Recent FDA approvals of regimens targeting programmed death 1 (PD-1) in combination with anti-CTLA-4 or with VEGF tyrosine kinase inhibitors are reshaping front-line therapy for metastatic kidney cancer. In parallel, therapeutics specific for programmed death ligand 1 (PD-L1), one of the two major ligands for PD-1, are under continued investigation. Surprisingly, not all PD-1 and PD-L1 agents lead to similar clinical outcomes, potentially due to biological differences in the cellular expression and regulation of these targets. Here, we review current clinical data on combination immune checkpoint inhibitor therapy in metastatic kidney cancer and discuss the relevant biology of PD-1 and PD-L1. The design of future rational combination therapy trials in metastatic renal cell carcinoma will rely upon an understanding of this biology, along with an evolving understanding of immune cell populations and their functional states in the tumor microenvironment.

The discovery of programmed death 1 (PD-1) and programmed death ligand 1 (PD-L1) as a mechanism of peripheral T-cell tolerance spurred the development of multiple therapeutics blocking their critical interaction (1). In the context of kidney cancer, the PD-1–specific therapies nivolumab (2, 3) and pembrolizumab (4), as well as the PD-L1 antibody avelumab (5) are FDA-approved in combination with other therapies for metastatic renal cell carcinoma (RCC). In the past 14 months, four phase III trials have tested the hypothesis that immunotherapy (I/O)-based combinations are efficacious as first-line therapy in metastatic RCC (Table 1). Combination therapies based on anti-PD-1 antibodies, pembrolizumab plus axitinib and nivolumab plus ipilimumab, have improved overall (OS) relative to sunitinib (3, 4). In contrast, combination therapies with anti-PD-L1 antibodies including avelumab plus axitinib (Javelin-101; ref. 5) or atezolizumab plus bevacizumab (ImMotion 151; ref. 6) have not yet demonstrated an overall survival benefit. Overall survival data in these anti-PD-L1 combination trials are still immature, and ultimately a survival benefit might be observed with further follow-up. Despite this finding, it is notable that occasional complete responses were reported with both anti-PD-1 and anti-PD-L1 combination regimens. Caution should be exercised in cross-trial comparison due to potential differences in baseline patient characteristics. However, the OS benefits documented in anti-PD-1 combination therapy trials, but not in anti-PD-L1 immunotherapy studies, highlights potential advantages to targeting PD-1 as compared with PD-L1 in specific clinical contexts.

Table 1.

Summary of completed phase III trials in metastatic RCC evaluating combination immune therapies.

Nivolumab + IpilimumabPembrolizumab + AxitinibAvelumab + AxitinibAtezolizumab + Bevacizumab
Trial CheckMate 214 Keynote 426 Javelin 101 ImMotion 151 
N 1,096 861 1,096 915 
PD-L1+ (%)a 23.0% 59.3% 61.0% 40.0% 
PD-L1 assay/cutoff Dako 28-8 (⁠|$ \ge $|1% TC) Dako 22C-3 (CPS |$ \ge$|1%) Ventana SP263 (⁠|$ \ge $|1% IC) Ventana SP-142 (>1% IC) 
Risk category 
 Favorable 23.0% 31.9% 21.3% 20.0% 
 Intermediate 61.0% 55.1% 61.3% 69.0% 
 Poor 17.0% 13.0% 16.3% 12.0% 
Liver metastases (%) 24.5% 15.0% NR 17.0% 
Median follow-up (months) 32.4 12.8 12.0 24.0 
ORRb 41.0% 59.3% 51.4% 37.0% 
CRb 11.0% 5.8% 3.4% 5.0% 
PFS (months) 
 Combination arm 9.7 15.1 13.8 11.2 
 Sunitinib arm 9.7 11.1 8.4 8.4 
 HR (CI) 0.85 (95.0% CI, 0.73–0.98) 0.69 (95% CI, 0.57–0.84) 0.69 (95.0% CI, 0.56–0.84) 0.83 (95.0% CI, 0.70–0.97) 
OS (months) 
 Combination arm NR NR NR 33.6 
 Sunitinib arm 37.9 NR NR 34.9 
 HR (CI) 0.71 (95.0% CI, 0.59–0.86) 0.53 (95% CI, 0.38–0.74) 0.78 (95% CI, 0.55–1.08) 0.93 (95% CI, 0.76–1.14) 
Nivolumab + IpilimumabPembrolizumab + AxitinibAvelumab + AxitinibAtezolizumab + Bevacizumab
Trial CheckMate 214 Keynote 426 Javelin 101 ImMotion 151 
N 1,096 861 1,096 915 
PD-L1+ (%)a 23.0% 59.3% 61.0% 40.0% 
PD-L1 assay/cutoff Dako 28-8 (⁠|$ \ge $|1% TC) Dako 22C-3 (CPS |$ \ge$|1%) Ventana SP263 (⁠|$ \ge $|1% IC) Ventana SP-142 (>1% IC) 
Risk category 
 Favorable 23.0% 31.9% 21.3% 20.0% 
 Intermediate 61.0% 55.1% 61.3% 69.0% 
 Poor 17.0% 13.0% 16.3% 12.0% 
Liver metastases (%) 24.5% 15.0% NR 17.0% 
Median follow-up (months) 32.4 12.8 12.0 24.0 
ORRb 41.0% 59.3% 51.4% 37.0% 
CRb 11.0% 5.8% 3.4% 5.0% 
PFS (months) 
 Combination arm 9.7 15.1 13.8 11.2 
 Sunitinib arm 9.7 11.1 8.4 8.4 
 HR (CI) 0.85 (95.0% CI, 0.73–0.98) 0.69 (95% CI, 0.57–0.84) 0.69 (95.0% CI, 0.56–0.84) 0.83 (95.0% CI, 0.70–0.97) 
OS (months) 
 Combination arm NR NR NR 33.6 
 Sunitinib arm 37.9 NR NR 34.9 
 HR (CI) 0.71 (95.0% CI, 0.59–0.86) 0.53 (95% CI, 0.38–0.74) 0.78 (95% CI, 0.55–1.08) 0.93 (95% CI, 0.76–1.14) 

