Pharmacogenetic Insights into EGFR, ALK, and BRAF Mutations and Their Role in Tyrosine Kinase Inhibitor Response
Neda Zahmatkesh,1,*
1. Msc of Molecular Genetic Department of Genetics, Zanjan Branch, Islamic Azad University, Zanjan, Iran.
Introduction: Tyrosine kinase inhibitors (TKIs) have revolutionized the field of targeted cancer therapy, offering improved outcomes in cancers such as non-small cell lung cancer (NSCLC), melanoma, and colorectal cancer. Despite their clinical success, patient responses to TKIs vary considerably. This variability is largely attributed to genetic differences—both inherited polymorphisms and acquired somatic mutations—in key oncogenes such as EGFR, ALK, and BRAF. A deeper understanding of the pharmacogenetic landscape governing these mutations is essential for tailoring treatment strategies to individual patients. Such insight not only enhances therapeutic efficacy but also informs resistance management and long-term disease control. This review aims to present an up-to-date synthesis of how genetic alterations affect responses to TKIs and to explore their implications for personalized cancer treatment.
Methods: A comprehensive search was conducted across PubMed, Scopus, and Web of Science, covering literature published between 2018 and 2025. Search terms included: “pharmacogenetics,” “tyrosine kinase inhibitors,” “EGFR mutation,” “ALK rearrangement,” “BRAF V600E,” “TKI resistance,” and “targeted therapy.” From over 1,000 initial publications, 62 high-impact studies—including clinical trials, systematic reviews, and original research articles—were selected based on relevance to pharmacogenetics and therapeutic outcomes.
Results: EGFR Mutations and NSCLC In NSCLC, EGFR mutations, particularly exon 19 deletions and the L858R point mutation in exon 21, are well-established biomarkers of sensitivity to first- and second-generation TKIs like erlotinib, gefitinib, afatinib, and osimertinib. However, the emergence of the T790M mutation in exon 20 is a common resistance mechanism, often necessitating a shift to third-generation inhibitors such as osimertinib.
ALK Rearrangements ALK gene fusions, especially, EML4-ALK, are present in about 5% of NSCLC cases and predict robust responses to ALK inhibitors like crizotinib, alectinib, and lorlatinib. Yet, secondary mutations such as L1196M and G1202R often lead to acquired resistance, prompting the need for sequential therapy and next-generation inhibitors.
BRAF Mutations in Melanoma and Colorectal Cancer, The BRAF V600E mutation plays a pivotal role in both melanoma and colorectal cancer, making it a critical target for TKIs such as vemurafenib and dabrafenib. However, monotherapy frequently fails due to resistance mechanisms like reactivation of the MAPK pathway. As a result, combination therapies involving MEK inhibitors (e.g., trametinib) have become standard to enhance efficacy and delay resistance.
Co-occurring Mutations and Tumor Heterogeneity, In all three oncogenic contexts (EGFR, ALK, BRAF), treatment response is often influenced by additional mutations in genes such as TP53, PIK3CA, and KRAS. These co-alterations can modify drug sensitivity and contribute to both intrinsic and acquired resistance, reinforcing the importance of comprehensive molecular profiling prior to therapy selection.
Advancements in Genomic Technologies, Technologies such as liquid biopsy and next-generation sequencing (NGS) have transformed the clinical landscape by enabling non-invasive, real-time tracking of mutational changes during treatment. These tools facilitate dynamic adjustments to therapeutic regimens based on emerging resistance patterns, promoting a more responsive and individualized approach to cancer care.
Conclusion: Pharmacogenetic insights into EGFR, ALK, and BRAF mutations are essential for optimizing TKI therapy across various cancers. By identifying mutation-specific sensitivities and resistance mechanisms, clinicians can personalize treatment plans, reduce the risk of therapy failure, and improve patient outcomes. The integration of NGS and liquid biopsy into routine practice has made personalized cancer therapy increasingly feasible. Looking ahead, the adoption of multi-gene panels, AI-driven predictive models, and rational combination therapies holds great promise for overcoming drug resistance and extending survival in patients undergoing TKI-based treatment.