Metabolic Associations of Reduced Proliferation and Oxidative Stress in Advanced Breast Cancer

Abstract

Aberrant metabolism is a hallmark of cancer, but whole metabolomic flux measurements remain scarce. To bridge this gap, we developed a novel metabolic phenotypic analysis (MPA) method that infers metabolic phenotypes based on the integration of transcriptomics or proteomics data within a human genome-scale metabolic model. MPA was applied to conduct the first genome-scale study of breast cancer metabolism based on the gene expression of a large cohort of clinical samples. The modeling correctly predicted cell lines' growth rates, tumor lipid levels, and amino acid biomarkers, outperforming extant metabolic modeling methods. Experimental validation was obtained in vitro. The analysis revealed a subtype-independent "go or grow" dichotomy in breast cancer, where proliferation rates decrease as tumors evolve metastatic capability. MPA also identified a stoichiometric tradeoff that links the observed reduction in proliferation rates to the growing need to detoxify reactive oxygen species. Finally, a fundamental stoichiometric tradeoff between serine and glutamine metabolism was found, presenting a novel hallmark of estrogen receptor (ER) þ versus ER À tumor metabolism. Together, our findings greatly extend insights into core metabolic aberrations and their impact in breast cancer. Cancer Res; 72 (22); 5712-20. Ó2012 AACR.

Predicting Drug Targets and Biomarkers of Cancer via Genome-Scale Metabolic Modeling

“…This platform makes it possible to infer the production, secretion, and uptake rates of different metabolites; to determine which reactions are active or inactive; assess reaction rates; and to determine gene and enzyme essentiality for proliferation or survival. By incorporating gene expression data, GSMMs can be used to identify reactions that have been subject to posttranscriptional regulation and specify whether their rate has been posttranscriptionally increased or decreased (32,33). As further elaborated in the following sections, when experimental data are collected from 2 types of cells, GSMMs can be used to identify knockouts (KO) that will be lethal only to one of the cells or KOs that will transform the metabolism of one of the cells to be as akin as possible to that of the other, as done via metabolic transformation analysis (MTA; Table 1).…”

“…We have recently applied this approach by utilizing a new method. The method, "Metabolic Phenotypic Analysis" (MPA), gauges the adaptive potential of cells to produce metabolites of essence in a given context (33). It was first validated by predicting amino acid biomarkers for breast cancer and confirming them based on measured plasma-free amino acid profiles of breast cancer patients and control subjects.…”

“…Recently, we addressed this task in 2 ways. By applying MPA, we described the metabolic state of different patients with breast cancer, providing a system-level view of generic and subtype-specific metabolic characteristics of breast cancer (33). We used MPA to assess growth rates, lipid production capacities, posttranscriptional regulation, and metabolic biomarkers in breast cancer, obtaining highly accurate results.…”

Haptoglobin Phenotype, Hp1-1: A Potential Risk Factor of Breast Cancer in Ghanaian Women.

“…In most cases, patients present the disease at the advanced stage and that poses a challenge to management leading to poor prognosis. Progression of BC has been associated with tumour microenvironmental alterations and is increasingly recognized as a major regulator (Jerby et al, 2012). Identified biological signatures that influence tumour microenvironment in patients could be useful in the prediction of disease outcome.…”

Synthetic dosage lethality in the human metabolic network is highly predictive of tumor growth and cancer patient survival

“…We introduce a method that uses a constraint-based genome-scale model of metabolism (GSMM) (21-25) to predict metabolic SDLs. GSMMs have successfully resolved a wide range of research questions in model organisms (23,(26)(27)(28)(29)(30) and have been the basis for many computational studies of cancer (7,8,(31)(32)(33)(34). Furthermore, they have contributed to a systematic understanding of the underlying mechanisms leading to lethality and SL (3-7).…”