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. 2025 Feb 2;21(1):2458948. doi: 10.1080/21645515.2025.2458948

Immune checkpoint inhibitors in cancer patients with autoimmune disease: Safety and efficacy

Jiayuan Le a,b,c,d,e, Yuming Sun a,b,c,d,e, Guangtong Deng a,b,c,d,e, Yating Dian a,b,c,d,e, Yanli Xie f,✉, Furong Zeng g,✉
PMCID: PMC11792813  PMID: 39894761

ABSTRACT

The utilization of immune-checkpoint inhibitors (ICIs) in cancer immunotherapy frequently leads to the occurrence of immune-related adverse events (irAEs), making it generally not recommended for patients with preexisting autoimmune diseases. Hence, we conducted a meta-analysis on safety and efficacy of ICIs in cancer patients with preexisting autoimmune diseases to provide further insights. PubMed, EMBASE, and Cochrane Library were systematically searched until December 20, 2024. The main summary measures used were pooled rate and risk ratio (RR) with 95% confidential interval (CI), which were analyzed using R statistic software. A total of 52 articles were included in the study. When cancer patients with preexisting autoimmune diseases received ICIs treatment, the overall incidence was 0.610 (95% CI: 0.531–0.686) for any grade irAEs, 0.295 (95% CI: 0.248–0.343) for flares, 0.325 (95% CI: 0.258–0.396) for de novo irAEs, 0.238 (95% CI: 0.174–0.309) for grade ≥3 irAEs, and 0.143 (95% CI: 0.109–0.180) for discontinuation due to immunotoxicity. Compared with those without autoimmune diseases, cancer patients with autoimmune diseases experienced a higher risk of any-grade irAEs (RR: 1.23, 95% CI: 1.12–1.35) and discontinuation due to immunotoxicity (1.40, 95% CI: 1.11–1.78). However, no statistically significant differences were observed in the incidence of grade ≥3 irAEs, objective response rate (ORR), disease control rate (DCR), overall survival (OS), and progression-free survival (PFS) between the two groups. During ICIs treatment, irAEs are common among cancer patients with autoimmune diseases, but severe irAEs is relatively low. ICIs are effective in this population, but should be strictly monitored when used to avoid immunotoxicity.

KEYWORDS: Immune checkpoint inhibitors, autoimmune diseases, safety and efficacy, tumor, meta-analysis

Introduction

Immune-checkpoint inhibitors (ICIs) are a revolutionized anti-malignancy therapy that activates immune microsystems and dramatically increases the survival of numerous tumor patients.1–3 ICIs are roughly classified into three types according to the specifically blocking immunomodulatory checkpoint receptor: cytotoxic T lymphocyte-associated protein 4 (CTLA-4) inhibitor, such as lpilimumab; programmed cell death 1 (PD-1) inhibitor, such as nivolumab and pembrolizumab; and programmed cell death-ligand 1 (PD-L1) inhibitor, such as atezolizumab, durvalumab, and avelumab.4–9 Despite the immense therapeutic potential of ICIs in tumor therapy, their use usually results in immune-related adverse events (irAEs), especially in patients with autoimmune diseases.10,11

Autoimmune diseases occur when the immune system excessively activates autoantigens,12,13 which can involve multiple systems, such as rheumatologic, dermatologic, endocrine, gastrointestinal, and neurologic systems. The use of ICIs may exacerbate autoimmune diseases and elevate the risk of severe irAEs.4–6 Consequently, patients with autoimmune diseases are often excluded from ICIs clinical trials.14 However, with the expanding utilization of ICIs and their demonstrated benefits in cancer treatment, there is a growing demand to establish the safety and efficacy of ICIs in individuals with both cancer and autoimmune diseases. Cancer patients with autoimmune diseases have a higher risk of adverse events following ICIs therapy in clinical practice.15–20 However, the efficacy of ICIs in these patients has been controversial.16–18,21–24 Notably, the available evidence has several limitations, including an absence of categorization for autoimmune diseases across various systems, which limits the assessment of the impact of specific disease subgroups; reliance on pooled overall response rate (ORR) and disease control rate (DCR) in patients with autoimmune diseases without conducting a comparative analysis between patients with autoimmune diseases and those without, which may introduce potential bias; and a lack of pooled incidence of irAEs across different severity and categories, which limits the understanding of these drug complications.

