Hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (ICC), and gallbladder cancer (GBC) are reported to be amongst leading causes of cancer related death globally [1-3]. Because the condition is generally not associated with symptoms, many a patient is diagnosed at a more advanced stage, hence, fails to capture the best therapeutic opportunity. Another factor is the high degree of heterogeneity of hepatobiliary tumors, low sensitivity to traditional chemotherapy drugs, and the relatively high rate of drug resistance pose significant treatment problems [4, 5]. Consequently, the emergence of new therapeutic approaches and drugs allowing to increase the survival rate and improve the quality of life of patients is becoming a pressing problem in medical science.
Organoid models have recently gained popularity in tumor research as one of the advanced in vitro models because of their in vivo like tumor microenvironment and biological properties [6-8]. Organoids are small structures developed from adult stem cells or pluripotent stem cells by 3D culture methods in vitro with self-stemness and multi-lineage differentiation potential [9-11]. These organelloid structures can preserve the histological, molecular, and genetic integrity of the original tumors and reconstruct only limited cellular components of the tumor microenvironment and biological activity, giving an artificial system that is more physiologically relevant for tumor studies [12].
In contrast to the ordinary 2D cell culture and animal models, organoid models have the following benefits. Organoid models are superior to 2D cultures for predicting molecular and biophysical properties of tumor cells and their relationships with neighbors, the extracellular matrix, and changes to the tumor microenvironment. Furthermore, high levels of individualization are possible in organoid models, which creates tissue-specific organoids using a patient’s tumor cell or tissue samples promoting personalized medicine [13-16]. Moreover, organoid cultures are more accessible to develop, cost-effective, and highly efficient for large-scale applications, particularly in drug discovery.
Organoid models are becoming popular for studying hepatobiliary tumors very quickly. These models are used to investigate tumor formation, testing potential drugs and providing opportunities to design individual therapeutic regimens [17, 18]. Applying organoid models, it is possible to quickly assess the cytotoxic response of tumour cells to the drug and determine the drug’s sensitivity and resistance, which allows to develop individual treatment strategies for patients [19]. In addition, organoid models can herein determine how the tumor microenvironment influences drug resistance, analyse pathways by which tumors evade immune recognition, and help identify novel immunotherapies [20]. The clinical treatment of hepatobiliary tumors has multiple difficulties and uncertainties. As a novel platform of research, organoid models bring fresh directions and approaches for the modal understanding and pharmacological treatment of HBTs. Based on the current progress and uses of organoid technology, it is possible to think that these models will be useful in the concept of precision medicine for HB tumors.

Organoid models are part of the most advanced technologies used in contemporary biomedical research and are characterized by the ability to reproduce the structure and function of native organs [21-23]. Functional identical in vitro, show high biological activity and organization in three dimensions, as well as can mimic the physiological and pathophysiological activity of the source organ/ tissue (Figure 1) [24, 25].
Organoid models require the use of three-dimensional (3D) culture techniques [26]. Unlike the conventional two dimensional with flat surface 2D culture system, 3D culture techniques allow cells to be closer to their physiological setting. Under these conditions, cells are capable of perceiving the extracellular space and substrate, and may form tissues with precise three-dimensional organization [27-29]. In addition, 3D cultures enable cell-cell communication and signaling required for preservation of cellular phenotype and function [30]. Organoid models also have highly valuable characteristic such as the self-renewal capability. In certain culture settings and with appropriate signaling control, the stem cells of the organoid models are capable of multiple rounds of symmetric division, so that the organoid models remain stable in the long term [31, 32]. The long-term expansion competency is invaluable not only for the study of stem cells but also for possible applications in regenerative medicine and cell therapy.
The features underscore that organoid models possess specific analytical capabilities in modeling liver pathophysiologic processes [33-36]. Liver is one of the most multifaceted organ of the organism its activity is associated with metabolism, detoxication, immune response etc. Conventional two-dimensional cell cultures lack proper representation of these physiological functions, while the organoids provide a partial representation of liver functions. For instance, liver organoids may exhibit hepatic enzymes and transporter proteins, and perform xenobiotic and drug metabolism and bile production [37]. Moreover, liver organoids are very helpful in understanding the development and progression of liver diseases, they are important models in explaining the molecular basis of liver diseases [38]. Nevertheless, there are still challenges when using liver organoid models for the simulation of liver pathophysiology despite the benefits. For instance, the problem of recreating the necessary conditions within the niche, such as blood and oxygen circulation, and interactions with immune cells in a liver microenvironment still persists today [39-41]. Furthermore, several questions remain unanswered in the field of organoid development: the enhancement of stability and reproducibility of organoids; and the use of organoid models for large-scale drug screening, toxicity tests [39]. Organoid models are emerging as a promising in vitro approach, particularly for simulating complex liver pathophysiological mechanisms. With ongoing advancements and refinements in this technology, organoid models are poised to provide increasingly accurate and efficient methodologies for liver disease research and treatment [40]. As we continue to explore and apply organoids in the context of hepatobiliary tumor investigations and therapeutics, these models are expected to significantly enhance our capabilities, ultimately offering patients more effective and personalized treatment options [41].

