Efficacy and safety of first-line immunotherapy in advanced renal cell carcinoma: a systematic review and network meta-analysis

Introduction

Background

Renal cell carcinoma (RCC) is one of the most common malignancies of the urinary system, and its incidence has been increasing worldwide. According to the 2022 GLOBOCAN data, RCC accounts for 2.2% of all cancer diagnoses globally and ranks as the 14th most common malignancy. In 2020, there were approximately 431,288 new cases of kidney cancer worldwide, resulting in more than 179,000 deaths (1,2). Despite advances in early detection, up to 30% of patients present with distant metastases at initial diagnosis. Even among patients with localized RCC who undergo radical surgery, nearly one-third eventually develop recurrence or metastasis, progressing to advanced RCC (aRCC). The prognosis of aRCC is extremely poor, with a 5-year survival rate of less than 15%, placing a substantial burden on both patients and society (1).

Over the past decades, the therapeutic landscape of aRCC has undergone a paradigm shift, evolving from early cytokine therapies to modern immune checkpoint inhibitors (ICIs). Initially, cytokines such as interleukin-2 (IL-2) and interferon-alpha (IFN-α) were the only systemic treatment options (3-5). Although these agents achieved durable remission in a small subset of patients, their severe toxicities and low overall response rates (5–20%) greatly limited their clinical utility (6), necessitating the development of more targeted approaches. The subsequent introduction of therapies targeting the vascular endothelial growth factor (VEGF) and mTOR pathways marked a major breakthrough, offering improved efficacy and manageable safety profiles, and thus replacing cytokines as the standard of care (7). However, the inevitable development of resistance and the transient nature of clinical benefit shifted research efforts toward more precise and effective immunotherapeutic strategies, ultimately leading to the advent of ICIs (8). By specifically blocking immunosuppressive pathways such as PD-1/PD-L1 and CTLA-4, ICIs restored the antitumor activity of T cells and ushered in a new era in the management of aRCC. Landmark clinical trials, including CheckMate-214 and KEYNOTE-426, have demonstrated that ICI-based combination regimens—either dual immune checkpoint blockade or immunotherapy combined with targeted agents—significantly improve overall survival (OS) and objective response rate (ORR) compared with the previous standard of single-agent targeted therapy (9-15).

Rationale and knowledge gap

While the frontline landscape for aRCC has become increasingly diverse, a critical challenge remains for clinicians: the absence of direct head-to-head randomized controlled trials (RCTs) comparing these modern ICI-based regimens. Existing network meta-analysis (NMA) has attempted to bridge this gap, but they are rapidly becoming outdated as they often lack the most recent high-quality evidence from 2024 and 2025 (16-18). Specifically, newly released data from the ETER100 trial (evaluating benmelstobart plus anlotinib) and long-term follow-up results from pivotal Phase III trials (such as KEYNOTE-581 and CheckMate 9ER) have not been comprehensively integrated into a single therapeutic hierarchy. This lack of up-to-date, synthesized evidence creates a knowledge gap regarding the relative efficacy and safety of the newest combinations versus established protocols, hindering the optimization of individualized treatment strategies in clinical practice.

Objective

To address these gaps, we conducted a systematic review and frequentist NMA to evaluate the efficacy and safety of all first-line immunotherapy-based regimens for aRCC. Our objective was to integrate the most contemporary clinical evidence—up to January 31, 2026—to provide a comprehensive ranking of treatments based on OS, progression-free survival (PFS), ORR, and treatment-related adverse events (TRAEs). By synthesizing data from 21 RCTs involving 11,779 patients, this study aims to provide high-level evidence to guide clinicians in navigating the complex array of therapeutic options and to facilitate the optimization of individualized treatment strategies (19,20). We present this article in accordance with the PRISMA reporting checklist (available at https://tau.amegroups.com/article/view/10.21037/tau-2026-0333/rc).

Methods

Search strategy and study selection

This systematic review and NMA were prospectively registered in the International Prospective Register of Systematic Reviews (PROSPERO; registration number CRD420261333477). We conducted a comprehensive search of all literature published in the PubMed, EMBASE, and Cochrane Library databases from their inception to January 31, 2026. The search strategy combined Medical Subject Headings (MeSH) with free-text keywords, covering terms such as “renal cell carcinoma,” “immunotherapy”, “Randomized clinical trial”, and specific drug names (e.g., “nivolumab”, “pembrolizumab”, “ipilimumab”, “interleukin-2”, “interferon alpha”). The search was limited to literature published in English. The detailed search strategy is provided in Table S1. Additionally, we manually screened the reference lists of included studies and relevant review articles to identify eligible trials that may have been missed by the database search.

