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

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.