Epitope immunogenicity prediction through repertoire-wide TCR-peptide contact profiles

16 Computational methodologies to predict epitopes for cytotoxic T lymphocytes 17 (CTLs) will galvanize vaccine research and pave the way toward targeted 18 immunotherapy of infections and cancer. However, the classification of immunogenic 19 epitopes and non-immunogenic major histocompatibility complex (MHC) class I ligands 20 in silico remains difficult. Here, we defined a novel framework quantifying the 21 interactions between a given peptide and T cell receptor (TCR) repertoire. Using 4738 22 peptide sequences and a pooled TCR repertoire, an epitope classifier with unprecedented 23 accuracy in hold-out validation was constructed. The classifier was applicable to multiple 24 human leucocyte antigen supertypes. The classifier was further validated independently 25 using pathogen epitope datasets and tumor neoepitope datasets. A panel of 26 neoepitope-rich genes were identified using The Cancer Genome Atlas (TCGA) datasets. 27 The R package Repitope was implemented to maximize code reusability. This is the first 28 study demonstrating in silico CTL epitope prediction with clinically meaningful 29 robustness, thus prospective validation is warranted. 30 31 32 not peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint (which was . http://dx.doi.org/10.1101/155317 doi: bioRxiv preprint first posted online Jun. 25, 2017;