) for the sample based on the sequence read data. In some embodiments, the method further comprises predicting, based on the determined CH burden score and the determined cTF, a survival prognosis for the subject. In some embodiments, the subject has been diagnosed with a form of cancer. In some embodiments, the subject has been diagnosed with lung cancer. In some embodiments, the subject has been diagnosed with non-small cell lung cancer. In some embodiments, the subject has been diagnosed with non-squamous non-small cell lung cancer (NSCLC).

[0021] In some embodiments, the method further comprises predicting, based on the determined CH burden score and the determined cTF, a clinical response of the subject to an immune checkpoint inhibitor. In some embodiments, the method further comprises predicting, based on the determined CH burden score and the determined cTF, a likelihood of an immune-related adverse event occurring for the subject. In some embodiments, the immune-related adverse event comprises an autoimmune disorder or an inflammatory condition. In some embodiments, the autoimmune disorder comprises arthritis. In some embodiments, the inflammatory condition comprises pneumonitis, myocarditis, or colitis. In some embodiments, the prediction is based on sf-5889500Docket No.: 197102014240 at least one of (i) a comparison of the determined CH burden score to a predetermined CH burden threshold or (ii) a comparison of the determined cTF to a predetermined cTF threshold. In some embodiments, the CH burden score is determined based on the total number of CH alterations in the sample, and the predetermined CH burden threshold is equal to 2, 3, 4, or 5 CH alterations. In some embodiments, determining the at least one of the total number of CH alterations, the number of pathogenic CH alterations, the number of clonal CH alterations, or the maximum CH allele fraction comprises determining the total number of CH, and the predetermined CH burden threshold is 3 CH alterations. In some embodiments, the predetermined cTF threshold is 0.01% or greater, 0.1% or greater, 0.5% or greater, 1.0% or greater, or 10% or greater. In some embodiments, the predetermined cTF threshold is 16% or 18%.

[0022] In some embodiments, the method further comprises classifying the subject into one of a set of clinical response classes based on the determined CH burden score and determined cTF. In some embodiments, a treatment decision is selected for the subject based on a clinical response class from the set of clinical response classes that the subject is classified into. In some embodiments, classifying the subject into one of the set of clinical response classes comprises: classifying the subject into: (i) a first clinical response class comprising first subjects, wherein the first subjects exhibit a CH burden score greater than the predetermined CH burden threshold and a cTF greater than the predetermined cTF threshold; (ii) a second clinical response class comprising second subjects, wherein the second subjects exhibit a CH burden score greater than the predetermined CH burden threshold and a cTF less than the predetermined cTF threshold; (iii) a third clinical response class comprising third subjects, wherein the third subjects exhibit a CH burden score less than the predetermined CH burden threshold and a cTF greater than the predetermined cTF threshold; and (iv) a fourth clinical response class comprising fourth subjects, wherein the fourth subjects exhibit a CH burden score less than the predetermined CH burden threshold and a cTF less than the predetermined cTF threshold.

[0023] In some embodiments, a first treatment decision is selected for the subject based on the subject being classified into one of the first clinical response class, the second clinical response class, or the third clinical response class. In some embodiments, a second treatment decision is selected for the subject based on the subject being classified into the fourth clinical response sf-5889500Docket No.: 197102014240 class. In some embodiments, the second treatment decision comprises providing an immune checkpoint inhibitor to the subject. In some embodiments, the sample comprises a liquid biopsy sample. In some embodiments, the liquid biopsy sample comprises a blood plasma sample. In some embodiments, the sequence read data is derived using one or more targeted sequencing methods.

[0024] In some embodiments, the method further comprises receiving sequence read data for a plurality of sequence reads derived from a control sample from the subject. In some embodiments, the control sample comprises peripheral blood mononuclear cells (PBMCs) from the subject. In some embodiments, the sequence read data derived from the control sample is used to identify CH alterations. In some embodiments, the CH burden score for the sample is generated using one or more machine learning models. In some embodiments, the one or more machine learning models are trained to output a CH burden score based on input data comprising at least one of a total number of clonal hematopoiesis (CH) alterations, a number of pathogenic CH alterations, a number of clonal CH alterations, or a maximum CH allele fraction. In some embodiments, one or more of the plurality of sequence reads overlap one or more gene loci within one or more subgenomic intervals in the sample. In some embodiments, the one or more gene loci comprise EGFR or ALK. In some embodiments, the one or more machine learning models are trained to output a consolidated CH burden score based on input data, wherein the input data comprises at least one of: a total number of clonal hematopoiesis (CH) alterations, a number of pathogenic CH alterations, a number of clonal CH alterations, or a maximum CH allele fraction, and a composite tumor fraction (cTF) for the sample based on the sequence read data.

[0025] In some embodiments, the method further comprises generating training data for training the one or more machine learning models, the training data comprising a plurality of tuples respectively associated with a plurality of subjects, wherein each tuple comprises at least one of a total number of clonal hematopoiesis (CH) alterations, a number of pathogenic CH alterations, a number of clonal CH alterations, or a maximum CH allele fraction determined for a sample obtained from a respective one of the plurality of subjects. In some embodiments, the training data further comprises, for each tuple, the cTF determined for a sample obtained from a respective one of the plurality of subjects. In some embodiments, determining at least one of a sf-5889500Docket No.: 197102014240 total number of clonal hematopoiesis (CH) alterations, a number of pathogenic CH alterations, a number of clonal CH alterations, or a maximum CH allele fraction in the sample comprises: (i) detecting an alteration based on the sequence read data; and (ii) performing a statistical analysis of sequence read data for a selected subset of the plurality of sequence reads to distinguish between tumor-derived alterations and CH alterations. In some embodiments, each sequence read of the selected subset of the plurality of sequence reads is selected based on a gene associated with the alteration. In some embodiments, performing the statistical analysis comprises: determining at least one feature characterizing the selected subset of the plurality of sequence reads; and generating, using a prediction model, a tumor origin score based on the at least one feature, wherein distinguishing between tumor-derived alterations and CH alterations is based on the tumor origin score.

[0026] In some embodiments, distinguishing between tumor-derived alterations and CH alterations comprises comparing the generated tumor origin score with one or more tumor origin score thresholds to classify the alteration as being a tumor-derived alteration or a CH alteration. In some embodiments, the one or more tumor origin score thresholds comprise a first tumor origin score threshold, the method further comprising: determining that the alteration is a CH alteration based on the tumor origin score being greater than or equal to the first tumor origin score threshold; or determining that the alteration is a tumor derived alteration based on the tumor origin score being less than the first tumor origin score threshold. In some embodiments, the one or more tumor origin score thresholds comprise a first tumor origin score threshold and a second tumor origin score threshold, the method further comprising: determining that the alteration is a CH alteration based on the tumor origin score being greater than or equal to the first tumor origin score threshold; determining that the alteration is a tumor derived alteration based on the tumor origin score being less than the second tumor origin score threshold; or determining that the alteration is of inconclusive origin based on the tumor origin score being less than the first tumor origin score threshold and greater than or equal to the second tumor origin score threshold.

[0027] In some embodiments, the method further comprises the prediction model is selected from a plurality of prediction models each associated with one of a plurality of CH alterations, wherein each prediction model is trained to predict whether an alteration is a tumor-derived sf-5889500Docket No.: 197102014240 alteration or a CH alteration. In some embodiments, the prediction model comprises a statistical model or a machine learning model. In some embodiments, the prediction model is a machine learning model and comprises a neural network. In some embodiments, the at least one feature comprises one or more fragmentomic characteristics. In some embodiments, the one or more fragmentomic characteristics comprises at least one of: a number of the selected subset of the plurality of sequence reads having a specified length, a mean fragment length of the selected subset of the plurality of sequence reads, a median fragment length of the selected subset of the plurality of sequence reads, an interquartile range of fragment lengths of the selected subset of the plurality of sequence reads, a distribution of fragment lengths of the selected subset of the plurality of sequence reads, one or more peaks of fragment lengths for the selected subset of the plurality of sequence reads, a fragment length for at least one of the selected subset of the plurality of sequence reads, a start position of a fragment for the selected subset of the plurality of sequence reads, an end position of a fragment for the selected subset of the plurality of sequence reads.

[0028] In some embodiments, the at least one feature comprises one or more additional features related to the alteration. In some embodiments, the one or more additional features comprise at least one of an alteration depth, an allele frequency, an alteration coding type, a somatic determination of the alteration, a germline determination of the alteration, a zygosity determination, a somatic-germline-zygosity (SGZ) determination of the alteration, an age of the subject providing the sample, a blood tumor mutational burden (bTMB) score of the sample, protein level data, or gene level data. In some embodiments, the CH burden score is used to diagnose or confirm a diagnosis of the disease in the subject. In some embodiments, the disease is cancer. In some embodiments, the cancer is non-squamous non-small cell lung cancer (NSCLC).

[0029] In some embodiments, the method further comprises selecting an anti-cancer therapy to administer to the subject based on the determination of the CH burden score. In some embodiments, the method further comprises determining an effective amount of an anti-cancer therapy to administer to the subject based on the determination of the CH burden score. In some embodiments, the method further comprises administering the anti-cancer therapy to the subject based on the determination of the CH burden score. In some embodiments, the anti-cancer sf-5889500Docket No.: 197102014240 therapy comprises chemotherapy, radiation therapy, immunotherapy, a targeted therapy, or surgery. In some embodiments, determining the cTF for the sample comprises: computing at least one of a propriety tumor fraction estimator (TFE) metric or a maximum somatic allele frequency (MSAF) metric based on the sequence read data. In some embodiments, the TFE metric is based on a measure of tumor aneuploidy for the sample; and the MSAF metric is based on an allele fraction for the sample.

[0030] Disclosed herein are methods for diagnosing a disease, the method comprising: diagnosing that a subject has the disease based on a determination of a CH burden score for a sample from the subject, wherein the CH burden score is determined according to any one of the methods described herein.

[0031] Disclosed herein are methods selecting an anti-cancer therapy, the method comprising: determining a CH burden score based on a sample from a subject; and selecting an anti-cancer therapy for the subject based on the CH burden score, wherein the CH burden score is determined according to any one of the methods described herein.

[0032] Disclosed herein are methods of treating a cancer in a subject, comprising: determining a CH burden score for a sample from the subject; and administering an effective amount of an anti- cancer therapy to the subject based on the CH burden score, wherein the CH burden score is determined according to any one of the methods described herein. In some embodiments, the anti-cancer therapy comprises an immune checkpoint inhibitor (ICH) monotherapy.

[0033] Disclosed herein are methods for monitoring cancer progression or recurrence in a subject, the method comprising: determining a first CH burden score in a first sample obtained from the subject at a first time point according to any one of the methods described herein; determining a second CH burden score in a second sample obtained from the subject at a second time point; and comparing the first CH burden score to the second CH burden score, thereby monitoring the cancer progression or recurrence. In some embodiments, the second CH burden score for the second sample is determined according to any one of the methods described herein.

[0034] In some embodiments, the method further comprises selecting an anti-cancer therapy for the subject in response to the cancer progression. In some embodiments, the method further comprises administering an anti-cancer therapy to the subject in response to the cancer progression. In some embodiments, the method further comprises adjusting an anti-cancer sf-5889500Docket No.: 197102014240 therapy for the subject in response to the cancer progression. In some embodiments, the method further comprises adjusting a dosage of the anti-cancer therapy or selecting a different anti- cancer therapy in response to the cancer progression. In some embodiments, the method further comprises administering the adjusted anti-cancer therapy to the subject. In some embodiments, 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. In some embodiments, 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. In some embodiments, the cancer is a solid tumor. In some embodiments, the cancer is a hematological cancer. In some embodiments, the anti-cancer therapy comprises chemotherapy, radiation therapy, immunotherapy, a targeted therapy, or surgery.

[0035] In some embodiments, the method further comprises determining, identifying, or applying a value of the CH burden score for the sample as a diagnostic value associated with the sample. In some embodiments, the method further comprises generating a genomic profile for the subject based on the determination of the CH burden score. In some embodiments, 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. In some embodiments, the genomic profile for the subject further comprises results from a nucleic acid sequencing-based test. In some embodiments, the method further comprises 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. In some embodiments, the determination of the CH burden score for the sample is used in making suggested treatment decisions for the subject. In some embodiments, the determination of CH burden score for the sample is used in applying or administering a treatment to the subject.

[0036] Disclosed herein are methods for treating a cancer in a subject, the method comprising: receiving, at one or more processors, sequence read data for a plurality of sequence reads derived from a sample from the subject; determining, using the one or more processors, a composite tumor fraction (cTF) for the sample based on the sequence read data; determining, using the one or more processors, a CH burden score representing the CH burden for the subject based on at sf-5889500Docket No.: 197102014240 least one of a total number of clonal hematopoiesis (CH) alterations, a number of pathogenic CH alterations, a number of clonal CH alterations, or a maximum CH allele fraction as determined for the sample based on the sequence read data; comparing, using the one or more processors, the CH burden score to a predetermined CH burden threshold and the cTF to a predetermined cTF threshold; selecting, using the one or more processors, a cancer treatment for the subject based on the comparison; and administering the selected cancer treatment to the subject.

[0037] In some embodiments, determining the cTF for the sample comprises computing at least one of a propriety tumor fraction estimator (TFE) metric or a maximum somatic allele frequency (MSAF) metric based on the sequence read data. In some embodiments, the TFE metric is based on a measure of tumor aneuploidy for the sample; and the MSAF metric is based on an allele fraction for the sample. In some embodiments, the method further comprises predicting, based on the determined CH burden score and the determined cTF, a survival prognosis for the subject. In some embodiments, the method further comprises predicting, based on the determined CH burden score and the determined cTF, a likelihood of an immune-related adverse event occurring for a subject. In some embodiments, the CH burden score is determined based on the total number of CH alterations in the sample, and the predetermined CH burden threshold is equal to 2, 3, 4, or 5 CH alterations. In some embodiments, determining the at least one of the total number of CH alterations, the number of pathogenic CH alterations, the number of clonal CH alterations, or the maximum CH allele fraction comprises determining the total number of CH, and the predetermined CH burden threshold is 3 CH alterations. In some embodiments, the predetermined cTF threshold is 0.01% or greater, 0.1% or greater, 0.5% or greater, 1.0% or greater, or 10% or greater. In some embodiments, the predetermined cTF threshold is 16% or 18%.

[0038] In some embodiments, selecting the cancer treatment comprises: classifying the subject into one of a set of clinical response classes based on the determined CH burden score and determined cTF. In some embodiments, a treatment decision is selected for the subject based on a clinical response class from the set of clinical response classes that the subject is classified into. In some embodiments, classifying the subject into one of the set of clinical response classes comprises: classifying the subject into: (i) a first clinical response class comprising first subjects, wherein the first subjects exhibit a CH burden score greater than the predetermined CH burden sf-5889500Docket No.: 197102014240 threshold and a cTF greater than the predetermined cTF threshold; (ii) a second clinical response class comprising second subjects, wherein the second subjects exhibit a CH burden score greater than the predetermined CH burden threshold and a cTF less than the predetermined cTF threshold; (iii) a third clinical response class comprising third subjects, wherein the third subjects exhibit a CH burden score less than the predetermined CH burden threshold and a cTF greater than the predetermined cTF threshold; and (iv) a fourth clinical response class comprising fourth subjects, wherein the fourth subjects exhibit a CH burden score less than the predetermined CH burden threshold and a cTF less than the predetermined cTF threshold.

[0039] In some embodiments, a first treatment decision is selected for the subject based on the subject being classified into one of the first clinical response class, the second clinical response class, or the third clinical response class. In some embodiments, the sequence read data is derived using one or more targeted sequencing methods. In some embodiments, the method further comprises receiving sequence read data for a plurality of sequence reads derived from a control sample from the subject. In some embodiments, the control sample comprises peripheral blood mononuclear cells (PBMCs) from the subject. In some embodiments, the sequence read data derived from the control sample is used to identify CH alterations. In some embodiments, the CH burden score for the sample is generated using one or more machine learning models.

[0040] In some embodiments, determining at least one of a total number of clonal hematopoiesis (CH) alterations, a number of pathogenic CH alterations, a number of clonal CH alterations, or a maximum CH allele fraction in the sample comprises: (i) detecting an alteration based on the sequence read data; and (ii) performing a statistical analysis of sequence read data for a selected subset of the plurality of sequence reads to distinguish between tumor-derived alterations and CH alterations. In some embodiments, each sequence read of the selected subset of sequence reads is selected based on a gene associated with the alteration. In some embodiments, performing the statistical analysis comprises: determining at least one feature characterizing the selected subset of sequence reads; generating, using a prediction model, a tumor origin score based on the at least one feature, wherein distinguishing between tumor-derived alterations and CH alterations is based on the tumor origin score. In some embodiments, distinguishing between tumor-derived alterations and CH alterations comprises comparing the generated tumor origin sf-5889500Docket No.: 197102014240 score with one or more tumor origin score thresholds to classify the alteration as being a tumor- derived alteration or a CH alteration.

[0041] In some embodiments, the one or more tumor origin score thresholds comprise a first tumor origin score threshold, the method further comprising: determining that the alteration is a CH alteration based on the tumor origin score being greater than or equal to the first tumor origin score threshold; or determining that the alteration is a tumor derived alteration based on the tumor origin score being less than the first tumor origin score threshold. In some embodiments, the one or more tumor origin score thresholds comprise a first tumor origin score threshold and a second tumor origin score threshold, the method further comprising: determining that the alteration is a CH alteration based on the tumor origin score being greater than or equal to the first tumor origin score threshold; determining that the alteration is a tumor derived alteration based on the tumor origin score being less than the second tumor origin score threshold; or determining that the alteration is of inconclusive origin based on the tumor origin score being less than the first tumor origin score threshold and greater than or equal to the second tumor origin score threshold. In some embodiments, the prediction model is selected from a plurality of prediction models each associated with one of a plurality of CH alterations, wherein each prediction model is trained to predict whether an alteration is a tumor-derived alteration or a CH alteration. In some embodiments, the prediction model comprises a statistical model or a machine learning model. In some embodiments, the prediction model is a machine learning model and comprises a neural network. In some embodiments, the at least one feature comprises one or more fragmentomic characteristics. In some embodiments, the cancer comprises non- squamous non-small cell lung cancer (NSCLC).

[0042] It should be appreciated that all combinations of the foregoing concepts and additional concepts discussed in greater detail below (provided such concepts are not mutually inconsistent) are contemplated as being part of the inventive subject matter disclosed herein. In particular, all combinations of claimed subject matter appearing at the end of this disclosure are contemplated as being part of the inventive subject matter disclosed herein. INCORPORATION BY REFERENCE

[0043] 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 sf-5889500Docket No.: 197102014240 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

[0044] The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee.

[0045] 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 instances and the accompanying drawings, of which:

[0046] FIG. 1 provides a non-limiting example of a process for determining a clonal hematopoiesis (CH) burden score, in accordance with various instances.

[0047] FIG. 2 provides a non-limiting example of a process for generating a tumor origin score indicating whether a gene alteration is a tumor-derived alteration or a CH alteration, in accordance with various instances.

[0048] FIG. 3 provides a non-limiting example of a plot showing the relationship between fragment length and origin of the fragment, in accordance with various instances.

[0049] FIG. 4 provides a non-limiting example of a process for predicting a survival prognosis, a clinical response, and / or an immune-related adverse event for a subject, in accordance with various instances.

[0050] FIG. 5 provides a non-limiting example of a pipeline implemented using one or more machine learning models for determining a CH burden score, in accordance with various instances.

[0051] FIG. 6 provides a non-limiting example of training process for training a machine learning model, in accordance with various instances.

[0052] FIG. 7 provides a non-limiting example of an overall survival (OS) plot for subjects based on a CH burden score and a cTF value, in accordance with various instances. sf-5889500Docket No.: 197102014240

[0053] FIG. 8 depicts an exemplary computing device or system in accordance with one instance of the present disclosure.

[0054] FIG. 9 depicts an exemplary computer system or computer network, in accordance with some instances of the systems described herein. DETAILED DESCRIPTION

[0055] Methods and systems for quantifying CH burden in a sample from a subject (e.g., a patient sample) and using the CH burden as a prognostic or predictive measure of clinical response when treating the subject (e.g., patient) for a disease (e.g., a cancer that may be susceptible to treatment with an immune checkpoint inhibitor) are described. The disclosed methods were developed based on the hypothesis that the presence and quantity of CH variants may drive upregulation of inflammatory signaling pathways that result in resistance to immune checkpoint inhibitors (ICI) in solid tumor malignancies.

[0056] Work is ongoing to optimize CH detection algorithms to better detect tumor-derived somatic alterations in different sample types. However, not much work has been done to quantify the CH burden for a given individual and investigate whether CH burden is associated with differential clinical treatment responses. The methods described herein thus enable the use of a quantitative determination of CH burden as a biomarker for patient outcomes.

