Emerging biomarkers for immunotherapy response in biliary tract cancers: a comprehensive review of immune checkpoint inhibitor strategies

Abstract

Biliary tract cancers (BTCs) have rising incidence and mortality rates. Chemotherapy’s limited efficacy has led to exploring new treatments like immunotherapy. which offers modest benefits. Moreover, the identification of reliable predictive biomarkers for immune checkpoint therapy in BTCs remains elusive, hindering personalized treatment strategies. This review provides an overview of the current landscape of emerging biomarkers for immunotherapy response in BTCs. We discuss the incremental benefits of combination therapy and the evolving role of immunotherapy in managing advanced BTC. Additionally, we highlight the need for robust predictive biomarkers to optimize treatment outcomes and foster a more individualized approach to patient care. We aim to identify promising research avenues and strategies to enhance therapeutic efficacy and patient survival in BTCs.

Graphical Abstract

Plain language summary

Article highlights.

Background

• Currently, the combination of immune checkpoint inhibitors with chemotherapy is emerging as a promising first-line treatment option for advanced biliary tract cancers; however, the exploration of biomarkers for immunotherapy remains underdeveloped.

Method

• In this review, we aim to consolidate the existing evidence and identify potential predictive biomarkers in advanced BTCs, with a focus on guiding personalized treatment strategies.

Identified biomarkers associated with BTC immunotherapy

• Currently, PD-L1, TMB, MSI/dMMR are well-known predictive biomarkers, but their applicability in BTC patients remains uncertain.

• Emerging biomarkers in BTCs include genetic mutational signatures, tumor microenvironment features, host infectious factors and blood parameters.

• Utilizing multiple markers in combination may offer improved predictive efficacy.

• The combination of immune checkpoint inhibitors with chemotherapy has emerged as a novel first-line treatment option for advanced BTCs. Future efforts are needed to enhance treatment efficacy and identify the optimal patient population through biomarker screening.

1. Introduction

Biliary tract cancers (BTCs) encompass a diverse array of malignancies, including cholangiocarcinoma (both intrahepatic cholangiocarcinoma [ICC] and extrahepatic cholangiocarcinoma [ECC]), gallbladder cancer (GBC) and ampulla of Vater cancer (AOV), with their incidence and mortality rates on the rise in recent years [1,2]. Despite radical resection being the sole curative option, the prognosis is poor due to high recurrence, with an ICC recurrence rate of 80% postsurgery [3]. Consequently, systemic therapy has become the cornerstone for managing unresectable disease [4]. Since 2010, gemcitabine plus cisplatin (GP) has been the standard first-line therapy, as established by the ABC-02 Trial [5], albeit with a modest objective response rate (ORR) of 26.0% [6].

Recently, immune checkpoint inhibitors (ICIs) have emerged as a promising therapy for advanced BTCs. ICIs target immune checkpoints, promoting immune activation and enhancing the antitumor response. Notably, patients with DNA mismatch repair-deficiency (dMMR), microsatellite instability-high (MSI-H) and tumor-mutational-burden-high (TMB-H) have received US FDA approval for ICI treatment [7,8]. The TOPAZ-1 study, presented at the 2022 ASCO-GI, demonstrated the widespread benefits of durvalumab (a programmed cell death 1 ligand 1 [PD-L1] monoclonal antibody) plus standard chemotherapy across all primary biliary tract tumors, particularly noteworthy in Asian populations. Accordingly, the latest NCCN guidelines (v.2023), as well as the ESMO guidelines, recommend considering cisplatin-gemcitabine with durvalumab as a potential first-line treatment for advanced cholangiocarcinoma (CCA) [9,10]. Additionally, the KEYNOTE-966 study revealed survival benefits with the combination of pembrolizumab (a PD-1 inhibitor) and chemotherapy as a first-line treatment for advanced BTCs [11]. Besides, experts from Italy [12], France [13] and Canada [14] similarly endorse the use of immunotherapy in both first and second-line settings for BTC treatment.

