Abstract 1083: Combinatorial strategies for tissue characterization with advanced image analysis and digital spatial profiling

Abstract

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.