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Updated: May 29, 2026

Next Generation Sequencing for the Detection of Actionable Mutations in Solid and Liquid Tumors
Published on: September 20, 2016
Impact of smoking status and pathologic type on epidermal growth factor receptor mutations in lung cancer
Yi-Sheng Huang1, Jin-Ji Yang, Xu-Chao Zhang
1Guangdong Lung Cancer Institute, Guangdong General Hospital & Guangdong Academy of Medical Sciences, Guangzhou, Guangdong 510080, China.
Background:
Epidermal growth factor receptor (EGFR) mutations in lung carcinomas can make the disease more responsive to the treatment with tyrosine kinase inhibitors. We aimed to evaluate the prevalence of EGFR mutations in a large series of lung carcinomas.
Methods:
We examined 1195 consecutive lung cancer patients for EGFR mutations in exons 18, 19, and 21 using direct sequencing of polymerase chain reaction products. A detailed smoking history was obtained. Patients were categorized as never smokers (< 100 lifetime cigarettes), former smokers (quit > 1 year ago), or current smokers (quit < 1 year ago).
Results:
There were EGFR mutations in 9 (4.5%) of 201 squamous carcinomas, in 1 (2%) of 50 large cell carcinomas, and in 1 (2.3%) of 44 small cell carcinomas that were investigated. Three hundred and twenty-seven mutations were found in the series of 858 adenocarcinomas (38.1%). Among 858 lung adenocarcinomas, we detected EGFR mutations in 250 (48.6%) of 514 never smokers, 39 (33.9%) of 115 former smokers, and 38 (16.6%) of 229 current smokers. Significantly fewer EGFR mutations were found in people who smoked for more than 15 pack-years (P = 0.0002) or stopped smoking less than 15 years ago (P = 0.033) compared with individuals who never smoked.
Conclusions:
Adenocarcinoma is the most frequent EGFR mutation pathologic type in lung cancer. The likelihood of EGFR mutations in exons 18, 19 and 21 decreases as the number of pack-years increases. Mutations were less common in people who smoked for more than 15 pack-years or who stopped smoking cigarettes less than 15 years ago. These data can assist clinicians in assessing the likelihood of exons 18, 19, or 21 EGFR mutations in Chinese patients with lung cancer when mutational analysis is not feasible.
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