Meanwhile, advances in molecular profiling of BTCs have led to a surge in targeted therapies for BTCs with alterations in IDH1, FGFR2 and MAP kinase signaling, with interest in combining these agents with immunotherapy. The molecular characteristics of ICC, ECC and GB differ significantly. ICC often has mutations in IDH and FGFR2 fusions, while ECC frequently shows alterations in KRAS, SMAD4 and ARID1A. GBC typically has mutations in TP53 and ERBB2, influencing its therapeutic response and prognosis.

Despite these advancements, the benefit of immune combination chemotherapy for advanced BTC remains modest, highlighting the need for further exploration into predictive biomarkers for immunotherapy response. Potential biomarkers such as tumor mutational burden (TMB), dMMR, PD-L1 expression and MSI-H have been identified [15–19]. However, research on biomarkers for ICIs treatment in advanced BTCs, particularly in molecular biology, is still evolving. This review aims to consolidate existing evidence and identify potential predictive biomarkers to guide personalized treatment strategies in advanced BTCs. Table 1 lists the clinical trials and studies on biomarkers.

Table 1.

biomarker analysis in clinical trials and studies.

Trials or studies	Biomarker	Treatment	Predictive factors	Source	Ref.
KEYNOTE-028(NCT02054806)	PD-L1 expression	Pembrolizumab	PD-L1 expression (CPS ≥ 1%)	Tumor samples	[20]
KEYNOTE-158(NCT02628067)	PD-L1 expression	Pembrolizumab	PD-L1 expression (CPS ≥ 1%)	Tumor samples	[21]
ChiCTR2100044476	PD-L1 expression	Lenvatinib plus anti-PD-1	PD-L1 expression (TPS ≥ 1%)	Tumor samples	[3]
NCT02829918	PD-L1 expression	Nivolumab plus chemotherapy	PD-L1 expression (TPS ≥ 1%)	Tumor samples	[22]
TOPAZ-1(NCT03046862)	PD-L1 expression	Gemcitabine and cisplatin plus immunotherapy	PD-L1 expression (TAP ≥ 1%)	Tumor samples	[23]
NCT03311789	PD-L1 expression	Gemcitabine and cisplatin plus nivolumab	PD-L1 expression (TAP ≥ 1%)	Tumor and blood samples	[24]
NCT03951597	PD-L1 expression	Toripalimab combined with levatinib and GEMOX	PD-L1 expression in ≥1%	Tumor samples	[25]
NCT03486678	PD-L1 expression, TMB, ctDNA	Camrelizumab and GEMOX	PD-L1 expression in ≥1%, negative post-treatment ctDNA	Tumor and blood samples	[26]
Zuo et al.	PD-L1 expression	Lenvatinib plus anti-PD-1	PD-L1 expression in ≥1%	Tumor samples	[27]
Zhu et al.	PD-L1 expression	PD-1 combined with levatinib and GEMOX	PD-L1 expression in ≥5%	Tumor samples	[28]
Liddell SS et al.	TMB	Anti-PD-1 or PD-L1	TMB >5 Muts/Mb	Tumor samples	[29]
Shi C	PD-L1 expression, TMB	Lenvatinib Plus anti-PD-1	PD-L1 expression ≥50% or in TMB ≥2.5 Muts/Mb	Tumor samples	[30]
Jin, B et al.	TMB, MSI	ICI	TMB ≥3.47Muts/Mb, MSI sensor score ≥0.36	Tissue samples	[31]
NCT03892577	MSI, PD-L1 expression	Immunotherapy	MSI-H and PD-L1 expression (CPS ≥ 5)	Tumor samples	[32]
NCT02829918	PD-L1 expression on tumor and TIL	Nivolumab	PD-L1 expression on tumor and PD-1 expression on TIL	Tumor biopsy samples	[22]
Qiu et al.	Genetic characterization	Immunotherapy	lower level of methylation changes	Tumor samples	[33]
Chen et al.	Genetic characterization	Immunotherapy	KRAS-TP53 co-mutations	Tumor samples	[6]
Spizzo et al.	Genetic characterization	Unknown	HRR pathway gene mutations	Tumors samples	[34]
Yoon JG et al.	Genetic characterization	Anti-PD-1/PD-L1	chromosomal alterations (19q amplification and 9p deletion)	Tumors samples	[35]
He et al.	Virus	Anti-PD-1	EBVaICC	Tumors and blood samples	[36]
Mao et al.	Microbiome	Anti-PD-1	Riched of Bacteroidetes phylum	Fresh stool samples	[37]
Du et al.	Blood testing	Anti-PD-1	Decreased NLR, SII and IP-10 and increased MIP-1β	Blood samples	[38]

Trials or studies: Specific clinical trials or studies where the biomarker analysis was conducted.

