Efficacy, safety, and patient-reported outcome of immune checkpoint inhibitor in gynecologic cancers: A systematic review and meta-analysis of randomized controlled trials

Abstract

Over the past decades, immune checkpoint inhibitors (ICIs) have shown dramatic efficacy in improving survival rates in multiple malignancies. Recently, gynecological cancer patients also showed to respond favorably to ICI treatment. This study aimed to evaluate the efficacy, safety, and patient-reported outcomes of ICI therapy in gynecological cancers. We conducted a systematic review and meta-analysis by retrieving literature from multiple electronic databases, such as MEDLINE, ScienceDirect, EBSCO, ProQuest, and Google Scholar. The protocol used in this study has been registered in PROSPERO (CRD42022369529). We included a total of 12 trials involving 8 therapies and 8,034 patients. ICI group demonstrated a longer OS (HR: 0.807; 95% CI: 0.719, 0.907; p = 0.000) and greater PFS improvement (HR: 0.809; 95% CI: 0.673, 0.973; p = 0.024) compared to the control group. There was no significant difference in the incidence of treatment-related adverse events [RR: 0.968; 95%CI: 0.936, 1.001; p = 0.061], but a higher incidence of immune-related adverse events (IRAEs) was observed in the ICI group (RR: 3.093; 95%CI: 1.933, 4.798; p = 0.000). Although the mean changes of QOL score from baseline was not significantly different between both groups (SMD: 0.048; 95% CI: -0.106, 0.202; p = 0.542), the time to definitive QOL deterioration was longer in the ICI group (HR: 0.508; 95% CI: 0.461, 0.560; p = 0.000). Despite having a higher incidence of IRAE, ICI was shown to improve survival rates and QOL of patients. Thus, it should be considered as a new standard of care for gynecologic cancers, especially in advanced stages.

Introduction

Immune checkpoint inhibitors (ICIs), such as cytotoxic T lymphocyte-associated antigen 4 (CTLA-4), programmed death-1 (PD-1), and its ligand (PD-L1), represent a breakthrough in cancer treatment that successfully increases the survival rate of the patients [1, 2]. These immune checkpoints naturally exist to control immune responses in the body, and blocking them allows the host’s T-cells to regain their immune activity against cancer cells that have developed ways to evade the immune system. However, inherent characteristics of the tumor, the composition of the tumor microenvironment (TME), and deficiencies in the host’s innate and adaptive immune systems can impede the efficacy of the anti-tumor immune response and impact the host’s response to ICI therapy [1, 3].

Patients with gynecological cancers possess the potential to respond favorably to ICI treatment. Pembrolizumab was approved by the Food and Drug Administration (FDA) as a treatment for advanced cervical and endometrial cancer [2, 4–12]. Existing evidence showed conflicting results on the benefits of ICI therapy, especially in ovarian cancer [13–17], but none had directly compared the efficacy of ICI across gynecological cancers. There is also a growing body of evidence suggesting the use of ICI in the early stages of cancer or in previously untreated patients [6, 7, 15], indicating the possibility of ICI as an alternative or neoadjuvant of current existing therapy. ICI treatment can potentially lead to immune-related adverse events (IRAEs), affecting organs such as the skin, gastrointestinal tract, lungs, liver, nervous system, and endocrine system [18]. Therefore, it is necessary to understand whether the risks outweigh the benefits of ICI therapy, thus this study aims to evaluate the efficacy, safety, and patient-reported outcomes of ICI therapy in gynecological malignancies.

Materials and methods

This meta-analysis was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) criteria [19]. As this paper did not directly involve human subjects, while only using data from publicly published articles, institutional review board approval was not required. The protocol of this study has been registered in the International Prospective Register of Systematic Reviews (PROSPERO) (CRD42022369529).

Eligibility criteria

Type of Study.

Only randomized controlled trials (RCT) evaluating the use of ICI in gynecologic cancer were included in this study. Nonrandomized clinical studies, single-arm studies, observational studies, case series, case reports, reviews, and commentary articles were excluded. Papers with unavailable full text were also omitted.

Population.

Subjects diagnosed with gynecologic cancer. There were no restrictions on age, race, stage, occupation, economy or social status, religion, country, underlying conditions, etc.

Intervention.

Studies evaluating ICI for the treatment of gynecologic cancer were included in this study.

Comparison.

Subjects treated with placebo and/or standard-of-care treatment were used as comparators.

Outcome.

Outcomes of interest were efficacy, safety, and patient-reported outcomes of ICI for the treatment of gynecologic cancer. The efficacy assessment included overall survival (OS), progression-free survival (PFS), objective response rate (ORR), disease control rate (DCR), and duration of response (DOR). Safety assessment included the incidence of adverse events of any causes (AE) and treatment-related adverse events (TRAE) reported during treatment with ICI. Patient-reported outcome assessment included the mean changes of health-related quality of life (HRQOL) score from baseline, the proportion of patients with significant HRQOL improvement, and time to definitive HRQOL deterioration (TTD). HRQOL was assessed using the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire-Core 30 (EORTC QLQ-C30) or EuroQol-5 dimension-5 level (EQ-5D) or Functional Assessment of Cancer Therapy–Ovarian (FACT-O) at baseline, during treatment, and after treatment.

Data sources and search strategy

The systematic literature search was performed by three researchers (FK, GG, GM) using multiple electronic databases, such as PubMed/MEDLINE, ScienceDirect, EBSCO, ProQuest, and Google Scholar to identify articles published up to December 2023. The keywords used were presented in the S1 Table. After removing duplicates and irrelevant articles, three reviewers independently screened the titles and abstracts according to the eligibility criteria described above. The decision for inclusion or exclusion of articles was reached by seven investigators. Any emerging discrepancies would be resolved by consensus among the review team. A PRISMA flow chart describing the study selection process was provided (S1 Fig).

Data extraction

Quality of Reporting of Meta-analyses and Cochrane Collaboration guidelines were used for data extraction. Seven authors (FK, GG, GM, HW, GP, TWU, TDA) independently extracted information from all included studies to ensure the quality of the data. The following data were extracted from the eligible studies: (1) study name, (2) ICI drugs and their mechanism of action, (3) study design, (4) type of cancer, (5) patient status, (6) \age of the patient, (7) treatment arm and size, (8) control arm and size, (9) dosage of ICI, and (10) median duration of follow up.

