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. 2022 May 25;13:883655. doi: 10.3389/fphar.2022.883655

Comprehensive Evaluation of Anti-PD-1, Anti-PD-L1, Anti-CTLA-4 and Their Combined Immunotherapy in Clinical Trials: A Systematic Review and Meta-analysis

Ze Xiang 1,†, Jiayuan Li 1,†, Zhengyu Zhang 2,†, Chao Cen 3, Wei Chen 4, Bin Jiang 5, Yiling Meng 6, Ying Wang 7, Björn Berglund 8, Guanghua Zhai 7,*, Jian Wu 7,*
PMCID: PMC9174611  PMID: 35694260

Abstract

Immunotherapy with immune checkpoint inhibitor (ICI) drugs is gradually becoming a hot topic in cancer treatment. To comprehensively evaluate the safety and efficacy of ICI drugs, we employed the Bayesian model and conducted a network meta-analysis in terms of progression-free survival (PFS), overall survival (OS) and severe adverse events (AEs). Our study found that treatment with ipilimumab was significantly worse than standard therapies in terms of PFS, whereas treatment with cemiplimab significantly improved PFS. The results also indicated that cemiplimab was the best choice for PFS. Treatment with nivolumab, pembrolizumab and nivolumab plus ipilimumab significantly improved OS compared to standard therapies. In terms of OS, cemiplimab was found to be the best choice, whereas avelumab was the worst. In terms of severe AEs, atezolizumab, avelumab, durvalumab, nivolumab, and pembrolizumab all significantly reduced the risk of grade 3 or higher AEs compared to standard therapy. The least likely to be associated with severe AEs were as follows: cemiplimab, avelumab, nivolumab, atezolizumab, and camrelizumab, with nivolumab plus ipilimumab to be the worst. Therefore, different ICI drug therapies may pose different risks in terms of PFS, OS and severe AEs. Our study may provide new insights and strategies for the clinical practice of ICI drugs.

Keywords: immune checkpoint inhibitor, cancer immunotherapy, programmed death-1 (PD-1), programmed death-ligand-1 (PD-L1), cytotoxic T lymphocyte antigen-4 (CTLA-4)

1 Introduction

Immunotherapy has become one of the most important breakthroughs in the treatment of cancer in recent years, and its development has promoted changes in many cancer treatment methods. As a series of co-inhibitory and co-stimulatory receptors and ligands, immune checkpoint inhibitors (ICI) drugs can block negative regulatory factors expressed by immune or tumor cells to enhance their immune function against cancer cells, mainly programmed death-1 (PD-1), programmed death-ligand-1 (PD-L1) and cytotoxic T lymphocyte antigen-4 (CTLA-4) (Rosenberg et al., 2004). In 2011, the CTLA-4 inhibitor ipilimumab was approved by the US Food and Drug Administration for the treatment of advanced melanoma (Hodi et al., 2010). Subsequently, several ICI drugs were also approved for the treatment of cancer (Topalian et al., 2012; Gong et al., 2018). Since then, interest for immunotherapy with ICI drugs has been increasing. Many studies focused on the prognosis and treatment for different cancers (Wu et al., 2015).

Chemotherapy is the first-line treatment for advanced cancer, and patients undergoing chemotherapy often experience severe adverse events (AEs). Although ICI drugs have achieved good anticancer effects in the treatment of many solid tumors, they may still cause severe treatment-related or drug-related AEs. Progression-free survival (PFS) and overall survival (OS) are usually efficacy end-points. In terms of PFS and OS, the therapeutic effects of ICI drugs remain unclear compared with standard therapies. Due to the limitations of randomized clinical trials, the overall safety evaluation of different ICI drugs for cancer treatment is not comprehensive, especially in terms of PFS and OS.

We conducted a systematic review and network meta-analysis of the therapeutic effects of ICI drugs targeting PD-1, PD-L1, and CTLA-4, focusing on PFS, OS and treatment-related severe AEs in patients receiving ICI drug monotherapy, combination therapies and standard therapies (chemotherapy, targeted therapies and their combination therapies included). This study comprehensively evaluated the safety and efficacy of different ICI drugs and their combination therapies, aiming to provide better guidance for the clinical application of various ICI drugs.

2 Methods

2.1 Search Methods and Study Selection

We searched PubMed, Embase, and Cochrane Library for English-language studies between January 2000 and September 2021, using keywords such as ipilimumab, tremelimumab, pembrolizumab, nivolumab, cemiplimab, camrelizumab, toripalimab, tislelizumab, spartalizumab, atezolizumab, avelumab, durvalumab, PD-1, PD-L1, and CTLA-4. The search strategy was described in Supplementary Table S1. The study search, selection and data extraction were independently conducted by two reviewers (ZX and ZZ), and discrepancies were evaluated by an independent reviewer (JL). The three authors (ZX, JL and ZZ) reviewed and discussed the full text of studies that may be eligible, and differences of opinions were resolved by consensus.

Only high-quality head-to-head phase 2 and 3 randomized controlled trials (RCTs) comparing two or more treatments including ICI drug monotherapy, ICI drug combination therapies and standard therapies were included. Some RCTs only presented interim results, as insufficient information may affect the final analysis, we selected the most recent results as much as possible. Data provided include at least one of the following: hazard ratios (HRs) of PFS, OS and treatment-related severe AEs. We excluded reviews, conference abstracts and posters. The study was performed based on the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guideline (Hutton et al., 2015; Wang et al., 2021). This study was approved by International prospective register of systematic reviews (PROSPERO) (registered ID: CRD42021278158).

2.2 Data Extraction

The authors (ZX and ZZ) independently extracted data according to the PRISMA guidelines. The first author, year of publication, national clinical trial identification number, trial name, phase, number of patients, type of cancer, drug used, follow-up time, number of severe AEs, HRs, and confidence interval (CI) of PFS and OS were summarized in standardized Tables 1–3.

TABLE 1.

List of the studies involving PFS in this meta-analysis.