To address these gaps, we pooled the incidence of adverse events and response rates based on the systems affected by autoimmune diseases and the type of tumor, grouped adverse events according to different severity, and compared the safety and efficacy of ICIs in patients with and without autoimmune diseases. Our study offers a synthesis of the current evidence for clinical decision-making and enriches the landscape of ICIs therapy in cancer patients with autoimmune diseases through a nuanced consideration.

Methods

This meta-analysis was performed and presented in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRIMSA) guidelines and AMSTAR Guidelines.25 The protocol for this meta-analysis was registered in PROSPERO (CRD42023459421).

Data sources and searches

We searched PubMed, Embase databases, and the Cochrane Library up to December 20, 2024, for full-text articles in English, using a combination of MeSH heading and text search strategies. MeSH headings included “neoplasms,” “immune checkpoint inhibitors,” “ipilimumab,” “nivolumab,” and “autoimmune diseases.” The detailed search strategies are shown in supplementary materials table S1.

Study selection

We focused on studies that investigated the safety and efficacy of ICIs in cancer patients with preexisting autoimmune diseases. Selected studies should meet the following inclusion criteria: (1) population, cancer patients with preexisting autoimmune diseases; (2) intervention, treatment with ICIs; (3) comparison, treatment without ICIs; (4) outcomes, the incidence of any grade irAEs, autoimmune disease flares, de novo irAEs, the discontinuation rate due to immunotoxicity, ORR, DCR, as well as hazard ratio (HR) values for overall survival (OS) or progression-free survival (PFS); (5) study design, randomized controlled trials, cohort studies, case-control studies, or case series. We categorized the autoimmune diseases included in the literature according to the system affected, as shown in supplementary materials table S2. We excluded studies if they did not report the administration of ICIs if they were reviews, commentaries, or case reports, or if the participant was diagnosed with an autoimmune disease after ICI therapy. For multiple articles reporting identical outcomes from the same cohort, we selected the study with the largest cohort or the most detailed information for analysis. Two researchers (YD and JL) independently screened titles and abstracts and reviewed articles in full text, and a third researcher (FZ or GD) resolved disagreements through consensus.

Data extraction and quality assessment

Using prespecified forms, two researchers (YD and JL) independently extracted data from eligible studies. The extracted data included first author, year of publication, country or region, sample size, age, sex, preexisting autoimmune diseases, ICIs therapy, and outcome variables of interest. The primary outcomes were the incidence of adverse events (including irAEs, flares, de novo irAEs, and discontinuation rate due to immunotoxicity) and response rates (including ORR and DCR) in cancer patients with autoimmune diseases after receiving ICIs therapy. The secondary outcomes included risk ratios (RR) for the incidence of adverse events and response rates, as well as HRs for OS and PFS in cancer patients with and without autoimmune diseases following ICIs therapy. The Newcastle-Ottawa Quality Assessment Scale was used for quality assessment of cohort studies.26 Studies were evaluated based on selection, comparability, and exposure/outcome using stars. We classify studies with 7 stars or more as good, 4–6 stars as fair, and less than 4 stars as poor.27

Certainty assessment

The Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) criteria was used to appraise the overall quality of evidence using GRADEpro GDT software.28 It divides findings into four categories: i) high quality, which indicates that there is little chance that future research will alter our conclusions; ii) moderate quality, which indicates that future research will probably have an impact on our conclusions; iii) low quality, which indicates that future research will likely have a significant impact on our conclusions; and iv) very low quality, which indicates that there is uncertainty in our conclusions. Findings based on observational studies are rated as low or very low quality of evidence with the GRADE system (supplementary materials table S3); however, they can lead to strong recommendations in clinical settings where randomized trials are not available.

Statistical analysis

All extracted data for the meta-analysis were analyzed using R statistical software version R 4.2.0. Random effects model was used as the primary method for pooling results across studies. The model accounts for interstudy variation and provides a more conservative effect than a fixed model.27,29 We grouped the results based on the tumor sites and systems affected by autoimmune diseases, including all (autoimmune diseases across various systems), rheumatologic, dermatologic, endocrine, gastrointestinal, and neurologic systems. Calculation of pooled incidence of adverse events and the pooled response rate in cancer patients with autoimmune diseases using the Freeman-Tukey double arcsine method. To investigate the differences in safety and efficacy of ICIs therapy in cancer patients with and without autoimmune diseases, we utilized RR [95% confidential interval (CI)] for any grade irAEs, grade ≥3 irAEs, discontinuation rates due to immunotoxicity, ORR, and DCR and used pooled HR (95% CI) for OS and PFS. We assessed statistical heterogeneity using the I2 statistic, and we considered heterogeneity significant when the p value was <0.1 or the I2 statistic was ≥50%. We used leave-one-out sensitivity analysis to assess robustness across studies. We also used funnel plots and Egger’s regression tests for any analysis that included at least 10 studies to estimate publication bias.30

