Multivariate modeling of metabolic state vulnerabilities across diverse cancer contexts reveals synthetically lethal associations.
Multivariate modeling of metabolic state vulnerabilities across diverse cancer contexts reveals synthetically lethal associations.
复制标题
不同癌症背景下代谢状态脆弱性的多变量模型揭示了综合致死关联。
DOI：
10.1101/2023.11.28.569098
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Fallahi-Sichani,Mohammad
中科院分区：
文献类型：
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作者：
Abecunas,Cara;Fallahi-Sichani,Mohammad
靶向肿瘤细胞的不同代谢需求最近已成为癌症治疗的一种有前途的策略。然而，癌细胞代谢的异质性、背景依赖性对确定有效的治疗干预提出了挑战。在这里，我们利用各种无监督和有监督的多变量建模方法来系统地确定数百种癌细胞系中的复发代谢状态，阐明它们与肿瘤谱系和生长环境的关联，并揭示与不同遗传和组织背景下的代谢状态相关的脆弱性。我们通过分析患者来源的肿瘤和药理学筛选的数据以及进行遗传和药理学实验来验证关键发现。我们的分析揭示了肿瘤代谢状态（例如，氧化磷酸化）、驱动突变（例如，肿瘤抑制因子PTEN的丧失），和可作用的生物靶点（例如，线粒体电子传递链）。研究这些关系的潜在机制可以为开发更精确和更具体的代谢靶向癌症疗法提供信息。
Targeting the distinct metabolic needs of tumor cells has recently emerged as a promising strategy for cancer therapy. The heterogeneous, context-dependent nature of cancer cell metabolism, however, poses challenges to identifying effective therapeutic interventions. Here, we utilize various unsupervised and supervised multivariate modeling approaches to systematically pinpoint recurrent metabolic states within hundreds of cancer cell lines, elucidate their association with tumor lineage and growth environments, and uncover vulnerabilities linked to their metabolic states across diverse genetic and tissue contexts. We validate key findings via analysis of data from patient-derived tumors and pharmacological screens and by performing genetic and pharmacological experiments. Our analysis uncovers synthetically lethal associations between the tumor metabolic state (e.g., oxidative phosphorylation), driver mutations (e.g., loss of tumor suppressor PTEN), and actionable biological targets (e.g., mitochondrial electron transport chain). Investigating the mechanisms underlying these relationships can inform the development of more precise and context-specific, metabolism-targeted cancer therapies.