Biomarker: This column specifies the biomarker(s) evaluated in each study.

Treatment: Describes the treatment regimen used in each respective study.

Predictive value: Indicates factors associated with a favorable response to treatment as identified by the study, such as specific biomarker thresholds.

Source: Specifies the origin of the samples used for biomarker testing in the study.

Ref: PMID number.

CPS: Combined positive score; ctDNA: Circulating tumor DNA; EBVaICC: Epstein–Barr virus associated intrahepatic cholangiocarcinoma; GEMOX: Gemcitabine and oxaliplatin; HRR: Homologous recombination repair; ICI: Immune checkpoint inhibitor; IP-10: IFN-inducible protein-10; MIP-1β: Macrophage inflammatory protein-1β; NLR: Neutrophils-to lymphocytes ratio; PD-L1: Programmed cell death 1 ligand 1; PD-1: Programmed cell death protein 1; SII: Systemic immune-inflammation index; TAP: Tumor area positivity score; TIL: Tumor-infiltrating leukocytes; TPS: Tumor cell proportion score.

2. Key biomarkers for immunotherapy response

2.1. PD-L1 expression

PD-1, an immune checkpoint protein found on T-cells, plays a pivotal role in immune evasion by binding to PD-L1 (B7–H1). PD-L1 is expressed on various cell types, including vascular endothelium, epithelium, mesenchymal stem cells, B-cells, T-cells, dendritic cells and macrophages, indicating its wide-ranging impact on immune regulation [39]. Clinical trials using immunohistochemical staining to evaluate PD-L1 expression on tumor and immune cells have highlighted its potential as an immunotherapy biomarker. Notably, in NSCLC, evaluating PD-L1 expression via immunohistochemistry has led to FDA approval for pembrolizumab.

Interest in immunotherapy for BTCs has surged recently. However, there remains a scarcity of studies and clinical trials investigating BTCs. Previous research indicates heterogeneous PD-L1 expression rates in BTC patients, ranging from 9% to 72%, with higher incidence in invasive disease associated with poorer survival [19,28,40,41]. Gani et al. analyzed the correlation between PD-L1 expression and clinical outcomes in ICCs, finding that 62.9% of tumor samples stained positive for PD-L1 on TAMs and 72.2% showed positivity on cells within the tumor front (TF+), with PD-L1 expression around the tumor front linked to a 60% reduction in survival rates [42]. Sabbatino et al. identified PD-L1 expression on tumor cells and tumor-infiltrating lymphocytes (TILs), as well as HLA class I antigens, finding that 60% of samples had HLA defects and 30% expressed PD-L1 expression, both associated with reduced survival [40]. In a phase II single-arm study (NCT03486678) evaluating the camrelizumab (a PD-1 inhibitor) combination therapy in advanced BTCs, higher objective response rates were observed in patients with PD-L1 ≥1% compared with PD-L1 <1% [26].

In ICI monotherapy, studies like KEYNOTE-028 and KEYNOTE-158 have reported varied response rates in PD-L1 positive BTC patients treated with pembrolizumab, indicating potential efficacy differences based on PD-L1 expression levels [20,21,43]. Similarly, in ICI-combined therapy, studies have shown improved survival outcomes in PD-L1-positive patients [25,27]. Additionally, the TOPAZ-1 study suggested further improvement in response rates, progression-free survival and overall survival with the addition of a PD-L1 monoclonal antibody, irrespective of PD-L1 expression levels [23]. Besides, results from a phase II study showed the efficacy of nivolumab (a PD-1 inhibitor) with GP was independent of PD-L1 expression level [24].

