Neoantigen feature selection for prioritization of potential immunotherapy targets

Abstract

Methods for identifying and prioritizing neoantigens as potential immunotherapy targets are described. The methods may comprise, for example, receiving one or more variant sequences identified in a tumor sample from a subject; receiving HLA genotype data for the subject; identifying a plurality of candidate neoantigens based on the one or more variant sequences and the HLA genotype data; and identifying one or more neoantigens from the plurality of candidate neoantigens by: removing a first subset of candidate neoantigens based on a comparison between a binding affinity threshold and a predicted binding affinity associated with each candidate neoantigen and a corresponding HLA type; removing a second subset of candidate neoantigens based on a comparison between a clonality threshold and a clonal estimation of each candidate neoantigen; and removing a third subset of candidate neoantigens based on an HLA LOH prediction for each candidate neoantigen.

Description

NEOANTIGEN FEATURE SELECTION FOR PRIORITIZATION OF POTENTIAL IMMUNOTHERAPY TARGETSCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the priority benefit of United States Provisional Patent Application Serial No. 63 / 535,263, filed August 29, 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 using genomic profiling data derived from a tumor sample from a subject (e.g. , a patient) to identify and prioritize subjectspecific neoantigens that may be potential immunotherapy targets.BACKGROUND

[0003] Neoantigens are tumor- specific proteins, primarily generated by somatic short variant (SV) mutations present in the tumor genome, that play a critical role in eliciting an anti-tumor immune response, and thus may be optimal targets for developing anti-cancer immunotherapies. However, despite the recent success of some cancer therapeutics that harness the immune system to target cells expressing neoantigens, including immune checkpoint inhibitors, personalized cancer vaccines, and T cell therapies, typically only a subset of patients treated with these therapies respond. Thus, there is a need for improved methods for identifying and prioritizing neoantigens, e.g., patient- specific neoantigens, as potential immunotherapy targets exhibiting greater efficacy in eliciting an immune response.BRIEF SUMMARY OF THE INVENTION

[0004] Disclosed herein are methods and systems for identifying and prioritizing neoantigens as potential immunotherapy targets. The methods are based on a combined analysis of: (i) the predicted binding affinity of candidate neoantigen peptides or proteins to Major Histocompatibility Complex (MHC) class I proteins (encoded in humans by the HumanLeukocyte Antigen (HLA) class I genes (e.g., the HLA-A, HLA-B, and HLA-C genes)), (ii) theclonality of the underlying somatic short variants, (iii) the genomic ancestry of the subject (e.g.. the patient), and / or (iv) patterns of loss of heterozygosity (LOH) in the HLA class I genes for the subject (z.e., HLA-I LOH), and arose from an investigation of the genomic landscape of neoantigens in advanced Non-Small Cell Lung Cancer (advNSCLC). The disclosed methods improve upon previously described approaches for identifying neoantigen candidates for development of anti-cancer therapies - based on estimates of neoantigen binding affinity for MHC class I molecules - by including several additional criteria (z.e., the aforementioned clonality, genomic ancestry, and HLA-I LOH criteria) that can be used to refine the list of candidate neoantigens to include only those neoantigens that are predicted to be the best presenting and that are most likely to have the highest efficacy for eliciting a patient response to a targeted immunotherapy. In some instances, the identified neoantigens may be used for, e.g., developing a personalized anti-cancer treatment and / or anti-cancer vaccine.

[0005] 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; receiving, at the one or more processors, one or more variant sequences identified based on the sequence read data; receiving, at the one or more processors, HLA genotype data for the subject indicative of one or more HLA gene alleles associated with the subject; identifying, using the one or more processors, a plurality of candidate neoantigens based on the one or more variant sequences and the HLA genotype data; and identifying one or more neoantigens as potential immunotherapy targets by: removing a first subset of candidate neoantigens from the plurality of candidate neoantigens based on a comparison between a binding affinity threshold and a predicted binding affinity associated with each candidate neoantigen in the first subset and a corresponding HLA protein; removing a second subset of candidate neoantigens from the plurality of candidate neoantigens based on a comparison between a clonality threshold and a clonal estimation of each candidate neoantigen in the secondsubset; and removing a third subset of candidate neoantigens from the plurality of candidate neoantigens based on an HLA LOH prediction for an HLA gene allele associated with each candidate neoantigen in the third subset.

[0006] In some embodiments, each candidate neoantigen of the plurality of candidate neoantigens is identified based a combination of a particular variant sequence of the one or more variant sequences and a particular HLA gene allele of the one or more HLA gene alleles. In some embodiments, the method further comprises obtaining the plurality of candidate neoantigens by: inputting the one or more variant sequences and the one or more HLA gene alleles into a machine learning model; and receiving, from the machine learning model, the plurality of candidate neoantigens and the predicted binding affinity associated with each of the candidate neoantigens. In some embodiments, the machine learning model comprises NetMHCpan.

[0007] 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, 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.

[0008] 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 ROS1gene 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 nonsmall cell lung cancer (with an EGFR T790M mutation), ovarian cancer, ovarian cancer (with a BRCA mutation), pancreatic cancer, a pancreatic, gastrointestinal, or lung origin neuroendocrine tumor, a pediatric neuroblastoma, a peripheral T-cell lymphoma, peritoneal cancer, prostate cancer, a renal cell carcinoma, rheumatoid arthritis, a small lymphocytic lymphoma, a soft tissue sarcoma, a solid tumor (MSI-H / dMMR), a squamous cell cancer of the head and neck, a squamous non- small cell lung cancer, thyroid cancer, a thyroid carcinoma, urothelial cancer, a urothelial carcinoma, or Waldenstrom's macroglobulinemia.

[0009] In some embodiments, the method further comprises treating the subject with an anticancer 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 (Haris), capmatinib hydrochloride (Tabrecta), carfilzomib (Kyprolis), cemiplimab-rwlc (Libtayo), ceritinib (LDK378 / Zykadia), cetuximab (Erbitux), cobimetinib (Cotellic), copanlisib hydrochloride (Aliqopa), crizotinib (Xalkori), dabrafenib (Tafinlar), dacomitinib (Vizimpro), daratumumab (Darzalex), daratumumab and hyaluronidase-fihj (Darzalex Faspro), darolutamide (Nubeqa), dasatinib (Sprycel), denileukin diftitox (Ontak), denosumab (Xgeva), dinutuximab (Unituxin), dostarlimab-gxly (Jemperli), durvalumab (Imfinzi), duvelisib (Copiktra), elotuzumab (Empliciti), enasidenib mesylate (Idhifa), encorafenib (Braftovi), enfortumab vedotin-ejfv (Padcev), entrectinib (Rozlytrek), enzalutamide(Xtandi), erdafitinib (Balversa), erlotinib (Tarceva), everolimus (Afinitor), exemestane (Aromasin), fam-trastuzumab deruxtecan-nxki (Enhertu), fedratinib hydrochloride (Inrebic), fulvestrant (Faslodex), gefitinib (Iressa), gemtuzumab ozogamicin (Mylotarg), gilteritinib (Xospata), glasdegib maleate (Daurismo), hyaluronidase-zzxf (Phesgo), ibrutinib (Imbruvica), ibritumomab tiuxetan (Zevalin), idecabtagene vicleucel (Abecma), idelalisib (Zydelig), imatinib mesylate (Gleevec), infigratinib phosphate (Truseltiq), inotuzumab ozogamicin (Besponsa), iobenguane 1131 (Azedra), ipilimumab (Yervoy), isatuximab-irfc (Sarclisa), ivosidenib (Tibsovo), ixazomib citrate (Ninlaro), lanreotide acetate (Somatuline Depot), lapatinib (Tykerb), larotrectinib sulfate (Vitrakvi), lenvatinib mesylate (Lenvima), letrozole (Femara), lisocabtagene maraleucel (Breyanzi), loncastuximab tesirine-lpyl (Zynlonta), lorlatinib (Eorbrena), lutetium Eu 177-dotatate (Eutathera), 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 (Eumakras), 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.

[0010] 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 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.

[0011] 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.

[0012] 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.

[0013] In some embodiments, the sequencing comprises use of a massively parallel sequencing(MPS) technique, whole genome sequencing (WGS), whole exome sequencing, targetedsequencing, 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.

[0014] In some embodiments, one or more of the plurality of sequencing 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 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.

[0015] 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, CYP17A1, DAXX, DDR1, DDR2, DIS3, DNMT3A, DOT1L, EED, EGFR, EMSY (Cllorf30), EP300, EPHA3, EPHB1, EPHB4, ERBB2, ERBB3, ERBB4, ERCC4, ERG, ERRFI1, ESRI, ETV4, ETV5, ETV6, EWSR1, EZH2, EZR, FAM46C, FANCA, FANCC, FANCG, FANCL, FAS, FBXW7, FGF10, FGF12, FGF14, FGF19, FGF23, FGF3, FGF4, FGF6, FGFR1, FGFR2, FGFR3, FGFR4, FH, FLCN, FLT1, FLT3, FOXL2, FUBP1, GABRA6, GATA3, GATA4, GATA6, GID4 (C17orf39), GNA11, GNA13, GNAQ, GNAS, GRM3, GSK3B, H3F3A, HDAC1, HGF, HNF1A, HRAS, HSD3B1, ID3, IDH1, IDH2, IGF1R, IKBKE, IKZF1, INPP4B, IRF2, IRF4, IRS2, JAK1, JAK2, JAK3, JUN, KDM5A, KDM5C, KDM6A, KDR, KEAP1, KEL, KIT, KLHL6, KMT2A (MLL), KMT2D (MLL2), KRAS, LTK, LYN, MAF, MAP2K1, MAP2K2, MAP2K4, MAP3K1, MAP3K13, MAPK1, MCL1, MDM2, MDM4, MED12, MEF2B, MEN1, MERTK, MET, MITF, MKNK1, MLH1, MPL, MRE11A, MSH2, MSH3, MSH6, MST1R, MTAP, MTOR, MUTYH, MYB, MYC, MYCL, MYCN, MYD88, NBN, NF1, NF2, NFE2L2, NFKBIA, NKX2-1, NOTCH1, NOTCH2, NOTCH3, NPM1, NRAS, NT5C2, NTRK1, NTRK2, NTRK3, NUTM1, P2RY8, PALB2, PARK2, PARP1, PARP2, PARP3, PAX5, PBRM1, PDCD1, PDCD1LG2, PDGFRA, PDGFRB, PDK1, PIK3C2B, PIK3C2G, PIK3CA, PIK3CB, PIK3R1, PIM1, PMS2, POLDI, POLE, PPARG, PPP2R1A, PPP2R2A, PRDM1, PRKAR1A, PRKCI, PTCHI, PTEN, PTPN11, PTPRO, QKI, RAC1, RAD21, RAD51, RAD51B, RAD51C, RAD51D, RAD52, RAD54L, RAFI, RARA, RBI, RBM10, REL, RET, RICTOR, RNF43, ROS1, RPTOR, RSPO2, SDC4, SDHA, SDHB, SDHC, SDHD, SETD2, SF3B1, SGK1, SLC34A2, SMAD2, SMAD4, SMARCA4, SMARCB1, SMO, SNCAIP, S0CS1, SOX2, SOX9, SPEN, SPOP, SRC, STAG2, STAT3, STK11, SUFU, SYK, TBX3, TEK, TERC, TERT, TET2, TGFBR2, TIPARP, TMPRSS2, TNFAIP3, TNFRSF14, TP53, TSC1, TSC2,TYRO3, U2AF1, VEGFA, VHL, WHSCI, WHSC1L1, WT1, XPO1, XRCC2, ZNF217, ZNF703, or any combination thereof.

[0016] In some embodiments, the one or more gene loci comprise ABL, ALK, ALL, B4GALNT1, BALE, BCL2, BRAE, BRCA, BTK, CD19, CD20, CD3, CD30, CD319, CD38, CD52, CDK4, CDK6, CML, CRACC, CS1, CTLA-4, dMMR, EGFR, ERBB1, ERBB2, FGFR1- 3, FLT3, GD2, HDAC, HER1, HER2, HR, IDH2, IL-ip, IL-6, IL-6R, JAK1, JAK2, JAK3, KIT, KRAS, MEK, MET, MSI-H, mTOR, PARP, PD-1, PDGFR, PDGFRa, PDGFRp, PD-L1, PI3K5, PIGF, PTCH, RAF, RANKL, RET, ROS1, SLAMF7, VEGF, VEGFA, VEGFB, or any combination thereof.

[0017] In some embodiments, the method further comprises generating, by the one or more processors, a report comprising a listing of the one or more neoantigens. 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.

[0018] Disclosed herein are methods for identifying one or more neoantigens as potential immunotherapy targets for a subject, comprising: receiving, at one or more processors, one or more variant sequences identified in a tumor sample from the subject; receiving, at the one or more processors, HLA genotype data for the subject indicative of one or more HLA gene alleles associated with the subject; identifying, using the one or more processors, a plurality of candidate neoantigens based on the one or more variant sequences and the HLA genotype data; and identifying the one or more neoantigens by: removing a first subset of candidate neoantigens from the plurality of candidate neoantigens based on a comparison between a binding affinity threshold and a predicted binding affinity associated with each candidate neoantigen in the first subset and a corresponding HLA protein; removing a second subset of candidate neoantigens from the plurality of candidate neoantigens based on a comparison between a clonality threshold and a clonal estimation of each candidate neoantigen in the second subset; and removing a third subset of candidate neoantigens from the plurality of candidate neoantigens based on an HLA LOH prediction for an HLA gene allele associated with each candidate neoantigen in the third subset.

[0019] In some embodiments, the one or more variant sequences are identified by: receiving, at the one or more processors, sequence read data for a plurality of sequence reads corresponding to the tumor sample from the subject; and identifying, using the one or more processors, the one or more variant sequences based on the sequence read data.

[0020] In some embodiments, the one or more HLA gene alleles comprise maternal and paternal copies of HLA-A gene alleles, HLA-B gene alleles, HLA-C gene alleles, or a combination thereof.

[0021] In some embodiments, each candidate neoantigen of the plurality of candidate neoantigens is identified based a combination of a particular variant sequence of the one or more variant sequences and a particular HLA gene allele of the one or more HLA gene alleles.

[0022] In some embodiments, the method further comprises: obtaining the plurality of candidate neoantigens by: inputting the one or more variant sequences and the one or more HLA gene alleles into a machine learning model; and receiving, from the machine learning model, the plurality of candidate neoantigens and the predicted binding affinity associated with each of the candidate neoantigens. In some embodiments, the machine learning model comprises NetMHCpan.

[0023] In some embodiments, the predicted binding affinity associated with each candidate neoantigen in the first subset is weaker than the binding affinity threshold. In some embodiments, the binding affinity threshold is between 1 and 500 nM.

[0024] In some embodiments, the clonal estimation of each candidate neoantigen in the second subset is smaller than the clonality threshold. In some embodiments, the clonal estimation of each candidate neoantigen is an estimate of a fraction of cancer cells in the sample carrying a variant sequence associated with a respective candidate neoantigen. In some embodiments, the clonal estimation of each candidate neoantigen comprises a cancer cell fraction (CCF) value. In f some embodiments, the CCF value is calculated by: — (pNT+ 2(1 — )), wherein / isindicative of an allele frequency of the variant sequence, m is indicative of a number of mutant copies of a gene, p is indicative of tumor purity, and NT is indicative of total copies of the gene.In some embodiments, the clonality threshold comprises a CCF value greater than 50%, 75%, or 90%.

[0025] In some embodiments, each candidate neoantigen in the third subset is associated with an HLA gene allele that is predicted to be HLA-LOH positive.

[0026] In some embodiments, the method further comprises: developing one or more anti-cancer therapies based on the one or more neoantigens identified for the subject. In some embodiments, the one or more anti-cancer therapies comprise a targeted anti-cancer therapy based on the one or more neoantigens. In some embodiments, the targeted anti-cancer therapy comprises a personalized cancer vaccine.

[0027] In some embodiments, the method further comprises obtaining the tumor sample from the subject. In some embodiments, the tumor sample comprises a tissue biopsy sample or a liquid biopsy sample. In some embodiments, the sample is a liquid biopsy sample and comprises blood, plasma, cerebrospinal fluid, sputum, stool, urine, or saliva.

