High-Throughput Prediction of MHC Class I and II Neoantigens with MHCnuggets.

Journal: Cancer immunology research
Published Date: Mar 1, 2020

Abstract

Computational prediction of binding between neoantigen peptides and major histocompatibility complex (MHC) proteins can be used to predict patient response to cancer immunotherapy. Current neoantigen predictors focus on estimation of MHC binding affinity and are limited by low 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 that predicts peptide-MHC binding. MHCnuggets can predict binding 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 faster than other methods. When compared with methods that integrate binding affinity and MHC-bound peptide (HLAp) data from mass spectrometry, MHCnuggets yields a 4-fold increase in positive predictive value on independent HLAp data. We applied MHCnuggets to 26 cancer types in The Cancer Genome Atlas, processing 26.3 million allele-peptide comparisons in under 2.3 hours, yielding 101,326 unique predicted immunogenic missense mutations (IMM). Predicted IMM hotspots occurred in 38 genes, including 24 driver genes. Predicted IMM load was significantly associated with increased immune cell infiltration ( < 2 × 10), including CD8 T cells. Only 0.16% of predicted IMMs were observed in more than 2 patients, with 61.7% of these derived from driver mutations. Thus, we describe a method for neoantigen prediction and its performance characteristics and demonstrate its utility in data sets representing multiple human cancers.

Authors

  • Xiaoshan M Shao
    Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland.
  • Rohit Bhattacharya
    Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland.
  • Justin Huang
    Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland.
  • I K Ashok Sivakumar
    Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland.
  • Collin Tokheim
    Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland.
  • Lily Zheng
    Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland.
  • Dylan Hirsch
    Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland.
  • Benjamin Kaminow
    Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland.
  • Ashton Omdahl
    Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland.
  • Maria Bonsack
    Immunotherapy and Immunoprevention, German Cancer Research Center (DKFZ), Heidelberg, Germany.
  • Angelika B Riemer
    Immunotherapy and Immunoprevention, German Cancer Research Center (DKFZ), Heidelberg, Germany.
  • Victor E Velculescu
    The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA. velculescu@jhmi.edu angiuoli@personalgenome.com.
  • Valsamo Anagnostou
    The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA.
  • Kymberleigh A Pagel
    Department of Computer Science and Informatics, Indiana University, Bloomington, IN, USA.
  • Rachel Karchin
    The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA.

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