Quality assurance in neuropathology: Experiences from the round robin trials on IDH mutation and MGMT promoter methylation testing launched by the Quality Assurance Initiative Pathology (QuIP) in 2018 and 2019

Riemenschneider, Markus J.; Fischer, Josephine; Grassow-Narlik, Maja; Mawrin, Christian; Deimling, Andreas von; Pietsch, Torsten; Reifenberger, Guido; Mueller, Wolf; Sommer, Clemens; Dietel, Manfred; Zoubaa, Saida; Lorenz, Julia; Rothhammer-Hampl, Tanja

doi:10.5414/np301278

Cited by 3 publications

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“…The patients' brain tumor diagnosis and WHO grading were determined at the local neuropathology department. Routine histopathology was accompanied by testing for IDH mutation (standard immunohistochemistry and Sanger sequencing) and MGMT promoter methylation status (methyQESD) [43,44]. Promoter methylation was considered as being present starting from a minimum of 3% methylation.…”

Section: Glioma Diagnosis and Molecular Parametersmentioning

confidence: 99%

Non-Invasive Prediction of IDH Mutation in Patients with Glioma WHO II/III/IV Based on F-18-FET PET-Guided In Vivo 1H-Magnetic Resonance Spectroscopy and Machine Learning

Bumes

Wirtz

Fellner

et al. 2020

Cancers

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Isocitrate dehydrogenase (IDH)-1 mutation is an important prognostic factor and a potential therapeutic target in glioma. Immunohistological and molecular diagnosis of IDH mutation status is invasive. To avoid tumor biopsy, dedicated spectroscopic techniques have been proposed to detect D-2-hydroxyglutarate (2-HG), the main metabolite of IDH, directly in vivo. However, these methods are technically challenging and not broadly available. Therefore, we explored the use of machine learning for the non-invasive, inexpensive and fast diagnosis of IDH status in standard 1H-magnetic resonance spectroscopy (1H-MRS). To this end, 30 of 34 consecutive patients with known or suspected glioma WHO grade II-IV were subjected to metabolic positron emission tomography (PET) imaging with O-(2-18F-fluoroethyl)-L-tyrosine (18F-FET) for optimized voxel placement in 1H-MRS. Routine 1H-magnetic resonance (1H-MR) spectra of tumor and contralateral healthy brain regions were acquired on a 3 Tesla magnetic resonance (3T-MR) scanner, prior to surgical tumor resection and molecular analysis of IDH status. Since 2-HG spectral signals were too overlapped for reliable discrimination of IDH mutated (IDHmut) and IDH wild-type (IDHwt) glioma, we used a nested cross-validation approach, whereby we trained a linear support vector machine (SVM) on the complete spectral information of the 1H-MRS data to predict IDH status. Using this approach, we predicted IDH status with an accuracy of 88.2%, a sensitivity of 95.5% (95% CI, 77.2–99.9%) and a specificity of 75.0% (95% CI, 42.9–94.5%), respectively. The area under the curve (AUC) amounted to 0.83. Subsequent ex vivo 1H-nuclear magnetic resonance (1H-NMR) measurements performed on metabolite extracts of resected tumor material (eight specimens) revealed myo-inositol (M-ins) and glycine (Gly) to be the major discriminators of IDH status. We conclude that our approach allows a reliable, non-invasive, fast and cost-effective prediction of IDH status in a standard clinical setting.

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Section: Glioma Diagnosis and Molecular Parametersmentioning

confidence: 99%

Non-Invasive Prediction of IDH Mutation in Patients with Glioma WHO II/III/IV Based on F-18-FET PET-Guided In Vivo 1H-Magnetic Resonance Spectroscopy and Machine Learning

Bumes

Wirtz

Fellner

et al. 2020

Cancers

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Clinical validation of a novel quantitative assay for the detection of MGMT methylation in glioblastoma patients

Rosas-Alonso¹,

Colmenarejo-Fernandez²,

Pernía³

et al. 2021

Clin Epigenet

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Background The promoter hypermethylation of the methylguanine-DNA methyltransferase gene is a frequently used biomarker in daily clinical practice as it is associated with a favorable prognosis in glioblastoma patients treated with temozolamide. Due to the absence of adequately standardized techniques, international harmonization of the MGMT methylation biomarker is still an unmet clinical need for the diagnosis and treatment of glioblastoma patients. Results In this study we carried out a clinical validation of a quantitative assay for MGMT methylation detection by comparing a novel quantitative MSP using double-probe (dp_qMSP) with the conventional MSP in 100 FFPE glioblastoma samples. We performed both technologies and established the best cutoff for the identification of positive-methylated samples using the quantitative data obtained from dp_qMSP. Kaplan–Meier curves and ROC time dependent curves were employed for the comparison of both methodologies. Conclusions We obtained similar results using both assays in the same cohort of patients, in terms of progression free survival and overall survival according to Kaplan–Meier curves. In addition, the results of ROC(t) curves showed that dp_qMSP increases the area under curve time-dependent in comparison with MSP for predicting progression free survival and overall survival over time. We concluded that dp_qMSP is an alternative methodology compatible with the results obtained with the conventional MSP. Our assay will improve the therapeutic management of glioblastoma patients, being a more sensitive and competitive alternative methodology that ensures the standardization of the MGMT-biomarker making it reliable and suitable for clinical use.

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Hirntumordiagnostik: Neue Entwicklungen und Zukunftsperspektiven

Mauch-Mücke¹,

Pohl²,

Riemenschneider³

2022

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Quality assurance in neuropathology: Experiences from the round robin trials on IDH mutation and MGMT promoter methylation testing launched by the Quality Assurance Initiative Pathology (QuIP) in 2018 and 2019

Cited by 3 publications

References 0 publications

Non-Invasive Prediction of IDH Mutation in Patients with Glioma WHO II/III/IV Based on F-18-FET PET-Guided In Vivo 1H-Magnetic Resonance Spectroscopy and Machine Learning

Non-Invasive Prediction of IDH Mutation in Patients with Glioma WHO II/III/IV Based on F-18-FET PET-Guided In Vivo 1H-Magnetic Resonance Spectroscopy and Machine Learning

Clinical validation of a novel quantitative assay for the detection of MGMT methylation in glioblastoma patients

Hirntumordiagnostik: Neue Entwicklungen und Zukunftsperspektiven

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