[0057] In some instances, for example, methods are described that comprise: providing a plurality of nucleic acid molecules obtained from a sample from a subject; ligating one or more adapters onto one or more nucleic acid molecules from the plurality of nucleic acid molecules; amplifying the one or more ligated nucleic acid molecules from the plurality of nucleic acid molecules; capturing amplified nucleic acid molecules from the amplified nucleic acid molecules; sequencing, by a sequencer, the captured nucleic acid molecules to obtain a plurality of sequence reads that represent the captured nucleic acid molecules; receiving, at one or more processors, sequence read data for the plurality of sequence reads; receiving, at one or more processors, sequence read data for a plurality of sequence reads derived from a sample from a subject; determining, using the one or more processors, at least one of a total number of clonal hematopoiesis (CH) alterations, a number of pathogenic CH alterations, a number of clonal CH alterations, or a maximum CH allele fraction in the sample based on the sequence read data; and determining, using the one or more processors, a CH burden score representing the CH burden sf-5889500Docket No.: 197102014240 for the subject based on at least one of the total number of CH alterations, the number of pathogenic CH alterations, the number of clonal CH alterations, or the maximum CH allele fraction in the sample.

[0058] In some instances, for example, methods are described that comprise: receiving, at one or more processors, sequence read data for a plurality of sequence reads derived from a sample from a subject; determining, using the one or more processors, at least one of a total number of clonal hematopoiesis (CH) alterations, a number of pathogenic CH alterations, a number of clonal CH alterations, or a maximum CH allele fraction in the sample based on the sequence read data; and determining, using the one or more processors, a CH burden score representing the CH burden for the subject based on at least one of the total number of CH alterations, the number of pathogenic CH alterations, the number of clonal CH alterations, or the maximum CH allele fraction in the sample.

[0059] In some instances, for example, methods are described that comprise: receiving, at one or more processors, sequence read data for a plurality of sequence reads derived from a sample from the subject; determining, using the one or more processors, a composite tumor fraction (cTF) for the sample based on the sequence read data; determining, using the one or more processors, a CH burden score representing the CH burden for the subject based on at least one of a total number of clonal hematopoiesis (CH) alterations, a number of pathogenic CH alterations, a number of clonal CH alterations, or a maximum CH allele fraction as determined for the sample based on the sequence read data; comparing, using the one or more processors, the CH burden score to a predetermined CH burden threshold and the cTF to a predetermined cTF threshold; selecting, using the one or more processors, a cancer treatment for the subject based on the comparison; and administering the selected cancer treatment to the subject, wherein the CH burden score provides a predictive measure of the subject’s clinical response to a treatment for a disease.

[0060] In some instances, the methods further comprise: determining, using the one or more processors, a composite tumor fraction (cTF) for the sample based on the sequence read data.

[0061] In some instances, the methods further comprise: predicting, based on the determined CH burden score and the determined cTF, a survival prognosis for the subject. sf-5889500Docket No.: 197102014240

[0062] In some instances, the methods further comprise: predicting, based on the determined CH burden score and the determined cTF, a clinical response of the subject to an immune checkpoint inhibitor.

[0063] In some instances, the methods further comprise: predicting, based on the determined CH burden score and the determined cTF, a likelihood of an immune-related adverse event occurring for the subject.

[0064] In some instances, the prediction is based on at least one of a comparison of the determined CH burden score to a predetermined CH burden threshold, or a comparison of the determined cTF to a predetermined cTF threshold.

[0065] In some instances, the methods further comprise: classifying the subject into one of a set of clinical response classes based on the determined CH burden score and determined cTF.

[0066] In some instances, classifying the subject into one of the set of clinical response classes comprises: classifying the subject into: (i) a first clinical response class comprising first subjects, wherein the first subjects exhibit a CH burden score greater than the predetermined CH burden threshold and a cTF greater than the predetermined cTF threshold; (ii) a second clinical response class comprising second subjects, wherein the second subjects exhibit a CH burden score greater than the predetermined CH burden threshold and a cTF less than the predetermined cTF threshold; (iii) a third clinical response class comprising third subjects, wherein the third subjects exhibit a CH burden score less than the predetermined CH burden threshold and a cTF greater than the predetermined cTF threshold; and (iv) a fourth clinical response class comprising fourth subjects, wherein the fourth subjects exhibit a CH burden score less than the predetermined CH burden threshold and a cTF less than the predetermined cTF threshold.

[0067] In some instances, a treatment decision for the subject is based on a clinical response class from the set of clinical response classes that the subject is classified into.

[0068] In some instances, the CH burden score for the sample is generated using one or more machine learning models.

[0069] In some instances, the one or more machine learning models are trained to output a CH burden score based on input data comprising at least one of a total number of clonal sf-5889500Docket No.: 197102014240 hematopoiesis (CH) alterations, a number of pathogenic CH alterations, a number of clonal CH alterations, or a maximum CH allele fraction.

[0070] In some instances, the one or more machine learning models are trained to output a consolidated CH burden score based on input data, wherein the input data comprises at least one of: a total number of clonal hematopoiesis (CH) alterations, a number of pathogenic CH alterations, a number of clonal CH alterations, or a maximum CH allele fraction, and a composite tumor fraction (cTF) for the sample based on the sequence read data. Definitions

[0071] 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.

[0072] 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.

[0073] “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.

[0074] 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.

[0075] 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 instances, the individual, patient, or subject herein is a human. sf-5889500Docket No.: 197102014240

[0076] 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.

[0077] 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 anti- cancer 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.

[0078] As used herein, the term “subgenomic interval” (or “subgenomic sequence interval”) refers to a portion of a genomic sequence.

[0079] 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).

[0080] 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.

[0081] 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.

[0082] 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. sf-5889500Docket No.: 197102014240

[0083] The term “allele coverage,” or simply “coverage” or “Cvg” as used herein, refers to the number of reads (e.g., unique reads) generated from DNA sequencing of a subgenomic interval in a sample. The term “allele intensity,” or simply “intensity,” as used herein, refers to the number of signals (e.g., unique signals) generated from a genomic hybridization at a subgenomic interval in a sample. It will be appreciated that “reads” or “signal” is intended to encompass situations in which there may exist duplicates of the same “unique read” or “unique signal” (i.e., duplicates are not removed prior to performing the methods described herein), but any ratios calculated using the described methods will yield a value very similar to “unique” read or signal ratios, since the duplicates will be represented in both the numerator and denominator.

[0084] The term “allele fraction,” as used herein, refers to the relative level (e.g., abundance) of an allele at a subgenomic interval in a sample. Allele fraction can be expressed as a fraction or percentage. For example, allele fraction can be expressed as the ratio of the number of one particular allele (e.g., A, T, C, or G) at a subgenomic interval relative to the number of all different alleles at that subgenomic interval. In some instances, allele fraction is measured by determining the ratio of the coverage or intensity from one particular allele (e.g., A, T, C, or G) to the total coverage or intensity from all different alleles at a given subgenomic interval. Sometimes, the terms “allele fraction” and “allele frequency” are used interchangeably herein. As used herein, a log ratio is typically measured by log2 (T / R), where T is the level (e.g., abundance) of one or more alleles associated with a subgenomic interval in a sample, and R is the level (e.g., abundance) of the one or more alleles associated with the subgenomic interval in a reference sample. The term, “allele,” as used herein, refers to one of the two or more alternative forms of a genomic sequence (e.g., a gene or any portion thereof). For example, if a “C” to “T” SNP is associated with a subgenomic interval, then the subgenomic interval can be described as being associated with alleles “C” and “T” with respect to the SNP.

[0085] In some instances, there are two or more different alleles associated with a subgenomic interval. If the two or more different alleles are present in a sample, the subgenomic interval is considered as heterozygous for the sample. If the subgenomic interval is not heterozygous for the sample, it can, in some instances, be homozygous, semizygous, or hemizygous.

[0086] The term, “abundance,” as used herein, refers to the amount, number, or quantity of an object. For example, the abundance of an allele associated with a subgenomic interval can mean sf-5889500Docket No.: 197102014240 the amount, number, or quantity of an allele associated with a subgenomic interval in a sample, for example, as determined by sequencing or array-based comprehensive genomic hybridization (aCGH). For example, if there are two alleles, “A” and “G,” associated with a particular subgenomic interval, and there are 10 copies of allele “A” and 20 copies of allele “G” in a sample, the abundance of allele “A” can be considered as 10 and the abundance of allele “G” can be considered as 20. In some instances, the abundance of an allele is measured by allele coverage or allele intensity. For example, the number of unique reads for allele “A” or “G” reflects how many copies of allele “A” or “G” are present in the sample.

[0087] The term “certainty metric,” as used herein, refers to a metric derived from a measure or value of a target variable. In some instances, the target variable may represent an abundance of a subgenomic interval, or an allele associated with the subgenomic interval, in a sample. In some examples, the certainty metric may be a deviation of an allele fraction from an expected allele fraction. In other examples, the certainty metric may be a measure of allele intensity. These examples are intended to be illustrative, and other certainty metrics may be used.

[0088] As used herein, a “single-nucleotide polymorphism,” or SNP, refers to an alteration of a single nucleotide that occurs at a specific position in the genome. In some instances, such alteration is present to some appreciable degree within a population (e.g., > 1%). Typically, a SNP is a germline alteration and is not a somatic single-nucleotide variant (SNV).

[0089] 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 CH burden and predicting clinical treatment outcomes

[0090] The disclosed methods for determining a clonal hematopoiesis (CH) burden score may include: receiving sequence read data for a plurality of sequence reads derived from a sample from a subject, determining at least one of a total number of clonal hematopoiesis (CH) alterations, a number of pathogenic CH alterations, a number of clonal CH alterations, or a maximum CH allele fraction in the sample based on the sequence read data, and determining a CH burden score representing the CH burden for the subject based on at least one of the total number of CH alterations, the number of pathogenic CH alterations, the number of clonal CH alterations, or the maximum CH allele fraction in the sample. 27 sf-5889500Docket No.: 197102014240

[0091] In some instances, a composite tumor fraction (cTF) may be determined for the sample based on the sequence read data. The CH burden score may be determined based on the cTF and the at least one of the total number of CH alterations, the number of pathogenic CH alterations, the number of clonal CH alterations, or the maximum CH allele fraction in the sample. In one or more examples, the CH burden score may be discrete values of 0, 1, 2, 3, etc.

[0092] In some instances, the CH burden score for the sample may be generated using one or more machine learning models. The machine learning models may be trained to output a CH burden score based on input data comprising at least one of a total number of clonal hematopoiesis (CH) alterations, a number of pathogenic CH alterations, a number of clonal CH alterations, or a maximum CH allele fraction.

[0093] Some instances relate to an algorithm for computational prediction of CH in liquid biopsies to identify which alterations in blood samples are CH alterations. The CH burden in each sample may be quantified and assessed as to whether the CH burden is associated with prognosis, response to therapy, and / or any immune-related adverse events. Initially the CH may be quantified from sequenced matched PBMCs during the algorithm’s development and, in the long term, may use a computational prediction algorithm of CH in liquid biopsies. CH burden may be quantified in a number of ways. Which of these ways are ultimately selected may be based on the most clinically relevant iteration of the biomarker. Some example ways include but are not limited to 1) Number of CH alterations, 2) Number of pathogenic / driver CH alterations, 3) Number of clonal CH alterations, and 4) Maximum CH Allele Fraction.

[0094] FIG. 1 provides a non-limiting example of a process 100 for determining a clonal hematopoiesis (CH) burden score, in accordance with various instances. Process 100 may be executed by one or more computing systems (e.g., one or more processers).

[0095] Clonal hematopoiesis (CH) is a phenomenon characterized by the clonal expansion of leukemogenic somatic mutations in hematopoietic stem cells. CH happens when a cell, called a hematopoietic stem cell, which can develop into different types of blood cells, starts making cells with a same genetic mutation. These blood cells have a different genetic pattern than the rest of a subject’s blood cells. It is a common occurrence that increases in prevalence with age, with an estimated 10-20% of individuals over the age of 70 having detectable CH. CH alterations can be found in individuals who do not have any detectable cancer but are associated sf-5889500Docket No.: 197102014240 with an increased risk of hematological malignancies as well as chronic inflammation. For instance, CH alterations are often associated with an increased risk of blook cancer and / or heart disease. Some example CH alterations that can be detected include short variants, focal copy number alterations, rearrangements, and genome-wide disruptions (e.g., larger aneuploidy). As described in greater detail herein, the presence and quantity of CH may drive upregulation of inflammatory signaling pathways that result in resistance to immune checkpoint inhibitors (ICI) in solid tumor malignancies. Some example signaling pathways that mediate the inflammatory response in immune cells include nuclear factor-κB (NF-κB) signaling pathway, Janus kinase / signal transducer and activator of transcription (JAK / STAT) signaling pathway, and mitogen-activated protein kinase (MAPK) signaling pathway.

[0096] Research is ongoing to better detect tumor-derived somatic alterations. Somatic alterations refer to alterations in DNA that occur after conception. Somatic mutations can occur in any of the cells of the body except the germ cells, and therefore are not passed on to children. However, little research has been performed in the area of using CH alterations to investigate whether CH burden is associated with clinical outcomes. Thus, quantifying the CH burden of a subject may be a predictive biomarker for ICI response.

[0097] In some instances, process 100 may begin at step 102. At step 102, sequence read data for a plurality of sequence reads derived from a sample from a subject may be received. The sequence read data may be received by a computing system executing process 100. The sample may be a liquid biopsy sample. In one or more examples, the liquid biopsy sample may be a blood plasma sample liquid biopsy sample, a whole blood sample, human peripheral blood mononuclear cells (PBMCs) sample, and / or a buffy coat sequencing sample. Human PBMCs refer to immune cells with a single, round nucleus that originate in bone marrow and are secreted into peripheral circulation. These cells are critical components of the immune system and are involved in both humoral and cell-mediated immunity. In one or more examples, the liquid biopsy sample comprises a blood plasma sample. In some instances, the sequence read data may be derived using one or more targeted sequencing methods. For instance, the sequence reads may comprise a nucleic acid sequence describing the order of nucleotides in DNA. In some instances, sequence read data may be stored as a BAM file. In some instances, the subject may sf-5889500Docket No.: 197102014240 have been diagnosed with a form of cancer. For example, the subject has been diagnosed with non-squamous non-small cell lung cancer (NSCLC).

[0098] In some instances, sequence read data for a plurality of sequence reads derived from a control sample from the subject may be received. In one or more examples, the control sample may include PBMCs from the subject. In some instances, the sequence read data derived from the control sample may be used to identify CH alterations. Some example CH alterations may include an insertion, a deletion, or a substitution, or a combination thereof.

[0099] At step 104, at least one of a total number of clonal hematopoiesis (CH) alterations, a number of pathogenic CH alterations, a number of clonal CH alterations, or a maximum CH allele fraction in the sample may be determined. The total number of clonal hematopoiesis (CH) alterations, number of pathogenic CH alterations, number of clonal CH alterations, and / or maximum CH allele fraction in the sample may be determined based on the sequence read data. In some instances, the total number of clonal hematopoiesis (CH) alterations, the number of pathogenic CH alterations, the number of clonal CH alterations, and / or the maximum CH allele fraction in the sample may be determined by: (i) detecting an alteration based on the sequence read data and (ii) performing a statistical analysis of sequence read data for a selected subset of the plurality of sequence reads to distinguish between tumor-derived alterations and CH alterations. In one or more examples, each sequence read of the selected subset of sequence reads may be selected based on a gene associated with the alteration. Additional details related to detecting alterations and determining whether those alterations are tumor-derived alterations or CH alterations are provided below with reference to FIG. 2.

[0100] The total number of CH alterations refers to a total amount of CH alterations detected from the sample. In other words, the total number of CH alterations is a count of all of the different types of CH alterations that are detected without specification of the type of CH alteration. Some example CH alterations that can be detected include short variants, focal copy number alterations, rearrangements, and genome-wide disruptions (e.g., larger aneuploidy).

[0101] The number of pathogenic CH alterations refers to a number of CH alterations associated with a disease of interest that are detected from the sample. A pathogenic CH alteration refers to an alteration that is known or likely to be associated with cancer detected from the sample. sf-5889500Docket No.: 197102014240

[0102] The number of clonal CH alterations refers to a number of CH alterations that occur in every, or approximately every, cancer cell detected from the sample. A clonal CH alteration refers to an alteration that occurs in the majority of all cancer cells detected from the sample.

[0103] The maximum CH allele fraction refers to the highest observed fraction of sequence reads corresponding to a particular variant relative to the total number of sequence reads for a genomic locus amongst all CH alterations detected from the sample.

[0104] At step 106, a CH burden score representing the CH burden for the subject may be determined based on the total number of CH alterations, the number of pathogenic CH alterations, the number of clonal CH alterations, and / or the maximum CH allele fraction in the sample. The CH burden score may provide a predictive measure of the subject’s clinical response to a treatment for a disease. In some instances, the CH burden score may be generated by inputting one or more of the total number of CH alterations, the number of pathogenic CH alterations, the number of clonal CH alterations, and / or the maximum CH allele fraction in the sample to a prediction model. The prediction model may be a machine learning model trained to generate a CH burden score based on the total number of CH alterations, the number of pathogenic CH alterations, the number of clonal CH alterations, and / or the maximum CH allele fraction in the sample. In some instances, the CH burden score may be generated by aggregating the total number of CH alterations, the number of pathogenic CH alterations, the number of clonal CH alterations, and / or the maximum CH allele fraction in the sample and comparing the aggregated value to one or more predetermined CH burden score thresholds. In one or more examples, the CH burden score may be discrete values of 0, 1, 2, 3, etc. In some examples, the CH burden score may be a continuous value between a lower limit and an upper limit.

[0105] In some instances, process 100 may further include determining a composite tumor fraction (cTF) for the sample based on the sequence read data. Tumor fraction can be an important indicator in monitoring for, diagnosing, and treating cancer. In some instances, tumor fraction is a measure of tumor genomic content, for example in a sample (e.g., biopsy), in proportion to the total genomic content regardless of cell origin. In general, it is advantageous to determine (e.g., estimate) tumor content or a change in tumor content from a sample since this can aid in both reporting alterations and informing on disease presence or progression. For example, liquid biopsies, which typically utilize blood samples from cancer patients, can be sf-5889500Docket No.: 197102014240 useful when solid biopsies are not possible or recommended. However, the described tumor fraction determination techniques may be used for various types of samples, including solid and liquid samples. In some instances, the visual screening methods may be used in addition to or instead of the tumor fraction determination techniques.

[0106] In some instances, tumor fraction in a cell-free sample comprises a measure of the tumor DNA that has shed into the vasculature or lymphatics from a primary tumor relative to the amount of total DNA (e.g., tumor and normal) shed into the blood stream, and is being carried around the body in the blood circulation. Tumor fraction can be used to monitor a patient at risk for cancer (with or without current diagnosis); as a factor used in diagnosing cancer; or to determine if a current treatment regimen is having an effect, e.g., a beneficial effect.

[0107] Traditional processes for measuring tumor fraction typically require that both purity and ploidy, modeled parameters, be inferred from either log ratio and allele frequency measurements, both log ratio and allele frequency measurements, or from pathology review. In some instances, tumor fraction can be considered as a modeled parameter of the fraction of cancer cells in a heterogeneous tumor sample and can take into account tumor purity or other measures. In some instances, tumor cell ploidy can refer to the average weighted copy number of all chromosomes (or portions thereof). The ploidy observed in a sample can be impacted by the varying degrees of aneuploidy of tumor cells, the heterogeneity of the sample (e.g., different ratios of tumor cells to normal cells), or both.

[0108] Traditional processes for predicting tumor fraction can be highly unreliable for low tumor content due to poorly fit models. In some instances, tumor fraction (and associated confidence levels) may be determined based on the effects of tumor cell aneuploidy. The tumor cell aneuploidy may be measured by the allele coverage or allele fraction at one or more subgenomic intervals in a sample. In some instances, the subgenomic interval comprises a heterozygous single nucleotide polymorphism (SNP) site. In some instances, the subgenomic interval comprises more than one nucleotide positions.

[0109] As an example, for a heterozygous SNP, an allele fraction value of 0.50 can indicate a typical diploid subgenomic interval; and an allele fraction that deviates from an expected value of 0.50 indicates aneuploidy at that site. In these examples, this deviation of allele coverage can be correlated with tumor fraction in a training set in order to build a model that determines (e.g., sf-5889500Docket No.: 197102014240 predicts or estimates) tumor fraction based on allele coverage. In some instances, the methods described herein correlate deviation of allele fraction or log ratio with tumor fraction, thereby eliminating the need to model tumor purity and ploidy. In some instances, the methods described herein allow for more accurate determination of tumor fraction of low level, e.g., less than 30%. In an instance, the allele fraction or log ratio is determined by a method comprising sequencing, e.g., next generation sequencing (NGS). It will be appreciated that the methods for determining allele fraction or log ratio are not limited to sequencing. Any method that measures, for example, SNP coverage or relative level (e.g., abundance) of SNPs, as well as any method that measures coverage from larger genomic regions can be used. In an instance, the allele fraction or log ratio is determined by a method other than sequencing, e.g., is determined by an array-based comprehensive genomic hybridization (aCGH). In an instance, the tumor fraction is, or is expected to be, less than or equal to 0.25, less than or equal to 0.2, less than or equal to 0.15, or less than or equal to 0.1, e.g., between 0.1 and 0.3, between 0.1 and 0.2, between 0.2 and 0.3, or between 0.15 and 0.25.