[0028] Disclosed herein are systems comprising: one or more processors; and a memory communicatively coupled to the one or more processors and configured to store instructions that, when executed by the one or more processors, cause the system to: receive one or more variant sequences identified in a tumor sample from a subject; receive HLA genotype data for the subject indicative of one or more HLA gene alleles associated with the subject; identify a plurality of candidate neoantigens based on the one or more variant sequences and the HLA genotype data; and identify one or more neoantigens by: removing a first subset of candidate neoantigens from the plurality of candidate neoantigens based on a comparison between a binding affinity threshold and a predicted binding affinity associated with each candidate neoantigen in the first subset and a corresponding HLA protein; removing a second subset of candidate neoantigens from the plurality of candidate neoantigens based on a comparison between a clonality threshold and a clonal estimation of each candidate neoantigen in the second subset; and removing a third subset of candidate neoantigens from the plurality of candidate neoantigens based on an HLA LOH prediction for an HLA gene allele associated with each candidate neoantigen in the third subset.

[0029] In some embodiments, each candidate neoantigen of the plurality of candidate neoantigens is identified based a combination of a particular variant sequence of the one or more variant sequences and a particular HLA gene allele of the one or more HLA gene alleles. In some embodiments, the system further comprises instructions for obtaining the plurality of candidate neoantigens by: inputting the one or more variant sequences and the one or more HLA gene alleles into a machine learning model; and receiving, from the machine learning model, the plurality of candidate neoantigens and the predicted binding affinity associated with each of the candidate neoantigens. In some embodiments, the machine learning model comprises NetMHCpan.

[0030] Also disclosed herein are non-transitory computer-readable storage media storing one or more programs, the one or more programs comprising instructions, which when executed by one or more processors of a system, cause the system to: receive one or more variant sequences identified in a tumor sample from the subject; receive HLA genotype data for the subject indicative of one or more HLA gene alleles associated with the subject; identify a plurality of candidate neoantigens based on the one or more variant sequences and the HLA genotype data; and identifying the one or more neoantigens by: removing a first subset of candidate neoantigens from the plurality of candidate neoantigens based on a comparison between a binding affinity threshold and a predicted binding affinity associated with each candidate neoantigen in the first subset and a corresponding HLA protein; removing a second subset of candidate neoantigens from the plurality of candidate neoantigens based on a comparison between a clonality threshold and a clonal estimation of each candidate neoantigen in the second subset; and removing a third subset of candidate neoantigens from the plurality of candidate neoantigens based on an HLA LOH prediction for an HLA gene allele associated with each candidate neoantigen in the third subset.

[0031] In some embodiments, each candidate neoantigen of the plurality of candidate neoantigens is identified based a combination of a particular variant sequence of the one or more variant sequences and a particular HLA gene allele of the one or more HLA gene alleles. In some embodiments, the non-transitory computer-readable storage medium further comprises instructions for obtaining the plurality of candidate neoantigens by: inputting the one or more variant sequences and the one or more HLA gene alleles into a machine learning model; andreceiving, from the machine learning model, the plurality of candidate neoantigens and the predicted binding affinity associated with each of the candidate neoantigens. In some embodiments, the machine learning model comprises NetMHCpan.

[0032] 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

[0033] All publications, patents, and patent applications mentioned in this specification are herein incorporated by reference in their entirety to the same extent as if each individual publication, patent, or patent application was specifically and individually indicated to be incorporated by reference in its entirety. In the event of a conflict between a term herein and a term in an incorporated reference, the term herein controls.BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Various aspects of the disclosed methods, devices, and systems are set forth with particularity in the appended claims. A better understanding of the features and advantages of the disclosed methods, devices, and systems will be obtained by reference to the following detailed description of illustrative embodiments and the accompanying drawings, of which:

[0035] FIG. 1 provides a non-limiting example of a process flowchart for identifying candidate neoantigens for personalized immunotherapy development, according to one implementation of the methods described herein.

[0036] FIG. 2 provides a non-limiting example of a plot of posterior probability as a function of cancer cell fraction (CCF) according to one method of estimating the clonality of short variant mutations described herein.

[0037] FIG. 3 provides a non-limiting schematic illustration of a process for detecting HLA-I loss of heterozygosity as described herein.

[0038] FIG. 4 provides another non-limiting example of a process flowchart for identifying candidate neoantigens for personalized immunotherapy development, according to an implementation of the methods described herein.

[0039] FIG. 5 depicts an exemplary computing device or system in accordance with one embodiment of the present disclosure.

[0040] FIG. 6 depicts an exemplary computer system or computer network, in accordance with some instances of the systems described herein.

[0041] FIG. 7 provides a non-limiting example of data for the prevalence of a specified set of short variant alterations in Non-Small Cell Lung Cancer (NSCLC).

[0042] FIG. 8 provides non-limiting examples of the clonal prevalence (> 0.5 CCF), genomic ancestry group prevalence, neoantigen prevalence, and the prevalence of the neoantigen’s presenting allele being lost by HLA-I LOH for the specified set of short variant alterations in Non-Small Cell Lung Cancer (NSCLC).

[0043] FIGS. 9A-E provide non-limiting examples of data for the prevalence of the specified set of short variant alterations in Non-Small Cell Lung Cancer (NSCLC) across genomic ancestry groups. FIG. 9A: African ancestry group. FIG. 9B: European ancestry group. FIG. 9C: Admixed American ancestry group. FIG. 9D: South Asian ancestry group. FIG. 9E: East Asian ancestry group.DETAILED DESCRIPTION

[0044] Methods and systems for identifying and prioritizing neoantigens as potential immunotherapy targets are described. The methods are based on a combined analysis of: (i) the predicted binding affinity of candidate neoantigen peptides or proteins to Major Histocompatibility Complex (MHC) class I proteins (encoded in humans by the Human Leukocyte Antigen (HLA) class I genes (e.g., the HLA-A, HLA-B, and HLA-C genes)), (ii) the clonality of the underlying somatic short variants, (iii) the genomic ancestry of the subject e.g., the patient), and / or (iv) patterns of loss of heterozygosity (LOH) in the HLA class I genes for the subject (z.e., HLA-I LOH), and arose from an investigation of the genomic landscape ofneoantigens in advanced Non-Small Cell Lung Cancer (advNSCLC). The disclosed methods improve upon previously described approaches for identifying neoantigen candidates for development of anti-cancer therapies - based on estimates of neoantigen binding affinity for MHC class I molecules - by including several additional criteria (z.e., the aforementioned clonality, genomic ancestry, and HLA-I LOH criteria) that can be used to refine the list of candidate neoantigens to include only those neoantigens that are predicted to be the best presenting and that are most likely to have the highest efficacy for eliciting a patient response to a targeted immunotherapy. In some instances, the identified neoantigens may be used for, e.g., developing a personalized anti-cancer treatment and / or anti-cancer vaccine.

[0045] In some instances, for example, computer-implemented methods for identifying one or more neoantigens as potential immunotherapy targets for a subject are described and can comprise: receiving one or more variant sequences identified in a tumor sample from the subject; receiving HLA genotype data for the subject indicative of one or more HLA gene alleles associated with the subject; identifying a plurality of candidate neoantigens based on the one or more variant sequences and the HLA genotype data; and identifying the one or more neoantigens by: removing a first subset of candidate neoantigens from the plurality of candidate neoantigens based on a comparison between a binding affinity threshold and a predicted binding affinity associated with each candidate neoantigen in the first subset and a corresponding HLA protein; removing a second subset of candidate neoantigens from the plurality of candidate neoantigens based on a comparison between a clonality threshold and a clonal estimation of each candidate neoantigen in the second subset; and removing a third subset of candidate neoantigens from the plurality of candidate neoantigens based on an HLA LOH prediction for an HLA gene allele associated with each candidate neoantigen in the third subset.

[0046] In some instances, the one or more variant sequences may be identified by: receiving sequence read data for a plurality of sequence reads corresponding to the tumor sample from the subject; and identifying the one or more variant sequences based on the sequence read data e.g., by mapping and comparing sequence reads to a reference genome sequence such as the GRCh38 / hg38 reference human genome sequence).

[0047] In some instances, the one or more HLA gene alleles may comprise maternal and paternal copies of HLA-A gene alleles, HLA-B gene alleles, HLA-C gene alleles, or a combination thereof.

[0048] In some instances, each candidate neoantigen of the plurality of candidate neoantigens may be identified based a combination of a particular variant sequence present in the one or more variant sequences and a particular HLA gene allele present in the one or more HLA gene alleles.Definitions

[0049] 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.

[0050] 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.

[0051] ‘ ‘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.

[0052] 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.

[0053] As used herein, the terms “individual,” “patient,” or “subject” are used interchangeably and refer to any single animal, e.g., a mammal (including such non-human animals as, for example, dogs, cats, horses, rabbits, zoo animals, cows, pigs, sheep, and non-human primates)for which treatment is desired. In particular embodiments, the individual, patient, or subject herein is a human.

[0054] 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.

[0055] As used herein, “treatment” (and grammatical variations thereof such as “treat” or “treating”) refers to clinical intervention (e.g., administration of an anti-cancer agent or anticancer therapy) in an attempt to alter the natural course of the individual being treated, and can be performed either for prophylaxis or during the course of clinical pathology. Desirable effects of treatment include, but are not limited to, preventing occurrence or recurrence of disease, alleviation of symptoms, diminishment of any direct or indirect pathological consequences of the disease, preventing metastasis, decreasing the rate of disease progression, amelioration or palliation of the disease state, and remission or improved prognosis.

[0056] As used herein, the term “subgenomic interval” (or “subgenomic sequence interval”) refers to a portion of a genomic sequence.

[0057] 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).

[0058] 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.

[0059] 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.

[0060] 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.

[0061] The section headings used herein are for organizational purposes only and are not to be construed as limiting the subject matter described.Methods for identifying and prioritizing neoantigens as potential immunotherapy targets

[0062] Tumor- specific neoantigens can serve as targets of both naturally occurring immune responses to developing cancers, and can also function as optimal targets for the development of anti-cancer immunotherapies (see, for example, Ward, et al. (2016), “The Role of Neoantigens in Naturally Occurring and Therapeutically Induced Immune Responses to Cancer”, Adv Immunol. 130: 25-74). Tumor elimination by the immune system is dependent upon T cell recognition of the neoantigen expressing cells, a process that in turn relies upon presentation of the tumorspecific neoantigens on the cell surface by MHC class I proteins, which are encoded by the HLA class I (HLA-I) genes HLA-A, HLA-B, and HLA-C. There are thousands of different MHC class I (HLA-I) proteins, each of which has a specific binding pocket where the neoantigen peptide / protein binds. The presence of the tumor-specific neoantigen / HLA-I complex on the surface of a cancer cell triggers an immune response when the complex is recognized as “nonself’ by the immune system. Reduced HLA-I functionality through mechanisms such as loss of heterozygosity (HLA-I LOH) can impact the efficacy of the immune response, thereby resulting in immune evasion by the cancer and immunotherapy resistance.

[0063] A number of anti-cancer immunotherapies have been developed in recent years with the objective of harnessing a patient’s immune system to target cancer cells that express tumorspecific neoantigens. Some of these therapies rely upon recurrent neoantigens, e.g., KIMMTRAK® (tebentafusp-tebn), a drug that was approved in January 2022 for patients with uveal melanoma who have the HLA-A*02:01 gene and melanoma cells that express the gplOO antigen. However, as noted above, typically only a subset of patients treated with these therapies respond well. Other anti-cancer immunotherapies are in development that are personalized (z.e., created uniquely for each patient), such as the mRNA-4157 / V940 vaccine produced by Modema, Inc. (Cambridge, MA) that is currently in clinical trials. For either type of therapy (z.e., thosebased on recurrent neoantigens and those based on patient- specific neoantigens), selecting optimal neoantigens to target can be crucial since neoantigens that are subclonal or are produced by cells with a disrupted immune presentation pathway (e.g., through a mechanism such as HLA-I LOH) are predicted to have reduced immune response efficacy.

[0064] Past efforts to identify neoantigens that are likely to provoke a strong immune response have relied upon neoantigen binding affinity prediction software, such as NetMHCpan (see, e.g., Nielsen, et al. (2007), “NetMHCpan, a Method for Quantitative Predictions of Peptide Binding to Any HLA-A and -B Locus Protein of Known Sequence”, PLoS ONE 8:e796), to identify whether or not a particular combination of gene alteration and HLA allele will result in a neoantigen that exhibits a strong binding affinity to an HLA-1 molecule. The disclosed methods improve upon previously described approaches by including several additional criteria that can be used to narrow down the list of candidate neoantigens to include only those neoantigens that are predicted to be the best presenting. According to some embodiments of the present disclosure, an analysis based on a combination of features, not just the predicted binding affinity of candidate neoantigens to MHC class I proteins, but also the clonality of the underlying short variants, the genomic ancestry of the subject e.g., a patient), and loss of function of the immune presentation pathway, can be used to identify and prioritize the neoantigens that provide the most optimal immunotherapy targets.

[0065] FIG. 1 provides a non-limiting example of a flowchart for a process 100 for identifying candidate neoantigens for personalized immunotherapy development. 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 asillustrated (and described in greater detail below) are exemplary by nature and, as such, should not be viewed as limiting.

[0066] At step 102 in FIG. 1, one or more variant sequences identified in a tumor sample from a subject are received (e.g., by one or more processors of a system configured to implement the disclosed methods.

[0067] In some instances, the tumor sample may comprise, e.g., a tissue biopsy sample or a liquid biopsy sample. In some instances, the sample may be a liquid biopsy sample and may comprise blood, plasma, cerebrospinal fluid, sputum, stool, urine, or saliva.

[0068] In some instances, the method may further comprise obtaining the tumor sample from the subject. In some instances, the method may further comprise identifying the one or more variant sequences by receiving sequence read data for a plurality of sequence reads corresponding to the tumor sample from the subject, and identifying the one or more variant sequences based on the sequence read data by mapping and comparing sequence reads to a reference sequence (e.g., an HLA reference sequence or the GRCh38 / hg38 human genome reference sequence). In some instances, the method may further comprise generating the plurality of sequence reads by sequencing nucleic acids extracted from the tumor sample.

[0069] In some instances, the one or more variant sequences may comprise, e.g., short variants. In some instances, the one or more variants may comprise variant sequences identified within at least 1, 5, 10, 50, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1,000, 2,000, 4,000, 6,000, 8,000, 10,000, 12,000, 14,000, 16,000, 18,000, or 20,000 gene loci.

[0070] At step 104 in FIG. 1, HLA genotype data for the subject that is indicative of one or more HLA gene alleles associated with the subject is received. The one or more HLA gene alleles (e.g., 1, 2, 3, 4, 5, or 6 HLA gene alleles) may comprise, for example, maternal and paternal copies of HLA-A gene alleles, HLA-B gene alleles, HLA-C gene alleles, or a combination thereof.

[0071] At step 106 in FIG. 1, a plurality of candidate neoantigens is identified based on the one or more variant sequences and the HLA genotype data. For example, each candidate neoantigen of the plurality of candidate neoantigens may be identified based on, e.g., a predicted bindingaffinity for a combination of a particular variant sequence of the one or more variant sequences and a particular HLA gene allele product of the one or more HLA gene alleles. In some instances, each candidate neoantigen of the plurality of candidate neoantigens may be identified based on, e.g., predicted binding affinities for all possible combinations of a particular variant sequence of the one or more variant sequences and a particular HLA gene allele product of the one or more HLA gene alleles. In some instances, binding affinity calculations may be performed for a portion of each variant sequence of the one or more variant sequences (e.g., an 8-mer, 9-mer, 10-mer, 11-mer, or 12-mer amino acid sequence sampled from each variant peptide / protein sequence) and each HLA gene allele product of the one or more HLA gene alleles.

