Heterogeneity, inherent and acquired drug resistance in patient-derived organoid models of primary liver cancer

Drug resistance in human cancers - Mechanisms and implications

Cancers have complex etiology and pose a significant impact from the health care perspective apart from the socio-economic implications. The enormity of challenge posed by cancers can be understood from the fact that clinical trials for cancer therapy has yielded minimum potential promises compared to those obtained for other diseases. Surgery, chemotherapy and radiotherapy continue to be the mainstay therapeutic options for cancers. Among the challenges posed by these options, induced resistance to chemotherapeutic drugs is probably the most significant contributor for poor prognosis and ineffectiveness of the therapy. Drug resistance is a property exhibited by almost all cancer types including carcinomas, leukemias, myelomas, sarcomas and lymphomas. The mechanisms by which drug resistance is induced include the factors within the tumor microenvironment, mutations in the genes responsible for drug metabolism, changes in the surface drug receptors and increased drug efflux. We present here comprehensively the drug resistance in cancers along with their mechanisms. Also, apart from resistance to regularly used chemotherapeutic drugs, we present resistance induction to new generation therapeutic agents such as monoclonal antibodies. Finally, we have discussed the experimental approaches to understand the mechanisms underlying induction of drug resistance and potential ways to mitigate induced drug resistance.

Human Hepatobiliary Organoids: Recent Advances in Drug Toxicity Verification and Drug Screening

Many drug and therapeutic modalities have emerged over the past few years. However, successful commercialization is dependent on their safety and efficacy evaluations. Several preclinical models are available for drug-screening and safety evaluations, including cellular- and molecular-level models, tissue and organoid models, and animal models. Organoids are three-dimensional cell cultures derived from primary tissues or stem cells that are structurally and functionally similar to the original organs and can self-renew, and they are used to establish various disease models. Human hepatobiliary organoids have been used to study the pathogenesis of diseases, such as hepatitis, liver fibrosis, hepatocellular carcinoma, primary sclerosing cholangitis and biliary tract cancer, as they retain the physiological and histological characteristics of the liver and bile ducts. Here, we review recent research progress in validating drug toxicity, drug screening and personalized therapy for hepatobiliary-related diseases using human hepatobiliary organoid models, discuss the challenges encountered in current research and evaluate the possible solutions.

Toward reproducible tumor organoid culture: focusing on primary liver cancer

Organoids present substantial potential for pushing forward preclinical research and personalized medicine by accurately recapitulating tissue and tumor heterogeneity in vitro. However, the lack of standardized protocols for cancer organoid culture has hindered reproducibility. This paper comprehensively reviews the current challenges associated with cancer organoid culture and highlights recent multidisciplinary advancements in the field with a specific focus on standardizing liver cancer organoid culture. We discuss the non-standardized aspects, including tissue sources, processing techniques, medium formulations, and matrix materials, that contribute to technical variability. Furthermore, we emphasize the need to establish reproducible platforms that accurately preserve the genetic, proteomic, morphological, and pharmacotypic features of the parent tumor. At the end of each section, our focus shifts to organoid culture standardization in primary liver cancer. By addressing these challenges, we can enhance the reproducibility and clinical translation of cancer organoid systems, enabling their potential applications in precision medicine, drug screening, and preclinical research.

SNHG1, interacting with SND1, contributes to sorafenib resistance of liver cancer cells by increasing m6A-mediated SLC7A11 expression and promoting aerobic glycolysis

Aerobic glycolysis plays an important role in multidrug resistance of cancer cells. Here, we screened different expressed lncRNAs associated with sorafenib resistance of liver cancer cells, by intersecting the bioinformatics analyses of TCGA and GEO (the GSE62813 dataset) databases. Our results revealed that the 18 upregulated lncRNAs in the intersection are associated with and enriched in metabolism of small molecule organic acids, suggesting their potential in glycolysis. The lncRNA small nucleolar RNA host gene 1 (Snhg1) was chosen as a potential regulator of aerobic glycolysis in liver cancer cells, for its significant promotion on lactate production. Gain- and loss-of-function experiments mediated by Crispr-Cas9 technique in HepG2 cells indicated that Snhg1 promoted cell proliferation, invasion, sorafenib resistance, and aerobic glycolysis. In the mechanism exploration, we found that Snhg1 can interact with SND1 protein, a famous RNA binding protein and recently identified "Reader" of N6-methyladenosine (m6A). SND1 was demonstrated to be positively regulated by Snhg1 and had similar promoting effects on proliferation, invasion, sorafenib resistance, and aerobic glycolysis of HepG2 cells. SND1 bound with and promoted the expression of SLC7A11, an aerobic glycolysis regulator. Furthermore, either silencing SLC7A11 or blocking aerobic glycolysis with 2-deoxy-d-glucose (2-DG) was able to reverse the promotion of Snhg1 overexpression on malignancy, sorafenib resistance, and aerobic glycolysis of HepG2 cells. Finally, in a liver cancer xenograft mouse model, we found that formed tumors with Snhg1-knocked-down HepG2 cells were more sensitive to sorafenib administration. Altogether, SNHG1 contributes to sorafenib resistance of liver cancer cells by promoting SND1-m6A-SLC7A11-mediated aerobic glycolysis.

