title: Good practice guidelines for biomarker discovery from array data: a case study for breast cancer prognosis.
abstract: BACKGROUND
  Biomarker discovery holds the promise for advancing personalized medicine as the biomarkers can help match patients to optimal treatment to improve patient outcomes. However, serious concerns have been raised because very few molecular biomarkers or signatures discovered from high dimensional array data can be successfully validated and applied to clinical use. We propose good practice guidelines as well as a novel tool for biomarker discovery and use breast cancer prognosis as a case study to illustrate the proposed approach.
  
  
  RESULTS
  We applied the proposed approach to a publicly available breast cancer prognosis dataset and identified small numbers of predictive markers for patient subpopulations stratified by clinical variables. Results from an independent cross-platform validation set show that our model compares favorably to other gene signature and clinical variable based prognostic tools. About half of the discovered candidate markers can individually achieve very good performance, which further demonstrate the high quality of feature selection. These candidate markers perform extremely well for young patient with estrogen receptor-positive, lymph node-negative early stage breast cancers, suggesting a distinct subset of these patients identified by these markers is actually at high risk of recurrence and may benefit from more aggressive treatment than current practice.
  
  
  CONCLUSIONS
  The results show that by following good practice guidelines, we can identify highly predictive genes in high dimensional breast cancer array data. These predictive genes have been successfully validated using an independent cross-platform dataset.
authors.forename: Jie
authors.initials: J
authors.lastname: Cheng
authors.forename: Joel
authors.initials: J
authors.lastname: Greshock
authors.forename: Leming
authors.initials: L
authors.lastname: Shi
authors.forename: Shu
authors.initials: S
authors.lastname: Zheng
authors.forename: Alan
authors.initials: A
authors.lastname: Menius
authors.forename: Kwan
authors.initials: K
authors.lastname: Lee
mesh_terms: D014408:Biomarkers, Tumor / Q000235:genetics*; D001943:Breast Neoplasms / Q000175:diagnosis* / Q000235:genetics*; D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D005260:Female; D006801:Humans; D008875:Middle Aged; D020411:Oligonucleotide Array Sequence Analysis; D011379:Prognosis; D012008:Recurrence
chemical_list: D014408:Biomarkers, Tumor