Note: HR with statistically significant confidence intervals are in bold.

Abbreviations: CPS, combined positive score calculated as the number of (total PD-L1 + TC and IC)/divided by total number of TC x 100; IC, immune Cells; NR, not reported; TC, tumor cells.

aPercent PD-L1 positive in combination I/O arm. PD-L1 cutoff and compartment evaluated differs in each trial.

bORR and CR rate in combination I/O arm.

Observations in other tumor types support the concept of non-equivalence between PD-1 and PD-L1 targeted therapeutics. In bladder cancer, the anti-PD-1 agent pembrolizumab showed an OS benefit compared with second-line treatment in Keynote-045 (7), whereas the anti-PD-L1 antibody atezolizumab did not show an OS benefit when compared with second-line chemotherapy in a similar patient population in the IMVigor211 trial (8). In NSCLC, the anti-PD-1 antibody pembrolizumab improved OS relative to second-line chemotherapy in Keynote-010 (9), whereas the anti-PD-L1 antibody avelumab failed to improve OS in a similar cohort of patients (10). Notwithstanding the limitations of cross-trial comparisons, the discrepancies in clinical outcome between PD-1 and PD-L1 antibodies beg a lingering question: are these therapeutics equivalent?

Fundamental differences in the biologic mechanisms of anti-PD-1 and anti-PD-L1 may underlay these potentially disparate clinical outcomes; thus, understanding these nuances is critical to the design of next-generation combinatorial strategies. Herein, we describe some key distinctions in PD-1 and PD-L1 biology in terms of cell type–specific expression, differential regulation, and the physiologic effects of blockade. The relative contribution of antibody directed cell cytotoxicity (ADCC) for PD-L1 therapeutics is also discussed. Finally, we summarize ongoing clinical activity using these therapeutics in combination regimens.

T-cell activation is initiated by the engagement of a T-cell receptor (TCR) with its cognate peptide-MHC complex—along with an appropriate costimulatory signal (Signal 2). In this setting, the primary biologic function of PD-1 is to maintain a desirable range of T-cell activation so as to prevent rampant autoimmunity (11). Upon T-cell activation, PD-1 is upregulated within 12 to 36 hours and its interaction with PD-L1 and/or PD-L2 downmodulates T-cell proliferation and effector function (Fig. 1; ref. 12). Biochemically, PD-1 binding to either PD-L1 or PD-L2 (13) activates the tyrosine phosphatase SHP-2 in the PD-1–expressing cell; this directly dampens T-cell activation by dephosphorylating the TCR and costimulatory molecules like CD28 (14). In the setting of chronic antigen stimulation, PD-1 preserves T-cell clones that might otherwise undergo activation-induced cell death. As a consequence of its biology, PD-1 expression is both a marker of initial T-cell activation as well as a marker of several states of functional exhaustion. Those states are defined in part by the coexpression of additional immune checkpoint molecules like LAG-3 and TIM-3 (15). Consequently, not all PD-1–expressing T cells behave as functionally exhausted T cells, and additional cell surface markers and epigenetic signatures are required to more completely define immune cell subsets with diminished effector capacity (recently reviewed in detail; ref. 16).

Figure 1.

PD-1/PD-L1 targeted therapeutics in renal cell carcinoma. Overview of current immunotherapy targets in renal cell carcinoma. The PD-1 antibodies pembrolizumab, nivolumab, and spartalizumab prevent interaction with PD-L1 and PD-L2. In contrast, the PD-L1 antibodies avelumab, atezolizumab, and durvalumab prevent PD1 ligation, but leave PD-1 and PD-L2 ligation unopposed.

Figure 1.

