Assessment of PD-L1 (programmed cell death 1 ligand 1) expression by immunohistochemical (IHC) analysis has been used as a predictive diagnostic test to identify responders and guide treatment in trials of the PD-1 (programmed cell death 1) axis inhibitors nivolumab, atezolizumab, durvalumab, and pembrolizumab in non–small cell lung cancer (NSCLC).^1-4 The PD-L1 IHC 22C3 PharmDx kit (Dako North America) was approved by the US Food and Drug Administration (FDA) as a companion diagnostic for pembrolizumab in NSCLC, whereas the PD-L1 28-8 PharmDx kit (Dako North America) and the PD-L1 SP142 Ventana test (Ventana Medical Systems Inc) were approved as complementary diagnostics for nivolumab and atezolizumab, repectively.

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.

Purpose: Protein expression in formalin-fixed, paraffin-embedded tissue is routinely measured by IHC or quantitative fluorescence (QIF) on a handful of markers on a single section. Digital spatial profiling (DSP) allows spatially informed simultaneous assessment of multiple biomarkers. Here we demonstrate the DSP technology using a 44-plex antibody cocktail to find protein expression that could potentially be used to predict response to immune therapy in melanoma. Experimental Design: The NanoString GeoMx DSP technology is compared with automated QIF (AQUA) for immune marker compartment-specific measurement and prognostic value in non–small cell lung cancer (NSCLC). Then we use this tool to search for novel predictive markers in a cohort of 60 patients with immunotherapy-treated melanoma on a tissue microarray using a 44-plex immune marker panel measured in three compartments (macrophage, leukocyte, and melanocyte) generating 132 quantitative variables. Results: The spatially informed variable assessment by DSP validates by both regression and variable prognostication compared with QIF for stromal CD3, CD4, CD8, CD20, and PD-L1 in NSCLC. From the 132 variables, 11 and 15 immune markers were associated with prolonged progression-free survival (PFS) and overall survival (OS). Notably, we find PD-L1 expression in CD68-positive cells (macrophages) and not in tumor cells was a predictive marker for PFS, OS, and response. Conclusions: DSP technology shows high concordance with QIF and validates based on both regression and outcome assessment. Using the high-plex capacity, we found a series of expression patterns associated with outcome, including that the expression of PD-L1 in macrophages is associated with response.

Purpose: Because durable response to programmed cell death 1 (PD-1) inhibition is limited to a subset of melanoma patients, new predictive biomarkers could have clinical utility. We hypothesize that pretreatment tumor-infiltrating lymphocyte (TIL) profiles could be associated with response. Experimental Design: Pretreatment whole tissue sections from 94 melanoma patients treated with anti–PD-1 therapy were profiled by multiplex immunofluorescence to perform TIL quantification (CD4, CD8, CD20) and assess TIL activation (CD3, GZMB, Ki67). Two independent image analysis technologies were used: inForm (PerkinElmer) to determine cell counts, and AQUA to measure protein by quantitative immunofluorescence (QIF). TIL parameters by both methodologies were correlated with objective response or disease control rate (ORR/DCR) by RECIST 1.1 and survival outcome. Results: Pretreatment lymphocytic infiltration, by cell counts or QIF, was significantly higher in complete or partial response than in stable or progressive disease, particularly for CD8 (P < 0.0001). Neither TIL activation nor dormancy was associated with outcome. CD8 associations with progression-free survival (HR > 3) were independently significant in multivariable analyses and accounted for similar CD3 associations in anti–PD-1-treated patients. CD8 was not associated with melanoma prognosis in the absence of immunotherapy. Predictive performance of CD8 cell count (and QIF) had an area under the ROC curve above 0.75 (ORR/DCR), which reached 0.83 for ipilimumab plus nivolumab. Conclusions: Pretreatment lymphocytic infiltration is associated with anti–PD-1 response in metastatic melanoma. Quantitative TIL analysis has potential for application in digital precision immuno-oncology as an “indicative” companion diagnostic.