Yani Ke1,2,3†
Yuyan Pan2,3,4†Xueru Huang1,2,3Xing Bai1,2,3Xiaojuan Liu1,2,3Mingsi Zhang1,2,3Yunhai Wei5Tao Jiang1,2,3*
Guangji Zhang1,2,3*- 1School of Basic Medical Sciences, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
- 2Key Laboratory of Blood-stasis-toxin Syndrome of Zhejiang Province, Hangzhou, China
- 3Traditional Chinese Medicine “Preventing Disease” Wisdom Health Project Research Center of Zhejiang, Hangzhou, China
- 4The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Province Hospital of Chinese Medicine), Hangzhou, Zhejiang, China
- 5Huzhou Central Hospital, Huzhou, Zhejiang, China
Background: Cancer remains a major global health burden. Combining traditional Chinese medicine (TCM) with immune checkpoint inhibitors (ICIs) may potentially mitigate treatment-related side effects and improve the quality of life for cancer patients. To critically evaluate the clinical efficacy of this combination therapy, a meta-analysis was performed.
Methods: A systematic search was conducted across six databases. Data were analyzed using RevMan 5.3 and Stata 12. Heterogeneity was explored through subgroup analysis and meta-regression. The robustness of results was assessed via sensitivity analysis and publication bias.
Results: 41 studies were included. The TCM + ICIs group demonstrated significantly superior outcomes compared to the ICIs group across multiple endpoints: Overall Response Rate (ORR) (RR: 1.34 [1.20, 1.49]), Disease Control Rate (DCR) (RR: 1.15 [1.10, 1.21]), CD4+/CD8+ T-cell ratio (WMD: 0.25 [0.15, 0.35]), Progression-Free Survival (PFS) (WMD: 0.96 [0.29, 1.63]), Overall Survival (OS) (WMD: 1.46 [0.62, 2.30]), Karnofsky Performance Status (KPS) (WMD: 6.35 [4.99, 7.70]), and TCM Therapeutic Evaluation (RR: 1.42 [1.30, 1.55]). Conversely, the TCM + ICIs group showed lower levels of tumor markers, including Alpha-Fetoprotein (AFP) (SMD: 0.75 [-1.49, −0.01]), Carcinoembryonic Antigen (CEA) (SMD: 0.72 [-1.08, −0.37]), Carbohydrate Antigen 125 (CA125) (SMD: 0.77 [-1.46, −0.08]), and a reduced incidence of adverse events (RR: 0.82 [0.69, 0.97]). There is high heterogeneity among CD4+T/CD8+T studies due to the type of tumor and whether it is combined with chemotherapy. The high heterogeneity among studies on KPS may be related to the type of ICIs. Sensitivity analysis and assessment of publication bias confirmed the robustness of the pooled results.
Conclusion: The combination of TCM with ICIs appears to enhance antitumor immunity, reduce adverse reactions, lower serum tumor marker levels, improve disease control, and ameliorate patient performance status. This combination strategy represents a promising therapeutic approach for various cancers and warrants further investigation.
Introduction
Cancer poses a significant global health challenge, exerting a profound impact on human life. According to Global Cancer Observatory (GCO) data, there were approximately 20 million new cancer cases and nearly 10 million cancer-related deaths worldwide in 2022. Projections indicate that by 2050, new cancer cases could exceed 35 million annually (Bray et al., 2024). Lung, liver, stomach, breast, and colon cancers are among the leading causes of cancer mortality (Cao et al., 2021). Current treatment modalities include local therapies such as surgical resection, radiotherapy, interventional therapy, and ablation, as well as systemic therapies like chemotherapy. However, these approaches often have limitations and can be associated with substantial side effects (Zeng, 2018).
Recent years have witnessed remarkable advances in cancer immunotherapy. Immune checkpoint inhibitors (ICIs), particularly monoclonal antibodies targeting programmed cell death protein 1 (PD-1), its ligand (PD-L1), and cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4), have become first-line treatments for various malignancies, significantly improving survival outcomes for many patients (Bagchi et al., 2021; Cunningham et al., 2024). ICIs function by blocking inhibitory immune signals, thereby potentiating T cell-mediated anti-tumor responses (Brahmer et al., 2021). Despite their efficacy, the widespread clinical application of ICIs has revealed a spectrum of immune-related adverse events (irAEs), such as myocarditis, pneumonitis, and hepatitis. The increasing incidence of these toxicities, along with the emergence of drug resistance, presents significant clinical challenges (Hu et al., 2022; Wang Z. et al., 2023).