PD-1/PD-L1 targeted therapeutics in renal cell carcinoma. Overview of current immunotherapy targets in renal cell carcinoma. The PD-1 antibodies pembrolizumab, nivolumab, and spartalizumab prevent interaction with PD-L1 and PD-L2. In contrast, the PD-L1 antibodies avelumab, atezolizumab, and durvalumab prevent PD1 ligation, but leave PD-1 and PD-L2 ligation unopposed.

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In general, PD-1 is expressed on activated/exhausted CD8 and CD4 T cells, although expression has been reported on a number of other populations including B cells and macrophages (17). PD-L1 may be expressed on both tumor cells or other cells in the TME, including dendritic cells, macrophages, and other myeloid populations (18). Controversy exists regarding the most important PD-L1 expressing population—with some studies suggesting that tumor expression is most critical (19, 20) and other studies highlighting expression on myeloid populations (21, 22). The second major PD-1 ligand, PD-L2, has a more restricted expression pattern with predominant expression on endothelial cells, monocytes, and dendritic cells. In a study evaluating PD-L2 expression in seven distinct tumor types, RCC had among the lowest level of tumor PD-L2 expression with relatively high stromal and endothelial cell expression of this ligand (23). The expression of PD-L2 in the TME is in general under-appreciated, especially as PD-1 has a higher binding affinity for PD-L2 than for PD-L1 (24). Indeed, the potential of PD-L2 to promote T-cell tolerance provides one potential explanation for the lack of OS benefit with PD-L1 combination therapies in kidney cancer.

As described above, PD-1 expression is initiated by T-cell activation. Expression is further modulated by a number of signals in the TME including TGFβ (25), and IFNα, which promote upregulation of PD-1 on both T cells and macrophages (26). PD-1 biology is somewhat complex, with at least 10 transcriptional factor complexes that function in modulating PD-1 activity dependent on the state of T-cell activation (reviewed in detail elsewhere; ref. 27). In acute infection, antigen clearance leads to eventual downregulation of PD-1, whereas in the context of cancer and chronic viral infection persistent antigen exposure drives continued PD-1 expression on antigen-specific T cells (28).

PD-L1 expression on immune and tumor cell subsets is largely induced by TH1 cytokines like IFNγ. Following IFNγ exposure, tumor and immune cells upregulate PD-L1 through a transcriptional program involving the JAK1/STAT signaling pathway (29). Clinically, this is important, because mutations in the JAK/STAT pathway and antigen presentation machinery have been implicated in primary and acquired resistance to PD-1 therapy in melanoma (30). At the genomic level, copy-number alterations (CNA) in the PD-L1 gene in tumor cells may also lead to increased levels of PD-L1 expression. CNA in PD-L1 at chromosome 9p24 are associated with increased tumor mutation burden (31) and are enriched in a rare but unique RCC subset with sarcomatoid pathologic features (32). The latter is of keen interest as gene signatures associated with sarcomatoid RCC pathology were enriched in patients responding to atezolizumab and bevacizumab in IMMotion 151 (33). Subgroup analysis of patients with sarcomatoid pathologic features from Checkmate 214 (nivolumab + ipilimumab; ref. 34) and Keynote 426 (pembrolizumab + axitinib; ref. 35) also demonstrated improved ORR and OS relative to sunitinib.

Additional tumor intrinsic factors may also drive PD-L1 expression to promote immune tolerance and tumor immune evasion. In clear cell RCC, HIF2α activation secondary to Von Hippel Landau (VHL) deficiency promotes PD-L1 expression in vitro (36, 37). However, clinical data supporting this association are not yet available. VHL inactivation is estimated to occur in >90% of patients with RCC either through direct mutation or promoter hyper-methylation, and one would anticipate the number of PD-L1 expressing tumor samples in RCC would be dramatically higher if this association were absolute (38). For example, in Checkmate 214, only 20% to 30% of patients with RCC had PD-L1 positive tumor cells (3). Similarly, in the COMPARZ trial evaluating pazopanib versus sunitinib, 36% of patients had PD-L1–positive specimens (39). As a consequence, the association between PD-L1 expression and VHL deficiency certainly requires additional investigation.

PD-1 ligation with PD-L1 or PD-L2 induces T cell functional exhaustion by causing distinct metabolic changes within the T cell. PD-1 binding switches the T cell energy source to fatty acid oxidation with concomitant attenuation of glycolysis (40). This metabolic switch assists in determination of T cell effector versus memory cell fates and promotes the maintenance of functional CD8 exhaustion. Similarly, attenuation of glycolysis in CD4 T cells, which may or may not be independent of PD-1 signaling, promotes regulatory T cell commitment (41). Thus PD-1 and additional costimulatory molecules, such as 4-1BB, are implicated in driving immune cell metabolic programs that lead to T cell dysfunction.