Although PD-L1 expression shows promise as a predictive biomarker in BTCs treated with ICIs, its precise role remains to be defined. Various factors, such as tumor types, sample lesions, scoring systems, detection methods and treatment decisions, can influence PD-L1 expression or its predictive efficacy.

2.2. Tumor mutational burden

Cancer is predominantly driven by genetic factors. Genetic changes, including nonsynonymous mutations, synonymous mutations, copy number variations and insertions or deletions, can transform normal cells into cancerous ones [44]. TMB is defined as the overall number of mutations per megabase in the coding regions of tumor cells and TMB-H is defined as a high number of mutations per megabase of DNA. In 2020, the FDA first approved pembrolizumab for the treatment of solid tumors with TMB-H [45].

The prevalence of TMB-H in BTCs is generally low. ranging from 4% to 6% [46,47]. Liddell et al. analyzed 47 advanced BTCs receiving ICIs and found that patients with TMB-H (defined as TMB: >5 Mut/Mb) exhibited improved Progression free survival (PFS) compared with those with lower TMB (median PFS: 6.4 months vs 2.2 months, p = 0.0027) [29]. In the KEYNOTE-028 phase Ib basket trials, advanced BTC patients with TMB-H demonstrated higher response rates and PFS. Additionally, an analysis from a phase II study investigating the combination of nivolumab and chemotherapy indicated that both TMB and tumor neoantigen mutation (TNM) could predict the efficacy of nivolumab plus GP in advanced BTCs. Furthermore, specific mutations in RYR2, APOB and MUC4 were found to occur more frequently in nonresponders [22]. Another recent phase Ib study of anlotinib (a small molecule tyrosine kinase inhibitor) and TQB2450 (a PD-L1 inhibitor) in pretreated advanced BTCs also suggested that high TMB could serve as a predictor of treatment efficacy (cut-off:5 Mut/Mb) [48]. However, TMB is a relatively new marker in BTCs and standardized criteria for identifying and reporting TMB have not yet been established. The FDA has approved pembrolizumab as a treatment for TMB-H solid tumors, using a cut-off value of 10 mut/Mb. This criterion is debated and another challenge is utilizing TMB with other factors influencing ICI response, such as specific mutations. Moreover, TMB does not always correlate with the efficacy of immunotherapeutic agents.

2.3. Microsatellite instability-high

Microsatellites are short, repetitive sequences of DNA, typically 1–6 base pairs in length, scattered throughout the genome. These sequences are prone to errors during DNA replication due to their repetitive nature. Microsatellite instability (MSI) occurs due to deficiency in the MMR system, resulting in accumulation of insertion or deletion errors in these microsatellite regions. Tumors are classified as MSI-H when a significant number of microsatellites show instability, or as microsatellite stable (MSS) when microsatellites remain largely unchanged. In 2019, the FDA approved anti-PD1 therapy for patients with MSI-H cancers based on the concept of TMB [7,49]. While most MSI-H patients with solid tumors exhibit a high TMB, only 16% of patients with TMB-H tumors demonstrate MSI-H [50]. The frequencies of MSI in BTCs remain controversial. A systematic review indicated that MSI-H was rarely observed in 3893 GBC patients (3.5%) [32]. However, data from a groundbreaking whole exome-sequencing report revealed a dMMR or MSI-H status in 36% of 260 BTC patients [46]. Tumors with dMMR or MSI-H primarily exhibit favorable responses to immunotherapy. However, research on the MSI of BTCs is limited. A recent study showed that in cholangiocarcinoma, MSI-H status and positive PD-L1 expression correlate with elevated TMB and are associated with improved Overall survival and PFS in patients receiving PD-1 inhibitor-based immunotherapy [51]. Besides, in a phase II trial involving nivolumab monotherapy for 54 BTC, the ORR was 11% (5 out of 46) and an intriguing finding emerged that all responders had tumors proficient in mismatch repair proteins [22]. In contrast, the KEYNOTE-158 phase II trial revealed that 95.2% of BTC patients treated with pembrolizumab had MSS tumors and all six responders also had MSS tumors. The KEYNOTE-028 phase Ib trial reported that only one out of 24 had MSI-H patients and achieved a PR. Despite the limited supporting data, the assessment of high microsatellite status appears to be a predictor in BTC. Nonetheless, some patients with MSS tumors still derive benefits from treatment from immunotherapy, both in monotherapy and combination therapy [22].

