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All issues Volume 6 (2025) Vis Cancer Med, 6 (2025) 10 Full HTML
Open Access
Review
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Vis Cancer Med
Volume 6, 2025
Article Number 10
Number of page(s) 6
DOI https://doi.org/10.1051/vcm/2025012
Published online 31 July 2025

© The Authors, published by EDP Sciences, 2025

Licence Creative CommonsThis is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Introduction

Primary liver cancer (PLC), comprising hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (ICC), and combined hepatocellular-cholangiocarcinoma (cHCC-ICC), ranks as the second leading cause of cancer-related mortality worldwide [1, 2]. Although early-stage patients benefit from surgical resection and liver transplantation, therapeutic options for advanced disease remain limited. Targeted therapies and immunotherapies have transformed the landscape of oncology, but confer durable responses to only a small subset of liver cancer patients [3, 4].

Inter- and intra-tumor heterogeneity (ITH) has been increasingly recognized as a major barrier to effective cancer treatment [5]. Substantial genomic diversity observed across HCC, ICC, and cHCC-ICC underscores the urgent need to elucidate the mechanisms underlying tumor heterogeneity [6, 7]. In addition, the complexity of the tumor immune microenvironment (TIME), shaped by diverse immune and stromal cell populations, contributes to metastasis, relapse, and therapeutic resistance [8, 9]. Although immunotherapies such as immune checkpoint blockade have demonstrated promise, their efficacy remains highly variable across patients [10]. Together, these challenges highlight the necessity for systematic characterization of the tumor and TIME landscapes to uncover novel therapeutic targets and strategies.

Our laboratory has systematically investigated the genomic, phenotypic, and immune heterogeneity of PLC and pioneered the use of patient-derived organoid models to capture tumor diversity and guide therapeutic discovery. Integrating single-cell multi-omics, organoid-based functional screening, and clinical cohort studies, we aim to advance precision oncology for liver cancer (Figure 1). This review highlights our key findings and outlines future strategies to translate biological insights into clinical benefit.

thumbnail Figure 1

Multidimensional strategies for dissecting liver cancer heterogeneity and guiding precision medicine. (A) Tumor heterogeneity: Multi-region sampling and genomic profiling reveal branched evolutionary trajectories among intrahepatic lesions in primary liver cancer. (B) TIME heterogeneity: Single-cell and spatial omics technologies uncover diverse immune and stromal cell subsets shaping the TIME. (C) Organoid-based drug screening: Patient-derived organoids enable high-throughput evaluation of drug sensitivity and resistance mechanisms. (D) Precision medicine: Integrating tumor and TIME profiling with organoid-guided screening facilitates personalized treatment strategies for liver cancer patients. The image was created by Biorender.

Tumor heterogeneity in primary liver cancer

Understanding the heterogeneity of liver cancer is crucial for elucidating tumor evolution and guiding therapeutic strategies [11, 12]. We have undertaken a series of studies to systematically dissect it at the tissue, single-cell, and phenotypic levels.

PLC patients exhibit high recurrence rates and poor prognosis, largely attributable to the frequent emergence of intrahepatic metastases. Nevertheless, the mechanisms driving intrahepatic metastasis and the clonal relationships among these distinct lesions have yet to be fully elucidated. We analyzed 43 tumor lesions from 10 hepatitis B virus (HBV)-related HCC patients by comparing the genomic information of different lesions within individual patients. We reconstructed an evolutionary tree illustrating the relationships among lesions, revealing a clear pattern of branched evolution among distinct lesions in liver cancer and supporting the notion that intrahepatic metastases often arise from a common ancestral clone [13]. Building on this, we systematically investigated the genomic features of liver cancer at the single-cell level. We proposed a dual-phase copy number evolution (DPCNE) model for the first time. Notably, we provided the single-cell evidence that polyploid liver cancers originate from diploid cells and identified CAD as a novel prognostic biomarker [14].

