Table S1: Immune marker panels used in NanoString DSP. CAB# is custom antibody ID, which uniquely identifies this reagent. Table S2: Clinicopathological characteristics of EGFR TKI treated NSCLC patients in Yale cohort A. Table S3: Clinicopathological characteristics of NSCLC patients in Yale cohort B. Table S4: Clinicopathological characteristics of ICIs treated melanoma patients in Yale cohort C. Table S5: Multivariate analysis of the predictive markers for PFS in Yale melanoma ICIs treated cohort C. Table S6: Multivariate analysis of the predictive markers for OS in Yale melanoma ICIs treated cohort C. Figure S1: Representative images of NanoString DSP and AQUA compartment selection in NSCLC. The epithelial compartment was defined by cytokeratin positivity (green) which allowed the measurement of targets by both assays in the same compartment. Stroma was defined by cytokeratin negative DNA positive regions (purple). Figure S2: NanoString DSP reproducibility across different cores in ITx melanoma cohort C. (A) CD8 reproducibility across 2 cores (B) CD68 reproducibility across 2 cores Figure S3: NanoString DSP sequential compartment assignment. CD8 counts by NanoString DSP in tumor, CD68+ and CD45+ compartment. Compartment definition accuracy is affected by the order of barcode collection from each compartment. In overlapping compartments, measurement of a marker can be assigned to only one compartment.
ARTICLE ABSTRACT
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.
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.
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.
