A strategy to build and validate a prognostic biomarker model based on RT-qPCR gene expression and clinical covariates. Issue 1 (December 2015)

• Record Type: Journal Article
• Authors: Tournoud, Maud; Larue, Audrey; Cazalis, Marie-Angelique; Venet, Fabienne; Pachot, Alexandre; Monneret, Guillaume; Lepape, Alain; Veyrieras, Jean-Baptiste
• Is Part Of: BMC bioinformatics. Volume 16:Issue 1(2015)
• Page Start: 1
• Page End: 15
• Publication Date: 2015-12
• Subjects: Prognostic survival model -- RT-qPCR gene expression measurement -- Cross-validation -- Performance estimation -- Model optimism; Bioinformatics -- Periodicals; Computational biology -- Periodicals; 570.285
• DOI: 10.1186/s12859-015-0537-9
• Languages: English
• ISSNs: 1471-2105

Abstract

Abstract Background Construction and validation of a prognostic model for survival data in the clinical domain is still an active field of research. Nevertheless there is no consensus on how to develop routine prognostic tests based on a combination of RT-qPCR biomarkers and clinical or demographic variables. In particular, the estimation of the model performance requires to properly account for the RT-qPCR experimental design. Results We present a strategy to build, select, and validate a prognostic model for survival data based on a combination of RT-qPCR biomarkers and clinical or demographic data and we provide an illustration on a real clinical dataset. First, we compare two cross-validation schemes: a classical outcome-stratified cross-validation scheme and an alternative one that accounts for the RT-qPCR plate design, especially when samples are processed by batches. The latter is intended to limit the performance discrepancies, also called the validation surprise, between the training and the test sets. Second, strategies for model building (covariate selection, functional relationship modeling, and statistical model) as well as performance indicators estimation are presented. Since in practice several prognostic models can exhibit similar performances, complementary criteria for model selection are discussed: the stability of the selected variables, the model optimism, and the impact of the omitted variables on the model performance. Conclusion On the training dataset, appropriate resampling methods are expected to prevent from any upward biases due to unaccounted technical and biological variability that may arise from the experimental and intrinsic design of the RT-qPCR assay. Moreover, the stability of the selected variables, the model optimism, and the impact of the omitted variables on the model performances are pivotal indicators to select the optimal model to be validated on the test dataset.