neoMS: Attention-based Prediction of MHC-I Epitope Presentation

Abstract

Personalised immunotherapy aims to (re-)activate the immune system of a given patient against its tumour. It relies extensively on the ability of tumour-derived neoantigens to trigger a T-cell immune reaction able to recognise and kill the tumour cells expressing them. Since only peptides presented on the cell surface can be immunogenic, the prediction of neoantigen presentation is a crucial step of any discovery pipeline. Limiting neoantigen presentation to MHC binding fails to take into account all other steps of the presentation machinery and therefore to assess the true potential clinical benefit of a given epitope. Indeed, research has uncovered that merely 5% of predicted tumour-derived MHC-bound peptides is actually presented on the cell surface, demonstrating that affinity-based approaches fall short from isolating truly actionable neoantigens. Here, we present neoMS, a MHC-I presentation prediction algorithm leveraging mass spectrometry-derived MHC ligandomic data to better isolate presented antigens from potentially very large sets. The neoMS model is a transformer-based, peptide-sequence-to-HLA-sequence neural network algorithm, trained on 386,647 epitopes detected in the ligandomes of 92 HLA-monoallelic datasets and 66 patient-derived HLA-multiallelic datasets. It leverages attention mechanisms in which the most relevant parts of both putative epitope and HLA alleles are isolated. This results in a positive predictive value of 0.61 at a recall of 40% on its patient-derived test dataset, considerably outperforming current alternatives. Predictions made by neoMS correlate with peptide identification confidence in mass spectrometry experiments and reliably identify binding motif preferences of individual HLA alleles thereby further consolidating the biological relevance of the model. Additionally, neoMS displays extrapolation capabilities, showing good predictive power for presentation by HLA alleles not present in its training dataset. Finally, it was found that neoMS results can help refine predictions of response to immune checkpoint inhibitor treatment in certain cancer indications. Taken together, these results establish neoMS as a considerable step forward in high-specificity isolation of clinically actionable antigens for immunotherapies.