Improving immunotherapy for the treatment of hepatocellular carcinoma: learning from patients and preclinical models

Abstract

Hepatocellular carcinoma (HCC), the third cause of cancer-related deaths worldwide, continues to present significant therapeutic challenges. Despite significant therapeutic advancement in the last decade, the efficacy of systemic treatments for patients with advanced HCC remains unsatisfactory. While the clinical introduction of immune checkpoint inhibitors has improved response rates and overall survival, their clinical success remains still limited. Here we provide a comprehensive review of current and emerging strategies aimed at enhancing the efficacy of immunotherapy for the treatment of HCC. Both clinical studies conducted in patients as well as studies in preclinical models have markedly advanced our understanding of resistance as well as uncovered novel approaches to overcome resistance. Recent progress is paving the way for improved efficacy and safety of novel approaches that will improve the dismal prognosis of patients with advanced HCC.

Unmet medical need to treat advanced liver cancer

Liver cancer is one of the most commonly diagnosed cancers and the third main cause of cancer-related deaths worldwide, with >900,000 new cases and >800,000 deaths in 20201. Globally, the annual number of new cases of liver cancer is predicted to increase, with 1.4 million people projected to be diagnosed in 20402. Progress in antiviral therapy combined with increased incidence in metabolic diseases leads to an etiological shift from virus-induced to metabolic-associated liver cancer within the next decades3,4. Hepatocellular carcinoma (HCC), accounting for 90% of liver cancers, is still difficult to treat. Key reasons include: (1) its development on a background of advanced chronic liver diseases in most cases, (2) its phenotypic, genomic, and molecular heterogeneity, and (3) late-stage detection due to absent or limited clinical symptoms at the early stages5,6. Early-stage (Barcelona Clinic Liver Cancer (BCLC) class A) patients can undergo surgical resection, percutaneous ablation, or liver transplantation, which can be considered curative, in well-selected patients, and yield median overall survival (mOS) of >5 and >10 years, respectively7,8.

Following the approval of sorafenib around 15 years ago9, marked progress has been made in the treatment of advanced HCC. Several novel treatment regimens have been approved4. The latest AASLD guidelines10 recommend the use of the atezolizumab plus bevacizumab combination; or tremelimumab plus durvalumab, as first line. Sorafenib, Lenvatinib or Durvalumab are alternatives in case of contraindications. Atezolizumab, durvalumab, and tremelimumab are immune checkpoints inhibitors (ICIs), the first two targeting programmed cell death ligand 1 (PD-L1) and the latest cytotoxic T-lymphocyte-associated protein 4 (CTLA-4). The two above mentioned combinations showed improved survival over sorafenib, with a mOS of 19.2 and 16.43 months, respectively11,12,13. However, regardless of the poor survival rate of these patients under treatment, the response rate is limited, with an objective response rate (ORR) of only 25% in both studies and a complete response in only 5% and 2% of patients respectively. Despite a significant improvement in patient survival, the response rate of ICIs remains unsatisfactory. To identify new therapeutic targets, it is critical to unveil the mechanisms of resistance to immunotherapy. Herein, we explore the known mechanism of resistance to ICIs and present the results of preclinical models using combination therapy to thwart ICI resistance.

Immune checkpoint inhibitors

The PD-1/PD-L1 pathway is a crucial regulator of immune tolerance within the tumor microenvironment. PD-1 is a type 1 transmembrane protein belonging to the CD28 immunoglobulin family. Structurally, it comprises a 288-amino-acid-long protein with an extracellular Ig-V-like N-terminal domain, a hydrophobic transmembrane region, and a cytoplasmic tail characterized by two tyrosine residues14. These residues form an immune receptor tyrosine-based inhibitory motif (ITIM) and an immune receptor tyrosine-based switch motif (ITSM). It has been elucidated through mutagenetic studies that PD-1’s inhibitory effect on T cells is predominantly mediated via the activated ITSM15. PD-1 expression is not limited to T cells but extends to B cells, NK cells, monocytes, dendritic cells (DCs), and various tumor-infiltrating lymphocytes. Importantly, PD-1 is also expressed on regulatory T cells (Tregs), where it inhibits their proliferation and contributes to the immune response’s suppression16,17. The PD-1 pathway includes two ligands, PD-L1 and PD-L2, expressed on a range of cells, including pancreatic islet cells, vascular endothelium, and various antigen-presenting cells (APCs). PD-L1’s expression is particularly notable in multiple tumor types, including gastric cancers, melanomas, and HCC, making it a critical target in immunotherapy. PD-L1 binds to PD-1 with a lower affinity compared to PD-L218, yet its widespread expression on tumor cells and significant association with clinical outcomes in different cancers, including HCC19,20, made it a better therapeutic target for immunotherapy. Recently, studies discovered the capacity of the co-activator CD80 to form a heterodimer with PD-1 on the surface of APCs, which inhibits its capacity to bind PD-L1 and could explain part of the resistance to therapies targeting immune checkpoints21. The engagement of PD-1 with its ligands leads to the inhibition of T-cell activation, which ultimately leads to T-cell exhaustion and tumor progression22,23. This process is facilitated by several pro-inflammatory molecules, including interferon-gamma, which play a significant role in upregulating PD-L1 expression24. Monoclonal antibodies targeting PD-1 or PD-L1, such as nivolumab, atezolizumab, or durvalumab, effectively prevent the inhibitory signaling and reactivate T-cell-mediated cytotoxicity against tumor cells. They were approved as single agents or as part of combination therapy for the treatment of advanced HCC, either as first-line or second line, and numerous alternative antibodies are currently under clinical trial (see Table 1).

