Systematic identification of cancer-type-specific drugs based on essential genes and validations in lung adenocarcinoma
Xiang Lian, Xia Kuang, Dongdong Zhang, Qian Xu, Anqiang Ye, Chengyu Wang, Hongtu Cui, Haixia Guo, Jiyun Zhang, Yuan Liu, Ge‐Fei Hao, Zhenshun Cheng, Feng-Biao Guo · Briefings in Bioinformatics · 2025
Depicting a global landscape of essential gene-targeting drugs would provide more opportunities for cancer therapy. However, a systematic investigation on drugs targeting essential genes still has not been reported. We suppose that drugs targeting cancer-type-specific essential genes would generally have less toxicity than those targeting pan-cancer essential genes. A scoring function-based strategy was developed to identify cancer-type-specific targets and drugs. The EssentialitySpecificityScore ranked the essential genes in 19 cancer types, and 1151 top genes were identified as cancer-type-specific targets. Combining target-drug interaction databases with research/marketing status, 370 cancer-type-specific drugs were identified, bound to 100 out of all identified targets. Profiles of applied cancer types of identified targets and drugs illustrate the scoring strategy's effectiveness: most drugs apply to cancer types <10. Seven drugs with no previous anticancer evidence were validated in 11 lung adenocarcinoma cell lines, and lower inhibition rates (from 9.4% to 44.0%) were observed in 10 normal cell lines. This difference is statistically significant (Student's t-test, P ≤ .0001), confirming the rationality of our supposition. Our built EGKG (Essential Gene Knowledge Graph) forms a computational basis to uncover essential gene targets and drugs for specific cancer types. It is available at http://gepa.org.cn/egkg/. Also, our experimental result suggests that combining drugs with orthogonal essentiality may be an alternative way to improve anticancer effects while maintaining biocompatibility. The code and data are available at https://github.com/KKINGA1/EGKG_data_process.