title: Molecular subtyping of breast cancer from traditional tumor marker profiles using parallel clustering methods.
abstract: OBJECTIVE
  Recent small-sized genomic studies on the identification of breast cancer bioprofiles have led to profoundly dishomogenous 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.
  
  
  METHODS
  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/MIB1 as 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.
authors.affiliation: Unit of Medical Statistics and Biometry, National Cancer Institute of Milano, Milan, Italy. federico.ambrogi@istitutotumori.mi.it
authors.forename: Federico
authors.initials: F
authors.lastname: Ambrogi
authors.forename: Elia
authors.initials: E
authors.lastname: Biganzoli
authors.forename: Patrizia
authors.initials: P
authors.lastname: Querzoli
authors.forename: Stefano
authors.initials: S
authors.lastname: Ferretti
authors.forename: Patrizia
authors.initials: P
authors.lastname: Boracchi
authors.forename: Saverio
authors.initials: S
authors.lastname: Alberti
authors.forename: Ettore
authors.initials: E
authors.lastname: Marubini
authors.forename: Italo
authors.initials: I
authors.lastname: Nenci
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D014408:Biomarkers, Tumor / Q000235:genetics*; D001943:Breast Neoplasms / Q000145:classification* / Q000235:genetics* / Q000473:pathology; D016000:Cluster Analysis; D018450:Disease Progression; D005260:Female; D015972:Gene Expression Regulation, Neoplastic; D018734:Genes, erbB-2 / Q000235:genetics; D005838:Genotype; D006801:Humans; D007150:Immunohistochemistry; D019394:Ki-67 Antigen / Q000235:genetics; D008875:Middle Aged; D011960:Receptors, Estrogen / Q000235:genetics; D011980:Receptors, Progesterone / Q000235:genetics; D012189:Retrospective Studies; D016019:Survival Analysis; D016159:Tumor Suppressor Protein p53 / Q000235:genetics
chemical_list: D014408:Biomarkers, Tumor; D019394:Ki-67 Antigen; D011960:Receptors, Estrogen; D011980:Receptors, Progesterone; D016159:Tumor Suppressor Protein p53