WO2019005764A1

WO2019005764A1 - Mhc-1 genotype restricts the oncogenic mutational landscape

Info

Publication number: WO2019005764A1
Application number: PCT/US2018/039455
Authority: WO
Inventors: Joan FONT-BURGADA; David ROSSELL; Hannah K. CARTER; Rachel MARTY
Original assignee: Institute For Cancer Research D/B/A The Research Institute Of Fox Chase Cancer Center; Universitat Pompeu Fabra; The Regents Of The University Of California
Priority date: 2017-06-27
Filing date: 2018-06-26
Publication date: 2019-01-03
Also published as: EP3645028A4; CA3068437A1; WO2019005764A9; US20200219586A1; EP3645028A1

Abstract

The present disclosure provides methods of determining the risk of a subject having or developing a cancer or autoimmne disorder based on the affinity of the subject's MHC-I alleles for oncogenic mutations or peptides linked with autoimmune disorders, methods for improving cancer diagnosis, and kits comprising agents that detect the oncogenic mutations in a subject.

Description

MHC-I Genotype Restricts The Oncogenic Mutational Landscape

Field

The present disclosure is directed, in part, to methods of determining the risk of a subject having or developing a cancer based on the affinity of MHC-I for oncogenic mutations, and to methods of detection of various cancers using oncogenic mutations that are not recognized by MHC-I, and to cancer diagnostic kits comprising agents that detect the oncogenic mutations. Background

Avoiding immune destruction is a hallmark of cancer (Hanahan and Weinberg, Cell,

2011, 144, 646-674), suggesting that the ability of the immune system to detect and eliminate neoplastic cells is a major deterrent to tumor progression. Recent studies have demonstrated that the immune system is capable of eliminating tumors when the mechanisms that tumor cells employ to evade detection are countered (Brahmer et al., N. Engl. J. Med., 2012, 366, 2455- 2465; Hodi et al., N. Engl. J. Med., 2010, 363, 711-723; and Topalian et al., N. Engl. J. Med.,

2012, 366, 2443-2454). This discovery has motivated new efforts to identify the characteristics of tumors that render them susceptible to immunotherapy (Rizvi et al., Science, 2015, 348, 124- 128; and Rooney et al., Cell, 2015, 160, 48-61). Less attention has been directed toward the role of the immune system in shaping the tumor genome prior to immune evasion; however, such early interactions may have important implications for the characteristics of the developing tumor.

While the potential of manipulating the immune system for treating cancer has now been clearly demonstrated, its role in determining characteristics of tumors remains poorly understood in humans. The theory of cancer immunosurveillance dictates that the immune system should exert a negative selective pressure on tumor cell populations through elimination of tumor cells that harbor antigenic mutations or aberrations. Under this model, tumor precursor cells with antigenic variants would be at higher risk for immune elimination and, conversely, tumor cell populations that continue to expand should be biased toward cells that avoid producing neoantigens.

One major mechanism by which tumor cells can be detected is the antigen presentation pathway. Endogenous peptides generated within tumor cells are bound to the MHC-I complex and displayed on the cell surface where they are monitored by T cells. Mutations in tumors that affect protein sequence have the potential to elicit a cytotoxic response by generating neoantigens. In order for this to happen, the mutated protein product must be cleaved into a peptide, transported to the endoplasmic reticulum, bound to an MHC-I molecule, transported to the cell surface, and recognized as foreign by a T cell (Schumacher and Schreiber, Science, 2015, 348, 69-74). According to the theory of cancer immunosurveillance, the immune system exerts a negative selective pressure on those tumor cells that harbor antigenic mutations or aberrations. Tumor precursor cells presenting antigenic variants would be at higher risk for immune elimination and, conversely, tumors that grow would be biased toward those that successfully avoid immune elimination. Immune evasion could be achieved by either losing or failing to acquire antigenic variants.

In model organisms, there is strong experimental evidence that immunosurveillance sculpts the genomes of tumors through detection and elimination of cancer cells early in tumor progression (DuPage et al., Nature, 2012, 482, 405-409; Kaplan et al., Proc. Natl. Acad. Sci. USA, 1998, 95, 7556-7561; Koebel et al., Nature, 2007, 450, 903-907; Matsushita et al., Nature, 2012, 482, 400-404; and Shankaran et al., Nature, 2001, 410, 1107-111). In humans, the observed frequency of neoantigens has been reported to be unexpectedly low in some tumor types (Rooney et al., Cell, 2015, 160, 48-61), suggesting that immunoediting could be taking place. However, this phenomenon has been challenging to study systematically, in part due to the highly polymorphic nature of the HLA locus where the genes that encode MHC-I proteins are located (over 10,000 distinct alleles for the three genes documented to date; Robinson et al., Nucleic Acids Res., 2015, 43, D423-D431).

The polymorphic nature of the HLA locus raises the possibility that the set of oncogenic mutations that create neoantigens may differ substantially among individuals. Indeed, neoantigens found to drive tumor regression in response to immunotherapy were almost always unique to the responding tumor (Lu et al., Int. Immunol., 2016, 28, 365-370). Several studies have also reported that nonsynonymous mutation burden, rather than the presence of any particular mutation, is the common factor among responsive tumors (Rizvi et al., Science, 2015, 348, 124-128). The paucity of recurrent oncogenic mutations driving effective responses to immunotherapy is suggestive that these mutations may less frequently be antigenic, possibly as a result of selective pressure by the immune system during tumor development. This suggests that that recurrent oncogenic mutations are immune- selected early on during tumor initiation and that this selection should strongly depend on the capability of the MHC-I to effectively present recurrent oncogenic mutations (see, Figure 1). A direct inference that can be drawn from this hypothesis is that the capability of the set of MHC-I alleles carried by an individual to present oncogenic mutations may play a key role in determining which oncogenic mutations can be recognized by that individual's immune system. Hence, determining the MHC-I genotype of any individual can lead directly to a prediction of the subset of the oncogenic peptidome that individual' s immune system would be able to detect, with important implications for predicting individual cancer susceptibility.

Accordingly, there is a need for an effective model capable of predicting which oncogenic mutations are detectable by an individual's MHC-I-based immunosurveillance system. Such a model would help assess an individual's susceptibility to various cancers. In addition, a need exists for a model capable of predicting oncogenic mutations that are not efficiently presented to the MHC-I-based immunosurveillance system. Such a model would help in the development of diagnostic assays aimed at early detection of oncogenic and pre-oncogenic conditions.

Summary

The present disclosure provides computer implemented methods for determining whether a subject is at risk of having or developing a cancer or an autoimmune disease, the method comprising: a) genotyping the subject's major histocompatibility complex class I (MHC- I); and b) scoring the ability of the subject's MHC-I to present a mutant cancer-associated peptide or an autoimmune-associated peptide based upon a library of known cancer-associated peptide sequences or autoimmune-associated peptide sequences derived from subjects, wherein the produced score is the MHC-I presentation score; wherein: i) if the subject is a poor MHC-I presenter of specific mutant cancer-associated peptides, the subject has an increased likelihood of having or developing the cancer for which the specific mutant cancer-associated peptides are associated; ii) if the subject is a good MHC-I presenter of specific mutant cancer-associated peptides, the subject has a decreased likelihood of having or developing the cancer for which the specific mutant cancer-associated peptides are associated; iii) if the subject is a poor MHC-I presenter of specific autoimmune-associated peptides, the subject has a decreased likelihood of having or developing autoimmunity for which the specific autoimmune-associated peptides are associated; or iv) if the subject is a good MHC-I presenter of specific autoimmune-associated peptides, the subject has an increased likelihood of having or developing autoimmunity for which the specific autoimmune-associated peptides are associated.

The present disclosure also provides computing systems for determining whether a subject is at risk of having or developing a cancer or an autoimmune disease, the system comprising: a) a communication system for using a library of cancer-associated peptides or autoimmune-associated peptides derived from subjects; and b) a processor for scoring the ability of the subject's major histocompatibility complex class I (MHC-I) to present a mutant cancer- associated peptide or an autoimmune-associated peptide based upon a library of cancer- associated peptides or autoimmune-associated peptides derived from subjects, wherein the produced score is the MHC-I presentation score.

The present disclosure also provides methods of detecting an early stage breast invasive carcinoma (BRCA) in a subject, the method comprising the steps of: a) obtaining a biological sample from the subject; and b) assaying the sample for the presence of any of the B-Raf Proto- Oncogene (BRAF) V600E mutation, Phosphatidylinositol-4,5-Bisphosphate 3-Kinase Catalytic Subunit Alpha (PIK3CA) E545K mutation, PIK3CA E542K mutation, PIK3CA H1047R mutation, Kirsten Rat Sarcoma Viral Oncogene Homolog (KRAS) G12D mutation, KRAS G13D mutation, KRAS G12V mutation, KRAS A146T mutation, TP53 R175H mutation, TP53 H179R mutation, TP53 mutation, TP53 R248Q mutation, TP53 R273C mutation, TP53 R273H mutation, TP53 R282W mutation, Keratin Associated Protein 4-11 (KRTAP4-11) L161V mutation, Mab-21 Domain Containing 2 (MB21D2) Q311E, mutation, HLA-A Q78R mutation, Harvey Rat Sarcoma Viral Oncogene Homolog (HRAS) G13V mutation, Isocitrate

Dehydrogenase (NADP(+)) 1 (IDH1) R132H mutation, IDH1 R132C mutation, IDH1 R132G mutation, IDH2 R172K mutation, IDH1 R132S mutation, Capicua Transcriptional Repressor (CIC) R215W mutation, Phosphoglucomutase 5 (PGM5) I98V mutation, Tripartite Motif Containing 48 (TRIM48) Y192H mutation, or F-Box And WD Repeat Domain Containing 7 (FBXW7) R465C mutation, wherein the presence of any one of these mutations indicates the presence of early stage breast invasive carcinoma.

The present disclosure also provides methods of detecting an early stage colon adenocarcinoma (CO AD) in a subject, the method comprising the steps of: a) obtaining a biological sample from the subject; and b) assaying the sample for the presence of any of the BRAF V600E mutation, Neuroblastoma RAS Viral Oncogene Homolog (NRAS) Q61R mutation, NRAS Q61K mutation, NRAS Q61L mutation, IDH1 R132S mutation, Mitogen- Activated Protein Kinase Kinase 1 (MAP2K1) P124S mutation, Rac Family Small GTPase 1 (RAC1) P29S mutation, Protein Phosphatase 6 Catalytic Subunit (PPP6C) R301C mutation, Cyclin Dependent Kinase Inhibitor 2A (CDKN2A) P114L mutation, Keratin Associated Protein 4-11 (KRTAP4-11) L161 V mutation, KRTAP4-11 M93 V mutation, HRAS Q61R mutation, HLA-A Q78R mutation, Zinc Finger Protein 799 (ZNF799) E589G mutation, Zinc Finger Protein 844 (ZNF844) R447P mutation, or RNA Binding Motif Protein 10 (RBM10) E184D mutation, wherein the presence of any one of these mutations indicates the presence of early stage colon adenocarcinoma. The present disclosure also provides methods of detecting an early stage head and neck squamous cell carcinoma (HNSC) in a subject, the method comprising the steps of: a) obtaining a biological sample from the subject; and b) assaying the sample for the presence of any of the IDH1 R132H mutation, IDH1 R132C mutation, IDH1 R132G mutation, IDH1 R132S mutation, IDH2 R172K mutation, TP53 H179R mutation, TP53 R273C mutation, TP53 R273H mutation, CIC R215W mutation, or HLA-A Q78R mutation, wherein the presence of any one of these mutations indicates the presence of early stage head and neck squamous cell carcinoma.

The present disclosure also provides methods of detecting an early stage brain lower grade glioma (LGG) in a subject, the method comprising the steps of: a) obtaining a biological sample from the subject; and b) assaying the sample for the presence of any of the IDH1 R132H mutation, IDH1 R132C mutation, IDH1 R132G mutation, IDH1 R132S mutation, IDH2 R172K mutation, TP53 H179R mutation, TP53 R273C mutation, TP53 R273H mutation, CIC R215W mutation, or HLA-A Q78R mutation, wherein the presence of any one of these mutations indicates the presence of early stage brain lower grade glioma.

The present disclosure also provides methods of detecting an early stage lung adenocarcinoma (LUAD), in a subject, the method comprising the steps of: a) obtaining a biological sample from the subject; and b) assaying the sample for the presence of any of the BRAF V600E mutation, PIK3CA E545K mutation, KRAS G12D mutation, KRAS G13D mutation, KRAS A146T mutation, TP53 R175H mutation, KRAS G12V mutation, TP53 R248Q mutation, TP53 R273C mutation TP53 R273H mutation, TP53 R282W mutation, PGM5 I98V mutation, TRIM48 Y192H mutation, PIK3CA E545K mutation, KRAS G13D mutation, PIK3CA H1047R mutation, or FBXW7 R465C mutation, wherein the presence of any one of these mutations indicates the presence of early stage lung adenocarcinoma.

The present disclosure also provides methods of detecting an early stage lung squamous cell carcinoma (LUSC) in a subject, the method comprising the steps of: a) obtaining a biological sample from the subject; and b) assaying the sample for the presence of any of the PIK3CA H1047R mutation, PIK3CA E545K mutation, PIK3CA E542K mutation, TP53 R175H mutation, PIK3CA N345K mutation, AKT Serine/Threonine Kinase 1 (AKT1) E17K mutation, Splicing Factor 3b Subunit 1 (SF3B1) K700E mutation, or PIK3CA H1047L mutation, wherein the presence of any one of these mutations indicates the presence of early stage lung squamous cell carcinoma.

The present disclosure also provides methods of detecting an early stage skin cutaneous melanoma (SKCM) in a subject, the method comprising the steps of: a) obtaining a biological sample from the subject; and b) assaying the sample for the presence of any of the BRAF V600E mutation, PIK3CA E545K mutation, KRAS G12D mutation, KRAS G13D mutation, KRAS A146T mutation, KRAS G12V mutation, TP53 R175H mutation, TP53 H179R mutation, TP53 R248Q mutation TP53 R273C mutation, TP53 R273H mutation, TP53 R282W mutation, IDH1 R132H mutation, IDH1 R132C mutation, IDH1 R132G mutation, IDH1 R132S mutation, IDH2 R172K mutation, CIC R215W mutation, or HLA-A Q78R mutation, NRAS Q61R mutation, NRAS Q61K mutation, NRAS Q61L mutation, MAP2K1 P124S mutation, RAC1 P29S mutation, PPP6C R301C mutation, CDKN2A P114L mutation, KRTAP4-11 L161V mutation, KRTAP4-11 M93V mutation, HRAS Q61R mutation, ZNF799 E589G mutation, ZNF844 R447P mutation, or RBM10 E184D mutation, wherein the presence of any one of these mutations indicates the presence of early stage skin cutaneous melanoma.

The present disclosure also provides methods of detecting an early stage stomach adenocarcinoma (STAD) in a subject, the method comprising the steps of: a) obtaining a biological sample from the subject; and b) assaying the sample for the presence of any of the KRAS G12C mutation, KRAS G12V mutation, Epidermal Growth Factor Receptor (EGFR) L858R mutation, KRAS G12D mutation, KRAS G12A mutation, U2 Small Nuclear RNA

Auxiliary Factor 1 (U2AF1) S34F mutation, KRTAP4-11 L16 IV mutation, KRTAP4-11 R121K mutation, Eukaryotic Translation Elongation Factor 1 Beta 2 (EEF1B2) R42H mutation, or KRTAP4-11 M93V mutation, wherein the presence of any one of these mutations indicates the presence of early stage stomach adenocarcinoma.

The present disclosure also provides methods of detecting an early stage thyroid carcinoma (THCA) in a subject, the method comprising the steps of: a) obtaining a biological sample from the subject; and b) assaying the sample for the presence of any of the BRAF V600E mutation, PIK3CA E545K mutation, KRAS G12D mutation, KRAS G13D mutation, TP53 R175H mutation, KRAS G12V mutation, TP53 R248Q mutation, KRAS A146T mutation, TP53 R273H mutation, HRAS Q61R mutation, HLA-A Q78R mutation, TP53 R282W mutation, NRAS Q61R mutation, NRAS Q61K mutation, IDH1 R132C mutation, MAP2K1 P124S mutation, RAC1 P29S mutation, NRAS Q61L mutation, PPP6C R301C mutation, CDKN2A P114L mutation, KRTAP4-11 L161V mutation, KRTAP4-11 M93V mutation, ZNF799 E589G mutation, ZNF844 R447P mutation, or RBM10 E184D mutation, wherein the presence of any one of these mutations indicates the presence of early stage thyroid carcinoma.

The present disclosure also provides methods of detecting an early stage uterine corpus endometrial carcinoma (UCEC) in a subject, the method comprising the steps of: a) obtaining a biological sample from the subject; and b) assaying the sample for the presence of any of the BRAF V600E mutation, PIK3CA H1047R mutation, PIK3CA E545K mutation, PIK3CA E542K mutation, TP53 R175H mutation, PIK3CA N345K mutation, AKT Serine/Threonine Kinase 1 (AKT1) E17K mutation, Splicing Factor 3b Subunit 1 (SF3B1) K700E mutation, KRAS G12C mutation, KRAS G12V mutation, Epidermal Growth Factor Receptor (EGFR) L858R mutation, KRAS G12D mutation, KRAS G12A mutation, KRAS G12V mutation, KRAS G13D mutation, TP53 R175H mutation, TP53 R248Q mutation, KRAS A146T mutation, TP53 R273H mutation, TP53 R282W mutation, U2 Small Nuclear RNA Auxiliary Factor 1 (U2AF1) S34F mutation, KRTAP4-11 L161V mutation, KRTAP4-11 R121K mutation, Eukaryotic Translation Elongation Factor 1 Beta 2 (EEF1B2) R42H mutation, or KRTAP4-11 M93V mutation, wherein the presence of any one of these mutations indicates the presence of early stage uterine corpus endometrial carcinoma.

Brief Description Of The Drawings

Figure 1 shows MHC-I genotype immune selection in cancer; schematic representing individuals and their combinations of MHCs; each individual's MHCs are better equipped to present specific mutations, rendering them less likely to develop cancer harboring those mutations.

Figure 2A shows a graphical representation of calculating the presentation score for a particular residue, each residue can be presented in 38 different peptides of differing lengths between 8 and 11.

Figure 2B shows single-allele MS data from Abelin et al. (Abelin et al., Mass

Immunity, 2017, 46, 315-326) compared to a random background of peptides to determine the best residue-centric score for quantifying of extracellular presentation (best rank score shown).

Figure 2C shows a ROC curve showing the accuracy of the best rank residue presentation score for classifying the extracellular presentation of a residue by an MHC allele; the aggregated presentation scores for MS data from 16 different alleles was compared to a random set of residues with the same 16 alleles.

Figure 2D shows the fraction of native residues found for the list of mutations identified in five different cancer cell lines for strong (rank <0.5) and weak (0.5% rank <2) binders; the mutated version of the residue is assumed to be presented if the mutation does not disrupt the binding motif.

Figure 3 A shows the number of 8-11-mer peptides that differed from the native sequence for recurrent in- frame indels pan-cancer.

Figure 3B shows the distribution of residue-centric presentation scores for MS-observed peptides and randomly selected residues for best rank. Figure 3C shows the distribution of residue-centric presentation scores for MS-observed peptides and randomly selected residues for summation (rank < 2).

Figure 3D shows the distribution of residue-centric presentation scores for MS-observed peptides and randomly selected residues for summation (rank <0.5).

Figure 3E shows the distribution of residue-centric presentation scores for MS-observed peptides and randomly selected residues for best rank with cleavage.

Figure 3F shows the log of the ratio between the fraction of MS-observed residues and the fraction of random residues detected over regular score intervals for best rank.

Figure 3G shows the log of the ratio between the fraction of MS-observed residues and the fraction of random residues detected over regular score intervals for summation (rank < 2).

Figure 3H shows the log of the ratio between the fraction of MS-observed residues and the fraction of random residues detected over regular score intervals for summation (rank <0.5).

Figure 31 shows the log of the ratio between the fraction of MS-observed residues and the fraction of random residues detected over regular score intervals for best rank with cleavage.

Figure 3 J shows a ROC curve revealing the accuracy of classification for several different presentation scoring schemes.

Figure 3K shows a heatmap showing the AUCs for the 16 alleles for each presentation scoring scheme.