[0110] While in some instances the methods described herein use allele fraction or log ratio to indicate expected coverage proportions, it will be appreciated that the present disclosure is generally intended to describe the correlation of tumor fraction to expected coverage deviations, without limitation to allele fraction, log ratio, or any other specific metric.

[0111] In some instances, tumor fraction may be represented numerically (e.g., fraction or percentage) indicating the amount of DNA from tumor cells versus the total amount of DNA (e.g., tumor and non-tumor DNA) in the sample. In some instances, the sequence read data may be updated to include the tumor fraction associated with the sample. For example, upon determining the tumor fraction for a sample, the sequence read data may be annotated to include tumor fraction information of the sample.

[0112] 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. sf-5889500Docket No.: 197102014240 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., microsatellite 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.

[0113] In some instances, a process for determining a tumor fraction of a sample from a subject may include: acquiring a plurality of values each indicative of an allele fraction at a corresponding locus within a subgenomic interval in the sample, determining a certainty metric indicative of a dispersion of the plurality of values, accessing a predetermined relationship between a stored certainty metric and a stored tumor fraction, and determining, from the certainty metric and the predetermined relationship, the tumor fraction of the sample. A value of the certainty metric may be indicative of a dispersion of a plurality of values, each indicative of an allele fraction at a gene locus within the sequence read data of the sample.

[0114] The value indicative of an allele fraction can be determined for each corresponding locus. The loci may include one or more nucleotide. In some instances, the loci may have a different maternal allele and paternal allele. In some instances, the loci may have the same maternal allele and paternal allele. In some instances, the values indicative of allele fraction at the corresponding loci may be a plurality of allele fractions at the corresponding loci in the sample. The allele fraction at each of the corresponding loci may be determined, for example, by sequencing nucleic acid molecules in the tumor sample and assigning an allele coverage for each allele at each locus. For example, the allele fraction at locus i (^^^) may be determined by:where ^^^^,^is the coverage of allele a at locus i, and ^^^^,^is the coverage of allele b at locusi. In some instances, allele a and allele b are assigned such that ^^^^,^≤ ^^^^,^, such that ^^^≤0.5.sf-5889500Docket No.: 197102014240

[0115] In some instances, the expected allele fraction is the allele fraction expected in a healthy individual or healthy sample (i.e., a non-tumor sample). For example, the allele fraction at a heterozygous locus (that is, having a different maternal allele and paternal allele) is expected to be 0.5, and the allele fraction at a homozygous locus (that is, wherein the maternal allele and the paternal allele are the same) is expected to be 1.0.

[0116] Allele fraction is an exemplary value for determining tumor fraction according to the methods described herein, although other values indicative of allele fraction may be used in some instances. In some instances, the value indicative of the allele fraction is a relative difference in allele frequency. For example, the value indicative of the allele fraction may be ratio of the difference in the abundance (e.g., a coverage or sequencing depth) between a maternal allele and a paternal allele relative to the abundance of the maternal allele or the paternal allele. That is, in some instances, the value can a relative difference:where ^^^^,^is the coverage of allele a at locus i, and ^^^^,^is the coverage of allele b at locus i. In a healthy individual or healthy sample, the difference between the allele frequency, as well as the relative difference, is expected to be 0. In some instances, a probability distribution function is determined for the plurality of values indicative of allele fraction. For example, in some instances, the probability distribution function is determined for the plurality of allele fractions at the plurality of corresponding loci in the sample. In some instances, the probability distribution function for the plurality of allele fractions is defined as:wherein ^^^^,^is the coverage of allele a at locus i, and ^^^^,^is the coverage of allele b at locus i.

[0117] The dispersion (or certainty metric) can be, for example, a deviation from the expected allele fraction (or value indicative of expected allele fraction) across the plurality of loci. In some instances, the certainty metric is a root mean squared deviation from the expected allele fraction 35 sf-5889500Docket No.: 197102014240 (or value indicative thereof). For example, in some instances, the certainty metric is a root mean squared deviation (RMSD) defined by:wherein ^^^is the allele frequency (or value indicative of the allele frequency, such as a relative difference ratio) at locus i, ^^12314516is the expected allele frequency at locus i, and N is the number of loci in the plurality of corresponding loci. For example, for some loci, ^^12314516may be 0.5, and at other loci ^^12314516may be 1. In some instances, the loci include only those loci having a different maternal allele and paternal allele. Thus, the ^^12314516may be defined as 0.5 across all loci, and the RMSD can be defined as:

[0118] In some instances, the value indicative of the allele fraction may be ratio of the difference in abundance (e.g., a coverage or sequencing depth) between a maternal allele and a paternal allele, relative to the abundance of the maternal allele or the paternal allele, and the ^^12314516may be defined as 0. Thus, the RMSD can be defined as:where ^^^^,^is the coverage of allele a at locus i, and ^^^^,^is the coverage of allele b at locus i.

[0119] In some instances, a probability distribution (e.g., a probability distribution function) can be determined for allele fractions across a plurality of loci. The certainty metric (e.g., a dispersion) can be a metric of the probability distribution, such as an entropy of the probability distribution. For example, in some instances, the entropy of an allele fraction probability distribution function ()DE"^^%F) may be defined as:sf-5889500Docket No.: 197102014240 where E"^^%is the allele fraction probability distribution function, and n is the log base. In some instances, the log base is 2 (i.e., log2). Accordingly, in some instances, the entropy of an allele fraction probability distribution function ()DE"^^%F) may be defined as:

[0120] In some instances, determining the tumor fraction may include: acquiring a plurality of values, each value indicative of a difference between an allele coverage of a locus in a tumor sample and an allele coverage of the same locus in a non-tumor sample at a plurality of loci within a subgenomic interval; determining a certainty metric indicative of a dispersion of the plurality of values; accessing a predetermined relationship between a stored certainty metric and a stored tumor fraction; and determining, from the certainty metric and the predetermined relationship, the tumor fraction of the sample. In some instances, the tumor sample and the non- tumor sample are obtained from the same individual (i.e., a matched normal control). In some instances, the tumor sample and the non-tumor sample are obtained from different individuals. The coverage may be a raw coverage (for example, a raw number of sequencing reads), a normalized coverage (for example, normalized to a mean or median sequencing depth), and / or otherwise bias-corrected coverage (for example, a GC-bias corrected coverage depth). In some instances, the allele coverage comprises the coverage of a maternal allele and a coverage of a paternal allele (such as a sum of the coverage of the maternal allele and the coverage of the paternal allele). In some instances, the allele coverage consists of the coverage of a maternal allele and a coverage of a paternal allele (such as a sum of the coverage of the maternal allele and the coverage of the paternal allele).

[0121] In some instances, each value indicative of the difference between an allele coverage of the locus in a tumor sample and an allele coverage of the same locus in the non-tumor sample comprises a ratio of the allele coverage of a locus in the tumor sample compared to the allele coverage of the same locus in the non-tumor sample. In some instances, the allele coverage comprises the coverage of a maternal allele and a coverage of a paternal allele (such as a sum of the coverage of the maternal allele and the coverage of the paternal allele). In some instances, the allele coverage consists of the coverage of a maternal allele and a coverage of a paternal allele (such as a sum of the coverage of the maternal allele and the coverage of the paternal allele). For example, in some instances, the ratio may be defined as: sf-5889500Docket No.: 197102014240is the coverage of the maternal allele at the locus i within the tumor sample, ^^^^^,^^H41Ris the coverage of the paternal allele at the locus i within the tumor sample, ^^^^9,^SRT^Uis the coverage of the maternal allele at the locus i within the non-tumor sample, and ^^^^9,^SRT^Uis the coverage of the paternal allele at the locus i within the non-tumor sample.

[0122] In some instances, each value may be indicative of the difference between an allele coverage of the locus in a tumor sample and an allele coverage of the same locus in the non- tumor sample is a log ratio (such as a log2 ratio) of the allele coverage of a locus in the tumor sample compared to the allele coverage of the same locus in the non-tumor sample. In some instances, the allele coverage comprises the coverage of a maternal allele and a coverage of a paternal allele (such as a sum of the coverage of the maternal allele and the coverage of the paternal allele). In some instances, the allele coverage consists of the coverage of a maternal allele and a coverage of a paternal allele (such as a sum of the coverage of the maternal allele and the coverage of the paternal allele). For example, the log ratio may be defined, in some instances, as: K^LKMN K^LKMlog ^^^^G = l / ^^^ ^ N^,^ ^^^^,^ 7H ogH / ^^^LONP^Q^,^ ^ ^^^LONP^Q^,^ 7, where logn is the log at base n, ^^^^^,^^H41Ris the coverage of the maternal allele at the locus i within the tumor sample, ^^^^^,^^H41Ris the coverage of the paternal allele at the locus i within the tumor sample, ^^^^9,^SRT^Uis the coverage of the maternal allele at the locus i within the non- tumor sample, and ^^^^9,^SRT^Uis the coverage of the paternal allele at the locus i within the non- tumor sample. For example, the log ratio may be a log2 ratio. In some instances, the log ratio is defined as:is the coverage of the maternal allele at the locus i within the tumor sample, ^^^^^,^^H41Ris the coverage of the paternal allele at the locus i within the tumor sample, sf-5889500Docket No.: 197102014240 ^^^^9,^SRT^Uis the coverage of the maternal allele at the locus i within the non-tumor sample, and ^^^^9,^SRT^Uis the coverage of the paternal allele at the locus i within the non-tumor sample.

[0123] In some instances, each value may be indicative of the difference between an allele coverage of the locus in a tumor sample and an allele coverage of the same locus in the non- tumor sample comprises a ratio of the difference between the allele coverage of a locus in the tumor sample compared to the allele coverage of the same locus in the non-tumor sample, relative to the allele coverage of the same locus in the non-tumor sample. In some instances, the allele coverage may include the coverage of a maternal allele and a coverage of a paternal allele (such as a sum of the coverage of the maternal allele and the coverage of the paternal allele). In some instances, the allele coverage may consist of the coverage of a maternal allele and a coverage of a paternal allele (such as a sum of the coverage of the maternal allele and the coverage of the paternal allele). For example, the ratio may be defined as:is the coverage of the maternal allele at the locus i within the tumor sample, ^^^^^,^^H41Ris the coverage of the paternal allele at the locus i within the tumor sample, ^^^^9,^SRT^Uis the coverage of the maternal allele at the locus i within the non-tumor sample, and ^^^^9,^SRT^Uis the coverage of the paternal allele at the locus i within the non-tumor sample.

[0124] In some instances, a probability distribution function may be determined for the plurality of values indicative of the difference between an allele coverage of the locus in a tumor sample and an allele coverage of the same locus in the non-tumor sample. In some instances, the allele coverage may include the coverage of a maternal allele and a coverage of a paternal allele (such as a sum of the coverage of the maternal allele and the coverage of the paternal allele). In some instances, the allele coverage may consist of the coverage of a maternal allele and a coverage of a paternal allele (such as a sum of the coverage of the maternal allele and the coverage of the paternal allele). For example, the probability distribution function may be determined for the plurality of ratios of the allele coverage of a locus in the tumor sample compared to the allele coverage of the same locus in the non-tumor sample (such as a log ratio, for example a log2sf-5889500Docket No.: 197102014240 ratio). In some instances, the probability distribution function for the plurality of allele fractions is defined by:where logn is the log at base n, ^^^^^,^^H41Ris the coverage of the maternal allele at the locus i within the tumor sample, ^^^^^,^^H41Ris the coverage of the paternal allele at the locus i within the tumor sample, ^^^^9,^SRT^Uis the coverage of the maternal allele at the locus i within the non-tumor sample, and ^^^^9,^SRT^Uis the coverage of the paternal allele at the locus i within the non-tumor sample. In some instance, log ratio is a log2 ratio. For example, the probability distribution function for the plurality of allele fractions is defined by:whereis the coverage of the maternal allele at the locus i within the tumor sample, ^^^^^,^^H41Ris the coverage of the paternal allele at the locus i within the tumor sample, ^^^^9,^SRT^Uis the coverage of the maternal allele at the locus i within the non-tumor sample, and ^^^^9,^SRT^Uis the coverage of the paternal allele at the locus i within the non-tumor sample.

[0125] The dispersion (or certainty metric) can be, for example, a deviation of each value within the plurality of values from an expected value across the corresponding loci. The expected value is the value that would be expected if the tumor sample were non-tumor (e.g., a healthy) sample. In some instances, the certainty metric is a root mean squared deviation from the expected value. For example, the certainty metric is a root mean squared deviation (RMSD) defined by:

[0126] In some instances, the value indicative of the allele fraction is a ratio of the difference between the allele coverage of a locus in the tumor sample compared to the allele coverage of the same locus in the non-tumor sample, relative to the allele coverage of the same locus in the non- tumor sample. Thus, the RMSD can be defined as: sf-5889500Docket No.: 197102014240

[0127] In some instances, a probability distribution (e.g., a probability distribution function) can be determined for the plurality of values indicative of the difference between an allele coverage of the locus in a tumor sample and an allele coverage of the same locus in the non-tumor sample. The certainty metric (e.g., a dispersion) can be a metric of the probability distribution, such as an entropy of the probability distribution. For example, in some instances, the entropy of an allele fraction probability distribution function ()DE"^^%F) may be defined as:where:where lognis a log having base n, ^^^^^,^^H41Ris the coverage of the maternal allele at the locus i within the tumor sample, ^^^^^,^^H41Ris the coverage of the paternal allele at the locus i within the tumor sample, ^^^^9,^SRT^Uis the coverage of the maternal allele at the locus i within the non-tumor sample, and ^^^^9,^SRT^Uis the coverage of the paternal allele at the locus i within the non-tumor sample. In some instances, the log base is 2 (i.e., log2). Accordingly, in some instances, the entropy of an allele fraction probability distribution function ()DE"^^%F) may be defined as:where:is the coverage of the maternal allele at the locus i within the tumor sample, ^^^^^,^^H41Ris the coverage of the paternal allele at the locus i within the tumor sample, ^^^^9,^SRT^Uis the coverage of the maternal allele at the locus i within the non-tumor sample, and ^^^^9,^SRT^Uis the coverage of the paternal allele at the locus i within the non-tumor sample. sf-5889500Docket No.: 197102014240

[0128] A relationship between one or more stored certainty metrics and one or more stored tumor fractions can be used to determine the tumor fraction based on the determined certainty metrics. In some instances, a model may be trained to using a training dataset that includes training certainty metrics and associated tumor fractions to determine the relationship between the certainty metrics and the tumor fractions. The training dataset may be determined, for example, using a plurality of clinical samples with known (i.e., training) tumor fractions (for example, as determined by maximum somatic allele frequency (MSAF), which filters germline variant calls from all calls in a tumor sample and compares residual variants (i.e., the maximum somatic variants) to the total variants (maximum somatic variants plus germline variants) to determine the maximum somatic allele frequency). Nucleic acid molecules in the clinical samples can be sequenced to determine allele frequency across a plurality of loci (or a value indicative of an allele frequency), as well as an associated training certainty metric. The training certainty metrics can be correlated with the training tumor fractions to determine the relationship between certainty metric and tumor fraction. In another method, serial dilutions may be made from one or more clinical samples to obtain a plurality of different tumor fractions, which can be correlated with the certainty metric for the serially diluted samples to determine the relationship.

[0129] In some instances, to determine (e.g., estimate) the tumor fraction, a training subprocess may first be performed. A dataset can be constructed from clinical specimens. Using the training set and in-silico dilutions of the training set, tumor fraction can be correlated to variations in allele fractions or log ratios corresponding to aneuploidy typically observed in tumors. In other examples, cell-line / clinical sample dilutions can be performed.

[0130] In some instances, the certainty metric may be functions of the coverage at particular SNP bins for particular alleles and / or an allele frequency (e.g., in the range of 0 to 0.5). In some examples, the training data uses as input a deviation metric (e.g., allele fraction deviation or log ratio deviation) and returns the estimated tumor fraction, along with lower and upper bounds. Values that deviate from (i.e., fall between) 0 and 1 and not 0.5 (exclusive) may be thought of as “noise,” and the averaged noise may be correlated with an expected or estimated tumor fraction. In other examples, the training data provides as input a log ratio deviation metric, or in general, any metric which quantifies coverage deviations from expectations. In either case, the allele sf-5889500Docket No.: 197102014240 coverage deviation metric or the log ratio deviation metric may be a measure of the tumor fraction.

[0131] Utilizing these correlations derived during training, a tumor fraction of a patient can be estimated or evaluated with upper and lower bounds. Coverage metrics, such as SNP allele coverage variation metrics, may be used in generating the correlation.

[0132] In some instances, the cTF may be determined by accounting for both variant allele frequencies (VAF) and the tumor fraction of the sample. The composite tumor fraction determination methodology may measure the ctDNA fraction of the total circulating cfDNA across a broad range of tumor content values, leveraging two complementary metrics: a proprietary tumor fraction estimator (TFE) metric and a maximum somatic allele frequency (MSAF) metric.

[0133] The TFE metric is based on a measure of tumor aneuploidy that incorporates observed deviations in, for example, SNP coverage across the genome for a given sample. Calculated values for this metric may be calibrated against a training set based on samples with well-defined tumor fractions to generate an estimate of the tumor fraction. The MSAF method may estimate the fraction of ctDNA versus that of all sources of cfDNA in plasma, including circulating germline DNA from patients. The MSAF metric may be determined based on the allele fraction for all known somatic, likely somatic, and variant of unknown significance substitution alterations detected at >2000x median unique coverage by non-PCR duplicate read pairs, excluding certain common and rare germline variants. A select list of variants associated with clonal hematopoiesis are not considered while determining MSAF.

[0134] The cTF measurement may default to the TFE metric’s value if the TFE metric has been calculated. When lack of tumor aneuploidy limits the TFE metric’s ability to return an informative estimate of ctDNA fraction, the MSAF metric’s value may be used to generate a ctDNA fraction value. In this case, only variants with allele fraction at or below TFE metric’s limit of detection may be used as input for MSAF. When the TFE’s estimate is unavailable or impacted by failed quality control measures, the cTF may be generated from the MSAF metric calculation, as previously described. In effect, the MSAF metric’s process can typically be used at low ctDNA fractions due to its higher reliability at lower ctDNA fractions, whereas the TFE sf-5889500Docket No.: 197102014240 metric’s process can be used at high ctDNA fractions due to its higher reliability at higher ctDNA fractions.

[0135] In some instances, the CH burden score may be generated based on the cTF and one or more of the total number of CH alterations, the number of pathogenic CH alterations, the number of clonal CH alterations, and / or the maximum CH allele fraction in the sample. For example, the CH burden score may be generated based on the cTF and the total number of CH alterations detected from the sample. In some instances, multiple CH burden scores may be generated each based on different combinations of the cTF and the total number of CH alterations, the number of pathogenic CH alterations, the number of clonal CH alterations, and / or the maximum CH allele fraction in the sample. For example, a first CH burden score may be computed based on the cTF and the total number of CH alterations, whereas a second CH burden score may be computed based on the cTF, the total number of CH alterations, and the maximum CH allele fraction in the sample. In one or more examples, the CH burden score may be discrete values of 0, 1, 2, 3, etc. In some examples, the CH burden score may be a continuous value between a lower limit and an upper limit.

[0136] In some instances, process 100 may further include predicting a survival prognosis for the subject based on the determined CH burden score and the determined cTF. The survival prognosis indicates a chance that a given subject will recover from the disease with which they are inflicted. The survival prognosis may serve as a basis for determining what treatment options, if any, are available for the subject. The survival prognosis may be quantified using metrics such as overall survival (OS). In some instances, the survival prognosis may be predicted further based on age, gender, or other factors.

[0137] In some instances, process 100 may further include a step of predicting a clinical response of the subject to an immune checkpoint inhibitor based on the determined CH burden score and the determined cTF. The clinical response of the subject prediction may indicate the likelihood that the subject will respond to a particular therapeutic. Some example checkpoint inhibitors may include first-line ICI monotherapies, such as Pembrolizumab, Nivolumab, Cemiplimab, Ipilimumab, Atezolizumab, Avelumab, Dostarlimab (a PH-1 inhibitor), Temelimumab (a CTLA-4 inhibitor), or Durvalumab. However, the checkpoint inhibitor may sf-5889500Docket No.: 197102014240 include therapies other than first-line ICI monotherapies, such as anti-PD-1 antibody second-line ICI therapies.

