title: An algorithm to discover gene signatures with predictive potential.
abstract: BACKGROUND
  The advent of global gene expression profiling has generated unprecedented insight into our molecular understanding of cancer, including breast cancer. For example, human breast cancer patients display significant diversity in terms of their survival, recurrence, metastasis as well as response to treatment. These patient outcomes can be predicted by the transcriptional programs of their individual breast tumors. Predictive gene signatures allow us to correctly classify human breast tumors into various risk groups as well as to more accurately target therapy to ensure more durable cancer treatment.
  
  
  RESULTS
  Here we present a novel algorithm to generate gene signatures with predictive potential. The method first classifies the expression intensity for each gene as determined by global gene expression profiling as low, average or high. The matrix containing the classified data for each gene is then used to score the expression of each gene based its individual ability to predict the patient characteristic of interest. Finally, all examined genes are ranked based on their predictive ability and the most highly ranked genes are included in the master gene signature, which is then ready for use as a predictor. This method was used to accurately predict the survival outcomes in a cohort of human breast cancer patients.
  
  
  CONCLUSIONS
  We confirmed the capacity of our algorithm to generate gene signatures with bona fide predictive ability. The simplicity of our algorithm will enable biological researchers to quickly generate valuable gene signatures without specialized software or extensive bioinformatics training.
authors.affiliation: Department of Biochemistry and Biomedical Sciences, Centre for Functional Genomics, McMaster University, 1200 Main Street West, Hamilton, Ontario L8N 3Z5, Canada. hassell@mcmaster.ca
authors.forename: Robin M
authors.initials: RM
authors.lastname: Hallett
authors.forename: Anna
authors.initials: A
authors.lastname: Dvorkin
authors.forename: Christine M
authors.initials: CM
authors.lastname: Gabardo
authors.forename: John A
authors.initials: JA
authors.lastname: Hassell
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000175:diagnosis / Q000235:genetics* / Q000473:pathology / Q000628:therapy; D005260:Female; D020869:Gene Expression Profiling / Q000379:methods*; D015972:Gene Expression Regulation, Neoplastic; D005820:Genetic Testing; D006801:Humans; D008875:Middle Aged; D020411:Oligonucleotide Array Sequence Analysis; D018579:Patient Selection; D057285:Precision Medicine; D011237:Predictive Value of Tests; D012372:ROC Curve; D015203:Reproducibility of Results; D016019:Survival Analysis; D013997:Time Factors; D016896:Treatment Outcome
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