Safety and Efficacy of Immune Checkpoint Inhibitors in Advanced Cancer patients with Autoimmune Disease: a Meta-Analysis

doi:10.21203/rs.3.rs-1537689/v1

Background

Cancer patients with autoimmune disease (AID) usually have been excluded from the clinical trials of immune checkpoint inhibitors (ICIs). The safety and effectiveness of ICIs for these patients remain uncertainty.

Methods

A systematic search was conducted in available electronic database. We collected the incidence of immune-related adverse events (irAEs), progression-free survival (PFS) and overall survival (OS) from included studies.

Results

11 studies including 5489 participants were incorporated in our meta-analysis. The pooled risk ratio (RR) for any grade and grade ≥ 3 irAEs were 1.79 (95% CI: 1.31–2.45) and 1.58 (95% CI: 1.06–2.36), respectively. When irAEs were from the same system as AID, we called the irAEs as AID-homogeneous irAEs, otherwise as AID-heterogeneous irAEs. Subgroup analysis found that the higher risk of AID-homogeneous irAEs contributed to the higher risk of overall irAEs of AID patients. The pooled hazard ratio (HR) for PFS and OS were 1.09 (95% CI: 0.96–1.25) and 1.10 (95% CI: 0.68–1.77), respectively. The results of subgroup analyses for PFS and OS matched the overall results well.

Conclusion

AID patients were significantly associated with a higher risk of irAEs in any grade and ≥ 3 grade under ICIs therapy. Specifically, AID-homogeneous irAEs but AID-heterogeneous irAEs were higher in AID patients than without AID patients. No statistically significant difference in PFS and OS were observed in patients with and without AID.

cancer

autoimmune disease

immune checkpoint inhibitors

immune-related adverse events

progression-free survival

overall survival

The therapeutic outlook for many malignancies has been fundamentally changed owing to the advent of immune checkpoints inhibitors (ICIs)^{[ 1–3]}. It is a radical transformation of antitumor concept from killing tumor cells directly to regulating immune microenvironment. ICIs aim to block negative co-stimulation of T lymphocytes, which can activate anergic T cells to restore their antitumor effects^[4]. It is different from chemotherapy and targeted therapy. However, the immune-related side effects causing by ICIs have gradually attracted clinicians’ attention.

In terms of distinguishing between self and non-self, the immune system is finely tuned in order to maintain host integrity^[5]. Autoimmune disease (AID) would occur when the immune system leads to overactivate to autoantigens^[6]. There are varieties of autoimmune diseases that can occur in any system. The common pathogenesis is the loss of tolerance to autoantigens, leading to own organs under attack^[7]. It still remains unclear that the immunological and molecular mechanisms of each autoimmune disease related to the breakdown of tolerance to autoantigens^[8]. The common clinical manifestations of autoimmune diseases resemble to immunological side effects related to ICIs^[9]. Fearing of the deterioration of their preexisting AID, patients with cancer and autoimmune disease are often excluded from clinical trials of ICIs^{[10, 11]}.

People with autoimmune diseases often have a higher risk of tumorigenesis^[12], which in turn increases the risk of autoimmune diseases^[13]. Owing to the abnormalities of immune system, whether AID patients will benefit or suffer from ICIs remains to be verified. Previous study reported that ICIs are effective in AID patients and irAEs are often manageable^[14]. However, this study did not compare the PFS and OS between patients with and without AID. Based on this background, the published data of several studies that treated AID patients with ICIs was systematically collected. We aim to assess the rate of irAEs and PFS and OS of AID patients under ICIs therapy.

2.1 Literature search

Electronic databases, including PubMed, Web of Science, EMBASE, and Google Scholar from inception until February 2022, were searched to identify eligible studies without language restrictions. The medical subject headings (MeSH) are following: cancer, melanoma, leukemia, lymphoma, multiple myeloma, sarcoma, malignant mesothelioma, immune-related adverse event, irAEs, autoimmune disease, autoimmune disorder, lupus, interstitial lung disease, autoimmune thyroiditis, inflammatory bowel disease, rheumatoid arthritis, CTLA-4, ipilimumab, PD-1, nivolumab, pembrolizumab, cemiplimab, PD-L1, atezolizumab, durvalumab, avelumab, PFS, OS. In addition, the related bibliographies of the retrieved articles were also searched in case any articles were missing.