[0138] In some instances, process 100 may further include a step of predicting, based on the determined CH burden score and the determined cTF, a likelihood of one or more immune- related adverse events occurring for the subject. For example, the immune-related adverse events may include autoimmune disorders (e.g., arthritis) or other inflammatory conditions (e.g., pneumonitis, myocarditis, colitis).

[0139] In some instances, the predictions of the survival, ICI response, and / or likelihood of immune-related adverse events may be based on a comparison of the determined CH burden score to a predetermined CH burden threshold. For example, the CH burden score may be determined based on a total number of CH alterations in the sample. In some examples, the predetermined CH burden threshold is equal to 2, 3, 4, or 5 CH alterations. In some instances, the predictions of the survival, ICI response, and / or likelihood of immune-related adverse events may be based on a comparison of the determined cTF to a predetermined cTF threshold. In one or more examples, the predetermined cTF threshold is 0.01% or greater, 0.1% or greater, 0.5% or greater, 1.0% or greater, or 10% or greater. In some instances, In some instances, the predictions of the survival, ICI response, and / or likelihood of immune-related adverse events may be based on (i) a comparison of the determined CH burden score to a predetermined CH burden threshold and (ii) a comparison of the determined cTF to a predetermined cTF threshold.

[0140] In some instances, process 100 may further include a step of classifying the subject into one of a set of clinical response classes based on the determined CH burden score and the determined cTF. The subject may be classified into one of the set of clinical response classes. Depending on the classification, different clinical response strategies may be selected for the subject. For example, one ICI therapy may be provided to the subject based on the determined CH burden score and the determined cTF, whereas another ICI therapy may be provided to another subject based on the determined CH burden score and the determined cTF of that subject.

[0141] The set of clinical response classes may include a first clinical response class comprising first subjects. Each of the first subjects may exhibit a CH burden score greater than the predetermined CH burden threshold and may also exhibit a cTF greater than the predetermined sf-5889500Docket No.: 197102014240 cTF threshold. The set of clinical response classes may further include a second clinical response class comprising second subjects. Each of the second subjects may exhibit a CH burden score that is greater than the predetermined CH burden threshold and a cTF that is less than the predetermined cTF threshold. The set of clinical response classes may still further include a third clinical response class comprising third subjects. Each of the third subjects may exhibit a CH burden score that is less than the predetermined CH burden threshold and a cTF that is greater than the predetermined cTF threshold. The set of clinical response classes may yet still further include a fourth clinical response class comprising fourth subjects. Each of the fourth subjects may exhibit a CH burden score that is less than the predetermined CH burden threshold and a cTF that is less than the predetermined cTF threshold. In some instances, the first subjects, the second subjects, the third subjects, and the fourth subjects may include a same or similar quantity of subjects. In one or more examples, the CH burden score may be discrete values of 0, 1, 2, 3, etc. In some examples, the CH burden score may be a continuous value between a lower limit and an upper limit. In one or more examples, the predetermined CH burden threshold may be a discreate value (e.g., 1 or more, 2 or more, 3 or more, etc.). In some examples, the predetermined CH burden threshold may be a threshold CH burden score, and a predicted CH burden score may be compared to the threshold CH burden score.

[0142] In some instances, a treatment decision for the subject may be selected based on a clinical response class from the set of clinical response classes that the subject is classified into. For example, subjects that have been classified into the first clinical response class may have a worse response to a particular ICI than compared to subjects that have been classified into the fourth clinical response class. Thus, if a subject is classified into the first clinical response class, that subject may not be provided with the same treatment options as a subject classified into the fourth clinical response class due to it being unlikely for that subject to have a positive response to the ICI.

[0143] In some instances, process 100 may include a step of determining a composite tumor fraction (cTF) for the sample based on the sequence read data. Process 100 may further include a step of comparing the cTF to a predetermined cTF threshold as well as comparing the CH burden score to a predetermined CH burden threshold. Process 100 may still further include sf-5889500Docket No.: 197102014240 steps for selecting a cancer treatment for the subject based on the comparisons and administering the selected cancer treatment to the subject.

[0144] In some instances, the CH burden score for the sample may be generated using one or more machine learning models. The machine learning models may be trained to output a CH burden score based on input data. The input data may include data representing a total number of clonal hematopoiesis (CH) alterations, a number of pathogenic CH alterations, a number of clonal CH alterations, or a maximum CH allele fraction of a corresponding sample. In some instances, the one or more machine learning models may be trained to output a consolidated CH burden score based on input data, wherein the input data comprises at least one of: a total number of clonal hematopoiesis (CH) alterations, a number of pathogenic CH alterations, a number of clonal CH alterations, or a maximum CH allele fraction, and a composite tumor fraction (cTF) for the sample based on the sequence read data. In some instances, the machine learning models may be trained to output a CH burden score based on sequence read data obtained for a subject. In one or more examples, the CH burden score may be discrete values of 0, 1, 2, 3, etc. In some examples, the CH burden score may be a continuous value between a lower limit and an upper limit. In one or more examples, the predetermined CH burden threshold may be a discreate value (e.g., 1 or more, 2 or more, 3 or more, etc.). In some examples, the predetermined CH burden threshold may be a threshold CH burden score, and a predicted CH burden score may be compared to the threshold CH burden score.

[0145] In some instances, process 100 may further include a step of generating training data for training the machine learning models. In some instances, the training data may be stored in a training data database for training of the machine learning models, re-training of the machine learning models, and the like. The training data may include a plurality of tuples respectively associated with a plurality of subjects. In particular, for each subject, a sample and sequence read data may be obtained. For each sample, a total number of CH alterations, a number of pathogenic CH alterations, a number of clonal CH alterations, and / or a maximum CH allele fraction may be determined. In one or more examples, each tuple may include at least one of the total number of CH alterations, the number of pathogenic CH alterations, the number of clonal CH alterations, and / or the maximum CH allele fraction determined for that sample. The training sf-5889500Docket No.: 197102014240 data may further include, for each tuple, the cTF determined for a sample obtained from a respective one of the plurality of subjects.

[0146] 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.

[0147] FIG. 2 provides a non-limiting example of a process 200 for generating a tumor origin score indicating whether a gene alteration is a tumor-derived alteration or a CH alteration, in accordance with various instances. Process 200 may be executed by one or more computing systems (e.g., one or more processers). In some instances, process 200 may be used to predict an origin of an alteration of interest in a sample from a patient. For example, the predicted origin may be based on the tumor origin score that has been generated.

[0148] Liquid biopsy tests provide healthcare providers with a less-invasive method of obtaining and analyzing patient sample for detecting potentially cancerous cells in order to diagnose and treat patient. Liquid biopsy tests examine cell free DNA (cfDNA) and can detect multiple categories of alterations, including germline alleles, somatic mutations from tumor cells, and alterations that drive clonal hematopoiesis (CH). Due to overlap in the underlying biological processes, the liquid biopsy tests can misclassify CH-derived alterations as tumor-derived alterations, which can inflate the number of tumor-derived alterations detected in the sample. A healthcare provider that recommends a therapy based on the results of a test that does not account for the potential misclassification of CH-derived alterations as tumor-derived alterations sf-5889500Docket No.: 197102014240 runs the risk of prescribing an ineffective treatment while exposing the patient to the risks and adverse side-effects associated with that treatment.

[0149] Process 200 can be used to determine an origin of an alteration in a sample. In particular, process 200 may be allow tumor-derived alterations to be distinguished from CH-derived alterations in liquid biopsy using genomic profiling data. In some instances, process 200 may be used to distinguish CH-derived alterations from tumor-derived alterations based on fragmentomic characteristics of cfDNA sequence reads from a sample. As an example of a fragmentomic characteristic of cfDNA sequence reads, the length distribution of cfDNA fragments may be correlated with its cell of origin. For example, cfDNA fragments arising from tumor cells tend to be shorter than cfDNA fragments from non-tumor cells. cfDNA fragments arising from tumor cells tend to be shorter than fragments arising from CH-derived alterations. As such, fragment length can be indicative of the origin of the cell (e.g., tumor-derived, or CH- derived) for individual and groups of alterations. FIG. 3 provides a non-limiting example of a plot 300 showing the relationship between fragment length and origin of the fragment, in accordance with various instances. In particular, plot 300 illustrates the fragment size distribution for a range of samples, including samples having a high percentage of circulating tumor DNA (ctDNA) to samples having a low percentage of circulating tumor DNA (ctDNA). As seen by plot 300, samples with low ctDNA tend to have longer fragment length distributions, akin to healthy, tumor-free samples, compared to samples with high ctDNA. Thus, generally, ctDNA has shorter fragment lengths than healthy-cell-derived cfDNA.

[0150] In some instances, process 200 may begin at step 202. At step 202, sequence read data for a plurality of sequence reads derived from a sample from a subject may be received. The sequence read data may be received by a computing system executing process 200. In some instances, the subject has been diagnosed with a form of cancer. For example, the subject may have been diagnosed with non-squamous non-small cell lung cancer (NSCLC). The sample may be a liquid biopsy sample. In one or more examples, the liquid biopsy sample may be a blood plasma sample liquid biopsy sample, a whole blood sample, peripheral blood mononuclear cells (PBMCs) sample, and / or a buffy coat sequencing sample. In one or more examples, the liquid biopsy sample comprises a blood plasma sample. In some instances, the sequence read data may be derived using one or more targeted sequencing methods. For example, the sequence read data sf-5889500Docket No.: 197102014240 may be derived from sequencing circulating tumor DNA in a liquid biopsy sample. In one or more examples, the sequence read data may be received by the computing system as a BAM file.

[0151] In one or more examples, the sequence read data may include one or more short variants (SVs) in a patient sample. In some instances, the sequence read data may be derived from targeted exome sequencing or whole exome sequencing. In one or more examples, the whole exome sequencing can increase the number of genomic features (e.g., the number of SVs) detected. In some instances, the sequence read data may also include other genomic features in addition to short variants, such as copy number alterations, rearrangements, chromosomal aneuploidy, whole genome doubling, Catalogue Of Somatic Mutations In Cancer (COSMIC) mutational signatures, or any combination thereof.

[0152] In some instances, sequence read data for a plurality of sequence reads derived from a control sample from the subject may be received. In one or more examples, the control sample may include peripheral blood mononuclear cells (PBMCs) from the subject. In one or more examples, the sequence read data derived from the control sample may be used to identify CH alterations. In some instances, step 202 of process 200 is the same or similar to step 102 of process 100, and the previous description may apply.

[0153] At step 204, an alteration may be detected based on the sequence read data. The alteration may be an alteration of interest. In one or more examples, the computing system executing process 200 may receive an indication of the alteration to be detected as an input from a user. The alteration can correspond to any alteration of interest in a sample. In one or more examples, the alteration can be determined based on one or more tests. In one or more examples, one or more indications of a plurality of alterations (e.g., a group of alterations) to be detected may be received. In such scenarios, process 200 may include additional steps, carried out sequentially or in parallel, to predict an origin of the cells for each of the alterations.

[0154] In one or more examples, the alteration can correspond to an insertion, a deletion, or a substitution, or a combination thereof. In one or more examples, the alteration may be associated with clonal hematopoiesis. In one or more examples the alteration can include at least one of a simple or complex insertion, a simple or a complex deletion, or a base substitution. In one or more examples, process 200 may be implemented for short variants, which can sf-5889500Docket No.: 197102014240 encompass any single nucleotide alterations. A short variant can refer to a variant sequence of less than about 50 base pairs in length.

[0155] At step 206, a subset of sequence reads may be selected from the plurality of sequence reads based on the gene associated with the alteration. In some instances, a pysam package may be used (e.g., by the computing system executing process 200) to select sequencing reads from a consensus BAM file in a genomic region where the alteration resides. Sequence reads that include a wild type or a mutant sequence may be selected unless the reads are secondary reads, supplement reads, or a mapping quality of the reads is less than a threshold or a range of thresholds. Fragment length may be estimated based on the length of the sequence read, one or more locations the sequence reads on a reference genome, and an alignment of the sequence reads. In one or more examples, the plurality of reads may be selected based on a gene associated with the alteration. In one or more examples, the system can identify a plurality of reads from the sequence data that overlap with the alteration.

[0156] At step 208, at least one feature characterizing the selected subset of sequence reads may be determined. In some instances, the at least one feature corresponds to a fragmentomic characteristic. For example, the fragmentomic characteristic may include fragment lengths for the selected plurality of reads, a start position of a fragment for the selected plurality of reads, an end position of a fragment for the selected plurality of reads, or a combination thereof. The fragment length can refer to a length of a single strand of cfDNA, the size of which may be determined by extraction of the reference genome alignment coordinates of the corresponding paired-end sequence read of the selected plurality of reads. In one or more examples, the at least one feature can include one or more additional features characterizing the selected plurality of reads of the sample.

[0157] In one or more examples, the at least one feature characterizing the selected plurality of reads can correspond to one or more fragmentomic characteristics. Some examples of the fragmentomic characteristics may include an amount of the selected reads having a specified length, a mean fragment length of the selected reads, a median fragment length of the selected reads, an interquartile range of fragment lengths of the selected reads, a distribution of fragment lengths of the selected reads, one or more peaks of fragment lengths for the selected reads, a fragment length for at least one of the selected reads, a start position of a fragment for the sf-5889500Docket No.: 197102014240 selected reads, and / or an end position of a fragment for the selected reads. Persons of ordinary skill in the art will recognize that additional fragmentomic characteristics can be determined and the aforementioned is merely exemplary.

[0158] In some instances, the amount of a fragment having a specified length can correspond to a total amount (i.e., count) of fragments at the specified length. For example, for a particular sample, the total number of fragments that have a length below 100bp, 101bp, 102 bp, . . ., 550bp, and / or 550bp may be determined. A skilled artisan will understand that these examples of specified lengths are exemplary and is not intended to limit the scope of the disclosure. For example, the specified length can correspond to a specific number of base pairs, a range of number of base pairs, or a combination thereof.

[0159] In some instances, the amount of a fragment having a specified length may correspond to a relative number of fragments of the selected reads (e.g., reads overlapping with the alteration or gene of interest) corresponding to the specified length. In some examples, the amount of a fragment having the specified length can comprise a fraction. For example, the amount of a fragment having the specified length can be determined based on the number of fragments with a specified length (e.g., length below 100bp, a length of 100bp, a length of 101bp, a length of 102 bp, . . ., a length of 550bp, and a length greater than 550bp) divided by the number of the selected reads. In some examples, the amount can correspond to the selected reads that include an alteration and / or the selected reads that include a wild type gene. A skilled artisan will understand that these examples of specified lengths are exemplary and is not intended to limit the scope of the disclosure.

[0160] In some instances, the mean fragment length of the selected reads can correspond to an average fragment length of the selected reads. In some examples, the median fragment length may correspond to the middle fragment length value of a sorted list of the fragment lengths of the selected reads. In some instances, the interquartile range of fragment lengths of the selected reads can correspond to a first fragment length value associated with the 25thpercentile of the fragment lengths of the selected plurality of reads and a second fragment length value associated with the 75thpercentile of the fragment lengths of the selected plurality of reads. In some instances, the peak fragment length can correspond to the mode or the fragment length value that appears most frequently in the length characteristics for the selected reads. In some instances, sf-5889500Docket No.: 197102014240 multiple peak fragment lengths may be determined. In some instances, the distribution of the fragment length can correspond to a summary statistics characterizing the distribution (e.g., maximum value, minimum value, standard deviation, shape, etc.).

[0161] In one or more examples, the fragmentomic characteristics of the selected reads may be determined by the computing system executing process 200. In one or more examples, a statistical model can determine one or more fragmentomic characteristics of the selected reads based on length characteristics characterizing one or more of the selected reads.

[0162] In some instances, additional and / or alternative features characterizing the selected reads may include an alteration depth, an allele frequency, an alteration coding type, a somatic determination of the alteration, a germline determination of the alteration, and a zygosity determination, a somatic-germline-zygosity (SGZ) determination of the alteration, an age of patient providing the sample, a blood tumor mutational burden (bTMB) score of the sample, protein level data, or gene level data, or a combination thereof. In one or more examples, the alteration depth can correspond to a number of times that the locus corresponding to the alteration of interest was sequenced. In one or more examples, the alteration depth can correspond to a sequencing depth of the alteration. In one or more examples, the allele frequency can correspond to the relative frequency of an allele at a particular locus. In one or more examples, the alteration coding type can correspond to at least one of a missense, a nonsense, a frameshift, a non-frameshift, and the like. In one or more examples, the SGZ determination can correspond to a determination of whether the alteration is germline or somatic based on the allele frequency and allele specific copy number modeling. In one or more examples, the SGZ determination can be a computational SGZ determination. In one or more examples, the bTMB score can correspond to a measure of a mutational burden extrapolated from baited region of one or more sequenced reads. In one or more examples, the protein level data can include a measure of how frequently the alteration of the same protein effect in this gene are seen in liquid biopsy compared to tissue and a measure of how frequently alterations of the same protein effect in this gene are seen in older patients compared to younger patients. In one or more examples, the gene level data can include a measure of how frequently alterations in this gene are observed to be mutated in liquid biopsy compared to tissue and a measure of how frequently alterations in this gene are seen in older patients compared to younger patients. In one sf-5889500Docket No.: 197102014240 or more examples, the age of the individual can correspond the age of the individual (e.g., patient or subject) from whom the sample was taken.

[0163] At step 210, a tumor origin score may be generated using a prediction model where the tumor origin score is generated based on the at least one feature. In some instances, the tumor origin score indicative of the origin of the alteration may be generated using a prediction model. In one or more examples, the prediction model comprises a statistical model, a machine learning model, or another type of model, or a combination thereof. In some instances, the at least one feature may be input to the prediction model. For example, the prediction model may represent a statistical model or a machine learning model trained to generate a tumor origin score for a given input feature. Some or all of the fragmentomic characteristics characterizing the selected reads, as described above, may be provided as input to the prediction model. In some instances, the prediction model can be configured to generate a score indicative of whether the selected plurality reads are tumor-derived. In one or more examples, the score can be expressed as a percentage likelihood of whether the selected reads are tumor derived. In one or more examples, the score can be expressed as a percentage likelihood of whether the selected reads are not tumor derived, (e.g., CH-derived). In some instances, data associated with the sample (e.g., the sequence read data) may be annotated to include a binary flag indicating whether, based on the score, a given sequence read is associated with a tumor-derived alteration or a CH-derived alteration. For example, samples associated with CH-derived alterations may include a logical 1 bit, whereas samples associated with tumor-derived alterations may include a logical 0 bit.

[0164] In some instances, the prediction model comprises a trained machine learning model and the tumor origin score of an alteration may be predicted using the trained machine learning model. The trained machine learning model may be configured to receive, as input, at least one feature characterizing the selected reads (e.g., one or more fragmentomic characteristics). The trained machine learning model may output a tumor origin score indicative of the origin of the alteration of interest based on the at least one feature. In some instances, the machine learning model may be configured to extract additional fragmentomic characteristics from the input data (e.g., the at least one feature). In one or more examples, the machine learning model can extract the fragmentomic characteristics instead of or in addition to the manual extraction of the fragmentomic characteristics from the length characteristics. sf-5889500Docket No.: 197102014240

[0165] In some instances, the prediction model can include an artificial intelligence (“AI”) learning model. In some examples, the prediction model can be at least one of a supervised model or an unsupervised model. In some examples, the prediction model can be a gradient boosting ensemble model. In some examples, the prediction model can be a convolutional neural network (CNN), an artificial neural network (ANN), a recurrent neural network (RNN), or other neural network. In some examples, the prediction model can be a Bayesian regression model, a random forest regression model, a support vector machine model, a linear regression model, a non-linear regression model, a multivariate regression machine learning model, a robust regression machine learning model, a neural network model, a nearest neighbor regression machine learning model, or a proportional hazards regression statistical model, or a combination or permutation thereof.

[0166] In one or more examples, the prediction model can be trained to predict the origin of multiple alterations corresponding to different genes. For example, the prediction model can be trained based on input data corresponding to a plurality of alterations. In one or more examples, the prediction model can be trained on a per gene basis, such that a single prediction model is configured to predict the origin of the selected reads for a particular alteration. In such examples, input data corresponding to a particular alteration of interest can be input into the corresponding prediction model trained to predict the origin of the particular alteration of interest.

[0167] In some instances, determining the total number of CH alterations, the number of pathogenic CH alterations, the number of clonal CH alterations, and / or the maximum CH allele fraction in the sample may include: (i) detecting an alteration based on the sequence read data and (ii) performing a statistical analysis of sequence read data for a selected subset of the plurality of sequence reads to distinguish between tumor-derived alterations and CH alterations. In one or more examples, each sequence read of the selected subset of sequence reads may be selected based on a gene associated with the alteration. Some example CH alterations that can be detected include short variants, focal copy number alterations, rearrangements, and genome- wide disruptions (e.g., larger aneuploidy).