2.4. Homologous recombination deficiency

In a recent biomarker trial within a phase I/II clinical study, ovarian cancer patients treated with niraparib (A PARP inhibitor) and pembrolizumab showed promising outcomes linked to defective Homologous recombination deficiency (HRD). HRD, a hallmark of various cancers including BTCs, remains a subject of interest due to its implications in tumorigenesis and therapeutic response. Ovarian cancer (40–50% with HRD), prostate cancer (20–25%) and breast cancer (18%) are among the cancers where HRD is prevalent [52,53]. Extensive gene analysis has revealed a high incidence of HRD in other solid tumors as well, including BTCs (28%), bladder cancers (23%), hepatocellular cancers (20%) and gastroesophageal cancers (20%) [54]. Another study reported an HRD frequency of 74.7% (347/501) across multiple tumor types, with an incidence of 75% (36/48) in BTCs [55]. The incidence of pathogenic HRD varies depending on the stage, histology and previous treatment [56]. Currently, there is no consensus on the testing methods for defining HRD in BTCs, including the selection and cut-off value for HR-related gene numbers.

Recent pan-cancer analyses have highlighted HRD’s association with an immune-sensitive tumor microenvironment (TME) and improved responses to ICIs [57]. However, the efficacy of HRD as a sole predictive biomarker for ICIs is inconclusive across different tumor types [55]. A study examined the response to ICIs in tumors with BRCA1 and BRCA2 mutations, revealing superior response with BRCA2 truncating mutations [58]. Spizzo et al. reported that BRCA mutations correlated with higher rates of MSI-H/deficient mismatch repair and tumors with elevated TMB, regardless of MMR/MSI status [34]. In a recent phase II clinical trial (ChiCTR2000036652) [59], predictive biomarkers for sintilimab (a PD-1 inhibitor) plus GP in advanced BTCs were investigated, indicating mutations in genes related to the homologous recombination repair pathway correlated with improved survival outcomes and tumor response.

2.5. Tumor microenvironment

The TME in BTCs (Figure 1) comprises tumor cells, mesenchymal cells and extracellular components, exhibiting varying characteristics across malignancies [60–63]. Cholangiocarcinoma typically manifests as a highly fibrotic and hypovascularized environment with abundant cancer-associated fibroblasts and pro-tumor immune cell infiltration, notably tumor-associated macrophages (TAMs) and Tregs [64,65]. Cancer-associated fibroblasts play a pivotal role in mesenchymal fibrosis and interact with various immune cell types, modulated by cytokines and extracellular matrix molecules. Myeloid-derived suppressor cells in the TME contribute to immune evasion and resistance to immunotherapy through interactions with regulatory T lymphocytes and macrophages, mediated by immunosuppressive cytokine [66–70].

Figure 1.

Characterization and main immune components in the microenvironment of BTC. In the TME various cell types promote tumor progression and regulate immune activity, including infiltrating immune cells and cancer-associated fibroblasts, CAFs and extracellular components. TAM can attract immunosuppressive cells by secreting IL-4, IL-8, IL-10, CCL-2, CCL-22 and CCL-17. Th1 cells mediate antitumor immunity by producing IFN-γ and IL-2, which activate CD8⁺ T-cells and Th2 cells produce IL-4, IL-5 and IL-13. Endogenous CXCL9 regulates tumor-infiltrating NK cells to enhance antitumor immune surveillance.

CAF: Cancer-associated fibroblast; CCL: Chemokine ligand: CXCL: Chemokine (C-X-C motif) ligand; IFN: Interferon; IL: Interleukin; NK: Natural killer; TAM: Tumor-associated macrophage; TME: Tumor microenvironment.

