Unsupervised Clustering of Immunohistochemical Markers to Define High-Risk Endometrial Cancer.

Fiche publication

Date publication

décembre 2017

Journal

Pathology oncology research : POR

Auteurs

Membres identifiés du Cancéropôle Est :
Pr GRAESSLIN Olivier

Tous les auteurs :
Laas E, Ballester M, Cortez A, Graesslin O, Daraï E

Lien Pubmed

https://www.ncbi.nlm.nih.gov/pubmed/29264761

Résumé

Considerable heterogeneity exists in outcomes of early endometrial cancer (EC) according to the type but also the histological grading. Our goal was to describe the immunohistochemical profiles of type I EC according to grades and type II EC, to identify groups of interacting proteins using principal component analysis (PCA) and unsupervised clustering. We studied 13 immunohistochemical markers (steroid receptors, pro/anti-apoptotic proteins, metalloproteinases (MMP) and tissue inhibitor of metalloproteinase (TIMP), and CD44 isoforms known for their role in endometrial pathology. Co-expressed proteins associated with the type, grade and outcome of EC were determined by PCA and unsupervised clustering. PCA identified three functional groups of proteins from 43 tissue samples (38 type I and 5 type II EC): the first was characterized by p53 expression; the second by MMPs, bcl-2, PR B and CD44v6; and the third by ER alpha, PR A, TIMP-2 and CD44v3. Unsupervised clustering found two main clusters of proteins, with both type I grade 3 and type II EC exhibiting the same cluster profile. PCA and unsupervised clustering of immunohistochemical markers in EC contribute to a better comprehension and classification of the disease.