Quality assessment

Cochrane risk of bias (RoB)2 tool was used to assess the quality of included studies based on random sequence generation, allocation concealment, blinding of participants and personnel, blinding of outcome assessment, insufficient outcome data, selective reporting of results, and other possible sources of bias [20]. The quality assessment was performed by all authors FK, GG, GM, HW, GP, TWU, and TDA independently with any discrepancies resolved by consensus among the review team.

Statistical analysis

Hazard ratio (HR), risk ratio (RR), standardized mean differences (SMDs), and odds ratio (OR) with a confidence interval (CI) of 95% were used to determine the efficacy, safety, and patient-reported outcome of ICI for treatment of gynecologic cancer. HR was used to evaluate the effect of OS, PFS, DOR, and TTD. RR was used to evaluate the effect of ORR, DCR, AE, and TRAE. SMD was used to evaluate mean changes in HRQOL score from baseline. OR was used to evaluate the proportion of patients with significant HRQOL improvement. The effect was considered significant if the value was not equal to 1 with p < 0.05. The combined effect size was plotted using a forest plot. All the meta-analyses were performed using the random-effects model. Cochrane Q test of homogeneity and Higgins I2 were used to detect the statistical heterogeneity in studies. If heterogeneity (p >0.05 or I2 >50%) was detected, subgroup analysis was conducted to explore the possible cause of heterogeneity [21]. The publication bias was assessed visually using a funnel plot and confirmed through the Begg and Mazumdar rank correlation test and Egger test of the intercept to determine the presence of publication bias statistically [22, 23]. Duval and Tweedie’s trim-and-fill method was used to adjust the effect size if publication bias was detected [24]. Sensitivity analysis was performed to confirm the robustness of this meta-analysis by excluding studies with a high risk of bias from the analysis. All statistical tests were performed using Comprehensive Meta-Analysis software (version 3; Biostat, Englewood, NJ, USA) [25].

Confidence in cumulative evidence

The confidence in cumulative evidence was determined using the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) framework. There are four domains assessed in the GRADE framework, namely the presence of bias, imprecision, inconsistency, and indirectness. The overall quality of evidence was shown as very low, low, moderate, and high quality.

Results

Search results

An initial search of electronic databases yielded 36,621 studies. After excluding 12,454 duplicate studies and 18,010 non-clinical studies, 5,797 studies were then screened. Another 5,782 studies were excluded as they were non-RCT studies, assessing other diseases, and assessing other drugs. After these rounds of exclusion, 15 studies were then assessed for eligibility for the present study. At last, 12 studies were included in our study and 3 studies were excluded due to insufficient data for extraction. The search strategy and selection methods of this study are illustrated in S1 Fig.

Study characteristics

The majority of included studies were conducted in patients with ovarian cancer (5 studies), followed by cervical cancer (4 studies), and endometrial cancer (3 studies). Most of the included studies were conducted only on advanced gynecological cancers, whereas five other studies included all stages of cancer. This study included 6 studies using anti-PD-1 and 6 studies using anti-PD-L1 as their ICI regimen with the majority of studies using it as a combination therapy. Seven studies used placebo as a comparison, while the other seven studies used other drugs, such as pemetrexed, pegylated liposomal doxorubicin, gemcitabine, topotecan, irinotecan, or vinorelbine. In this study, 6 studies were only conducted on previously treated patients, 4 studies were only conducted on previously untreated patients, and 2 studies included all patients regardless of their treatment status. All studies showed a low risk of possible bias (S2 Fig). Characteristics of the included studies are presented in Table 1.

[Figure omitted. See PDF.]

Overall survival

The pooled data from 12 studies, involving 8,336 patients demonstrated the ICI group had a longer OS than the control group (HR: 0.807; 95% CI: 0.719, 0.907; p = 0.000; S3 Fig). However, a substantial heterogeneity (I² = 60.80; p = 0.001) was observed. Therefore, subgroup analyses were done and summarized in Table 2. Subgroup analyses based on age, ECOG performance status, race, cancer stage, number of prior lines of therapy, and prior use of bevacizumab showed no significant differences in OS. Subgroup analysis demonstrated a longer OS in patients treated with anti-PD-1 drugs (HR: 0.715; 95% CI: 0.622, 0.822; p = 0.000; I² = 47.54), patients with PD-L1 positive status (HR: 0.747; 95% CI: 0.637, 0.876; p = 0.000; I² = 20.01), patients with cervical cancer (HR: 0.726; 95% CI: 0.646, 0.815; p = 0.000; I² = 0) and endometrial cancer (HR: 0.658; 95% CI: 0.573, 0.755; p = 0.000; I² = 0), previously treated patients (HR: 0.828; 95% CI: 0.719, 0.953; p = 0.009; I² = 65.08), patients who treated with combination therapy (HR: 0.775; 95% CI: 0.685, 0.877; p = 0.000; I² = 51.81), patients that had history of prior radiotherapy (HR: 0.731; 95% CI: 0.541, 0.988; p = 0.042; I² = 3.23), and when ICI was compared with placebo as control regimen (HR: 0.768; 95% CI: 0.672, 0.877; p = 0.000; I² = 35.33). The Egger’s weighted regression test (p = 0.397) and the Begg-Mazumdar Kendall’s Tahu (p = 0.620) showed no evidence of publication bias.

[Figure omitted. See PDF.]

Progression-free survival

The pooled data from 12 studies, involving 8,336 patients revealed a greater PFS improvement in the ICI group compared to the control group (HR: 0.809; 95% CI: 0.673, 0.973; p = 0.024; S3 Fig). However, a substantial heterogeneity (I² = 90.15; p = 0.000) was observed. Therefore, subgroup analyses were done and summarized in Table 3. to explore the heterogeneity. Subgroup analyses based on age, race, PD-L1 status, treatment status, number of prior lines of therapy, prior use of bevacizumab, and history of prior radiotherapy showed no significant differences in PFS. Subgroup analysis showed significant improvement in PFS for patients with ECOG score of 0 (HR: 0.774; 95% CI: 0.608, 0.985; p = 0.037; I² = 82.31), patients treated with anti-PD-1 drugs (HR: 0.654; 95% CI: 0.494, 0.865; p = 0.003; I² = 91.13), patients with cervical cancer (HR: 0.708; 95% CI: 0.625, 0.800; p = 0.000; I² = 27.30) and endometrial cancer (HR: 0.528; 95% CI: 0.429, 0.653; p = 0.000; I² = 64.12), patients with advanced stage cancer (HR: 0.758; 95% CI: 0.612, 0.938; p = 0.011; I² = 94.22), patients who treated with combination therapy (HR: 0.729; 95% CI: 0.616, 0.864; p = 0.000; I² = 84.75), and when ICI was compared with non-placebo as control regimen (HR: 0.970; 95% CI: 0.713, 1.319; p = 0.000; I² = 81.97). The Egger’s weighted regression test (p = 0.773) and the Begg-Mazumdar Kendall’s Tahu (p = 1.000) showed no evidence of publication bias.