Inclusion criteria

The inclusion criteria for this review are as follows: the study population consisted of adult patients diagnosed with previously untreated, locally advanced (stage IV) clear cell RCC, with or without sarcomatoid features. Patients who had received prior treatment or had early-stage disease were excluded. The interventions of interest included monotherapy or combination therapy with the following drugs: pembrolizumab, atezolizumab, ipilimumab, nivolumab, avelumab, high-dose IL-2, bempegaldesleukin, and IFN-α. Acceptable comparators included targeted therapies (e.g., sunitinib, sorafenib), cytokines (e.g., IFN-α, IL-2), placebo, or best supportive care. Studies comparing two non-immunotherapy regimens (e.g., targeted therapy vs. cytokine) were included only if they served as a necessary bridge to connect the network geometry. Studies comparing two targeted therapies directly against each other without contributing to the immunotherapy network connection were excluded. The primary efficacy outcomes of interest included OS, PFS, ORR, and TRAEs. Regarding study design, only RCTs were included, encompassing parallel group and cross-over trials, as well as post-hoc analyses, open-label extensions, and pooled analyses of RCTs. All non-randomized controlled studies, cohort studies, case-control studies, cross-sectional studies, single-arm studies, analyses based on hospital records or databases, and case studies/series/reports were excluded. No other restrictions were applied.

Data extraction

Two researchers independently screened the titles and abstracts of the retrieved literature to make a preliminary judgment on their eligibility. Subsequently, the full texts of potentially relevant studies were obtained for a final inclusion assessment. Any disagreements between the two researchers were resolved through discussion or consultation with a third researcher.

We used a standardized data extraction form to collect the following information from each included study: first author, year of publication, trial name, number of subjects, outcome data values, follow-up duration, National Clinical Trial (NCT) number, and the specific drugs used in the interventions.

End points

Outcome data—the primary endpoints assessed were OS and PFS. Secondary endpoints included ORR and the incidence of grade ≥3 TRAEs. For time-to-event outcomes (OS and PFS), we extracted the hazard ratios (HRs) and their 95% confidence intervals (CIs); for dichotomous outcomes (ORR and TRAEs), we extracted the raw event data (the absolute number of events and the total number of randomized patients) in each treatment arm.

Quality assessment

The quality of the identified evidence was assessed using the Cochrane Risk of Bias 2 (RoB 2) tool. We evaluated six key domains: random sequence generation, allocation concealment, blinding of participants and personnel, blinding of outcome assessment, incomplete outcome data, and selective reporting.

Statistical analysis

We performed a frequentist NMA for all assessed outcomes (OS, PFS, ORR, and TRAEs) using R software (version 4.5.1) utilizing the “netmeta” package. For the dichotomous outcome of ORR, log odds ratios (ORs) and their standard errors were not directly extracted as reported effect sizes; instead, they were calculated by constructing standard 2×2 contingency tables using the raw number of responders (complete or partial response) and non-responders (stable disease, progressive disease, or death) relative to the total sample size in each arm via the pairwise function in R. Both fixed-effect and random-effects models were considered for each analysis, and the random-effects model was ultimately selected to provide more conservative and generalizable estimates. The results for OS and PFS are presented as HRs with their 95% CIs; the results for ORR and TRAEs are reported as ORs and relative risks (RRs) alongside their 95% CIs, respectively. We used the surface under the cumulative ranking (SUCRA) curve to estimate the relative ranking of each treatment regimen for each outcome. SUCRA values range from 0 to 1, where a higher value indicates a higher probability that the treatment is the most effective or safest option.

To ensure the validity of the random-effects NMA, the assumption of consistency was rigorously evaluated through both global and local approaches. Global heterogeneity and inconsistency across the network were assessed using the design-by-treatment interaction model. Local inconsistency between direct and indirect evidence for each split-able pairwise comparison was scrutinized using the node-splitting method via the separate indirect from direct evidence (SIDE) approach. Furthermore, to evaluate the stability of our primary survival findings and mitigate the potential impact of immature data, a sensitivity analysis was performed for the OS endpoint by systematically excluding the novel ETER100 trial from the evidence network.

