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

Tamasin Doig, David A Hume, Thanos Theocharidis, John Goodlad, Christopher D Gregory, Tom C Freeman

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

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.

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.

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.
Original languageEnglish
Article number469
JournalBMC Genomics
Issue numbern/a
Publication statusPublished - 11 Jul 2013

Keywords / Materials (for Non-textual outputs)

  • Cancer
  • Transcriptomics
  • Coexpression
  • Disease networks
  • Clustering
  • Modules
  • Gene signatures
  • Stroma


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