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

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 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.

MaREA: Metabolic feature extraction, enrichment and visualization of RNAseq data

“…For each reaction of a given metabolic network, MaREA computes a Reaction Activity Score (RAS), which describes the extent of its activity in a given condition, as a function of the expression level of the genes encoding for the subunits and/ or the isoforms of the enzyme catalyzing such reaction. This score provides a more refined information than the mere list of genes associated to a reaction, without requiring to set any arbitrary threshold, nor to binarize data (gene present or absent), as required by other approaches [11]. Besides, MaREA does not perform FBA simulation, but only employs the RAS as a static representation of the metabolic behavior of a given sample, which can be then used to compare different sample sets (e.g., different patient cohorts, or control vs. tumor), identifying over (or under) expressed reactions.…”

Can a systems approach produce a better understanding of mood disorders?

“…Even once an individual is correctly diagnosed with an affective disorder, the known heterogeneity in presentation (and potentially mechanistic underpinning) may cause issues. Analogous to the study of breast cancer, when tumour heterogeneity is a major concern [30], analysis of affective disorders without further stratification will almost certainly confound biological insight.…”

“…It should be noted that while these models are qualitative in design, experimental parameters can be added to constrain the system, producing more realistic simulations. Such an approach can be seen in the integration of omics level data and a GSMN, tuning it to a 8 particular cell-type or biological context [30,31]. Essentially, any reaction in the network catalysed by a protein not present within a particular cell-type is switched off, helping the GSMN to represent the cellular phenotype [32].…”

“…Heterogeneity is a significant issue in both affective disorders and cancer, and studies are now emerging in the area of oncology on how systems biology can be used to help stratify patients [30]. This should lead to improved understanding of the disease itself, improved stratification of patients, and ultimately improved selection of the most appropriate therapeutic regimens.…”

Framework and resource for more than 11,000 gene-transcript-protein-reaction associations in human metabolism

“…To date, a series of comprehensive human GEMs has been released, including Recon 1 (19), Recon 2 (20), a revised Recon 2 (hereafter, Recon 2Q) (13), and Recon 2.2 (21), as well as human metabolic reaction (HMR) series (22,23). These human GEMs have been employed to predict anticancer targets (24)(25)(26) and oncometabolites (27), characterize metabolism of abnormal human myocyte with type 2 diabetes (28), investigate roles of gut microbiota in host glutathione metabolism (29), predict biomarkers in response to drugs (30), predict essentiality of human genes having diverse numbers of transcript variants (31), identify poor prognosis in patients with breast cancer (32), and predict tumor sizes and overall survival rates of patients with breast cancer (33). Despite such a wide application scope, currently available human GEMs cannot be used to address transcriptphenotype associations beyond genotype-phenotype links because the human GEMs have incomplete gene-protein-reaction (GPR) associations that cannot be integrated with transcriptlevel data.…”