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

Seibold, Petra; Webb, Adam; Aguado-Barrera, Miguel E.; Azria, D.; Bourgier, C.; Brengues, Muriel; Briers, Erik; Bultijnck, Renée; Calvo-Crespo, Patricia; Carballo, Ana; Choudhury, Ananya; Cicchetti, A.; Claßen, Johannes; Delmastro, Elena; Dunning, Alison M.; Elliott, Rebecca; Fachal, Laura; Farcy-Jacquet, Marie-Pierre; Pietro, Gabriele; Garibaldi, E.; Gómez-Caamaño, Antonio; Gutiérrez‐Enríquez, Sara; Higginson, Daniel S.; Johnson, Kerstie; Lobato-Busto, Ramón; Mollà, Meritxell; Müller, Anusha; Payne, Debbie; Peleteiro, Paula; Post, Giselle; Rancati, T.; Rattay, Tim; Reyes, Victoria; Rosenstein, Barry S.; Ruysscher, Dirk De; Santis, Maria Carmen De; Schäfer, Jörg; Schnabel, Thomas; Sperk, Elena; Symonds, R. Paul; Stobart, Hilary; Taboada-Valladares, Begoña; Talbot, Christopher J.; Valdagni, Riccardo; Vega, Ana; Veldeman, Liv; Ward, Tim; Weißenberger, Christian; West, Catharine M L; Chang‐Claude, Jenny; Lievens, Yolande; Eijkeren, M. Van; Vandecasteele, Katrien; Elhamin, Elhaseen; Ost, Piet; Fonteyne, Valérie; Swimberghe, Martijn; Deseyne, Pieter; Neve, Wilfried De; Duprez, Frédéric; Mareel, Marcus; Monten, Christel; Greveling, Annick Van; Vercauteren, Tom; Paelinck, Leen; Defraene, Gilles; Aerts, Robina; Arredouani, Soumia; Lambrecht, Maarten; Vanneste, Ben; Draghici, Roxana; Giordano, Frank A.; Herskind, Carsten; Veldwijk, Marlon R.; Helmbold, Irmgard; Giesche, Ulrich; Stegmaier, Petra; Weiß, Christian; Blaschke, Thomas; Neu, Burkhard; Lozza, Laura; Avuzzi, B.; Morlino, S.; Sangalli, Claudia; Franceschini, Marzia; Rodriguez-Lage, Belina; Fernández‐Tajes, Juan; Fuentes-Ríos, Olivia; Dominguez-Rios, Isabel; Fajardo-Paneque, Irene; Sosa-Fajardo, Paloma; Torrado-Moya, Laura; Ramos-Albiac, Mónica; Giraldo, Alexandra; Altabas, M.; Piqué-Leiva, Bibiana; García-Relancio, David; Seoane-Ramallo, Alejandro; Lavers, Samuel; Wright, Simon K.; Dobbelaere, Hannah; Appleton, Donna; Kaushik, Monika; Kenny, Frances; Khout, Hazem; Krupa, Jarosław; Lambert, Kelly; Pilgrim, Simon; Shokuhi, Sheila; Valassiadou, Kalliope; Aznar-Garcia, L.; Boiangui, Ion; Kancherla, Kiran; Kent, Christopher; Sampson, Kufre; Osman, A. E.; Sridhar, T. M.; Vasanthan, Subramaniam; Faivre-Finn, Corinne; Harrop, V.; Keni, Manjusha; Foweraker, K.; Pascoe, A.; Esler, C.; Stock, Richard G.; Green, Sheryl; Golchin, Ava; Li, William

doi:10.1016/j.radonc.2019.04.034

Cited by 56 publications

(47 citation statements)

