Consistency of predictive signature genes and classifiers generated using different microarray platforms - Fan, X; Lobenhofer, E K; Chen, M; Shi, W; Huang, J; Luo, J; Zhang, J; Walker, S J; Chu, T-M; Li, L; Wolfinger, R; Bao, W; et al. (The pharmacogenomics journal, 2010)
Microarray-based classifiers and associated signature genes generated from various platforms are abundantly reported in the literature; however, the utility of the classifiers and signature genes in cross-platform prediction applications remains largely uncertain. As part of the MicroArray Quality Control Phase II (MAQC-II) project, we show in this study 80–90% cross-platform prediction consistency using a large toxicogenomics data set by illustrating that: (1) the signature genes of a classifier generated from one platform can be directly applied to another platform to develop a predictive classifier; (2) a classifier developed using data generated from one platform can accurately predict samples that were profiled using a different platform. The results suggest the potential utility of using published signature genes in cross-platform applications and the possible adoption of the published classifiers for a variety of applications. The study reveals an opportunity for possible translation of biomarkers identified using microarrays to clinically validated non-array gene expression assays.