Liver cancer is one of the most common malignant tumors worldwide. Currently, the available treatment methods cannot fully control its recurrence and mortality rate. Establishing appropriate animal models for liver cancer is crucial for developing new treatment technologies and strategies. The patient-derived xenograft (PDX) model preserves the tumor's microenvironment and heterogeneity, which makes it advantageous for biological research, drug evaluation, personalized medicine, and other purposes. This article reviews the development, preparation techniques, application fields, and challenges of PDX models in liver cancer, providing insights for the research and exploration of PDX models in diagnostic and therapeutic strategies of liver cancer.

Cyclin-dependent kinases (CDKs) are pivotal regulators of the cell cycle and transcriptional machinery, making them attractive targets for cancer therapy. While CDK inhibitors have demonstrated promising clinical outcomes, they also face challenges in enhancing efficacy, particularly in overcoming drug resistance. Combination therapies have emerged as a key strategy to augment the effectiveness of CDK inhibitors when used alongside other kinase inhibitors or non-kinase-targeted agents. Dual-target inhibitors that simultaneously inhibit CDKs and other oncogenic drivers are gaining attention, offering novel avenues to optimize cancer therapy. Based on the structural characterization and biological functions of CDKs, this review comprehensively examines the structure-activity relationship (SAR) of existing dual-target CDK inhibitors from a drug design perspective. We also thoroughly investigate the preclinical studies and clinical translational potential of combination therapies and dual-target inhibitors. Tailoring CDK inhibitors to specific cancer subtypes and therapeutic settings will inspire innovative approaches for the next generation of CDK-related therapies, ultimately improving patient survival.

Bortezomib, as a proteasome inhibitor, is used in clinical trials related to solid cancers. However, its use is not always associated with a good response to treatment. Taking into account the above, we decided to analyze the effect of the time-dependency (24 vs. 48 h) and the dose-dependency of bortezomib (2, 4, 8 and 16 nM) on apoptosis and activities of antioxidant enzymes such as catalase (CAT), superoxide dismutase (SOD), glutathione reductase (GR), glutathione peroxidase (GPx) and glutathione transferase (GST), as well as concentrations of reduced glutathione (GSH) and malondialdehyde (MDA) in hepatoblastoma cell line (HepG2) cells. We have shown that increasing concentrations of bortezomib caused (I) a gradual decrease in the levels of GSH; (II) changes in MDA concentrations and antioxidant enzymes activities; (III) increase in apoptosis levels in HepG2 cells. We did not find significant association between antioxidant parameters and number of apoptotic cells. Our study showed that the analyzed parameters (such as: CAT, SOD, GR, GPx, GST, GSH, MDA) changed after bortezomib treatment. It is important to search for new anti-cancer therapies based on next-generation proteasome inhibitors. It is possible that the use of proteins associated with oxidative stress will help enhance the action of these inhibitors and will provide a better treatment effect.

Soft tissue sarcomas (STS) are rare, heterogeneous tumors with poor survival outcomes, primarily due to reliance on cytotoxic chemotherapy and lack of targeted therapies. Given the uniquely individualized nature of STS, we hypothesized that the ex vivo drug sensitivity platform, quadratic phenotypic optimization platform (QPOP), can predict treatment response and enhance combination therapy design for STS. Using QPOP, we screened 45 primary STS patient samples, and showed improved or concordant patient outcomes that are attributable to QPOP predictions. From a panel of approved and investigational agents, QPOP identified AZD5153 (BET inhibitor) and pazopanib (multi-kinase blocker) as the most effective combination with superior efficacy compared to standard regimens. Validation in a panel of established patient lines and in vivo models supported its synergistic interaction, accompanied by repressed oncogenic MYC and related pathways. These findings provide preliminary clinical evidence for QPOP to predict STS treatment outcomes and guide the development of novel therapeutic strategies for STS patients.