At the phenotypic level of tumor heterogeneity, cHCC-ICC contains both hepatocellular carcinoma and cholangiocarcinoma components and represents the subtype of liver cancer with the poorest prognosis. Currently, its molecular characteristics remain largely unknown, and accurate diagnostic biomarkers are lacking. Therefore, we profiled 133 cases of cHCC-ICC patients’ samples, identifying distinct molecular and clinical features across subtypes. Additionally, combined and mixed cHCC-ICCs exhibited monoclonal origins and high Nestin expression, suggesting potential diagnostic value [15]. What’s more, the latest study has shown that large duct (LD) and small duct (SD) subtypes of intrahepatic cholangiocarcinoma (iCCA) are characterized by distinct molecular alterations. LD-iCCA is commonly associated with mutations in KRAS and TP53, whereas SD-iCCA more frequently harbors IDH1/2 mutations and FGFR2 fusions [16].

The heterogeneity of tumor immune microenvironment in liver cancer

The clinical relevance of distinct TIME subtypes in liver cancer remains poorly understood, despite growing recognition of their potential impact on disease progression and therapeutic response [17]. Recently, we utilized single-cell RNA sequencing on 189 samples from 124 liver cancer patients and 8 mice, dissecting TIME heterogeneity [18]. Specifically, a total of 160 samples were collected from 124 liver cancer patients, including 10 blood samples, 14 adjacent tissues, 116 tumor tissues, and 20 metastatic samples. Additionally, 19 samples were obtained from 8 mice, comprising 9 tumor tissues, 4 blood samples, 4 adjacent liver tissues, and 2 bone marrow samples. Our study systematically characterized tumor-associated neutrophils (TANs), a key cellular subset in liver cancer, in terms of their heterogeneity and immunosuppressive functions. Neutrophils are highly fragile cells that typically survive no more than a week in peripheral circulation, and previous single-cell studies in liver cancer have largely overlooked this population [19, 20]. Leveraging a rapid processing workflow and antibody-free enrichment strategy, our study successfully captured over 30,000 neutrophils and identified 11 distinct TAN subpopulations, delineating their intratumoral differentiation trajectories. Furthermore, five TIME subtypes and their molecular features were defined in liver cancer. Our study highlighted the role of TANs, particularly in the myeloid-cell-enriched subtype, which is linked to poor prognosis due to their immunosuppressive activities. Evidence from mouse models suggested that targeting TANs could be an effective strategy for immunotherapy, as neutrophil depletion reduced tumor progression.

Recently, the functional states and composition of tumor-infiltrating T cells [21], B cells [22–24], NK cells [25], neutrophils [26], and myeloid cells [27] across pan-cancer cohorts have been characterized. Tumor-specific immune subsets, such as CXCL13+ T cells [21], FCRL4+ tumor-associated atypical B cells [22], ITGAX+ atypical B cells [23], SPP1+ macrophages [27], DNAJB1+ tumor-associated NK cells [25], HLA-DR+ neutrophil [26], and LAMP3⁺ dendritic cells [28], have been identified and linked to patient prognosis and immunotherapy response, providing critical insights into tumor ecosystem heterogeneity. With the accumulation of single-cell sequencing data, large-scale integration and tumor microenvironment subtyping are becoming feasible, offering new opportunities for precision immunotherapy. In addition, emerging technologies such as spatial omics, single-cell multi-omics, and CRISPR-based single-cell screens are further advancing our understanding of tumor microenvironment composition and function.

In addition to immune cell heterogeneity, vascular heterogeneity also plays a critical role in shaping the tumor microenvironment and treatment responses in liver cancer. Distinct vascular phenotypes, including capillarized, sinusoidal-like, and arterialized vasculature, are differentially associated with tumor subtypes and therapeutic vulnerabilities. For instance, tumors with arterialized vasculature may respond differently to anti-angiogenic therapies compared to those with sinusoidal remodeling [29]. Moreover, recent advances in imaging and histopathology have highlighted how the spatial and functional heterogeneity of liver tumor vasculature impacts drug delivery and immune infiltration [30, 31]. While this review focuses on immunologic aspects, the interplay between vascular architecture and immune contexture represents an important frontier in liver cancer research.

