Strategies for discerning chemotherapy response and resistance in ovarian cancer

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Project Summary/Abstract Ovarian cancer is the fifth leading cause of cancer death among females. Most cases of ovarian cancer are high- grade serous ovarian cancer (HGSOC), which accounts for most ovarian cancer mortality. In spite of the increasing knowledge of this tumor type, the use of “old” DNA-damaging chemotherapeutic drugs remains the first option for ovarian cancer patients. The standard treatment of HGSOC involves de-bulking surgery followed by platinum-based chemotherapy with the aim to eliminate all the remaining micro-metastases. Unfortunately, a significant number of HGSOC patients do not respond or only partially respond to these treatments. A patient who does not respond to platinum chemotherapies has to go through the same treatment and experience the toxicity without meaningful benefit. Despite the rapid advances in cancer genomics and precision medicine in the last decade, what remains surprising is that no robust molecular biomarker that can foretell patient response to platinum agents has been identified in ovarian cancer, pointing to the need for the incorporation of functional metrics and early-stage molecular changes into the predictive framework. We hypothesize that early-stage molecular and functional alterations upon drug exposure that underlie how tumor cells orchestrate a response to the drug treatment are dictating longer-term therapy responses and thus critical to the development of predictive models. We further hypothesize that the drug susceptibility and bioenergetic dependency of the patient tumors are important functional readouts for such a prediction. We propose to integrate early-stage temporal genome, transcriptome, metabolic phenotypes, drug susceptibility, and histology information into machine learning models to discern a set of metrics most predictive for patient-specific responses to platinum chemotherapy in HGSOC. This is enabled by a strategic integration of a freshly prepared, precision-cut tumor slice culture model, an innovative single-cell on-chip metabolic cytometry assay, and a novel in situ spatial metabolic profiling assay on live tissues. Upon successful completion, this study could deliver a predictive diagnostic assay for identifying HGSOC patients with a higher risk of resistance to platinum chemotherapy, as well as patients who may benefit from such treatments, prior to the onset of therapy. These results will provide innovative molecular and functional information complementary to tumor genetics and other clinical factors for more informative clinical decision-making and help re-direct platinum-refractory patients to other therapeutic approaches or clinical trials of novel therapies before the treatment.