Table 1 Published and ongoing phase III randomized control trials using immune checkpoint inhibitors in unresectable or metastatic hepatocellular carcinoma

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CTLA-4 is a transmembrane protein with a crucial role in regulating the amplitude of T-cell responses during the early priming phase in lymphoid organs. It is actually the first immune checkpoint protein to be discovered25, in 1987, and the first one to be successfully targeted with monoclonal antibody in a preclinical model of cancer26. Similar to PD-1, CTLA-4 is part of the CD28 immunoglobulin family and binds to B7 ligands (CD80 and CD86) on APCs. The expression of CTLA-4 is not detectable in naive T cells but is upregulated upon T-cell activation and is also expressed in Tregs. CTLA-4 outcompetes CD28 for B7 ligands, resulting in T-cell anergy27,28. In APCs, CTLA-4 binding results in reduced CD28 binding and impeded T-cell activation29. Moreover, CTLA-4 engagement has been shown to inhibit IL-2 production and T-cell proliferation and to induce cell cycle arrest30. This occurs through interaction with PP2A, SHP-2, and PI3K, which transduce downstream signaling that inhibits T-cell receptor signaling, together with the inhibition of other pathways linked to cell proliferation and survival, such as the nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB), PI3K, and MAP kinase pathways31,32,33. Furthermore, accumulating evidence points to an important role of CTLA-4 signaling in mediating the suppressive functions of Tregs. Anti-CTLA-4 antibodies bind to CTLA-4 molecules with high affinity, leading to Tregs functional blockade, resulting in enhanced T-cell activation and immunological responses to cancer34. CTLA-4 inhibitors have shown success in treating metastatic melanomas and are being explored for HCC treatment. CTLA-4 positive T cells and DCs are associated with impaired T-cell functionality35,36, and CTLA-4 inhibitors showed some potential in early clinical trials and ex-vivo models37,38. Importantly, CTLA-4 inhibition could have some synergetic effects with anti-PD-117. CTLA-4 inhibition by tremelimumab or ipilimumab showed some potential in phase II and phase III clinical trials, either as first or second-line therapy11,39. More, a virtual clinical trial of the nivolumab and ipilimumab combinations in >5000 virtual patients confirmed the therapeutic potential of CLTA-4 inhibition in HCC40. Numerous clinical trials are ongoing to test CLTA-4 inhibition in combination with other targets for the treatment of HCC (Table 1).

Approaches to improve the efficacy of ICIs

Among the different strategies to improve the efficacy of ICIs, one of the most promising is neoantigen vaccination. Neoantigens derive from proteins coding gene mutations or non-mutational sources such as RNA alternative splicing or epigenetic remodeling41. Tumor-associated antigens (TAA) are exclusive to tumors, they can bind to major histocompatibility complex (MHC) molecules and are recognized by T cells, triggering robust anti-tumor responses. Hence, TAA-based vaccines have the potential to be used alone or in combination with ICIs to trigger a potent antitumour immune response and potentially display some curative capacity. Several clinical trials are ongoing in different cancer types using this technology and some of them showed promising results42. Importantly, a recent study from Yarchoan and colleagues used a plasmid encoding a combination of 40 neoantigens in combination with pembrolizumab in 36 patients with advanced HCC, previously treated with multikinase inhibitor. The treatment showed no severe adverse effect, an ORR of ~30%, and ~8% of complete response43. These results, especially the complete response rate, are very promising but they need to be validated in larger cohorts.

An alternative strategy to improve the efficacy of ICIs is to combine them with loco-regional therapies (LRTs) such as percutaneous ablation, transarterial chemoembolization (TACE), or radiation therapy44. All these procedures lead to tumor cell death, which releases TAA and pro-inflammatory mediators, which in turn may induce tumor-infiltrating immune cell activation and synergize with ICIs45. Interestingly, early results suggest promising efficacy: in a recent study with 110 patients receiving atezolizumab plus bevacizumab as first-line treatment for unresectable intermediate-stage HCC, alone or in combination with LRTs, among the 38 patients achieving complete response, 35 had received ICIs together with LRTs and only 3 ICIs alone46. Another approach to improve ICIs efficacy uses irreversible electroporation following tumor resection to trigger an immune response in HCC. In several studies, it was shown that this technique induces a protective immune response, which could synergize with ICIs47,48. Many combinations involving LRTs and ICIs are under clinical trial44 but preliminary results highlight the complexity of accurately assessing the clinical benefits and potential harms of such therapeutic combination, as shown in the Imbrave 050 trial49. The extent to which neoadjuvant ICI applied before curative procedures may improve outcomes is also currently tested in clinical trials50.

Overall, numerous therapeutic approaches have been tested in combination with ICIs. As an example, in a recent phase II study, we tested a strategy using an antibody targeting phosphatidylserine in combination with pembrolizumab in HCC patients51. Phosphatidylserine is a cytoplasmic-facing phospholipid with an important role in the regulation of inflammation and immune escape. Our data suggest a synergistic effects with PD-1 blockade, which needs to be further confirmed.