Ongoing PD-1 engagement with its cognate ligands also results in epigenetic reprogramming of T cells, which may prevent effective rescue by immune checkpoint blockade. These observations were initially based on murine studies utilizing the LCMV virus that mimics chronic antigen stimulation as is observed in cancerous states (42). In LCMV murine models, functionally exhausted T-cell remained in a PD-1HI exhausted state even after clearance of antigen, demonstrating that epigenetic mechanisms likely underlie long-lived functional exhaustion and PD-1 expression (43).

More recent data suggest that distinct epigenetic profiles define states of functional T-cell exhaustion (16). Elegant work identified the nuclear transcription factor TOX as a central regulator of epigenetic and transcriptional programs driving T-cell exhaustion (44, 45). TOX expression increases following chronic antigen stimulation, leading to a decrease in markers of self-renewal in T cells—including the key transcription factor TCF1 (46). Conversely, deletion of TOX restored CD8 T-cell function and differentiation to effector and memory phenotypes. Taken together, these studies show that that TOX is a critical driver of early T-cell exhaustion. Advancements in single-cell analysis (47) and epigenetic profiling will be critical in further defining the functional and phenotypic heterogeneity within these exhausted states, and clinical interventions aimed at altering the epigenetic phenotype of T cells remains an area of active interest (48–50).

Anti-PD-1 agents can restore the functionality of exhausted T cells through direct ligation of PD-1 on CD4 and CD8 T lymphocytes, and based on that principle may rescue an immune response relatively independent of tumor PD-L1 expression (Fig. 2A). Direct T-cell binding by an anti-PD-1 therapeutic may afford significant advantages relative to an anti-PD-L1 treatment, potentially via more rapid T-cell expansion. In patients with melanoma treated with anti-PD-1, peripheral blood profiling showed that expansion of a PD-1+ effector T-cell pool after immune checkpoint blockade correlated with clinical response (51). Relevant neoadjuvant studies illustrate peripheral occupancy of PD-1, with peripheral blood responses detected within 3 weeks on therapy (52). Peripheral blood profiling also shows that anti-PD-1 therapeutics rapidly stimulate T cells in the periphery, enabling tumor cell lysis, relatively independent of tumor volume/burden. The kinetics of T-cell expansion mediated by direct engagement of PD-1 on effector T cells may not be achievable with anti-PD-L1 agents targeting tumor and immune cells. With adoption of immunotherapy into the neoadjuvant setting in clinical trials of kidney cancer including PROSPER-RCC (53), we will gain further insights into the mechanistic and kinetic differences in PD-1 and PD-L1 occupancy and immune blockade. Similarly, adjuvant trials for high-risk RCC evaluating atezolizumab (NCT03024996) and pembrolizumab (NCT03142334) have completed accrual, and peripheral blood studies from these trials will enhance our understanding of the relative benefit of perioperative PD-L1 and PD-1 blockade.

Figure 2.

Mechanisms of PD-1 and PD-L1 targeting immunotherapy. A, PD-1 blockade exerts direct effects on immune cells upon ligation by driving distinct metabolic and epigenetic programs that reverse T-cell dysfunction. B, PD-L1 blockade on immune cells masks PD-1 ligation. PD-1 can bind PD-L2. C, ADCC from PD-L1 ligation on tumor cells permits tumor cell killing. D, PD-L1 expression on T cells permits ADCC of immune cells overexpressing PD-L1.

Figure 2.

Mechanisms of PD-1 and PD-L1 targeting immunotherapy. A, PD-1 blockade exerts direct effects on immune cells upon ligation by driving distinct metabolic and epigenetic programs that reverse T-cell dysfunction. B, PD-L1 blockade on immune cells masks PD-1 ligation. PD-1 can bind PD-L2. C, ADCC from PD-L1 ligation on tumor cells permits tumor cell killing. D, PD-L1 expression on T cells permits ADCC of immune cells overexpressing PD-L1.

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PD-L1–targeted therapies, in contrast, can induce immune tumor rejection through multiple mechanisms (summarized in Fig. 2BD). First, anti-PD-L1 therapies prevent ligation with PD-1 on immune cells like anti-PD-1 therapeutics. PD-L1 blockade also prevents ligation with the costimulatory molecule B7.1 (CD80) either in cis or in trans, which may provide a secondary mechanism for T-cell reinvigoration (54). Second, anti-PD-L1 therapeutics may also drive direct tumor cell killing through antibody-dependent cellular cytotoxicity (ADCC), in this case via PD-L1 expressed on tumor cells (Fig. 2C). In murine models, anti-PD-L1 that bind Fc receptors that mediate ADCC led to tumor regression, whereas a similar effect was not observed with anti-PD-1 therapies in those models (55).

