Abstract
Background
Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality worldwide, and advanced-stage disease presents significant therapeutic challenges. Combining transarterial chemoembolization (TACE) with tyrosine kinase inhibitors (TKIs) and immune checkpoint inhibitors (ICIs) has emerged as a promising strategy to enhance treatment efficacy. This meta-analysis evaluates efficacy and safety of TACE + TKIs + ICIs compared to TKIs + ICIs alone in patients with HCC.
Methods
A systematic search was conducted across “PubMed”, “Web of Science”, “Cochrane Library”, “Scopus”, “Google Scholar”, and “Embase” to screen studies up to November 2024. Studies comparing TACE + TKIs + ICIs with TKIs + ICIs alone in advanced HCC were included. Outcomes of interest included objective response rate (ORR), disease control rate (DCR), overall survival (OS), progression-free survival (PFS), and adverse events. Results were reported as relative risk (RR) or hazard ratios (HR) with 95% confidence intervals (CI). Funnel plots was used to assess publication bias.
Results
Ten studies comprising 1999 patients were included. The combination of TACE + TKIs + ICIs marked improved ORR (RR = 1.81, 95%CI:1.57–2.09, P < 0.00001) and DCR (RR = 1.32, 95%CI: 1.19–1.46, P < 0.00001) comparing with TKIs + ICIs alone. OS and PFS were also significantly prolonged in combination group, with HR of 0.55 (95%CI:0.48–0.63, P < 0.00001) and 0.73 (95%CI:0.65–0.82, P < 0.00001), respectively. Adverse events such as pain (RR = 3.94, 95%CI:2.40–6.47, P < 0.001) and nausea/vomiting (RR = 2.28, 95% CI:1.56–3.33, P < 0.001) were more frequent in the TACE + TKIs + ICIs group, though rates of hypertension, diarrhea, and rash were similar between groups. Funnel plots indicated minimal publication bias for primary outcomes.
Conclusions
The combination of TACE, TKIs, and ICIs significantly improves ORR, DCR, OS, and PFS compared to TKIs and ICIs alone, demonstrating superior efficacy with an acceptable safety profile. These findings provide evidence for the integration of TACE with systemic therapies in the management of HCC.
Keywords: Hepatocellular carcinoma; Transarterial chemoembolization; Tyrosine kinase inhibitors; Immune checkpoint inhibitors; Meta-analysis, survival; Adverse events
Introduction
Hepatocellular carcinoma (HCC) is a serious health burden, ranking as the 6 th most common cancer and 3rd leading cause of cancer-related deaths worldwide, responsible for about 0.8 million deaths every year [1, 2]. Managing advanced HCC remains a significant challenge due to complex biology, poor diagnosis, and limited treatment options [1]. Transarterial chemoembolization (TACE), a locoregional therapy, has long been commonly employed for treating intermediate-stage HCC [3]. However, its efficacy in advanced-stage disease is limited when used as monotherapy. Recent advancements in systemic therapies, particularly tyrosine kinase inhibitors (TKIs) such as sorafenib and lenvatinib, and immune checkpoint inhibitors (ICIs) such as nivolumab and pembrolizumab, have revolutionized landscape for treating advanced HCC by improving overall survival (OS) and tumor response rates [4, 5]. Combining these modalities with TACE has emerged as a potential therapeutic strategy to enhance antitumor efficacy, leveraging the localized tumor control provided by TACE and systemic benefits of ICIs and TKIs [6].
Combining TACE with ICIs and TKIs is hypothesized to provide synergistic benefits by addressing multiple mechanisms of tumor progression. TACE induces ischemic tumor necrosis, which enhances antigen release and promotes immune activation, while TKIs inhibit angiogenesis and tumor cell proliferation [7, 8]. Meanwhile, ICIs restore T-cell-mediated immunity by blocking immune checkpoints enhancing systemic antitumor response [9]. This multimodal approach is thought to enhance objective response rates (ORR), delay disease progression, and potentially improve OS. Despite promising early evidence, the safety and efficacy of combining TACE, TKIs, and ICIs remain areas of active investigation, with studies reporting inconsistent results regarding clinical outcomes and adverse events [10–12]. A comprehensive evaluation of these findings is therefore warranted to guide developing effective and tolerable strategies for advanced HCC.
