Ovarian cancer, the most lethal gynecologic malignancy in the United States, will result in an estimated 22,280 new cases and 14,240 deaths in 2016. Paclitaxel and carboplatin have remained the standard treatment for over twenty years but cause debilitating side effects in patients. Furthermore, 70-90% of patients with high- grade serous ovarian cancer (HGSOC), the most common epithelial ovarian cancer (EOC), will relapse after standard treatment and succumb to chemoresistant disease. Poly(ADP-ribose) polymerase inhibitors (PARPis) represent a promising new targeted therapy for ovarian cancer and have a milder side effect profile than standard therapy. PARPis have been recently FDA approved for patients with relapsed, platinum sensitive EOC harboring BRCA1/2 mutations. However, as is the case with standard therapy, over 70% of patients who initially respond to PARPis later develop resistant disease. Understanding the mechanisms of this resistance as PARPis move forward in clinical practice is critical from two standpoints: i) to inform potential biomarkers for PARPi sensitivity and ii) to help identify co-therapeutic targets to potentially prevent or overcome PARPi resistance. The Kaufmann laboratory has developed in vitro and in vivo model systems that will be used to generate hypotheses regarding mechanisms of acquired PARPi resistance. The in vitro model system consists of a BRCA2-mutant PARPi sensitive parental ovarian cancer cell line and four separate PARPi resistant clones. The in vivo model system represents the first patient derived xenograft (PDX) mouse model of acquired PARPi resistance and consists of four paired resistant and parental tumor models. Because the resistant and parental samples share the same genetic and transcriptional background, these models are ideally suited to a systems biology approach. Genomic, transcriptomic, and proteomic profiling of the parental and resistant models is currently underway. To identify biologically relevant targets from this sea of omics data, a network-based systems biology approach will be taken. The Li laboratory has recently developed NetDecoder, a novel network-based context-dependent systems biology platform for assessing the significance of genes and proteins within a biological network. This platform will be expanded and enhanced during the integration of this omics data. Predicted drivers of resistance identified from the omics analyses will be prioritized based on their potential as drug co-therapy targets and their mechanistic plausibility. These prioritized targets will be validated in vitro and in vivo. This work represents an interdisciplinary approach to elucidating mechanisms of acquired PARPi resistance that will contribute to the goal of combatting PARPi resistance in patients. Moreover, this project will provide outstanding training at the intersection of cancer pharmacology and systems biology to prepare me for a research career in individualized medicine.
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NIH ExPorter