title: Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer.
abstract: BACKGROUND
  Genome-wide measures of gene expression can identify patterns of gene activity that subclassify tumours and might provide a better means than is currently available for individual risk assessment in patients with lymph-node-negative breast cancer.
  
  
  METHODS
  We analysed, with Affymetrix Human U133a GeneChips, the expression of 22000 transcripts from total RNA of frozen tumour samples from 286 lymph-node-negative patients who had not received adjuvant systemic treatment.
  
  
  RESULTS
  In a training set of 115 tumours, we identified a 76-gene signature consisting of 60 genes for patients positive for oestrogen receptors (ER) and 16 genes for ER-negative patients. This signature showed 93% sensitivity and 48% specificity in a subsequent independent testing set of 171 lymph-node-negative patients. The gene profile was highly informative in identifying patients who developed distant metastases within 5 years (hazard ratio 5.67 [95% CI 2.59-12.4]), even when corrected for traditional prognostic factors in multivariate analysis (5.55 [2.46-12.5]). The 76-gene profile also represented a strong prognostic factor for the development of metastasis in the subgroups of 84 premenopausal patients (9.60 [2.28-40.5]), 87 postmenopausal patients (4.04 [1.57-10.4]), and 79 patients with tumours of 10-20 mm (14.1 [3.34-59.2]), a group of patients for whom prediction of prognosis is especially difficult.
  
  
  CONCLUSIONS
  The identified signature provides a powerful tool for identification of patients at high risk of distant recurrence. The ability to identify patients who have a favourable prognosis could, after independent confirmation, allow clinicians to avoid adjuvant systemic therapy or to choose less aggressive therapeutic options.
authors.affiliation: Veridex LLC, a Johnson & Johnson Company, San Diego, CA, USA.
authors.forename: Yixin
authors.initials: Y
authors.lastname: Wang
authors.forename: Jan G M
authors.initials: JG
authors.lastname: Klijn
authors.forename: Yi
authors.initials: Y
authors.lastname: Zhang
authors.forename: Anieta M
authors.initials: AM
authors.lastname: Sieuwerts
authors.forename: Maxime P
authors.initials: MP
authors.lastname: Look
authors.forename: Fei
authors.initials: F
authors.lastname: Yang
authors.forename: Dmitri
authors.initials: D
authors.lastname: Talantov
authors.forename: Mieke
authors.initials: M
authors.lastname: Timmermans
authors.forename: Marion E
authors.initials: ME
authors.lastname: Meijer-van Gelder
authors.forename: Jack
authors.initials: J
authors.lastname: Yu
authors.forename: Tim
authors.initials: T
authors.lastname: Jatkoe
authors.forename: Els M J J
authors.initials: EM
authors.lastname: Berns
authors.forename: David
authors.initials: D
authors.lastname: Atkins
authors.forename: John A
authors.initials: JA
authors.lastname: Foekens
mesh_terms: D000328:Adult; D000368:Aged; D001943:Breast Neoplasms / Q000737:chemistry / Q000145:classification / Q000235:genetics* / Q000473:pathology; D005260:Female; D020869:Gene Expression Profiling; D006801:Humans; D008207:Lymphatic Metastasis; D008875:Middle Aged; D009362:Neoplasm Metastasis; D020411:Oligonucleotide Array Sequence Analysis; D011379:Prognosis; D012372:ROC Curve; D011960:Receptors, Estrogen / Q000032:analysis; D011980:Receptors, Progesterone / Q000032:analysis; D012680:Sensitivity and Specificity
chemical_list: D011960:Receptors, Estrogen; D011980:Receptors, Progesterone