Apart from these strategies based on a combination of multiple treatments with ICIs, other approaches try to improve ICI treatment by themselves. First, recent insights pointed out how IgG subclass and posttranslational modifications impact IgG activity, suggesting potential improvements for PD-1, PD-L1, or CTLA-4 antibodies regarding Fc targets52. Notably, different Fc targets of CTLA-4 antibodies play an important role in Tregs inhibition and this could be a way to significantly improve CTLA-4 targeting therapy. Otherwise, a study focused on PD-1 isoforms and identified the Δ42PD-1 as expressed in a subtype of T cells. In different mouse models, they were able to show that antibodies targeting Δ42PD-1 were more efficient than nivolumab53. Alternative strategies are based on the delivery of ICI molecules to the tumor. Nanoparticles are sub-micron-sized structures with a diameter of 150–500 nm54. Liu and colleagues used nanoparticles to co-deliver PD-L1 antibody in addition to a sonodynamic agent (Chlorin E6) in a syngeneic mouse model55. This led to an efficient delivery of the compound and a synergistic effect of the two approaches boosting the effect of PD-L1 antibody. Similarly, peptide-based nanoparticles were used to co-deliver compounds targeting β-catenin and PD-1 simultaneously in syngeneic mouse models56.

CAR T-cell therapy

Chimeric antigen receptor (CAR) T cells represent a revolutionary approach in cancer immunotherapy57. This technology involves genetically engineering a patient’s own T cells to express a CAR that targets a specific antigen present in the tumoral cells. These modified T cells are then expanded in the laboratory and reinfused into the patient, where they can directly recognize and kill cancer cells. Unlike traditional T-cell therapies that rely on the natural ability of T cells to recognize cancer cells, CAR T cells are designed to target tumors with high specificity and potency. In HCC, the application of CAR T-cell therapy holds significant promise. Glypican-3 (GPC3), a protein overexpressed in most HCC cells but not in normal tissues, has been identified as a promising target for CAR T-cell therapy58,59,60. Studies have shown that GPC3-targeted CAR T cells can effectively recognize and eliminate GPC3-positive HCC cells. Several other promising CAR T-cell tumor targets were identified, including alpha-fetoprotein (AFP), Epithelial cell adhesion molecule (EpCAM), Claudin18.2 (CLD18), CD133, and c-MET61. Ongoing clinical trials are still on phase 1 and 2 but some preliminary results are encouraging, notably suggesting a promising anti-tumor activity for CD133-directed CAR T cell therapy in advanced HCC62.

Additional immunotherapies

In the past 10 years, the development of novel immunotherapies has been enormously successful, resulting in the identification of novel immune checkpoints such as T-cell immunoglobulin and ITIM domain (TIGIT)63, T-cell immunoglobulin and mucin-domain containing-3 (TIM3)64, lymphocyte activation gene-3 (LAG3)65, CD4766 and B7 homolog 3 protein (B7-H3)67, among others (for a more extensive list, see68). Each of these proteins has distinct ligands and suppress T-cell function through several mechanisms to inhibit T-cell response (Fig. 1). Yet for the moment, phase III clinical trials are missing to evaluate the therapeutic potential of these new targets in HCC patients (Table 1).

Fig. 1: Overview of immune checkpoint inhibitor targets in cancer immunotherapy.

figure 1

This schematic illustrates various immune checkpoint inhibitors (ICIs) and their respective targets within the tumor microenvironment. ICIs include various anti-CTLA-4, anti-PD-1 targeting PD-1 receptors, and anti-PD-L1 antibodies. Magrolimab is depicted targeting CD47 to prevent the ‘don’t eat me’ signal on cancer cells, while TIGIT inhibitors interact with TIGIT receptors on T cells. Other ICIs, such as Enoblituzumab and Orlotamab, are shown to target unspecified antigens, Relatlimab binds LAG3 on T cells, and Cobolimab targets TIM3. The figure underscores the complex interplay between the immune system and cancer cells and highlights the multiplicity of potential therapeutic targets for ICIs.

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Hypothetical mechanisms and immune cells involved in resistance to checkpoint inhibitors

The advent of ICIs has marked a significant breakthrough in the treatment of various malignancies, with substantial clinical efficacy across several cancer types69. Particularly in Hodgkin’s disease and desmoplastic melanoma, immune checkpoint inhibitor therapy has achieved outstanding success, with ORR > 70% with the nivolumab plus pembrolizumab combination; but for most cancers (including HCC), treatment responses remain unsatisfactory. Moreover, ~10% of HCC patients under ICIs experience faster and more aggressive tumor progression than expected (known as hyperprogressive disease) for which the mechanisms of action are poorly understood70,71. Resistance to immunotherapy can occur in two different ways. Either patients are primary non-responders, or resistance is acquired after a period of documented response to therapy72. Of note, challenges remain in defining responders and non-responders, given the heterogeneity in patterns of response to ICI, such as spatial or temporal heterogeneity, manifesting within a given patient as mixed responses73.

While the exact mechanisms of innate and/or acquired resistance to ICI remain to be fully unveiled, several mechanisms have been hypothesized based on the known mechanisms of action of these therapies and preclinical studies74. These include: decreased neoantigen expression, impaired antigen recognition, ineffective antigen presentation, insufficient priming and activation of tumor-specific T cells, inadequate expansion of T cells or lack of co-stimulation, poor trafficking of the activated effector T cells to the tumor site, insufficient cancer cell recognition by T cells, presence of T-cell inhibitory factors or other T-cell inhibitory immune cells in the tumor microenvironment (TME) (Fig. 2)75. In HCC, the liver’s unique immune microenvironment, characterized by a high prevalence of immunosuppressive cells like Kupffer cells and hepatic stellate cells, also contributes to resistance by creating an immunotolerant environment that diminishes the efficacy of ICIs76. The mechanisms at stake in ICI resistance are complex and involve the crosstalk between many cell populations and subpopulations within the TME.