[0072] In some instances, the method may further comprise obtaining the plurality of candidate neoantigens by: inputting the one or more variant sequences and the one or more HLA gene alleles into a machine learning model; and receiving the plurality of candidate neoantigens, and the predicted binding affinity associated with each of the candidate neoantigens, as output from the machine learning model. In some instances, the machine learning model may comprise, for example, NetMHCpan, NetMHCIIpan, TEPITOPEpan, BasicMHCl, and MHCflurry (see, e.g., Farrell (2021), “Epitopepredict: A Tool for Integrated MHC Binding Prediction”, Gigabyte, 2021, 1-14).

[0073] In some instances, the plurality of candidate neoantigens may comprise at least 10,000, 20,000, 30,000, 40,000, 50,000, 60,000, 70,000, 80,000, 90,000, 100,000, or more than 100,000 candidate neoantigens.

[0074] At step 108 in FIG. 1, the one or more neoantigens can be identified by:• removing a first subset of candidate neoantigens from the plurality of candidate neoantigens based on a comparison between a binding affinity threshold and a predicted binding affinity associated with each candidate neoantigen (e.g., as obtained using a model such as NetMHC) in the first subset and a corresponding HLA protein;removing a second subset of candidate neoantigens from the plurality of candidate neoantigens based on a comparison between a clonality threshold and a clonal estimation of each candidate neoantigen in the second subset; and• removing a third subset of candidate neoantigens from the plurality of candidate neoantigens based on an HLA LOH prediction for an HLA gene allele associated with each candidate neoantigen in the third subset (see, e.g., FIG. 3 and the description thereof).

[0075] In some instances, the predicted binding affinity associated with each candidate neoantigen in the first subset is weaker than the binding affinity threshold. In some instances, the binding affinity threshold may be between 1 and 500 nM. In some instances, for example, the binding affinity threshold may be 500 nM, 450 nM, 400 nM, 350 nM, 300 nM, 250 nM, 200 nM, 150 nM, 100 nM, 90 nM, 80 nM, 70 nM, 60 nM, 50 nM, 40 nM, 30 nM, 20 nM, 10 nM, 8 nM, 6 nM, 4 nM, 2 nM, or 1 nM. In some instances, the binding affinity threshold may be lowered to identify stronger binding neoantigens that are therefore potentially better immunotherapy targets.

[0076] In some instances, the clonal estimation for each candidate neoantigen in the second subset may be smaller than the clonality threshold. In some instances, the clonal estimation of each candidate neoantigen may be an estimate of a fraction of cancer cells in the sample carrying a variant sequence associated with a respective candidate neoantigen. In some instances, for example, the clonal estimation for each candidate neoantigen may comprise a cancer cell fraction (CCF) value.

[0077] In some instances, the CCF value may be calculated by:(see Tarabichi, el al. (2021), “A Practical Guide to Cancer Subclonal Reconstruction from DNA Sequencing”, Nature Methods 18:144-155) wherein / is indicative of an allele frequency of the variant sequence, m is indicative of a number of mutant copies of a gene, p is indicative of tumor purity, and NT is indicative of total copies of the gene.

[0078] In some instances, the CCF value may be calculated based on a probabilistic model of allele frequency. For example, the observed allele frequency (AF) is given by a / N, where a is the number of altered sequence reads detected and N is the sequencing coverage at the short variant locus. The expected allele frequency, expressed as a function of CCF, may be given by:(see McGranahan, et al. (2015), “Clonal Status of Actionable Driver Events and the Timing of Mutational Processes in Cancer Evolution”, Science Translational Medicine 7(283):283ra54;Tarabichi, et al. (2021), ibid.).

[0079] By setting the relationships for observed AF and expected AF equal to each other and solving for CCF, one may obtain a probabilistic relationship for CCF given by:P(CCF) = binomial(a\N,AF CCFy)

[0080] Table 1 provides a non-limiting example of calculated values of CCF probability and posterior probability for different CCF values calculated for the TP53 C238S variant (with p = 0.85, N = 1049, AF = 0.1459). The expected AF value as determined from the distribution is CCF = 0.347 (95% confidence interval = 0.345 - 0.349).Table 1. Calculated values of CCF probability and posterior probability.

[0081] In some instances, the method may further comprise developing one or more anti-cancer therapies based on the one or more neoantigens identified for the subject. In some instances, the one or more anti-cancer therapies comprise a targeted anti-cancer therapy based on the one or more neoantigens. In some instances, the targeted anti-cancer therapy comprises a personalized cancer vaccine.

[0082] FIG. 2 provides a non-limiting example of posterior probability plotted as a function of cancer cell fraction (CCF) based on the parameters used to generate the data summarized in Table 1.

[0083] In some instances, the clonality threshold may comprise a CCF value greater than 50%, 75%, or 90%. In some instances, the clonality threshold may be set to 100%. In some instances, the clonality threshold may be set such that a lower confidence interval of CCF may be greater than or equal to 50%. In some instances, the clonality threshold may be set such that the probability that the neoantigen has a CCF greater than or equal to 0.5, 0.75, or 0.9 is greater than or equal to, e.g., 0.75, 0.90, or 0.95.

[0084] In some instances, each candidate neoantigen in the third subset is associated with an HLA gene allele that is predicted to be HLA-LOH negative. FIG. 3 provides a non-limitingschematic illustration of a process 300 for detecting HLA-I loss of heterozygosity. Process 300 can be performed, for example, using one or more electronic devices implementing a software platform. In some examples, process 300 is performed using a client-server system, and the blocks of process 300 are divided up in any manner between the server and a client device. In other examples, the blocks of process 300 are divided up between the server and multiple client devices. Thus, while portions of process 300 are described herein as being performed by particular devices of a client-server system, it will be appreciated that process 300 is not so limited. In other examples, process 300 is performed using only a client device or only multiple client devices. In process 300, 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 300. Accordingly, the operations as illustrated (and described in greater detail below) are exemplary by nature and, as such, should not be viewed as limiting.

[0085] At step 302 in FIG. 3, sequence read data for a sample from a subject is received (e.g., by one or more processors of a system configured to implement the disclosed methods) in the form of, e.g., a BAM file.

[0086] At step 304 in FIG. 3, sequence reads that map to HLA genes are extracted from the BAM file along with unmapped sequence reads.

[0087] At step 306 in FIG. 3, an HLA reference comprising imputed introns is accessed.

[0088] At step 308 in FIG. 3, the sequence reads that mapped to HLA genes (along with unmapped sequence reads) are compared to the HLA reference to perform genotyping and identify the HLA type for the subject (z.e., to identify the HLA gene alleles associated with the subject). In some instances, the OptiType algorithm may be used to perform the genotyping (see, e.g., Szolek, el al. (2014), “OptiType: Precision HLA Typing from Next- Generation Sequencing Data”, Bioinformatics 30(23):3310-3316).

[0089] At step 310 in FIG. 3, the IMGT HLA reference is accessed. IMGT HLA is a database of human MHC (or HLA) sequences and comprises an international reference for HLA alleles.

[0090] At step 312 in FIG. 3, the subject’s HLA type data generated at step 308 and the IM GT HLA reference are used to build a subject- specific (e.g., patient-specific) HLA reference.

[0091] At step 314 in FIG. 3, the subject’s HLA sequence reads are realigned to the subjectspecific (e.g., patient- specific) HLA reference.

[0092] At step 316 in FIG. 3, a set of bait set binding adjustments are determined (z.e., for the bait set used to perform targeted sequencing to generate the sequence read data in the BAM file) based on the relative hybridization efficiencies determined for different bait molecules and their target sequences.

[0093] At step 318 in FIG. 3, the bait set binding adjustments are used to correct the number of sequence reads mapped to different HLA gene loci and calculate HLA gene allele frequencies for the detected alleles.

[0094] At step 320 in FIG. 3, copy number estimates for the detected HLA alleles are determined using a copy number modeling algorithm (see, for example, PCT International Patent Application Publication No. WO 2023 / 060236, the contents of which are incorporated herein by reference in its entirety).

[0095] At step 322 in FIG. 3, a degree of contamination in the sequence read data is estimated using a contamination detection algorithm (see, for example, PCT International Patent Application Publication No. WO 2023 / 060261, the contents of which are incorporated herein by reference in its entirety).

[0096] At step 324 in FIG. 3, a zygosity determination (z.e., an HLA LOH determination) is made based on the HLA gene allele frequencies determined at step 318 after taking into account the copy number modeling and contamination detection results determined at steps 320 and 322, respectively. Methods for determining HLA LOH are described further in PCT International Patent Application No. PCT / US23 / 70866, the contents of which are incorporated herein by reference in their entirety, and include methods for identifying HLA-I LOH when allelic imbalance information is available but copy number variation information is unavailable, as well as methods for identifying HLA-I LOH when copy number variation information is available but allelic imbalance information is unavailable. 1

[0097] Determination of HLA-I LOH, as described above, is one approach that may be used to identify tumor samples comprising a loss of function alteration in the antigen presentation pathway. In some instances, an alternative approach may be used instead of, or in addition to, the HLA-I LOH determination, e.g., identification of HLA-I SV alterations and / or B2M gene alterations that directly impact the antigen presentation pathway.

[0098] FIG. 4 provides another non-limiting example of a flowchart for a process 400 for identifying candidate neoantigens for personalized immunotherapy development. 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.

[0099] At step 402 in FIG. 4, a patient’s HLA genotype data is received (e.g., by one or more processors of a system configured to implement the disclosed methods). As described above in reference to FIG. 1, the HLA genotype data for the subject may be indicative of one or more HLA gene alleles associated with the subject is received. The one or more HLA gene alleles (e.g., 1, 2, 3, 4, 5, or 6 HLA gene alleles) may comprise, for example, maternal and paternal copies of HLA-A gene alleles, HLA-B gene alleles, HLA-C gene alleles, or a combination thereof. In some instances, the HLA genotype data may be obtained based on an analysis of sequence read data derived from a sample (e.g., a tumor sample) collected from the patient using, e.g., the OptiType algorithm, as described elsewhere herein.

[0100] At step 404 in FIG. 4, short variant sequences identified in a sample (e.g., a tumor sample) collected from the patient are received, where the short variant sequences may be identified based on, e.g., sequence read data derived from the patient sample. In some instances, the sequence read data may be generated by a genomic profiling pipeline (e.g., the Foundation Medicine genomic profiling pipeline). As described above in reference to FIG. 1, in some instances one or more short variant sequences may be identified based on the sequence read data by mapping and comparing sequence reads to a reference sequence (e.g., an HLA reference sequence or the GRCh38 / hg38 human genome reference sequence).

[0101] At step 406 in FIG. 4, a plurality of candidate neoantigens are identified based on HLA genotype data received in step 402 and the short variant alteration data received in step 404, e.g., by inputting the HLA genotype data and short variant data into a machine learning-based model, such as NetMHCpan, and outputting a list of neoantigens corresponding to the short variant sequences and an estimate of their binding affinities to an HLA protein.

[0102] At step 408 in FIG. 4, a first subset of candidate neoantigens are selected from the original plurality of identified neoantigens based on comparing their estimated binding affinities to a binding affinity threshold and retaining those candidate neoantigens that exhibit a stronger estimated binding affinity than the specified threshold. As described above in reference to FIG. 1, the binding affinity threshold may, in some instance, be set to 500 nM, 450 nM, 400 nM, 350 nM, 300 nM, 250 nM, 200 nM, 150 nM, 100 nM, 90 nM, 80 nM, 70 nM, 60 nM, 50 nM, 40 nM, 30 nM, 20 nM, 10 nM, 8 nM, 6 nM, 4 nM, 2 nM, or 1 nM. In some instances, the binding affinity threshold may be lowered to identify stronger binding neoantigens that are therefore potentially better immunotherapy targets.

[0103] At step 410 in FIG. 4, a clonality estimate is calculated for each of the remaining candidate neoantigens. In some instances, clonality may be determined based on, e.g., a calculation of cancer cell fraction (CCF). Exemplary methods for determining CCF have been described above in reference to FIG. 1.

[0104] At step 412 in FIG. 4, a second subset of candidate neoantigens are selected from the first subset based on comparing their CCF values to a CCF threshold and retaining those candidate neoantigens that exhibit a clonality (CCF value) of greater than the specified threshold.As described in reference to FIG. 1, the clonality threshold may, in some instances, comprise a CCF value greater than 50%, 75%, or 90%. In some instances, the clonality threshold may be set to 100%. In some instances, the clonality threshold may be set such that a lower confidence interval of CCF may be greater than or equal to 50%. In some instances, the clonality threshold may be set such that the probability that the neoantigen has a CCF greater than or equal to 0.5, 0.75, or 0.9 is greater than or equal to, e.g., 0.75, 0.90, or 0.95.

[0105] At step 414 in FIG. 4, an HEA-I EOH prediction is made for the HEA gene allele associated with each of the remaining candidate neoantigens. Exemplary methods for determining HEA-I EOH have been described above in reference to FIG. 1. In some instances, an alternative approach may be used instead of, or in addition to, the HLA-I LOH determination, e.g., identification of HLA-I SV alterations and / or B2M gene alterations that directly impact the antigen presentation pathway.

[0106] At step 416 in FIG. 4, a third subset of candidate neoantigens are selected from the second subset based on a prediction that the presenting HLA-I gene allele has not undergone loss of heterozygosity.

[0107] At step 418 in FIG. 4, the remaining set of one or more neoantigens are classified as optimal neoantigens for use in pursuing development of anti-cancer immunotherapies.

[0108] In any of the methods disclosed herein, the one or more variants e.g., short variants) may include variants identified in the ABL1, ACVR1B, AKT1, AKT2, AKT3, ALK, ALOX12B, AMER1, APC, AR, ARAF, ARFRP1, ARID1A, ASXL1, ATM, ATR, ATRX, AURKA, AURKB, AXIN1, AXL, BAP1, BARD1, BCL2, BCL2L1, BCL2L2, BCL6, BCOR, BCORL1, BCR, BRAF, BRCA1, BRCA2, BRD4, BRIP1, BTG1, BTG2, BTK, CALR, CARD11, CASP8, CBFB, CBL, CCND1, CCND2, CCND3, CCNE1, CD22, CD274, CD70, CD74, CD79A, CD79B, CDC73, CDH1, CDK12, CDK4, CDK6, CDK8, CDKN1A, CDKN1B, CDKN2A, CDKN2B, CDKN2C, CEBPA, CHEK1, CHEK2, CIC, CREBBP, CRKL, CSF1R, CSF3R, CTCF, CTNNA1, CTNNB1, CUL3, CUL4A, CXCR4, CYP17A1, DAXX, DDR1, DDR2, DIS3, DNMT3A, DOT1L, EED, EGFR, EMSY (Cllorf30), EP300, EPHA3, EPHB1, EPHB4, ERBB2, ERBB3, ERBB4, ERCC4, ERG, ERRFI1, ESRI, ETV4, ETV5, ETV6,EWSR1, EZH2, EZR, FAM46C, FANCA, FANCC, FANCG, FANCL, FAS, FBXW7, FGF10,FGF12, FGF14, FGF19, FGF23, FGF3, FGF4, FGF6, FGFR1, FGFR2, FGFR3, FGFR4, FH, FLCN, FLT1, FLT3, F0XL2, FUBP1, GABRA6, GATA3, GATA4, GATA6, GID4 (C17orf39), GNA11, GNA13, GNAQ, GNAS, GRM3, GSK3B, H3F3A, HDAC1, HGF, HNF1A, HRAS, HSD3B1, ID3, IDH1, IDH2, IGF1R, IKBKE, IKZF1, INPP4B, IRF2, IRF4, IRS2, JAK1, JAK2, JAK3, JUN, KDM5A, KDM5C, KDM6A, KDR, KEAP1, KEL, KIT, KLHL6, KMT2A (MLL), KMT2D (MLL2), KRAS, LTK, LYN, MAF, MAP2K1, MAP2K2, MAP2K4, MAP3K1, MAP3K13, MAPK1, MCL1, MDM2, MDM4, MED12, MEF2B, MEN1, MERTK, MET, MITF, MKNK1, MLH1, MPL, MRE11A, MSH2, MSH3, MSH6, MST1R, MTAP, MTOR, MUTYH, MYB, MYC, MYCL, MYCN, MYD88, NBN, NF1, NF2, NFE2L2, NFKBIA, NKX2-1, NOTCH1, NOTCH2, NOTCH3, NPM1, NRAS, NT5C2, NTRK1, NTRK2, NTRK3, NUTM1, P2RY8, PALB2, PARK2, PARP1, PARP2, PARP3, PAX5, PBRM1, PDCD1, PDCD1LG2, PDGFRA, PDGFRB, PDK1, PIK3C2B, PIK3C2G, PIK3CA, PIK3CB, PIK3R1, PIM1, PMS2, POLDI, POLE, PPARG, PPP2R1A, PPP2R2A, PRDM1, PRKAR1A, PRKCI, PTCHI, PTEN, PTPN11, PTPRO, QKI, RAC1, RAD21, RAD51, RAD51B, RAD51C, RAD51D, RAD52, RAD54L, RAFI, RARA, RBI, RBM10, REL, RET, RICTOR, RNF43, ROS1, RPTOR, RSPO2, SDC4, SDHA, SDHB, SDHC, SDHD, SETD2, SF3B1, SGK1, SLC34A2, SMAD2, SMAD4, SMARCA4, SMARCB1, SMO, SNCAIP, SOCS1, SOX2, SOX9, SPEN, SPOP, SRC, STAG2, STAT3, STK11, SUFU, SYK, TBX3, TEK, TERC, TERT, TET2, TGFBR2, TIPARP, TMPRSS2, TNFAIP3, TNFRSF14, TP53, TSC1, TSC2, TYRO3, U2AF1, VEGFA, VHL, WHSCI, WHSC1L1, WT1, XPO1, XRCC2, ZNF217, or ZNF703 gene locus, or any combination thereof.