Results

Included studies

A total of 4,152 records were initially identified through database searches. After the removal of 216 duplicate records, 3,936 articles proceeded to the title screening stage, during which 2,259 were excluded. Subsequently, the abstracts of 1,677 articles were screened, leading to the further exclusion of 1,147 records. The remaining 530 full-text articles were assessed for eligibility, and those that did not meet the Population, Intervention, Comparator, and Outcome (PICO) inclusion criteria for this study were excluded for the following reasons: population (n=115), study design (n=64), outcome (n=87), intervention (n=93), or other (n=144). Ultimately, 27 articles reporting on 21 unique RCTs met the eligibility criteria and were included in this systematic review and NMA (7,10-15,21-40). Notably, this analysis incorporates recently released data from the ETER100 trial and long-term follow-up results of pivotal phase III trials (e.g., KEYNOTE-581 and CheckMate 9ER), providing the most up-to-date evidence through January 2026. The detailed selection process is illustrated in the PRISMA flow diagram shown in Figure 1.

Figure 1 PRISMA flow diagram.

The detailed baseline characteristics of the 21 included trials (derived from the 27 publications) are summarized in Table 1. These trials collectively enrolled 11,779 patients with aRCC. The studies evaluated first-line immunotherapy strategies for RCC, with a primary focus on combination regimens involving ICIs. These included ICI plus tyrosine kinase inhibitor (TKI) combinations (e.g., pembrolizumab plus lenvatinib, nivolumab plus cabozantinib, avelumab plus axitinib, pembrolizumab plus axitinib), a dual ICI combination (nivolumab plus ipilimumab), and an ICI plus an anti-VEGF antibody combination (atezolizumab plus bevacizumab). The control arms in these trials primarily utilized standard-of-care TKIs (most commonly sunitinib) or earlier cytokine-based therapies, such as IFN-α. A subset of trials also investigated earlier treatment regimens, including IL-2 and IFN-α, as monotherapy or in combination. The primary efficacy endpoints assessed across the trials were ORR, PFS, and OS. The median follow-up duration varied considerably among the studies, ranging from 6.0 to 99.1 months. The methodological quality of the included trials was evaluated using the Cochrane RoB 2 tool. The comprehensive risk of bias assessment for each study is presented in Figure 2.

Figure 2 Quality assessment.

OS (Figures 3-5)

Figure 3 Evidence network. (A) Overall survival; (B) progression-free survival; (C) objective response rate; (D) treatment-related adverse events. IFN-alfa, interferon-alpha; IL-2, interleukin-2; VEGFR-TKI, vascular endothelial growth factor receptor tyrosine kinase inhibitor.

Figure 4 Forest plot of the meta-analysis. (A) Overall survival; (B) progression-free survival; (C) objective response rate; (D) treatment-related adverse events. CI, confidence interval; HR, hazard ratio; IFN-alfa, interferon-alpha; IL-2, interleukin-2; VEGFR-TKI, vascular endothelial growth factor receptor tyrosine kinase inhibitor.

Figure 5 SUCRA ranking of treatments. (A) Overall survival; (B) progression-free survival; (C) objective response rate; (D) treatment-related adverse events. IFN-alfa, interferon-alpha; IL-2, interleukin-2; SUCRA, surface under the cumulative ranking; VEGFR-TKI, vascular endothelial growth factor receptor tyrosine kinase inhibitor.