Fig. 2: Mechanisms of resistance to immune checkpoint inhibitors in hepatocellular carcinoma.

figure 2

Illustration of the various mechanisms through which HCC cells may develop resistance to immune checkpoint inhibitors (ICIs). 1) Mutation or altered expression of antigen-presenting molecules on tumor cells leading to reduced T-cell recognition. 2) Impaired antigen presentation. 3) Alterations in the tumor microenvironment, or secretion of immunosuppressive cytokines by tumor cells. 4) Impaired T-cell phenotype, or a physical barrier repressing T-cell infiltration. 5) Altered tumor recognition by T cells. 6) Recruitment of immunosuppressive macrophages, myeloid-derived suppressor cells (MDSCs), or regulatory T cells (Tregs) to the tumor microenvironment, suppressing the activation and proliferation of T cells. 7) Upregulation of alternate immune checkpoints in tumor cells, rendering standard ICIs ineffective. 8) T-cell exhaustion is characterized by the overexpression of multiple inhibitory receptors, diminishing the immune response. The figure underscores the complexity of the tumor-immune microenvironment and the multifaceted nature of immune resistance in HCC, highlighting the need for multi-targeted approaches.

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One of the mediators of this process is tumor-associated macrophages (TAMs). In theory, macrophages are dichotomized into M1 and M2 phenotypes, with M1 macrophages exerting pro-inflammatory effects, while M2 macrophages contribute to immunosuppression and tissue repair. However, these subclasses have been difficult to identify in vivo and single-cell RNAseq analysis identified a more complex pattern of distribution of TAMs77. Yet the prevalence of immunosuppressive “M2-like” macrophages, characterized by markers CD163 and CD206, is associated with an aggressive HCC phenotype, advanced tumor stage, and poor survival outcomes78. The presence of these immunosuppressive TAMs, especially at the tumor margin, is linked to adverse clinical features like vascular invasion, tumor multiplicity, and fibrous capsule formation79. Even if the role of the TAMs in ICI resistance has not been fully established in HCC, TAMs remain on the top of the suspects list. Regarding the potential mechanisms, studies in other cancers suggest that “M2-like” macrophages inhibit cytotoxic T cell through IL-10 secretion, and promote immunosuppressive phenotype in other macrophages, NK cells, and Tregs by IL-6, VEGF, and CSF-180,81,82.

Tregs are another cell population with a crucial role in immune exhaustion and resistance to ICIs. Naturally occurring FoxP3 + CD25 + CTLA-4 + CD4+ Tregs are indispensable for immunological self-tolerance. They come from the thymus and derive from the differentiation of T cells with intermediate T-cell receptor (TCR) affinity for self-peptide/MHC ligands while T cells with low TCR affinity differentiate into naive conventional T cells17,83. Tregs are thus able to recognize self-antigens, which may also be tumor-associated neoantigens. In addition to these thymus-derived Tregs, different sets of FoxP3 positive or negative immunosuppressive Tregs can differentiate in the tumor tissue from conventional T cells; the mechanisms at stake are not fully understood but involve IL-2 and TGF-β84. In general, high Treg/CD8 + T-cell ratios in tumors correlate with tumor progression and poor survival85,86. Tregs inhibit antigen presentation on APCs via CTLA-4 and have other immunosuppressive functions through the cell surface molecules, CD25, CD39, and CD73, and the cytokines IL-10, IL-35, and TGF-β17. PD-1 also inhibits Tregs activity as PD-1 blockade can result in increased Tregs activation87. PD-1 positive Tregs in tumors may undermine PD-1 blockade immunotherapy, as shown by the positive correlation between PD-1 positive Treg and hyperprogressive disease in gastric cancer88.

In addition to TAMs and Tregs, tumor-associated neutrophils (TANs) can also be immunosuppressive. Under activation by cancer-associated fibroblasts (CAFs) or HCC cells, they can express PD-L1 and release anti-inflammatory molecules, such as IL8, TNF, and CCl2, which inhibits T-cell activation89, or promotes Tregs recruitment90. Myeloid-derived suppressor cells (MDSCs) are a heterogeneous subset of myeloid cells that have been shown to inhibit T-cell responses in cancer and HCC. MDSCs can inhibit CD8 + T-cell proliferation and their accumulation is associated with poor survival rates in HCC patients91. More, MDSCs can promote Tregs and repress NK cell cytotoxicity by NKp30-dependent cell contact92,93.

