The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models

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Abstract
The Microarray Quality Control consortium pitted 36 teams against each other to evaluate methods for creating genomic classifiers, computational tools for interpreting gene expression profiles. The performance of the classifiers on blinded validation data—and metadata on the analytic methods—reveal the challenges facing the field. Gene expression data from microarrays are being applied to predict preclinical and clinical endpoints, but the reliability of these predictions has not been established. In the MAQC-II project, 36 independent teams analyzed six microarray data sets to generate predictive models for classifying a sample with respect to one of 13 endpoints indicative of lung or liver toxicity in rodents, or of breast cancer, multiple myeloma or neuroblastoma in humans. In total, >30,000 models were built using many combinations of analytical methods. The teams generated predictive models without knowing the biological meaning of some of the endpoints and, to mimic clinical reality, tested the models on data that had not been used for training. We found that model performance depended largely on the endpoint and team proficiency and that different approaches generated models of similar performance. The conclusions and recommendations from MAQC-II should be useful for regulatory agencies, study committees and independent investigators that evaluate methods for global gene expression analysis.