T cell immunity directed against tumor-encoded amino acid substitutions (AAS) occurs in some melanoma patients. This implicates missense mutations (MM) as a source of patient-specific neoantigens. However, a systematic evaluation of these putative neoantigens as validated targets of anti-tumor immunity is lacking. Moreover, whether vaccination can augment such responses is unknown. Here we show that a dendritic cell vaccine increased naturally occurring and revealed new HLA class I-restricted neoantigens in patients with advanced melanoma. The presentation of neoantigens by HLA-A*02:01 in human melanoma was confirmed by mass spectrometry. Vaccination promoted a diverse neoantigen-specific T cell receptor repertoire in terms of both TCRVβ usage and clonal composition. Our results demonstrate that vaccination directed at tumor AAS broadens the antigenic breadth and clonal diversity of anti-tumor immunity.

Genetic studies associate Parkinson’s disease with alleles of the major histocompatibility complex1–3. We find that a defined set of peptides derived from α-synuclein, a protein aggregated in Parkinson’s disease4, act as antigenic epitopes displayed by these alleles and drive helper and cytotoxic T cell responses in Parkinson’s disease patients. These responses may explain the association of Parkinson’s disease with alleles of the acquired immune system.

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SUMMARY MHC-I molecules expose the intracellular protein content on the cell surface, allowing T cells to detect foreign or mutated peptides. The combination of six MHC-I alleles each individual carries defines the sub-peptidome that can be effectively presented. We applied this concept to human cancer, hypothesizing that oncogenic mutations could arise in gaps in personal MHC-I presentation. To validate this hypothesis, we developed and applied a residue-centric patient presentation score to 9,176 cancer patients across 1,018 recurrent oncogenic mutations. We found that patient MHC-I genotype-based scores could predict which mutations were more likely to emerge in their tumor. Accordingly, poor presentation of a mutation across patients was correlated with higher frequency among tumors. These results support that MHC-I genotype-restricted immunoediting during tumor formation shapes the landscape of oncogenic mutations observed in clinically diagnosed tumors and paves the way for predicting personal cancer susceptibilities from knowledge of MHC-I genotype.

Major Histocompatibility Complex II (MHC II) molecules play a vital role in the onset and control of cellular immunity. In a highly selective process, MHC II presents peptides derived from exogenous antigens on the surface of antigen-presenting cells for T cell scrutiny. Understanding the rules defining this presentation holds critical insights into the regulation and potential manipulation of the cellular immune system. Here, we apply the NNAlign_MA machine learning framework to analyse and integrate large-scale eluted MHC II ligand mass spectrometry (MS) data sets to advance prediction of CD4+ epitopes. NNAlign_MA allows integration of mixed data types, handling ligands with multiple potential allele annotations, encoding of ligand context, leveraging information between data sets, and has pan-specific power allowing accurate predictions outside the set of molecules included in the training data. Applying this framework, we identified accurate binding motifs of more than 50 MHC class II molecules described by MS data, particularly expanding coverage for DP and DQ beyond that obtained using current MS motif deconvolution techniques. Further, in large-scale benchmarking, the final model termed NetMHCIIpan-4.0, demonstrated improved performance beyond current state-of-the-art predictors for ligand and .