Accumulating evidence suggests that various forms of Traditional Chinese Medicine (TCM) can inhibit the proliferation and metastasis of diverse tumor cells. TCM has shown notable benefits in treating cancers such as breast, lung, liver, and gastric cancer, potentially extending survival, improving quality of life, and enhancing the efficacy while reducing the toxicity of combined radiotherapy and chemotherapy (Wang K. et al., 2021; Xiang et al., 2019). Preclinical and clinical studies have begun to verify the synergistic effects of TCM and ICIs in cancers including lung cancer, breast cancer, and melanoma. Potential underlying mechanisms include modulation of the tumor microenvironment and regulation of gut microbiota (Yu YX. et al., 2023). Consequently, the combination of TCM and ICIs is gaining acceptance, although the precise mechanisms of action remain incompletely elucidated. This meta-analysis aims to clarify the efficacy and potential mechanisms of the TCM + ICIs combination in cancer treatment, with the goal of informing clinical practice and providing new perspectives for therapeutic development.
Methods
Literature search
The protocol for this study has been registered on the PROSPERO website (https://www.crd.york.ac.uk/prospero/) with number CRD42024582055 in Additional File 1. Two independent investigators systematically searched six databases (PubMed, Embase, Cochrane Library, CNKI, Wanfang, and CBM) for literature published up to 7 October 2024. The search strategy combined Medical Subject Headings (MeSH) terms with free words, incorporating key concepts such as ‘Traditional Chinese Medicine’ (TCM), ‘immune checkpoint inhibitors’, ‘PD-1′, ‘PD-L1′, and ‘ICIs’. The detailed search strategy is provided in Additional File 2. Relevant references from the retrieved articles were also screened for inclusion. The entire process adhered strictly to the PRISMA guidelines (Moher et al., 2009) (see Additional File 3). Botanical drugs included in this study were verified using the Medicinal Plant Names Services (MPNS) portal (http://mpns.kew.org/mpns-portal/), while other traditional medicines were authenticated via the Zhong Hui Zhong Yao Wang database (https://www.zhzyw.com/).
Study selection
Two reviewers independently screened the titles and abstracts of retrieved records, followed by full-text assessment for eligibility. A third reviewer resolved any discrepancies and made final judgments based on the pre-defined protocol. The specific inclusion criteria were as follows:
All included literature must meet the following: 1) Study participants were clinically diagnosed cancer patients aged 18 years or older, with both an experimental and a control group; 2) The intervention consisted of ICIs combined with TCM, which could include TCM monomers, single herbs, herb pairs, prescriptions, or moxibustion/fumigation therapies; 3) The control group received ICIs for cancer; 4) Reported outcomes included at least one of the following: Overall Response Rate (ORR), Disease Control Rate (DCR), CD4+/CD8+ T-cell ratio, Progression-Free Survival (PFS), Overall Survival (OS), Karnofsky Performance Status (KPS), levels of Alpha-Fetoprotein (AFP), Carcinoembryonic Antigen (CEA), Carbohydrate Antigen 125 (CA125), Carbohydrate Antigen 19-9 (CA199), incidence of adverse effects, or TCM therapeutic evaluation; 5) Study design was a randomized controlled trial (RCT) comparing ICI combination therapy with TCM versus ICI monotherapy. Concurrent conventional or basic supportive care was permitted in both groups. Common ICIs considered included Pembrolizumab, Nivolumab, Sintilimab, Tislelizumab, Camrelizumab, and so on.
Data extraction and quality assessment
Data extraction and quality assessment were performed independently by two reviewers. A third reviewer was consulted to reconcile disagreements and finalize the data synthesis. Extracted data encompassed: publication year, corresponding author’s country/region, cancer diagnostic criteria, baseline characteristics of participants (sample size, age, gender), details of interventions (types, dosage, administration of ICIs; types, formulation, administration of TCM; other concomitant treatments; treatment duration), and all pre-specified outcomes (ORR, DCR, CD4+/CD8+ ratio, PFS, OS, KPS, AFP, CEA, CA125, CA19-9, adverse effects, TCM evaluation). Corresponding authors were contacted for missing or unclear data. Furthermore, ORR was defined as the proportion of patients achieving a complete response (CR) or partial response (PR). DCR was defined as the proportion of patients achieving CR, PR, or stable disease (SD) (Upadhyay et al., 2025; Bae et al., 2022). Both ORR and DCR are key metrics for tumor response evaluation.
The methodological quality of the included RCTs was assessed using the Cochrane Risk of Bias tool (RoB 2.0) (Higgins et al., 2011). This tool evaluates six domains: i) randomization process, ii) allocation concealment, iii) blinding of participants and personnel, iv) blinding of outcome assessment, v) incomplete outcome data, and vi) selective reporting. Each domain was judged as ‘low risk of bias’, ‘some concerns’, or ‘high risk of bias’. To enhance the reporting transparency and reproducibility of the included TCM interventions, the ConPhyMP tool was referenced, following the approach of Heinrich et al.
