ESTRO 35 Abstract Book

S276 ESTRO 35 2016 _____________________________________________________________________________________________________ SP-0581 Integrative data analysis for PRO M.A. Gambacorta 1 Gambacorta Maria Antonietta, Roma, Italy 1 2 German Cancer Research Center DKFZ, Heidelberg and German Cancer Consortium DKTK partner site Dresden, Dresden, Germany 3 Helmholtz-Zentrum Dresden – Rossendorf, Institute of Radiooncology, Dresden, Germany

5 Faculty of Medicine and University Hospital Carl Gustav Carus- Technische Universität Dresden, Department of Radiation Oncology, Dresden, Germany 6 Faculty of Medicine and University Hospital Carl Gustav Carus- Technische Universität Dresden, Department of Otorhinolaryngology, Dresden, Germany 7 Faculty of Medicine and University Hospital Carl Gustav Carus- Technische Universität Dresden, Department of Oral and Maxillofacial Surgery, Dresden, Germany 8 German Cancer Research Center DKFZ, Heidelberg and German Cancer Consortium DKTK partner site Berlin, Berlin, Germany 9 Charité University Hospital, Department of Radiooncology and Radiotherapy, Berlin, Germany 10 German Cancer Research Center DKFZ, Heidelberg and German Cancer Consortium DKTK partner site Essen, Essen, Germany 11 Medical Faculty- University of Duisburg-Essen, Department of Radiotherapy, Essen, Germany 12 Goethe-University Frankfurt, Department of Radiotherapy and Oncology, Frankfurt am Main, Germany 13 German Cancer Research Center DKFZ, Heidelberg and German Cancer Consortium DKTK partner site Frankfurt, Frankfurt am Main, Germany 14 Department of Radiotherapy and Oncology, Goethe- University Frankfurt, Frankfurt am Main, Germany 15 German Cancer Research Center DKFZ, Heidelberg and German Cancer Consortium DKTK partner site Freiburg, Freiburg, Germany 16 University of Freiburg- Germany, Department of Radiation Oncology- Clinical Study Section, Freiburg, Germany 17 University of Freiburg, Department of Radiation Oncology, Freiburg, Germany 18 German Cancer Research Center DKFZ, Heidelberg and German Cancer Consortium DKTK partner site Heidelberg, Heidelberg, Germany 19 University of Heidelberg Medical School and German Cancer Research Center DKFZ, Translational Radiation Oncology, Heidelberg, Germany 20 University of Heidelberg Medical School and German Cancer Research Center DKFZ, National Center for Tumor Diseases NCT, Heidelberg, Germany 21 University of Heidelberg Medical School- Heidelberg Ion Therapy Center HIT, Department of Radiation Oncology, Heidelberg, Germany 22 University of Heidelberg Medical School and German Cancer Research Center DKFZ, Heidelberg Institute of Radiation Oncology HIRO- National Center for Radiation Research in Oncology NCRO, Heidelberg, Germany 23 University of Heidelberg Medical School and German Cancer Research Center DKFZ, Clinical Cooperation Unit Radiation Oncology, Heidelberg, Germany 24 German Cancer Research Center DKFZ, Heidelberg and German Cancer Consortium DKTK partner site Munich, München, Germany 25 Ludwig-Maximilians-Universität, Department of Radiotherapy and Radiation Oncology, München, Germany 26 Technische Universität München, Department of Radiation Oncology, München, Germany 27 Department of Radiation Oncology, Technische Universität München, München, Germany 28 German Cancer Research Center DKFZ, Heidelberg and German Cancer Consortium DKTK partner site Tübingen, Tübingen, Germany 29 Faculty of Medicine and University Hospital Tübingen- Eberhard Karls Universität Tübingen, Department of Radiation Oncology, Tübingen, Germany 30 University Hospital Carl Gustav Carus- Technische Universität Dresden, Tumour- and Normal Tissue Bank- University Cancer Centre UCC, Dresden, Germany 31 Faculty of Medicine and University Hospital Carl Gustav Carus- Technische Universität Dresden, Institute of Pathology, Dresden, Germany

Personalized Radiation Oncology (PRO) integrating omics technology is a rapidly developing concept that will have an enormous impact on oncologic treatments and specifically radiation therapy in the near future. Tumor behaviour and outcomes related to oncologic treatments are related to several factors of which connections are nowdays poorly known. Different branches of medicine have developed their own lines of research which are sometimes difficult to be interpreted, difficult to be integrated with classical clinical factors and for these reasons, difficult to be applied in clinical practice. In clinical prediction and decision making process, results provided by omics are rarely used, whereas clinicians usually use clinical and imaging data for understanding tumor behaviour, predicting patients' outcomes and for choosing the the most suitable treatment. The clinical decision is usually based on general guidelines which extrapolate information from randomized clinical trial. Moreover independent factors derived from several RCT are used by the Radiation Oncologist to make his prevision on tumor behaviour and consequently to choose the „right treatment“ for a specific patient. Randomized clinical trials enclose patients with characteristics chosen beforehand and usually omics informations are rarely or never included. This lead to a potential missing of several information that could refine prediction and thus promote personalized treatments and to an erroneous outcomes prediction that can lead to un- appropriate treatment decision for a specific patient. Integrative data analysis has the potential to correlate data of different origins (genetic, radiology, clinic...) with patient’s outcomes and to create a consistent dataset useful to obtain a trustful analysis for the Decision Support System. The DSS can easily be applied in clinical practice helping the Radiation Oncologist to utilize several information that otherwise would be excluded in the process of decision making. The possibility to predict the outcome for a certain patient in combination with a specific treatment with more accuracy, will lead to better identification of risk groups and thus better treatment decisions in individual patients, but it will also stimulate research focused on specific risk groups which try to find new treatment options or other combinations of treatment options for these subgroups. These treatments will be more personalized, which will not only save patients from unnecessary toxicity and inconvenience, but will also facilitate the choice of the most appropriate treatment . The resulting predictive models, based on patient features, enable a more patient specific selection from the treatment options menu and a possibility to share decisions with patients based on an objective evaluation of risks and benefits. Finally, considering the important role that predictive models could play in the clinical practice, clinicians must be aware of the limits of these prediction models. They need to be internally validated taking into account the quality of the collected data. An external validation of models is also essential to support general applicability of the prediction model. Therefore structural collaboration between different groups is crucial to generate enough anonymized large databases from patients included or not in clinical trials. OC-0582 Gene signatures predict loco-regional control after postoperative radiochemotherapy in HNSCC S. Schmidt 1,2,3,4 , A. Linge 1,2,4,5 , F. Lohaus 1,2,5 , V. Gudziol 6 , A. Nowak 7 , I. Tinhofer 8,9 , V. Budach 8,9 , A. Sak 10,11 , M. Stuschke 10,11 , P. Balermpas 12 , C. Rödel 13,14 , M. Avlar 15,16 , A.L. Grosu 15,17 , A. Abdollahi 18,19,20,21,22 , J. Debus 18,20,21,22,23 , C. Belka 24,25 , S. Pigorsch 24,26 , S.E. Combs 24,27 , D. Mönnich 28,29 , D. Zips 28,29 , G.B. Baretton 2,30,31 , F. Buchholz 2,32 , M. Baumann 1,2,3,5 , M. Krause 1,2,3,5 , S. Löck 1,2,3,5 1 OncoRay – National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus- Technische Universität Dresden, Dresden, Germany