Despite these theoretical advantages, there are now two randomized phase III trials in metastatic RCC using anti-PD-L1 therapeutics that have not yet shown an OS benefit (Table 2). OS data in these trials is still immature and longer follow-up is awaited. Although the mechanisms underlying this difference may be challenging to dissect, one possibility is that interaction between PD-1 and PD-L2 is unaffected by PD-L1 blockade, such that interactions between PD-L2 in the TME and PD-1 on T cells provides some level of ongoing suppression. A second theoretical concern involves binding to nontumor cell expressing isoforms of PD-L1, sequestering antibody that might be important in blocking the PD-1/PD-L1 interaction. Accordingly, relevant data suggest that PD-L1 expression on exosomes (56) and secreted variants of PD-L1 (57) may suppress anti-PD-L1 responses. A final potential mechanism of interest is that expression of PD-L1 on immune cells might deplete immune effector cells through ADCC in certain circumstances (Fig. 2D, reviewed below).

Table 2.

Prospects and limitations of anti-PD-1 and anti-PD-L1 immunotherapies for combination therapy in RCC.

AdvantagesDisadvantages
Anti-PD-1 
  • Targets T cells directly

  • Distinct metabolic and epigenetic changes upon PD-1 binding reverse T-cell exhaustion

  • Does not require tumor PD-L1 expression for activity

  • PD-1 occupancy on T cells observed within 3 weeks on treatment

 
  • No direct tumor effects from antibody

  • In vitro VEGF TKIs increase PD-1 expression on immune cells providing a potential resistance mechanism

 
Anti-PD-L1 
  • Targets tumor cells directly

  • May permit ADCC of tumor cells

  • May also target immunosuppressive TAMs that express PD-L1 in the TME

 
  • Does not block PD-1 binding to PD-L2

  • PD-L1 exists on exosomes and in soluble forms which may act as a “decoy” receptor for antibody therapy

  • VEGF TKI treatment permits PD-L1 upregulation on tumor cells providing a potential resistance mechanism

  • Limited engagement of anti-PD-L1 with PD-L1+ tumor T cells allows for continued T cell dysfunction

  • Potential for ADCC and elimination of PD-L1+ positive immune cells subsets (NK cells, DCs, antitumor TAM)

  • Kinetics of PD-L1 occupancy are not yet defined and complete saturation to block PD-1 ligation may not be possible

 
AdvantagesDisadvantages
Anti-PD-1 
  • Targets T cells directly

  • Distinct metabolic and epigenetic changes upon PD-1 binding reverse T-cell exhaustion

  • Does not require tumor PD-L1 expression for activity

  • PD-1 occupancy on T cells observed within 3 weeks on treatment

 
  • No direct tumor effects from antibody

  • In vitro VEGF TKIs increase PD-1 expression on immune cells providing a potential resistance mechanism

 
Anti-PD-L1 
  • Targets tumor cells directly

  • May permit ADCC of tumor cells

  • May also target immunosuppressive TAMs that express PD-L1 in the TME

 
  • Does not block PD-1 binding to PD-L2

  • PD-L1 exists on exosomes and in soluble forms which may act as a “decoy” receptor for antibody therapy

  • VEGF TKI treatment permits PD-L1 upregulation on tumor cells providing a potential resistance mechanism

  • Limited engagement of anti-PD-L1 with PD-L1+ tumor T cells allows for continued T cell dysfunction

  • Potential for ADCC and elimination of PD-L1+ positive immune cells subsets (NK cells, DCs, antitumor TAM)

  • Kinetics of PD-L1 occupancy are not yet defined and complete saturation to block PD-1 ligation may not be possible

 

An underappreciated aspect of immune checkpoint blockade is the relative contribution of T-cell–mediated tumor killing versus the potential for ADCC or complement-dependent cytotoxicity. In ADCC, FC gamma receptors (primarily FcγRIII) on the surface of macrophages and NK cells bind to the Fc portion of antibodies resulting in depletion of tumor or subsets of immune cells (Fig. 2C and D). Specific IgG subtypes are more likely to promote ADCC, with IgG1 and IgG3 antibody subtypes with a higher binding affinity for Fc receptors (Supplementary Table S1; ref. 58). Thus, anti-PD-L1 antibodies of the IgG1 isotype may lead to depletion of both tumor and immune cells. Indeed, avelumab, an IgG1 isotype antibody, can mediate ADCC and lead to effective direct tumor cell killing. In theory, ADCC can also occur on PD-L1–positive CD8 effector cells leading to elimination of immune effectors. However, no definitive evidence of the latter phenomena has been appreciated clinically. Importantly, recently presented subgroup analysis from the Javelin-101 showed no difference in activity of combination avelumab plus axitinib treatment in patients with FcγRIII polymorphisms, demonstrating that ADCC may be only a minor mechanism in anti-PD-L1 immunotherapy (59).

Ongoing efforts are focused on improving the efficiency of antibody-induced cellular cytotoxicity with immune checkpoint blockade antibodies. Through modification of glycosylation and fucosylation sites, antibodies can be engineered to have differential effects on ADCC and cellular depletion (60). To promote ADCC, an Fc-modified (non-fucosylated version) of anti-CTLA-4 is in early-phase clinical trials with the goal of regulatory T-cell depletion (NCT#03110107). Future immunotherapy combination approaches may leverage the ability to selectively deplete immunosuppressive cell subsets and potentiate antitumor responses.

