Analytical performance of the ThyroSeq v3 genomic classifier for cancer diagnosis in thyroid nodules.
Analytical performance of the ThyroSeq v3 genomic classifier for cancer diagnosis in thyroid nodules.
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DOI：
10.1002/cncr.31245
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发表时间：
2018-04-15
期刊：
影响因子：
6.2
通讯作者：
Nikiforov YE
中科院分区：
文献类型：
--
作者：
Nikiforova MN;Mercurio S;Wald AI;Barbi de Moura M;Callenberg K;Santana-Santos L;Gooding WE;Yip L;Ferris RL;Nikiforov YE
分子检测对细针穿刺（FNA）细胞学不确定的甲状腺结节具有临床实用性，尽管其性能需要进一步改进。在这项研究中，我们评估了新创建的ThyroSeq v3测试的分析性能。ThyroSeq第3版是一种基于DNA和RNA的下一代测序检测试剂盒，可分析112个基因的各种遗传改变，包括点突变、插入缺失、基因融合、拷贝数改变和异常基因表达，并使用基因组分类器（GC）将恶性病变与良性病变分开。在已知手术随访的238个组织和175个FNA样本中进行了验证。进行了分析性能研究。使用训练组织集，ThyroSeq GC检测到>100种遗传改变，包括BRAF、RAS、TERT、DICER 1突变、NTRK 1/3、BRAF和RET融合、22 q缺失和基因表达改变。建立GC临界值以区分癌性结节和良性结节，灵敏度为93.9%，特异性为89.4%，准确性为92.1%。这正确地分类了大多数乳头状、滤泡状和Hurthle细胞病变、甲状腺髓样癌和甲状旁腺病变。在FNA验证集中，GC敏感性为98.0%，特异性为81.8%，准确性为90.9%。分析准确度研究证明，在可变强制降解条件下，所需的最低核酸输入量为2.5 ng，最低可接受肿瘤含量为12%，检测结果具有重现性。ThyroSeq v3 GC分析了五种不同类型的分子改变，并为检测所有常见类型的甲状腺癌和甲状旁腺病变提供了高准确性。成功验证了检测的分析灵敏度、特异性和耐用性，表明其适用于临床使用。
Molecular tests have clinical utility for thyroid nodules with indeterminate fine-needle aspiration (FNA) cytology, although their performance requires further improvement. In this study, we evaluated the analytical performance of the newly created ThyroSeq v3 test. ThyroSeq version 3 is a DNA and RNA-based next-generation sequencing assay that analyzes 112 genes for a variety of genetic alterations including point mutations, indels, gene fusions, copy number alterations, and abnormal gene expression and uses a Genomic Classifier (GC) to separate malignant from benign lesions. It was validated in 238 tissue and 175 FNA samples with known surgical follow-up. Analytical performance studies were conducted. Using the training tissue set, ThyroSeq GC detected >100 genetic alterations, including BRAF, RAS, TERT, DICER1 mutations, NTRK1/3, BRAF and RET fusions, 22q loss, and gene expression alterations. GC cutoffs were established to distinguish cancer from benign nodules with 93.9% sensitivity, 89.4% specificity, and 92.1% accuracy. This correctly classified most papillary, follicular, and Hurthle cell lesions, medullary thyroid carcinomas and parathyroid lesions. In the FNA validation set, the GC sensitivity was 98.0%, specificity 81.8%, and accuracy 90.9%. Analytical accuracy studies demonstrated minimal required nucleic acid input of 2.5 ng, a 12% minimal acceptable tumor content, and reproducible test results under variable stress conditions. ThyroSeq v3 GC analyzes five different classes of molecular alterations and provides high accuracy for detecting all common types of thyroid cancer and parathyroid lesions. Analytical sensitivity, specificity, and robustness of the test were successfully validated, indicating its suitability for clinical use.
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影响因子：
64.5
作者：
Cancer Genome Atlas Research Network
通讯作者：
Cancer Genome Atlas Research Network
DOI：
10.1016/j.jmoldx.2017.01.011
发表时间：
2017-05
期刊：
The Journal of molecular diagnostics : JMD
影响因子：
--
作者：
Jennings LJ;Arcila ME;Corless C;Kamel-Reid S;Lubin IM;Pfeifer J;Temple-Smolkin RL;Voelkerding KV;Nikiforova MN
通讯作者：
Nikiforova MN
影响因子：
3.5
作者：
Cibas, Edmund S.;Ali, Syed Z.
通讯作者：
Ali, Syed Z.
DOI：
10.1073/pnas.1321937111
发表时间：
2014-03-18
影响因子：
11.1
作者：
Kelly, Lindsey M.;Barila, Guillermo;Nikiforov, Yuri E.
通讯作者：
Nikiforov, Yuri E.
影响因子：
1.8
作者：
Agresti, A;Coull, BA
通讯作者：
Coull, BA