Abstract 2670: Combining automated image analysis and digital spatial profiling to investigate prognostic immune signatures in clear cell renal cell carcinoma

Abstract

Abstract Introduction Renal Cell Carcinoma (RCC) is the deadliest urological malignancy. Profiling its complex microenvironment (TME) in situ is crucial to understand the mechanisms of progression and immune evasion that lead to metastasis and death. NanoString® GeoMx™ Digitial Spatial Profiling (DSP) platform facilitates these studies by enabling highly multiplexed, spatially resolved characterisation of proteins and RNA from FFPE tissue. DSP visualises and quantifies targets from areas of interest (AOI) using oligonucleotide-conjugated antibodies. Here, DSP is combined with automated image analysis (IA). When coupled with multiplexed immunofluorescence (IF), IA is able to automatically segment tumor from stroma and profile marker co-expression at single cell level. We present the advantages of using a combinatorial strategy, applied to clear cell RCC (ccRCC) tissue sections, in order to predict patient outcome. Methods 165 patients, grouped into 11 tumour microarray (TMA) slides were labelled with multiplex IF and scanned with a Zeiss Axioscan.z1. Scans were imported into Definiens Tissue Studio® IA software. Multiple TMA cores were sampled from matched non-cancerous kidney, primary, and venous thrombus (VTT) ccRCC. Tumor regions (labelled with Pan-cadherin and CA9) and stroma were segmented prior to automated immune quantification, where CD3, CD163, PD-1 and PD-L1 antibodies were used to profile the immune contexture. DSP was performed on the corresponding serial sections, where a 60-plex antibody panel was applied to each TMA core. Statistical analysis was performed on R Studio, where cox-proportional hazard ratios and Kaplan-Meier curves were used to correlate marker densities to risk of metastasis and cancer-related death. Results Both IA and DSP associated M2 macrophages (CD163) and T cells (CD3) to increased risk of metastasis and poor survival. IA demonstrated that tumor/stroma segmentation and single cell marker co-registration complements DSP analysis by allowing a more detailed profiling of the TME. In particular, a high density of PD-L1 positive tumor cells and PD-1 positive T-cells were correlated to poor survival in VTT and non-cancerous cores, respectively. DSP's high-plex ability is useful to investigate the relationship among the proteins of interest. It confirmed the T-cell exhaustion marker TIM-3 as a poor prognostic factor, thus demonstrating that quantifying only CD3 positive T cells may be insufficient to predict a precise prognosis. Conclusions This data demonstrates that both co-registration of cellular protein expression and highly plexed analysis can add value to the prediction of patient outcome and the risk of metastasis. We further report the prognostic significance of analysing the molecular signature of the immune contexture in both ccRCC tumorous and its adjacent non-cancerous tissue. Citation Format: Raffaele De Filippis, Sarah Warren, Youngmi Kim, Andrew White, Jason Reeves, Grant D. Stewart, David J. Harrison, Joe M. Beechem, Peter D. Caie. Combining automated image analysis and digital spatial profiling to investigate prognostic immune signatures in clear cell renal cell carcinoma [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2670.