Dataset Information

Screening of Methylation Signature and Gene Functions Associated With the Subtypes of Isocitrate Dehydrogenase-Mutation Gliomas.

ABSTRACT: Isocitrate dehydrogenase (IDH) is an oncogene, and the expression of a mutated IDH promotes cell proliferation and inhibits cell differentiation. IDH exists in three different isoforms, whose mutation can cause many solid tumors, especially gliomas in adults. No effective method for classifying gliomas on genetic signatures is currently available. DNA methylation may be applied to distinguish cancer cells from normal tissues. In this study, we focused on three subtypes of IDH-mutation gliomas by examining methylation data. Several advanced computational methods were used, such as Monte Carlo feature selection (MCFS), incremental feature selection (IFS), support machine vector (SVM), etc. The MCFS method was adopted to analyze methylation features, resulting in a feature list. Then, the IFS method incorporating SVM was applied to the list to extract important methylation features and construct an optimal SVM classifier. As a result, several methylation features (sites) were found to relate to glioma subclasses, which are annotated onto multiple genes, such as FLJ37543, LCE3D, FAM89A, ADCY5, ESR1, C2orf67, REST, EPHA7, etc. These genes are enriched in biological functions, including cellular developmental process, neuron differentiation, cellular component morphogenesis, and G-protein-coupled receptor signaling pathway. Our results, which are supported by literature reports and independent dataset validation, showed that our identified genes and functions contributed to the detailed glioma subtypes. This study provided a basic research on IDH-mutation gliomas.

SUBMITTER: Pan X

PROVIDER: S-EPMC6871504 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Screening of Methylation Signature and Gene Functions Associated With the Subtypes of Isocitrate Dehydrogenase-Mutation Gliomas.

Pan XiaoYong X Zeng Tao T Yuan Fei F Zhang Yu-Hang YH Chen Lei L Zhu LiuCun L Wan SiBao S Huang Tao T Cai Yu-Dong YD

Frontiers in bioengineering and biotechnology 20191114

Isocitrate dehydrogenase (IDH) is an oncogene, and the expression of a mutated IDH promotes cell proliferation and inhibits cell differentiation. IDH exists in three different isoforms, whose mutation can cause many solid tumors, especially gliomas in adults. No effective method for classifying gliomas on genetic signatures is currently available. DNA methylation may be applied to distinguish cancer cells from normal tissues. In this study, we focused on three subtypes of IDH-mutation gliomas by ...[more]

PMID: 31803734

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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:BACKGROUND:Mutations in the isocitrate dehydrogenase 1 (IDH1) and IDH2 genes are important for both the integrated diagnosis and the prognosis of diffuse gliomas. The p.R132H mutation of IDH1 is the most frequently observed IDH mutation, while IDH2 mutations were relatively rarely studied. The aim of the study was to determine the pathological and genetic characteristics of lower-grade gliomas that carry IDH2 mutations. METHODS:Data from 238 adult patients with lower-grade gliomas were retrospectively analyzed. The status of IDH1/2 gene mutations, telomerase reverse transcriptase (TERT) promoter mutations, O-methylguanine-DNA-methyltransferase (MGMT) promoter methylation, 1p/19q co-deletion and the expressions of IDH1 R132H, alpha-thalassemia X-linked mental retardation, and p53 were evaluated. Progression-free survival (PFS) and overall survival (OS) were calculated via Kaplan-Meier estimation using the log-rank test. RESULTS:Totally, 71% (169/238) of patients were positive for IDH mutations, including 12 patients harboring mutations in IDH2. Among the 12 patients with IDH2 mutations, ten patients harbored the R172K mutation, one patient harbored the R172S mutation and one harbored the R172W mutation. Of these, 11 tumors occurred in the frontal lobe and showed morphology typical of oligodendroglioma. The proportion of grade II tumors was higher than that of grade III tumors in IDH2 mutant-gliomas. IDH2 mutations were frequently associated with TERT promoter mutations, 1p/19q co-deletion and MGMT promoter methylation. IDH2 mutations were associated with better outcomes compared with IDH wild-type gliomas (P?<?0.05). However, the PFS and OS did not differ from that of IDH1 mutant patients (P?=?0.95 and P?=?0.60, respectively). CONCLUSIONS:IDH2 mutations are more frequent in oligodendrogliomas and associated with a better prognosis. IDH2 mutations may segregate in distinct clinico-pathological and genetic subtypes of gliomas, and therefore may merit routine investigation.

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

Screening of Methylation Signature and Gene Functions Associated With the Subtypes of Isocitrate Dehydrogenase-Mutation Gliomas.

Publications

Screening of Methylation Signature and Gene Functions Associated With the Subtypes of Isocitrate Dehydrogenase-Mutation Gliomas.

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