Dataset Information

Methylation associated genes contribute to the favorable prognosis of gliomas with isocitrate dehydrogenase 1 mutation.

ABSTRACT: Gliomas, the most common primary brain tumors, are characterized by isocitrate dehydrogenase 1 mutation (IDH1-M). High mutation frequency of IDH1 indicates it's promoting role in tumorgenesis. However, the observation that patients with IDH1-M have better survival comparing with patients with IDH1 wild-type (IDH1-W) suggests that this alteration has other significant beneficial features for patients. Currently, temozolomide (TMZ) is a standard of care for patients which play a major role in DNA methylation that is similar with the role of IDH1-M in genome-wide methylation. In this study, we collected 323 gliomas samples with genome-wide methylation microarray, 502 samples with genome-wide mRNA expression microarray and 295 samples with RNA-seq. By significance analysis of microarray (SAM), we identified 18 genes which are hypermethylation and low expression in samples with IDH1-M comparing with IDH1-W (FDR<0.01). Furthermore, 18 candidate genes were downregulated in TMZ-treated samples. Finally, we obtained two candidate genes, F3 and RBP1. Survival analysis showed that hypermethylation or low expression of the two genes indicated a favorable prognosis, which was consistent with IDH1-M and administration of TMZ in glioma patients. F3 and RBP1 were further validated by qPCR on an independent validation cohort containing 145 samples. Our data suggest that these candidate genes were suppressed by TMZ or IDH1-M induced hypermethylation, resulting in the favorable prognosis of patients with gliomas.

SUBMITTER: Liu Y

PROVIDER: S-EPMC4633397 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Methylation associated genes contribute to the favorable prognosis of gliomas with isocitrate dehydrogenase 1 mutation.

Liu Yanwei Y Hu Huimin H Zhang Chuanbao C Wang Zheng Z Li Mingyang M Zhang Wei W Jiang Tao T

American journal of cancer research 20150815 9

Gliomas, the most common primary brain tumors, are characterized by isocitrate dehydrogenase 1 mutation (IDH1-M). High mutation frequency of IDH1 indicates it's promoting role in tumorgenesis. However, the observation that patients with IDH1-M have better survival comparing with patients with IDH1 wild-type (IDH1-W) suggests that this alteration has other significant beneficial features for patients. Currently, temozolomide (TMZ) is a standard of care for patients which play a major role in DNA ...[more]

PMID: 26609481

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Project description:Isocitrate dehydrogenase 1 gene mutations are found in most World Health Organization grade II and III gliomas and secondary glioblastomas. Isocitrate dehydrogenase 1 mutations are known to have prognostic value in high-grade gliomas. However, their prognostic significance in low-grade gliomas remains controversial. We determined the predictive and prognostic value of isocitrate dehydrogenase 1 status in low-grade gliomas. The association of isocitrate dehydrogenase 1 status with clinicopathological and genetic factors was also evaluated. Clinical information and genetic data including isocitrate dehydrogenase 1 mutation, O 6-methylguanine DNA methyltransferase promoter methylation, 1p/19q chromosome loss, and TP53 mutation of 417 low-grade gliomas were collected from the Chinese Glioma Genome Atlas database. Kaplan-Meier and Cox proportional hazards regression analyses were performed to evaluate the prognostic effect of clinical characteristics and molecular biomarkers. Isocitrate dehydrogenase 1 mutation was identified as an independent prognostic factor for overall, but not progression-free, survival. Notably, isocitrate dehydrogenase 1 mutation was found to be a significant prognostic factor in patients with oligodendrogliomas, but not in patients with astrocytomas. Furthermore, O 6-methylguanine DNA methyltransferase promoter methylation (p = 0.017) and TP53 mutation (p < 0.001), but not 1p/19q loss (p = 0.834), occurred at a higher frequency in isocitrate dehydrogenase 1-mutated tumors than in isocitrate dehydrogenase 1 wild-type tumors. Younger patient age (p = 0.041) and frontal lobe location (p = 0.010) were significantly correlated with isocitrate dehydrogenase 1 mutation. Chemotherapy did not provide a survival benefit in patients with isocitrate dehydrogenase 1-mutated tumors. Isocitrate dehydrogenase 1 mutation was an independent prognostic factor in low-grade gliomas, whereas it showed no predictive value for chemotherapy response. Isocitrate dehydrogenase 1 mutation was highly associated with O 6-methylguanine DNA methyltransferase promoter methylation and TP53 mutation.

