多原发肺腺癌与多发性肺肿瘤患者肺内转移的组织分子联合算法鉴别诊断的建议。
Proposal for a Combined Histomolecular Algorithm to Distinguish Multiple Primary Adenocarcinomas from Intrapulmonary Metastasis in Patients with Multiple Lung Tumors.
机构信息
Department of Pathology, Hôpitaux Universitaire Paris Centre, Cochin Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France; Institut National de la Santé et de la Recherche Médicale 1138, Team Cancer, Immune Control, and Escape, Centre de Recherche des Cordeliers, Université; Paris Descartes-Paris 5, Paris, France.
Department of Biochemistry, Unit of Pharmacogenetic and Molecular Oncology, Georges Pompidou European Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France; INSERM UMR-S1147, Paris Sorbonne Cite University, Paris, France.
出版信息
J Thorac Oncol. 2019 May;14(5):844-856. doi: 10.1016/j.jtho.2019.01.017. Epub 2019 Feb 2.
INTRODUCTION
Multiple nodules in the lung are being diagnosed with an increasing frequency thanks to high-quality computed tomography imaging. In patients with lung cancer, this situation represents up to 10% of patients who have an operation. For clinical management, it is important to classify the disease as intrapulmonary metastasis or multiple primary lung carcinoma to define TNM classification and optimize therapeutic options. In the present study, we evaluated the respective and combined input of histological and molecular classification to propose a classification algorithm for multiple nodules.
METHODS
We studied consecutive patients undergoing an operation with curative intent for lung adenocarcinoma (N = 120) and harboring two tumors (N = 240). Histological diagnosis according to the WHO 2015 classification and molecular profiling using next-generation sequencing targeting 22 hotspot genes allowed classification of samples as multiple primary lung adenocarcinomas or as intrapulmonary metastasis.
RESULTS
Next-generation sequencing identified molecular mutations in 91% of tumor pairs (109 of 120). Genomic and histological classification showed a fair agreement when the κ test was used (κ = 0.43). Discordant cases (30 of 109 [27%]) were reclassified by using a combined histomolecular algorithm. EGFR mutations (p = 0.03) and node involvement (p = 0.03) were significantly associated with intrapulmonary metastasis, whereas KRAS mutations (p = 0.00005) were significantly associated with multiple primary lung adenocarcinomas. EGFR mutations (p = 0.02) and node involvement (p = 0.004) were the only independent prognostic factors.
CONCLUSION
We showed that combined histomolecular algorithm represents a relevant tool to classify multifocal lung cancers, which could guide adjuvant treatment decisions. Survival analysis underlined the good prognosis of EGFR-mutated adenocarcinoma in patients with intrapulmonary metastasis.
简介
由于高质量的计算机断层扫描成像，肺部的多个结节的诊断率正在不断提高。在肺癌患者中，这种情况占手术患者的 10%左右。为了进行临床管理，将疾病分类为肺内转移或多原发肺癌以确定 TNM 分类并优化治疗选择非常重要。在本研究中，我们评估了组织学和分子分类的各自和联合输入，以提出一种用于多结节的分类算法。
方法
我们研究了连续接受根治性手术治疗肺腺癌（N=120）且存在两个肿瘤（N=240）的患者。根据 2015 年 WHO 分类进行组织学诊断，以及使用靶向 22 个热点基因的下一代测序进行分子分析，允许将样本分类为多原发肺腺癌或肺内转移。
结果
下一代测序在 91%的肿瘤对（109/120）中鉴定出分子突变。当使用 κ 检验时，基因组和组织学分类显示出良好的一致性（κ=0.43）。使用组合组织分子算法重新分类了 30 例（109 例中的 27%）不一致的病例。肺内转移与 EGFR 突变（p=0.03）和淋巴结受累（p=0.03）显著相关，而 KRAS 突变（p=0.00005）与多原发肺腺癌显著相关。EGFR 突变（p=0.02）和淋巴结受累（p=0.004）是唯一的独立预后因素。
结论
我们表明，组合组织分子算法是一种用于分类多灶性肺癌的相关工具，它可以指导辅助治疗决策。生存分析强调了 EGFR 突变型腺癌在肺内转移患者中的良好预后。
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