文章目录

为癌症基因组图谱构建翻译癌症依赖图谱

原文发布日期：2024-07-15

英文摘要：

摘要翻译：

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文章：

为癌症基因组图谱构建翻译癌症依赖图谱

Building a translational cancer dependency map for The Cancer Genome Atlas

原文发布日期：2024-07-15

英文摘要：

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.

摘要翻译：

癌症依赖图加速了发现可翻译到患者的肿瘤易变性。癌基因组图谱（TCGA）汇总了癌症发生过程中遗传、表观遗传和分子变化的“地图”，但缺少依赖图来翻译基因重要性。我们利用机器学习构建了患者的依赖图，识别出预测药物反应和疾病结果的肿瘤易变点。类似的方法用于绘制健康组织中基因耐受性的“地图”，以优先考虑治疗窗中的肿瘤易变点。我们还测试了一些患者可转移的合成致死基因对，包括PAPSS1/PAPSS12和CNOT7/CNOT78，并在体内外进行了验证。特别地，PAPSS1的合成致死性与其旁路删除PAPSS2、同时涉及PTEN有关，并与患者的生存率相关。最后，提供了一个基于 web 的应用来探索肿瘤易变性。