Impact of liver metastasis on the efficacy of immune checkpoint inhibitors for advanced colorectal cancer

Abstract

BACKGROUND

Liver metastasis is common in advanced colorectal cancer (CRC) and may influence the response to immune checkpoint inhibitors (ICIs). However, the prognostic impact of liver metastasis on ICI efficacy remains uncertain.

AIM

To evaluate the association between liver metastasis and survival outcomes in patients with metastatic CRC (mCRC) treated with ICIs in a meta-analysis.

METHODS

We systematically searched PubMed, EMBASE, and Web of Science up to May 14, 2025, for studies comparing survival outcomes in patients with mCRC with vs without liver metastasis receiving ICIs. Hazard ratios (HRs) for overall survival (OS) and/or progression-free survival (PFS) were extracted and pooled using random-effects models. Subgroup and sensitivity analyses were conducted to explore heterogeneity and result stability.

RESULTS

Sixteen studies comprising 1203 patients were included. Liver metastasis was associated with significantly worse PFS [HR = 1.94, 95% confidence interval (95%CI): 1.56-2.41, P < 0.001; I² = 38%] and OS (HR = 2.10, 95%CI: 1.66-2.65, P < 0.001; I² = 23%) among patients with mCRC treated with ICIs. Subgroup analyses showed consistent results across study design, microsatellite status, age, follow-up duration, and statistical adjustment (P for subgroup difference all > 0.05). Sensitivity analyses performed by excluding one study at a time showed consistent results, which further confirmed the robustness of the findings.

CONCLUSION

Liver metastasis is associated with worse survival outcomes in patients with mCRC receiving ICIs. These results suggest that liver metastasis may serve as a negative prognostic factor in the context of immunotherapy for mCRC.

Key Words: Advanced colorectal cancer; Liver metastasis; Progression; Survival; Immune checkpoint inhibitors

Core Tip: This meta-analysis evaluated the impact of liver metastasis on the efficacy of immune checkpoint inhibitors (ICIs) in advanced colorectal cancer (CRC). By pooling data from 16 studies involving 1203 patients, we found that liver metastasis was significantly associated with poorer progression-free and overall survival among ICI-treated patients. The adverse effect persisted across microsatellite instability subtypes and study designs, highlighting liver metastasis as a robust negative prognostic factor. These findings suggest that liver metastasis may impair systemic antitumor immunity and should be considered when optimizing immunotherapy strategies for metastatic CRC.

INTRODUCTION

Colorectal cancer (CRC) remains one of the most common malignancies worldwide and is a leading cause of cancer-related mortality[1,2]. Approximately 20%-25% of patients are diagnosed with metastatic CRC (mCRC) at initial presentation, and nearly 50% will eventually develop distant metastases during the course of their disease[3]. Despite advances in systemic therapies, the prognosis of mCRC remains poor, with a 5-year survival rate of less than 20%[4]. CRC is a biologically heterogeneous disease that can be classified into two major molecular subtypes based on mismatch repair status: Microsatellite instability-high (MSI-H) or deficient mismatch repair (dMMR)[5] and microsatellite stable (MSS) or proficient mismatch repair (pMMR)[6]. While the MSI-H/dMMR subtype accounts for only about 5% of metastatic cases, it is characterized by a high tumor mutational burden and increased immunogenicity, making it particularly responsive to immune checkpoint inhibitors (ICIs)[7]. In contrast, MSS/pMMR tumors, which represent the vast majority of mCRC, tend to be less responsive to ICIs due to their relatively immunologically “cold” tumor microenvironment[8].

ICIs have emerged as an important treatment modality in mCRC, particularly for patients with MSI-H/dMMR tumors, where agents such as pembrolizumab and nivolumab have provided durable responses and improved survival[9,10]. However, responses to ICIs can be variable, and there is growing interest in identifying reliable prognostic or predictive factors that may influence treatment outcomes[11]. Liver metastasis is present in more than 50% of patients with mCRC and is associated with worse overall outcomes[12]. Preclinical and translational studies suggest that liver metastases may impair systemic anti-tumor immune responses by creating an immunosuppressive microenvironment and promoting T-cell dysfunction, potentially reducing the efficacy of ICIs[13,14]. Although individual studies have examined the impact of liver metastasis on ICI outcomes in mCRC, their results are inconsistent[15-30], and a comprehensive synthesis of available evidence is lacking. In view of this knowledge gap, in this study, we performed a meta-analysis aiming to evaluate the association between liver metastasis and survival outcomes in patients with mCRC treated with ICIs.