Liver cancer poses a global health challenge with limited therapeutic options. Notably, the limited success of current therapies in patients with primary liver cancers (PLCs) may be attributed to the high heterogeneity of both hepatocellular carcinoma (HCCs) and intrahepatic cholangiocarcinoma (iCCAs). This heterogeneity evolves over time as tumor-initiating stem cells, or cancer stem cells (CSCs), undergo (epi)genetic alterations or encounter microenvironmental changes within the tumor microenvironment. These modifications enable CSCs to exhibit plasticity, differentiating into various resistant tumor cell types. Addressing this challenge requires urgent efforts to develop personalized treatments guided by biomarkers, with a specific focus on targeting CSCs. The lack of effective precision treatments for PLCs is partly due to the scarcity of ex vivo preclinical models that accurately capture the complexity of CSC-related tumors and can predict therapeutic responses. Fortunately, recent advancements in the establishment of patient-derived liver cancer cell lines and organoids have opened new avenues for precision medicine research. Notably, patient-derived organoid (PDO) cultures have demonstrated self-assembly and self-renewal capabilities, retaining essential characteristics of their respective in vivo tissues, including both inter- and intratumoral heterogeneities. The emergence of PDOs derived from PLCs serves as patient avatars, enabling preclinical investigations for patient stratification, screening of anticancer drugs, efficacy testing, and thereby advancing the field of precision medicine. This review offers a comprehensive summary of the advancements in constructing PLC-derived PDO models. Emphasis is placed on the role of CSCs, which not only contribute significantly to the establishment of PDO cultures but also faithfully capture tumor heterogeneity and the ensuing development of therapy resistance. The exploration of PDOs' benefits in personalized medicine research is undertaken, including a discussion of their limitations, particularly in terms of culture conditions, reproducibility, and scalability.

Precision oncology relies on detailed molecular analysis of how diverse tumours respond to various therapies, with the aim to optimize treatment outcomes for individual patients. Patient-derived xenograft (PDX) models have been key to preclinical validation of precision oncology approaches, enabling the analysis of each tumour's unique genomic landscape and testing therapies that are predicted to be effective based on specific mutations, gene expression patterns or signalling abnormalities. To extend these standard precision oncology approaches, the field has strived to complement the otherwise static and often descriptive measurements with functional assays, termed functional precision oncology (FPO). By utilizing diverse PDX and PDX-derived models, FPO has gained traction as an effective preclinical and clinical tool to more precisely recapitulate patient biology using in vivo and ex vivo functional assays. Here, we explore advances and limitations of PDX and PDX-derived models for precision oncology and FPO. We also examine the future of PDX models for precision oncology in the age of artificial intelligence. Integrating these two disciplines could be the key to fast, accurate and cost-effective treatment prediction, revolutionizing oncology and providing patients with cancer with the most effective, personalized treatments.

Radiotherapy is the primary treatment modality for most head and neck cancers (HNCs). Despite the addition of chemotherapy to radiotherapy to enhance its tumoricidal effects, almost a third of HNC patients suffer from locoregional relapses. Salvage therapy options for such recurrences are limited and often suboptimal, partly owing to divergent tumor and microenvironmental factors underpinning radioresistance. In this study, we utilized a combinatorial functionomics approach, the Quadratic Phenotypic Optimization Platform (QPOP), to rationally design drug pairings that exploit the molecular fingerprint and vulnerability of established in vitro isogenic radioresistant (RR)-HNC models.

A QPOP-specific protocol was applied to RR-HNC models to rank and compare all possible drug combinations from a 12-drug set comprising standard chemotherapy, small molecule inhibitors and targeted therapies specific to HNC. Drug combination efficacy was evaluated by computing combination index scores, and by measuring apoptotic response. Drug targeting was validated by western blot analyses, and the Comet assay was used to quantify DNA damage. Enhanced histone deacetylase inhibitor (HDACi) efficacy in RR models was further examined by in vivo studies, and genetic and chemical inhibition of major Class I/II HDACs. Regulatory roles of HDAC6/SP1 axis were investigated using immunoprecipitation, gel shift and ChIP-qPCR assays. Comparative transcriptomic analyses were employed to determine the prognostic significance of targeting HDAC6.

We report the therapeutic potential of combining panobinostat (pan-HDAC inhibitor) with AZD7762 (CHK1/2 inhibitor; AstraZeneca) or ionizing radiation (IR) to re-sensitize RR-HNC cells and showed increased DNA damage underlying enhanced synergy. We further refined this RR-specific drug combination and prioritized HDAC6 as a targetable dependency in reversing radioresistance. We provide mechanistic insights into HDAC6-mediated regulation via a crosstalk involving SP1 and oncogenic and repair genes. From two independent patient cohorts, we identified a four-gene signature that may have discriminative ability to predict for radioresistance and amenable to HDAC6 inhibition.