[Figure omitted. See PDF.]

Objective response, disease control, and duration of response

The clinical response was evaluated using ORR, DCR, and DOR. A total of 11 studies demonstrated that the ICI group had a significantly higher ORR than the control group (RR: 1.186; 95% CI: 1.065, 1.321; p = 0.002; S3 Fig). Subgroup analysis (Table 4) was done and demonstrated a higher ORR in patients treated with anti-PD-1 drugs (HR: 1.448; 95% CI: 1.036, 2.023; p = 0.030; I² = 89.93), patients with cervical cancer (HR: 1.244; 95% CI: 1.041, 1.486; p = 0.016; I² = 86.77), patients with early and advanced stage cancer (HR: 1.628; 95% CI: 1.078, 2.459; p = 0.021; I² = 83.01), previously treated patients (HR: 1.392; 95% CI: 1.043, 1.856; p = 0.025; I² = 72.71), patients treated with combination therapy (HR: 1.164; 95% CI: 1.051, 1.290; p = 0.004; I² = 82.74), and when ICI was compared with non-placebo as control regimen (HR: 1.408; 95% CI: 1.072, 1.850; p = 0.014; I² = 83.94). In contrast, there were no significant DCR differences among these two groups (RR: 1.021; 95% CI: 0.930, 1.120; p = 0.667; S3 Fig). However, subgroup analysis (Table 4) showed a higher DCR in patients with cervical cancer (HR: 1.051; 95% CI: 1.009, 1.095; p = 0.018; I² = 0), patients treated with combination therapy (HR: 1.096; 95% CI: 1.006, 1.195; p = 0.036; I² = 88.71), and when ICI was compared with placebo as control regimen (HR: 1.044; 95% CI: 1.044, 1.086; p = 0.030; I² = 0). However, there was a lower DCR in previously treated patients (HR: 0.951; 95% CI: 0.708, 1.276; p = 0.000; I² = 93.21). The DOR was longer in the ICI group than in the control group (HR: 0.581; 95% CI: 0.440, 0.767; p = 0.000; S3 Fig), though there was a moderate heterogeneity observed (I² = 57.22; p = 0.071). Subgroup analysis (Table 4) was done and demonstrated a longer DOR in patients with cervical cancer (HR: 0.643; 95% CI: 0.529, 0.782; p = 0.000; I² = 0), patients with early and advanced stage cancer (HR: 0.572; 95% CI: 0.470, 0.697; p = 0.000; I² = 0), and when ICI was compared with placebo as control regimen (HR: 0.615; 95% CI: 0.477, 0.794; p = 0.000; I² = 33.95). The Egger’s weighted regression test and the Begg-Mazumdar Kendall’s Tahu showed no evidence of publication bias for these meta-analyses.

[Figure omitted. See PDF.]

Safety

There was no significant difference in the probability of adverse events of any cause (AE) [RR: 1.000; 95%CI: 0.988, 1.012; p = 0.972; I² = 82.62; S4 Fig] and treatment-related adverse events (TRAE) [RR: 0.968; 95%CI: 0.936, 1.001; p = 0.061; I² = 91.74; S4 Fig]. Moreover, there was also no significant difference in the probability of grade 3–5 AE (RR: 1.024; 95%CI: 0.940, 1.116; p = 0.588; I² = 88.92) and grade 3–5 TRAE (RR: 0.869; 95%CI: 0.738, 1.023; p = 0.091; I² = 92.66). The probability of immune-related AE was significantly higher in the ICI group than in the control group (RR: 3.093; 95%CI: 1.933, 4.798; p = 0.000; I² = 94.74). On the contrary, there were no significant differences in the immune-related TRAE in the ICI group than in the control group (RR: 1.725; 95%CI: 0.882, 3.373; p = 0.111; I² = 91.87). However, the probability of TRAE-related discontinuation was significantly higher in the ICI group than in the control group (RR: 1.711; 95%CI: 1.224, 2.390; p = 0.002; I² = 79.42). The Egger’s weighted regression test and the Begg-Mazumdar Kendall’s Tahu showed no evidence of publication bias for these meta-analyses. In addition, to explore substantial heterogeneity in these meta-analyses. subgroup analyses were also done and summarized in Table 5.

[Figure omitted. See PDF.]

Patients-reported outcomes

The pooled data from 6 studies revealed that mean changes in QOL score from baseline were not significantly different between both groups (SMD: 0.048; 95% CI: -0.106. 0.202; p = 0.542; I² = 40.82; S5 Fig). Subgroup analysis (Table 6) showed a higher mean change of QOL score from baseline in patients with endometrial cancer (SMD: 0.306; 95% CI: 0.040. 0.572; p = 0.024; I² = 0). However, there was a lower mean change of QOL score from baseline in patients with ovarian cancer (SMD: -0.177; 95% CI: -0.353. -0.002; p = 0.048; I² = 0). Moreover, a meta-analysis of 4 studies also showed that the proportion of patients with significant improvement of QOL was not significantly different between both groups (OR: 1.106; 95% CI: 0.961. 1.272; p = 0.159; I² = 11.48; S5 Fig). However, subgroup analysis (Table 6) showed a higher proportion of QOL improvement in patients treated with anti-PD-1 and patients with cervical cancer (OR: 1.498; 95% CI: 1.248, 1.799; p = 0.000; I² = 0). Time to definitive QOL deterioration was longer in the ICI group than in the control group (HR: 0.508; 95% CI: 0.461, 0.560; p = 0.000). The Egger’s weighted regression test and the Begg-Mazumdar Kendall’s Tahu showed no evidence of publication bias for these meta-analyses.

[Figure omitted. See PDF.]