MATERIALS AND METHODS

This study followed the PRISMA 2020[31,32] and Cochrane Handbook guidelines[32] for conducting systematic reviews and meta-analyses, covering study design, data collection, statistical methods, and interpretation of results. The protocol was also registered in PROSPERO under the ID CRD420251075437.

Database search

To identify studies pertinent to this meta-analysis, we searched PubMed, EMBASE, and Web of Science databases using an extensive array of search terms, which involved the combined terms of (1): “Colorectal” OR “colon” OR “rectal” OR “colorectum” OR “rectum”; (2) “Neoplasms” OR “cancer” OR “tumor” OR “carcinoma” OR “malignancy” OR “adenocarcinoma”; (3) “Programmed cell death protein 1” OR “PD-1” OR “PD-L1” OR “immune checkpoint inhibitors” OR “ICI” OR “cytotoxic t-lymphocyte-associated protein 4” OR “CTLA-4” OR “nivolumab” OR “pembrolizumab” OR “atezolizumab” OR “ipilimumab” OR “durvalumab” OR “cemiplimab” OR “avelumab” OR “tremelimumab”; (4) “Liver” OR “hepatic”; (5) “Metastatic” OR “metastasis” OR “metastases” OR “advanced” OR “advance”; and (6) “Survival” OR “prognosis” OR “mortality” OR “death” OR “progression” OR “overall survival” OR “progression-free survival” OR “PFS”. Only full-text articles involving human participants and published in English-language peer-reviewed journals were considered for inclusion. We also manually checked the references of related original and review articles to find additional relevant studies. The search covered all records from database inception up to May 14, 2025.

Study eligible criteria

We applied the PICOS framework to define the inclusion criteria: (1) P (population): Adult patients with confirmed diagnosis of advanced (stage IV) CRC receiving ICIs, regardless of the histological type and location of the original cancer; (2) I (exposure): With liver metastasis; (3) C (comparison): Without liver metastasis; (4) O (outcome): Overall survival (OS) and/or progression-free survival (PFS), compared between patients with and without liver metastasis at baseline. OS was commonly defined as the duration from initiation of treatment to death from any cause. PFS referred to the time from treatment commencement to either disease progression or death, whichever occurred first; and (5) S (study design): Observational studies with longitudinal follow-up, such as cohort studies, nested case-control analyses, and secondary analyses of clinical trial data.

We excluded reviews, editorials, other meta-analyses, preclinical studies, studies not in patients with mCRC, studies not including patients receiving ICIs, studies that did not assess liver metastasis as an exposure, and those that did not report survival outcomes. If studies had overlapping populations, we included the one with the largest sample size in the meta-analysis.

Study quality evaluation

Two reviewers independently conducted the literature search, screened studies, assessed methodological quality, and extracted data. Any discrepancies were resolved through discussion with the corresponding author. The quality of included studies was evaluated using the Newcastle-Ottawa Scale (NOS)[33], which assesses aspects such as participant selection, adjustment for confounders, and outcome ascertainment. The NOS yields scores from 1 to 9, with a score of 7 or above indicating high methodological quality.

Data collection

The data collected for analysis included the study details (author, year, study country, and design), participant characteristics (diagnosis, number of patients included in each study, mean age, sex distribution, number of patients with liver metastasis, ICIs used, and other concurrent anticancer treatments), median follow-up durations, outcomes reported, and covariates adjusted for in the regression models.

Statistical analysis

We used hazard ratios (HRs) and 95% confidence intervals (95%CIs) to assess the association between liver metastasis and survival of patients with mCRC receiving ICIs, including PFS and OS, compared between patients with and without liver metastasis. HRs and standard errors were directly extracted or calculated from 95%CIs or P values, then log-transformed to stabilize variance and normalize the data[32]. If multiple HRs were reported from different models, we used the one with the most complete adjustment. Heterogeneity was assessed using the Cochrane Q test and I² statistic[34], with a P value < 0.10 suggesting significant heterogeneity and I² values of < 25%, 25%-75%, and > 75% indicating low, moderate, and high heterogeneity. A random-effects model was used to pool the data, accounting for heterogeneity between studies[32]. Sensitivity analyses were done by removing one study at a time, in order to validate the robustness of the finding. Predefined subgroup analyses were conducted based on study design (prospective vs retrospective or post-hoc analysis), type of mCRC (MSI-H/dMMR vs MSS/pMMR), mean ages of the patients, mean follow-up durations, and the analytic models (univariate vs multivariate). Medians of continuous variables were used to divide subgroups evenly. Publication bias was assessed using funnel plots and visual inspection for asymmetry, along with Egger’s test[35]. All analyses were performed using RevMan (Version 5.1; Cochrane Collaboration, Oxford, United Kingdom) and Stata (Version 12.0; Stata Corporation, College Station, TX, United States).