The meta-analysis aimed to systematically evaluate efficacy and safety of TACE combined with TKIs and ICIs compared to ICIs and TKIs alone in patients with HCC. Key clinical outcomes included ORR, disease control rate (DCR), OS, and progression-free survival (PFS). Additionally, adverse event profiles were evaluated to determine the tolerability of this multimodal approach. By synthesizing data from multiple studies, the meta-analysis shades evidence-based insights into clinical utility of combining locoregional and systemic therapies, informing future research and clinical decision-making in the management of advanced HCC.
Methods
Search strategy
A systematic search was conducted in databases including “PubMed”, “Web of Science”, “Cochrane Library”, “Scopus”, “Google Scholar”, and “Embase”. Search aimed to identify studies evaluating efficacy and safety of TACE combined with ICIs and TKIs in HCC patients. The search covered studies published up to November 2024, without language or publication type restrictions. Keywords included combinations of"TACE,""transarterial chemoembolization,""TKIs,""tyrosine kinase inhibitors,""ICIs,""immune checkpoint inhibitors,"and"HCC"or"hepatocellular carcinoma."Boolean operators such as"AND"and"OR"were employed to improve search strategy. Additionally, reference lists of included studies were manually screened for supplementary studies. This multi-faceted approach ensured a comprehensive collection of potentially relevant studies.
Eligibility criteria
Inclusion criteria were as follows: (a) Study design: cohort studies (prospective or retrospective) that directly compared TACE + TKIs + ICIs with TKIs + ICIs in HCC patients. (b) Population: adult patients (≥ 18 years) diagnosed with intermediate or advanced HCC based on histological, imaging, or clinical criteria. (c) Outcomes: reported following outcomes: ORR, DCR, OS, PFS, or adverse events; the tumor response was assessed according to RECIST 1.1 or mRECIST [13]. (c) Data availability: relative risk (RR) or hazard ratio (HR) with 95% confidence intervals (CI) were available. Exclusion criteria included: (a) Studies involving animal or in vitro experiments. (b) Reviews, systematic reviews, meta-analyses, or opinion pieces. (c) Conference abstracts or posters that lacked sufficient numerical data for analysis. (d) Duplicate publications from the same cohort without new information.
Data extraction process
Two independent reviewers evaluated titles and abstracts. Full texts were retrieved and evaluated for eligibility. Data extraction was using a standardized data extraction form. Rxtracted information included: (a) study characteristics: “author”, “publication year”, “country”, and “study design”. (b) Patient demographics: “sample size”, “age”, “gender”, baseline performance status, and disease stage. (c) Treatment details: intervention group (TACE + TKIs + ICIs) and control group (TKIs + ICIs). (d) Outcome measures: “complete response (CR)”, “progressive disease (PD)”, “partial response (PR)”, and “stable disease (SD)”, ORR, DCR, OS, PFS, and adverse events.
Quality assessment
Included studies quality was assessed using Newcastle–Ottawa Scale (NOS) for cohort studies. Each study was assessed across three domains: “selection of study groups”, “comparability of groups”, and “ascertainment of outcomes”. Scores ranged from 0 to 9, with studies scoring ≥ 7 considered high quality. Assessed results were cross-verified, and disagreements were reached through consensus.
Statistical analysis
Statistical analyses were performed using RevMan software (version 5.4) for forest plot and funnel plot generation. P < 0.05 was considered statistically significant. Meta-analyses were conducted to synthesize data across studies for the primary and secondary outcomes. Primary outcomes were ORR and DCR, and secondary outcomes included OS, PFS, and adverse events. Dichotomous outcomes were analyzed using RR with 95%CI, while time-to-event outcomes (OS and PFS) were analyzed using HR with 95%CI. Statistical heterogeneity was assessed using the I2 statistic. The Cochran Q test was also conducted, with a P-value < 0.10 indicating significant heterogeneity. Fixed-effects models were used for outcomes with low heterogeneity (I2 < 50%), while random-effects model was applied when heterogeneity was moderate or high (I2 ≥ 50%). Publication bias was evaluated visually by funnel plots. The sensitivity analysis was conducted by sequentially excluding each study from the pooled analysis.