Mechanisms of resistance to checkpoint inhibitors: learning from patients

In addition to cell-based studies to characterize the mechanisms of resistance to ICIs, an alternative strategy is to analyze tumor samples to compare the molecular, genetic, and clinical features of patient responders with non-responders. In an elegant study by Zhu and colleagues, tumor samples from 358 HCC patients treated with atezolizumab plus bevacizumab were enrolled from the GO30140 phase 1b and IMbrave 150 phase 3 trials94. The authors discovered that pre-existing immunity, marked by dense intratumoural CD8 + T cells, was linked to improved clinical outcomes. Conversely, reduced benefits were associated with a high ratio of Tregs to effector T cells and the expression of oncofetal genes, as well as with β-catenin mutation. The study also indicated that improved outcomes from the combination therapy, as opposed to atezolizumab alone, were linked to high expression of VEGF Receptor 2, Tregs, and myeloid inflammation signatures. These findings, validated through analyses of pre- and post-treatment biopsies, in situ analyses, and in vivo mouse models, suggest that the anti-VEGF component might synergize with anti-PD-L1 therapy by targeting angiogenesis, Tregs proliferation, and myeloid cell inflammation. The study also analyzed the potential effect of the tumor mutational burden (TMB) on the response to ICIs and showed some inconclusive results. In theory, elevated TMB, often caused by DNA repair deficiency, leads to high level of neoantigens, which increases the immune response and the capacity to target ICIs95,96. However, a recent study in lung and colon cancer showed that high mutational burden can at the same time leads to strong tumor heterogeneity, which jeopardizes the efficacy of ICIs97. In turn, TMB are insufficient to predict response to ICIs and the presence of clonal neoantigens appears to be a better alternative. Overall, the study by Zhu and colleagues confirmed the potential of analyzing the association between treatment response and the patient molecular profiles. Their study also identified a gene signature predicting progression-free survival after atezolizumab-bevacizumab initiation (called the atezolizumab-bevacizumab response signature). Later, this signature was validated in some independent cohorts by Zeng and colleagues and the signature was even predicted based on the analysis of patient pathology slides using artificial intelligence-based algorithms98. This technic could be used in the future to predict ICIs outcome in patients and better assign future treatments.

Another study from Liu and colleagues performed spatial transcriptomics analysis in 11 HCC samples treated with atezolizumab plus bevacizumab (6 non-responders, 5 responders) and highlighted a histological structure called “immune barrier”, only in non-responders99. This structure, composed of TAMs expressing osteopontin interacting with CAFs could act as a physical barrier to T-cell infiltration. Osteopontin is highly expressed in HCC cells and is a well-established predictor of tumor progression and poor outcome100,101. Osteopontin secretion by tumoural cells activates macrophages migration through CD44 signaling102; these activated macrophages further secrete CSF-1, which eventually induces PD-L1 expression in HCC cells82. Moreover, single-cell analysis of HCC patients identified ostepontin as a marker of a subpopulation of cancer-specific macrophages103, which is consistent with the data from Liu and colleagues on a potential immune barrier in HCC99. The concept of the immune barrier can be linked with the concept of “fibrous nest”104, or “fibrotic niche”, an histological structure observed in a subset of HCC patients, that we recently characterized by matrisome analysis105. We showed that this phenotype is associated with cancer-specific extracellular matrix remodeling, signatures of Wnt and TGFβ signaling, and immune evasion. All this is consistent with the immune classification of HCC, initiated by Sia and recently refined by Montironi and colleagues106,107. For long HCCs have been classified based on transcriptomic profile, and works from different teams yields to different classifications108,109,110,111,112,113, ultimately integrated into a system where HCC are divided into two groups: proliferative, associated with poor outcome and TP53 mutation and non-proliferative, associated with better outcome and β-catenin (CTNNB1) mutation. In the immune-based classification, patients are first divided into two groups: inflamed and non-inflamed, based on the expression of a series of markers of immune activation. Within the inflamed group, patients are subdivided into immune-active, immune-exhausted, and immune-like. Patients expressing the Hoshida’s S1/Wnt/TGFβ signature111 are found mostly within the immune active and immune exhausted groups, confirming the link between activation of Wnt/β-Catenin and TGF-β signaling pathways with immune evasion. Both Wnt and TGF-β signaling are known to promote an immunosuppressive TME in HCC. Wnt signaling leads to T-cell exclusion and resistance to ICI therapy through a decrease in chemokines secretion114. Elevated TGF-β signaling increase PD-L1 expression and stimulate Tregs expansion, thus disrupting the effectiveness of both anti-PD-L1 and anti-CTLA-4 antibodies115,116,117. In other cancer types, the decrease in TGF-β-induced collagen deposition has been shown to reactivate adaptative immune response and to improve the efficacy of ICIs, confirming the link between intratumour fibrosis and resistance to immunotherapy118,119,120. Overall, it is likely that TGF-β secretion by tumor cells in immune exhausted patients induce an immunosuppressive TME and activate CAFs, which form a collagen-based fibrous nest, acting as an immune barrier for infiltrating T cells.

Within the non-inflamed group, patients are subdivided into intermediate and immune-excluded. Patients with CTNNB1 mutation, which represent ~30% of HCCs5, are mostly associated with the immune-excluded subclass, consistent with their known resistance to ICIs121,122. Even if the mechanisms of immune exclusion driven by CTNNB1 mutation are not fully understood, a model based on hydrodynamic tail vein injection (HDTV) of a mutated CTNNB1 showed that CTNNB1 mutation impairs the recruitment of DCs and subsequent T-cell activation, mediated by a reduction in CCL5122. Another study showed that the immunosuppressive phenotype in CTNNB1 mutated HCC was mediated by TNFRSF19-mediated repression of cytokines secretion123. Of note, HCC with CTNNB1 mutation was not resistant to ICIs in an HDTV-based mouse model94. Overall, the association between the immune-based molecular classification and response to immunotherapy is appealing but needs to be confirmed in large prospective patient cohorts. This could lead to improving our comprehension of the molecular mechanisms of resistances in different patient subsets and to the rise of personalized medicine in advanced HCCs.

