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

L. De Mattos-Arruda, M. Vazquez, F. Finotello, R. Lepore, E. Porta, J. Hundal, P. Amengual-Rigo, C. K.Y. Ng, A. Valencia, J. Carrillo, T. A. Chan, V. Guallar, N. McGranahan, J. Blanco, M. Griffith

Research output: Contribution to journalReview articlepeer-review

48 Scopus citations

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.

Original languageEnglish
Pages (from-to)978-990
Number of pages13
JournalAnnals of Oncology
Volume31
Issue number8
DOIs
StatePublished - Aug 2020

Keywords

  • cancer
  • computational
  • immunotherapy
  • mutation
  • neoantigen
  • personalised vaccine

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