Developing clinical predictors based on high-dimensional genomics data, pathway information and directed experimentation
- Date from - to
- 01 Jan 2005 - 01 Nov 2009
- Project leader(s)
- Wessels, Lodewyk prof. Dr.
- Goeman, Jelle J. Dr.
In this project we develop statistical techniques that modify the gene selection strategy such that it takes particular known relationships between genes, defining e.g. a particular pathway, into account. Testing the association of a set of gene with the outcome variable becomes very important. This is augmented with approaches that interesting gene sets from data obtained from compendiums of cancer samples and specific data from model systems subjected to directed perturbations. The aim is to identify subtypes in breast, colon and melanoma tumour series that have a clinically relevant predictive value. Finally, the gene expression based prediction will be augmented with prognostic predictors derived from proteomics data originating from the same patient series. These approaches promise to improve the performance of the predictors, allow better treatment choices, and also advance our understanding of the biology of cancer.
- A novel approach to subtype tumors by association with single gene perturbation fingerprints
- Analysis of Mass Spectrometry data using Wavelets and Sub-spectra
- Somatic loss of BRCA1 and p53 in mice induces mammary tumors with features of human BRCA1-mutated basal-like breast cancer
- Module-based outcome prediction using breast cancer compendia
- Oncogenic pathways impinging on the G(2)-restriction point
- Domain organization of human chromosomes revealed by mapping of nuclear lamina interactions
- Biclustering Sparse Binary Genomic Data
- Molecular maps of the reorganization of genome-nuclear lamina interactions during differentation
- A comprehensive analysis of prognostic signatures reveals the high predictive capacity of the Proliferation, Immune response and RNA splicing modules in breast cancer
- Pooling breast cancer datasets has a synergetic effect on classification performance and improves signature stability
- Knowledge driven decomposition of tumor expression profiles
- Analysis of mass spectrometry data using sub-spectra