In vitro models of the liver : disease modeling, drug discovery and clinical applications

In vitro models of the liver have led to important insights into the pathogenesis of liver disease. These models are essential tools in the discovery and preclinical stages of drug development. The clinical application of these models is also emerging as a promising avenue for validating genetic target-matched treatments, in a precision medicine approach to treatment. Recent advances in ‘liver-on-a-chip’ technology and liver organoid research have opened up new opportunities for the functional and clinical use of organotypic in vitro models. This chapter focuses on the currently available in vitro liver models and the opportunities and limitations they present in the context of evaluating their use in disease modeling, drug discovery, and clinical application

Cellular Genomic Sites of Hepatitis B Virus DNA Integration

Infection with the Hepatitis B Virus (HBV) is one of the strongest risk-factors for liver cancer (hepatocellular carcinoma, HCC). One of the reported drivers of HCC is the integration of HBV DNA into the host cell genome, which may induce pro-carcinogenic pathways. These reported pathways include: induction of chromosomal instability; generation of insertional mutagenesis in key cancer-associated genes; transcription of downstream cancer-associated cellular genes; and/or formation of a persistent source of viral protein expression (particularly HBV surface and X proteins). The contribution of each of these specific mechanisms towards carcinogenesis is currently unclear. Here, we review the current knowledge of specific sites of HBV DNA integration into the host genome, which sheds light on these mechanisms. We give an overview of previously-used methods to detect HBV DNA integration and the enrichment of integration events in specific functional and structural cellular genomic sites. Finally, we posit a theoretical model of HBV DNA integration during disease progression and highlight open questions in the field

Accumulation of Deleterious Passenger Mutations Is Associated with the Progression of Hepatocellular Carcinoma

<div><p>In hepatocellular carcinoma (HCC), somatic genome-wide DNA mutations are numerous, universal and heterogeneous. Some of these somatic mutations are drivers of the malignant process but the vast majority are passenger mutations. These passenger mutations can be deleterious to individual protein function but are tolerated by the cell or are offset by a survival advantage conferred by driver mutations. It is unknown if these somatic deleterious passenger mutations (DPMs) develop in the precancerous state of cirrhosis or if it is confined to HCC. Therefore, we studied four whole-exome sequencing datasets, including patients with non-cirrhotic liver (n = 12), cirrhosis without HCC (n = 6) and paired HCC with surrounding non-HCC liver (n = 74 paired samples), to identify DPMs. After filtering out putative germline mutations, we identified 187±22 DPMs per non-diseased tissue. DPMs number was associated with liver disease progressing to HCC, independent of the number of exonic mutations. Tumours contained significantly more DPMs compared to paired non-tumour tissue (258–293 per HCC exome). Cirrhosis- and HCC-associated DPMs do not occur predominantly in specific genes, chromosomes or biological pathways and the effect on tumour biology is presently unknown. Importantly, for the first time we have shown a significant increase in DPMs with HCC.</p></div

Novel Aspects of the Liver Microenvironment in Hepatocellular Carcinoma Pathogenesis and Development

Hepatocellular carcinoma (HCC) is a prevalent primary liver cancer that is derived from hepatocytes and is characterised by high mortality rate and poor prognosis. While HCC is driven by cumulative changes in the hepatocyte genome, it is increasingly recognised that the liver microenvironment plays a pivotal role in HCC propensity, progression and treatment response. The microenvironmental stimuli that have been recognised as being involved in HCC pathogenesis are diverse and include intrahepatic cell subpopulations, such as immune and stellate cells, pathogens, such as hepatitis viruses, and non-cellular factors, such as abnormal extracellular matrix (ECM) and tissue hypoxia. Recently, a number of novel environmental influences have been shown to have an equally dramatic, but previously unrecognized, role in HCC progression. Novel aspects, including diet, gastrointestinal tract (GIT) microflora and circulating microvesicles, are now being recognized as increasingly important in HCC pathogenesis. This review will outline aspects of the HCC microenvironment, including the potential role of GIT microflora and microvesicles, in providing new insights into tumourigenesis and identifying potential novel targets in the treatment of HCC

Frequency distribution of DPMs.

<p>A frequency distribution of the genes containing DPMs in 1000G and WES 1 (A), WES 2 (B), WES 3 (C), and WES 4 (D) shows that most are unique to a given patient. Each gene containing a DPM was grouped based on the number of patients in which that gene contained a DPM (x-axis).</p

Absolute number of exonic variants and mutation subtypes in 1000G, liver injury, cirrhosis and HCC.

<p>The exonic variants in each of the 5 datasets were enumerated (A and B) and then subdivided into 5 groups (missense, frameshift ins/del, stop-gain/-loss and non-frameshift ins/del) (C and D, expressed as a percentage of all somatic exonic mutations). 1000G and WES 1 (A and C) contain unpaired samples, while WES 2–4 (B and D) are composed of paired tumour and non-tumour samples taken from the same individual. Data are expressed as median (interquartile range). * p<0.05, ** p<0.01, *** p<0.001 and **** p<0.0001, Mann-Whitney U test (1000G and WES 1) or Wilcoxon matched-pairs signed-rank test (WES 2–4). NC-non-cirrhosis; C-cirrhosis; NT-non-tumour; T-tumour.</p

Bioinformatics analysis pipeline.

<p>Each resultant data file is indicated by a sloped rectangle and each process represented by a square rectangle. Our pipeline contains 3 stages: alignment and calibration; variant calling and filtering; and variants annotation and filtration of putative germline mutations.</p

Hypothetical model of HCC progression.

<p>HCC progression is presented here as multiple waves of driver sweeps within hepatocyte subclones. The equilibrium between DPM accumulation and negative selection on the hepatocyte subclones are shown in the top row. A schematic model of the liver (with each circle representing a hepatocyte and the colour gradient representing the DPM load within each hepatocyte) is shown in the centre row. The average DPM load for the tissue is depicted in the bottom row.</p

Summary statistics for normalised DPMs between datasets.

<p>Summary statistics for normalised DPMs between datasets.</p

Summary data of publicly-available WES datasets used in this study.

<p>Summary data of publicly-available WES datasets used in this study.</p