In any case, the hepatobiliary tumor organoid models represent a pioneering step forward for tumor biology and, more broadly, for personalized medicine. These models give new vision in proliferation of tumors and gives potential tools for therapeutic actions and drug identification the [43-46]. The generation of hepatobiliary tumor organoid models involves extracting cells from fresh tumor tissues of patients and subsequently culturing them in suitable media within a 3D system to grow tumorigenic tissue-like structures known as organoids. These models accurately recreate histological architecture, cell composition and most microenvironments of primary carcinoma, making it a near perfect model for studying tumor behavior.
In the context of disease models, hepatobiliary tumor organoid models are widely used to analyze tumor formation, progression, metastasis and chemoresistance. Tumor cells can be implanted in a system which is nearest to vivo, and researchers can see cell growth, division and behavior. Also, organsoid models allow understanding of cell interactions within the tumor niche and how tumors can escape immune recognition and destruction [47].
The strength in utilizing organoid models is in maintaining the phenotypic profile of primary tumors. Compared to 2D cell culture and animal models organoids recapitulate tumours’ 3D architecture and cell-to-cell communication [48, 49]. Thus, this highly mimetic feature makes organoid models perfect for investigating tumor heterogeneity and the effect of the milieu. Also, as organoid culture models employed a single-cell approach, they also enable personalized medicine because of individual differences. Organoids generated from a patient’s tumor cells imply that the development of treatment plan tailor to the patient’s case is made possible [48, 49].
Specifically worth mentioning are Patient-Derived Hepatobiliary Organoids (PDHO). PDHO models can apply at high levels for the prediction of drug sensitivity of hepatobiliary tumors [50-52]. Thanks to the usage of different drugs for tumor cells within the frameworks of PDHO models, investigators can predict the effectiveness of drugs and their toxic effects in the treatment of patients. Furthermore, the PDHO topological models are useful for the HTS in drug discovery [53-55]. By using such models, it is easy for the researchers to discover compounds with antitumor activity since the effects of several compounds can be tested on the models at once, hence shortening the process of identifying new drugs. However, the development and implementation of PDHO models are not problem-free either. For instance, the culture conditions used for organoids must be made to assume the various types of tumors in order to guarantee model stability and replicability [53, 54]. Furthermore, due to tumorigenesis heterogeneity, the specific organoid model may not capture all aspects of the tumor behavior, which requires multiple vehicle models towards a comprehensive understanding of tumorigenesis. However, there are still many points to improve, and thus the application prospects of PDHO models in hepatobiliary tumor research and treatment are still very brilliant [55].
As technology and knowledge of tumour biology progress, hepatobiliary tumor organoid models will be progressively important in tumour research and therapy. They include essential knowledge of the tumor notion and contribute to the establishment of individualized and effective treatment approaches to promote benefits in patients in terms of quality of life.

