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

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 unsuper...

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Published inCell 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
LanguageEnglish
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
Online AccessGet full text
ISSN2211-1247
2639-1856
2211-1247
DOI10.1016/j.celrep.2024.114775

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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 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.
AbstractList 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.
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. 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.
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.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.
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.
ArticleNumber 114775
Author Fallahi-Sichani, Mohammad
Abecunas, Cara
Kidd, Audrey D.
Jiang, Ying
Zong, Hui
AuthorAffiliation 4 UVA Comprehensive Cancer Center, University of Virginia, Charlottesville, VA 22908, USA
2 Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
5 Present address: Novartis Institutes for BioMedical Research, Cambridge, MA 02139, USA
6 Lead contact
3 Department of Microbiology, Immunology, and Cancer Biology, University of Virginia, Charlottesville, VA 22908, USA
1 Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA
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Issue 10
Keywords multivariate modeling
cancer metabolism
cancer therapies
mitochondrial electron transport chain
metabolic state vulnerabilities
CP: Metabolism
CP: Cancer
synthetic lethality
PTEN
glioma
oxidative phosphorylation
Language English
License This is an open access article under the CC BY-NC-ND license.
Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.
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AUTHOR CONTRIBUTIONS
C.A. and M.F.-S. conceived and designed the study. C.A. performed the computational modeling work. A.D.K. and Y.J. performed the experiments. A.D.K. analyzed the experimental data. C.A., A.D.K., Y.J., H.Z., and M.F.-S. wrote and edited the manuscript. M.F.-S. supervised the work.
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Snippet Targeting the distinct metabolic needs of tumor cells has recently emerged as a promising strategy for cancer therapy. The heterogeneous, context-dependent...
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SubjectTerms 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
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Title Multivariate analysis of metabolic state vulnerabilities across diverse cancer contexts reveals synthetically lethal associations
URI https://dx.doi.org/10.1016/j.celrep.2024.114775
https://www.ncbi.nlm.nih.gov/pubmed/39305483
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https://doi.org/10.1016/j.celrep.2024.114775
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