TILs, including macrophages, NK cells, CD4⁺ and CD8⁺ T-cells, represent predictive factors for immunotherapy response. These cells execute distinct functions within the TME, with CD4⁺ cells mediating cellular immunity, CD8⁺ cytotoxic T-cells directly destroying tumor cells and NK cells employing similar mechanisms. Tregs exert immunosuppressive effects by secreting cytokines, contributing to immune evasion. Various molecular mechanisms regulate TIL functions, including signaling pathways implicated in malignancy and immune evasion [71,72].

TAMs, the predominant immune cell population in the TME, correlate with poor prognosis in BTC patients [73]. Research indicates that macrophages primarily regulate immune function, hematopoiesis, metabolism, tissue repair and embryonic tissue maturation [74–76]. A significant number of TAMs in ICC exhibit gene expression patterns associated with an immunosuppressed state [77]. In a mouse model, interactions between PD-1 or PD-L1 inhibitors and macrophages, in the absence of T-cells, B-cells and NK cells, led to an antitumor immune response [78].

Several cytokines, including interleukins, interferons, TNF and TGF-β [79–81] have been reported as predictive biomarkers for cancer immunotherapy. IL-6, IL-15 have been reported to have a predictive effect on the prognosis of BTCs [79,82].

The immune landscape in BTCs is heterogeneous, with distinct immune classifications. Studies have identified immune subtypes characterized by differences in immune activation, antigen processing and immune cell infiltration. In a study investigating the TME characteristics of ICC patients [83], four immune subtypes were identified: immune-desert, immunogenic, myeloid and mesenchymal. Another study identified three immune subgroups: IG1 (immunosuppressive), IG2 (immune exclusion) and IG3 (immune-activated). Tumors with specific mutations, such as KRAS mutations, are enriched in certain immune subgroups [60].

2.6. Genetic characterization

Epigenetic modifications play a pivotal role in regulating immune activity in ICC. A recent study categorized ICC tumor samples into high and low immune clusters, employing the MIRA score to compare immune activity profiles. The findings unveiled higher promoter methylation levels in genes associated with tumor immune pathways in the high-immune cluster. Moreover, this cluster exhibited increased interactions between tumor and immune cells, elevated immune cell infiltration and upregulated immune pathways, including T-cell receptor signaling and cytokine-cytokine receptor interaction pathways. Additionally, these tumors displayed elevated interferon signature expression, suggesting heightened immune activity [84].

Previous research suggests DNA methylation changes can serve as predictive biomarkers for improved responses to immunotherapy in cholangiocarcinoma and GBC. Minimal methylation changes in BTCs are associated with an inflammatory tumor microenvironment and increased PD-L1 expression, indicating potentially favorable outcomes with ICIs. In high methyl-risk cholangiocarcinoma, PD-L1 expression correlates with oncogenic pathways, whereas in low methyl-risk cases, it is linked with immune responses [85]. Demethylating agents may enhance T-cell recruitment and activation in high methyl-risk patients, augmenting the efficacy of immunotherapy. This is achieved through the upregulation of tumor antigen presentation [86] and the concurrent downregulation of immune inhibitory signals [87], ultimately bolstering the efficacy of immunotherapeutic interventions [88].

Mutations in isocitrate dehydrogenase 1 mutations (mIDH1) are frequently observed in CCA and lead to the production of (R)-2-hydroxyglutarate (R-2HG), which inhibits enzymes involved in epigenetic regulation and DNA repair [89,90]. mIDH1 is associated with a genetic signature indicative of low immune infiltration in CCA [83,91]. Inhibition of mIDH1 can potentially transform ‘cold’ tumors into ‘hot’ tumors, restoring responsiveness to immunological signals. Moreover, mIDH1 promotes immune evasion by inhibiting IFNγ-TET signaling, suggesting a therapeutic strategy to enhance immunotherapy efficacy [91,92]. A recent study demonstrated that IDH1 mutations might indicate a better treatment response in CCA [93].

Despite the different anatomic locations of BTCs, they exhibit similar genomic and epigenomic features. Comparative exome sequencing studies have identified common characteristics across BTCs, including mutations in TP53, KRAS and KMT2C, irrespective of their specific location [32,94,95], which are associated with immunotherapy effectiveness in BTCs. KRAS-TP53 co-mutations predict poor prognosis and immunotherapy outcomes in CCA, while a genomic signature composed of KRAS signaling genes is linked to unfavorable clinical outcomes [6,48]. Chromosomal alterations, such as 19q amplification and 9p deletion, containing genes involved in immune checkpoint regulation, may serve as potential markers of immunotherapy resistance [35].