Results
Study selection and screening process
Database search across “PubMed”, “Web of Science”, “Cochrane Library”, “Scopus”, “Google Scholar”, and “Embase” yielded 212 records, with another 5 identified via other sources, resulting in 217 records. By excluding duplicates, 104 records were screened, and 84 were excluded based on the following criteria: unrelated references (n = 45), review or systematic review articles (n = 22), and animal experiments (n = 6). Subsequently, 20 articles were assessed for eligibility, of which 10 were excluded due to conference articles without relevant data (n = 5), duplicates of the same clinical studies (n = 2), and failure to meet inclusion criteria (n = 3). Ultimately, 10 studies were selected for meta-analysis, as detailed in flowchart (Fig. 1).
Fig. 1.
Flow chart of the study selection process
Baseline characteristics of included studies
Table 1 summarizes characteristics of included studies, which primarily consisted of retrospective cohort studies and propensity score-matched (PSM) studies evaluating various treatment strategies for patients with HCC. The treatments compared across studies included TACE combined with TKIs and ICIs versus TKIs and ICIs alone. The studies reported a wide range of sample sizes, with the smallest cohort having 23 patients and the largest including 802 patients. The age of participants varied, with most studies reporting a mean or median age around 50 to 60 years. Gender distribution was predominantly male, with male-to-female ratios exceeding 3:1 in most cohorts. Patient classifications, such as Child–Pugh class and ECOG-PS, were reported, demonstrating a mix of early to advanced disease stages (BCLC stages A-C).
Table 1.
Characteristics of the included studies
| Study | Study design | Treatment strategy (cases) | Age (years) | Gender (M/F) | Child–Pugh class (A/B) | ECOG-PS (0/1/2) | BCLC stage (A/B/C) |
|---|---|---|---|---|---|---|---|
| Dai 2021 [14] | Retrospective cohort study | TACE + Sorafenib + Sintilimab (n = 40) | 56.5 ± 10.2 | 35/5 | 19/16 | 20/15/0 | 0/14/21 |
| Sorafenib + Sintilimab (n = 23) | 54.0 ± 15.0 | 21/2 | 12/11 | 12/11/0 | 0/5/18 | ||
| Guo 2022 [15] | Retrospective cohort study | TACE + Sorafenib/Lenvatinib/Apatinib + Camrelizumab (n = 31) | < 60 (77.4%) | 26/5 | 21/10 | 22 (0–1)/9 (2) | 2/5/24 |
| Sorafenib/Lenvatinib/Apatinib + Camrelizumab (n = 23) | < 60 (52.2%) | 22/1 | 14/9 | 15 (0–1)/8 (2) | 1/3/19 | ||
| Hu 2023 [16] | Retrospective cohort study | TACE + Sorafenib/Lenvatinib + Nivolumab/Pembrolizumab/Camrelizumab (n = 98) | 52 [42–62] | 87/11 | 75/23 | 42/56/0 | 0/12/86 |
| Sorafenib/Lenvatinib + Nivolumab/Pembrolizumab/Camrelizumab (n = 49) | 53 [47–63] | 47/2 | 33/16 | 21/28/0 | 0/7/42 | ||
| Huang 2022 [17] | PSM study | TACE + Sorafenib/Lenvatinib + Camrelizumam/Sintilimab (n = 24) | 58.0 ± 10.7 | 20/4 | 18/6 | 11/13 | - |
| Sorafenib/Lenvatinib + Camrelizumam/Sintilimab (n = 24) | 58.0 ± 10.7 | 21/3 | 14/10 | 9/15 | - | ||
| Jin 2024 [18] | Retrospective cohort study | TACE + Sorafenib/Lenvatinib/Donafenib/Apatinib + Atezolizumab/Camrelizumab/Sintilimab/Pembrolizumab/Nivolumab/Tislelizumab (n = 802) | 54(48–62) | 683/119 | 659/143 | 455/320/27 | - |
| Sorafenib/Lenvatinib/Donafenib/Apatinib + Atezolizumab/Camrelizumab/Sintilimab/Pembrolizumab/Nivolumab/Tislelizumab (n = 442) | 56 (48–63) | 384/58 | 360/82 | 254/162/26 | - | ||
| Lang 2024 [19] | PSM study | TACE + Lenvatinib + Sintilimab (n = 75) | ≤ 65 (76.0%) | 66/9 | 59/16 | 48/24/3 | 0/32/43 |
| Lenvatinib + Sintilimab (n = 39) | ≤ 65 (74.4%) | 34/5 | 30/9 | 28/8/3 | 0/14/25 | ||
| Li 2023 [20] | PSM study | TACE + Lenvatinib + Sintilimab (n = 46) | < 65 (80.0%) | 41/5 | 35/10/1 | - | 3/5/38 |