Applications of tumor organoids in precision medicine

Organoids faithfully retain the structural and genomic features of their tissues of origin and have become indispensable three-dimensional models in cancer research [32–37]. In recent years, compared to traditional two-dimensional cell lines and patient-derived xenograft (PDX) models, organoids have demonstrated unique advantages in drug screening and precision medicine. Notably, gene expression profiles at the transcriptomic level provide an important basis for drug sensitivity assessment using organoids [35, 36, 38].

We established a biobank of 399 tumor organoids from 144 patients to model the phenotypic and pharmacological heterogeneity of liver cancer. Through integrative analysis, we identified gene signatures predictive of treatment response and uncovered the role of c-Jun in lenvatinib resistance. Our study led to the development of PKUF-01, a novel compound showing synergistic effects in overcoming resistance [39]. Importantly, by comparing the drug responses of liver cancer organoids and corresponding patients undergoing standard clinical treatments, we found that the in vitro drug sensitivity of organoids closely mirrored the clinical responses observed in patients, highlighting the potential of organoid models to predict individual patient outcomes. Additionally, patient-derived organoids have been shown to faithfully recapitulate clinical drug responses, demonstrating high sensitivity, specificity, and predictive accuracy in forecasting patient outcomes to both targeted therapies and chemotherapeutic agents in breast and gastrointestinal cancer [36, 40].

Furthermore, organoids have become valuable tools for large-scale drug and toxicity screening. Integration with microfluidic systems, such as organoid-on-a-chip platforms, enables high-throughput drug testing while preserving key features of the tumor microenvironment [41]. In addition to identifying effective therapies, patient-derived organoids allow tracking of drug resistance evolution, as demonstrated by models replicating regorafenib resistance in colorectal cancer [40]. Moreover, organoids from adjacent normal tissues have been employed to predict drug-induced hepatotoxicity, nephrotoxicity, and cardiotoxicity, offering a comprehensive approach to optimizing both efficacy and safety in personalized treatment strategies [42–45].

Recent developments in immune-organoid co-culture systems have enabled more accurate modeling of immune responses. For example, Jacob et al. co-cultured glioblastoma organoids with CAR-T cells targeting EGFRvIII, demonstrating antigen-specific T cell activation and tumor cell killing [46]. Similarly, Neal et al. established immune-enhanced tumor organoids to evaluate the efficacy of the anti-PD-1 antibody nivolumab, showing induction of cytotoxic T cells, consistent with clinical responses. These studies underscore the potential of organoid platforms for rapid evaluation of immunotherapeutic strategies [47].

Discussion

Our recent studies have significantly deepened the understanding of genomic, phenotypic, and immunological heterogeneity underlying PLC. Through integrative approaches combining multi-region sequencing, single-cell technologies, and patient-derived organoid models, our studies have delineated the evolutionary dynamics of tumor lesions, uncovered novel prognostic biomarkers such as CAD. Also, we identified Nestin as the biomarker of cHCC-ICC. Concurrently, dissection of the TIME has revealed diverse and functionally pro-tumor neutrophils, highlighting their roles in shaping therapeutic outcomes. Notably, organoid platforms have emerged as powerful tools for modeling tumor diversity, predicting patient-specific drug responses, and enabling the functional evaluation of targeted and immunotherapies. Together, these findings provide a comprehensive framework for understanding the complexity of PLC and offer promising avenues for translating biological insights into personalized clinical strategies.

