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Published on: May 27, 2021
Multivariate analysis of metabolic state vulnerabilities across diverse cancer contexts reveals synthetically lethal
Cara Abecunas1,2,3, Audrey D Kidd1, Ying Jiang4
1Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908.
Abstract:
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 in 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 new genetic and pharmacological experiments. Our analysis uncovers new 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.
Insights
This study identifies distinct cancer cell metabolic states and links them to genetic mutations, revealing new therapeutic targets for precision cancer medicine. These findings pave the way for developing context-specific, metabolism-targeted cancer therapies.
Area of Science:
- Oncology
- Metabolomics
- Computational Biology
Background:
- Targeting cancer cell metabolism is a promising therapeutic strategy.
- Cancer cell metabolism is heterogeneous and context-dependent, posing challenges for therapy development.
Purpose of the Study:
- To systematically identify recurrent metabolic states in cancer cells.
- To associate these metabolic states with tumor lineage, growth environments, and genetic contexts.
- To uncover vulnerabilities and synthetic lethality for targeted cancer therapies.
Main Methods:
- Unsupervised and supervised multivariate modeling of cancer cell line data.
- Analysis of patient-derived tumor data and pharmacological screens.
- New genetic and pharmacological experiments for validation.
Main Results:
- Recurrent metabolic states in cancer cells were pinpointed.
- Associations between metabolic states, tumor lineage, and genetic drivers (e.g., PTEN loss) were elucidated.
- New synthetically lethal associations were uncovered, linking metabolic state (e.g., oxidative phosphorylation) to actionable targets (e.g., mitochondrial electron transport chain).
Conclusions:
- Metabolism-targeted cancer therapies can be made more precise and context-specific.
- Understanding tumor metabolic states and their vulnerabilities can guide the development of novel therapeutic strategies.
- Identified synthetic lethalities offer potential avenues for combination therapies in cancer treatment.
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