Figure 4A shows a bar chart representing the number of peptides recovered from the mass spectrometry data for each HLA allele (cell lines: HeLa, FHIOSE, SKOV3, 721.221, A2780, and OV90).

Figure 4B shows a bar chart representing the fraction of select residues with high and low presentation scores from the mass spectrometry data from the HLA-A*01 :02 allele; values are shown for both the randomly selected residues and the oncogenic residues.

Figure 5 A shows a non-parametric estimate of GAM-based mutation probability vs. affinity.

Figure 5B shows a non-parametric estimate of GAM-based lo git- mutation probability vs. log-affinity.

Figure 5C shows a non-parametric estimate of frequency of mutation for affinity in groups.

Figure 6 A shows a within-residues analysis odds ratio and 95% CIs by cancer type. Figure 6B shows a within-subjects analysis odds ratio and 95% CIs by cancer type. Figure 7 A shows a within-residues analysis odds ratio and 95% CIs by cancer type for cancer types with > 100 subjects. Figure 7B shows a within-subjects analysis odds ratio and 95% CIs by cancer type for cancer types with > 100 subjects.

Description Of Embodiments

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting. Various terms relating to aspects of disclosure are used throughout the specification and claims. Such terms are to be given their ordinary meaning in the art, unless otherwise indicated. Other specifically defined terms are to be construed in a manner consistent with the definition provided herein.

Unless otherwise expressly stated, it is in no way intended that any method or aspect set forth herein be construed as requiring that its steps be performed in a specific order.

Accordingly, where a method claim does not specifically state in the claims or descriptions that the steps are to be limited to a specific order, it is in no way intended that an order be inferred, in any respect. This holds for any possible non-express basis for interpretation, including matters of logic with respect to arrangement of steps or operational flow, plain meaning derived from grammatical organization or punctuation, or the number or type of aspects described in the specification.

As used herein, the singular forms "a," "an" and "the" include plural referents unless the context clearly dictates otherwise.

As used herein, the terms "subject" and "subject" are used interchangeably. A subject may include any animal, including mammals. Mammals include, without limitation, farm animals (e.g., horse, cow, pig), companion animals (e.g., dog, cat), laboratory animals (e.g., mouse, rat, rabbits), and non-human primates. In some embodiments, the subject is a human being.

As used herein, the term "genotype" refers to the identity of the alleles present in an individual or a sample. In the context of the present disclosure, a genotype preferably refers to the description of the human leukocyte antigen (HLA) alleles present in an individual or a sample. The term "genotyping" a sample or an individual for an HLA allele consists of determining the specific allele or the specific nucleotide carried by an individual at the HLA locus.

A mutation is "correlated" or "associated" with a specified phenotype (e.g. cancer susceptibility, etc.) when it can be statistically linked (positively or negatively) to the phenotype. Methods for determining whether a polymorphism or allele is statistically linked are well known in the art and described below. The cancer or autoimmune disease-associated mutation may result in a substitution, insertion, or deletion of one or more amino acids within a protein. In some embodiments, the mutant peptides described herein carry known oncogenic mutations that have poor MHC-I- mediated presentation to the immune system due to low affinity of a subject' s HLA allele for that particular mutation.

As used herein, the term "oncogene" refers to a gene which is associated with certain forms of cancer. Oncogenes can be of viral origin or of cellular origin. An oncogene is a gene encoding a mutated form of a normal protein (i.e., having an "oncogenic mutation") or is a normal gene which is expressed at an abnormal level (e.g., over-expressed). Over-expression can be caused by a mutation in a transcriptional regulatory element (e.g., the promoter), or by chromosomal rearrangement resulting in subjecting the gene to an unrelated transcriptional regulatory element. The normal cellular counterpart of an oncogene is referred to as "proto- oncogene." Proto-oncogenes generally encode proteins which are involved in regulating cell growth, and are often growth factor receptors. Numerous different oncogenes have been implicated in tumorigenesis. Tumor suppressor genes (e.g., p53 or p53-like genes) are also encompassed by the term "proto-oncogene." Thus, a mutated tumor suppressor gene which encodes a mutated tumor suppressor protein or which is expressed at an abnormal level, in particular an abnormally low level, is referred to herein as "oncogene." The terms "oncogene protein" refer to a protein encoded by an oncogene.

As used herein, the term "mutation" refers to a change introduced into a parental sequence, including, but not limited to, substitutions, insertions, and deletions (including truncations). The consequences of a mutation include, but are not limited to, the creation of a new character, property, function, phenotype or trait not found in the protein encoded by the parental sequence.

Methods of detection of cancer-associated mutations are well known in the art and comprise detection of the nucleic acid and/or protein having a known oncogenic mutation in a test sample or a control sample.

In some embodiments, the methods rely on the detection of the presence or absence of an oncogenic mutation in a population of cells in a test sample relative to a standard (for example, a control sample). In some embodiments, such methods involve direct detection of oncogenic mutations via sequencing known oncogenic mutations loci. In some embodiments, such methods utilize reagents such as oncogenic mutation-specific polynucleotides and/or oncogenic mutation-specific antibodies. In particular, the presence or absence of an oncogenic mutation may be determined by detecting the presence of mutated messenger RNA (mRNA), for example, by DNA-DNA hybridization, RNA-DNA hybridization, reverse transcription- polymerase chain reaction (PGR), real time quantitative PCR, differential display, and/or TaqMan PCR. Any one or more of hybridization, mass spectroscopy (e.g., MALDI-TOF or

SELDI-TOF mass spectroscopy), serial analysis of gene expression, or massive parallel signature sequencing assays can also be performed. Non-limiting examples of hybridization assays include a singleplex or a multiplexed aptamer assay, a dot blot, a slot blot, an RNase protection assay, microarray hybridization, Southern or Northern hybridization analysis and in situ hybridization (e.g., fluorescent in situ hybridization (FISH)).

For example, these techniques find application in microarray-based assays that can be used to detect and quantify the amount of gene transcripts having oncogenic mutations using cDNA-based or oligonucleotide-based arrays. Microarray technology allows multiple gene transcripts having oncogenic mutations and/or samples from different subjects to be analyzed in one reaction. Typically, mRNA isolated from a sample is converted into labeled nucleic acids by reverse transcription and optionally in vitro transcription (cDNAs or cRNAs labelled with, for example, Cy3 or Cy5 dyes) and hybridized in parallel to probes present on an array (see, for example, Schulze et al., Nature Cell. Biol., 2001, 3, E190; and Klein et al., J. Exp. Med., 2001, 194, 1625-1638). Standard Northern analyses can be performed if a sufficient quantity of the test cells can be obtained. Utilizing such techniques, quantitative as well as size-related differences between oncogenic transcripts can also be detected.

In some embodiments, oncogenic mutations are detected using reagents that are specific for these mutations. Such reagents may bind to a target gene or a target gene product (e.g., mRNA or protein), gene product having an oncogenic mutation can be specifically detected. Such reagents may be nucleic acid molecules that hybridize to the mRNA or cDNA of target gene products. Alternatively, the reagents may be molecules that label mRNA or cDNA for later detection, e.g., by binding to an array. The reagents may bind to proteins encoded by the genes of interest. For example, the reagent may be an antibody or a binding protein that specifically binds to a protein encoded by a target gene having an oncogenic mutation of interest. Alternatively, the reagent may label proteins for later detection, e.g., by binding to an antibody on a panel. In some embodiments, reagents are used in histology to detect histological and/or genetic changes in a sample.

Numerous cohorts of mutations associated with particular cancers have been identified in human cancer subjects (e.g., The Cancer Genome Atlas (TCGA) Research Network (world wide web at "cancergenome.nih.gov/"), Nature, 2014, 507, 315-22; and Jiang et al.,

Bioinformatics, 2007, 23, 306-13). TCGA contains complete exomes of numerous cancer subject cohorts having particular cancer types.

In some embodiments, a custom cancer or autoimmune disease library is obtained by whole genome sequencing of a cohort of at least 100 subjects having cancer or autoimmune disease of interest. In some embodiments, a custom cancer or autoimmune disease library is obtained by whole genome sequencing of a cohort of at least 90 subjects having cancer or autoimmune disease of interest. In some embodiments, a custom cancer or autoimmune disease library is obtained by whole genome sequencing of a cohort of at least 80 subjects having cancer or autoimmune disease of interest. In some embodiments, a custom cancer or autoimmune disease library is obtained by whole genome sequencing of a cohort of at least 70 subjects having cancer or autoimmune disease of interest. In some embodiments, a custom cancer or autoimmune disease library is obtained by whole genome sequencing of a cohort of at least 60 subjects having cancer or autoimmune disease of interest. In some embodiments, a custom cancer or autoimmune disease library is obtained by whole genome sequencing of a cohort of at least 50 subjects having cancer or autoimmune disease of interest. In some embodiments, a custom cancer or autoimmune disease library is obtained by whole genome sequencing of a cohort of at least 40 subjects having cancer or autoimmune disease of interest. In some embodiments, a custom cancer or autoimmune disease library is obtained by whole genome sequencing of a cohort of at least 30 subjects having cancer or autoimmune disease of interest. In some embodiments, a custom cancer or autoimmune disease library is obtained by whole genome sequencing of a cohort of at least 25 subjects having cancer or autoimmune disease of interest. In some embodiments, a custom cancer or autoimmune disease library is obtained by whole genome sequencing of a cohort of at least 20 subjects having cancer or autoimmune disease of interest. In some embodiments, a custom cancer or autoimmune disease library is obtained by whole genome sequencing of a cohort of at least 15 subjects having cancer or autoimmune disease of interest.

In some embodiments, a custom cancer or autoimmune disease library is obtained by Genome Wide Association Studies (GWAS) using approaches well known in the art. For example, association of a mutation to a phenotype optionally includes performing one or more statistical tests for correlation. Many statistical tests are known, and most are computer- implemented for ease of analysis. A variety of statistical methods of determining

associations/correlations between phenotypic traits and biological markers are known and can be applied to the methods described herein (e.g., Hartl, A Primer of Population Genetics

Washington University, Saint Louis Sinauer Associates, Inc. Sunderland, Mass., 1981, ISBN: 0- 087893-271-2). A variety of appropriate statistical models are described in Lynch and Walsh, Genetics and Analysis of Quantitative Traits, Sinauer Associates, Inc. Sunderland Mass., 1998, ISBN 0-87893-481-2. These models can, for example, provide for correlations between genotypic and phenotypic values, characterize the influence of a locus on a phenotype, sort out the relationship between environment and genotype, determine dominance or penetrance of genes, determine maternal and other epigenetic effects, determine principle components in an analysis (via principle component analysis, or "PCA"), and the like. The references cited in these texts provide considerable further detail on statistical models for correlating markers and phenotype.

In some embodiments, all the tumor associated mutations are evaluated in the analysis according to the methods described herein. In some embodiments, only the driver mutations are evaluated in the analysis. As used herein, the term "driver mutation" refers to the subset of mutations within a tumor cell that confer a growth advantage. Methods of identifying driver mutations are known in the art and are described in, for example, PCT Publication No. WO 2012/159754. Alternatively, other criteria for driver mutation selection may be used. For example, the mutations that occur in known oncogenes and have been observed in multiple TCGA samples or in genomic sequences of multiple subjects can be selected.

In some embodiments, the mutations that occur in the 100 most highly ranked oncogenes and observed in at least one TCGA sample or in at least one subject genomic sequence are selected as driver mutations. In some embodiments, the mutations that occur in the 100 most highly ranked oncogenes (e.g., as described by Davoli et al., Cell, 2013, 155, 948-962) and observed in at least two TCGA samples or in at least two subject genomic sequences are selected as driver mutations. In some embodiments, the mutations that occur in the 100 most highly ranked oncogenes and observed in at least three TCGA samples or in at least three subject genomic sequences are selected as driver mutations. In some embodiments, the mutations that occur in the 100 most highly ranked oncogenes and observed in at least four TCGA samples or in at least four subject genomic sequences are selected as driver mutations. In some

embodiments, the mutations that occur in the 100 most highly ranked oncogenes and observed in at least five TCGA samples or in at least five subject genomic sequences are selected as driver mutations. In some embodiments, the mutations that occur in the 50 most highly ranked oncogenes and observed in at least one TCGA sample or in at least one subject genomic sequence are selected as driver mutations. In some embodiments, the mutations that occur in the 50 most highly ranked oncogenes and observed in at least two TCGA samples or in at least two subject genomic sequences are selected as driver mutations. In some embodiments, the mutations that occur in the 50 most highly ranked oncogenes and observed in at least three TCGA samples or in at least three subject genomic sequences are selected as driver mutations. In some embodiments, the mutations that occur in the 50 most highly ranked oncogenes and observed in at least four TCGA samples or in at least four subject genomic sequences are selected as driver mutations. In some embodiments, the mutations that occur in the 50 most highly ranked oncogenes and observed in at least five TCGA samples or in at least five subject genomic sequences are selected as driver mutations. In some embodiments, the mutations that occur in the 20 most highly ranked oncogenes and observed in at least one TCGA sample or in at least one subject genomic sequence are selected as driver mutations. In some embodiments, the mutations that occur in the 20 most highly ranked oncogenes and observed in at least two TCGA samples or in at least two subject genomic sequences are selected as driver mutations. In some embodiments, the mutations that occur in the 20 most highly ranked oncogenes and observed in at least three TCGA samples or in at least three subject genomic sequences are selected as driver mutations. In some embodiments, the mutations that occur in the 20 most highly ranked oncogenes and observed in at least four TCGA samples or in at least four subject genomic sequences are selected as driver mutations. In some embodiments, the mutations that occur in the 20 most highly ranked oncogenes and observed in at least five TCGA samples or in at least five subject genomic sequences are selected as driver mutations. In some embodiments, the mutations that occur in the 10 most highly ranked oncogenes and observed in at least one TCGA sample or in at least one subject genomic sequence are selected as driver mutations. In some embodiments, the mutations that occur in the 10 most highly ranked oncogenes and observed in at least two TCGA samples or in at least two subject genomic sequences are selected as driver mutations. In some embodiments, the mutations that occur in the 10 most highly ranked oncogenes and observed in at least three TCGA samples or in at least three subject genomic sequences are selected as driver mutations. In some embodiments, the mutations that occur in the 10 most highly ranked oncogenes and observed in at least four TCGA samples or in at least four subject genomic sequences are selected as driver mutations. In some embodiments, the mutations that occur in the 10 most highly ranked oncogenes and observed in at least five TCGA samples or in at least five subject genomic sequences are selected as driver mutations.

In some embodiments, the selected mutations are further limited to those that would result in predictable protein sequence changes that could generate neoantigens, including missense mutations and in- frame insertions and deletions. In some embodiments, the set of 1018 mutations occurring in one of the 100 most highly ranked oncogenes or tumor suppressors, observed in at least three TCGA samples, and resulting in predictable protein sequence changes that could generate neoantigens, including missense mutations and in-frame insertions and deletions can be selected (see, Tables 24 and 25).

The MHC-I presentation scores for the driver mutation sites can be determined through a residue-centric approach using prediction algorithms. These prediction algorithms can either scan an existing protein sequence from a pathogen for putative T-cell epitopes, or they can predict, whether de novo designed peptides bind to a particular MHC molecule. Many such prediction algorithms are commonly known. Examples include, but are not limited to,

SVRMHCdb (world wide web at "svrmhc.umn.edu/SVRMHCdb"; Wan et al., BMC

Bioinformatics, 2006, 7, 463), SYFPEITHI (world wide web at "syfpeithi.de"), MHCPred (world wide web at "jenner.ac.uk/MHCPred"), motif scanner (world wide web at

"hcv.lanl.gov/content/immuno/motif_scan/motif_scan"), and NetMHCpan (world wide web at "cbs.dtu.dk services/ NetMHCpan") for MHC I binding epitopes. In some embodiments, the MHC-I presentation scores are obtained using the NetMHCPan 3.0 tool. The values obtained using this tool reflect the affinity of a peptide encompassing an oncogenic mutation for that subject's MHC-I allele, and thereby predict the likelihood of that peptide to be presented by the subject's MHC-I allele, thus generating neoantigens.

In some embodiments the ability of the subject' s MHC-I to present a mutant cancer- associated peptide or an autoimmune-associated peptide is determined through fitting a statistical model. In some embodiments, the statistical model is a logistic regression model. Logistic regression is part of a category of statistical models called generalized linear models. Logistic regression can allow one to predict a discrete outcome, such as group membership, from a set of variables that may be continuous, discrete, dichotomous, or a mix of any of these. The dependent or response variable is dichotomous, for example, one of two possible types of cancer. Logistic regression models the natural log of the odds ratio, i.e., the ratio of the probability of belonging to the first group (P) over the probability of belonging to the second group (1-P), as a linear combination of the different expression levels (in log-space). The logistic regression output can be used as a classifier by prescribing that a case or sample will be classified into the first type if P is large, such as a usual default where P is greater than 0.5 or 50% but depending on the desired sensitivity or specificity or the diagnostic test, thresholds other than 0.5 can be considered. Alternatively, the calculated probability P can be used as a variable in other contexts, such as a ID or 2D threshold classifier.

In some embodiments, the statistical model is a binary logistic regression model, wherein MHC-I affinities for a cancer or autoimmune disease- associated mutations are evaluated as independent variables. In some embodiments, the statistical model is an additive logistic regression model correlating affinity of a subject' s MHC-I allele for a peptide encompassing an oncogenic mutation and the probability of mutations occurring across subjects "across-subject model". In some embodiments, the statistical model is a random effects logistic regression model that follows a model equation:

logit (Piyy = 1 I x_i})) = β, + ylog(x_y) (3), wherein yy is a binary mutation matrix yy E {0,1 } indicating whether a subject i has a mutation j; xy is a binary mutation matrix indicating predicted MHC-I binding affinity of subject i having mutation j; γ measures the effect of the log-affinities on the mutation probability; and β, ~ N(0, ^ ) are random effects capturing mutation specific effects (e.g., different occurrence frequencies among mutations).

In some embodiments, the statistical model is a mixed-effects logistic regression model that follows a model equation:

logit (Piyy = 1 I y)) = η,- + ylog(xy) ( 1 ) , wherein yy is a binary mutation matrix y E {0,1 } indicating whether a subject i has a mutation j; xy is a binary mutation matrix indicating predicted MHC-I binding affinity of subject i having mutation j; γ measures the effect of the log-affinities on the mutation probability; and ~ N(0, ^η) are random effects capturing residue- specific effects, wherein the model tests the null hypothesis that γ = 0 and calculates odds ratios for MHC-I affinity of a mutation and presence of a cancer or autoimmune disease. This model correlates the affinity of a subject's MHC-I allele for a peptide

encompassing an oncogenic mutation and the probability of mutations occurring within subjects "within-subject model." In other words, the model is testing whether the affinity of a subject's MHC-I allele for a particular oncogenic mutation has any impact on probability this mutation occurring within a subject, or which mutation a subject is more likely to undergo.

In some embodiments, the predicted MHC-I affinity for a given mutation (represented in the above equations with the term x_i}) is obtained by aggregating MHC-I binding affinities of a set comprising one or more mutant cancer-associated peptides or a set comprising one or more autoimmune disorder-associated peptides by referring to a pre-determined dataset of peptides binding to MHC-I molecules encoded by at least 16 different HLA alleles. In some

embodiments, the predicted MHC-I affinity is obtained by aggregating MHC-I binding affinities of a set comprising one or more mutant cancer-associated peptides or a set comprising one or more autoimmune-associated peptides by referring to a pre-determined dataset of peptides binding to MHC-I molecules encoded by at least six common HLA alleles. In some

embodiments, the predicted MHC-I affinity is the simple sum of six values of the MHC-I binding affinities for six common HLA alleles. In some embodiments, the predicted MHC-I affinity is the sum of the inverse of the six values of the MHC-I binding affinities for six common HLA alleles. In some embodiments, the predicted MHC-I affinity is the inverse of sum of the inverse of the six values of the MHC-I binding affinities for six common HLA alleles. In some embodiments, MHC-I affinity is a Subject Harmonic-mean Best Rank (PHBR) score, which is the harmonic mean of the six common HLA alleles.

