scholarly journals MRI-BASED DEEP LEARNING METHOD FOR DETERMINING METHYLATION STATUS OF THE O6-METHYLGUANINE–DNA METHYLTRANSFERASE PROMOTER OUTPERFORMS TISSUE BASED METHODS IN BRAIN GLIOMAS

2020 ◽  
Author(s):  
Chandan Ganesh Bangalore Yogananda ◽  
Bhavya R. Shah ◽  
Sahil S. Nalawade ◽  
Gowtham K. Murugesan ◽  
Frank F. Yu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Dna Methyltransferase ◽  
Cross Validation ◽  
Brain Mri ◽  
Methylation Status ◽  
Magnetic Resonance Images ◽  
The Cancer Genome Atlas ◽  
Tumor Segmentation ◽  
Methylguanine Dna Methyltransferase ◽  
Mgmt Promoter

ABSTRACTPURPOSEMethylation of the O6-Methylguanine-DNA Methyltransferase (MGMT) promoter results in epigenetic silencing of the MGMT enzyme and confers an improved prognosis and treatment response in gliomas. The purpose of this study was to develop a deep-learning network for determining the methylation status of the MGMT Promoter in gliomas using T2-w magnetic resonance images only.METHODSBrain MRI and corresponding genomic information were obtained for 247 subjects from The Cancer Imaging Archive (TCIA) and The Cancer Genome Atlas (TCGA). 163 subjects had a methylated MGMT promoter. A T2-w image only network (MGMT-net) was developed to determine MGMT promoter methylation status and simultaneous single label tumor segmentation. The network was trained using 3D-Dense-UNets. Three-fold cross-validation was performed to generalize the networks’ performance. Dice-scores were computed to determine tumor segmentation accuracy.RESULTSMGMT-net demonstrated a mean cross validation accuracy of 94.73% across the 3 folds (95.12%, 93.98%, and 95.12%, standard dev=0.66) in predicting MGMT methylation status with a sensitivity and specificity of 96.31% ±0.04 and 91.66% ±2.06, respectively and a mean AUC of 0.93 ±0.01. The whole tumor segmentation mean Dice-score was 0.82 ± 0.008.CONCLUSIONWe demonstrate high classification accuracy in predicting the methylation status of the MGMT promoter using only T2-w MR images that surpasses the sensitivity, specificity, and accuracy of invasive histological methods such as pyrosequencing, methylation-specific PCR, and immunofluorescence methods. This represents an important milestone toward using MRI to predict glioma histology, prognosis, and response to treatment.

scholarly journals A Novel Fully Automated Mri-Based Deep Learning Method for Classification of Idh Mutation Status in Brain Gliomas

2019 ◽  
Author(s):  
Chandan Ganesh Bangalore Yogananda ◽  
Bhavya R Shah ◽  
Maryam Vejdani-Jahromi ◽  
Sahil S Nalawade ◽  
Gowtham K Murugesan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cross Validation ◽  
Brain Mri ◽  
The Cancer Genome Atlas ◽  
Tumor Segmentation ◽  
Idh Mutation ◽  
Mr Images ◽  
Post Contrast ◽  
Mutation Status ◽  
Brain Gliomas

