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Multistate Gene Cluster Switches Determine the Adaptive Mitochondrial and Metabolic Landscape of Breast Cancer

Michela Menegollo1, Robert B Bentham2, Tiago Henriques1

  • 1Department of Biomedical Sciences, University of Padova, Padova, Italy.

Cancer Research
|June 26, 2024
PubMed

Insights

Researchers identified gene expression patterns linked to metabolic switches in cells. This method predicts tumor characteristics and outcomes, aiding in cancer subtype discovery and treatment strategies.

Area of Science:

  • Biochemistry
  • Molecular Biology
  • Bioinformatics

Background:

  • Metabolic adaptations are crucial for cellular state transitions in development and cancer.
  • Understanding metabolic switches is key for developing targeted cancer therapies.

Purpose of the Study:

  • To develop a method for identifying metabolic switches through gene expression patterns.
  • To predict metabolic states and their clinical relevance in breast cancer.

Main Methods:

  • Developed a biclustering method to identify interspersed gene sets.
  • Applied the method to breast cancer transcriptome data.
  • Validated predictions using bioenergetic profiling and metabolic flux analysis.

Main Results:

  • Identified gene sets exhibiting switch-like behavior predicting mitochondrial content, metabolic activity, and carbon flux.
  • Metabolic switch positions correlated with tumor pathology, prognosis, and chemosensitivity.
  • Stratified breast cancer into distinct metabolic subtypes.

Conclusions:

  • The developed method effectively identifies transcriptomic signatures of metabolic switches.
  • This approach enables the discovery of metabolic subtypes with predictive clinical value.
  • The method is broadly applicable to heterogeneous transcriptome datasets for uncovering metabolic and pathophysiological states.

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