Evaluation of gene expression profiles in thyroid nodule biopsy material to diagnose thyroid cancer
Evaluation of gene expression profiles in thyroid nodule biopsy material to diagnose thyroid cancer
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DOI：
10.1210/jc.2007-1571
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发表时间：
2008-04-01
影响因子：
5.8
通讯作者：
Rousset, Bernard
中科院分区：
文献类型：
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作者：
Durand, Stephanie;Ferraro-Peyret, Carole;Rousset, Bernard
背景：目前依靠细胞学检查的细针抽吸活检（FNAB）对良性结节中甲状腺癌的检测预计将通过基因组数据建立的新诊断测试得到改进。目的：本研究的目的是使用一组根据表达水平区分良性和恶性肿瘤的基因，建立肿瘤分类器并评估其预测能力 FNAB 上的恶性肿瘤。设计：我们使用尼龙宏阵列分析了 56 个甲状腺组织样本（良性或恶性肿瘤以及配对的正常组织）中 200 个潜在信息基因的表达水平。基因表达数据经过加权投票算法来生成肿瘤分类器。分类器的性能在一系列 26 个假 FNAB（即手术切除后对甲状腺结节进行 FNAB）上进行评估。结果：一系列 19 个在滤泡性腺瘤和正常组织中表达相似的基因，并将滤泡性腺瘤 + 正常组织与以下组织区分开来：1）滤泡性甲状腺癌（FTC），2） 甲状腺乳头状癌 (PTC)，或 3) FTC 和 PTC。这些用于生成四个分类器：FTC、PTC、通用（FTC + PTC）和全局分类器。在 26 例假 FNAB 中的 23 例中，四个分类器得出的诊断与用作参考的病理学家的诊断一致；在其他三个病例中，四个分类器中的三个给出了正确的诊断。结论：我们开发了一种适用于 FNAB 收集的材料的良性肿瘤与恶性肿瘤的分子诊断程序。分子测试符合临床前验证阶段；现在必须在大规模前瞻性研究中通过超声引导 FNAB 对其进行评估。
Context: Detection of thyroid cancer among benign nodules on fine-needle aspiration biopsies (FNAB), which presently relies on cytological examination, is expected to be improved by new diagnostic tests set up from genomic data.Objective: The aim of the study was to use a set of genes discriminating benign from malignant tumors, on the basis of their expression levels, to build tumor classifiers and evaluate their capacity to predict malignancy on FNAB.Design: We analyzed the level of expression of 200 potentially informative genes in 56 thyroid tissue samples (benign or malignant tumors and paired normal tissue) using nylon macroarrays. Gene expression data were subjected to a weighted voting algorithm to generate tumor classifiers. The performances of the classifiers were evaluated on a series of 26 sham FNAB, i.e. FNAB carried out on thyroid nodules after surgical resection.Results: A series of 19 genes with a similar expression in follicular adenomas and normal tissue and discriminating follicular adenomas + normal tissue from the following: 1) follicular thyroid carcinomas (FTCs), 2) papillary thyroid carcinomas (PTCs), or 3) both FTCs and PTCs. These were used to generate four classifiers, the FTCs, PTCs, common (FTC + PTCs), and global classifiers. In 23 of the 26 sham FNAB, the four classifiers yielded a diagnosis in agreement with the diagnosis of the pathologist used as reference; in the three other cases, the correct diagnosis was given by three of four classifiers.Conclusions: We developed a procedure of molecular diagnosis of benign vs. malignant tumors applicable to the material collected by FNAB. The molecular test complied with a preclinical validation stage; it must be now evaluated on ultrasound-guided FNAB in a large-scale prospective study.