scholarly journals Recycling drug screen repurposes hydroxyurea as a sensitizer of glioblastomas to temozolomide targeting de novo DNA synthesis, irrespective of molecular subtype

2017 ◽  
Vol 20 (5) ◽  
pp. 642-654 ◽  
Author(s):  
Jian Teng ◽  
Seyedali Hejazi ◽  
Lotte Hiddingh ◽  
Litia Carvalho ◽  
Mark C de Gooijer ◽  
...  
Keyword(s):  
Promoter Methylation ◽  
Dna Methyltransferase ◽  
Methylation Status ◽  
Primary Cultures ◽  
Mgmt Promoter Methylation ◽  
In Vivo Models ◽  
Promoter Methylation Status ◽  
Mgmt Promoter ◽  
Mgmt Promoter Methylation Status

Abstract Background Glioblastoma (GBM) is the most common and most aggressive primary malignant brain tumor. Standard-of-care treatment involves maximal surgical resection of the tumor followed by radiation and chemotherapy (temozolomide [TMZ]). The 5-year survival rate of patients with GBM is <10%, a colossal failure that has been partially attributed to intrinsic and/or acquired resistance to TMZ through O6-methylguanine DNA methyltransferase (MGMT) promoter methylation status in the tumor. Methods A drug screening aimed at evaluating the potential recycling and repurposing of known drugs was conducted in TMZ-resistant GBM cell lines and primary cultures of newly diagnosed GBM with different MGMT promoter methylation status, phenotypic/genotypic background and subtype, and validated with sphere formation, cell migration assays, and quantitative invasive orthotopic in vivo models. Results We identified hydroxyurea (HU) to synergize with TMZ in GBM cells in culture and in vivo, irrespective of MGMT promoter methylation status, subtype, and/or stemness. HU acts specifically on the S-phase of the cell cycle by inhibiting the M2 unit of enzyme ribonucleotide reductase. Knockdown of this enzyme using RNA interference and other known chemical inhibitors exerted a similar effect to HU in combination with TMZ both in culture and in vivo. Conclusions We demonstrate preclinical efficacy of repurposing hydroxyurea in combination with TMZ for adjuvant GBM therapy. This combination benefit is of direct clinical interest given the extensive use of TMZ and the associated problems with TMZ-related resistance and treatment failure.

scholarly journals XGBoost Improves Classification of MGMT Promoter Methylation Status in IDH1 Wildtype Glioblastoma

2020 ◽  
Vol 10 (3) ◽  
pp. 128 ◽  
Author(s):  
Nguyen Quoc Khanh Le ◽  
Duyen Thi Do ◽  
Fang-Ying Chiu ◽  
Edward Kien Yee Yapp ◽  
Hui-Yuan Yeh ◽  
...  
Keyword(s):  
Promoter Methylation ◽  
Dna Methyltransferase ◽  
Methylation Status ◽  
Mgmt Promoter Methylation ◽  
Gradient Boosting ◽  
Mgmt Methylation ◽  
Promoter Methylation Status ◽  
Extreme Gradient Boosting ◽  
Mgmt Promoter ◽  
Mgmt Promoter Methylation Status

Approximately 96% of patients with glioblastomas (GBM) have IDH1 wildtype GBMs, characterized by extremely poor prognosis, partly due to resistance to standard temozolomide treatment. O6-Methylguanine-DNA methyltransferase (MGMT) promoter methylation status is a crucial prognostic biomarker for alkylating chemotherapy resistance in patients with GBM. However, MGMT methylation status identification methods, where the tumor tissue is often undersampled, are time consuming and expensive. Currently, presurgical noninvasive imaging methods are used to identify biomarkers to predict MGMT methylation status. We evaluated a novel radiomics-based eXtreme Gradient Boosting (XGBoost) model to identify MGMT promoter methylation status in patients with IDH1 wildtype GBM. This retrospective study enrolled 53 patients with pathologically proven GBM and tested MGMT methylation and IDH1 status. Radiomics features were extracted from multimodality MRI and tested by F-score analysis to identify important features to improve our model. We identified nine radiomics features that reached an area under the curve of 0.896, which outperformed other classifiers reported previously. These features could be important biomarkers for identifying MGMT methylation status in IDH1 wildtype GBM. The combination of radiomics feature extraction and F-core feature selection significantly improved the performance of the XGBoost model, which may have implications for patient stratification and therapeutic strategy in GBM.

scholarly journals NIMG-38. NON-INVASIVE PREDICTION OF MGMT PROMOTER METHYLATION USING COMBINED FET PET/MRI RADIOMICS

2020 ◽  
Vol 22 (Supplement_2) ◽  
pp. ii156-ii156
Author(s):  
Philipp Lohmann ◽  
Anna-Katharina Meissner ◽  
Jan-Michael Werner ◽  
Gabriele Stoffels ◽  
Martin Kocher ◽  
...  
Keyword(s):  
Promoter Methylation ◽  
Methylation Status ◽  
Mgmt Promoter Methylation ◽  
Test Dataset ◽  
Fet Pet ◽  
Promoter Methylation Status ◽  
Non Invasive ◽  
Mgmt Promoter ◽  
Amino Acid Pet ◽  
Mgmt Promoter Methylation Status

Abstract BACKGROUND Recently, the Response Assessment in Neuro-Oncology (RANO) Working Group emphasized the additional diagnostic value of amino acid PET in addition to MRI. However, the number of studies using amino acid PET/MRI radiomics is still low. We investigated the potential of combined O-(2-[18F]fluoroethyl)-L-tyrosine (FET) PET/MRI radiomics for the non-invasive prediction of the O6-methylguanine-DNA methyl-transferase (MGMT) promoter methylation status in glioma patients. METHODS Seventy-one patients with newly diagnosed glioma (predominantly WHO grade III and IV glioma, 82%) underwent a hybrid FET PET/MRI scan. Forty-six patients (65%) had a methylated MGMT promoter. The tumor and tumor subregions were manually segmented on conventional MRI. In total, 199 standardized features were obtained from FET PET, contrast-enhanced T1-weighted, T2-weighted, and fluid attenuated inversion recovery (FLAIR) MRI. After feature extraction and data normalization, patients were randomly assigned to a training and a test dataset for final model evaluation in a ratio of 70/30, with a balanced distribution of the MGMT promoter methylation status. Feature selection was performed by recursive feature elimination using random forest regressors. For the final model generation, the number of features was limited to seven to avoid data overfitting. Different algorithms for model generation were compared, and the model performance in the training data was assessed by 5-fold cross-validation. Finally, the best performing models were applied to the test dataset to evaluate the robustness of the models. RESULTS In the test dataset, the best radiomics signatures obtained from MRI or FET PET alone achieved diagnostic accuracies for the prediction of the MGMT promoter methylation of 64% and 70%, respectively. In contrast, the highest diagnostic accuracy of 83% was obtained by combining FET PET and MRI features. CONCLUSION Combined FET PET/MRI radiomics allows the non-invasive prediction of the MGMT promoter methylation status in patients with gliomas, providing more diagnostic information than either modality alone.

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