In some embodiments, the predicted MHC-I affinity (such as the PHBR score) is determined for a peptide encompassing a driver mutation. In some embodiments, the peptide used to obtain a predicted MHC-I affinity (such as the PHBR score) is 6 amino acids long, and the driver mutation position is located at or near the center of the peptide. In some embodiments, the peptide used to obtain a predicted MHC-I affinity (such as the PHBR score) is 7 amino acids long, and the driver mutation position is located at or near the center of the peptide. In some embodiments, the peptide used to obtain a predicted MHC-I affinity (such as the PHBR score) is 8 amino acids long, and the driver mutation position is located at or near the center of the peptide. In some embodiments, the peptide used to obtain a predicted MHC-I affinity (such as the PHBR score) is 9 amino acids long, and the driver mutation position is located at or near the center of the peptide. In some embodiments, the peptide used to obtain a predicted MHC-I affinity (such as the PHBR score) is 10 amino acids long, and the driver mutation position is located at or near the center of the peptide. In some embodiments, the peptide used to obtain a predicted MHC-I affinity (such as the PHBR score) is 11 amino acids long, and the driver mutation position is located at or near the center of the peptide. In some embodiments, the peptide used to obtain a predicted MHC-I affinity (such as the PHBR score) is 12 amino acids long, and the driver mutation position is located at or near the center of the peptide. In some embodiments, the peptide used to obtain a predicted MHC-I affinity (such as the PHBR score) is 13 amino acids long, and the driver mutation position is located at or near the center of the peptide.

In some embodiments, the predicted MHC-I affinity (such as the PHBR score) represents an aggregate of MHC-I binding affinities of all 6-amino acid-long peptides encompassing a driver mutation, wherein the driver mutation is located at any position along the peptide. In some embodiments, the predicted MHC-I affinity (such as the PHBR score) represents an aggregate of MHC-I binding affinities of all 7-amino acid-long peptides encompassing a driver mutation, wherein the driver mutation is located at any position along the peptide. In some embodiments, the predicted MHC-I affinity (such as the PHBR score) represents an aggregate of MHC-I binding affinities of all 8-amino acid-long peptides encompassing a driver mutation, wherein the driver mutation is located at any position along the peptide. In some embodiments, the predicted MHC-I affinity (such as the PHBR score) represents an aggregate of MHC-I binding affinities of all 9-amino acid-long peptides encompassing a driver mutation, wherein the driver mutation is located at any position along the peptide. In some embodiments, the predicted MHC-I affinity (such as the PHBR score) represents an aggregate of MHC-I binding affinities of all 10 amino acid-long peptides encompassing a driver mutation, wherein the driver mutation is located at any position along the peptide. In some embodiments, the predicted MHC-I affinity (such as the PHBR score) represents an aggregate of MHC-I binding affinities of all 11-amino acid-long peptides encompassing a driver mutation, wherein the driver mutation is located at any position along the peptide. In some embodiments, the predicted MHC-I affinity (such as the PHBR score) represents an aggregate of MHC-I binding affinities of all 12-amino acid-long peptides encompassing a driver mutation, wherein the driver mutation is located at any position along the peptide. In some embodiments, the predicted MHC-I affinity (such as the PHBR score) represents an aggregate of MHC-I binding affinities of all 13-amino acid-long peptides encompassing a driver mutation, wherein the driver mutation is located at any position along the peptide.

In some embodiments, the predicted MHC-I affinity (such as the PHBR score) represents a combination of aggregate MHC-I binding affinity scores of all 6- and 7-amino acid peptides encompassing a driver mutation, wherein the driver mutation is located at any position along the peptide. In some embodiments, the predicted MHC-I affinity (such as the PHBR score) represents a combination of aggregate MHC-I binding affinity scores of all 7- and 8-amino acid peptides encompassing a driver mutation, wherein the driver mutation is located at any position along the peptide. In some embodiments, the predicted MHC-I affinity (such as the PHBR score) represents a combination of aggregate MHC-I binding affinity scores of all 8- and 9-amino acid peptides encompassing a driver mutation, wherein the driver mutation is located at any position along the peptide. In some embodiments, the predicted MHC-I affinity (such as the PHBR score) represents a combination of aggregate MHC-I binding affinity scores of all 9- and 10-amino acid peptides encompassing a driver mutation, wherein the driver mutation is located at any position along the peptide. In some embodiments, the predicted MHC-I affinity (such as the PHBR score) represents a combination of aggregate MHC-I binding affinity scores of all 10- and 11 -amino acid peptides encompassing a driver mutation, wherein the driver mutation is located at any position along the peptide. In some embodiments, the predicted MHC-I affinity (such as the PHBR score) represents a combination of aggregate MHC-I binding affinity scores of all 11- and 12-amino acid peptides encompassing a driver mutation, wherein the driver mutation is located at any position along the peptide. In some embodiments, the predicted MHC-I affinity (such as the PHBR score) represents a combination of aggregate MHC-I binding affinity scores of all 12- and 13-amino acid peptides encompassing a driver mutation, wherein the driver mutation is located at any position along the peptide. In some embodiments, the predicted MHC-I affinity (such as the PHBR score) ore represents a combination of aggregate MHC-I binding affinity scores of any two length-determined sets of peptides encompassing a driver mutation, wherein the driver mutation is located at any position along the peptide, and wherein each set comprises equal length 6- to 13-amino acids long peptides.

In some embodiments, the predicted MHC-I affinity (such as the PHBR score) represents a combination of aggregate MHC-I binding affinity scores of all 6-, 7-, and 8-amino acid peptides encompassing a driver mutation, wherein the driver mutation is located at any position along the peptide. In some embodiments, the predicted MHC-I affinity (such as the PHBR score) represents a combination of aggregate MHC-I binding affinity scores of all 7-, 8-, and 9-amino acid peptides encompassing a driver mutation, wherein the driver mutation is located at any position along the peptide. In some embodiments, the predicted MHC-I affinity (such as the PHBR score) represents a combination of aggregate MHC-I binding affinity scores of all 8-, 9-, and 10-amino acid peptides encompassing a driver mutation, wherein the driver mutation is located at any position along the peptide. In some embodiments, the predicted MHC- I affinity (such as the PHBR score) represents a combination of aggregate MHC-I binding affinity scores of all 9-, 10-, and ll-amino acid peptides encompassing a driver mutation, wherein the driver mutation is located at any position along the peptide. In some embodiments, the predicted MHC-I affinity (such as the PHBR score) represents a combination of aggregate MHC-I binding affinity scores of all 10-, 11-, and 12-amino acid peptides encompassing a driver mutation, wherein the driver mutation is located at any position along the peptide. In some embodiments, the predicted MHC-I affinity (such as the PHBR score) represents a combination of aggregate MHC-I binding affinity scores of all 11-, 12-, and 13-amino acid peptides encompassing a driver mutation, wherein the driver mutation is located at any position along the peptide. In some embodiments, the predicted MHC-I affinity (such as the PHBR score) represents a combination of aggregate MHC-I binding affinity scores of any three length- determined sets of peptides encompassing a driver mutation, wherein the driver mutation is located at any position along the peptide, and wherein each set comprises equal length 6-to 13- amino acids long peptides.

In some embodiments, the predicted MHC-I affinity (such as the PHBR score) represents a combination of aggregate MHC-I binding affinity scores of all 6-, 7-, 8- and 9- amino acid peptides encompassing a driver mutation, wherein the driver mutation is located at any position along the peptide. In some embodiments, the predicted MHC-I affinity (such as the PHBR score) represents a combination of aggregate MHC-I binding affinity scores of all 7-, 8- 9-, and 10-amino acid peptides encompassing a driver mutation, wherein the driver mutation is located at any position along the peptide. In some embodiments, the predicted MHC-I affinity (such as the PHBR score) represents a combination of aggregate MHC-I binding affinity scores of all 8-, 9-, 10-, and 11- amino acid peptides encompassing a driver mutation, wherein the driver mutation is located at any position along the peptide. In some embodiments, the predicted MHC- I affinity (such as the PHBR score) represents a combination of aggregate MHC-I binding affinity scores of all 9-, 10- 11-, and 12-amino acid peptides encompassing a driver mutation, wherein the driver mutation is located at any position along the peptide. In some embodiments, the predicted MHC-I affinity (such as the PHBR score) represents a combination of aggregate MHC-I binding affinity scores of all 10- 11-, 12-, and 13-amino acid peptides encompassing a driver mutation, wherein the driver mutation is located at any position along the peptide. In some embodiments, the predicted MHC-I affinity (such as the PHBR score) represents a combination of aggregate MHC-I binding affinity scores of any four length-determined sets of peptides encompassing a driver mutation, wherein the driver mutation is located at any position along the peptide, and wherein each set comprises equal length 6-to 13-amino acids long peptides. In some embodiments, the predicted MHC-I affinity (such as the PHBR score) represents a combination of aggregate MHC-I binding affinity scores of any five length-determined sets of peptides encompassing a driver mutation, wherein the driver mutation is located at any position along the peptide, and wherein each set comprises equal length 6-to 13- amino acids long peptides. In some embodiments, the predicted MHC-I affinity (such as the PHBR score) represents a combination of aggregate MHC-I binding affinity scores of any six length-determined sets of peptides encompassing a driver mutation, wherein the driver mutation is located at any position along the peptide, and wherein each set comprises equal length 6-to 13-amino acids long peptides. In some embodiments, the predicted MHC-I affinity (such as the PHBR score) represents a combination of aggregate MHC-I binding affinity scores of all 6-, 7-, 8-, 9-, 10-, 11, 12-, and 13-amino acids long encompassing a driver mutation, wherein the driver mutation is located at any position along the peptide.

In some embodiments, the predicted MHC-I affinity (such as the PHBR score) is obtained using wild type peptide sequences. In some embodiments, the predicted MHC-I affinity (such as the PHBR score) is obtained using peptide sequences containing a driver mutation. In some embodiments, the predicted MHC-I affinity (such as the PHBR score) is obtained using peptides containing wild-type sequences and a driver mutation.

The individual peptides' the predicted MHC-I affinities can be combined in several ways. In some embodiments, the predicted MHC-I affinities are combined through assigning the best rank among the peptides in a set. In some embodiments, predicted MHC-I affinities are combined through calculating the number of peptides having MHC-I affinity below a certain threshold (e.g., <2 for MHC-I binders and <0.5 for MHC-I strong binders). In some embodiments, predicted MHC-I affinities are combined through assigning the best rank weighted by predicted proteasomal cleavage. In some embodiments, predicted MHC-I affinities are combined by referring to a pre-determined dataset of peptides binding to MHC-I molecules encoded by at least 16 different HLA alleles. In some embodiments, predicted MHC-I affinities are combined by referring to a pre-determined dataset of peptides binding to MHC-I molecules encoded by at least 6 common HLA alleles.

In some embodiments, the mixed-effects logistic regression model following the model equation (1) can be used to evaluate a subject's risk of developing or having a pre-detection stage of many types cancer. As used herein, the term "cancer" refers to refers to a cellular disorder characterized by uncontrolled or disregulated cell proliferation, decreased cellular differentiation, inappropriate ability to invade surrounding tissue, and/or ability to establish new growth at ectopic sites. The term "cancer" further encompasses primary and metastatic cancers. Specific examples of cancers include, but are not limited to, Acute Lymphoblastic Leukemia, Adult; Acute Lymphoblastic Leukemia, Childhood; Acute Myeloid Leukemia, Adult;

Adrenocortical Carcinoma; Adrenocortical Carcinoma, Childhood; AIDS-Related Lymphoma; AIDS-Related Malignancies; Anal Cancer; Astrocytoma, Childhood Cerebellar; Astrocytoma, Childhood Cerebral; Bile Duct Cancer, Extrahepatic; Bladder Cancer; Bladder Cancer,

Childhood; Bone Cancer, Osteosarco ma/Malignant Fibrous Histiocytoma; Brain Stem Glioma, Childhood; Brain Tumor, Adult; Brain Tumor, Brain Stem Glioma, Childhood; Brain Tumor, Cerebellar Astrocytoma, Childhood; Brain Tumor, Cerebral Astrocytoma/Malignant Glioma, Childhood; Brain Tumor, Ependymoma, Childhood; Brain Tumor, Medulloblastoma, Childhood; Brain Tumor, Supratentorial Primitive Neuroectodermal Tumors, Childhood; Brain Tumor,

Visual Pathway and Hypothalamic Glioma, Childhood; Brain Tumor, Childhood (Other); Breast Cancer; Breast Cancer and Pregnancy; Breast Cancer, Childhood; Breast Cancer, Male;

Bronchial Adenomas/Carcinoids, Childhood: Carcinoid Tumor, Childhood; Carcinoid Tumor, Gastrointestinal; Carcinoma, Adrenocortical; Carcinoma, Islet Cell; Carcinoma of Unknown Primary; Central Nervous System Lymphoma, Primary; Cerebellar Astrocytoma, Childhood; Cerebral Astrocytoma/Malignant Glioma, Childhood; Cervical Cancer; Childhood Cancers; Chronic Lymphocytic Leukemia; Chronic Myelogenous Leukemia; Chronic Myeloproliferative Disorders; Clear Cell Sarcoma of Tendon Sheaths; Colon Cancer; Colorectal Cancer, Childhood; Cutaneous T-Cell Lymphoma; Endometrial Cancer; Ependymoma, Childhood; Epithelial Cancer, Ovarian; Esophageal Cancer; Esophageal Cancer, Childhood; Ewing's Family of Tumors; Extracranial Germ Cell Tumor, Childhood; Extragonadal Germ Cell Tumor;

Extrahepatic Bile Duct Cancer; Eye Cancer, Intraocular Melanoma; Eye Cancer,

Retinoblastoma; Gallbladder Cancer; Gastric (Stomach) Cancer; Gastric (Stomach) Cancer, Childhood; Gastrointestinal Carcinoid Tumor; Germ Cell Tumor, Extracranial, Childhood; Germ Cell Tumor, Extragonadal; Germ Cell Tumor, Ovarian; Gestational Trophoblastic Tumor;

Glioma. Childhood Brain Stem; Glioma. Childhood Visual Pathway and Hypothalamic; Hairy Cell Leukemia; Head and Neck Cancer; Hepatocellular (Liver) Cancer, Adult (Primary);

Hepatocellular (Liver) Cancer, Childhood (Primary) ; Hodgkin' s Lymphoma, Adult; Hodgkin' s Lymphoma, Childhood; Hodgkin' s Lymphoma During Pregnancy; Hypopharyngeal Cancer; Hypothalamic and Visual Pathway Glioma, Childhood; Intraocular Melanoma; Islet Cell Carcinoma (Endocrine Pancreas); Kaposi's Sarcoma; Kidney Cancer; Laryngeal Cancer;

Laryngeal Cancer, Childhood; Leukemia, Acute Lymphoblastic, Adult; Leukemia, Acute Lymphoblastic, Childhood; Leukemia, Acute Myeloid, Adult; Leukemia, Acute Myeloid, Childhood; Leukemia, Chronic Lymphocytic; Leukemia, Chronic Myelogenous; Leukemia, Hairy Cell; Lip and Oral Cavity Cancer; Liver Cancer, Adult (Primary); Liver Cancer,

Childhood (Primary); Lung Cancer, Non-Small Cell; Lung Cancer, Small Cell; Lymphoblastic Leukemia, Adult Acute; Lymphoblastic Leukemia, Childhood Acute; Lymphocytic Leukemia, Chronic; Lymphoma, AIDS-Related; Lymphoma, Central Nervous System (Primary);

Lymphoma, Cutaneous T-Cell; Lymphoma, Non-Hodgkin's, Adult; Lymphoma, Non-

Hodgkin's, Childhood; Lymphoma, Non-Hodgkin's During Pregnancy; Lymphoma, Primary Central Nervous System; Macroglobulinemia, Waldenstrom's; Male Breast Cancer; Malignant Mesothelioma, Adult; Malignant Mesothelioma, Childhood; Malignant Thymoma;

Medulloblastoma, Childhood; Melanoma; Melanoma, Intraocular; Merkel Cell Carcinoma; Mesothelioma, Malignant; Metastatic Squamous Neck Cancer with Occult Primary; Multiple Endocrine Neoplasia Syndrome, Childhood; Multiple Myeloma/Plasma Cell Neoplasm; Mycosis Fungoides; Myelodysplasia Syndromes; Myelogenous Leukemia, Chronic; Myeloid Leukemia, Childhood Acute; Myeloma, Multiple; Myeloproliferative Disorders, Chronic; Nasal Cavity and Paranasal Sinus Cancer; Nasopharyngeal Cancer; Nasopharyngeal Cancer, Childhood;

Neuroblastoma; Neurofibroma; Non-Hodgkin' s Lymphoma, Adult; Non-Hodgkin' s Lymphoma, Childhood; Non-Hodgkin's Lymphoma During Pregnancy; Non-Small Cell Lung Cancer; Oral Cancer, Childhood; Oral Cavity and Lip Cancer; Oropharyngeal Cancer;

Osteosarcoma/Malignant Fibrous Histiocytoma of Bone; Ovarian Cancer, Childhood; Ovarian Epithelial Cancer; Ovarian Germ Cell Tumor; Ovarian Low Malignant Potential Tumor;

Pancreatic Cancer; Pancreatic Cancer, Childhood, Pancreatic Cancer, Islet Cell; Paranasal Sinus and Nasal Cavity Cancer; Parathyroid Cancer; Penile Cancer; Pheochromocytoma; Pineal and Supratentorial Primitive Neuroectodermal Tumors, Childhood; Pituitary Tumor; Plasma Cell Neoplasm/Multiple Myeloma; Pleuropulmonary Blastoma; Pregnancy and Breast Cancer;

Pregnancy and Hodgkin's Lymphoma; Pregnancy and Non-Hodgkin's Lymphoma; Primary Central Nervous System Lymphoma; Primary Liver Cancer, Adult; Primary Liver Cancer, Childhood; Prostate Cancer; Rectal Cancer; Renal Cell (Kidney) Cancer; Renal Cell Cancer, Childhood; Renal Pelvis and Ureter, Transitional Cell Cancer; Retinoblastoma;

Rhabdomyosarcoma, Childhood; Salivary Gland Cancer; Salivary Gland Cancer, Childhood; Sarcoma, Ewing's Family of Tumors; Sarcoma, Kaposi's; Sarcoma (Osteosarcoma)/Malignant Fibrous Histiocytoma of Bone; Sarcoma, Rhabdomyosarcoma, Childhood; Sarcoma, Soft Tissue, Adult; Sarcoma, Soft Tissue, Childhood; Sezary Syndrome; Skin Cancer; Skin Cancer,

Childhood; Skin Cancer (Melanoma); Skin Carcinoma, Merkel Cell; Small Cell Lung Cancer; Small Intestine Cancer; Soft Tissue Sarcoma, Adult; Soft Tissue Sarcoma, Childhood; Squamous Neck Cancer with Occult Primary, Metastatic; Stomach (Gastric) Cancer; Stomach (Gastric) Cancer, Childhood; Supratentorial Primitive Neuroectodermal Tumors, Childhood; T-Cell Lymphoma, Cutaneous; Testicular Cancer; Thymoma, Childhood; Thymoma, Malignant;

Thyroid Cancer; Thyroid Cancer, Childhood; Transitional Cell Cancer of the Renal Pelvis and Ureter; Trophoblastic Tumor, Gestational; Unknown Primary Site, Cancer of, Childhood;

Unusual Cancers of Childhood; Ureter and Renal Pelvis, Transitional Cell Cancer; Urethral Cancer; Uterine Sarcoma; Vaginal Cancer; Visual Pathway and Hypothalamic Glioma,

Childhood; Vulvar Cancer; Waldenstrom's Macro globulinemia; and Wilms' Tumor. Many additional types of cancer are known in the art. As used herein, cancer cells, including tumor cells, refer to cells that divide at an abnormal (increased) rate or whose control of growth or survival is different than for cells in the same tissue where the cancer cell arises or lives. Cancer cells include, but are not limited to, cells in carcinomas, such as squamous cell carcinoma, basal cell carcinoma, sweat gland carcinoma, sebaceous gland carcinoma, adenocarcinoma, papillary carcinoma, papillary adenocarcinoma, cystadenocarcinoma, medullary carcinoma,

undifferentiated carcinoma, bronchogenic carcinoma, melanoma, renal cell carcinoma, hepatoma- liver cell carcinoma, bile duct carcinoma, cholangiocarcinoma, papillary carcinoma, transitional cell carcinoma, choriocarcinoma, semonoma, embryonal carcinoma, mammary carcinomas, gastrointestinal carcinoma, colonic carcinomas, bladder carcinoma, prostate carcinoma, and squamous cell carcinoma of the neck and head region; sarcomas, such as fibrosarcoma, myxosarcoma, liposarcoma, chondrosarcoma, osteogenic sarcoma,

chordosarcoma, angiosarcoma, endotheliosarcoma, lymphangiosarcoma, synoviosarcoma and mesotheliosarcoma; hematologic cancers, such as myelomas, leukemias (e.g., acute

myelogenous leukemia, chronic lymphocytic leukemia, granulocytic leukemia, monocytic leukemia, lymphocytic leukemia), and lymphomas (e.g., follicular lymphoma, mantle cell lymphoma, diffuse large cell lymphoma, malignant lymphoma, plasmocytoma, reticulum cell sarcoma, or Hodgkin's disease); and tumors of the nervous system including glioma, meningioma, medulloblastoma, schwannoma, or epidymoma.

