Challenges targeting cancer neoantigens in 2021: a systematic literature review

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.

KEYWORDS:

• Cancer neoantigen
• cancer immunotherapy
• neoantigen vaccine
• sequencing
• epitope prediction
• MHC class I
• binding affinity
• immune checkpoint inhibition

Article highlights

• Strategies targeting cancer neoantigens, such as neoantigen vaccines, rely on accurate identification of cancer neoantigens.

• ‘Off-the-shelf’ immune therapies targeting shared tumor antigens have had limited success, emphasizing the need to target cancer neoantigens. Recent studies demonstrate that cancer neoantigens are important targets of immune checkpoint inhibition, adoptive cell therapy and other cancer immunotherapies.

• The two most common strategies to identify cancer neoantigens are: (1) direct identification based on proteomic analysis of ligands eluted from peptide-MHC complexes and (2) indirect identification based on sequencing and bioinformatic pipelines. Since direct identification is currently too cumbersome for widespread use, most clinical trials targeting cancer neoantigens have relied on indirect identification based on sequencing.

• Next-generation sequencing is currently in clinical use, facilitating identification of the genetic alterations encoding cancer neoantigens.

• While neoantigen vaccines have successfully generated neoantigen-specific immune responses in preclinical models and early phase clinical trials, most candidate neoantigens do not generate immune responses. This suggests that additional study is necessary to improve current neoantigen prediction algorithms.

• Neoantigen identification and prioritization pipelines will likely improve in the future, benefitting from insights into the mechanisms of antigen processing and presentation within tumor cells, advances in sequencing and machine-learning technologies, and collaborative efforts.

• In addition to improving strategies targeting neoantigens, accurate neoantigen prediction is likely to enhance our mechanistic understanding of cancer immunotherapies.

Reviewer disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Declaration of interest

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Additional information

Funding

This manuscript was funded by the Washington University School of Medicine Surgical Oncology Basic Science and Translational Research Training Program grant T32CA009621, and the Washington University School of Medicine Cancer Center Support Grant 2P30CA091842-19 from the National Cancer Institute (NCI). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.