Statistical analysis
Data analysis was conducted using RevMan (version 5.3) and Stata (version 12). For dichotomous outcomes, data were pooled and expressed as Risk Ratios (RR) with 95% confidence intervals (CI). Continuous outcomes were categorized based on their distribution. Data following a normal distribution were presented as mean ± standard deviation (SD). Non-normally distributed data were converted to mean ± SD using established methods (Shi et al., 2023; Luo et al., 2018; Wan et al., 2014) via https://www.math.hkbu.edu.hk/∼tongt/pages/median2mean.html. The Weighted Mean Difference (WMD) and 95% CI were applied when outcomes were measured on the same unit across studies; otherwise, the Standardized Mean Difference (SMD) and 95% CI were used. Heterogeneity among studies was quantitatively assessed using the I2 statistic and Cochran’s Q test, with results visualized using forest plots. An I2 value less than 50% indicated low heterogeneity, warranting the use of a fixed-effects model. An I2 value greater than 50% suggested substantial heterogeneity, leading to the adoption of a random-effects model (Schmidt et al., 2009; Higgins et al., 2003). If sufficient studies were available (n > 10), subgroup analysis or meta-regression was planned to explore potential sources of heterogeneity. Sensitivity analysis was performed by sequentially excluding each study to evaluate the robustness of the pooled results. Publication bias was assessed using Egger’s test (Egger et al., 1997) when more than five studies were included in a meta-analysis. A p-value <0.05 indicated potential publication bias, in which case the trim-and-filling method was employed to assess their stability.
Results
Study selection
A total of 6,826 articles were retrieved from the six databases. After removal of duplicates, 4,948 articles remained for screening by two independent reviewers based on the inclusion and exclusion criteria. Ultimately, 41 studies (Chai, 2023; Chen, 2022; Ding et al., 2024; Du and Liu, 2022; Fang, 2023; Jiang and Fang, 2024; Lin et al., 2022; Lin, 2023; Liu et al., 2022; Luo, 2023; Qu, 2023; Wang L. et al., 2023; Wang Q., 2023; Wang et al., 2024; Wang XH., 2023; Wang, 2021; Xiao et al., 2022; Yang, 2023; Yao, 2023; Zhang J., 2023; Zhang, 2022; Zhao et al., 2023; Zhao, 2024; Zhou, 2023; Zhu, 2024; Cao et al., 2024; Cao, 2023; Huang et al., 2024; Ye and Fang, 2022; Yu D. et al., 2023; Zhang LL., 2023; Zhong, 2023; Dou, 2022; Liu and Wang, 2024; Liu and Xia, 2024; Ye, 2022; Huang et al., 2023; Wang, 2024; Xu, 2023; Zhang et al., 2023; Zhou and Xie, 2024) were included in the meta-analysis. All included studies were conducted in China, with only three published in English databases. This analysis encompassed 2,612 cancer patients, including cases of lung cancer (n = 1,631), liver cancer (n = 490), gastric cancer (n = 154), esophageal cancer (n = 227), colorectal cancer (n = 50), and ovarian cancer (n = 60). Various ICIs were used, such as Sintilimab, Nivolumab, Camrelizumab, and Tislelizumab. TCM interventions included prescriptions, moxibustion, injections, and other formulations. The publication years of the included studies ranged from 2020 to 2024, indicating a focus on recent research. The characteristics of the included studies are summarized in Table 1, and the study selection flow diagram is presented in Figure 1. The TCM included in each study are detailed in Additional File 4, with all drugs having been verified.
Table 1. Baseline characteristics of studies included.
Figure 1. Flowchart of study inclusions and exclusions.
Quality assessment and data extraction
The methodological quality of the 41 included studies, assessed using the Cochrane RoB 2.0 tool, is summarized in Figure 2 and detailed in Additional File 5. Most studies reported random allocation of participants to the case or control group; however, only two studies specified the involvement of a third researcher in the randomization process. Blinding procedures were seldom mentioned, leading to potential performance and detection bias. Several studies reported participant dropouts, with reasons for attrition not always being consistent between groups. All studies adhered to their pre-specified outcome measures without selective reporting. Overall, the quality of the included studies was variable, with some demonstrating robust methodology and others having unclear reporting of design elements. The assessment of TCM preparation reporting quality using the ConPhyMP tool is provided in Additional Files 6 and 7.
