Literature DB >> 30377561

Predicting T cell recognition of MHC class I restricted neoepitopes.

Zeynep Koşaloğlu-Yalçın1, Manasa Lanka1, Angela Frentzen1, Ashmitaa Logandha Ramamoorthy Premlal1, John Sidney1, Kerrie Vaughan1, Jason Greenbaum1, Paul Robbins2, Jared Gartner2, Alessandro Sette1,3, Bjoern Peters1,3.   

Abstract

Epitopes that arise from a somatic mutation, also called neoepitopes, are now known to play a key role in cancer immunology and immunotherapy. Recent advances in high-throughput sequencing have made it possible to identify all mutations and thereby all potential neoepitope candidates in an individual cancer. However, most of these neoepitope candidates are not recognized by T cells of cancer patients when tested in vivo or in vitro, meaning they are not immunogenic. Especially in patients with a high mutational load, usually hundreds of potential neoepitopes are detected, highlighting the need to further narrow down this candidate list. In our study, we assembled a dataset of known, naturally processed, immunogenic neoepitopes to dissect the properties that make these neoepitopes immunogenic. The tools to use and thresholds to apply for prioritizing neoepitopes have so far been largely based on experience with epitope identification in other settings such as infectious disease and allergy. Here, we performed a detailed analysis on our dataset of curated immunogenic neoepitopes to establish the appropriate tools and thresholds in the cancer setting. To this end, we evaluated different predictors for parameters that play a role in a neoepitope's immunogenicity and suggest that using binding predictions and length-rescaling yields the best performance in discriminating immunogenic neoepitopes from a background set of mutated peptides. We furthermore show that almost all neoepitopes had strong predicted binding affinities (as expected), but more surprisingly, the corresponding non-mutated peptides had nearly as high affinities. Our results provide a rational basis for parameters in neoepitope filtering approaches that are being commonly used. Abbreviations: SNV: single nucleotide variant; nsSNV: nonsynonymous single nucleotide variant; ROC: receiver operating characteristic; AUC: area under ROC curve; HLA: human leukocyte antigen; MHC: major histocompatibility complex; PD-1: Programmed cell death protein 1; PD-L1 or CTLA-4: cytotoxic T-lymphocyte associated protein 4.

Entities:  

Keywords:  HLA binding; bioinformatics; cancer; immunotherapy; neoantigen; neoepitope

Year:  2018        PMID: 30377561      PMCID: PMC6204999          DOI: 10.1080/2162402X.2018.1492508

Source DB:  PubMed          Journal:  Oncoimmunology        ISSN: 2162-4011            Impact factor:   8.110


  74 in total

Review 1.  Towards a systems understanding of MHC class I and MHC class II antigen presentation.

Authors:  Jacques Neefjes; Marlieke L M Jongsma; Petra Paul; Oddmund Bakke
Journal:  Nat Rev Immunol       Date:  2011-11-11       Impact factor: 53.106

Review 2.  Alternative translational products and cryptic T cell epitopes: expecting the unexpected.

Authors:  On Ho; William R Green
Journal:  J Immunol       Date:  2006-12-15       Impact factor: 5.422

3.  Mutant MHC class II epitopes drive therapeutic immune responses to cancer.

Authors:  Sebastian Kreiter; Mathias Vormehr; Niels van de Roemer; Mustafa Diken; Martin Löwer; Jan Diekmann; Sebastian Boegel; Barbara Schrörs; Fulvia Vascotto; John C Castle; Arbel D Tadmor; Stephen P Schoenberger; Christoph Huber; Özlem Türeci; Ugur Sahin
Journal:  Nature       Date:  2015-04-22       Impact factor: 49.962

Review 4.  Lowering the tone: mechanisms of immunodominance among epitopes with low affinity for MHC.

Authors:  P J Fairchild; D C Wraith
Journal:  Immunol Today       Date:  1996-02

5.  Measurement of MHC/peptide interactions by gel filtration or monoclonal antibody capture.

Authors:  John Sidney; Scott Southwood; Carrie Moore; Carla Oseroff; Clemencia Pinilla; Howard M Grey; Alessandro Sette
Journal:  Curr Protoc Immunol       Date:  2013-02

Review 6.  Evolving synergistic combinations of targeted immunotherapies to combat cancer.

Authors:  Ignacio Melero; David M Berman; M Angela Aznar; Alan J Korman; José Luis Pérez Gracia; John Haanen
Journal:  Nat Rev Cancer       Date:  2015-08       Impact factor: 60.716

Review 7.  Neoepitopes as cancer immunotherapy targets: key challenges and opportunities.

Authors:  Cory A Brennick; Mariam M George; William L Corwin; Pramod K Srivastava; Hakimeh Ebrahimi-Nik
Journal:  Immunotherapy       Date:  2017-03       Impact factor: 4.196

8.  A quantitative analysis of the variables affecting the repertoire of T cell specificities recognized after vaccinia virus infection.

