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Multistate gene cluster switches determine the adaptive mitochondrial and metabolic landscape of breast cancer.

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journal contribution
posted on 2024-09-04, 10:37 authored by Michela Menegollo, Robert B Bentham, Tiago Henriques, Seow Qi Ng, Ziyu Ren, Clarinde Esculier, Sia Agarwal, Emily TY Tong, Clement Lo, Sanjana Ilangovan, Zorka Szabadkai, Matteo Suman, Neill Patani, Avinash Ghanate, Kevin Bryson, Robert C Stein, Mariia Yuneva, Gyorgy Szabadkai
Adaptive metabolic switches are proposed to underlie conversions between cellular states during normal development as well as in cancer evolution. Metabolic adaptations represent important therapeutic targets in tumors, highlighting the need to characterize the full spectrum, characteristics, and regulation of the metabolic switches. To investigate the hypothesis that metabolic switches associated with specific metabolic states can be recognized by locating large alternating gene expression patterns, we developed a method to identify interspersed gene sets by massive correlated biclustering (MCbiclust) and to predict their metabolic wiring. Testing the method on breast cancer transcriptome datasets revealed a series of gene sets with switch-like behavior that could be used to predict mitochondrial content, metabolic activity, and central carbon flux in tumors. The predictions were experimentally validated by bioenergetic profiling and metabolic flux analysis of 13C-labelled substrates. The metabolic switch positions also distinguished between cellular states, correlating with tumor pathology, prognosis, and chemosensitivity. The method is applicable to any large and heterogeneous transcriptome dataset to discover metabolic and associated pathophysiological states.

Funding

Crick (Grant ID: CC2082, Grant title: Yuneva CC2082) Crick (Grant ID: CC1107, Grant title: STP Bioinformatics & Biostatistics) Cancer Research UK (Grant ID: 25043, Grant title: CRUK C57633/A25043)

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