title: A machine learned classifier that uses gene expression data to accurately predict estrogen receptor status.
abstract: BACKGROUND
  Selecting the appropriate treatment for breast cancer requires accurately determining the estrogen receptor (ER) status of the tumor. However, the standard for determining this status, immunohistochemical analysis of formalin-fixed paraffin embedded samples, suffers from numerous technical and reproducibility issues. Assessment of ER-status based on RNA expression can provide more objective, quantitative and reproducible test results.
  
  
  METHODS
  To learn a parsimonious RNA-based classifier of hormone receptor status, we applied a machine learning tool to a training dataset of gene expression microarray data obtained from 176 frozen breast tumors, whose ER-status was determined by applying ASCO-CAP guidelines to standardized immunohistochemical testing of formalin fixed tumor.
  
  
  RESULTS
  This produced a three-gene classifier that can predict the ER-status of a novel tumor, with a cross-validation accuracy of 93.17±2.44%. When applied to an independent validation set and to four other public databases, some on different platforms, this classifier obtained over 90% accuracy in each. In addition, we found that this prediction rule separated the patients' recurrence-free survival curves with a hazard ratio lower than the one based on the IHC analysis of ER-status.
  
  
  CONCLUSIONS
  Our efficient and parsimonious classifier lends itself to high throughput, highly accurate and low-cost RNA-based assessments of ER-status, suitable for routine high-throughput clinical use. This analytic method provides a proof-of-principle that may be applicable to developing effective RNA-based tests for other biomarkers and conditions.
authors: Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada.
authors: Meysam
authors: M
authors: Bastani
authors: Larissa
authors: L
authors: Vos
authors: Nasimeh
authors: N
authors: Asgarian
authors: Jean
authors: J
authors: Deschenes
authors: Kathryn
authors: K
authors: Graham
authors: John
authors: J
authors: Mackey
authors: Russell
authors: R
authors: Greiner
mesh_terms: D001185:Artificial Intelligence; D056808:Biostatistics; D001943:Breast Neoplasms / Q000378:metabolism; D019295:Computational Biology / Q000379:methods*; D020869:Gene Expression Profiling; D006801:Humans; D011960:Receptors, Estrogen / Q000378:metabolism*
chemical_list: D011960:Receptors, Estrogen