Cancer drug discovery as a low rank tensor completion problem
Vasanth Siruvallur Murali, Didem Ağaç Çobanoğlu, Michael Hsieh, Meyer Zinn, Venkat S. Malladi, Jonathan R. Gesell, Noelle Sevilir Williams, Erik S. Welf, Ganesh V. Raj, Murat Can Çobanoğlu · bioRxiv (Cold Spring Harbor Laboratory) · 2021
Abstract The heterogeneity of cancer necessitates developing a multitude of targeted therapies. We propose the view that cancer drug discovery is a low rank tensor completion problem. We implement this vision by using heterogeneous public data to construct a tensor of drug-target-disease associations. We show the validity of this approach computationally by simulations, and experimentally by testing drug candidates. Specifically, we show that a novel drug candidate, SU11652, controls melanoma tumor growth, including BRAF WT melanoma. Independently, we show that another molecule, TC-E 5008, controls tumor proliferation on ex vivo ER+ human breast cancer. Most importantly, we identify these chemicals with only a few computationally selected experiments as opposed to brute-force screens. The efficiency of our approach enables use of ex vivo human tumor assays as a primary screening tool. We provide a web server, the Cancer Vulnerability Explorer (accessible at https://cavu.biohpc.swmed.edu ), to facilitate the use of our methodology.