Analytical performance of the ThyroSeq v3 genomic classifier for cancer diagnosis in thyroid nodules
Аналитические характеристики геномного классификатора ThyroSeq v3 для диагностики рака в узлах щитовидной железы
2018-01-18
SCID: 54.1/5p49uphb
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ThyroSeq v3 genomic classifierfine-needle aspiration cytologymolecular alterationsnext-generation sequencingthyroid nodule cancer diagnosis
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Abstract (AI)
BACKGROUND: Molecular tests have clinical utility for thyroid nodules with indeterminate fine-needle aspiration (FNA) cytology, although their performance requires further improvement. This study evaluated the analytical performance of the newly created ThyroSeq v3 test. METHODS: ThyroSeq v3 is a DNA- and RNA-based next-generation sequencing assay that analyzes 112 genes for a variety of genetic alterations, including point mutations, insertions/deletions, gene fusions, copy number alterations, and abnormal gene expression, and it uses a genomic classifier (GC) to separate malignant lesions from benign lesions. It was validated in 238 tissue samples and 175 FNA samples with known surgical follow-up. Analytical performance studies were conducted. RESULTS: In the training tissue set of samples, ThyroSeq GC detected more than 100 genetic alterations, including BRAF, RAS, TERT, and 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%, the specificity was 81.8%, and the accuracy was 90.9%. Analytical accuracy studies demonstrated a minimal required nucleic acid input of 2.5 ng, a 12% minimal acceptable tumor content, and reproducible test results under variable stress conditions. CONCLUSIONS: The ThyroSeq v3 GC analyzes 5 different classes of molecular alterations and provides high accuracy for detecting all common types of thyroid cancer and parathyroid lesions. The analytical sensitivity, specificity, and robustness of the test have been successfully validated and indicate its suitability for clinical use. Cancer 2018;124:1682-90. © 2018 American Cancer Society.
Key Findings
1
Analytical studies showed reliable performance with 2.5 ng minimum nucleic-acid input, 12% minimum tumor content, and reproducibility under variable stress conditions.
2
In FNA samples, the classifier achieved 98.0% sensitivity, 81.8% specificity, and 90.9% accuracy with surgical follow-up validation.
3
In tissue samples, established classifier cutoffs achieved 93.9% sensitivity, 89.4% specificity, and 92.1% accuracy for distinguishing cancer from benign nodules.
4
The genomic classifier detected diverse alterations, including BRAF, RAS, TERT, and DICER1 mutations; gene fusions; copy-number loss; and abnormal gene expression.
5
ThyroSeq v3 is a DNA- and RNA-based next-generation sequencing assay analyzing 112 genes and five classes of molecular alterations.
Research Object
ThyroSeq v3 genomic classifier applied to thyroid nodule tissue and fine-needle aspiration samples
Research Subject
Analytical performance, diagnostic accuracy, sensitivity, specificity, reproducibility, and robustness for distinguishing malignant from benign thyroid and parathyroid lesions
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2018-01-18
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