Background: Predictive factors of benefit from specific chemotherapy regimens are not currently available in triple-negative breast cancer (TNBC). MGMT (O(6)-methylguanine-DNA methyltransferase) controls DNA repair pathways, and its epigenetic silencing is used for predicting the response to the alkylating drug temozolomide in patients with glioma.
Materials And Methods: The study population was composed of 84 patients with TNBC treated with alkylating agents and evaluated for clinicopathologic parameters (tumor shrinkage and pathologic complete response [pCR]). MGMT methylation status was assessed in formalin-fixed, paraffin-embedded tumor specimens by pyrosequencing. The samples were categorized as methylated (mean methylation value > 5%), indeterminate (4%-5%), and unmethylated (≤ 3%).
Results: MGMT methylation status was successfully evaluated in all the cases: 58.3% were methylated; 27.4%, unmethylated; and 14.3%, indeterminate. MGMT methylation was observed in 80%, 62%, and 29% of patients showing a 100%, 99% to 30%, and < 30% tumor reduction, respectively, a trend not achieving statistical significance (P = .23). There was no association between MGMT methylation status and pCR.
Conclusion: The present study provided evidence that pyrosequencing performs well for the evaluation of MGMT methylation even in small bioptic samples, suggesting that it could be reliably used in translational studies of preoperative clinical trials. Although there was an association trend between high methylation levels and clinical response to therapy, no statistically significant association with the pCR was found. Further studies in larger series of patients are warranted for ascertaining the putative clinical role of MGMT in patients with TNBC.
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http://dx.doi.org/10.1016/j.clbc.2014.02.010 | DOI Listing |
Biomedicines
December 2024
Life and Health Sciences Research Group, Graduate School, CES University, Medellín 050021, Colombia.
Introduction: The treatment for patients with high-grade gliomas includes surgical resection of tumor, radiotherapy, and temozolomide chemotherapy. However, some patients do not respond to temozolomide due to a methylation reversal mechanism by the enzyme O-methylguanine-DNA-methyltransferase (MGMT). In patients receiving treatment with temozolomide, this biomarker has been used as a prognostic factor.
View Article and Find Full Text PDFBiomedicines
November 2024
Cancer Epidemiology and Cancer Services Research, Centre for Cancer, Society & Public Health, Bermondsey Wing, King's College London, 3rd Floor, Guy's Hospital, Great Maze Pond, London SE1 9RT, UK.
Molecular profiles can predict which patients will respond to current standard treatment and new targeted therapy regimens. Using data from a highly diverse population of approximately three million in Southeast London and Kent, this study aims to evaluate the prevalence of IDH1 mutation and MGMT promoter methylation in the gliomas diagnosed in adult patients and to explore correlations with patients' demographic and clinicopathological characteristics. Anonymised data on 749 adult patients diagnosed with a glioma in 2015-2019 at King's College Hospital were extracted.
View Article and Find Full Text PDFBiomedicines
November 2024
Department of Neurosurgery, University of Florida, Gainesville, FL 32608, USA.
Glioblastoma multiforme (GBM), a WHO grade 4 glioma, is the most common and aggressive primary brain tumor, characterized by rapid progression and poor prognosis. The heterogeneity of GBM complicates diagnosis and treatment, driving research into molecular biomarkers that can offer insights into tumor behavior and guide personalized therapies. This review explores recent advances in molecular biomarkers, highlighting their potential to improve diagnosis and treatment outcomes in GBM patients.
View Article and Find Full Text PDFCancers (Basel)
December 2024
Department of Neurosurgery, Mount Sinai Health System, New York, NY 10029, USA.
Background/objectives: Glioblastoma (GBM) is the most common malignant primary central nervous system tumor with extremely poor prognosis and survival outcomes. Non-invasive methods like radiomic feature extraction, which assess sub-visual imaging features, provide a potentially powerful tool for distinguishing molecular profiles across groups of patients with GBM. Using consensus clustering of MRI-based radiomic features, this study aims to investigate differential gene expression profiles based on radiomic clusters.
View Article and Find Full Text PDFBioinform Biol Insights
January 2025
Department of Pathology & Clinical Bioinformatics, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands.
While deep learning (DL) is used in patients' outcome predictions, the insufficiency of patient samples limits the accuracy. In this study, we investigated how transfer learning (TL) alleviates the small sample size problem. A 2-step TL framework was constructed for a difficult task: predicting the response of the drug temozolomide (TMZ) in glioblastoma (GBM) cell cultures.
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