Identifying vulnerabilities of ovarian cancer persister cells through integrated single-cell analyses - PROJECT SUMMARY/ABSTRACT Recurrence of cancer cells that evade therapy is a leading cause of death. Given that residual disease can persist for years to decades following therapy, it presents a second therapeutic window where the vulnerabilities of residual cells can be exploited to identify novel, actionable targets, thus reducing or delaying recurrence. Unfortunately, mechanisms of residual disease survival remain under-studied, partly due to the lack of tools and models to precisely study the evolutionary nature of residual disease. To this end, metabolic and vascular reprogramming have been observed to occur in parallel to therapy resistance and precede anatomical changes after treatment, and thus hold promise as targets to be leveraged to improve clinical outcomes. This proposal will develop novel functional imaging tools to understand the dynamic behaviors of residual tumors (F99 phase) and how metabolic and vascular vulnerabilities can be targeted during residual disease to mitigate recurrence (K00 phase). The emergence of residual disease reflects the spatiotemporal heterogeneities of the tumor microenvironment and the evolutionary property of cancer cells to adapt to therapy-induced selective pressures. Therefore, to effectively monitor treatment responses, a systems-level approach to image metabolism and the associated vasculature of the tumor microenvironment at a spatial resolution capable of visualizing intra-tumoral heterogeneity in vivo is necessary, but currently unavailable. In the F99 phase of this proposal, I aim to design novel optical imaging methodologies to track metabolic and vascular shifts to identify metabolically distinct residual tumor subpopulations that emerge following chemotherapy. In aim 1.1 (previous work), I show that longitudinal assessment of bulk tumor metabolism and intra-tumoral heterogeneity enables chemotherapy induced metabolic shifts to be captured during disease regression, residual disease, and recurrence. In aim 1.2 (proposed work), I will develop image segmentation approaches to quantify 1) cellular-level metabolic features and 2) vascular characteristics that lead to poor perfusion. I will examine whether resistance to treatment leads to the emergence of specialized niches of metabolically distinct residual tumor subpopulations that could be targeted. While functional imaging approaches are desirable due to their ability to reflect cellular, and tissue- level dynamics, they are insufficient to elucidate all the molecular mechanisms that drive recurrence. In the K00 phase of this proposal, I will take a molecular approach to delve into the mechanisms of residual disease in PDX models of Triple Negative Breast Cancer (TNBC). I will focus on targeting MYC oncogenic signaling pathways to identify novel, actionable metabolic and vascular targets of residual disease for a subset of TNBC tumors. In addition to filling a critical need for the treatment of residual disease in TNBC, this training plan will provide exceptional training by leaders in the imaging and cancer biology fields, positioning me to become an accomplished independent researcher at the interface of these two fields.