Abstract Background Isocitrate dehydrogenase (IDH) mutation status has emerged as an important prognostic marker in gliomas. Currently, reliable IDH mutation determination requires invasive surgical procedures. The purpose of this study was to develop a highly-accurate, MRI-based, voxel-wise deep-learning IDH-classification network using T2-weighted (T2w) MR images and compare its performance to a multi-contrast network. Methods Multi-parametric brain MRI data and corresponding genomic information were obtained for 214 subjects (94 IDH-mutated, 120 IDH wild-type) from The Cancer Imaging Archive (TCIA) and The Cancer Genome Atlas (TCGA). Two separate networks were developed including a T2w image only network (T2-net) and a multi-contrast (T2w, FLAIR, and T1 post-contrast) network (TS-net) to perform IDH classification and simultaneous single label tumor segmentation. The networks were trained using 3D-Dense-UNets. Three-fold cross-validation was performed to generalize the networks’ performance. ROC analysis was also performed. Dice-scores were computed to determine tumor segmentation accuracy. Results T2-net demonstrated a mean cross-validation accuracy of 97.14% ±0.04 in predicting IDH mutation status, with a sensitivity of 0.97 ±0.03, specificity of 0.98 ±0.01, and an AUC of 0.98 ±0.01.  TS-net achieved a mean cross-validation accuracy of 97.12% ±0.09, with a sensitivity of 0.98 ±0.02, specificity of 0.97 ±0.001, and an AUC of 0.99 ±0.01. The mean whole tumor segmentation Dice-scores were 0.85 ±0.009 for T2-net and 0.89 ±0.006 for TS-net. Conclusion We demonstrate high IDH classification accuracy using only T2-weighted MR images. This represents an important milestone towards clinical translation.

scholarly journals A novel fully automated MRI-based deep-learning method for classification of 1p/19q co-deletion status in brain gliomas

2020 ◽  
Vol 2 (1) ◽  
Author(s):  
Chandan Ganesh Bangalore Yogananda ◽  
Bhavya R Shah ◽  
Frank F Yu ◽  
Marco C Pinho ◽  
Sahil S Nalawade ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cross Validation ◽  
Brain Mri ◽  
Area Under The Curve ◽  
The Cancer Genome Atlas ◽  
Response To Treatment ◽  
Tumor Segmentation ◽  
Idh Mutation ◽  
Characteristic Analysis ◽  
Genomic Marker

Abstract Background One of the most important recent discoveries in brain glioma biology has been the identification of the isocitrate dehydrogenase (IDH) mutation and 1p/19q co-deletion status as markers for therapy and prognosis. 1p/19q co-deletion is the defining genomic marker for oligodendrogliomas and confers a better prognosis and treatment response than gliomas without it. Our group has previously developed a highly accurate deep-learning network for determining IDH mutation status using T2-weighted (T2w) MRI only. The purpose of this study was to develop a similar 1p/19q deep-learning classification network. Methods Multiparametric brain MRI and corresponding genomic information were obtained for 368 subjects from The Cancer Imaging Archive and The Cancer Genome Atlas. 1p/19 co-deletions were present in 130 subjects. Two-hundred and thirty-eight subjects were non-co-deleted. A T2w image-only network (1p/19q-net) was developed to perform 1p/19q co-deletion status classification and simultaneous single-label tumor segmentation using 3D-Dense-UNets. Three-fold cross-validation was performed to generalize the network performance. Receiver operating characteristic analysis was also performed. Dice scores were computed to determine tumor segmentation accuracy. Results 1p/19q-net demonstrated a mean cross-validation accuracy of 93.46% across the 3 folds (93.4%, 94.35%, and 92.62%, SD = 0.8) in predicting 1p/19q co-deletion status with a sensitivity and specificity of 0.90 ± 0.003 and 0.95 ± 0.01, respectively and a mean area under the curve of 0.95 ± 0.01. The whole tumor segmentation mean Dice score was 0.80 ± 0.007. Conclusion We demonstrate high 1p/19q co-deletion classification accuracy using only T2w MR images. This represents an important milestone toward using MRI to predict glioma histology, prognosis, and response to treatment.

scholarly journals Tim-3 protein expression with MGMT methylation status in glioblastoma and association with survival

2020 ◽  
Author(s):  
ji zhang ◽  
Xiaoli Wang ◽  
Shengquan Ye ◽  
Lijiao Liang ◽  
Yi Zhou ◽  
...  
Keyword(s):  
Protein Expression ◽  
Promoter Methylation ◽  
Immune Checkpoint ◽  
Dna Methyltransferase ◽  
Methylation Status ◽  
Prognostic Significance ◽  
Mgmt Promoter Methylation ◽  
Mgmt Methylation ◽  
Methylguanine Dna Methyltransferase ◽  
Mgmt Promoter