2.2 Eligibility criteria

Studies were eligible if following conditions were met: (1) participants were diagnosed with advanced cancer or other malignant diseases, (2) they had pre-existing autoimmune disease, (3) they were treated with ICIs, including PD-1 or PD-L1, CTLA-4 agents or other ICIs, (4) the studies must reported: incidence of irAEs, exact values of HR or survival curve for PFS and/or OS. Studies were excluded if (1) the reported outcomes only reported irAEs without PFS or OS (2) PFS and OS were reported as survival rates in percentage terms, (3) autoimmune diseases were diagnosed after the treatment of ICIs, (4) they talked about autoimmune antibodies instead of autoimmune diseases, (5) case reports.

2.3 Data extraction and quality assessment

Two authors (QC and GW H) who reviewed and screened all eligible studies for content according to a predefined data extraction form. All the patients in each study were divided into AID group (cancer patients with AID) and non-AID group (cancer patients without AID). We calculated total number of AID group, total number of non-AID group, number of patients developing any or ≥ G3 irAEs, and the incidence of irAEs was reported as RR (risk ratio). The outcome measures PFS and OS were assessed, and consistently reported as hazard ratio (HR). The following information was collected independently: the first author’s name, publication time, country, patients’ characteristics such as: number of patients, proportion of gender, type of autoimmune disease, class of malignancy, and category of immunotherapy. We used New Castle–Ottawa Scale to evaluate the quality of each study. Any disagreements would be resolved by an experienced reviewer (FY Z) or discussion.

2.4 Statistical analysis

The R software (version 4.1.3) was used for data analyses. We used RR to reflect the incidence of irAEs of any grade and grade ≥ 3^[15–21]. If the studies recorded HR for PFS or OS, we adopted its results. If some studies ^{[15–19, 22]} that lacked HR for PFS or OS values, we downloaded concerning Kaplan–Meier curves, then we used drawing tools to extract the curves’ data and Jayne F Tierney’s spreadsheet to estimate HR^[23]. The calculations were independently repeated twice for precise results^[24]. We reported the pooled RR with 95% CI for irAEs and the pooled HR with 95% CI for PFS and OS. The forest plots were applied to represent these pooled results. Statistical heterogeneity was estimated using I² test. I² < 50% was considered as low heterogeneity and > 50% was considered high heterogeneity. And p < 0.05 was statistically significant.

3.1 Literature search results

5754 relevant articles were initially retrieved through database searches. 5502studies were excluded, either because they were repetitive or because they did not meet the requirements after screening the title and the abstract. 252 studies were considered potentially eligible for further assessment. 11 studies were finally included in our analysis. Details of inclusion and exclusion process were presented in the form of a flowchart.(Fig. 1)

3.2 Characteristics of the selected studies

A total of 11 eligible citations were included and 9 of them adopted a retrospective design, and there were only 2 cohort studies. All articles were published between 2014 and 2021. 8,277 participants were included and males were 5156 (accounting for 62.3%) and a detailed description of the characteristics of the patients included in the meta-analysis was presented in Table 1. Of these, 5489 participants were treated with ICIs. These studies mainly took place in Asia and Europe, only one in US. Among these, 3 studies were from in Japan^{[15, 16, 22]}, 1 in Korea ^[17], 2 in the Italy ^{[18, 25]}, 1 in Germany^[26], 1 in UK^[19], 1 in Switzerland^[20] and 1 in Holland^[27], and 1 in US^[21]. 7 studies paid attention to non-small cell lung cancer ^{[15–17, 19, 22, 26]}, 4 talked about melanoma^{[18, 21, 25, 27]}, 2 involved in metastatic renal cell cancer and urothelial carcinoma ^{[18, 20]}. For autoimmune diseases, pneumonic AID and endocrine AID were the most mentioned. Specific information on irAEs, survival and quality characteristics are presented in Table 2,3,4,5 respectively.

