title: Prediction potential of candidate biomarker sets identified and validated on gene expression data from multiple datasets.
abstract: BACKGROUND
  Independently derived expression profiles of the same biological condition often have few genes in common. In this study, we created populations of expression profiles from publicly available microarray datasets of cancer (breast, lymphoma and renal) samples linked to clinical information with an iterative machine learning algorithm. ROC curves were used to assess the prediction error of each profile for classification. We compared the prediction error of profiles correlated with molecular phenotype against profiles correlated with relapse-free status. Prediction error of profiles identified with supervised univariate feature selection algorithms were compared to profiles selected randomly from a) all genes on the microarray platform and b) a list of known disease-related genes (a priori selection). We also determined the relevance of expression profiles on test arrays from independent datasets, measured on either the same or different microarray platforms.
  
  
  RESULTS
  Highly discriminative expression profiles were produced on both simulated gene expression data and expression data from breast cancer and lymphoma datasets on the basis of ER and BCL-6 expression, respectively. Use of relapse-free status to identify profiles for prognosis prediction resulted in poorly discriminative decision rules. Supervised feature selection resulted in more accurate classifications than random or a priori selection, however, the difference in prediction error decreased as the number of features increased. These results held when decision rules were applied across-datasets to samples profiled on the same microarray platform.
  
  
  CONCLUSIONS
  Our results show that many gene sets predict molecular phenotypes accurately. Given this, expression profiles identified using different training datasets should be expected to show little agreement. In addition, we demonstrate the difficulty in predicting relapse directly from microarray data using supervised machine learning approaches. These findings are relevant to the use of molecular profiling for the identification of candidate biomarker panels.
authors.affiliation: School of Biomedical Engineering, Drexel University, Philadelphia, PA, USA. mpg33@drexel.edu
authors.forename: Michael
authors.initials: M
authors.lastname: Gormley
authors.forename: William
authors.initials: W
authors.lastname: Dampier
authors.forename: Adam
authors.initials: A
authors.lastname: Ertel
authors.forename: Bilge
authors.initials: B
authors.lastname: Karacali
authors.forename: Aydin
authors.initials: A
authors.lastname: Tozeren
mesh_terms: D001185:Artificial Intelligence; D014408:Biomarkers, Tumor; D004268:DNA-Binding Proteins / Q000235:genetics; D030541:Databases, Genetic; D003661:Decision Support Techniques; D003951:Diagnostic Errors; D020869:Gene Expression Profiling / Q000379:methods*; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D009369:Neoplasms / Q000175:diagnosis / Q000235:genetics; D020411:Oligonucleotide Array Sequence Analysis; D010363:Pattern Recognition, Automated / Q000379:methods*; D011237:Predictive Value of Tests; D051560:Proto-Oncogene Proteins c-bcl-6; D012372:ROC Curve; D011960:Receptors, Estrogen / Q000235:genetics; D012107:Research Design
chemical_list: C084103:BCL6 protein, human; D014408:Biomarkers, Tumor; D004268:DNA-Binding Proteins; D051560:Proto-Oncogene Proteins c-bcl-6; D011960:Receptors, Estrogen