title: Consistent metagenes from cancer expression profiles yield agent specific predictors of chemotherapy response.
abstract: BACKGROUND
  Genome scale expression profiling of human tumor samples is likely to yield improved cancer treatment decisions. However, identification of clinically predictive or prognostic classifiers can be challenging when a large number of genes are measured in a small number of tumors.
  
  
  RESULTS
  We describe an unsupervised method to extract robust, consistent metagenes from multiple analogous data sets. We applied this method to expression profiles from five "double negative breast cancer" (DNBC) (not expressing ESR1 or HER2) cohorts and derived four metagenes. We assessed these metagenes in four similar but independent cohorts and found strong associations between three of the metagenes and agent-specific response to neoadjuvant therapy. Furthermore, we applied the method to ovarian and early stage lung cancer, two tumor types that lack reliable predictors of outcome, and found that the metagenes yield predictors of survival for both.
  
  
  CONCLUSIONS
  These results suggest that the use of multiple data sets to derive potential biomarkers can filter out data set-specific noise and can increase the efficiency in identifying clinically accurate biomarkers.
authors.affiliation: Center for Biological Sequence Analysis, Department of Systems Biolology, Technical University of Denmark, 2800 Lyngby, Denmark.
authors.forename: Qiyuan
authors.initials: Q
authors.lastname: Li
authors.forename: Aron C
authors.initials: AC
authors.lastname: Eklund
authors.forename: Nicolai J
authors.initials: NJ
authors.lastname: Birkbak
authors.forename: Christine
authors.initials: C
authors.lastname: Desmedt
authors.forename: Benjamin
authors.initials: B
authors.lastname: Haibe-Kains
authors.forename: Christos
authors.initials: C
authors.lastname: Sotiriou
authors.forename: W Fraser
authors.initials: WF
authors.lastname: Symmans
authors.forename: Lajos
authors.initials: L
authors.lastname: Pusztai
authors.forename: Søren
authors.initials: S
authors.lastname: Brunak
authors.forename: Andrea L
authors.initials: AL
authors.lastname: Richardson
authors.forename: Zoltan
authors.initials: Z
authors.lastname: Szallasi
mesh_terms: D000230:Adenocarcinoma / Q000188:drug therapy / Q000235:genetics; D000077192:Adenocarcinoma of Lung; D000970:Antineoplastic Agents / Q000627:therapeutic use*; D014408:Biomarkers, Tumor / Q000032:analysis*; D001943:Breast Neoplasms / Q000188:drug therapy / Q000235:genetics*; D015331:Cohort Studies; D047628:Estrogen Receptor alpha / Q000378:metabolism; D005260:Female; D020869:Gene Expression Profiling; D006801:Humans; D008175:Lung Neoplasms / Q000188:drug therapy / Q000235:genetics; D020360:Neoadjuvant Therapy; D009369:Neoplasms / Q000188:drug therapy* / Q000235:genetics*; D010051:Ovarian Neoplasms / Q000188:drug therapy / Q000235:genetics; D011379:Prognosis; D018719:Erb-b2 Receptor Tyrosine Kinases / Q000378:metabolism
chemical_list: D000970:Antineoplastic Agents; D014408:Biomarkers, Tumor; D047628:Estrogen Receptor alpha; D018719:Erb-b2 Receptor Tyrosine Kinases; C506487:ESR1 protein, human; C508053:ERBB2 protein, human