Purpose: Protein expression in formalin-fixed, paraffinembedded 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 (macro-phage, 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.

Next-generation tissue-based biomarkers for immunotherapy will likely include the simultaneous analysis of multiple cell types and their spatial interactions, as well as distinct expression patterns of immunoregulatory molecules. Here, we introduce a comprehensive platform for multispectral imaging and mapping of multiple parameters in tumor tissue sections with high-fidelity single-cell resolution. Image analysis and data handling components were drawn from the field of astronomy. Using this “AstroPath” whole-slide platform and only six markers, we identified key features in pretreatment melanoma specimens that predicted response to anti–programmed cell death-1 (PD-1)–based therapy, including CD163+PD-L1– myeloid cells and CD8+FoxP3+PD-1low/mid T cells. These features were combined to stratify long-term survival after anti–PD-1 blockade. This signature was validated in an independent cohort of patients with melanoma from a different institution.

An anti-metastatic drug, NAMI-A ((ImH)[Ru(III) Cl4 (Im)(dmso)]; Im=imidazole, dmso=S-bound dimethylsulfoxide), and a cytotoxic drug, KP1019 ((IndH)[Ru(III) Cl4 (Ind)2 ]; Ind=indazole), are two Ru-based anticancer drugs in human clinical trials. Their reactivities under biologically relevant conditions, including aqueous buffers, protein solutions or gels (e.g, albumin, transferrin and collagen), undiluted blood serum, cell-culture medium and human liver (HepG2) cancer cells, were studied by Ru K-edge X-ray absorption spectroscopy (XAS). These XAS data were fitted from linear combinations of spectra of well-characterised Ru compounds. The absence of XAS data from the parent drugs in these fits points to profound changes in the coordination environments of Ru(III) . The fits point to the presence of Ru(IV/III) clusters and binding of Ru(III) to S-donor groups, amine/imine and carboxylato groups of proteins. Cellular uptake of KP1019 is approximately 20-fold higher than that of NAMI-A under the same conditions, but it diminishes drastically after the decomposition of KP1019 in cell-culture media, which indicate that the parent complex is taken in by cells through passive diffusion.

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 tumorinfiltrating 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 progressionfree 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.a Cox proportional hazards model included age, sex, mutation status, stage, treatment, and prior immune checkpoint blockade as covariates.

Assessment of tumor infiltrating lymphocytes (TILs) as a prognostic variable in melanoma has not seen broad adoption due to lack of standardization. Automation could represent a solution. Here, using open source software, we build an algorithm for image-based automated assessment of TILs on hematoxylin-eosin stained sections in melanoma. Using a retrospective collection of 641 melanoma patients comprising four independent cohorts; one training set (N = 227) and three validation cohorts (N = 137, N = 201, N = 76) from 2 institutions, we show that the automated TIL scoring algorithm separates patients into favorable and poor prognosis cohorts, where higher TILs scores were associated with favorable prognosis. In multivariable analyses, automated TIL scores show an independent association with disease-specific overall survival. Therefore, the open source, automated TIL scoring is an independent prognostic marker in melanoma. With further study, we believe that this algorithm could be useful to define a subset of patients that could potentially be spared immunotherapy.