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Genomic and Transcriptomic Hallmarks of Poorly-Differentiated and Anaplastic Thyroid Cancers

GSE76039 Homo sapiens Expression profiling by array 37 samples Submitted 2016/02/15 Platform GPL570
Summary
BACKGROUND. Poorly-differentiated (PDTC) and anaplastic (ATC) thyroid cancers are rare and frequently lethal tumors, which so far have not been subjected to comprehensive genetic characterization. METHODS. We performed next generation sequencing of 341 cancer genes in 117 PDTCs and ATCs, and a transcriptomic analysis of a representative subset of 37 tumors. Results were analyzed in the context of The Cancer Genome Atlas (TCGA) study of papillary thyroid cancers (PTC). RESULTS. ATCs have a greater mutation burden than PDTCs, and higher mutation frequency of TP53, TERT promoter, PI3K/AKT/mTOR pathway effectors, SWI/SNF subunits and histone methyltransferases. BRAF and RAS are the predominant drivers, and dictate remarkably distinct tropism for nodal vs. distant metastases in PDTC. RAS and BRAF sharply distinguish between PDTCs defined by the Turin (PDTC-Turin) vs. MSKCC (PDTC-MSK) criteria, respectively. Mutations of EIF1AX, a component of the translational preinitiation complex, are markedly enriched in PDTCs and ATCs, and have a striking pattern of co-occurrence with RAS. TERT promoter mutations are rare and subclonal in PTCs, whereas they are clonal and highly prevalent in advanced cancers. Application of the TCGA-derived BRAF-RAS score (a measure of MAPK transcriptional output) shows a preserved relationship with BRAF/RAS mutation in PDTCs, whereas ATCs are BRAF-like irrespective of driver mutation. CONCLUSIONS. These data support a model of tumorigenesis whereby PDTCs and ATCs arise from well-differentiated tumors through the accumulation of key additional genetic abnormalities, many of which have prognostic and possible therapeutic relevance. The widespread genomic disruptions in ATC compared to PDTC underscore their greater virulence and higher mortality.
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Also filed as BioProject PRJNA307343. Searching any of these in the dataset finder brings you back here.

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