[0109] In some instances, the one or more variants may comprise variants identified in the ABL, ALK, ALL, B4GALNT1, BAFF, BCL2, BRAF, BRCA, BTK, CD19, CD20, CD3, CD30, CD319, CD38, CD52, CDK4, CDK6, CML, CRACC, CS1, CTLA-4, dMMR, EGFR, ERBB1, ERBB2, FGFR1-3, FLT3, GD2, HDAC, HER1, HER2, HR, IDH2, IL-ip, IL-6, IL-6R, JAK1, JAK2, JAK3, KIT, KRAS, MEK, MET, MSI-H, mTOR, PARP, PD-1, PDGFR, PDGFRa, PDGFRP, PD-L1, PI3K5, PIGF, PTCH, RAF, RANKL, RET, ROS1, SLAMF7, VEGF, VEGFA, or VEGFB gene locus, or any combination thereof.Methods of use

[0110] 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, webbased, 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 displayedin 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.

[0111] 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).

[0112] 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.

[0113] In some instances, the disclosed methods for identifying and prioritizing neoantigens may be used to select a subject (e.g., a patient) for a clinical trial based on the specific neoantigens identified. In some instances, patient selection for clinical trials based on, e.g., identification of specific neoantigens derived from mutations (e.g., short variants) at one or more gene loci, may accelerate the development of targeted therapies and improve the healthcare outcomes for treatment decisions.

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

[0115] In some instances, the anti-cancer therapy or treatment may comprise a targeted anticancer therapy or treatment (e.g., a monoclonal antibody-based therapy, an enzyme inhibitorbased 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 (Haris), 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), 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.

[0116] 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- PD-1 or anti-PD-Ll 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).

[0117] 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 antigenbinding 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.

[0118] In some instances, the disclosed methods for identifying and / or prioritizing neoantigens may be used in treating a disease e.g., a cancer) in a subject. For example, in response to determining that a specific neoantigen, or set of neoantigens, is present in a tumor sample fromthe subject 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.

[0119] In some instances, the disclosed methods for identifying and / or prioritizing neoantigens 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 identifying and / or prioritizing neoantigens as part of a genomic profiling process (or inclusion of the output from the disclosed methods for identifying and / or prioritizing neoantigens as part of the genomic profile of the subject) can improve the validity of, e.g., disease detection calls and treatment decisions, made on the basis of the genomic profile by, for example, independently confirming the presence of a specific neoantigen, or set of neoantigens, in a tumor sample from a given patient sample.

[0120] 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.

[0121] 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.

[0122] 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 subjectbased 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

[0123] 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 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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 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.

[0128] 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 asample based on the presence of the detected genetic alteration. In some instances, multiple samples (e.g., from different subjects) are processed simultaneously.

[0129] 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.

[0130] 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 fragments of DNA that are released from cancerous cells and tumors that circulate in the blood stream and / or accumulate in other bodily fluids.

[0131] 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.

[0132] 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.

[0133] 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 nontumor cells). In some instances, the tumor content of the sample may constitute a sample metric. In some instances, the sample may comprise a tumor content of at least 5-50%, 10-40%, 15-25%, or 20-30% tumor cell nuclei. In some instances, the sample may comprise a tumor content of at least 5%, at least 10%, at least 20%, at least 30%, at least 40%, or at least 50% tumor cell nuclei. In some instances, the percent tumor cell nuclei (e.g., sample fraction) is determined (e.g., calculated) by dividing the number of tumor cells in the sample by the total number of all cells within the sample that have nuclei. In some instances, for example when the sample is a liver sample comprising hepatocytes, a different tumor content calculation may be required due to the presence of hepatocytes having nuclei with twice, or more than twice, the DNA content of other, e.g., non-hepatocyte, somatic cell nuclei. In some instances, the sensitivity of detection of a genetic alteration, e.g., a variant sequence, or a determination of, e.g., micro satellite instability, may depend on the tumor content of the sample. For example, a sample having a lower tumor content can result in lower sensitivity of detection for a given size sample.

[0134] 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

[0135] 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) orsuspected 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.

[0136] 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).

[0137] In some instances, the subject (e.g., a patient) is being treated, or has been previously treated, with one or more targeted therapies. In some instances, e.g., for a patient who has been previously treated with a targeted therapy, a post-targeted therapy sample (e.g., specimen) is obtained (e.g., collected). In some instances, the post-targeted therapy sample is a sample obtained after the completion of the targeted therapy.

[0138] 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

[0139] 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, nonHodgkin lymphoma (NHL), soft-tissue sarcoma, fibrosarcoma, myxosarcoma, liposarcoma, osteogenic sarcoma, chordoma, angiosarcoma, endotheliosarcoma, lymphangiosarcoma, lymphangioendotheliosarcoma, synovioma, mesothelioma, Ewing's tumor, leiomyosarcoma, rhabdomyosarcoma, squamous cell carcinoma, basal cell carcinoma, adenocarcinoma, sweat gland carcinoma, sebaceous gland carcinoma, papillary carcinoma, papillary adenocarcinomas, medullary carcinoma, bronchogenic carcinoma, renal cell carcinoma, hepatoma, bile duct carcinoma, choriocarcinoma, seminoma, embryonal carcinoma, Wilms' tumor, bladder carcinoma, epithelial carcinoma, glioma, astrocytoma, medulloblastoma, craniopharyngioma, ependymoma, pinealoma, hemangioblastoma, acoustic neuroma, oligodendroglioma, meningioma, neuroblastoma, retinoblastoma, follicular lymphoma, diffuse large B-cell lymphoma, mantle cell lymphoma, hepatocellular carcinoma, thyroid cancer, gastric cancer, head and neck cancer, small cell cancers, essential thrombocythemia, agnogenic myeloid metaplasia, hypereosinophilic syndrome, systemic mastocytosis, familiar hypereosinophilia, chronic eosinophilic leukemia, neuroendocrine cancers, carcinoid tumors, and the like.

[0140] 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 17pdeletion), 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 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.

[0141] 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), chronicmyelogenous 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, Sezary syndrome, Waldenstrom macroglobulinemia), chronic myeloproliferative neoplasm, Langerhans cell histiocytosis, multiple myeloma / plasma cell neoplasm, myelodysplastic syndrome, or myelodysplastic / myeloproliferative neoplasm.Nucleic acid extraction and processing

[0142] 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, 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] As noted above, in some instances the sample may comprise a formalin-fixed (also known as formaldehyde-fixed, or paraformaldehyde-fixed), paraffin-embedded (FFPE) tissue preparation. For example, the FFPE sample may be a tissue sample embedded in a matrix, e.g., an FFPE block. Methods to isolate nucleic acids (e.g., DNA) from formaldehyde- or paraformaldehyde-fixed, paraffin-embedded (FFPE) tissues are disclosed in, e.g., Cronin, et al., (2004) Am J Pathol. 164(l):35-42; Masuda, et al., (1999) Nucleic Acids Res. 27 (22): 4436-4443;Specht, et al., (2001) Am J Pathol. 158(2):419-429; the Ambion RecoverAll™ Total Nucleic Acid Isolation Protocol (Ambion, Cat. No. AM1975, September 2008); the Maxwell® 16 FFPE Plus 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 pm sections of FFPE tissue. DNA is purified using silica-clad paramagnetic particles (PMPs), and eluted in low elution volume. The E.Z.N.A.® FFPE DNA Kit uses a spin column and buffer system for isolation of genomic DNA. QIAamp® DNA FFPE Tissue Kit uses QIAamp® DNA Micro technology for purification of genomic and mitochondrial DNA.

[0149] 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.

[0150] After isolation, the nucleic acids are typically dissolved in a slightly alkaline buffer, e.g., Tris-EDTA (TE) buffer, or in ultra-pure water. In some instances, the isolated nucleic acids (e.g., 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

[0151] 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.

[0152] In some instances, the resulting nucleic acid library may contain all or substantially all of the complexity of the genome. The term “substantially all” in this context refers to the possibility that there can in practice be some unwanted loss of genome complexity during the initial steps of the procedure. The methods described herein also are useful in cases where the nucleic acid library comprises a portion of the genome, e.g., where the complexity of the genome is reduced by design. In some instances, any selected portion of the genome can be used with a method described herein. For example, in certain embodiments, the entire exome or a subset thereof is isolated. In some instances, the library may include at least 95%, 90%, 80%, 70%, 60%, 50%, 40%, 30%, 20%, 10%, or 5% of the 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.

[0153] 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.

[0154] 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. 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

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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

[0159] The methods described herein may comprise contacting a nucleic acid library with a plurality of target capture reagents in order to select and capture a plurality of specific target sequences (e.g., gene sequences or fragments thereof) for analysis. In some instances, a target capture reagent (i.e., a molecule which can bind to and thereby allow capture of a target molecule) is used to select the subject intervals to be analyzed. For example, a target capturereagent 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.

[0160] The methods described herein provide for optimized sequencing of a large number of genomic loci (e.g., genes or gene products (e.g., mRNA), micro satellite loci, etc.) from samples (e.g., cancerous tissue specimens, liquid biopsy samples, and the like) from one or more subjects by the appropriate selection of target capture reagents to select the target nucleic acid molecules to be sequenced. In some instances, a target capture reagent may hybridize to a specific target locus, e.g., a specific target gene locus or fragment thereof. In some instances, a target capture reagent may hybridize to a specific group of target loci, e.g., a specific group of gene loci or fragments thereof. In some instances, a plurality of target capture reagents comprising a mix of target- specific and / or group- specific target capture reagents may be used.

[0161] 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.

[0162] In some instances, the overall length of the target capture reagent sequence can be between about 70 nucleotides and 1000 nucleotides. In one instance, the target capture reagent length is between about 100 and 300 nucleotides, 110 and 200 nucleotides, or 120 and 170 nucleotides, in length. In addition to those mentioned above, intermediate oligonucleotide lengths of about 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 300, 400, 500, 600, 700, 800, and 900 nucleotides in length can be used in the methods described herein. In some embodiments, oligonucleotides of about 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, or 230 bases can be used.

[0163] In some instances, each target capture reagent sequence can include: (i) a target- specific capture sequence (e.g., a gene locus or micro satellite locus- specific complementary sequence), (ii) an adapter, primer, barcode, and / or unique molecular identifier sequence, and (iii) universal tails on one or both ends. As used herein, the term "target capture reagent" can refer to the targetspecific target capture sequence or to the entire target capture reagent oligonucleotide including the target- specific target capture sequence.

[0164] In some instances, the target- specific capture sequences in the target capture reagents are between about 40 nucleotides and 1000 nucleotides in length. In some instances, the targetspecific capture sequence is between about 70 nucleotides and 300 nucleotides in length. In some instances, the target- specific sequence is between about 100 nucleotides and 200 nucleotides in length. In yet other instances, the target- specific sequence is between about 120 nucleotides and 170 nucleotides in length, typically 120 nucleotides in length. Intermediate lengths in addition to those mentioned above also can be used in the methods described herein, such as target- specific sequences of about 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 300, 400, 500, 600, 700, 800, and 900 nucleotides in length, as well as target- specific sequences of lengths between the above-mentioned lengths.

[0165] 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 knownjuncture sequence, complementary target capture reagents can be designed to recognize the juncture sequence to increase the selection efficiency.

[0166] 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.

[0167] 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.

[0168] 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 saidplurality 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).

[0169] 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.

[0170] 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

[0171] 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 (z.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.

[0172] 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.

[0173] 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

[0174] The methods and systems disclosed herein can be used in combination with, or as part of, a method or system for sequencing nucleic acids (e.g., a next-generation sequencing system) to generate a plurality of sequence reads that overlap one or more gene loci within a subgenomic interval in the sample and thereby determine, e.g., gene allele sequences at a plurality of gene loci. “Next-generation sequencing” (or “NGS”) as used herein may also be referred to as “massively parallel sequencing” (or “MPS”), and refers to any sequencing method that determines the nucleotide sequence of either individual nucleic acid molecules (e.g., as in single molecule sequencing) or clonally expanded proxies for individual nucleic acid molecules in a high throughput fashion (e.g., wherein greater than 103, 104, 105or more than 105molecules are sequenced simultaneously).

[0175] 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.

[0176] 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.

[0177] In certain instances, the disclosed methods comprise one or more of the steps of: (a) acquiring a library comprising a plurality of normal and / or tumor nucleic acid molecules from a sample; (b) simultaneously or sequentially contacting the library with one, two, three, four, five, or more than five pluralities of target capture reagents under conditions that allow hybridization of the target capture reagents to the target nucleic acid molecules, thereby providing a selected set of captured normal and / or tumor nucleic acid molecules (z.e., a library catch); (c) separating the selected subset of the nucleic acid molecules (e.g., the library catch) from the hybridization mixture, e.g., by contacting the hybridization mixture with a binding entity that allows for separation of the target capture reagent / nucleic acid molecule hybrids from the hybridization mixture, (d) sequencing the library catch to acquiring a plurality of reads (e.g., sequence reads) that overlap one or more subject intervals (e.g., one or more target sequences) from said library catch that may comprise a mutation (or alteration), e.g., a variant sequence comprising a somatic mutation or germline mutation; (e) aligning said sequence reads using an alignment method as described elsewhere herein; and / or (f) assigning a nucleotide value for a nucleotide position in the subject interval (e.g., calling a mutation using, e.g., a Bayesian method or other method described herein) from one or more sequence reads of the plurality.

[0178] 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 maycomprise 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.

[0179] In some instances, acquiring a sequence read for one or more subject intervals comprises sequencing a subject interval with a sequencing method that provides a sequence read length (or average sequence read length) of at least 20 bases, at least 30 bases, at least 40 bases, at least 50 bases, at least 60 bases, at least 70 bases, at least 80 bases, at least 90 bases, at least 100 bases, at least 120 bases, at least 140 bases, at least 160 bases, at least 180 bases, at least 200 bases, at least 220 bases, at least 240 bases, at least 260 bases, at least 280 bases, at least 300 bases, at least 320 bases, at least 340 bases, at least 360 bases, at least 380 bases, or at least 400 bases. In some instances, acquiring a sequence read for the one or more subject intervals may comprise sequencing a subject interval with a sequencing method that provides a sequence read length (or average sequence read length) of any number of bases within the range described in this paragraph, e.g., a sequence read length (or average sequence read length) of 56 bases.

