Literature DB >> 32610166

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

L De Mattos-Arruda1, M Vazquez2, F Finotello3, R Lepore2, E Porta4, J Hundal5, P Amengual-Rigo2, C K Y Ng6, A Valencia7, J Carrillo8, T A Chan9, V Guallar7, N McGranahan10, J Blanco11, M Griffith12.   

Abstract

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

Entities:  

Keywords:  cancer; computational; immunotherapy; mutation; neoantigen; personalised vaccine

Mesh:

Substances:

Year:  2020        PMID: 32610166      PMCID: PMC7885309          DOI: 10.1016/j.annonc.2020.05.008

Source DB:  PubMed          Journal:  Ann Oncol        ISSN: 0923-7534            Impact factor:   32.976


  122 in total

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Journal:  Science       Date:  2015-04-02       Impact factor: 47.728

2.  Landscape of immunogenic tumor antigens in successful immunotherapy of virally induced epithelial cancer.

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Journal:  Science       Date:  2017-04-14       Impact factor: 47.728

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

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Journal:  Nature       Date:  2015-04-22       Impact factor: 49.962

4.  Prospective identification of neoantigen-specific lymphocytes in the peripheral blood of melanoma patients.

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Review 5.  Novel tools to assist neoepitope targeting in personalized cancer immunotherapy.

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Review 8.  Cancer immunoediting: integrating immunity's roles in cancer suppression and promotion.

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Journal:  Science       Date:  2011-03-25       Impact factor: 47.728

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Authors:  Matthew M Gubin; Xiuli Zhang; Heiko Schuster; Etienne Caron; Jeffrey P Ward; Takuro Noguchi; Yulia Ivanova; Jasreet Hundal; Cora D Arthur; Willem-Jan Krebber; Gwenn E Mulder; Mireille Toebes; Matthew D Vesely; Samuel S K Lam; Alan J Korman; James P Allison; Gordon J Freeman; Arlene H Sharpe; Erika L Pearce; Ton N Schumacher; Ruedi Aebersold; Hans-Georg Rammensee; Cornelis J M Melief; Elaine R Mardis; William E Gillanders; Maxim N Artyomov; Robert D Schreiber
Journal:  Nature       Date:  2014-11-27       Impact factor: 49.962

10.  A library of Neo Open Reading Frame peptides (NOPs) as a sustainable resource of common neoantigens in up to 50% of cancer patients.

Authors:  Jan Koster; Ronald H A Plasterk
Journal:  Sci Rep       Date:  2019-04-29       Impact factor: 4.379

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2.  Requirements of integrated computational approach for developing personalized cancer vaccines.

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Review 7.  Tracking Cancer Evolution through the Disease Course.

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