| Lenvatinib + Sintilimab (n = 46) | < 65 (76.0%) | 42/4 | 32/13/1 | - | 4/6/36 | ||
| Yang 2024 [21] | PSM study | TACE + Regorafenib + Camrelizumam/Sintilimab (n = 23) | 53 [43.0–65.0] | 20/3 | 17/6 | 10/13/0 | 0/9/14 |
| Regorafenib + Camrelizumam/Sintilimab (n = 23) | 49 [45.0–56.0] | 19/4 | 18/5 | 5/18/0 | 0/5/18 | ||
| Zhang 2024 [22] | Retrospective cohort study | TACE + Sorafenib/Lenvatinib + Camrelizumab (n = 106) | ≤ 60 (71.7%) | 92/14 | 69/37 | 70/36/0 | - |
| Sorafenib/Lenvatinib + Camrelizumab (n = 78) | ≤ 60 (65.4%) | 70/8 | 56/22 | 45/33/0 | - | ||
| Zhang 2024a [23] | PSM study | TACE + Sorafenib/Lenvatinib + Camrelizumab/Sintilimab (n = 54) | ≤ 60 (72.5%) | 46/8 | - | 43/11/0 | 0/23/31 |
| Sorafenib/Lenvatinib + Camrelizumab/Sintilimab (n = 54) | ≤ 60 (68.5%) | 47/7 | - | 41/13/0 | 0/21/33 |
Quality assessment of included studies
Table 2 presents quality assessment of studies using NOS method. All studies demonstrated robust methodological rigor, consistently scoring high across the selection, comparability, and outcome domains. Most studies achieved the maximum score of 9, indicating high quality in representativeness of exposed cohort, selection of controls, ascertainment of exposure, and absence of outcomes. Furthermore, comparability based on design or analysis and adequacy of follow-up were particularly strong across studies. However, two studies (Hu 2023 and Zhang 2024) received slightly lower scores of 8 due to limited comparability or slightly insufficient follow-up [16, 22]. Jin 2024 had the lowest score of 7, reflecting deficiencies in follow-up adequacy [24]. Overall, these studies presented high level of methodological quality, supporting reliability of these findings.
Table 2.
Assessment of the cohort studies using the Newcastle‑Ottawa scale
| Study | Represent activeness of the exposed cohort | Selection of the control cohort | Ascertainment of exposure | Outcome not present at the start | Comparability of cohorts on the basis of the design or analysis | Assessment of the outcome | Sufficient follow-up | Adequacy of the follow-up of cohorts | Total score (out of 9) |
|---|---|---|---|---|---|---|---|---|---|
| Dai 2021 [14] | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 9 |
| Guo 2022 [15] | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 9 |
| Hu 2023 [16] | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 8 |
| Huang 2022 [17] | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 9 |
| Jin 2024 [18] | 1 | 1 | 1 | 1 | 2 | 1 | 0 | 0 | 7 |
| Lang 2024 [19] | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 9 |
| Li 2023 [20] | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 9 |
| Yang 2024 [21] | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 9 |
| Zhang 2024 [22] | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 8 |
| Zhang 2024a [23] | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 9 |
Treatment response: CR, PD, PR, and SD
Figure 2 summarizes the meta-analysis results for treatment response outcomes: CR, PD, PR, and SD. Across the included studies, the experimental group (TACE + TKIs + ICIs) demonstrated no significant changes for CR (RR = 1.50, 95%CI:0.58–3.88, P = 0.40; Fig. 2A) compared to the control group (TKIs + ICIs). For PD, the forest plot indicated a reduced progression rate in experimental group (RR = 0.51, 95%CI:0.40–0.65, P < 0.0001; Fig. 2C). Similarly, PR rate was significantly better in the experimental group (RR = 1.85, 95%CI:1.48–2.31, P < 0.00001; Fig. 2E), while the SD rate exhibited no difference (RR = 0.99, 95%CI:0.82–1.21, P = 0.96; Fig. 2G). Funnel plots for CR, PD, PR and SD were symmetrical, indicating low publication bias (Fig. 2B, 2D, 2 F and 2H).
Fig. 2.