First author Year NCT Trial name Total number Phase Canner type Treatment 1 Patient number Treatment 2 Patient number Follow-up time PFS HR PFS CI lower limit PFS CI upper limit
Fehrenbacher L et al. (Fehrenbacher et al., 2018) 2018 NCT02008227 OAK 1225 3 Non small cell lung cancer Atezolizumab 613 Docetaxel 612 21 0.96 0.85 1.08
McDermott DF et al. (McDermott et al., 2018) 2018 NCT01984242 IMmotion150 204 2 Renal cell carcinoma Atezolizumab 103 Sunitinib 101 20.7 1.19 0.82 1.71
Powles T et al. (Powles et al., 2018) 2018 NCT02302807 IMvigor211 234 3 Urothelial carcinoma Atezolizumab 116 Chemotherapy 118 17.3 1.01 0.75 1.34
Eng C et al. (Eng et al., 2019) 2019 NCT02788279 IMblaze370 180 3 Colorectal cancer Atezolizumab 90 Regorafenib 90 7.3 1.39 1.00 1.94
Pujol JL et al. (Pujol et al., 2019) 2019 NCT03059667 IFCT-1603 73 2 Small Cell Lung Cancer Atezolizumab 49 Chemotherapy 24 13.7 2.26 1.3 3.93
Herbst RS et al. (Herbst et al., 2020) 2020 NCT02409342 IMpower110 554 3 Non small cell lung cancer Atezolizumab 277 Chemotherapy 277 13.4 0.77 0.63 0.94
Bang YJ et al. (Bang et al., 2018) 2018 NCT02625623 JAVELIN Gastric 300 371 3 Gastric/gastrooesophageal junction cancer Avelumab 185 Chemotherapy 186 10.6 1.73 1.4 2.2
Barlesi F et al. (Barlesi et al., 2018) 2018 NCT02395172 JAVELIN Lung 200 529 3 Non small cell lung cancer Avelumab 264 Docetaxel 265 T1:18.9 1.01 0.80 1.28
T2:17.8
Pujade-Lauraine E et al. (Pujade-Lauraine et al., 2021) 2021 NCT02580058 JAVELIN Ovarian 200 378 3 Ovarian cancer Avelumab 188 Pegylated liposomal doxorubicin 190 T1:18.2 1.68 1.32 2.60
T2:17.4
Huang J et al. (Huang et al., 2020) 2020 NCT03099382 ESCORT 448 3 Squamous cell carcinoma Camrelizumab 228 Chemotherapy 220 8.3 0.69 0.56 0.86
Sezer A et al. (Sezer et al., 2021) 2021 NCT03088540 EMPOWER-Lung 1 563 3 Non small cell lung cancer Cemiplimab 283 Chemotherapy 280 T1:10.8 0.54 0.43 0.68
T2:10.9
Siu LL et al. (Siu et al., 2019) 2019 NCT02319044 CONDOR 267 2 Squamous cell carcinoma Durvalumab+ 133 Durvalumab 67 T1:6.5 1.13 0.82 1.56
Tremelimumab T2:6.0
2 Squamous cell carcinoma Durvalumab+ 133 Tremelimumab 67 T1:6.5 0.73 0.53 1.01
Tremelimumab T2:5.2
Ferris RL et al. (Ferris et al., 2020) 2020 NCT02369874 EAGLE 736 3 Squamous cell carcinoma Durvalumab 240 Standard of care 249 T1:7.6 1.02 0.84 1.25
T2:7.8
3 Squamous cell carcinoma Durvalumab+ 247 Standard of care 249 T1:6.3 1.09 0.90 1.33
Tremelimumab T2:7.8
Planchard D et al. (Planchard et al., 2020) 2020 NCT02352948 ARCTIC 595 3 Non small cell lung cancer Durvalumab 62 Standard of care 64 9.1 0.71 0.49 1.04
3 Non small cell lung cancer Durvalumab+ 174 Standard of care 118 9.1 0.77 0.59 1.01
Tremelimumab
3 Non small cell lung cancer Durvalumab 117 Standard of care 118 9.1 0.87 0.65 1.16
3 Non small cell lung cancer Tremelimumab 60 Standard of care 118 9.1 1.25 0.88 1.77
Rizvi NA et al. (Rizvi et al., 2020) 2020 NCT02453282 MYSTIC 488 3 Non small cell lung cancer Durvalumab 163 Chemotherapy 162 10.6 0.87 0.59 1.29
3 Non small cell lung cancer Durvalumab+ 163 Chemotherapy 162 10.6 1.05 0.72 1.53
Tremelimumab
Bachelot T et al. (Bachelot et al., 2021) 2021 NCT02299999 SAFIR02-BREAST IMMUNO 199 2 Breast cancer Durvalumab 68 Chemotherapy 131 19.7 1.40 1.00 1.96
Bang YJ et al. (Bang et al., 2017) 2017 NCT01585987 NA 108 2 Gastric/gastrooesophageal junction cancer Ipilimumab 57 Best supportive care 51 24 1.44 1.09 1.91
Borghaei H et al. (Borghaei et al., 2015) 2015 NCT01673867 CheckMate 057 582 3 Non small cell lung cancer Nivolumab 292 Docetaxel 290 13.2 0.92 0.77 1.11
Brahmer J et al. (Brahmer et al., 2015) 2015 NCT01642004 CheckMate 017 272 3 Non small cell lung cancer Nivolumab 135 Docetaxel 137 11 0.62 0.47 0.81
Motzer RJ et al. (Motzer et al., 2015) 2015 NCT01668784 CheckMate 025 821 3 Renal cell carcinoma Nivolumab 410 Everolimus 411 14 0.88 0.75 1.03
Ferris RL et al. (Ferris et al., 2016) 2016 NCT02105636 CheckMate 141 361 3 Squamous cell carcinoma Nivolumab 240 Standard therapy 121 5.1 0.89 0.70 1.13
Hodi FS et al. (Hodi et al., 2016) 2016 NCT01927419 CheckMate 069 142 2 Melanoma Nivolumab+ 95 Ipilimumab 47 24.5 0·36 0.22 0.56
Ipilimumab
Carbone DP et al. (Carbone et al., 2017) 2017 NCT02041533 CheckMate 026 541 3 Non small cell lung cancer Nivolumab 271 Chemotherapy 270 13.5 1.19 0.97 1.46
Hodi FS et al. (Hodi et al., 2018) 2018 NCT01844505 CheckMate 067 945 3 Melanoma Nivolumab+ 314 Ipilimumab 315 T1：46.9 0.42 0.35 0.51
Ipilimumab T2:18.6
3 Melanoma Nivolumab 316 Ipilimumab 315 T1:18.6 0.53 0.44 0.64
T2:36
Larkin J et al. (Larkin et al., 2018) 2018 NCT01721746 CheckMate 037 405 3 Melanoma Nivolumab 272 Chemotherapy 133 24 1.00 0.78 1.44
Hellmann MD et al. (Hellmann et al., 2019) 2019 NCT02477826 CheckMate 227 299 3 Non small cell lung cancer Nivolumab+ 139 Chemotherapy 160 11.2 0.58 0.41 0.81
Ipilimumab
Kato K et al. (Kato et al., 2019) 2019 NCT02569242 ATTRACTION-3 419 3 Squamous cell carcinoma Nivolumab 210 Chemotherapy 209 17.6 1.08 0.87 1.34
Wu YL et al. (Wu et al., 2019) 2019 NCT02613507 CheckMate 078 504 3 Non small cell lung cancer Nivolumab 338 Docetaxel 166 8.8 0.77 0.62 0.95
Motzer RJ et al. (Motzer et al., 2020) 2020 NCT02231749 CheckMate 214 1096 3 Renal cell carcinoma Nivolumab+ 550 Sunitinib 546 42 0.88 0.75 1.04
Ipilimumab
Reardon DA et al. (Reardon et al., 2020) 2020 NCT02017717 CheckMate 143 369 3 Glioblastoma Nivolumab 184 Bevacizumab 185 9.5 1.97 1.57 2.48
Robert C et al. (Robert et al., 2020) 2020 NCT01721772 CheckMate 066 418 3 Melanoma Nivolumab 210 Dacarbazine 208 60 0.40 0.33 0.54
Zamarin D et al. (Zamarin et al., 2020) 2020 NCT02498600 NRG GY003 100 2 Ovarian Cancer Nivolumab+ 51 Nivolumab 49 NA 0.53 0.34 0.82
Ipilimumab
Baas P et al. (Baas et al., 2021) 2021 NCT02899299 CheckMate 743 605 3 Malignant pleural mesothelioma Nivolumab+ 303 Chemotherapy 302 29.7 1.00 0.82 1.21
Ipilimumab
Spigel DR et al. (Spigel et al., 2021) 2021 NCT02481830 CheckMate 331 569 3 Small cell lung cancer Nivolumab 284 Chemotherapy 285 15.8 1.41 1.18 1.69
Tannir NM et al. (Tannir et al., 2021) 2021 NA CheckMate 214 139 3 Renal cell carcinoma Nivolumab+ 74 Sunitinib 65 42 0.54 0.33 0.86
Ipilimumab
Herbst RS et al. (Herbst et al., 2016) 2016 NCT01905657 KEYNOTE-010 687 2/3 Non small cell lung cancer Pembrolizumab 344 Docetaxel 343 13.1 0.88 0.74 1.05
Hamid O et al. (Hamid et al., 2017) 2017 NCT01704287 KEYNOTE-002 359 2 Melanoma Pembrolizumab 180 Chemotherapy 179 28 0.58 0.46 0.73
Shitara K et al. (Shitara et al., 2018) 2018 NCT02370498 KEYNOTE-061 395 3 Gastric/gastrooesophageal junction cancer Pembrolizumab 196 Paclitaxel 199 8.5 1.27 1.03 1.57
Cohen EEW et al. (Cohen et al., 2019) 2019 NCT02252042 KEYNOTE-040 495 3 Ssquamous cell carcinoma Pembrolizumab 247 Standard of care 248 T1:7.5 0.96 0.79 1.16
T2:7.1
Fradet Y et al. (Fradet et al., 2019) 2019 NCT02256436 KEYNOTE-045 542 3 Urothelial cancer Pembrolizumab 270 Chemotherapy 272 27.7 0.96 0.79 1.16
Mok TSK et al. (Mok et al., 2019) 2019 NCT02220894 KEYNOTE-042 1274 3 Non small cell lung cancer Pembrolizumab 637 Chemotherapy 637 12.8 1.07 0.94 1.21
Reck M et al. (Reck et al., 2019) 2019 NCT02142738 KEYNOTE-024 305 3 Non small cell lung cancer Pembrolizumab 154 Chemotherapy 151 11.2 0.50 0.37 0.68
Robert C et al. (Robert et al., 2019) 2019 NCT01866319 KEYNOTE-006 834 3 Melanoma Pembrolizumab 556 Ipilimumab 278 57.7 0.57 0.48 0.67
André T et al. (André et al., 2020) 2020 NCT02563002 KEYNOTE-177 307 3 Colorectal cancer Pembrolizumab 153 Chemotherapy 154 32.4 0.60 0.45 0.80
Kojima T et al. (Kojima et al., 2020) 2020 NCT02564263 KEYNOTE-181 628 3 Esophageal Cancer Pembrolizumab 314 Chemotherapy 314 T1:7.1 1.11 0.94 1.31
T2:6.9
Popat S et al. (Popat et al., 2020) 2020 NCT02991482 ETOP 9-15 144 3 Malignant pleural mesothelioma Pembrolizumab 73 Chemotherapy 71 17.5 1.06 0.73 1.53
Shitara K et al. (Shitara et al., 2020) 2020 NCT02494583 KEYNOTE-062 506 3 Gastric/gastrooesophageal junction cancer Pembrolizumab 256 Chemotherapy 250 29.4 1.66 1.37 2.01
Kuruvilla J et al. (Kuruvilla et al., 2021) 2021 NCT02684292 KEYNOTE-204 304 3 Hodgkin lymphoma Pembrolizumab 151 Brentuximab vedotin 153 24 0.65 0.48 0.88
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PFS = Progression-free survival. HR = Hazard ratio. CI = Confidence interval.