Confidence in cumulative evidence

According to the Cochrane risk of bias tool (ROB-2), all included studies were judged to have a low risk of bias, thus plausible bias was unlikely to alter the outcomes. There was also no indication of indirectness in all the outcomes of this study. In contrast, the inconsistency was serious in almost all outcomes as there was a substantial heterogeneity observed among the included studies. However, by conducting subgroup analyses, we were able to reduce the heterogeneity of each outcome and address this issue. There were also some imprecision observed especially when assessing the incidence of IRAE, immune-related TRAE, TRAE-related discontinuation, and the impact of ICIs on QOL improvement because the majority of the included studies in these outcomes had wide confidence intervals. Moreover, in the outcomes with more than 10 included studies, no publication bias was found. Overall, we had moderate quality of evidence for all the outcomes, except the outcomes for incidence of IRAE, immune-related TRAE, and TRAE-related discontinuation which were considered as low quality of evidence. GRADE evidence profile is generated as shown in S2 Table.

Discussion

In terms of efficacy, there was a marked improvement in the clinical outcomes in the ICI group compared to control. Pooled data showed higher OS, PFS, and ORR among patients treated with ICI. The effects were mainly observed in cervical cancer, treatment with anti-PD-1 drugs, use of combination therapy, and advanced cancers. According to a model proposed by Yarchoan et al, cervical cancer had a higher tumor mutational burden compared to endometrial and ovarian cancers, suggesting that there are more neoantigens present in the tumor cells and microenvironment that leads to increased response to checkpoint inhibition. This hypothesis is grounded in the rationale that a heightened presence of neoantigens improves the detection of tumor cells by the immune system [26, 27].

Immunotherapy with anti-PD-1 seems to show superior OS, PFS, and ORR compared to anti-PD-L1 probably as a result of the simultaneous inhibition of PD-1 binding to PD-L1 and PD-L2. On the contrary, anti-PD-L1 specifically interacts with PD-1, hence only blocks the binding of PD-1 to PD-L1 and does not affect the interaction between PD-1 and PD-L2. This process may lead to the inhibition of T-cell activation and further enhance the immunologic escape of the tumor cells. Anti-PD-1 and anti-PD-L1 therapy may have similar efficacy in cases of patients having high expression of PD-L1. However, anti-PD-L1 treatment may be insufficient for patients with low-to-none PD-L1 expression or high PD-L2 expression [28, 29].

The benefits of immunotherapy with ICI are also enhanced as part of the combination regimen. This strategy involves the synergistic effect of combination treatments to generate an anti-tumor response, prevent the immunologic escape of the tumor cells, and reduce the adverse events that arise during the treatment. The combinations are designed based on the hallmark mechanisms of cancer immunotherapy, which include immunogenic cell death, enhanced antigen-presenting cells, activation of primed T cells and reversion of exhaustion, increased lymphocyte infiltration, and depletion of immunosuppressive cells [30].

It is possible that patients who responded well to ICIs are more likely to experience autoimmune toxicities [31], however, this meta-analysis showed that the safety profile of the treatment was comparable between the two groups. The occurrence of immune-related adverse events (IRAEs) is in accordance with the action of the checkpoint blockade and may be related to the antitumor responses. IRAEs mostly occur during the first three months of the treatment and frequently involve organs with significant exposure to the environment (skin, lungs, gastrointestinal tract) or those known with pre-existing or smoldering autoimmunity (thyroid, joints). Evidence suggests that patients who developed IRAEs had favorable OS, PFS, ORR, and DCR compared to those who experienced minimal toxicity [18, 31]. As shown in this meta-analysis, the occurrence of IRAEs was significantly higher in the ICI group, suggesting the potential link between IRAEs and antitumor immunity. Although we did not observe significant changes in QoL between the two groups, immunotherapy with ICI can mitigate significant QoL deterioration, as seen in the longer TTD in the ICI group. There is also a trend of superior clinical benefits after ICI therapy coupled with favorable patient-reported outcomes.

Moreover, we acknowledge some limitations in this study. The included studies in this meta-analysis are heterogeneous, thus it can be difficult to conduct a meaningful comparison. However, we mitigate this issue by using a random effects model and conducting subgroup analyses. In addition, due to the limited of studies available, our included studies were dominated by advanced-stage cancer, previously treated patients, and the use of ICI as a combination therapy, which could impact the generalizability of this study. Finally, there were some studies that were not included in our analysis because they had not been published at the time of our literature search. Nevertheless, despite these limitations, this study remains the first and most comprehensive meta-analysis addressing the use of ICI in gynecologic cancer.

Conclusions

This meta-analysis suggested the favorable effect of ICI on gynecological cancer, especially when compared to those who received a placebo. Although the safety profile was remarkable, there is a trend of increasing IRAEs in the ICI group, highlighting the possibility of IRAE and antitumor response that generate markedly improved clinical outcomes.

Supporting information

S1 Table. Search keywords.

https://doi.org/10.1371/journal.pone.0307800.s001

(DOCX)

S2 Table. GRADE evidence profile.

https://doi.org/10.1371/journal.pone.0307800.s002

(DOCX)

S1 Fig. PRISMA flowchart.

The diagram provided a summary of the search strategy and selection process used to include eligible articles for this meta-analysis.

https://doi.org/10.1371/journal.pone.0307800.s003

(DOCX)

S2 Fig. Quality assessment.

Cochrane risk of bias tool (ROB-2) was used to evaluate the risk of bias.

https://doi.org/10.1371/journal.pone.0307800.s004

(DOCX)

S3 Fig. Overall survival, progression-free survival, objective response rate, disease control rate, and duration of response of ICI for gynecologic cancer.

(Left) Forest plot. The horizontal line indicates 95% CI of a study. The square represents the result of each individual study. The size of the square varies according to the weight of a particular study. The diamond at the bottom of the plot represents the pooled analysis of all included studies. The outer edges of the diamond indicates the CIs. CI, confidence interval. (Right) Funnel plot. Asymmetrical plot indicated that publication bias was present.

https://doi.org/10.1371/journal.pone.0307800.s005

(DOCX)

S4 Fig. Adverse events of any causes and treatment-related adverse events of ICI for gynecologic cancer.