Overexpression of Aurora Kinase B Is Correlated with Diagnosis and Poor Prognosis in Hepatocellular Carcinoma

Aurora kinase B (AURKB) overexpression promotes tumor initiation and development by participating in the cell cycle. In this study, we focused on the mechanism of AURKB in hepatocellular carcinoma (HCC) progression and on AURKB's value as a diagnostic and prognostic biomarker in HCC. We used data from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) to analyze AURKB expression in HCC. We found that the expression levels of AURKB in HCC samples were higher than those in the corresponding control group. R packages were used to analyze RNA sequencing data to identify AURKB-related differentially expressed genes (DEGs), and these genes were found to be significantly enriched during the cell cycle. The biological function of AURKB was verified, and the results showed that cell proliferation was slowed down and cells were arrested in the G2/M phase when AURKB was knocked down. AURKB overexpression resulted in significant differences in clinical symptoms, such as the clinical T stage and pathological stage. Kaplan-Meier survival analysis, Cox regression analysis, and Receiver Operating Characteristic (ROC) curve analysis suggested that AURKB overexpression has good diagnostic and prognostic potential in HCC. Therefore, AURKB may be used as a potential target for the diagnosis and cure of HCC.

Disulfidptosis-related lncRNA signatures assess immune microenvironment and drug sensitivity in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is associated with a high mortality rate, where resistance to immunotherapy and chemotherapy plays a crucial role. A newly identified form of cell death called disulfidptosis shows promise, but its biological mechanism in HCC remains uncertain. In this study, a prognostic model was developed for Disulfidptosis-related long non-coding RNAs (DRLs) from 370 HCC patients sourced from TCGA-LIHC, utilizing five key features: AC026356.1, AC073254.1, PXN-AS1 expression, AC026412.3, and AC099066.2. High-risk HCC patients had lower survival, CD4+ T cell infiltration, and elevated immune checkpoint gene expression. Furthermore, based on the features of DRLs, HCC was classified into three subtypes. Notably, patients belonging to different subtypes demonstrated varying overall survival rates, immune cell infiltration patterns, and sensitivity to immune therapy. Moreover, the novel DRL AC026412.3 (HR = 40.207) emerged as the most significant prognostic factor, exhibiting high expression across all HCC cells. Elevated expression of AC026412.3 promoted HCC cell proliferation and induced resistance to gefitinib. In conclusion, we have discovered five DRLs and constructed a prognostic risk model. Our findings validate the correlation between DRL-related prognostic models, tumor subtypes, and the HCC immune microenvironment along with its implications for immunotherapy. Moreover, further investigation into the molecular mechanisms of key biomarkers like AC026412.3 in the future will contribute significantly to advancing our comprehension of HCC's pathogenesis and drug resistance mechanisms.

Advancements and application prospects of three-dimensional models for primary liver cancer: a comprehensive review

