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Background And Aim: Glioblastoma (GBM) is the most aggressive primary brain tumor in adults, with poor prognosis despite maximal surgical resection, radiotherapy (RT), and temozolomide (TMZ) per the Stupp protocol. IDH-wild-type GBM, the predominant molecular subtype, frequently harbors EGFR amplification and is resistant to therapy, while MGMT promoter methylation predicts improved TMZ response. This study aimed to assess the prognostic impact of EGFR and MGMT status on survival and recurrence patterns in IDH-wild-type GBM.
Materials And Methods: We retrospectively analyzed 218 patients with IDH-wild-type GBM treated at the Azienda Ospedaliero-Universitaria Senese (2016-2024). All patients underwent maximal safe surgical resection whenever feasible. The cohort includes patients who received gross total resection (GTR), subtotal resection (STR), or biopsy only, depending on tumor location and clinical condition, followed by intensity-modulated RT (59.4-60 Gy) with concurrent and adjuvant TMZ. EGFR amplification was assessed via FISH/NGS and immunohistochemistry; MGMT promoter methylation was determined using methylation-specific PCR. Progression-free survival (PFS), overall survival (OS), and recurrence patterns (in-field, marginal, out-field) were evaluated using Kaplan-Meier, Cox regression, and logistic regression analyses.
Results: Among patients (64.7% male; mean age 61.8), 58.7% had EGFR amplification and 49.1% showed MGMT methylation. Median OS and PFS were 14 and 8 months, respectively. EGFR non-amplified/MGMT methylated tumors had the best outcomes (OS: 22.0 months, PFS: 10.5 months), while EGFR-amplified/MGMT unmethylated tumors fared worst (OS: 10.0 months, PFS: 5.0 months; < 0.001). MGMT methylation was an independent positive prognostic factor (HR: 0.48, < 0.001), while EGFR amplification predicted worse survival (HR: 1.57, = 0.02) and higher marginal recurrence (OR: 2.42, = 0.01).
Conclusions: EGFR amplification and MGMT methylation significantly influence survival and recurrence dynamics in IDH-wild-type GBM. Incorporating these biomarkers into treatment planning may enable tailored therapeutic strategies, potentially improving outcomes in this challenging disease. Prospective studies are needed to validate biomolecularly guided management approaches.
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http://dx.doi.org/10.3390/brainsci15070713 | DOI Listing |
Hum Vaccin Immunother
December 2025
Department of Oncology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China.
Trastuzumab-containing therapy remains a treatment option for patients with HER2-positive gastric cancer (GC). However, primary resistance to trastuzumab is a challenge. Therefore, it is essential to identify biomarkers for predicting the efficacy of trastuzumab-based treatment.
View Article and Find Full Text PDFLung cancer is the most common cause of cancer-related death worldwide. Recent advancements in targeted therapies and immunotherapies have achieved remarkable success. However, patient responses to treatments with lung cancer vary substantially.
View Article and Find Full Text PDFJ Clin Oncol
September 2025
Department of Translational Research and New Technologies in Medicine, University of Pisa, Pisa, Italy.
Purpose: Human epidermal growth factor receptor 2 (HER2) amplification/overexpression (HER2-pos) is detected in 5% of wild-type metastatic colorectal cancers (mCRCs). Its prognostic/predictive role in terms of benefit from anti-EGFR/bevacizumab (bev) is debated. Similarly, the role of activating mutations (mut) is unclear.
View Article and Find Full Text PDFMedicine (Baltimore)
August 2025
Department of Cancer Center, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Rationale: Human epidermal growth factor receptor 2 (HER2)-positive cholangiocarcinoma is a rare disease with a low incidence and high degree of malignancy. Trastuzumab deruxtecan (T-DXd) has been approved for the treatment of HER2-positive breast and gastric cancer. However, it is still in the initial exploration period for HER2-positive cholangiocarcinoma.
View Article and Find Full Text PDFDespite promising results in using deep learning to infer genetic features from histological whole-slide images (WSIs), no prior studies have specifically applied these methods to lung adenocarcinomas from subjects who have never smoked tobacco (NS-LUAD) - a molecularly and histologically distinct subset of lung cancer. Existing models have focused on LUAD from predominantly smoker populations, with limited molecular scope and variable performance. Here, we propose a customized deep convolutional neural network based on ResNet50 architecture, optimized for multilabel classification for NS-LUAD, enabling simultaneous prediction of 16 molecular alterations from a single H&E-stained WSI.
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