In some embodiments, mixed-effects logistic regression model following the model equation (1) can be used to evaluate a subject's risk of developing or having a pre-detection stage of an adrenocortical carcinoma (ACC), a bladder urothelial carcinoma (BLCA), a breast invasive carcinoma (BRCA), a cervical squamous cell carcinoma and endocervical

adenocarcinoma (CESC), a colon adenocarcinoma (CO AD), a lymphoid neoplasm diffuse large B-cell lymphoma (DLBC), a glioblastoma multiforme (GBM), a head and neck squamous cell carcinoma (HNSC), a kidney chromophobe (KICH), a kidney renal clear cell carcinoma (KIRC), a kidney renal papillary cell carcinoma (KIRP), an acute myeloid leukemia (LAML), a brain lower grade glioma (LGG), a liver hepatocellular carcinoma (LIHC), a lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), a mesothelioma (MESO), an ovarian serous cystadenocarcinoma (OV), a pancreatic adenocarcinoma (PAAD), a pheochromocytoma and paraganglioma (PCPG), a prostate adenocarcinoma (PRAD), a rectum adenocarcinoma (READ), a sarcoma (SARC), a skin cutaneous melanoma (SKCM), a stomach adenocarcinoma (ST AD), a testicular germ cell tumors (TGCT), a thyroid carcinoma (THCA), a uterine corpus endometrial carcinoma (UCEC), a uterine carcinosarcoma (UCS), or a uveal melanoma (UVM).

The mixed-effects logistic regression model following the model equation (1) can be also used to evaluate a subject's risk of developing or having a pre-detection stage of an autoimmune disease. As used herein, the term "autoimmune disease" refers to disorders wherein the subjects own immune system mistakenly attacks itself, thereby targeting the cells, tissues, and/or organs of the subjects own body, for example through MHC-I- mediated presentation of subject's proteins (see e.g., Matzaraki et al., Genome Biol., 2017, 18, 76). For example, the autoimmune reaction is directed against the nervous system in multiple sclerosis and the gut in Crohn's disease, in other autoimmune disorders such as systemic lupus erythematosus (lupus), affected tissues and organs may vary among individuals with the same disease. One person with lupus may have affected skin and joints whereas another may have affected skin, kidney, and lungs. Ultimately, damage to certain tissues by the immune system may be permanent, as with destruction of insulin-producing cells of the pancreas in Type 1 diabetes mellitus. Specific autoimmune disorders whose risk can be assessed using methods of this disclosure include without limitation, autoimmune disorders of the nervous system (e.g., multiple sclerosis, myasthenia gravis, autoimmune neuropathies such as Guillain-Barre, and autoimmune uveitis), autoimmune disorders of the blood (e.g., autoimmune hemolytic anemia, pernicious anemia, and autoimmune thrombocytopenia), autoimmune disorders of the blood vessels (e.g., temporal arteritis, anti-phospholipid syndrome, vasculitides such as Wegener's granulomatosis, and Bechet's disease), autoimmune disorders of the skin (e.g., psoriasis, dermatitis herpetiformis, pemphigus vulgaris, and vitiligo), autoimmune disorders of the gastrointestinal system (e.g., Crohn's disease, ulcerative colitis, primary biliary cirrhosis, and autoimmune hepatitis), autoimmune disorders of the endocrine glands (e.g., Type 1 or immune-mediated diabetes mellitus, Grave's disease, Hashimoto's thyroiditis, autoimmune oophoritis and orchitis, and autoimmune disorder of the adrenal gland); and autoimmune disorders of multiple organs (including connective tissue and musculoskeletal system diseases) (e.g., rheumatoid arthritis, systemic lupus erythematosus, scleroderma, polymyositis, dennatomyositis,

spondyloarthropathies such as ankylosing spondylitis, and Sjogren's syndrome). In addition, other immune system mediated diseases, such as graft- versus-host disease and allergic disorders, are also included in the definition of immune disorders herein.

Using the mixed-effects logistic regression model following the model equation (1) it has been surprisingly and unexpectedly found that oncogenic mutations associated with one cancer type are predictive of other cancer types. Thus, for example, the 10 residues highly mutated in a breast invasive carcinoma (BRCA), specifically, PIK3CA_H1047R,

PIK3CA_E545K, PIK3CA_E542K, TP53_R175H, PIK3CA_N345K, AKT1_E17K,

SF3B1_K700E, PIK3CA_H1047L, TP53_R273H, and TP53_Y220C, are predictive (odds ratio >1.2, p value <0.05) of a colon adenocarcinoma (CO AD), a head and neck squamous cell carcinoma (HNSC), a glioblastoma multiforme (GBM), a brain lower grade glioma (LGG), an ovarian serous cystadenocarcinoma (OV), a pancreatic adenocarcinoma (PAAD), a stomach adenocarcinoma (STAD), and a uterine carcinosarcoma (UCS). At the same time, surprisingly and unexpectedly, the set of BRCA-associated mutations was not predictive of BRCA (see, Example 4 and Tables 12-23).

The present disclosure also provides methods of detecting a cancer, such as an early stage cancer, in a subject, the method comprising the steps of: a) obtaining a biological sample from the subject; b) assaying the sample for the presence of a cancer-associated mutation, c) genotyping the HLA locus of the subject; and d) scoring the likelihood of the MHC-I-mediated presentation of the mutations found in step (b) by the subject' s MHC-I allele as determined in step (c), wherein the poor presentation score indicates the presence of cancer, such as early stage cancer, in the subject.

The present disclosure also provides methods of detecting an autoimmune disease, such as an early stage autoimmune disease, in a subject, the method comprising the steps of: a) obtaining a biological sample from the subject; b) assaying the sample for the presence of an autoimmune-associated peptide, c) genotyping the HLA locus of the subject; and d) scoring the likelihood of the MHC-I-mediated presentation of the autoimmune-associated peptides found in step (b) by the subject's MHC-I allele as determined in step (c), wherein the poor presentation score indicates the presence of an autoimmune disease, such as an early stage autoimmune disease, in the subject.

As used herein, "biological sample" refers to any sample that can be from or derived from a human subject, e.g., bodily fluids (blood, saliva, urine etc.), biopsy, tissue, and/or waste from the subject. Thus, tissue biopsies, stool, sputum, saliva, blood, lymph, tears, sweat, urine, vaginal secretions, or the like can be screened for the presence of one or more specific mutations, as can essentially any tissue of interest that contains the appropriate nucleic acids. These samples are typically taken, following informed consent, from a subject by standard medical laboratory methods. The sample may be in a form taken directly from the subject, or may be at least partially processed (purified) to remove at least some non-nucleic acid material.

In some embodiments, the cancer is a breast invasive carcinoma (BRCA), and the corresponding predictive mutations comprise one or more of B-Raf Proto-Oncogene (BRAF) V600E mutation, Phosphatidylinositol-4,5-Bisphosphate 3-Kinase Catalytic Subunit Alpha (PIK3CA) E545K mutation, PIK3CA E542K mutation, PIK3CA H1047R mutation, Kirsten Rat Sarcoma Viral Oncogene Homolog (KRAS) G12D mutation, KRAS G13D mutation, KRAS G12V mutation, KRAS A146T mutation, TP53 R175H mutation, TP53 H179R mutation, TP53 mutation, TP53 R248Q mutation, TP53 R273C mutation, TP53 R273H mutation, TP53 R282W mutation, Keratin Associated Protein 4-11 (KRTAP4-11) L161V mutation, Mab-21 Domain Containing 2 (MB21D2) Q31 IE, mutation, HLA-A Q78R mutation, Harvey Rat Sarcoma Viral Oncogene Homolog (HRAS) G13V mutation, Isocitrate Dehydrogenase (NADP(+)) 1 (IDHl) R132H mutation, IDHl R132C mutation, IDHl R132G mutation, IDH2 R172K mutation, IDHl R132S mutation, Capicua Transcriptional Repressor (CIC) R215W mutation,

Phosphoglucomutase 5 (PGM5) I98V mutation, Tripartite Motif Containing 48 (TRIM48) Y192H mutation, or F-Box And WD Repeat Domain Containing 7 (FBXW7) R465C mutation, wherein the presence of any one of these mutations indicates the presence of breast invasive carcinoma.

In some embodiments, the cancer is a colon adenocarcinoma (CO AD) and the corresponding predictive mutations comprise one or more of BRAF V600E mutation,

Neuroblastoma RAS Viral Oncogene Homolog (NRAS) Q61R mutation, NRAS Q61K mutation, NRAS Q61L mutation, IDHl R132S mutation, Mitogen-Activated Protein Kinase Kinase 1 (MAP2K1) P124S mutation, Rac Family Small GTPase 1 (RAC1) P29S mutation, Protein Phosphatase 6 Catalytic Subunit (PPP6C) R301C mutation, Cyclin Dependent Kinase Inhibitor 2A (CDKN2A) P114L mutation, Keratin Associated Protein 4-11 (KRTAP4-11) L161V mutation, KRTAP4-11 M93V mutation, HRAS Q61R mutation, HLA-A Q78R mutation, Zinc Finger Protein 799 (ZNF799) E589G mutation, Zinc Finger Protein 844 (ZNF844) R447P mutation, or RNA Binding Motif Protein 10 (RBM10) E184D mutation, wherein the presence of any one of these mutations indicates the presence of colon adenocarcinoma.

In some embodiments, the cancer is a head and neck squamous cell carcinoma (HNSC) and the corresponding predictive mutations comprise one or more of IDH1 R132H mutation, IDH1 R132C mutation, IDH1 R132G mutation, IDH1 R132S mutation, IDH2 R172K mutation, TP53 H179R mutation, TP53 R273C mutation, TP53 R273H mutation, CIC R215W mutation, or HLA-A Q78R mutation, wherein the presence of any one of these mutations indicates the presence of head and neck squamous cell carcinoma.

In some embodiments, the cancer is a brain lower grade glioma (LGG) and the corresponding predictive mutations comprise one or more of IDH1 R132H mutation, IDH1 R132C mutation, IDH1 R132G mutation, IDH1 R132S mutation, IDH2 R172K mutation, TP53 H179R mutation, TP53 R273C mutation, TP53 R273H mutation, CIC R215W mutation, or HLA-A Q78R mutation, wherein the presence of any one of these mutations indicates the presence of brain lower grade glioma.

In some embodiments, the cancer is a lung adenocarcinoma (LUAD) and the corresponding predictive mutations comprise one or more of BRAF V600E mutation, PIK3CA E545K mutation, KRAS G12D mutation, KRAS G13D mutation, KRAS A146T mutation, TP53 R175H mutation, KRAS G12V mutation, TP53 R248Q mutation, TP53 R273C mutation TP53 R273H mutation, TP53 R282W mutation, PGM5 I98V mutation, TRIM48 Y192H mutation, PIK3CA E545K mutation, KRAS G13D mutation, PIK3CA H1047R mutation, or FBXW7 R465C mutation, wherein the presence of any one of these mutations indicates the presence of lung adenocarcinoma.

In some embodiments, the cancer is a lung squamous cell carcinoma (LUSC) and the corresponding predictive mutations comprise one or more of PIK3CA H1047R mutation, PIK3CA E545K mutation, PIK3CA E542K mutation, TP53 R175H mutation, PIK3CA N345K mutation, AKT Serine/Threonine Kinase 1 (AKTl) E17K mutation, Splicing Factor 3b Subunit 1 (SF3B1) K700E mutation, or PIK3CA H1047L mutation, wherein the presence of any one of these mutations indicates the presence of lung squamous cell carcinoma.

In some embodiments, the cancer is a skin cutaneous melanoma (SKCM) and the corresponding predictive mutations comprise one or more of BRAF V600E mutation, PIK3CA E545K mutation, KRAS G12D mutation, KRAS G13D mutation, KRAS A146T mutation, KRAS G12V mutation, TP53 R175H mutation, TP53 H179R mutation, TP53 R248Q mutation TP53 R273C mutation, TP53 R273H mutation, TP53 R282W mutation, IDH1 R132H mutation, IDH1 R132C mutation, IDH1 R132G mutation, IDH1 R132S mutation, IDH2 R172K mutation, CIC R215W mutation, or HLA-A Q78R mutation, NRAS Q61R mutation, NRAS Q61K mutation, NRAS Q61L mutation, MAP2K1 P124S mutation, RAC1 P29S mutation, PPP6C R301C mutation, CDKN2A PI 14L mutation, KRTAP4-11 L161 V mutation, KRTAP4-11 M93 V mutation, HRAS Q61R mutation, ZNF799 E589G mutation, ZNF844 R447P mutation, or RBM10 E184D mutation, wherein the presence of any one of these mutations indicates the presence of skin cutaneous melanoma.

In some embodiments, the cancer is a stomach adenocarcinoma (STAD) and the corresponding predictive mutations comprise one or more of KRAS G12C mutation, KRAS G12V mutation, Epidermal Growth Factor Receptor (EGFR) L858R mutation, KRAS G12D mutation, KRAS G12A mutation, U2 Small Nuclear RNA Auxiliary Factor 1 (U2AF1) S34F mutation, KRTAP4-11 L161V mutation, KRTAP4-11 R121K mutation, Eukaryotic Translation Elongation Factor 1 Beta 2 (EEF1B2) R42H mutation, or KRTAP4-11 M93V mutation, wherein the presence of any one of these mutations indicates the presence of stomach adenocarcinoma.

In some embodiments, the cancer is a thyroid carcinoma (THCA) and the corresponding predictive mutations comprise one or more of BRAF V600E mutation, PIK3CA E545K mutation, KRAS G12D mutation, KRAS G13D mutation, TP53 R175H mutation, KRAS G12V mutation, TP53 R248Q mutation, KRAS A146T mutation, TP53 R273H mutation, HRAS Q61R mutation, HLA-A Q78R mutation, TP53 R282W mutation, NRAS Q61R mutation, NRAS Q61K mutation, IDH1 R132C mutation, MAP2K1 P124S mutation, RAC1 P29S mutation, NRAS Q61L mutation, PPP6C R301C mutation, CDKN2A P114L mutation, KRTAP4-11 L161V mutation, KRTAP4-11 M93V mutation, ZNF799 E589G mutation, ZNF844 R447P mutation, or RBM10 E184D mutation, wherein the presence of any one of these mutations indicates the presence of thyroid carcinoma.

In some embodiments, the cancer is a uterine corpus endometrial carcinoma (UCEC) and the corresponding predictive mutations comprise one or more of BRAF V600E mutation, PIK3CA H1047R mutation, PIK3CA E545K mutation, PIK3CA E542K mutation, TP53 R175H mutation, PIK3CA N345K mutation, AKT Serine/Threonine Kinase 1 (AKT1) E17K mutation, Splicing Factor 3b Subunit 1 (SF3B1) K700E mutation, KRAS G12C mutation, KRAS G12V mutation, Epidermal Growth Factor Receptor (EGFR) L858R mutation, KRAS G12D mutation, KRAS G12A mutation, KRAS G12V mutation, KRAS G13D mutation, TP53 R175H mutation, TP53 R248Q mutation, KRAS A146T mutation, TP53 R273H mutation, TP53 R282W mutation, U2 Small Nuclear RNA Auxiliary Factor 1 (U2AF1) S34F mutation, KRTAP4-11 L161V mutation, KRTAP4-11 R121K mutation, Eukaryotic Translation Elongation Factor 1 Beta 2 (EEF1B2) R42H mutation, or KRTAP4-11 M93V mutation, wherein the presence of any one of these mutations indicates the presence of uterine corpus endometrial carcinoma.

In any of the embodiments described herein, the presence of any one of the mutations may indicate the presence of an early stage cancer.

The present disclosure also provides diagnostic kits comprising detection agents for one or more cancer or autoimmune disease-associated mutations. A kit may optionally further comprise a container with a predetermined amount of one or more purified molecules, either protein or nucleic acid having a cancer or autoimmune disease-associated mutation according to the present disclosure, for use as positive controls. Each kit may also include printed instructions and/or a printed label describing the methods disclosed herein in accordance with one or more of the embodiments described herein. Kit containers may optionally be sterile containers. The kits may also be configured for research use only applications whether on clinical samples, research use samples, cell lines and/or primary cells.

Suitable detection agents comprise any organic or inorganic molecule that specifically bind to or interact with proteins or nucleic acids having a cancer or autoimmune disease- associated mutation. Non-limiting examples of detection agents include proteins, peptides, antibodies, enzyme substrates, transition state analogs, cofactors, nucleotides, polynucleotides, aptamers, lectins, small molecules, ligands, inhibitors, drugs, and other biomolecules as well as non-biomolecules capable of specifically binding the analyte to be detected.

In some embodiments, the detection agents comprise one or more label moiety(ies). In embodiments employing two or more label moieties, each label moiety can be the same, or some, or all, of the label moieties may differ.

In some embodiments, the label moiety comprises a chemiluminescent label. The chemiluminescent label can comprise any entity that provides a light signal and that can be used in accordance with the methods and devices described herein. A wide variety of such chemiluminescent labels are known (see, e.g., U.S. Patent Nos. 6,689,576, 6,395,503, 6,087,188, 6,287,767, 6,165,800, and 6,126,870). Suitable labels include enzymes capable of reacting with a chemiluminescent substrate in such a way that photon emission by chemiluminescence is induced. Such enzymes induce chemiluminescence in other molecules through enzymatic activity. Such enzymes may include peroxidase, beta-galactosidase, phosphatase, or others for which a chemiluminescent substrate is available. In some embodiments, the chemiluminescent label can be selected from any of a variety of classes of luminol label, an isoluminol label, etc. In some embodiments, the detection agents comprise chemiluminescent labeled antibodies. Likewise, the label moiety can comprise a bioluminescent compound. Bioluminescence is a type of chemiluminescence found in biological systems in which a catalytic protein increases the efficiency of the chemiluminescent reaction. The presence of a bioluminescent compound is determined by detecting the presence of luminescence. Suitable bioluminescent compounds include, but are not limited to luciferin, luciferase, and aequorin.

In some embodiments, the label moiety comprises a fluorescent dye. The fluorescent dye can comprise any entity that provides a fluorescent signal and that can be used in accordance with the methods and devices described herein. Typically, the fluorescent dye comprises a resonance-delocalized system or aromatic ring system that absorbs light at a first wavelength and emits fluorescent light at a second wavelength in response to the absorption event. A wide variety of such fluorescent dye molecules are known in the art. For example, fluorescent dyes can be selected from any of a variety of classes of fluorescent compounds, non-limiting examples include xanthenes, rhodamines, fluoresceins, cyanines, phthalocyanines, squaraines, bodipy dyes, coumarins, oxazines, and carbopyronines. In some embodiments, for example, where detection agents contain fluorophores, such as fluorescent dyes, their fluorescence is detected by exciting them with an appropriate light source, and monitoring their fluorescence by a detector sensitive to their characteristic fluorescence emission wavelength. In some embodiments, the detection agents comprise fluorescent dye labeled antibodies.

In embodiments using two or more different detection agents, which bind to or interact with different analytes, different types of analytes can be detected simultaneously. In some embodiments, two or more different detection agents, which bind to or interact with the one analyte, can be detected simultaneously. In embodiments using two or more different detection agents, one detection agent, for example a primary antibody, can bind to or interact with one or more analytes to form a detection agent-analyte complex, and second detection agent, for example a secondary antibody, can be used to bind to or interact with the detection agent-analyte complex.

