Research article

The effect of the stromal component of breast tumours on prediction of clinical outcome using gene expression microarray analysis

Susan J Cleator^1,2 , Trevor J Powles³ , Tim Dexter¹ , Laura Fulford¹ , Alan Mackay¹ , Ian E Smith⁴ , Haukur Valgeirsson¹ , Alan Ashworth¹ and Mitch Dowsett^1,5

¹  Breakthrough Breast Cancer Research Centre, The Institute of Cancer Research, Fulham Road, SW3 6JB, London, UK

²  Current address: Department of Oncology, St. Mary's Hospital, Praed Street, London W2 1NY, UK

³  Parkside Hospital, Parkside, SW19 5NX, London, UK

⁴  Breast Unit, Royal Marsden Hospital, Fulham Road, London, SW3 6JJ, UK

⁵  Department of Academic Biochemistry, Royal Marsden Hospital, Fulham Road, SW3 6JJ, London, UK

author email corresponding author email

Breast Cancer Research 2006, 8:R32doi:10.1186/bcr1506

Published:	21 June 2006

Abstract

Introduction

The aim of this study was to examine the effect of the cellular composition of biopsies on the error rates of multigene predictors of response of breast tumours to neoadjuvant adriamycin and cyclophosphamide (AC) chemotherapy.

Materials and methods

Core biopsies were taken from primary breast tumours of 43 patients prior to AC, and subsequent clinical response was recorded. Post-chemotherapy (day 21) samples were available for 16 of these samples. Frozen sections of each core were used to estimate the proportion of invasive cancer and other tissue components at three levels. Transcriptional profiling was performed using a cDNA array containing 4,600 elements.

Results

Twenty-three (53%) patients demonstrated a 'good' and 20 (47%) a 'poor' clinical response. The percentage invasive tumour in core biopsies collected from these patients varied markedly. Despite this, agglomerative clustering of sample expression profiles showed that almost all biopsies from the same tumour aggregated as nearest neighbours. SAM (significance analysis of microarrays) regression analysis identified 144 genes which distinguished high- and low-percentage invasive tumour biopsies at a false discovery rate of not more than 5%. The misclassification error of prediction of clinical response using microarray data from pre-treatment biopsies (on leave-one-out cross-validation) was 28%. When prediction was performed on subsets of samples which were more homogeneous in their proportions of malignant and stromal cells, the misclassification error was considerably lower (8%–13%, p < 0.05 on permutation).

Conclusion

The non-tumour content of breast cancer samples has a significant effect on gene expression profiles. Consideration of this factor improves accuracy of response prediction by expression array profiling. Future gene expression array prediction studies should be planned taking this into account.