Abstract
BACKGROUND: Anti-programmed cell death 1 (anti-PD-1) and PD ligand 1 (PD-L1) immune checkpoint therapies (ICTs) provided durable responses only in a subset of cancer patients. Thus, biomarkers are needed to predict nonresponders and offer them alternative treatments. We recently implicated discoidin domain receptor tyrosine kinase 2 (DDR2) as a contributor to anti-PD-1 resistance in animal models; therefore, we sought to investigate whether this gene family may provide ICT response prediction. METHODS: We assessed mRNA expression of DDR2 and its family member DDR1. Transcriptome analysis of bladder cancer (BCa) models in which DDR1 and 2 were perturbed was used to derive DDR1- and DDR2-driven signature scores. DDR mRNA expression and gene signature scores were evaluated using BCa-The Cancer Genome Atlas (n = 259) and IMvigor210 (n = 298) datasets, and their relationship to BCa subtypes, pathway enrichment, and immune deconvolution analyses was performed. The potential of DDR-driven signatures to predict ICT response was evaluated and independently validated through a statistical framework in bladder and lung cancer cohorts. All statistical tests were 2-sided. RESULTS: DDR1 and DDR2 showed mutually exclusive gene expression patterns in human tumors. DDR2high BCa exhibited activation of immune pathways and a high immune score, indicative of a T-cell-inflamed phenotype, whereas DDR1high BCa exhibited a non-T-cell-inflamed phenotype. In IMvigor210 cohort, tumors with high DDR1 (hazard ratio [HR] = 1.53, 95% confidence interval [CI] = 1.16 to 2.06; P = .003) or DDR2 (HR = 1.42, 95% CI = 1.01 to 1.92; P = .04) scores had poor overall survival. Of note, DDR2high tumors from IMvigor210 and CheckMate 275 (n = 73) cohorts exhibited poorer overall survival (HR = 1.56, 95% CI = 1.20 to 2.06; P < .001) and progression-free survival (HR = 1.77 95%, CI = 1.05 to 3.00; P = .047), respectively. This result was validated in independent cancer datasets. CONCLUSIONS: These findings implicate DDR1 and DDR2 driven signature scores in predicting ICT response.