Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Deep Neural Networks Predict MHC-I Epitope Presentation and Transfer Learn Neoepitope Immunogenicity

View ORCID ProfileBenjamin Alexander Albert, Yunxiao Yang, Xiaoshan M. Shao, Dipika Singh, Kellie N. Smith, Valsamo Anagnostou, View ORCID ProfileRachel Karchin
doi: https://doi.org/10.1101/2022.08.29.505690
Benjamin Alexander Albert
1Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
2Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Benjamin Alexander Albert
Yunxiao Yang
1Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
2Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Xiaoshan M. Shao
1Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Dipika Singh
3The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
4Bloomberg~Kimmel Institute for Cancer Immunotherapy, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Kellie N. Smith
3The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
4Bloomberg~Kimmel Institute for Cancer Immunotherapy, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Valsamo Anagnostou
3The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
4Bloomberg~Kimmel Institute for Cancer Immunotherapy, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Rachel Karchin
1Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
2Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA
3The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
5Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Rachel Karchin
  • For correspondence: karchin@jhu.edu
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Data/Code
  • Preview PDF
Loading

Abstract

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 Statement

Under 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.

Footnotes

  • Updated BigMHC architecture, training procedure, and immunogenicity evaluation protocol. Updated the conceptual framework to include more endogenous peptide processing. The revised experimental procedure and architecture are illustrated in the revised Fig. 1 and Fig. 2. Included more fine-grain analysis of presentation prediction performance and added statistical tests to establish significance in Fig. 3. Updated immunogenicity test data to not use negative EL background to avoid false negative bias from mass spectrometry data; the immunogenicity positives and negatives are validated with immunoassays. Included comparisons of immunogenicity prediction to additional models: MixMHCpred-2.2, PRIME-2.0, and HLAthena. Included both types of outputs of prior models: percentile ranks and raw scores. Expanded discussion and explored many limitations of the proposed method. Added identifiers for the three clinical trials in which BigMHC is being used.

  • https://github.com/KarchinLab/bigmhc

  • https://doi.org/10.17632/dvmz6pkzvb.1

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
Back to top
PreviousNext
Posted January 18, 2023.
Download PDF
Data/Code
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Deep Neural Networks Predict MHC-I Epitope Presentation and Transfer Learn Neoepitope Immunogenicity
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Deep Neural Networks Predict MHC-I Epitope Presentation and Transfer Learn Neoepitope Immunogenicity
Benjamin Alexander Albert, Yunxiao Yang, Xiaoshan M. Shao, Dipika Singh, Kellie N. Smith, Valsamo Anagnostou, Rachel Karchin
bioRxiv 2022.08.29.505690; doi: https://doi.org/10.1101/2022.08.29.505690
Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools
Deep Neural Networks Predict MHC-I Epitope Presentation and Transfer Learn Neoepitope Immunogenicity
Benjamin Alexander Albert, Yunxiao Yang, Xiaoshan M. Shao, Dipika Singh, Kellie N. Smith, Valsamo Anagnostou, Rachel Karchin
bioRxiv 2022.08.29.505690; doi: https://doi.org/10.1101/2022.08.29.505690

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Bioinformatics
Subject Areas
All Articles
  • Animal Behavior and Cognition (4665)
  • Biochemistry (10324)
  • Bioengineering (7649)
  • Bioinformatics (26274)
  • Biophysics (13489)
  • Cancer Biology (10659)
  • Cell Biology (15386)
  • Clinical Trials (138)
  • Developmental Biology (8474)
  • Ecology (12795)
  • Epidemiology (2067)
  • Evolutionary Biology (16811)
  • Genetics (11377)
  • Genomics (15443)
  • Immunology (10589)
  • Microbiology (25111)
  • Molecular Biology (10183)
  • Neuroscience (54295)
  • Paleontology (399)
  • Pathology (1663)
  • Pharmacology and Toxicology (2886)
  • Physiology (4330)
  • Plant Biology (9221)
  • Scientific Communication and Education (1585)
  • Synthetic Biology (2548)
  • Systems Biology (6766)
  • Zoology (1459)