[0180] In some instances, acquiring a sequence read for one or more subject intervals may comprise sequencing with at least lOOx or more coverage (or depth) on average. In some instances, acquiring a sequence read for one or more subject intervals may comprise sequencing with at least lOOx, at least 150x, at least 200x, at least 250x, at least 500x, at least 750x, at least l,000x, at least 1,500 x, at least 2,000x, at least 2,500x, at least 3,000x, at least 3,500x, at least 4,000x, at least 4,500x, at least 5,000x, at least 5,500x, or at least 6,000x or more coverage (or depth) on average. In some instances, acquiring a sequence read for one or more subject intervals may comprise sequencing with an average coverage (or depth) having any value within the range of values described in this paragraph, e.g., at least 160x.

[0181] In some instances, acquiring a read for the one or more subject intervals comprises sequencing with an average sequencing depth having any value ranging from at least lOOx to at least 6,000x for greater than about 90%, 92%, 94%, 95%, 96%, 97%, 98%, or 99% of the 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 subjectinterval comprises sequencing with an average sequencing depth of at least 4,100x for at least 95% of the gene loci sequenced.

[0182] 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.

[0183] 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).

[0184] 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

[0185] 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.

[0186] 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 repetitivesequence) 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.

[0187] In some instances, the methods and systems disclosed herein may integrate the use of multiple, individually-tuned, alignment methods or algorithms to optimize base-calling performance in sequencing methods, particularly in methods that rely on massively parallel sequencing (MPS) of a large number of diverse genetic events at a large number of diverse genomic loci. In some instances, the disclosed methods and systems may comprise the use of one or more global alignment algorithms. In some instances, the disclosed methods and systems may comprise the use of one or more local alignment algorithms. Examples of alignment algorithms that may be used include, but are not limited to, the Burrows-Wheeler Alignment (BWA) software bundle (see, e.g., Li, et al. (2009), “Fast and Accurate Short Read Alignment with Burrows-Wheeler Transform”, Bioinformatics 25: 1754-60; Li, et al. (2010), Fast and Accurate Long-Read Alignment with Burrows-Wheeler Transform”, Bioinformatics epub.PMID: 20080505), the Smith- Waterman algorithm (see, e.g., Smith, et al. (1981), "Identification of Common Molecular Subsequences", J. Molecular Biology 147(1): 195-197), the Striped Smith-Waterman algorithm (see, e.g., Farrar (2007), “Striped Smith-Waterman 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.

[0188] 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).

[0189] 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, micro satellite locus, or other subject interval) being sequenced, (ii) the tumor type associated with the sample, (iii) the variant being sequenced, or (iv) a characteristic of the sample or the subject. The selection or use of alignment conditions that are individually tuned to a number of specific subject intervals to be sequenced allows optimization of speed, sensitivity, and specificity. The method is particularly effective when the alignment of reads for a relatively large number of diverse subject intervals are optimized.

[0190] 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.

[0191] 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).

[0192] 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.

[0193] In some instances, alternative methods may be used to align troublesome reads. These methods are particularly effective when the alignment of reads for a relatively large number of diverse subject intervals is optimized. By way of example, a method of analyzing a sample can comprise: (i) performing a comparison (e.g., an alignment comparison) of a read using a first set of parameters (e.g., using a first mapping algorithm, or by comparison with a first reference sequence), and determining if said read meets a first alignment criterion (e.g., the read can be aligned with said first reference sequence, e.g., with less than a specific number of mismatches); (ii) if said read fails to meet the first alignment criterion, performing a second alignment comparison using a second set of parameters, (e.g., using a second mapping algorithm, or by comparison with a second reference sequence); and (iii) optionally, determining if said read meets said second criterion (e.g., the read can be aligned with said second reference sequence, e.g., with less than a specific number of mismatches), wherein said second set of parameters comprises use of, e.g., said second reference sequence, which, compared with said first set of parameters, is more likely to result in an alignment with a read for a variant (e.g., a rearrangement, insertion, deletion, or translocation).

[0194] 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 remediedwith 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. CaT 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).

[0195] Reduced specificity (increased false positive rate) in the evaluated subject intervals due to misalignment can be assessed by manual or automated examination of all mutation calls in the sequencing data. Those regions found to be prone to spurious mutation calls due to misalignment can be subjected to alignment remedies as discussed above. In cases where no algorithmic remedy is found possible, “mutations” from the problem regions can be classified or screened out from the panel of targeted loci.Mutation calling

[0196] 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.

[0197] 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,micro satellite 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.

[0198] 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 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.

[0199] 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).

[0200] 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.

[0201] 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 basecalling error alone. Mutations can be called if this comparison is sufficiently strongly supportive of the presence of a mutation.

[0202] 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 ~le-6. The likelihood of specific mutations occurring at many sites in, for example, a typical multigenic cancer genome panel can be orders of magnitude higher. These likelihoods can be derived from public databases of cancer mutations (e.g., COSMIC).

[0203] 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 metric. Methods of indel calling can include the steps of identifying candidate indels, calculating genotype likelihood through local re-alignment, and performing LD-based genotype inference and calling. Typically, a Bayesian approach is used to obtain potential indel candidates, and then these candidates are tested together with the reference sequence in a Bayesian framework.

[0204] Algorithms to generate candidate indels are described in, e.g., McKenna, A., et al., Genome Res. 2010; 20(9): 1297-303; Ye, K., et al., Bioinformatics, 2009; 25(21):2865-71; Lunter, G., and Goodson, M., Genome Res. 2011; 21(6):936-9; and Li, H., et al. (2009), Bioinformatics 25(16):2078-9.

[0205] Methods for generating indel calls and individual-level genotype likelihoods include, e.g., the Dindel algorithm (Albers, C.A., et al., Genome Res. 2011 ;21 (6):961-73). For example, the Bayesian EM algorithm can be used to analyze the reads, make initial indel calls, and generate genotype likelihoods for each candidate indel, followed by imputation of genotypes using, e.g., QCALL (Le S.Q. and Durbin R. Genome Res. 2011 ;21(6):952-60). Parameters, such as prior expectations of observing the indel can be adjusted e.g., increased or decreased), based on the size or location of the indels.

[0206] Methods have been developed that address limited deviations from allele frequencies of 50% or 100% for the analysis of cancer DNA. (see, e.g., SNVMix -Bioinformatics. 2010 March 15; 26(6): 730-736.) Methods disclosed herein, however, allow consideration of the possibility of the presence of a mutant allele at frequencies (or allele fractions) ranging from 1% to 100% (z.e., allele fractions ranging from 0.01 to 1.0), and especially at levels lower than 50%. This approach is particularly important for the detection of mutations in, for example, low-purity FFPE samples of natural (multi-clonal) tumor DNA.

[0207] In some instances, the mutation calling method used to analyze sequence reads is not individually customized or fine-tuned for detection of different mutations at different genomic loci. In some instances, different mutation calling methods are used that are individually customized or fine-tuned for at least a subset of the different mutations detected at different genomic loci. In some instances, different mutation calling methods are used that are individually customized or fine-tuned for each different mutant detected at each different genomic loci. The customization or tuning can be based on one or more of the factors described herein, e.g., the type of cancer in a sample, the gene or locus in which the subject interval to be sequenced is located, or the variant to be sequenced. This selection or use of mutation calling methods individually customized or fine-tuned for a number of subject intervals to be sequenced allows for optimization of speed, sensitivity and specificity of mutation calling.

[0208] In some instances, a nucleotide value is assigned for a nucleotide position in each of X unique subject intervals using a unique mutation calling method, and X is at least 2, at least 3, at least 4, at least 5, at least 10, at least 15, at least 20, at least 30, at least 40, at least 50, at least 60, at least 70, at least 80, at least 90, at least 100, at least 200, at least 300, at least 400, at least 500, at least 1000, at least 1500, at least 2000, at least 2500, at least 3000, at least 3500, at least 4000, at least 4500, at least 5000, or greater. The calling methods can differ, and thereby be unique, e.g., by relying on different Bayesian prior values.

[0209] In some instances, assigning said nucleotide value is a function of a value which is or represents the prior e.g., literature) expectation of observing a read showing a variant, e.g., a mutation, at said nucleotide position in a tumor of type.

[0210] In some instances, the method comprises assigning a nucleotide value (e.g., calling a mutation) for at least 10, 20, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, or 1,000 nucleotide positions, wherein each assignment is a function of a unique value (as opposed to the value for the other assignments) which is or represents the prior (e.g., literature) expectation of observing a read showing a variant, e.g., a mutation, at said nucleotide position in a tumor of type.

[0211] In some instances, assigning said nucleotide value is a function of a set of values which represent the probabilities of observing a read showing said variant at said nucleotide position if the variant is present in the sample at a specified frequency (e.g., 1%, 5%, 10%, etc.) and / or if the variant is absent (e.g., observed in the reads due to base-calling error alone).

[0212] In some instances, the mutation calling methods described herein can include the following: (a) acquiring, for a nucleotide position in each of said X subject intervals: (i) a first value which is or represents the prior (e.g., literature) expectation of observing a read showing a variant, e.g., a mutation, at said nucleotide position in a tumor of type X; and (ii) a second set of values which represent the probabilities of observing a read showing said variant at said nucleotide position if the variant is present in the sample at a frequency (e.g., 1%, 5%, 10%, etc.) and / or if the variant is absent (e.g., observed in the reads due to base-calling error alone); and (b) responsive to said values, assigning a nucleotide value (e.g., calling a mutation) from said reads for each of said nucleotide positions by weighing, e.g., by a Bayesian method described herein, the comparison among the values in the second set using the first value (e.g. , computing the posterior probability of the presence of a mutation), thereby analyzing said sample.

[0213] Additional description of exemplary nucleic acid sequencing methods, mutation calling methods, and methods for analysis of genetic variants is provided in, e.g., U.S. Patent No. 9,340,830, U.S. Patent No. 9,792,403, U.S. Patent No. 11,136,619, U.S. Patent No. 11,118,213, and International Patent Application Publication No. WO 2020 / 236941, the entire contents of each of which is incorporated herein by reference.Systems

[0214] Also disclosed herein are systems designed to implement any of the disclosed methods for identifying and prioritizing neoantigens as potential immunotherapy targets in a sample from a subject. The systems may comprise, e.g., one or more processors, and a memory unit communicatively coupled to the one or more processors and configured to store instructions that, when executed by the one or more processors, cause the system to: receive one or more variant sequences identified in a tumor sample from the subject; receive HLA genotype data for the subject indicative of one or more HLA gene alleles associated with the subject; identify a plurality of candidate neoantigens based on the one or more variant sequences and the HLA genotype data; and identify one or more neoantigens from the plurality of candidate neoantigens by: removing a first subset of candidate neoantigens from the plurality of candidate neoantigens based on a comparison between a binding affinity threshold and a predicted binding affinity associated with each candidate neoantigen in the first subset and a corresponding HLA potein; removing a second subset of candidate neoantigens from the plurality of candidate neoantigens based on a comparison between a clonality threshold and a clonal estimation of each candidate neoantigen in the second subset; and removing a third subset of candidate neoantigens from the plurality of candidate neoantigens based on an HLA LOH prediction for an HLA gene allele associated with each candidate neoantigen in the third subset.

[0215] In some instances, the disclosed systems may further comprise a sequencer, e.g., a next generation sequencer (also referred to as a massively parallel sequencer). Examples of next generation (or massively parallel) sequencing platforms include, but are not limited to, Roche / 454’s Genome Sequencer (GS) FLX system, Illumina / Solexa’ s Genome Analyzer (GA), Illumina’s HiSeq® 2500, HiSeq® 3000, HiSeq® 4000 and NovaSeq® 6000 sequencing systems, Life / APG’s Support Oligonucleotide Ligation Detection (SOLiD) system, Polonator’s G.007 system, Helicos BioSciences’ HeliScope Gene Sequencing system, ThermoFisher Scientific’s Ion Torrent Genexus system, or Pacific Biosciences’ PacBio® RS system.

[0216] In some instances, the disclosed systems may be used for identifying and prioritizing neoantigens as potential immunotherapy targets based on genomic data derived from any of avariety of samples as described herein (e.g., a tissue sample, biopsy sample, hematological sample, or liquid biopsy sample derived from the subject).

[0217] In some instances, the genomic data may be derived by sequencing a plurality of gene or genomic loci. In some instances, the plurality of gene or genomic loci for which sequencing data is processed may comprise at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 0, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, or more than 1000 gene or genomic loci (or any number of gene or genomic loci within the range of 1 to more than 1000 gene or genomic loci).

[0218] In some instance, the nucleic acid sequence data is acquired using a next generation sequencing technique (also referred to as a massively parallel sequencing technique) having a read-length of less than 400 bases, less than 300 bases, less than 200 bases, less than 150 bases, less than 100 bases, less than 90 bases, less than 80 bases, less than 70 bases, less than 60 bases, less than 50 bases, less than 40 bases, or less than 30 bases.

[0219] In some instances, the identification of one or more optimal neoantigens using the disclosed methods and systems is used to develop, select, initiate, adjust, or terminate a treatment for cancer in the subject (e.g., a patient) from which the sample was derived, as described elsewhere herein.

[0220] In some instances, the disclosed systems may further comprise sample processing and library preparation workstations, microplate-handling robotics, fluid dispensing systems, temperature control modules, environmental control chambers, additional data storage modules, data communication modules (e.g., Bluetooth®, WiFi, intranet, or internet communication hardware and associated software), display modules, one or more local and / or cloud-based software packages (e.g., instrument / system control software packages, sequencing data analysis software packages), etc., or any combination thereof. In some instances, the systems may comprise, or be part of, a computer system or computer network as described elsewhere herein.Machine learning

[0221] Any of a variety of machine learning approaches & algorithms (where a machine learning model, as referred to herein, comprises a trained machine learning algorithm) may be used in implementing the disclosed methods. For example, the machine learning model may comprise asupervised learning model (z.e., a model trained using labeled sets of training data), an unsupervised learning model (z.e., a model trained using unlabeled sets of training data), a semisupervised learning model (z.e., a model trained using a combination of labeled and unlabeled training data), a self- supervised learning model, or any combination thereof. In some examples, the machine learning model can comprise a deep learning model (z.e., a model comprising many layers of coupled "nodes" that may be trained in a supervised, unsupervised, or semi-supervised manner).

[0222] In some instances, one or more machine learning models (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more than 10 machine learning models), or a combination thereof, may be utilized to implement the disclosed methods.

[0223] In some instances, the one or more machine learning models may comprise statistical methods for analyzing data. The machine learning models may be used for classification and / or regression of data. The machine learning models can include, for example, neural networks, support vector machines, decision trees, ensemble learning (e.g., bagging-based learning, such as random forest, and / or boosting-based learning), ^-nearest neighbors algorithms, linear regression-based models, and / or logistic regression-based models. The machine learning models can comprise regularization, such as LI regularization and / or L2 regularization. The machine learning models can include the use of dimensionality reduction techniques (e.g., principal component analysis, matrix factorization techniques, and / or autoencoders) and / or clustering techniques (e.g., hierarchical clustering, / .-means clustering, distribution-based clustering, such as Gaussian mixture models, or density -based clustering, such as DBSCAN or OPTICS). The one or more machine learning models can comprise solving, e.g., optimizing, an objective function over multiple iterations based on a training data set. The iterative solving approach can be used even when the machine learning model comprises a model for which there exists a closed-form solution (e.g., linear regression).

[0224] In some instances, the machine learning models can comprise artificial neural networks (ANNs), e.g., deep learning models. For example, the one or more machine learning models / algorithms used for implementing the disclosed methods may include an ANN which can comprise any of a variety of computational motifs / architectures known to those of skill in theart, including, but not limited to, feedforward connections (e.g., skip connections), recurrent connections, fully connected layers, convolutional layers, and / or pooling functions (e.g., attention, including self-attention). The artificial neural networks can comprise differentiable non-linear functions trained by backpropagation.