In some embodiments, two different detection agents, for example antibodies for both phospho and non-phospho forms of analyte of interest can enable detection of both forms of the analyte of interest. In some embodiments, a single specific detection agent, for example an antibody, can allow detection and analysis of both phosphorylated and non-phosphorylated forms of a analyte, as these can be resolved in the fluid path. In some embodiments, multiple detection agents can be used with multiple substrates to provide color-multiplexing. For example, the different chemiluminescent substrates used would be selected such that they emit photons of differing color. Selective detection of different colors, as accomplished by using a diffraction grating, prism, series of colored filters, or other means allow determination of which color photons are being emitted at any position along the fluid path, and therefore determination of which detection agents are present at each emitting location. In some embodiments, different chemiluminescent reagents can be supplied sequentially, allowing different bound detection agents to be detected sequentially.

Throughout the specification the word "comprising," or variations such as "comprises" or "comprising," will be understood to imply the inclusion of a stated element, integer or step, or group of elements, integers or steps, but not the exclusion of any other element, integer or step, or group of elements, integers or steps. The methods, systems, and kits described herein may suitably "comprise", "consist of, or "consist essentially of, the steps, elements, and/or reagents recited herein.

In order that the subject matter disclosed herein may be more efficiently understood, examples are provided below. It should be understood that these examples are for illustrative purposes only and are not to be construed as limiting the claimed subject matter in any manner.

Examples

Example 1: MHC-I Affinity-Based Scoring Scheme for Mutated Residues

To study the influence of MHC-I genotype in shaping the genomes of tumors, a qualitative residue-centric presentation score was developed, and its potential to predict whether a sequence containing a residue will be presented on the cell surface was evaluated. The score relies on aggregating MHC-I binding affinities across possible peptides that include the residue of interest. MHC-I peptide binding affinity predictions were obtained using the NetMHCPan3.0 tool (Vita et al., Nucleic Acids Res., 2015, 43, D405-D412), and following published recommendations (Nielsen and Andreatta, Genome Med., 2016, 8, 33), peptides receiving a rank threshold <2 and <0.5 were designated MHC-I binders and strong binders respectively. For evaluation of missense mutations, the score was based on the affinities of all 38 possible peptides of length 8-11 that incorporate the amino acid position of interest (Figure 2A), while for insertions and deletions, any resulting novel peptides of length 8-11 were considered (Figure 3A).

Several strategies were evaluated for combining peptide affinities to approximate presentation of a specific residue on the cell surface using an existing dataset of peptides bound to MHC-I molecules encoded by 16 different HLA alleles in monoallelic lymphoblastoid cell lines determined using mass spectrometry (MS) (Abelin et al., Mass Immunity, 2017, 46, 315- 326), the most comprehensive database of cell surface presented peptides currently available. These strategies included assigning the best rank among peptides, the total number of peptides with rank <2, the total number of peptides with rank <0.5, and the best rank weighted by predicted proteasomal cleavage (Figures 3B-3K). The ability of these scores to discriminate these MS-derived residues from a size-matched set of randomly selected residues (STAR Methods) were compared. The best rank score (Figure 2B) provided the most reliable prediction that a particular residue position would be included in a sequence presented by the MHC-I on the cell surface (Figure 2C); thus, this score was used for all subsequent analysis.

To test the best rank score's ability to assess the presentation of cancer-related mutations, sets of expressed mutations in 5 cancer cell lines (A375, A2780, OV90, HeLa, and SKOV3) were scored to predict which would be presented by an HLA-A*02:01-derived MHC-I (see, Tables 1A and IB for A375 ; Tables 2A and 2B for A2780; Tables 3A and 3B for OV90; Tables 4A and 4B for HeLa; and Tables 5A and 5B for SKOV3). Unless a mutation affects an anchor position, a peptide harboring a single amino acid change has a modest impact on peptide binding affinity and should be presented on the cell surface provided that the corresponding native sequence is presented.

Table 1A: A375 Peptide Panel

Peptide # A375 (High) Allele Rank

1 PLEC_A398T HLA-A*02:01 WT 5.3

HLA-A*02:01 MUT 8.2

2 PLEC_A398T HLA-A*02:01 WT 0.2

HLA-A*02:01 MUT 0.3

A375 (Med) Allele Rank

3 MYOF_I353T HLA-A*Q2:Q1 WT 1.5

HLA-A*02:01 MUT 1.8

5 RSF1_V956I HLA-A*02:01 MUT 1.5

HLA-A*02:01 WT 1.6 SEC24C N944S HLA-A*02:01 MUT 2,6

HLA-A*02:01 WT 3.1

Two different peptides (Peptides 1 and 2) are presented from this source protein, overlapping the residue of interest. In none of them the residue is at an anchor position. For Peptides 3, 5, and 6, the residue is not at an anchor position.

Table IB: A375 Predicted Binders

Strong ; binders Weak binders

Gene Residue Gene Residue

ABCC10 A88 ABCC10 A45

ADTRP S95 ADTRP S113

ARHGEF2 G538 ANK2 A1359

CCDC27 R125 APOBEC3D E163

CD5 V289 ARHGEF2 G537

COL6A6 R37 ARID4B H766

CRELD1 L14 ASNSD1 P551

DCAF4L2 D84 BTN2A1 V185

F2RL3 L83 BTNL3 S231

FOSL2 V266 CD1A S147

GRIK2 T740 CD1D R92

GTF3C2 P605 CYP24A1 P449

HERC2 13905 DDX43 1283

HIST3H2A V108 DOCK11 E1549

ILDR2 S308 FAM46D S66

LGR6 S654 LHX8 S108

LGR6 S741 MAGEB6 1316

LGR6 S793 MTUS1 D297

LOXHD1 1768 MYOF* 1353

METTL8 HI 05 NBEAL2 D1092

NIPA1 V310 NELL1 V237

OR4A16 P282 NKAIN3 D92

OR51V1 S252 NLRP3 K942

PAPPA2 N1344 PLCE1 K2110

PCDHB2 G331 PLEC A239

PHC2 R312 PLXDC2 T451

PLEC* A398 PPP4R1L T271

PROKR2 A283 PTGES2 A272

SLC2A14 N67 PTPRD G262

SLC36A4 Li n PXDNL P1432

SNAP47 P94 RALGAPA2 S1164

TACC3 S190 RSF1* V956

TBX15 S238 SCN11A M1707 THBS3 V747 SEC24C* N944

TLR8 F346 SEMA3F E216

TRRAP S722 SLA T66

TTN P28517 SLC20A1 P270

UBQLN2 R249 SLIT2 P266

USP19 N697 SLITRK2 P60

STK11IP A955

TGIF1 S4

TM9SF4 P463

TTN D4445

TTN 126997

TTN K8183

TTN P2812

TTN P28515

TTN P9639

UBQLN2 N250

WDR19 S555

XDH G1007

ZFHX4 A60

ZNF431 R145

ZNF814 K162

Observed from MS (*).

Table 2A: A2780 Peptide Panel

A2780 (High) Allele Rank

MAP3K5_M375V HLA-A*02:01 WT 0.6

HLA-A*02:01 MUT 0.6

NET1_M159T HLA-A*02:01 WT 1.1

HLA-A*02:01 MUT 1.2

3 NET1_M159T HLA-A*02:01 WT 14

HLA-A*02:01 MUT 15

NET1_M159T HLA-A*02:01 WT 2.5

HLA-A*02:01 MUT 2.6 A2780 (Med) Allele Rank

GYS1 L353F HLA-A*02:01 WT 0.5

HLA-A*02:01 MUT 4.9

For Peptide 1 , the residue is not at an anchor position. Three different peptides (Peptides 2, 3, and 4) are presented from this source protein, overlapping the residue of interest. In none of them the residue is at an anchor position. For Peptide 5, the residue is at an anchor position.

Table 2B: A2780 Predicted Binders

Strong ; binders Weak binders

Gene Residue Gene Residue

ADAM21 D101 ATG16L1 Q136

CRAT A610 BIRC6 R4218

HHIPL1 R237 C2orfl6 F731

IFI44L P280 CCDC82 R383

MAP3K5* M375 CFTR G314

MAP7D2 T682 COL6A3 D773

NET1 M105 COL9A1 Ml 84

NET1* M159 CRIPAK R250

NHS LI V501 DNAH10 S1076

NHS LI V505 DNAH10 S894

NSUN4 Q331 DYSF L960

NUPL2 P314 EPB41L3 R375

PHGDH S277 GNAS P335

PROM1 D200 GYS1* L353

KANK1 S860

KCND1 F363

KIFC1 R210

LRP5 M637

NPHP1 V623

PBX1 E250

PHGDH S311

SMARCA4 T910

TTLL12 R425

UAP1L1 G275

WDR76 K450

Observed from MS (*). Table 3A: OV90 Peptide Panel

OV90 (High) Allele Rank

AMMECR1L_P124A HLA-A*02:01 WT 1.7

HLA-A*02:01 MUT 2

IFI27L2_V82F HLA-A*02:01 MUT 1.8

HLA-A*02:01 WT 3.7

3

For Peptide 1 , the residue is not at an anchor position. Two different peptides (Peptides and 3) are presented from this source protein, overlapping the residue of interest. In none of them the residue is at an anchor position.

Table 3B: OV90 Predicted Binders

Strong binders Weak binders

Gene Residue Gene Residue

AHNAK2 K4708 ABCA9 P1447

AMMECR1L* P124 APOB M495

ATP8B2 D1078 CRHBP T71

CDKN2A A86 CRISPLD1 M17

FBXW11 S521 E2F2 R256

GPR153 T48 FAM193A T616

HUNK R168 FGFR4 P352

IFI27L2* V82 MLKL M122

KIDINS220 F1047 NEK4 R788

VRTN T152 SLC12A8 G190

SLC12A8 L366

ZFYVE26 R385

Observed from MS (*). Table 4A: HeLA Peptide Panel Peptide # HeLa (High) Allele

For Peptide 1 , the residue is not at an anchor position.

Table 4B: HeLa Predicted Binders

Strong ; binders Weak binders

Gene Residue Gene Residue

CRB1* P876 ADCY1 K348

DIP2B C934 BAZ2B A1146

FAM86C1 R64 CCDC142 V549

FUT10 S89 CCDC142 V556

TPTE2 R407 CRIPAK P208

DCC S383

DOCK3 K520

FAM98C E181

GRIK2 A490

MPDU1 T89

NDST2 V297

OBSCN A7599

PCLO T3520

PDE3A Y814

PLEC C4071

RABGGTA R486

RIPK4 H231

SASS6 A452

SLC16A5 N284

SNRNP200 S1087

UGGT1 S126

USP35 L581

ZNF500 P249

Observed from MS (*). Table 5A: SKOV3 Peptide Panel

SKOV3 (High) Allele Rank

SKOV3 (Med) Allele Rank

Two different peptides (Peptides 1 and 2) are presented from this source protein, overlapping the residue of interest. In Peptide 1, the residue is not at an anchor position. In Peptide 2, the residue is at an anchor position. For Peptides 3, 4, 5, and 6, the residue is not at an anchor position.

Table 5B: SKOV3 Predicted Binders

Strong binders Weak binders

Gene Residue Gene Residue

ABCD1 S342 ABCD1 SI 57

ADRA2A A63 AHSA1 E220 B4GALNT2 V510 AN07 C875

CUL4B 1663 ASPRV1 E322

DHX38* L812 BAAT G72

DNAAF1 P571 C17orf53 N563

FZD3 F8 CLIP3 F318

HCN4 V319 CTDP1 F816

KLHL26 R252 CUL4B 1668

LIMK2 G499 CUL4B 1681

LIMK2 G520 DISP1 A562

MANBA E745 DOCK10 P358

MEF2D* Y33 DOCK10* P364

NPHP4 V883 FBXW7 R266

PIGN F5 FBXW7 R505

PTGER4 A180 FKBP10 V337

SLC18A1 T39 HSF1 N65

TCF7L2 N452 IRGQ M241

TMEM175 A471 ITGA8 A100

TREML2 C115 KRTAP13-4 A138

TUFM G29 LPIN2 L763

UBE4B* E936 3-Mar R143

ZFHX3 1935 MED13L T28

ZNF233 D384 MTMR2 1544

MVK A270

ONECUT2 R407

OR5AC2 Y253

PDE6A R102

RBM47* R251

SELENBP1 S354

SLC24A3 G613

STRA6 C256

TBC1D17 Y326

TCEANC2 R187

WRNIP1 V429

ZC3H7B T226

Observed from MS (*).

Analyzing a database of native peptides found in complex with an HLA-A*02:01 MHC-I in these 5 cell lines, across cell lines, 9.8% of mutations predicted to strongly bind and 4.0% of mutations predicted to bind an HLA-A*02:01 MHC-I at any strength were also supported by MS-derived peptides (Figure 2D). These experimental results validate the ability of a score derived from MHC-I binding affinities to identify mutations with a higher likelihood of generating neoantigens and support the application of this score to evaluate MHC-I genotype as a determinant of the antigenic potential of recurrent mutations in tumors. The formation of a stable complex is a prerequisite for antigen presentation, but does not ensure that an antigen will be displayed on the cell surface. The presentation score was experimentally validated for different peptides using three of the most common HLA alleles. HLA alleles A*24:02, A*02:01 , and B*57:01 were overexpressed in six cell lines (HeLa, FHIOSE, SKOV3 , 721.221, A2780, and OV90). HLA-peptide complexes were purified from the cell surface, and the bound peptides were isolated. Their sequence was determined using mass spectrometry (Patterson et al., Mol. Cancer Ther., 2016, 15, 313-322; and Trolle et al., J.

Immunol., 2016, 196, 1480-1487). The amount of mass spectrometry (MS) data obtained for each allele differed substantially, rendering A*24:02 and B*57 :01 underpowered to detect differences (Figure 4A). First, balanced numbers of random human peptides to bind or not bind these HLA-alleles were selected based on the score. Residues with high HLA allele- specific presentation scores were far more likely to be detected in complex with the MHC-I molecule on the cell surface than residues with low presentation scores (p = 3.3xl0^"7, Figure 4B, Table 6). Next, the presentation of balanced numbers of recurrent oncogenic mutations predicted to bind or not bind these same HLA alleles were evaluated. It was observed that recurrent oncogenic mutations receiving a high presentation score were also more likely to generate peptides observed in complex with the MHC-I molecule on the cell surface (p = 0.0003, Figure 4B). Thus, these experimental results validate the expectation that when considering a given amino acid residue, a higher number of peptides containing the residue that are predicted to stably bind to an MHC-I allele will correlate with a higher number of peptide neoantigens displayed on the cell surface by that allele and therefore a greater potential to engage T cell receptors.

Example 2: Statistical Analysis of Affinity Score vs. Presence Of Mutation

The data consists of a 9176x1018 binary mutation matrix yy £ {0, 1 } , indicating that subject i has/does not have a mutation in residue j. Another 9176x1018 matrix containing the predicted affinity xy of subject i for mutation j. All analyses below are restricted to the 412 residues that presented mutations in > 5 subjects.

The question considered was whether xy have an effect on yy within subjects, or, in other words whether affinity scores help predict, within a given subject, which residues are likely to undergo mutations.

To address the above question, logistic regression models were used. An important issue in such models is to capture adequately the type of effect that xy has on y , e.g. is it linear (in some sense), or all that matters is whether the affinity is beyond a certain threshold. To this end an additive logistic regression with non-linear effects for the affinity, was fitted via function gam in R package mgcv. The estimated mutation probability as a function of affinity, P(j¾ = 1 I y), is portrayed in Figure 5A. The corresponding logit mutation probabilities as a function of the log-affinity is shown in Figure 5B, revealing that the association between the two is linear. This justifies considering a linear effect of log(x_y) on the logit mutation probability. As a check, Figure 5C shows the estimated mutation probabilities based on discretizing the affinity scores into groups, = showing a similar pattern than the top panel (i.e. reinforcing that the GAM provides a good fit for the data).

The following random-effects model was considered:

logit (P(yij = 1 I Xij)) = η, + ylog(Xy), (1) where yy is a binary mutation matrix yy £ {0,1 } indicating whether a subject i has a mutation j; xy is a binary mutation matrix indicating predicted MHC-I binding affinity of subject i having mutation j; γ measures the effect of the log-affinities on the mutation probability; and ~ N(0, ^η) are random effects capturing residue-specific effects.

The question corresponds testing the null hypothesis that γ = 0 in the model above. This mixed effects logistic regression gave a highly significant result (R output in Table 6), indicating that the affinity score does have a within- subjects impact on the occurrence of mutation. The estimated random effects standard deviation was φ_Ά = 0:505, indicating that overall mutation rates differ across subjects.

Table 6: Model (1) R output

As a final check the following model with both subject and residue random effects was considered:

logit (P(yy = 1 I xy)) = η, + β> ylog(xy), (2) where ~ N(0, _Ά), β, ~ N(0, ^_β) The results are analogous to the previous analyses. The R output is in Table 7.

Table 7: Model (2) R output

Table 8 summarizes the results in terms of odds ratios (i.e. the increase in the odds of mutation for a +1 increase in log-affinity). The odds-ratio for the within- subjects model (Question 3) is virtually identical to the global model, the predictive power of a_nity within a subject is similar to the overall predictive power. A unit increase in log-a_nity (equivalently, a 2.7 fold increase in the affinity) increases the odds of mutation by 15.9%. In contrast, the odds- ratio for the within-residues model is close to 1 , signaling that within residues the a_nity score has practically negligible predictive power.

Table 8: Odds ratios for log- affinity

Global: model with no random effects. Within-residues: model with residue random effects. Within-subjects: model with subject random effects.

Example 3: Separate Analysis for Each Cancer Type

The within-residues and within-subjects analyses were carried out, selecting only the subjects with a specific cancer type (the number of subjects with each cancer type are indicated in Table 9). Following random-effects model was considered.

logit (Piyy = 1 I x_i})) = + y\og(_Xij), (3) where γ measures the effect of the log- affinities on the mutation probability and β, ~ N(0, ^β) are random effects capturing residue- specific effects (e.g. whether one residue has an overall higher probability of mutation than another). The null hypothesis γ = 0 was tested. The model in (3) was fitted via function glmer from R package lme4. The analysis was restricted to residues with > 5 mutations, as the remaining residues contain little information and result in an unmanageable increase in the computational burden (> 3 and > 10 mutations, were also checked, obtaining similar results).

Table 9: The number of subjects analyzed for each cancer type in model (3)

Cancer Number of subjects

ACC 91

BLCA 409

BRCA 897

CESC 55

COAD 396

DLBC 36

GBM 390

HNSC 503

KICH 66

KIRC 333

KIRP 281

LAML 138

LGG 506

LIHC 361

LUAD 565

LUSC 487

MESO 82

OV 403

PAAD 175

PCPG 179

PRAD 492

SARC 172

SKCM 467 STAD 435

TGCT 144

THCA 484

UCEC 359

UCS 57

UVM 78

Tables 10 and 11 report odds-ratios, 95% intervals and P- values. Figures 6A and 6B display these 95% intervals, and Figures 7 A and 7B repeat the same display using only the cancer types with >100 subjects. The salient feature is that in the within-residues analysis most intervals contain the value OR=l (which corresponds to no predictive power), whereas in the within- subjects analysis they're focused on OR> 1 for more than half of the cancer types. As expected, the 95% intervals are wider for those cancer types with less subjects.

