Experimental Models of Hepatocellular Carcinoma-A Preclinical Perspective.
TL;DR: In this article, a review of the currently available preclinical models frequently applied for the study of hepatocellular carcinoma in terms of initiation, development, and progression, as well as for the discovery of efficient treatments, highlighting the advantages and the limitations of each model.
Abstract: Hepatocellular carcinoma (HCC), the most frequent form of primary liver carcinoma, is a heterogenous and complex tumor type with increased incidence, poor prognosis, and high mortality. The actual therapeutic arsenal is narrow and poorly effective, rendering this disease a global health concern. Although considerable progress has been made in terms of understanding the pathogenesis, molecular mechanisms, genetics, and therapeutical approaches, several facets of human HCC remain undiscovered. A valuable and prompt approach to acquire further knowledge about the unrevealed aspects of HCC and novel therapeutic candidates is represented by the application of experimental models. Experimental models (in vivo and in vitro 2D and 3D models) are considered reliable tools to gather data for clinical usability. This review offers an overview of the currently available preclinical models frequently applied for the study of hepatocellular carcinoma in terms of initiation, development, and progression, as well as for the discovery of efficient treatments, highlighting the advantages and the limitations of each model. Furthermore, we also focus on the role played by computational studies (in silico models and artificial intelligence-based prediction models) as promising novel tools in liver cancer research.
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TL;DR: In this article , a review of the current preclinical mouse models that are frequently used to study hepatocellular carcinoma (HCC) can be found, where a wide range of mouse models of HCC has been established using various approaches including chemotoxic agents, genetic modifications, special diet administration, and tumor cells transplantation.
6 citations
TL;DR: The synthesis, characterization, and toxicological assessment of two RUT bioconjugates obtained by enzymatic esterification with oleic acid and linoleic acid, as flavonoid precursors with improved physicochemical and biological properties are focused on.
Abstract: Rutin (RUT) is considered one the most attractive flavonoids from a therapeutic perspective due to its multispectral pharmacological activities including antiradical, anti-inflammatory, antiproliferative, and antimetastatic among others. Still, this compound presents a low bioavailability what narrows its clinical applications. To overcome this inconvenience, the current paper was focused on the synthesis, characterization, and toxicological assessment of two RUT bioconjugates obtained by enzymatic esterification with oleic acid (OA) and linoleic acid (LA)—rutin oleate (RUT-O) and rutin linoleate (RUT-L), as flavonoid precursors with improved physicochemical and biological properties. Following the enzymatic synthesis in the presence of Novozyme® 435, the two bioconjugates were obtained, their formation being confirmed by RAMAN and FT-IR spectroscopy. The in vitro and in ovo toxicological assessment of RUT bioconjugates (1–100 µM) was performed using 2D consecrated cell lines (cardiomyoblasts - H9c2(2-1), hepatocytes—HepaRG, and keratinocytes—HaCaT), 3D reconstructed human epidermis tissue (EpiDerm™), and chick chorioallantoic membranes, respectively. The results obtained were test compound, concentration—and cell-type dependent, as follows: RUT-O reduced the viability of H9c2(2-1), HepaRG, and HaCaT cells at 100 µM (to 77.53%, 83.17%, and 78.32%, respectively), and induced cell rounding and floating, as well as apoptotic-like features in the nuclei of all cell lines, whereas RUT-L exerted no signs of cytotoxicity in all cell lines in terms of cell viability, morphology, and nuclear integrity. Both RUT esters impaired the migration of HepaRG cells (at 25 µM) and lack irritative potential (at 100 µM) in vitro (tissue viability >50%) and in ovo (irritation scores of 0.70 for RUT-O, and 0.49 for RUT-L, respectively). Computational predictions revealed an increased lipophilicity, and reduced solubility, drug-likeness and drug score of RUT-O and RUT-L compared to their parent compounds—RUT, OA, and LA. In conclusion, we report a favorable toxicological profile for RUT-L, while RUT-O is dosage-limited since at high concentrations were noticed cytotoxic effects.
4 citations
TL;DR: Using a cocktail of biologicals that mimic the stem cell niche signaling, hepatocellular carcinoma (HCC) organoids could be generated from tissue samples of both human and murine origin this article .
4 citations
TL;DR: In this article , the authors summarize the various methods in which liver organoids may be generated and describe their biological and clinical applications, present challenges, and prospects for their integration with emerging technologies.
3 citations
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21 Jan 2021
TL;DR: In this paper, the authors used non-invasive criteria to diagnose hepatocellular carcinoma (HCC) and showed that 25% of all HCCs present potentially actionable mutations, which are yet to translate into clinical practice.
