ESTRO 36 Abstract Book

S249 ESTRO 36 2017 _______________________________________________________________________________________________

(TCGA) that are providing unprecedented information about the human genome. NGS has the potential to identify all kinds of genetic variation (not only SNPs) at base-pair resolution throughout the human genome in a single experiment. The identification of rare genomic variants through these new sequencing technologies, should allow us to unravel the heritability of complex traits, such as radiation sensitivity. The genetic variants identified through Radiogenomic studies could lead to the development of an assay to predict a patient's risk of toxicity. Such an assay could help to individualize radiotherapy protocols leading to safer and more effective outcomes. SP-0474 From radiotherapy & dosimetry data to better plans M. Hoogeman 1 , S. Breedveld 1 , R. Bijman 1 , B. Heijmen 1 1 Erasmus MC Cancer Institute, Department of Radiation Oncology, Rotterdam, The Netherlands In radiotherapy patients are treated with a personalized treatment plan, which optimizes the linear accelerator settings for the delivery of a curative dose to the target while sparing surrounding healthy tissues. Those settings are calculated following a mathematical optimization, balancing the treatment goals as specified by the physician regarding target prescription dose and tolerable doses to healthy tissues. The generation of a radiotherapy treatment plan is a complex procedure. The quality is not only highly dependent on the planner skills, but also on the physician or institutional preferences regarding the prioritization of the sparing of organs-at-risk in relation to the coverage of the planning target volume. A complicating factor is that the semi-automated optimization can take up to hours or even a day to complete and that it is often not know a- priori what can be ultimately achieved in the trade-off between organs-at-risk sparing and target coverage. Another challenge in treatment planning is that the quality of a treatment plan is only partly captured by quantitative metrics such as DVH parameters and that prediction models of toxicity are not yet integrated in the process of treatment planning. In this presentation the above challenges will be addressed. It will be discussed if (big) radiotherapy data of plans and dosimetry can make treatment plans better. Prediction models combined with comparative treatment planning can be used for decision making and further personalizing the treatment. Some practical examples will be presented regarding the selection of patients for proton therapy. In addition, we will address to what extent the accuracy of the prediction models impact such clinical decisions. Finally, the role of treatment-plan automation will be discussed in improving the quality of treatment planning and different approaches of automation will be compared. SP-0475 Moving Big Data into Clinical Practice – A positive outlook S. Vinod 1 1 Liverpool Hospital, Cancer Therapy Centre, Liverpool BC, Australia The evidence-base underlying treatment of oncology patients is derived from the 2-3% of patients enrolled in prospective clinical trials. Outcomes in these highly selected patients are then applied to the general patient population. However, adherence to guideline treatment varies from 44% in lung cancer to 91% in breast cancer due to clinician uncertainty about the efficacy and toxicity of evidence-based treatments in individual patients. An alternative source of evidence is Big Data. This is what we already collect in routine clinical practice including clinical data, imaging data and genomic data. The type and nature of data collected and the platform of

collection varies, however current systems can overcome this to successfully enable distributed learning. Multi- institutional data can be used to develop predictive models relating outcomes to specific patient, tumour and treatment characteristics. The strength of Big Data is in the sheer number of patients and hence applicability of findings to the general clinic population. Moving Big Data into clinical practice requires translation of model outputs to decision support systems to enable shared decision making between clinicians and patients. It also requires trust of the model by patients and clinicians. There is a need to demonstrate that model predictions based on objective parameters are superior to clinician’s subjective judgement alone. Clinical trials of decision support systems are necessary to evaluate whether Big Data can change clinical practice. Only then can we truly deliver personalised medicine tailored to an individual patient’s specific parameters. 1 Aarhus University, Department of Clinical Medicine - The Department of Pathology, Aarhus, Denmark Genomic profiling has unveiled the heterogeneity of breast cancer, and revealed prognostic differences and prediction on benefit from systemic therapy. Although the literature on gene expression profiles related to prediction for response to different systemic treatment strategies has been substantial, only a limited number of studies have described molecular signatures associated with local control and benefit from radiotherapy (RT). The use of single markers or combinations of immunohistochemical markers to divide patients according to risk of LRR is potentially easily applicable in a daily clinical setting. Especially, the immunohistochemical approximations of the intrinsic subtypes (based on e.g. ER, PR, HER2 and Ki67) have attracted attention. Most consistently, the Luminal A subtype has been associated with a low risk of loco-regional recurrence (LRR). In a subgroup analysis of the Canadian hypofractionation trial, it has also been examined, if different treatment schemes may be more or less suitable for the various subtypes, but no interaction between hypofractionation and intrinsic subtypes was found. A number of molecular signatures prognostic of LRR have also been identified, but until recently, the majority of these signatures have failed to validate in independent cohorts. Two studies by a Dutch group did not succeed in identifying a specific gene-set predicting risk of recurrence after breast conserving therapy (BCT), though a gene-profile based on the wound response signature was described as being of independent prognostic value. Later, the same group developed a 111-gene signature, but it did not show independent prognostic value in multivariate analysis, and lost prognostic impact when tested in other cohorts. A Swedish gene-profile aiming to identify patients developing LRR despite of RT after BCT has also not been independently validated. The ideal setting for identification of a prognostic factor is in a non-treated study population. Gene profiles predicting LRR after BCT are, however, not strictly prognostic, but include an element of prediction of benefit from RT, since the vast majority of patients treated with BCT have been treated with RT. A few prognostic gene-expression profiles predicting risk of LRR after mastectomy have, however, also been published. One of these, the 18-gene classifier, was developed from 135 non-irradiated patients treated with Symposium: Locally advanced breast cancer SP-0476 Personalised local and locoregional radiotherapy in breast cancer T. Tramm 1