Abstract Book

S1256

ESTRO 37

Purpose or Objective There are several radiobiological models that attempt to predict normal tissue complication probability (NTCP) for a given dose of radiotherapy and fractionation schedule. Parameter sets available from published studies are commonly used to allow adaptation of these models to locally acquired data sets, which can be helpful in assessing patient response to radiotherapy. It is estimated that between 15-37% of lung cancer patients will develop radiation pneumonitis (RP) following radiotherapy treatment. The aim of this work was to test the hypothesis that existing NTCP models, when parameterised with published data sets and dose volume summary measures, can be used to predict RP. Material and Methods A cohort of 69 non-small cell lung (NSCLC) cancer patients[NB1] were selected to assess the performance of existing radiobiological models for predicting RP. Patients were treated with a 6 MV photon beam using a Varian linear accelerator (Varian Medical Systems, Inc. Palo Alto, CA, USA). All 3-field treatment plans were produced using the Eclipse treatment planning system v10 using a pencil beam convolution (PCB v10.0.28) dose calculation algorithm and dose volume histograms were extracted. Bespoke software implementation of the Lyman-Kutcher-Burman (LKB) and Relative Seriality (RS) models were written in Matlab to calculate the NTCP for each patient. Univariate analysis was used to test for independent associations between RP and NTCP, dose per fraction, maximum dose, V 20 , V 10, V 5 , and mean lung dose (MLD). A Pearson goodness of fit chi-squared (χ 2 ) test was used to assess whether there was a difference between expected and observed complication rates. Results The results of the univariate analysis are displayed in Table 1. The dosimetric factors that were significant were MLD (both lungs), odds ratio 1.186 (CI 1.036-1.573, p-value 0.013), V 10, odds ratio 1.049 (CI 1.005-1.095, p- value 0.029), and V 5, odds ratio 1.054 (CI 1.010-1.101, p- value 0.017). In the patient population studied none of the NTCP values were statistically significant in predicting RP, demonstrating that these calculations alone were not sufficient to do so. The results of the goodness of fit chi-squared test (χ 2 ) are displayed in Table 2. A p-value of 1 indicates a perfect fit, whereas a p-value of zero means that the radiobiological model and the parameters do not describe the clinical outcome very well. The Seppenwoolde et al. parameters gave the best fit for both the LKB and RS models (single lung), with a p- value of 0.72 & 0.88 respectively.

RT-qPCR analysis. We assume that a noticeable activation of DDR is particularly enhanced during treatment with IR. Conclusion We found that hiPSCs differentiated toward ChiPS undergo a stress that leads to activation of DDR mechanisms. The differentiated cells are very prone to exposure to genotoxic agents. Thus, they demonstrate extremely high level of members taking part in DNA damage and repair during IR treatment. The increased expression level of genes involved in DDR and p53- mediated mechanisms also raises concern about the potential genetic instability of obtained ChiPS. EP-2274 Fast and binary assay for predicting radiosensitivity based on the theory of the ATM nucleoshuttling S. DENEUVE 1 , C. Mirjolet 2 , M. Duclos 3 , G. Vogin 4 , T. Bastogne 5 , S. Pereira 6 1 CENTRE LEON BERARD, surgery, Lyon cedex 08, France 2 centre georges Leclerc, radiotherapy, dijon, France 3 NEolys diagnostics, Research, lyon, France 4 institut de cancérologie de Lorraine, radiotherapy, nancy, France 5 CYBERnano, research, villers les nancy, France 6 Inserm, UMR 1052 radiobiology group, lyon, France Purpose or Objective The societal and clinical impact of post-radiotherapy Adverse Events (AE) have highlighted the need of molecular and cellular parameters that can predict the outcome of the treatment. Recent studies have stressed the role of the nucleoshuttling of the ATM protein in the response to radiation, and the statistical performances of this molecular endpoint to predict individual radiosensitivity. The purpose of this study was to develop a predictive assay based on the quantification of the pATM proteins by ELISA method Material and Methods This study was performed on 40 skin fibroblasts from 14 radioresistant and 26 AE patients. Patients were divided in 2 groups, radioresistant (CTCAE<2) and radiosensitive (CTCAE≥2). The quantity of nuclear pATM molecules was assessed by ELISA method compared to the pATM immunofluorescence foci data. The statistical analysis is based on a binary decision diagram built with the CART (Classification And Regression Tree) method proposed by L. Breiman et al. (1984). The Ward distance was used to perform the classification analysis. Results For each cell line, the resulted quantities of nuclear pATM molecules were found in agreement with the immunofluorescence data, in order to provide a reliable evaluation of the statistical performance of the assay. Sensitivity, specificity and AUC values: Sens=0.68, Spec=0.95 and AUC=0.82, emphasize promising results in terms of classification performance. Conclusion This study showed that the assessment of nuclear pATM quantity by an ELISA assay can be the basis of a radiosensitivity predictive test with promising performances. EP-2275 Predicting radiation pneumonitis in non-small cell lung cancer patients: a radiobiological analysis. T. McMullan 1 , S. Campbell 2 , W.H. Nailon 1 1 Edinburgh Cancer Centre, Oncology Physics, Edinburgh, United Kingdom 2 Edinburgh Cancer Centre, Clinical Oncology, Edinburgh, United Kingdom

Table 1 : Univariate Analysis