Authors
Mohammad Noor Shaik, Goutham Yerrakula, Kabbathy Raghunathachar Sahana, Devi Aruna Jyothi Bommareddy, Adepu Himavarshini, Badvel Jeevan Kumar, Swathi Swaroopa Borra, Nagaraju Bandaru
Published in
Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico. Jul 13, 2026. Epub Jul 13, 2026.
Abstract
Non-small cell lung cancer (NSCLC) is the most common cancer-related cause of death among all countries globally, mostly because of late diagnosis, heterogeneity of tumors, and poor response to standard therapy. Innovations in the field of pharmacogenomics have completely revolutionized the management of NSCLC by facilitating the application of precision oncology, which matches the treatment to tumor-related molecular changes and host genomic elements. Using comprehensive genomic profiling, clinically actionable driver mutations, such as EGFR, ALK, KRAS, BRAF, MET, ROS1, RET, and HER2, have been identified, making it possible to use targeted therapies that lead to improved progression-free and overall survival compared to chemotherapy. A combination of predictive biomarkers such as PD-L1 expression, tumor mutational load, circulating tumor DNA, and immune gene signatures also narrows down patient selection to immunotherapy and combination therapies. The pharmacogenomic understanding of gene-immune interactions, clonal evolution, and resistance mechanisms is useful toward adaptive treatment plans and real-time therapeutic optimization. In the real world, genomically directed treatment regimens have demonstrated an increase in median overall survival to approximately 36 months and a decrease in treatment-related toxicity and healthcare costs. New technologies, such as artificial intelligence, integration of multi-omics, and personalized combination therapeutics, are poised to increase treatment accuracy and sustainability even further. Together, pharmacogenomics can be viewed as a shift in paradigm in the treatment of NSCLC, since it allows the development of dynamic and biology-based treatment models that enhance clinical outcomes and promise the future of personalized cancer treatment.
PMID:
42440255
Bibliographic data and abstract were imported from PubMed on 13 Jul 2026.
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