Variants in Epithelial-Mesenchymal Transition and Immune Checkpoint Genes Are Associated With Immune Cell Profiles and Predict Survival in Non–Small Cell Lung Cancer

Abstract

Context.—Identification of gene mutations that are indicative of epithelial-mesenchymal transition and a noninflammatory immune phenotype may be important for predicting response to immune checkpoint inhibitors.Objective.—To evaluate the utility of multiplex immunofluorescence for immune profiling and to determine the relationships among tumor immune checkpoint and epithelial-mesenchymal transition genomic profiles and the clinical outcomes of patients with nonmetastatic non–small cell lung cancer.Design.—Tissue microarrays containing 164 primary tumor specimens from patients with stages I to IIIA non–small cell lung carcinoma were examined by multiplex immunofluorescence and image analysis to determine the expression of programmed death ligand-1 (PD-L1) on malignant cells, CD68+ macrophages, and cells expressing the immune markers CD3, CD8, CD57, CD45RO, FOXP3, PD-1, and CD20. Immune phenotype data were tested for correlations with clinicopathologic characteristics, somatic and germline genetic variants, and outcome.Results.—A high percentage of PD-L1+ malignant cells was associated with clinicopathologic characteristics, and high density of CD3+PD-1+ T cells was associated with metastasis, suggesting that these phenotypes may be clinically useful to identify patients who will likely benefit from immunotherapy. We also found that ZEB2 mutations were a proxy for immunologic ignorance and immune tolerance microenvironments and may predict response to checkpoint inhibitors. A multivariate Cox regression model predicted a lower risk of death for patients with a high density of CD3+CD45RO+ memory T cells, carriers of allele G of CTLA4 variant rs231775, and those whose tumors do not have ZEB2 mutations.Conclusions.—Genetic variants in epithelial-mesenchymal transition and immune checkpoint genes are associated with immune cell profiles and may predict patient outcomes and response to immune checkpoint blockade.