RT Journal Article
SR Electronic
T1 High-throughput prediction of MHC Class I and Class II neoantigens with MHCnuggets
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 752469
DO 10.1101/752469
A1 XM Shao
A1 R Bhattacharya
A1 J Huang
A1 IKA Sivakumar
A1 C Tokheim
A1 L Zheng
A1 D Hirsch
A1 B Kaminow
A1 A Omdahl
A1 M Bonsack
A1 AB Riemer
A1 VE Velculescu
A1 V Anagnostou
A1 KA Pagel
A1 R Karchin
YR 2019
UL http://biorxiv.org/content/early/2019/10/04/752469.abstract
AB Computational prediction of binding between neoantigen peptides and major histocompatibility complex (MHC) proteins is an emerging biomarker for predicting patient response to cancer immunotherapy. Current neoantigen predictors focus on in silico estimation of MHC binding affinity and are limited by low positive predictive value for actual peptide presentation, inadequate support for rare MHC alleles and poor scalability to high-throughput data sets. To address these limitations, we developed MHCnuggets, a deep neural network method to predict peptide-MHC binding. MHCnuggets is the only method to handle binding prediction for common or rare alleles of MHC Class I or II, with a single neural network architecture. Using a long short-term memory network (LSTM), MHCnuggets accepts peptides of variable length and is capable of faster performance than other methods. When compared to methods that integrate binding affinity and HLAp data from mass spectrometry, MHCnuggets yields a fourfold increase in positive predictive value on independent MHC-bound peptide (HLAp) data. We applied MHCnuggets to 26 cancer types in TCGA, processing 26.3 million allele-peptide comparisons in under 2.3 hours, yielding 101,326 unique candidate immunogenic missense mutations (IMMs). Predicted-IMM hotspots occurred in 38 genes, including 24 driver genes. Predicted-IMM load was significantly associated with increased immune cell infiltration (p<2e−16) including CD8+ T cells. Notably, only 0.16% of predicted immunogenic missense mutations were observed in >2 patients, with 61.7% of these derived from driver mutations. Our results provide a new method for neoantigen prediction with high performance characteristics and demonstrate its utility in large data sets across human cancers.Synopsis We developed a new in silico predictor of Major Histocompatibility Complex (MHC) ligand binding and demonstrated its utility to assess potential neoantigens and immunogenic missense mutations (IMMs) in 6613 TCGA patients.