[0225] Artificial neural networks, e.g., deep learning models, generally comprise an interconnected group of nodes organized into multiple layers of nodes. For example, the ANN architecture may comprise at least an input layer, one or more hidden layers (i.e., intermediate layers), and an output layer. The ANN or deep learning model may comprise any total number of layers (e.g., 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, or more than 20 layers in total), and any number of hidden layers (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, or more than 20 hidden layers), where the hidden layers function as trainable feature extractors that allow mapping of a set of input data to a preferred output value or set of output values. Each layer of the neural network comprises a plurality of nodes (e.g., at least 10, 25, 50, 75 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10,000, or more than 10,000 nodes). A node receives input data (e.g., genomic feature data (such as variant sequence data, methylation status data, etc.), non-genomic feature data (e.g., digital pathology image feature data), or other types of input data (e.g., patient- specific clinical data)) that comes either directly from one or more input data nodes or from the output of one or more nodes in previous layers, and performs a specific operation, e.g., a summation operation. In some cases, a connection from an input to a node is associated with a weight (or weighting factor). In some cases, the node may, for example, sum up the products of all pairs of inputs, Xi, and their associated weights, Wi. In some cases, the weighted sum is offset with a bias, b. In some cases, the output of a node may be gated using a threshold or activation function, / , where / may be a linear or non-linear function. The activation function may be, for example, a rectified linear unit (ReLU) activation function or other function such as a saturating hyperbolic tangent, identity, binary step, logistic, arcTan, softsign, parameteric rectified linear unit, exponential linear unit, softPlus, bent identity, softExponential, Sinusoid, Sine, Gaussian, or sigmoid function, or any combination thereof.

[0226] The weighting factors, bias values, and threshold values, or other computational parameters of the neural network (or other machine learning architecture), can be "taught" or "learned" in a training phase using one or more sets of training data (e.g., 1, 2, 3, 4, 5, or morethan 5 sets of training data) and a specified training approach configured to solve, e.g., minimize, a loss function. For example, the adjustable parameters for an ANN (e.g., deep learning model) may be determined based on input data from a training data set using an iterative solver (such as a gradient-based method, e.g., backpropagation), so that the output value(s) that the ANN computes (e.g., a classification of a sample or a prediction of a disease outcome) are consistent with the examples included in the training data set. The training of the model (i.e., determination of the adjustable parameters of the model using an iterative solver) may or may not be performed using the same hardware as that used for deployment of the trained model.

[0227] In some instances, the disclosed methods may comprise retraining any of the machine learning models (e.g., iteratively retraining a previously trained model using one or more training data sets that differ from those used to train the model initially). In some instances, retraining the machine learning model may comprise using a continuous, e.g., online, machine learning model, i.e., where the model is periodically or continuously updated or retrained based on new training data. The new training data may be provided by, e.g., a single deployed local operational system, a plurality of deployed local operational systems, or a plurality of deployed, geographically-distributed operational systems. In some instances, the disclosed methods may employ, for example, pre-trained ANNs, and the pre-trained ANNs can be fine-tuned according to an additional dataset that is inputted into the pre-trained ANN.Computer systems and networks

[0228] FIG. 5 illustrates an example of a computing device or system in accordance with one embodiment. Device 500 can be a host computer connected to a network. Device 500 can be a client computer or a server. As shown in FIG. 5, device 500 can be any suitable type of microprocessor-based device, such as a personal computer, workstation, server or handheld computing device (portable electronic device) such as a phone or tablet. The device can include, for example, one or more processor(s) 510, input devices 520, output devices 530, memory or storage devices 540, communication devices 560, and nucleic acid sequencers 570. Software 550 residing in memory or storage device 540 may comprise, e.g., an operating system as well as software for executing the methods described herein. Input device 520 and output device 530 cangenerally correspond to those described herein, and can either be connectable or integrated with the computer.

[0229] Input device 520 can be any suitable device that provides input, such as a touch screen, keyboard or keypad, mouse, or voice-recognition device. Output device 530 can be any suitable device that provides output, such as a touch screen, haptics device, or speaker.

[0230] Storage 540 can be any suitable device that provides storage (e.g., an electrical, magnetic or optical memory including a RAM (volatile and non-volatile), cache, hard drive, or removable storage disk). Communication device 560 can include any suitable device capable of transmitting and receiving signals over a network, such as a network interface chip or device. The components of the computer can be connected in any suitable manner, such as via a wired media (e.g., a physical system bus 580, Ethernet connection, or any other wire transfer technology) or wirelessly (e.g., Bluetooth®, Wi-Fi®, or any other wireless technology).

[0231] Software module 550, which can be stored as executable instructions in storage 540 and executed by processor(s) 510, can include, for example, an operating system and / or the processes that embody the functionality of the methods of the present disclosure (e.g., as embodied in the devices as described herein).

[0232] Software module 550 can also be stored and / or transported within any non-transitory computer-readable storage medium for use by or in connection with an instruction execution system, apparatus, or device, such as those described herein, that can fetch instructions associated with the software from the instruction execution system, apparatus, or device and execute the instructions. In the context of this disclosure, a computer-readable storage medium can be any medium, such as storage 540, that can contain or store processes for use by or in connection with an instruction execution system, apparatus, or device. Examples of computer- readable storage media may include memory units like hard drives, flash drives and distribute modules that operate as a single functional unit. Also, various processes described herein may be embodied as modules configured to operate in accordance with the embodiments and techniques described above. Further, while processes may be shown and / or described separately, those skilled in the art will appreciate that the above processes may be routines or modules within other processes.

[0233] Software module 550 can also be propagated within any transport medium for use by or in connection with an instruction execution system, apparatus, or device, such as those described above, that can fetch instructions associated with the software from the instruction execution system, apparatus, or device and execute the instructions. In the context of this disclosure, a transport medium can be any medium that can communicate, propagate or transport programming for use by or in connection with an instruction execution system, apparatus, or device. The transport readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic or infrared wired or wireless propagation medium.

[0234] Device 500 may be connected to a network (e.g., network 604, as shown in FIG. 6 and / or described below), which can be any suitable type of interconnected communication system. The network can implement any suitable communications protocol and can be secured by any suitable security protocol. The network can comprise network links of any suitable arrangement that can implement the transmission and reception of network signals, such as wireless network connections, T1 or T3 lines, cable networks, DSL, or telephone lines.

[0235] Device 500 can be implemented using any operating system, e.g., an operating system suitable for operating on the network. Software module 550 can be written in any suitable programming language, such as C, C++, Java or Python. In various embodiments, application software embodying the functionality of the present disclosure can be deployed in different configurations, such as in a client / server arrangement or through a Web browser as a Web-based application or Web service, for example. In some embodiments, the operating system is executed by one or more processors, e.g., processor(s) 510.

[0236] Device 500 can further include a sequencer 570, which can be any suitable nucleic acid sequencing instrument.

[0237] FIG. 6 illustrates an example of a computing system in accordance with one embodiment. In system 600, device 500 e.g., as described above and illustrated in FIG. 5) is connected to network 604, which is also connected to device 606. In some embodiments, device 606 is a sequencer. Exemplary sequencers can include, without limitation, Roche / 454’s Genome Sequencer (GS) FLX System, Illumina / Solexa’ s Genome Analyzer (GA), Illumina’s HiSeq® 2500, HiSeq® 3000, HiSeq® 4000 and NovaSeq® 6000 Sequencing Systems, Life / APG’sSupport Oligonucleotide Ligation Detection (SOLiD) system, Polonator’s G.007 system, Helicos BioSciences’ HeliScope Gene Sequencing system, or Pacific Biosciences’ PacBio® RS system.

[0238] Devices 500 and 606 may communicate, e.g., using suitable communication interfaces via network 604, such as a Local Area Network (LAN), Virtual Private Network (VPN), or the Internet. In some embodiments, network 604 can be, for example, the Internet, an intranet, a virtual private network, a cloud network, a wired network, or a wireless network. Devices 500 and 606 may communicate, in part or in whole, via wireless or hardwired communications, such as Ethernet, IEEE 802.1 lb wireless, or the like. Additionally, devices 500 and 606 may communicate, e.g., using suitable communication interfaces, via a second network, such as a mobile / cellular network. Communication between devices 500 and 606 may further include or communicate with various servers such as a mail server, mobile server, media server, telephone server, and the like. In some embodiments, Devices 500 and 606 can communicate directly (instead of, or in addition to, communicating via network 604), e.g., via wireless or hardwired communications, such as Ethernet, IEEE 802.11b wireless, or the like. In some embodiments, devices 500 and 606 communicate via communications 608, which can be a direct connection or can occur via a network (e.g., network 604).

[0239] One or all of devices 500 and 606 generally include logic (e.g., http web server logic) or are programmed to format data, accessed from local or remote databases or other sources of data and content, for providing and / or receiving information via network 604 according to various examples described herein.EXAMPLESExample 1 - Evaluation of the Short Variant / Potential Neoantigen Landscape in Non-Small Cell Lung Cancer (NSCLC

[0240] The impact of short variant clonality, genomic ancestry, and HLA-I LOH - the additional selection criteria underlying the disclosed methods for neoantigen prioritization - on the neoantigen landscape was first assessed for patients diagnosed with advanced Non-Small Cell Lung Cancer (advNSCLC). In an analysis of 14,450 patients, 63,674 unique neoantigens were identified. Of these neoantigens, more than half (51%) were considered to be suboptimal becausethey arose from subclonal short variants or occurred in tumors exhibiting HLA-I LOH of the presenting allele.

[0241] FIG. 7 provides a non-limiting example of data for the prevalence of the top 30 most frequently altered short variants (SV) in NSCLC. The KRAS G12C mutation, for example occurred in about 11% of tumor samples for the patient cohort.

[0242] FIG. 8 provides non-limiting examples of the prevalence of the short variant being clonal (z.e., exhibiting a cancer cell fraction (CCF) of greater than 0.5; upper panel), the prevalence of the short variant within each main genomic ancestry group (middle panel), the prevalence of the short variant being a neoantigen (lower panel), and the prevalence of the neoantigen’s presenting allele being lost by HLA-I LOH (lower panel). The inset provides a heatmap intensity scale for the upper (left), middle (center), and lower (right) panels, respectively. AFR = African ancestry group. AMR = Admixed American ancestry group. EAS = East Asian ancestry group. EUR = European ancestry group. SAS = South Asian ancestry group.

[0243] FIGS. 9A-E provide non-limiting examples of data for the prevalence of the top 30 most frequently altered short variants in NSCLC across genomic ancestry groups. FIG. 9A: African ancestry group. FIG. 9B: European ancestry group. FIG. 9C: Admixed American ancestry group. FIG. 9D: South Asian ancestry group. FIG. 9E: East Asian ancestry group. The SV mutational landscape differs between genomic ancestry groups, with only 8 of the top 30 SV alterations being shared by all five groups: KRAS G12C / D / V, TERT promoter -124C>T, PIK3CA E542K, EGFR L858R, EGFR E746_A750del, and EGFR T790M.

[0244] Within the 14,450 specimens analyzed, there were 63,674 different neoantigens identified. There was a median of 4 neoantigens (IQR: 2-7) identified per specimen (the highest number observed in one specimen was 114 neoantigens). 11.8% of the identified neoantigens were generated by known / likely pathogenic alterations, and the total number of recurring neoantigens (identified in more than 1 sample) was 5,850 (9.2%).

[0245] Optimal neoantigens for development of immunotherapies were identified based on three initial criteria: (i) a neoantigen binding affinity of <500 nM (as estimated using NetMHCpan),(ii) the clonality of the underlying short variant (using a threshold of CCF > 0.5), and (iii) the presenting allele was not lost through HLA-I LOH.

[0246] Using these criteria, there were 31,063 different optimal neoantigens identified (48.8% of the total number of neoantigens identified), with a median of 2 optimal neoantigens (IQR: 1-3) identified per specimen (the highest number observed in one specimen was 71 optimal neoantigens). 13.1% of the optimal neoantigens were generated by known / likely pathogenic alterations, and the total number of recurring optimal neoantigens was 2,134 (6.9%).

[0247] These results suggest that implementing the more stringent selection criteria disclosed herein can be used to substantially decrease the pool of candidate neoantigen targets and to hone in on those neoantigens that are most likely to elicit a strong immune response as optimal targets for anti-cancer immunotherapy. There may also be additional factors that impact neoantigen selection, for example, genomic ancestry. Genomic ancestry-based differences in neoantigen presentation can be framed as a research observation. If hypothesis generating retrospective studies like the one described here are carried out in a widespread fashion (e.g., across multiple tumor types and large sets of diverse patient groups), a knowledge pool of genomic ancestryspecific neoantigens may be identified. This information could then be used prospectively as part of neoantigen target selection strategies (e.g., by implementing more stringent selection criteria that include genomic-ancestry) in clinical trial design.EXEMPLARY IMPLEMENTATIONS

