RT Journal Article
SR Electronic
T1 Deep Neural Networks Predict MHC-I Epitope Presentation and Transfer Learn Neoepitope Immunogenicity
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2022.08.29.505690
DO 10.1101/2022.08.29.505690
A1 Benjamin Alexander Albert
A1 Yunxiao Yang
A1 Xiaoshan M. Shao
A1 Dipika Singh
A1 Kellie N. Smith
A1 Valsamo Anagnostou
A1 Rachel Karchin
YR 2023
UL http://biorxiv.org/content/early/2023/01/18/2022.08.29.505690.abstract
AB Identifying neoepitopes that elicit an adaptive immune response is a major bottleneck to developing personalized cancer vaccines. Experimental validation of candidate neoepitopes is extremely resource intensive, and the vast majority of candidates are non-immunogenic, making their identification a needle-in-a-haystack problem. To address this challenge, we present computational methods for predicting MHC-I epitopes and identifying immunogenic neoepitopes with high precision. The BigMHC method comprises an ensemble of seven pan-allelic deep neural networks trained on peptide-MHC eluted ligand data from mass spectrometry assays and transfer learned on data from assays of antigen-specific immune response. Compared with four state-of-the-art classifiers, BigMHC significantly improves the prediction of epitope presentation on a test set of 45,409 MHC ligands among 900,592 random negatives (AUROC=0.9733, AUPRC=0.8779). After transfer learning on immunogenicity data, BigMHC yields significantly higher precision than seven state-of-the-art models in identifying immunogenic neoepitopes, making BigMHC effective in clinical settings. All data and code are freely available at https://github.com/KarchinLab/bigmhc.Competing Interest StatementUnder a license agreement between Genentech and the Johns Hopkins University, X.M.S., and R.K., and the University are entitled to royalty distributions related to MHCnuggets technology discussed in this publication. This arrangement has been reviewed and approved by the Johns Hopkins University in accordance with its conflict-of-interest policies. V.A. receives research funding to her institution from Astra Zeneca and has received research funding to her institution from Bristol Myers Squibb over the past five years. The remaining authors have declared no conflicts of interest.