Figure 2. Risk of bias analysis across studies: Risk of bias item presented as a percentage across all included trials (n = 41).
TCM + ICIs vs. ICIs in cancer—Overall Response Rate (ORR)
The comparison of ORR between two groups of patients is shown in Figure 3. The heterogeneity among 31 studies was low (p = 0.79, I2 = 0%), therefore a fixed-effect model was chosen. The results showed that the patients in TCM + ICIs group had higher ORR values (RR = 1.34 [1.20–1.49]) compared to those in ICIs group, with RR = 1 as the reference. In addition, exploration on different cancers were also conducted (see Table 2), and the results showed that the TCM + ICIs group had higher ORR values in lung cancer, gastric cancer, esophageal cancer, and other tumors (RR = 1.37 [1.19,1.58], RR = 2.07 [1.05,4.08], RR = 1.63 [1.10,2.41], RR = 2.43 [1.09,5.40], respectively).
Figure 3. Meta-analysis and Galbr analysis of TCM + ICIs vs. ICIs in cancer—ORR, DCR, TCM Therapeutic Evaluation: (a) Meta-analysis of TCM + ICIs vs. ICIs in cancer—ORR; (b) Galbr analysis of TCM + ICIs vs. ICIs in cancer—ORR; (c) Meta-analysis of TCM + ICIs vs. ICIs in cancer—DCR; (d) Galbr analysis of TCM + ICIs vs. ICIs in cancer—DCR; (e) Meta-analysis of TCM + ICIs vs. ICIs in cancer—TCM Therapeutic Evaluation; (f) Galbr analysis of TCM + ICIs vs. ICIs in cancer—TCM Therapeutic Evaluation.
Table 2. Subgroup analysis of TCM + ICIs vs. ICIs in cancer. (A)ORR; (B)DCR; (C)TCM Therapeutic evaluation.
TCM + ICIs vs. ICIs in cancer—Disease Control Rate (DCR)
There are a total of 29 studies involving DCR, and the results of meta-analysis and heterogeneity analysis are shown in Figure 3. The fixed-effect model was applied (p = 0.46, I2 = 0%). The results showed that the DCR values of TCM + ICIs group were significantly higher than those of ICIs group, with RR 1.15 (1.10, 1.21). The subgroup analysis results of different cancers also demonstrated that regardless of the type of cancer, patients in TCM + ICIs group have higher DCR in Table 2.
TCM + ICIs vs. ICIs in cancer—Cd4+T/CD8+T
The meta-analysis and heterogeneity analysis of 16 studies are shown in Figure 4. There is obvious heterogeneity (p < 0.05, I2 = 96%), so the random-effect model is the best choice. The CD4+T/CD8+T ratio in TCM + ICIs group was significantly higher than that in ICIs group, with WMD 0.25 (0.15, 0.35). Due to the presence of high heterogeneity, subgroup analysis and meta-regression were performed to further explore its sources. The results in Table 3 showed that heterogeneity among subgroups decreased when grouped based on different cancers or ICIs, but it cannot be determined whether it is the source of heterogeneity. Therefore, the results of meta-regression are particularly important. The results in Table 4 suggest that whether it is lung cancer and whether both groups receive chemotherapy regimens may be sources of high heterogeneity (p < 0.05). Unfortunately, further multi-meta-regression was not performed.
Figure 4. Meta-analysis and Galbr analysis of TCM + ICIs vs. ICIs in cancer—CD4+T/CD8+T or KPS: (a) Meta-analysis of TCM + ICIs vs. ICIs in cancer—CD4+T/CD8+T; (b) Galbr analysis of TCM + ICIs vs. ICIs in cancer—CD4+T/CD8+T; (c) Meta-analysis of TCM + ICIs vs. ICIs in cancer—KPS; (d) Galbr analysis of TCM + ICIs vs. ICIs in cancer—KPS.
Table 3. Subgroup analysis of TCM + ICIs vs. ICIs in cancer—CD4+T/CD8+T by different factors. (A) Different kinds of cancers; (B) Different kinds of ICIs; (C) Different combination therapies.
Table 4. Meta-regression of TCM + ICIs vs. ICIs in cancer—CD4+T/CD8+T.
TCM + ICIs vs. ICIs in cancer—Progression-Free Survival (PFS)/Overall Survival (OS)
Table 5 contains a comparison of PFS and OS between two groups of patients. The meta-analysis of PFS used a random-effect model (p < 0.05, I2 = 91%), and the results showed that the PFS of TCM + ICIs group was significantly higher than that of ICIs group (WMD = 0.96 [0.29, 1.63]). Due to the small number of studies included (n = 4), heterogeneity exploration was not conducted. Meanwhile, only two studies involved OS, and the heterogeneity among them was not high (p = 0.48, I2 = 0%). After applying the fixed-effect model, the results showed that TCM + ICIs group had a longer OS period, with WMD 1.46 (0.62, 2.30).
