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Article

Identifying Personalized Metabolic Signatures in Breast Cancer

1
Institute for Systems Biology, Seattle, WA 98109, USA
2
Department of Oncology, Sydney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
3
Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY 10021, USA
4
Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD 21205, USA
*
Authors to whom correspondence should be addressed.
Metabolites 2021, 11(1), 20; https://doi.org/10.3390/metabo11010020
Received: 16 November 2020 / Revised: 23 December 2020 / Accepted: 28 December 2020 / Published: 30 December 2020
(This article belongs to the Special Issue Metabolic Modelling: Methods, Applications and Future Perspectives)
Cancer cells are adept at reprogramming energy metabolism, and the precise manifestation of this metabolic reprogramming exhibits heterogeneity across individuals (and from cell to cell). In this study, we analyzed the metabolic differences between interpersonal heterogeneous cancer phenotypes. We used divergence analysis on gene expression data of 1156 breast normal and tumor samples from The Cancer Genome Atlas (TCGA) and integrated this information with a genome-scale reconstruction of human metabolism to generate personalized, context-specific metabolic networks. Using this approach, we classified the samples into four distinct groups based on their metabolic profiles. Enrichment analysis of the subsystems indicated that amino acid metabolism, fatty acid oxidation, citric acid cycle, androgen and estrogen metabolism, and reactive oxygen species (ROS) detoxification distinguished these four groups. Additionally, we developed a workflow to identify potential drugs that can selectively target genes associated with the reactions of interest. MG-132 (a proteasome inhibitor) and OSU-03012 (a celecoxib derivative) were the top-ranking drugs identified from our analysis and known to have anti-tumor activity. Our approach has the potential to provide mechanistic insights into cancer-specific metabolic dependencies, ultimately enabling the identification of potential drug targets for each patient independently, contributing to a rational personalized medicine approach. View Full-Text
Keywords: breast cancer; genome-scale metabolic models; constraint-based analysis; divergence analysis; gene expression; metabolism; drug targets; personalized metabolic networks breast cancer; genome-scale metabolic models; constraint-based analysis; divergence analysis; gene expression; metabolism; drug targets; personalized metabolic networks
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MDPI and ACS Style

Baloni, P.; Dinalankara, W.; Earls, J.C.; Knijnenburg, T.A.; Geman, D.; Marchionni, L.; Price, N.D. Identifying Personalized Metabolic Signatures in Breast Cancer. Metabolites 2021, 11, 20. https://doi.org/10.3390/metabo11010020

AMA Style

Baloni P, Dinalankara W, Earls JC, Knijnenburg TA, Geman D, Marchionni L, Price ND. Identifying Personalized Metabolic Signatures in Breast Cancer. Metabolites. 2021; 11(1):20. https://doi.org/10.3390/metabo11010020

Chicago/Turabian Style

Baloni, Priyanka, Wikum Dinalankara, John C. Earls, Theo A. Knijnenburg, Donald Geman, Luigi Marchionni, and Nathan D. Price 2021. "Identifying Personalized Metabolic Signatures in Breast Cancer" Metabolites 11, no. 1: 20. https://doi.org/10.3390/metabo11010020

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