Table 10: Odds ratios, 95% intervals and P-value of the within-residues analysis separately for each cancer subtype

OR 95% CI P-value

ACC 1.110 0.770,1.599 0.5767

BLCA 1.072 0.976,1.177 0.1477

BRCA 1.099 1.011,1.196 0.0274

CESC 1.100 0.818,1.480 0.5291

COAD 0.986 0.914,1.064 0.7250

DLBC 1.920 0.786,4.692 0.1522

GBM 1.025 0.913,1.152 0.6715

HNSC 1.086 0.990,1.190 0.0798

KICH 1.046 0.690,1.586 0.8328

KIRC 0.812 0.573,1.151 0.2423

KIRP 1.327 0.835,2.108 0.2319

LAML 1.068 0.869,1.314 0.5312

LGG 0.965 0.880,1.059 0.4547

LIHC 1.215 1.054,1.401 0.0074

LUAD 1.038 0.950,1.134 0.4100

LUSC 0.969 0.891,1.054 0.4610

MESO 1.264 0.804,1.989 0.3101 ov 1.037 0.912,1.179 0.5793

PAAD 0.908 0.783,1.052 0.1989

PCPG 1.487 0.937,2.361 0.0922

PRAD 1.072 0.887,1.295 0.4740

SARC 0.967 0.736,1.270 0.8077

SKCM 0.976 0.906,1.050 0.5104

STAD 1.054 0.955,1.163 0.2988

TGCT 0.977 0.634,1.506 0.9168

THCA 0.991 0.870,1.129 0.8959

UCEC 1.020 0.956,1.088 0.5434

UCS 1.058 0.872,1.282 0.5685

UVM 0.664 0.441,0.998 0.0487

Table 11: Odds ratios, 95% intervals and P- value of the within- subjects analysis separately for each cancer subtype

OR 95% CI P-value

ACC 1.155 0.842,1.583 0.3715

BLCA 1.151 1.069,1.240 0.0002

BRCA 1.224 1.152,1.300 0.0000

CESC 1.082 0.864,1.353 0.4930

COAD 1.252 1.183,1.326 0.0000

DLBC 1.671 0.985,2.836 0.0570

GBM 1.137 1.039,1.244 0.0050

HNSC 1.155 1.077,1.240 0.0001

KICH 1.046 0.690,1.586 0.8328

KIRC 0.812 0.573,1.151 0.2422

KIRP 1.463 1.016,2.107 0.0408

LAML 0.989 0.849,1.151 0.8825

LGG 1.460 1.379,1.546 0.0000

LIHC 1.206 1.077,1.349 0.0011

LUAD 1.151 1.079,1.228 0.0000

LUSC 0.982 0.918,1.049 0.5846 MESO 1.275 0.804,2.020 0.3014

OV 1.106 1.007,1.214 0.0356

PAAD 1.306 1.185,1.439 0.0000

PCPG 1.635 1.144,2.336 0.0070

PRAD 1.188 1.025,1.376 0.0219

SARC 0.961 0.780,1.185 0.7118

SKCM 1.171 1.106,1.239 0.0000

STAD 1.146 1.062,1.237 0.0005

TGCT 1.202 0.862,1.676 0.2784

THCA 1.914 1.752,2.091 0.0000

UCEC 1.079 1.028,1.132 0.0021

UCS 1.131 0.978,1.308 0.0966

UVM 0.640 0.475,0.862 0.0033

Example 4: Groups of High-Frequency Mutation Residues

The global and cancer-type specific analyses were repeated selecting only highly- mutated sets of residues (listed below). For instance, the 10 residues highly mutated in BRCA were selected and fit the within-subjects model, fist using all subjects (global OR) and then using only subjects with each cancer subtype. These odds-ratios are listed in Tables 12-23. In a number of instances the number of mutations in the selected residues/subjects was too small to obtain reliable estimates, in these instances no estimate is reported.

Table 12: Within-subjects analysis for residues with

high mutation frequency in BRCA

OR CLlow CLhigh pvalue

Global 1.254 1.182 1.331 0.0000

ACC

BLCA 1.179 0.933 1.490 0.1673

BRCA 1.072 0.967 1.189 0.1880

CESC 1.607 0.835 3.096 0.1557

COAD 1.262 1.053 1.512 0.0117

DLBC

GBM 2.005 1.302 3.086 0.0016 HNSC 1.420 1.154 1.748 0.0009

KICH

KIRC 0.314 0.082 1.207 0.0918

KIRP 1.062 0.378 2.982 0.9086

LAML

LGG 2.059 2.053 2.065 0.0000

LIHC 1.504 0.831 2.722 0.1775

LUAD 1.427 0.893 2.279 0.1370

LUSC 1.104 0.832 1.464 0.4935

MESO

OV 2.160 1.498 3.114 0.0000

PAAD 2.104 1.081 4.097 0.0286

PCPG

PRAD 0.718 0.429 1.199 0.2051

SARC 1.237 0.638 2.400 0.5293

SKCM 0.853 0.463 1.574 0.6118

STAD 1.578 1.232 2.022 0.0003

TGCT 0.943 0.342 2.598 0.9095

THCA 0.265 0.090 0.787 0.0168

UCEC 1.116 0.905 1.376 0.3036

UCS 2.056 1.144 3.696 0.0160

UVM

Table 13: Within- subjects analysis for residues with high mutation frequency in COAD

OR CLlow CLhigh pvalue

Global 1.047 0.993 1.105 0.0902

ACC

BLCA 0.627 0.467 0.841 0.0018

BRCA 0.892 0.720 1.104 0.2916

CESC 1.828 0.795 4.200 0.1554

COAD 1.034 0.903 1.184 0.6274 DLBC

GBM 0.759 0.529 1.089 0.1346

HNSC 1.032 0.786 1.354 0.8223

KICH

KIRC

KIRP 1.465 0.633 3.395 0.3727

LAML 1.838 0.693 4.875 0.2213

LGG 0.811 0.569 1.156 0.2465

LIHC 1.400 0.681 2.878 0.3605

LUAD 0.795 0.626 1.009 0.0592

LUSC 0.895 0.607 1.320 0.5761

MESO

OV 0.847 0.605 1.186 0.3331

PAAD 0.832 0.676 1.024 0.0827

PCPG

PRAD 0.536 0.274 1.049 0.0685

SARC 0.847 0.306 2.349 0.7503

SKCM 1.263 1.085 1.470 0.0026

STAD 1.196 0.928 1.543 0.1675

TGCT 0.723 0.270 1.933 0.5176

THCA 1.477 1.291 1.690 0.0000

UCEC 0.844 0.659 1.082 0.1815

UCS 1.153 0.695 1.915 0.5814

UVM

Table 14: Within- subjects analysis for residues with high mutation frequency in HNSC

OR CLlow CLhigh pvalue

Global 1.115 1.048 1.187 0.0006

ACC

BLCA 1.047 0.847 1.294 0.6707

BRCA 1.090 0.967 1.229 0.1565 CESC 1.908 0.905 4.023 0.0896

COAD 1.022 0.857 1.218 0.8090

DLBC

GBM 1.184 0.766 1.828 0.4467

HNSC 1.077 0.896 1.296 0.4294

KICH

KIRC

KIRP 0.945 0.342 2.606 0.9127

LAML

LGG 1.298 1.288 1.308 0.0000

LIHC 1.196 0.621 2.304 0.5927

LUAD 0.796 0.553 1.146 0.2199

LUSC 0.982 0.754 1.281 0.8957

MESO

OV 1.187 0.763 1.848 0.4468

PAAD 1.592 0.869 2.916 0.1325

PCPG

PRAD 0.776 0.482 1.250 0.2973

SARC 0.996 0.368 2.691 0.9933

SKCM 2.004 0.454 8.846 0.3590

STAD 1.421 1.094 1.845 0.0085

TGCT 1.438 0.355 5.828 0.6107

THCA

UCEC 1.192 0.948 1.500 0.1332

UCS 1.569 0.956 2.572 0.0745

UVM

Table 15: Within- subjects analysis for residues with high mutation frequency in KIRC

OR CLlow CLhigh pvalue

Global 0.892 0.534 1.489 0.6616

ACC BLCA

BRCA

CESC

COAD

DLBC

GBM

HNSC

KICH

KIRC 0.829 0.492 1.396 0.4809

KIRP

LAML

LGG

LIHC

LUAD

LUSC

MESO

OV

PAAD

PCPG

PRAD

READ

SARC

SKCM

STAD

TGCT

THCA

UCEC

UCS

UVM Table 16: Within- subjects analysis for residues with high mutation frequency in LGG

OR CLlow CLhigh pvalue

Global 1.247 1.136 1.369 0.0000

ACC

BLCA 1.264 0.620 2.577 0.5186

BRCA 1.021 0.663 1.571 0.9251

CESC

COAD 1.069 0.706 1.617 0.7532

DLBC

GBM 1.678 1.084 2.598 0.0202

HNSC 1.182 0.738 1.893 0.4873

KICH

KIRC

KIRP

LAML 1.640 0.901 2.984 0.1054

LGG 1.131 1.025 1.248 0.0140

LIHC 1.680 0.717 3.939 0.2324

LUAD 1.813 0.505 6.509 0.3613

LUSC 0.878 0.425 1.813 0.7249

MESO 1.250 0.307 5.088 0.7557

OV 1.085 0.659 1.785 0.7486

PAAD 0.721 0.348 1.495 0.3791

PCPG

PRAD 0.673 0.282 1.604 0.3716

SARC

SKCM 1.682 0.959 2.949 0.0696

STAD 1.360 0.865 2.139 0.1826

TGCT

THCA

UCEC 1.105 0.642 1.901 0.7182

UCS 2.208 0.872 5.593 0.0947

Table 17: Within- subjects analysis for residues with high mutation frequency in LUAD

OR CLlow CLhigh pvalue

Global 1.400 1.275 1.538 0.0000

ACC

BLCA 1.110 0.591 2.086 0.7452

BRCA 2.102 0.674 6.557 0.2008

CESC 3.952 0.964 16.207 0.0563

COAD 1.700 1.363 2.120 0.0000

DLBC

GBM 56.989 0.024 132782.426 0.3068

HNSC

KICH

KIRC

KIRP 2.730 1.010 7.381 0.0478

LAML 4.266 1.238 14.699 0.0215

LGG

LIHC 4.777 1.103 20.694 0.0365

LUAD 1.112 0.949 1.303 0.1876

LUSC 1.797 0.373 8.644 0.4647

MESO

OV 1.541 0.508 4.668 0.4448

PAAD 1.515 1.191 1.928 0.0007

PCPG

PRAD

SARC

SKCM 2.282 0.472 11.028 0.3048

STAD 2.060 1.130 3.758 0.0184

TGCT 1.917 0.641 5.731 0.2442

THCA UCEC 1.321 0.968 1.801 0.0791

UCS 2.429 0.882 6.686 0.0859

UVM

Table 18: Within- subjects analysis for residues with high mutation frequency in LUSC

OR CLlow CLhigh pvalue

Global 1.108 1.102 1.114 0.0000

ACC

BLCA 1.173 0.934 1.475 0.1702

BRCA 1.256 1.057 1.494 0.0097

CESC 1.781 0.894 3.549 0.1009

COAD 1.182 0.933 1.497 0.1661

DLBC

GBM 1.278 0.565 2.889 0.5562

HNSC 1.096 0.887 1.355 0.3970

KICH

KIRC

KIRP

LAML

LGG 0.913 0.484 1.722 0.7777

LIHC 1.142 0.579 2.253 0.7017

LUAD 0.776 0.588 1.024 0.0733

LUSC 0.916 0.787 1.067 0.2619

MESO

OV 0.895 0.622 1.289 0.5526

PAAD

PCPG

PRAD

SARC

SKCM 1.547 0.524 4.563 0.4292

STAD 1.295 0.846 1.983 0.2346 TGCT 1.340 0.470 3.820 0.5845

THCA

UCEC 1.239 0.837 1.832 0.2838

UCS 1.306 0.636 2.682 0.4667

UVM

Table 19: Within- subjects analysis for residues with high mutation frequency in PRAD

OR CLlow CLhigh pvalue

Global 0.982 0.754 1.279 0.8917

ACC

BLCA

BRCA

CESC

COAD

DLBC

GBM

HNSC

KICH

KIRC

KIRP

LAML

LGG

LIHC

LUAD

LUSC

MESO

OV

PAAD

PCPG

PRAD 0.980 0.753 1.275 0.8780

READ

SARC SKCM

STAD

TGCT

THCA

UCEC

UCS

Table 20: Within- subjects analysis for residues with high mutation frequency in SKCM

OR CLlow CLhigh pvalue

Global 1.642 1.637 1.647 0.0000

ACC

BLCA 1.390 0.760 2.545 0.2852

BRCA

CESC

COAD 1.512 1.250 1.829 0.0000

DLBC

GBM 1.428 0.893 2.284 0.1371

HNSC 1.547 0.672 3.561 0.3047

KICH

KIRC

KIRP 1.675 0.524 5.352 0.3844

LAML 1.208 0.835 1.748 0.3157

LGG 1.482 1.098 2.002 0.0102

LIHC 2.116 0.825 5.426 0.1187

LUAD 1.431 0.974 2.103 0.0681

LUSC 1.007 0.593 1.709 0.9803

MESO

OV 1.084 0.558 2.106 0.8116

PAAD

PCPG

PRAD 1.240 0.513 2.998 0.6330

READ 1.555 0.849 2.848 0.1527 SARC

SKCM 1.334 1.245 1.430 0.0000

STAD 1.093 0.478 2.497 0.8336

TGCT 1.040 0.548 1.972 0.9043

THCA 1.881 1.704 2.076 0.0000

UCEC 1.076 0.646 1.793 0.7789

UCS

UVM

Table 21: Within- subjects analysis for residues with high mutation frequency in STAD

OR CLlow CLhigh pvalue

Global 0.999 0.924 1.080 0.9795

ACC 0.957 0.191 4.798 0.9572

BLCA 0.780 0.567 1.072 0.1258

BRCA 0.697 0.593 0.819 0.0000

CESC 2.626 0.989 6.968 0.0526

COAD 1.171 0.978 1.403 0.0863

DLBC

GBM 1.190 0.716 1.979 0.5018

HNSC 1.022 0.756 1.382 0.8863

KICH

KIRC

KIRP 5.501 1.266 23.897 0.0229

LAML 34.584 0.542 2205.582 0.0947

LGG 0.913 0.688 1.213 0.5311

LIHC 2.583 1.077 6.193 0.0334

LUAD 1.565 1.554 1.576 0.0000

LUSC 0.690 0.374 1.275 0.2362

MESO 1.302 0.218 7.772 0.7723

OV 1.102 0.710 1.710 0.6650

PAAD 1.458 1.067 1.993 0.0180

PCPG PRAD 0.564 0.224 1.420 0.2243

SARC 0.762 0.283 2.051 0.5899

SKCM 2.200 0.875 5.532 0.0939

STAD 1.001 0.774 1.294 0.9940

TGCT 0.969 0.171 5.483 0.9715

THCA

UCEC 0.904 0.685 1.191 0.4720

UCS 0.838 0.474 1.481 0.5430

UVM

Table 22: Within- subjects analysis for residues with high mutation frequency in THCA

OR CLlow CLhigh pvalue

Global 1.363 1.281 1.451 0.0000

ACC

BLCA 0.947 0.425 2.113 0.8944

BRCA

CESC

COAD 1.350 1.071 1.702 0.0112

DLBC

GBM 1.026 0.525 2.004 0.9412

HNSC

KICH

KIRC

KIRP 1.397 0.374 5.223 0.6192

LAML 0.347 0.090 1.335 0.1235

LGG 1.127 0.558 2.277 0.7385

LIHC 2.378 0.484 11.674 0.2861

LUAD 1.267 0.750 2.140 0.3758

LUSC 0.940 0.373 2.370 0.8962

MESO

OV 0.790 0.313 1.992 0.6171 PAAD

PCPG 1.511 0.889 2.569 0.1269

PRAD 0.771 0.305 1.949 0.5823

SARC

SKCM 1.354 1.222 1.500 0.0000

STAD 0.719 0.223 2.316 0.5807

TGCT 0.707 0.281 1.777 0.4609

THCA 1.589 1.423 1.773 0.0000

UCEC 0.905 0.408 2.010 0.8073

UCS

UVM

Table 23: Within- subjects analysis for residues with high mutation frequency in UCEC