In RCC, the addition of a VEGF TKI to an anti-PD-1 or PD-L1 antibody exploits a number of potentially synergistic mechanisms. VEGF in the tumor ecosystem promotes immunosuppression by decreasing T-cell trafficking to tumors, increasing immunosuppressive cytokines and initially increasing regulatory T cells. Treatment with anti-angiogenic therapies mitigates a number of the immunosuppressive effects of VEGF in preclinical models (61, 62). For example, the use of sunitinib in a preclinical RCC model decreases immunosuppressive myeloid-derived suppressor cells (MDSC), a potential mechanism of adaptive immune resistance to PD-1 immunotherapy (63). More recent preclinical data with axitinib showed antitumor efficacy not only through vascular remodeling but also through depletion of tumor-promoting mast cells and tumor-associated macrophages (64). In human RCC specimens, treatment with antiangiogenic therapy increased infiltration of CD4 and CD8 effector T cells, supporting the hypothesis that VEGF inhibition might potentiate the response to immune checkpoint blockade by promoting T-cell infiltration (65). Clearly, the immune effects of VEGF TKIs support nonredundant mechanisms of immune activation distinct from the PD1/PD-L1 axis, with TKI immune remodeling affecting the myeloid and T-cell compartment.

Although the MDSC-specific effects of TKIs provide a good rationale for combining anti-PD-L1 with VEGF therapy, there are some data suggesting that VEGF TKIs may dampen a T-cell response to cancer. Chronic inhibition of VEGF with TKIs can induce a hypoxic state within the TME with a concomitant accumulation of HIF1α (66). Multiple studies show that HIF1α accumulation induces a compensatory immunosuppressive state through recruitment of MDSCs (67), tumor-associated macrophages (68, 69), and Tregs (70). In addition, accumulation of HIF1α alters PD-L1 expression on immune cell subsets (71). That observation is supported by data from human RCC samples showing that TKIs may decrease PD-L1 expression, rendering anti-PD-L1 blockade more challenging. Of note, on-treatment biopsies from patients treated with pazopanib or sunitinib showed transient decreases in PD-L1 expression by IHC (72).

In some model systems, VEGF TKIs also decrease immune cell PD-1 expression (73). The decrease in PD-1 expression, however, is not absolute, and blockade of remaining PD-1 on T cells with anti-PD-1 therapeutics may explain the improved OS noted with combination anti-PD-1 with VEGF TKIs. Taken together, these collected observations lend support to a hypothesis that limited or intermittent VEGF TKI therapy in combination regimens might allow an even greater immune response, but at present all TKI combinations in the phase III setting have been taken continuously. Further, HIF1 inhibition may be a therapeutic approach to enhance the clinical benefit of VEGF TKI-based combinations.

Both nivolumab plus ipilimumab and pembrolizumab plus axitinib are now consensus first-line treatments for metastatic RCC. At present, the choice of first-line therapy for a given patient is not driven by a randomized, comparative trial, but rather by treatment side-effect profile, prognostic risk group, perceived benefits of complete and overall response rate and MD/patient preference. Avelumab plus axitinib is also FDA approved for first-line RCC, but so far, an OS benefit relative to sunitinib has not been documented. Finally, the FDA application for drug approval of atezolizumab plus bevacizumab was withdrawn by the manufacturer, although there may in fact be subgroups of patients with specific gene signatures that benefit from this combination (74). Taken together, these data support the use of a PD-1–based immunotherapy combination, either with pembrolizumab plus axitinib or nivolumab plus ipilimumab for first-line therapy of metastatic RCC.

The impressive response rates and OS for patients treated with combination anti-PD-1 plus anti-CTLA-4 or anti-PD-1 plus VEGF TKI therapy with a favorable side-effect profile and tolerability begs the question of utilizing a triplet therapy in the first-line setting (75). Combination nivolumab, ipilimumab, and cabozantinib has been administered safely across GU malignancies, and the activity of this triplet will be tested in a phase II expansion cohort and a randomized, phase III trial (76). Triplet therapy, however, likely over treats some patients, such that biomarker-based strategies to select patients for the appropriate mechanism and intensity of therapy is an unmet need. One additional combination for first-line treatment, pembrolizumab + lenvatinib, is currently being tested in large phase III trials. Table 3 provides a complete listing of trials currently accruing for RCC.

Table 3.

On-going immunotherapy trials in RCC.

