Identifying Personalized Metabolic Signatures in Breast Cancer
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 u...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-12-01
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| Series: | Metabolites |
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| Online Access: | https://www.mdpi.com/2218-1989/11/1/20 |
| _version_ | 1827699008195264512 |
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| author | Priyanka Baloni Wikum Dinalankara John C. Earls Theo A. Knijnenburg Donald Geman Luigi Marchionni Nathan D. Price |
| author_facet | Priyanka Baloni Wikum Dinalankara John C. Earls Theo A. Knijnenburg Donald Geman Luigi Marchionni Nathan D. Price |
| author_sort | Priyanka Baloni |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T13:40:03Z |
| format | Article |
| id | doaj.art-c6979fc6d9de47fdb5b9d41935dd96c7 |
| institution | Directory Open Access Journal |
| issn | 2218-1989 |
| language | English |
| last_indexed | 2024-03-10T13:40:03Z |
| publishDate | 2020-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Metabolites |
| spelling | doaj.art-c6979fc6d9de47fdb5b9d41935dd96c72023-11-21T03:09:11ZengMDPI AGMetabolites2218-19892020-12-011112010.3390/metabo11010020Identifying Personalized Metabolic Signatures in Breast CancerPriyanka Baloni0Wikum Dinalankara1John C. Earls2Theo A. Knijnenburg3Donald Geman4Luigi Marchionni5Nathan D. Price6Institute for Systems Biology, Seattle, WA 98109, USADepartment of Oncology, Sydney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USAInstitute for Systems Biology, Seattle, WA 98109, USAInstitute for Systems Biology, Seattle, WA 98109, USADepartment of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD 21205, USADepartment of Oncology, Sydney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USAInstitute for Systems Biology, Seattle, WA 98109, USACancer 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.https://www.mdpi.com/2218-1989/11/1/20breast cancergenome-scale metabolic modelsconstraint-based analysisdivergence analysisgene expressionmetabolism |
| spellingShingle | Priyanka Baloni Wikum Dinalankara John C. Earls Theo A. Knijnenburg Donald Geman Luigi Marchionni Nathan D. Price Identifying Personalized Metabolic Signatures in Breast Cancer Metabolites breast cancer genome-scale metabolic models constraint-based analysis divergence analysis gene expression metabolism |
| title | Identifying Personalized Metabolic Signatures in Breast Cancer |
| title_full | Identifying Personalized Metabolic Signatures in Breast Cancer |
| title_fullStr | Identifying Personalized Metabolic Signatures in Breast Cancer |
| title_full_unstemmed | Identifying Personalized Metabolic Signatures in Breast Cancer |
| title_short | Identifying Personalized Metabolic Signatures in Breast Cancer |
| title_sort | identifying personalized metabolic signatures in breast cancer |
| topic | breast cancer genome-scale metabolic models constraint-based analysis divergence analysis gene expression metabolism |
| url | https://www.mdpi.com/2218-1989/11/1/20 |
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