TABLE 2.

List of the studies involving OS in this meta-analysis.

First author Year NCT Trial name Total number Phase Canner type Treatment 1 Patient number Treatment 2 Patient number Follow-up time OS HR OS CI lower limit OS CI upper limit
Fehrenbacher L et al. (Fehrenbacher et al., 2016) 2016 NCT01903993 POPLAR 287 2 Non small cell lung cancer Atezolizumab 144 Docetaxel 143 13 0.73 0.53 0.99
Fehrenbacher L et al. (Fehrenbacher et al., 2018) 2018 NCT02008227 OAK 1225 3 Non small cell lung cancer Atezolizumab 613 Docetaxel 612 26 0.80 0.70 0.92
Powles T et al. (Powles et al., 2018) 2017 NCT02302807 IMvigor211 234 3 Urothelial carcinoma Atezolizumab 116 Chemotherapy 118 17.3 0.87 0.63 1.21
Eng C et al. (Eng et al., 2019) 2019 NCT02788279 IMblaze370 180 3 Colorectal cancer Atezolizumab 90 Regorafenib 90 7.3 1.19 0.83 1.71
Pujol JL et al. (Pujol et al., 2019) 2018 NCT03059667 IFCT-1603 73 2 Small cell lung cancer Atezolizumab 49 Chemotherapy 24 13.7 0.84 0.45 1.58
Herbst RS et al. (Herbst et al., 2020) 2020 NCT02409342 IMpower110 554 3 Non small cell lung cancer Atezolizumab 277 Chemotherapy 277 13.4 0.83 0.65 1.07
Bang YJ er al (Bang et al., 2018) 2018 NCT02625623 JAVELIN Gastric 300 371 3 Gastric/gastrooesophageal junction cancer Avelumab 185 Chemotherapy 186 10.6 1.1 0.9 1.4
Park K et al. (Park et al., 2021) 2021 NCT02395172 JAVELIN Lung 200 529 3 Non small cell lung cancer Avelumab 264 Docetaxel 265 24 0.87 0.71 1.05
Pujade-Lauraine E et al. (Pujade-Lauraine et al., 2021) 2021 NCT02580058 JAVELIN Ovarian 200 378 3 Ovarian cancer Avelumab 188 Pegylated liposomal doxorubicin 190 T1:18.2 1.14 0.95 1.58
T2:17.4
Huang J et al. (Huang et al., 2020) 2020 NCT03099382 ESCORT 448 3 Squamous cell carcinoma Camrelizumab 228 Chemotherapy 220 8.3 0.71 0.57 0.87
Sezer A et al. (Sezer et al., 2021) 2021 NCT03088540 EMPOWER-Lung 1 563 3 Non small cell lung cancer Cemiplimab 283 Chemotherapy 280 T1:10.8 0.57 0.42 0.77
T2:10.9
Siu LL et al. (Siu et al., 2019) 2019 NCT02319044 CONDOR 267 2 Squamous cell carcinoma Durvalumab+ 133 Durvalumab 67 T1:6.5 0.99 0.69 1.43
Tremelimumab T2:6.0
2 Squamous cell carcinoma Durvalumab+ 133 Tremelimumab 67 T1:6.5 0.72 0.51 1.03
Tremelimumab T2:5.2
Ferris RL et al. (Ferris et al., 2020) 2020 NCT02369874 EAGLE 736 3 Squamous cell carcinoma Durvalumab 240 Standard of care 249 T1:7.6 T2:7.8 0.88 0.72 1.08
3 Squamous cell carcinoma Durvalumab+ 247 Standard of care 249 T1:6.3 1.04 0.85 1.26
Tremelimumab T2:7.8
Planchard D et al. (Planchard et al., 2020) 2020 NCT02352948 ARCTIC 595 3 Non small cell lung cancer Durvalumab 62 Standard of care 64 9.1 0.63 0.42 0.93
3 Non small cell lung cancer Durvalumab+ Tremelimumab 174 Standard of care 118 9.1 0.80 0.61 1.05
3 Non small cell lung cancer Durvalumab 117 Standard of care 118 9.1 0.80 0.59 1.08
3 Non small cell lung cancer Tremelimumab 60 Standard of care 118 9.1 1.02 0.71 1.46
Powles T et al. (Powles et al., 2020) 2020 NCT02516241 DANUBE 1032 3 Urothelial carcinoma Durvalumab 346 Chemotherapy 344 41.2 0.99 0.83 1.17
3 Urothelial carcinoma Durvalumab+ 342 Chemotherapy 344 41.2 0.85 0.72 1.02
Tremelimumab
Rizvi NA et al. (Rizvi et al., 2020) 2020 NCT02453282 MYSTIC 488 3 Non small cell lung cancer Durvalumab 163 Chemotherapy 162 30.2 0.76 0.56 1.02
3 Non small cell lung cancer Durvalumab+ 163 Chemotherapy 162 30.2 0.85 0.61 1.17
Tremelimumab
Bachelot T et al. (Bachelot et al., 2021) 2021 NCT02299999 SAFIR02-BREAST IMMUNO 199 2 Breast cancer Durvalumab 68 Chemotherapy 131 19.7 0.84 0.54 1.29
Hodi FS et al. (Hodi et al., 2010) 2010 NCT00094653 MDX010-20 273 3 Melanoma Ipilimumab 137 Gp100 136 T1:27.8 T2:17.2 0.66 0.51 0.87
Tarhini AA et al. (Tarhini et al., 2020) 2020 NA E1609 1159 3 Melanoma Ipilimumab 523 Interferon Alfa-2b 636 57.4 0.78 0.61 0.99
Borghaei H et al. (Borghaei et al., 2015) 2015 NCT01673867 CheckMate 057 582 3 Non small cell lung cancer Nivolumab 292 Docetaxel 290 13.2 0.73 0.59 0.89
Brahmer J et al. (Brahmer et al., 2015) 2015 NCT01642004 CheckMate 017 272 3 Non small cell lung cancer Nivolumab 135 Docetaxel 137 11 0.59 0.44 0.79
Motzer RJ et al. (Motzer et al., 2015) 2015 NCT01668784 CheckMate 025 821 3 Renal cell carcinoma Nivolumab 410 Everolimus 411 14 0.73 0.57 0.93
Ferris RL et al. (Ferris et al., 2016) 2016 NCT02105636 CheckMate 141 361 3 Squamous cell carcinoma Nivolumab 240 Standard therapy 121 5.1 0.70 0.51 0.96
