REQUITE: A prospective multicentre cohort study of patients undergoing radiotherapy for breast, lung or prostate cancer.

Petra Seibold, Adam Webb, Miguel E Aguado-Barrera, David Azria, Celine Bourgier, Muriel Brengues, Erik Briers, Renée Bultijnck, Patricia Calvo-Crespo, Ana Carballo, Ananya Choudhury, Alessandro Cicchetti, Johannes Claßen, Elena Delmastro, Alison M Dunning, Rebecca M Elliott, Laura Fachal, Marie-Pierre Farcy-Jacquet, Pietro Gabriele, Elisabetta GaribaldiAntonio Gómez-Caamaño, Sara Gutiérrez-Enríquez, Daniel S Higginson, Kerstie Johnson, Ramón Lobato-Busto, Meritxell Mollà, Anusha Müller, Debbie Payne, Paula Peleteiro, Giselle Post, Tiziana Rancati, Tim Rattay, Victoria Reyes, Barry S Rosenstein, Dirk De Ruysscher, Maria Carmen De Santis, Jörg Schäfer, Thomas Schnabel, Elena Sperk, R Paul Symonds, Hilary Stobart, Begoña Taboada-Valladares, Christopher J Talbot, Riccardo Valdagni, Ana Vega, Liv Veldeman, Tim Ward, Christian Weißenberger, Catharine M L West, Jenny Chang-Claude, REQUITE consortium

Research output: Contribution to journalArticlepeer-review


REQUITE aimed to establish a resource for multi-national validation of models and biomarkers that predict risk of late toxicity following radiotherapy. The purpose of this article is to provide summary descriptive data. An international, prospective cohort study recruited cancer patients in 26 hospitals in eight countries between April 2014 and March 2017. Target recruitment was 5300 patients. Eligible patients had breast, prostate or lung cancer and planned potentially curable radiotherapy. Radiotherapy was prescribed according to local regimens, but centres used standardised data collection forms. Pre-treatment blood samples were collected. Patients were followed for a minimum of 12 (lung) or 24 (breast/prostate) months and summary descriptive statistics were generated. The study recruited 2069 breast (99% of target), 1808 prostate (86%) and 561 lung (51%) cancer patients. The centralised, accessible database includes: physician- (47,025 forms) and patient- (54,901) reported outcomes; 11,563 breast photos; 17,107 DICOMs and 12,684 DVHs. Imputed genotype data are available for 4223 patients with European ancestry (1948 breast, 1728 prostate, 547 lung). Radiation-induced lymphocyte apoptosis (RILA) assay data are available for 1319 patients. DNA (n = 4409) and PAXgene tubes (n = 3039) are stored in the centralised biobank. Example prevalences of 2-year (1-year for lung) grade ≥2 CTCAE toxicities are 13% atrophy (breast), 3% rectal bleeding (prostate) and 27% dyspnoea (lung). The comprehensive centralised database and linked biobank is a valuable resource for the radiotherapy community for validating predictive models and biomarkers. Up to half of cancer patients undergo radiation therapy and irradiation of surrounding healthy tissue is unavoidable. Damage to healthy tissue can affect short- and long-term quality-of-life. Not all patients are equally sensitive to radiation "damage" but it is not possible at the moment to identify those who are. REQUITE was established with the aim of trying to understand more about how we could predict radiation sensitivity. The purpose of this paper is to provide an overview and summary of the data and material available. In the REQUITE study 4400 breast, prostate and lung cancer patients filled out questionnaires and donated blood. A large amount of data was collected in the same way. With all these data and samples a database and biobank were created that showed it is possible to collect this kind of information in a standardised way across countries. In the future, our database and linked biobank will be a resource for research and validation of clinical predictors and models of radiation sensitivity. REQUITE will also enable a better understanding of how many people suffer with radiotherapy toxicity.
Original languageUndefined/Unknown
Pages (from-to)59-67
Number of pages9
JournalRadiotherapy and Oncology
Publication statusPublished - Sept 1 2019


  • Biomarkers
  • Breast cancer
  • Late radiotherapy side effects
  • Lung cancer
  • Prediction models
  • Prostate cancer

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