Table 1

Detailed description of the characteristics of included studies
Author	Year	Gender Male(%) Female(%)	Age	Type of Autoimmune Disease	Type of Cancer	Immunotherapy Regimen
U. Bottoni et al.	2014	48(24.1%) 151(75.9%)	< 60 56.3% ≥ 60 43.7%	Autoimmune thyroiditis 55.1% Rheumatoid arthritis12.2% Others32.7%	melanoma	CTLA-4
Osamu Kanai et al.	2018	154(71.3%) 62(28.7%)	69 (30–89)	Interstitial lung disease	NSCLC	PD-1 (nivolumab)
Alessio Cortellini et al.	2019	499(66.4%) 252(33.6%)	69(24–92)	Thyroid disorders60% Dermatologic16.4% Rheumatologic11.8% Others 11.8%	NSCLC65.5% Melanoma21.2% Kidney cancer 12.5% Others0.8%	PD-1 (pembrolizumab) (nivolumab)
Ryota Shibaki et al.	2019	223(62.3%) 135(37.7%)	62(27–84)	Interstitial lung disease	NSCLC	PD-1 (nivolumab) (pembrolizumab)
Seonggyu Byeon et al.	2020	167(70.5%) 70(29.5%)	60(35–80)	Rheumatoid arthritis7.1% Behcet’s disease7.1% Interstitial lung disease85.3%	NSCLC	PD-1 (pembrolizumab) (nivolumab)
Yuri Tasaka et al.	2020	337(73.1%) 124(26.9%)	69(34–88)	Interstitial lung disease	NSCLC	PD-1 (atezolizumab)
Yohann Loriot et al. ^a	2020	772(77.4%) 225(22.6%)	69(41–82) 68(34–93)	psoriasis	urinary tract carcinoma	PD-L1 (atezolizumab)
Martin Faehling et al.	2020	82(65.1%) 44(34.9%)	62.4(33.5–81.6)	NA	NSCLC	PD-L1 (durvalumab)
Sonam Ansel et al.	2020	41(50%) 41(50%)	65(60.6–73.2)	Rheumatoid Arthritis Psoriasis Idiopathic pulmonary fibrosis Raynaud’s Disease Fibromyalgia Post-polio Syndrome Multiple sclerosis	NSCLC	PD-1 (pembrolizumab)
Nicholas Gulati et al. ^a	2021	295(61.1%) 188(38.9%)	Mean 63.29	Asthma Inflammatory bowel dsease Psoriasis Rheumatoid arthritis Eczema Polymyalgia rheumatic Other	Melanoma	CTLA-4 CTLA-4 + PD-1 PD-1
van der Kooij et al.	2021	2538(58.1%) 1829(41.9%)	< 65 67.8% ≥ 65 32.2%	RheumatologicAID Endocrine AID Inflammatory bowel disease other	Melanoma	CTLA-4 (ipilimumab) PD-1 (pembrolizumab) (nivolumab) CTLA-4 + PD-1
^a: We chose baseline characteristics of cohort studies; NSCLC: non-small cell lung cancer; PD-1: programmed cell death protein 1; PD-L1: programmed cell death protein ligand 1; CTLA-4: cytotoxic T-lymphocyte–associated protein 4 ; NA: not available

Table 2

Detailed information of irAEs of included studies
Author	Sum AID	Any irAEs (%)	≥G3 irAEs (%)	Sum non-AID	Any irAEs (%)	≥G3 irAEs (%)
Osamu Kanai et al.	26	8(31%)	5(19%)	190	22(12%)	10(5%)
Alessio Cortellini et al.	85	56(65.9%)	8(9.4%)	666	266(39.9%)	59(8.8%)
Ryota Shibaki et al.	14	4(29%)	1(7.1%)	196	22(11%)	8(4.1%)
Yuri Tasaka et al.	49	15(30.6%)	NA	412	39(9.5%)	NA
Yohann Loriot et al.	35	24(69%)	9(26%)	962	506(53%)	112(12%)
Sonam Ansel et al.	10	9(90%)	1(10%)	72	51(70.8%)	5(6.9%)
van der Kooij et al. ^b	187	NA	31(16.6%)	1540	NA	206(13.4%)
^b: We chose the patients treated with anti-PD-1 because they are the most in this study; AID: autoimmune disease; irAEs: immune-related adverse events; G3: grade 3; NA: not available.

