2018 Recipient David Sabatini, MD, PhD

David Sabatini, MD, PhD

A Personalized Approach to Identify and Target Ovarian Cancer Liabilities

Project Summary

Ovarian cancer is the fifth leading cause of cancer death in women in the United States. Even after initial responsiveness to treatment with chemotherapeutic drugs, over 80% of patients with advanced stage ovarian cancer relapse. As a result, there is a desperate need for novel therapies, particularly those that can mitigate or overcome the resistance of the cancer cells to chemotherapy. We propose to identify new vulnerabilities in ovarian cancer cells by using CRISPR/Cas9 technology, which we have successfully used to identify vulnerabilities in other cancer types, to systematically mutate every gene in a panel of ovarian cancer cells taken from patient tumors. These cells are isolated from relapses of high-grade serous ovarian cancer, which is the most common subtype and has the poorest outcome for patients. We will also use our CRISPR/Cas9 approach in patient-derived ovarian cancer cells treated with the routinely used chemotherapy carboplatin, which will allow us to identify new drug targets that can increase sensitivity to this treatment. It is critical to identify new therapies that increase the effectiveness of carboplatin, because recurrent tumors often become resistant to the initial treatment. We will also focus on targets that may be effective at all stages of disease progression to maximize the therapeutic potential of our findings. Furthermore, we will work toward treating this disease using synthetic nanoparticles that target the vulnerabilities we identify, using a model system that closely mimics patient treatment.

Our efforts will increase our understanding of how the vulnerabilities of ovarian cancer cells change throughout the course of treatment, so that we may vastly improve the development and use of targeted and combination therapies for treatment of recurrent tumors. Together, our combined expertise in identifying new cancer liabilities, modeling chemoresistant recurrent ovarian cancer, and developing nanoparticles for therapeutic intervention, put us in an excellent position to make a tremendous leap forward in understanding and overcoming this disease. Given the current prognosis for ovarian cancer, this project—which is designed to find new, more effective ways to target this type of cancer—has the potential to make a huge impact on patient treatment and survival.

This grant was made possible by a generous donation from Ovarian Cancer Alliance of Greater Cincinnati made in memory of Debbie Walter, and by Turn the Towns Teal.

Bio

David Sabatini is a Member of the Whitehead Institute for Biomedical Research, and a Professor of Biology at the Massachusetts Institute of Technology. He is also an Investigator of the Howard Hughes Medical Institute, a Senior Associate Member at the Broad Institute and a member of the Koch Institute for Integrative Cancer Research. David received his B.S. from Brown University magna cum laude and his M.D./Ph.D. from Johns Hopkins University in 1997. David was appointed a Whitehead Fellow later that year. He became a Member of the Whitehead Institute and Assistant Professor of Biology at the Massachusetts Institute of Technology in 2002, and received tenure in 2012. David has received several awards, including the 2009 Paul Marks Prize for Cancer Research, the 2012 Earl and Thressa Stadtman Scholar Award, the 2017 Lurie Prize in Biomedical Sciences, and the 2017 Dickson Prize in Medicine. He was elected to the National Academy of Sciences in 2016. The Sabatini laboratory has a longstanding interest in cell signaling and metabolism in cancer, as well as in technology development. His lab has recently adapted the CRISPR/Cas9 system for large-scale genetic screening in mammalian cells, with the goal of identifying novel dependencies in cancer. This can be used to detect new vulnerabilities in ovarian cancer cells, as well as help identify new targets that can increase chemotherapeutic sensitivity in ovarian cancer cells.