癌症依赖性图谱加速了肿瘤脆弱性的发现，当这些脆弱性转化为患者时，可以将其用作药物靶标。癌症基因组图谱 (TCGA) 是一个“地图”概要，详细介绍了癌症发病过程中发生的遗传、表观遗传和分子变化，但它缺乏依赖图来翻译患者肿瘤中基因的重要性。在这里，我们使用机器学习为患者肿瘤构建翻译依赖性图，该图确定了预测药物反应和疾病结果的肿瘤脆弱性。使用类似的方法来绘制健康组织中的基因耐受性，以优先考虑肿瘤的脆弱性和最佳治疗窗口。对患者可翻译的合成致死率的子集进行了实验测试，包括 PAPSS1/PAPSS12 和 CNOT7/CNOT78，并在体外和体内进行了验证。值得注意的是，PAPSS1 合成致死率是由 PTEN 附带删除 PAPSS2 驱动的，并且与患者生存相关。最后，翻译依赖图作为基于网络的应用程序提供，用于探索肿瘤漏洞。© 2024。作者。

Cancer dependency maps have accelerated the discovery of tumor vulnerabilities that can be exploited as drug targets when translatable to patients. The Cancer Genome Atlas (TCGA) is a compendium of 'maps' detailing the genetic, epigenetic and molecular changes that occur during the pathogenesis of cancer, yet it lacks a dependency map to translate gene essentiality in patient tumors. Here, we used machine learning to build translational dependency maps for patient tumors, which identified tumor vulnerabilities that predict drug responses and disease outcomes. A similar approach was used to map gene tolerability in healthy tissues to prioritize tumor vulnerabilities with the best therapeutic windows. A subset of patient-translatable synthetic lethalities were experimentally tested, including PAPSS1/PAPSS12 and CNOT7/CNOT78, which were validated in vitro and in vivo. Notably, PAPSS1 synthetic lethality was driven by collateral deletion of PAPSS2 with PTEN and was correlated with patient survival. Finally, the translational dependency map is provided as a web-based application for exploring tumor vulnerabilities.© 2024. The Author(s).