The Combination of RET, BRAF and Demographic Data Identifies Subsets of Patients with Aggressive Papillary Thyroid Cancer View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-03-22

AUTHORS

Jose R. W. Martínez, Sergio Vargas-Salas, Soledad Urra Gamboa, Estefanía Muñoz, José Miguel Domínguez, Augusto León, Nicolás Droppelmann, Antonieta Solar, Mark Zafereo, F. Christopher Holsinger, Hernán E. González

ABSTRACT

The use of BRAFV600E and RET/PTC1 as biomarkers to guide the extent of surgery in patients with papillary thyroid cancer (PTC) remains controversial. We assessed the combined use of demographic data (sex and age) with mRNA expression levels and/or mutational status (BRAFV600E and RET/PTC1) to identify potential subsets of patients with aggressive histopathological features (lymph node metastases and extrathyroidal extension). In a cohort of 126 consecutive patients, BRAFV600E and RET/PTC1 mutations were found in 52 and 18%, respectively. By conditional bivariate analysis (CBVA), a 'high activity' profile of BRAF (BRAFV600E positive or high expression) and 'low activity' profile of RET (RET/PTC1 negative or low expression) was associated with extrathyroidal extension (ETE) (OR 4.48). Alternatively, a 'high activity' profile of RET (RET/PTC1 positive or high expression) and 'low activity' profile of BRAF (BRAFV600E negative or low expression) were associated with lymph node metastasis (LNM) (OR 12.80). Furthermore, in patients younger than 55 years, a low expression of BRAF was associated with LNM (OR 17.65) and the presence of BRAFV600E mutation was associated with ETE (OR 2.76). Our results suggest that the analysis of demographic and molecular variables by CBVA could contribute to identify subsets of patients with aggressive histopathologic features, providing a potential guide to personalised surgical management of PTC. More... »

PAGES

1-10

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s12672-019-0359-8

DOI

http://dx.doi.org/10.1007/s12672-019-0359-8

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1112945211

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30903583


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