Gene expression profiling

The most important prognostic indicators to predict patient outcome in breast cancer are hormone receptor status, axillary lymph node status and histological grade; although these give a broad overview of outcome, they cannot predict for all patients. The recent development of cDNA array analysis provides the opportunity for a more refined approach based on molecular classification of individual tumours (see Additional information [1]). A previous study by the same authors identified molecular profiles in a cohort of nine benign and 72 malignant breast tumours (see Additional information [2]). The aim of this work was to use this technology to identify patients at increased risk of tumour recurrence.

Hierarchical cluster analysis of data revealed two clusters, class A - which consisted of a high proportion of node-negative patients with metastases at time of diagnosis - and non-class A (all other patients). Median follow up data (23.5 months) from 55 of these patients were analysed. Of 22 patients in class A, 11 progressed to metastatic disease and 9 out of 20 had recurrences, compared to 3 out of 27 of non-class A. Additionally, three out of five patients originally classified as N0 in class A developed metastatic disease.

Although based on a small number of patients, these results illustrate the potential use of cluster analysis in identifying patients at greater risk of recurrence. Larger scale studies are warranted to examine the impact of gene expresion profiling in predicting patient outcome and response to therapy on a more individualised basis. The data presented here and in another related study (see Additional information [3]) are promising and suggest that this technology could be used routinely in a clinical setting in the not too distant future.

DNA arrays

1.Perou CM, Sorlie T, Eisen MB, van de Rijn M, Jeffrey SS, Rees CA, Pollack JR, Ross DT, Johnsen H, Akslen LA, Fluge O, Pergamenschikov A, Williams C, Zhu SX, Lonning PE, Borresen-Dale AL, Brown PO, Botstein D: Molecular portraits of human breast tumours. Nature 2000,406: 747-752 (PubMed abstract)

2.Ahr A, Holtrich U, Solbach C, Scharl A, Strebhardt K, Karn T, Kaufmann M: Molecular classification of breast cancer patients by gene expression profiling. J Pathol 2001, 195: 312-320 (PubMed abstract)

3. van 't Veer LJ, Dai H, van De Vijver MJ, He YD, Hart AA, Mao M, Peterse HL, van Der Kooy K, Marton MJ, Witteveen AT, Schreiber GJ, Kerkhoven RM, Roberts C, Linsley PS, Bernards R, Friend SH: Gene expression profiling predicts clinical outcome of breast cancer. Nature, 2002 415:530-536 (Paper report)