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Incorporating single-nucleotide polymorphisms into the Lyman model to improve prediction of radiation pneumonitis.

ABSTRACT: To determine whether single-nucleotide polymorphisms (SNPs) in genes associated with DNA repair, cell cycle, transforming growth factor-?, tumor necrosis factor and receptor, folic acid metabolism, and angiogenesis can significantly improve the fit of the Lyman-Kutcher-Burman (LKB) normal-tissue complication probability (NTCP) model of radiation pneumonitis (RP) risk among patients with non-small cell lung cancer (NSCLC).Sixteen SNPs from 10 different genes (XRCC1, XRCC3, APEX1, MDM2, TGF?, TNF?, TNFR, MTHFR, MTRR, and VEGF) were genotyped in 141 NSCLC patients treated with definitive radiation therapy, with or without chemotherapy. The LKB model was used to estimate the risk of severe (grade?3) RP as a function of mean lung dose (MLD), with SNPs and patient smoking status incorporated into the model as dose-modifying factors. Multivariate analyses were performed by adding significant factors to the MLD model in a forward stepwise procedure, with significance assessed using the likelihood-ratio test. Bootstrap analyses were used to assess the reproducibility of results under variations in the data.Five SNPs were selected for inclusion in the multivariate NTCP model based on MLD alone. SNPs associated with an increased risk of severe RP were in genes for TGF?, VEGF, TNF?, XRCC1 and APEX1. With smoking status included in the multivariate model, the SNPs significantly associated with increased risk of RP were in genes for TGF?, VEGF, and XRCC3. Bootstrap analyses selected a median of 4 SNPs per model fit, with the 6 genes listed above selected most often.This study provides evidence that SNPs can significantly improve the predictive ability of the Lyman MLD model. With a small number of SNPs, it was possible to distinguish cohorts with >50% risk vs <10% risk of RP when they were exposed to high MLDs.

SUBMITTER: Tucker SL

PROVIDER: S-EPMC3521878 | biostudies-literature | 2013 Jan

REPOSITORIES: biostudies-literature

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Incorporating single-nucleotide polymorphisms into the Lyman model to improve prediction of radiation pneumonitis.

Tucker Susan L SL Li Minghuan M Xu Ting T Gomez Daniel D Yuan Xianglin X Yu Jinming J Liu Zhensheng Z Yin Ming M Guan Xiaoxiang X Wang Li-E LE Wei Qingyi Q Mohan Radhe R Vinogradskiy Yevgeniy Y Martel Mary M Liao Zhongxing Z

International journal of radiation oncology, biology, physics 20120427 1

<h4>Purpose</h4>To determine whether single-nucleotide polymorphisms (SNPs) in genes associated with DNA repair, cell cycle, transforming growth factor-β, tumor necrosis factor and receptor, folic acid metabolism, and angiogenesis can significantly improve the fit of the Lyman-Kutcher-Burman (LKB) normal-tissue complication probability (NTCP) model of radiation pneumonitis (RP) risk among patients with non-small cell lung cancer (NSCLC).<h4>Methods and materials</h4>Sixteen SNPs from 10 differen ...[more]

PMID: 22541966

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Project description:Objective The study aims to establish and validate an effective CT-based radiation pneumonitis (RP) prediction model using the multiomics method of radiomics and EQD2-based dosiomics. Materials and Methods The study performed a retrospective analysis on 91 nonsmall cell lung cancer patients who received radiotherapy from 2019 to 2021 in our hospital. The patients with RP grade ≥1 were labeled as 1, and those with RP grade < 1 were labeled as 0. The whole lung excluding clinical target volume (lung-CTV) was used as the region of interest (ROI). The radiomic and dosiomic features were extracted from the lung-CTV area's image and dose distribution. Besides, the equivalent dose of the 2 Gy fractionated radiation (EQD2) model was used to convert the physical dose to the isoeffect dose, and then, the EQD2-based dosiomic (eqd-dosiomic) features were extracted from the isoeffect dose distribution. Four machine learning (ML) models, including DVH, radiomics combined with DVH (radio + DVH), radiomics combined with dosiomics (radio + dose), and radiomics combined with eqd-dosiomics (radio + eqdose), were established to construct the prediction model via eleven different classifiers. The fivefold cross-validation was used to complete the classification experiment. The area under the curve (AUC) of the receiver operating characteristics (ROC), accuracy, precision, recall, and F1-score were calculated to assess the performance level of the prediction models. Results Compared with the DVH, radio + DVH, and radio + dose model, the value of the training AUC, accuracy, and F1-score of radio + eqdose was higher, and the difference was statistically significant (p < 0.05). Besides, the average value of the precision and recall of radio + eqdose was higher, but the difference was not statistically significant (p > 0.05). Conclusion The performance of using the ML-based multiomics method of radiomics and eqd-dosiomics to predict RP is more efficient and effective.

