Identifying Personalized Metabolic Signatures in Breast Cancer.

Priyanka Baloni,John C Earls,Donald Geman,Nathan D Price,Wikum Dinalankara,Theo A Knijnenburg,Luigi Marchionni

doi:10.3390/metabo11010020

Abstract

Cancer cells are adept at reprogramming energy metabolism, and the precise manifestation of this metabolic reprogramming exhibits heterogeneity across individuals (and from cell to cell). In this study, we analyzed the metabolic differences between interpersonal heterogeneous cancer phenotypes. We used divergence analysis on gene expression data of 1156 breast normal and tumor samples from The Cancer Genome Atlas (TCGA) and integrated this information with a genome-scale reconstruction of human metabolism to generate personalized, context-specific metabolic networks. Using this approach, we classified the samples into four distinct groups based on their metabolic profiles. Enrichment analysis of the subsystems indicated that amino acid metabolism, fatty acid oxidation, citric acid cycle, androgen and estrogen metabolism, and reactive oxygen species (ROS) detoxification distinguished these four groups. Additionally, we developed a workflow to identify potential drugs that can selectively target genes associated with the reactions of interest. MG-132 (a proteasome inhibitor) and OSU-03012 (a celecoxib derivative) were the top-ranking drugs identified from our analysis and known to have anti-tumor activity. Our approach has the potential to provide mechanistic insights into cancer-specific metabolic dependencies, ultimately enabling the identification of potential drug targets for each patient independently, contributing to a rational personalized medicine approach.

Highlights

The physiological state of a cell is influenced by underlying metabolic processes which exhibit high degrees of heterogeneity across patients and across cells
Some of the metabolic and genetic changes that are reported in tumor cells are enhanced glycolysis, differential expression of lactate dehydrogenase A (LDH), which is linked with cancer growth and metastasis, mutations in metabolic enzymes such as isocitrate dehydrogenase 1 (IDH1), succinate dehydrogenase (SDH) and fumarate hydratase (FH) involved in initiating tumors [3]
The main contributions of the present work are three-fold: (1) we generate contextspecific metabolic networks for 1156 cancer and normal samples by integrating their divergence profiles with a global human metabolic network reconstruction; (2) we develop a framework for identifying key metabolic and regulatory signatures and used it to classify the samples in breast cancer based on their metabolic state; (3) we perform in silico gene knockout in these 1156 context-specific metabolic networks and identify genes that can perturb the system, many of which correspond to known drug targets

Summary

Introduction

The physiological state of a cell is influenced by underlying metabolic processes which exhibit high degrees of heterogeneity across patients and across cells. Some of the metabolic and genetic changes that are reported in tumor cells are enhanced glycolysis, differential expression of lactate dehydrogenase A (LDH), which is linked with cancer growth and metastasis, mutations in metabolic enzymes such as isocitrate dehydrogenase 1 (IDH1), succinate dehydrogenase (SDH) and fumarate hydratase (FH) involved in initiating tumors [3]. These findings suggest that metabolism is fundamental in determining cell fate in cancer and should be explored further. Integration of these omics measurements with computational models increases the accuracy of predictions

Methods

Results

Conclusion