Results

Study retrieved, characteristics and quality assessment

We obtained 11,496 citations through a literature search and assessed the full text of 213 studies after initial title and abstract screening. The final 52 articles were eligible for data extraction and quantitative analysis. The PRISMA flow chart of the meta-analysis is shown in Figure 1. These studies included 16 retrospective observational studies,7,21,23,31–43 prospective observational studies,44–47 and 32 case series.8,9,22,48-73 Eleven studies involved only one type of autoimmune disease, and the remaining 37 studies involved multiple different autoimmune diseases. The tumor types were mostly melanoma and non-small cell lung cancer, as these two cancers have the largest number of ICIs clinical trials.74 Except for 11 studies that reported treatment outcomes for a single immune checkpoint inhibitor, the rest were all based on mixed ICIs. Descriptive features and characteristics of the included studies are summarized in supplementary materials table S4. According to quality assessment criteria, the quality of cohort studies is divided into two levels: 14 studies were graded as good, and 4 studies were graded as fair. The score for each study is shown in supplementary materials table S5.

Figure 1.

Figure 1.

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PRISMA flow diagram for search and selection of studies.

Primary outcomes: the pooled incidence of adverse events and pooled response rates

Forty-seven studies reported data on the incidence of adverse events following ICIs therapy in cancer patients with autoimmune disease. The results were divided into subgroups according to systems affected by autoimmune diseases and different cancer types. Figure 2 shows the pooled rates of adverse events and each indicator represents a specific analysis. The overall incidence was 0.610 (95% CI: 0.531–0.686) for any grade irAEs,0.295 (95% CI: 0.248–0.343) for flares, 0.325 (95% CI: 0.258–0.396) for de novo irAEs, 0.238 (95% CI: 0.174–0.309) for grade ≥3 irAEs, and 0.143 (95% CI: 0.109–0.180) for discontinuation due to immunotoxicity. Data analysis from patients with various autoimmune diseases affecting different body systems revealed that the incidence of any grade irAEs ranged from 0.590 (95%CI: 0.445–0.727) to 0.740 (95%CI: 0.650–0.824). However, the incidence of other adverse events (such as flares, de novo irAEs, grade ≥3 irAEs, discontinuation due to immunotoxicity) was comparatively lower, ranging from 0.059 (95%CI: 0–0.333) to 0.432 (95%CI: 0.258–0.614). Consistent with the previous findings, when the data was stratified by cancer type, the incidence of other adverse events (ranging from 0.627, 95%CI: 0.391–0.837 to 0.669, 95%CI: 0.561–0.769) remained lower than that of any grade irAEs, whatever in melanoma and non-small cell lung cancer (ranging from 0.122, 95%CI: 0.070–0.183 to 0.376, 95%CI: 0.275–0.481). The detailed analysis process for each indicator is shown in supplementary materials figures S1 to S40.

Figure 2.

Figure 2.

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Forest plot of pooled incidence of adverse events in different autoimmune diseases and cancers. ALL: patients with autoimmune diseases of various systems; irAes: immune-related adverse events; NSCLC: non-small cell lung cancer. CI: confidential interval.

Twenty-three studies provided data on response rates in cancer patients with autoimmune diseases following ICIs therapy (Figure 3). Random-effects models showed that the overall pooled ORR and DCR were 0.333 (95%CI: 0.262–0.407) and 0.554 (95%CI: 0.426–0.678). According to the stratified analysis based on autoimmune diseases affecting different systems and different cancer types, the pooled ORR ranged from 0.021 (95%CI: 0.000–0.243) to 0.400 (95%CI: 0.159–0.662), and the pooled DCR ranged from 0.352 (95%CI: 0.180–0.543) to 0.756 (95%CI: 0.192–1.000). The detailed analysis process for each indicator is shown in supplementary figures S41 to S56.

Figure 3.

Figure 3.

