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Generation of 2,000 breast cancer metabolic landscapes reveals a poor prognosis group with active serotonin production

Leoncikas, V, Wu, H, Ward, LT, kierzek, A and Plant, NJ (2016) Generation of 2,000 breast cancer metabolic landscapes reveals a poor prognosis group with active serotonin production Scientific Reports, 6.

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Abstract

A major roadblock in the effective treatment of cancers is their heterogeneity, whereby multiple molecular landscapes are classified as a single disease. To explore contribution of cellular metabolism to cancer heterogeneity, we analyse the Metabric dataset, a landmark genomic and transcriptomic study of 2,000 individual breast tumours, in the context of the human genome-scale metabolic network. We create personalized metabolic landscapes for each tumour by exploring sets of active reactions that satisfy constraints derived from human biochemistry and maximize congruency with the Metabric transcriptome data. Classification of the personalized landscapes derived from 997 tumour samples within the Metabric discovery dataset reveals a novel poor prognosis cluster, reproducible in the 995-sample validation dataset. We experimentally follow mechanistic hypotheses resulting from the computational study and establish that active serotonin production is a major metabolic feature of the poor prognosis group. These data support the reconsideration of concomitant serotonin-specific uptake inhibitors treatment during breast cancer chemotherapy.

Item Type: Article
Divisions : Faculty of Health and Medical Sciences
Authors :
AuthorsEmailORCID
Leoncikas, VUNSPECIFIEDUNSPECIFIED
Wu, HUNSPECIFIEDUNSPECIFIED
Ward, LTUNSPECIFIEDUNSPECIFIED
kierzek, AUNSPECIFIEDUNSPECIFIED
Plant, NJUNSPECIFIEDUNSPECIFIED
Date : 27 January 2016
Identification Number : 10.1038/srep19771
Copyright Disclaimer : This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
Additional Information : This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
Depositing User : Symplectic Elements
Date Deposited : 18 Feb 2016 10:23
Last Modified : 18 Feb 2016 10:23
URI: http://epubs.surrey.ac.uk/id/eprint/809607

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