The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models.
ABSTRACT 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.
Article: Microarrays: retracing steps.Nature Medicine 12/2007; 13(11):1276-7; author reply 1277-8. · 22.86 Impact Factor
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ABSTRACT: Rejection diagnosis by endomyocardial biopsy (EMB) is invasive, expensive and variable. We investigated gene expression profiling of peripheral blood mononuclear cells (PBMC) to discriminate ISHLT grade 0 rejection (quiescence) from moderate/severe rejection (ISHLT > or = 3A). Patients were followed prospectively with blood sampling at post-transplant visits. Biopsies were graded by ISHLT criteria locally and by three independent pathologists blinded to clinical data. Known alloimmune pathways and leukocyte microarrays identified 252 candidate genes for which real-time PCR assays were developed. An 11 gene real-time PCR test was derived from a training set (n = 145 samples, 107 patients) using linear discriminant analysis (LDA), converted into a score (0-40), and validated prospectively in an independent set (n = 63 samples, 63 patients). The test distinguished biopsy-defined moderate/severe rejection from quiescence (p = 0.0018) in the validation set, and had agreement of 84% (95% CI 66% C94%) with grade ISHLT > or = 3A rejection. Patients >1 year post-transplant with scores below 30 (approximately 68% of the study population) are very unlikely to have grade > or = 3A rejection (NPV = 99.6%). Gene expression testing can detect absence of moderate/severe rejection, thus avoiding biopsy in certain clinical settings. Additional clinical experience is needed to establish the role of molecular testing for clinical event prediction and immunosuppression management.American Journal of Transplantation 01/2006; 6(1):150-60. · 6.19 Impact Factor
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ABSTRACT: DNA microarrays are a potentially powerful technology for improving diagnostic classification, treatment selection and prognostic assessment. There are, however, many potential pitfalls in the use of microarrays that result in false leads and erroneous conclusions. Effective use of this technology requires new levels of interdisciplinary collaboration with statistical and computational scientists. This paper provides a review of the key features to be observed in developing diagnostic and prognostic classification systems based upon gene expression profiling. It also attempts to outline some of the steps needed to develop initial microarray research findings into classification systems suitable for broad clinical application.Expert Review of Molecular Diagnostics 10/2003; 3(5):587-95. · 4.09 Impact Factor