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Forest plot of pooled incidence of objective response rate and disease control rate in different autoimmune diseases and cancers. ALL: patients with autoimmune diseases of various systems; ORR: objective response rate; DCR: disease control rate; NSCLC: non-small cell lung cancer; CI: confidential interval.

Secondary outcomes: adverse events, response rates, and survival outcomes in cancer patients with and without autoimmune diseases

Based on sixteen studies, random-effects models showed that cancer patients with autoimmune disease had a 23% increased overall risk of any grade irAEs compared with patients without autoimmune disease (risk ratio 1.23, 95% CI: 1.12–1.351) (Figure 4), with significant heterogeneity (I2 = 61%, p < .01). Additionally, the overall risk ratio was 1.08 (95% CI: 0.95–1.23) for grade≥ 3 irAEs and 1.40 (95% CI: 1.11–1.78) for discontinuation due to immunotoxicity.

Figure 4.

Figure 4.

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Forest plot of adverse events in patients receiving immune checkpoint inhibitors with and without autoimmune diseases. AID: autoimmune disease; irAes: immune-related adverse events; RR: risk ratio; CI: confidential interval.

Seven and four studies reported ORR and DCR in cancer patients with and without autoimmune disease respectively. As shown in Figure 5, random effects model analysis showed that the pooled ORR was 0.94 (95%CI: 0.83–1.07), with insignificant heterogeneity (I2 = 0%, p = .64). Moreover, the pooled DCR was 1.00 (95%CI: 0.91–1.09), with insignificant heterogeneity (I2 = 50%, p = .11).

Figure 5.

Figure 5.

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Forest plot of efficacy of immune checkpoint inhibitors in patients with and without autoimmune diseases. AID: autoimmune disease; ORR: objective response rate; DCR: disease control rate; CI: confidential interval.

Eight and four studies, respectively, reported HR for OS or PFS in cancer patients with and without autoimmune disease. The pooled results showed no significant difference between cancer patients with and without autoimmune disease in OS (HR: 0.97, 95%CI: 0.88–1.08) and PFS (HR: 0.79, 95%CI: 0.52–1.20). The heterogeneity was detected in OS (I2 = 45%, p = .08) and PFS (I2 = 69%, p = .02). The forest map is shown in Figure 6.

Figure 6.

Figure 6.

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Forest plot of pooled hazard ratio of overall survival and progression-free survival in patients receiving immune checkpoint inhibitors with and without autoimmune diseases. HR: hazard ratio; OS: overall survival; PFS: progress free survival; CI: confidential interval.

Publication bias and sensitivity analysis

Funnel plot and Egger’s test were conducted for all analyses involving 10 or more studies. These analyses aimed to assess the presence of publication bias. Except for one pooled analysis examining any grade irAEs in cancer patients with and without autoimmune diseases, the results indicated no evidence of publication bias for the remaining relevant findings (supplementary materials figures S57 and S58). Trim and fill analysis was performed to adjust for the publication bias and after adding 7 studies, the funnel plot showed relative symmetry. Further leave-one-out sensitivity analyses indicated that the pooled estimates were robust and not excessively influenced (supplementary materials figures S59 to S65).

Discussion

This meta-analysis provides evidence that the occurrence of any grade irAEs is common in cancer patients with autoimmune disease and does not exhibit substantial variations based on the specific system affected by autoimmune disease or the type of tumor. However, the incidence of other adverse events, such as flares, de novo irAEs, grade ≥3 irAEs, and discontinuation due to immunotoxicity, appears to be relatively lower compared with patients without autoimmune diseases. Cai et al. reported an increased risk of any grade irAEs in cancer patients with autoimmune disease compared to those without autoimmune disease.18 Furthermore, they observed a higher risk specifically for grade≥ 3 irAEs in cancer patients with autoimmune disease. However, our study, which had a larger sample size, did not find a significant increase in the risk of grade ≥3 irAEs in cancer patients with autoimmune disease. These findings are consistent with our previous statement. While ICIs may be associated with a higher incidence of any grade irAEs in cancer patients with preexisting autoimmune diseases, there is insufficient evidence to suggest an increased risk of severe irAEs.

