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

نویسندگان

  • Salvatore Terrazzino
  • Letizia Deantonio
  • Sarah Cargnin
  • Laura Donis
  • Carla Pisani
  • Laura Masini
  • Giuseppina Gambaro
  • Pier Luigi Canonico
  • Armando A. Genazzani
  • Marco Krengli
چکیده

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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عنوان ژورنال:

دوره 49  شماره 

صفحات  -

تاریخ انتشار 2017