2.7. Blood parameters

Blood testing parameters and serum cytokines can be used for predicting the efficacy of immunotherapy in several malignancies. A study assessing ICI treatment efficacy in advanced BTCs has identified significant differences in circulating hematologic characteristics, such as systemic immune-inflammation index (SII), neutrophils-to lymphocytes ratio (NLR) and in serum cytokines including cytokine interferon-inducible protein-10 (IP-10), macrophage inflammatory protein-1β (MIP-1β) between response and nonresponse [38]. Decreased neutrophils-to lymphocytes ratio, SII and IP-10 and increased MIP-1β were related to better overall benefit rates [38]. Moreover, the circulating tumor DNA had shown potential as a predictive biomarker for the response to camrelizumab combined treatment. ORR was lower in patients with positive post-treatment circulating tumor DNA than in those with negative [26].

2.8. Microbiome & viral factors

The microbiome profoundly influences the immune system through diverse mechanisms, impacting inflammation, DNA integrity and metabolic processes, thereby influencing immunotherapy efficacy [96]. Within the TME, the microbiome shapes antitumor immunity by modulating various immune cells and cytokines [97–99]. Research on epithelial tumors has highlighted the gut microbiome as a predictor of immunotherapy response and toxicity [100]. Furthermore, fecal microbiota transplantation from immunotherapy-responsive donors has enhanced antitumor immunity and overcome resistance to ICIs in patients with advanced melanoma [101]. Studies comparing the microbiome composition in bile fluid samples from patients with choledocholithiasis and cholangiocarcinoma have identified microbial dysbiosis, suggesting a role in tumor pathogenesis [102,103].

Recent investigations unveiled associations between the gut microbiome and anti-PD-1 immunotherapy response in hepatobiliary cancers, proposing taxonomic signatures as biomarkers for predicting treatment outcomes and survival benefits [37]. Specifically, certain microbial taxa, such as Veillonellaceae bacterium-GAM79 and Alistipes sp. Marseille-P5997, exhibit potential as biomarkers for favorable immunotherapy responses [37]. In BTCs, the enrichment of specific bacterial taxa from the Bacteroidetes phylum correlates with enhanced response to immunotherapy, likely mediated through secondary bile acid metabolism and modulation of host immunity [104,105].

Regarding viral factors, the incidence of EBV infection in BTCs remains uncertain and varies based on tumor location [106,107]. Studies showed that EBV-associated ICCs (EBVaICC) have distinct immunogenomic profiles characterized by an enlarged tumor immune microenvironment, increased infiltration of T-cells and elevated PD-1/PD-L1 expression [36,107,108]. Conversely, HBV infection is a known risk factor for BTCs, particularly ICC, where HBV-positive tumors exhibit specific immune signatures associated with responsiveness to immunotherapy [60].

3. Challenges & future directions

Patients undergoing ICI therapy frequently encounter resistance, whether inherent or acquired, due to diverse mechanisms. Overcoming this resistance involves several strategic approaches, including mitigating T-cell exhaustion, reshaping the TME, targeting alternative immune checkpoints, restoring a healthy gut microbiome, employing oncolytic viruses and utilizing tumor vaccines [109,110]. Additionally, combining ICIs with conventional therapies such as chemotherapy, radiation therapy, or agents that induce immunogenic cell death shows promise [111,112]. Research has demonstrated efficacy in overcoming resistance by combining anti-CTLA-4 and anti-PD1 therapies with gemcitabine/cisplatin chemotherapy in ICC models. This approach enhances survival outcomes by boosting TILs and normalizing ICC vessels [113]. Besides, IL-6 was found to induce PD-L1 expression, contributing to immune evasion and ICI resistance [82].