Table 3

Detailed information for PFS and OS of included studies
Author		Country	Patients AID (%) Non-AID (%)	PFS(month) (AID: Non AID)	OS(month) (AID: Non AID)	HR for PFS (95%CI)	HR for OS (95%CI)	Quality
U. Bottoni et al.		Italy	49 24.6% 150 75.4%	NA	109:187	NA	3.01 (1.26–7.19)	7
Osamu Kanai et al.		Japan	26 12% 190 88%	2.7:2.9	NA	2.99 (1.04–8.62)	NA	7
Alessio Cortellini et al.		Italy	85 11.3 666 88.7%	6.8:8.0	9.8:16.5	2.31 (1.19–4.45)	1.04 (0.73–1.48)	6
Ryota Shibaki et al.		Japan	14 6.6% 196 93.4%	4.3:5.3	NA	0.97 (0.67–1.44)	NA	6
Seonggyu Byeon et al.		Korea	14 5.9% 223 94.1%	3.6:2.3	NA	1.28 (0.61–2.69)	NA	6
Yuri Tasaka et al.	Japan		49 10.6% 412 89.4%	5.9:3.5	27.8:25.2	0.94 (0.63–1.42)	1.70 (0.75–3.82)	6
Yohann Loriot et al.	Switzerland		35 3.5% 962 96.5%	NA	8.2:8.8	NA	1.80 (0.85–3.81)	7
Martin Faehling et al.	Germany		9 7.1% 116 92.9% 1 UN	NA	NA	1.01 (0.41–2.54)	1.34 (0.42–4.28)	7
Sonam Ansel et al.	UK		10 12% 72 88%	33.32:6.4	42:10.7	1.68 (0.15–18.22)	0.5 (0.19–1.3)	6
Nicholas Gulati et al.	US		74 15.3% 409 84.7%	NA	NA	0.49 (0.26–0.91)	0.21 (0.07–0.65)	8
van der Kooij et al. ^b	Holland		187 10.8% 1540 89.2%	NA	NA	1.11 (0.92–1.34)	1.08 (0.87–1.34)	8
^b: We chose the patients treated with anti-PD-1 they are the most in this study; UN: whether he/she had AID or not was uncertain; UK: the united kingdom; US: the united states; NA: not available.

Table 4

Quality assessment of the included retrospective studies with New Castle –Ottawa quality assessment scale
Study	Case definition adequate	Representativeness of the case	Selection of Controls	Definition of Controls	Comparability of cases and controls on the basis of the design or analysis	Assessment of exposure	Same method of ascertainment for cases and controls	Non-Response rate
U. Bottoni et al.	√	√	√	√	√		√
Osamu Kanai et al.	√	√	√	√	√		√
Alessio Cortellini et al.	√	√	√	√	√
Ryota Shibaki et al.		√	√	√	√		√
Seonggyu Byeon et al.		√	√	√	√	√
Yuri Tasaka et al.		√	√	√	√	√
Martin Faehling et al.	√	√	√	√	√			√
Sonam Ansel et al.		√	√	√	√	√
van der Kooij et al.	√	√	√	√	√	√	√
√ Adequacy of criteria and its absence represents inadequacy

Table 5

Quality assessment of the included cohort studies with New Castle –Ottawa quality assessment scale
Study	Representativeness of exposed cohort	Selection of Non exposed cohort	Ascertainment of exposure	Demonstration that outcome of interest was not present at start of study	Comparability of cohorts on the basis of the design or analysis	Assessment of outcome	Was follow-up long enough for outcomes to occur	Adequacy of follow-up completion of cohorts
Yohann Loriot et al.	√	√	√	√	√			√
Nicholas Gulati et al.	√	√	√	√	√	√	√
√ Adequacy of criteria and its absence represents inadequacy

3.3 Safety

7 studies reported irAEs including 406 patients in AID group and 4038 patients in non-AID group. 6 studies reported any grade irAEs, and 6 of these reported grade ≥ 3 irAEs. 2 studies reported mortality caused by ICIs. The incidence of any grade irAEs ranged from 29–90% in AID group and 9.5–70.8% in non-AID group, whereas the incidence of grade ≥ 3 irAEs ranged from 7.1–26% in AID group and 4.1–13.4% in non-AID group. AID group (RR: 1.79, 95%CI 1.31–2.45, p < 0.01, Fig. 2) was significantly at a higher risk to suffer irAEs. Nevertheless, significant heterogeneity was detected (I² = 69.0%, p < 0.01). The incidence of grade ≥ 3 irAEs (RR: 1.58, 95%CI 1.06–2.36, p = 0.01, Fig. 3) was also higher in AID group and low heterogeneity was found (I² = 27.3%; p = 0.23).

Then, a subgroup analysis for any grade irAEs was carried out to find the reason for the high heterogeneity. We found that autoimmune disease tended to aggravate irAEs in the same systems, but generally did not exacerbate irAEs of other systems. When irAEs are from the same system as AID, we called the irAEs as AID-homogeneous irAE, otherwise as AID-heterogeneous irAEs. For example, 3 studies mainly talked about interstitial lung disease, and pneumonia-related side effects were higher than non-interstitial lung disease group (RR = 2.93, 95%CI 2.01–4.27). When autoimmune disease was psoriasis, cutaneous side effects were AID-homogeneous irAEs (RR = 1.64, 95%CI 1.10–2.47). For AID-unrelated irAEs, the pneumonic side effects (RR = 1.78, 95%CI 0.75–4.22), the endocrine side effects (RR = 0.91, 95%CI 0.38–2.19). Detailed information is presented in Fig. 4.

