ESTRO 36 Abstract Book

S226 ESTRO 36 2017 _______________________________________________________________________________________________

SP-0434 State of the art in prostate tumour radiobiology C. Peitzsch 1,2 1 OncoRay - Center for Radiation Research in Oncology, University Hospital Carl Gustav Carus- Technische Universität Dresden, Dresden, Germany 2 Nationales Centrum for Tumor diseases NCT- Dresden, German Cancer Center DKFZ- Heidelberg, Dresden, Germany Prostate tumorigenesis is a multistep process from intraepithelial neoplasia (PIN) and localized adenocarcinoma, to castration-resistant prostate cancer (CRPC) and further into an invasive and metastatic disease stage with poor prognosis. Several driver and passenger mutations e.g. within the androgen receptor ( AR ), ETS , TP53 , PTEN , BRCA1/2, CTNNB1 or ATM were identified, so far, to be involved in this developmental process. Beside this specific genetic features of prostate cancer cells, cellular heterogeneity within prostate cancer describes the observation that malignant cells differ within their phenotypic features and functional properties. This tumor heterogeneity and cellular plasticity of tumor cells are the main driving forces for tumor growth, metastasis and therapy resistance and can be explained by the cancer stem cell (CSC) hypothesis in combination with clonal evolution and epigenetic regulation. CSC-specific molecular mechanisms of radioresistance mainly based on increased DNA repair capacity, enhanced reactive oxygen species (ROS) scavenging and induced epithelial- mesenchymal transition (EMT) and is regulated e.g. by the androgen-receptor signaling, the tumor microenvironment, growth factors and cytokines. Data from our own group indicating that ionizing radiation themself is modulating epigenetic mechanisms in prostate cancer cells and thereby cellular plasticity. To translate these basic research findings into clinically relevant data primary model systems and mouse models can be used for pre-clinical validation of radiosensitizer and biomarker discovery. SP-0435 Novel developments in molecular targeting of prostate cancer R. Bristow 1 1 Princess Margaret Cancer Centre University Health Network, Radiation Oncology - Room 5-964, Toronto, Canada Prostate cancer (CaP) remains the most common male malignancy worldwide. Although some localized cancers can be indolent, others can manifest aggressive biology with abnormal cancer metabolism and genetic instability. These men need intensified treatment to prevent metastatic castrate-resistant disease (mCRPC). Recent studies have started to define the genomic landscape of prostatic cancer heterogeneity in which mCRPC is associated with increasing androgen receptor aberrations, DNA repair deficiencies, mutations in PI3K and tumour suppressor gene pathways, aberrant WNT-beta-catenin signaling and defects in cell cycle control. For localized disease amenable to radiotherapy,we have previously shown that genetic instability and hypoxia are strong prognostic factors for prostate cancer outcome. Subsequently, we have gone on to analyze the whole- genomes and methylomes of 194 men and the exomes of 479 men to discover multimodal genetic signatures for responders and non-responders following precision radiotherapy and surgery. We observed that intermediate risk prostate cancers have a paucity of clinically- actionable mutations; in distinct contrast to that reported for mCRPC. However, all patients with an DDR-associated ATM mutation failed therapy. A significant proportion of tumours harbour recurrent non-coding aberrations, important genomic rearrangements, and a novel mechanism of PTEN inactivation whereby a local inversion

provide valuable diagnostic, prognostic or predictive information for oncological diseases. This information aims at improving individual patients’ outcomes by a better treatment selection. SP-0431 Radiomics in radiotherapy. How is it used to personalise treatment and to predict toxicity and/or tumour control C. Gani 1 1 University Hospital Tübingen Eberhard Karls University Tübingen, Radiation Oncology Department, Tübingen, Germany Radiomics is defined as the automated or semi-automated extraction of a large number of features from imaging datasets resulting an individual “imaging phenotype”. These features and the imaging phenotype can then be correlated with a variety of other parameters: from genetic phenotypes to oncological outcome data. Radiomics as a non-invasive procedure is of particular interest for the radiation oncologist in times of precision radiation oncology: The radiomics phenotype might help to identify patients at high risk for treatment failure and therefore candidates for more aggressive treatment. Furthermore radiomics can also be a helpful tool to predict the risk for radiation-induced toxicities and guide the dose distribution within normal tissues. This lecture will give an overview about the existing data on radiomics in the field of radiation oncology. SP-0432 Uncertainties in imaging -how they should be reported and propagated in prediction models using radiomics L. Muren 1 Aarhus University Hospital - Aarhus University, Medical Physics, Aarhus, Denmark An imaging biobank can be defined as an organised database of medical images and associated imaging biomarkers (radiology and beyond) shared among multiple researchers, and linked to other biorepositories. An imaging biobank is designed for scientific use. Image data are systematically analysed visually, manual, or (semi)- automated with the main aim to extract imaging biomarkers than can be related to patient characteristics like medical history, genomic data, and outcome or disease characteristics like genomic data, biomaterials or response to treatment. The data storage is structured in a way that the database can be queried and retrieved based on available metadata. In order to exploit the available information interactions with other databases are a perquisite. General requirements with respect to the data collection are therefore a database facilitating storage of image data and metadata, storage of derived image-based measurements, and storage of associated non-imaging data, taking into account the need to deal with longitudinal data, and to cope with multiple file formats. Finally, automated retrieval is needed for image analysis pipelines that extract image features for radiomics signatures or for hypothesis free deep learning algorithms. Abstract not received SP-0433 Imaging biobanks: challenges and opportunities A. Van der Lugt 1 1 Erasmus MC University Medical Center Rotterdam, Department of Radiology, Rotterdam, The Netherlands

Symposium with Proffered Papers: Novel approaches in prostate tumour control