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| Home > Publications database > Treatment time and circadian genotype interact to influence radiotherapy side-effects. A prospective European validation study using the REQUITE cohort. > print |
| 001 | 181851 | ||
| 005 | 20240229145659.0 | ||
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| 100 | 1 | _ | |a Webb, Adam J |b 0 |
| 245 | _ | _ | |a Treatment time and circadian genotype interact to influence radiotherapy side-effects. A prospective European validation study using the REQUITE cohort. |
| 260 | _ | _ | |a Amsterdam [u.a.] |c 2022 |b Elsevier |
| 336 | 7 | _ | |a article |2 DRIVER |
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| 520 | _ | _ | |a Circadian rhythm impacts broad biological processes, including response to cancer treatment. Evidence conflicts on whether treatment time affects risk of radiotherapy side-effects, likely because of differing time analyses and target tissues. We previously showed interactive effects of time and genotypes of circadian genes on late toxicity after breast radiotherapy and aimed to validate those results in a multi-centre cohort.Clinical and genotype data from 1690 REQUITE breast cancer patients were used with erythema (acute; n=340) and breast atrophy (two years post-radiotherapy; n=514) as primary endpoints. Local datetimes per fraction were converted into solar times as predictors. Genetic chronotype markers were included in logistic regressions to identify primary endpoint predictors.Significant predictors for erythema included BMI, radiation dose and PER3 genotype (OR 1.27(95%CI 1.03-1.56); P < 0.03). Effect of treatment time effect on acute toxicity was inconclusive, with no interaction between time and genotype. For late toxicity (breast atrophy), predictors included BMI, radiation dose, surgery type, treatment time and SNPs in CLOCK (OR 0.62 (95%CI 0.4-0.9); P < 0.01), PER3 (OR 0.65 (95%CI 0.44-0.97); P < 0.04) and RASD1 (OR 0.56 (95%CI 0.35-0.89); P < 0.02). There was a statistically significant interaction between time and genotypes of circadian rhythm genes (CLOCK OR 1.13 (95%CI 1.03-1.23), P < 0.01; PER3 OR 1.1 (95%CI 1.01-1.2), P < 0.04; RASD1 OR 1.15 (95%CI 1.04-1.28), P < 0.008), with peak time for toxicity determined by genotype.Late atrophy can be mitigated by selecting optimal treatment time according to circadian genotypes (e.g. treat PER3 rs2087947C/C genotypes in mornings; T/T in afternoons). We predict triple-homozygous patients (14%) reduce chance of atrophy from 70% to 33% by treating in mornings as opposed to mid-afternoon. Future clinical trials could stratify patients treated at optimal times compared to those scheduled normally.EU-FP7. |
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| 650 | _ | 7 | |a Breast cancer |2 Other |
| 650 | _ | 7 | |a Circadian rhythm |2 Other |
| 650 | _ | 7 | |a Genetics |2 Other |
| 650 | _ | 7 | |a Radiotherapy |2 Other |
| 700 | 1 | _ | |a Harper, Emily |b 1 |
| 700 | 1 | _ | |a Rattay, Tim |b 2 |
| 700 | 1 | _ | |a Aguado-Barrera, Miguel E |b 3 |
| 700 | 1 | _ | |a Azria, David |b 4 |
| 700 | 1 | _ | |a Bourgier, Celine |b 5 |
| 700 | 1 | _ | |a Brengues, Muriel |b 6 |
| 700 | 1 | _ | |a Briers, Erik |b 7 |
| 700 | 1 | _ | |a Bultijnck, Renée |b 8 |
| 700 | 1 | _ | |a Chang-Claude, Jenny |0 P:(DE-He78)c259d6cc99edf5c7bc7ce22c7f87c253 |b 9 |u dkfz |
| 700 | 1 | _ | |a Choudhury, Ananya |b 10 |
| 700 | 1 | _ | |a Cicchetti, Alessandro |b 11 |
| 700 | 1 | _ | |a De Ruysscher, Dirk |b 12 |
| 700 | 1 | _ | |a De Santis, Maria Carmen |b 13 |
| 700 | 1 | _ | |a Dunning, Alison M |b 14 |
| 700 | 1 | _ | |a Elliott, Rebecca M |b 15 |
| 700 | 1 | _ | |a Fachal, Laura |b 16 |
| 700 | 1 | _ | |a Gómez-Caamaño, Antonio |b 17 |
| 700 | 1 | _ | |a Gutiérrez-Enríquez, Sara |b 18 |
| 700 | 1 | _ | |a Johnson, Kerstie |b 19 |
| 700 | 1 | _ | |a Lobato-Busto, Ramón |b 20 |
| 700 | 1 | _ | |a Kerns, Sarah L |b 21 |
| 700 | 1 | _ | |a Post, Giselle |b 22 |
| 700 | 1 | _ | |a Rancati, Tiziana |b 23 |
| 700 | 1 | _ | |a Reyes, Victoria |b 24 |
| 700 | 1 | _ | |a Rosenstein, Barry S |b 25 |
| 700 | 1 | _ | |a Seibold, Petra |0 P:(DE-He78)fd17a8dbf8d08ea5bb656dfef7398215 |b 26 |u dkfz |
| 700 | 1 | _ | |a Seoane, Alejandro |b 27 |
| 700 | 1 | _ | |a Sosa-Fajardo, Paloma |b 28 |
| 700 | 1 | _ | |a Sperk, Elena |b 29 |
| 700 | 1 | _ | |a Taboada-Valladares, Begoña |b 30 |
| 700 | 1 | _ | |a Valdagni, Riccardo |b 31 |
| 700 | 1 | _ | |a Vega, Ana |b 32 |
| 700 | 1 | _ | |a Veldeman, Liv |b 33 |
| 700 | 1 | _ | |a Ward, Tim |b 34 |
| 700 | 1 | _ | |a West, Catharine M |b 35 |
| 700 | 1 | _ | |a Symonds, R Paul |b 36 |
| 700 | 1 | _ | |a Talbot, Christopher J |b 37 |
| 700 | 1 | _ | |a Consortium, REQUITE |b 38 |e Collaboration Author |
| 773 | _ | _ | |a 10.1016/j.ebiom.2022.104269 |g Vol. 84, p. 104269 - |0 PERI:(DE-600)2799017-5 |p 104269 |t EBioMedicine |v 84 |y 2022 |x 2352-3964 |
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