The evidence network for OS, summarizing the direct and indirect comparisons among all included first-line treatment strategies, is presented in Figure 3A. The analysis revealed significant differences among the various first-line treatment strategies. Figure 4A presents a forest plot comparing the network estimates of each treatment regimen against sunitinib as the common reference, while the comprehensive pairwise comparisons among all treatment strategies are presented in the league table (Figure S1). Notably, six immunotherapy-based combination regimens demonstrated a statistically significant improvement in OS compared to sunitinib in the pooled network synthesis. The most pronounced benefits were observed with benmelstobart plus anlotinib (HR: 0.66, 95% CI: 0.48–0.91) and pembrolizumab plus lenvatinib (HR: 0.66, 95% CI: 0.49–0.88). Notably, the OS data for benmelstobart plus anlotinib were derived from the ETER100 trial, where OS was reported as a secondary endpoint with immature follow-up. Other combination regimens that also yielded significant OS improvements in the network comparison included nivolumab plus ipilimumab (HR: 0.72, 95% CI: 0.62–0.83), pembrolizumab plus axitinib (HR: 0.73, 95% CI: 0.60–0.88), the triplet regimen of cabozantinib plus nivolumab and ipilimumab (HR: 0.73, 95% CI: 0.58–0.93), and nivolumab plus cabozantinib (HR: 0.79, 95% CI: 0.65–0.96). In contrast, the combinations of atezolizumab plus bevacizumab, avelumab plus axitinib, and bempegaldesleukin plus nivolumab did not yield a statistically significant network OS benefit over sunitinib. Furthermore, earlier therapeutic regimens, including IFN-α and bevacizumab-based combinations, resulted in significantly inferior OS outcomes compared to sunitinib.

The SUCRA analysis provided a hierarchical ranking of the treatment regimens based on their probability of offering the best OS benefit (Figure 5A). Pembrolizumab plus lenvatinib ranked highest with a SUCRA score of 0.859, indicating it is the most effective treatment strategy. This was followed closely by benmelstobart plus anlotinib (SUCRA: 0.851), nivolumab plus ipilimumab (SUCRA: 0.780), pembrolizumab plus axitinib (SUCRA: 0.753), and cabozantinib plus nivolumab and ipilimumab (SUCRA: 0.738). The lowest-ranked treatments included bevacizumab-based combinations and IFN-α (SUCRA: 0.029). Specifically, bevacizumab plus everolimus and bevacizumab plus temsirolimus demonstrated poor performance in terms of OS.

To evaluate the potential impact and robustness of the network findings given the immature nature of the OS data from the ETER100 trial, we performed a pre-planned sensitivity analysis by completely excluding the benmelstobart plus anlotinib regimen from the OS network. The results demonstrated that the overall network hierarchy and relative treatment effects remained highly stable and consistent. Following the exclusion of ETER100, pembrolizumab plus lenvatinib retained its top rank with an increased SUCRA score of 0.889 (vs. 0.859 in the main analysis), followed by nivolumab plus ipilimumab (SUCRA: 0.819 vs. 0.780), pembrolizumab plus axitinib (SUCRA: 0.791 vs. 0.753), and the triplet regimen of cabozantinib plus nivolumab and ipilimumab (SUCRA: 0.775 vs. 0.738). Crucially, the network HRs for all remaining active regimens versus sunitinib remained virtually identical, preserving both their numerical values and statistical significance (e.g., pembrolizumab plus lenvatinib: HR: 0.66, 95% CI: 0.49–0.88; nivolumab plus ipilimumab: HR: 0.72, 95% CI: 0.62–0.83). This indicates that the core conclusions of our NMA are robust and do not heavily rely on the inclusion of this single novel data point. The detailed forest plot, SUCRA ranking stability plot, and league table for this sensitivity analysis are provided in the supplementary material (Figures S2-S4 and Table S2).

PFS (Figures 3-5)

The network structure for PFS comparisons is shown in Figure 3B. Results indicated that most immunotherapy-based combination regimens were superior to sunitinib, which served as the common comparator (Figure 4B), while the comprehensive pairwise comparisons among all treatment strategies are presented in the league table (Figure S5). Specifically, seven combination therapies demonstrated a statistically significant improvement in PFS over sunitinib. The most pronounced benefit was observed with pembrolizumab plus lenvatinib (HR: 0.47, 95% CI: 0.38–0.58). This was followed by benmelstobart plus anlotinib (HR: 0.53, 95% CI: 0.42–0.67), which also showed robust efficacy. Other highly effective combinations included nivolumab plus cabozantinib (HR: 0.58, 95% CI: 0.49–0.69), avelumab plus axitinib (HR: 0.66, 95% CI: 0.57–0.77), pembrolizumab plus axitinib (HR: 0.68, 95% CI: 0.58–0.80), and cabozantinib plus nivolumab and ipilimumab (HR: 0.72, 95% CI: 0.57–0.91). Atezolizumab plus bevacizumab also showed a statistically significant PFS benefit (HR: 0.86, 95% CI: 0.75–1.00). Notably, the combination of nivolumab plus ipilimumab did not result in a statistically significant improvement in PFS compared to sunitinib (HR: 0.88, 95% CI: 0.75–1.03). Conversely, older therapeutic strategies, such as IFN-α and bevacizumab-based regimens, were significantly inferior to sunitinib in terms of PFS prolongation.

