Triple negative breast cancer (TNBC) is an aggressive form characterized by the lack of ER, PR, and HER2 receptors. There are currently limited treatment options for TNBC. TNBC tumors present a diverse range of phenotypes, such as basal-like, mesenchymal stem-like, and respond differentially to treatments depending on the immune cell content and cell states. Dissecting the molecular and spatial heterogeneity of cell populations in TNBC tumors becomes necessary for understanding treatment response. Current efforts to dissect the molecular heterogeneity of TNBC employ single-cell RNA sequencing yet these approaches lose the spatial information that underlie cell-to-cell interactions in the tum...

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Triple negative breast cancer (TNBC) is an aggressive form characterized by the lack of ER, PR, and HER2 receptors. There are currently limited treatment options for TNBC. TNBC tumors present a diverse range of phenotypes, such as basal-like, mesenchymal stem-like, and respond differentially to treatments depending on the immune cell content and cell states. Dissecting the molecular and spatial heterogeneity of cell populations in TNBC tumors becomes necessary for understanding treatment response. Current efforts to dissect the molecular heterogeneity of TNBC employ single-cell RNA sequencing yet these approaches lose the spatial information that underlie cell-to-cell interactions in the tumor niche. Advances in proteomic technologies enable a dissection of cell-to-cell interactions between the tumor and the immune cells but these cover only a handful of protein markers. The relative lack of spatial methods and computational analytic methods that directly interrogate the spatial tumor ecosystems in TNBC has impeded progress in our understanding of TNBC pathology and outcomes. It is likely that finding predictive biomarkers of TNBC outcomes will require complementary technologies to be employed, and multiple layers of information, such as transcriptomics, spatial positions, and morphology, to be integrated. The goal of my proposed research is to develop and apply methods to identify spatial transcriptomic signatures of the tumor ecosystem of TNBC and to analyze the clinical significance and molecular underpinnings of morphology features of H&E images, in order to better understand the source of intra-tumor heterogeneity, and TNBC treatment response. My postdoctoral research is dedicated to developing tools to enable diverse spatially related analyses including the detection of spatial domains and spatially enriched cell-cell interactions. I plan to apply these to study spatially associated tumor ecosystems in TNBC, and hope to translate my findings to the clinic.

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