Increasing clinical usefulness of gene signature prediction rules through simplification and validation

• Applicants: Professorin Dr. Anne-Laure Boulesteix; Professor Dr. Wilhelm Sauerbrei
• Subject Area: Medical Informatics and Medical Bioinformatics
• Term: from 2011 to 2022
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 208375936

Hundreds of gene signatures based on high-dimensional data (HDD) have been proposed in the biomedical literature and hundreds of methodological papers are published on issues around deriving, validating and applying prediction methods for such data sets. Nevertheless it is realized that the clinical value of information from HDD is limited and that important principles widely recognized by biometricians in low-dimensional settings tend to be often ignored in the HDD context. However, even in low-dimensional data (LDD) several issues require additional research and some investigations will concentrate in the LD world and consider adoption to HDD. The main emphasis of this project is on key statistical issues related to the development and validation of multivariable regression models. Findings from the methodological part will be used to consider possible improvements attributed to the combined value of clinical and genetic data. For some gene signatures proposed as useful prediction rules in patients with breast cancer we aim to evaluate critically the evidence for their clinical utility. The latter requires systematic reviews including assessment of quality of reporting and suitability of the methods used for meta-analysis. In work package 1 (WP1) we study the problem of identifying influential observations and their impact on selecting a multivariable model. First, approaches proposed in the low-dimensional context will be compared in several examples. In a second step we will consider their usefulness for HDD. Adaption of key parameters will be required. WP2 focuses on methodological issues related to resampling techniques for model selection in multivariate regression, which have been demonstrated to be useful for deriving interpretable prediction models. In particular, methods are developed to extend these techniques to the HD context with a particular emphasis on model averaging procedures and to support decisions in the case of apparently equally important (possibly correlated) predictors. WP3, a direct extension of a work package from the first funding period, is devoted to the construction and evaluation of prediction models making use of both clinical and omics information. WP4 aims to assess the clinical utility of some gene signatures by critically considering available evidence published in the literature. Relevant methodological issues are the quality of reporting, and the suitability of methods used for validation and meta-analysis.

• DFG Programme: Research Grants