OR CLlow CLhigh pvalue

Global 1.288 1.203 1.378 0.0000

ACC

BLCA 1.269 0.818 1.968 0.2881

BRCA 1.180 1.016 1.369 0.0302

CESC 4.522 1.009 20.268 0.0487

COAD 1.507 1.269 1.790 0.0000

DLBC

GBM 1.330 0.771 2.296 0.3057

HNSC 0.994 0.684 1.446 0.9763

KICH

KIRC

KIRP 2.973 1.065 8.301 0.0375

LAML 5.034 1.288 19.671 0.0201

LGG 1.223 0.588 2.546 0.5899

LIHC 3.518 0.986 12.547 0.0525

LUAD 1.561 1.229 1.983 0.0003

LUSC 1.265 0.680 2.355 0.4582 MESO

OV 0.886 0.538 1.459 0.6346

PAAD 1.654 1.360 2.013 0.0000

PCPG

PRAD 0.965 0.464 2.009 0.9252

SARC 0.573 0.189 1.733 0.3241

SKCM 2.500 0.550 11.370 0.2356

STAD 1.287 0.970 1.706 0.0801

TGCT 1.493 0.524 4.255 0.4527

THCA

UCEC 0.965 0.863 1.078 0.5258

UCS 0.881 0.619 1.253 0.4802

UVM

Table 24: The cohort of cancer-associated substitution

mutations used in the present study

Gene Residue Gene Residue Gene Residue Gene Residue

BRAF V600E NRAS Q61L ATM R337C TP53 A159V

IDH1 R132H TP53 Y163C TP53 G245D SMAD4 R361C

PIK3CA H1047R EGFR L858R GNAS R201H PIK3CA R93Q

PIK3CA E545K KRAS G12S ERBB2 V842I FBXW7 R689W

KRAS G12D TP53 M237I IDH2 R172K TP53 P278S

KRAS G12V TP53 R158L CTNNB1 S37C PIK3R1 G376R

TP53 R175H FGFR2 S252W PIK3CA R108H FGFR2 N549K

PIK3CA E542K ERBB3 V104M TP53 H214R ERBB2 L755S

TP53 R273C FBXW7 R505G PIK3CA Q546K CTNNB1 G34R

TP53 R248Q TP53 I195T KRT15 V205I BRAF K601E

NRAS Q61R CTNNB 1 S37F NFE2L2 R34G CTNNB1 S33Y

KRAS G12C PPP2R1A P179R SMAD4 R361H PIK3CA H1047Y

TP53 R273H KRAS Q61H PIK3CA M1043I SF3B1 R625H

TP53 R282W RAC1 P29S TP53 C238Y IDH2 R140Q

TP53 R248W PIK3CA C420R TP53 L194R HRAS Q61K

NRAS Q61K TP53 Y234C TP53 C238F TP53 G245C

KRAS G13D EGFR A289V CTNNB1 S45F TP53 V216M

TP53 Y220C CTNNB 1 S45P TP53 E286K PPP6C R264C

PIK3CA R88Q PIK3CA Q546R TP53 R280K TP53 H193Y

IDH1 R132C BCOR N1459S PIK3CA E545A TP53 R110L

AKT1 E17K TP53 V272M TP53 C141Y TP53 A159P BRAF V600M TP53 S241F TP53 G266V TP53 C242F

PTEN R130Q PIK3CA G118D MAP2K1 P124S FBXW7 R505C

KRAS G12A KRAS A146T TP53 R337C TP53 P250L

TP53 G245S TP53 K132N NFE2L2 D29H TP53 H193L

TP53 H179R CTNNB 1 T41A SF3B1 K700E HRAS G13V

KRAS G12R EGFR G598V TP53 P151S CIC R215W

PTEN R130G TP53 E285K KRAS G13C EP300 D1399N

FBXW7 R465C MB21D2 Q311E IDH1 R132G TP53 P152L

PIK3CA N345K TP53 C176Y CDKN2A P114L KRAS Q61L

TP53 V157F PIK3CA E453K TP53 E271K PIK3CA K111E

ERBB2 S310F TP53 R280T TP53 V173L CTNNB1 T41I

HRAS Q61R TP53 R158H TP53 V173M TP53 S 127F

PIK3CA H1047L TP53 Y205C CDKN2A H83Y SOX 17 S403I

TP53 H193R TP53 Y236C ERBB2 R678Q BRAF G469A

TP53 R249S FBXW7 R479Q NRAS G12D PIK3CA Q546P

TP53 R273L TP53 C275Y CTNNB1 S33C CDKN2A D108Y

FBXW7 R465H TP53 G245V TP53 H179Y PIK3CA Y1021C

TP53 C176F GNAS R201C CTNNB1 S33F TP53 G262V

PIK3CA E726K PPP2R1A R183W MAPK1 E322K NFE2L2 E79Q

DNMT3A R882H SPOP W131G PTEN R173H PIK3CA E545G

CHD4 R975H NRAS Q61H PIK3CA R38H BTBD11 A561V

TP53 G266R MYC S 146L ABCB1 R467W KCND3 S438L

PTEN R173C CTNNB 1 S33P MS4A8 S3L CTNNB1 R587Q

RRAS2 Q72L CTNNB 1 D32Y TP53 R175G CTNNB1 G34V

CTNNB1 D32G SF3B1 R625C MYH2 R1051C PPP2R1A S256F

PIK3CA E81K TP53 P278L NFE2L2 R34P CHD4 R1105W

CTNNB1 G34E FLT3 D835Y KRAS L19F PIK3CA R93W

PIK3CA M1043V MYCN P44L DKK2 R230H GRM5 S406L

TP53 R249G MTOR S2215Y KRAS Q61R ERBB2 V777L

TP53 G266E MAX R60Q GATA3 A395T ACADS R330H

LUM E240K NFE2L2 E82D TP53 A161T PIK3R1 L56V

IDH1 R132S CHD4 R1338I CREBBP R1446C CTNNB1 K335I

HRAS G13R NFE2L2 E79K TP53 G244C PIK3CA E542A

TP53 C135Y NRAS G13D TP53 R249M HRAS G12D

TP53 R213Q RAC1 A159V TP53 R273S RHOA E40Q

TP53 P278A GRXCR1 R262Q TP53 K132R PIK3CA G1049R

TP53 C275F TP53 I195F TP53 P151H EGFR L861Q

TP53 D281Y ZNF117 R185I CASP8 R233W CSMD3 R100Q

CDKN2A D84N EGFR L62R TP53 S215R SPOP F133V

PIK3R1 N564D FGFR2 C382R TP53 P278R LHFPL1 R69C

PTEN G132D PIK3CA E545Q TP53 R280G CSMD3 R334Q

TP53 G279E RHOA E47K MAP3K1 S1330L KRAS K117N

TP53 R248L PIK3CA V344M FBXW7 S582L EGFR R108K

TP53 R337L EGFR R222C TP53 P278T EGFR V774M

TP53 G154V TP53 H193P TP53 G105C CAPRIN2 E13K

SMARCA4 R1192C CTNNB 1 D32V TP53 Q331H TP53 D281E ARID2 S297F PTEN C136R DNMT3A R882C PTEN P246L

TP53 G244S TP53 S241Y TP53 D259Y TP53 LI 30V

TP53 S241C TP53 Y163H TP53 R156P SMARCA4 T910M

TP53 G244D SMARCA4 R1192H SF3B1 E902K FUBP1 R430C

PIK3CA G106V TP53 K132E EGFR R252C SMARCA4 G1232S

HRAS Q61L ARID2 R314C KCNQ5 G273E TP53 E224D

HRAS G12S TP53 V274F CSMD3 P258S TP53 E286G

MBOAT2 R43Q TP53 N239D SPOP F133L FBXW7 G423V

TP53 R283P TP53 P190L ZNF117 R157I CTCF R377C

NRAS G13R PIK3CA R38C CHD4 R1162W TP53 R267W

BRAF D594N MTOR E1799K PTPN11 G503V CREBBP R1446H

CTNNB1 D32N TP53 Q136E MFGE8 D170N TP53 C135F

BRAF G466V INTS7 R106I NFE2L2 G31A CASP8 R68Q

TUSC3 R334C TP53 R175C KRAS Q61K BRAF N581S

CDKN2A P48L PGM5 T442M APC S2307L SMAD2 R120Q

CTNNB1 S37A BRAF G469V TP53 D281V ATM R337H

EGFR E114K NSMCE1 D244N TP53 V216L TP53 G334V

MYD88 L265P COL4A2 R1410Q RASA1 R194C TP53 S215I

MYH2 R1388H ABCB1 R41C KMT2C R56Q PTEN D92E

NFE2L2 D29G TP53 N239S MAP2K4 S184L CHD8 F668L

NFE2L2 D29N NOTCH 1 A465T PTEN G165E FBXW7 R14Q

BRAF G466E CIC R202W MY06 R928H EP300 R580Q

NFE2L2 D29Y PIK3CA K111N TP53 G105V DNMT3A R736H

MYH2 E1421K MFGE8 E168K TGFBR2 R528H CIC R1515C

NFE2L2 L30F KCNQ5 R426C SMAD4 D537H TP53 S 106R

PIK3CA E453Q PIK3CA G1007R TP53 P151T TP53 H179N

RIT1 M90I TP53 F270S TP53 C135W TP53 Y220S

TRIM23 R289Q TP53 R280I BCOR E1076K PTEN R130P

TP53 R213L TP53 L265P CDKN2A D108N ZC3H13 R1261Q

MAP3K1 R306H TP53 T155N SMARCA4 E920K CHD8 R1092C

LZTR1 G248R TP53 H179D NOTCH 1 E455K FAT1 K2413N

MAX H28R TP53 T155P KEAP1 G480W ZFP36L2 D240N

KEAP1 R470C TP53 R267P TP53 E258K TP53 E286Q

TP53 C141W TP53 A161S TP53 Y205S CIC R215Q

FAT1 E4454K PBRM1 R876C TP53 D281H NOTCH 1 G310R

ERBB3 D297Y ARID 1 A G2087R TGFBR2 R528C TP53 C242S

PPP2R1A R183Q TP53 D259V TRIP 12 A761V PTEN H93R

CTNNB1 H36P PTEN R130L NF1 R1306Q TP53 V272G

LSM11 R180W CIC R201W PTEN G129E PTEN R142W

ABCB1 R404Q TP53 C277F TP53 C242Y ARHGAP35 V1317M

PTPN11 T468M ERBB2 D769Y TP53 M246I TP53 F109C

ERBB3 E332K PIK3CA E365K KEAP1 V271L CDKN2A M53I

EGFR A289T INTS7 R940C CTCF S354F TRIP 12 S 1840L

EGFR A289D CSMD3 R3127Q TP53 Y126C PTEN S 170N

ERBB3 E928G NFE2L2 R34Q PIK3R1 K567E TP53 L130F

CTNNB1 I35S EP300 A1629V NF2 R418C TP53 N131I CTNNB1 S45Y PIK3CA V344G ATRX R781Q TP53 T211I

PIK3CA D350G MAP2K4 R134W NF1 R1276Q STAG2 V465F

NRAS G12C PIK3CA N1044K SETD2 R2109Q TP53 P151R

MYH2 E1382K TP53 R273P TP53 H193N ARID2 R285Q

RAC1 P29L CIC R1512H TP53 S127Y CDK12 R890H

PIK3CA E600K NF1 R1870Q SMARCA4 R885C TP53 P177R

PIK3CA C901F TP53 G199V TP53 F134L RUNX1 R177Q

CSMD3 S1090Y KANSL1 A7T TP53 I195N FAT1 R881H

ERBB3 V104L TGFBR2 E519K FBXW7 Y545C TAF1 R843W

MYCN R302C SPOP F102V RRAS2 A70T CRIPAK R430C

CSMD3 R683C TUSC3 F66V KMT2D R5351L TP53 L257Q

CSMD3 R1529H BTBD11 K1003T KMT2D R5432Q EP300 Y1414C

MYH2 D756N PIK3CA E542G CDKN2A D84Y TP53 V218G

MYH2 R793Q KCNQ5 R909Q CHD8 R578H CREBBP P2094L

HRAS G13D BRAF V600G ARID IB P1411Q DDX3X E285K

ERBB3 M91I CTNNB 1 D32H CCAR1 R549C TP53 Y205H

MAP2K1 P124L ERBB2 S310Y TP53 V143M APC E136K

BRAF G469R GRXCR1 R19Q TP53 C176S TP53 R181H

SPOP F133C UBQLN2 S 196L CHD8 R1889H PTEN H123Y

SF3B1 R425Q MYF5 E104K EP300 C1164Y PIK3R1 G353W

KCNQ5 T693M PIK3CA M1004I KEAP1 R554Q PTEN C136F

PRKCI R480C FAM8A1 E94K ELF3 E262Q APC S2601R

CSMD3 G1941E EZH2 E740K PBRM1 M1487I KMT2C H367Y

MED 12 L1224F HRAS K117N ARHGAP35 R1147H CASP8 S99F

CSMD3 P184S GNAS R356C KANSL1 R891L TP53 V157D

DCLK1 R60C CTCF R377H EP300 S964Y ATRX L14F

ERBB2 I767M ATM S2812Y PTEN C124S ATM R2691C

METTL14 R298P PGM5 T476M TP53 V172F NCOR1 G1801V

EGFR T263P PTEN P38S KMT2B E324K ATM R23Q

PIK3CA D939G SPOP M117V NCOR1 P1081L TP53 V143G

FLT3 R387Q TRIM23 N92I KMT2C G3665A ACVR2A R400H

MAGI2 LI 14V CAPRIN2 R215Q CASP8 I333M TET2 A347V

LUM E187K MAP2K1 K57N TRIP 12 E1803K NSD1 A2144T

SULT1C4 R85Q LZTR1 F243L CHD8 S1632L MLLT4 S 15 ION

MYH2 E878K FGFR2 M537I ELF3 P30S STK11 G242W

ERBB3 A245V ZNF799 R297Q THRAP3 R504W KMT2C F357L

DKK2 E226K PIK3CA E39K TP53 Y220H SETD2 R1625C

MYF5 E27K DCLK1 R45C KMT2C W430C APC S 1400L

KRAS A59T ABCB1 S696F KMT2B R1597Q SETD2 H1629Y

GRXCR1 R190Q CSMD3 G1195W PIK3R1 L573P CHD8 N2372H

EP300 R1627W HIST1H2BF E77K KMT2C D4425Y KANSL1 R1066H

CAPRIN2 E905K PIK3CA E418K SETD2 R2077Q ASXL1 A611T

MAP2K1 E203K BRAF S467L TCF12 R589H NF1 L844F

IDH1 P33S PIK3CA R357Q TP53 A161D SMARCA4 R381Q

CHD4 R1105Q PIK3CA E970K KEAP1 V155F VHL H115N

PIK3CA N345T MYC P59L FAT1 R1627Q NOTCH2 R1726C MYH2 R1506Q ERBB3 R475W NF1 P1990Q KANSL1 E647K

DCLK1 A18V TAF1 R539Q PBRM1 R1096C CDKN1A D33N

MYH2 R1668W TUSC3 R82Q FBXW7 R479G KMT2D R5214C

MFAP5 R153C MYH2 E347K TP53 V274G NOTCH 1 A1918T

ATM G1663C TP53 D281N TP53 R158G IDH1 R132L

ATM L1408I MEN1 W428L RASA1 R194H NFE2L2 G81C

CDH1 E243K ZC3H13 R453Q TP53 I255F FGFR2 K659N

PTEN G129V USP28 R141C TP53 L194H FGFR2 K659E

TP53 L111P VHL N131K TP53 R248P MS4A8 A183V

ATM N2875S TP53 R196P VHL R205C PPP2R1A A273V

SMARCBl R374W BAP1 V99M USP28 P235L JAKMIP2 D338N

LARP4B E486K SETD2 R1335C ARID IB A987V EGFR T363I

RNF43 S607L TP53 K120E GATA3 S407L CSMD3 L2481I

TP53 H179L ARID IB D1734E TP53 A276D CSMD3 P3166H

NCOR1 R330W CDK12 S475Y WT1 R462L CTNNB1 N387K

MY06 A91T PTEN T277I SMARCA4 E882K CSMD3 E531K

KMT2C A135T NOTCH 1 R353C ACVR2A R478I SPOP W131C

STAG2 A300V TP53 I232T TP53 F134V ZNF844 D436N

KDM6A R1255W CDK12 R1008W VHL L128H JAKMIP2 A334T

TP53 V274D KMT2D R5214H VHL V74D KRAS A59G

KANSL1 S808L CREBBP A259T KMT2B H1226Y RIT1 R86L

GATA3 M293K COL4A2 R1651C TP53 S215G EGFR S645C

CASP8 R248W THRAP3 R723H TBX3 E275K CHD4 R877W

NCOR1 R2214C ATM R3008H TP53 M237V MYH2 R1181C

FBXW7 R505L TP53 I232S ARID 1 A R1262C MTOR P2158Q

TP53 T125M APC G1767C CREBBP W1472C ALK R292C

GATA3 R305Q TP53 R280S FAT1 T3356M ARF4 R99I

SETD2 R2024Q NCOR1 K482N CDKN2A D84G SF3B1 E862K

TP53 A138V TP53 E271V TP53 R249W MYH2 R1787Q

TP53 S215N TP53 C141G APC S1696N KCND3 V94M

TP53 E285V KMT2B R2332C TP53 Y126D CTNNB1 A391S

ELF3 R126Q TP53 E258D ACVR2A E214K COL5A2 R1453W

TP53 K139N APC S2026Y TP53 Y126N IDH2 R172M

ZC3H18 R520C TP53 E171K CDKN2A P81L ABCB 1 R489C

FBXW7 R658Q ARID2 P1590Q SMAD4 D537E NFE2L2 T80K

TP53 K164E PTEN C71Y TP53 C176W KCNQ5 A704V

TP53 C135R CCAR1 R383H FAT1 R1506C KCNQ5 R187Q

ARHGAP35 R863C TP53 P27S PTEN C136Y TAF1 A445V

MY06 R1169H HLA-A R243W FAT1 A2289V OR5I1 S95F

TP53 G245R COL4A2 P123Q PTEN G165R MYH2 E868K

DDX3X R263H CDH1 R732Q ARID2 V179I TAF1 A1287V

CDH1 D254Y RERE K176N GATA3 M442I PTN E130K

MEN1 R337H TP53 P151A ERBB3 R103H LUM G248E

TP53 L265R VHL S H IN KMT2B R2567C ABCB 1 R41H

RB I R451C RPL22 R113C PTPN11 D146Y PTPN11 F71L

TUSC3 H189N MYH2 S337R FAM8A1 E94Q MS4A8 A91V COL5A2 A592V CHD4 R572Q SPOP Y87C GRXCR1 G91S

MAGI2 L450M GNAS R389C TAF1 R1442L MBOAT2 E147K

HRAS G13C MAGI2 L603R CSMD3 T2652M UBQLN2 S62L

BTBD11 R421C FGFR2 R210Q MYH2 R709H ABCB 1 R286I

MYH2 P228L GRM5 R128C SF3B1 V1192A TAF1 R342C

CSMD3 G2578E EGFR S229C PPP6C E180K PPP2R1A R258H

MYF5 R93Q CHD4 R1177H ALK G452W TBX18 S206L

UBQLN2 R309S CSMD3 R1946C GRXCR1 R191Q AKT1 L52R

TBX18 H401Y CSMD3 R2168Q ABCB1 E468K PPP2R1A W257L

JAKMIP2 E155K MYCN R373Q KCNQ5 S280L CSMD3 M729I

PTN E68D CSMD3 E171K KCND3 E626K MTOR T1977R

HGF R178Q CHD4 F1112L RHOA F106L MFGE8 A280V

CSMD3 G165R GRM5 R834C EZH2 R679H GRID1 R221W

KCND3 T231M SPOP R121Q PIK3CA D725G GRID1 R631H

KCNQ5 E455K NFE2L2 G81V CSMD3 L2370I BTBD11 G699E

XYLT1 E804K MBOAT2 R170C SF3B1 K666T COL5A2 D1241N

SF3B1 G740E PIK3CA E542V MTOR I2500F CTNNB1 R515Q

PIK3CA H1047Q PIK3CA R115L MTOR I2500M METTL14 R228Q

KRTAP4-11 R41H FGFR2 E777K SMAD2 R321Q RHOA E172K

CSMD3 R2231Q MTOR R2152C TP53 M246V KRT15 G232S

PLK2 F363L NFE2L2 W24R EP300 E1514K PIK3CA C604R

GNAS A109T SPOP E50K CDH1 R598Q ERBB2 G222C

GNAS R160C CSMD3 R3025C TP53 F113C CSMD3 G742E

CAPRIN2 R727Q COL5A2 D1414N SMARCA4 R1243W PTPN11 Q510L

PIK3CA P539R MYF5 R129C CTCF P378L SPOP E47K

PDE7B El IK CTNNB 1 S33A DDX3X R528C CSMD3 D285N

TRIM48 M17I PIK3CA C378F SMARCA4 Al l 86V ABCB 1 R1085W

PIK3CA P471L GRXCR1 R14Q DNMT3A R659H PTPN11 R512Q

DCLK1 R93Q PTPN11 R498W PTEN R14M RHOA R5W

LUM R330C CDKN2A E88K TP53 P278H RHOA Y42C

ERBB3 T355I MYH2 S 1741F KMT2C R4693Q MYH2 E900K

ERBB3 A232V MED 12 E79D EGFR R252P RHOA G62E

TRIM23 R549Q OR5I1 R231C PTEN G36R PIK3CA Ml 004V

SF3B1 R957Q MAGI2 P876S SMAD2 S276L BRAF H725Y

TAF1 R1221Q JAKMIP2 R283I FBXW7 R505H TRIM48 E28K

PPP2R1A S256Y DCLK1 R80W TGFBR2 D446N KRT15 E455K

PIK3CA D350N EGFR S752F GRXCR1 R147C GRM5 T906P

MED 12 D23Y ABCB1 G610E MAGI2 D843N GRID1 S388L

CHD4 R1068C PRKCI R278C OR5I1 L294F CSMD3 R395Q

PIK3CA T1025A TUSC3 R170I TAF1 R1163H HGF E199K

FGFR2 R664W EGFR H304Y NFE2L2 W24C XYLT1 R754H

ABCB1 R958Q PTPN11 G409W OR5I1 S89L TP53 I254S

MB21D2 R288W MYH2 M858I CSMD3 E2280K

MTOR F1888L CSMD3 R3551C XYLT1 R754C

PIK3CA G364R PIK3CA D186H PIK3CA P104L Table 25: The Cohort of Cancer- Associated In- Frame Insertion and Deletion Mutations used in the Present Study

Example 5: Materials and Methods

Peptide Binding Affinity

Peptide binding affinity predictions for peptides of length 8-11 were obtained for various HLA alleles using the NetMHCPan-3.0 tool, downloaded from the Center for Biological Sequence Analysis on March 21, 2016 (Nielsen and Andreatta, Genome Med., 2016, 8, 33). NetMHCPan-3.0 returns IC₅₀ scores and corresponding allele-based ranks, and peptides with rank < 2 and < 0.5 are considered to be weak and strong binders respectively (Nielsen and Andreatta, Genome Med., 2016, 8, 33). Allele-based ranks were used to represent peptide binding affinity.

Residue Presentation Scoring Schemes

To create a residue-centric presentation score, allele-based ranks for the set of kmers of length 8-11 incorporating the residue of interest were evaluated, resulting in 38 peptides for single amino acid positions (Figure 2A). Insertion and deletion mutations were modeled by the total number of 8- 11-mer peptides differing from the native sequence (Figure 3J). Several approaches to combine the HLA allele- specific ranks for residue/mutation-derived peptides into a single score representing the likelihood of being presented by MHC-I were evaluated:

Summation (rank < 2): The summation score is the total number out of 38 possible peptides that had rank < 2. This scoring system results in an integer value from 0 to 38, with residues of 0 being very unlikely to be presented and higher numbers being more likely to be presented.

Summation (rank < 0.5): The summation score is the total number out of 38 possible peptides that had rank < 0.5. This scoring system results in an integer value from 0 to 38, with residues of 0 being very unlikely to be presented and higher numbers being more likely to be presented.

Best Rank: The best rank score is the lowest rank of all of the 38 peptides. Best Rank with cleavage: The best rank score was modified by first filtering the 38 possible peptides to remove those unlikely to be generated by proteasomal cleavage as predicted by the NetChop tool (Kesxmir et al., Protein Eng., 2002, 15, 287-296). Netchop relies on a neural network trained on observed MHC-I ligands cleaved by the human proteasome and returns a cleavage score ranging between 0 and 1 for the C terminus of each amino acid. A threshold of 0.5 is recommended by the NetChop software manual to designate peptides as likely to be generated by proteasomal cleavage. Thus, only the peptides receiving a cleavage score greater than 0.5 just prior to the first residue and just after the last residue were retained. The best rank with cleavage score is the lowest rank of the remaining peptides.

MS-based Presentation Score Validation

MS data was acquired from Abelin et al. (Abelin et al., Mass Immunity, 2017, 46, 315-

326) that catalogs peptides observed in complex with MHC-I on the cell surface across 16 HLA alleles, with between 923 and 3609 peptides observed bound to each. These data were combined with a set of random peptides to construct a benchmark for evaluating the performance of scoring schemes for identifying residues presented on the cell surface as follows:

Converting MS peptide data to residues: The Abelin et al. MS data provides peptide observed in complex with the MHC-I, whereas the presentation score is residue-centric. For each peptide in the MS data, the residue at the center (or one residue before the center in the case of peptides of even length) was selected as the residue for calculating the residue-centric presentation score.