(Left) Forest plot. The horizontal line indicates 95% CI of a study. The square represents the result of each individual study. The size of the square varies according to the weight of a particular study. The diamond at the bottom of the plot represents the pooled analysis of all included studies. The outer edges of the diamond indicates the CIs. CI, confidence interval. (Right) Funnel plot. Asymmetrical plot indicated that publication bias was present.

https://doi.org/10.1371/journal.pone.0307800.s006

(DOCX)

S5 Fig. Patient-reported outcomes of ICI for gynecologic cancer.

(Left) Forest plot. The horizontal line indicates 95% CI of a study. The square represents the result of each individual study. The size of the square varies according to the weight of a particular study. The diamond at the bottom of the plot represents the pooled analysis of all included studies. The outer edges of the diamond indicates the CIs. CI, confidence interval. (Top right) Funnel plot. Asymmetrical plot indicated that publication bias was present.

https://doi.org/10.1371/journal.pone.0307800.s007

(DOCX)

S1 Checklist.

https://doi.org/10.1371/journal.pone.0307800.s008

(DOCX)

References

1. 1. Tan S, Day D, Nicholls SJ, Segelov E. Immune Checkpoint Inhibitor Therapy in Oncology: Current Uses and Future Directions: JACC: CardioOncology State-of-the-Art Review. JACC CardioOncology. 2022 Dec;4(5):579–97.

* View Article

* Google Scholar

2. 2. Ma W, Xue R, Zhu Z, Farrukh H, Song W, Li T, et al. Increasing cure rates of solid tumors by immune checkpoint inhibitors. Exp Hematol Oncol. 2023 Jan 16;12(1):10. pmid:36647169

* View Article

* PubMed/NCBI

* Google Scholar

3. 3. Morad G, Helmink BA, Sharma P, Wargo JA. Hallmarks of response, resistance, and toxicity to immune checkpoint blockade [published correction appears in Cell. 2022 Feb 3;185(3):576. 10.1016/j.cell.2022.01.008]. Cell. 2021;184(21):5309–5337. pmid:34624224

* View Article

* PubMed/NCBI

* Google Scholar

4. 4. Colombo N, Dubot C, Lorusso D, Caceres MV, Hasegawa K, Shapira-Frommer R, et al. Pembrolizumab for Persistent, Recurrent, or Metastatic Cervical Cancer. N Engl J Med. 2021 Nov 11;385(20):1856–67. pmid:34534429

* View Article

* PubMed/NCBI

* Google Scholar

5. 5. Grau JF, Farinas-Madrid L, Oaknin A. A randomized phase III trial of platinum chemotherapy plus paclitaxel with bevacizumab and atezolizumab versus platinum chemotherapy plus paclitaxel and bevacizumab in metastatic (stage IVB), persistent, or recurrent carcinoma of the cervix: the BEATcc study (ENGOT-Cx10/GEICO 68-C/JGOG1084/GOG-3030). Int J Gynecol Cancer Off J Int Gynecol Cancer Soc. 2020 Jan;30(1):139–43. pmid:31645423

* View Article

* PubMed/NCBI

* Google Scholar

6. 6. Monk BJ, Toita T, Wu X, Vázquez Limón JC, Tarnawski R, Mandai M, et al. Durvalumab versus placebo with chemoradiotherapy for locally advanced cervical cancer (CALLA): a randomised, double-blind, phase 3 trial. Lancet Oncol. 2023 Dec 1;24(12):1334–48. pmid:38039991

* View Article

* PubMed/NCBI

* Google Scholar

7. 7. Monk BJ, Tewari KS, Dubot C, Caceres MV, Hasegawa K, Shapira-Frommer R, et al. Health-related quality of life with pembrolizumab or placebo plus chemotherapy with or without bevacizumab for persistent, recurrent, or metastatic cervical cancer (KEYNOTE-826): a randomised, double-blind, placebo-controlled, phase 3 trial. Lancet Oncol. 2023 Apr;24(4):392–402. pmid:36878237

* View Article

* PubMed/NCBI

* Google Scholar

8. 8. Oaknin A, Monk BJ, Vergote I, Cristina de Melo A, Kim YM, Lisyanskaya AS, et al. EMPOWER CERVICAL-1: Effects of cemiplimab versus chemotherapy on patient-reported quality of life, functioning and symptoms among women with recurrent cervical cancer. Eur J Cancer Oxf Engl 1990. 2022 Oct;174:299–309. pmid:35922251

* View Article

* PubMed/NCBI

* Google Scholar

9. 9. Tewari KS, Monk BJ, Vergote I, Miller A, de Melo AC, Kim HS, et al. Survival with Cemiplimab in Recurrent Cervical Cancer. N Engl J Med. 2022 Feb 10;386(6):544–55. pmid:35139273

* View Article

* PubMed/NCBI

* Google Scholar

10. 10. Eskander RN, Sill MW, Beffa L, Moore RG, Hope JM, Musa FB, et al. Pembrolizumab plus Chemotherapy in Advanced Endometrial Cancer. N Engl J Med. 2023 Jun 8;388(23):2159–70. pmid:36972022

* View Article

* PubMed/NCBI

* Google Scholar

11. 11. Makker V, Colombo N, Casado Herráez A, Santin AD, Colomba E, Miller DS, et al. Lenvatinib plus Pembrolizumab for Advanced Endometrial Cancer. N Engl J Med. 2022 Feb 3;386(5):437–48. pmid:35045221

* View Article

* PubMed/NCBI

* Google Scholar

12. 12. Mirza MR, Chase DM, Slomovitz BM, dePont Christensen R, Novák Z, Black D, et al. Dostarlimab for Primary Advanced or Recurrent Endometrial Cancer. N Engl J Med. 2023 Jun 8;388(23):2145–58. pmid:36972026

* View Article

* PubMed/NCBI

* Google Scholar

13. 13. Hamanishi J, Takeshima N, Katsumata N, Ushijima K, Kimura T, Takeuchi S, et al. Nivolumab Versus Gemcitabine or Pegylated Liposomal Doxorubicin for Patients With Platinum-Resistant Ovarian Cancer: Open-Label, Randomized Trial in Japan (NINJA). J Clin Oncol Off J Am Soc Clin Oncol. 2021 Nov 20;39(33):3671–81. pmid:34473544

