Anti-cancer Drug Synergy Prediction in Understudied Tissues using Transfer Learning

Motivation: Exploring an exponentially increasing yet more promising space, high-throughput combinatorial drug screening has advantages in identifying cancer treatment options with higher efficacy without degradation in terms of safety. A key challenge is that accumulated number of observations in in-vitro drug responses varies greatly among different cancer types, where some tissues (such as bone and prostate) are understudied than the others. Thus, we aim to develop a drug synergy prediction model for understudied data-poor tissues as overcoming data scarcity problem. Results: We collected a comprehensive set of genetic, molecular, phenotypic features for cancer cell lines from six different databases. We developed a drug synergy prediction model based on deep neural networks to integrate multi-modal input and utilize transfer learning from data-rich tissues to data-poor tissues. We showed improved accuracy in predicting drug synergy in understudied tissues without enough drug combination screening data nor after-treatment transcriptome. Our synergy prediction model can be used to rank synergistic drug combinations in understudied tissues and thus help prioritizing future in-vitro experiments.