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The evolving landscape of biomarkers for checkpoint inhibitor immunotherapy

Abstract

Checkpoint inhibitor-based immunotherapies that target cytotoxic T lymphocyte antigen 4 (CTLA4) or the programmed cell death 1 (PD1) pathway have achieved impressive success in the treatment of different cancer types. Yet, only a subset of patients derive clinical benefit. It is thus critical to understand the determinants driving response, resistance and adverse effects. In this Review, we discuss recent work demonstrating that immune checkpoint inhibitor efficacy is affected by a combination of factors involving tumour genomics, host germline genetics, PD1 ligand 1 (PDL1) levels and other features of the tumour microenvironment, as well as the gut microbiome. We focus on recently identified molecular and cellular determinants of response. A better understanding of how these variables cooperate to affect tumour–host interactions is needed to optimize the implementation of precision immunotherapy.

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Fig. 1: Immune checkpoint blockade and somatic mutations.
Fig. 2: Multiple sources of diversity converge to influence tumour immunity.

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Acknowledgements

The authors thank the Chan laboratory, members of the Immunogenomics and Precision Oncology Platform and members of the Immunology Program at Memorial Sloan Kettering Cancer Center for helpful discussions. The work was supported in part by US National Institutes of Health (NIH) National Cancer Institute (NCI) Cancer Center Support Grant P30 CA008748.

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Nature Reviews Cancer thanks I. Melero, M. Smyth and other anonymous reviewer(s) for their contribution to the peer review of this work.

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All authors researched data for the article, substantially contributed to the discussion of content and wrote, reviewed and edited the manuscript.

Corresponding author

Correspondence to Timothy A. Chan.

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Competing interests

T.A.C. is a co-founder of Gritstone Oncology and holds equity. T.A.C. holds equity in An2H. T.A.C. acknowledges grant funding from An2H, AstraZeneca, Bristol-Myers Squibb, Eisai, Illumina and Pfizer. T.A.C. has served as an adviser for An2H, Bristol-Myers Squibb, Eisai, Illumina and MedImmune. T.A.C. holds ownership of intellectual property on using tumour mutation burden to predict immunotherapy response, with a pending patent, which has been licensed to Personal Genome Diagnostics. J.J.H.’s spouse is a full-time employee of Regeneron Pharmaceuticals.

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FoundationOne CDx panel: https://www.fda.gov/medicaldevices/productsandmedicalprocedures/deviceapprovalsandclearances/recently-approveddevices/ucm590331.htm

Friends of Cancer Research Consortium: https://www.focr.org/tmb

MSK-IMPACT panel: https://www.mskcc.org/msk-impact

Glossary

Predictive biomarkers

Measurable biological entities or phenotypes that are associated with response to a specific therapy.

MHC class I

(MHC I). A group of cell surface molecules (major histocompatibility complex (MHC) molecules) present on essentially all nucleated cells that bind and present peptides of approximately 8–10 amino acids in length to CD8+ T cells.

MHC class II

(MHC II). A group of cell surface molecules (major histocompatibility complex (MHC) molecules) expressed primarily on professional antigen-presenting cells such as dendritic cells and macrophages that bind and present peptides of approximately 13–25 amino acids in length to CD4+ T cells.

Central tolerance

The elimination of developing self-reactive T cell clones in order to prevent autoimmunity.

Static biomarkers

Biomarkers determined at a single point in time.

Dynamic biomarkers

Biomarkers determined by the relative change in a measurement over time.

Sensitivity

A measure of the proportion of patients who respond to therapy who were predicted to respond according to a biomarker, for example, biomarker-positive patients who actually respond divided by all patients who actually respond.

Specificity

A measure of the proportion of patients who do not respond to therapy who were predicted not to respond according to a biomarker, for example, biomarker-negative patients who do not respond divided by all patients who do not respond.

Positive predictive value

The probability that a biomarker-positive patient will benefit from a therapy.

Clonal mutations

Mutations acquired early in tumorigenesis that are present in all or most clones. These are also sometimes referred to as truncal mutations.

Subclonal mutations

Mutations acquired later in tumorigenesis that are present in a much smaller percentage of clones relative to clonal mutations.

Somatic copy number alterations

(SCNAs). Focal or chromosome-level deletions and amplifications that arise in tumours.

Epistatic interactions

Interactions of multiple genes that influence a phenotype.

HLA-I supertypes

Individual human leukocyte antigen class I (HLA-I) alleles can be classified into discrete HLA-I supertypes on the basis of similar peptide anchor binding specificities.

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Havel, J.J., Chowell, D. & Chan, T.A. The evolving landscape of biomarkers for checkpoint inhibitor immunotherapy. Nat Rev Cancer 19, 133–150 (2019). https://doi.org/10.1038/s41568-019-0116-x

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