A Qualitative Signature to Identify TERT Promoter Mutant High-Risk Tumors in Low-Grade Gliomas

Zheng, Wenfang; Zhang, Ruolan; Huang, Ziru; Li, Jianpeng; Wu, Haonan; Zhou, Yuwei; Zhu, Jin; Wang, Xianlong

doi:10.3389/fmolb.2022.806727

Cited by 3 publications

(1 citation statement)

References 43 publications

(58 reference statements)

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“…TERT-p mutations are frequently found in low-grade and glioblastoma tumors, but they are associated with a contrary prognosis. In low-grade gliomas (Grades 2 and 3), TERT-p mutant patients have a better prognosis than wildtype patients, but in GBs (Grade 4), TERT-p mutations are associated with poor outcomes [ 25 , 26 ]. Approximately 83% of primary GBs [ 27 ] have a specific mutation where a C nucleotide is changed to a T nucleotide in the promoter region of the TERT gene [ 28 ].…”

Section: Introductionmentioning

confidence: 99%

Genetic Prognostic Factors in Adult Diffuse Gliomas: A 10-Year Experience at a Single Institution

Barzegar Behrooz,

Darzi Ramandi,

Latifi-Navid

et al. 2024

Cancers

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Gliomas are primary brain lesions involving cerebral structures without well-defined boundaries and constitute the most prevalent central nervous system (CNS) neoplasms. Among gliomas, glioblastoma (GB) is a glioma of the highest grade and is associated with a grim prognosis. We examined how clinical variables and molecular profiles may have affected overall survival (OS) over the past ten years. A retrospective study was conducted at Sina Hospital in Tehran, Iran and examined patients with confirmed glioma diagnoses between 2012 and 2020. We evaluated the correlation between OS in GB patients and sociodemographic as well as clinical factors and molecular profiling based on IDH1, O-6-Methylguanine-DNA Methyltransferase (MGMT), TERTp, and epidermal growth factor receptor (EGFR) amplification (EGFR-amp) status. Kaplan–Meier and multivariate Cox regression models were used to assess patient survival. A total of 178 patients were enrolled in the study. The median OS was 20 months, with a 2-year survival rate of 61.0%. Among the 127 patients with available IDH measurements, 100 (78.7%) exhibited mutated IDH1 (IDH1-mut) tumors. Of the 127 patients with assessed MGMT promoter methylation (MGMTp-met), 89 (70.1%) had MGMT methylated tumors. Mutant TERTp (TERTp-mut) was detected in 20 out of 127 cases (15.7%), while wildtype TERTp (wildtype TERTp-wt) was observed in 107 cases (84.3%). Analyses using multivariable models revealed that age at histological grade (p < 0.0001), adjuvant radiotherapy (p < 0.018), IDH1 status (p < 0.043), and TERT-p status (p < 0.014) were independently associated with OS. Our study demonstrates that patients with higher tumor histological grades who had received adjuvant radiotherapy exhibited IDH1-mut or presented with TERTp-wt experienced improved OS. Besides, an interesting finding showed an association between methylation of MGMTp and TERTp status with tumor location.

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Section: Introductionmentioning

confidence: 99%

Genetic Prognostic Factors in Adult Diffuse Gliomas: A 10-Year Experience at a Single Institution

Barzegar Behrooz,

Darzi Ramandi,

Latifi-Navid

et al. 2024

Cancers

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Implications of TERT promoter mutations and telomerase activity in solid tumors with a focus on genitourinary cancers

Marchese

Mollica

Tassinari

et al. 2022

Expert Review of Molecular Diagnostics

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Prediction of TERT mutation status in gliomas using conventional MRI radiogenomic features

Tang,

Chen,

et al. 2024

Front. Neurol.

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ObjectiveTelomerase reverse transcriptase (TERT) promoter mutation status in gliomas is a key determinant of treatment strategy and prognosis. This study aimed to analyze the radiogenomic features and construct radiogenomic models utilizing medical imaging techniques to predict the TERT promoter mutation status in gliomas.MethodsThis was a retrospective study of 304 patients with gliomas. T1-weighted contrast-enhanced, apparent diffusion coefficient, and diffusion-weighted imaging MRI sequences were used for radiomic feature extraction. A total of 3,948 features were extracted from MRI images using the FAE software. These included 14 shape features, 18 histogram features, 24 gray level run length matrix, 14 gray level dependence matrix, 16 gray level run length matrix, 16 gray level size zone matrix (GLSZM), 5 neighboring gray tone difference matrix, and 744 wavelet transforms. The dataset was randomly divided into training and testing sets in a ratio of 7:3. Three feature selection methods and six classification algorithms were used to model the selected features. Predictive performance was evaluated using receiver operating characteristic curve analysis.ResultsAmong the evaluated classification algorithms, the combination model of recursive feature elimination (RFE) with linear regression (LR) using six features showed the best diagnostic performance (area under the curve: 0.733, 0.562, and 0.633 in the training, validation, and testing sets, respectively). The next best-performing models were naive Bayes, linear discriminant analysis, autoencoder, and support vector machine. Regarding the three feature selection algorithms, RFE showed the most consistent performance, followed by relief and ANOVA. T1-enhanced entropy and GLSZM derived from T1-enhanced images were identified as the most critical radiomics features for distinguishing TERT promoter mutation status.ConclusionThe LR and LRLasso models, mainly based on T1-enhanced entropy and GLSZM, showed good predictive ability for TERT promoter mutations in gliomas using radiomics models.

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A Qualitative Signature to Identify TERT Promoter Mutant High-Risk Tumors in Low-Grade Gliomas

Cited by 3 publications

References 43 publications

Genetic Prognostic Factors in Adult Diffuse Gliomas: A 10-Year Experience at a Single Institution

Genetic Prognostic Factors in Adult Diffuse Gliomas: A 10-Year Experience at a Single Institution

Implications of TERT promoter mutations and telomerase activity in solid tumors with a focus on genitourinary cancers

Prediction of TERT mutation status in gliomas using conventional MRI radiogenomic features

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