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| Format: | Recurso digital |
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Zenodo
2025
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| Online-Zugang: | https://doi.org/10.5281/zenodo.17898008 |
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Inhaltsangabe:
- <p><strong><span>Background:</span></strong><span> Breast cancer remains a leading cause of cancer-related mortality worldwide, with triple-negative breast cancer (TNBC) representing a particularly aggressive subtype comprising 15–20% of all cases and characterized by poor prognosis, high recurrence rates, and limited targeted therapy options. Epidermal growth factor receptor (EGFR), a receptor tyrosine kinase, is frequently overexpressed in TNBC (prevalence 13–89% across cohorts) and drives oncogenesis through dysregulated signaling pathways such as PI3K/AKT and MAPK, promoting proliferation, survival, and metastasis. EGFR expression and mutations serve as promising biomarkers for prognostic stratification and therapeutic selection, yet their clinical translation has been hampered by heterogeneous responses and resistance mechanisms.</span></p> <p><strong><span>Focus:</span></strong><span> This review emphasizes clinical trials and patient stratification strategies based on EGFR expression and mutation status, highlighting biomarker-enriched designs that enhance trial efficiency and patient outcomes.</span></p> <p><strong><span>Key Points:</span></strong><span> In TNBC, EGFR overexpression correlates with chemotherapy resistance and inferior overall survival, positioning it as a key biomarker for high-risk patient identification. The therapeutic landscape includes monoclonal antibodies (e.g., cetuximab) and tyrosine kinase inhibitors (TKIs; e.g., gefitinib, erlotinib), with recent trials demonstrating modest efficacy in EGFR-stratified cohorts, such as improved objective response rates (21%) when combined with chemotherapy. Emerging innovations, including decentralized clinical trials (DCTs) and AI-assisted patient selection, address recruitment barriers in oncology; AI algorithms optimize stratification by predicting EGFR status from omics data, reducing screen failures by up to 40%, while DCTs enable remote biomarker testing to broaden access in underserved populations.</span></p> <p><strong><span>Conclusions:</span></strong><span> Precision targeting of EGFR in breast cancer, particularly TNBC, holds transformative potential for improving progression-free and overall survival through stratified trial designs and innovative tools. Integrating DCTs and AI will accelerate evidence generation, fostering equitable, biomarker-driven care and underscoring the need for adaptive, multi-omics approaches to overcome resistance.</span></p> <p><strong><span>Keywords: </span></strong><span>EGFR; Triple-Negative Breast Cancer; Biomarker Stratification; Precision Oncology; Clinical Trials; Monoclonal Antibodies; Tyrosine Kinase Inhibitors; Decentralized Clinical Trials; AI-Assisted Patient Selection</span></p> <p><strong><span> </span></strong></p>