Biomarkers for Response to Anti–PD-1/Anti–PD-L1 Immune Checkpoint Inhibitors: A Large Meta-Analysis

Abstract

BACKGROUND: Immune checkpoint inhibitors (ICIs) that block PD-1/PD-L1 have consistently demonstrated durable clinical activity across multiple histologies but have low overall response rates for many cancers—indicating that too few patients benefit from ICIs. Many studies have explored potential predictive biomarkers (eg, PD-1/PD-L1 expression, tumor mutational burden [TMB]), no consensus biomarker has been identified. METHODS: This meta-analysis combined predictive accuracy metrics for various biomarkers, across multiple cancer types, to determine which biomarkers are most accurate for predicting ICI response. Data from 18,792 patients from 100 peer-reviewed studies that evaluated putative biomarkers for response to anti–PD-1/anti-PD-L1 treatment were meta-analyzed using bivariate linear mixed models. Biomarker performance was assessed based on the global area under the receiver operating characteristic curve (AUC) and 95% bootstrap confidence intervals. RESULTS: PD-L1 immunohistochemistry, TMB, and multimodal biomarkers discriminated responders and nonresponders better than random assignment (AUCs >.50). Excluding multimodal biomarkers, these biomarkers correctly classified at least 50% of the responders (sensitivity 95% CIs, >.50). Notably, variation in biomarker performance was observed across cancer types. CONCLUSIONS: Although some biomarkers consistently performed better, heterogeneity in performance was observed across cancer types, and additional research is needed to identify highly accurate and precise biomarkers for widespread clinical use.