Zoe Drigot, a Junior at the University of Colorado, Boulder studying Neuroscience, Psychology, and Leadership, investigated the genetic basis between Major Depressive Disorder and cancer.
Both Major Depressive Disorder (MDD) and Cancer affect a subsational amount of people. Previous literature indicates patients with MDD are more likely to get certain types of cancer.This experience has been undoubtedly one of the most influential experiences in my academic career. I came into this experience with a substantial interest in research but not a wide range of experience. The Broad’s collaborative environment encouraged me to be creative with my hypotheses and then supported me as I navigated different paths of testing them. Here, I learned how to use data analytics to enhance many different types of research. I found I genuinely enjoy computational work and hope to find ways to continually incorporate it into my future research. I am deeply grateful for the opportunity to learn these skills so early on in my scientific career. Needless to say, this experience would not have been as wonderful without my cohort and mentors. It has been an honor to work alongside them and I cannot wait to see what the future holds for all of them! This project aims to investigate if there is a genetic basis for this trend. Investigating the genetic link between MDD and Cancer is crucial to understanding how to prevent the concurrence of both diagnoses. If we believe there is said genetic relationship between the two, then upregulated MDD genes may be oncogenes with higher copy numbers. Hence, assisting cancer to proliferate. This pattern also applies conversely with down regulated MDD genes and tumor suppressors. For this project, I identified genes that followed either of the two patterns. Using the coding language R, I isolated genes most associated with MDD from a meta-analysis dataset. To be included in the analysis, they had to have a p-Value of <0.0005. Then, using CRISPR knockout data, I determined if a cell line died or proliferated after specific genes were knocked out using their dependency score. This helped me identify potential oncogenes and tumor suppressor genes. To ensure statistical significance for the dependency values, I performed a t-test between cancerous cell lines and normal cell lines. I determined the outliers knockout data and compared that list to the extremely significant MDD genes. In comparison of two lists, the results demonstrate specific genes such as AURKA, OIP5, and BFR2 were both highly associated with MDD and Cancer. A correlation matrix comparing genes’ dependency scores and the regulation of MDD genes, did not indicate any statistical significance. Because the overall statistical association was inconclusive, we reject the notion that MDD is linked to cancer on a predominantly genetic basis. However, we recommend further investigation into the final list of genes as they specifically have demonstrated an association. Identifying genes highly associated with MDD and cancer helps us understand possible ways to treat both simultaneously.
Project: Computational approach for identifying genes associated with cancer and depression
Mentors: Cooper Galvin, Matt Tegtmyer, Stanley Center for Psychiatric Research