Tison et al. found that flares occurrence varied in different types of autoimmune disease, and cancer patients with psoriatic arthritis had the highest risk of flares, followed by cancer patients with rheumatoid arthritis or polymyalgia rheumatica.75 Conversely, Xie et al. found no statistically significant increase in the risk of flares among cancer patients with rheumatoid arthritis compared to cancer patients with other autoimmune diseases.16 Our study provided a more complete analysis, and we observed no significant difference in the incidence of various adverse events among cancer patients with autoimmune diseases across different systems. Furthermore, our study also investigated the impact of tumor sites on the incidence of irAEs. Interestingly, we observed that the incidence of irAEs did not vary much across different tumor types. However, it is important to note that much of the existing research in this area has primarily focused on melanoma and non-small cell lung cancer.74 Therefore, definitive conclusions regarding the influence of tumor type on irAEs incidence cannot be drawn at this time. Further research is needed to provide a more comprehensive understanding of this relationship in a wider range of tumor types.

Through a pooled analysis of response rate data, it has been observed that cancer patients with autoimmune diseases can achieve a certain level of remission following ICIs therapy. Hence, the utilization of ICIs in this patient population under vigilant management is justified, as there is no significant reduction in ORR, DCR, or survival outcomes compared to cancer patients without autoimmune diseases. Notably, it is crucial to enhance the identification and management of irAEs, which could involve implementing comprehensive patient education programs, standardizing guidelines for irAE management, and refining the selection criteria for immunosuppressive therapies.76

Previous studies have observed a correlation between antitumor response and the heightened occurrence of irAEs among cancer patients.77,78 Based on this observation, the increased incidence of irAEs in individuals with preexisting autoimmune diseases may suggest a potentially enhanced anti-tumor capability. However, our study found that cancer patients with autoimmune diseases did not exhibit a significant enhancement in ORR and DCR in comparison to those without autoimmune diseases. This phenomenon may be attributed to methodological considerations, such as the presence of statistical biases like immortal time bias or publication bias, which could have led to an overestimation of the association in question.79 More prospective clinical trials are needed to provide evidence in the future.

The study has several limitations. Primarily, a large proportion of the investigations focused solely on individuals diagnosed with autoimmune diseases, resulting in a small sample size for comparative analysis of outcome variables between cancer patients with and without autoimmune diseases. Moreover, the lack of studies investigating the use or absence of ICIs in autoimmune diseases hinders the ability to compare the impact of autoimmune diseases on the effectiveness of ICIs treatment. Furthermore, the retrospective nature of the majority of studies introduces potential sources of confusion and bias, and the majority of studies focused on melanoma and non-small cell lung cancer, with a limited representation of other malignancies. Given the above, there are several aspects that need to be improved in future research. Firstly, the scope of research should be expanded to cover more types of cancer and rare autoimmune diseases. Secondly, the study design should be optimized by enhancing data quality through multicenter, prospective randomized controlled trials. Thirdly, the follow-up time needs to be extended to dynamically assess the long-term safety and efficacy of ICIs on cancer patients with autoimmune diseases. Finally, further research is needed on how to prevent and manage adverse events related to ICIs, reducing risks and maintaining efficacy through prophylactic medication and optimized management strategies. We expect that the application of ICIs in cancer patients with autoimmune diseases will be further improved in the future.

Conclusion

In conclusion, our study suggests that irAE is prevalent but usually mild in cancer patients with autoimmune diseases treated with ICIs. Additionally, preexisting autoimmune diseases do not affect the efficacy of ICIs. Therefore, ICIs should be used under rigorous monitoring and management.

Supplementary Material

PRISMA_2020_checklist.docx
KHVI_A_2458948_SM0083.docx (58.3KB, docx)
Supplementary Materials.pdf
KHVI_A_2458948_SM0082.pdf (5.1MB, pdf)

Biographies

Yanli Xie is a rheumatologist and doctoral supervisor in Xiangya Hospital, Central South University. Her research expertise is investigating the pathogenesis of inflammatory diseases.

Furong Zeng is an oncologist and doctoral supervisor in Xiangya Hospital, Central South University. Her research expertise is investigating the mechanisms of cell death, as well as the pathogenesis of skin tumors and inflammatory diseases.

Funding Statement

The work was supported by the Huxiang Youth Talent Support Program [2024RC3043].

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Ethical Statement

Relevant ethical approval does not apply to this study.

Supplementary material

Supplemental data for this article can be accessed on the publisher’s website at https://doi.org/10.1080/21645515.2025.2458948

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

PRISMA_2020_checklist.docx
KHVI_A_2458948_SM0083.docx (58.3KB, docx)
Supplementary Materials.pdf
KHVI_A_2458948_SM0082.pdf (5.1MB, pdf)

Data Availability Statement

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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