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

• Published in: Cancer research (Chicago, Ill.) Vol. 84; no. 17; pp. 2911 - 2925
• Main Authors: Menegollo, Michela, Bentham, Robert B, Henriques, Tiago, Ng, Seow Q, Ren, Ziyu, Esculier, Clarinde, Agarwal, Sia, Tong, Emily T Y, Lo, Clement, Ilangovan, Sanjana, Szabadkai, Zorka, Suman, Matteo, Patani, Neill, Ghanate, Avinash, Bryson, Kevin, Stein, Robert C, Yuneva, Mariia, Szabadkai, Gyorgy
• Format: Journal Article
• Language: English
• Published: United States American Association for Cancer Research 04.09.2024
• Subjects: Breast Cancer; Breast Neoplasms - genetics; Breast Neoplasms - metabolism; Breast Neoplasms - pathology; Computational Cancer Biology and Technology; Energy Metabolism - genetics; Female; Gene Expression Profiling - methods; Gene Expression Regulation, Neoplastic; Genome Biology; Humans; Metabolism; Mitochondria - genetics; Mitochondria - metabolism; Multigene Family; Prognosis; Transcriptome
• ISSN: 0008-5472; 1538-7445; 1538-7445
• DOI: 10.1158/0008-5472.CAN-23-3172

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 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-labeled 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. Significance: A method for identifying the transcriptomic signatures of metabolic switches underlying divergent routes of cellular transformation stratifies breast cancer into metabolic subtypes, predicting their biology, architecture, and clinical outcome.