Abstract Background Understanding the molecular landscape of glioblastoma (GBM) is increasingly crucial for its therapy. Immune checkpoint molecules motivated the emergence of immune checkpoint-targeting therapeutic strategies. However, the prognostic significance of the immune checkpoint molecule T cell immunoglobulin mucin-3 (Tim-3) on tumor-infiltrating immune cells (TIICs) and O-6-Methylguanine-DNA methyltransferase (MGMT) methylation status remains to be fully elucidated. We aimed to develop an MGMT methylation status-associated immune prognostic signature for predicting prognosis in GBMs.Patients and Methods: A total of 84 patients with newly diagnosed GBM were involved. MGMT methylation status was retrospectively analyzed and the expression level of Tim-3 protein was investigated using immunohistochemistry (IHC). The correlation between Tim-3 protein expression and MGMT methylation status, and the prognosis was explored.Results The obtained data showed that Tim-3 protein was expressed at different levels in GBMs. Mesenchymal expression of Tim-3 protein in these tissues was 73.81% (62/84), including low 15.48% (13/84), moderate 7.14% (6/84) and strong expression 51.19% (43/84), respectively. Of the 48 patients whose tumors tested positive for MGMT methylation, the remaining 36 patients was negative.Conclusions We profiled the immune status in GBM with MGMT promoter methylation and established a local immune signature for GBM, which could independently identify patients with a favorable prognosis, indicating the relationship between prognosis and immune. MGMT promoter methylation with lower Tim-3 protein expression was statistically significantly associated with better survival.

Consistency of O6-methylguanine-DNA methyltransferase in intracranial and extracranial lesions and the therapeutic effect of temozolomide in patients with advanced lung cancer and brain metastasis.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e14001-e14001
Author(s):  
Ying Fan ◽  
Qian Yun Shan ◽  
Jia Li Gong ◽  
Na Han ◽  
Hongyang Lu
Keyword(s):  
Lung Cancer ◽  
Side Effects ◽  
Promoter Methylation ◽  
Dna Methyltransferase ◽  
Methylation Status ◽  
Small Cell ◽  
Advanced Lung Cancer ◽  
Small Cell Lung ◽  
Methylguanine Dna Methyltransferase ◽  
Mgmt Promoter

e14001 Background: Targeted drugs have made a major breakthrough except small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC) of brain metastasis (BM) patients with negative driving gene. Because of the difficulty to obtain pathology of intracranial metastases lesions and the low acceptance of patients, the heterogeneity of O6-methylguanine-DNA methyltransferase (MGMT) between extracranial lesions and intracranial metastatic lesions worth exploring. Methods: 10 patients were administered TMZ after the progression of radiotherapy and chemotherapy and included in one group; the efficacy and safety of TMZ were assessed. Moreover, 15 patients who underwent resection of the lung and brain without neoadjuvant therapy formed another cohort. The promoter methylation status of MGMT was analyzed and compared in the paired primary and BM lesions. Results: The overall response (OS) rate of TMZ was 50% and the disease control rate was 80% in patients with BM lesions. The median progression-free survival was 13.6 months, and the median overall survival was 18.9 months after a median follow-up of 120 months. No grade 3 or higher side effects were reported, but grade 1 or 2 side effects, such as leucopenia, nausea, or vomiting, occurred. However, in another cohort, MGMT promoter methylation was detected in 2 patients (with primary lesions; unmethylation was detected in BM lesions, and the methylation status of MGMT promoter was consistent in paired primary and BM lesions).Conclusions: TMZ was effective against SCLC and NSCLC combined with BM having negative driving genes. Whether primary lesions can replace the MGMT methylation level of BM lesions, as well as the consistency of the promoter methylation status of MGMT in patients with advanced lung cancer, needs exploration.[Table: see text]

scholarly journals NCOG-29. O6-METHYLGUANINE DNA METHYLTRANSFERASE (MGMT) PROMOTER METHYLATION STATUS AND THE INCIDENCE OF TEMOZOLOMIDE-INDUCED HEMATOLOGIC TOXICITY: AN ASSOCIATION STUDY

