A systematic flux analysis approach to identify metabolic vulnerabilities in human breast cancer cell lines

Sheree D. Martin,Sean L. McGee

doi:10.1186/s40170-019-0207-x

Abstract

BackgroundIncreased flux through both glycolytic and oxidative metabolic pathways is a hallmark of breast cancer cells and is critical for their growth and survival. As such, targeting this metabolic reprograming has received much attention as a potential treatment approach. However, the heterogeneity of breast cancer cell metabolism, even within classifications, suggests a necessity for an individualised approach to treatment in breast cancer patients.MethodsThe metabolic phenotypes of a diverse panel of human breast cancer cell lines representing the major breast cancer classifications were assessed using real-time metabolic flux analysis. Flux linked to ATP production, pathway reserve capacities and specific macromolecule oxidation rates were quantified. Suspected metabolic vulnerabilities were targeted with specific pathway inhibitors, and relative cell viability was assessed using the crystal violet assay. Measures of AMPK and mTORC1 activity were analysed through immunoblotting.ResultsBreast cancer cells displayed heterogeneous energy requirements and utilisation of non-oxidative and oxidative energy-producing pathways. Quantification of basal glycolytic and oxidative reserve capacities identified cell lines that were highly dependent on individual pathways, while assessment of substrate oxidation relative to total oxidative capacity revealed cell lines that were highly dependent on individual macromolecules. Based on these findings, mild mitochondrial inhibition in ESH-172 cells, including with the anti-diabetic drug metformin, and mild glycolytic inhibition in Hs578T cells reduced relative viability, which did not occur in non-transformed MCF10a cells. The effects on viability were associated with AMPK activation and inhibition of mTORC1 signalling. Hs578T were also found to be highly dependent on glutamine oxidation and inhibition of this process also impacted viability.ConclusionsTogether, these data highlight that systematic flux analysis in breast cancer cells can identify targetable metabolic vulnerabilities, despite heterogeneity in metabolic profiles between individual cancer cell lines.

Highlights

Increased flux through both glycolytic and oxidative metabolic pathways is a hallmark of breast cancer cells and is critical for their growth and survival
As cancer cells are highly dependent on metabolic reprogramming for their proliferation and survival, targeting tumours with therapies that inhibit specific metabolic pathways has been touted as a new treatment approach [5]
The aim of the present study was to characterise the metabolic phenotypes across a panel of breast cancer cell lines before using a standardised, yet comprehensive, approach in an effort to identify potential metabolic vulnerabilities in major metabolic pathways coupled to ATP production using real-time metabolic flux analysis

Summary

Introduction

Increased flux through both glycolytic and oxidative metabolic pathways is a hallmark of breast cancer cells and is critical for their growth and survival. Cancer cells reprogram their metabolism to drive high rates of proliferation and ensure their survival under conditions of fluctuating nutrient availability [1] Characterisation of these alterations in metabolism suggested that cancer cells exclusively increased glycolytic flux to maintain high rates of ATP production [2]. It is recognised that metabolism within an individual tumour can be spatially heterogeneous due to factors such as nutrient and oxygen penetrance [12, 13], and the site of sampling can have a profound impact on conclusions generated using this approach Another method used to identify metabolic vulnerabilities is real-time flux analysis in isolated and cultured cancer cells [6]. The methods used to identify metabolic vulnerabilities in cancer cells using real-time flux analysis have been ad hoc, and there are no clear stepwise protocols to identify metabolic vulnerabilities in cancer cells using this approach

Objectives

Methods

Results

Conclusion