We have uncovered HDAC6 as a promising molecular vulnerability that should be explored to treat RR-HNC.

Liver cancer is a leading cause of cancer-related deaths worldwide, highlighting the need for innovative approaches to understand its complex biology and develop effective treatments. While traditional in vivo animal models have played a vital role in liver cancer research, ethical concerns and the demand for more human-relevant systems have driven the development of advanced in vitro models. Spheroids and organoids have emerged as powerful tools due to their ability to replicate tumor microenvironment and facilitate preclinical drug development. Spheroids are simpler 3D culture models that partially recreate tumor structure and cell interactions. They can be used for drug penetration studies and high-throughput screening. Organoids derived from stem cells or patient tissues that accurately emulate the complexity and functionality of liver tissue. They can be generated from pluripotent and adult stem cells, as well as from liver tumor specimens, providing personalized models for studying tumor behavior and drug responses. Liver organoids retain the genetic variability of the original tumor and offer a robust platform for high-throughput drug screening and personalized treatment strategies. However, both organoids and spheroids have limitations, such as the absence of functional vasculature and immune components, which are essential for tumor growth and therapeutic responses. The field of preclinical modeling is evolving, with ongoing efforts to develop more predictive and personalized models that reflect the complexities of human liver cancer. By integrating these advanced in vitro tools, researchers can gain deeper insights into liver cancer biology and accelerate the development of novel treatments.

Blood cancers, including leukemia, multiple myeloma, and lymphoma, pose significant challenges owing to their heterogeneous nature and the limitations of traditional treatments. Precision medicine has emerged as a transformative approach that offers tailored therapeutic strategies based on individual patient profiles. Ex vivo drug sensitivity analysis is central to this advancement, which enables testing of patient-derived cancer cells against a panel of therapeutic agents to predict clinical responses. This review provides a comprehensive overview of the latest advancements in ex vivo drug sensitivity analyses and their application in blood cancers. We discuss the development of more comprehensive drug response metrics and the evaluation of drug combinations to identify synergistic interactions. Additionally, we present evaluation of the advanced therapeutics such as antibody-drug conjugates using ex vivo assays. This review describes the critical role of ex vivo drug sensitivity analyses in advancing precision medicine by examining technological innovations and clinical applications. Ultimately, these innovations are paving the way for more effective and individualized treatments, improving patient outcomes, and establishing new standards for the management of blood cancers.

Primary liver cancer is a leading cause of death worldwide. To create advanced treatments for primary liver cancer, studies have utilized models such as 2D cell culture and in vivo animal models. Recent developments in cancer organoids have created the possibility for 3D in vitro cultures that recapitulates the cancer cell structure and operation as well as the tumor microenvironment (TME). However, before organoids can be directly translated to clinical use, tissue processing and culture medium must be standardized with unified protocols to decrease variability in results. Herein, we present the wide variety of published methodologies used to derive liver cancer organoids from patient tumor tissues. Additionally, we summarize validation methodologies for organoids in terms of marker expression levels with immunohistochemistry as well as the presence of mutations and variants through RNA-sequencing. Primary liver cancer organoids have exciting applications allowing for faster drug testing at a larger scale. Primary liver cancer organoids also assisit in uncovering new mechanisms. Through the coculture of different immune cells and cancer organoids, organoids are now better able to recapitulate the liver cancer TME. In addition, it further aids in the investigation of drug development and drug resistance. Lastly, we posit that the usage of liver cancer organoids in animal models provides researchers a methodology to overcome the current limitations of culture systems.

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.