Selection of background peptides: 3000 residues at random were selected from the Ensembl human protein database (Release 89) (Aken et al., Nucleic Acids Res., 2017, 45 (Dl), D635-D642) to ensure balanced representation of MS-bound and random residues. Since the majority of residues are expected not be presented by the MHC (Nielsen and Andreatta, Genome Med., 2016, 8, 33), the randomly selected residues may represent a reasonable approximation of a true negative set of residues that would not be presented on the cell surface.

Scoring benchmark set residues: Presentation scores were calculated with each scoring scheme for all of the selected residues from the Abelin et al. data and the 3000 random residues against each of the 16 HLA alleles.

Evaluating scoring scheme performance using the benchmark: For each scoring scheme, scores were pooled across the 16 alleles. The distribution of scores for the MS-observed residues was compared to the distribution of scores for the random residues for each score formulation (Figure 3). For the best rank, residues were grouped at score intervals of 0.25 and for the summation, residues were grouped at integer values between 0 and 38. At each scoring interval, the fraction of MS-observed residues falling was divided into the interval by the fraction of random residues falling into that interval.

Visualizing score performance with Receiver Operating Characteristic (ROC) Curves: ROC curves (Figures 3J and 3K) were plotted and compared for each score formulation by calculating the True Positive Rate (% of observed MS residues predicted to bind at a given threshold) and the False Positive Rate (% of random residues predicted to bind at a given threshold) across a range of thresholds as follows:

Summation (rank < 2): 0 through 38 by increments of 1

Summation (rank < 0.5): 0 through 38 by increments of 1

Best Rank: 0 through 100 by increments of 0.1

Best Rank with Cleavage: 0 through 100 by increments of 0.1

Overall score performance was assessed using the area under the curve (AUC) statistic. The best rank presentation score was selected for all subsequent analyses.

MS-based Evaluation of the Presentation of Mutated Residues Present in Cancer Cell Lines

The list of somatic mutations present in the genomes of five cancer cell lines (SKOV3, A2780, OV90, HeLa and A375) was acquired from the Cosmic Cell Lines Project (Forbes et al., Nucleic Acids Res., 2015, 43, D805-D811). The mutations were restricted to the missense mutations observed in genes present in the Ensembl protein database and removed all known common germline variants reported by the Exome Variant Server. Furthermore, the cell line expression data from the Genomics of Drug Sensitivity Center was used to exclude mutations observed in genes that are expressed in the lowest quantile of the specific cell line. For each of these mutated residues, the presentation score for HLA-A*02:01 , an allele which had previously been studied in these cell lines, was calculated (Method Details). Then the database of MS- derived peptides from each cell line was searched to determine whether the mutation was observed in complex with the MHC-I on the cell surface. Since the database only contains peptides mapping to the consensus human proteome reference, the native versions of the peptides were searched. As long as the mutation does not disrupt the peptide binding motif, the mutated version should still be presented by the MHC allele which can be determined using MHC binding predictions in IEDB (Marsh, S.G.E., Parham, P., and Barber, L.D., 1999, The HLA FactsBook, Academic Press). For each cell line, the fraction of mutations predicted to be strong and weak binders that should be presented based on the corresponding native sequences observed in the MS data was evaluated (see, Tables 1A, IB, 2A, 2B, 3A, 3B, 4A, 4B, 5A, and 5B).

Various modifications of the described subject matter, in addition to those described herein, will be apparent to those skilled in the art from the foregoing description. Such modifications are also intended to fall within the scope of the appended claims. Each reference (including, but not limited to, journal articles, U.S. and non-U.S. patents, patent application publications, international patent application publications, gene bank accession numbers, and the like) cited in the present application is incorporated herein by reference in its entirety.

Claims

What Is Claimed Is:

1. A computer implemented method for determining whether a subject is at risk of having or developing a cancer or an autoimmune disease, the method comprising:

a) geno typing the subject's major histocompatibility complex class I (MHC-I); and b) scoring the ability of the subject's MHC-I to present a mutant cancer-associated peptide or an autoimmune-associated peptide based upon a library of known cancer-associated peptide sequences or autoimmune-associated peptide sequences derived from subjects, wherein the produced score is the MHC-I presentation score;

wherein:

i) if the subject is a poor MHC-I presenter of specific mutant cancer- associated peptides, the subject has an increased likelihood of having or developing the cancer for which the specific mutant cancer- associated peptides are associated;

ii) if the subject is a good MHC-I presenter of specific mutant cancer- associated peptides, the subject has a decreased likelihood of having or developing the cancer for which the specific mutant cancer- associated peptides are associated;

iii) if the subject is a poor MHC-I presenter of specific autoimmune- associated peptides, the subject has a decreased likelihood of having or developing autoimmunity for which the specific autoimmune-associated peptides are associated; or

iv) if the subject is a good MHC-I presenter of specific autoimmune- associated peptides, the subject has an increased likelihood of having or developing autoimmunity for which the specific autoimmune-associated peptides are associated.

2. The method according to claim 1, further comprising:

c) determining whether a liquid biopsy sample obtained from the subject comprises DNA encoding a mutant cancer-associated peptide or an autoimmune-associated peptide based upon a library of cancer-associated mutations or autoimmune disease peptides obtained from subjects.

3. The method of claim 2, wherein the liquid biopsy sample is blood, saliva, urine, or other body fluid.

4. The method according to claim 2, wherein the library of cancer-associated mutations is obtained by whole genome sequencing of subjects.

5. The method according to claim 2, wherein the library of autoimmune disease peptides is obtained by whole exome sequencing of subjects.

6. The method according to any one of claims 1 to 5, wherein the step of scoring the ability of the subject' s MHC-I to present a mutant cancer-associated peptide or an autoimmune- associated peptide comprises using a predicted MHC-I affinity for a given mutation xy, where x is the MHC-I affinity of subject i for mutation j to fit a mixed-effects logistic regression model that follows a model equation obtained from a large dataset of subjects from which MHC-I genotypes and presence of peptides of interest can be obtained:

logit (P(yi_j = 1 I xy)) = η,- + ylog( y)

wherein:

yt_j is a binary mutation matrix yy £ {0, 1 } indicating whether a subject i has a mutation j; xy is a binary mutation matrix indicating predicted MHC-I binding affinity of subject i having mutation j;

γ measures the effect of the log-affinities on the mutation probability; and

r ~ N(0, φ_Ά) are random effects capturing residue-specific effects,

wherein the model tests the null hypothesis that γ = 0 and calculates odds ratios for MHC-I affinity of a mutation and presence of a cancer or autoimmune disease.

7. The method according to claim 6, wherein the predicted MHC-I affinity for a given mutation xy is a Subject Harmonic-mean Best Rank (PHBR) score.

8. The method according to claim 7, wherein the PHBR score is obtained by aggregating MHC-I binding affinities of a set of mutant cancer-associated peptides or a set of autoimmune- associated peptides by referring to a pre-determined dataset of peptides binding to MHC-I molecules encoded by at least 16 different HLA alleles.

9. The method according to claim 6, wherein the mutant cancer-associated peptide or the autoimmune-associated peptide contains an amino acid substitution, and wherein the set of peptides consists of at least 38 of all possible 8-, 9-, 10- and 11-amino acid long peptides incorporating the substitution at every position along the peptide.

10. The method according to claim 8, wherein the mutant cancer-associated peptide or the autoimmune-associated peptide contains an amino acid insertion or deletion, and wherein the set of peptides consists of at least 38 of all possible 8-, 9-, 10- and 11-amino acid long peptides incorporating the insertion or deletion at every position along the peptide.

11. The method according to any one of claims 1 to 10, wherein the cancer is an adrenocortical carcinoma (ACC), a bladder urothelial carcinoma (BLCA), a breast invasive carcinoma (BRCA), a cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), a colon adenocarcinoma (CO AD), a lymphoid neoplasm diffuse large B-cell lymphoma (DLBC), a glioblastoma multiforme (GBM), a head and neck squamous cell carcinoma (HNSC), a kidney chromophobe (KICH), a kidney renal clear cell carcinoma (KIRC), a kidney renal papillary cell carcinoma (KIRP), an acute myeloid leukemia (LAML), a brain lower grade glioma (LGG), a liver hepatocellular carcinoma (LIHC), a lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), a mesothelioma (MESO), an ovarian serous

cystadenocarcinoma (OV), a pancreatic adenocarcinoma (PAAD), a pheochromocytoma and paraganglioma (PCPG), a prostate adenocarcinoma (PRAD), a rectum adenocarcinoma (READ), a sarcoma (SARC), a skin cutaneous melanoma (SKCM), a stomach adenocarcinoma (ST AD), a testicular germ cell tumors (TGCT), a thyroid carcinoma (THCA), a uterine corpus endometrial carcinoma (UCEC), a uterine carcinosarcoma (UCS), or a uveal melanoma (UVM).

12. The method according to any one of claims 8 to 11, wherein the set of mutant cancer- associated peptides comprises any one or more of B-Raf Proto-Oncogene (BRAF) V600E mutation, Phosphatidylinositol-4,5-Bisphosphate 3-Kinase Catalytic Subunit Alpha (PIK3CA) E545K mutation, PIK3CA E542K mutation, PIK3CA H1047R mutation, Kirsten Rat Sarcoma Viral Oncogene Homolog (KRAS) G12D mutation, KRAS G13D mutation, KRAS G12V mutation, KRAS A146T mutation, TP53 R175H mutation, TP53 H179R mutation, TP53 mutation, TP53 R248Q mutation, TP53 R273C mutation, TP53 R273H mutation, TP53 R282W mutation, Keratin Associated Protein 4-11 (KRTAP4-11) L161V mutation, Mab-21 Domain Containing 2 (MB21D2) Q31 IE, mutation, HLA-A Q78R mutation, Harvey Rat Sarcoma Viral Oncogene Homolog (HRAS) G13V mutation, Isocitrate Dehydrogenase (NADP(+)) 1 (IDHl) R132H mutation, IDHl R132C mutation, IDHl R132G mutation, IDH2 R172K mutation, IDHl R132S mutation, Capicua Transcriptional Repressor (CIC) R215W mutation,

Phosphoglucomutase 5 (PGM5) I98V mutation, Tripartite Motif Containing 48 (TRIM48) Y192H mutation, and F-Box And WD Repeat Domain Containing 7 (FBXW7) R465C mutation, wherein the presence of any one of these mutations indicates the presence of or increased risk of developing breast invasive carcinoma.

13. The method according to any one of claims 8 to 11, wherein the set of mutant cancer- associated peptides comprises any one or more of BRAF V600E mutation, Neuroblastoma RAS Viral Oncogene Homolog (NRAS) Q61R mutation, NRAS Q61K mutation, NRAS Q61L mutation, IDHl R132S mutation, Mitogen- Activated Protein Kinase Kinase 1 (MAP2K1) P124S mutation, Rac Family Small GTPase 1 (RAC1) P29S mutation, Protein Phosphatase 6 Catalytic Subunit (PPP6C) R301C mutation, Cyclin Dependent Kinase Inhibitor 2A (CDKN2A) P114L mutation, Keratin Associated Protein 4-11 (KRTAP4-11) L16 IV mutation, KRTAP4-11 M93V mutation, HRAS Q61R mutation, HLA-A Q78R mutation, Zinc Finger Protein 799 (ZNF799) E589G mutation, Zinc Finger Protein 844 (ZNF844) R447P mutation, and RNA Binding Motif Protein 10 (RBM10) E184D mutation, wherein the presence of any one of these mutations indicates the presence of or increased risk of developing colon adenocarcinoma.

14. The method according to any one of claims 8 to 11, wherein the set of mutant cancer- associated peptides comprises any one or more of IDH1 R132H mutation, IDH1 R132C mutation, IDH1 R132G mutation, IDH1 R132S mutation, IDH2 R172K mutation, TP53 H179R mutation, TP53 R273C mutation, TP53 R273H mutation, CIC R215W mutation, and HLA-A Q78R mutation, wherein the presence of any one of these mutations indicates the presence of or increased risk of developing head and neck squamous cell carcinoma.

15. The method according to any one of claims 8 to 11, wherein the set of mutant cancer- associated peptides comprises any one or more of IDH1 R132H mutation, IDH1 R132C mutation, IDH1 R132G mutation, IDH1 R132S mutation, IDH2 R172K mutation, TP53 H179R mutation, TP53 R273C mutation, TP53 R273H mutation, CIC R215W mutation, and HLA-A Q78R mutation, wherein the presence of any one of these mutations indicates the presence of or increased risk of developing brain lower grade glioma.

16. The method according to any one of claims 8 to 11, wherein the set of mutant cancer- associated peptides comprises any one or more of BRAF V600E mutation, PIK3CA E545K mutation, KRAS G12D mutation, KRAS G13D mutation, KRAS A146T mutation, TP53 R175H mutation, KRAS G12V mutation, TP53 R248Q mutation, TP53 R273C mutation TP53 R273H mutation, TP53 R282W mutation, PGM5 I98V mutation, TRIM48 Y192H mutation, PIK3CA E545K mutation, KRAS G13D mutation, PIK3CA H1047R mutation, and FBXW7 R465C mutation, wherein the presence of any one of these mutations indicates the presence of or increased risk of developing lung adenocarcinoma.

17. The method according to any one of claims 8 to 11, wherein the set of mutant cancer- associated peptides comprises any one or more of PIK3CA H1047R mutation, PIK3CA E545K mutation, PIK3CA E542K mutation, TP53 R175H mutation, PIK3CA N345K mutation, AKT Serine/Threonine Kinase 1 (AKT1) E17K mutation, Splicing Factor 3b Subunit 1 (SF3B1) K700E mutation, and PIK3CA H1047L mutation, wherein the presence of any one of these mutations indicates the presence of or increased risk of developing lung squamous cell carcinoma.

18. The method according to any one of claims 8 to 11, wherein the set of mutant cancer- associated peptides comprises any one or more of BRAF V600E mutation, PIK3CA E545K mutation, KRAS G12D mutation, KRAS G13D mutation, KRAS A146T mutation, KRAS G12V mutation, TP53 R175H mutation, TP53 H179R mutation, TP53 R248Q mutation TP53 R273C mutation, TP53 R273H mutation, TP53 R282W mutation, IDH1 R132H mutation, IDH1 R132C mutation, IDH1 R132G mutation, IDH1 R132S mutation, IDH2 R172K mutation, CIC R215W mutation, or HLA-A Q78R mutation, NRAS Q61R mutation, NRAS Q61K mutation, NRAS Q61L mutation, MAP2K1 P124S mutation, RAC1 P29S mutation, PPP6C R301C mutation, CDKN2A P114L mutation, KRTAP4-11 L161V mutation, KRTAP4-11 M93V mutation, HRAS Q61R mutation, ZNF799 E589G mutation, ZNF844 R447P mutation, and RBM10 E184D mutation, wherein the presence of any one of these mutations indicates the presence of or increased risk of developing skin cutaneous melanoma.

19. The method according to any one of claims 8 to 11, wherein the set of mutant cancer- associated peptides comprises any one or more of KRAS G12C mutation, KRAS G12V mutation, Epidermal Growth Factor Receptor (EGFR) L858R mutation, KRAS G12D mutation, KRAS G12A mutation, U2 Small Nuclear RNA Auxiliary Factor 1 (U2AF1) S34F mutation, KRTAP4-11 L161V mutation, KRTAP4-11 R121K mutation, Eukaryotic Translation Elongation Factor 1 Beta 2 (EEF1B2) R42H mutation, and KRTAP4-11 M93V mutation, wherein the presence of any one of these mutations indicates the presence of or increased risk of developing stomach adenocarcinoma.

20. The method according to any one of claims 8 to 11 , wherein the set of mutant cancer- associated peptides comprises any one or more of BRAF V600E mutation, PIK3CA E545K mutation, KRAS G12D mutation, KRAS G13D mutation, TP53 R175H mutation, KRAS G12V mutation, TP53 R248Q mutation, KRAS A146T mutation, TP53 R273H mutation, HRAS Q61R mutation, HLA-A Q78R mutation, TP53 R282W mutation, NRAS Q61R mutation, NRAS Q61K mutation, IDH1 R132C mutation, MAP2K1 P124S mutation, RAC1 P29S mutation, NRAS Q61L mutation, PPP6C R301C mutation, CDKN2A P114L mutation, KRTAP4-11 L161V mutation, KRTAP4-11 M93V mutation, ZNF799 E589G mutation, ZNF844 R447P mutation, and RBM10 E184D mutation, wherein the presence of any one of these mutations indicates the presence of or increased risk of developing thyroid carcinoma.

21. The method according to any one of claims 8 to 11, wherein the set of mutant cancer- associated peptides comprises any one or more of BRAF V600E mutation, PIK3CA H1047R mutation, PIK3CA E545K mutation, PIK3CA E542K mutation, TP53 R175H mutation, PIK3CA N345K mutation, AKT Serine/Threonine Kinase 1 (AKT1) E17K mutation, Splicing Factor 3b Subunit 1 (SF3B1) K700E mutation, KRAS G12C mutation, KRAS G12V mutation, Epidermal Growth Factor Receptor (EGFR) L858R mutation, KRAS G12D mutation, KRAS G12A mutation, KRAS G12V mutation, KRAS G13D mutation, TP53 R175H mutation, TP53 R248Q mutation, KRAS A146T mutation, TP53 R273H mutation, TP53 R282W mutation, U2 Small Nuclear RNA Auxiliary Factor 1 (U2AF1) S34F mutation, KRTAP4-11 L161V mutation, KRTAP4-11 R121 K mutation, Eukaryotic Translation Elongation Factor 1 Beta 2 (EEF1B2) R42H mutation, and KRTAP4-11 M93V mutation, wherein the presence of any one of these mutations indicates the presence of or increased risk of developing uterine corpus endometrial carcinoma.

22. A computing system for determining whether a subject is at risk of having or developing a cancer or an autoimmune disease, the system comprising:

a) a communication system for using a library of cancer-associated peptides or autoimmune-associated peptides derived from subjects; and

b) a processor for scoring the ability of the subject' s major histocompatibility complex class I (MHC-I) to present a mutant cancer-associated peptide or an autoimmune-associated peptide based upon a library of cancer-associated peptides or autoimmune-associated peptides derived from subjects, wherein the produced score is the MHC-I presentation score.

23. The computing system according to claim 21 , wherein the step of scoring the ability of the subject' s MHC-I to present a mutant cancer-associated peptide or an autoimmune-associated peptide comprises using a predicted MHC-I affinity for a given mutation xy, where x is the MHC-I affinity of subject i for mutation j to fit a mixed-effects logistic regression model that follows a model equation obtained from a large dataset of subjects from which MHC-I genotypes and presence of peptides of interest can be obtained:

logit (P(yi_j = 1 I xy)) = η,- + ylog( y)

wherein:

yy is a binary mutation matrix yy £ {0, 1 } indicating whether a subject i has a mutation j; xy is a binary mutation matrix indicating predicted MHC-I binding affinity of subject i having mutation j;

γ measures the effect of the log-affinities on the mutation probability; and

,- ~ N(0, φ_Ά) are random effects capturing residue-specific effects,

wherein the model tests the null hypothesis that γ = 0 and calculates odds ratios for

MHC-I affinity of a mutation and presence of a cancer or autoimmune disease.

24. The computing system according to claim 23 , wherein the predicted MHC-I affinity for a given mutation xy is a Subject Harmonic-mean Best Rank (PHBR) score.

25. The computing system according to claim 23 , wherein the PHBR score is obtained by aggregating MHC-I binding affinities of a set of mutant cancer-associated peptides or a set of autoimmune-associated peptide by referring to a pre-determined dataset of peptides binding to MHC-I molecules encoded by at least 16 different HLA alleles.

26. The computing system according to claim 25, wherein the mutant cancer-associated peptide or the autoimmune-associated peptide contains an amino acid substitution, and wherein the set of peptides consists of at least 38 of all possible 8-, 9-, 10- and 1 l-amino acid long peptides incorporating the substitution at every position along the peptide.

27. The computing system according to claim 25, wherein the mutant cancer-associated peptide or the autoimmune-associated peptide contains an amino acid insertion or deletion, and wherein the set of peptides consists of at least 38 of all possible 8-, 9-, 10- and 11 -amino acid long peptides incorporating the insertion or deletion at every position along the peptide.