Multi-omics technologies are also essential to organoid models, offering unique insights into the molecular regulation and drug effects of hepatobiliary tumors. The types of technologies these studies used include whole-genome sequencing (WGS), RNA sequencing (RNA-seq), mass spectrometry (MS), and single-cell RNA sequencing (scRNA-seq), which provide different aspects of tumor cells [56-60].
Whole-genome sequencing (WGS) is playing an increasingly important role in cancer research. Through WGS, researchers can gain a comprehensive understanding of the genomic information of both tumor and normal cells, providing strong support for the diagnosis and treatment of cancer [56]. For example, in a study, WGS was used to identify germline variants associated with the susceptibility to colorectal cancer (CRC), such as mutations in the POLE and POLD1 genes, which are related to a high penetrance of CRC [57]. Additionally, WGS has been used to identify other genes associated with cancer susceptibility, such as TP53 and ATM gene mutations [58].
RNA sequencing (RNA-seq) technology is crucial in analyzing the transcriptome of tumor cells. It can reveal the adaptability of tumor cells to changes in their microenvironment and therapeutic interventions, and by identifying differential gene expression profiles, it provides a basis for the molecular classification and prognostic assessment of tumors [59]. For instance, in a study on oral cancer, RNA-Seq was used to explore the genetic basis related to the disease [60]. Moreover, RNA-seq has also been used to analyze miRNAs and other non-coding small RNAs in formalin-fixed paraffin-embedded (FFPE) tumor samples, which may contain important information about the pathogenesis and outcomes of cancer [61].
Mass spectrometry (MS) technology provides important information at the proteomic level for tumor research. It can quantitatively profile the expression and post-translational modifications of proteins in tumor cells, revealing the metabolic characteristics of tumor cells and the dynamic activities of drugs [62]. For example, in one study, MS technology was used to analyze the glycoforms of prostate-specific antigen (PSA) to improve the accuracy of prostate cancer diagnosis [63]. Additionally, MS technology has been used to explore biomarkers in plasma, providing potential targets for the early diagnosis and treatment of cancer [64].
The development of single-cell RNA sequencing (scRNA-seq) technology has provided a new perspective for the study of tumor heterogeneity. Through single-cell resolution transcriptome analysis, researchers can reveal the unique characteristics and states of different cellular subsets within tumors, which is crucial for understanding tumor development, metastasis, and therapeutic resistance [65]. For example, in a study, scRNA-seq was used to analyze the tumor microenvironment of esophageal squamous cell carcinoma (ESCC), revealing the heterogeneity of lymphoid and myeloid cell lineages, and identifying a tumor-specific subset of CST1+ myofibroblasts with significant prognostic value and potential biological significance [66]. Furthermore, the CancerSCEM database developed by the Beijing Institute of Genomics, Chinese Academy of Sciences, integrates single-cell data from various types of cancers, providing rich resources for the study of the cancer microenvironment [67].
Combined with these multi-omics technologies, it is possible to systematically decipher the developmental processes of hepatobiliary tumors on the molecular level. For example, WGS and RNA-seq can jointly allow for the discovery of mutations that exist in the tumor cells as well as examination of the effects of such mutations on gene expression and the behavior of the tumor [68]. The integration of MS and scRNA-seq reveals the proteins and single cell heterogeneity within the tumor cells that can be used as leads to new treatment interventions. Moreover, the use of multi-omics in organoid models allow for an assessment of drug response and an orientation toward individualized therapy. A quantitative analysis of the signaling response of tumors to various drugs enables investigators to discover the biomarkers of drugs’ efficacy and toxicity. They harbour the patient’s clinical data along with multi-omics data of desired organoid models for treatment planning leading to enhancement in treatment efficacies [69].

Drug response prediction is important in cancer treatment because it determines the treatment outcomes and survival of patients in cancer. Organoid models, a new in vitro model, taking a more and more important position in drug screening and efficacy assessment. These models recreate the tumour surrounding and the actual biological properties and offer more detailed and specific information for the drug response prediction as depicted above in Figure 2 [70, 71].
The applicability of the organoid models in drug scrutiny and assessment of effectiveness is clear. With organoid models, researchers are able to work with a tumor cells in order to evaluate the effectiveness of the drugs and, at the same time, measure potential toxicity of the drug. This damage comprises the immediate lethal actions upon tumor cells and the effects on the tumor milieu, including angiogenesis and immune modulation [72]. More significantly, organoid models can model the tumor heterogeneity, meaning that the model can show the response of the multiple subpopulations of tumor cells to drugs and therefore provide a richer information set for drug testing.
The potential of intensively investigated in vitro screening platforms based on organoids with respect to neoantigen peptide activity has also emerged in the field of personalised immunotherapies. The tumour neoepitopes that are generated from tumour associated nonsynonymous mutations can activate the immune response against tumour cells [73, 74]. By doing so, researchers can pinpoint positive, or neoantigen, peptides that can activate the immune system with the use of organoid models. Thus, this method can extend and improve immunotherapy and decrease negative outcomes. Therefore, methods for predicting drug-target interactions, including the wSDTNBI method, also have an important role in drug discovery. The wSDTNBI method, an acronym for Weighted Short-Driven Time New Bariccentric Index, employs a sophisticated network-based strategy to forecast the efficacy of drugs in engaging their molecular targets [73]. By incorporating the weighted influences and temporal dynamics of drug-target interactions, this method extends its utility beyond mere prediction, offering a nuanced assessment of the interaction's intensity and specificity [74]. This level of detail is crucial for optimizing drug design, as it allows researchers to tailor compounds to more effectively interact with their intended targets. The wSDTNBI method thus serves as a powerful tool in the arsenal of drug discovery, enabling the development of more precise and potent therapeutics [75].
Indeed, the wSDTNBI method has potential application in the prediction of the activity of several drugs against diverse targets [75]. Originally, by employing this approach, researchers can easily-screen compounds which might possess activity, saving time and increasing the process of drug identification. In addition, no matter target or negative samples, the wSDTNBI method does not necessitate the 3D structure information, so that it can be used broadly in the drug discovery field.
Of course, both, organoid models, and drug-target interaction prediction methods have many opportunities in predicting drug responses. Ex situ culture and maintenance of organoids require intricate procedures and conditions in their formation. Additionally, the substantial interpatient variability of tumor cells diminishes the model’s representative and reproducible nature [76, 77]. Furthermore, even though several computer-aided drug-target interaction prediction methods can give relatively fast predictions, the prediction quality and necessity of further optimization remain questionable.
Altogether, organoid models and drug-target interaction prediction approaches have a great potential for enhancing the prediction of drugs’ effect. As technology ceases not to progress and these methods continually get optimized, more accurate and individualized approaches of cancer will be developed hence coming up with better results by increasing the survival rates of the patient. In future, through integration of multi-disciplinary fields and technological advancement, many higher progressive results are foreseen in treating cancer.