Protein homeostasis is the basis of normal life activities, and the proteasome family plays an extremely important function in this process. The proteasome 20S is a concentric circle structure with two α rings and two β rings overlapped. The proteasome 20S can perform both ATP-dependent and non-ATP-dependent ubiquitination proteasome degradation by binding to various subunits (such as 19S, 11S, and 200 PA), which is performed by its active subunit β1, β2, and β5. The proteasome can degrade misfolded, excess proteins to maintain homeostasis. At the same time, it can be utilized by tumors to degrade over-proliferate and unwanted proteins to support their growth. Proteasomes can affect the development of tumors from several aspects including tumor signaling pathways such as NF-κB and p53, cell cycle, immune regulation, and drug resistance. Proteasome-encoding genes have been found to be overexpressed in a variety of tumors, providing a potential novel target for cancer therapy. In addition, proteasome inhibitors such as bortezomib, carfilzomib, and ixazomib have been put into clinical application as the first-line treatment of multiple myeloma. More and more studies have shown that it also has different therapeutic effects in other tumors such as hepatocellular carcinoma, non-small cell lung cancer, glioblastoma, and neuroblastoma. However, proteasome inhibitors are not much effective due to their tolerance and singleness in other tumors. Therefore, further studies on their mechanisms of action and drug interactions are needed to investigate their therapeutic potential.

Membrane associated ubiquitin ligase MARCH2 majorly involves in inflammation response and protein trafficking. However, its comprehensive role in hepatocellular carcinoma (HCC) is largely unknown.

Firstly, multiple bioinformatic analyses were applied to determine MARCH2 mRNA level, its expression comparison in diverse molecular and immune subtypes, and diagnostic value in HCC. Subsequently, RNA-seq, real-time quantitative PCR, immunohistochemistry and cell proliferation assay are used to explore the epithelial-mesenchymal transition (EMT) and proliferation by gene-silencing or overexpressing in cultured HCC cells or in vivo xenograft. Moreover, dual luciferase reporter assay and immunoblotting are delved into verify the transcription factor that activating MARCH2 promoter.

Multiple bioinformatic analyses demonstrate that MARCH2 is upregulated in multiple cancer types and exhibits startling diagnostic value as well as distinct molecular and immune subtypes in HCC. RNA-seq analysis reveals MARCH2 may promote EMT, cell proliferation and migration in HepG2 cells. Furthermore, overexpression of MARCH2 triggers EMT and significantly enhances HCC cell migration, proliferation and colony formation in a ligase activity-dependent manner. Additionally, above observations are validated in the HepG2 mice xenografts. For up-stream mechanism, transcription factor KLF15 is highly expressed in HCC and activates MARCH2 expression.

KLF15 activated MARCH2 triggers EMT and serves as a fascinating biomarker for precise diagnosis of HCC. Consequently, MARCH2 emerges as a promising candidate for target therapy in cancer management.

Hepatic cancer is widely regarded as the leading cause of cancer-related mortality worldwide. Despite recent advances in treatment options, the prognosis of liver cancer remains poor. Therefore, there is an urgent need to develop more representative in vitro models of liver cancer for pathophysiology and drug screening studies. Fortunately, an exciting new development for generating liver models in recent years has been the advent of organoid technology. Organoid models hold huge potential as an in vitro research tool because they can recapitulate the spatial architecture of primary liver cancers and maintain the molecular and functional variations of the native tissue counterparts during long-term culture in vitro. This review provides a comprehensive overview and discussion of the establishment and application of liver organoid models in vitro. Bioengineering strategies used to construct organoid models are also discussed. In addition, the clinical potential and other relevant applications of liver organoid models in different functional states are explored. In the end, this review discusses current limitations and future prospects to encourage further development.

Globally, colorectal cancer (CRC) is the third most frequently occurring cancer. Progression on to an advanced metastatic malignancy (metCRC) is often indicative of poor prognosis, as the 5-year survival rates of patients decline rapidly. Despite the availability of many systemic therapies for the management of metCRC, the long-term efficacies of these regimens are often hindered by the emergence of treatment resistance due to intratumoral and intertumoral heterogeneity. Furthermore, not all systemic therapies have associated biomarkers that can accurately predict patient responses. Hence, a functional personalised oncology (FPO) approach can enable the identification of patient-specific combinatorial vulnerabilities and synergistic combinations as effective treatment strategies. To this end, we established a panel of CRC patient-derived organoids (PDOs) as clinically relevant biological systems, of which three pairs of matched metCRC PDOs were derived from the primary sites (ptCRC) and metastatic lesions (mCRC). Histological and genomic characterisation of these PDOs demonstrated the preservation of histopathological and genetic features found in the parental tumours. Subsequent application of the phenotypic-analytical drug combination interrogation platform, Quadratic Phenotypic Optimisation Platform, in these pairs of PDOs identified patient-specific drug sensitivity profiles to epigenetic-based combination therapies. Most notably, matched PDOs from one patient exhibited differential sensitivity patterns to the rationally designed drug combinations despite being genetically similar. These findings collectively highlight the limitations of current genomic-driven precision medicine in guiding treatment strategies for metCRC patients. Instead, it suggests that epigenomic profiling and application of FPO could complement the identification of novel combinatorial vulnerabilities to target synchronous ptCRC and mCRC.