Project description:BackgroundWe examined the effects of the rs1800469 and rs1800470 single nucleotide polymorphisms (SNPs) of the transforming growth factor-β1 (TGFβ1) gene and the rs189037 and rs373759 SNPs of the ataxia telangiectasia mutated (ATM) gene on the risk of radiation-induced pneumonitis (RP) in patients who underwent radiotherapy for various thoracic malignancies.MethodsWe determined the genotype and allele distributions of rs1800469 (C-509T), rs1800470 (C869T), rs189037 (A-111G), and rs373759 (126713 G>A) in 141 Han Chinese patients who underwent definitive thoracic radiotherapy (50 to 77 Gy, 5 days/wk) for lung cancer (small cell or non-small cell tumors, n = 97), esophageal squamous cell carcinoma (ESCC, n = 27), or mediastinal cancer (n = 17). Clinical variables were evaluated using multivariate logistic regression models to calculate the relative risk of RP associated with the clinical variables, and a Pearson correlation analysis was used to evaluate the relationship between the SNP genotypes and alleles and the incidence of RP for the various risk factors.ResultsThe T alleles of rs1800470 (CT/TT) and rs1800469 (CT/TT) and the G allele of rs189037 (GA/GG) were associated with the risk of ≥ grade-2 RP in the ESCC patients (P = 0.0006, P = 0.0127, and P = 0.0412, respectively), and that the A alleles of rs189037 (AG/AA) and rs373759 (AG/AA) were associated with the risk of ≥ grade-2 RP in the patients with mediastinal cancer (P = 0.0063 and P = 0.0003, respectively). None of the SNP genotypes were associated with the risk of RP in lung cancer patients.ConclusionThe T alleles of the rs1800470 (CT/TT) and rs1800469 (CT/TT) SNPs of TGFβ1 and the G allele of the rs189037 (GA/GG) SNP of ATM are independent risk factors for RP in Chinese ESCC patients, and the A alleles of the rs189037 (AG/AA) and rs373759 (AG/AA) SNPs of ATM are independent risk factors for RP in Chinese patients with mediastinal cancer. These SNPs might represent useful biomarkers for personalizing radiotherapy regimens for Chinese patients with ESCC or mediastinal cancer to reduce the incidence of RP. Large-cohort studies of these SNPs in thoracic cancer patients are warranted.

Project description:Radiation pneumonitis (RP) is one of the major side effects of thoracic radiotherapy. The aim of this study is to build a dose distribution based prediction model, and investigate the correlation of RP incidence and high-order features of dose distribution. A convolution 3D (C3D) neural network was used to construct the prediction model. The C3D network was pre-trained for action recognition. The dose distribution was used as input of the prediction model. With the C3D network, the convolution operation was performed in 3D space. The guided gradient-weighted class activation map (grad-CAM) was utilized to locate the regions of dose distribution which were strongly correlated with grade?2 and grade<2 RP cases, respectively. The features learned by the convolution filters were generated with gradient ascend to understand the deep network. The performance of the C3D prediction model was evaluated by comparing with three multivariate logistic regression (LR) prediction models, which used the dosimetric, normal tissue complication probability (NTCP) or dosiomics factors as input, respectively. All the prediction models were validated using 70 non-small cell lung cancer (NSCLC) patients treated with volumetric modulated arc therapy (VMAT). The area under curve (AUC) of C3D prediction model was 0.842. While the AUC of the three LR models were 0.676, 0.744 and 0.782, respectively. The guided grad-CAM indicated that the low-dose region of contralateral lung and high-dose region of ipsilateral lung were strongly correlated with the grade?2 and grade<2 RP cases, respectively. The features learned by shallow filters were simple and globally consistent, and of monotonous color. The features of deeper filters displayed more complicated pattern, which was hard or impossible to give strict mathematical definition. In conclusion, we built a C3D model for thoracic radiotherapy toxicity prediction. The results demonstrate its performance is superior over the classical LR models. In addition, CNN also offers a new perspective to further understand RP incidence.

Project description:Background:DNA prediction of eye color represent one application of the externally visible characteristics (EVC), which attained growing interest in the field of DNA forensic phenotyping. This is mainly due to its ability to narrow the pool of suspects without the need to compare any retrieved DNA material from the crime scene to a reference DNA. Several methods and multiplex genetic panel were proposed with variable prediction accuracy between different populations. However, such panel was not previously tested in the Saudi population, nor any populations of the Middle East and North Africa origin. Method:A panel of eleven single nucleotide polymorphisms (SNPs) was tested for their association with three eye colors (brown, hazel, and intermediate) in 80 volunteer Saudi individuals. SNPs and haplotype association test with eye colors were performed to identify the top significant SNPs with the three eye colors. Also, multinomial logistic regression was used to construct the prediction model using a training set of 60 subjects, and a validation set of 20 subjects. The goodness of fit parameter of the model to correctly predicts each eye color as compared to the other was performed. Results:Eye color was significantly associated with rs12913832, rs7170852, and rs916977 that are located within HERC2. SNP rs12913832 was the top significant SNP (p-value?=?1.78E-15) that accounted for the association in this region, as the other SNPs were not significant after adjusting for rs12913832. A prediction model containing five SNPs showed high prediction accuracy with Area Under the receiver operating characteristic Curves (AUC) equals to 0.95 and 0.83 for brown and intermediate eye colors, respectively. However, the model's performance was very low for predicting the hazel eye color with AUC equals 0.75. Discussion:Despite the small sample size of our study, we reported very significant SNP associations with eye color. Our model to predict eye colors based on DNA material showed high accuracy for brown and intermediate eye colors. The eye color prediction-model underperformed for the hazel eye colors, suggesting that larger sample size, as well as more comprehensive set of SNPs, could improve the model-prediction accuracy.

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Incorporating single-nucleotide polymorphisms into the Lyman model to improve prediction of radiation pneumonitis.

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Incorporating single-nucleotide polymorphisms into the Lyman model to improve prediction of radiation pneumonitis.

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