[0168] In some instances, performing the statistical analysis may include determining at least one feature characterizing the selected subset of sequence reads and generating, using a sf-5889500Docket No.: 197102014240 prediction model, a tumor origin score based on the at least one feature. In one or more examples, distinguishing between tumor-derived alterations and CH alterations may be based on the tumor origin score. In one or more examples, distinguishing between tumor-derived alterations and CH alterations may include comparing the generated tumor origin score with one or more tumor origin score thresholds to classify the alteration as being a tumor-derived alteration or a CH alteration.

[0169] In some instances, the origin of the alteration in the sample may be predicted based on the generated tumor origin score. For example, the origin of the alteration may be determined by comparing the tumor origin score to one or more predefined tumor origin score thresholds. For example, generated tumor origin score may be compared to one or more predefined tumor origin score thresholds to determine whether the alteration is tumor-derived or CH-derived.

[0170] In some instances, the one or more tumor origin score thresholds may include a first tumor origin score threshold. Generated tumor origin scores greater than or equal to the first tumor origin score threshold may indicate that the corresponding alteration is a CH alteration. For example, a determination may be made as to whether the alteration is a CH alteration based on the tumor origin score being greater than or equal to the first tumor origin score threshold. Process 200 may alternatively include a step of determining that the alteration is a tumor derived alteration based on the tumor origin score being less than the first tumor origin score threshold.

[0171] In some instances, the one or more tumor origin score thresholds may include a first tumor origin score threshold and a second tumor origin score threshold. Process 200 may further include a step of determining that the alteration is a CH alteration based on the tumor origin score being greater than or equal to the first tumor origin score threshold. Process 200 may alternatively or additionally include a step of determining that the alteration is a tumor derived alteration based on the tumor origin score being less than the second tumor origin score threshold. Process 200 may alternatively or additionally include a step of determining that the alteration is of inconclusive origin based on the tumor origin score being less than the first tumor origin score threshold and greater than or equal to the second tumor origin score threshold.

[0172] In one or more examples, if the tumor origin score is below the first tumor origin score threshold, the sequence read data of the selected reads may be inconclusive. In such examples, additional testing (e.g., a normal paired test or an orthogonal test) may be recommended. The sf-5889500Docket No.: 197102014240 additional testing should be completed to confirm the origin of the sequence read data. In one or more examples, if the tumor origin score is less than the second threshold, the alteration may be classified as a tumor-derived alteration. In one or more examples, if the score falls between the first tumor origin score threshold and the second tumor origin score threshold, the sequence read data may be inconclusive. In such examples, further testing (e.g., a normal paired test or an orthogonal test) may be recommended to confirm the origin of the sequence read data.

[0173] In one or more examples, the predetermined tumor origin score thresholds can be determined by maximizing or minimizing a function of sensitivity and specificity (such as the sum). For example, a loss function associated with performance metrics (e.g., whether the tumor origin score corresponds to an accurate prediction) can be maximized or minimized. In some examples, the tumor origin score threshold can be set to maximize sensitivity and specificity.

[0174] In one or more examples, the predetermined tumor origin score thresholds can be determined based on the area under the prediction function’s receiver operating characteristic (ROC) curve. The area under the ROC curve can be used in statistics to measure the prediction accuracy of a binary classifier system. In one or more examples, the predetermined tumor origin score thresholds can be determined using one or more statistical techniques combined with predetermined confidence levels.

[0175] In one or more examples, each alteration (e.g., corresponding to a different gene) can be associated with one or more different tumor origin score thresholds. Alternatively, some examples include each alteration being associated with the same tumor origin score threshold.

[0176] In one or more examples, the prediction model can be selected from a plurality of prediction models each associated with one of a plurality of CH alterations. Each prediction model may be trained to predict whether an alteration is a tumor-derived alteration or a CH alteration. Some example CH alterations that can be detected include short variants, focal copy number alterations, rearrangements, and genome-wide disruptions (e.g., larger aneuploidy). The prediction model may be a statistical model or a machine learning model. In an example where the prediction model is a machine learning model, the machine learning model may be a neural network.

[0177] In some instances, the at least one feature may include one or more fragmentomic characteristics. In one or more examples, the one or more fragmentomic characteristics may sf-5889500Docket No.: 197102014240 include at least one of: a number of the selected subset of sequence reads having a specified length, a mean fragment length of the selected subset of sequence reads, a median fragment length of the selected subset of sequence reads, an interquartile range of fragment lengths of the selected subset of sequence reads, a distribution of fragment lengths of the selected subset of sequence reads, one or more peaks of fragment lengths for the selected subset of sequence reads, a fragment length for at least one of the selected subset of sequence reads, a start position of a fragment for the selected subset of sequence reads, an end position of a fragment for the selected subset of sequence reads.

[0178] In some instances, the at least one feature comprises one or more additional features related to the alteration. In one or more examples, the additional features may include at least one of an alteration depth, an allele frequency, an alteration coding type, a somatic determination of the alteration, a germline determination of the alteration, a zygosity determination, a somatic- germline-zygosity (SGZ) determination of the alteration, an age of the subject providing the sample, a blood tumor mutational burden (bTMB) score of the sample, protein level data, or gene level data.

[0179] Process 200 can be performed, for example, using one or more electronic devices implementing a software platform. In some examples, process 200 is performed using a client- server system, and the blocks of process 200 are divided up in any manner between the server and a client device. In other examples, the blocks of process 200 are divided up between the server and multiple client devices. Thus, while portions of process 200 are described herein as being performed by particular devices of a client-server system, it will be appreciated that process 200 is not so limited. In other examples, process 200 is performed using only a client device or only multiple client devices. In process 200, 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 200. Accordingly, the operations as illustrated (and described in greater detail below) are exemplary by nature and, as such, should not be viewed as limiting.

[0180] FIG. 4 provides a non-limiting example of a process 400 for predicting a survival prognosis, a clinical response, and / or an immune-related adverse event for a subject, in accordance with various instances. Process 400 may be executed by one or more computing sf-5889500Docket No.: 197102014240 systems (e.g., one or more processers). In some instances, process 400 may begin at step 402. At step 402, sequence read data for a plurality of sequence reads derived from a sample from a subject may be received. The sequence read data may be received by a computing system executing process 400. In some instances, the subject has been diagnosed with a form of cancer. For example, the subject has been diagnosed with non-squamous non-small cell lung cancer (NSCLC). The sample may be a liquid biopsy sample. In one or more examples, the liquid biopsy sample may be a blood plasma sample liquid biopsy sample, a whole blood sample, peripheral blood mononuclear cells (PBMCs) sample, and / or a buffy coat sequencing sample. In one or more examples, the liquid biopsy sample comprises a blood plasma sample. In some instances, the sequence read data may be derived using one or more targeted sequencing methods.

[0181] In some instances, sequence read data for a plurality of sequence reads derived from a control sample from the subject may be received. In one or more examples, the control sample may include peripheral blood mononuclear cells (PBMCs) from the subject. In one or more examples, the sequence read data derived from the control sample may be used to identify CH alterations.

[0182] In some instances, the sequence read data received at step 402 may be the same or similar to the sequence read data described above with respect to step 102 of FIG. 1, and the previous description may apply.

[0183] At step 404, at least one of a total number of clonal hematopoiesis (CH) alterations, a number of pathogenic CH alterations, a number of clonal CH alterations, or a maximum CH allele fraction in the sample may be determined. The total number of clonal hematopoiesis (CH) alterations, number of pathogenic CH alterations, number of clonal CH alterations, and / or maximum CH allele fraction in the sample may be determined based on the sequence read data. In some instances, the total number of clonal hematopoiesis (CH) alterations, the number of pathogenic CH alterations, the number of clonal CH alterations, and / or the maximum CH allele fraction in the sample may be determined by: (i) detecting an alteration based on the sequence read data and (ii) performing a statistical analysis of sequence read data for a selected subset of the plurality of sequence reads to distinguish between tumor-derived alterations and CH alterations. In one or more examples, each sequence read of the selected subset of sequence sf-5889500Docket No.: 197102014240 reads may be selected based on a gene associated with the alteration. Additional details related to detecting alterations and determining whether those alterations are tumor-derived alterations or CH alterations are provided below with reference to FIG. 2.

[0184] In some instances, the determination performed at step 404 may be the same or similar to the determination performed above with respect to step 104 of FIG. 1, and the previous description may apply.

[0185] At step 406, a CH burden score representing the CH burden for the subject may be determined based on the total number of CH alterations, the number of pathogenic CH alterations, the number of clonal CH alterations, and / or the maximum CH allele fraction in the sample. The CH burden score may provide a predictive measure of the subject’s clinical response to a treatment for a disease. In some instances, the CH burden score may be generated by inputting one or more of the total number of CH alterations, the number of pathogenic CH alterations, the number of clonal CH alterations, and / or the maximum CH allele fraction in the sample to a prediction model. For example, the prediction model may be a machine learning model trained to generate a CH burden score based on the total number of CH alterations, the number of pathogenic CH alterations, the number of clonal CH alterations, and / or the maximum CH allele fraction in the sample. In some instances, the CH burden score may be generated by aggregating the total number of CH alterations, the number of pathogenic CH alterations, the number of clonal CH alterations, and / or the maximum CH allele fraction in the sample and comparing the aggregated value to one or more predetermined CH burden score thresholds.

[0186] In some instances, the CH burden score generated at step 406 may be the same or similar to the CH burden score described above with respect to step 106 of FIG. 1, and the previous description may apply.

[0187] At step 408, a composite tumor fraction (cTF) for the sample may be determined based on the sequence read data. Tumor fraction can be an important indicator in monitoring for, diagnosing, and treating cancer. In some instances, tumor fraction is a measure of tumor genomic content, for example in a sample (e.g., biopsy), in proportion to the total genomic content regardless of cell origin. In general, it is advantageous to determine (e.g., estimate) tumor content, or a change in tumor content, from a sample, since this can aid in both reporting alterations and informing on disease presence or progression. For example, liquid biopsies, sf-5889500Docket No.: 197102014240 which typically utilize blood samples from cancer patients, can be useful when solid biopsies are not possible or recommended. However, the described tumor fraction determination techniques may be used for various types of samples, including solid and liquid samples. In some instances, the visual screening methods may be used in addition to or instead of the tumor fraction determination techniques.

[0188] In some instances, tumor fraction in a cell-free sample comprises a measure of the tumor DNA that has shed into the vasculature or lymphatics from a primary tumor relative to the amount of total DNA (e.g., tumor and normal) shed into the blood stream, and is being carried around the body in the blood circulation. Tumor fraction can be used to monitor a patient at risk for cancer (with or without current diagnosis); as a factor used in diagnosing cancer; or to determine if a current treatment regimen is having an effect, e.g., a beneficial effect.

[0189] Traditional processes for measuring tumor fraction typically require that both purity and ploidy, modeled parameters, be inferred from either log ratio and allele frequency measurements, both log ratio and allele frequency measurements, or from pathology review. In some instances, tumor fraction can be considered as a modeled parameter of the fraction of cancer cells in a heterogeneous tumor sample and can take into account tumor purity or other measures. In some instances, tumor cell ploidy can refer to the average weighted copy number of all chromosomes (or portions thereof). The ploidy observed in a sample can be impacted by the varying degrees of aneuploidy of tumor cells, the heterogeneity of the sample (e.g., different ratios of tumor cells to normal cells), or both.

[0190] Traditional processes for predicting tumor fraction can be highly unreliable for low tumor content due to poorly fit models. In some instances, tumor fraction (and associated confidence levels) may be determined based on the effects of tumor cell aneuploidy. The tumor cell aneuploidy may be measured by the allele coverage or allele fraction at one or more subgenomic intervals in a sample. In some instances, the subgenomic interval comprises a heterozygous single nucleotide polymorphism (SNP) site. In some instances, the subgenomic interval comprises more than one nucleotide positions.

[0191] As an example, for a heterozygous SNP, an allele fraction value of 0.50 can indicate a typical diploid subgenomic interval; and an allele fraction that deviates from an expected value of 0.50 indicates aneuploidy at that site. In these examples, this deviation of allele coverage can sf-5889500Docket No.: 197102014240 be correlated with tumor fraction in a training set in order to build a model that determines (e.g., predicts or estimates) tumor fraction based on allele coverage. In some instances, the methods described herein correlate deviation of allele fraction or log ratio with tumor fraction, thereby eliminating the need to model tumor purity and ploidy. In some instances, the methods described herein allow for more accurate determination of tumor fraction of low level, e.g., less than 30%. In an instance, the allele fraction or log ratio is determined by a method comprising sequencing, e.g., next generation sequencing (NGS). It will be appreciated that the methods for determining allele fraction or log ratio are not limited to sequencing. Any method that measures, for example, SNP coverage or relative level (e.g., abundance) of SNPs, as well as, any method that measures coverage from larger genomic regions can be used. In an instance, the allele fraction or log ratio is determined by a method other than sequencing, e.g., is determined by an array-based comprehensive genomic hybridization (aCGH). In an instance, the tumor fraction is, or is expected to be, less than or equal to 0.25, less than or equal to 0.2, less than or equal to 0.15, or less than or equal to 0.1, e.g., between 0.1 and 0.3, between 0.1 and 0.2, between 0.2 and 0.3, or between 0.15 and 0.25.

[0192] While in some instances the methods described herein use allele fraction or log ratio to indicate expected coverage proportions, it will be appreciated that the present disclosure is generally intended to describe the correlation of tumor fraction to expected coverage deviations, without limitation to allele fraction, log ratio, or any other specific metric.

[0193] In some instances, tumor fraction may be represented numerically (e.g., fraction or percentage) indicating the amount of DNA from tumor cells versus the total amount of DNA (e.g., tumor and non-tumor DNA) in the sample.

[0194] In some instances, determining a tumor fraction of a sample from a subject may include: acquiring a plurality of value each indicative of an allele fraction at a corresponding locus within a subgenomic interval in the sample, determining a certainty metric indicative of a dispersion of the plurality of values, accessing a predetermined relationship between a stored certainty metric and a stored tumor fraction, and determining, from the certainty metric and the predetermined relationship, the tumor fraction of the sample

[0195] A value indicative of an allele fraction can be determined for each corresponding locus. The loci may include one or more nucleotide. In some instances, the loci may have a different sf-5889500Docket No.: 197102014240 maternal allele and paternal allele. In some instances, the loci may have the same maternal allele and paternal allele. In some instances, the values indicative of allele fraction at the corresponding loci may be a plurality of allele fractions at the corresponding loci in the sample. The allele fraction at each of the corresponding loci may be determined, for example, by sequencing nucleic acid molecules in the tumor sample and assigning an allele coverage for each allele at each locus. For example, the allele fraction at locus i (^^^) may be determined by:where ^^^^,^is the coverage of allele a at locus i, and ^^^^,^is the coverage of allele b at locusi. In some instances, allele a and allele b are assigned such that ^^^^,^≤ ^^^^,^, such that ^^^≤0.5.

[0196] In some instances, the expected allele fraction is the allele fraction expected in a healthy individual or healthy sample (i.e., a non-tumor sample). For example, the allele fraction at a heterozygous locus (that is, having a different maternal allele and paternal allele) is expected to be 0.5, and the allele fraction at a homozygous locus (that is, wherein the maternal allele and the paternal allele are the same) is expected to be 1.0.

[0197] Allele fraction is an exemplary value for determining tumor fraction according to the methods described herein, although other values indicative of allele fraction may be used in some instances. In some instances, the value indicative of the allele fraction is a relative difference in allele frequency. For example, the value indicative of the allele fraction may be ratio of the difference in the abundance (e.g., a coverage or sequencing depth) between a maternal allele and a paternal allele relative to the abundance of the maternal allele or the paternal allele. That is, in some instances, the value can a relative difference:where ^^^^,^is the coverage of allele a at locus i, and ^^^^,^is the coverage of allele b at locus i. In a healthy individual or healthy sample, the difference between the allele frequency, as well as the relative difference, is expected to be 0.In some instances, a probability distribution function is determined for the plurality of values indicative of allele fraction. For example, in some instances, the probability distribution function is determined for the plurality of allele sf-5889500Docket No.: 197102014240 fractions at the plurality of corresponding loci in the sample. In some instances, the probability distribution function for the plurality of allele fractions is defined as:wherein ^^^^,^is the coverage of allele a at locus i, and ^^^^,^is the coverage of allele b at locus i.

[0198] The dispersion (or certainty metric) can be, for example, a deviation from the expected allele fraction (or value indicative of expected allele fraction) across the plurality of loci. In some instances, the certainty metric is a root mean squared deviation from the expected allele fraction (or value indicative thereof). For example, in some instances, the certainty metric is a root mean squared deviation (RMSD) defined by:wherein ^^^is the allele frequency (or value indicative of the allele frequency, such as a relative difference ratio) at locus i, ^^12314516is the expected allele frequency at locus i, and N is the number of loci in the plurality of corresponding loci. For example, for some loci, ^^12314516may be 0.5, and at other loci ^^12314516may be 1. In some instances, the loci include only those loci having a different maternal allele and paternal allele. Thus, the ^^12314516may be defined as 0.5 across all loci, and the RMSD can be defined as:

[0199] In some instances, the value indicative of the allele fraction may be ratio of the difference in abundance (e.g., a coverage or sequencing depth) between a maternal allele and a paternal allele, relative to the abundance of the maternal allele or the paternal allele, and the ^^12314516may be defined as 0. Thus, the RMSD can be defined as:sf-5889500Docket No.: 197102014240 where ^^^^,^is the coverage of allele a at locus i, and ^^^^,^is the coverage of allele b at locus i.

[0200] In some instances, a probability distribution (e.g., a probability distribution function) can be determined for allele fractions across a plurality of loci. The certainty metric (e.g., a dispersion) can be a metric of the probability distribution, such as an entropy of the probability distribution. For example, in some instances, the entropy of an allele fraction probability distribution function ()DE"^^%F) may be defined as:where E"^^%is the allele fraction probability distribution function, and n is the log base. In some instances, the log base is 2 (i.e., log2). Accordingly, in some instances, the entropy of an allele fraction probability distribution function ()DE"^^%F) may be defined as: )DE"^^%F = ∑;^.JI:;E"^^%^G^8 / E"^^%7.

[0201] In some instances, determining the tumor fraction may include: acquiring a plurality of values, each value indicative of a difference between an allele coverage of a locus in a tumor sample and an allele coverage of the same locus in a non-tumor sample at a plurality of loci within a subgenomic interval; determining a certainty metric indicative of a dispersion of the plurality of values; accessing a predetermined relationship between a stored certainty metric and a stored tumor fraction; and determining, from the certainty metric and the predetermined relationship, the tumor fraction of the sample. In some instances, the tumor sample and the non- tumor sample are obtained from the same individual (i.e., a matched normal control). In some instances, the tumor sample and the non-tumor sample are obtained from different individuals. The coverage may be a raw coverage (for example, a raw number of sequencing reads), a normalized coverage (for example, normalized to a mean or median sequencing depth), and / or otherwise bias-corrected coverage (for example, a GC-bias corrected coverage depth). In some instances, the allele coverage comprises the coverage of a maternal allele and a coverage of a paternal allele (such as a sum of the coverage of the maternal allele and the coverage of the paternal allele). In some instances, the allele coverage consists of the coverage of a maternal allele and a coverage of a paternal allele (such as a sum of the coverage of the maternal allele and the coverage of the paternal allele). sf-5889500Docket No.: 197102014240

[0202] In some instances, each value indicative of the difference between an allele coverage of the locus in a tumor sample and an allele coverage of the same locus in the non-tumor sample comprises a ratio of the allele coverage of a locus in the tumor sample compared to the allele coverage of the same locus in the non-tumor sample. In some instances, the allele coverage comprises the coverage of a maternal allele and a coverage of a paternal allele (such as a sum of the coverage of the maternal allele and the coverage of the paternal allele). In some instances, the allele coverage consists of the coverage of a maternal allele and a coverage of a paternal allele (such as a sum of the coverage of the maternal allele and the coverage of the paternal allele). For example, in some instances, the ratio may be defined as:is the coverage of the maternal allele at the locus i within the tumor sample, ^^^^^,^^H41Ris the coverage of the paternal allele at the locus i within the tumor sample, ^^^^9,^SRT^Uis the coverage of the maternal allele at the locus i within the non-tumor sample, and ^^^^9,^SRT^Uis the coverage of the paternal allele at the locus i within the non-tumor sample.

