Molecular Subtyping of Breast Cancer from Traditional Tumor Marker Profiles Using Parallel Clustering Methods

Abstract

Purpose: Recent small-sized genomic studies on the identification of breast cancer bioprofiles have led to profoundly dishomogeneous results.Thus, we sought to identify distinct tumor profiles with possible clinical relevance based on clusters of immunohistochemical molecular markers measured on a large, single institution, case series. Experimental Design: Tumor biological profiles were explored on 633 archival tissue samples analyzed by immunohistochemistry. Five validated markers were considered, i.e., estrogen receptors (ER), progesterone receptors (PR), Ki-67/MIB1as a proliferation marker, HER2/NEU, and p53 in their original scale of measurement. The results obtained were analyzed by three different clustering algorithms. Four different indices were then used to select the different profiles (number of clusters). Results: The best classification was obtained creating four clusters. Notably, three clusters were identified according to low, intermediate, and high ER/PR levels. A further subdivision in two biologically distinct subtypes was determined by the presence/absence of HER2/NEU and of p53. As expected, the cluster with high ER/PR levels was characterized by a much better prognosis and response to hormone therapy compared to that with the lowest ER/PR values. Notably, the cluster characterized by high HER2/NEU levels showed intermediate prognosis, but a rather poor response to hormone therapy. Conclusions: Our results show the possibility of profiling breast cancers by means of traditional markers, and have novel clinical implications on the definition of the prognosis of cancer patients. These findings support the existence of a tumor subtype that responds poorly to hormone therapy, characterized by HER2/NEU overexpression.Breast cancer patients with apparently similar clinical and pathologic features can experience rather different disease dynamics or response to adjuvant therapies. This prognostic heterogeneity is considerable, and suggests a corresponding heterogeneity of the underlying biological variables. Hence, it should be possible to identify reliable, novel prognostic/ predictive markers via proteomic or transcriptomic phenotyping, or by genetic analysis. Thus, molecular phenotyping might play an important role as an adjunct to classical clinical/pathologic staging procedures. Recent work focused on the quantitative identification of biological profiles of tumors from transcriptomic data. Transcriptomic profiles were then correlated with clinical behavior, according to the hypothesis that specific profiles could identify breast cancer subtypes (1 -3). Cluster analysis (4) was applied as a statistical method capable of splitting data into subgroups (clusters) based on the relationships between the subjects and the measured variables. In breast cancer studies, cluster analyses have been conducted almost exclusively by hierarchic techniques. However, only a fraction of cases have clearly distinct features; whereas some others show a less clear-cut profile, rendering assignment to one g...

Trop-2 Induces Tumor Growth Through AKT and Determines Sensitivity to AKT Inhibitors

“…Differential correlation of membrane and storage Trop-2 with distinct risk parameters in breast cancer was previously shown (25). IHC analysis showed that membrane Trop-2 was marginally correlated with grade.…”

“…Patients were selected according to the following criteria: tumor diameter: T1, T2; absence of nodal diffusion (23,24); absence of distant metastases at diagnosis. Clinical and pathologic status, tumor histotype, histologic grade, levels of expression of estrogen receptor a and progesterone receptor (PGR), and fraction of proliferating cells (% Ki-67) were recorded (25). Tumor samples were analyzed for correlated expression of Trop-2, AKT, and TP53 (26,27) …”

“…ERa and PGR were expressed by 76.6% and 58.5% of the patients tumors, respectively. Invasive ductal carcinomas were 79.7%, invasive lobular carcinomas were 13%, other histotypes represented 7.3% of the cases, as broadly representative of the natural appearance and progression of breast cancer (25). IHC analysis identified ERa, PGR, and ERBB2 (HER2) as expressed by 76.6%, 58.5%, and 15.6% of the breast cancers, respectively.…”

A quantifier-based fuzzy classification system for breast cancer patients

“…In recent years, several studies have been carried out investigating the application of protein biomarker panels (with known relevance to breast cancer), to large numbers of cases using tissue microarrays, exploring the existence and clinical significance of distinct breast cancer classes [13][14][15][16]. In particular, Abd El-Rehim et al [46] identified and characterised five breast cancer classes, with a sixth group of only four cases also identified but considered too small for further detailed assessment.…”

“…Moreover, an alternative approach to gene expression profiling is to use established robust laboratory technology, such as immunocytochemistry on formalin fixed paraffin embedded patient tumour samples. We and others have applied protein biomarker panels, with known relevance to breast cancer, to large numbers of cases using tissue microarrays, exploring the existence and clinical significance of distinct breast cancer classes through clustering approaches [13][14][15][16].…”