ESTRO 2021 Abstract Book
Group Personalized Radiotherapy in Head and Neck Cancer, Helmholtz Zentrum Munich, Neuherberg, Munich, Germany; 26 Department of Radiation Oncology, Technische Universität München, Munich, Germany; 27 Department of Radiation Oncology, Technische Universität München, Munich, Germany; 28 Department of Radiation Sciences (DRS), Institut für Innovative Radiotherapie (iRT), Helmholtz Zentrum Munich, Neuherberg, Munich, Germany; 29 German Cancer Research Center (DKFZ), Heidelberg, Germany, German Cancer Consortium (DKTK), partner site Tübingen, Tübingen, Germany; 30 Department of Radiation Oncology, Faculty of Medicine and University Hospital Tübingen, Eberhard Karls Universität Tübingen, Tübingen, Germany; 31 Tumour- and Normal Tissue Bank, University Cancer Centre (UCC), University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany Purpose or Objective The aim of this study was to identify and validate a gene signature combining machine learning approaches and biological information in order to predict loco-regional control (LRC) in patients with HPV-negative, locally advanced HNSCC who received postoperative radio(chemo)therapy (PORT(-C)). Materials and Methods Gene expression analysis was performed using the GeneChip Human Transcriptome Array 2.0 on a multicentre retrospective training cohort of 128 patients and an independent validation cohort of 114 patients studied by the German Cancer Consortium Radiation Oncology Group (DKTK-ROG) after treatment with PORT(-C) (figure A). Genes were filtered based on differential gene expression analysis and Cox regression. The identified gene signature was combined with clinical features and with previously identified genes related to cancer stem cells [1-2] and hypoxia . Model performance was evaluated by the concordance index (ci) and Kaplan-Meier analyses. Results We identified a 6-gene signature consisting of four individual genes CAV1 , GPX8 , IGLV3-25 , TGFBI and one metagene combining the highly correlated genes INHBA and SERPINE1 . A multivariable Cox model combining the 6-gene classifier and clinical parameters was fit to the training data (ci=0.81) and was successfully validated (ci=0.66). It stratified patients into two risk groups that significantly differed in the primary endpoint LRC in training (p<0.001) and in validation (p=0.039) (figure B, C). The prognostic value of the corresponding gene classifier was additionally supported by the TCGA data set, leading to a validation ci of 0.59 (0.51-0.67) for the endpoint OS (figure D, E). Extending the 6-gene signature with the putative cancer stem cell marker CD44 [1-2] and the 15 genes of a hypoxia-associated signature  further improved performance on the validation cohort (ci=0.70) including patient stratification (p<0.001) (figure F, G).
Conclusion We have identified and validated a novel 6-gene signature for LRC that is prognostic for patients with HPV- negative HNSCC treated by PORT(-C). After successful prospective validation the signature could be applied in clinical trials to further individualize radiotherapy. References:  Linge et al. Clin Cancer Res 22: 2639 (2016).  Linge et al. Clin Transl Radiat Oncol 1: 19 (2016).  Toustrup et al. Cancer Res 7: 5923 (2011). OC-0278 Accelerated CH-RT with/without nimorazole for p16- HNSCC: the randomized DAHANCA 29- EORTC 1219 trial V. Grégoire 1 , Y. Tao 2 , J. Kaanders 3 , J. Machiels 4 , N. Vulquin 5 , S. Nuyts 6 , C. Fortpied 7 , H. Lmalem 7 , S. Marreaud 8 , J. Overgaard 9 1 Centre Léon Bérard, Radiation Oncology, Lyon, France; 2 Institut Gustave-Roussy, Radiation Oncology, Villejuif, France; 3 RadboudUMC, Radiation Oncology, Nijmegen, The Netherlands; 4 Cliniques Universitaires St- Luc, Medical Oncology, Brussels, Belgium; 5 Centre Georges Francois Leclerc, Radiation Oncology, Dijon, France; 6 UZ Gasthuisberg, , Radiation Oncology, Leuven, Belgium; 7 EORTC , Headquarter, Brussels, Belgium; 8 EORTC, Headquarter, Brussels, Belgium; 9 Aarhus University Hospital, Experimental Clinical Oncology, Aarhus, Denmark
Purpose or Objective
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