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Updated: Jul 24, 2025

An Orthotopic Mouse Model of Anaplastic Thyroid Carcinoma
Published on: April 17, 2013
A clinically applicable molecular classification of oncocytic cell thyroid nodules
Elizabeth J de Koster1,2, Willem E Corver3, Lioe-Fee de Geus-Oei1,2,4
1Department of Medical Imaging, Nuclear Medicine, Radboud University Medical Centre, Nijmegen, the Netherlands.
Abstract:
Whole chromosome instability with near-whole genome haploidization (GH) and subsequent endoreduplication is considered a main genomic driver in the tumorigenesis of oncocytic cell thyroid neoplasms (OCN). These copy number alterations (CNA) occur less frequently in oncocytic thyroid adenoma (OA) than in oncocytic carcinoma (OCA), suggesting a continuous process. The current study described the CNA patterns in a cohort of 30 benign and malignant OCN, observed using a next-generation sequencing (NGS) panel that assesses genome-wide loss of heterozygosity (LOH) and chromosomal imbalances using 1500 single-nucleotide polymorphisms (SNPs) across all autosomes and the X chromosome in DNA derived from cytological and histological samples. Observed CNA patterns were verified using multiparameter DNA flow cytometry with or without whole-genome SNP array analysis and lesser-allele intensity-ratio (LAIR) analysis. On CNA-LOH analysis using the NGS panel, GH-type CNA were observed in 4 of 11 (36%) OA and in 14 of 16 OCA (88%). Endoreduplication was suspected in 8 of 16 (50%) OCA, all with more extensive GH-type CNA (P < 0.001). Reciprocal chromosomal imbalance type CNA, characterized by (imbalanced) chromosomal copy number gains and associated with benign disease, were observed in 6 of 11 (55%) OA and one equivocal case of OCA. CNA patterns were different between the histopathological subgroups (P < 0.001). By applying the structured interpretation and considerations provided by the current study, CNA-LOH analysis using an NGS panel that is feasible for daily practice may be of great added value to the widespread application of molecular diagnostics in the diagnosis and risk stratification of OCN.
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