Pan-Tumor Genomic Biomarkers for PD-1 Based Immunotherapy
Ontology highlight
ABSTRACT: Tumor mutation burden (TMB) and a T-cell-inflamed gene expression profile (GEP) independently predict clinical outcome in pembrolizumab-treated patients in 22 indications and stratify distinct targets of resistance biology.
REPOSITORIES: dbGaP
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| GapExchange_phs001572.v1.p1.xml | Xml | |||
| dbGaPEx2.1.5.xsd | Other | |||
| Study_Report.phs001572.PD1.v1.p1.MULTI.pdf | ||||
| manifest_phs001572.PD1.v1.p1.c3.DS-CA-IRB.pdf | ||||
| datadict_v2.xsl | Other |
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Project description: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.
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