Analytical performance of the ThyroSeq v3 genomic classifier for cancer diagnosis in thyroid nodules.

Abstract

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.

• Authors: Marina N Nikiforova, Stephanie Mercurio, Abigail I Wald, Michelle Barbi de Moura, Keith Callenberg, Lucas Santana-Santos, William E Gooding, Linwah Yip, Robert L Ferris, Yuri E Nikiforov
• Journal: Cancer (Cancer) Vol. 124 Issue 8 Pg. 1682-1690 (04 15 2018) ISSN: 1097-0142 [Electronic] United States
• PMID: 29345728 (Publication Type: Journal Article, Research Support, N.I.H., Extramural, Research Support, Non-U.S. Gov't, Validation Study)
• Copyright: © 2018 American Cancer Society.

Chemical References

• Biomarkers, Tumor

Topics

• Biomarkers, Tumor (genetics)
• Biopsy, Fine-Needle
• DNA Mutational Analysis (methods)
• Diagnosis, Differential
• Follow-Up Studies
• High-Throughput Nucleotide Sequencing (methods)
• Humans
• Parathyroid Glands (pathology)
• Parathyroid Neoplasms (diagnosis, genetics, pathology)
• Reproducibility of Results
• Sensitivity and Specificity
• Thyroid Gland (pathology)
• Thyroid Neoplasms (diagnosis, genetics, pathology)
• Thyroid Nodule (diagnosis, genetics, pathology)