Identifying vulnerabilities of ovarian cancer persister cells through integrated single-cell analyses

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, there is a pressing need to define therapy-resistant tumors more comprehensively from a broad spectrum of samples to characterize cell types/states associated with resistance, thus enabling the identification of druggable targets of residual disease. This proposal will integrate molecular and multi-spectral imaging tools to extensively characterize High Grade Serious Ovarian Cancer (HGSOC) residual cells that escape therapy, thus identifying tumor cell-intrinsic and - extrinsic vulnerabilities that might be targeted to improve initial response and either delay or prevent recurrence. 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 holistic approach to quantify cell states/types of the tumor microenvironment at a spatial resolution capable of visualizing intra-tumoral heterogeneity is necessary. In the first aim of this proposal, I will define the spectrum of heterogeneous cell states in HGSOC patient tumors following treatment to identify states associated with resistance. I will combine multiplexed cyclic immunofluorescence and spatial transcriptomics approaches to identify cellular programs/pathways (including but not limited to metabolism) and spatial architecture that are associated with resistance. The second aim will capture dynamic shifts in cell states using time series data in vivo to provide insights into the behaviors of different tumor subpopulations following treatment. This approach will enable a mechanistic understanding of the non- genetic/epigenetic adaptations that occur during resistance. In addition to filling a critical need for the treatment of residual disease in HGSOC, 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.