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Open Access 01-12-2010 | Research article

Merging transcriptomics and metabolomics - advances in breast cancer profiling

Authors: Eldrid Borgan, Beathe Sitter, Ole Christian Lingjærde, Hilde Johnsen, Steinar Lundgren, Tone F Bathen, Therese Sørlie, Anne-Lise Børresen-Dale, Ingrid S Gribbestad

Published in: BMC Cancer | Issue 1/2010

Abstract

Background

Combining gene expression microarrays and high resolution magic angle spinning magnetic resonance spectroscopy (HR MAS MRS) of the same tissue samples enables comparison of the transcriptional and metabolic profiles of breast cancer. The aim of this study was to explore the potential of combining these two different types of information.

Methods

Breast cancer tissue from 46 patients was analyzed by HR MAS MRS followed by gene expression microarrays. Two strategies were used to combine the gene expression and metabolic data; first using multivariate analyses to identify different groups based on gene expression and metabolic data; second correlating levels of specific metabolites to transcripts to suggest new hypotheses of connections between metabolite levels and the underlying biological processes. A parallel study was designed to address experimental issues of combining microarrays and HR MAS MRS.

Results

In the first strategy, using the microarray data and previously reported molecular classification methods, the majority of samples were classified as luminal A. Three subgroups of luminal A tumors were identified based on hierarchical clustering of the HR MAS MR spectra. The samples in one of the subgroups, designated A2, showed significantly lower glucose and higher alanine levels than the other luminal A samples, suggesting a higher glycolytic activity in these tumors. This group was also enriched for genes annotated with Gene Ontology (GO) terms related to cell cycle and DNA repair. In the second strategy, the correlations between concentrations of myo-inositol, glycine, taurine, glycerophosphocholine, phosphocholine, choline and creatine and all transcripts in the filtered microarray data were investigated. GO-terms related to the extracellular matrix were enriched among the genes that correlated the most to myo-inositol and taurine, while cell cycle related GO-terms were enriched for the genes that correlated the most to choline. Additionally, a subset of transcripts was identified to have slightly altered expression after HR MAS MRS and was therefore removed from all other analyses.

Conclusions

Combining transcriptional and metabolic data from the same breast carcinoma sample is feasible and may contribute to a more refined subclassification of breast cancers as well as reveal relations between metabolic and transcriptional levels.

See Commentary: http://www.biomedcentral.com/1741-7015/8/73

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The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2407/10/628/prepub

Title: Merging transcriptomics and metabolomics - advances in breast cancer profiling
Authors: Eldrid Borgan
Beathe Sitter
Ole Christian Lingjærde
Hilde Johnsen
Steinar Lundgren
Tone F Bathen
Therese Sørlie
Anne-Lise Børresen-Dale
Ingrid S Gribbestad
Publication date: 01-12-2010
Publisher: BioMed Central
Published in: BMC Cancer / Issue 1/2010
Electronic ISSN: 1471-2407
DOI: https://doi.org/10.1186/1471-2407-10-628

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