We would like to remind you that the monthly online meeting of Synthetic Biology Club (SB club) will be held TODAY Monday,  June 27, 2022 at 12h00 (EST).. Please, Click here to register after which you will receive the meeting link (zoom).

Title: “Interpretable Machine Learning for Discovery in Cancer”

Presenter: Haitham Elmarakeby, Instructor at the Dana-Farber Cancer Institute, Harvard Medical School, Broad Institute of MIT and Harvard, United States

Time: Monday, June 27, 2022 at 12h00 (EST).

Place: Online (via Zoom). The zoom link of the meeting is provided by email after your registration. Click here to register.

Abstract: Despite advances in prostate cancer treatment, including androgen deprivation therapy, metastatic castration-resistant prostate cancer (mCRPC) remains largely incurable. Recent advances in collecting and sharing large quantities of genomic records from patients with primary and metastatic prostate cancer have not yet been matched with advances in computational model development to shed light on the underlying biology of mCRPC. Here we developed a biologically informed deep learning model (P-NET) that can accurately identify advanced prostate cancer samples based on their genomic profiles. By using a sparse model architecture that encodes different biological entities including genes, pathways, and biological processes, we were able to interpret the model in a way that is not matched by typical deep learning models. In a systematic unbiased way, P-NET recovered known biology of mCRPC via AR, TP53, RB1, and PTEN disruption, as well as less expected genes such as MDM4. We showed experimentally that MDM4 mediates enzalutamide resistance, showing that it may be a potential therapeutic target. We envision that our model will be helpful in both predicting clinical outcomes of cancer patients and generating biological hypotheses to better understand the underlying cancer biology.