Characterization of breast cancer subtypes by quantitative assessment of biological parameters: Relationship with clinicopathological characteristics, biological features and prognosis
Received 6 July 2007; received in revised form 10 April 2008; accepted 11 July 2008. published online 15 August 2008.
Abstract
Objectives
Gene expression analysis has identified several breast cancer subtypes, including luminal, epidermal growth factor receptor-2 positive (HER2+), and basal-like. To determine if our proposed molecular taxonomy correlates with biological and clinical behavior. This is based on four biological markers: estrogen and progesterone receptors (ER and PR, respectively), HER2 and the epidermal growth factor receptor-1 (HER1), all of them being determined by quantitative assays.
Study design
The biological parameters were examined by enzyme immunoassay, radioligand-binding assay or ELISA, in tumors from 787 patients with invasive breast cancer. Patients were prospectively evaluated over a median follow-up period of 50 months. Subtype definitions were as follows: luminal (ER+), HER2+ (HER2+, ER−, PgR−) and basal-like (HER2−, ER−, PgR−). In addition, we divided basal tumors into two groups based on their HER1 status.
Results
A 55.8% of tumors were of luminal type, 11.9% basal-like HER1+, 10.7 basal-like HER1−, and the remainder 21.6% HER2+. Both HER2+ and basal-like subtypes were more frequent in younger and premenopausal women, showing a higher percentage of cases of poorly differentiated tumors and higher S-phase fraction, when compared with those of luminal subtype. Multivariate analysis demonstrated that the subtype of tumor was related to both relapse and overall survival, being those of luminal subtype associated with the best prognosis.
Conclusions
Through the classification of breast tumors in four groups, according to their ER, PgR, HER2 and HER1 status, it is possible to obtain a major division of breast tumors associated with significant differences in biological features and clinical behavior.
ABSTRACT