Primary liver cancer (PLC) is one of the most commonly diagnosed cancers worldwide and a leading cause of cancer-related deaths. However, traditional liver cancer models fail to replicate tumor heterogeneity and the tumor microenvironment, limiting the study and personalized treatment of liver cancer. To overcome these limitations, scientists have introduced three-dimensional (3D) culture models as an emerging research tool. These 3D models, utilizing biofabrication technologies such as 3D bioprinting and microfluidics, enable more accurate simulation of the in vivo tumor microenvironment, replicating cell morphology, tissue stiffness, and cell-cell interactions. Compared to traditional two-dimensional (2D) models, 3D culture models better mimic tumor heterogeneity, revealing differential sensitivity of tumor cell subpopulations to targeted therapies or immunotherapies. Additionally, these models can be used to assess the efficacy of potential treatments, providing guidance for personalized therapy. 3D liver cancer models hold significant value in tumor biology, understanding the mechanisms of disease progression, and drug screening. Researchers can gain deeper insights into the impact of the tumor microenvironment on tumor cells and their interactions with the surrounding milieu. Furthermore, these models allow for the evaluation of treatment responses, offering more accurate guidance for clinical interventions. In summary, 3D models provide a realistic and reliable tool for advancing PLC research. By simulating tumor heterogeneity and the microenvironment, these models contribute to a better understanding of the disease mechanisms and offer new strategies for personalized treatment. Therefore, 3D models hold promising prospects for future PLC research.

The combined characteristics of cholesterol metabolism and the immune microenvironment may serve as valuable biomarkers for both the prognosis and treatment of hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) being a complex disease, commonly exhibits multifaceted presentations, rendering its treatment challenging and necessitating specific approaches. The tumor immune microenvironment is crucial in cancer treatment, and cholesterol metabolism is a key component that helps cells grow and produce vital metabolites. However, the reprogramming of cholesterol metabolism in the tumor microenvironment (TME) can promote HCC development, and cancer classifiers relating to cholesterol metabolism are currently limited. Despite significant progress, further research is needed to improve early detection, liver function, and treatment options to improve patient outcomes.

Methods

To evaluate the expression abundance of tumor immune microenvironment (TIME) and cholesterol metabolism in 8 types of liver cancer cells, we comprehensively evaluated the immune cell composition, extracellular matrix alterations, and activity of relevant signaling pathways in the TIME through nine liver cancer patients, stromal scoring, immune scoring, tumor purity scoring, immune infiltration analysis, and pathway enrichment. Subsequently, we utilized machine learning techniques to construct prognostic models for both cholesterol metabolism and the tumor immune microenvironment, further exploring the tumor mutation burden, immune infiltration levels, and drug sensitivity in different subtypes of HCC patients.

Results

Our study constructed three cancer screening models to identify HCC patients with high cholesterol metabolism and low TIME, who have a poorer prognosis. On the contrary, patients with low cholesterol metabolism and high TIME often have better prognosis. Furthermore, we identified chemical compounds, such as BPD-00008900, ML323, Doramapimod, and AZD2014, which display better chemotherapy results for high-risk patients in specific sub-groups.

Progress in patient-derived liver cancer cell models: a step forward for precision medicine

The development of effective precision treatments for liver cancers has been hindered by the scarcity of preclinical models that accurately reflect the heterogeneity of this disease. Recent progress in developing patient-derived liver cancer cell lines and organoids has paved the way for precision medicine research. These expandable resources of liver cancer cell models enable a full spectrum of pharmacogenomic analysis for liver cancers. Moreover, patient-derived and short-term cultured two-dimensional tumor cells or three-dimensional organoids can serve as patient avatars, allowing for the prediction of patients' response to drugs and facilitating personalized treatment for liver cancer patients. Furthermore, the current novel techniques have expanded the scope of cancer research, including innovative organoid culture, gene editing and bioengineering. In this review, we provide an overview of the progress in patient-derived liver cancer cell models, focusing on their applications in precision and personalized medicine research. We also discuss the challenges and future perspectives in this field.

Pharmaco-proteogenomic characterization of liver cancer organoids for precision oncology

Organoid models have the potential to recapitulate the biological and pharmacotypic features of parental tumors. Nevertheless, integrative pharmaco-proteogenomics analysis for drug response features and biomarker investigation for precision therapy of patients with liver cancer are still lacking. We established a patient-derived liver cancer organoid biobank (LICOB) that comprehensively represents the histological and molecular characteristics of various liver cancer types as determined by multiomics profiling, including genomic, epigenomic, transcriptomic, and proteomic analysis. Proteogenomic profiling of LICOB identified proliferative and metabolic organoid subtypes linked to patient prognosis. High-throughput drug screening revealed distinct response patterns of each subtype that were associated with specific multiomics signatures. Through integrative analyses of LICOB pharmaco-proteogenomics data, we identified the molecular features associated with drug responses and predicted potential drug combinations for personalized patient treatment. The synergistic inhibition effect of mTOR inhibitor temsirolimus and the multitargeted tyrosine kinase inhibitor lenvatinib was validated in organoids and patient-derived xenografts models. We also provide a user-friendly web portal to help serve the biomedical research community. Our study is a rich resource for investigation of liver cancer biology and pharmacological dependencies and may help enable functional precision medicine.

