Quantitative Spatial Profiling of PD-1/PD-L1 Interaction and HLA-DR/IDO-1 Predicts Improved Outcomes of anti-PD-1 Therapies in Metastatic Melanoma

Purpose: PD-1/L1 axis-directed therapies produce clinical responses in a subset of patients, therefore, biomarkers of response are needed. We hypothesized quantifying key immunosuppression mechanisms within the tumor microenvironment by multiparameter algorithms would identify strong predictors of anti-PD1 response. Methods: Pre-treatment tumor biopsies from 166 patients treated with anti-PD-1 across 10 academic cancer centers were fluorescently stained with multiple markers in discovery (n = 24) and validation (n = 142) cohorts. Biomarker positive cells and their co-localization were spatially profiled in pathologist-selected tumor regions using novel Automated Quantitative Analysis (AQUA) algorithms. Selected biomarker signatures, PD-1/PD-L1 interaction score and IDO-1/HLA-DR co-expression were evaluated for anti-PD-1 treatment outcomes. Results: In the discovery cohort, PD-1/PD-L1 interaction score and/or IDO-1/HLA-DR co-expression was strongly associated with anti-PD-1 response (P = .0005). In contrast, individual biomarkers (PD-1, PD-L1, IDO-1, HLA-DR) were not associated with response or survival. This finding was replicated in an independent validation cohort: patients with high PD-1/PD-L1 and/or IDO-1/HLA-DR were more likely to respond (P = .0096). These patients also experienced significantly improved progression free survival (PFS; hazard ratio [HR] = 0.36; P = .0004) and overall survival (OS; HR = 0.39; P = .0011). In the combined cohort, 80% of patients exhibiting higher levels of PD-1/PD-L1 interaction scores and IDO-1/HLA-DR responded to PD-1 blockers (P = .000004). In contrast, PD-L1 expression was not predictive of survival. Conclusion: Quantitative spatial profiling of key tumor-immune suppression pathways by novel digital pathology algorithms could help more reliably select melanoma patients for PD-1 monotherapy.

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