Dataset Information

Differences in Mutational Profile between Follicular Thyroid Carcinoma and Follicular Thyroid Adenoma Identified Using Next Generation Sequencing.

ABSTRACT: We aimed to identify differences in mutational status between follicular thyroid adenoma (FTA) and follicular thyroid cancer (FTC). The study included 35 patients with FTA and 35 with FTC. DNA was extracted from formalin-fixed paraffin-embedded (FFPE) samples from thyroidectomy. Next-generation sequencing (NGS) was performed with the 50-gene Ion AmpliSeq Cancer Hotspot Panel v2. Potentially pathogenic mutations were found in 14 (40%) FTA and 24 (69%) FTC patients (OR (95%CI) = 3.27 (1.22-8.75)). The number of mutations was higher in patients with FTC than FTA (p-value = 0.03). SMAD4 and STK11 mutations were present only in patients with FTA, while defects in FBXW7, JAK3, KIT, NRAS, PIK3CA, SMARCB1, and TP53 were detected exclusively in FTC patients. TP53 mutations increased the risk of FTC; OR (95%CI) = 29.24 (1.64-522.00); p-value = 0.001. FLT3-positivity was higher in FTC than in the FTA group (51.4% vs. 28.6%; p-value = 0.051). The presence of FLT3 and TP53 with no RET mutations increased FTC detectability by 17.1%, whereas the absence of FLT3 and TP53 with a presence of RET mutations increased FTA detectability by 5.7%. TP53 and FLT3 are candidate markers for detecting malignancy in follicular lesions. The best model to predict FTA and FTC may consist of FLT3, TP53, and RET mutations considered together.

SUBMITTER: Borowczyk M

PROVIDER: S-EPMC6651591 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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Differences in Mutational Profile between Follicular Thyroid Carcinoma and Follicular Thyroid Adenoma Identified Using Next Generation Sequencing.

Borowczyk Martyna M Szczepanek-Parulska Ewelina E Dębicki Szymon S Budny Bartłomiej B Verburg Frederik A FA Filipowicz Dorota D Więckowska Barbara B Janicka-Jedyńska Małgorzata M Gil Lidia L Ziemnicka Katarzyna K Ruchała Marek M

International journal of molecular sciences 20190626 13

We aimed to identify differences in mutational status between follicular thyroid adenoma (FTA) and follicular thyroid cancer (FTC). The study included 35 patients with FTA and 35 with FTC. DNA was extracted from formalin-fixed paraffin-embedded (FFPE) samples from thyroidectomy. Next-generation sequencing (NGS) was performed with the 50-gene Ion AmpliSeq Cancer Hotspot Panel v2. Potentially pathogenic mutations were found in 14 (40%) FTA and 24 (69%) FTC patients (OR (95%CI) = 3.27 (1.22-8.75)). ...[more]

PMID: 31248021

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Project description:Molecular mechanisms underlying the pathogenesis and progression of malignant thyroid cancers, such as follicular thyroid carcinomas (FTCs), and how these differ from benign thyroid lesions, are poorly understood. In this study, we employed network-based integrative analyses of FTC and benign follicular thyroid adenoma (FTA) lesion transcriptomes to identify key genes and pathways that differ between them. We first analysed a microarray gene expression dataset (Gene Expression Omnibus GSE82208, n = 52) obtained from FTC and FTA tissues to identify differentially expressed genes (DEGs). Pathway analyses of these DEGs were then performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) resources to identify potentially important pathways, and protein-protein interactions (PPIs) were examined to identify pathway hub genes. Our data analysis identified 598 DEGs, 133 genes with higher and 465 genes with lower expression in FTCs. We identified four significant pathways (one carbon pool by folate, p53 signalling, progesterone-mediated oocyte maturation signalling, and cell cycle pathways) connected to DEGs with high FTC expression; eight pathways were connected to DEGs with lower relative FTC expression. Ten GO groups were significantly connected with FTC-high expression DEGs and 80 with low-FTC expression DEGs. PPI analysis then identified 12 potential hub genes based on degree and betweenness centrality; namely, TOP2A, JUN, EGFR, CDK1, FOS, CDKN3, EZH2, TYMS, PBK, CDH1, UBE2C, and CCNB2. Moreover, transcription factors (TFs) were identified that may underlie gene expression differences observed between FTC and FTA, including FOXC1, GATA2, YY1, FOXL1, E2F1, NFIC, SRF, TFAP2A, HINFP, and CREB1. We also identified microRNA (miRNAs) that may also affect transcript levels of DEGs; these included hsa-mir-335-5p, -26b-5p, -124-3p, -16-5p, -192-5p, -1-3p, -17-5p, -92a-3p, -215-5p, and -20a-5p. Thus, our study identified DEGs, molecular pathways, TFs, and miRNAs that reflect molecular mechanisms that differ between FTC and benign FTA. Given the general similarities of these lesions and common tissue origin, some of these differences may reflect malignant progression potential, and include useful candidate biomarkers for FTC and identifying factors important for FTC pathogenesis.

