© 2007

Freund/OncoLab

The group

The Department of Theoretical Bioinformatics at the German Cancer Research Center (DKFZ) is developing computer methods and algorithms to improve molecular diagnostics for cancerous diseases. We apply these to molecular data from different techniques in molecular biology, e.g. transcriptomics, proteomics, genome sequencing and microRNA profiling. The aim is to improve the prediction of the course of disease (i.e., prognosis) and the response to certain therapies at the time of diagnosis, which may help to choose among several possible therapeutic strategies on more rational grounds.
The methods that we use are derived from artificial intelligence research in computer science and are called “learning algorithms”. They try to determine a complex pattern from a training set, where – for instance – the course of disease is already exactly known. Once the predictive patterns have been identified, they are used prospectively for new samples. Methods that we are using include support vector machines and nearest-centroid classifiers. We are also developing bioinformatics methods to understand the complex changes in signal transduction pathways during different steps in tumorigenesis. Finally, we provide IT infrastructure for this research, in particular databases such as iCHIP (http://www.ichip.de) that hold high-dimensional datasets from gene expression profiling or proteomics, together with accompanying clinical information.
The Department is headed by Prof. Dr. Roland Eils. In EET, the research groups on Computational Oncology (PI: Dr. Benedikt Brors) and on Databases and Computational Infrastructure (PI: Jürgen Eils) are involved.

Our role in EET-Pipeline

The Department of Theroretical Bioinformatics, DKFZ, will provide a database and data models for unified storage of clinical information and molecular data from tumors included in the study collection (i.e. neuroblastoma, Wilms' tumor, medulloblastoma, retinoblastoma and Ewing’s sarcoma family of tumors). This is important to have consistent criteria to integrate clinical and experimental data from several studies. We will also provide a virtual biobank providing a source of information about the existing collections of tumor material at different EET partners. Finally, we will use the combined molecular data to determine common predictors of prognosis as well as understand the common molecular features of embryonic tumors as well as their entity-specific traits. For this, we will use machine learning algorithms together with methods for meta-analysis as well as integrative data analysis.

Staff Member

People in Photograph:
The Institute of Theoretical Bioinformatics, include the research group of R. Eils

Top 5 publications

1. Schramm A, Schulte JH, Klein-Hitpass L, Havers W, Sieverts H, Berwanger B, Christiansen H, Warnat P, Brors B, Eils J, Eils R, Eggert A. Prediction of clinical outcome and biological characterization of neuroblastoma by expression profiling. Oncogene (2005), 24:7902-7912.

2. Warnat P, Eils R, Brors B. Cross-platform analysis of cancer microarray data improves gene expression based classification of phenotypes. BMC Bioinformatics (2005), 6:265.

3. Oberthür A, Berthold F, Warnat P, Hero B, Kahlert Y, Spitz R, Ernestus K, König R, Haas S, Eils R, Schwab M, Brors B, Westermann F, Fischer M. Gene-expression based classification of neuroblastoma patients using a customized oligonucleotide-microarray outperforms current clinical risk stratification. J Clin Oncol (2006), 24, 5070-5078.

4. Schramm A, Vandesompele J, Schulte JH, Dreesmann S, Kaderali L, Brors B, Eils R, Speleman F, Eggert A. Translating expression profiling into a clinically feasible test to predict neuroblastoma outcome. Clin Cancer Res (2007), 13, 1459-1465.

5. Warnat P, Oberthür A, Fischer M, Westermann F, Eils R, Brors B. Cross-study analysis of gene expression data for intermediate neuroblastoma identifies two biological subtypes. BMC Cancer (2007), 7, 89.