TherapyNumberPhaseTrial IDEstimated completion date
First-line metastatic RCC trials 
 Pembrolizumab + Lenvatinib or Everolimus + Lenvatinib vs. Sunitinib (CLEAR) 1,050 III NCT02811861 February 2021 
 Nivolumab + Ipilimumab Followed by Nivolumab ± Cabozantinib (PDIGREE) 1,046 III NCT03793166 September 2021 
 Nivolumab + Ipilimumab ± Cabozantinib (COSMIC-313) 676 III NCT03937219 November 2021 
 Nivolumab + Cabozantinib vs. Sunitinib 638 III NCT03141177 May 2024 
 Nivolumab + bempegaldesleukin (CD122 agonist) vs. Cabozantinib or Sunitinib 600 III NCT03729245 June 2024 
 Nivolumab + Ipilimumab vs. Nivo/IDO vs. Nivo/Anti-Lag3 (Relatlimab) vs. Nivolumab + CCR2/CCR5 dual agonist (BMS936558) FRACTION-RCC 200 Ib/II NCT02996110 January 2022 
 Nivolumab + Cabozantinib ± Ipilimumab 152 NCT02496208 Early 2020 
 Nivolumab + Ipilimumab or Pazopanib or Sunitinib (BIONIKK Biomarker Guided Trial) 150 II NCT02960906 May 2020 
 Nivolumab with Salvage Nivolumab + Ipilimumab 120 II NCT03117309 February 2021 
 Nivolumab + Bempegaldesleukin (CD122 agonist) ± Ipilimumab 90 Ib/II NCT02983045 June 2021 
 Pembrolizumab + Cabozantinib 55 Ib/II NCT03149822 June 2020 
Advanced (second-line or later) metastatic RCC trials 
 Arginase Inhibitor (INCB001158) + Pembrolizumab 424 Ib/II NCT02903914 January 2020 
 TLR 7/8 agonist (NKTR 262) + bempegaldesleukin ± Nivolumab 393 Ib/II NCT03435640 December 2023 
 Anti-CD73 (CPI-006) ± A2AR Antagonist or Pembrolizumab 378 Ib/II NCT03454451 December 2023 
 Glutaminase Inhibitor (CB-839) + Nivolumab 299 Ib/II NCT02771626 Early 2020 
 Anti-TIM3 (MBG453) ± Spartalizumab (Anti-PD1) 250 Ib/II NCT02608268 Early 2020 
 Durvalumab ± Tremelimumab or Savolitinib 195 II NCT02819596 Early 2020 
 ApoE Agonist (RGX104) + Nivolumab 150 Ib/II NCT02922764 Early 2020 
 Anti-CSF1R (Cabiralizumab) + Anti-CD40 (APX005M) ± Nivolumab 120 Ib/II NCT03502330 October 2024 
 HIF-2a Inhibitor (PT2977) + Cabozantinib 118 II NCT03634540 September 2022 
 Axitinib + Nivolumab 98 Ib/II NCT03172754 April 2024 
 Sitravatinib + Nivolumab 60 Ib/II NCT03015740 April 2023 
 Angiopoietin-2 inhibitor (Trebananib) + Pembrolizumab 60 Ib/II NCT03239145 August 2024 
 Anti-IL1β (Gevokizumab) + Cabozantinib 60 Ib NCT03798626 December 2023 
 Guadecitabine + Durvalumab 58 Ib/II NCT03308396 December 2020 
 177Lu-J591 Anti-PSMA Radiolabeled Antibody 50 NCT00967577 December 2019 
 Anti-CD25 pyrrolobenzodiazepine toxin conjugate (Camidanlumab Tesirine) 50 NCT03621982 July 2021 
 IL-2 (Aldesleukin) + Pembrolizumab 27 NCT03260504 March 2021 
Perioperative (neoadjuvant RCC trials) 
 Nivolumab – PROSPER RCC 805 III NCT03055013 November 2023 
  MSKCC 29 Pilot NCT02595918 August 2020 
  Royal Marsden 19 Pilot NCT02446860 Late 2019 
 Avelumab + Axitinib 40 Pilot NCT03341845 August 2025 
 Durvalumab ± Tremelimumab 45 Ib NCT02762006 January 2020 
 Nivolumab + Sitravatinib 25 II NCT03680521 December 2019 
 Anti-IL1β (Canakinumab) + Spartalizumab (anti-PD1) 14 Pilot NCT04028245 2021 
Adjuvant RCC immunotherapy trials 
 Durvalumab vs. Durvalumab/Tremelimumab vs. Observation 1,750 III NCT03288532 December 2037 
 Pembrolizumab vs. Observation 950 III NCT03142334 December 2025 
 Nivolumab + Ipilimumab vs. Observation 800 III NCT03138512 July 2023 
 Atezolizumab vs. Observation 778 III NCT03024996 April 2024 
TherapyNumberPhaseTrial IDEstimated completion date
First-line metastatic RCC trials 
 Pembrolizumab + Lenvatinib or Everolimus + Lenvatinib vs. Sunitinib (CLEAR) 1,050 III NCT02811861 February 2021 
 Nivolumab + Ipilimumab Followed by Nivolumab ± Cabozantinib (PDIGREE) 1,046 III NCT03793166 September 2021 
 Nivolumab + Ipilimumab ± Cabozantinib (COSMIC-313) 676 III NCT03937219 November 2021 
