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Updated: Dec 28, 2025

Detection of Targetable Alterations in Non-small Cell Lung Cancer using Next-generation Sequencing
Published on: October 10, 2025
Precision medicine and actionable alterations in lung cancer: A single institution experience
Isa Mambetsariev1, Yingyu Wang2, Chen Chen2
1Department of Medical Oncology and Therapeutics Research, City of Hope, Duarte, California, United States of America.
Objectives:
Oncology has become more reliant on new testing methods and a greater use of electronic medical records, which provide a plethora of information available to physicians and researchers. However, to take advantage of vital clinical and research data for precision medicine, we must initially make an effort to create an infrastructure for the collection, storage, and utilization of this information with uniquely designed disease-specific registries that could support the collection of a large number of patients.
Materials And Methods:
In this study, we perform an in-depth analysis of a series of lung adenocarcinoma patients (n = 415) with genomic and clinical data in a recently created thoracic patient registry.
Results:
Of the 415 patients with lung adenocarcinoma, 59% (n = 245) were female; the median age was 64 (range, 22-92) years with a median OS of 33.29 months (95% CI, 29.77-39.48). The most common actionable alterations were identified in EGFR (n = 177/415 [42.7%]), ALK (n = 28/377 [7.4%]), and BRAF V600E (n = 7/288 [2.4%]). There was also a discernible difference in survival for 222 patients, who had an actionable alteration, with a median OS of 39.8 months as compared to 193 wild-type patients with a median OS of 26.0 months (P<0.001). We identified an unprecedented number of actionable alterations [53.5% (222/415)], including distinct individual alteration rates, as compared with 15.0% and 22.3% in TCGA and GENIE respectively.
Conclusion:
The use of patient registries, focused genomic panels and the appropriate use of clinical guidelines in community and academic settings may influence cohort selection for clinical trials and improve survival outcomes.
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