[0203] In some instances, each value may be indicative of the difference between an allele coverage of the locus in a tumor sample and an allele coverage of the same locus in the non- tumor sample is a log ratio (such as a log2ratio) of the allele coverage of a locus in the tumor sample compared to the allele coverage of the same locus in the non-tumor sample. In some instances, the allele coverage comprises the coverage of a maternal allele and a coverage of a paternal allele (such as a sum of the coverage of the maternal allele and the coverage of the paternal allele). In some instances, the allele coverage consists of the coverage of a maternal allele and a coverage of a paternal allele (such as a sum of the coverage of the maternal allele and the coverage of the paternal allele). For example, the log ratio may be defined, in some instances, as:where logn is the log at base n, ^^^^^,^^H41Ris the coverage of the maternal allele at the locus i within the tumor sample, ^^^^^,^^H41Ris the coverage of the paternal allele at the locus i within the sf-5889500Docket No.: 197102014240 tumor sample, ^^^^9,^SRT^Uis the coverage of the maternal allele at the locus i within the non- tumor sample, and ^^^^9,^SRT^Uis the coverage of the paternal allele at the locus i within the non- tumor sample. For example, the log ratio may be a log2 ratio. In some instances, the log ratio is defined as:is the coverage of the maternal allele at the locus i within the tumor sample, ^^^^^,^^H41Ris the coverage of the paternal allele at the locus i within the tumor sample, ^^^^9,^SRT^Uis the coverage of the maternal allele at the locus i within the non-tumor sample, and ^^^^9,^SRT^Uis the coverage of the paternal allele at the locus i within the non-tumor sample.

[0204] In some instances, each value may be indicative of the difference between an allele coverage of the locus in a tumor sample and an allele coverage of the same locus in the non- tumor sample comprises a ratio of the difference between the allele coverage of a locus in the tumor sample compared to the allele coverage of the same locus in the non-tumor sample, relative to the allele coverage of the same locus in the non-tumor sample. In some instances, the allele coverage may include the coverage of a maternal allele and a coverage of a paternal allele (such as a sum of the coverage of the maternal allele and the coverage of the paternal allele). In some instances, the allele coverage may consist of the coverage of a maternal allele and a coverage of a paternal allele (such as a sum of the coverage of the maternal allele and the coverage of the paternal allele). For example, the ratio may be defined as:is the coverage of the maternal allele at the locus i within the tumor sample, ^^^^^,^^H41Ris the coverage of the paternal allele at the locus i within the tumor sample, ^^^^9,^SRT^Uis the coverage of the maternal allele at the locus i within the non-tumor sample, and ^^^^9,^SRT^Uis the coverage of the paternal allele at the locus i within the non-tumor sample.

[0205] In some instances, a probability distribution function may be determined for the plurality of values indicative of the difference between an allele coverage of the locus in a tumor sample and an allele coverage of the same locus in the non-tumor sample. In some instances, the allele sf-5889500Docket No.: 197102014240 coverage may include the coverage of a maternal allele and a coverage of a paternal allele (such as a sum of the coverage of the maternal allele and the coverage of the paternal allele). In some instances, the allele coverage may consist of the coverage of a maternal allele and a coverage of a paternal allele (such as a sum of the coverage of the maternal allele and the coverage of the paternal allele). For example, the probability distribution function may be determined for the plurality of ratios of the allele coverage of a locus in the tumor sample compared to the allele coverage of the same locus in the non-tumor sample (such as a log ratio, for example a log2 ratio). In some instances, the probability distribution function for the plurality of allele fractions is defined by:where logn is the log at base n, ^^^^^,^^H41Ris the coverage of the maternal allele at the locus i within the tumor sample,is the coverage of the paternal allele at the locus i within the tumor sample, ^^^^9,^SRT^Uis the coverage of the maternal allele at the locus i within the non-tumor sample, and ^^^^9,^SRT^Uis the coverage of the paternal allele at the locus i within the non-tumor sample. In some instance, log ratio is a log2 ratio. For example, the probability distribution function for the plurality of allele fractions is defined by:whereis the coverage of the maternal allele at the locus i within the tumor sample, ^^^^^,^^H41Ris the coverage of the paternal allele at the locus i within the tumor sample, ^^^^9,^SRT^Uis the coverage of the maternal allele at the locus i within the non-tumor sample, and ^^^^9,^SRT^Uis the coverage of the paternal allele at the locus i within the non-tumor sample.

[0206] The dispersion (or certainty metric) can be, for example, a deviation of each value within the plurality of values from an expected value across the corresponding loci. The expected value is the value that would be expected if the tumor sample were non-tumor (e.g., a healthy) sample. In some instances, the certainty metric is a root mean squared deviation from the expected value. For example, the certainty metric is a root mean squared deviation (RMSD) defined by: sf-5889500Docket No.: 197102014240

[0207] In some instances, the value indicative of the allele fraction is a ratio of the difference between the allele coverage of a locus in the tumor sample compared to the allele coverage of the same locus in the non-tumor sample, relative to the allele coverage of the same locus in the non- tumor sample. Thus, the RMSD can be defined as:

[0208] In some instances, a probability distribution (e.g., a probability distribution function) can be determined for the plurality of values indicative of the difference between an allele coverage of the locus in a tumor sample and an allele coverage of the same locus in the non-tumor sample. The certainty metric (e.g., a dispersion) can be a metric of the probability distribution, such as an entropy of the probability distribution. For example, in some instances, the entropy of an allele fraction probability distribution function ()DE"^^%F) may be defined as:where:where lognis a log having base n,is the coverage of the maternal allele at the locus within the tumor sample, ^^^^^,^^H41Ris the coverage of the paternal allele at the locus i within the tumor sample, ^^^^9,^SRT^Uis the coverage of the maternal allele at the locus i within the non-tumor sample, and ^^^^9,^SRT^Uis the coverage of the paternal allele at the locus i within the non-tumor sample. In some instances, the log base is 2 (i.e., log2). Accordingly, in some instances, the entropy of an allele fraction probability distribution function ()DE"^^%F) may be defined as:where: sf-5889500Docket No.: 197102014240whereis the coverage of the maternal allele at the locus i within the tumor sample, ^^^^^,^^H41Ris the coverage of the paternal allele at the locus i within the tumor sample, ^^^^9,^SRT^Uis the coverage of the maternal allele at the locus i within the non-tumor sample, and ^^^^9,^SRT^Uis the coverage of the paternal allele at the locus i within the non-tumor sample.

[0209] A relationship between one or more stored certainty metrics and one or more stored tumor fractions can be used to determine the tumor fraction based on the determined certainty metrics. In some instances, a model may be trained to using a training dataset that includes training certainty metrics and associated tumor fractions to determine the relationship between the certainty metrics and the tumor fractions. The training dataset may be determined, for example, using a plurality of clinical samples with known (i.e., training) tumor fractions (for example, as determined by maximum somatic allele frequency (MSAF), which filters germline variant calls from all calls in a tumor sample and compares residual variants (i.e., the maximum somatic variants) to the total variants (maximum somatic variants plus germline variants) to determine the maximum somatic allele frequency). Nucleic acid molecules in the clinical samples can be sequenced to determine allele frequency across a plurality of loci (or a value indicative of an allele frequency), as well as an associated training certainty metric. The training certainty metrics can be correlated with the training tumor fractions to determine the relationship between certainty metric and tumor fraction. In another method, serial dilutions may be made from one or more clinical samples to obtain a plurality of different tumor fractions, which can be correlated with the certainty metric for the serially diluted samples to determine the relationship.

[0210] In some instances, to determine (e.g., estimate) the tumor fraction, a training subprocess may first be performed. A dataset can be constructed from clinical specimens. Using the training set and in-silico dilutions of the training set, tumor fraction can be correlated to variations in allele fractions or log ratios corresponding to aneuploidy typically observed in tumors. In other examples, cell-line / clinical sample dilutions can be performed.

[0211] In some instances, the certainty metric may be functions of the coverage at particular SNP bins for particular alleles and / or an allele frequency (e.g., in the range of 0 to 0.5). In some examples, the training data uses as input a deviation metric (e.g., allele fraction deviation or log sf-5889500Docket No.: 197102014240 ratio deviation) and returns the estimated tumor fraction, along with lower and upper bounds. Values that deviate from (i.e., fall between) 0 and 1 and not 0.5 (exclusive) may be thought of as “noise,” and the averaged noise may be correlated with an expected or estimated tumor fraction. In other examples, the training data provides as input a log ratio deviation metric, or in general, any metric which quantifies coverage deviations from expectations. In either case, the allele coverage deviation metric or the log ratio deviation metric may be a measure of the tumor fraction.

[0212] Utilizing these correlations derived during training, a tumor fraction of a patient can be estimated or evaluated with upper and lower bounds. Coverage metrics, such as SNP allele coverage variation metrics, may be used in generating the correlation.

[0213] In some instances, the cTF may be determined by accounting for both variant allele frequencies (VAF) and the tumor fraction of the sample. The composite tumor fraction determination methodology may measure the ctDNA fraction of the total circulating cfDNA across a broad range of tumor content values, leveraging two complementary metrics: a proprietary tumor fraction estimator (TFE) metric and a maximum somatic allele frequency (MSAF) metric.

[0214] The TFE metric is based on a measure of tumor aneuploidy that incorporates observed deviations in, for example, SNP coverage across the genome for a given sample. Calculated values for this metric may be calibrated against a training set based on samples with well-defined tumor fractions to generate an estimate of the tumor fraction. The MSAF method may estimate the fraction of ctDNA versus that of all sources of cfDNA in plasma, including circulating germline DNA from patients. The MSAF metric may be determined based on the allele fraction for all known somatic, likely somatic, and variant of unknown significance substitution alterations detected at >2000x median unique coverage by non-PCR duplicate read pairs, excluding certain common and rare germline variants. A select list of variants associated with clonal hematopoiesis are not considered while determining MSAF.

[0215] The cTF measurement may default to the TFE metric’s value if the TFE metric has been calculated. When lack of tumor aneuploidy limits the TFE metric’s ability to return an informative estimate of ctDNA fraction, the MSAF metric’s value may be used to generate a ctDNA fraction value. In this case, only variants with allele fraction at or below TFE metric’s sf-5889500Docket No.: 197102014240 limit of detection may be used as input for MSAF. When the TFE’s estimate is unavailable or impacted by failed quality control measures, the cTF may be generated from the MSAF metric calculation, as previously described. In effect, the MSAF metric’s process can typically be used at low ctDNA fractions due to its higher reliability at lower ctDNA fractions, whereas the TFE metric’s process can be used at high ctDNA fractions due to its higher reliability at higher ctDNA fractions.

[0216] At step 410, a survival prognosis, a clinical response, and / or an immune-related adverse event for the subject may be predicted based on the CH burden score, the cTF, or both the CH burden score and the cTF. In some instances, the clinical response may refer to a predicted response of the subject to an immune checkpoint inhibitor. In some instances, predicting the immune-related adverse event may include predicting a likelihood of one or more immune- related adverse events occurring for the subject. For example, the immune-related adverse events may include autoimmune disorders (e.g., arthritis) or other inflammatory conditions (e.g., pneumonitis, myocarditis, colitis).

[0217] In some instances, the prediction may be based on at least one of a comparison of the determined CH burden score to a predetermined CH burden threshold, a comparison of the determined cTF to a predetermined cTF threshold, or both comparisons of the determined CH burden score to the predetermined CH burden threshold and the determined cTF to the predetermined cTF. For example, the CH burden score may be determined based on a total number of CH alterations in the sample. Some example CH alterations that can be detected include short variants, focal copy number alterations, rearrangements, and genome-wide disruptions (e.g., larger aneuploidy). In one or more examples, the CH burden score may be discrete values of 0, 1, 2, 3, etc. In some examples, the CH burden score may be a continuous value between a lower limit and an upper limit. In one or more examples, the predetermined CH burden threshold may be a discreate value (e.g., 1 or more, 2 or more, 3 or more, etc.). In some examples, the predetermined CH burden threshold may be a threshold CH burden score, and a predicted CH burden score may be compared to the threshold CH burden score. In some examples, the predetermined CH burden threshold is equal to 2, 3, 4, or 5 CH alterations. In one or more examples, the predetermined cTF threshold is 0.01% or greater, 0.1% or greater, 0.5% or greater, 1.0% or greater, or 10% or greater. sf-5889500Docket No.: 197102014240

[0218] In some instances, process 400 may further include a step of classifying the subject into one of a set of clinical response classes based on the determined CH burden score and the determined cTF. The subject may be classified into one of the set of clinical response classes. For example, the set of clinical response classes may include (i) a first clinical response class comprising first subjects, wherein the first subjects exhibit a CH burden score greater than the predetermined CH burden threshold and a cTF greater than the predetermined cTF threshold, (ii) a second clinical response class comprising second subjects, wherein the second subjects exhibit a CH burden score greater than the predetermined CH burden threshold and a cTF less than the predetermined cTF threshold, (iii) a third clinical response class comprising third subjects, wherein the third subjects exhibit a CH burden score less than the predetermined CH burden threshold and a cTF greater than the predetermined cTF threshold, and (iv) a fourth clinical response class comprising fourth subjects, wherein the fourth subjects exhibit a CH burden score less than the predetermined CH burden threshold and a cTF less than the predetermined cTF threshold.

[0219] In some instances, a treatment decision for the subject may be selected based on a clinical response class from the set of clinical response classes that the subject is classified into.

[0220] In some instances, the CH burden score for the sample may be generated using one or more machine learning models. The machine learning models may be trained to output a CH burden score based on input data. The input data may include data representing a total number of clonal hematopoiesis (CH) alterations, a number of pathogenic CH alterations, a number of clonal CH alterations, or a maximum CH allele fraction of a corresponding sample. In some instances, the one or more machine learning models may be trained to output a consolidated CH burden score based on input data, wherein the input data comprises at least one of: a total number of clonal hematopoiesis (CH) alterations, a number of pathogenic CH alterations, a number of clonal CH alterations, or a maximum CH allele fraction, and a composite tumor fraction (cTF) for the sample based on the sequence read data. Additionally, or alternatively, the machine learning models may be trained to predict the survival prognosis, clinical response, and / or immune-related adverse event based on the total number of CH alterations, the number of pathogenic CH alterations, the number of clonal CH alterations, and / or the maximum CH allele fraction of the corresponding sample. For example, the machine learning model may output a sf-5889500Docket No.: 197102014240 treatment decision for the subject based on the input total number of CH alterations, number of pathogenic CH alterations, number of clonal CH alterations, and / or the maximum CH allele fraction. The machine learning models may select the treatment response based on a clinical response class from the set of clinical response classes that the subject is classified into. In some instances, the machine learning model may output the predicted survival prognosis, clinical response, and / or immune-related adverse event based on a comparison of the generated CH burden score and one or more predetermined CH burden score thresholds.

[0221] In some instances, process 400 may further include a step of generating training data for training the one or more machine learning models. The training data may include a plurality of tuples respectively associated with a plurality of subjects. In one or more examples, each tuple may include at least one of a total number of clonal hematopoiesis (CH) alterations, a number of pathogenic CH alterations, a number of clonal CH alterations, or a maximum CH allele fraction determined for a sample obtained from a respective one of the plurality of subjects. The training data may further include, for each tuple, the cTF determined for a sample obtained from a respective one of the plurality of subjects.

[0222] Process 400 can be performed, for example, using one or more electronic devices implementing a software platform. In some examples, process 400 is performed using a client- server system, and the blocks of process 400 are divided up in any manner between the server and a client device. In other examples, the blocks of process 400 are divided up between the server and multiple client devices. Thus, while portions of process 400 are described herein as being performed by particular devices of a client-server system, it will be appreciated that process 400 is not so limited. In other examples, process 400 is performed using only a client device or only multiple client devices. In process 400, 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 400. Accordingly, the operations as illustrated (and described in greater detail below) are exemplary by nature and, as such, should not be viewed as limiting.

[0223] FIG. 5 provides a non-limiting example of a pipeline 500 implemented using one or more machine learning models for determining a CH burden score, in accordance with various instances. In some instances, pipeline 500 may multiple prediction models. For example, sf-5889500Docket No.: 197102014240 pipeline 500 may include a first prediction model 504 configured to determine one or more of a total number of clonal hematopoiesis (CH) alterations 508a, a number of pathogenic CH alterations 508b, a number of clonal CH alterations 508c, or a maximum CH allele fraction 508d determined for a sample obtained from a respective one of the plurality of subjects. In some instances, sequence read data 502 may be input to first prediction model 504, which may output total number of clonal hematopoiesis (CH) alterations 508a, number of pathogenic CH alterations 508b, number of clonal CH alterations 508c, and / or maximum CH allele fraction 508d.

[0224] Sequence read data 502 may be received by a computing system that is used to implement first prediction model 504. In some instances, the subject has been diagnosed with a form of cancer. For example, the subject has been diagnosed with non-squamous non-small cell lung cancer (NSCLC). The sample may be a liquid biopsy sample. In one or more examples, the liquid biopsy sample may be a blood plasma sample liquid biopsy sample, a whole blood sample, peripheral blood mononuclear cells (PBMCs) sample, and / or a buffy coat sequencing sample. In one or more examples, the liquid biopsy sample comprises a blood plasma sample. In some instances, sequence read data 502 may be derived using one or more targeted sequencing methods.

[0225] In some instances, sequence read data 502 may be derived from a control sample from the subject. In one or more examples, the control sample may include peripheral blood mononuclear cells (PBMCs) from the subject. In one or more examples, sequence read data 502 derived from the control sample may be used to identify CH alterations. Some example CH alterations that can be detected include short variants, focal copy number alterations, rearrangements, and genome-wide disruptions (e.g., larger aneuploidy).

[0226] In some instances, pipeline 500 further includes a cTF determination module 506. cTF determination module 506 may be configured to receive sequence read data 502 and determine a composite tumor fraction (cTF) 510 for the subject. cTF determination module 506 may be effectuated based on execution of one or more software programs by a computing system. In some instances, cTF determination module 506 may determine cTF 510 based on both variant allele frequencies (VAF) and the tumor fraction of the sample. cTF determination module 506 may be configured to measure the ctDNA fraction of the total circulating cfDNA across a broad sf-5889500Docket No.: 197102014240 range of tumor content values, leveraging two complementary metrics: a proprietary tumor fraction estimator (TFE) metric and a maximum somatic allele frequency (MSAF) metric.

[0227] The TFE metric is based on a measure of tumor aneuploidy that incorporates observed deviations in, for example, SNP coverage across the genome for a given sample. Calculated values for this metric may be calibrated against a training set based on samples with well-defined tumor fractions to generate an estimate of the tumor fraction. The MSAF method may estimate the fraction of ctDNA versus that of all sources of cfDNA in plasma, including circulating germline DNA from patients. The MSAF metric may be determined based on the allele fraction for all known somatic, likely somatic, and variant of unknown significance substitution alterations detected at >2000x median unique coverage by non-PCR duplicate read pairs, excluding certain common and rare germline variants. A select list of variants associated with clonal hematopoiesis are not considered while determining MSAF.

[0228] In some instances, cTF determination module 506 may assign the TFE metric’s value as cTF 510 based on if the TFE metric has been calculated. When lack of tumor aneuploidy limits the TFE metric’s ability to return an informative estimate of ctDNA fraction, cTF determination module 506 may use the MSAF metric’s value to generate a ctDNA fraction value. In this case, only variants with allele fraction at or below TFE metric’s limit of detection may be used as input for MSAF. When the TFE’s estimate is unavailable or impacted by failed quality control measures, cTF determination module 506 may be configured to generate cTF 510 from the MSAF metric calculation, as previously described. In effect, the MSAF metric’s process can typically be used at low ctDNA fractions due to its higher reliability at lower ctDNA fractions, whereas the TFE metric’s process can be used at high ctDNA fractions due to its higher reliability at higher ctDNA fractions. In some instances, cTF determination module 506 may implement one or more machine learning models to determine cTF 510 from sequence read data 502.

[0229] In some instances, pipeline 500 may further include a second prediction model 512. Second prediction model 512 may be configured to generate a CH burden score 514 for the subject based on total number of clonal hematopoiesis (CH) alterations 508a, number of pathogenic CH alterations 508b, number of clonal CH alterations 508c, and / or maximum CH allele fraction 508d. In some instances, second prediction model 512 may be configured to sf-5889500Docket No.: 197102014240 generate CH burden score 514 for the subject based on cTF 510 and at least one of total number of clonal hematopoiesis (CH) alterations 508a, number of pathogenic CH alterations 508b, number of clonal CH alterations 508c, or maximum CH allele fraction 508d. The CH burden score may provide a predictive measure of the subject’s clinical response to a treatment for a disease. In some instances, cTF 510 and at least one of total number of clonal hematopoiesis (CH) alterations 508a, number of pathogenic CH alterations 508b, number of clonal CH alterations 508c, or maximum CH allele fraction 508d may be received by a computing system that is used to implement second prediction model 512.