RESULTS

Study inclusion

The study selection process is shown in Figure 1. We first identified 1336 records from the three databases. After removing 422 duplicates, 914 articles were screened by title and abstract. Of these, 879 were excluded for not meeting the aims of the meta-analysis. The full texts of the remaining 35 articles were reviewed by two independent authors, and 19 were excluded for various reasons (Figure 1). In the end, 16 studies were included in the quantitative analysis[15-30].

Figure 1 Flowchart of database search and study inclusion. mCRC: Metastatic colorectal cancer; ICI: Immune checkpoint inhibitor; HR: Hazard ratio.

Summary of study characteristics

Table 1 summarizes the key characteristics of the 16 studies included in this meta-analysis, which collectively enrolled 1203 patients with mCRC. The studies were published between 2019 and 2024 and were conducted across diverse countries, including the United States, China, Italy, Canada, and France. Most studies (n = 13) were retrospective cohort designs or post-hoc analysis[15,17-20,22-25,27-30], with three being prospective[16,21,26]. The mean age of participants ranged from 52 years to 81 years. The proportion of male participants varied from 29% to 68.5%. Across studies, patients received ICIs, including pembrolizumab, nivolumab, sintilimab, and others, either alone or in combination with agents such as regorafenib or fruquintinib. The number of patients with liver metastases varied widely, from 10 to 93 per study. Accordingly, 639 (53.1%) patients had liver metastasis at baseline. Follow-up durations ranged from 5.3 months to 42 months. Fifteen studies reported PFS[15-19,21-30], and 10 studies also reported OS[15,16,20,21,23,25,26,28-30]. Most studies adjusted for critical prognostic variables including age, Eastern Cooperative Oncology Group performance status, tumor site, and molecular characteristics[15,17-19,22-28], although several provided only unadjusted data[16,20,21,29,30]. Study quality was assessed using the NOS scores (Table 2), with total scores ranging from 6 to 9, indicating moderate to high methodological quality. Thirteen studies scored ≥ 7, reflecting good quality with adequate follow-up and outcome assessment[15-19,21-28]. Three studies scored 6 due to limited adjustment for confounders and lack of representativeness[20,29,30]. All studies demonstrated robust ascertainment of exposure and outcomes and had adequate follow-up rates, supporting the reliability of survival data used in this meta-analysis.

Table 1 Characteristics of the included studies.

