Computational Diagnostics We are a new research group in the department of Computational Molecular Biology at the Max Planck Institute for Molecular Genetics in Berlin-Dahlem. Our group is part of the Berlin Center for Genome Based Bioinformatics and participates in the NGFN ( National Genome Research Network ). Research A comprehensive understanding of the mostly subtle differences in gene expression in patient specific cell samples is crucial for elucidating the molecular characteristics of diseases as well as for the optimal choice of treatment. Large scale gene expression profiling allow for a systematic investigation of the molecular characteristics of diseases. Recently, there was tremendous progress in the development of technologies that allows for the parallel measurement of expression levels for tens of thousands of genes. However, it is still very challenging to interpret the data, and use it in clinical decision processes. The focus of this group is to develop statistical methodology for the use of gene expression profiles in medical diagnostics. We aim to identify pattern in expression profiles that improve or facilitate diagnosis, help to predict clinical outcome or refine common diagnostic schemes. Members Stefan Bentink Web: Fon:( ) Claudio Lottaz Web: Fon: ( ) Florian Markowetz Web: Fon: ( ) Rainer Spang (head) Web: Fon: ( ) Stefanie Scheid Web: Fon: ( ) Publications Prediction and uncertainty in the analysis of gene expression profiles Rainer Spang, Carrie Blanchette, Harry Zuzan, Jeffrey R. Marks, Joseph Nevins and Mike West Proceedings of the German Conference on Bioinformatics GCB 2001 Predicting the clinical status of human breast cancer by using gene expression profiles West M, Blanchette C, Dressman H, Huang E, Ishida S, Spang R, Zuzan H, Olson JA Jr, Marks JR, Nevins JR. Proc Natl Acad Sci U S A Sep 25;98(20): Role for E2F in control of both DNA replication and mitotic functions as revealed from DNA microarray analysis Ishida S, Huang E, Zuzan H, Spang R, Leone G, West M, Nevins JR. Mol Cell Biol Jul;21(14): Estrogen Receptor Status 7000 genes 49 breast tumors 25 ER+ 24 ER- Breast Cancer, Expression Profiles and Binary Regression in 7000 Dimensions 7000 Numbers Are More Numbers Than We Need Overfitting: We Can Not Identify a Model There are many different models that assign high probabilities for ER+ tumors and low probabilities for ER- tumors in the training set For a new patient we find among these models some that support that she is ER+ and others that predict she is ER- Informative Priors Likelihood Prior Posterior Prior Choice Center Orientation Not to wide not to narrow Assumptions on the model correspond to assumptions on the diagnosis orthogonal super-genes auto adjusting model hyper-parameters with their own priors What are the additional assumptions that came in by the prior? The model can not be dominated by only a few super-genes ( genes! ) The diagnosis is done based on global changes in the expression profiles influenced by many genes The assumptions are neutral with respect to the individual diagnosis GeneWeight nuclear factor 3 alpha0.853 cysteine rich heart protein0.842 estrogen receptor0.840 intestinal trefoil factor0.840 x box binding protein gata ps liv many many more Which Genes Have Driven the Prediction ? Rainer Spang, Harry Zuzan, Carrie Blanchette, Erich Huang, Holly Dressman, Jeff Marks, Joe Nevins, Mike West Duke Medical Center & Duke University