2020 ◽  
Vol 22 (Supplement_2) ◽  
pp. ii135-ii135
Author(s):  
Alyssa Strohbusch ◽  
Heather Pound ◽  
Patrick Regis ◽  
Robert Cavaliere
Keyword(s):  
Promoter Methylation ◽  
Dna Methyltransferase ◽  
Statistical Difference ◽  
Methylation Status ◽  
Mgmt Promoter Methylation ◽  
Hematologic Toxicity ◽  
Methylguanine Dna Methyltransferase ◽  
Mgmt Promoter ◽  
Grade 3 ◽  
Mgmt Promoter Methylation Status

Abstract BACKGROUND Despite the growing body of evidence demonstrating an increased clinical benefit of alkylating agents in glioblastoma patients with O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation, little is known regarding the effect of MGMT status on toxicity. The purpose of this investigation is to provide insight into the potential association between MGMT promoter methylation and the development of Grade 3/4 hematologic toxicities in patients receiving temozolomide. METHODS A total of 63 patients with documented glioblastoma or anaplastic astrocytoma diagnoses were evaluated retrospectively. A chi-square test of independence was utilized to evaluate the incidence of hematologic toxicities and patient overall survival was determined by univariate analysis estimated through use of Kaplan-Meier curves. Relative frequencies of treatment discontinuation secondary to myelosuppression were also compared. RESULTS At the time of study completion, 71.4% of patients with MGMT promoter hypermethylation had survived compared with 50% of patients with hypomethylation (HR 0.86; 95% CI 0.71 to 1.05; P>0.05). The percentage of patients who experienced Grade 3/4 hematologic toxicities during concurrent treatment was 20% and 5.1% within the hypermethylated and hypomethylated groups, respectively, and 26.7% and 17.9% throughout standard therapy. While a statistical difference was not found between the cumulative incidences of Grade 3/4 hematologic toxicity events throughout both phases of treatment, a statistical difference was noted in the incidence of Grade 4 occurrences with ten events occurring in the hypermethylated group and zero in those with hypomethylated promoter regions (P< 0.01). Furthermore, use of temozolomide in hypermethylated patients resulted in fewer completed cycles of standard therapy and higher rates of treatment delays and drug discontinuation. CONCLUSIONS This study showed marked differences in the frequency of temozolomide-induced hematologic toxicities and treatment discontinuation based on tumor MGMT promoter methylation status. Further research is warranted in larger patient populations to both validate and determine the clinical significance of these findings.

scholarly journals Methylation of MGMT promoter does not predict response to temozolomide in patients with glioblastoma in Donostia Hospital

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Larraitz Egaña ◽  
Jaione Auzmendi-Iriarte ◽  
Joaquin Andermatten ◽  
Jorge Villanua ◽  
Irune Ruiz ◽  
...  
Keyword(s):  
Promoter Methylation ◽  
Dna Methyltransferase ◽  
Methylation Status ◽  
Progression Free Survival ◽  
Mgmt Promoter Methylation ◽  
Mgmt Methylation ◽  
Free Survival ◽  
Methylguanine Dna Methyltransferase ◽  
Mgmt Promoter ◽  
Newly Diagnosed Glioblastoma

Abstract O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status has been considered a prognostic factor in newly diagnosed glioblastoma (GBM). In this study, we evaluated the prognostic and predictive value of MGMT promoter methylation in patients with glioblastoma in Donostia Hospital. Surprisingly, methylation of MGMT promoter did not predict response to temozolomide in patients with glioblastoma in Donostia Hospital. Specifically, overall survival (OS) and progression-free survival (PFS) did not differ significantly by MGMT methylation status in our cohort. In contrast, both were longer in patients who received treatment, received more TMZ cycles, had a better general status and perform at least a partial resection. No association was detected between methylation of MGMT promoter and molecular markers such as ATRX, IDH, p53 and Ki67. These results indicate that MGMT methylation did not influence in patient survival in our cohort.

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