Ref.	Country	Design	Diagnosis	Number of patients	Mean age (years)	Men (%)	Number of patients with LM	ICIs used	Concurrent treatment	Median follow-up duration (months)	Outcomes reported	Variables adjusted
Schrock et al[15], 2019	United States	RC	MSI-H mCRC	22	52	45.5	10	Pembrolizumab, nivolumab/ipilimumab, or durvalumab/tremelimumab	NR	18	PFS and OS	Age, sex, tumor stage, grade, location, and TMB
Loupakis et al[16], 2020	Italy	PC	MSI-H mCRC	80	60	56.2	25	Nivolumab, pembrolizumab, or nivolumab/ipilimumab	NR	22.8	PFS and OS	None
Yang et al[19], 2022	China	RC	mCRC without confirmed MSI-H/dMMR status	84	63	60	55	Sintilimab, nivolumab, toripalimab, camrelizumab, pembrolizumab, and tislelizumab	Regorafenib	5.5	PFS	Age, ECOG-PS, RAS mutation status and site of primary tumor
Sun et al[18], 2021	China	RC	MSS mCRC	51	54.2	52.9	38	Toripalimab, nivolumab, sintilimab, or camrelizumab	Fruquintinib or regorafenib	6.2	PFS	Age, sex, ECOG-PS at baseline, first diagnosis time, tumor location, RAS mutation status, and previous therapy
Sahin et al[17], 2021	United States	RC	dMMR MSI-H mCRC	60	NR (> 50)	55	11	Pembrolizumab, nivolumab, or nivolumab/ipilimumab	NR	28.3	PFS	Age, MMR genes, and BRAF V600E mutation
Li et al[20], 2022	China	RC	pMMR/MSS mCRC	103	56	54.4	59	Nivolumab, pembrolizumab, camrelizumab, sintilimab, or toripalimab	Regorafenib	5.3	OS	None
Nie et al[21], 2022	China	PC	MSS mCRC	72	57	50	44	Sintilimab	Fruquintinib or regorafenib	13	PFS and OS	None
Zhang et al[22], 2022	China	RC	MSS mCRC	110	53	57.3	60	Sintilimab, camrelizumab, toripalimab, tislelizumab, and pembrolizumab	Fruquintinib	22.4	PFS	Age, sex, baseline ECOG-PS, site of primary tumor, lines of treatment, RAS and BRAF mutation status, previous treatment, ALP, and TMB
Chen et al[23], 2023	Canada	Post-hoc analysis of RCT	mCRC unselected for MSI status	119	65	67.2	80	Durvalumab plus tremelimumab	NR	15	PFS and OS	Age, sex, and TMB
Saberzadeh-Ardestani et al[24], 2023	United States	RC	dMMR mCRC	41	81	29	14	Pembrolizumab	None	23	PFS	Age, sex, primary tumor location, histological grade, PS at baseline, the number of sites of metastatic disease and CEA
Yang et al[25], 2023	China	RC	MSS/pMMR mCRC	70	59	52.9	51	Nivolumab, pembrolizumab, tislelizumab, sintilimab, and toripalimab	Fruquintinib	17.2	PFS and OS	Age, sex, metastatic site, KRAS mutant, low BMI, primary lesion resected, and lines of ICIs treatment
Zhao et al[28], 2024	China	RC	MSS/pMMR mCRC	143	59	68.5	93	Pembrolizumab, nivolumab, sintilimab, tislelizumab, camrelizumab, and durvalumab	TKI, bevacizumab or cetuximab, or chemotherapy	23.1	PFS and OS	Age, sex, ECOG-PS, primary tumor site, number of metastases, RAS and BRAF mutation status, previous treatment, and lines of ICIs treatment
Kim et al[26], 2024	United States	PC	MSS/pMMR mCRC	51	56	53.8	37	Nivolumab	Regorafenib	15	PFS and OS	Age, sex, race, obesity, albumin, ECOG-PS, primary tumor side, RAS mutation status, and previous treatment
Saberzadeh-Ardestani et al[27], 2024	France	RC	dMMR mCRC	66	64	45	17	Pembrolizumab, nivolumab, or avelumab	NR	24.3	PFS	Age, sex, PS, primary tumor site, and number of metastatic sites
Fakih et al[29], 2024	United States	RC	MSS mCRC	96	58	55.2	33	Nivolumab alone, or ipilimumab and nivolumab	Regorafenib	18	PFS and OS	None
Fakih et al[30], 2024	United States	RC	MSI-H mCRC	35	56	51.4	12	Pembrolizumab, nivolumab and ipilimumab, or durvalumab in combination with tremelimumab	NR	42	PFS and OS	None

NR: Not reported; RC: Retrospective cohort; PC: Prospective cohort; RCT: Randomized controlled trial; mCRC: Metastatic colorectal cancer; MSI-H: Microsatellite instability-high; MSS: Microsatellite stable; dMMR: Mismatch repair-deficient; pMMR: Mismatch repair-proficient; ICIs: Immune checkpoint inhibitors; PFS: Progression-free survival; OS: Overall survival; ECOG-PS: Eastern Cooperative Oncology Group performance status; TMB: Tumor mutational burden; ALP: Alkaline phosphatase; CEA: Carcinoembryonic antigen; TKI: Tyrosine kinase inhibitor; BMI: Body mass index; PS: Performance status.

Table 2 Study quality evaluation via the Newcastle-Ottawa Scale.