In addition to these approaches based on patient clustering, alternative signaling pathways activated in HCC have been shown to have some immunomodulatory features and appear as interesting therapeutic targets. The EGFR‐P38 MAPK axis in HCC cells also enhances immunosuppression by upregulating PD‐L1 expression and suppressing HLA‐I expression124. The loss of PTEN, a tumor suppressor gene frequently mutated in HCC125, can activate the PI3K signaling pathway, leading to decreased T-cell infiltration and increased immunosuppression. This loss impairs the stimulation of pathways like type I interferon and NF-κB, contributing to tumor progression due to the immunosuppressive TME126,127. All these proteins identified as immunomodulators could be targeted, which could improve the efficacy of ICIs in HCC.

Combination therapy: learning from preclinical models

The advancement of ICI therapy in cancer has been greatly facilitated by the recent evolution of preclinical models. Traditionally, the study of liver cancer relied on cell-based models and in vivo xenografts in immunodeficient mice. These models, while invaluable in understanding cancer biology and evaluating the therapeutic potential of chemotherapies, fell short in elucidating the complex interactions between tumor cells and the immune system, which is critical to assessing ICI efficacy. The growing need to accurately assess the potential of ICIs necessitated a shift toward models that embody the intricacies of the tumor-immune microenvironment. Coculture systems mark a step forward, allowing direct interactions between tumor cells and some immune cells in a controlled environment. In theory, they could offer insights into the immunomodulatory effects of ICIs, but these systems are limited by their lack of architectural complexity and the absence of a full physiological immune system. Patient-derived organoids, represent a more physiologically relevant model, preserving the three-dimensional architecture and cellular heterogeneity of tumors128. They serve as a bridge between traditional cell culture and in vivo studies, providing a more accurate platform for drug screening. Their limitation is the absence of a competent immune system, which can be bypassed by combining coculture systems and organoids129,130. Another alternative is the use of patient-derived spheroids, cultured in patients-derived serum131. This system provides all the advantages of the previous one but displays a complete immune system, which can be used to assess the effects of ICIs. Using such a system, we discovered a previously undiscovered immunomodulatory capacity of a therapy targeting Claudin-1, a protein expressed on the cell surface of HCC cells. Treatment with CLDN1-specific antibodies has been shown to markedly inhibit HCC growth by inhibiting pro-carcinogenic signaling and reprogramming the TME132. Syngeneic mouse models, wherein tumor cells from a mouse strain are implanted into a genetically identical host, preserve an intact immune system, thus providing an invaluable context for studying ICIs133. They offer insights into the antitumour immune response and the development of resistance. However, these models often lack the genetic diversity and complexity of human HCC, potentially oversimplifying the immune TME. Chemically induced HCC models in mice replicate the multistage development of liver cancer, providing a spectrum of diseases from dysplasia to carcinoma134,135,136. They are valuable for studying the natural evolution of HCC and the immune responses at different stages. Yet, the long latency and the variability in tumor development are significant drawbacks, posing challenges for timely and uniform study designs. Diethylnitrosamine, in particular, the most widely used model of chemically induced HCC, apart from its long time to tumor development (8–12 months), generates tumors with genetic mutations that poorly recapitulate the mutational landscape of human HCC137 and harboring poor immune infiltration138. This model is often associated with chemically induced or diet-induced liver fibrosis, either with carbon tetrachloride or Western diet in order to accelerate the tumor development and to get closer to the physiopathology of HCC development134,139. Similarly, genetically engineered mouse models with transgenic overexpression of an oncogene and/or ablation of an anti-oncogene, offer the possibility to evaluate the effect of ICIs in immunocompetent animal, but by definition, they can only recapitulate a narrow spectrum of genetic alterations, which can be problematic for the translation to human140. The HDTV technique is a relatively recent innovation, enabling the study of gene function in liver carcinogenesis through the rapid introduction of genetic material into hepatocytes141. This method can mimic the genetic alterations seen in human HCC and allows for the study of tumor-immune interactions in an intact immune system. By the choice of the oncogenes overexpression, or the anti-oncogenes deletion, they also provide a variety of cancer phenotypes, associated with different immune profiles, which mimics, to a certain extend, the variability of human HCC142,143. More, models using some plasmid combinations of oncogenes are sensitive to ICIs, while others are resistant143,144. Humanized mice, engineered to possess a human immune system, provide a critical platform for evaluating the efficacy of ICIs in a context that closely mimics human immune responses145. They are particularly useful for studying ICIs targeting human-specific antigens. The cons include the high cost, the need for specialized facilities, and the fact that the reconstituted human immune system may not fully recapitulate the diversity and functionality of its natural counterpart.

