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        DNA Methyltransferase Gene Polymorphisms for Prediction of Radiation-Induced Skin Fibrosis after Treatment of Breast Cancer: A Multifactorial Genetic Approach

        Salvatore Terrazzino,Letizia Deantonio,Sarah Cargnin,Laura Donis,Carla Pisani,Laura Masini,Giuseppina Gambaro,Pier Luigi Canonico,Armando A. Genazzani,Marco Krengli 대한암학회 2017 Cancer Research and Treatment Vol.49 No.2

        Purpose This study was conducted to investigate the role of four polymorphic variants of DNA methyltransferase genes as risk factors for radiation-induced fibrosis in breast cancer patients. We also assessed their ability to improve prediction accuracy when combined with mitochondrial haplogroup H, which we previously found to be independently associated with a lower hazard of radiation-induced fibrosis. Materials and Methods DNMT1 rs2228611, DNMT3A rs1550117, DNMT3A rs7581217, and DNMT3B rs2424908 were genotyped by real-time polymerase chain reaction in 286 Italian breast cancer patients who received radiotherapy after breast conserving surgery. Subcutaneous fibrosis was scored according to the Late Effects of Normal Tissue–Subjective Objective Management Analytical (LENT-SOMA) scale. The discriminative accuracy of genetic models was assessed by the area under the receiver operating characteristic curves (AUC). Results Kaplan-Meier curves showed significant differences among DNMT1 rs2228611 genotypes in the cumulative incidence of grade  2 subcutaneous fibrosis (log-rank test p-value= 0.018). Multivariate Cox regression analysis revealed DNMT1 rs2228611 as an independent protective factor for moderate to severe radiation-induced fibrosis (GG vs. AA; hazard ratio, 0.26; 95% confidence interval [CI], 0.10 to 0.71; p=0.009). Adding DNMT1 rs2228611 to haplogroup H increased the discrimination accuracy (AUC) of the model from 0.595 (95% CI, 0.536 to 0.653) to 0.655 (95% CI, 0.597 to 0.710). Conclusion DNMT1 rs2228611 may represent a determinant of radiation-induced fibrosis in breast cancer patients with promise for clinical usefulness in genetic-based predictive models.

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