* View Article

* PubMed/NCBI

* Google Scholar

14. 14. Kurtz JE, Pujade-Lauraine E, Oaknin A, Belin L, Leitner K, Cibula D, et al. Atezolizumab Combined With Bevacizumab and Platinum-Based Therapy for Platinum-Sensitive Ovarian Cancer: Placebo-Controlled Randomized Phase III ATALANTE/ENGOT-ov29 Trial. J Clin Oncol Off J Am Soc Clin Oncol. 2023 Oct 20;41(30):4768–78. pmid:37643382

* View Article

* PubMed/NCBI

* Google Scholar

15. 15. Monk BJ, Colombo N, Oza AM, Fujiwara K, Birrer MJ, Randall L, et al. Chemotherapy with or without avelumab followed by avelumab maintenance versus chemotherapy alone in patients with previously untreated epithelial ovarian cancer (JAVELIN Ovarian 100): an open-label, randomised, phase 3 trial. Lancet Oncol. 2021 Sep;22(9):1275–89. pmid:34363762

* View Article

* PubMed/NCBI

* Google Scholar

16. 16. Moore KN, Bookman M, Sehouli J, et al. Atezolizumab, Bevacizumab, and Chemotherapy for Newly Diagnosed Stage III or IV Ovarian Cancer: Placebo-Controlled Randomized Phase III Trial (IMagyn050/GOG 3015/ENGOT-OV39) [published correction appears in J Clin Oncol. 2021 Jul 20;39(21):2420. 10.1200/JCO.21.01441]. J Clin Oncol. 2021;39(17):1842–1855. pmid:33891472

* View Article

* PubMed/NCBI

* Google Scholar

17. 17. Pujade-Lauraine E, Fujiwara K, Ledermann JA, Oza AM, Kristeleit R, Ray-Coquard IL, et al. Avelumab alone or in combination with chemotherapy versus chemotherapy alone in platinum-resistant or platinum-refractory ovarian cancer (JAVELIN Ovarian 200): an open-label, three-arm, randomised, phase 3 study. Lancet Oncol. 2021 Jul;22(7):1034–46. pmid:34143970

* View Article

* PubMed/NCBI

* Google Scholar

18. 18. Johnson DB, Nebhan CA, Moslehi JJ, Balko JM. Immune-checkpoint inhibitors: long-term implications of toxicity. Nat Rev Clin Oncol. 2022 Apr;19(4):254–67. pmid:35082367

* View Article

* PubMed/NCBI

* Google Scholar

19. 19. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021 Mar 29;372:n71. pmid:33782057

* View Article

* PubMed/NCBI

* Google Scholar

20. 20. Sterne JAC, Savović J, Page MJ, Elbers RG, Blencowe NS, Boutron I, et al. RoB 2: a revised tool for assessing risk of bias in randomised trials. BMJ. 2019 Aug 28;366:l4898. pmid:31462531

* View Article

* PubMed/NCBI

* Google Scholar

21. 21. Higgins JPT, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, Welch VA (editors). Cochrane Handbook for Systematic Reviews of Interventions version 6.4 (updated August 2023). Cochrane, 2023. www.training.cochrane.org/handbook.

22. 22. Begg CB, Mazumdar M. Operating characteristics of a rank correlation test for publication bias. Biometrics. 1994 Dec;50(4):1088–101. pmid:7786990

* View Article

* PubMed/NCBI

* Google Scholar

23. 23. Egger M, Davey Smith G, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. BMJ. 1997 Sep 13;315(7109):629–34. pmid:9310563

* View Article

* PubMed/NCBI

* Google Scholar

24. 24. Duval S, Tweedie R. Trim and fill: A simple funnel-plot-based method of testing and adjusting for publication bias in meta-analysis. Biometrics. 2000 Jun;56(2):455–63. pmid:10877304

* View Article

* PubMed/NCBI

* Google Scholar

25. 25. Borenstein M. Comprehensive Meta-Analysis Software. In: Systematic Reviews in Health Research [Internet]. 2022 [cited 2024 Apr 15]. p. 535–48. Available from:

* View Article

* Google Scholar

26. 26. Lawlor RT, Mattiolo P, Mafficini A, Hong SM, Piredda ML, Taormina SV, et al. Tumor Mutational Burden as a Potential Biomarker for Immunotherapy in Pancreatic Cancer: Systematic Review and Still-Open Questions. Cancers. 2021 Jun 22;13(13). pmid:34206554

* View Article

* PubMed/NCBI

* Google Scholar

27. 27. Yarchoan M, Hopkins A, Jaffee EM. Tumor Mutational Burden and Response Rate to PD-1 Inhibition. N Engl J Med. 2017 Dec 21;377(25):2500–1. pmid:29262275

* View Article

* PubMed/NCBI

* Google Scholar

28. 28. Chen L, Han X. Anti-PD-1/PD-L1 therapy of human cancer: past, present, and future. J Clin Invest. 2015 Sep;125(9):3384–91. pmid:26325035

* View Article

* PubMed/NCBI

* Google Scholar

29. 29. Duan J, Cui L, Zhao X, Bai H, Cai S, Wang G, et al. Use of Immunotherapy With Programmed Cell Death 1 vs Programmed Cell Death Ligand 1 Inhibitors in Patients With Cancer: A Systematic Review and Meta-analysis. JAMA Oncol. 2020 Mar 1;6(3):375–84. pmid:31876895

* View Article

* PubMed/NCBI

* Google Scholar

30. 30. Kyi C, Postow MA. Immune checkpoint inhibitor combinations in solid tumors: opportunities and challenges. Immunotherapy. 2016 Jun;8(7):821–37. pmid:27349981

* View Article

* PubMed/NCBI

* Google Scholar

31. 31. Das S, Johnson DB. Immune-related adverse events and anti-tumor efficacy of immune checkpoint inhibitors. J Immunother Cancer. 2019 Nov 15;7(1):306. pmid:31730012

* View Article

* PubMed/NCBI

* Google Scholar

Citation: Kusuma F, Glenardi G, Mangkuliguna G, Winarto H, Purwoto G, Utami TW, et al. (2024) Efficacy, safety, and patient-reported outcome of immune checkpoint inhibitor in gynecologic cancers: A systematic review and meta-analysis of randomized controlled trials. PLoS ONE 19(8): e0307800. https://doi.org/10.1371/journal.pone.0307800

About the Authors:

Fitriyadi Kusuma

Contributed equally to this work with: Fitriyadi Kusuma, Glenardi Glenardi, Ghea Mangkuliguna