The SUCRA analysis provided a probabilistic ranking of each treatment regimen’s efficacy in improving PFS (Figure 5B). The results confirmed that pembrolizumab plus lenvatinib was the most effective strategy, achieving a SUCRA score of 0.980. Notably, benmelstobart plus anlotinib ranked second with a high SUCRA score of 0.921, reflecting its superior competitive position in PFS. These were followed by other ICI + TKI combinations, including nivolumab plus cabozantinib (SUCRA: 0.868), avelumab plus axitinib (SUCRA: 0.769), and pembrolizumab plus axitinib (SUCRA: 0.744). The IFN-α regimen remained one of the poorest-performing treatments for PFS, with a SUCRA score of only 0.053.

ORR (Figures 3-5)

The network comparisons for ORR are depicted in Figure 3C. As shown in Figure 4C, the analysis demonstrated that several immunotherapy-based combination regimens significantly increased the likelihood of tumor response compared to sunitinib, with the full pairwise comparisons detailed in the league table (Figure S6). Five combination therapies exhibited a statistically significant improvement in ORR. The most pronounced benefit was observed with benmelstobart plus anlotinib, which yielded an OR of 7.55 (95% CI: 3.99–14.28), followed by pembrolizumab plus lenvatinib (OR: 4.18, 95% CI: 2.29–7.61). Other regimens that also significantly improved ORR included avelumab plus axitinib (OR: 3.12, 95% CI: 1.75–5.57), nivolumab plus cabozantinib (OR: 3.03, 95% CI: 1.65–5.56), and pembrolizumab plus axitinib (OR: 2.33, 95% CI: 1.31–4.11). Notably, in the present analysis, the improvements observed with nivolumab plus ipilimumab (OR: 1.26, 95% CI: 0.72–2.21) and cabozantinib plus nivolumab and ipilimumab (OR: 1.67, 95% CI: 0.73–3.83) did not reach statistical significance. Conversely, older regimens such as sorafenib (OR: 0.16, 95% CI: 0.04–0.69), sorafenib plus IFN-α (OR: 0.13, 95% CI: 0.02–0.78), and IFN-α monotherapy (OR: 0.28, 95% CI: 0.14–0.58) remained significantly inferior to sunitinib.

The SUCRA analysis provided a probabilistic ranking of the regimens’ effectiveness in achieving tumor response (Figure 5C). Benmelstobart plus anlotinib was identified as the most effective strategy with a SUCRA score of 0.993, followed by pembrolizumab plus lenvatinib (SUCRA: 0.921) and avelumab plus axitinib (SUCRA: 0.862). In contrast, regimens such as sorafenib plus IFN-α (SUCRA: 0.047) and sorafenib monotherapy (SUCRA: 0.063) were ranked as the least effective.

TRAEs (Figures 3-5)

The evidence network for TRAEs (Grade ≥3) is summarized in Figure 3D. Pairwise comparisons from the forest plot (Figure 4D) elucidated the RR of adverse events (AEs) among the treatment regimens compared to sunitinib, while the complete comparisons between all pairs of treatments are provided in the league table (Figure S7). Several regimens were associated with a significantly lower risk of high-grade AEs: atezolizumab (RR: 0.30, 95% CI: 0.18–0.52), bempegaldesleukin plus nivolumab (RR: 0.46, 95% CI: 0.31–0.67), and atezolizumab plus bevacizumab (RR: 0.76, 95% CI: 0.59–1.00). The risk of AEs with the benmelstobart plus anlotinib regimen was comparable to sunitinib (RR: 1.01, 95% CI: 0.72–1.40). In contrast, treatments such as IFN-α plus IL-2 (RR: 21.11, 95% CI: 2.58–172.59) and IL-2 monotherapy (RR: 18.80, 95% CI: 2.31–152.78) posed a substantially higher risk of severe toxicity.

