Dataset Information

Genome-Wide Analysis of Glioblastoma Patients with Unexpectedly Long Survival.

ABSTRACT: Glioblastoma (GBM), representing WHO grade IV astrocytoma, is a relatively common primary brain tumor in adults with an exceptionally dismal prognosis. With an incidence rate of over 10 000 cases in the United States annually, the median survival rate ranges from 10-15 months in IDH1/2-wildtype tumors and 24-31 months in IDH1/2-mutant tumors, with further variation depending on factors such as age, MGMT methylation status, and treatment regimen. We present a cohort of 4 patients, aged 37-60 at initial diagnosis, with IDH1-mutant GBMs that were associated with unusually long survival intervals after the initial diagnosis, currently ranging from 90 to 154 months (all still alive). We applied genome-wide profiling with a methylation array (Illumina EPIC Array 850k) and a next-generation sequencing panel to screen for genetic and epigenetic alterations in these tumors. All 4 tumors demonstrated methylation patterns and genomic alterations consistent with GBM. Three out of four cases showed focal amplification of the CCND2 gene or gain of the region on 12p that included CCND2, suggesting that this may be a favorable prognostic factor in GBM. As this study has a limited sample size, further evaluation of patients with similar favorable outcome is warranted to validate these findings.

SUBMITTER: Richardson TE

PROVIDER: S-EPMC9891105 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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Genome-Wide Analysis of Glioblastoma Patients with Unexpectedly Long Survival.

Richardson Timothy E TE Patel Seema S Serrano Jonathan J Sathe Adwait Amod AA Daoud Elena V EV Oliver Dwight D Maher Elizabeth A EA Madrigales Alejandra A Mickey Bruce E BE Taxter Timothy T Jour George G White Charles L CL Raisanen Jack M JM Xing Chao C Snuderl Matija M Hatanpaa Kimmo J KJ

Journal of neuropathology and experimental neurology 20190601 6

Glioblastoma (GBM), representing WHO grade IV astrocytoma, is a relatively common primary brain tumor in adults with an exceptionally dismal prognosis. With an incidence rate of over 10 000 cases in the United States annually, the median survival rate ranges from 10-15 months in IDH1/2-wildtype tumors and 24-31 months in IDH1/2-mutant tumors, with further variation depending on factors such as age, MGMT methylation status, and treatment regimen. We present a cohort of 4 patients, aged 37-60 at i ...[more]

PMID: 31034050

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Project description:Glioblastoma multiforme (GBM) has been recognized as the most lethal type of malignant brain tumor. Despite efforts of the medical and research community, patients' survival remains extremely low. Multi-omic profiles (including DNA sequence, methylation and gene expression) provide rich information about the tumor. These profiles are likely to reveal processes that may be predictive of patient survival. However, the integration of multi-omic profiles, which are high dimensional and heterogeneous in nature, poses great challenges. The goal of this work was to develop models for prediction of survival of GBM patients that can integrate clinical information and multi-omic profiles, using multi-layered Bayesian regressions. We apply the methodology to data from GBM patients from The Cancer Genome Atlas (TCGA, n = 501) to evaluate whether integrating multi-omic profiles (SNP-genotypes, methylation, copy number variants and gene expression) with clinical information (demographics as well as treatments) leads to an improved ability to predict patient survival. The proposed Bayesian models were used to estimate the proportion of variance explained by clinical covariates and omics and to evaluate prediction accuracy in cross validation (using the area under the Receiver Operating Characteristic curve, AUC). Among clinical and demographic covariates, age (AUC = 0.664) and the use of temozolomide (AUC = 0.606) were the most predictive of survival. Among omics, methylation (AUC = 0.623) and gene expression (AUC = 0.593) were more predictive than either SNP (AUC = 0.539) or CNV (AUC = 0.547). While there was a clear association between age and methylation, the integration of age, the use of temozolomide, and either gene expression or methylation led to a substantial increase in AUC in cross-validaton (AUC = 0.718). Finally, among the genes whose methylation was higher in aging brains, we observed a higher enrichment of these genes being also differentially methylated in cancer.

Project description:Glioblastoma multiforme (GBM) is the most common and aggressive adult primary brain cancer, with <10% of patients surviving for more than 3 years. Demographic and clinical factors (e.g. age) and individual molecular biomarkers have been associated with prolonged survival in GBM patients. However, comprehensive systems-level analyses of molecular profiles associated with long-term survival (LTS) in GBM patients are still lacking. We present an integrative study of molecular data and clinical variables in these long-term survivors (LTSs, patients surviving >3 years) to identify biomarkers associated with prolonged survival, and to assess the possible similarity of molecular characteristics between LGG and LTS GBM. We analyzed the relationship between multivariable molecular data and LTS in GBM patients from the Cancer Genome Atlas (TCGA), including germline and somatic point mutation, gene expression, DNA methylation, copy number variation (CNV) and microRNA (miRNA) expression using logistic regression models. The molecular relationship between GBM LTS and LGG tumors was examined through cluster analysis. We identified 13, 94, 43, 29, and 1 significant predictors of LTS using Lasso logistic regression from the somatic point mutation, gene expression, DNA methylation, CNV, and miRNA expression data sets, respectively. Individually, DNA methylation provided the best prediction performance (AUC = 0.84). Combining multiple classes of molecular data into joint regression models did not improve prediction accuracy, but did identify additional genes that were not significantly predictive in individual models. PCA and clustering analyses showed that GBM LTS typically had gene expression profiles similar to non-LTS GBM. Furthermore, cluster analysis did not identify a close affinity between LTS GBM and LGG, nor did we find a significant association between LTS and secondary GBM. The absence of unique LTS profiles and the lack of similarity between LTS GBM and LGG, indicates that there are multiple genetic and epigenetic pathways to LTS in GBM patients.

Dataset Information

Genome-Wide Analysis of Glioblastoma Patients with Unexpectedly Long Survival.

Publications

Genome-Wide Analysis of Glioblastoma Patients with Unexpectedly Long Survival.

Similar Datasets

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