However, significant challenges remain in both the clinical and translational aspects of liver cancer research. Firstly, although organoids offer significant advantages over traditional cancer models and have developed rapidly, issues such as limited passaging capacity, low cryopreservation recovery rates, and restricted drug screening opportunities for patients persist. Challenges also exist in balancing fibroblast inclusion to better mimic the tumor microenvironment without allowing fibroblast overgrowth. Also, while liver cancer organoids have significantly advanced our understanding of tumor heterogeneity, drug response, and immune interactions, their ability to faithfully recapitulate metastatic behavior remains limited. Metastasis is a critical determinant of patient prognosis and the primary cause of liver cancer-related mortality, yet most organoid models are derived from primary tumor tissues and are cultured in isolation, lacking the cellular and structural components necessary for modeling the metastatic cascade. Recent efforts have attempted to address this limitation by deriving organoids from metastatic lesions, incorporating stromal or immune cell co-cultures, and applying organoid-on-a-chip or microfluidic platforms to mimic circulation and invasion dynamics. However, a robust and standardized organoid model capable of simulating the full metastatic potential of liver cancer cells is still lacking. Future work should prioritize the integration of spatial, mechanical, and immune cues into organoid systems, as well as the use of longitudinal patient sampling to trace clonal evolution during disease progression and metastasis. Secondly, the multi-omics datasets have only been partially analyzed. A large portion of the data remains unexplored and needs in-depth mining to uncover novel mechanisms of tumor immune regulation. Moreover, our study has identified several promising therapeutic targets, including PD-L1+TANs, the cHCC-ICC biomarker Nestin, and the molecular marker CAD linked to tumor heterogeneity. While these findings offer potential for therapeutic intervention, they still require validation in large-scale clinical cohorts. Further efforts are needed to develop diagnostic kits and conduct comprehensive translational studies, ultimately bridging basic research and clinical application to benefit patients.

Thus, our laboratory will focus on three key areas of research in the future. Firstly, we will investigate novel mechanisms by integrating single-cell multi-omics data with animal models and clinical data. This will enable a deeper understanding of the TIME, focusing on tumor-associated neutrophils [26, 48–50], fibroblasts [51–55], B cells [22, 23, 56–59], and NK cells [25, 60–62]. Secondly, we aim to identify new therapeutic targets and strategies. By mapping single-cell profiles across disease stages and treatment responses, and combining organoid models [63, 64] with CRISPR screening [65–67], we will validate candidate targets and evaluate them in representative mouse models. Lastly, we will enhance clinical translation by building a larger cancer cohort with major hospitals. Integrating clinical and preclinical data will support target validation and early clinical trials, aiming to improve clinical outcomes and advance precision oncology.

Funding

This project was supported by grants from the National Natural Science Foundation of China (No. 82030079, 81972656, 82341005, and 81988101 to N.Z.), the National Science and Technology Major Project of China (2022YFC3400900 to N.Z.), Baidu Foundation of China (No. 2020BD015 to N.Z.).

Conflicts of interest

The authors declare no conflicts of interest.

Data availability statement

No new data were generated or analyzed in this study.

Author contribution statement

Yinuo Wang and Liheng Chen contributed to the conceptualization and writing of the manuscript. Ning Zhang provided supervision and guidance throughout the writing process. All authors reviewed and approved the final version of the manuscript.

Ethics approval

This article is a review and does not contain any studies with human participants or animals performed by any of the authors. Therefore, ethics approval was not required.

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Cite this article as: Wang Y, Chen L & Zhang N. Advances in basic and translational research on the heterogeneity and metastatic mechanisms of primary liver cancer. Visualized Cancer Medicine. 2025; 6, 10. https://doi.org/10.1051/vcm/2025012.

All Figures

thumbnail Figure 1

Multidimensional strategies for dissecting liver cancer heterogeneity and guiding precision medicine. (A) Tumor heterogeneity: Multi-region sampling and genomic profiling reveal branched evolutionary trajectories among intrahepatic lesions in primary liver cancer. (B) TIME heterogeneity: Single-cell and spatial omics technologies uncover diverse immune and stromal cell subsets shaping the TIME. (C) Organoid-based drug screening: Patient-derived organoids enable high-throughput evaluation of drug sensitivity and resistance mechanisms. (D) Precision medicine: Integrating tumor and TIME profiling with organoid-guided screening facilitates personalized treatment strategies for liver cancer patients. The image was created by Biorender.
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