Hodi FS et al. (Hodi et al., 2016) 2016 NCT01927419 CheckMate 069 142 2 Melanoma Nivolumab+ 95 Ipilimumab 47 24.5 0.74 0.43 1.26
Ipilimumab
Carbone DP et al. (Carbone et al., 2017) 2017 NCT02041533 CheckMate 026 541 3 Non small cell lung cancer Nivolumab 271 Chemotherapy 270 13.5 1.08 0.87 1.34
Hodi FS et al. (Hodi et al., 2018) 2018 NCT01844505 CheckMate 067 945 3 Melanoma Nivolumab+ Ipilimumab 314 Ipilimumab 315 T1:46.9 T2:18.6 0.54 0.44 0.67
3 Melanoma Nivolumab 316 Ipilimumab 315 T1: 36 T2:18.6 0.65 0.53 0.79
Larkin J et al. (Larkin et al., 2018) 2018 NCT01721746 CheckMate 037 405 3 Melanoma Nivolumab 272 Chemotherapy 133 24 0.95 0.73 1.24
Hellmann MD et al. (Hellmann et al., 2019) 2019 NCT02477826 CheckMate 227 1166 3 Non small cell lung cancer Nivolumab+ Ipilimumab 583 Chemotherapy 583 29.3 0.73 0.64 0.84
Kato K et al. (Kato et al., 2019) 2019 NCT02569242 ATTRACTION-3 419 3 Squamous cell carcinoma Nivolumab 210 Chemotherapy 209 17.6 0.77 0.62 0.96
Wu YL et al. (Wu et al., 2019) 2019 NCT02613507 CheckMate 078 504 3 Non small cell lung cancer Nivolumab 338 Docetaxel 166 8.8 0.68 0.52 0.90
Motzer RJ et al. (Motzer et al., 2020) 2020 NCT02231749 CheckMate 214 1096 3 Renal cell carcinoma Nivolumab+ 550 Sunitinib 546 42 0.72 0.61 0.86
Ipilimumab
Reardon DA et al. (Reardon et al., 2020) 2020 NCT02017717 CheckMate 143 369 3 Glioblastoma Nivolumab 184 Bevacizumab 185 9.5 1.04 0.83 1.3
Robert C et al. (Robert et al., 2020) 2020 NCT01721772 CheckMate 066 418 3 Melanoma Nivolumab 210 Dacarbazine 208 60 0.50 0.40 0.63
Zamarin D et al. (Zamarin et al., 2020) 2020 NCT02498600 NRG GY003 100 2 Ovarian cancer Nivolumab+ 51 Nivolumab 49 NA 0.79 0.44 1.42
Ipilimumab
Baas P et al. (Baas et al., 2021) 2021 NCT02899299 CheckMate 743 605 3 Malignant pleural mesothelioma Nivolumab+ 303 Chemotherapy 302 29.7 0.74 0.60 0.91
Ipilimumab
Spigel DR et al. (Spigel et al., 2021) 2021 NCT02481830 CheckMate 331 569 3 Small cell lung cancer Nivolumab 284 Chemotherapy 285 15.8 0.86 0.72 1.04
Tannir NM et al. (Tannir et al., 2021) 2021 NA CheckMate 214 139 3 Renal cell carcinoma Nivolumab+ 74 Sunitinib 65 42 0.45 0.30 0.70
Ipilimumab
Ribas A et al. (Ribas et al., 2013) 2013 NCT00257205 NA 655 3 Melanoma Tremelimumab 328 Chemotherapy 327 NA 0.88 NA NA
Herbst RS et al. (Herbst et al., 2016) 2016 NCT01905657 KEYNOTE-010 687 2/3 Non small cell lung cancer Pembrolizumab 344 Docetaxel 343 13.1 0.71 0.58 0.88
Hamid O et al. (Hamid et al., 2017) 2017 NCT01704287 KEYNOTE-002 359 2 Melanoma Pembrolizumab 180 Chemotherapy 179 28 0.86 0.67 1.10
Shitara K et al. (Shitara et al., 2018) 2018 NCT02370498 KEYNOTE-061 395 3 Gastric/gastrooesophageal junction cancer Pembrolizumab 196 Paclitaxel 199 8.5 0.82 0.66 1.03
Cohen EEW et al. (Cohen et al., 2019) 2019 NCT02252042 KEYNOTE-040 495 3 Squamous cell carcinoma Pembrolizumab 247 Standard of care 248 7.5 0.80 0.65 0.98
Fradet Y et al. (Fradet et al., 2019) 2019 NCT02256436 KEYNOTE-045 542 3 Urothelial cancer Pembrolizumab 270 Chemotherapy 272 27.7 0.70 0.57 0.85
Mok TSK et al. (Mok et al., 2019) 2019 NCT02220894 KEYNOTE-042 1274 3 Non small cell lung cancer Pembrolizumab 637 Chemotherapy 637 12.8 0.81 0.71 0.93
Reck M et al. (Reck et al., 2019) 2019 NCT02142738 KEYNOTE-024 305 3 Non small cell lung cancer Pembrolizumab 154 Chemotherapy 151 25.2 0.63 0.47 0.86
Robert C et al. (Robert et al., 2019) 2019 NCT01866319 KEYNOTE-006 834 3 Melanoma Pembrolizumab 556 Ipilimumab 278 57.7 0.73 0.61 0.88
Kojima T et al. (Kojima et al., 2020) 2020 NCT02564263 KEYNOTE-181 628 3 Esophageal cancer Pembrolizumab 314 Chemotherapy 314 7.1 0.89 0.75 1.05
Popat S et al. (Popat et al., 2020) 2020 NCT02991482 ETOP 9-15 144 3 Malignant pleural mesothelioma Pembrolizumab 73 Chemotherapy 71 17.5 1.04 0.66 1.67
Shitara K et al. (Shitara et al., 2020) 2020 NCT02494583 KEYNOTE-062 506 3 Gastric/gastrooesophageal junction cancer Pembrolizumab 256 Chemotherapy 250 29.4 0.91 0.69 1.18
Powles T et al. (Powles et al., 2021) 2021 NCT02853305 KEYNOTE-361 659 3 Urothelial carcinoma Pembrolizumab 307 Chemotherapy 352 31.7 0.92 0.77 1.11
Winer EP et al. (Winer et al., 2021) 2021 NCT02555657 KEYNOTE-119 622 3 Breast cancer Pembrolizumab 312 Chemotherapy 310 31.4 0.97 0.82 1.15
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OS = Overall survival. HR = Hazard ratio. CI = Confidence interval.

TABLE 3.

List of the studies involving serious AEs in this meta-analysis.