Meta-analysis of CR, PD, PR and SD. A Forest plot of the meta-analysis results of CR between Experimental group (TACE + TKIs + ICIs) and Control group (TKIs + ICIs). B Funnel plot of the included studies for CR. C Forest plot of the meta-analysis results of PD between Experimental group (TACE + TKIs + ICIs) and Control group (TKIs + ICIs). D Funnel plot of the included studies for PD. E Forest plot of the meta-analysis results of PR between Experimental group (TACE + TKIs + ICIs) and Control group (TKIs + ICIs). F Funnel plot of the included studies for PR. G Forest plot of the meta-analysis results of SD between Experimental group (TACE + TKIs + ICIs) and Control group (TKIs + ICIs). H Funnel plot of the included studies for SD
ORR
Figure 3 evaluates ORR, defined as sum of CR and PR rates. Meta-analysis included 10 studies with 1999 patients (experimental group (TACE + TKIs + ICIs): 1248; control group (TKIs + ICIs: 751). The forest plot demonstrated improvement in ORR for experimental group compared to controls (RR = 1.81, 95%CI:1.57–2.09, P < 0.00001; Fig. 3A). This finding highlights the enhanced efficacy of adding TACE to TKIs and ICIs. The funnel plot for ORR revealed minimal asymmetry, suggesting a low risk of publication bias, further supporting reliability of pooled results (Fig. 3B).
Fig. 3.
Meta-analysis of objective response rate. A Forest plot of the meta-analysis results between Experimental group (TACE + TKIs + ICIs) and Control group (TKIs + ICIs). B Funnel plot of the included studies
DCR
The DCR, which includes CR, PR, and SD, is illustrated in Fig. 4A total of 755 patients (experimental group: 446; control group: 309) were analyzed. The forest plot indicated a significantly higher DCR in experimental group (RR = 1.32, 95%CI:1.19–1.46, P < 0.00001; Fig. 4A). Included studies exhibited low heterogeneity (I2 = 18%, P = 0.29; Fig. 4A), and the funnel plot was symmetrical, indicating low publication bias (Fig. 4B). These results underscore the efficacy of combination therapy in achieving disease control across a diverse patient population.
Fig. 4.
Meta-analysis of disease control rate. A Forest plot of the meta-analysis results between Experimental group (TACE + TKIs + ICIs) and Control group (TKIs + ICIs). B Funnel plot of the included studies
OS
Figure 5 highlights the meta-analysis results for OS. The experimental group achieved significantly prolonged OS compared to controls, with a pooled HR of 0.55 (95% CI:0.48–0.63, P < 0.00001; Fig. 5A). Heterogeneity among studies was moderate (I2 = 30%, P = 0.12; Fig. 5A), indicating consistent survival benefits across different study settings. The funnel plot was largely symmetrical, suggesting minimal publication bias (Fig. 5B). The subgroup analysis based on follow-up duration was further performed. For follow-up duration ≤ 20 months, experimental group had significantly better OS compared to controls (HR = 0.64, 95%CI:0.37–0.57, P < 0.0001; Supplementary Fig.S1 A); for follow-up duration > 20 months, experimental group also had prolonged OS comparing with controls (HR = 0.62, 95%CI:0.52–0.74, P < 0.0001; Supplementary Fig.S1 A).
Fig. 5.
Meta-analysis of overall survival. A Forest plot of the meta-analysis results between Experimental group (TACE + TKIs + ICIs) and Control group (TKIs + ICIs). B Funnel plot of the included studies
PFS
Figure 6 highlights the meta-analysis results for OS. The meta-analysis demonstrated a clear benefit in PFS for the experimental group, with a pooled HR of 0.75 (95%CI:0.65–0.82, P < 0.00001; Fig. 6A). Heterogeneity was low (I2 = 20%, P = 0.22; Fig. 6A), and the funnel plot revealed symmetry, reflecting consistent and reliable results across the included studies (Fig. 6B). A subgroup analysis based on follow-up duration was conducted. For patients with a follow-up duration of ≤ 20 months, the experimental group showed significantly better PFS compared to controls (HR = 0.60, 95%CI:0.50–0.73, P < 0.0001; Supplementary Fig. S1B). Similarly, for those with a follow-up duration of > 20 months, the experimental group also exhibited prolonged PFS compared to controls (HR = 0.73, 95%CI: 0.65–0.82, P < 0.0001; Supplementary Fig. S1B).
Fig. 6.
