Abstract 3140: Computational modeling of stromal-immune cell interactions in triple-negative breast cancer

Abstract

Abstract Breast cancer is the most common cancer type in women worldwide, and it represents the second most common cause of cancer deaths. Compared with other breast cancer subtypes, triple-negative breast cancer (TNBC) has the worst prognosis. The tumor microenvironment (TME) of TNBC is a complex ecosystem, characterized by the dynamic interactions between stromal, immune, extracellular matrices (ECM), and cancer cells. Understanding the fibroblasts as a heterogeneous population with pleiotropic actions on TME, mediated by ECM formation and interactions with immune cells, is necessary to develop approaches to interfere with the loss of ECM homeostasis, promote immunity, and improve the outcome of concomitant therapeutic approaches in TNBC and other cancer types. Our recent studies suggested distinct fibroblast sub cell types of varied biosynthesis of ECM components in the TME of TNBC, which have significant associations with the infiltration and activity of immune cells. In this study, we developed advanced computational methods to characterize the role of fibroblast sub cell types, their functional variations, and stromal-immune cell interactions in TNBC, by integrative analysis of tissue, single-cell, and spatial multi-omics data. We hypothesized that the genes involved in sub cell types and functional variations of fibroblast cells may form distinct data structures in omics data, which are identifiable by carefully designed pattern recognition methods. We also hypothesized that different subtypes and functional activities of fibroblast cells lead to varied ECM formations, metabolic shifts, and affect the infiltration and functions of immune cells, which could be identified as the spatial regions with distinct expression changes of stromal and immune marker genes.We first reconstructed TNBC TME context-specific gene sets of fibroblast sub cell types and functions. We assemble a series of computational methods including tissue data deconvolution, functional module detection on scRNA-seq data, and metabolic flux analysis into an integrative data analysis framework to characterize fibroblast cells and their functions in the TME of TNBC and reconstruct context-specific pathways of fibroblast sub cell types and functions. We further detected spatially dependent ECM formation and stromal-immune cell interactions in TNBC by using spatial resolved transcriptomics data. We observed spatially dependent variations of (1) sub cell type and cell type-specific functions, and (2) stromal-immune cell interactions, in the TME of TNBC. Citation Format: Yuhui Wei, Haiqi Zhu, Xiao Wang, Xinyu Zhou, Jia Wang, Tingbo Guo, Pengtao Dang, Sha Cao, Chi Zhang. Computational modeling of stromal-immune cell interactions in triple-negative breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3140.