Open Access Highly Accessed Research article

Coexpression analysis of large cancer datasets provides insight into the cellular phenotypes of the tumour microenvironment

Tamasin N Doig13, David A Hume3, Thanasis Theocharidis3, John R Goodlad2, Christopher D Gregory1 and Tom C Freeman3*

Author Affiliations

1 Centre for Inflammation Research, University of Edinburgh, The Queen’s Medical Research Institute, 47 Little France Crescent, Edinburgh EH16 4JT, UK

2 Department of Pathology, Lothian University NHS Trust, Western General Hospital, Crewe Road, Edinburgh EH4 2XU, UK

3 The Roslin Institute, R(D)SVS, 741 University of Edinburgh, Easter Bush, Midlothian, Scotland EH25 9RG, UK

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BMC Genomics 2013, 14:469  doi:10.1186/1471-2164-14-469

Published: 11 July 2013

Abstract

Background

Biopsies taken from individual tumours exhibit extensive differences in their cellular composition due to the inherent heterogeneity of cancers and vagaries of sample collection. As a result genes expressed in specific cell types, or associated with certain biological processes are detected at widely variable levels across samples in transcriptomic analyses. This heterogeneity also means that the level of expression of genes expressed specifically in a given cell type or process, will vary in line with the number of those cells within samples or activity of the pathway, and will therefore be correlated in their expression.

Results

Using a novel 3D network-based approach we have analysed six large human cancer microarray datasets derived from more than 1,000 individuals. Based upon this analysis, and without needing to isolate the individual cells, we have defined a broad spectrum of cell-type and pathway-specific gene signatures present in cancer expression data which were also found to be largely conserved in a number of independent datasets.

Conclusions

The conserved signature of the tumour-associated macrophage is shown to be largely-independent of tumour cell type. All stromal cell signatures have some degree of correlation with each other, since they must all be inversely correlated with the tumour component. However, viewed in the context of established tumours, the interactions between stromal components appear to be multifactorial given the level of one component e.g. vasculature, does not correlate tightly with another, such as the macrophage.

Keywords:
Cancer; Transcriptomics; Coexpression; Disease networks; Clustering; Modules; Gene signatures; Stroma