3.4 Efficacy

9 studies reported PFS and 8 studies reported OS. 5489 participants were treated with ICIs, of these, 4293 participants for PFS and 4826 participants for OS, respectively. There was no significant difference in PFS (HR: 1.09, 95% CI 0.96–1.25) and OS (HR: 1.10, 95% CI 0.68–1.77) between AID patients and non-AID when they were treated with ICIs. There was a low heterogeneity (I² = 30.8%; p = 0.17) in PFS, however, high heterogeneity was found (I² = 63.6%; p < 0.01) in OS. The forest plots of these outcomes are showed in Fig. 5 and Fig. 6.

Furthermore, we analyzed the sources of heterogeneity using subgroup analysis, such as cancer types, regional factors and quality characteristics of these studies. Example for the type of cancer in PFS, the pooled HR was 1.09(95% CI 0.86–1.37) in NSCLC group. For melanoma, the pooled HR: 0.95(95% CI 0.57–1.59). The results of each subgroup matched the overall results. The forest plots of subgroup analysis are showed in Fig. 7.

3.5 Publication bias and sensitivity analysis

Publication bias can be verified by the funnel plot (Supplemental material: Fig S1(a), Fig S1(b), Fig S1(c), Fig S1(d)) and Egger linear regression test. There were no obvious publication bias in any grade irAEs(t = 1.66, p = 0.17), ≥G3 irAEs(t = 0.68, p = 0.53), PFS (t = − 0.18, p = 0.86)and OS (t = − 0.02, p = 0.99). However, the funnel plots for any grade irAEs and OS were asymmetrical in visual inspection, indicating underlying publication bias should be considered. By sensitivity analysis, the results of our study were reliable because the pooled results of any grade irAEs, ≥G3 irAEs, PFS and OS still remained significant regardless of which study was omitted. The forest plots of sensitivity analysis are showed in Fig S2(a), Fig S2(b), Fig S2(c), Fig S2(d).

According to our findings, the incidence of irAEs is higher in AID group than non-AID group under ICIs treatment. Specifically, AID-homogeneous irAEs are more likely to occur in AID group than non-AID group, whereas no significant difference was detected on AID-heterogeneous irAEs. The survival outcomes in AID group are almost unaffected compared to non-AID group. As far as we know, we are the first to study what kinds of irAEs AID patients are prone to and compare PFS and OS in cancer patients with and without autoimmune disease under ICIs therapy, filling a gap in previous studies of this kind.

As mentioned above, there is a close relationship among cancer, autoimmune diseases and immune-related adverse events^[28]. Immune checkpoint is a class of immunosuppressive molecules expressed on immune cells to regulate the degree of immune activation^[29]. Normally, effector function of CD4⁺ T cell remains stable due to the presence of immune checkpoints^[30]. Once the negative signal regulating T cell activation is suppressed, it would strengthen the T cells killing function. At the same time, it may also enhance the immune response to the body’s normal tissues^[31]. This process resembles to that of autoimmune diseases recognized by existing theories although the pathogenesis of autoimmune diseases is still incompletely understood. Hence, it is reasonable to worry about exacerbating autoimmune diseases in the administration of ICIs. Owing to the abnormalities of immune system in patients with autoimmune disease, T cell activation levels are higher than normal people. ICIs further promoted T cell function so that the balance of immune system was broken, which can easily lead to irAEs. It may partly explain the high incidence of irAEs in AID patients. The major pathological and clinical manifestations of autoimmune diseases are mainly manifested in which system, indicating that the immune cells of this system are in abnormal activation state. The immune cells of the other systems are generally in a normal state, so the frequency of irAEs is similar to that in normal people. Clinicians are suggested to pay more attention to irAEs of the same system as the pre-existing AID.