Roles: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

E-mail: [email protected] (FK); [email protected] (GG)

Affiliation: Division of Oncology Gynecology, Department of Obstetrics and Gynecology, Dr. Cipto Mangunkusumo Hospital, Greater Jakarta, Daerah Khusus Ibukota Jakarta, Indonesia

Glenardi Glenardi

Contributed equally to this work with: Fitriyadi Kusuma, Glenardi Glenardi, Ghea Mangkuliguna

Roles: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

E-mail: [email protected] (FK); [email protected] (GG)

Affiliations: Division of Oncology Gynecology, Department of Obstetrics and Gynecology, Dr. Cipto Mangunkusumo Hospital, Greater Jakarta, Daerah Khusus Ibukota Jakarta, Indonesia, School of Medicine and Health Sciences, Department of Medicine, Atma Jaya Catholic University of Indonesia, North Jakarta, Daerah Khusus Ibukota Jakarta, Indonesia, Lewoleba General Hospital, Lembata Island, East Nusa Tenggara, Indonesia

ORICD: https://orcid.org/0000-0001-7360-6634

Ghea Mangkuliguna

Contributed equally to this work with: Fitriyadi Kusuma, Glenardi Glenardi, Ghea Mangkuliguna

Roles: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

Affiliation: School of Medicine and Health Sciences, Department of Medicine, Atma Jaya Catholic University of Indonesia, North Jakarta, Daerah Khusus Ibukota Jakarta, Indonesia

ORICD: https://orcid.org/0000-0002-3436-0624

Hariyono Winarto

Roles: Conceptualization, Data curation, Formal analysis, Supervision, Visualization, Writing – original draft, Writing – review & editing

¶‡ HW, GP, TWU and TDA also contributed equally to this work.

Affiliation: Division of Oncology Gynecology, Department of Obstetrics and Gynecology, Dr. Cipto Mangunkusumo Hospital, Greater Jakarta, Daerah Khusus Ibukota Jakarta, Indonesia

ORICD: https://orcid.org/0000-0002-2081-9238

Gatot Purwoto

Roles: Conceptualization, Data curation, Formal analysis, Supervision, Visualization, Writing – original draft, Writing – review & editing

¶‡ HW, GP, TWU and TDA also contributed equally to this work.

Affiliation: Division of Oncology Gynecology, Department of Obstetrics and Gynecology, Dr. Cipto Mangunkusumo Hospital, Greater Jakarta, Daerah Khusus Ibukota Jakarta, Indonesia

Tofan Widya Utami

Roles: Conceptualization, Data curation, Formal analysis, Supervision, Visualization, Writing – original draft, Writing – review & editing

¶‡ HW, GP, TWU and TDA also contributed equally to this work.

Affiliation: Division of Oncology Gynecology, Department of Obstetrics and Gynecology, Dr. Cipto Mangunkusumo Hospital, Greater Jakarta, Daerah Khusus Ibukota Jakarta, Indonesia

Tricia Dewi Anggraeni

Roles: Conceptualization, Data curation, Formal analysis, Supervision, Visualization, Writing – original draft, Writing – review & editing

¶‡ HW, GP, TWU and TDA also contributed equally to this work.

Affiliation: Division of Oncology Gynecology, Department of Obstetrics and Gynecology, Dr. Cipto Mangunkusumo Hospital, Greater Jakarta, Daerah Khusus Ibukota Jakarta, Indonesia

[/RAW_REF_TEXT]

References

1. Tan S, Day D, Nicholls SJ, Segelov E. Immune Checkpoint Inhibitor Therapy in Oncology: Current Uses and Future Directions: JACC: CardioOncology State-of-the-Art Review. JACC CardioOncology. 2022 Dec;4(5):579–97.

2. Ma W, Xue R, Zhu Z, Farrukh H, Song W, Li T, et al. Increasing cure rates of solid tumors by immune checkpoint inhibitors. Exp Hematol Oncol. 2023 Jan 16;12(1):10. pmid:36647169

3. Morad G, Helmink BA, Sharma P, Wargo JA. Hallmarks of response, resistance, and toxicity to immune checkpoint blockade [published correction appears in Cell. 2022 Feb 3;185(3):576. 10.1016/j.cell.2022.01.008]. Cell. 2021;184(21):5309–5337. pmid:34624224

4. Colombo N, Dubot C, Lorusso D, Caceres MV, Hasegawa K, Shapira-Frommer R, et al. Pembrolizumab for Persistent, Recurrent, or Metastatic Cervical Cancer. N Engl J Med. 2021 Nov 11;385(20):1856–67. pmid:34534429

5. Grau JF, Farinas-Madrid L, Oaknin A. A randomized phase III trial of platinum chemotherapy plus paclitaxel with bevacizumab and atezolizumab versus platinum chemotherapy plus paclitaxel and bevacizumab in metastatic (stage IVB), persistent, or recurrent carcinoma of the cervix: the BEATcc study (ENGOT-Cx10/GEICO 68-C/JGOG1084/GOG-3030). Int J Gynecol Cancer Off J Int Gynecol Cancer Soc. 2020 Jan;30(1):139–43. pmid:31645423

6. Monk BJ, Toita T, Wu X, Vázquez Limón JC, Tarnawski R, Mandai M, et al. Durvalumab versus placebo with chemoradiotherapy for locally advanced cervical cancer (CALLA): a randomised, double-blind, phase 3 trial. Lancet Oncol. 2023 Dec 1;24(12):1334–48. pmid:38039991

7. Monk BJ, Tewari KS, Dubot C, Caceres MV, Hasegawa K, Shapira-Frommer R, et al. Health-related quality of life with pembrolizumab or placebo plus chemotherapy with or without bevacizumab for persistent, recurrent, or metastatic cervical cancer (KEYNOTE-826): a randomised, double-blind, placebo-controlled, phase 3 trial. Lancet Oncol. 2023 Apr;24(4):392–402. pmid:36878237

8. Oaknin A, Monk BJ, Vergote I, Cristina de Melo A, Kim YM, Lisyanskaya AS, et al. EMPOWER CERVICAL-1: Effects of cemiplimab versus chemotherapy on patient-reported quality of life, functioning and symptoms among women with recurrent cervical cancer. Eur J Cancer Oxf Engl 1990. 2022 Oct;174:299–309. pmid:35922251

