High-plex predictive marker discovery for melanoma immunotherapy-treated patients using digital spatial profiling

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Abstract

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 (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.

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Toki, M. I., Merritt, C. R., Wong, P. F., Smithy, J. W., Kluger, H. M., Syrigos, K. N., … Rimm, D. L. (2019). High-plex predictive marker discovery for melanoma immunotherapy-treated patients using digital spatial profiling. Clinical Cancer Research, 25(18), 5503–5512. https://doi.org/10.1158/1078-0432.CCR-19-0104

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