[0248] Exemplary implementations of the methods and systems described herein include:1. A method comprising: providing a plurality of nucleic acid molecules obtained from a sample from a subject; ligating one or more adapters onto one or more nucleic acid molecules from the plurality of nucleic acid molecules; amplifying 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 the one or more processors, one or more variant sequences identified based on the sequence read data; receiving, at the one or more processors, HLA genotype data for the subject indicative of one or more HLA gene alleles associated with the subject; identifying, using the one or more processors, a plurality of candidate neoantigens based on the one or more variant sequences and the HLA genotype data; and identifying one or more neoantigens as potential immunotherapy targets by: removing a first subset of candidate neoantigens from the plurality of candidate neoantigens based on a comparison between a binding affinity threshold and a predicted binding affinity associated with each candidate neoantigen in the first subset and a corresponding HLA protein; removing a second subset of candidate neoantigens from the plurality of candidate neoantigens based on a comparison between a clonality threshold and a clonal estimation of each candidate neoantigen in the second subset; and removing a third subset of candidate neoantigens from the plurality of candidate neoantigens based on an HLA LOH prediction for an HLA gene allele associated with each candidate neoantigen in the third subset.2. The method of clause 1, wherein each candidate neoantigen of the plurality of candidate neoantigens is identified based a combination of a particular variant sequence of the one or more variant sequences and a particular HLA gene allele of the one or more HLA gene alleles.3. The method of clause 1 or clause 2, further comprising obtaining the plurality of candidate neoantigens by: inputting the one or more variant sequences and the one or more HLA gene alleles into a machine learning model; andreceiving, from the machine learning model, the plurality of candidate neoantigens and the predicted binding affinity associated with each of the candidate neoantigens.4. The method of any one of clauses 1 to 3, wherein the machine learning model comprises NetMHCpan.5. The method of any one of clauses 1 to 4, wherein the subject is suspected of having or is determined to have cancer.6. The method of clause 5, wherein the cancer is a B cell cancer (multiple myeloma), a melanoma, breast cancer, lung cancer, bronchus cancer, colorectal cancer, prostate cancer, pancreatic cancer, stomach cancer, ovarian cancer, urinary bladder cancer, brain cancer, central nervous system cancer, peripheral nervous system cancer, esophageal cancer, cervical cancer, uterine cancer, endometrial cancer, cancer of an oral cavity, cancer of a pharynx, liver cancer, kidney cancer, testicular cancer, biliary tract cancer, small bowel cancer, appendix cancer, salivary gland cancer, thyroid gland cancer, adrenal gland cancer, osteosarcoma, chondrosarcoma, a cancer of hematological tissue, an adenocarcinoma, an inflammatory myofibroblastic tumor, a gastrointestinal stromal tumor (GIST), colon cancer, multiple myeloma (MM), myelodysplastic syndrome (MDS), myeloproliferative disorder (MPD), acute lymphocytic leukemia (ALL), acute myelocytic leukemia (AML), chronic myelocytic leukemia (CML), chronic lymphocytic leukemia (CLL), polycythemia Vera, Hodgkin lymphoma, nonHodgkin lymphoma (NHL), soft-tissue sarcoma, fibrosarcoma, myxosarcoma, liposarcoma, osteogenic sarcoma, chordoma, angiosarcoma, endotheliosarcoma, lymphangiosarcoma, lymphangioendotheliosarcoma, synovioma, mesothelioma, Ewing's tumor, leiomyosarcoma, rhabdomyosarcoma, squamous cell carcinoma, basal cell carcinoma, adenocarcinoma, sweat gland carcinoma, sebaceous gland carcinoma, papillary carcinoma, papillary adenocarcinomas, medullary carcinoma, bronchogenic carcinoma, renal cell carcinoma, hepatoma, bile duct carcinoma, choriocarcinoma, seminoma, embryonal carcinoma, Wilms' tumor, bladder carcinoma, epithelial carcinoma, glioma, astrocytoma, medulloblastoma, craniopharyngioma, ependymoma, pinealoma, hemangioblastoma, acoustic neuroma, oligodendroglioma, meningioma, neuroblastoma, retinoblastoma, follicular lymphoma, diffuse large B-cell lymphoma, mantle cell lymphoma, hepatocellular carcinoma, thyroid cancer, gastric cancer, headand 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.7. The method of clause 5, wherein the cancer comprises acute lymphoblastic leukemia (Philadelphia chromosome positive), acute lymphoblastic leukemia (precursor B-cell), acute myeloid leukemia (FLT3+), acute myeloid leukemia (with an IDH2 mutation), anaplastic large cell lymphoma, basal cell carcinoma, B-cell chronic lymphocytic leukemia, bladder cancer, breast cancer (HER2 overexpressed / amplified), breast cancer (HER2+), breast cancer (HR+, HER2-), cervical cancer, cholangiocarcinoma, chronic lymphocytic leukemia, chronic lymphocytic leukemia (with 17p deletion), chronic myelogenous leukemia, chronic myelogenous leukemia (Philadelphia chromosome positive), classical Hodgkin lymphoma, colorectal cancer, colorectal cancer (dMMR / MSI-H), colorectal cancer (KRAS wild type), cryopyrin-associated periodic syndrome, a cutaneous T-cell lymphoma, dermatofibrosarcoma protuberans, a diffuse large B-cell lymphoma, fallopian tube cancer, a follicular B-cell 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 tumor, a pediatric neuroblastoma, a peripheral T-cell lymphoma, peritoneal cancer, prostatecancer, 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.8. The method of clause 7, further comprising treating the subject with an anti-cancer therapy.9. The method of clause 8, wherein the anti-cancer therapy comprises a targeted anti-cancer therapy.10. The method of clause 9, wherein the targeted anti-cancer therapy comprises abemaciclib (Verzenio), abiraterone acetate (Zytiga), acalabrutinib (Calquence), ado-trastuzumab emtansine (Kadcyla), afatinib dimaleate (Gilotrif), aldesleukin (Proleukin), alectinib (Alecensa), alemtuzumab (Campath), alitretinoin (Panretin), alpelisib (Piqray), amivantamab-vmjw (Rybrevant), anastrozole (Arimidex), apalutamide (Erleada), asciminib hydrochloride (Scemblix), atezolizumab (Tecentriq), avapritinib (Ayvakit), avelumab (Bavencio), axicabtagene ciloleucel (Yescarta), axitinib (Inlyta), belantamab mafodotin-blmf (Blenrep), belimumab (Benlysta), belinostat (Beleodaq), belzutifan (Welireg), bevacizumab (Avastin), bexarotene (Targretin), binimetinib (Mektovi), blinatumomab (Blincyto), bortezomib (Velcade), bosutinib (Bosulif), brentuximab vedotin (Adcetris), brexucabtagene autoleucel (Tecartus), brigatinib (Alunbrig), cabazitaxel (Jevtana), cabozantinib (Cabometyx), cabozantinib (Cabometyx, Cometriq), canakinumab (Haris), capmatinib hydrochloride (Tabrecta), carfilzomib (Kyprolis), cemiplimab-rwlc (Libtayo), ceritinib (LDK378 / Zykadia), cetuximab (Erbitux), cobimetinib (Cotellic), copanlisib hydrochloride (Aliqopa), crizotinib (Xalkori), dabrafenib (Tafinlar), dacomitinib (Vizimpro), daratumumab (Darzalex), daratumumab and hyaluronidase-fihj (Darzalex Faspro), darolutamide (Nubeqa), dasatinib (Sprycel), denileukin diftitox (Ontak), denosumab (Xgeva), dinutuximab (Unituxin), dostarlimab-gxly (Jemperli), durvalumab (Imfinzi), duvelisib (Copiktra), elotuzumab (Empliciti), enasidenib mesylate (Idhifa), encorafenib (Braftovi), enfortumab vedotin-ejfv (Padcev), entrectinib (Rozlytrek), enzalutamide (Xtandi), erdafitinib (Balversa), erlotinib (Tarceva), everolimus (Afinitor), exemestane (Aromasin), fam-trastuzumab deruxtecan-nxki (Enhertu), fedratinib hydrochloride (Inrebic), fulvestrant (Faslodex), gefitinib (Iressa), gemtuzumab ozogamicin (Mylotarg), gilteritinib(Xospata), glasdegib maleate (Daurismo), hyaluronidase-zzxf (Phesgo), ibrutinib (Imbruvica), ibritumomab tiuxetan (Zevalin), idecabtagene vicleucel (Abecma), idelalisib (Zydelig), imatinib mesylate (Gleevec), infigratinib phosphate (Truseltiq), inotuzumab ozogamicin (Besponsa), iobenguane 1131 (Azedra), ipilimumab (Yervoy), isatuximab-irfc (Sarclisa), ivosidenib (Tibsovo), ixazomib citrate (Ninlaro), lanreotide acetate (Somatuline Depot), lapatinib (Tykerb), larotrectinib sulfate (Vitrakvi), lenvatinib mesylate (Lenvima), letrozole (Femara), lisocabtagene maraleucel (Breyanzi), loncastuximab tesirine-lpyl (Zynlonta), lorlatinib (Lorbrena), lutetium Lu 177-dotatate (Lutathera), margetuximab-cmkb (Margenza), midostaurin (Rydapt), mobocertinib succinate (Exkivity), mogamulizumab-kpkc (Poteligeo), moxetumomab pasudotox-tdfk (Lumoxiti), naxitamab-gqgk (Danyelza), necitumumab (Portrazza), neratinib maleate (Nerlynx), nilotinib (Tasigna), niraparib tosylate monohydrate (Zejula), nivolumab (Opdivo), obinutuzumab (Gazyva), ofatumumab (Arzerra), olaparib (Lynparza), olaratumab (Lartruvo), osimertinib (Tagrisso), palbociclib (Ibrance), panitumumab (Vectibix), panobinostat (Farydak), pazopanib (Votrient), pembrolizumab (Keytruda), pemigatinib (Pemazyre), pertuzumab (Perjeta), pexidartinib hydrochloride (Turalio), polatuzumab vedotin-piiq (Polivy), ponatinib hydrochloride (Iclusig), pralatrexate (Folotyn), pralsetinib (Gavreto), radium 223 dichloride (Xofigo), ramucirumab (Cyramza), regorafenib (Stivarga), ribociclib (Kisqali), ripretinib (Qinlock), rituximab (Rituxan), rituximab and hyaluronidase human (Rituxan Hycela), romidepsin (Istodax), rucaparib camsylate (Rubraca), ruxolitinib phosphate (Jakafi), sacituzumab govitecan-hziy (Trodelvy), seliciclib, selinexor (Xpovio), selpercatinib (Retevmo), selumetinib sulfate (Koselugo), siltuximab (Sylvant), sipuleucel-T (Provenge), sirolimus protein-bound particles (Fyarro), sonidegib (Odomzo), sorafenib (Nexavar), sotorasib (Lumakras), sunitinib (Sutent), tafasitamab-cxix (Monjuvi), tagraxofusp-erzs (Elzonris), talazoparib tosylate (Talzenna), tamoxifen (Nolvadex), tazemetostat hydrobromide (Tazverik), tebentafusp-tebn (Kimmtrak), temsirolimus (Torisel), tepotinib hydrochloride (Tepmetko), tisagenlecleucel (Kymriah), tisotumab vedotin-tftv (Tivdak), tocilizumab (Actemra), tofacitinib (Xeljanz), tositumomab (Bexxar), trametinib (Mekinist), trastuzumab (Herceptin), tretinoin (Vesanoid), tivozanib hydrochloride (Fotivda), toremifene (Fareston), tucatinib (Tukysa), umbralisib tosylate (Ukoniq), vandetanib (Caprelsa), vemurafenib (Zelboraf), venetoclax (Venclexta), vismodegib (Erivedge), vorinostat (Zolinza), zanubrutinib (Brukinsa), ziv-aflibercept (Zaltrap), or any combination thereof.11. The method of any one of clauses 1 to 10, further comprising obtaining the sample from the subject.12. The method of any one of clauses 1 to 11, wherein the sample comprises a tissue biopsy sample, a liquid biopsy sample, or a normal control.13. The method of clause 12, wherein the sample is a liquid biopsy sample and comprises blood, plasma, cerebrospinal fluid, sputum, stool, urine, or saliva.14. The method of clause 12, wherein the sample is a liquid biopsy sample and comprises circulating tumor cells (CTCs).15. The method of clause 12, wherein the sample is a liquid biopsy sample and comprises cell- free DNA (cfDNA), circulating tumor DNA (ctDNA), or any combination thereof.16. The method of any one of clauses 1 to 15, wherein the plurality of nucleic acid molecules comprises a mixture of tumor nucleic acid molecules and non-tumor nucleic acid molecules.17. The method of clause 16, wherein the tumor nucleic acid molecules are derived from a tumor portion of a heterogeneous tissue biopsy sample, and the non-tumor nucleic acid molecules are derived from a normal portion of the heterogeneous tissue biopsy sample.18. The method of clause 16, wherein the sample comprises a liquid biopsy sample, and wherein the tumor nucleic acid molecules are derived from a circulating tumor DNA (ctDNA) fraction of the liquid biopsy sample, and the non-tumor nucleic acid molecules are derived from a non- tumor, cell-free DNA (cfDNA) fraction of the liquid biopsy sample.19. The method of any one of clauses 1 to 18, wherein the one or more adapters comprise amplification primers, flow cell adaptor sequences, substrate adapter sequences, or sample index sequences.20. The method of any one of clauses 1 to 19, wherein the captured nucleic acid molecules are captured from the amplified nucleic acid molecules by hybridization to one or more bait molecules.21. The method of clause 20, wherein the one or more bait molecules comprise one or more nucleic acid molecules, each comprising a region that is complementary to a region of a captured nucleic acid molecule.22. The method of any one of clauses 1 to 21, wherein amplifying nucleic acid molecules comprises performing a polymerase chain reaction (PCR) amplification technique, a non-PCR amplification technique, or an isothermal amplification technique.23. The method of any one of clauses 1 to 22, wherein the sequencing comprises use of a massively parallel sequencing (MPS) technique, whole genome sequencing (WGS), whole exome sequencing, targeted sequencing, direct sequencing, or Sanger sequencing technique.24. The method of clause 23, wherein the sequencing comprises massively parallel sequencing, and the massively parallel sequencing technique comprises next generation sequencing (NGS).25. The method of any one of clauses 1 to 24, wherein the sequencer comprises a next generation sequencer.26. The method of any one of clauses 1 to 25, wherein one or more of the plurality of sequencing reads overlap one or more gene loci within one or more subgenomic intervals in the sample.27. The method of clause 26, wherein the one or more gene loci comprises between 10 and 20 loci, between 10 and 40 loci, between 10 and 60 loci, between 10 and 80 loci, between 10 and 100 loci, between 10 and 150 loci, between 10 and 200 loci, between 10 and 250 loci, between 10 and 300 loci, between 10 and 350 loci, between 10 and 400 loci, between 10 and 450 loci, between 10 and 500 loci, between 20 and 40 loci, between 20 and 60 loci, between 20 and 80 loci, between 20 and 100 loci, between 20 and 150 loci, between 20 and 200 loci, between 20 and 250 loci, between 20 and 300 loci, between 20 and 350 loci, between 20 and 400 loci, between 20 and 500 loci, between 40 and 60 loci, between 40 and 80 loci, between 40 and 100 loci, between 40 and 150 loci, between 40 and 200 loci, between 40 and 250 loci, between 40 and 300 loci, between 40 and 350 loci, between 40 and 400 loci, between 40 and 500 loci, between 60 and 80 loci, between 60 and 100 loci, between 60 and 150 loci, between 60 and 200 loci, between 60 and 250 loci, between 60 and 300 loci, between 60 and 350 loci, between 60 and 400 loci, between 60 and 500 loci, between 80 and 100 loci, between 80 and 150 loci,between 80 and 200 loci, between 80 and 250 loci, between 80 and 300 loci, between 80 and 350 loci, between 80 and 400 loci, between 80 and 500 loci, between 100 and 150 loci, between 100 and 200 loci, between 100 and 250 loci, between 100 and 300 loci, between 100 and 350 loci, between 100 and 400 loci, between 100 and 500 loci, between 150 and 200 loci, between 150 and 250 loci, between 150 and 300 loci, between 150 and 350 loci, between 150 and 400 loci, between 150 and 500 loci, between 200 and 250 loci, between 200 and 300 loci, between 200 and 350 loci, between 200 and 400 loci, between 200 and 500 loci, between 250 and 300 loci, between 250 and 350 loci, between 250 and 400 loci, between 250 and 500 loci, between 300 and 350 loci, between 300 and 400 loci, between 300 and 500 loci, between 350 and 400 loci, between 350 and 500 loci, or between 400 and 500 loci.28. The method of clause 26 or clause 27, wherein the one or more gene loci comprise ABL1, ACVR1B, AKT1, AKT2, AKT3, ALK, ALOX12B, AMER1, APC, AR, ARAF, ARFRP1, ARID1A, ASXL1, ATM, ATR, ATRX, AURKA, AURKB, AXIN1, AXL, BAP1, BARD1, BCL2, BCL2L1, BCL2L2, BCL6, BCOR, BCORL1, BCR, BRAF, BRCA1, BRCA2, BRD4, BRIP1, BTG1, BTG2, BTK, CALR, CARD11, CASP8, CBFB, CBL, CCND1, CCND2, CCND3, CCNE1, CD22, CD274, CD70, CD74, CD79A, CD79B, CDC73, CDH1, CDK12, CDK4, CDK6, CDK8, CDKN1A, CDKN1B, CDKN2A, CDKN2B, CDKN2C, CEBPA, CHEK1, CHEK2, CIC, CREBBP, CRKL, CSF1R, CSF3R, CTCF, CTNNA1, CTNNB1, CUL3, CUL4A, CXCR4, CYP17A1, DAXX, DDR1, DDR2, DIS3, DNMT3A, DOT1L, EED, EGFR, EMSY (Cl lorf30), EP300, EPHA3, EPHB1, EPHB4, ERBB2, ERBB3, ERBB4, ERCC4, ERG, ERRFI1, ESRI, ETV4, ETV5, ETV6, EWSR1, EZH2, EZR, FAM46C, FANCA, FANCC, FANCG, FANCL, FAS, FBXW7, FGF10, FGF12, FGF14, FGF19, FGF23, FGF3, FGF4, FGF6, FGFR1, FGFR2, FGFR3, FGFR4, FH, FLCN, FLT1, FLT3, FOXL2, FUBP1, GABRA6, GATA3, GATA4, GATA6, GID4 (C17orf39), GNA11, GNA13, GNAQ, GNAS, GRM3, GSK3B, H3F3A, HDAC1, HGF, HNF1A, HRAS, HSD3B1, ID3, IDH1, IDH2, IGF1R, IKBKE, IKZF1, INPP4B, IRF2, IRF4, IRS2, JAK1, JAK2, JAK3, JUN, KDM5A, KDM5C, KDM6A, KDR, KEAP1, KEL, KIT, KLHL6, KMT2A (MLL), KMT2D (MLL2), KRAS, LTK, LYN, MAF, MAP2K1, MAP2K2, MAP2K4, MAP3K1, MAP3K13, MAPK1, MCL1, MDM2, MDM4, MED12, MEF2B, MEN1, MERTK, MET, MITF, MKNK1, MLH1, MPL, MRE11A, MSH2, MSH3, MSH6, MST1R, MTAP, MTOR, MUTYH, MYB, MYC, MYCL, MYCN, MYD88,NBN, NF1, NF2, NFE2L2, NFKBIA, NKX2-1, N0TCH1, NOTCH2, NOTCH3, NPM1, NRAS, NT5C2, NTRK1, NTRK2, NTRK3, NUTM1, P2RY8, PALB2, PARK2, PARP1, PARP2, PARP3, PAX5, PBRM1, PDCD1, PDCD1LG2, PDGFRA, PDGFRB, PDK1, PIK3C2B, PIK3C2G, PIK3CA, PIK3CB, PIK3R1, PIM1, PMS2, POLDI, POLE, PPARG, PPP2R1A, PPP2R2A, PRDM1, PRKAR1A, PRKCI, PTCHI, PTEN, PTPN11, PTPRO, QKI, RAC1, RAD21, RAD51, RAD51B, RAD51C, RAD51D, RAD52, RAD54L, RAFI, RARA, RBI, RBM10, REL, RET, RICTOR, RNF43, ROS1, RPTOR, RSPO2, SDC4, SDHA, SDHB, SDHC, SDHD, SETD2, SF3B1, SGK1, SLC34A2, SMAD2, SMAD4, SMARCA4, SMARCB1, SMO, SNCAIP, SOCS1, SOX2, SOX9, SPEN, SPOP, SRC, STAG2, STAT3, STK11, SUFU, SYK, TBX3, TEK, TERC, TERT, TET2, TGFBR2, TIPARP, TMPRSS2, TNFAIP3, TNFRSF14, TP53, TSC1, TSC2, TYRO3, U2AF1, VEGFA, VHL, WHSCI, WHSC1L1, WT1, XPO1, XRCC2, ZNF217, ZNF703, or any combination thereof.29. The method of clause 26 or clause 27, wherein the one or more gene loci comprise ABL, ALK, ALL, B4GALNT1, BAFF, BCL2, BRAF, BRCA, BTK, CD19, CD20, CD3, CD30, CD319, CD38, CD52, CDK4, CDK6, CML, CRACC, CS1, CTLA-4, dMMR, EGFR, ERBB1, ERBB2, FGFR1-3, FLT3, GD2, HDAC, HER1, HER2, HR, IDH2, IL-ip, IL-6, IL-6R, JAK1, JAK2, JAK3, KIT, KRAS, MEK, MET, MSI-H, mTOR, PARP, PD-1, PDGFR, PDGFRa, PDGFRP, PD-L1, PI3K5, PIGF, PTCH, RAF, RANKL, RET, ROS1, SLAMF7, VEGF, VEGFA, VEGFB, or any combination thereof.30. The method of any one of clauses 1 to 29, further comprising generating, by the one or more processors, a report comprising a listing of the one or more neoantigens.31. The method of clause 30, further comprising transmitting the report to a healthcare provider.32. The method of clause 31, wherein the report is transmitted via a computer network or a peer- to-peer connection.33. A method for identifying one or more neoantigens as potential immunotherapy targets for a subject, comprising: receiving, at one or more processors, one or more variant sequences identified in a tumor sample from the subject;receiving, at the one or more processors, HLA genotype data for the subject indicative of one or more HLA gene alleles associated with the subject; identifying, using the one or more processors, a plurality of candidate neoantigens based on the one or more variant sequences and the HLA genotype data; and identifying the one or more neoantigens by: removing a first subset of candidate neoantigens from the plurality of candidate neoantigens based on a comparison between a binding affinity threshold and a predicted binding affinity associated with each candidate neoantigen in the first subset and a corresponding HLA protein; removing a second subset of candidate neoantigens from the plurality of candidate neoantigens based on a comparison between a clonality threshold and a clonal estimation of each candidate neoantigen in the second subset; and removing a third subset of candidate neoantigens from the plurality of candidate neoantigens based on an HLA LOH prediction for an HLA gene allele associated with each candidate neoantigen in the third subset.34. The method of clause 33, wherein the one or more variant sequences are identified by: receiving, at the one or more processors, sequence read data for a plurality of sequence reads corresponding to the tumor sample from the subject; identifying, using the one or more processors, the one or more variant sequences based on the sequence read data.35. The method of clause 33 or 34, wherein the one or more HLA gene alleles comprise maternal and paternal copies of HLA-A gene alleles, HLA-B gene alleles, HLA-C gene alleles, or a combination thereof.36. The method of any one of the preceding clauses, wherein each candidate neoantigen of the plurality of candidate neoantigens is identified based a combination of a particular variant sequence of the one or more variant sequences and a particular HLA gene allele of the one or more HLA gene alleles.37. The method of any one of the preceding clauses, further comprising: obtaining the plurality of candidate neoantigens by: inputting the one or more variant sequences and the one or more HLA gene alleles into a machine learning model; and receiving, from the machine learning model, the plurality of candidate neoantigens and the predicted binding affinity associated with each of the candidate neoantigens.38. The method of clause 37, wherein the machine learning model comprises NetMHCpan.39. The method of any one of the preceding clauses, wherein the predicted binding affinity associated with each candidate neoantigen in the first subset is weaker than the binding affinity threshold.40. The method of any one of the preceding clauses, wherein the binding affinity threshold is between 1 and 500 nM.41. The method of any one of the preceding clauses, wherein the clonal estimation of each candidate neoantigen in the second subset is smaller than the clonality threshold.42. The method of any one of the preceding clauses, wherein the clonal estimation of each candidate neoantigen is an estimate of a fraction of cancer cells in the sample carrying a variant sequence associated with a respective candidate neoantigen.43. The method of clause 42, wherein the clonal estimation of each candidate neoantigen comprises a cancer cell fraction (CCF) value.44. The method of clause 43, wherein the CCF value is calculated by: (pNT+ 2(1 — )),wherein / is indicative of an allele frequency of the variant sequence, m is indicative of a number of mutant copies of a gene, p is indicative of tumor purity, and NTis indicative of total copies of the gene.45. The method of any one of the clauses 42-44, wherein the clonality threshold comprises a CCF value greater than 50%, 75%, or 90%.46. The method of any one of the preceding clauses, wherein each candidate neoantigen in the third subset is associated with an HLA gene allele that is predicted to be HLA-LOH positive.47. The method of any one of the preceding clauses, further comprising: developing one or more anti-cancer therapies based on the one or more neoantigens identified for the subject.48. The method of clause 47, wherein the one or more anti-cancer therapies comprise a targeted anti-cancer therapy based on the one or more neoantigens.49. The method of clause 48, wherein the targeted anti-cancer therapy comprises a personalized cancer vaccine.50. The method of any one of the preceding clauses, further comprising obtaining the tumor sample from the subject.51. The method of any one of the preceding clauses, wherein the tumor sample comprises a tissue biopsy sample or a liquid biopsy sample.52. The method of clause 51, wherein the sample is a liquid biopsy sample and comprises blood, plasma, cerebrospinal fluid, sputum, stool, urine, or saliva.53. A system comprising: one or more processors; and a memory communicatively coupled to the one or more processors and configured to store instructions that, when executed by the one or more processors, cause the system to: receive one or more variant sequences identified in a tumor sample from a subject; receive HLA genotype data for the subject indicative of one or more HLA gene alleles associated with the subject; identify a plurality of candidate neoantigens based on the one or more variant sequences and the HLA genotype data; and identify one or more neoantigens by:removing a first subset of candidate neoantigens from the plurality of candidate neoantigens based on a comparison between a binding affinity threshold and a predicted binding affinity associated with each candidate neoantigen in the first subset and a corresponding HLA protein; removing a second subset of candidate neoantigens from the plurality of candidate neoantigens based on a comparison between a clonality threshold and a clonal estimation of each candidate neoantigen in the second subset; and removing a third subset of candidate neoantigens from the plurality of candidate neoantigens based on an HLA LOH prediction for an HLA gene allele associated with each candidate neoantigen in the third subset.54. The system of clause 53, wherein each candidate neoantigen of the plurality of candidate neoantigens is identified based a combination of a particular variant sequence of the one or more variant sequences and a particular HLA gene allele of the one or more HLA gene alleles.55. The system of any one of the preceding clauses, further comprising instructions for obtaining the plurality of candidate neoantigens by: inputting the one or more variant sequences and the one or more HLA gene alleles into a machine learning model; and receiving, from the machine learning model, the plurality of candidate neoantigens and the predicted binding affinity associated with each of the candidate neoantigens.56. The system of clause 55, wherein the machine learning model comprises NetMHCpan.57. A non-transitory computer-readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by one or more processors of a system, cause the system to: receive one or more variant sequences identified in a tumor sample from the subject; receive HLA genotype data for the subject indicative of one or more HLA gene alleles associated with the subject;identify a plurality of candidate neoantigens based on the one or more variant sequences and the HLA genotype data; and identifying the one or more neoantigens by: removing a first subset of candidate neoantigens from the plurality of candidate neoantigens based on a comparison between a binding affinity threshold and a predicted binding affinity associated with each candidate neoantigen in the first subset and a corresponding HLA protein; removing a second subset of candidate neoantigens from the plurality of candidate neoantigens based on a comparison between a clonality threshold and a clonal estimation of each candidate neoantigen in the second subset; and removing a third subset of candidate neoantigens from the plurality of candidate neoantigens based on an HLA LOH prediction for an HLA gene allele associated with each candidate neoantigen in the third subset.58. The non-transitory computer-readable storage medium of clause 57, wherein each candidate neoantigen of the plurality of candidate neoantigens is identified based a combination of a particular variant sequence of the one or more variant sequences and a particular HLA gene allele of the one or more HLA gene alleles.59. The non-transitory computer-readable storage medium of any one of the preceding clauses, further comprising instructions for obtaining the plurality of candidate neoantigens by: inputting the one or more variant sequences and the one or more HLA gene alleles into a machine learning model; and receiving, from the machine learning model, the plurality of candidate neoantigens and the predicted binding affinity associated with each of the candidate neoantigens.60. The non-transitory computer-readable storage medium of clause 59, wherein the machine learning model comprises NetMHCpan.

