Breast tumours with intermediate histological grade can be reclassified into prognostically distinct groups by gene expression profiling.

Sotiriou C, Wirapati P, Loi S, Viale G, Harris A, Bergh J, Smeds J, Farmer P, Praz V, Haibe-Kains B, Lallemand F, Desmedt C, Durbecq V, Larsimont D, Cardoso F, Buyse M, Delorenzi M, Piccart M

Background: Histological grade (HG) in breast cancer provides important prognostic information. Low and high grade (HG1 and HG3, respectively) tumours are known to have good and poor prognosis. However, intermediate grade (HG2) tumours present a difficulty in clinical decision making, because their survival profile is not different from that of the total (non-graded) population and their proportion is large (40%-50%). The aim of this study was to determine whether the prognostic value of grade could be refined by using gene expression profiling.

Methods: Gene expression profiles (GEP) from Affymetrix U133A Genechips were contrasted between HG 1 (low grade) and 3 (high grade) tumours using a training set of 64 estrogen receptor (ER)-positive breast cancer samples. A score called the gene-expression grade index (GGI) based on the 128 most significant genes was defined and tested on a independent validation dataset of 129 tumour samples. A multiple independent external validation was also performed using three publicly available datasets from van de Vijver, M. J. et al. 2002, Sorlie et al. 2001 and Sotiriou et al. 2003 using different populations and microarray platforms (total=474 tumour samples).

Results: HG 1 and 3 tumours were associated with distinct GEP and GGI values. The GGI clearly splits HG 2 (intermediate grade) tumours into high-risk (gene-expression grade GG 3) and low-risk (GG 1) groups with significantly different clinical outcomes (HR: 3.49, 95% CI 2.14-5.69, p<0.0001), similar to that of HG 3 and 1, respectively (HR: 3.48, 95% CI 2.26-5.36, p<0.0001). Replacing HG with genomic grade (GG) in the Nottingham Prognostic Index significantly improved prognostic classification. Similar results were found when we applied GG to the 3 external independent datasets. We are currently validating our findings in the TransBig series of 302 tumour samples from 5 different European institutions from which grading was determined based on central pathology review in Milan.

Conclusions: Gene-expression-based grading can significantly improve current grading systems for the prognostic assessment of breast cancer. Reproduction of these findings across multiple independent datasets and across different platforms suggests our findings are robust. Refined grading based on gene expression measurements can have important clinical implications for breast cancer management in the near future.