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87 Monday25thAugustTuesday26thAugustThursday28thAugustAuthorIndexPostersWednesday27thAugustSunday24thAugust M2 Genomics-based Personalized Medicine Organizers: Andreas Ziegler and Georg Heinze M2.1 Application of genomic tests in breast cancer management M Filipits1 1 Medical University of Vienna, Institute of Cancer Research, Vienna, Austria   Breast cancer is a heterogeneous disease at the clinical, biological and par- ticularly at the molecular level. Gene expression profiling has improved the knowledge on the complex molecular background of this disease and allows a more accurate prognostication and patient stratification for therapy. Several genomic tests have been developed with the aim of im- proving prognostic information beyond that provided by classic clinico- pathologic parameters. Some of these tests are currently available in the clinic and are used to determine prognosis and more importantly to assist in determining the optimal treatment in patients with hormone receptor- positive breast cancer. Available data suggest that information generated from genomic tests has resulted in a change in decision making in approximately 25%-30% of cases. The clinical relevance of genomic tests and their ability to define prognosis and determine treatment benefit will be discussed. M2.2 Risk prediction models using family and genomic data JE Bailey-Wilson1 1 National Human Genome Research Institute, NIH, Baltimore, United States   Advances in our ability to model personal risk of developing a disease have accelerated as large epidemiologic and genomic studies have in- creased our understanding of disease causation. Prediction of disease risk can be based on personal history of environmental exposures, family history of disease and personal genotypes at genetic susceptibility loci. Approaches to predicting risk of disease that utilize familial and genetic information will be discussed for a range of different causal models from simple Mendelian disorders that are caused by variants in a single gene to diseases caused by complex actions of multiple risk factors. The utility of adding family history and personal genotypes into disease risk models will be covered. Accurate disease risk prediction can be important to individual health since it can encourage individuals to have more frequent screening proce- dures, to undertake environmental risk reduction, and to undergo preven- tive medical procedures and treatments.   M2.3 The importance of appropriate quality control in -omics studies as required for personalized and stratified medicine B Müller-Myhsok1,2,3 1 Max Planck Institute of Psychiatry, Munich, Germany, 2 Munich Cluster for Systems Neurology (SyNergy), Munich, Germany, 3 Institute for Translational Medicine, University of Liverpool, Liverpool, United Kingdom   Both personalized and stratified medicine are an important avenue for re- search at present and more likely so even in the future. The importance of deriving good predictors usually necessitates incorporating data from var- ious -omics sources into the model, which has implications for the quality Thursday, 28th August 2014 • 9:00-12:30ISCB 2014 Vienna, Austria • Minisymposia control of the corresponding data sets. I will discuss some of these, including the need to very carefully take into account undesirable structure in the data and differing reliability of data from different data sets. I will also show how some of the demonstrated procedures may as a con- sequence lead to better understanding and predictors. M2.4 Study designs for predictive biomarkers A Ziegler1,2 1 U Lübeck, Institute of Medical Biometry and Statistics, Lübeck, Germany, 2 U Lübeck, Center for Clinical Trials, Lübeck, Germany   Biomarkers are of increasing importance for personalized medicine, in- cluding diagnosis, prognosis and targeted therapy of a patient. Examples are provided for current use of biomarkers in applications. It is shown that their use is extremely diverse, and it varies from pharma- codynamics to treatment monitoring. The particular features of biomark- ers are discussed. Before biomarkers are used in clinical routine, several phases of research need to be successfully passed, and important aspects of these phases are considered. Some biomarkers are intended to predict the likely response of a patient to a treatment in terms of efficacy and/or safety, and these biomarkers are termed predictive biomarkers or, more generally, companion diagnostic tests. Using examples from the literature, different clinical trial designs are introduced for these biomarkers, and their pros and cons are discussed in detail.  

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