Challenges targeting cancer neoantigens in 2021: a systematic literature review. (3rd July 2021)

• Record Type: Journal Article
• Authors: Chen, Ina; Chen, Michael Y.; Goedegebuure, S. Peter; Gillanders, William E.
• Is Part Of: Expert review of vaccines. Volume 20:Number 7(2021)
• Page Start: 827
• Page End: 837
• Publication Date: 2021-07-03
• Subjects: Cancer neoantigen -- cancer immunotherapy -- neoantigen vaccine -- sequencing -- epitope prediction -- MHC class I -- binding affinity -- immune checkpoint inhibition; Vaccines -- Periodicals; Vaccination -- Periodicals; 615.37205
• DOI: 10.1080/14760584.2021.1935248
• Languages: English
• ISSNs: 1476-0584

Abstract

ABSTRACT: Introduction : Cancer neoantigens represent important targets of cancer immunotherapy. The goal of cancer neoantigen vaccines is to induce neoantigen-specific immune responses and antitumor immunity while minimizing the potential for autoimmune toxicity. Advances in sequencing technologies, neoantigen prediction algorithms, and other technologies have dramatically improved the ability to identify and prioritize cancer neoantigens. Unfortunately, results from preclinical studies and early phase clinical trials highlight important challenges to the successful clinical translation of neoantigen cancer vaccines. Areas covered : In this review, we provide an overview of current strategies for the identification and prioritization of cancer neoantigens with a particular emphasis on the two most common strategies used for neoantigen identification: (1) direct identification of peptide ligands eluted from peptide-MHC complexes, and (2) next-generation sequencing combined with neoantigen prediction algorithms. We highlight the limitations of current neoantigen prediction pipelines, and discuss broader challenges associated with cancer neoantigen vaccines including tumor purity/heterogeneity and the immunosuppressive tumor microenvironment. Expert opinion : Despite current limitations, neoantigen prediction is likely to improve rapidly based on advances in sequencing, machine learning, and information sharing. The successful development of robust cancer neoantigen prediction strategies is likely to have a significant impact, with the potential to facilitate cancer neoantigen vaccine design.