Djenebou Semega Diani

Djenebou Semega Diani, a junior biomedical engineering major at Columbia University, School of Engineering and Applied Science (SEAS), used statistical analysis methods to nominate clinically approved drugs to be repurposed for potential lung cancer treatment

Lung cancer is one of the most common and fatal cancer types, however, the number of new cases each year has decreased as a result of early detection and treatment. BSRP was an eye-opening experience for me, especially as someone who has never conducted computational research. Through the courses and workshops, I’ve acquired skills that are beneficial to me as a biomedical engineering student as well as an underrepresented minority in STEM. In addition to research skills, the program helped me to become more confident in my achievements and endeavors, to practice self-advocacy, and to seek supportive mentors and advisors. Working with the rest of the cohort during activities, coworking, and research meetings allowed me to form meaningful connections with an intelligent and driven group of people, and it also heightened my excitement for future collaborative biomedical research. I am extremely grateful for this unique and fulfilling opportunity at the Broad Institute.The process for approving drugs for the treatment of lung cancer can be made more efficient by nominating drugs that have already been approved for clinical use by the Food and Drug Administration and filtering for those that effectively target lung cancer cell lines. To contribute to treatment studies, we used pre-existing datasets from cancer genomics projects provided by the Broad Institute, the Cancer Cell Line Encyclopedia and the PRISM Repurposing dataset. These datasets provide numerical results on drug response from 578 cancer cell lines after drug treatment and genomic features such as cell line lineage, respectively. To nominate a small number of drugs, we identified the targeted cancer type of each drug by visualizing the distribution of drug viability data and computing bimodality coefficients, which can provide insight on two underlying distributions for each drug. We then determined the effectiveness of each drug against lung cancer cell lines by conducting t-tests on the top fifty bimodal drugs to compare the means of cell viability values of lung cancer cell lines and non-lung cancer cell lines. Through these analyses, we have concluded that drugs ARQ-621, CB-839, and Cavipaubulin have the potential to effectively target lung cancer cells. To further our studies and refine our list of drugs, we will assess drug activity at various doses and we hope that our work will increase studies on whether the drugs nominated could be used in the clinic to treat lung cancer patients, potentially saving time for safety and efficacy trials.

Project: Repurposing Drugs to Effectively Target Lung Cancer Cells

Mentors: Lena Joesch-Cohen and Peter Tsvetkov, Ph.D., Golub Lab