Gene Expression Profiling in Breast Cancer: Understanding the Molecular Basis of Histologic Grade To Improve Prognosis

Christos Sotiriou(SIB Swiss Institute of Bioinformatics), Pratyaksha Wirapati(SIB Swiss Institute of Bioinformatics), Sherene Loi(SIB Swiss Institute of Bioinformatics), Adrian L. Harris(SIB Swiss Institute of Bioinformatics), Steve Fox(SIB Swiss Institute of Bioinformatics), Johanna Smeds(SIB Swiss Institute of Bioinformatics), Hans Nordgren(SIB Swiss Institute of Bioinformatics), Pierre Farmer(SIB Swiss Institute of Bioinformatics), Viviane Praz(SIB Swiss Institute of Bioinformatics), Benjamin Haibe‐Kains(SIB Swiss Institute of Bioinformatics), Christine Desmedt(SIB Swiss Institute of Bioinformatics), Denis Larsimont(SIB Swiss Institute of Bioinformatics), Fátima Cardoso(SIB Swiss Institute of Bioinformatics), Hans Peterse(SIB Swiss Institute of Bioinformatics), Dimitry S.A. Nuyten(SIB Swiss Institute of Bioinformatics), Marc Buyse(SIB Swiss Institute of Bioinformatics), Marc J. van de Vijver(SIB Swiss Institute of Bioinformatics), Jonas Bergh(SIB Swiss Institute of Bioinformatics), Martine Piccart(SIB Swiss Institute of Bioinformatics), Mauro Delorenzi(SIB Swiss Institute of Bioinformatics)
JNCI Journal of the National Cancer Institute
February 14, 2006
10.1093/jnci/djj052
Cited by 2,127Open Access
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Abstract

Background: Histologic grade in breast cancer provides clinically important prognostic information. However, 30%–60% of tumors are classified as histologic grade 2. This grade is associated with an intermediate risk of recurrence and is thus not informative for clinical decision making. We examined whether histologic grade was associated with gene expression profiles of breast cancers and whether such profiles could be used to improve histologic grading. Methods: We analyzed microarray data from 189 invasive breast carcinomas and from three published gene expression datasets from breast carcinomas. We identified differentially expressed genes in a training set of 64 estrogen receptor (ER)–positive tumor samples by comparing expression profiles between histologic grade 3 tumors and histologic grade 1 tumors and used the expression of these genes to define the gene expression grade index. Data from 597 independent tumors were used to evaluate the association between relapse-free survival and the gene expression grade index in a Kaplan–Meier analysis. All statistical tests were two-sided. Results: We identified 97 genes in our training set that were associated with histologic grade; most of these genes were involved in cell cycle regulation and proliferation. In validation datasets, the gene expression grade index was strongly associated with histologic grade 1 and 3 status; however, among histologic grade 2 tumors, the index spanned the values for histologic grade 1–3 tumors. Among patients with histologic grade 2 tumors, a high gene expression grade index was associated with a higher risk of recurrence than a low gene expression grade index (hazard ratio = 3.61, 95% confidence interval = 2.25 to 5.78; P <.001, log-rank test). Conclusions: Gene expression grade index appeared to reclassify patients with histologic grade 2 tumors into two groups with high versus low risks of recurrence. This approach may improve the accuracy of tumor grading and thus its prognostic value.

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