Dataset Information

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

ABSTRACT: 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.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).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).DNMT1 rs2228611 may represent a determinant of radiation-induced fibrosis in breast cancer patients with promise for clinical usefulness in genetic-based predictive models.

SUBMITTER: Terrazzino S

PROVIDER: S-EPMC5398398 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature

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

Terrazzino Salvatore S Deantonio Letizia L Cargnin Sarah S Donis Laura L Pisani Carla C Masini Laura L Gambaro Giuseppina G Canonico Pier Luigi PL Genazzani Armando A AA Krengli Marco M

Cancer research and treatment 20160823 2

<h4>Purpose</h4>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.<h4>Materials and methods</h4><i>DNMT1</i> rs2228611,<i>DNMT3A</i> rs1550117,<i>DNMT3 ...[more]

PMID: 27554481

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Project description:PurposeRadiation-induced skin toxicity is a common and distressing side effect of breast radiation therapy (RT). We investigated the use of quantitative spectrophotometric markers as input parameters in supervised machine learning models to develop a predictive model for acute radiation toxicity.Methods and materialsOne hundred twenty-nine patients treated for adjuvant whole-breast radiotherapy were evaluated. Two spectrophotometer variables, i.e. the melanin (IM) and erythema (IE) indices, were used to quantitatively assess the skin physical changes. Measurements were performed at 4-time intervals: before RT, at the end of RT and 1 and 6 months after the end of RT. Together with clinical covariates, melanin and erythema indices were correlated with skin toxicity, evaluated using the Radiation Therapy Oncology Group (RTOG) guidelines. Binary group classes were labeled according to a RTOG cut-off score of ≥ 2. The patient's dataset was randomly split into a training and testing set used for model development/validation and testing (75%/25% split). A 5-times repeated holdout cross-validation was performed. Three supervised machine learning models, including support vector machine (SVM), classification and regression tree analysis (CART) and logistic regression (LR), were employed for modeling and skin toxicity prediction purposes.ResultsThirty-four (26.4%) patients presented with adverse skin effects (RTOG ≥2) at the end of treatment. The two spectrophotometric variables at the beginning of RT (IM,T0 and IE,T0), together with the volumes of breast (PTV2) and boost surgical cavity (PTV1), the body mass index (BMI) and the dose fractionation scheme (FRAC) were found significantly associated with the RTOG score groups (p<0.05) in univariate analysis. The diagnostic performances measured by the area-under-curve (AUC) were 0.816, 0.734, 0.714, 0.691 and 0.664 for IM, IE, PTV2, PTV1 and BMI, respectively. Classification performances reported precision, recall and F1-values greater than 0.8 for all models. The SVM classifier using the RBF kernel had the best performance, with accuracy, precision, recall and F-score equal to 89.8%, 88.7%, 98.6% and 93.3%, respectively. CART analysis classified patients with IM,T0 ≥ 99 to be associated with RTOG ≥ 2 toxicity; subsequently, PTV1 and PTV2 played a significant role in increasing the classification rate. The CART model provided a very high diagnostic performance of AUC=0.959.ConclusionsSpectrophotometry is an objective and reliable tool able to assess radiation induced skin tissue injury. Using a machine learning approach, we were able to predict grade RTOG ≥2 skin toxicity in patients undergoing breast RT. This approach may prove useful for treatment management aiming to improve patient quality of life.

Project description:BackgroundRadiation-induced skin injury, which may progress to fibrosis, is a severe side effect of radiotherapy in patients with cancer. However, currently, there is a lack of preventive or curative treatments for this injury. Meanwhile, the mechanisms underlying this injury remain poorly understood. Here, we elucidated whether autophagy is essential for the development of radiation-induced skin injury and the potential molecular pathways and mechanisms involved.Methods and resultsWe used the myofibroblast-specific Atg7 knockout (namely, conditional Atg7 knockout) mice irradiated with a single electron beam irradiation dose of 30 Gy. Vaseline-based 0.2% rapamycin ointment was topically applied once daily from the day of irradiation for 30 days. On day 30 post irradiation, skin tissues were harvested for further analysis. In vitro, human foreskin fibroblast cells were treated with rapamycin (100 nM) for 24 h and pretreated with 3-MA (5 mM) for 12 h. Macroscopic skin manifestations, histological changes, and fibrosis markers at the mRNA and protein expression levels were measured. Post irradiation, the myofibroblast-specific autophagy-deficient (Atg7Flox/Flox Cre+ ) mice had increased fibrosis marker (COL1A1, CTGF, TGF-β1, and α-SMA) levels in the irradiated area and had more severe macroscopic skin manifestations than the control group (Atg7Flox/Flox Cre- ) mice. Treatment with an autophagy agonist rapamycin attenuated macroscopic skin injury scores and skin fibrosis marker levels with decreased epidermal thickness and dermal collagen deposition in Atg7Flox/Flox Cre+ mice compared with the vehicle control. Moreover, in vitro experiment results were consistent with the in vivo results. Together with studies at the molecular level, we found that these changes involved the Akt/mTOR pathway. In addition, this phenomenon might also relate to Nrf2-autophagy signaling pathway under oxidative stress conditions.ConclusionIn conclusion, Atg7 and autophagy-related mechanisms confer radioprotection, and reactivation of the autophagy process can be a novel therapeutic strategy to reduce and prevent the occurrence of radiodermatitis, particularly skin fibrosis, in patients with cancer.

Dataset Information

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

Publications

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

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