Cohen Ziv, Petrenko Ekaterina, Barisaac Alma Sophia, Abu-Zhayia Enas R, Yanovich-Ben-Uriel Chen, Ayoub Nabieh, Aran Dvir
The Taub Faculty of Computer Science, Technion-Israel Institute of Technology, Haifa 3200003, Israel.
Faculty of Biology, Technion-Israel Institute of Technology, Haifa 3200003, Israel.
NAR Genom Bioinform. 2025 Apr 24;7(2):lqaf052. doi: 10.1093/nargab/lqaf052. eCollection 2025 Jun.
Synthetic lethality represents a promising therapeutic approach in precision oncology, yet systematic identification of clinically relevant synthetic lethal interactions remains challenging. Here, we present SLAYER (Synthetic Lethality AnalYsis for Enhanced taRgeted therapy), a computational framework that integrates cancer genomic data and genome-wide CRISPR knockout screens to identify potential synthetic lethal interactions. SLAYER employs parallel analytical approaches examining both direct mutation-dependency associations and pathway-mediated relationships across 1080 cancer cell lines. Our integrative method identified 682 putative interactions, which were refined to 148 high-confidence candidates through stringent filtering for effect size, druggability, and clinical prevalence. Systematic validation against protein interaction databases revealed an ∼14-fold enrichment of known associations among SLAYER predictions compared with random gene pairs. Through pathway-level analysis, we identified inhibition of the aryl hydrocarbon receptor (AhR) as potentially synthetically lethal with RB1 mutations in bladder cancer. Experimental studies demonstrated selective sensitivity to AhR inhibition in RB1-mutant versus wild-type bladder cancer cells, which probably operates through indirect pathway-mediated mechanisms rather than direct genetic interaction. In summary, by integrating mutation profiles, gene dependencies, and pathway relationships, our approach provides a resource for investigating genetic vulnerabilities across cancer types.
合成致死性是精准肿瘤学中一种很有前景的治疗方法，但系统识别临床相关的合成致死相互作用仍然具有挑战性。在此，我们介绍了SLAYER（用于增强靶向治疗的合成致死性分析），这是一个整合癌症基因组数据和全基因组CRISPR敲除筛选以识别潜在合成致死相互作用的计算框架。SLAYER采用并行分析方法，在1080个癌细胞系中检查直接的突变依赖性关联和通路介导的关系。我们的整合方法识别出682个推定的相互作用，通过对效应大小、可成药性质和临床普遍性进行严格筛选，将其细化为148个高可信度候选相互作用。与蛋白质相互作用数据库进行的系统验证显示，与随机基因对相比，SLAYER预测中已知关联的富集度约高14倍。通过通路水平分析，我们确定在膀胱癌中，芳烃受体（AhR）的抑制与RB1突变可能具有合成致死性。实验研究表明，RB1突变型与野生型膀胱癌细胞对AhR抑制具有选择性敏感性，这可能是通过间接的通路介导机制而非直接的基因相互作用起作用。总之，通过整合突变谱、基因依赖性和通路关系，我们的方法为研究不同癌症类型的遗传脆弱性提供了一种资源。