Table 2 provide an extended list of the preclinical studies that used immunocompetent mouse models to study the possibility of combination therapy of ICIs with other drugs. They were performed using a variety of mouse models including syngeneic, HDTV, chemically induced, or transgenic mouse models. The most widely used model was the subcutaneous injection of Hepa1-6 cells. This HCC mouse cell line originates from the BW7756 cells, generated by Jackson laboratory from C57L/J mice in the 60’s and first used in research in 1971146, before it was in vitro subcloned into different Hepa1 variants147 and that the Hepa1-6 was identified148. Since then, the Hepa1-6 cell line has emerged as a reliable tool to easily and cost-effectively generate fast-growing tumors, both in C57L and C57/BL6, upon subcutaneous or orthotopic (i.e., intrahepatic) injection. Tumors from this model partly respond to ICIs but are sensitive to a broad spectrum of combination therapies, which improve the efficacy of ICIs. Recently, Zabransky and colleagues performed cytometry by time of flight to profile the TME of different syngeneic mouse models of HCC, including Hepa1-6149. They highlighted a rather important CD8 T-cell infiltration as well as PD-L1 expression in Hepa1-6 orthotopic model. They also performed integration with human data and found only a few HCC samples with immune profiles matching the one of Hepa1-6, which questions the translationality of data using this model. Of note, while Hepa1-6 subcutaneously injected were sensitive to anti-PD-1 therapy, it was not the case for orthotopic injection in this study, underlying the importance of orthotopic models to better recapitulate the characteristics of the liver TME for ICIs evaluation.

Table 2 Summary of preclinical models of combination therapy to improve ICIs for HCC

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As shown in Table 2, a broad spectrum of therapeutic targets has shown a potential to improve the efficacy of ICIs, which underlines the wide possibilities for refining current treatments. First, some studies have been using ICIs in combination with standard chemotherapy such as oxaliplatin, or with multikinase inhibitors already approved as single agents for the treatment of HCC (sorafenib or Lenvatinib). These studies suggest some potential in combining ICIs with standard treatments, as recently performed for the treatment of cholangiocarcinoma, where ICIs have been approved in combination with chemotherapy12,150. Alternatively, studies tried some molecules from traditional medicine like Scutellarin151 or YIV-906152, which highlight the potential of drugs already known to have anticancer properties to improve ICIs-based therapy. Studies like the one from Bao and colleagues also confirm that vascular normalization strategies can enhance ICI efficacy153. By a combination of VEGF/VEGFR2 inhibitors with anti-PD-1 therapy, they showed a significant increase in CD8 T-cell infiltration, a significant reduction in tumor size, and improved survival rates in treated mice. Another study used regorafenib, a multikinase inhibitor known for its anti-angiogenic properties, with PD-1 blockade and found that regorafenib not only hindered angiogenesis but also altered the TME to make it more receptive to ICIs154. As expected, a majority of studies showing improvement of ICIs used immunomodulatory agents, like antibodies targeting TIGIT144, CXCR2134, CSF-1-R82,155, osteopontin99 or triggering receptor expressed on myeloid cells-1 (TREM-1)156. Others used nonsteroidal anti-inflammatory drugs (meloxicam)157 or interferon-α158,159, or approaches closer to a vaccination strategy, by the use of components triggering an immunization in the mouse, by using polyinosinic-polycytidylic acid160, DCs vaccine161 or AFP immunization162. Another strategy is to focus on the metabolic pathways associated with HCC and highlight that modulating these networks has the potential to improve immunotherapy. For example, Luo and colleagues identified Prmt5, encoding protein arginine N-methyltransferase 5, as a key metabolic modulator of MYC-induced HCC; and by disrupting this pathway they could enhance the response to anti-PD-1 therapy163. Other drugs targeting liver metabolisms showed some potential, like cholecystokinina antagonist164 or peroxisome proliferator-activated receptor gamma antagonist165. More analysis focused on novel molecular and genetic targets with unknown roles in liver cancer, such as N6-methyladenosine reader YTHDF1, Poly(ADP-ribose) glycohydrolase, or Cholecystokinin-B Receptor. These three new therapeutic targets were shown to be targetable and to synergize with ICIs164,166,167. Collectively, all these studies demonstrate the potential of combining ICIs with a variety of agents targeting different aspects of tumor biology and the TME. Each approach offers unique insights into enhancing the efficacy of immunotherapy in HCC, emphasizing the need for multifaceted and tailored treatment strategies to overcome the complex nature of the disease.

Conclusion and future avenues

Emerging strategies in the treatment of HCC through ICIs reveal the multifaceted nature of cancer therapy. The development and utilization of immunocompetent preclinical models are instrumental in understanding the complex interactions between tumor cells and the immune system, but significant challenges arise when translating these findings into clinical practice. Syngeneic mouse models, genetically engineered mice, chemically induced, and diet-induced models only partially capture the diversity of the tumor-immune microenvironment from human HCC. Bridging the gap between preclinical research and clinical application remains a significant challenge in cancer research. Nevertheless, the exploitation of combination therapies using these models targeting the TME from multiple angles has revealed important new information to improve cancer therapy. Investigating the molecular diversity of HCC has uncovered new therapeutic targets. The potential of CAR T-cell therapy remains still to be determined for HCC but may open a perspective for a more personalized treatment approach. As we move forward, the integration of these diverse therapeutic strategies promises to enhance the efficacy of current therapies. The combination of discovery and innovation will bring the field closer to more effective, personalized treatments for HCC patients—a key unmet medical need. The collective effort of clinicians and scientists will undoubtedly pave the way for improved efficacy and safety and, ultimately, improved prognosis for patients.

Data availability

No datasets were generated or analyzed during the current study.

