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Lung cancer mutation profile of EGFR, ALK, and KRAS: Meta-analysis and comparison of never and ever smokers

医学 肺癌 克拉斯 内科学 肿瘤科 优势比 癌症 结直肠癌
作者
Aaron Chapman,Kathie Sun,Peter Ruestow,Dallas M. Cowan,Amy K. Madl
出处
期刊:Lung Cancer [Elsevier]
卷期号:102: 122-134 被引量:176
标识
DOI:10.1016/j.lungcan.2016.10.010
摘要

Lung cancer is the leading cause of cancer-related mortality. While the majority of lung cancers are associated with tobacco smoke, approximately 10–15% of U.S. lung cancers occur in never smokers. Evidence suggests that lung cancer in never smokers appears to be a distinct disease caused by driver mutations which are different than the genetic pathways observed with lung cancer in smokers. A meta-analysis of human epidemiologic data was conducted to evaluate the profile of common or therapy-targetable mutations in lung cancers of never and ever smokers. Epidemiologic studies (N = 167) representing over 63,000 lung cancer cases were identified and used to calculate summary odds ratios for lung cancer in never and ever smokers containing gene mutations: EGFR, chromosomal rearrangements and fusion of EML4 and ALK, and KRAS. This analysis also considered the effect of histopathology, smoking status, sex, and ethnicity. There were significantly increased odds of presenting the EGFR and ALK-EML4 mutations in 1) adenocarcinomas compared to non-small cell lung cancer and 2) never smokers compared to ever smokers. The prevalence of EGFR mutations was higher in Asian women as compared to women of Caucasian/Mixed ethnicity. As the smoking history increased, there was a decreased odds for exhibiting the EGFR mutation, particularly for cases >30 pack-years. Compared to ever smokers, never smokers had a decreased odds of KRAS mutations among those of Caucasian/Mixed ethnicity (OR = 0.22, 95% CI: 0.17–0.29) and those of Asian ethnicity (OR = 0.39, 95% CI: 0.30–0.50). Our findings show that key driver mutations and several patient features are highly prevalent in lung cancers of never smokers. These associations may be helpful as patient demographic models are developed to predict successful outcomes of targeted therapeutic interventions NSCLC.
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