A computer implemented method for determining whether a subject is at risk of having or developing a cancer, 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 based upon a library of known cancer-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; or 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; wherein the step of scoring the ability of the subject's MHC-I to present a mutant cancer-associated peptide comprises using a predicted MHC-I affinity for a given mutation x ij , 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 y ij = 1 x ij = η i + γlog x ij wherein: y ij is a binary mutation matrix y ij ∈ indicating whether a subject i has a mutation j ; x ij 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 η i ~ N(0, φ η ) are random effects capturing different mutation rates among subjects, 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. 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 based upon a library of known cancer-associated peptide sequences derived from subjects, wherein the produced score is the MHC-I presentation score; 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; or 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; y ij is a binary mutation matrix y ij ∈ indicating whether a subject i has a mutation j ; x ij 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 η i ~ N(0, φ η ) are random effects capturing different mutation rates among subjects, 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. <EOS>