[0230] Second prediction model 512 may be a statistical model, a machine learning model, or other type of prediction model. In some instances, second prediction model 512 may be a machine learning model trained to output a CH burden score based on input data comprising at least one of a total number of clonal hematopoiesis (CH) alterations, a number of pathogenic CH alterations, a number of clonal CH alterations, or a maximum CH allele fraction. In some instances, second prediction model 512 may be a machine learning model trained to output a consolidated CH burden score based on input data, wherein the input data comprises at least one of total number of clonal hematopoiesis (CH) alterations 508a, number of pathogenic CH alterations 508b, number of clonal CH alterations 508c, or maximum CH allele fraction 508d, and cTF 510 for the sample based on sequence read data 502.

[0231] Pipeline 500 may further include a classifier 516. Classifier 516 may be trained to determine a clinical response class 518 of the subject based CH burden score 514. In some instances, classifier 516 may classify the subject into one of set of clinical response classes 518. The set of clinical response classes 518 may include a plurality of clinical response classes. For example, the set of clinical response classes 518 may include a first clinical response class, a second clinical response class, a third clinical response class, and a fourth clinical response class, however the set may include additional or fewer clinical response classes. The first clinical response class may include first subjects. The first subjects may each exhibit a CH burden score that is greater than or equal to a predetermined CH burden threshold and a cTF that is greater than or equal to a predetermined cTF threshold. The second clinical response class may include second subjects. The second subjects may each exhibit a CH burden score that is greater than or equal to the predetermined CH burden threshold and a cTF that is less than the predetermined sf-5889500Docket No.: 197102014240 cTF threshold. The third clinical response class may include third subjects. The third subjects may each exhibit a CH burden score that is less than the predetermined CH burden threshold and a cTF that is greater than or equal to the predetermined cTF threshold. The fourth clinical response class may include fourth subjects. The fourth subjects may each exhibit a CH burden score that is less than the predetermined CH burden threshold and a cTF that is less than the predetermined cTF threshold.

[0232] In some instances, a treatment decision for the subject may be selected based on clinical response class 518 from the set of clinical response classes that the subject is classified into. In some instances, clinical response class 518 may be used to predict a likelihood of an immune- related adverse event occurring for the subject. The immune-related adverse event may be an autoimmune disorder or an inflammatory condition. For example, the autoimmune disorder comprises arthritis, and wherein the inflammatory condition comprises pneumonitis, myocarditis, or colitis.

[0233] In some instances, classifier 516 may determine clinical response class 518 based on at least one of a comparison of CH burden score 514 to a predetermined CH burden threshold, or a comparison of cTF 510 to a predetermined cTF threshold. As an example, CH burden score 514 may be determined based on total number of CH alterations 508a in the sample, and the predetermined CH burden threshold may be equal to 2, 3, 4, or 5 CH alterations. In one or more examples, the predetermined cTF threshold is 0.01% or greater, 0.1% or greater, 0.5% or greater, 1.0% or greater, or 10% or greater. Some example CH alterations that can be detected include short variants, focal copy number alterations, rearrangements, and genome-wide disruptions (e.g., larger aneuploidy). In one or more examples, the CH burden score may be discrete values of 0, 1, 2, 3, etc. In some examples, the CH burden score may be a continuous value between a lower limit and an upper limit. In one or more examples, the predetermined CH burden threshold may be a discreate value (e.g., 1 or more, 2 or more, 3 or more, etc.). In some examples, the predetermined CH burden threshold may be a threshold CH burden score, and a predicted CH burden score may be compared to the threshold CH burden score.

[0234] In some instances, first prediction model 504, second prediction model 512, and / or classifier 516 may be implemented using one or more of the following: Ordinary Least Squares Regression (OLSR), Linear Regression, Logistic Regression, Stepwise Regression, Multivariate sf-5889500Docket No.: 197102014240 Adaptive Regression Splines (MARS), Locally Estimated Scatterplot Smoothing (LOESS), Instance-based Algorithms, k-Nearest Neighbor (KNN), Learning Vector Quantization (LVQ), Self-Organizing Map (SOM), Locally Weighted Learning (LWL), Regularization Algorithms, Ridge Regression, Least Absolute Shrinkage and Selection Operator (LASSO), Elastic Net, Least-Angle Regression (LARS), Decision Tree Algorithms, Classification and Regression Tree (CART), Iterative Dichotomizer 3 (ID3), C4.5 and C5.0 (different versions of a powerful approach), Chi-squared Automatic Interaction Detection (CHAID), Decision Stump, M5, Conditional Decision Trees, Naive Bayes, Gaussian Naive Bayes, Causality Networks (CN), Multinomial Naive Bayes, Averaged One-Dependence Estimators (AODE), Bayesian Belief Network (BBN), Bayesian Network (BN), k-Means, k-Medians, K-cluster, Expectation Maximization (EM), Hierarchical Clustering, Association Rule Learning Algorithms, A-priori algorithm, Eclat algorithm, Artificial Neural Network Algorithms, Perceptron, Back- Propagation, Hopfield Network, Radial Basis Function Network (RBFN), Deep Learning Algorithms, Deep Boltzmann Machine (DBM), Deep Belief Networks (DBN), Convolutional Neural Network (CNN), Deep Metric Learning, Stacked Auto-Encoders, Dimensionality Reduction Algorithms, Principal Component Analysis (PCA), Principal Component Regression (PCR), Partial Least Squares Regression (PLSR), Collaborative Filtering (CF), Latent Affinity Matching (LAM), Cerebri Value Computation (CVC), Multidimensional Scaling (MDS), Projection Pursuit, Linear Discriminant Analysis (LDA), Mixture Discriminant Analysis (MDA), Quadratic Discriminant Analysis (QDA), Flexible Discriminant Analysis (FDA), Ensemble Algorithms, Boosting, Bootstrapped Aggregation (Bagging), AdaBoost, Stacked Generalization (blending), Gradient Boosting Machines (GBM), Gradient Boosted Regression Trees (GBRT), Random Forest, Computational intelligence (evolutionary algorithms, etc.), Computer Vision (CV), Recommender Systems, Reinforcement Learning, Graphical Models, or separable convolutions (e.g., depth-separable convolutions, spatial separable convolutions).

[0235] FIG. 6 provides a non-limiting example of training process 600 for training a machine learning model, in accordance with various instances. In some instances, first prediction model 504, second prediction model 512, and / or classifier 516 may be trained using training 600. Persons of ordinary skill in the art will recognize that other training processes may be used to train first prediction model 504, second prediction model 512, and / or classifier 516. For example, some models may be trained using contrastive learning, self-supervised learning, sf-5889500Docket No.: 197102014240 unsupervised learning, and / or supervised learning. Thus, training process 600 should not be construed as limiting the disclosed instances to particular training processes. Furthermore, training process 600 is not restricted to one type of machine learning model. In training process 600, training data 604 may be retrieved from training data database 610. Different training data 604 may be used to train different types of machine learning models. Furthermore, validation data may also be stored in training data database 610. Training data 604 and the validation data may be identified and retrieved prior to training process 600 beginning.

[0236] In some instances, training data 604 may include a plurality of tuples respectively associated with a plurality of subjects, wherein each tuple comprises at least one of a total number of clonal hematopoiesis (CH) alterations, a number of pathogenic CH alterations, a number of clonal CH alterations, or a maximum CH allele fraction determined for a sample obtained from a respective one of the plurality of subjects. In some instances, training data 604 may also include, for each tuple, a cTF determined for a sample obtained from a respective one of the plurality of subjects.

[0237] In some instances, machine learning model 602 may be selected for training using training process 600. Machine learning model 602 may be retrieved from model database 612. Machine learning model 602 may be selected based on a total number of clonal hematopoiesis (CH) alterations, a number of pathogenic CH alterations, a number of clonal CH alterations, or a maximum CH allele fraction determined for a sample obtained from a subject, a cTF, and / or other criteria. Some example CH alterations that can be detected include short variants, focal copy number alterations, rearrangements, and genome-wide disruptions (e.g., larger aneuploidy). In some instances, training data 60 may be retrieved from training data database 610.

[0238] In some instances, training data 604 may be provided to machine learning model 602. Training data 604 may include tuples respectively associated with a plurality of subjects, wherein each tuple comprises at least one of a total number of clonal hematopoiesis (CH) alterations, a number of pathogenic CH alterations, a number of clonal CH alterations, a maximum CH allele fraction determined for a sample obtained from a respective one of the plurality of subjects, and / or a cTF. Training data 604 may be input to machine learning model 602, which may generate a prediction 606. Prediction 606 may indicate, amongst other information, a predicted CH burden score for each tuple of training data 604. sf-5889500Docket No.: 197102014240

[0239] Prediction 606 may be compared to a ground truth identified from training data 604. As mentioned above, training data 604 may include labels indicating a ground truth CH burden score. Therefore, prediction 606 may be used to indicate whether machine learning model 602 correctly identified the characteristics. Training process 600 may compare a ground truth CH burden score for a tuple from training data 604 with prediction 606 generate by model 602 based on the input tuple from training data 604. One or more adjustments 608 may be made to one or more hyper-parameters of machine learning model 602. The adjustments to the hyper-parameters may be to improve predictive capabilities of machine learning model 602. For example, based on the comparison, one or more weights and / or biases of one or more nodes of machine learning model 602 may be adjusted. Training process 600 may repeat until an accuracy of machine learning model 602 reaches a predefined accuracy level (e.g., 95% accuracy or greater, 99% accuracy or greater, etc.), at which point machine learning model 602 may be stored in model database 612 as a trained machine learning model. The accuracy of machine learning model 602 may be determined based on a number of correct predictions (e.g., prediction 606).

[0240] FIG. 7 provides a non-limiting example of OS plot 700 for subjects based on a CH burden score and a cTF value, in accordance with various instances. The data used to produce OS plot 700 may correspond to example experimental data. The sequence read data analyzed to produce OS plot 700 may be for samples of patients in a clinico-genomic database. These samples may be liquid biopsy samples. Samples for 199 non-squamous NSCLC patients were assessed (all wild-type for known / likely EGFR and ALK alterations) that had a composite tumor fraction (cTF) of at least 1%. For each alteration detected in a patient’s sample, the inferred CHIP (iCH) status was determined and the number of iCH-positive alterations were summed up to calculate the sample’s iCH burden. Overall survival was then assessed from the start of first- line ICI monotherapy, and patients were stratified based on cTF and iCH status using empirically determined thresholds (cTF high threshold >=10% and iCH high threshold >=3 iCH variants).

[0241] As shown in plot 700, patients who were both iCH high and cTF high (n=2), as indicated by trace 702, had the worst median overall survival (mOS) amongst the four cohorts (mOS = 0.7 months; confidence interval = not applicable [NA-NA]), and that patients who were iCH low and cTF low (n=142), as indicated by trace 708, had the best mOS amongst the four cohorts (mOS = 14.7 months; confidence interval = [13.0-20.8]). Patients who were either iCH high and cTF low sf-5889500Docket No.: 197102014240 (n=14) or iCH low and cTF high (n=41), as indicated by traces 704 and 706, respectively, had comparable median mOS values at 8.9 months [5.3-NA] and 9.4 months [3.6-NA], respectively. Overall, this association was found to be significant (logrank P = 0.01). The results suggest that patients stratified into iCH low and cTF low respond best to the ICI monotherapy when compared to the other stratified classes of patients.

[0242] Importantly, as high cTF is known to be associated with worse outcomes, the median cTF value of the two cTF high groups were comparable (18% vs 16%). Additionally, there was no significant difference in the age of the patient at the time of the sample biopsy in these cohorts (iCH high / cTF high = 69, iCH high / cTF low = 74, iCH low / cTF high = 71, iCH low / cTF low = 73).

[0243] In some instances, the disclosed methods for quantifying CH burden may rely on detection of CH variants for a specific panel of genes. 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, at least 50, at least 60, at least 70, at least 80, at least 90, 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, or more than 500 genes.

[0244] In some instances, the disclosed methods for quantifying CH burden may rely on detecting CH variants 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, at least 50, at least 60, at least 70, at least 80, at least 90, 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, or more than 500 genomic loci.

[0245] 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, sf-5889500Docket No.: 197102014240 EMSY (C11orf30), EP300, EPHA3, EPHB1, EPHB4, ERBB2, ERBB3, ERBB4, ERCC4, ERG, ERRFI1, ESR1, ETV4, ETV5, ETV6, EWSR1, EZH2, EZR, FAM46C, FANCA, FANCC, FANCG, FANCL, FAS, FBXW7, FGF10, FGF12, FGF14, FGF19, FGF23, FGF3, FGF4, FGF6, FGFR1, FGFR2, FGFR3, FGFR4, FH, FLCN, FLT1, 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, POLD1, POLE, PPARG, PPP2R1A, PPP2R2A, PRDM1, PRKAR1A, PRKCI, PTCH1, PTEN, PTPN11, PTPRO, QKI, RAC1, RAD21, RAD51, RAD51B, RAD51C, RAD51D, RAD52, RAD54L, RAF1, RARA, RB1, RBM10, REL, RET, RICTOR, RNF43, ROS1, RPTOR, RSPO2, SDC4, SDHA, SDHB, SDHC, SDHD, SETD2, SF3B1, SGK1, SLC34A2, SMAD2, SMAD4, SMARCA4, SMARCB1, SMO, SNCAIP, SOCS1, SOX2, SOX9, SPEN, SPOP, SRC, STAG2, STAT3, STK11, SUFU, SYK, TBX3, TEK, TERC, TERT, TET2, TGFBR2, TIPARP, TMPRSS2, TNFAIP3, TNFRSF14, TP53, TSC1, TSC2, TYRO3, U2AF1, VEGFA, VHL, WHSC1, WHSC1L1, WT1, XPO1, XRCC2, ZNF217, or ZNF703 gene locus, or any combination thereof.

[0246] 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-1β, IL-6, IL-6R, JAK1, JAK2, JAK3, KIT, KRAS, MEK, MET, MSI-H, mTOR, PARP, PD-1, PDGFR, PDGFRα, PDGFRβ, PD-L1, PI3Kδ, PIGF, PTCH, RAF, RANKL, RET, ROS1, SLAMF7, VEGF, VEGFA, or VEGFB gene locus, or any combination thereof. sf-5889500Docket No.: 197102014240 Methods of use

[0247] 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 nucleic acid sequence data (including, e.g., variant data, copy number data, methylation status data, etc., of the sequenced nucleic acid molecules) with other biomarker data modalities including, but not limited to, proteomics-based biomarker data (e.g., the detection of specific polypeptides, such as proteins) or fragmentomics-based biomarker data (e.g., the detection 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 in the sample and / or to determine a diagnostic, prognostic, and / or treatment response prediction for the subject, 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 sf-5889500Docket No.: 197102014240 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.

[0248] 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). In some instances, the cell-free DNA (cfDNA), or a portion thereof, may comprise circulating tumor DNA (ctDNA). In some instances, the liquid biopsy sample may comprise a combination of cell-free DNA (cfDNA) and circulating tumor DNA (ctDNA).

[0249] 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.

[0250] In some instances, the disclosed methods for determining a CH burden score 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, are relevant 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.

[0251] In some instances, the disclosed methods for determining a CH burden score may be used to select an appropriate therapy or treatment (e.g., an anti-cancer therapy or anti-cancer treatment) for a subject. In some instances, for example, the anti-cancer therapy or treatment sf-5889500Docket No.: 197102014240 may comprise use of a poly (ADP-ribose) polymerase inhibitor (PARPi), a platinum compound, chemotherapy, radiation therapy, a targeted therapy, an immunotherapy, a neoantigen-based therapy, surgery, or any combination thereof.

[0252] In some instances, the anti-cancer therapy or treatment may comprise a targeted anti- cancer therapy) or treatment (e.g., a monoclonal antibody-based therapy, an enzyme inhibitor- based therapy, an antibody-drug conjugate therapy, a hormone therapy, and / or a targeted radiotherapy) that targets specific molecules required for cancer cell growth, division, and spreading. In some instances, the targeted anti-cancer therapy or treatment may comprise abemaciclib (Verzenio), abiraterone acetate (Zytiga), acalabrutinib (Calquence), ado-trastuzumab emtansine (Kadcyla), afatinib dimaleate (Gilotrif), 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 (Ilaris), capmatinib hydrochloride (Tabrecta), carfilzomib (Kyprolis), cemiplimab- rwlc (Libtayo), ceritinib (LDK378 / Zykadia), cetuximab (Erbitux), cobimetinib (Cotellic), 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), sf-5889500Docket No.: 197102014240 inotuzumab ozogamicin (Besponsa), ipilimumab (Yervoy), isatuximab-irfc (Sarclisa), ivosidenib (Tibsovo), ixazomib citrate (Ninlaro), lanreotide acetate (Somatuline Depot), lapatinib (Tykerb), larotrectinib sulfate (Vitrakvi), lenvatinib mesylate (Lenvima), letrozole (Femara), 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), 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), 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.

[0253] In some instances, the anti-cancer therapy or treatment may comprise an immunotherapy (e.g., a cancer treatment that acts by stimulating the immune system to fight cancer). In some instances, the immunotherapy can be, for example, an immune system modulator (e.g., a cytokine, such as an interferon or interleukin), an immune checkpoint inhibitor (such as an anti- sf-5889500Docket No.: 197102014240 PD-1 or anti-PD-L1 antibody), a T-cell transfer therapy (e.g., a tumor infiltrating lymphocyte (TIL) therapy in lymphocytes extracted from a patient’s tumor are selected for their ability to recognize tumor cells and propagated prior to reintroduction into the patient, or a CAR T-cell therapy in which a patient’s T-cells are modified to express the CAR protein prior to reintroduction into the patient), a monoclonal antibody-based therapy (e.g., a monoclonal antibody that binds to cell surface markers on cancer cells to facilitate recognition by the immune system), or a cancer treatment vaccine (e.g., a vaccine based on tumor cells, tumor- associated neoantigens, or dendritic cells, etc., that stimulates the immune system to fight cancer).

[0254] In some instances, the anti-cancer therapy or treatment may comprise a neoantigen-based therapy. Non-limiting examples of neoantigen-based therapies include T-cell receptor (TCR) engineered T-cell (TCR-T) therapies, chimeric antigen receptor T-cell (CAR-T) therapies, TCR bispecific antibody therapies, and cancer vaccines. TCR-T therapies are produced by genetically engineering a patient’s T-cells to express T-cell receptors that are specific to neoantigens of interest, and then infusing them back into the patient. CAR-T therapies are produced by genetically engineering a patient’s T-cells to express chimeric antigen receptor molecules which contain an intracellular signaling and co-signaling domain as well as an extracellular antigen- binding domain; CAR-T therapies don’t always rely on neoantigen presentation, but can be designed to be directed towards neoantigens. TCR bispecific antibody therapies are small, engineered antibody molecules that comprise a neoantigen-specific TCR on one end and a CD3- directed single-chain variable fragment on the other end. Cancer vaccines can include RNA molecules, DNA molecules, peptides, or a combination thereof that are designed to boost the immune system’s ability to find and destroy neoantigen-presenting cells.

[0255] In some instances, the disclosed methods for determining a CH burden score may be used in treating a disease (e.g., a cancer) in a subject. For example, in response to determining the CH burden score 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.

[0256] In some instances, the disclosed methods for determining a CH burden score 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 sf-5889500Docket No.: 197102014240 a CH burden score in a first sample obtained from the subject at a first time point, and used to determine a CH burden score in a second sample obtained from the subject at a second time point, where comparison of the first determination of the CH burden score and the second determination of the CH burden score allows one to monitor disease progression 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.

[0257] 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 the CH burden score.

[0258] In some instances, the value of the CH burden score 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.

[0259] In some instances, the disclosed methods for determining a CH burden score 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 a CH burden score as part of a genomic profiling process (or inclusion of the output from the disclosed sf-5889500Docket No.: 197102014240 methods for determining a CH burden score 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 CH in a given patient sample.

[0260] 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.

[0261] 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.

[0262] 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 anti-cancer 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

[0263] 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, or 90 sf-5889500Docket No.: 197102014240 other 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.

[0264] 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.

[0265] 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.

[0266] 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).

[0267] 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 further comprise 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 sf-5889500Docket No.: 197102014240 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.

[0268] 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.

[0269] 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.

[0270] 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, mitochondrial DNA or fragments thereof, cell-free DNA (cfDNA), and circulating tumor DNA (ctDNA). Cell-free DNA (cfDNA) is comprised of fragments 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 sf-5889500Docket No.: 197102014240 fragments of DNA that are released from cancerous cells and tumors that circulate in the blood stream and / or accumulate in other bodily fluids.

[0271] 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.

[0272] In some instances, the nucleic acids extracted from the sample may comprise ribonucleic acid (RNA) molecules. Examples of RNA that may be suitable for analysis by the disclosed methods include, but are not limited to, total cellular RNA, total cellular RNA after depletion of 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.

[0273] 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, sf-5889500Docket No.: 197102014240 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., microsatellite 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.

[0274] 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

[0275] 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.

[0276] In some instances, the subject has a cancer or is at risk of having a cancer. 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 some instances, 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).