First author Year NCT number Trail name Total number Cancer type Trial phase Treatment Patient number Total number surveyed Grade 3 or higher AEs
Fehrenbacher L et al. (Fehrenbacher et al., 2016) 2016 NCT01903993 POPLAR 287 Non small cell lung cancer 2 Atezolizumab 144 142 17
2 Standard 143 135 55
Fehrenbacher L et al. (Fehrenbacher et al., 2018) 2018 NCT02008227 OAK 1225 Non small cell lung cancer 3 Atezolizumab 613 609 91
3 Standard 612 578 246
McDermott DF et al. (McDermott et al., 2018) 2018 NCT01984242 IMmotion150 204 Renal cell carcinoma 2 Atezolizumab 103 103 17
2 Standard 101 100 57
Powles T et al. (Powles et al., 2018) 2018 NCT02302807 IMvigor211 234 Urothelial carcinoma 3 Atezolizumab 116 114 11
3 Standard 118 112 43
Eng C et al. (Eng et al., 2019) 2019 NCT02788279 IMblaze370 180 Colorectal cancer 3 Atezolizumab 90 90 28
3 Standard 90 80 46
Herbst RS et al. (Herbst et al., 2020) 2020 NCT02409342 IMpower110 554 Non small cell lung cancer 3 Atezolizumab 277 286 97
3 Standard 277 263 149
Bang YJ et al. (Bang et al., 2018) 2018 NCT02625623 JAVELIN Gastric 300 371 Gastric/gastrooesophageal junction cancer 3 Avelumab 185 184 17
3 Standard 186 177 56
Park K et al. (Park et al., 2021) 2021 NCT02395172 JAVELIN Lung 200 529 Non small cell lung cancer 3 Avelumab 264 393 41
3 Standard 265 365 180
Pujade-Lauraine E et al. (Pujade-Lauraine et al., 2021) 2021 NCT02580058 JAVELIN Ovarian 200 378 Ovarian cancer 3 Avelumab 188 187 30
3 Standard 190 177 56
Huang J et al. (Huang et al., 2020) 2020 NCT03099382 ESCORT 448 Squamous cell carcinoma 3 Camrelizumab 228 228 44
3 Standard 220 220 87
Sezer A et al. (Sezer et al., 2021) 2021 NCT03088540 EMPOWER-Lung 1 563 Non small cell lung cancer 3 Cemiplimab 283 355 50
3 Standard 280 342 134
O'Reilly EM et al. (O'Reilly et al., 2019) 2019 NCT02558894 NA 65 Pancreatic ductal adenocarcinoma 2 Durvalumab+Tremelimumab 32 32 7
2 Durvalumab 33 32 2
Siu LL et al. (Siu et al., 2019) 2019 NCT02319044 CONDOR 267 Squamous Cell Carcinoma 2 Durvalumab+Tremelimumab 133 133 21
2 Durvalumab 67 65 8
2 Tremelimumab 67 65 11
Ferris RL et al. (Ferris et al., 2020) 2020 NCT02369874 EAGLE 736 Squamous cell carcinoma 3 Durvalumab 240 237 24
3 Durvalumab+Tremelimumab 247 246 40
3 Standard 249 240 58
Planchard D et al. (Planchard et al., 2020) 2020 NCT02352948 ARCTIC 595 Non small cell lung cancer 3 Durvalumab 62 62 6
3 Durvalumab+Tremelimumab 174 173 38
3 Durvalumab 117 117 14
3 Tremelimumab 60 60 14
3 Standard 64 63 28
3 Standard 118 110 40
Powles T et al. (Powles et al., 2020) 2020 NCT02516241 DANUBE 1032 Urothelial carcinoma 3 Durvalumab 346 345 49
3 Durvalumab+Tremelimumab 342 340 95
3 Standard 344 313 189
Rizvi NA et al. (Rizvi et al., 2020) 2020 NCT02453282 MYSTIC 488 Non small cell lung cancer 3 Durvalumab 163 369 55
3 Durvalumab+Tremelimumab 163 371 85
3 Standard 162 352 119
Bachelot T et al. (Bachelot et al., 2021) 2021 NCT02299999 SAFIR02-BREAST IMMUNO 199 Breast cancer 2 Durvalumab 68 63 10
2 Standard 131 129 17
Hodi FS et al. (Hodi et al., 2010) 2010 NCT00094653 MDX010-20 273 Melanoma 3 Ipilimumab 137 131 30
3 Standard 136 132 15
Bang YJ et al. (Bang et al., 2017) 2017 NCT01585987 NA 108 Gastric/gastrooesophageal junction cancer 2 Ipilimumab 57 57 13
2 Standard 51 45 4
Borghaei H et al. (Borghaei et al., 2015) 2015 NCT01673867 CheckMate 057 582 Non small cell lung cancer 3 Nivolumab 292 287 30
3 Standard 290 268 144
Brahmer J et al. (Brahmer et al., 2015) 2015 NCT01642004 CheckMate 017 272 Non small cell lung cancer 3 Nivolumab 135 131 9
3 Standard 137 129 71
Motzer RJ et al. (Motzer et al., 2015) 2015 NCT01668784 CheckMate 025 821 Renal cell carcinoma 3 Nivolumab 410 406 76
3 Standard 411 397 145
Ferris RL et al. (Ferris et al., 2016) 2016 NCT02105636 CheckMate 141 361 Squamous cell carcinoma 3 Nivolumab 240 236 31
3 Standard 121 111 39
Hodi FS et al. (Hodi et al., 2016) 2016 NCT01927419 CheckMate 069 142 Melanoma 2 Nivolumab+Ipilimumab 95 94 51
2 Ipilimumab 47 46 9
Carbone DP et al. (Carbone et al., 2017) 2017 NCT02041533 CheckMate 026 541 Non small cell lung cancer 3 Nivolumab 271 267 47
3 Standard 270 263 133
Weber J et al. (Weber et al., 2017) 2017 NCT02388906 CheckMate 238 906 Melanoma 3 Nivolumab 453 452 65
3 Ipilimumab 453 453 208
Amaria RN et al. (Amaria et al., 2018) 2018 NCT02519322 NA 23 Melanoma 2 Nivolumab 12 12 1
2 Nivolumab+Ipilimumab 11 11 8
Hodi FS et al. (Hodi et al., 2018) 2018 NCT01844505 CheckMate 067 945 Melanoma 3 Nivolumab+Ipilimumab 314 313 185
3 Ipilimumab 315 311 86
3 Nivolumab 316 313 70
Larkin J et al. (Larkin et al., 2018) 2018 NCT01721746 CheckMate 037 405 Melanoma 3 Nivolumab 272 268 37
3 Standard 133 102 84
Ascierto PA et al. (Ascierto et al., 2019) 2019 NCT01721772 CheckMate 066 418 Melanoma 3 Nivolumab 210 206 31
3 Standard 208 205 36
Hellmann MD et al. (Hellmann et al., 2019) 2019 NCT02477826 CheckMate 227 1166 Non small cell lung cancer 3 Nivolumab+Ipilimumab 583 576 189
3 Standard 583 570 205
Kato K et al. (Kato et al., 2019) 2019 NCT02569242 ATTRACTION-3 419 Squamous cell carcinoma 3 Nivolumab 210 209 38
3 Standard 209 208 133
Scherpereel A et al. (Scherpereel et al., 2019) 2019 NCT02716272 IFCT-1501 MAPS2 125 Malignant pleural mesothelioma 2 Nivolumab 63 63 9
2 Nivolumab+Ipilimumab 62 61 16
Wu YL et al. (Wu et al., 2019) 2019 NCT02613507 CheckMate 078 504 Non small cell lung cancer 3 Nivolumab 338 337 35
3 Standard 166 156 74
Motzer RJ et al. (Motzer et al., 2020) 2020 NCT02231749 CheckMate 214 1096 Renal cell carcinoma 3 Nivolumab+Ipilimumab 550 547 259
3 Standard 546 535 343
Reardon DA et al. (Reardon et al., 2020) 2020 NCT02017717 CheckMate 143 369 Glioblastoma 3 Nivolumab 184 182 33
3 Standard 185 165 25
Zimmer L et al. (Zimmer et al., 2020) 2020 NCT02523313 IMMUNED 115 Melanoma 2 Nivolumab+Ipilimumab 56 55 39
2 Nivolumab 59 56 15
Baas P et al. (Baas et al., 2021) 2021 NCT02899299 CheckMate 743 605 Malignant pleural mesothelioma 3 Nivolumab+Ipilimumab 303 300 91
3 Standard 302 284 91
Owonikoko TK et al. (Owonikoko et al., 2021) 2021 NCT02538666 CheckMate 451 559 Small cell lung cancer 3 Nivolumab+Ipilimumab 279 278 145
3 Nivolumab 280 279 32
Spigel DR et al. (Spigel et al., 2021) 2021 NCT02481830 CheckMate 331 569 Small cell lung cancer 3 Nivolumab 284 282 39
3 Standard 285 265 194
Tannir NM et al. (Tannir et al., 2021) 2021 NA CheckMate 214 139 Renal cell carcinoma 3 Nivolumab+Ipilimumab 74 73 36
3 Standard 65 65 29
Ribas A et al. (Ribas et al., 2013) 2013 NCT00257205 NA 655 Melanoma 3 Tremelimumab 328 325 192
3 Standard 327 319 132
Herbst RS et al. (Herbst et al., 2016) 2016 NCT01905657 KEYNOTE-010 687 Non small cell lung cancer 2/3 Pembrolizumab 344 339 43
2/3 Standard 343 309 109
Hamid O et al. (Hamid et al., 2017) 2017 NCT01704287 KEYNOTE-002 359 Melanoma 2 Pembrolizumab 180 178 24
2 Standard 179 171 45
Shitara K et al. (Shitara et al., 2018) 2018 NCT02370498 KEYNOTE-061 395 Gastric/gastrooesophageal junction cancer 3 Pembrolizumab 196 294 42
3 Standard 199 276 96
Cohen EEW et al. (Cohen et al., 2019) 2019 NCT02252042 KEYNOTE-040 495 Squamous cell carcinoma 3 Pembrolizumab 247 246 33
3 Standard 248 234 85
Fradet Y et al. (Fradet et al., 2019) 2019 NCT02256436 KEYNOTE-045 542 Urothelial cancer 3 Pembrolizumab 270 266 44
3 Standard 272 255 128
Mok TSK et al. (Mok et al., 2019) 2019 NCT02220894 KEYNOTE-042 1274 Non-small cell lung cancer 3 Pembrolizumab 637 636 113
3 Standard 637 615 252
Reck M et al. (Reck et al., 2019) 2019 NCT02142738 KEYNOTE-024 305 Non-small cell lung cancer 3 Pembrolizumab 154 154 48
3 Standard 151 150 80
Robert C et al. (Robert et al., 2019) 2019 NCT01866319 KEYNOTE-006 834 Melanoma 3 Pembrolizumab 556 555 103
3 Ipilimumab 278 256 54
André T et al. (André et al., 2020) 2020 NCT02563002 KEYNOTE-177 307 Colorectal cancer 3 Pembrolizumab 153 153 86
3 Standard 154 143 111
Kojima T et al. (Kojima et al., 2020) 2020 NCT02564263 KEYNOTE-181 628 Esophageal cancer 3 Pembrolizumab 314 314 57
3 Standard 314 296 121
Popat S et al. (Popat et al., 2020) 2020 NCT02991482 ETOP 9-15 144 Malignant pleural mesothelioma 3 Pembrolizumab 73 72 14
3 Standard 71 70 18
Kuruvilla J et al. (Kuruvilla et al., 2021) 2021 NCT02684292 KEYNOTE-204 304 Hodgkin lymphoma 3 Pembrolizumab 151 148 29
3 Standard 153 152 38
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AEs = Adverse events.