Authors:  Erika Assarsson; John Sidney; Carla Oseroff; Valerie Pasquetto; Huynh-Hoa Bui; Nicole Frahm; Christian Brander; Bjoern Peters; Howard Grey; Alessandro Sette
Journal:  J Immunol       Date:  2007-06-15       Impact factor: 5.422

9.  Efficient identification of mutated cancer antigens recognized by T cells associated with durable tumor regressions.

Authors:  Yong-Chen Lu; Xin Yao; Jessica S Crystal; Yong F Li; Mona El-Gamil; Colin Gross; Lindy Davis; Mark E Dudley; James C Yang; Yardena Samuels; Steven A Rosenberg; Paul F Robbins
Journal:  Clin Cancer Res       Date:  2014-07-01       Impact factor: 12.531

10.  The immune epitope database (IEDB) 3.0.

Authors:  Randi Vita; James A Overton; Jason A Greenbaum; Julia Ponomarenko; Jason D Clark; Jason R Cantrell; Daniel K Wheeler; Joseph L Gabbard; Deborah Hix; Alessandro Sette; Bjoern Peters
Journal:  Nucleic Acids Res       Date:  2014-10-09       Impact factor: 16.971

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  35 in total

1.  A comprehensive review and performance evaluation of bioinformatics tools for HLA class I peptide-binding prediction.

Authors:  Shutao Mei; Fuyi Li; André Leier; Tatiana T Marquez-Lago; Kailin Giam; Nathan P Croft; Tatsuya Akutsu; A Ian Smith; Jian Li; Jamie Rossjohn; Anthony W Purcell; Jiangning Song
Journal:  Brief Bioinform       Date:  2020-07-15       Impact factor: 11.622

2.  Expression of MHC class I, HLA-A and HLA-B identifies immune-activated breast tumors with favorable outcome.

Authors:  María Del Mar Noblejas-López; Cristina Nieto-Jiménez; Sara Morcillo García; Javier Pérez-Peña; Miriam Nuncia-Cantarero; Fernando Andrés-Pretel; Eva M Galán-Moya; Eitan Amir; Atanasio Pandiella; Balázs Győrffy; Alberto Ocana
Journal:  Oncoimmunology       Date:  2019-07-03       Impact factor: 8.110

3.  Impact of Cysteine Residues on MHC Binding Predictions and Recognition by Tumor-Reactive T Cells.

Authors:  Abraham Sachs; Eugene Moore; Zeynep Kosaloglu-Yalcin; Bjoern Peters; John Sidney; Steven A Rosenberg; Paul F Robbins; Alessandro Sette
Journal:  J Immunol       Date:  2020-06-22       Impact factor: 5.422

4.  A machine learning model for ranking candidate HLA class I neoantigens based on known neoepitopes from multiple human tumor types.

Authors:  Jared J Gartner; Maria R Parkhurst; Alena Gros; Eric Tran; Mohammad S Jafferji; Amy Copeland; Ken-Ichi Hanada; Nikolaos Zacharakis; Almin Lalani; Sri Krishna; Abraham Sachs; Todd D Prickett; Yong F Li; Maria Florentin; Scott Kivitz; Samuel C Chatmon; Steven A Rosenberg; Paul F Robbins
Journal:  Nat Cancer       Date:  2021-05-03

5.  A computational algorithm to assess the physiochemical determinants of T cell receptor dissociation kinetics.

Authors:  Zachary A Rollins; Jun Huang; Ilias Tagkopoulos; Roland Faller; Steven C George
Journal:  Comput Struct Biotechnol J       Date:  2022-06-25       Impact factor: 6.155

6.  Combining Three-Dimensional Modeling with Artificial Intelligence to Increase Specificity and Precision in Peptide-MHC Binding Predictions.

Authors:  Michelle P Aranha; Yead S M Jewel; Robert A Beckman; Louis M Weiner; Julie C Mitchell; Jerry M Parks; Jeremy C Smith
Journal:  J Immunol       Date:  2020-09-02       Impact factor: 5.422

Review 7.  Harnessing neoantigen specific CD4 T cells for cancer immunotherapy.

Authors:  Spencer E Brightman; Martin S Naradikian; Aaron M Miller; Stephen P Schoenberger
Journal:  J Leukoc Biol       Date:  2020-03-14       Impact factor: 4.962

Review 8.  MUCIN-4 (MUC4) is a novel tumor antigen in pancreatic cancer immunotherapy.

Authors:  Shailendra K Gautam; Sushil Kumar; Vi Dam; Dario Ghersi; Maneesh Jain; Surinder K Batra
Journal:  Semin Immunol       Date:  2020-01-14       Impact factor: 11.130

Review 9.  Determinants for Neoantigen Identification.

Authors:  Andrea Garcia-Garijo; Carlos Alberto Fajardo; Alena Gros
Journal:  Front Immunol       Date:  2019-06-24       Impact factor: 7.561

Review 10.  Antigen processing and presentation in cancer immunotherapy.

Authors:  Maxwell Y Lee; Jun W Jeon; Cem Sievers; Clint T Allen
Journal:  J Immunother Cancer       Date:  2020-08       Impact factor: 13.751

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