9. Tewari KS, Monk BJ, Vergote I, Miller A, de Melo AC, Kim HS, et al. Survival with Cemiplimab in Recurrent Cervical Cancer. N Engl J Med. 2022 Feb 10;386(6):544–55. pmid:35139273

10. Eskander RN, Sill MW, Beffa L, Moore RG, Hope JM, Musa FB, et al. Pembrolizumab plus Chemotherapy in Advanced Endometrial Cancer. N Engl J Med. 2023 Jun 8;388(23):2159–70. pmid:36972022

11. Makker V, Colombo N, Casado Herráez A, Santin AD, Colomba E, Miller DS, et al. Lenvatinib plus Pembrolizumab for Advanced Endometrial Cancer. N Engl J Med. 2022 Feb 3;386(5):437–48. pmid:35045221

12. Mirza MR, Chase DM, Slomovitz BM, dePont Christensen R, Novák Z, Black D, et al. Dostarlimab for Primary Advanced or Recurrent Endometrial Cancer. N Engl J Med. 2023 Jun 8;388(23):2145–58. pmid:36972026

13. Hamanishi J, Takeshima N, Katsumata N, Ushijima K, Kimura T, Takeuchi S, et al. Nivolumab Versus Gemcitabine or Pegylated Liposomal Doxorubicin for Patients With Platinum-Resistant Ovarian Cancer: Open-Label, Randomized Trial in Japan (NINJA). J Clin Oncol Off J Am Soc Clin Oncol. 2021 Nov 20;39(33):3671–81. pmid:34473544

14. Kurtz JE, Pujade-Lauraine E, Oaknin A, Belin L, Leitner K, Cibula D, et al. Atezolizumab Combined With Bevacizumab and Platinum-Based Therapy for Platinum-Sensitive Ovarian Cancer: Placebo-Controlled Randomized Phase III ATALANTE/ENGOT-ov29 Trial. J Clin Oncol Off J Am Soc Clin Oncol. 2023 Oct 20;41(30):4768–78. pmid:37643382

15. Monk BJ, Colombo N, Oza AM, Fujiwara K, Birrer MJ, Randall L, et al. Chemotherapy with or without avelumab followed by avelumab maintenance versus chemotherapy alone in patients with previously untreated epithelial ovarian cancer (JAVELIN Ovarian 100): an open-label, randomised, phase 3 trial. Lancet Oncol. 2021 Sep;22(9):1275–89. pmid:34363762

16. Moore KN, Bookman M, Sehouli J, et al. Atezolizumab, Bevacizumab, and Chemotherapy for Newly Diagnosed Stage III or IV Ovarian Cancer: Placebo-Controlled Randomized Phase III Trial (IMagyn050/GOG 3015/ENGOT-OV39) [published correction appears in J Clin Oncol. 2021 Jul 20;39(21):2420. 10.1200/JCO.21.01441]. J Clin Oncol. 2021;39(17):1842–1855. pmid:33891472

17. Pujade-Lauraine E, Fujiwara K, Ledermann JA, Oza AM, Kristeleit R, Ray-Coquard IL, et al. Avelumab alone or in combination with chemotherapy versus chemotherapy alone in platinum-resistant or platinum-refractory ovarian cancer (JAVELIN Ovarian 200): an open-label, three-arm, randomised, phase 3 study. Lancet Oncol. 2021 Jul;22(7):1034–46. pmid:34143970

18. Johnson DB, Nebhan CA, Moslehi JJ, Balko JM. Immune-checkpoint inhibitors: long-term implications of toxicity. Nat Rev Clin Oncol. 2022 Apr;19(4):254–67. pmid:35082367

19. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021 Mar 29;372:n71. pmid:33782057

20. Sterne JAC, Savović J, Page MJ, Elbers RG, Blencowe NS, Boutron I, et al. RoB 2: a revised tool for assessing risk of bias in randomised trials. BMJ. 2019 Aug 28;366:l4898. pmid:31462531

21. Higgins JPT, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, Welch VA (editors). Cochrane Handbook for Systematic Reviews of Interventions version 6.4 (updated August 2023). Cochrane, 2023. www.training.cochrane.org/handbook.

22. Begg CB, Mazumdar M. Operating characteristics of a rank correlation test for publication bias. Biometrics. 1994 Dec;50(4):1088–101. pmid:7786990

23. Egger M, Davey Smith G, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. BMJ. 1997 Sep 13;315(7109):629–34. pmid:9310563

24. Duval S, Tweedie R. Trim and fill: A simple funnel-plot-based method of testing and adjusting for publication bias in meta-analysis. Biometrics. 2000 Jun;56(2):455–63. pmid:10877304

25. Borenstein M. Comprehensive Meta-Analysis Software. In: Systematic Reviews in Health Research [Internet]. 2022 [cited 2024 Apr 15]. p. 535–48. Available from:

26. Lawlor RT, Mattiolo P, Mafficini A, Hong SM, Piredda ML, Taormina SV, et al. Tumor Mutational Burden as a Potential Biomarker for Immunotherapy in Pancreatic Cancer: Systematic Review and Still-Open Questions. Cancers. 2021 Jun 22;13(13). pmid:34206554

27. Yarchoan M, Hopkins A, Jaffee EM. Tumor Mutational Burden and Response Rate to PD-1 Inhibition. N Engl J Med. 2017 Dec 21;377(25):2500–1. pmid:29262275

28. Chen L, Han X. Anti-PD-1/PD-L1 therapy of human cancer: past, present, and future. J Clin Invest. 2015 Sep;125(9):3384–91. pmid:26325035

29. Duan J, Cui L, Zhao X, Bai H, Cai S, Wang G, et al. Use of Immunotherapy With Programmed Cell Death 1 vs Programmed Cell Death Ligand 1 Inhibitors in Patients With Cancer: A Systematic Review and Meta-analysis. JAMA Oncol. 2020 Mar 1;6(3):375–84. pmid:31876895

30. Kyi C, Postow MA. Immune checkpoint inhibitor combinations in solid tumors: opportunities and challenges. Immunotherapy. 2016 Jun;8(7):821–37. pmid:27349981

31. Das S, Johnson DB. Immune-related adverse events and anti-tumor efficacy of immune checkpoint inhibitors. J Immunother Cancer. 2019 Nov 15;7(1):306. pmid:31730012