Constitutive activation of the transcription factor STAT3 (signal transducer and activator of transcription 3) contributes to the malignancy of many cancers such as hepatocellular carcinoma (HCC) and is associated with poor prognosis. STAT3 activity is increased by the reversible palmitoylation of Cys108 by the palmitoyltransferase DHHC7 (encoded by ZDHHC7). Here, we investigated the consequences of S-palmitoylation of STAT3 in HCC. Increased ZDHHC7 abundance in HCC cases was associated with poor prognosis, as revealed by bioinformatics analysis of patient data. In HepG2 cells in vitro, DHHC7-mediated palmitoylation enhanced the expression of STAT3 target genes, including HIF1A, which encodes the hypoxia-inducible transcription factor HIF1α. Inhibiting DHHC7 decreased the S-palmitoylation of STAT3 and decreased HIF1α abundance. Furthermore, stabilization of HIF1α by cyclin-dependent kinase 5 (CDK5) enabled it to promote the expression of ZDHHC7, which generated a positive feedback loop between DHHC7, STAT3, and HIF1α. Perturbing this loop reduced the growth of HCC cells in vivo. Moreover, DHHC7, STAT3, and HIF1α were all abundant in human HCC tissues. Our study identifies a pathway connecting these proteins that is initiated by S-palmitoylation, which may be broadly applicable to understanding the role of this modification in cancer.

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.

Hepatocellular carcinoma (HCC) is a common malignant tumor with insidious early symptoms, easy metastasis, postoperative recurrence, poor drug efficacy, and a high drug resistance rate when surgery is missed, leading to a low 5-year survival rate. Research on the pathogenesis and drugs is particularly important for clinical treatment. Animal models are crucial for basic research, which is conducive to studying pathogenesis and drug screening more conveniently and effectively. An appropriate animal model can better reflect disease occurrence and development, and the process of anti-tumor immune response in the human body. This review summarizes the classification, characteristics, and advances in experimental animal models of HCC to provide a reference for researchers on model selection.

Hepatocellular carcinoma (HCC) is one of the most common cancerous tumors and one of the leading causes of death among cancer-related disorders. Chemotherapy is ineffective in HCC patients, and the number of drugs that are in use is limited. Thus, new molecules are needed that could increase the effectiveness of anti-HCC regimens. Here, we show that AT7519, a CDK inhibitor, exerts positive effects on HCC cells: it inhibits proliferation, migration and clonogenicity. Detailed analysis of the transcriptomes of cells treated with this compound indicated that AT7519 affects a substantial portion of genes that are associated with HCC development and progression. Moreover, we showed that the concomitant use of AT7519 with gefitinib or cabozantinib sensitized HCC cells to these drugs. Thus, our research indicates that AT7519 is worth considering in monotherapy for hepatocellular carcinoma patients or in combination with other drugs, e.g., gefitinib or cabozantinib.

Hepatocellular carcinoma (HCC) is one of the most lethal malignant tumours worldwide. The mortality-to-incidence ratio is up to 91.6% in many countries, representing the third leading cause of cancer-related deaths. Systemic drugs, including the multikinase inhibitors sorafenib and lenvatinib, are first-line drugs used in HCC treatment. Unfortunately, these therapies are ineffective in most cases due to late diagnosis and the development of tumour resistance. Thus, novel pharmacological alternatives are urgently needed. For instance, immune checkpoint inhibitors have provided new approaches targeting cells of the immune system. Furthermore, monoclonal antibodies against programmed cell death-1 have shown benefits in HCC patients. In addition, drug combinations, including first-line treatment and immunotherapy, as well as drug repurposing, are promising novel therapeutic alternatives. Here, we review the current and novel pharmacological approaches to fight HCC. Preclinical studies, as well as approved and ongoing clinical trials for liver cancer treatment, are discussed. The pharmacological opportunities analysed here should lead to significant improvement in HCC therapy.

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.