[0277] 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 sf-5889500Docket No.: 197102014240 obtained (e.g., collected). In some instances, the post-targeted therapy sample is a sample obtained after the completion of the targeted therapy.

[0278] 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

[0279] 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, 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 sf-5889500Docket No.: 197102014240 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.

[0280] 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, 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 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 sf-5889500Docket No.: 197102014240 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 cell lung cancer, thyroid cancer, a thyroid carcinoma, urothelial cancer, a urothelial carcinoma, or Waldenstrom's macroglobulinemia.

[0281] 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-lymphoblastic lymphoma)), primary central nervous system lymphoma, Sézary syndrome, Waldenström macroglobulinemia), chronic myeloproliferative neoplasm, Langerhans cell histiocytosis, multiple myeloma / plasma cell neoplasm, myelodysplastic syndrome, or myelodysplastic / myeloproliferative neoplasm. Nucleic acid extraction and processing

[0282] DNA or RNA 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, sf-5889500Docket No.: 197102014240 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).

[0283] 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 (i.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.

[0284] 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.

[0285] 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.

[0286] 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.

[0287] 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 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). 98 sf-5889500Docket No.: 197102014240

[0288] 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(1):35–42; Masuda, et al., (1999) Nucleic Acids Res. 27(22):4436–4443; Specht, et al., (2001) Am J Pathol. 158(2):419–429; the Ambion RecoverAll™ Total Nucleic Acid Isolation Protocol (Ambion, Cat. No. AM1975, September 2008); the Maxwell® 16 FFPE Plus LEV DNA Purification Kit Technical Manual (Promega Literature #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, the RecoverAll™ Total Nucleic Acid Isolation Kit uses xylene at elevated temperatures to solubilize paraffin- 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 genomic DNA from 1 to 10 µm 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 genomic DNA. QIAamp® DNA FFPE Tissue Kit uses QIAamp® DNA Micro technology for purification of genomic and mitochondrial DNA.

[0289] 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.

[0290] 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 sf-5889500Docket No.: 197102014240 (e.g., genomic DNA) may be fragmented or sheared by using any of a variety of techniques known to those of skill in the art. For example, genomic 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

[0291] 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 chain end 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), 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 strand-displacement amplification. Examples of nucleic acid library preparation techniques for next-generation sequencing are described in, e.g., van Dijk, et al. (2014), Exp. Cell Research 322:12 – 20, and Illumina’s genomic DNA sample preparation kit.

[0292] 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 sf-5889500Docket No.: 197102014240 described herein. For example, in certain instances, 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 genomic DNA. In some instances, the library may consist of cDNA copies of genomic DNA that includes copies of at least 95%, 90%, 80%, 70%, 60%, 50%, 40%, 30%, 20%, 10%, or 5% of the genomic 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.

[0293] 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 a target 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.

[0294] 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 genomic DNA molecule. sf-5889500Docket No.: 197102014240 For example, a subgenomic interval can correspond to a fragment of genomic 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, e.g., subgenomic intervals in cDNA can include exon- exon junctions formed as a result of splicing. 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

[0295] 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.

[0296] 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.

[0297] 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.

[0298] 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 capture reagents

[0299] 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 sf-5889500Docket No.: 197102014240 capture reagent (i.e., a molecule which can bind to and thereby allow capture of a target molecule) 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 (i.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 genomic DNA molecule, an RNA molecule, a cDNA molecule derived from an RNA molecule, a microsatellite 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.

[0300] The methods described herein provide for optimized sequencing of a large number of genomic loci (e.g., genes or gene products (e.g., mRNA), microsatellite 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.

[0301] 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 to capture 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. sf-5889500Docket No.: 197102014240

[0302] 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 instances, 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.

[0303] In some instances, each target capture reagent sequence can include: (i) a target-specific capture sequence (e.g., a gene locus or microsatellite 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.

[0304] 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 target- specific 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 900 nucleotides in length, as well as target-specific sequences of lengths between the above-mentioned lengths.

[0305] 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. sf-5889500Docket No.: 197102014240

[0306] 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 of genomic DNA 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.

[0307] 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.

[0308] 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 acid molecules 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). sf-5889500Docket No.: 197102014240

[0309] 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.

[0310] 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

[0311] 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.

[0312] 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.

[0313] 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

[0314] 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 sf-5889500Docket No.: 197102014240 “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).

[0315] 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.

[0316] The disclosed methods and systems may be implemented using sequencing platforms such as the Roche / 454 Genome Sequencer (GS) FLX System, Illumina / Solexa Genome Analyzer (GA), Illumina’s HiSeq® 2500, HiSeq® 3000, HiSeq® 4000 and NovaSeq® 6000 Sequencing Systems, Life / APG’s Support Oligonucleotide Ligation Detection (SOLiD) system, Polonator’s G.007 system, Helicos BioSciences’ HeliScope Gene Sequencing system, or Pacific Biosciences’ PacBio® RS platform. In some 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.

[0317] 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 sf-5889500Docket No.: 197102014240 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 (i.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.

[0318] 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 least 800, at least 850, at least 900, at least 950, at least 1,000, at least 1,250, at least 1,500, at least 1,750, at least 2,000, at least 2,250, at least 2,500, at least 2,750, at least 3,000, at least 3,500, at least 4,000, at least 4,500, or at least 5,000 loci, 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.

[0319] 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 sf-5889500Docket No.: 197102014240 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.

[0320] In some instances, acquiring a sequence read for one or more subject intervals may comprise sequencing with at least 100x 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 100x, at least 150x, at least 200x, at least 250x, at least 500x, at least 750x, at least 1,000x, 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.

[0321] 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 100x to at least 6,000x for greater than about 90%, 92%, 94%, 95%, 96%, 97%, 98%, or 99% of the gene loci 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.

[0322] 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.

[0323] 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). sf-5889500Docket No.: 197102014240

[0324] 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

[0325] 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 is provided in, e.g., International Patent Application Publication No. WO 2020 / 236941, the entire content of which is incorporated herein by reference.

[0326] 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.

[0327] 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 sf-5889500Docket No.: 197102014240 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 Speeds Database 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.

[0328] 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).

[0329] 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 111 sf-5889500Docket No.: 197102014240 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.

[0330] 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.

[0331] 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 interval being 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).

[0332] 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.

[0333] 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 sf-5889500Docket No.: 197102014240 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).

[0334] 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).

[0335] 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 sf-5889500Docket No.: 197102014240 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

[0336] 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).

[0337] 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).

[0338] 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- sf-5889500Docket No.: 197102014240 Methylcytosine and 5-Hydroxymethylcytosine at Base Resolution”, Nature Biotechnology, vol. 37, pp. 424-429).

[0339] In some instances, the sequence read data may be obtained using a nucleic acid sequencing method comprising the use of Methylated DNA Immunoprecipitation (MeDIP).

[0340] Examples of alignment tools optimized for aligning sequence reads for converted DNA include, but are not limited to, NovoAlign (Novocraft Technologies, Selangor, Malaysia), and the Bismark tool (Krueger, et al. (2011), “Bismark: A Flexible Aligner and Methylation Caller for Bisulfite-Seq Applications”, Bioinformatics 27(11):1571-1572). Mutation calling

[0341] Base calling refers to the raw output of a sequencing device, e.g., the determined sequence of nucleotides in an oligonucleotide molecule. Mutation calling refers to the process of selecting a nucleotide value, e.g., A, G, T, or C, for a given nucleotide position being sequenced. Typically, the sequence reads (or base calling) for a position will provide more than one value, e.g., some reads will indicate a T and some will indicate a G. Mutation calling is the process of assigning a correct nucleotide value, e.g., one of those values, to the sequence. Although it is referred to as “mutation” calling, it can be applied to assign a nucleotide value to any nucleotide position, e.g., positions corresponding to mutant alleles, wild-type alleles, alleles that have not been characterized as either mutant or wild-type, or to positions not characterized by variability.

[0342] In some instances, the disclosed methods may comprise the use of customized or tuned mutation calling algorithms or parameters thereof to optimize performance when applied to sequencing data, 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 (e.g., gene loci, microsatellite regions, etc.) in samples, e.g., samples from a subject having cancer. Optimization of mutation calling is described in the art, e.g., as set out in International Patent Application Publication No. WO 2012 / 092426.

[0343] Methods for mutation calling can include one or more of the following: making independent calls based on the information at each position in the reference sequence (e.g., examining the sequence reads; examining the base calls and quality scores; calculating the probability of observed bases and quality scores given a potential genotype; and assigning sf-5889500Docket No.: 197102014240 genotypes (e.g., using Bayes’ rule)); removing false positives (e.g., using depth thresholds to reject SNPs with read depth much lower or higher than expected; local realignment to remove false positives due to small indels); and performing linkage disequilibrium (LD) / imputation- based analysis to refine the calls.

[0344] Equations used to calculate the genotype likelihood associated with a specific genotype and position are described in, e.g., Li, H. and Durbin, R. Bioinformatics, 2010; 26(5): 589-95. The prior expectation for a particular mutation in a certain cancer type can be used when evaluating samples from that cancer type. Such likelihood can be derived from public databases of cancer mutations, e.g., Catalogue of Somatic Mutation in Cancer (COSMIC), HGMD (Human Gene Mutation Database), The SNP Consortium, Breast Cancer Mutation Data Base (BIC), and Breast Cancer Gene Database (BCGD).

[0345] Examples of LD / imputation based analysis are described in, e.g., Browning, B.L. and Yu, Z. Am. J. Hum. Genet. 2009, 85(6):847-61. Examples of low-coverage SNP calling methods are described in, e.g., Li, Y., et al., Annu. Rev. Genomics Hum. Genet. 2009, 10:387-406.

[0346] After alignment, detection of substitutions can be performed using a mutation calling method (e.g., a Bayesian mutation calling method) which is applied to each base in each of the subject intervals, e.g., exons of a gene or other locus to be evaluated, where presence of alternate alleles is observed. This method will compare the probability of observing the read data in the presence of a mutation with the probability of observing the read data in the presence of base- calling error alone. Mutations can be called if this comparison is sufficiently strongly supportive of the presence of a mutation.

[0347] An advantage of a Bayesian mutation detection approach is that the comparison of the probability of the presence of a mutation with the probability of base-calling error alone can be weighted by a prior expectation of the presence of a mutation at the site. If some reads of an alternate allele are observed at a frequently mutated site for the given cancer type, then presence of a mutation may be confidently called even if the amount of evidence of mutation does not meet the usual thresholds. This flexibility can then be used to increase detection sensitivity for even rarer mutations / lower purity samples, or to make the test more robust to decreases in read coverage. The likelihood of a random base-pair in the genome being mutated in cancer is ~1e-6. The likelihood of specific mutations occurring at many sites in, for example, a typical multigenic sf-5889500Docket No.: 197102014240 cancer genome panel can be orders of magnitude higher. These likelihoods can be derived from public databases of cancer mutations (e.g., COSMIC).

[0348] Indel calling is a process of finding bases in the sequencing data that differ from the reference sequence by insertion or deletion, typically including an associated confidence score or statistical evidence...

Claims

Docket No.: 197102014240 CLAIMS What is claimed is:

1. A method for determining a clonal hematopoiesis (CH) burden score, comprising: receiving, at one or more processors, sequence read data for a plurality of sequence reads derived from a sample from a subject; determining, using the one or more processors, at least one of a total number of clonal hematopoiesis (CH) alterations, a number of pathogenic CH alterations, a number of clonal CH alterations, or a maximum CH allele fraction in the sample based on the sequence read data; and determining, using the one or more processors, a CH burden score representing the CH burden for the subject based on at least one of the total number of CH alterations, the number of pathogenic CH alterations, the number of clonal CH alterations, or the maximum CH allele fraction in the sample, wherein the CH burden score provides a predictive measure of the subject’s clinical response to a treatment for a disease.

2. The method of claim 1, further comprising: determining, using the one or more processors, a composite tumor fraction (cTF) for the sample based on the sequence read data.

3. The method of claim 1, further comprising: predicting, based on the determined CH burden score and the determined cTF, a survival prognosis for the subject.

4. The method of claim 1, wherein the subject has been diagnosed with a form of cancer.

5. The method of claim 4, wherein the subject has been diagnosed with lung cancer, non- small cell lung cancer, or non-squamous non-small cell lung cancer (NSCLC).

6. The method of claim 2, further comprising: predicting, based on the determined CH burden score and the determined cTF, a clinical response of the subject to an immune checkpoint inhibitor. sf-5889500Docket No.: 197102014240 7. The method of claim 2, further comprising: predicting, based on the determined CH burden score and the determined cTF, a likelihood of an immune-related adverse event occurring for the subject.

8. The method of claim 7, wherein the immune-related adverse event comprises an autoimmune disorder or an inflammatory condition.

9. The method of claim 2, wherein the prediction is based on at least one of (i) a comparison of the determined CH burden score to a predetermined CH burden threshold or (ii) a comparison of the determined cTF to a predetermined cTF threshold.

10. The method of claim 9, further comprising: classifying the subject into one of a set of clinical response classes based on the determined CH burden score and determined cTF.

11. The method of claim 10, wherein a treatment decision is selected for the subject based on a clinical response class from the set of clinical response classes that the subject is classified into.

12. The method of claim 10, wherein classifying the subject into one of the set of clinical response classes comprises: classifying the subject into: (i) a first clinical response class comprising first subjects, wherein the first subjects exhibit a CH burden score greater than the predetermined CH burden threshold and a cTF greater than the predetermined cTF threshold; (ii) a second clinical response class comprising second subjects, wherein the second subjects exhibit a CH burden score greater than the predetermined CH burden threshold and a cTF less than the predetermined cTF threshold; (iii) a third clinical response class comprising third subjects, wherein the third subjects exhibit a CH burden score less than the predetermined CH burden threshold and a cTF greater than the predetermined cTF threshold; and (iv) a fourth clinical response class comprising fourth subjects, wherein the fourth subjects exhibit a CH burden score less than the predetermined CH burden threshold and a cTF less than the predetermined cTF threshold. sf-5889500Docket No.: 197102014240 13. The method of claim 1, wherein the sample comprises a liquid biopsy sample.

14. The method of claim 1, wherein the sequence read data is derived using one or more targeted sequencing methods.

15. The method of claim 1, wherein the CH burden score for the sample is generated using one or more machine learning models.

16. The method of claim 15, wherein the one or more machine learning models are trained to output a CH burden score based on input data comprising at least one of a total number of clonal hematopoiesis (CH) alterations, a number of pathogenic CH alterations, a number of clonal CH alterations, or a maximum CH allele fraction.

17. The method of claim 16, wherein the one or more machine learning models are trained to output a consolidated CH burden score based on input data, wherein the input data comprises at least one of: a total number of clonal hematopoiesis (CH) alterations, a number of pathogenic CH alterations, a number of clonal CH alterations, or a maximum CH allele fraction, and a composite tumor fraction (cTF) for the sample based on the sequence read data.

18. The method of claim 1, wherein determining at least one of a total number of clonal hematopoiesis (CH) alterations, a number of pathogenic CH alterations, a number of clonal CH alterations, or a maximum CH allele fraction in the sample comprises: (i) detecting an alteration based on the sequence read data; and (ii) performing a statistical analysis of sequence read data for a selected subset of the plurality of sequence reads to distinguish between tumor-derived alterations and CH alterations.

19. The method of claim 18, wherein performing the statistical analysis comprises: determining at least one feature characterizing the selected subset of the plurality of sequence reads; generating, using a prediction model, a tumor origin score based on the at least one feature, wherein distinguishing between tumor-derived alterations and CH alterations is based on the tumor origin score. sf-5889500Docket No.: 197102014240 20. The method of claim 19, wherein distinguishing between tumor-derived alterations and CH alterations comprises comparing the generated tumor origin score with one or more tumor origin score thresholds to classify the alteration as being a tumor-derived alteration or a CH alteration.

21. The method of claim 19, wherein the prediction model is selected from a plurality of prediction models each associated with one of a plurality of CH alterations, wherein each prediction model is trained to predict whether an alteration is a tumor-derived alteration or a CH alteration.

22. The method of claim 19, wherein the prediction model comprises a statistical model or a machine learning model.

23. The method of claim 19, wherein the at least one feature comprises one or more fragmentomic characteristics.

24. The method of claim 23, wherein the one or more fragmentomic characteristics comprises at least one of: a number of the selected subset of the plurality of sequence reads having a specified length, a mean fragment length of the selected subset of the plurality of sequence reads, a median fragment length of the selected subset of the plurality of sequence reads, an interquartile range of fragment lengths of the selected subset of the plurality of sequence reads, a distribution of fragment lengths of the selected subset of the plurality of sequence reads, one or more peaks of fragment lengths for the selected subset of the plurality of sequence reads, a fragment length for at least one of the selected subset of the plurality of sequence reads, a start position of a fragment for the selected subset of the plurality of sequence reads, an end position of a fragment for the selected subset of the plurality of sequence reads.

25. The method of claim 19, wherein the at least one feature comprises one or more additional features related to the alteration.

26. The method of claim 25, wherein the one or more additional features comprise at least one of an alteration depth, an allele frequency, an alteration coding type, a somatic determination of the alteration, a germline determination of the alteration, a zygosity determination, a somatic- germline-zygosity (SGZ) determination of the alteration, an age of the subject providing the 154 sf-5889500Docket No.: 197102014240 sample, a blood tumor mutational burden (bTMB) score of the sample, protein level data, or gene level data.

27. The method of claim 1, wherein the CH burden score is used to diagnose or confirm a diagnosis of cancer in the subject.

28. The method of claim 27, wherein the cancer is non-squamous non-small cell lung cancer (NSCLC).

29. The method of claim 1, further comprising selecting an anti-cancer therapy to administer to the subject, determining an effective amount of an anti-cancer therapy to administer to the subject, or administering the anti-cancer therapy to the subject based on the determination of the CH burden score.

30. The method of claim 2, wherein determining the cTF for the sample comprises: computing at least one of a propriety tumor fraction estimator (TFE) metric or a maximum somatic allele frequency (MSAF) metric based on the sequence read data.

31. The method of claim 30, wherein: the TFE metric is based on a measure of tumor aneuploidy for the sample; and the MSAF metric is based on an allele fraction for the sample.

32. A method for treating a cancer in a subject, the method comprising: receiving, at one or more processors, sequence read data for a plurality of sequence reads derived from a sample from the subject; determining, using the one or more processors, a composite tumor fraction (cTF) for the sample based on the sequence read data; determining, using the one or more processors, a CH burden score representing the CH burden for the subject based on at least one of a total number of clonal hematopoiesis (CH) alterations, a number of pathogenic CH alterations, a number of clonal CH alterations, or a maximum CH allele fraction as determined for the sample based on the sequence read data; comparing, using the one or more processors, the CH burden score to a predetermined CH burden threshold and the cTF to a predetermined cTF threshold; selecting, using the one or more processors, a cancer treatment for the subject based on 155 sf-5889500Docket No.: 197102014240 the comparison; and administering the selected cancer treatment to the subject.

33. The method of claim 32, wherein determining the cTF for the sample comprises computing at least one of a propriety tumor fraction estimator (TFE) metric or a maximum somatic allele frequency (MSAF) metric based on the sequence read data.

34. The method of claim 33, wherein: the TFE metric is based on a measure of tumor aneuploidy for the sample; and the MSAF metric is based on an allele fraction for the sample.

35. The method of claim 32, further comprising: predicting, based on the determined CH burden score and the determined cTF, a survival prognosis for the subject.

36. The method of claim 32, further comprising: predicting, based on the determined CH burden score and the determined cTF, a likelihood of an immune-related adverse event occurring for a subject.

37. The method of claim 32, wherein the CH burden score is determined based on the total number of CH alterations in the sample, and the predetermined CH burden threshold is equal to 2, 3, 4, or 5 CH alterations.

38. The method of claim 32, wherein determining the at least one of the total number of CH alterations, the number of pathogenic CH alterations, the number of clonal CH alterations, or the maximum CH allele fraction comprises determining the total number of CH, and the predetermined CH burden threshold is 3 CH alterations.

39. The method of claim 32, wherein selecting the cancer treatment comprises: classifying the subject into one of a set of clinical response classes based on the determined CH burden score and determined cTF.

40. The method of claim 39, wherein a treatment decision is selected for the subject based on a clinical response class from the set of clinical response classes that the subject is classified into. sf-5889500Docket No.: 197102014240 41. The method of claim 32, wherein the CH burden score for the sample is generated using one or more machine learning models.

42. The method of claim 32, wherein determining at least one of a total number of clonal hematopoiesis (CH) alterations, a number of pathogenic CH alterations, a number of clonal CH alterations, or a maximum CH allele fraction in the sample comprises: (i) detecting an alteration based on the sequence read data; and (ii) performing a statistical analysis of sequence read data for a selected subset of the plurality of sequence reads to distinguish between tumor-derived alterations and CH alterations. sf-5889500

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