[0249] It should be understood from the foregoing that, while particular implementations of the disclosed methods and systems have been illustrated and described, various modifications can bemade thereto and are contemplated herein. It is also not intended that the invention be limited by the specific examples provided within the specification. While the invention has been described with reference to the aforementioned specification, the descriptions and illustrations of the preferable embodiments herein are not meant to be construed in a limiting sense. Furthermore, it shall be understood that all aspects of the invention are not limited to the specific depictions, configurations or relative proportions set forth herein which depend upon a variety of conditions and variables. Various modifications in form and detail of the embodiments of the invention will be apparent to a person skilled in the art. It is therefore contemplated that the invention shall also cover any such modifications, variations and equivalents.

Claims

CLAIMSWhat is claimed is:

1. A method for identifying one or more neoantigens as potential immunotherapy targets for a subject, comprising: receiving, at one or more processors, one or more variant sequences identified in a tumor sample from the subject; receiving, at the one or more processors, HLA genotype data for the subject indicative of one or more HLA gene alleles associated with the subject; identifying, using the one or more processors, a plurality of candidate neoantigens based on the one or more variant sequences and the HLA genotype data; and identifying the one or more neoantigens by: removing a first subset of candidate neoantigens from the plurality of candidate neoantigens based on a comparison between a binding affinity threshold and a predicted binding affinity associated with each candidate neoantigen in the first subset and a corresponding HLA protein; removing a second subset of candidate neoantigens from the plurality of candidate neoantigens based on a comparison between a clonality threshold and a clonal estimation of each candidate neoantigen in the second subset; and removing a third subset of candidate neoantigens from the plurality of candidate neoantigens based on an HLA LOH prediction for an HLA gene allele associated with each candidate neoantigen in the third subset.

2. The method of claim 1, wherein the one or more variant sequences are identified by: receiving, at the one or more processors, sequence read data for a plurality of sequence reads corresponding to the tumor sample from the subject; identifying, using the one or more processors, the one or more variant sequences based on the sequence read data.

3. The method of claim 1, wherein the one or more HLA gene alleles comprise maternal and paternal copies of HLA-A gene alleles, HLA-B gene alleles, HLA-C gene alleles, or a combination thereof.

4. The method of claim 1, wherein each candidate neoantigen of the plurality of candidate neoantigens is identified based a combination of a particular variant sequence of the one or more variant sequences and a particular HLA gene allele of the one or more HLA gene alleles.

5. The method of claim 1, further comprising: obtaining the plurality of candidate neoantigens by: inputting the one or more variant sequences and the one or more HLA gene alleles into a machine learning model; and receiving, from the machine learning model, the plurality of candidate neoantigens and the predicted binding affinity associated with each of the candidate neoantigens.

6. The method of claim 5, wherein the machine learning model comprises NetMHCpan.

7. The method of claim 1, wherein the predicted binding affinity associated with each candidate neoantigen in the first subset is weaker than the binding affinity threshold.

8. The method of claim 1, wherein the binding affinity threshold is between 1 and 500 nM.

9. The method of claim 1, wherein the clonal estimation of each candidate neoantigen in the second subset is smaller than the clonality threshold.

10. The method of claim 1, wherein the clonal estimation of each candidate neoantigen is an estimate of a fraction of cancer cells in the sample carrying a variant sequence associated with a respective candidate neoantigen.

11. The method of claim 10, wherein the clonal estimation of each candidate neoantigen comprises a cancer cell fraction (CCF) value. f12. The method of claim 11, wherein the CCF value is calculated by: — (pNT+ 2(1 — )),wherein / is indicative of an allele frequency of the variant sequence, m is indicative of a numberof mutant copies of a gene, p is indicative of tumor purity, and NTis indicative of total copies of the gene.

13. The method of claim 1, wherein the clonality threshold comprises a CCF value greater than 50%, 75%, or 90%.

14. The method of claim 1, wherein each candidate neoantigen in the third subset is associated with an HLA gene allele that is predicted to be HLA-LOH positive.

15. The method of claim 1, further comprising: developing one or more anti-cancer therapies based on the one or more neoantigens identified for the subject.

16. The method of claim 15, wherein the one or more anti-cancer therapies comprise a targeted anti-cancer therapy based on the one or more neoantigens.

17. The method of claim 16, wherein the targeted anti-cancer therapy comprises a personalized cancer vaccine.

18. The method of claim 1, further comprising obtaining the tumor sample from the subject.

19. The method of claim 1, wherein the tumor sample comprises a tissue biopsy sample or a liquid biopsy sample.

20. The method of claim 19, wherein the sample is a liquid biopsy sample and comprises blood, plasma, cerebrospinal fluid, sputum, stool, urine, or saliva.

21. A system comprising: one or more processors; and a memory communicatively coupled to the one or more processors and configured to store instructions that, when executed by the one or more processors, cause the system to: receive one or more variant sequences identified in a tumor sample from a subject; receive HLA genotype data for the subject indicative of one or more HLA gene alleles associated with the subject;identify a plurality of candidate neoantigens based on the one or more variant sequences and the HLA genotype data; and identify one or more neoantigens by: removing a first subset of candidate neoantigens from the plurality of candidate neoantigens based on a comparison between a binding affinity threshold and a predicted binding affinity associated with each candidate neoantigen in the first subset and a corresponding HLA protein; removing a second subset of candidate neoantigens from the plurality of candidate neoantigens based on a comparison between a clonality threshold and a clonal estimation of each candidate neoantigen in the second subset; and removing a third subset of candidate neoantigens from the plurality of candidate neoantigens based on an HLA LOH prediction for an HLA gene allele associated with each candidate neoantigen in the third subset.

22. A non-transitory computer-readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by one or more processors of a system, cause the system to: receive one or more variant sequences identified in a tumor sample from the subject; receive HLA genotype data for the subject indicative of one or more HLA gene alleles associated with the subject; identify a plurality of candidate neoantigens based on the one or more variant sequences and the HLA genotype data; and identifying the one or more neoantigens by: removing a first subset of candidate neoantigens from the plurality of candidate neoantigens based on a comparison between a binding affinity threshold and a predicted binding affinity associated with each candidate neoantigen in the first subset and a corresponding HLA protein;removing a second subset of candidate neoantigens from the plurality of candidate neoantigens based on a comparison between a clonality threshold and a clonal estimation of each candidate neoantigen in the second subset; and removing a third subset of candidate neoantigens from the plurality of candidate neoantigens based on an HLA LOH prediction for an HLA gene allele associated with each candidate neoantigen in the third subset.

Images