References 41 publications

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“…It has become evident that the risk of developing side-effects is predominantly influenced by genetic factors, presumably those regulating DNA repair and relevant pathways [13][14][15][16][26][27][28][29][30][31][32]. Despite huge efforts and long-term research, the real progress in this area has been achieved only recently, when 'big data' became available in the framework of large-scale, international projects devoted to the validation of suggested biomarkers of the normal tissue radiotoxicity for clinical use [16,17,20,22,23,25,[33][34][35]. Working-out and validation of molecular genetic predictors of radiosensitivity were carried out by consolidated efforts of scientific consortiums in the framework of international programs and projects: EURATOM, Multidisciplinary European Low-Dose Initiative of the European Joint Programme for the Integration of Radiation Protection Research (MELODI/CONCERT), RadGenomics,"Genetic Predictors of Adverse Radiotherapy" (Gene-PARE), "Genetic Pathways for the Prediction of the Effects of Irradiation" (GENEPI), "Assessment of Polymorphisms for Predicting the Effects of Radiotherapy" (RAPPER) or the most recent "Validating Predictive Models and Biomarkers of Radiotherapy Toxicity to Reduce Side-Effects and Improve Quality of Life in Cancer Survivors" (REQUITE) [22,23,26,[33][34][35].…”

Section: Introductionmentioning

confidence: 99%

Prediction of the Acute or Late Radiation Toxicity Effects in Radiotherapy Patients Using Ex Vivo Induced Biodosimetric Markers: A Review

Vinnikov

Hande

Wilkins

et al. 2020

JPM

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A search for effective methods for the assessment of patients’ individual response to radiation is one of the important tasks of clinical radiobiology. This review summarizes available data on the use of ex vivo cytogenetic markers, typically used for biodosimetry, for the prediction of individual clinical radiosensitivity (normal tissue toxicity, NTT) in cells of cancer patients undergoing therapeutic irradiation. In approximately 50% of the relevant reports, selected for the analysis in peer-reviewed international journals, the average ex vivo induced yield of these biodosimetric markers was higher in patients with severe reactions than in patients with a lower grade of NTT. Also, a significant correlation was sometimes found between the biodosimetric marker yield and the severity of acute or late NTT reactions at an individual level, but this observation was not unequivocally proven. A similar controversy of published results was found regarding the attempts to apply G2- and γH2AX foci assays for NTT prediction. A correlation between ex vivo cytogenetic biomarker yields and NTT occurred most frequently when chromosome aberrations (not micronuclei) were measured in lymphocytes (not fibroblasts) irradiated to relatively high doses (4–6 Gy, not 2 Gy) in patients with various grades of late (not early) radiotherapy (RT) morbidity. The limitations of existing approaches are discussed, and recommendations on the improvement of the ex vivo cytogenetic testing for NTT prediction are provided. However, the efficiency of these methods still needs to be validated in properly organized clinical trials involving large and verified patient cohorts.

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Section: Introductionmentioning

confidence: 99%

Prediction of the Acute or Late Radiation Toxicity Effects in Radiotherapy Patients Using Ex Vivo Induced Biodosimetric Markers: A Review

Vinnikov

Hande

Wilkins

et al. 2020

JPM

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“…REQUITE prostate cancer patients treated with external beam radiotherapy (with/without hormonal therapy, with/without a previous prostatectomy, no brachytherapy) and complete 2-year follow-up were included. Details on the REQUITE population are given in Seibold et al ( 14 ).…”

Section: Methodsmentioning

confidence: 99%

“…A number of different approaches have been explored (5)(6)(7)(8)(9)(10)(11)(12)(13), however, the developed models and biomarkers have failed to progress to routine clinical use due to the lack of thorough independent validation. REQUITE (validating pREdictive models and biomarkers of radiotherapy toxicity to reduce side effects and improve QUalITy of lifE in cancer survivors) was established with the aim of validating models and biomarkers for the prediction of adverse effects following radiotherapy (14)(15)(16). In order to address previous limitations in pooling data, in using common toxicity scoring systems and in collecting standardized data, REQUITE carried out an international, multi-center, prospective observational study.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Approach Validates Genetic Risk Factors for Late Toxicity After Prostate Cancer Radiotherapy in a REQUITE Multi-National Cohort

