Abstract
BackgroundBiopsies 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.ResultsUsing 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.ConclusionsThe 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.
Highlights
Biopsies taken from individual tumours exhibit extensive differences in their cellular composition due to the inherent heterogeneity of cancers and vagaries of sample collection
A significant proportion of a tumour mass is comprised of stromal cells [15]; these non-transformed cells forming the microenvironment in which tumour cell growth is contained and supported
We demonstrate using individual cancer datasets that global expression patterns can be divided into biologically meaningful clusters defining tumour cell and stromal elements, and that many of these gene signatures are conserved across multiple unrelated human cancer datasets
Summary
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 means that the level of expression of genes expressed 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 be correlated in their expression. In recent years the field of cancer research has seen an increasing number of large gene expression studies of primary human tumours Analysis of these datasets has tended to focus on the identification of markers able to divide disease samples into prognostically relevant classifications [1,2,3,4,5]. The tumour stroma is increasingly seen as an alternative target for therapeutics with potential treatments targeting angiogenesis [16,17], the extracellular matrix [18] or immune cells [16,19]
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