Abbreviations

APCs:

antigen-presenting cells

B7-H3:

B7 homolog 3 protein

BCLC:

Barcelona clinic liver cancer

CAFs:

cancer-associated fibroblasts

CAR:

Chimeric antigen receptor

CTLA-4:

cytotoxic T-lymphocyte-associated protein 4

DCs:

dendritic cells

HCC:

Hepatocellular carcinoma

HDTV:

hydrodynamic tail vein injection

ICIs:

immune checkpoints inhibitors

ITSM:

immune receptor tyrosine-based switch motif

ITIM:

immune receptor tyrosine-based inhibitory motif

LAG3:

lymphocyte activation gene-3

LRTs:

loco-regional therapies

MDSCs:

myeloid-derived suppressor cells

MHC:

major histocompatibility complex

mOS:

median overall survival

NF-κB:

nuclear factor kappa-light-chain-enhancer of activated B cells

ORR:

objective response rate

PD-L1:

programmed cell death ligand 1

TAA:

Tumor-associated antigens

TACE:

transarterial chemoembolization

TAMs:

tumor-associated macrophages

TANs:

tumor-associated neutrophils

TCR:

T-cell receptor

TIGIT:

T-cell immunoglobulin and ITIM domain

TIM3:

T-cell immunoglobulin and mucin-domain containing-3

Tregs:

regulatory T cells

TME:

tumor microenvironment

TMB:

tumor mutational burden

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Acknowledgements

The authors acknowledge the following financial support: European Research Council Grant ERC-AdG-2020-FIBCAN #101021417 (T.F.B. and Y.H.); ERC-2022-PoC1 CANDY #101069276 (T.F.B.), EU HORIZON-HLTH-2021-DISEASE-04-07 D-SOLVE #101057917 (T.F.B.), ARC Grant TheraHCC2.0 IHU201301187 (T.F.B.); the French National Research Agency RHU DELIVER (ANR-21-RHUS-0001) (T.F.B. and P.N.) and LABEX ANR-10-LABX-0028_HEPSYS (T.F.B); the US National Institute of Health (R01CA233794, Y.H., T.F.B), the University of Strasbourg Foundation and Alsace Cancer Foundation (T.F.B). This work of the Interdisciplinary Thematic Institute IMCBio, as part of the ITI 2021-2028 program of the University of Strasbourg, CNRS and Inserm, was further supported by IdEx Unistra (ANR-10-IDEX-0002), and by SFRI-STRAT’US project (ANR 20-SFRI-0012) and EUR IMCBio (ANR-17-EURE-0023) under the framework of the French Investments for the Future Program and the France 2030 program. The figures have been created using BioRender.com.

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Authors and Affiliations

Inserm, Institute of Translational Medicine and Liver Disease, Strasbourg, France

Romain Desert, Fabio Gianonne, Antonio Saviano & Thomas F. Baumert

University of Strasbourg, Strasbourg, France

Romain Desert, Fabio Gianonne, Antonio Saviano & Thomas F. Baumert

Service d’hépato-gastroentérologie, Pôle Hépato-digestif, Institut-Hospitalo-Universitaire, Hôpitaux Universitaires de Strasbourg, Strasbourg, France

Fabio Gianonne, Antonio Saviano & Thomas F. Baumert

Division of Digestive and Liver Diseases, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, USA

Yujin Hoshida

Division of Chronic Inflammation and Cancer, German Cancer Research Centre Heidelberg (DKFZ), Heidelberg, Germany

Mathias Heikenwälder

AP-HP, Hôpitaux Universitaires Paris Seine Saint-Denis, Liver Unit, University Sorbonne Paris Nord, UFR SMBH, Bobigny, France

Pierre Nahon

Inserm, UMR-1138 “Functional Genomics of Solid Tumours”, Centre de Recherche des Cordeliers, Université de Paris, Paris, France

Pierre Nahon

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Contributions

R.D. wrote the first draft of the manuscript and assembled the figures. F.G. assembled Table 1. A.S., Y.H., M.H., and P.N. revised and edited the manuscript. T.F.B. led the project and participated in the writing and revisions.

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The authors declare no competing interests.

Disclosures

Inserm, the University of Strasbourg, the Strasbourg University Hospitals and the IHU Strasbourg have filed patent applications for the use of anti-claudin-1 monoclonal antibodies for the treatment of fibrosis and cancers (T.F.B. inventor) which have been licensed to Alentis Therapeutics. The same organisations have also filed patent applications for a method for diagnosis and/or prognosis of liver disease progression and risk of hepatocellular carcinoma and discovery of therapeutic compounds and targets to treat liver disease and cancer as well as a clinical gene signature-based human cell culture model and uses thereof (T.F.B and Y.H. co-inventors). T.F.B. is founder, shareholder, and advisor for Alentis Therapeutics. P.N. has received honoraria from and/or consults for AstraZeneca, Bayer, Bristol-Myers Squibb, Eisai, Gilead, Guerbet, Ipsen, and Roche. He has also received research grants from AstraZeneca, AbbVie, Bristol-Myers Squibb, and Eisai.

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Desert, R., Gianonne, F., Saviano, A. et al. Improving immunotherapy for the treatment of hepatocellular carcinoma: learning from patients and preclinical models. npj Gut Liver 2, 8 (2025). https://doi.org/10.1038/s44355-025-00018-y

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Received:25 October 2024

Accepted:28 January 2025

Published:03 April 2025

DOI:https://doi.org/10.1038/s44355-025-00018-y

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