This study evaluates the predictive value of tumor mutational burden (TMB) and a T cell–inflamed gene expression profile (GEP) in identifying responders and nonresponders to pembrolizumab, an anti-PD-1 monoclonal antibody. The analysis includes over 300 patient samples from 22 tumor types across four KEYNOTE clinical trials. TMB and GEP were found to be independently predictive of response, with higher levels of both biomarkers associated with greater objective response rates and longer progression-free survival. The study also explores the relationship between TMB and GEP in a large molecular database (The Cancer Genome Atlas, TCGA), revealing that these biomarkers can jointly stratify tumors into distinct subgroups with different clinical responses to pembrolizumab monotherapy. The findings suggest that combining TMB and inflammatory biomarkers may provide a precision medicine framework for rational selection of anti-PD-1/PD-L1-based combination therapy regimens.This study evaluates the predictive value of tumor mutational burden (TMB) and a T cell–inflamed gene expression profile (GEP) in identifying responders and nonresponders to pembrolizumab, an anti-PD-1 monoclonal antibody. The analysis includes over 300 patient samples from 22 tumor types across four KEYNOTE clinical trials. TMB and GEP were found to be independently predictive of response, with higher levels of both biomarkers associated with greater objective response rates and longer progression-free survival. The study also explores the relationship between TMB and GEP in a large molecular database (The Cancer Genome Atlas, TCGA), revealing that these biomarkers can jointly stratify tumors into distinct subgroups with different clinical responses to pembrolizumab monotherapy. The findings suggest that combining TMB and inflammatory biomarkers may provide a precision medicine framework for rational selection of anti-PD-1/PD-L1-based combination therapy regimens.