Organoid models are increasingly being recognized for their broad applicability in clinical settings, particularly for their ability to pave the way towards personalized treatment strategies and precise drug sensitivity assessments. These models, derived from patient tumor biopsies, encapsulate the inherent heterogeneity and the surrounding microenvironment, which is crucial for accurately mirroring tumor physiological behaviors and predicting drug responses [78]. The direct testing of drugs on patient-derived organoids promises to refine individualized therapy approaches and potentially reduce the incidence of adverse effects.
Despite their potential, organoid models present certain challenges when translated to clinical use. A primary concern is the complexity of their internal structure, which necessitates a delicate balance between tumor cell characteristics and microenvironmental variations. Replicating the intricacies of the natural tumor milieu within these models is a complex endeavor. Furthermore, the significant inter-patient heterogeneity observed within the same cancer type adds another layer of complexity; organoid models must capture the unique features of each patient's tumor to be clinically relevant.
To surmount these challenges, a variety of strategies have been implemented to refine culture techniques and integrate multi-omics data [79]. By optimizing the composition of culture media and conditions, organoid models can achieve greater stability and fidelity. Additionally, the integration of diverse omics layers, including genomics, transcriptomics, proteomics, and metabolomics, deepens our understanding of the molecular underpinnings of tumors and bolsters the predictive power of these models. Looking ahead, future research is poised to enhance our grasp of the evolution of organoid models and drug response prediction technologies, especially within the field of hepatobiliary tumor research [79]. The synergy between cutting-edge technological advancements and a profound comprehension of tumor biology is anticipated to elevate the capacity of these models to emulate the tumor microenvironment and biological behaviors. This, in turn, will bolster drug screening and pave the way for more tailored treatment approaches. Continued progress in this domain will likely stem from interdisciplinary collaboration and technological innovation, with the convergence of bioinformatics, systems biology, and clinical medicine potentially yielding more robust and reliable models and predictive methodologies.

There can be no doubt regarding the significance of the organoid models as a tool in the study of hepatobiliary tumour as well as predicting drug responses. These models thus offer new approaches for deconstructing tumor heterogeneity and designing specific treatment and drug design approaches. Thus, additional investigations and developments should be performed to expound the utility of organoid models adequately. Potential directions for further research include method development for culture-based systems, the incorporation of multi-omics data, creation of new algorithms in predictive microbiology, and improving model robustness and reuse. Further development of these fields is expected to bring major improvements in cancer therapies, making available to the patients radiation treatments which are more effective from the therapeutic standpoint and substantially safer for their health.

Availability of data and materials

Data and materials are available on request from the authors.

Ethical policy

Not applicable.

Author contributions

CHZ and HTL conceptualized, designed, conducted research, and wrote the first draft; YXL contributed to the revision and figure production; SC provided supervision and revision of the draft.

Competing interests

The authors have no conflicts of interest regarding the publication of this paper.

Funding

None.

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