Understanding the Impact of Microscale and Nanoscale Heterogeneity and Resistance

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ABSTRACT- Project 3 The goals of this Project are to use a spatial systems approach to identify molecular networks that control development of resistance-associated heterogeneity in triple negative breast cancers (TNBCs) and to use this information to devise multidrug treatments that will be effective in heterogeneous TNBCs. Our focus is on heterogeneity that arises from epigenomic plasticity intrinsic to cancer cells and from extrinsic signals from the diverse microenvironments into which TNBC cells disperse. Individual cells within a TNBC exhibit variable phenotypes and respond variably to treatment so that establishing durable control of TNBCs is notoriously difficult. We will explore the mechanisms by which individual cells in TNBC tissues respond to perturbations induced by microenvironment interactions and/or drugs. Our approach is based on the concept that the phenotype and response to therapy of every cell in a heterogeneous TNBC tissue is influenced by its intrinsic epigenomic status and by the microenvironmental signals it receives. In short, every cancer cell- microenvironment-drug interaction in a heterogeneous experimental tissue or clinical specimen is an independent experiment of nature. We propose to analyze ensembles of such interactions in TNBC tissues before and after treatment to determine the impact of local environmental signals on cancer cell phenotype and therapeutic response. We will accomplish this using cmIF to stain cancer cells for quantitative analysis of proliferative status, differentiation state, and expression levels of proteins that report on control network activity. We will quantify cancer cell-microenvironment interactions at the microscale using multicolor fluorescence microscopy and at the nanoscale using multispectral super resolution fluorescence microscopy (MSSRM) and 3D scanning electron microscopy. We will use custom image analysis techniques developed in the Imaging Core to quantify cell and microenvironment components and machine/deep learning strategies to identify microenvironment-cancer cell interactions that influence phenotype. This work will guide development of dynamic models of spatially dependent control network-microenvironment interactions that can be used to devise therapeutic strategies to control TNBCs. The approach is statistically powerful since every tissue section contains details about tens of thousands of cell-microenvironment interactions. This work is encompassed in three Aims. Aim 1 will develop cyclic multiplex immunofluorescence (cmIF), multiscale image analysis, and machine learning procedures needed to identify molecular control networks in individual cells in TNBC tissues that respond to signals from microenvironmental cells and proteins (MEPs) and that influence phenotype and/or therapeutic response. Aim 2 will elucidate the effects of microenvironmental cells and high impact proteins on TNBC control network activity, phenotype, and therapeutic response in bioprinted tissues. Aim 3 will elucidate the effects of microenvironmental cells and high impact proteins on TNBC control network activity, phenotype, and therapeutic response in TNBC xenografts and clinical TNBC specimens.