PFS is considered to be the primary endpoint of randomized clinical trials evaluating patients with solid tumors (Korn and Crowley, 2013). OS is defined as the time from the start of treatment to death or the last follow-up. The HRs of PFS and OS represent HRs between treatment 1 and 2. In assessing AEs, we chose treatment-related or drug-related AEs as the main results. If there were no treatment or drug-related AEs in studies, we included all AEs. The classification of AEs is often used to evaluate the type and severity of AEs in clinical trials. According to AE classification, grade 3 or higher AEs are considered as severe AEs. The risk of severe AEs is the focus of the evaluation of therapeutic effectiveness, so the number of AEs surveyed and severe AEs were both extracted (Xu et al., 2018).

2.3 Data Synthesis and Statistical Analysis

2.3.1 Adverse Events Analysis

We used gemtc and pcnetmeta packages in R v4.0.3 and called JAGS v4.3.0 to perform statistical analysis in a Bayesian framework based on Markov Chain Monte Carlo (MCMC) methods, and generated the graph depicting the network geometry (Wang et al., 2019).

Firstly, we made a rough comparison between the fit of the consistent model with the inconsistent model. Secondly, the inconsistency on the specific comparison was tested by node splitting analysis. p < 0.05 was considered as indicating a significant inconsistency. Outstanding consistency is the key to robust results, as evidenced by the consistency between direct and indirect results. We compared the results of network meta-analysis (indirect results) with those of pairwise analysis (direct results) to explore the sources of inconsistency. Additionally, if there existed significant heterogeneity, we used the random-effect model. Otherwise, we used the fixed-effect model (Dias et al., 2011). We used non-information prior distributions and overdispersed initial values (scaling 2.5) in 3 chains to fit the model. 56 independent randomized controlled experiments yielded 100,000 iterations (including 20,000 optimization iterations) with 10 refinement intervals for each chain. This method was used to generate a posterior distribution of model parameters. The convergence of iterations was evaluated by using the Gelman-Rubin-Brooks statistics, all of which converge near 1. Based on the odds ratio (OR) advantage ratio and posterior probability, we ranked probabilities of each treatment as the safest, followed by the second, third and so on.

2.4 Progression-free Survival and Overall Survival Analysis

For the consistency and heterogeneity analysis of PFS and OS, we chose to use R’s netmeta package in the Frequentist framework to make a preliminary judgment using the traditional frequency method, avoiding the artificial bias caused by complex prior settings, settings of dummy variables and variance-covariance matrices of regression models in Bayesian statistics, which would simplify the operator’s parameter setting. The I2 test was used to evaluate the heterogeneity between studies, with the significance level set as p < 0.05. I2 greater than 25, 50 or 75% indicated low, medium and high heterogeneity respectively. If significant heterogeneity exists, the random-effect model was used. Otherwise, we employed the fixed-effect model (Higgins and Thompson, 2002).

Since Bayesian statistics are more accurate and the results are highly consistent with those in the frequency model, we subsequently chose the Bayesian framework by using the MCMC method in WinBUGS v1.4.3 for network meta-analysis. We used the consistency model (due to I2 < 25) to calculate HRs and 95% CIs. We simulated 3 different chains, each with 45,000 built-in samples, resulting in 15 iterations with a refinement rate of 15 (3 different chains with 15,000 iterations and 45,000 burn-in samples and 50 thinning rates). The model fitting was further determined according to the deviation information criterion. The output was a posterior distribution of relative effect size, and we got the estimated average of HR and 95% CI (95% CI as the 2.5th and 97.5th percentiles) (van Valkenhoef et al., 2012). The ranking probability distribution was calculated, ranking the probabilities of each treatment as the safest, followed by the second, third and so on.

3 Results

3.1 Literature Search and Study Characteristics

After a preliminary search, a total of 2,841 related articles were identified. After the screening of the title and abstract, 2,495 studies were excluded because they did not meet the corresponding standards. We carefully reviewed the remaining studies and then incorporated 63 RCTs for final analysis (2,14–75). The literature selection flowchart is shown in Figure 1. Of these, 48 RCTs involving 22,519 patients were analyzed for HRs of PFS, 51 RCTs involving 27,150 patients were analyzed for HRs of OS, and 55 RCTs involving 26,747 patients were analyzed for severe AEs (Figure 2).

