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.
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.
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.
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.
This research output has an Altmetric Attention Score of 18. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 12 November 2024.
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