Spatial and Temporal Heterogeneity of PD-L1 Expression and Tumor Mutational Burden in Gastroesophageal Adenocarcinoma at Baseline Diagnosis and after Chemotherapy

PUBLICATIONS

December 1, 2020

Clinical Cancer Research, Tempus-acknowledged —

Purpose: Intrapatient heterogeneity of programmed death ligand 1 (PD-L1) expression and tumor mutational burden (TMB) in gastroesophageal adenocarcinoma (GEA) could influence their roles as predictive biomarkers for response to immune checkpoint inhibitors (ICI). In this retrospective analysis, we evaluated the spatiotemporal heterogeneity and prognostic relevance of PD-L1 expression and TMB in GEA.

Experimental Design: A cohort of 211 patients with stage II–IV GEA was retrospectively reviewed for a total of 407 tumor samples with PD-L1 expression data and 319 tumor samples with TMB data. PD-L1 status was defined as positive if combined positive score (CPS) ≥1 using the 22C3 pharmDx assay. TMB levels were categorized as low, intermediate, or high (≤5, 5–15, or >15 mutations/Mb), or using a single threshold (<10 or ≥10 mutation/Mb), determined by next-generation sequencing using a targeted gene panel.

Results: Of 407 tumors, 56% were PD-L1 negative and 44% PD-L1 positive. Of 319 tumors, 50% were TMB-low, 45% TMB-intermediate, and 5% TMB-high; 86% had <10 and 14% ≥10 mutations/Mb. TMB level was significantly associated with MSI-status. PD-L1 expression and TMB exhibited marked spatial heterogeneity between baseline primary and metastatic tumors (61% and 69% concordance), and temporal heterogeneity between tumors before and after chemotherapy (57%–63% and 73%–75% concordance). PD-L1 expression and TMB were not significantly associated with overall survival.

Conclusions: PD-L1 expression and TMB exhibit marked spatial and temporal heterogeneity in GEA. This heterogeneity should be considered when obtaining tumor samples for molecular testing and when deciding whether ICI therapy is appropriate.

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Authors: Katherine I. Zhou, Bryan Peterson, Anthony Serritella, Joseph Thomas, Natalie Reizine, Stephanie Moya, Carol Tan, Yan Wang and Daniel V.T. Catenacci