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

De Mattos-Arruda, L; Vazquez, M; Finotello, F; Lepore, R; Porta, E; Hundal, J; Amengual-Rigo, P; Ng, C K Y; Valencia, A; Carrillo, J; Chan, T A; Guallar, V; McGranahan, N; Blanco, J; Griffith, M (2020). Neoantigen prediction and computational perspectives towards clinical benefit: recommendations from the ESMO Precision Medicine Working Group. Annals of oncology, 31(8), pp. 978-990. Elsevier 10.1016/j.annonc.2020.05.008

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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.

Item Type:

Journal Article (Review Article)

Division/Institute:

04 Faculty of Medicine > Pre-clinic Human Medicine > BioMedical Research (DBMR)

UniBE Contributor:

Ng, Kiu Yan Charlotte

Subjects:

600 Technology > 610 Medicine & health

ISSN:

1569-8041

Publisher:

Elsevier

Language:

English

Submitter:

Marla Rittiner

Date Deposited:

29 Dec 2020 10:03

Last Modified:

29 Dec 2020 10:03

Publisher DOI:

10.1016/j.annonc.2020.05.008

PubMed ID:

32610166

Uncontrolled Keywords:

cancer computational immunotherapy mutation neoantigen personalised vaccine

BORIS DOI:

10.48350/150227

URI:

https://boris.unibe.ch/id/eprint/150227

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