Table 5. Meta-analysis of TCM + ICIs vs. ICIs in cancer.
TCM + ICIs vs. ICIs in cancer—Karnofsky Performance Status (KPS)
14 studies are related to KPS, and there is significant heterogeneity among them (p < 0.05, I2 = 72%). As shown in Figure 4, the results of the random-effect model demonstrate that the KPS score of TCM + ICIs group is significantly higher than that of the ICIs group, with WMD 6.35 (4.99, 7.70). The Galbr plot also showed high heterogeneity among studies. Thus, subgroup analysis and meta-regression were also performed. Through subgroup analysis of different cancers and ICIs, heterogeneity of each group has decreased, shown in Table 6, suggesting that they may be the source of heterogeneity (Table 7). Univariate meta-regression suggests that different types of ICIs are indeed sources of high heterogeneity (p < 0.05), which deserves further research.
Table 6. Subgroup analysis of TCM + ICIs vs. ICIs in cancer—KPS by different factors. (A) Different kinds of cancers; (B) Different kinds of ICIs.
Table 7. Meta-regression of TCM + ICIs vs. ICIs in cancer—KPS.
TCM + ICIs vs. ICIs in cancer—Alpha-Fetoprotein (AFP)
Only four studies involved AFP (Table 5), and there was significant heterogeneity (p < 0.05, I2 = 89%). After the application of the random-effect model, the AFP level in TCM + ICIs group was significantly lower than that in ICIs group, with SMD -0.75 (- 1.49, − 0.01). The source of heterogeneity has not been explored.
TCM + ICIs vs. ICIs in cancer—Carcinoembryonic Antigen (CEA)/carbohydrate antigen 125 (CA125)/carbohydrate antigen 199 (CA199)
Nine studies are included in this section (Table 5), including seven on CEA, five on CA125, and two on CA199. Due to the presence of high heterogeneity, they all adopted a random-effect model (p < 0.05, I2 = 66%; p < 0.05, I2 = 87%; p < 0.05, I2 = 95%, respectively). All results demonstrated that the levels of CEA and CA125 in TCM + ICIs group were significantly lower than those in ICIs group (SMD = −0.72 [-1.08, −0.37]; SMD = −0.77 [-1.46, −0.08], respectively), and there was no significant difference in CA199 levels between the two groups (SMD = −0.95 [-2.68, 0.78]). However, the number of studies included in this section is limited, which also affects the reliability of the conclusion.
TCM + ICIs vs. ICIs in cancer—Adverse event
The results of the adverse events of the two groups are presented in Table 5. There was high heterogeneity among the 12 included studies (p = 0.0005, I2 = 67%), therefore a random-effect model was selected. The incidence of adverse events in TCM + ICIs group was significantly lower than that in ICIs group (RR = 0.82 [0.69, 0.97]). Further analysis was conducted based on different adverse events, including gastrointestinal reactions, myelosuppression, hypertension, thyroid dysfunction, liver dysfunction and kidney dysfunction. The final results showed that the various adverse reactions in TCM + ICIs group were significantly lower than those in ICIs group shown in Table 8.
Table 8. Subgroup analysis of TCM + ICIs vs. ICIs in cancer by different adverse events.
TCM + ICIs vs. ICIs in cancer—TCM therapeutic evaluation
Figure 3 shows the TCM Therapeutic Evaluation of two groups, which includes 16 related studies. Due to insignificant heterogeneity (p = 0.65, I2 = 0%), a fixed-effect model was chosen. The overall TCM therapeutic evaluation of TCM + ICIs group was higher than that of ICIs group, with RR 1.42 (1.30, 1.55). Regardless of the type of cancer, the TCM therapeutic evaluation of TCM + ICIs group was significantly increased shown in Table 2.
Sensitivity analysis
The results of all sensitivity analyses can be found in Additional File 8. The stability of each meta-analysis can be determined by whether the changes of the results are significant or whether there is a reversal after removing each study one by one. After comprehensive evaluation, it was found that all 11 meta-analyses in this study have good stability (with no significant changes in the results).
Publication bias
Only meta-analyses with more than 5 included studies were evaluated for publication bias. The publication bias results of these seven meta-analyses were based on Eggar’s Test (Additional File 9). When it comes to ORR, DCR, CD4+T/CD8+T, Adverse Event, and TCM therapeutic evaluation, significant publication bias cannot be ignored (p < 0.05), while the other two meta-analyses are not. Therefore, the trim-and-filling method was executed to further evaluate whether the presence of publication bias affects the reliability and robustness of the results. This adjustment did not substantially alter or reverse the original pooled results, supporting the robustness and representativeness of the findings despite the presence of publication bias.
