Validating predictive models of radiotherapy toxic.. (REQUITE)
Validating predictive models of radiotherapy toxicity to improve quality-of-life and reduce side-effects in cancer survivors
Date du début: 1 oct. 2013,
Date de fin: 30 sept. 2018
Long-term side-effects of radiotherapy impact on the quality-of-life (QoL) of cancer survivors. These side-effects could be reduced if predicted in advance. Previous work identified clinical and biological predictors but a major, coordinated approach is needed to validate them so they can be used clinically. The EU has ~17.8 million people living with a prior diagnosis of cancer of whom ~7 million received radiotherapy. In the long-term, potentially 20% of those suffering with mild to severe side-effects (~1.4 million) might benefit from alleviation of symptoms, with resulting reductions in the cost of care in the EU.REQUITE aims to develop validated clinical models and incorporate biomarkers to identify before treatment cancer patients at risk of side-effects and use the models to design interventional trials aimed at reducing side-effects and improving QoL in cancer survivors who underwent radiotherapy.REQUITE will:1. carry out a multi-centre, longitudinal, observational study to collect standardised data and samples in breast, prostate and lung cancer patients;2. validate biomarkers with published evidence of predictive value;3. replicate published clinical models and incorporate replicated biomarkers to create validated predictive algorithms;4. use the prospectively validated models and biomarkers to design interventional trial protocols aiming to reduce side-effects and improve QoL in high-risk patients.REQUITE builds on collaborations with a proven history of data sharing, enlarged to a consortium with expertise in patient recruitment, knowledge management, biomarker testing and predictive model development. SME involvement for biomarker assays will facilitate future clinical implementation and commercial exploitation. The outcome of this project will be validated predictive models for three common cancers and trial protocols using the models to investigate interventions aimed at reducing long-term side-effects and improving the QoL of cancer survivors.
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