- Paper Report
- Open Access
Gene expression signatures may predict clinical outcome
- Valerie Speirs1
© Biomed Central Ltd 2002
- Received: 5 February 2002
- Accepted: 6 February 2002
- Published: 1 December 2002
- DNA arrays, metastasis, recurrence
Traditional pathological classification of breast tumours is based on a panel of established markers, especially hormone receptor status, axillary lymph node status and histological grade. Unfortunately these fail to accurately predict clinical outcome in some patients. As a result, many patients go on to receive unnecessary adjuvant therapy. The aim of this work was to use gene expression signatures to predict clinical outcome in a cohort of breast tumours.
Approximately 25,000 genes were analysed in 98 primary breast tumours, 5000 of these showed altered expression. Hierarchical cluster analysis revealed two distinct groups that were broadly classified into tumours of good or poor prognosis. Using knowledge of clinical outcome, a three-step 'supervised' cluster analysis identified genes that correctly predicted the development of metastasis. This poor prognosis signature consisted of genes regulating cell cycle, invasion, metastasis and angiogenesis. Interestingly, this was found in small primary tumours without node metastasis at presentation, suggesting these tumours are already "hard-wired" for a metastatic phenotype. To validate this, an additional cohort of 19 tumours was studied and resulted in only two incorrect classifications, indicating the predictive power of the prognosis classification.
DNA arrays, cluster analysis
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