Targeted next-generation sequencing panel (ThyroSeq) for detection of mutations in thyroid cancer

Abstract

Objectives: Next-generation sequencing (NGS) allows for high-throughput sequencing analysis of large regions of the human genome. We explored the use of targeted NGS for simultaneous testing for multiple mutations in thyroid cancer.

Design: A custom panel (ThyroSeq) was designed to target 12 cancer genes with 284 mutational hot spots. Sequencing was performed to analyze DNA from 228 thyroid neoplastic and nonneoplastic samples including 105 frozen, 72 formalin-fixed, and 51 fine-needle aspiration samples representing all major types of thyroid cancer.

Results: Only 5-10 ng of input DNA was sufficient for successful analysis of 99.6% of samples. The analytical accuracy for mutation detection was 100% with the sensitivity of 3%-5% of mutant allele. ThyroSeq DNA assay identified mutations in 19 of 27 of classic papillary thyroid carcinomas (PTCs) (70%), 25 of 30 follicular variant PTCs (83%), 14 of 18 conventional (78%) and 7 of 18 oncocytic follicular carcinomas (39%), 3 of 10 poorly differentiated carcinomas (30%), 20 of 27 anaplastic (ATCs) (74%), and 11 of 15 medullary thyroid carcinomas (73%). In contrast, 5 of 83 benign nodules (6%) were positive for mutations. Most tumors had a single mutation, whereas several ATCs and PTCs demonstrated two or three mutations. The most common mutations detected were BRAF and RAS followed by PIK3CA, TP53, TSHR, PTEN, GNAS, CTNNB1, and RET. The BRAF mutant allele frequency was 18%-48% in PTCs and was lower in ATCs.

Conclusions: The ThyroSeq NGS panel allows simultaneous testing for multiple mutations with high accuracy and sensitivity, requires a small amount of DNA and can be performed in a variety of thyroid tissue and fine-needle aspiration samples, and provides quantitative assessment of mutant alleles. Using this approach, the point mutations were detected in 30%-83% of specific types of thyroid cancer and in only 6% of benign thyroid nodules and were shown to be present in the majority of cells within the cancer nodule.

Figures

Figure 1.

ThyroSeq work flow. DNA from FFPE tissue, frozen tissue, or FNA samples is amplified for enrichment of 34 target regions in 12 thyroid cancer genes in a multiplex PCR reaction. Then the library is prepared by ligating the PCR amplicons into platform-specific adapters and adding bar codes for specimen multiplexing. Finally, the library is enriched by clonal amplification (emPCR) and sequenced by massively parallel sequencing on the Ion Torrent PGM. The data analysis and variant calling are performed using bioinformatic pipelines followed by a custom SeqReporter algorithm for filtering and annotation of genetic variants.

Figure 2.

Mutational profiles of various types of thyroid cancers identified by ThyroSeq.

Figure 3.

Identification of mutations by ThyroSeq NGS, as visualized in integrative genomic viewer (top panel) and confirmed by Sanger sequencing (bottom panel). A, BRAF V600E mutation in PTC. B, TSHR F631L mutation in FTC. C, TP53 R175H mutation in ATC.