Modeling the Mechanisms of Tumor Progression in Low Grade Human Hepatocellular Carcinomas
Résumé du sujet : Hepatocellular carcinoma (HCC) is the 3rd cause of cancer death worldwide and its heterogeneity challenges patient management. Prediction of early recurrence and of the potential for cancer aggressiveness are key factors governing patient eligibility for potentially curative therapies.1 After analysis of 1133 human HCCs, we reported a 2-dimensional distribution explaining HCC heterogeneity.2, 3 We defined a 1st dimension across the proliferation/differentiation ratio, with progressive tumor dedifferentiation and development of tumor fibrosis and cancer stem cells, in accordance with our previous modeling.4-6 In the 2nd dimension, we showed that well-differentiated HCCs distribute across an HNF4α/β-catenin-driven metabolic, hepatocyte-specific axis. As a result, we identified four HCC subclasses: PERIPORTAL, PERIVENOUS, EXTRACELLULAR MATRIX and STEM CELL, that form a continuum of increasing malignancy.7 The salient finding of this work was the discovery of the least aggressive of all HCC subclasses (PERIPORTAL-TYPE HCCs). PERIPORTAL-TYPE HCCs represent 30% of all resected HCCs. They are defined by the high expression of eight liver-specific gene markers involved in periportal liver metabolic functions, such as gluconeogenesis and amino-acid catabolism (INSERM PATENT: WO/2018/189215). Seventy percent of PERIPORTAL-TYPE HCCs and 60% of PERIVENOUS-TYPE HCCs did not recur two years after resection.2 This tumor behavior indicates low malignancy HCCs. However, 30% of PERIPORTAL-TYPE and 40% of PERIVENOUS-TYPE HCCs progress to high-grade cancers. We hypothesize that low grade progress to high grade HCCs resistant to therapy via microevolution to a cancer stem cell phenotype8 and that microevolution may result from metabolic reprogramming.9 Therefore, the Master Internship aims to be a step forward in a program investigating how low grade cancers acquire further malignancy and end up killing the patients. This project is being carried out by researchers in bio-statistics, research engineers, molecular biologists and molecular pathologists, with both heuristics and translational purposes in cancer research. The Master candidate will team up with a PhD student in bioinformatics and a post-doc in anatomical pathology both interested in high dimensional data analysis.
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Laboratoire d'accueil : Institut NuMeCanEquipe d'accueil : EXPRESResponsable(s) scientifique(s) : Orlando MUSSOContact(s) : email@example.com
Proposé le 30-07-2020