Project description:BackgroundAlthough much is known about molecular and chromosomal characteristics that distinguish glioma histological subtypes, DNA methylation patterns of gliomas and their association with other tumor features such as mutation of isocitrate dehydrogenase (IDH) genes have only recently begun to be investigated.MethodsDNA methylation of glioblastomas, astrocytomas, oligodendrogliomas, oligoastrocytomas, ependymomas, and pilocytic astrocytomas (n = 131) from the Brain Tumor Research Center at the University of California San Francisco, as well as nontumor brain tissues (n = 7), was assessed with the Illumina GoldenGate methylation array. Methylation data were subjected to recursively partitioned mixture modeling (RPMM) to derive methylation classes. Differential DNA methylation between tumor and nontumor was also assessed. The association between methylation class and IDH mutation (IDH1 and IDH2) was tested using univariate and multivariable analysis for tumors (n = 95) with available substrate for sequencing. Survival of glioma patients carrying mutant IDH (n = 57) was compared with patients carrying wild-type IDH (n = 38) using a multivariable Cox proportional hazards model and Kaplan-Meier analysis. All statistical tests were two-sided.ResultsWe observed a statistically significant association between RPMM methylation class and glioma histological subtype (P < 2.2 × 10(-16)). Compared with nontumor brain tissues, across glioma tumor histological subtypes, the differential methylation ratios of CpG loci were statistically significantly different (permutation P < .0001). Methylation class was strongly associated with IDH mutation in gliomas (P = 3.0 × 10(-16)). Compared with glioma patients whose tumors harbored wild-type IDH, patients whose tumors harbored mutant IDH showed statistically significantly improved survival (hazard ratio of death = 0.27, 95% confidence interval = 0.10 to 0.72).ConclusionThe homogeneity of methylation classes for gliomas with IDH mutation, despite their histological diversity, suggests that IDH mutation is associated with a distinct DNA methylation phenotype and an altered metabolic profile in glioma.

Project description:BackgroundAccurate classification of diffuse gliomas, the most common tumors of the central nervous system in adults, is important for appropriate treatment. However, detection of isocitrate dehydrogenase (IDH) mutation and chromosome1p/19q codeletion, biomarkers to classify gliomas, is time- and cost-intensive and diagnostic discordance remains an issue. Adenosine to inosine (A-to-I) RNA editing has emerged as a novel cancer prognostic marker, but its value for glioma classification remains largely unexplored. We aim to (1) unravel the relationship between RNA editing and IDH mutation and 1p/19q codeletion and (2) predict IDH mutation and 1p/19q codeletion status using machine learning algorithms.ResultsBy characterizing genome-wide A-to-I RNA editing signatures of 638 gliomas, we found that tumors without IDH mutation exhibited higher total editing level compared with those carrying it (Kolmogorov-Smirnov test, p?<?0.0001). When tumor grade was considered, however, only grade IV tumors without IDH mutation exhibited higher total editing level. According to 10-fold cross-validation, support vector machines (SVM) outperformed random forest and AdaBoost (DeLong test, p?<?0.05). The area under the receiver operating characteristic curve (AUC) of SVM in predicting IDH mutation and 1p/19q codeletion were 0.989 and 0.990, respectively. After performing feature selection, AUCs of SVM and AdaBoost in predicting IDH mutation were higher than that of random forest (0.985 and 0.983 vs. 0.977; DeLong test, p?<?0.05), but AUCs of the three algorithms in predicting 1p/19q codeletion were similar (0.976-0.982). Furthermore, 67% of the six continuously misclassified samples by our 1p/19q codeletion prediction models were misclassifications in the original labelling after inspection of 1p/19q status and/or pathology report, highlighting the accuracy and clinical utility of our models.ConclusionsThe study represents the first genome-wide analysis of glioma editome and identifies RNA editing as a novel prognostic biomarker for glioma. Our prediction models provide standardized, accurate, reproducible and objective classification of gliomas. Our models are not only useful in clinical decision-making, but also able to identify editing events that have the potential to serve as biomarkers and therapeutic targets in glioma management and treatment.

Project description:BackgroundIsocitrate dehydrogenase (IDH) mutation status is an important biomarker for the treatment strategy selection and prognosis evaluation of glioma. The purpose of this study is to predict the IDH mutation status of gliomas based on multicenter magnetic resonance (MR) images using radiomic models, which were composed from the selected radiomics features and logistic regression (LR), support vector machine (SVM), and LR least absolute shrinkage and selection operator (LASSO) classifiers.MethodsWe retrospectively reviewed the medical records of 205 patients with gliomas. We enrolled 78 patients from Shandong Provincial Hospital from January 2018 to December 2019 as testing sets and 127 patients from The Cancer Genome Atlas (TCGA) as training sets. Preoperative MR images were stratified according to their IDH status, and the participants formed a consecutive and random series. Four MR modalities, including T1C, T2, T1 fluid-attenuated inversion recovery (FLAIR), and T2 FLAIR, were used for analysis. Five-fold cross-validation was adopted to train the models, and the models' performances were verified through the testing set. Tumor volumes of interest (VOI) were delineated on the 4 MR modalities. A total of 428 radiomics features were extracted. Two feature selection algorithms, Pearson correlation coefficient (PCC) and recursive feature elimination (RFE), were used to select radiomics features. These features were fed into 3 machine learning classifiers, which were LR, SVM, and LR LASSO, to construct prediction models. The accuracy (ACC), sensitivity (SEN), specificity (SPEC), and area under the curve (AUC) were applied to measure the predictive performance of the radiomics models.ResultsThe LR (SVM and LR LASSO) classifier predicted IDH mutation status with an average testing set ACC of 80.77% (80.64% and 80.41%), a SEN of 73.68% (84.21% and 89.47%), a SPEC of 87.50% (67.50% and 62.50%), and an AUC of 0.8572 (0.8217 and 0.8164).ConclusionsThe radiomics models based on MR modalities demonstrated the potential to be used as tools across different data sets for the noninvasive prediction of the IDH mutation status in glioma.

Dataset Information

Methylation associated genes contribute to the favorable prognosis of gliomas with isocitrate dehydrogenase 1 mutation.

Publications

Methylation associated genes contribute to the favorable prognosis of gliomas with isocitrate dehydrogenase 1 mutation.

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