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September 2016

Biomarkers for the Clinical Use of PD-1/PD-L1 Inhibitors in Non–Small-Cell Lung Cancer: A Review

Author Affiliations
  • 1New York–Presbyterian/Columbia University Medical Center, New York
  • 2Lowe Center for Thoracic Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
  • 3Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts
JAMA Oncol. 2016;2(9):1217-1222. doi:10.1001/jamaoncol.2016.0639

Importance  The development of programmed cell death 1 (PD-1)/PD-1 ligand 1 (PD-L1) checkpoint inhibitors has changed the landscape of non–small-cell lung cancer (NSCLC) therapy, with 2 approvals from the US Food and Drug Administration of PD-1 inhibitors for second-line therapy. However, the rational use of these agents has been limited by the lack of a definitive predictive biomarker.

Observations  Tumor PD-L1 expression is associated with an increased likelihood of NSCLC response to these agents, although responses can still occur at a low rate in PD-L1–negative tumors. The use of PD-L1 as a predictive biomarker for use of PD-1/PD-L1 inhibitors is limited by the multitude of PD-L1 antibodies, assays, scoring systems, and thresholds for positivity currently used. Alternative biomarkers such as tumor neoantigens identified through whole-exome sequencing and clinical parameters (eg, smoking or oncogene driver status) may also have predictive value. Biomarkers that can direct the rational use of PD-1/PD-L1 checkpoint inhibitors are crucial given the risk of life-threatening immune-related complications associated with these therapies and the reality that most patients still do not benefit from their use.

Conclusions and Relevance  The refinement of existing biomarkers and identification of novel predictive biomarkers will be key to ensuring the effective and safe use of these agents. Since most patients still do not benefit from these agents, it is critical to continue to work to define the select patient population who will derive durable benefit from PD-1/PD-L1 inhibition and identify markers that could have predictive value for combination therapies that could expand the population who benefit.

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