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除烟草外因素:吸烟者和不吸烟者肺癌的基因组差异。

Beyond tobacco: genomic disparities in lung cancer between smokers and never-smokers.

机构信息

Centro Genética y Genómica, Instituto de Ciencias E Innovación en Medicina, Facultad de Medicina Clínica Alemana, Universidad del Desarrollo, Santiago, Chile.

CORFO Center of Excellence in Precision Medicine Pfizer, Santiago, Chile.

出版信息

BMC Cancer. 2024 Aug 3;24(1):951. doi: 10.1186/s12885-024-12737-1.

DOI:10.1186/s12885-024-12737-1
PMID:39097719
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11297669/
Abstract

BACKGROUND

Tobacco use is one of the main risk factors for Lung Cancer (LC) development. However, about 10-20% of those diagnosed with the disease are never-smokers. For Non-Small Cell Lung Cancer (NSCLC) there are clear differences in both the clinical presentation and the tumor genomic profiles between smokers and never-smokers. For example, the Lung Adenocarcinoma (LUAD) histological subtype in never-smokers is predominately found in young women of European, North American, and Asian descent. While the clinical presentation and tumor genomic profiles of smokers have been widely examined, never-smokers are usually underrepresented, especially those of a Latin American (LA) background. In this work, we characterize, for the first time, the difference in the genomic profiles between smokers and never-smokers LC patients from Chile.

METHODS

We conduct a comparison by smoking status in the frequencies of genomic alterations (GAs) including somatic mutations and structural variants (fusions) in a total of 10 clinically relevant genes, including the eight most common actionable genes for LC (EGFR, KRAS, ALK, MET, BRAF, RET, ERBB2, and ROS1) and two established driver genes for malignancies other than LC (PIK3CA and MAP2K1). Study participants were grouped as either smokers (current and former, n = 473) or never-smokers (n = 200) according to self-report tobacco use at enrollment.

RESULTS

Our findings indicate a higher overall GA frequency for never-smokers compared to smokers (58 vs. 45.7, p-value < 0.01) with the genes EGFR, KRAS, and PIK3CA displaying the highest prevalence while ERBB2, RET, and ROS1 the lowest. Never-smokers present higher frequencies in seven out of the 10 genes; however, smokers harbor a more complex genomic profile. The clearest differences between groups are seen for EGFR (15.6 vs. 21.5, p-value: < 0.01), PIK3CA (6.8 vs 9.5) and ALK (3.2 vs 7.5) in favor of never-smokers, and KRAS (16.3 vs. 11.5) and MAP2K1 (6.6 vs. 3.5) in favor of smokers. Alterations in these genes are comprised almost exclusively by somatic mutations in EGFR and mainly by fusions in ALK, and only by mutations in PIK3CA, KRAS and MAP2K1.

CONCLUSIONS

We found clear differences in the genomic landscape by smoking status in LUAD patients from Chile, with potential implications for clinical management in these limited-resource settings.

摘要

背景

吸烟是肺癌(LC)发展的主要危险因素之一。然而,约 10-20%的确诊患者从不吸烟。对于非小细胞肺癌(NSCLC),吸烟者和从不吸烟者在临床特征和肿瘤基因组特征方面存在明显差异。例如,从不吸烟者的肺腺癌(LUAD)组织学亚型主要发生在欧洲、北美和亚洲血统的年轻女性中。虽然吸烟者的临床表现和肿瘤基因组特征已被广泛研究,但从不吸烟者通常代表性不足,尤其是拉丁美洲(LA)背景的从不吸烟者。在这项工作中,我们首次描述了来自智利的吸烟者和从不吸烟者 LC 患者之间基因组特征的差异。

方法

我们根据吸烟状态对 10 个临床相关基因中的基因组改变(GA)频率进行比较,包括 8 个最常见的 LC 靶向治疗基因(EGFR、KRAS、ALK、MET、BRAF、RET、ERBB2 和 ROS1)和 2 个非 LC 恶性肿瘤的既定驱动基因(PIK3CA 和 MAP2K1)。根据入组时的自我报告吸烟情况,研究参与者分为吸烟者(现吸烟者和既往吸烟者,n=473)或从不吸烟者(n=200)。

结果

我们的研究结果表明,从不吸烟者的总体 GA 频率高于吸烟者(58%比 45.7%,p 值<0.01),其中 EGFR、KRAS 和 PIK3CA 基因的患病率最高,而 ERBB2、RET 和 ROS1 基因的患病率最低。在 10 个基因中有 7 个基因中,从不吸烟者的频率更高;然而,吸烟者的基因组特征更复杂。两组之间最明显的差异是 EGFR(15.6%比 21.5%,p 值:<0.01)、PIK3CA(6.8%比 9.5%)和 ALK(3.2%比 7.5%)有利于从不吸烟者,而 KRAS(16.3%比 11.5%)和 MAP2K1(6.6%比 3.5%)有利于吸烟者。这些基因的改变几乎完全由 EGFR 的体细胞突变组成,主要由 ALK 的融合组成,仅由 PIK3CA、KRAS 和 MAP2K1 的突变组成。

结论

我们发现了来自智利的 LUAD 患者中由吸烟状态引起的基因组图谱的明显差异,这可能对这些资源有限的环境中的临床管理产生影响。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f1e/11297669/8ccc19c2b69f/12885_2024_12737_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f1e/11297669/5f7c35a24c9a/12885_2024_12737_Fig1_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f1e/11297669/7b649c22231f/12885_2024_12737_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f1e/11297669/c7da15f842ca/12885_2024_12737_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f1e/11297669/8ccc19c2b69f/12885_2024_12737_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f1e/11297669/5f7c35a24c9a/12885_2024_12737_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f1e/11297669/07ffd5d77075/12885_2024_12737_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f1e/11297669/7f21e6519b4e/12885_2024_12737_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f1e/11297669/7b649c22231f/12885_2024_12737_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f1e/11297669/c7da15f842ca/12885_2024_12737_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f1e/11297669/8ccc19c2b69f/12885_2024_12737_Fig7_HTML.jpg

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