Discussion
The immune system plays a critical role in anti-tumor defense by identifying and eliminating mutated cells through immune surveillance, thereby preventing tumor development (Gao et al., 2022). Cancer immunotherapy represents a milestone in oncology, shifting the therapeutic focus from solely targeting malignant cells to modulating the tumor microenvironment (Rui et al., 2023). ICIs, a class of immunotherapy, function by reactivating T cells in the tumor microenvironment and enhancing natural killer (NK) cell activity. This process prevents tumor immune escape and facilitates the conversion of immunologically “cold” tumors to “hot” tumors (Zhang et al., 2022). ICIs are monoclonal antibodies targeting inhibitory checkpoint molecules expressed on antigen-presenting cells (APCs) and CD4+ T cells (Marei et al., 2023). Currently, FDA-approved ICIs include CTLA-4 inhibitors (e.g., ipilimumab), PD-1 inhibitors (e.g., nivolumab, pembrolizumab, cemiplimab), and PD-L1 inhibitors (e.g., atezolizumab, durvalumab, avelumab) (Vafaei et al., 2022). By blocking ligand-receptor interactions on T cells, ICIs reverse immunosuppression and inhibit tumor growth (Andrews et al., 2019). Evidence indicates that ICIs enhance anti-tumor immunity by modulating the PD-1/PD-L1 and CTLA-4/CD80/86 pathways, strengthening tumor antigen recognition and ultimately inducing tumor cell death (Seto et al., 2019). However, ICIs can disrupt peripheral self-tolerance, triggering autoimmune-like inflammatory responses known as immune-related adverse events (irAEs) (Zhou et al., 2024). These irAEs may involve multiple organ systems, such as cardiac, endocrine, gastrointestinal, dermatologic, and renal, and can lead to significant inflammation and visceral toxicity (Ramos-Casals et al., 2022). Identifying strategies to mitigate the incidence and severity of irAEs remains a key research priority.
In China, Traditional Chinese Medicine (TCM) represents a major therapeutic modality for cancer treatment. TCM derives from three primary sources: botanical, animal, and microbial materials. Its therapeutic effects are not attributable to isolated drugs but arise from the synergistic interactions among drug combinations and complex interactions, producing multi-target regulatory activities that collectively exert anti-tumor effects. Numerous studies have confirmed that various traditional Chinese medicines and their main metabolites can inhibit tumor progression, alleviate radiotherapy- and chemotherapy-induced side effects, and improve survival in cancer patients (Li et al., 2012). Proposed anti-tumor mechanisms of TCM include the suppression of cancer cell proliferation, migration, and invasion, promotion of tumor vascular normalization, and interference with metastatic processes (Cheng et al., 2021; Chen et al., 2022). A growing body of clinical and preclinical evidence supports the combined use of TCM with immunotherapy, demonstrating that such combinations can enhance efficacy, reverse drug resistance, and reduce adverse reactions (Zhang et al., 2020). It was confirmed through in vivo and in vitro experiments that bufonidin can reverse the activation of phosphatidylinositol 3-kinase (PI3K)/(Serine/Threonine Kinase B) AKT/(mammalian target of rapamycin) mTOR signaling pathway induced by bifunctional apoptosis regulator (BFAR), and enhance the efficacy of combination with ICIs (Chen G. et al., 2023). Cordycepin, combined with anti–CTLA-4 therapy, alleviated CD8+ T cell exhaustion in the tumor microenvironment by upregulating chemokine expression (Chen L. et al., 2023). Similarly, the combination of evodiamine and anti–PD-1 therapy more effectively suppressed Lewis lung cancer growth by increasing CD8+ T cell infiltration in blood, tumor, and spleen, reducing Treg proportions, and promoting the secretion of cytokines such as Tumor Necrosis Factor-alpha (TNF-α), Recombinant Granzyme B (GZMB), and Interferon-gamma (IFN-γ) (Jiang et al., 2020). These findings collectively indicate that TCM and ICIs can act synergistically, suggesting a promising approach for enhancing cancer treatment outcomes.
