The tumor therapy landscape of synthetic lethality
Biyu Zhang, Chen Tang, Yanli Yao, Xiaohan Chen, Chi Zhou, Zhiting Wei, Feiyang Xing, Lan Chen, Xiang Cai, Zhiyuan Zhang, Shuyang Sun, Qi Liu
Abstract
Synthetic lethality is emerging as an important cancer therapeutic paradigm, while the comprehensive selective treatment opportunities for various tumors have not yet been explored. We develop the Synthetic Lethality Knowledge Graph (SLKG), presenting the tumor therapy landscape of synthetic lethality (SL) and synthetic dosage lethality (SDL). SLKG integrates the large-scale entity of different tumors, drugs and drug targets by exploring a comprehensive set of SL and SDL pairs. The overall therapy landscape is prioritized to identify the best repurposable drug candidates and drug combinations with literature supports, in vitro pharmacologic evidence or clinical trial records. Finally, cladribine, an FDA-approved multiple sclerosis treatment drug, is selected and identified as a repurposable drug for treating melanoma with CDKN2A mutation by in vitro validation, serving as a demonstrating SLKG utility example for novel tumor therapy discovery. Collectively, SLKG forms the computational basis to uncover cancer-specific susceptibilities and therapy strategies based on the principle of synthetic lethality.