The safety ranking based on SUCRA probabilities (Figure 5D) further clarifies the tolerability profiles, where higher scores indicate a lower risk of Grade ≥3 AEs. Immunotherapy monotherapies were ranked as the most tolerable options, with nivolumab ranking first (SUCRA: 0.974), followed by atezolizumab (SUCRA: 0.964) and bempegaldesleukin plus nivolumab (SUCRA: 0.908). The safety profile of benmelstobart plus anlotinib (SUCRA: 0.618) was ranked similarly to sunitinib (SUCRA: 0.629). Conversely, regimens containing IFN-α plus IL-2 (SUCRA: 0.022) and IL-2 monotherapy (SUCRA: 0.050) demonstrated the poorest safety profiles.

Assessment of heterogeneity and consistency

Global and local consistency and heterogeneity were systematically evaluated using the design-by-treatment interaction model and the node-splitting method (SIDE approach). For efficacy outcomes, low heterogeneity and no significant global or local inconsistency were observed for both ORR (Cochran’s Q=6.71, df =4, P=0.15, I²=40.4%; between-designs inconsistency Q=3.92, P=0.14) and PFS (Cochran’s Q=1.99, df =3, P=0.575, I²=0%; between-designs inconsistency Q=0.22, P=0.64), with all splittable node-splitting comparisons yielding non-significant differences between direct and indirect evidence (all P values >0.05). For OS, the evaluation of inconsistency and node-splitting was not applicable [I²= not available (NA), df =0] due to the absence of closed loops within the network geometry, indicating intrinsic consistency. Regarding safety (AEs), moderate global heterogeneity was detected (I²=51.1%; global Cochran’s Q=8.18, df =4, P=0.09) with significant within-design heterogeneity for the bevacizumab + IFN-α vs. IFN-α comparison (Q=4.16, P=0.042); however, the overall between-designs inconsistency remained non-significant (Q=3.33, P=0.19) and local consistency was well-maintained across all pairwise comparisons (all P values >0.05). The corresponding local consistency forest plots are provided in the supplementary material (Figures S8-S10), collectively confirming that the consistency assumption held robustly across all network models.

Discussion

This NMA provides a comprehensive and up-to-date evaluation of first-line immunotherapy-based regimens for the unstratified overall population of aRCC, synthesizing evidence from 21 RCTs involving 11,779 patients. Our findings confirm the paradigm shift in aRCC treatment, demonstrating that modern immunotherapy-based combinations offer substantial efficacy benefits over the previous standard of care, sunitinib, and historical cytokine therapies (7,20). Crucially, our findings contextualize and extend prior foundational meta-analyses, such as that by Massari et al. [2021] (41), which systematically established the superior efficacy of immune-based combination strategies over historical monotherapies in metastatic RCC. Within this overall cohort context, the combination of pembrolizumab plus lenvatinib consistently ranks as one of the most effective first-line treatments across all survival and response endpoints (14). However, the emergence of benmelstobart plus anlotinib (based on the ETER100 trial) introduces a potent new contender, particularly regarding tumor response and OS (40).

The supremacy of pembrolizumab plus lenvatinib was highlighted in the SUCRA rankings, where it achieved the highest probability of being the optimal treatment for both OS (SUCRA: 0.859) and PFS (SUCRA: 0.980). This robust efficacy is likely attributable to the synergistic mechanisms between lenvatinib, a potent multi-targeted TKI, and pembrolizumab, a PD-1 inhibitor (14). Notably, benmelstobart plus anlotinib demonstrated the most significant ORR (OR: 7.55, 95% CI: 3.99–14.28) and a competitive OS benefit (SUCRA: 0.851), ranking second only to pembrolizumab plus lenvatinib. Contrary to some preliminary observations, our analysis confirms that this combination also provides a statistically significant improvement in PFS compared to sunitinib (HR: 0.53, 95% CI: 0.42–0.67). This profile—combining the highest ORR with top-tier OS and PFS—suggests that benmelstobart plus anlotinib is a highly effective strategy for achieving both rapid tumor shrinkage and long-term survival (40).

