Gene expression profiling in breast cancer challenges the existence of intermediate histological grade
© BioMed Central 2005
Published: 17 June 2005
The histological grade (HG) in breast cancer provides important prognostic information. However, its interobserver variability and poor reproducibility, especially for tumours of intermediate grade, have limited its clinical potential. We hypothesized that molecular characterization of the grade may allow for full exploitation of the association between the grade and relapse beyond the ability of traditional grading procedures.
Six datasets totalling about 700 primary breast cancers, mostly publicly available data, were used in the analysis. Gene expression profiles (GEP) from Affymetrix U133A GeneChips were contrasted between HG 1 (low grade) and HG 3 (high grade) tumours on a training set of 64 estrogen-receptor-positive breast cancer samples. A set of genes positively and negatively correlated with grade was identified on this training set and chosen as grade reporting genes. A scoring system called the 'gene-expression grade index' (GGI), which essentially summarizes the grade reporting genes by their average expression level, was introduced. The GGI was applied to patients not used in the gene selection to test its prognostic value.
Similar observations were made in the different datasets analysed, in untreated as well as in systemically treated patients, and on the three different main types of microarray platforms, with substantial variability in the number of reporter genes available. Almost all known clinicopathological variables were significantly associated with clinical outcome in univariate analysis, while in a multivariate model only the GG, tumour size and nodal status were significant factors. Replacing the HG with the GG significantly improved the prognostic two-group classification obtained with the Nottingham Prognostic Index.
Gene-expression-based grading has the potential to significantly improve current grading systems by rendering them more objectively measurable and improving their prognostic value. The superior performance of the two-grade GG system challenges the purpose of classifying tumors as of intermediate grade. Reproduction of these findings in four independent datasets, and across different platforms and with a simple computational system, gives hope that the approach will prove robust and reliable.