The therapeutic efficacy of temozolomide (TMZ) is hindered by inherent and acquired resistance. Biomarkers such as MGMT expression and MMR proficiency are used as predictors of response. However, not all MGMT/MMR patients benefit from TMZ treatment, indicating a need for additional patient selection criteria. We explored the role of ATR in mediating TMZ resistance and whether ATR inhibitors (ATRi) could reverse this resistance in multiple cancer lines. We observed that only 31% of MGMT/MMR patient-derived and established cancer lines are sensitive to TMZ at clinically relevant concentrations. TMZ treatment resulted in DNA damage signaling in both sensitive and resistant lines, but prolonged G/M arrest and cell death were exclusive to sensitive models. Inhibition of ATR but not ATM, sensitized the majority of resistant models to TMZ and resulted in measurable DNA damage and persistent growth inhibition. Also, compromised homologous recombination (HR) via RAD51 or BRCA1 loss only conferred sensitivity to TMZ when combined with an ATRi. Furthermore, low REV3L mRNA expression correlated with sensitivity to the TMZ and ATRi combination and . This suggests that HR defects and low REV3L levels could be useful selection criteria for enhanced clinical efficacy of an ATRi plus TMZ combination.
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http://dx.doi.org/10.18632/oncotarget.28090 | DOI Listing |
Neuroendocrinology
January 2025
Background: Temozolomide (TMZ), a non-classical alkylating agent, possesses lipophilic properties that allow it to cross the blood-brain barrier, making it active within the central nervous system. Furthermore, the adverse reactions of the TMZ are relatively mild, which is why it is currently recommended as a first-line chemotherapy drug for refractory pituitary adenomas (RPAs) and pituitary carcinomas (PCs).
Summary: Systematic evaluations indicate a radiological response rate of 41% and a hormonal response rate of 53%, underscoring TMZ clinical efficacy, particularly when combined with radiotherapy.
Biomedicine (Taipei)
December 2024
School of Chinese Medicine, College of Chinese Medicine, China Medical University, Taichung, Taiwan.
Introduction: Our previous research demonstrated that a large language model (LLM) based on the transformer architecture, specifically the MegaMolBART encoder with an XGBoost classifier, effectively predicts the blood-brain barrier (BBB) permeability of compounds. However, the permeability coefficients of compounds that can traverse this barrier remain unclear. Additionally, the absorption, distribution, metabolism, and excretion (ADME) characteristics of substances obtained from the Natural Product Research Laboratory (NPRL) at China Medical University Hospital (CMUH) have not yet been determined.
View Article and Find Full Text PDFBiomedicines
December 2024
Laboratory of Pharmacognosy, School of Pharmacy, Faculty of Health Sciences, Aristotle University of Thessaloniki, 541 24 Thessaloniki, Greece.
Background/objectives: Glioblastoma (GBM) is the most aggressive type of brain tumor in adults. Currently, the only treatments available are surgery, radiotherapy, and chemotherapy based on temozolomide (TMZ); however, the prognosis is dismal. Several natural substances are under investigation for cancer treatment.
View Article and Find Full Text PDFCancers (Basel)
December 2024
Department of Neurology, Faculty of Health Sciences, Medical University of Warsaw, 03-242 Warsaw, Poland.
Gliomas are a wide group of common brain tumors, with the most aggressive type being glioblastoma multiforme (GBM), with a 5-year survival rate of less than 5% and a median survival time of approximately 12-14 months. The standard treatment of GBM includes surgical excision, radiotherapy, and chemotherapy with temozolomide (TMZ). However, tumor recurrence and progression are common.
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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