1. ¹Department of Academic Surgery, University College Cork, Cork, Ireland
2. ²Department of Biochemistry, University College Cork, Cork, Ireland
3. ³Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA
4. ⁴Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA
5. ⁵Conway Institute, University College Dublin, Belfield, Dublin, Ireland

Correspondence: Professor HP Redmond, Department of Academic Surgery, University College Cork, Cork University Hospital, Wilton, Cork, Ireland. E-mail: Henry.Redmond@hse.ie

Received 15 September 2008; Revised 3 December 2008; Accepted 15 January 2009.

Abstract

Tumour stroma gene expression in biopsy specimens may obscure the expression of tumour parenchyma, hampering the predictive power of microarrays. We aimed to assess the utility of fluorescence-activated cell sorting (FACS) for generating cell populations for gene expression analysis and to compare the gene expression of FACS-purified tumour parenchyma to that of whole tumour biopsies. Single cell suspensions were generated from colorectal tumour biopsies and tumour parenchyma was separated using FACS. Fluorescence-activated cell sorting allowed reliable estimation and purification of cell populations, generating parenchymal purity above 90%. RNA from FACS-purified and corresponding whole tumour biopsies was hybridised to Affymetrix oligonucleotide microarrays. Whole tumour and parenchymal samples demonstrated differential gene expression, with 289 genes significantly overexpressed in the whole tumour, many of which were consistent with stromal gene expression (e.g., COL6A3, COL1A2, POSTN, TIMP2). Genes characteristic of colorectal carcinoma were overexpressed in the FACS-purified cells (e.g., HOX2D and RHOB). We found FACS to be a robust method for generating samples for gene expression analysis, allowing simultaneous assessment of parenchymal and stromal compartments. Gross stromal contamination may affect the interpretation of cancer gene expression microarray experiments, with implications for hypotheses generation and the stability of expression signatures used for predicting clinical outcomes.