Therapeutic Management of Lineage- and Differentiation-state Plasticity

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ABSTRACT – Project 1 Intratumoral heterogeneity is a major cause of therapeutic resistance in breast cancer. Studies have demonstrated that a subset of cancer cells within a heterogeneous tumor can escape therapy-induced death due to innate or adaptive resistance, resulting in recurrence, disease progression, and poor patient survival. Triple negative breast cancer (TNBC) is an aggressive disease characterized by high intratumor heterogeneity and poor patient outcome. In preliminary experiments, we identified subpopulations of tumor cells in primary TNBC as well as in basal-like TNBC cell lines that are characterized by differential expression of luminal, basal, and mesenchymal differentiation-state markers. We have observed that distinct classes of targeted therapeutics have the capacity to eliminate or enrich specific differentiation-state subpopulations within these lines, steering heterogeneous cancer cell populations toward increased homogeneity. Importantly, we identified synergistic combinatorial treatments that targeted either pathway dependencies predicted by master regulator analysis of residual cells or epigenetic regulators found to contribute to a cell's transition to a resistant state. The overall goal of this project, therefore, is to understand cell intrinsic regulation of therapeutic response in phenotypically heterogeneous TNBC in order to develop targeting strategies to kill all co-existing subpopulations. We focus on phenotypic heterogeneity, as this can represent the combination of genetic and epigenetic factors, and we will take advantage of clinically relevant therapeutics that drive heterogeneous populations toward homogeneity. We hypothesize that a systems biology approach of measuring and computationally modeling the functional pathways underlying phenotypic state changes in response to state-aggregating therapeutics will reveal common escape routes and regulators of cell plasticity, which will allow us to predict effective combinatorial therapeutic strategies that eliminate all cancer subpopulations. We will address this hypothesis by (1) examining and computationally modeling phenotype state changes in multiple genetically diverse, heterogeneous TNBC cell lines in response to targeted therapeutics that induce homogeneity using high-content imaging and single cell expression analysis, (2) determining whether clonal expansion or differentiation state plasticity drives the dynamic phenotype changes following targeted therapy and modeling the molecular network changes that underlie these transitions, and (3) determining epigenetic regulation underlying state transitions and developing combinatorial strategies that overcome therapeutic resistance in heterogeneous TNBC cells in vitro and in vivo. Together, these aims support our goal to measure and model cell intrinsic responses to clinically relevant targeted therapeutics and to predict synergistic drug combinations that more effectively control heterogeneous TNBC. Integration of this work with Projects 2 and 3 in the M2CH-CSBC will allow us to incorporate extrinsic regulators of these intrinsic mechanisms and to iteratively refine control strategies for this devastating disease. !