Pan-tumor genomic biomarkers for PD-1 checkpoint blockade–based immunotherapy

TLDR

PD‑1/PD‑L1 checkpoint blockade produces durable antitumor responses in many cancers, yet response rates vary across patients. The study analyzed over 300 samples from 22 tumor types in four KEYNOTE trials to assess predictive biomarkers. Tumor mutational burden and a T‑cell‑inflamed gene expression profile independently predict pembrolizumab response, are weakly correlated, and together delineate distinct resistance mechanisms that can guide trial design.

Abstract

Programmed cell death protein-1 (PD-1) and programmed cell death ligand-1 (PD-L1) checkpoint blockade immunotherapy elicits durable antitumor effects in multiple cancers, yet not all patients respond. We report the evaluation of >300 patient samples across 22 tumor types from four KEYNOTE clinical trials. Tumor mutational burden (TMB) and a T cell-inflamed gene expression profile (GEP) exhibited joint predictive utility in identifying responders and nonresponders to the PD-1 antibody pembrolizumab. TMB and GEP were independently predictive of response and demonstrated low correlation, suggesting that they capture distinct features of neoantigenicity and T cell activation. Analysis of The Cancer Genome Atlas database showed TMB and GEP to have a low correlation, and analysis by joint stratification revealed biomarker-defined patterns of targetable-resistance biology. These biomarkers may have utility in clinical trial design by guiding rational selection of anti-PD-1 monotherapy and combination immunotherapy regimens.