Ref.	Representativeness of the exposed cohort	Selection of the non-exposed cohort	Ascertainment of exposure	Outcome not present at baseline	Control for age	Control for other confounding factors	Assessment of outcome	Enough long follow-up duration	Adequacy of follow-up of cohorts	Total
Schrock et al[15], 2019	0	1	1	1	1	1	1	1	1	8
Loupakis et al[16], 2020	1	1	1	1	0	0	1	1	1	7
Yang et al[19], 2022	0	1	1	1	1	1	1	0	1	7
Sun et al[18], 2021	0	1	1	1	1	1	1	0	1	7
Sahin et al[17], 2021	0	1	1	1	1	1	1	1	1	8
Li et al[20], 2022	0	1	1	1	0	0	1	1	1	6
Nie et al[21], 2022	1	1	1	1	0	0	1	1	1	7
Zhang et al[22], 2022	1	1	1	1	1	1	1	1	1	9
Chen et al[23], 2023	0	1	1	1	1	1	1	1	1	8
Saberzadeh-Ardestani et al[24], 2023	1	1	1	1	1	1	1	1	1	9
Yang et al[25], 2023	0	1	1	1	1	1	1	1	1	8
Zhao et al[28], 2024	0	1	1	1	1	1	1	1	1	8
Kim et al[26], 2024	0	1	1	1	1	1	1	1	1	8
Saberzadeh-Ardestani et al[27], 2024	0	1	1	1	1	1	1	1	1	8
Fakih et al[29], 2024	0	1	1	1	0	0	1	1	1	6
Fakih et al[30], 2024	0	1	1	1	0	0	1	1	1	6

Association between liver metastasis and PFS

A total of 15 cohorts[15-19,21-30] reported the association between liver metastasis and PFS in patients with mCRC who received ICIs. Moderate heterogeneity was observed (P for Cochrane Q test = 0.07; I² = 38%). Pooled results with a random-effects model showed that overall, liver metastasis was associated with poor PFS in patients with mCRC receiving ICIs (HR = 1.94, 95%CI: 1.56-2.41, P < 0.001; Figure 2A). Sensitivity analyses were performed by removing one dataset at a time, and the results remained stable (HR = 1.84-2.10, all P < 0.05). Further subgroup analyses indicated that the association between liver metastasis and poor PFS was consistent in prospective and retrospective/post-hoc studies (HR = 1.63 vs 2.03, P for subgroup difference = 0.47; Figure 2B), in patients with MSI-H/dMMR or MSS/pMMR mCRC (HR = 1.64 vs 1.96, P for subgroup difference = 0.53; Figure 2C), between patients with mean ages < or ≥ 59 years (HR = 1.74 vs 2.14, P for subgroup difference = 0.40; Figure 3A), in studies with follow-up durations ≤ or > 18 months (HR = 1.96 vs 1.90, P for subgroup difference = 0.88; Figure 3B), and in studies with univariate or multivariate analyses (HR = 1.77 vs 1.99, P for subgroup difference = 0.73; Figure 3C).

Figure 2 Forest plots for the meta-analysis of the association between liver metastasis and progression-free survival in patients with metastatic colorectal cancer treated with immune checkpoint inhibitors. A: Overall meta-analysis; B: Subgroup analysis according to study design; C: Subgroup analysis according to the type of metastatic colorectal cancer. 95%CI: 95% confidence interval.

Figure 3 Forest plots for the subgroup analyses of the association between liver metastasis and progression-free survival in patients with metastatic colorectal cancer treated with immune checkpoint inhibitors. A: Subgroup analysis according to mean age of the patients; B: Subgroup analysis according to follow-up duration; C: Subgroup analysis according to analytic model. 95%CI: 95% confidence interval.

Association between liver metastasis with OS

Further meta-analysis involving ten cohort studies[15,16,20,21,23,25,26,28-30] suggested that liver metastasis was also associated with a poor OS of patients with mCRC receiving ICIs (HR = 2.10, 95%CI: 1.66-2.65, P < 0.001; Figure 4A) with mild heterogeneity (P for Cochrane Q test = 0.23; I² = 23%). Sensitivity analyses, excluding one dataset at a time, did not significantly change the results (HR = 1.90-2.23, P all < 0.05). Further subgroup analyses showed similar results in prospective and retrospective/post-hoc studies (HR = 2.16 vs 2.11, P for subgroup difference = 0.93; Figure 4B), in patients with MSI-H/dMMR or MSS/pMMR mCRC (HR = 1.69 vs 2.27, P for subgroup difference = 0.47; Figure 4C), between patients with mean ages < or ≥ 58 years (HR = 1.81 vs 2.35, P for subgroup difference = 0.30; Figure 5A), in studies with follow-up durations < or ≥ 18 months (HR = 1.97 vs 2.29, P for subgroup difference = 0.58; Figure 5B), and studies with univariate or multivariate analyses (HR = 2.06 vs 2.05, P for subgroup difference = 0.99; Figure 5C).