Abstract: Liver cancer remains a global health challenge, with an estimated incidence of >1 million cases by 2025 Hepatocellular carcinoma (HCC) is the most common form of liver cancer and accounts for ~90% of cases Infection by hepatitis B virus and hepatitis C virus are the main risk factors for HCC development, although non-alcoholic steatohepatitis associated with metabolic syndrome or diabetes mellitus is becoming a more frequent risk factor in the West Moreover, non-alcoholic steatohepatitis-associated HCC has a unique molecular pathogenesis Approximately 25% of all HCCs present with potentially actionable mutations, which are yet to be translated into the clinical practice Diagnosis based upon non-invasive criteria is currently challenged by the need for molecular information that requires tissue or liquid biopsies The current major advancements have impacted the management of patients with advanced HCC Six systemic therapies have been approved based on phase III trials (atezolizumab plus bevacizumab, sorafenib, lenvatinib, regorafenib, cabozantinib and ramucirumab) and three additional therapies have obtained accelerated FDA approval owing to evidence of efficacy New trials are exploring combination therapies, including checkpoint inhibitors and tyrosine kinase inhibitors or anti-VEGF therapies, or even combinations of two immunotherapy regimens The outcomes of these trials are expected to change the landscape of HCC management at all evolutionary stages
2,038 citations
TL;DR: Given the growing trend on the application of ML methods in cancer research, this work presents here the most recent publications that employ these techniques as an aim to model cancer risk or patient outcomes.
Abstract: Cancer has been characterized as a heterogeneous disease consisting of many different subtypes. The early diagnosis and prognosis of a cancer type have become a necessity in cancer research, as it can facilitate the subsequent clinical management of patients. The importance of classifying cancer patients into high or low risk groups has led many research teams, from the biomedical and the bioinformatics field, to study the application of machine learning (ML) methods. Therefore, these techniques have been utilized as an aim to model the progression and treatment of cancerous conditions. In addition, the ability of ML tools to detect key features from complex datasets reveals their importance. A variety of these techniques, including Artificial Neural Networks (ANNs), Bayesian Networks (BNs), Support Vector Machines (SVMs) and Decision Trees (DTs) have been widely applied in cancer research for the development of predictive models, resulting in effective and accurate decision making. Even though it is evident that the use of ML methods can improve our understanding of cancer progression, an appropriate level of validation is needed in order for these methods to be considered in the everyday clinical practice. In this work, we present a review of recent ML approaches employed in the modeling of cancer progression. The predictive models discussed here are based on various supervised ML techniques as well as on different input features and data samples. Given the growing trend on the application of ML methods in cancer research, we present here the most recent publications that employ these techniques as an aim to model cancer risk or patient outcomes.
1,991 citations
TL;DR: 2D and 3D cell culture methods are reviewed, advantages and limitations of these techniques in modeling physiologically and pathologically relevant processes are discussed, and directions for future research are suggested.
Abstract: Cell culture has become an indispensable tool to help uncover fundamental biophysical and biomolecular mechanisms by which cells assemble into tissues and organs, how these tissues function, and how that function becomes disrupted in disease. Cell culture is now widely used in biomedical research, tissue engineering, regenerative medicine, and industrial practices. Although flat, two-dimensional (2D) cell culture has predominated, recent research has shifted toward culture using three-dimensional (3D) structures, and more realistic biochemical and biomechanical microenvironments. Nevertheless, in 3D cell culture, many challenges remain, including the tissue-tissue interface, the mechanical microenvironment, and the spatiotemporal distributions of oxygen, nutrients, and metabolic wastes. Here, we review 2D and 3D cell culture methods, discuss advantages and limitations of these techniques in modeling physiologically and pathologically relevant processes, and suggest directions for future research.
1,048 citations
TL;DR: In this Review, Drost and Clevers discuss the recent advances in organoid models of cancer and how they can be exploited to drive the translation of basic cancer research into novel patient-specific treatment regimens in the clinic.
Abstract: The recent advances in in vitro 3D culture technologies, such as organoids, have opened new avenues for the development of novel, more physiological human cancer models. Such preclinical models are essential for more efficient translation of basic cancer research into novel treatment regimens for patients with cancer. Wild-type organoids can be grown from embryonic and adult stem cells and display self-organizing capacities, phenocopying essential aspects of the organs they are derived from. Genetic modification of organoids allows disease modelling in a setting that approaches the physiological environment. Additionally, organoids can be grown with high efficiency from patient-derived healthy and tumour tissues, potentially enabling patient-specific drug testing and the development of individualized treatment regimens. In this Review, we evaluate tumour organoid protocols and how they can be utilized as an alternative model for cancer research.
955 citations
TL;DR: Common approaches to 3D culture are reviewed, the significance of 3D cultures in drug resistance and drug repositioning is discussed and some of the challenges of applying 3D cell cultures to high-throughput drug discovery are addressed.
Abstract: Drug development is a lengthy and costly process that proceeds through several stages from target identification to lead discovery and optimization, preclinical validation and clinical trials culminating in approval for clinical use. An important step in this process is high-throughput screening (HTS) of small compound libraries for lead identification. Currently, the majority of cell-based HTS is being carried out on cultured cells propagated in two-dimensions (2D) on plastic surfaces optimized for tissue culture. At the same time, compelling evidence suggests that cells cultured in these non-physiological conditions are not representative of cells residing in the complex microenvironment of a tissue. This discrepancy is thought to be a significant contributor to the high failure rate in drug discovery, where only a low percentage of drugs investigated ever make it through the gamut of testing and approval to the market. Thus, three-dimensional (3D) cell culture technologies that more closely resemble in vivo cell environments are now being pursued with intensity as they are expected to accommodate better precision in drug discovery. Here we will review common approaches to 3D culture, discuss the significance of 3D cultures in drug resistance and drug repositioning and address some of the challenges of applying 3D cell cultures to high-throughput drug discovery.
911 citations