A study published in Science (2018) explores the use of tumor mutational burden (TMB) and a T cell-inflamed gene expression profile (GEP) as predictive biomarkers for response to PD-1 checkpoint blockade immunotherapy. The research analyzed over 300 patient samples from 22 tumor types across four KEYNOTE clinical trials. TMB and GEP were independently predictive of response to pembrolizumab, with TMB reflecting tumor antigenicity and GEP indicating a T cell-inflamed tumor microenvironment. The study found that patients with high TMB and GEP had the strongest response rates, while those with low levels of both had minimal responses. TMB and GEP showed only modest correlation, suggesting they capture distinct aspects of neoantigenicity and T cell activation. The analysis of The Cancer Genome Atlas (TCGA) data confirmed the low correlation between TMB and GEP, highlighting their potential to jointly stratify transcriptomic and genomic features across cancer types. The study also identified specific gene expression patterns associated with TMB, GEP, or both, and found that TMB and GEP could be used to guide the development of combination immunotherapy regimens. The findings suggest that TMB and inflammatory biomarkers such as GEP and PD-L1 can jointly stratify human cancers into groups with different clinical responses to pembrolizumab and identify targetable biology related to these groups. TMB and inflammatory biomarkers independently predict response and may capture distinct features of neoantigenicity and T cell activation, respectively. This approach may provide a precision medicine framework for rationally constructing and evaluating anti-PD-1 and/or anti-PD-L1-based combination therapy regimens.A study published in Science (2018) explores the use of tumor mutational burden (TMB) and a T cell-inflamed gene expression profile (GEP) as predictive biomarkers for response to PD-1 checkpoint blockade immunotherapy. The research analyzed over 300 patient samples from 22 tumor types across four KEYNOTE clinical trials. TMB and GEP were independently predictive of response to pembrolizumab, with TMB reflecting tumor antigenicity and GEP indicating a T cell-inflamed tumor microenvironment. The study found that patients with high TMB and GEP had the strongest response rates, while those with low levels of both had minimal responses. TMB and GEP showed only modest correlation, suggesting they capture distinct aspects of neoantigenicity and T cell activation. The analysis of The Cancer Genome Atlas (TCGA) data confirmed the low correlation between TMB and GEP, highlighting their potential to jointly stratify transcriptomic and genomic features across cancer types. The study also identified specific gene expression patterns associated with TMB, GEP, or both, and found that TMB and GEP could be used to guide the development of combination immunotherapy regimens. The findings suggest that TMB and inflammatory biomarkers such as GEP and PD-L1 can jointly stratify human cancers into groups with different clinical responses to pembrolizumab and identify targetable biology related to these groups. TMB and inflammatory biomarkers independently predict response and may capture distinct features of neoantigenicity and T cell activation, respectively. This approach may provide a precision medicine framework for rationally constructing and evaluating anti-PD-1 and/or anti-PD-L1-based combination therapy regimens.