Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Identifying personalized metabolic signatures in breast cancer

Version 1 : Received: 16 November 2020 / Approved: 18 November 2020 / Online: 18 November 2020 (12:29:02 CET)

How to cite: Baloni, P.; Dinalankara, W.; Earls, J.C.; Knijnenburg, T.A.; Geman, D.; Marchionni, L.; Price, N.D. Identifying personalized metabolic signatures in breast cancer. Preprints 2020, 2020110474 (doi: 10.20944/preprints202011.0474.v1). Baloni, P.; Dinalankara, W.; Earls, J.C.; Knijnenburg, T.A.; Geman, D.; Marchionni, L.; Price, N.D. Identifying personalized metabolic signatures in breast cancer. Preprints 2020, 2020110474 (doi: 10.20944/preprints202011.0474.v1).

Abstract

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 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.

Subject Areas

Breast cancer, genome-scale metabolic models, constraint-based analysis, divergence analysis, gene expression, metabolism, drug targets, personalized metabolic networks.

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our diversity statement.

Leave a public comment
Send a private comment to the author(s)
Views 0
Downloads 0
Comments 0
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.