Figure 4 Forest plots for the meta-analysis of the association between liver metastasis and overall survival in patients with metastatic colorectal cancer treated with immune checkpoint inhibitors. A: Overall meta-analysis; B: Subgroup analysis according to study design; C: Subgroup analysis according to the type of metastatic colorectal cancer. 95%CI: 95% confidence interval.

Figure 5 Forest plots for the subgroup analyses of the association between liver metastasis and overall survival in patients with metastatic colorectal cancer treated with immune checkpoint inhibitors. A: Subgroup analysis according to mean age of the patients; B: Subgroup analysis according to follow-up duration; C: Subgroup analysis according to analytic model. 95%CI: 95% confidence interval.

Publication bias

Funnel plots for the meta-analyses of liver metastasis and survival outcomes of patients with mCRC receiving ICIs are shown in Figure 6. The plots appeared symmetrical, suggesting a low risk of publication bias. Egger’s test also showed no evidence of publication bias (PFS: P = 0.58; OS: P = 0.34).

Figure 6 Funnel plots for estimating the potential publication biases underlying the meta-analyses of the association between liver metastasis and survival outcomes in patients with metastatic colorectal cancer treated with immune checkpoint inhibitors. A: Funnel plots for the meta-analysis of the association between liver metastasis and progression-free survival in patients with metastatic colorectal cancer (mCRC) treated with immune checkpoint inhibitors (ICIs); B: Bunnel plots for the meta-analysis of the association between liver metastasis and overall survival in patients with mCRC treated with ICIs.

DISCUSSION

This meta-analysis provides pilot evidence that the presence of liver metastasis in patients with mCRC treated with ICIs is associated with inferior PFS and OS. These associations remained consistent across a wide range of predefined subgroup analyses stratified by study design, microsatellite instability status, patient age, follow-up duration, and statistical adjustment. Sensitivity analyses further confirmed the stability of these findings, suggesting that liver metastasis may be an independent and clinically meaningful prognostic factor for poor outcomes in patients with mCRC undergoing ICI therapy.

The biological mechanisms linking liver metastasis to reduced ICI efficacy are supported by increasing evidence from preclinical and translational studies[36,37]. The liver is a uniquely immunoregulatory organ that promotes peripheral immune tolerance to gut-derived antigens[38]. However, in the setting of malignancy, liver metastasis can hijack these tolerogenic properties to escape immune surveillance[39].

Mechanistically, liver metastasis impairs systemic antitumor immunity through several interconnected immunological pathways. The liver’s tolerogenic environment facilitates expansion of regulatory T cells and myeloid-derived suppressor cells, leading to broad suppression of effector T-cell responses[37,40,41]. Within metastatic niches, CD8⁺ T cells undergo deletion or functional exhaustion mediated by hepatic antigen-presenting cells, Kupffer cells, and macrophage-derived signals[37,40,41]. In parallel, cytokine shifts such as elevated interleukin-10 and transforming growth factor beta further dampen cytotoxic lymphocyte activation and hinder infiltration of effector cells into both hepatic and extrahepatic tumors[42,43]. These processes disrupt the priming and trafficking of T cells and attenuate immune memory formation, thereby reducing the capacity of ICIs to restore antitumor activity even in immunogenic subtypes such as MSI-H/dMMR tumors[44].

The consistency of our findings across various subgroup analyses reinforces the broad relevance of liver metastasis as a negative prognostic factor. Notably, the association remained significant in both MSI-H/dMMR and MSS/pMMR subtypes. This implies that the immunosuppressive effects of liver metastasis may act independently of tumor mutational burden or intrinsic tumor immunogenicity[45]. Similarly, results were consistent in both prospective and retrospective studies, indicating that the observed effects likely were not driven by methodological biases. Subgroup stratification by patient age, follow-up duration, and use of multivariate vs univariate statistical models yielded no significant effect modification, suggesting that the impact of liver metastasis on ICI outcomes is generalizable across a wide range of clinical scenarios. Importantly, sensitivity analyses excluding individual studies one at a time yielded robust and directionally consistent estimates, further supporting the validity of our findings.