Other ICI + TKI combinations, including nivolumab plus cabozantinib and pembrolizumab plus axitinib, also demonstrated improvements in OS and PFS compared to sunitinib, further solidifying the role of this class (10,11). Notably, our updated analysis demonstrates that pembrolizumab plus axitinib also provides a statistically significant improvement in ORR (OR: 2.33, 95% CI: 1.31–4.11), aligning it with other highly active ICI + TKI regimens. An important finding remains the distinct profile of the dual checkpoint blockade, nivolumab plus ipilimumab (25). While it provides a durable OS benefit (HR: 0.72, 95% CI: 0.62–0.83), its effect on PFS was not statistically superior to sunitinib in the overall population (HR: 0.88, 95% CI: 0.75–1.03). These distinct therapeutic profiles underscore the value of biomarker-driven approaches, particularly risk-stratified treatment selection. In clinical practice, the International Metastatic RCC Database Consortium (IMDC) risk categories serve as a critical biomarker; for instance, dual ICI combinations historically yield maximum durable survival benefits in intermediate- and poor-risk patients, whereas ICI + TKI regimens provide rapid, protocol-independent tumor responses across all risk categories, making tailored patient selection paramount (18,25).

While efficacy is paramount, safety remains a critical consideration. Our analysis revealed a clear efficacy-safety trade-off. The treatments with the most favorable safety profiles were the ICI monotherapies, nivolumab and atezolizumab, which ranked highest in the SUCRA analysis for Grade ≥3 AEs. In the era of combination therapies, understanding and managing the unique toxicity spectrum of checkpoint inhibitors is essential. As characterized by Ciccarese et al. [2016] (42), immune-related adverse events (irAEs) involve a novel spectrum of autoimmune-like toxicities across various organ systems, requiring meticulous clinical monitoring and prompt management, such as corticosteroid intervention, to maintain treatment compliance. In our network, the new combination of benmelstobart plus anlotinib showed a safety profile comparable to sunitinib (RR: 1.01, 95% CI: 0.72–1.40), suggesting it is a tolerable alternative among the high-efficacy regimens. In contrast, older cytokine-based therapies exhibited both inferior efficacy and extreme toxicity, reinforcing their displacement from clinical practice (20).

The strengths of our study include the inclusion of the latest clinical trial data, such as the ETER100 trial, which enhances the contemporary relevance of our findings. Nevertheless, several limitations layer our findings. Crucially, a notable limitation involves the maturity of the survival data for the benmelstobart plus anlotinib regimen from the ETER100 trial. In its primary publication, OS was a secondary endpoint with relatively immature follow-up and did not achieve statistical significance (P=0.07). While our network meta-analysis ranks this regimen highly based on the currently available data, this finding should be interpreted with clinical caution. Reassuringly, our sensitivity analysis excluding the ETER100 trial yielded identical HRs and ranking hierarchies for all other combination therapies, demonstrating the statistical robustness of the broader network. Nevertheless, future updates with mature OS data from the ETER100 trial are warranted to definitively confirm the long-term survival benefits of this novel combination. Crucially, another distinct limitation regarding the ETER100 trial is its geographic and ethnic uniformity, as the trial cohort consists solely of Chinese (ethnically Asian) patients. Consequently, it intrinsically differs from global multi-center phase III trials in terms of racial composition, healthcare insurance structures, and underlying socioeconomic factors, which may restrict the global generalizability of its top-ranked outcomes. Furthermore, the use of aggregate data precludes subgroup analyses based on IMDC risk categories, which are known to influence treatment outcomes (43). Finally, the lack of head-to-head trials between several modern combinations means conclusions are based on indirect evidence, a fundamental limitation of NMA (18).

Conclusions

In conclusion, this systematic review and NMA establishes a comparative hierarchy of efficacy and safety among first-line immunotherapy-based treatments specifically within the unstratified, overall population of aRCC. Within this global context, pembrolizumab plus lenvatinib and benmelstobart plus anlotinib demonstrate top-tier performance for survival and tumor response, respectively. Crucially, these network estimates must be interpreted with clinical caution and should not be viewed as unqualified or absolute clinical recommendations, as treatment superiority in aRCC is heavily driven by distinct IMDC risk categories. In clinical practice, therapeutic decisions must remain highly individualized and strictly tailored to the patient’s specific IMDC risk profile rather than relying solely on global rankings.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the PRISMA reporting checklist. Available at https://tau.amegroups.com/article/view/10.21037/tau-2026-0333/rc

Peer Review File: Available at https://tau.amegroups.com/article/view/10.21037/tau-2026-0333/prf

Funding: This work was supported by the Science and Technology Research Project of Jiangxi Provincial Department of Education (No. GJJ2403607).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tau.amegroups.com/article/view/10.21037/tau-2026-0333/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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