Organoids: The current status and biomedical applications

Organoids are three-dimensional (3D) miniaturized versions of organs or tissues that are derived from cells with stem potential and can self-organize and differentiate into 3D cell masses, recapitulating the morphology and functions of their in vivo counterparts. Organoid culture is an emerging 3D culture technology, and organoids derived from various organs and tissues, such as the brain, lung, heart, liver, and kidney, have been generated. Compared with traditional bidimensional culture, organoid culture systems have the unique advantage of conserving parental gene expression and mutation characteristics, as well as long-term maintenance of the function and biological characteristics of the parental cells in vitro. All these features of organoids open up new opportunities for drug discovery, large-scale drug screening, and precision medicine. Another major application of organoids is disease modeling, and especially various hereditary diseases that are difficult to model in vitro have been modeled with organoids by combining genome editing technologies. Herein, we introduce the development and current advances in the organoid technology field. We focus on the applications of organoids in basic biology and clinical research, and also highlight their limitations and future perspectives. We hope that this review can provide a valuable reference for the developments and applications of organoids.

Updates on Organoid Model for the Study of Liver Cancer

Liver cancer remains one of the most common cancers worldwide with limited therapy options. The main risk factors for hepatocellular carcinoma (HCC), the most common form of liver cancer, include chronic infection with hepatitis B or hepatitis C viruses, alcohol abuse, and metabolic disease. Current systemic therapies for advanced HCCs have greatly improved in the last decade, but there is still a need to develop more targeted drug therapy for HCCs. The development of liver organoids, a self-organising and self-renewal three-dimensional cell culture model, has greatly improved cancer research, including liver cancer. The generation of liver organoids provides a physiologically relevant model to study cancer drug screening and development, personalized medicine, liver disease modeling, and liver regeneration. However, the advent of organoid development also comes with few shortcomings that must be overcome, including the high cost of the model, the availability of origin tissues, and the need for multilineage liver organoids to replicate the true cellular heterogeneity of the liver. Despite all the limitations, liver organoids provide a reliable in vitro model for translational applications to develop more effective HCC therapy and to understand the underlying pathogenic mechanism in various liver diseases.

Opportunities and challenges of hepatocellular carcinoma organoids for targeted drugs sensitivity screening

Hepatocellular carcinoma is one of the malignancies worldwide with a high mortality rate and an increasing incidence. Molecular Targeted agents are its common first-line treatment. Organoid technology, as a cutting-edge technology, is gradually being applied in the development of therapeutic oncology. Organoid models can be used to perform sensitivity screening of targeted drugs to facilitate the development of innovative therapeutic agents for the treatment of hepatocellular carcinoma. The purpose of this review is to provide an overview of the opportunities and challenges of hepatocellular carcinoma organoids in targeted drug sensitivity testing as well as a future outlook.

Overcoming the therapeutic resistance of hepatomas by targeting the tumor microenvironment

Hepatocellular carcinoma (HCC) accounts for the majority of primary liver cancers and is the third leading cause of cancer-related mortality worldwide. Multifactorial drug resistance is regarded as the major cause of treatment failure in HCC. Accumulating evidence shows that the constituents of the tumor microenvironment (TME), including cancer-associated fibroblasts, tumor vasculature, immune cells, physical factors, cytokines, and exosomes may explain the therapeutic resistance mechanisms in HCC. In recent years, anti-angiogenic drugs and immune checkpoint inhibitors have shown satisfactory results in HCC patients. However, due to enhanced communication between the tumor and TME, the effect of heterogeneity of the microenvironment on therapeutic resistance is particularly complicated, which suggests a more challenging research direction. In addition, it has been reported that the three-dimensional (3D) organoid model derived from patient biopsies is more intuitive to fully understand the role of the TME in acquired resistance. Therefore, in this review, we have focused not only on the mechanisms and targets of therapeutic resistance related to the contents of the TME in HCC but also provide a comprehensive description of 3D models and how they contribute to the exploration of HCC therapies.