Among the strengths of this meta-analysis is the rigorous methodological approach, including comprehensive database searching, clear inclusion criteria, and systematic quality assessment using the NOS scale. The inclusion of 16 studies encompassing over 1200 patients across multiple countries enhances the generalizability of the results. Most included studies were of moderate to high quality, and several provided adjusted estimates accounting for known prognostic variables, such as age, performance status, tumor location, and molecular characteristics. Additionally, the use of random-effects modeling, thorough subgroup exploration, and formal publication bias assessment strengthened the robustness and credibility of our conclusions. Nonetheless, some limitations should be acknowledged. The majority of included studies were retrospective, which may introduce selection bias, residual confounding, and heterogeneity in data collection[46]. Although most studies adjusted for relevant clinical variables, not all confounders could be accounted for uniformly. Moreover, there was variability in ICI regimens, treatment lines, and concurrent therapies across studies, which may affect outcomes but could not be fully standardized in the analysis. Data on the extent and anatomical distribution of liver metastasis—such as unilobar vs bilobar involvement—were lacking, preventing stratified analyses by metastatic burden[47]. Additionally, key immune-related biomarkers (e.g., PD-L1 expression, tumor mutational burden, and tumor-infiltrating lymphocytes) were inconsistently reported or absent from most studies, precluding deeper mechanistic insights or predictive modeling. Finally, although no significant publication bias was detected statistically, small-study effects cannot be entirely excluded, particularly for the outcome of OS, which included only ten studies[48].

Clinically, these findings highlight the importance of accounting for liver metastasis when assessing prognosis and considering immunotherapy for patients with mCRC. While ICIs remain a valuable treatment option, particularly for MSI-H/dMMR tumors, our results suggest that patients with liver metastasis may derive attenuated benefit and could require additional therapeutic strategies to improve outcomes. Rational combinations of ICIs with anti-angiogenic agents, chemotherapy, or liver-directed therapies such as radiotherapy or hepatic arterial infusion may help overcome liver-induced immune resistance. These results also support the inclusion of liver metastasis status in clinical trial stratification criteria and real-world decision tools to better personalize immunotherapy strategies. Future research should focus on unraveling the cellular and molecular mechanisms by which liver metastasis impairs ICI responsiveness. Prospective trials specifically designed to evaluate ICI efficacy in patients with vs without liver metastasis—ideally incorporating biomarker analyses—are warranted. Efforts to characterize the immune microenvironment of liver metastasis and identify modifiable resistance pathways may also yield therapeutic targets. Furthermore, the role of locoregional treatments, such as stereotactic body radiotherapy or embolization, in modulating the immunosuppressive niche created by liver metastasis deserves further investigation as part of a multimodal immunotherapeutic approach[45].

In addition to conventional immunotherapy approaches, recent studies have explored strategies to overcome the immunosuppressive milieu created by liver metastases. Combination regimens targeting additional immune checkpoints or the tumor microenvironment have shown encouraging activity. In the phase II CheckMate-142 trial, nivolumab combined with the lymphocyte-activation gene 3 (LAG-3) inhibitor relatlimab achieved an objective response rate (ORR) of 39% among patients with liver metastases, exceeding historical benchmarks and suggesting that LAG-3 inhibition may enhance immune responsiveness in this subset[49]. Similarly, in the CAMILLA trial, the vascular endothelial growth factor receptor-2 and mesenchymal epithelial transition factor kinase inhibitor cabozantinib plus durvalumab produced an ORR of 27.6% in pMMR/MSS CRC, including objective responses in patients with liver involvement[50]. Beyond these combination strategies, next-generation immunotherapies—such as CAR T-cell therapy, bispecific T-cell engagers, tumor-infiltrating lymphocyte therapy, and oncolytic viruses—are being actively investigated to enhance systemic antitumor immunity and tumor specificity[51]. Collectively, these developments indicate that research priorities should now extend beyond confirming the prognostic value of liver metastasis toward identifying and implementing therapeutic strategies capable of reversing liver-induced immune tolerance. Future clinical trials should integrate these multimodal approaches and biomarker-guided designs to optimize outcomes for patients with mCRC harboring liver metastases.

CONCLUSION

In conclusion, this meta-analysis demonstrates that liver metastasis is significantly associated with worse survival outcomes in patients with mCRC treated with ICIs. These findings underscore the importance of liver metastasis as a clinically relevant prognostic factor and provide a strong rationale for optimizing treatment strategies in this challenging subgroup. As immunotherapy continues to evolve in the treatment landscape of CRC, integrating liver metastasis status into clinical and research frameworks may help refine patient selection and improve therapeutic outcomes.