A major roadblock in the effective treatment of cancers is their heterogeneity, 
whereby multiple molecular landscapes are classified as a single disease. To 
explore the 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.