Meta-analysis of PFS. A Forest plot of the meta-analysis results between Experimental group (TACE + TKIs + ICIs) and Control group (TKIs + ICIs). B Funnel plot of the included studies
Adverse events
Table 3 summarizes results of adverse events comparing the TACE + TKIs + ICIs group with the TKIs + ICIs group. Across the studies, adverse events were reported with varying frequencies and RR. No significant difference was observed for hypertension (RR = 0.93, P = 0.39), hand-foot skin reactions (RR = 0.95, P = 0.73), diarrhea (RR = 0.98, P = 0.88), fatigue (RR = 1.01, P = 0.94), rash (RR = 0.86, P = 0.32), proteinuria (RR = 0.80, P = 0.18), or hypothyroidism (RR = 0.84, P = 0.28). Notably, pain (any grade; RR = 3.94, P < 0.00001) and nausea/vomiting (any grade; RR = 2.28, P < 0.0001) were significantly more common in the TACE + TKIs + ICIs group, while decreased appetite and abnormal liver function showed higher heterogeneity (I2 > 50%) and borderline significance (P = 0.28 and P = 0.06, respectively). Further analysis revealed that no significant difference was observed for pain (grade ≥ 3, RR = 3.24, P = 0.27) and nausea/vomiting (grade ≥ 3, RR = 2.64, P = 0.37). Our findings imply that while overall adverse event profiles were comparable between the groups, specific events like pain and nausea/vomiting were more pronounced in the combination therapy group. Figure 7 presents funnel plots for adverse events reported across the included studies, including hypertension, pain, hand-foot skin reactions, diarrhea, fatigue, nausea/vomiting, rash, and others. Funnel plots revealed symmetry, reflecting consistent and reliable results across the included studies.
Table 3.
Meta-analysis of adverse events between TACE + TKIs + ICIs group and TKIs + ICIs group
| Adverse events | No. of studies | Rate of events | RR | P-value | I2 | df, P | Effect model | |
|---|---|---|---|---|---|---|---|---|
| TACE + TKIs + ICIs | TKIs + ICIs | |||||||
| Hypertension | 8 | 21.90% | 24.20% | 0.93 [0.79, 1.10] | 0.39 | 0% | df = 7 (P = 0.94) | Fixed |
| Pain (any grade) | 3 | 13.30% | 3.40% | 3.94 [2.40, 6.47] | < 0.00001 | 0% | df = 2 (P = 0.54) | Fixed |
| Pain (grade ≥ 3) | 3 | 0.55% | 0% | 3.24 [0.39, 26.66] | 0.27 | 0% | df = 1 (P = 0.86) | Fixed |
| Hand-foot skin reactions | 7 | 25.9 | 27.4 | 0.95 [0.72, 1.25] | 0.73 | 0% | df = 5 (P = 0.69) | Fixed |
| Diarrhea | 8 | 25.60% | 27.40% | 0.98 [0.77, 1.25] | 0.88 | 17% | df = 7 (P = 0.3) | Fixed |
| Fatigue | 8 | 16.70% | 16.80% | 1.01 [0.82, 1.23] | 0.94 | 10% | df = 7 (P = 0.35) | Fixed |
| Nausea and/or vomiting (any grade) | 4 | 12.50% | 5.40% | 2.28 [1.56, 3.33] | < 0.0001 | 41% | df = 3 (P = 0.17) | Fixed |
| Nausea and/or vomiting (grade ≥ 3) | 4 | 0.4% | 0% | 2.64 [0.31, 22.42] | 0.37 | 0% | df = 1 (P = 0.97) | Fixed |
| RCCEP | 5 | 14.30% | 16.70% | 0.89 [0.58, 1.38] | 0.6 | 0% | df = 4 (P = 1.00) | Fixed |
| Decreased appetite | 7 | 12.50% | 10.30% | 1.43 [0.74, 2.74] | 0.28 | 72% | df = 6 (P = 0.002) | Random |
| Rash | 9 | 10.70% | 11.50% | 0.86 [0.64, 1.16] | 0.32 | 0% | df = 7 (P = 0.86) | Fixed |
| Proteinuria | 6 | 7.60% | 9.70% | 0.80 [0.58, 1.10] | 0.18 | 0% | df = 5 (P = 0.99) | Fixed |
| Hypothyroidism | 6 | 7.90% | 10% | 0.84 [0.62, 1.15] | 0.28 | 0% | df = 5 (P = 0.72) | Fixed |
| Abnormal liver function | 3 | 24.60% | 15.10% | 1.71 [0.97, 3.01] | 0.06 | 59% | df = 2 (P = 0.08) | Random |
Fig. 7.
