Using graph-based model to identify cell specific synthetic lethal effects

• Published in: Computational and structural biotechnology journal Vol. 21; pp. 5099 - 5110
• Main Authors: Pu, Mengchen, Cheng, Kaiyang, Li, Xiaorong, Xin, Yucui, Wei, Lanying, Jin, Sutong, Zheng, Weisheng, Peng, Gongxin, Tang, Qihong, Zhou, Jielong, Zhang, Yingsheng
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.01.2023; Research Network of Computational and Structural Biotechnology; Elsevier
• Subjects: Cell specific target identification; Deep learning; GNN; Method; Multi-omics; Synthetic lethality; Deep learning; Cell specific target identification; Synthetic lethality; GNN; Multi-omics
• ISSN: 2001-0370; 2001-0370
• DOI: 10.1016/j.csbj.2023.10.011

Synthetic lethal (SL) pairs are pairs of genes whose simultaneous loss-of-function results in cell death, while a damaging mutation of either gene alone does not affect the cell’s survival. This makes SL pairs attractive targets for precision cancer therapies, as targeting the unimpaired gene of the SL pair can selectively kill cancer cells that already harbor the impaired gene. Limited by the difficulty of finding true SL pairs, especially on specific cell types, current computational approaches provide only limited insights because of overlooking the crucial aspects of cellular context dependency and mechanistic understanding of SL pairs. As a result, the identification of SL targets still relies on expensive, time-consuming experimental approaches. In this work, we applied cell-line specific multi-omics data to a specially designed deep learning model to predict cell-line specific SL pairs. Through incorporating multiple types of cell-specific omics data with a self-attention module, we represent gene relationships as graphs. Our approach achieves the prediction of SL pairs in a cell-specific manner and demonstrates the potential to facilitate the discovery of cell-specific SL targets for cancer therapeutics, providing a tool to unearth mechanisms underlying the origin of SL in cancer biology. The code and data of our approach can be found at https://github.com/promethiume/SLwise