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Review
November 2016

Genomics- and Transcriptomics-Based Patient Selection for Cancer Treatment With Immune Checkpoint Inhibitors: A Review

Author Affiliations
  • 1Division of Molecular Oncology, Antoni van Leeuwenhoek, Netherlands Cancer Institute, Amsterdam, the Netherlands
  • 2Division of Immunology, Antoni van Leeuwenhoek, Netherlands Cancer Institute, Amsterdam, the Netherlands
 

Copyright 2016 American Medical Association. All Rights Reserved.

JAMA Oncol. 2016;2(11):1490-1495. doi:10.1001/jamaoncol.2016.2214
Abstract

Importance  Checkpoint blockade therapy targeting cytotoxic T-lymphocyte–associated antigen 4 (CTLA-4) and the programmed cell death protein 1 pathways (PD-1/PD-L1) have achieved success in treating a number of malignancies. However, only a subset of patients responds to these therapies, and optimization of patient selection for treatment is imperative to avoid adverse effects without clinical benefit and keep costs manageable.

Observations  The past few years have witnessed checkpoint inhibition becoming a first-line treatment option with US Food and Drug Administration approvals for various tumor types. Genomic analyses (whole genome, exome, and transcriptome) have been instrumental in identifying a genetic profile associated with sensitivity to checkpoint inhibitors. Therapy outcome is determined at various levels: (1) the degree of tumor “foreignness,” as reflected by mutational burden and expression of viral genes, (2) the composition and activity of a preexisting immune infiltrate, and (3) mechanisms of tumor escape from immune surveillance. In addition, there are opportunities for genomic analyses of genetic polymorphisms and the gut microbiome that may be associated with clinical response to therapy.

Conclusions and Relevance  Genomics provides powerful tools for the identification of biomarkers for response to immune checkpoint blockade, given their potential to analyze multiple parameters simultaneously in an unbiased manner. This offers the opportunity for genomics- and transcriptomics-based selection of patients for rationally designed therapy with immune checkpoint inhibitors.

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