Although immune checkpoint inhibitors (ICIs) have significantly improved the oncological outcomes, about one-third of patients affected by clear cell renal cell carcinoma (ccRCC) still experience recurrence. Current prognostic algorithms, such as the Leibovich score (LS), rely on morphological features manually assessed by pathologists and are therefore subject to bias. Moreover, these tools do not consider the heterogeneous molecular milieu present in the Tumour Microenvironment (TME), which may have prognostic value. We systematically developed a semi-automated method to investigate 62 markers and their combinations in 150 primary ccRCCs using Multiplex Immunofluorescence (mIF), NanoString GeoMx® Digital Spatial Profiling (DSP) and Artificial Intelligence (AI)-assisted image analysis in order to find novel prognostic signatures and investigate their spatial relationship. We found that coexpression of cancer stem cell (CSC) and epithelial-to-mesenchymal transition (EMT) markers such as OCT4 and ZEB1 are indicative of poor outcome. OCT4 and the immune markers CD8, CD34, and CD163 significantly stratified patients at intermediate LS. Furthermore, augmenting the LS with OCT4 and CD34 improved patient stratification by outcome. Our results support the hypothesis that combining molecular markers has prognostic value and can be integrated with morphological features to improve risk stratification and personalised therapy. To conclude, GeoMx® DSP and AI image analysis are complementary tools providing high multiplexing capability required to investigate the TME of ccRCC, while reducing observer bias.

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.

Although Immune Checkpoint Inhibitors (ICIs) have significantly improved Clear Cell Renal Cell Carcinoma (ccRCC) prognosis, about one third of patients experience recurrence. Current prognostic algorithms like the Leibovich Score (LS) rely on morphological features manually assessed by pathologists, and are therefore subject to bias. Moreover, these tools do not consider the heterogeneous molecular milieu present in the Tumour Microenvironment (TME), which may have prognostic value. We systematically developed a semi-automated method to investigate 62 markers and their combinations in 150 primary ccRCCs using multiplex Immunofluorescence (mIF), NanoString GeoMx ® Digital Spatial Profiling (DSP) and Artificial Intelligence (AI)-assisted image analysis in order to find novel prognostic signatures and investigate their spatial relationship. We found that coexpression of Cancer Stem Cell (CSC) and Epithelial-to-Mesenchymal Transition (EMT) markers such as OCT4 and ZEB1 are indicative of poor outcome. OCT4 and the immune markers CD8, CD34 and CD163 significantly stratified patients at intermediate LS. Furthermore, augmenting the LS with OCT4 and CD34 improved patient stratification by outcome. Our results support the hypothesis that combining molecular markers has prognostic value and can be integrated with morphological features to improve risk stratification and personalised therapy. To conclude, GeoMx ® DSP and AI image analysis are complementary tools providing high multiplexing capability required to investigate the TME of ccRCC, while reducing observer bias.

Characterization of the spatial distribution and molecular profiles of diverse cell populations in the tumor microenvironment is important to understand the conditions required for the development of metastatic disease. The NanoString® GeoMx™ Digitial Spatial Profiling (DSP) platform facilitates these studies by enabling highly multiplexed, spatially resolved characterization of proteins and RNA from FFPE tissue (for Research Use Only). The platform leverages cocktails of fluorophore- and oligonucleotide-conjugated antibodies to visualize and digitally quantify targets from areas of interest (AOI). Here, DSP is combined with advanced tissue profiling using machine learning algorithms to define AOIs based on expression of multiple markers. This strategy enables targeted DSP sampling based on the digital segmentation of heterogeneous cell populations within the tumor microenvironment. We present a series of applications of this combined profiling strategy to enable deep characterization of renal, colon, and bladder cancers to address molecular and cellular interactions between host and tumor that facilitate metastasis and poor patient outcome. Whole slide digital scanning of multiplexed immunofluorescence labelled slides was performed on a Zeiss Axioscan.z1. Digitized scans were imported in Definiens Tissue Studio® software and machine learning-based analysis was performed. The image analysis segmented heterogeneous subpopulations, dependent on biomarker expression and spatial resolution, which acted as a digital spatial map to direct DSP sampling. DSP was performed on a serial section of tissue by selecting AOI within bladder and colorectal cancer sections and a geometric DSP profiling strategy was employed to collect all the protein content from the AOI in a single sample. In another application of the method, a clear cell renal cell carcinoma (ccRCC) tissue microarray comprised of cores from patient matched healthy, primary, venous thrombi and distant metastatic sites was profiled with DSP. We present highly-plexed in situ proteomic data captured from several AOIs across bladder and colorectal cancer tissue sections. This data describes the molecular profile from the densely distributed lymphocytes and macrophages in close proximity to tumor subpopulations such as tumor buds and PDL-1 positive cancer cells. We demonstrate that both tumor and immune cell subpopulation profiles within bladder and CRC alter dependent on their proximity to each other. We further present the altering proteomic landscape of ccRCC as it progresses from primary to metastatic disease. This work demonstrates the feasibility and applicability of combining machine learning and advanced image analysis with multiplexed digital spatial profiling to enable deep characterization of tissues. Analysis approaches such as these will have utility in a variety of pathological research settings. Citation Format: Raffaele De Filippis. Combinatorial strategies for tissue characterization with advanced image analysis and digital spatial profiling [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1083.

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