FIGURE 1.

FIGURE 1

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Flowchart of selection criteria and study design.

FIGURE 2.

FIGURE 2

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Network plots of comparisons for PFS (A), OS (B) and AEs (C) of different types of treatment-based network meta-analysis. Each node represents a treatment. The size of the circle is in proportion to the number of patients. The line width is in proportion to the number of patients included in the direct comparison of two treatments.

In terms of PFS, ICI drugs included nivolumab (n = 13), pembrolizumab (n = 13), atezolizumab (n = 6), durvalumab (n = 6), ipilimumab (n = 5), avelumab (n = 3), tremelimumab (n = 2), camrelizumab (n = 1), cemiplimab (n = 1), nivolumab plus ipilimumab (n = 7), durvalumab plus tremelimumab (n = 5). Cancer types tested in these studies include lung cancer (n = 16), melanoma (n = 6), squamous cell carcinoma (n = 6), gastric/gastrooesophageal junction cancer (n = 4), renal cell carcinoma (n = 4), colorectal cancer (n = 2), malignant pleural mesothelioma (n = 2), ovarian cancer (n = 2), urothelial cancer (n = 2), breast cancer (n = 1), esophageal cancer (n = 1), glioblastoma (n = 1), Hodgkin lymphoma (n = 1) (Figure 2A).

In terms of OS, ICI drugs included nivolumab (n = 13), pembrolizumab (n = 13), durvalumab (n = 7), atezolizumab (n = 6), ipilimumab (n = 6), avelumab (n = 3), tremelimumab (n = 3), camrelizumab (n = 1), cemiplimab (n = 1), nivolumab plus ipilimumab (n = 7), durvalumab plus tremelimumab (n = 6). Cancer types tested in these studies include lung cancer (n = 17), melanoma (n = 9), squamous cell carcinoma (n = 6), urothelial cancer (n = 4), gastric/gastrooesophageal junction cancer (n = 3), renal cell carcinoma (n = 3), breast cancer (n = 2), colorectal cancer (n = 1), malignant pleural mesothelioma (n = 2), ovarian cancer (n = 2), esophageal cancer (n = 1), glioblastoma (n = 1) (Figure 2B).

In terms of severe AEs, ICI drugs included nivolumab (n = 17), pembrolizumab (n = 12), durvalumab (n = 8), atezolizumab (n = 6), ipilimumab (n = 6), avelumab (n = 3), tremelimumab (n = 3), camrelizumab (n = 1), cemiplimab (n = 1), nivolumab plus ipilimumab (n = 11), durvalumab plus tremelimumab (n = 7). Cancer types tested in these studies include lung cancer (n = 17), melanoma (n = 11), squamous cell carcinoma (n = 6), renal cell carcinoma (n = 4), gastric/gastrooesophageal junction cancer (n = 3), malignant pleural mesothelioma (n = 3), urothelial cancer (n = 3), colorectal cancer (n = 2), breast cancer (n = 1), esophageal cancer (n = 1), glioblastoma (n = 1), hodgkin lymphoma (n = 1), ovarian cancer (n = 1), pancreatic ductal adenocarcinoma (n = 1) (Figure 2C).

3.2 Progression-free Survival

In analyzing PFS, no significant heterogeneity (I2 = 19%) or inconsistency was observed (p = 0.97) (Supplementary Table S2). Therefore, the Bayesian fixed-effect model was used. HRs and 95% CI from the network meta-analysis are shown in Figure 3A. Treatment with ipilimumab was significantly worse in terms of PFS than standard therapies, whereas treatment with cemiplimab significantly improved PFS. According to the probability ranking diagram, the results showed that cemiplimab was the best choice in terms of PFS, camrelizumab ranked the second safest and nivolumab plus ipilimumab ranked the third safest, whereas ipilimumab was the worst (Figure 3B). Additionally, treatment with ipilimumab was significantly worse than most other treatments in terms of PFS. Interestingly, nivolumab plus ipilimumab significantly improved PFS compared to ipilimumab, which suggested that treatment with combinations of ICI drugs may benefit PFS compared to monotherapy. The results calculated according to the frequency model were highly consistent with the results of the Bayesian fixed-effect model.

FIGURE 3.

FIGURE 3

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Results of network meta-analysis (NMA), safety profile (A) and probability ranking diagram (B) in the Bayesian model. In the safety profile, efficacy of treatment for progression-free survival (PFS) is represented as hazard ratios (HRs) with 95% confidence intervals. All comparisons are made as column versus row. Statistically significant results are in bold. Probability ranking diagram shows the probability of the safety of different therapies ranking the first to the last for PFS.

Additionally, we performed subgroup analyses based on treatment of different cancer types, particularly lung cancer and melanoma. The safety profile and probability ranking diagram for lung cancer and melanoma are shown in Supplementary Figures S1, S4 in the Supplement respectively. Cemiplimab was also the best choice in terms of PFS in treating lung cancer, and nivolumab plus ipilimumab ranked the second safest. Compared with standard therapies, HR (95% CI) for cemiplimab was 0.77 (0.61–0.96). Treatment with cemiplimab also significantly improved PFS compared to nivolumab. Tremelimumab was considered the worst choice in terms of PFS in treating lung cancer. In terms of melanoma, our results showed that nivolumab plus ipilimumab was the best choice for PFS. HR (95% CI) for nivolumab plus ipilimumab was 0.69 (0.52–0.92) compared with standard therapies. In addition, HRs (95% CI) for nivolumab and pembrolizumab were 0.78 (0.65–0.94) and 0.80 (0.64–0.99) respectively. The probability ranking diagram of melanoma indicated that ipilimumab was the worst choice for PFS.

3.3 Overall Survival

In analyzing OS, no consistency (I2 = 0%) or inconsistency (p = 0.60) (Supplementary Table S2) was observed, and so the Bayesian fixed-effects model was applied. HRs and 95% CI are shown in Figure 4A. Treatment with nivolumab, pembrolizumab and nivolumab plus ipilimumab significantly improved OS compared to standard therapies. According to the probability ranking diagram, cemiplimab was the best choice in terms of OS, and durvalumab ranked the second safest, whereas avelumab was the worst (Figure 4B). Of note, nivolumab plus ipilimumab may improve OS compared with nivolumab and ipilimumab monotherapy, which was similar to durvalumab plus tremelimumab compared with durvalumab and tremelimumab monotherapy. The results calculated based on the frequency model were also highly similar to the results of the Bayesian fixed-effect model.

FIGURE 4.

FIGURE 4

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Results of NMA, safety profile (A) and probability ranking diagram (B). In the safety profile, efficacy of treatment for overall survival (OS) is represented as HRs with 95% confidence intervals. All comparisons are made as column versus row. Statistically significant results are in bold. Probability ranking diagram shows the probability of the safety of different therapies ranking the first to the last for OS.

We also conducted subgroup analyses for lung cancer and melanoma. The safety profiles and probability ranking diagrams of lung cancer and melanoma are shown in Supplementary Figures S5, S8 in the Supplement. For lung cancer treatment, cemiplimab was the best option for OS and durvalumab ranked the second safest. Safety profile of lung cancer suggested that compared with standard therapies, HRs (95% CI) for nivolumab, nivolumab plus ipilimumab and pembrolizumab were 0.91 (0.82–0.99), 0.87 (0.76–0.99), and 0.89 (0.80–0.99) respectively. Standard therapies were considered to be the worst option for lung cancer in terms of OS whereas. For melanoma treatment, nivolumab plus ipilimumab was the best option. Safety profiles showed that HR (95% CI) for nivolumab was 0.84 (0.71–0.99) compared with standard therapies. Our results also indicated that standard therapies were the worst choice for melanoma in terms of OS.