Funnel plots of the included studies for (A) hypertension, (B) pain, (C) hand-foot skin reactions, (D) diarrhea, (E) fatigue, (F) nausea and/or vomiting, (G) RCCEP, (H) decreased appetite, (I) rash, (J) proteinuria, (K) hypothyroidism, (L) abnormal liver function
Sensitivity analysis
For the meta-analysis, a sensitivity analysis was conducted by sequentially excluding each study from the pooled analysis. The findings indicated that the exclusion of any individual study did not significantly impact the overall results.
Discussion
This meta-analysis demonstrates that combining TACE with ICIs and TKIs significantly improves clinical outcomes in patients with advanced HCC compared to TKIs and ICIs alone. The enhanced ORR and DCR observed in the combination therapy group align with previous studies suggesting synergistic effects of integrating locoregional and systemic treatments [18, 25–27]. Similar studies reported that combining TACE with systemic therapies enhanced tumor necrosis and immune activation, likely due to the immunostimulatory effects of TACE-induced antigen release [6, 28, 29]. Our findings corroborate these results, providing stronger evidence through pooled data analysis.
The survival benefits observed in this study, with hazard ratios of 0.55 for OS and 0.73 for PFS, are consistent with those reported in prior trials such as the IMbrave150 study, where the combination of atezolizumab and bevacizumab demonstrated prolonged OS and PFS compared to sorafenib alone [4]. However, while the IMbrave150 trial focused on systemic therapies without TACE, our findings suggest that adding TACE further enhances these benefits by providing localized tumor control. These results also align with the findings of Liu et al. [24], who demonstrated that triple combination of TACE, TKIs, and ICIs significantly delayed disease progression compared to dual therapy with TKIs and ICIs alone.
HCC's immunosuppressive tumor microenvironment (TME) enables immune evasion, making the combination of TACE, ICIs, and TKIs a promising strategy for synergistic immune modulation. TACE induces ischemic tumor necrosis, releasing tumor-associated antigens that enhance antigen presentation and T-cell activation [30]. ICIs further amplify immune responses by blocking immune checkpoints, while TKIs modulate the TME by reducing immunosuppressive factors like VEGF and TGF-β, promoting immune infiltration and improving ICI efficacy [31]. Studies suggest that TACE upregulates immune-related gene expression, supporting the observed improvements in ORR, DCR, OS, and PFS. However, HCC’s histopathological heterogeneity influences treatment response, as aggressive, poorly differentiated tumors exhibit rapid progression, high vascularity, and increased immunosuppressive signaling [32]. TKIs counteract VEGF-driven angiogenesis, TACE disrupts vascular supply, and ICIs may be particularly effective in patients with high tumor mutational burden or PD-L1 expression [32]. Our findings suggest that the improved OS and PFS with combination therapy may be due to its ability to mitigate aggressive tumor behavior, though inconsistent histopathological classification in included studies limits definitive conclusions.
One of the proposed mechanisms behind the efficacy of TACE + TKIs + ICIs is its ability to enhance immune response through synergistic effects, where TACE induces tumor necrosis and antigen release, TKIs inhibit angiogenesis and immunosuppressive signaling, and ICIs boost immune activation [33]. However, not all patients respond equally, underscoring the need for predictive biomarkers. While markers such as PD-L1 expression, tumor mutational burden, inflammatory markers (e.g., neutrophil-to-lymphocyte ratio, cytokine levels), and tumor-infiltrating lymphocytes have shown promise in predicting response to ICIs in various cancers [34], data on these markers were inconsistently reported in the included studies. The lack of biomarker-driven stratification limits the ability to identify patients most likely to benefit from TACE + TKIs + ICIs. Future studies should integrate biomarker analyses into clinical trials to refine patient selection and better stratify responders, while also investigating dynamic changes in immune markers before and after treatment to gain insights into mechanisms of response and resistance.
While most of the included studies investigated the combination of TACE, TKIs, and ICIs as a first-line therapy, we acknowledge that the study by Yang et al. [21] evaluated this approach in patients receiving second-line treatment. The rationale for its inclusion was to capture a broader clinical context, as treatment sequences in real-world practice can vary depending on patient-specific factors and prior therapeutic responses. Given the potential influence of prior treatments on treatment efficacy, we conducted a sensitivity analysis excluding this study. The results remained consistent, demonstrating that the exclusion of Yang et al. [21] did not significantly impact the pooled outcomes, thereby supporting the robustness of our findings.
