Neoantigen prediction and computational perspectives towards clinical benefit: recommendations from the ESMO Precision Medicine Working Group.


Journal

Annals of oncology : official journal of the European Society for Medical Oncology
ISSN: 1569-8041
Titre abrégé: Ann Oncol
Pays: England
ID NLM: 9007735

Informations de publication

Date de publication:
08 2020
Historique:
received: 22 01 2020
revised: 01 05 2020
accepted: 07 05 2020
pubmed: 2 7 2020
medline: 7 1 2021
entrez: 2 7 2020
Statut: ppublish

Résumé

The use of next-generation sequencing technologies has enabled the rapid identification of non-synonymous somatic mutations in cancer cells. Neoantigens are mutated peptides derived from somatic mutations not present in normal tissues that may result in the presentation of tumour-specific peptides capable of eliciting antitumour T-cell responses. Personalised neoantigen-based cancer vaccines and adoptive T-cell therapies have been shown to prime host immunity against tumour cells and are under clinical trial development. However, the optimisation and standardisation of neoantigen identification, as well as its delivery as immunotherapy are needed to increase tumour-specific T-cell responses and, thus, the clinical efficacy of current cancer immunotherapies. In this recommendation article, launched by the European Society for Medical Oncology (ESMO), we outline and discuss the available framework for neoantigen prediction and present a systematic review of the current scientific evidence. A number of computational pipelines for neoantigen prediction are available. Most of them provide peptide major histocompatibility complex (MHC) binding affinity predictions, but more recent approaches incorporate additional features like variant allele fraction, gene expression, and clonality of mutations. Neoantigens can be predicted in all cancer types with high and low tumour mutation burden, in part by exploiting tumour-specific aberrations derived from mutational frameshifts, splice variants, gene fusions, endogenous retroelements and other tumour-specific processes that could yield more potently immunogenic tumour neoantigens. Ongoing clinical trials will highlight those cancer types and combinations of immune therapies that would derive the most benefit from neoantigen-based immunotherapies. Improved identification, selection and prioritisation of tumour-specific neoantigens are needed to increase the scope of benefit from cancer vaccines and adoptive T-cell therapies. Novel pipelines are being developed to resolve the challenges posed by high-throughput sequencing and to predict immunogenic neoantigens.

Sections du résumé

BACKGROUND
The use of next-generation sequencing technologies has enabled the rapid identification of non-synonymous somatic mutations in cancer cells. Neoantigens are mutated peptides derived from somatic mutations not present in normal tissues that may result in the presentation of tumour-specific peptides capable of eliciting antitumour T-cell responses. Personalised neoantigen-based cancer vaccines and adoptive T-cell therapies have been shown to prime host immunity against tumour cells and are under clinical trial development. However, the optimisation and standardisation of neoantigen identification, as well as its delivery as immunotherapy are needed to increase tumour-specific T-cell responses and, thus, the clinical efficacy of current cancer immunotherapies.
METHODS
In this recommendation article, launched by the European Society for Medical Oncology (ESMO), we outline and discuss the available framework for neoantigen prediction and present a systematic review of the current scientific evidence.
RESULTS
A number of computational pipelines for neoantigen prediction are available. Most of them provide peptide major histocompatibility complex (MHC) binding affinity predictions, but more recent approaches incorporate additional features like variant allele fraction, gene expression, and clonality of mutations. Neoantigens can be predicted in all cancer types with high and low tumour mutation burden, in part by exploiting tumour-specific aberrations derived from mutational frameshifts, splice variants, gene fusions, endogenous retroelements and other tumour-specific processes that could yield more potently immunogenic tumour neoantigens. Ongoing clinical trials will highlight those cancer types and combinations of immune therapies that would derive the most benefit from neoantigen-based immunotherapies.
CONCLUSIONS
Improved identification, selection and prioritisation of tumour-specific neoantigens are needed to increase the scope of benefit from cancer vaccines and adoptive T-cell therapies. Novel pipelines are being developed to resolve the challenges posed by high-throughput sequencing and to predict immunogenic neoantigens.

Identifiants

pubmed: 32610166
pii: S0923-7534(20)39824-0
doi: 10.1016/j.annonc.2020.05.008
pmc: PMC7885309
mid: NIHMS1629136
pii:
doi:

Substances chimiques

Antigens, Neoplasm 0
Cancer Vaccines 0

Types de publication

Journal Article Research Support, Non-U.S. Gov't Systematic Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

978-990

Subventions

Organisme : Wellcome Trust
Pays : United Kingdom
Organisme : NHGRI NIH HHS
ID : R00 HG007940
Pays : United States

Informations de copyright

Copyright © 2020 European Society for Medical Oncology. Published by Elsevier Ltd. All rights reserved.

Déclaration de conflit d'intérêts

Disclosures J.B. is CEO and co-founder and J.C. is CSO and co-founder of AlbaJuna Therapeutics SL. N.M. has received consultancy fees from Achilles Therapeutics. L.D.M.A. has received honoraria for participation in a speaker's bureau/consultancy from Roche. V.G. is CTO and founder of Nostrum Biodiscovery. T.A.C is a co-founder of Gritstone Oncology and holds stock. T.A.C. has received grant support from BMS, AstraZeneca, Eisai, Illumina, An2H, and Pfizer. T.A.C has been on the scientific advisory boards of BMS, AstraZeneca, Merck, Illumina, and An2H. All remaining authors have declared no conflicts of interest.

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Auteurs

L De Mattos-Arruda (L)

IrsiCaixa, Hospital Universitari Trias i Pujol, Badalona, Spain; Germans Trias i Pujol Research Institute (IGTP), Badalona, Spain. Electronic address: education@esmo.org.

M Vazquez (M)

Barcelona Supercomputing Center, Barcelona, Spain.

F Finotello (F)

Biocenter, Institute of Bioinformatics, Medical University of Innsbruck, Innsbruck, Austria.

R Lepore (R)

Barcelona Supercomputing Center, Barcelona, Spain.

E Porta (E)

Barcelona Supercomputing Center, Barcelona, Spain; Josep Carreras Leukaemia Research Institute (IJC), Badalona, Spain.

J Hundal (J)

The McDonnell Genome Institute, Washington University in St Louis, USA.

P Amengual-Rigo (P)

Barcelona Supercomputing Center, Barcelona, Spain.

C K Y Ng (CKY)

Department for BioMedical Research, University of Bern, Bern, Switzerland.

A Valencia (A)

Barcelona Supercomputing Center, Barcelona, Spain; Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain.

J Carrillo (J)

IrsiCaixa, Hospital Universitari Trias i Pujol, Badalona, Spain; Germans Trias i Pujol Research Institute (IGTP), Badalona, Spain.

T A Chan (TA)

Center for Immunotherapy and Precision-Immuno-Oncology, Cleveland Clinic, Cleveland, USA.

V Guallar (V)

Barcelona Supercomputing Center, Barcelona, Spain; Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain.

N McGranahan (N)

Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, University College, London, UK; Cancer Genome Evolution Research Group, University College London Cancer Institute, University College London, London, UK.

J Blanco (J)

IrsiCaixa, Hospital Universitari Trias i Pujol, Badalona, Spain; Germans Trias i Pujol Research Institute (IGTP), Badalona, Spain; Universitat de Vic-Universitat Central de Catalunya (UVic-UCC), Vic, Spain.

M Griffith (M)

Department of Medicine, Washington University School of Medicine, St. Louis, USA.

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