Challenges and Frontiers of Targeted Gene Panels in Precision Oncology | ||||
| Journal of the Medical Research Institute | ||||
| Article 2, Volume 46, Issue 1, March 2025, Page 12-25 PDF (859.71 K) | ||||
| Document Type: Review Article | ||||
| DOI: 10.21608/jmalexu.2025.349090.1045 | ||||
 View on SCiNiTO | ||||
| Author | ||||
Omneya Abdelkarem 
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| Department of Chemical Pathology, Medical Research Institute, Alexandria University, Alexandria, Egypt | ||||
| Abstract | ||||
| Targeted gene panels (TGPs) have redefined precision oncology by bridging the gap between genomic insights and personalized treatments. Their ability to detect actionable mutations, optimize therapeutic strategies, and provide robust tumor profiling underscores their pivotal role in modern oncology. However, the rapid evolution of genomic knowledge demands continuous innovation in panel design, bioinformatics, and clinical integration. Addressing limitations such as standardization and accessibility are critical for maximizing their global impact. The integration of artificial intelligence, enhanced clinical decision support systems, and real-time updates from genomic databases promises a future where TGPs drive more equitable, precise, and effective cancer care. Through collaborative efforts across scientific, clinical, and technological domains, TGPs will continue to shape the landscape of precision medicine, ensuring better outcomes for diverse patient populations. This review delves into the pivotal role of targeted gene panels (TGPs) in cancer genomics, exploring their methodologies for target enrichment, diverse applications, and the challenges they face in clinical implementation. As this review navigates the intricacies of TGPs, it will also highlight future directions that promise to enhance their utility in genetically guided precision oncology, aiming to improve patient outcomes through personalized treatment approaches. | ||||
| Keywords | ||||
| Genomic profiling; Enrichment approaches; Polygenic risk scores; Tumor mutation burden; Clinical decision support systems | ||||
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