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Abstract Background Breast cancer is a heterogeneous disease, presenting with a wide range of histologic, clinical, and genetic features. Microarray technology has shown promise in predicting outcome in these patients. Methods We profiled 162 breast tumors using expression microarrays to stratify tumors based on gene expression. A subset of 55 tumors with extensive follow-up was used to identify gene sets that predicted outcome. The predictive gene set was further tested in previously published data sets. Results We used different statistical methods to identify three gene sets associated with disease free survival. A fourth gene set, consisting of 21 genes in common to all three sets, also had the ability to predict patient outcome. To validate the predictive utility of this derived gene set, it was tested in two published data sets from other groups. This gene set resulted in significant separation of patients on the basis of survival in these data sets, correctly predicting outcome in 62–65% of patients. By comparing outcome prediction within subgroups based on ER status, grade, and nodal status, we found that our gene set was most effective in predicting outcome in ER positive and node negative tumors. Conclusion This robust gene selection with extensive validation has identified a predictive gene set that may have clinical utility for outcome prediction in breast cancer patients.

Background: Breast cancer is a heterogeneous disease, presenting with a wide range of histologic, clinical, and genetic features. Microarray technology has shown promise in predicting outcome in these patients. Methods: We profiled 162 breast tumors using expression microarrays to stratify tumors based on gene expression. A subset of 55 tumors with extensive follow-up was used to identify gene sets that predicted outcome. The predictive gene set was further tested in previously published data sets. Results: We used different statistical methods to identify three gene sets associated with disease free survival. A fourth gene set, consisting of 21 genes in common to all three sets, also had the ability to predict patient outcome. To validate the predictive utility of this derived gene set, it was tested in two published data sets from other groups. This gene set resulted in significant separation of patients on the basis of survival in these data sets, correctly predicting outcome in 62-65% of patients. By comparing outcome prediction within subgroups based on ER status, grade, and nodal status, we found that our gene set was most effective in predicting outcome in ER positive and node negative tumors. Conclusion: This robust gene selection with extensive validation has identified a predictive gene set that may have clinical utility for outcome prediction in breast cancer patients. [ABSTRACT FROM AUTHOR]

A response by Hugh M. D. Gurling and colleagues to a letter to the editor about their article "Genetic association and brain morphology studies and the chromosome 8p22 pericentriolar material 1 (PCM1) gene in susceptibility to schizophrenia" is presented.

In order to expand our understanding of the molecular changes underlying the complex pathology of pancreatic malignancy, global gene expression profiling of pancreatic adenocarcinoma compared with normal pancreatic tissue was performed. Human cDNA arrays comprising 9932 elements were interrogated with fluorescence-labelled normal and adenocarcinoma samples (nine tumours, three normal pancreata, and three cell lines). The data were analysed for differential gene expression, which was confirmed by serial analysis of gene expression (SAGE), digital differential display (DDD) analysis, and immunohistochemistry for selected cases. The array data were filtered to produce lists of a total of 75 genes significantly up-regulated or down-regulated in pancreatic adenocarcinoma. Two of those showing the highest differential were members of the S100 family of Ca-binding proteins, namely S100P and S100A6, and therefore the S100 genes were studied in more detail. By immunohistochemical analysis of custom-built, pancreas-specific tissue arrays and commercially available, normal/cancer tissue arrays that included a wide variety of different tumour types, differential expression of S100P protein was found to be almost exclusive to pancreatic cancer. S100P could therefore represent a useful biomarker for pancreatic adenocarcinomas., (Copyright 2003 John Wiley & Sons, Ltd.)