Adverse event profiles in this meta-analysis largely paralleled those reported in previous studies, with hypertension, hand-foot skin reactions, and diarrhea occurring at similar rates in both the combination and control groups. However, specific adverse events, such as pain and nausea/vomiting, were significantly more common in the TACE + TKIs + ICIs group (RR = 3.94 and 2.28, respectively). These findings align with previous studies, which highlighted the added toxicity burden of combining locoregional and systemic therapies [35–37]. While the increased toxicity underscores the importance of careful patient monitoring, the benefits in survival and tumor control may justify the trade-off in selected patient populations. The heterogeneity observed in some outcomes, particularly decreased appetite and abnormal liver function, reflects variations in study populations and treatment protocols. This variability is consistent with prior meta-analyses, which noted differences in patient characteristics and follow-up durations as major contributors to heterogeneity [38–40].
Despite its strengths, this meta-analysis shares limitations with earlier reviews, such as the reliance on non-randomized studies for much of the data. While the NOS confirmed the high quality of included studies, retrospective designs remain susceptible to selection bias and reporting inconsistencies. Unlike the prospective trials [38], our pooled analysis includes studies with varying definitions of response criteria and adverse events, which may have influenced the results. Future studies should aim to validate our findings in more standardized settings to guide clinical practice. Although our funnel plot analysis suggested minimal publication bias, the reliance solely on cohort studies introduces the potential for selection bias, residual confounding, and variability in treatment assignment, which are inherent to observational study designs. To improve the quality of evidence, future research should prioritize prospective, multicenter randomized clinical trials to minimize selection bias and confounding while providing higher-level evidence on the efficacy and safety of TACE + TKIs + ICIs. Additionally, systematic efforts to include unpublished and gray literature data in future meta-analyses would enhance the transparency and generalizability of findings. While our meta-analysis demonstrated a significant benefit of TACE + TKIs + ICIs over systemic therapy alone, some outcomes exhibited moderate to high heterogeneity (I2 > 50%), suggesting that underlying differences among the included studies may have influenced the pooled results. Although we assessed heterogeneity using I2 statistics and performed sensitivity analyses, we were unable to fully explore the impact of potential confounding factors, such as prior treatments, comorbidities, and variations in patient selection criteria, due to the lack of detailed patient-level data in the included studies. To address these limitations, future studies should incorporate individual patient-level data to allow for more precise stratification of treatment effects based on prior therapy exposure, comorbidities, and liver function.
The generalizability of our findings to real-world clinical practice is influenced by variations in baseline patient characteristics, including liver function (Child–Pugh class), tumor burden (BCLC stage), and prior treatment history. While our meta-analysis demonstrates that TACE + TKIs + ICIs improves survival compared to systemic therapy alone, the extent of these benefits across different subgroups remains uncertain due to limited stratification data. Additionally, some included studies may have enrolled patients with prior systemic therapy exposure, potentially influencing treatment response, but a lack of detailed data precluded subgroup analysis. To refine patient selection, future studies should stratify outcomes based on liver function, tumor stage, and treatment history, while prospective trials incorporating biomarkers, genomic profiling, and clinical stratification will be crucial for optimizing treatment sequencing in HCC management.
Conclusions
In conclusion, this meta-analysis demonstrates that the combination of TACE, TKIs, and ICIs significantly improves ORR, DCR, OS, and PFS compared to TKIs and ICIs alone, with results comparable to or exceeding those of prior studies focused solely on systemic therapies. The combination therapy showed an acceptable safety profile despite an increased incidence of pain and nausea/vomiting, underscoring the importance of patient selection and toxicity management. These findings provide a robust basis for integrating TACE with systemic therapies in advanced HCC, while further prospective studies are needed to refine treatment protocols and confirm these results.
Acknowledgements
None.
Authors’ contributions
HT and CW: Conceptualization, data collection, data analysis, manuscript writing. HT and CW: Data collection, critical review of the manuscript. HT and CW: Statistical analysis, interpretation of results. All authors read and approved the final manuscript.
Funding
None.
Data availability
No datasets were generated or analysed during the current study.
Declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
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Data Availability Statement
No datasets were generated or analysed during the current study.