BCCMA: Overcoming chemoresistance in ovarian cancer: Identification and validation of biomarkers and targetable drivers of platinum resistance

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This Collaborative Merit Award application (CMA), consisting of three projects (CMA1-3), addresses a critical challenge in the clinical management of ovarian cancer (OC). The most common and lethal subtype of OC is high-grade serous ovarian carcinoma (HGSOC). Standard treatment for HGSOC combines surgical cytoreduction with platinum-based chemotherapy. Patients diagnosed with HGSOC often suffer from disease relapse associated with the emergence of chemotherapy resistance. The clinically necessary key to increasing survival in HGSOC is to prevent the development of platinum resistance (PtR) or identify alternative means of targeting PtR tumors. The main goal of this interdisciplinary and collaborative project is to identify novel targets and biomarkers of therapeutic efficacy for HGSOC. This requires a better understanding of the mechanisms that result in transformation of HGSOC cells to an aggressive, therapy-resistant phenotype. Increasing evidence supports the hypothesis that a key contributor to PtR is the reprogramming of cancer cells into a less differentiated and metabolically adaptable state. This collaborative proposal by three established OC researchers will leverage their interdisciplinary expertise and rich resources to define new mechanisms of resistance in OC. CMA1 will use digital spatial profiling and systems biology to provide a holistic understanding of PtR as well as prioritize cell-intrinsic and microenvironmental clinically relevant underlying molecular pathways. Unique preclinical immunocompetent mouse models and co-culture models will be used to study the role of the tumor microenvironment in PtR. CMA2 will study metabolic adaptation associated with the emergence of PtR focusing on a shift to fatty acid oxidation in PtR HGSOC tumors. CMA2 will use resources shared with CMA1&3 and cellular biology and novel single cell metabolic imaging to define unique metabolic dependencies of PtR HGSOC. As PtR tumors are highly susceptible to death induced by oxidized lipid membranes, mechanisms of ferroptosis will be examined in PtR models treated with novel metabolism targeting agents, which will be tested with CMA1. CMA3 will explore the reprogramming of recurrent HGSOC cells into more dedifferentiated tumor subpopulations with neuroendocrine (NE)-like features. Mounting evidence in other tumors suggest progression to a NE-like state results in therapy resistance - a concept yet to be explored in OC. To identify NE-like cells in OC, the transcriptome, proteome, gene vulnerability, and drug sensitivity landscape of matched patient tumors and model systems will be evaluated for the emergence of NE-like cells under chemotherapeutic pressures. Existing drug dependency databases will be mined for identification of druggable targets in NE-like cells. Drugs effective against these cells will be tested alone or in combination as targeted therapy for PtR OCs utilizing patient derived organoid and xenograft models. Hallmarks of NE-like cells including metabolic adaptations and histologic characteristics will be explored with CMA2 & CMA1 respectively. CMA1 rationale: The evaluation of histopathology slides is the main method to establish a HGSOC diagnosis and guide treatment decisions. However, the rich data embedded within histopathology slides have not been fully exploited to understand the underlying causes of PtR and develop personalized treatment strategies. Prior research on PtR mostly used cancer cell lines and focused on cell-intrinsic events. However, data from clinical samples suggest that the tissue microenvironment (TME) is a major driver of PtR. Specifically, cancer- associated fibroblasts (CAFs) were shown to induce PtR in adjacent cancer cells. We hypothesize that higher- order features extracted from pathology slide images are associated with distinct molecular profiles, such as specific gene and protein expression, metabolism, and sensitivity to specific drugs. We will combine spatial image profiling with assays in mouse models and CAF/cancer cell co-cultures to mechanistically dissect the role of the TME in PtR. The integration of image data from patient slides with other -omics data will add a key layer of information to prioritize biologically- and clinically-relevant biomarkers and drivers of PtR.