3.4 Severe Adverse Events

In the network meta-analyses of severe AEs, high heterogeneity was found (Supplememntary Table S3), and the random-effect model was employed. Safety profile in the consistency model is shown in Figure 5A. Atezolizumab, avelumab, durvalumab, nivolumab, and pembrolizumab all significantly reduced the risk of grade 3 or higher AEs compared to standard therapies. Compared with standard therapies, ORs (95% CI) for atezolizumab, avelumab, durvalumab, nivolumab, and pembrolizumab were 0.23 (0.13–0.42), 0.22 (0.10–0.49), 0.30 (0.17–0.52), 0.21 (0.14–0.31), and 0.37 (0.25–0.56) respectively. It is worth noting that there was no direct evidence that durvalumab plus tremelimumab could reduce the risk of severe AEs compared to durvalumab and tremelimumab monotherapy. Similarly, there was no evidence that the combination of nivolumab and ipilimumab could significantly reduce the risk of AEs compared with nivolumab and ipilimumab monotherapy. Even combination therapies increased the risk of severe AEs (durvalumab vs. durvalumab plus tremelimumab: [OR], 0.52%; 95% CI, 0.29–0.94; nivolumab vs. nivolumab plus ipilimumab: [OR], 0.17%; 95% CI, 0.10–0.29).

FIGURE 5.

FIGURE 5

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Results of NMA, safety profile (A) and probability ranking diagram (B). In the safety profile, efficacy of treatment for grade 3–5 adverse events is represented as odd ratios (ORs) with 95% confidence intervals. All comparisons are made as column versus row. Statistically significant results are in bold. Probability ranking diagram shows the probability of the safety of different therapies ranking the first to the last for severe adverse events.

Figure 5B shows the probability ranking diagram of 12 interventions. The probabilities of becoming the safest choice for severe AEs were as follows: cemiplimab (26.3%), avelumab (25.9%), nivolumab (18.2%), atezolizumab (14.5%), and camrelizumab (11.8%). The remaining interventions were all less than 5% likely to be the safest option, with nivolumab plus ipilimumab appearing to be the worst choice.

Through node splitting analysis, significant inconsistency could not be detected for most comparisons (Supplementary Table S4). There was significant inconsistency between ipilimumab and nivolumab plus ipilimumab, and ipilimumab and standard therapies (p < 0.05). The comparison between nivolumab plus ipilimumab and standard therapies also showed a degree of inconsistency (p = 0.08). In the direct comparisons, patients receiving nivolumab plus ipilimumab were more likely to have severe AEs than those receiving ipilimumab, and patients receiving ipilimumab were more likely to have severe AEs than those receiving standard therapies. However, patients receiving standard therapies were more likely to have severe AEs than those receiving nivolumab plus ipilimumab. The comparison between the above three groups may be the main reason for the inconsistency.

We performed subgroup analyses of lung cancer, melanoma, and squamous cell carcinoma treatment. The respective safety profiles and probability ranking diagram are shown in Supplementary Figures S9, S14 in the Supplement. Interestingly, the results suggested that nivolumab was the joint best choice for lung cancer, melanoma and squamous cell carcinoma. Standard therapies, based on the probability ranking diagram, were considered to be the worst for lung cancer and squamous cell carcinoma treatment, and nivolumab plus ipilimumab was the worst for melanoma treatment.

4 Discussion

In order to comprehensively evaluate the safety and efficacy of ICI drug monotherapy and combination therapies, we conducted a network meta-analysis combining HRs of PFS and OS, and the risk of severe AEs, and performed subgroup analyses particularly for lung cancer and melanoma. The application of bioinformatics is often used to analyze published data (Wu et al., 2017). To our knowledge, this study is the first comprehensive report comparing PFS HRs, OS HRs, and corresponding treatment-related severe AEs among ICI drug monotherapy, combination therapies and standard therapies.

In terms of PFS and OS, we first tested the heterogeneity and consistency of network meta-analysis based on the frequency method. No significant heterogeneity and consistency were found, indicating that this network meta-analysis was consistent in PFS and OS. We used the frequency model and the Bayesian model separately. The results of the frequency model and the Bayesian model agree well. In view of the greater accuracy of the Bayesian model, our final results were presented by the Bayesian model.

In terms of PFS, treatment with ipilimumab was significantly worse than standard therapies, whereas treatment with cemiplimab significantly improved PFS. The results also indicated that cemiplimab was the best choice for PFS. Treatment with nivolumab, pembrolizumab and nivolumab plus ipilimumab significantly improved OS compared to standard therapies. For OS, cemiplimab was considered to be the best choice, whereas avelumab was the worst. Since few studies compared cemiplimab and camrelizumab with other therapies, nivolumab plus ipilimumab ranked the third safest in PFS and durvalumab ranked the second safest in terms of OS. In comparing ICI drug combination therapies with monotherapy, we found that nivolumab plus ipilimumab significantly improved PFS compared to ipilimumab. Additionally, nivolumab plus ipilimumab may improve OS compared with nivolumab and ipilimumab monotherapy, similar to durvalumab plus tremelimumab compared with durvalumab, and tremelimumab monotherapy. However, further studies are needed to compare the safety and efficacy of ICI drug combination treatment with monotherapy. Although abundant included studies may lead to low significance of the overall results in terms of PFS and OS, we thought that the results would be more reliable and further performed subgroup analysis.

Severe AEs were examined as the measure of the toxicity of different ICI drug therapies and standard therapies. In terms of severe AEs, there was a large inconsistency in the comparison between the above three groups, which is similar to inconsistency reported by Chen et al (Xu et al., 2018), but the degree of inconsistency was more obvious in the comparison between ipilimumab and standard therapies in this study. We considered the main reasons as follows: In spite that the inclusion and exclusion criteria in our study are similar to those of Chen et al, eligible studies with inconsistent results were relatively abundant, which may lead to higher inconsistency. Of note, Chen et al combined durvalumab plus tremelimumab and nivolumab plus ipilimumab as the two ICI drug group, however, we grouped them in our study to assess the safety and efficacy more precisely.

4.1 Strengths and Limitations

The main strengths of our studies are as follows: we used the Bayesian model to conduct network meta-analysis and then employed the frequency model for inconsistency test and result verification in terms of PFS and OS. We found that the results of the frequency model were highly consistent with those of the Bayesian model, and we represented our final results from the Bayesian model, which greatly enhanced the reliability of conclusions. To comprehensively investigate the safety and efficacy of various ICI drugs, we analyzed three different indicators PFS, OS and severe AEs. Of note, we included enough studies to ensure the accuracy of the results. Despite that some studies represent the data of the same RCTs at different times, we chose the most recent results as much as possible.

This study also has several limitations. Firstly, enrolled studies showed high heterogeneity. In order to avoid publication bias, we tested the heterogeneity and used different models accordingly. Secondly, the number of RCTs that meet the requirements for inclusion is different among ICI drugs at present, and there is obvious inconsistency in severe AEs, which require more studies for higher-level verification. Thirdly, despite randomization of the eligible studies, there are still characteristic imbalances between the groups in trials.

5 Conclusion

In the present study, the Bayesian model was used to comprehensively assess survival data and the risk of severe AEs for ICI drugs, which showed that different ICI drug therapies may pose different risks in terms of PFS, OS and severe AEs. Our study may provide new insights and strategies for the clinical practice of ICI drugs.

Data Availability Statement

The raw data supporting the conclusion of this article will be made available by the authors, without undue reservation.

Author Contributions

The authors (ZX, JL and ZZ) contributed equally to this work. ZX and JL contributed to the design of the study, literature search and data analysis. ZX and ZZ identified eligible trials, extracted the data and assessed the quality of clinical trials. CC and WC provided some ideas for this study. BJ and GZ processed the data and generated the tables and figures. YW and YM contributed to the interpretation of the data. BB and JW drafted and critically revised the manuscript.

Funding

This study was supported by the Youth Medical Talent of Jiangsu Province (grant no. QNRC2016475).

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.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fphar.2022.883655/full#supplementary-material

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