title: Supervised risk predictor of breast cancer based on intrinsic subtypes.
abstract: PURPOSE To improve on current standards for breast cancer prognosis and prediction of chemotherapy benefit by developing a risk model that incorporates the gene expression-based "intrinsic" subtypes luminal A, luminal B, HER2-enriched, and basal-like. METHODS A 50-gene subtype predictor was developed using microarray and quantitative reverse transcriptase polymerase chain reaction data from 189 prototype samples. Test sets from 761 patients (no systemic therapy) were evaluated for prognosis, and 133 patients were evaluated for prediction of pathologic complete response (pCR) to a taxane and anthracycline regimen.
  
  
  RESULTS
  The intrinsic subtypes as discrete entities showed prognostic significance (P = 2.26E-12) and remained significant in multivariable analyses that incorporated standard parameters (estrogen receptor status, histologic grade, tumor size, and node status). A prognostic model for node-negative breast cancer was built using intrinsic subtype and clinical information. The C-index estimate for the combined model (subtype and tumor size) was a significant improvement on either the clinicopathologic model or subtype model alone. The intrinsic subtype model predicted neoadjuvant chemotherapy efficacy with a negative predictive value for pCR of 97%. CONCLUSION Diagnosis by intrinsic subtype adds significant prognostic and predictive information to standard parameters for patients with breast cancer. The prognostic properties of the continuous risk score will be of value for the management of node-negative breast cancers. The subtypes and risk score can also be used to assess the likelihood of efficacy from neoadjuvant chemotherapy.
authors.affiliation: Lineberger Comprehensive Cancer Center, Carolina Center for Genome Sciences, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
authors.forename: Joel S
authors.initials: JS
authors.lastname: Parker
authors.forename: Michael
authors.initials: M
authors.lastname: Mullins
authors.forename: Maggie C U
authors.initials: MC
authors.lastname: Cheang
authors.forename: Samuel
authors.initials: S
authors.lastname: Leung
authors.forename: David
authors.initials: D
authors.lastname: Voduc
authors.forename: Tammi
authors.initials: T
authors.lastname: Vickery
authors.forename: Sherri
authors.initials: S
authors.lastname: Davies
authors.forename: Christiane
authors.initials: C
authors.lastname: Fauron
authors.forename: Xiaping
authors.initials: X
authors.lastname: He
authors.forename: Zhiyuan
authors.initials: Z
authors.lastname: Hu
authors.forename: John F
authors.initials: JF
authors.lastname: Quackenbush
authors.forename: Inge J
authors.initials: IJ
authors.lastname: Stijleman
authors.forename: Juan
authors.initials: J
authors.lastname: Palazzo
authors.forename: J S
authors.initials: JS
authors.lastname: Marron
authors.forename: Andrew B
authors.initials: AB
authors.lastname: Nobel
authors.forename: Elaine
authors.initials: E
authors.lastname: Mardis
authors.forename: Torsten O
authors.initials: TO
authors.lastname: Nielsen
authors.forename: Matthew J
authors.initials: MJ
authors.lastname: Ellis
authors.forename: Charles M
authors.initials: CM
authors.lastname: Perou
authors.forename: Philip S
authors.initials: PS
authors.lastname: Bernard
mesh_terms: D000328:Adult; D000368:Aged; D001943:Breast Neoplasms / Q000145:classification* / Q000188:drug therapy / Q000209:etiology / Q000401:mortality; D017024:Chemotherapy, Adjuvant; D005260:Female; D006801:Humans; D008875:Middle Aged; D009364:Neoplasm Recurrence, Local / Q000209:etiology; D011379:Prognosis; D018719:Erb-b2 Receptor Tyrosine Kinases / Q000032:analysis; D011960:Receptors, Estrogen / Q000032:analysis; D020133:Reverse Transcriptase Polymerase Chain Reaction; D012306:Risk
chemical_list: D018719:Erb-b2 Receptor Tyrosine Kinases; D011960:Receptors, Estrogen; C508053:ERBB2 protein, human