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Understanding the Molecular Basis of Histologic GradeIgnatiadis M. · Sotiriou C.
Translational Research Unit, Jules Bordet Institute, Brussels, Belgium Corresponding Author
Christos Sotiriou, MD, PhD
Translational Research Unit, Jules Bordet Institute
121, boulevard de Waterloo
BE–1000 Brussels (Belgium)
Tel. +32 2 541 3428, Fax +32 2 538 0858, E-Mail email@example.com
Histologic grading in breast cancer is based on the evaluation of 3 morphologic features (tubule formation, nuclear pleomorphism and mitotic count), is essentially describing proliferation and differentiation in breast cancer, and is considered an important prognostic factor for this disease. It has been suggested that histologic grade 1 and 3 breast tumors are 2 different diseases that may have distinct molecular origins, pathogenesis and natural history. Different single markers like Ki-67, thymidine labeling index and S phase fraction/flow cytometry have been studied as markers of proliferation, but none of them, with the possible exception of Ki-67, is currently employed routinely in clinical practice. The advent of the powerful microarray technology has enabled scientists to comprehensively study proliferation in breast cancer on a genome-wide scale. A gene expression grade index (GGI) was developed that challenges the existence and clinical relevance of an intermediate grade 2 classification. The GGI could reclassify patients with histologic grade 2 tumors into 2 groups with high versus low risks of recurrence. GGI has also been used to define 2 clinically relevant subgroups in estrogen receptor-positive breast carcinomas. Finally, in the largest meta-analysis of publicly available gene expression and clinical data, 4 stable molecular subgroups of breast cancer have been identified, namely ER–/HER–, HER2+ and ER+/HER2–, which was divided into 2 subgroups (ER+/low proliferation and ER+/high proliferation). In this same meta-analysis, proliferation was shown to be the common driving force responsible for the performance of various breast cancer prognostic signatures.
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