This meta-analysis evaluated 41 randomized controlled trials investigating the efficacy of TCM combined with ICIs in cancer treatment. All included studies were conducted in China and published within the past 5 years. Although most reported random allocation, blinding procedures were rarely described. The results showed that the ORR, DCR, CD4+T/CD8+T, PFS, OS, KPS, adverse events, and TCM therapeutic evaluation of the TCM + ICIs group were significantly higher than those of the ICIs group. On the contrary, the AFP, CEA, and CA125 levels in TCM + ICIs group were significantly lower than those in ICIs group, while CA199 showed no significant difference. These findings suggest that TCM combination therapy may enhance immune function, improve performance status, reduce tumor marker levels, and ameliorate treatment-related toxicity. However, the efficacy of TCM + ICIs appears to vary by cancer type. The present analysis primarily included studies on respiratory and digestive tract tumors. For respiratory cancers, TCM + ICIs consistently outperformed ICIs monotherapy. Among gastrointestinal cancers, effects were more heterogeneous; notably, in liver cancer, no significant differences were observed in ORR, DCR, or CD4+/CD8+ ratio between groups, whereas benefits were evident in other digestive malignancies. These discrepancies may reflect the limited number of available studies or differential tumor biology and TCM sensitivity, highlighting the need for further clinical validation. Furthermore, the overall incidence of adverse reactions and the incidence of adverse reactions in each system in TCM + ICIs group were lower than those in ICIs group. From the perspective of modern pharmacological research, some active metabolites in TCM can exert a synergistic effect with immunosuppressants to reduce the occurrence of adverse reactions. TCM can affect enzyme activity, regulating the metabolic rate of immunosuppressants in the body, avoiding drug accumulation, and reducing adverse reactions such as liver and kidney damage (Wang S. et al., 2021). Meanwhile, it can alleviate excessive inflammation caused by immunosuppressants and reduce inflammation related tissue damage by relying on its own anti-inflammatory and oxygen free radical scavenging effects (Zhu et al., 2022). In addition, the protective effect of TCM on organs can weaken the direct stimulation and damage of immunosuppressants, thereby reducing adverse reactions (Ren et al., 2021).
The heterogeneity among studies on ORR, DCR, OS, and TCM therapeutic evaluation is not high, so fixed-effect models have been well applied. However, significant heterogeneity was observed in other outcome analyses, necessitating investigation into its sources. Given the varying number of studies across outcomes, subgroup analysis and meta-regression were only feasible for selected endpoints. When CD4+T/CD8+T is grouped based on tumor type and ICIs type, the heterogeneity among different subgroups decreases. The conclusion drawn from meta-regression is that whether it is lung cancer and whether chemotherapy treatment is applied may both be sources of heterogeneity, suggesting that TCM treatment may be more effective for lung cancer and the possible clinical efficacy of TCM with immunotherapy and chemotherapy. During the subgroup analysis of KPS, the heterogeneity among subgroups of different tumors or ICIs was significantly reduced. Further meta-regression analysis shows that the source of heterogeneity is the type of ICIs. These findings suggest that TCM’s therapeutic effects may vary by tumor type and ICI agent. Tumor-specific pathophysiology and ICI mechanism may influence TCM compatibility and efficacy. Currently, TCM + ICIs show consistent benefit in lung cancer across ICI types. As ICI diversity and indication breadth continue to expand, future research should clarify which ICIs are most suitable for specific patient subgroups and baseline characteristics, enabling treatment personalization and optimized resource use. If more systematic and comprehensive research can be conducted in the later stage, it is an essential part of improving clinical efficacy and avoiding resource waste. The reliability and robustness of the results are crucial for drawing correct conclusions. Sensitivity analysis confirmed the stability of all meta-analyses. However, Egger’s test indicated potential publication bias for ORR, DCR, CD4+/CD8+T, adverse events, and TCM therapeutic evaluation. Subsequent trim-and-filling analysis confirmed that the pooled results remained robust despite such bias, underscoring the clinical relevance of this study.
As the first meta-analysis to assess the clinical efficacy of TCM combined with ICIs for cancer, this review has several limitations. First, the number of included studies remains limited, with strong representation of lung cancer but relatively few trials on other common malignancies such as liver or gastric cancer. Second, all studies were conducted in China; although they cover multiple regions, potential ethnic variations in treatment response cannot be ruled out. As numerous preclinical studies support TCM’s anti-tumor properties, international clinical trials are warranted to assess the generalizability of TCM + ICI therapy. Third, in the quality assessment, most studies were rated as having “unclear” risk of bias across several domains, which may compromise the overall credibility of the evidence and limit its direct clinical applicability. Finally, although this meta-analysis evaluated multiple efficacy endpoints, incomplete reporting in many publications constrained deeper subgroup analysis. It is suggested that future clinical studies should adopt more comprehensive and standardized reporting to facilitate more precise evidence synthesis.
