Study of the RET receptor dysfunctions caused by mutations associated with human neoplastic disorders and developmental diseases

Abstract

The ret proto-oncogene encodes a membrane spanning glycoprotein which is a member of the receptor tyrosine kinase family (Hanks et al. 1988). RET is the signaling component of multi-subunit receptor complexes for the GDNF of family ligands, including GDNF, neurturin, artemin and persephin. The binding components of these receptor complexes are glycosyl-phosphatidylinositol (GPI)-membrane anchored molecules, known as GDNF family receptor α (GFRαs). Four different GFRαs (GFRα1–4) dictate ligand specificity. Germline point mutations of RET are responsible for the inheritance of MEN2 (Multiple Endocrine Neoplasia type 2) cancer syndromes which are usually divided into three different clinical subtypes: MEN2A, MEN2B and FMTC (familial medullary thyroid carcinoma), which are all autosomal dominant cancer syndromes. Inactivating mutations of RET cause an impaired development of the enteric nervous system which is responsible for the Congenital megacolon or Hirschprung's disease (HSCR). The aim of my work was to study the expression of different RET mutants in order to highlight their biological role in diverse cellular context. In particular, we focused on gain of function cysteine mutations that are responsible for medullary thyroid carcinoma (MTC) by causing covalent RET dimerisation, leading to ligand-independent activation of its tyrosine kinase. In this context, the association of Cys609 and Cys620 activating mutations with HSCR is still an unresolved paradox. To address this issue, we have developed a transgenic model for human diseases (specifically, Multiple Endocrine Neoplasia type 2 and Hirschsprung disease) through the insertion of a gain and loss of function RET mutation, the RETC620R in the mouse genome. We have also studied the in vitro effects of a tyrosine kinase inhibitor PP1, which we propose could represent a potential treatment strategy and merits further testing, using in vivo models such as the one we have generated

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