Epithelial ovarian cancer is the most lethal gynecological tumor worldwide, and it is the fifth common cause of female cancer death in the United States. The majority of women diagnosed with advanced-stage epithelial ovarian cancer experience tumor recurrence associated with the development of chemoresistance. It is widely recognized that the ovarian cancer cells have a significantly different metabolism than normal cells. In order to provide effective chemotherapies for ovarian cancers, novel drugs that target cancer cell metabolism directly are urgently required. In this proposal, we aim to identify these novel drugs that selectively target the dysregulated metabolic machinery in ovarian cancer cells while sparing normal ovarian cells.

In order to achieve this goal, we have created a strategic alliance of three multidisciplinary labs to screen novel drugs for ovarian cancers in a physiological relevant 3D preclinical model and to identify altered metabolic genes and pathways via genome-wide changes to RNA synthesis and stability assessed by Bru-seq and BruChase-seq. Additionally, we have the unique opportunity to test 650 novel potent small molecules drugs that target ovarian cancer cells that we have screened from a library of 28 million candidates, none of which have been previously explored for ovarian cancer therapy. At least 100 novel drugs that are not yet commercially available, but found to be potent in 2D screens with chemoresistant ovarian cancers, will be screened in a 3D physiologically relevant spheroid platform that preserves the patient diversity and heterogeneity of cancer cells in small cell number primary and metastatic ovarian tumor spheroids. This platform for preclinical screening has advantages of high-throughput multiplexing, easy liquid handling, and increased robustness of droplet stability to allow long-term spheroid culture.

Newly developed and validated next-generation sequencing technologies (Bru-seq and BruChase-seq) will quantify the changes in the rates of both synthesis and degradation of RNA globally after the treatment with most effective novel drugs to provide a comprehensive and complex picture of the contribution of transcriptional and post-transcriptional regulation after treatment with nanomolar potency novel drugs. Aberrant metabolic pathways in the novel drug-treated, patient-derived spheroids that are responsible for chemoresistance will be discovered. These pathways will help identify the most relevant targets that will be utilized for developing drugs that only target ovarian cancers while sparing normal ovarian cells. Therefore, given these unique advantages, our integrated platform will be an effective and time-saving preclinical physiologically relevant approach for discovering the new generation of ovarian cancer drugs that target cancer cells' metabolism directly.

Metabolic reprograming is widely recognized as the key to carcinogenesis, and metabolism has been suggested as a potential target for chemotherapy-resistant cells and cancer stem cells. Mitochondria play an important role in cell survival and cell death, and mitochondrial uncoupling may be responsible for increased resistance to chemotherapy. Thus, designing therapeutic strategies for specifically killing cancer cells by exploiting their metabolic alterations is a promising approach that will be explored in this proposal. This innovative approach will identify the novel drugs that target different aspects of cancer bioenergetics and mitochondrial functions to provide synergistic therapies for ovarian cancer, while at the same time, reducing any off-target effects and increasing the potency.

Short-Term Impact: With our physiologically relevant novel approach, we will discover novel drugs that target cellular metabolism in patient-derived ovarian cancer cells, and we will identify the altered metabolic pathways after novel drug treatments. Our integrated platform is user friendly and high-throughput amenable, and it can be easily adopted by both basic and clinical oncology researchers. By combining a physiological relevant model that preserves ovarian cancer patient heterogeneity with genetic analysis after novel drug treatments, we will enhance the predictive potency of preclinical drug testing. By optimizing concurrent and sequential chemotherapeutic strategy with novel drugs or their combination, we will reduce tumor growth.

Long-Term Impact: With the screening of novel drugs that directly target cancer cell metabolism and subsequent Bru-sequencing, ovarian cancer patient cells can be screened for effective individualized therapy to control and eliminate ovarian cancer. We anticipate that the novel drugs that most successfully target cancer cell metabolism will prove to be effective in the treatment of ovarian cancers in the future, whether alone or as part of a combination therapy. After successful completion of the proposed project, we hope to establish a clinical trial to directly assess the ability of our unique approach to predict patient responses to therapies. By creating individualized therapies catering to the unique needs of each ovarian cancer patient, we hope to contribute to elimination of ovarian cancers.