Multivariate analysis of metabolic state vulnerabilities across diverse cancer contexts reveals synthetically lethal associations

• Published in: Cell reports (Cambridge) Vol. 43; no. 10; p. 114775
• Main Authors: Abecunas, Cara, Kidd, Audrey D., Jiang, Ying, Zong, Hui, Fallahi-Sichani, Mohammad
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 22.10.2024; Elsevier
• Subjects: Animals; cancer metabolism; cancer therapies; Cell Line, Tumor; CP: Cancer; CP: Metabolism; glioma; Humans; metabolic state vulnerabilities; Mitochondria - metabolism; mitochondrial electron transport chain; Multivariate Analysis; multivariate modeling; Neoplasms - genetics; Neoplasms - metabolism; Neoplasms - pathology; Oxidative Phosphorylation; PTEN; Synthetic Lethal Mutations; synthetic lethality; multivariate modeling; cancer metabolism; cancer therapies; mitochondrial electron transport chain; metabolic state vulnerabilities; CP: Metabolism; CP: Cancer; synthetic lethality; PTEN; glioma; oxidative phosphorylation
• ISSN: 2211-1247; 2211-1247
• DOI: 10.1016/j.celrep.2024.114775

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. [Display omitted] •Most metabolic pathway heterogeneities are cancer type specific; Oxphos variability is not•OxphosHigh and OxphosLow states show unique vulnerabilities independent of growth condition•PTEN loss increases OxphosHigh state’s dependency on mitochondrial electron transport chain•Oxphos inhibitors show higher potency in PTENNull glioma cells than in PTEN-expressing cells Abecunas et al. use multivariate modeling to explore recurrent metabolic states in cancer cells and uncover synthetically lethal interactions across diverse metabolic, oncogenic, and tissue contexts. They validate key findings via independent data analysis and new experiments. They find that PTEN loss predicts increased dependency on mitochondrial respiratory chain in OxphosHigh tumor cells.