Conclusion
Compared with ICIs monotherapy, the combination of TCM and ICIs significantly improves ORR, DCR, CD4+/CD8+ T-cell ratio, PFS, OS, KPS, and TCM therapeutic evaluation, while reducing levels of AFP, CEA, and CA125 and the incidence of adverse events. Subgroup analysis and meta-regression revealed that heterogeneity in CD4+/CD8+ T-cell ratio was influenced by tumor type and concomitant chemotherapy, while heterogeneity in KPS was associated with ICI type. Combining sensitivity analysis, publication bias analysis, and the trim-and-filling method, the results of our studies are robust and reliable. However, our research is still limited in terms of quantity and region, and we hope more clinicians and researchers will engage in this field to contribute higher-quality data and more comprehensive insights.
Data availability statement
The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding authors.
Author contributions
YK: Software, Formal Analysis, Writing – original draft. YP: Formal Analysis, Software, Writing – original draft. XH: Writing – original draft, Data curation, Validation. XB: Writing – original draft, Data curation, Validation. XL: Writing – original draft, Investigation, Methodology. MZ: Methodology, Investigation, Writing – original draft. YW: Writing – original draft, Methodology, Investigation. TJ: Supervision, Project administration, Writing – review and editing, Conceptualization, Writing – original draft. GZ: Writing – review and editing, Supervision, Writing – original draft, Conceptualization, Project administration.
Funding
The author(s) declare that financial support was received for the research and/or publication of this article. Youth Fund of the National Natural Science Foundation of China, No. 82204950. Zhejiang Provincial Natural Science Foundation Youth Project, No. LQ23H270013. Zhejiang Province Traditional Chinese Medicine Science and Technology Plan Youth Talent Support Program Project, No. 2025ZR102. State Administration of Traditional Chinese Medicine Science and Technology Department - Zhejiang Provincial Administration of Traditional Chinese Medicine joint science and technology plan key research project, No. GZY-ZJ-KJ-23094. Chunyan Traditional Chinese Medicine Development Special Fund Achievement Transformation Research Project, No. CY202302.
Acknowledgments
We would like to deliver our thanks to Shan XL and Jie Hu from the first affiliated hospital of Zhejiang Chinese Medical University for their encouragement and support.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fphar.2025.1661503/full#supplementary-material
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Glossary
GCO Global Clinical Operations
ICIs Immune Checkpoint Inhibitors
PD-1 Programmed Cell Death Protein 1
PD-L1 Programmed Death Ligand 1
CTLA-4 Cytotoxic T Lymphocyte Antigen-4
TCM Traditional Chinese Medicine
ORR Overall Response Rate
DCR Disease Control Rate
PFS Progression-Free Survival
OS Overall Survival
KPS Karnofsky Performance Status
AFP Alpha-Fetoprotein
CEA Carcinoembryonic Antigen
CA125 Carbohydrate antigen 125
CA199 Carbohydrate antigen 199
CR Complete remission
PR Partial remission
SD Stable disease
RR Relative Risk
WMD Weighted Mean Difference
CI Confidence Intervals
SMD Standardized Mean Difference
mAb Monoclonal Antibody
APC Antigen Presenting Cells
FDA Food and Drug Administration
irAEs immune related Adverse Events
PI3K Phosphatidylinositol 3-kinase
AKT Serine/Threonine Kinase B
mTOR mammalian target of rapamycin
BFAR Bifunctional Apoptosis Regulator
TME Tumor Microenvironment
TNF-α Tumor Necrosis Factor-alpha
GZMB Recombinant Granzyme B
IFN-γ Interferon-gamma
Keywords: TCM, traditional Chinese medicine, ICIS, immune checkpoint inhibitors, cancer, meta-analysis
Citation: Ke Y, Pan Y, Huang X, Bai X, Liu X, Zhang M, Wei Y, Jiang T and Zhang G (2025) Efficacy and safety of traditional Chinese medicine (TCM) combined with immune checkpoint inhibitors (ICIs) for the treatment of cancer: a systematic review and meta-analysis. Front. Pharmacol. 16:1661503. doi: 10.3389/fphar.2025.1661503
Received: 23 July 2025; Accepted: 17 October 2025;
Published: 31 October 2025.
Edited by:
Rajiv Pathak, Albert Einstein College of Medicine, United StatesReviewed by:
Anusha Aditya, Columbia University, United StatesSoundhar Ramasamy, Kyoto University, Japan
Copyright © 2025 Ke, Pan, Huang, Bai, Liu, Zhang, Wei, Jiang and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Guangji Zhang, emdqdGNtQHpjbXUuZWR1LmNu; Tao Jiang, anR0Y21AemNtdS5lZHUuY24=
†These authors share first authorship