Modeling interactions to unravel biomarkers for disease progression and treatment response
The aim of this project is to explore the use of statistical interaction models to quantify cross-omics and omics-drug interactions in large-scale clinical and experimental datasets
- Coen van Hasselt
Discovering cross-omics and omics-drugs interactions in large clinical studies
Large biobanking studies of healthy volunteers and patients are increasingly conducted for analyzing using molecular high-throughput molecular profiling (“omics”) technologies such as genomics, transcriptomics and metabolomics to obtain insights in molecular alterations underlying disease.
A major challenge for the analysis of such large clinical datasets associated with multiple high-dimensional datasets represents the integration of multiple omics technologies and typically longitudinally measured clinical data in a statistically and biologically meaningful way.
We explore the use of statistical interaction models to quantify cross-omics and omics-drug interactions in large-scale clinical and experimental datasets. The statistical models will be grounded in a biological prior knowledge framework to link identified interactions to underlying biological pathways and pharmacological drug mechanisms. The developed approaches may enable identification of novel biomarkers for disease progression and treatment response in individual patients, which can further enable personalized medicine approaches.
This project involved a unique inter-disciplinary collaboration between the Leiden Academic Centre for Drug Research (LACDR) and the Mathematical Institute (MI). Expertise from the LACDR perspective concerns the development of computational systems pharmacology modeling to quantify drug treatment effects and metabolomics approaches. The statistical group of the MI supports this project in development and application of state-of-the-art statistical modeling approaches.