et al. 2020

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Background: REQUITE (validating pREdictive models and biomarkers of radiotherapy toxicity to reduce side effects and improve QUalITy of lifE in cancer survivors) is an international prospective cohort study. The purpose of this project was to analyse a cohort of patients recruited into REQUITE using a deep learning algorithm to identify patient-specific features associated with the development of toxicity, and test the approach by attempting to validate previously published genetic risk factors. Methods: The study involved REQUITE prostate cancer patients treated with external beam radiotherapy who had complete 2-year follow-up. We used five separate late toxicity endpoints: ≥grade 1 late rectal bleeding, ≥grade 2 urinary frequency, ≥grade 1 haematuria, ≥ grade 2 nocturia, ≥ grade 1 decreased urinary stream. Forty-three single nucleotide polymorphisms (SNPs) already reported in the literature to be associated with the toxicity endpoints were included in the analysis. No SNP had been studied before in the REQUITE cohort. Deep Sparse AutoEncoders (DSAE) were trained to recognize features (SNPs) identifying patients with no toxicity and tested on a different independent mixed population including patients without and with toxicity. Results: One thousand, four hundred and one patients were included, and toxicity rates were: rectal bleeding 11.7%, urinary frequency 4%, haematuria 5.5%, nocturia 7.8%, decreased urinary stream 17.1%. Twenty-four of the 43 SNPs that were associated with the toxicity endpoints were validated as identifying patients with toxicity. Twenty of the 24 SNPs were associated with the same toxicity endpoint as reported in the literature: 9 SNPs for urinary symptoms and 11 SNPs for overall toxicity. The other 4 SNPs were associated with a different endpoint. Conclusion: Deep learning algorithms can validate SNPs associated with toxicity after radiotherapy for prostate cancer. The method should be studied further to identify polygenic SNP risk signatures for radiotherapy toxicity. The signatures could then be included in integrated normal tissue complication probability models and tested for their ability to personalize radiotherapy treatment planning.

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“…The expected profile of radiotherapy toxicity is quite known according to the irradiated tissue, delivered dose, volume of treatment, amongst other factors [171]. Radioprotectors, such as Amifostine, have proven to be efficient in management, for example, of head and neck cancer treatment-related toxicity; however, its availability and implementation in clinical routine due to logistics or elevated costs could impact its accessibility [172,173].…”

Section: Implicancies In Radiation Protectionmentioning

confidence: 99%

Genoprotective activities of plant natural substances in cancer and chemopreventive strategies in the context of 3P medicine

et al. 2020

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Severe durable changes may occur to the DNA structure caused by exogenous and endogenous risk factors initiating the process of carcinogenesis. By evidence, a large portion of malignancies have been demonstrated as being preventable. Moreover, the targeted prevention of cancer onset is possible, due to unique properties of plant bioactive compounds. Although genoprotective effects of phytochemicals have been well documented, there is an evident lack of articles which would systematically present the spectrum of anticancer effects by phytochemicals, plant extracts, and plant-derived diet applicable to stratified patient groups at the level of targeted primary (cancer development) and secondary (cancer progression and metastatic disease) prevention. Consequently, clinical implementation of knowledge accumulated in the area is still highly restricted. To stimulate coherent co-development of the dedicated plant bioactive compound investigation on one hand and comprehensive cancer preventive strategies on the other hand, the current paper highlights and deeply analyses relevant evidence available in the area. Key molecular mechanisms are presented to detail genoprotective and anticancer activities of plants and phytochemicals. Clinical implementation is discussed. Based on the presented evidence, advanced chemopreventive strategies in the context of 3P medicine are considered.

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

Cited by 56 publications

References 41 publications

Prediction of the Acute or Late Radiation Toxicity Effects in Radiotherapy Patients Using Ex Vivo Induced Biodosimetric Markers: A Review

Prediction of the Acute or Late Radiation Toxicity Effects in Radiotherapy Patients Using Ex Vivo Induced Biodosimetric Markers: A Review

A Deep Learning Approach Validates Genetic Risk Factors for Late Toxicity After Prostate Cancer Radiotherapy in a REQUITE Multi-National Cohort

Genoprotective activities of plant natural substances in cancer and chemopreventive strategies in the context of 3P medicine

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