Heterogeneity of the cancer cell line metabolic landscape. Issue 11 (2nd November 2022)

• Record Type: Journal Article
• Authors: Shorthouse, David; Bradley, Jenna; Critchlow, Susan E; Bendtsen, Claus; Hall, Benjamin A
• Is Part Of: Molecular systems biology. Volume 18:Issue 11(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-11-02
• Subjects: cancer -- heterogeneity -- metabolomics -- mutation; Molecular biology -- Periodicals; Systems biology -- Periodicals; 572.8
• DOI: 10.15252/msb.202211006
• Languages: English
• ISSNs: 1744-4292

Abstract

Abstract: The unravelling of the complexity of cellular metabolism is in its infancy. Cancer‐associated genetic alterations may result in changes to cellular metabolism that aid in understanding phenotypic changes, reveal detectable metabolic signatures, or elucidate vulnerabilities to particular drugs. To understand cancer‐associated metabolic transformation, we performed untargeted metabolite analysis of 173 different cancer cell lines from 11 different tissues under constant conditions for 1, 099 different species using mass spectrometry (MS). We correlate known cancer‐associated mutations and gene expression programs with metabolic signatures, generating novel associations of known metabolic pathways with known cancer drivers. We show that metabolic activity correlates with drug sensitivity and use metabolic activity to predict drug response and synergy. Finally, we study the metabolic heterogeneity of cancer mutations across tissues, and find that genes exhibit a range of context specific, and more general metabolic control. Synopsis: Analyses of the metabolic landscape of 173 cancer cell lines reveal correlations between genomic factors and metabolic pathways and show that cancer genes exhibit a range of tissue dependent and global mutational effects on metabolism. 173 cell lines from 11 tissues are profiled for 1, 099 metabolites. Associations of mutations with metabolites/metabolic pathways are identified. Correlations of transcription factor activity with metabolic pathways are reported. Analysis of heterogeneity of cancer metabolism across tissues is performed. Abstract : Analyses of the metabolic landscape of 173 cancer cell lines reveal correlations between genomic factors and metabolic pathways and show that cancer genes exhibit a range of tissue dependent and global mutational effects on metabolism.