Novel patient-derived preclinical models of liver cancer

可药性 癌症 医学 恶性肿瘤 临床试验 肝癌 生物信息学 人口 鉴定(生物学) 肿瘤微环境 计算生物学 癌症研究 生物 病理 内科学 基因 环境卫生 植物 生物化学
作者
Erin Bresnahan,Pierluigi Ramadori,Mathias Heikenwälder,Lars Zender,Amaia Lujambio
出处
期刊:Journal of Hepatology [Elsevier BV]
卷期号:72 (2): 239-249 被引量:54
标识
DOI:10.1016/j.jhep.2019.09.028
摘要

Preclinical models of cancer based on the use of human cancer cell lines and mouse models have enabled discoveries that have been successfully translated into patients. And yet the majority of clinical trials fail, emphasising the urgent need to improve preclinical research to better interrogate the potential efficacy of each therapy and the patient population most likely to benefit. This is particularly important for liver malignancies, which lack highly efficient treatments and account for hundreds of thousands of deaths around the globe. Given the intricate network of genetic and environmental factors that contribute to liver cancer development and progression, the identification of new druggable targets will mainly depend on establishing preclinical models that mirror the complexity of features observed in patients. The development of new 3D cell culture systems, originating from cells/tissues isolated from patients, might create new opportunities for the generation of more specific and personalised therapies. However, these systems are unable to recapitulate the tumour microenvironment and interactions with the immune system, both proven to be critical influences on therapeutic outcomes. Patient-derived xenografts, in particular with humanised mouse models, more faithfully mimic the physiology of human liver cancer but are costly and time-consuming, which can be prohibitive for personalising therapies in the setting of an aggressive malignancy. In this review, we discuss the latest advances in the development of more accurate preclinical models to better understand liver cancer biology and identify paradigm-changing therapies, stressing the importance of a bi-directional communicative flow between clinicians and researchers to establish reliable model systems and determine how best to apply them to expanding our current knowledge.

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