Although AID patients are at a higher risk to develop irAEs, no statistically significant reduction in PFS and OS were observed in AID group compared to non-AID group. In other words, the curative effect of ICIs in patients with AID is worthy of affirmation. This may suggest that the abnormal immune system of autoimmune disease does not affect the killing function of T lymphocyte on tumor cells, or the aggravation of irAEs does not shorten the survival of AID patients. Considering some of the irAEs could be fatal, one question is how to control the irAEs occurring during treatment. Clinically, immunosuppressants like steroids are commonly used to control irAEs. Since only 2 studies have talked about the use of immunosuppressants to control irAEs, we cannot rely on the limited data to analyze the accurate results^{[15, 19]}. Zhang et al. finding showed that the OS and PFS were significantly shortened in the administration of corticosteroids ^[32]. However, Fausto et al. results indicated that using steroids in cancer patients to control irAEs did not shorten OS^{[33, 34]}. There are no definitive answers on whether, when and how to use steroids in the treatment of ICIs. Therefore, it is necessary to perform patient stratification strategies depending on irAEs severity to determine next treatment^[35].

Previous studies reported that irAEs were significantly related to a better curative effect in cancer patients^[36–40]. So some clinicians doubt that AID patients may benefit more under ICIs for there is an immune-activated tendency in these patients^[41]. Although our findings showed AID patients had a higher risk to develop irAEs, we did not draw a conclusion that the survival would be prolonged in AID group. All of the above studies were conducted in non-AID cancer patients, unlike the population we studied. Moreover, many factors can affect the final PFS and OS. First, ECOG status of cancer patients, it is necessary to evaluate the survival time separately according to the performance of ECOG status. Second, different autoimmune diseases could have mild or severe effects on one’s body. For instance, systemic lupus erythematosus (SLE) can affect nearly all kinds of organs and produces multiple autoantibodies^[42], whereas psoriasis does relatively minor impairment for other systems.

The limitations are following. First, because most studies did not use ICIs in AID patients, the number of included studies was not sufficient. Second, most of the data collected were from retrospective studies rather than prospective clinical trials, so the veracity of the information may be not objective enough. Third, the included studies were mainly conducted in Asia and Europe, so geographical and ethnic differences could not be excluded. Fourth, most of the malignancies included in the study were NSCLC and melanoma, but few of the other cancers were involved. In addition, 6 of the studies only reported survival curves, so HR was estimated from the survival curve using specialized tools, so there may be a certain degree of deviation from the actual situation.

In summary, AID patients are at a higher risk to suffer irAEs and AID-homogeneous irAEs were higher in AID group than non-AID group, whereas no significant difference was detected in AID-heterogeneous irAEs. No statistically significant reduction in PFS and OS were observed in cancer patients with autoimmune disease. In other words, the irAEs could be detected and controlled purposefully, so that AID patients could also benefit fromICIs. Therefore, ICIs treatment should be appropriate to broaden the indications for autoimmune disease populations during routine clinic practice under more attention. Further large-scale prospective studies are warranted to validate our findings.

ICIs	immune checkpoint inhibitors
AID	autoimmune disease
irAEs	immune-related adverse events
EMBASE	Excerpta Medica Database
PFS	progression-free survival
OS	overall survival
CI	confidence interval
RR HR	risk ratio hazard ratio
PD-1	programmed cell death protein 1
PD-L1	programmed cell death protein ligand 1
CTLA-4	cytotoxic T-lymphocyte–associated protein 4
MeSH	medical subject headings
NSCLC	Non-small cell lung cancer
ECOG	Eastern Cooperative Oncology Group
SLE	systemic lupus erythematosus

Acknowledgments

The authors would like to acknowledge Prof. Peng Chen and Tianjin Medical University Cancer Institute and Hospital

Authors’ contributions

QC and GW H conducted literature search, data extraction, risk of bias assessment. QC performed statistical analysis and wrote the original manuscript. GW H revised the manuscript and rectified some information of tables and Figs. FY Z resolved differences in data collection and revised the manuscript critically. P Y and DQ Y advised on the writing and revised the manuscript critically. All authors read and approved the final manuscript.

Funding

None.

Conflict of interests

The authors declare no conflict of interest.

Data Availability

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

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No competing interests reported.

supplementarymaterials.pdf

Safety and Efficacy of Immune Checkpoint Inhibitors in Advanced Cancer patients with Autoimmune Disease: a Meta-Analysis

Status:

Version 1

Abstract

Background

Methods

Results

Conclusion

Figures

1. Introduction

2. Methods

2.1 Literature search

2.2 Eligibility criteria

2.3 Data extraction and quality assessment

2.4 Statistical analysis

3. Result

3.1 Literature search results

3.2 Characteristics of the selected studies

3.3 Safety

3.4 Efficacy

3.5 Publication bias and sensitivity analysis

Discussion

Conclusion

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1