scholarly journals Breast Cancer Cell Subtypes Display Different Metabolic Phenotypes That Correlate with Their Clinical Classification

Biology ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1267
Author(s):  
Consuelo Ripoll ◽  
Mar Roldan ◽  
Maria J. Ruedas-Rama ◽  
Angel Orte ◽  
Miguel Martin

Metabolic reprogramming of cancer cells represents an orchestrated network of evolving molecular and functional adaptations during oncogenic progression. In particular, how metabolic reprogramming is orchestrated in breast cancer and its decisive role in the oncogenic process and tumor evolving adaptations are well consolidated at the molecular level. Nevertheless, potential correlations between functional metabolic features and breast cancer clinical classification still represent issues that have not been fully studied to date. Accordingly, we aimed to investigate whether breast cancer cell models representative of each clinical subtype might display different metabolic phenotypes that correlate with current clinical classifications. In the present work, functional metabolic profiling was performed for breast cancer cell models representative of each clinical subtype based on the combination of enzyme inhibitors for key metabolic pathways, and isotope-labeled tracing dynamic analysis. The results indicated the main metabolic phenotypes, so-called ‘metabophenotypes’, in terms of their dependency on glycolytic metabolism or their reliance on mitochondrial oxidative metabolism. The results showed that breast cancer cell subtypes display different metabophenotypes. Importantly, these metabophenotypes are clearly correlated with the current clinical classifications.

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Rotem Katzir ◽  
Ibrahim H. Polat ◽  
Michal Harel ◽  
Shir Katz ◽  
Carles Foguet ◽  
...  

AbstractAltered metabolism is a hallmark of cancer, but little is still known about its regulation. In this study, we measure transcriptomic, proteomic, phospho-proteomic and fluxomics data in a breast cancer cell-line (MCF7) across three different growth conditions. Integrating these multiomics data within a genome scale human metabolic model in combination with machine learning, we systematically chart the different layers of metabolic regulation in breast cancer cells, predicting which enzymes and pathways are regulated at which level. We distinguish between two types of reactions, directly and indirectly regulated. Directly-regulated reactions include those whose flux is regulated by transcriptomic alterations (~890) or via proteomic or phospho-proteomics alterations (~140) in the enzymes catalyzing them. We term the reactions that currently lack evidence for direct regulation as (putative) indirectly regulated (~930). Many metabolic pathways are predicted to be regulated at different levels, and those may change at different media conditions. Remarkably, we find that the flux of predicted indirectly regulated reactions is strongly coupled to the flux of the predicted directly regulated ones, uncovering a tiered hierarchical organization of breast cancer cell metabolism. Furthermore, the predicted indirectly regulated reactions are predominantly reversible. Taken together, this architecture may facilitate rapid and efficient metabolic reprogramming in response to the varying environmental conditions incurred by the tumor cells. The approach presented lays a conceptual and computational basis for mapping metabolic regulation in additional cancers.


PLoS ONE ◽  
2013 ◽  
Vol 8 (7) ◽  
pp. e69023 ◽  
Author(s):  
Nuno Bernardes ◽  
Ana Sofia Ribeiro ◽  
Sofia Abreu ◽  
Bruna Mota ◽  
Rute G. Matos ◽  
...  

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Chengcheng Niu ◽  
Long Wang ◽  
Zhigang Wang ◽  
Yan Xu ◽  
Yihe Hu ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document