title: Protein expression based multimarker analysis of breast cancer samples.
abstract: BACKGROUND
  Tissue microarray (TMA) data are commonly used to validate the prognostic accuracy of tumor markers. For example, breast cancer TMA data have led to the identification of several promising prognostic markers of survival time. Several studies have shown that TMA data can also be used to cluster patients into clinically distinct groups. Here we use breast cancer TMA data to cluster patients into distinct prognostic groups.
  
  
  METHODS
  We apply weighted correlation network analysis (WGCNA) to TMA data consisting of 26 putative tumor biomarkers measured on 82 breast cancer patients. Based on this analysis we identify three groups of patients with low (5.4%), moderate (22%) and high (50%) mortality rates, respectively. We then develop a simple threshold rule using a subset of three markers (p53, Na-KATPase-β1, and TGF β receptor II) that can approximately define these mortality groups. We compare the results of this correlation network analysis with results from a standard Cox regression analysis.
  
  
  RESULTS
  We find that the rule-based grouping variable (referred to as WGCNA*) is an independent predictor of survival time. While WGCNA* is based on protein measurements (TMA data), it validated in two independent Affymetrix microarray gene expression data (which measure mRNA abundance). We find that the WGCNA patient groups differed by 35% from mortality groups defined by a more conventional stepwise Cox regression analysis approach.
  
  
  CONCLUSIONS
  We show that correlation network methods, which are primarily used to analyze the relationships between gene products, are also useful for analyzing the relationships between patients and for defining distinct patient groups based on TMA data. We identify a rule based on three tumor markers for predicting breast cancer survival outcomes.
authors.affiliation: Department of Biostatistics, UCLA, Los Angeles, CA 90095, USA. apresson@ucla.edu
authors.forename: Angela P
authors.initials: AP
authors.lastname: Presson
authors.forename: Nam K
authors.initials: NK
authors.lastname: Yoon
authors.forename: Lora
authors.initials: L
authors.lastname: Bagryanova
authors.forename: Vei
authors.initials: V
authors.lastname: Mah
authors.forename: Mohammad
authors.initials: M
authors.lastname: Alavi
authors.forename: Erin L
authors.initials: EL
authors.lastname: Maresh
authors.forename: Ayyappan K
authors.initials: AK
authors.lastname: Rajasekaran
authors.forename: Lee
authors.initials: L
authors.lastname: Goodglick
authors.forename: David
authors.initials: D
authors.lastname: Chia
authors.forename: Steve
authors.initials: S
authors.lastname: Horvath
mesh_terms: D014408:Biomarkers, Tumor / Q000096:biosynthesis* / Q000235:genetics; D001943:Breast Neoplasms / Q000235:genetics / Q000378:metabolism* / Q000401:mortality; D016000:Cluster Analysis; D005260:Female; D015972:Gene Expression Regulation, Neoplastic; D016158:Genes, p53; D006801:Humans; D009363:Neoplasm Proteins / Q000096:biosynthesis* / Q000235:genetics; D011379:Prognosis; D016016:Proportional Hazards Models; D040081:Protein Array Analysis; D017346:Protein Serine-Threonine Kinases / Q000096:biosynthesis* / Q000235:genetics; D000077294:Receptor, Transforming Growth Factor-beta Type II; D018125:Receptors, Transforming Growth Factor beta / Q000096:biosynthesis* / Q000235:genetics; D000254:Sodium-Potassium-Exchanging ATPase / Q000096:biosynthesis* / Q000235:genetics; D016159:Tumor Suppressor Protein p53 / Q000096:biosynthesis*
chemical_list: C503551:ATP1B1 protein, human; D014408:Biomarkers, Tumor; D009363:Neoplasm Proteins; D018125:Receptors, Transforming Growth Factor beta; C495901:TP53 protein, human; D016159:Tumor Suppressor Protein p53; D017346:Protein Serine-Threonine Kinases; D000077294:Receptor, Transforming Growth Factor-beta Type II; D000254:Sodium-Potassium-Exchanging ATPase