Mouse models for liver cancer

Abstract

Hepatocellular carcinoma (HCC), the most common form of primary liver cancer is the third leading cause of cancer-related cell death in human and the fifth in women worldwide. The incidence of HCC is increasing despite progress in identifying risk factors, understanding disease etiology and developing anti-viral strategies. Therapeutic options are limited and survival after diagnosis is poor. Therefore, better preventive, diagnostic and therapeutic tools are urgently needed, in particular given the increased contribution from systemic metabolic disease to HCC incidence worldwide. In the last three decades, technological advances have facilitated the generation of genetically engineered mouse models (GEMMs) to mimic the alterations frequently observed in human cancers or to conduct intervention studies and assess the relevance of candidate gene networks in tumor establishment, progression and maintenance. Because these studies allow molecular and cellular manipulations impossible to perform in patients, GEMMs have improved our understanding of this complex disease and represent a source of great potential for mechanism-based therapy development. In this review, we provide an overview of the current state of HCC modeling in the mouse, highlighting successes, current challenges and future opportunities.

Figure 1

Integration of data obtained from mouse models and human patient analyses toward better liver cancer prevention, diagnostic and treatment. Cross‐species comparative studies, either large‐scale, unbiased (omic‐type) or knowledge‐based (candidate approaches) should be carried out at every step along the process of improving mouse modeling and patient analyses. Improved mouse models ‐ GEMMs, mosaic, xenografts ‐ can be designed to recapitulate specific features of human HCC, while improved human disease characterization will allow better patient and disease stage stratification. Improved disease characterization and animal modeling will allow better, ultimately personalized, design of studies that aim at chemoprevention, biomarker discovery and drug testing.