Platform for Quantitative Evaluation of Spatial Intratumoral Heterogeneity in Multiplexed Fluorescence Images

Abstract

We introduce THRIVE (Tumor Heterogeneity Research Interactive Visualization Environment), an open-source tool developed to assist cancer researchers in interactive hypothesis testing. The focus of this tool is to quantify spatial intratumoral heterogeneity (ITH), and the interactions between different cell phenotypes and noncellular constituents. Specifically, we foresee applications in phenotyping cells within tumor microenvironments, recognizing tumor boundaries, identifying degrees of immune infiltration and epithelial/stromal separation, and identification of heterotypic signaling networks underlying microdomains. The THRIVE platform provides an integrated workflow for analyzing whole-slide immunofluorescence images and tissue microarrays, including algorithms for segmentation, quantification, and heterogeneity analysis. THRIVE promotes flexible deployment, a maintainable code base using open-source libraries, and an extensible framework for customizing algorithms with ease. THRIVE was designed with highly multiplexed immunofluorescence images in mind, and, by providing a platform to efficiently analyze high-dimensional immunofluorescence signals, we hope to advance these data toward mainstream adoption in cancer research. Cancer Res; 77(21); e71-74. ©2017 AACR.

Figure 1. Tumor Heterogeneity Research Interactive Visualization Environment (THRIVE)

(A) For a given panel of images, a cell segmentation algorithm is run to obtain single-cell resolution. Then, biomarker intensity statistics (e.g. mean, median) are computed for each cell from the segmentation results. These statistics are used to discover cell phenotypes via pattern recognition. Heterogeneity metrics are used to quantify the spatial relationships between cell phenotypes. The bar graph shows the heterogeneity of cell phenotypes discovered from ERα expression for two different tumor ROIs (shown in red and blue). Phenotype heterogeneity is quantified by quadratic entropy summarized over the whole slide and statistics from ROIs. (B) Pointwise mutual information (PMI) maps capture the relative spatial co-occurrences of cell phenotypes (denoted by various cell colors) in a multiplexed IF image (1). The diagonal elements of the PMI map denote globally heterogeneous and locally homogenous interactions, while off-diagonal elements capture locally heterogeneous interactions. PMI is scaled from -1 (negative association) to 1 (positive association) where 0 is the background co-occurrence of cell phenotypes.