Additionally, an important question in BTC immunotherapy is whether continuing first-line treatment beyond initial chemo-immunotherapy progression benefits patients. Exploring the efficacy of ICI dual therapy or alternative modalities remains crucial. The ongoing GEMINI II study (NCT05775159) investigates bispecific antibodies (a CTLA-4/PD-1 bispecific antibody/a PD-1 and TIGIT bispecific antibody) in advanced BTC. Another trial (NCT04298008) examines durvalumab with AZD6738, an oral ATR inhibitor, as second-line therapy for BTCs [114,115].

Despite progress in identifying biomarkers like PD-L1, TMB and MSI/dMMR, their relevance in BTCs remains uncertain. Researchers are exploring novel biomarkers, sophisticated molecular profiling for identifying specific markers and integrated approaches combining genomic data with insights into immune evasion mechanisms. Looking ahead, the focus will be on advancing personalized medicine through robust predictive models tailored to individual patient profiles. Technological innovations in sequencing technologies and bioinformatics are expected to accelerate biomarker discovery and therapeutic targeting. Furthermore, global collaboration will play a crucial role in validating biomarkers and conducting large-scale clinical trials to enhance treatment outcomes in BTCs.

4. Conclusion

While PD-L1, TMB and MSI/dMMR have emerged as well-known predictive biomarkers in various cancer types, their applicability in BTC patients remains unclear. Currently, FDA approval for anti-PD-1 therapy is limited to patients with MSI-H or dMMR cancers, highlighting the need for further investigation into biomarkers specific to BTCs. Although TMB and PD-L1 expression hold potential as predictive biomarkers, their utility in BTC patients requires additional validation and treatment methods based on these criteria are lacking. It is hypothesized that combining multiple biomarkers may enhance predictive accuracy and treatment efficacy in BTCs.

Ongoing clinical trials and studies are evaluating a range of emerging biomarkers, including genetic mutational signatures, features of the tumor microenvironment, host infectious factors and blood parameters. Understanding the intricate crosstalk between cancer angiogenesis, adaptive immune cells and cancer endothelial cells is crucial for elucidating mechanisms underlying tumor immune surveillance and immunotherapy response.

As ICI combined with chemotherapy emerges as a promising first-line treatment option for advanced biliary malignancies, the current repertoire of predictive molecules for BTCs remains inadequate. The identification of reliable predictors of response to immunotherapy is therefore imperative in the context of limited treatment options. Moving forward, comprehensive research efforts aimed at elucidating novel biomarkers and refining existing predictive models are essential for advancing personalized treatment approaches and improving patient outcomes in BTCs.

5. Future Perspective

Looking ahead, the exploration and validation of novel biomarkers specific to BTCs will be essential in enhancing the precision of immunotherapy. The development of multi-biomarker strategies could potentially improve the predictive accuracy and treatment efficacy for BTC patients. Future research should focus on integrating genetic, molecular, and environmental factors to identify reliable predictive markers. Furthermore, a deeper understanding of the tumor microenvironment and its interaction with immune responses will be crucial in advancing personalized treatment approaches. This will involve investigating the complex interplay between cancer angiogenesis, immune cells, and endothelial cells to better comprehend the mechanisms of immune surveillance and therapeutic response.

Funding Statement

Major Projects of Hangzhou Health Science and Technology Plan (Z20240016), Hangzhou Biomedical and Health Industry Development Support Science and Technology Special Fund (2022WJC101) and Laboratory of Clinical Cancer Pharmacology and Toxicology Research of Zhejiang Province.

Author contributions

Data curation and writing (original draft) was done by YD Lou. Data curation of the original draft was done by YJ Chen. Data curation and writing (original draft) was done by KB Guo. Data curation (original draft) was done by BB Li. Conceptualization, funding acquisition and writing (review and editing) was done by S Zheng.

Financial disclosure

Major Projects of Hangzhou Health Science and Technology Plan (Z20240016), Hangzhou Biomedical and Health Industry Development Support Science and Technology Special Fund (2022WJC101) and Laboratory of Clinical Cancer Pharmacology and Toxicology Research of Zhejiang Province. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

Competing interests disclosure

The authors have no competing interests or relevant affiliations with any organization or entity with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, stock ownership or options and expert testimony.

Writing disclosure

No writing assistance was utilized in the production of this manuscript.