scholarly journals Clinical implications of TERT promoter mutation on IDH mutation and MGMT promoter methylation in diffuse gliomas

2018 ◽  
Vol 214 (6) ◽  
pp. 881-888 ◽  
Author(s):  
Hyun Sik Kim ◽  
Mi Jung Kwon ◽  
Joon Ho Song ◽  
Eun Soo Kim ◽  
Ho Young Kim ◽  
...  
2020 ◽  
Vol 22 (Supplement_2) ◽  
pp. ii14-ii14
Author(s):  
Dorothee Gramatzki ◽  
Jörg Felsberg ◽  
Bettina Hentschel ◽  
Marietta Wolter ◽  
Gabriele Schackert ◽  
...  

Abstract BACKGROUND Benefit from temozolomide (TMZ) chemotherapy in the treatment of isocitrate dehydrogenase (IDH)-wildtype glioblastoma is essentially limited to patients with O6-methylguanine DNA methyltransferase (MGMT) promoter-methylated tumors. Recent studies suggest that the impact of the MGMT status on chemosensitivity may be modulated by telomerase reverse transcriptase (TERT) promoter hotspot mutations. METHODS MGMT promoter methylation and TERT promoter mutation status were assessed in an exploratory prospective cohort of IDH-wildtype glioblastoma patients of the German Glioma Network (GGN) (n=298) and validated in a retrospective cohort from Düsseldorf, Germany, and Zurich, Switzerland (n=302). RESULTS In the prospective GGN discovery cohort of patients with MGMT promoter-unmethylated tumors, TERT promoter mutation showed inferior outcome (p=0.044). In contrast, TERT promoter mutations were not associated with improved outcome in patients with MGMT promoter-methylated tumors. Different TERT promoter hotspot mutations were not associated with distinct outcomes. The association of TERT promoter mutation in MGMT promoter-unmethylated tumors was not confirmed in the retrospective validation cohort. CONCLUSIONS Analysis of two independent cohorts of glioblastoma patients, including the prospective GGN cohort, did not confirm previous data suggesting that TERT promoter mutations confer an enhanced benefit from TMZ in patients with MGMT promoter-methylated glioblastoma. Thus, diagnostic testing for TERT promoter mutations may not be required for prediction of TMZ sensitivity in IDH-wildtype glioblastoma patients.


2017 ◽  
Vol 471 (5) ◽  
pp. 641-649 ◽  
Author(s):  
Ekkehard Hewer ◽  
Nadine Prebil ◽  
Sabina Berezowska ◽  
Marielena Gutt-Will ◽  
Philippe Schucht ◽  
...  

Author(s):  
Beomseok Sohn ◽  
Chansik An ◽  
Dain Kim ◽  
Sung Soo Ahn ◽  
Kyunghwa Han ◽  
...  

Abstract Purpose In glioma, molecular alterations are closely associated with disease prognosis. This study aimed to develop a radiomics-based multiple gene prediction model incorporating mutual information of each genetic alteration in glioblastoma and grade 4 astrocytoma, IDH-mutant. Methods From December 2014 through January 2020, we enrolled 418 patients with pathologically confirmed glioblastoma (based on the 2016 WHO classification). All selected patients had preoperative MRI and isocitrate dehydrogenase (IDH) mutation, O-6-methylguanine-DNA methyltransferase (MGMT) promoter methylation, epidermal growth factor receptor amplification, and alpha-thalassemia/mental retardation syndrome X-linked (ATRX) loss status. Patients were randomly split into training and test sets (7:3 ratio). Enhancing tumor and peritumoral T2-hyperintensity were auto-segmented, and 660 radiomics features were extracted. We built binary relevance (BR) and ensemble classifier chain (ECC) models for multi-label classification and compared their performance. In the classifier chain, we calculated the mean absolute Shapley value of input features. Results The micro-averaged area under the curves (AUCs) for the test set were 0.804 and 0.842 in BR and ECC models, respectively. IDH mutation status was predicted with the highest AUCs of 0.964 (BR) and 0.967 (ECC). The ECC model showed higher AUCs than the BR model for ATRX (0.822 vs. 0.775) and MGMT promoter methylation (0.761 vs. 0.653) predictions. The mean absolute Shapley values suggested that predicted outcomes from the prior classifiers were important for better subsequent predictions along the classifier chains. Conclusion We built a radiomics-based multiple gene prediction chained model that incorporates mutual information of each genetic alteration in glioblastoma and grade 4 astrocytoma, IDH-mutant and performs better than a simple bundle of binary classifiers using prior classifiers’ prediction probability.


Oncotarget ◽  
2015 ◽  
Vol 6 (38) ◽  
pp. 40896-40906 ◽  
Author(s):  
Pei Yang ◽  
Wei Zhang ◽  
Yinyan Wang ◽  
Xiaoxia Peng ◽  
Baoshi Chen ◽  
...  

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