Project description:BACKGROUND:Next-Generation Sequencing (NGS) technology has exceptionally increased the ability to sequence DNA in a massively parallel and cost-effective manner. Nevertheless, NGS data analysis requires bioinformatics skills and computational resources well beyond the possibilities of many "wet biology" laboratories. Moreover, most of projects only require few sequencing cycles and standard tools or workflows to carry out suitable analyses for the identification and annotation of genes, transcripts and splice variants found in the biological samples under investigation. These projects can take benefits from the availability of easy to use systems to automatically analyse sequences and to mine data without the preventive need of strong bioinformatics background and hardware infrastructure. RESULTS:To address this issue we developed an automatic system targeted to the analysis of NGS data obtained from large-scale transcriptome studies. This system, we named NGS-Trex (NGS Transcriptome profile explorer) is available through a simple web interface http://www.ngs-trex.org and allows the user to upload raw sequences and easily obtain an accurate characterization of the transcriptome profile after the setting of few parameters required to tune the analysis procedure. The system is also able to assess differential expression at both gene and transcript level (i.e. splicing isoforms) by comparing the expression profile of different samples.By using simple query forms the user can obtain list of genes, transcripts, splice sites ranked and filtered according to several criteria. Data can be viewed as tables, text files or through a simple genome browser which helps the visual inspection of the data. CONCLUSIONS:NGS-Trex is a simple tool for RNA-Seq data analysis mainly targeted to "wet biology" researchers with limited bioinformatics skills. It offers simple data mining tools to explore transcriptome profiles of samples investigated taking advantage of NGS technologies.

Project description:BACKGROUND:Fine-needle aspiration (FNA) cytology is a common approach to evaluating thyroid nodules, although 20% to 30% of FNAs have indeterminate cytology, which hampers the appropriate management of these patients. Follicular (or oncocytic) neoplasm/suspicious for a follicular (or oncocytic) neoplasm (FN/SFN) is a common indeterminate diagnosis with a cancer risk of approximately 15% to 30%. In this study, the authors tested whether the most complete next-generation sequencing (NGS) panel of genetic markers could significantly improve cancer diagnosis in these nodules. METHODS:The evaluation of 143 consecutive FNA samples with a cytologic diagnosis of FN/SFN from patients with known surgical outcomes included 91 retrospective samples and 52 prospective samples. Analyses were performed on a proprietary sequencer using the targeted ThyroSeq v2 NGS panel, which simultaneously tests for point mutations in 13 genes and for 42 types of gene fusions that occur in thyroid cancer. The expression of 8 genes was used to assess the cellular composition of FNA samples. RESULTS:In the entire cohort, histologic analysis revealed 104 benign nodules and 39 malignant nodules. The most common point mutations involved the neuroblastoma RAS viral oncogene homolog (NRAS), followed by the Kirsten rat sarcoma viral oncogene homolog (KRAS), the telomerase reverse transcriptase (TERT) gene, and the thyroid-stimulating hormone receptor (TSHR) gene. The identified fusions involved the thyroid adenoma associated (THADA) gene; the peroxisome proliferator-activated receptor ? (PPARG) gene; and the neurotrophic tyrosine kinase, receptor, type 3 (NTRK3) gene. Performance characteristics were similar in the retrospective and prospective groups. Among all FN/SFN nodules, preoperative ThyroSeq v2 performed with 90% sensitivity (95% confidence interval [CI], 80%-99%), 93% specificity (95% CI, 88%-98%), a positive predictive value of 83% (95% CI, 72%-95%), a negative predictive value of 96% (95% CI, 92%-100%), and 92% accuracy (95% CI, 88%-97%). CONCLUSIONS:The current results indicate that comprehensive genotyping of thyroid nodules using a broad NGS panel provides a highly accurate diagnosis for nodules with FN/SFN cytology and should facilitate the optimal management of these patients.

Dataset Information

Differences in Mutational Profile between Follicular Thyroid Carcinoma and Follicular Thyroid Adenoma Identified Using Next Generation Sequencing.

Publications

Differences in Mutational Profile between Follicular Thyroid Carcinoma and Follicular Thyroid Adenoma Identified Using Next Generation Sequencing.

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