 Nivolumab + Cabozantinib vs. Sunitinib 638 III NCT03141177 May 2024 
 Nivolumab + bempegaldesleukin (CD122 agonist) vs. Cabozantinib or Sunitinib 600 III NCT03729245 June 2024 
 Nivolumab + Ipilimumab vs. Nivo/IDO vs. Nivo/Anti-Lag3 (Relatlimab) vs. Nivolumab + CCR2/CCR5 dual agonist (BMS936558) FRACTION-RCC 200 Ib/II NCT02996110 January 2022 
 Nivolumab + Cabozantinib ± Ipilimumab 152 NCT02496208 Early 2020 
 Nivolumab + Ipilimumab or Pazopanib or Sunitinib (BIONIKK Biomarker Guided Trial) 150 II NCT02960906 May 2020 
 Nivolumab with Salvage Nivolumab + Ipilimumab 120 II NCT03117309 February 2021 
 Nivolumab + Bempegaldesleukin (CD122 agonist) ± Ipilimumab 90 Ib/II NCT02983045 June 2021 
 Pembrolizumab + Cabozantinib 55 Ib/II NCT03149822 June 2020 
Advanced (second-line or later) metastatic RCC trials 
 Arginase Inhibitor (INCB001158) + Pembrolizumab 424 Ib/II NCT02903914 January 2020 
 TLR 7/8 agonist (NKTR 262) + bempegaldesleukin ± Nivolumab 393 Ib/II NCT03435640 December 2023 
 Anti-CD73 (CPI-006) ± A2AR Antagonist or Pembrolizumab 378 Ib/II NCT03454451 December 2023 
 Glutaminase Inhibitor (CB-839) + Nivolumab 299 Ib/II NCT02771626 Early 2020 
 Anti-TIM3 (MBG453) ± Spartalizumab (Anti-PD1) 250 Ib/II NCT02608268 Early 2020 
 Durvalumab ± Tremelimumab or Savolitinib 195 II NCT02819596 Early 2020 
 ApoE Agonist (RGX104) + Nivolumab 150 Ib/II NCT02922764 Early 2020 
 Anti-CSF1R (Cabiralizumab) + Anti-CD40 (APX005M) ± Nivolumab 120 Ib/II NCT03502330 October 2024 
 HIF-2a Inhibitor (PT2977) + Cabozantinib 118 II NCT03634540 September 2022 
 Axitinib + Nivolumab 98 Ib/II NCT03172754 April 2024 
 Sitravatinib + Nivolumab 60 Ib/II NCT03015740 April 2023 
 Angiopoietin-2 inhibitor (Trebananib) + Pembrolizumab 60 Ib/II NCT03239145 August 2024 
 Anti-IL1β (Gevokizumab) + Cabozantinib 60 Ib NCT03798626 December 2023 
 Guadecitabine + Durvalumab 58 Ib/II NCT03308396 December 2020 
 177Lu-J591 Anti-PSMA Radiolabeled Antibody 50 NCT00967577 December 2019 
 Anti-CD25 pyrrolobenzodiazepine toxin conjugate (Camidanlumab Tesirine) 50 NCT03621982 July 2021 
 IL-2 (Aldesleukin) + Pembrolizumab 27 NCT03260504 March 2021 
Perioperative (neoadjuvant RCC trials) 
 Nivolumab – PROSPER RCC 805 III NCT03055013 November 2023 
  MSKCC 29 Pilot NCT02595918 August 2020 
  Royal Marsden 19 Pilot NCT02446860 Late 2019 
 Avelumab + Axitinib 40 Pilot NCT03341845 August 2025 
 Durvalumab ± Tremelimumab 45 Ib NCT02762006 January 2020 
 Nivolumab + Sitravatinib 25 II NCT03680521 December 2019 
 Anti-IL1β (Canakinumab) + Spartalizumab (anti-PD1) 14 Pilot NCT04028245 2021 
Adjuvant RCC immunotherapy trials 
 Durvalumab vs. Durvalumab/Tremelimumab vs. Observation 1,750 III NCT03288532 December 2037 
 Pembrolizumab vs. Observation 950 III NCT03142334 December 2025 
 Nivolumab + Ipilimumab vs. Observation 800 III NCT03138512 July 2023 
 Atezolizumab vs. Observation 778 III NCT03024996 April 2024 

A potential approach to mitigate the toxicities of I/O–I/O combinations is to incorporate other anti-inflammatory medications into the first-line treatment regimens. To this end, clinical trials are on-going exploring cytokine targets including anti-IL1β (NCT04028245), anti-IL6, and anti-IL8 (NCT03400332; ref. 77) to augment the immune response and potentially improve regimen tolerability. Another potential approach might be to block TNFα in the combination therapy setting. A recent publication in animal models highlighted this approach, demonstrating increased activity of combination immunotherapy when TNFα blockade was added to anti-PD-1 plus anti-CTLA-4 (78). The wealth of treatment options available for RCC also raises the questions of optimal therapeutic sequencing which will be addressed in an upcoming trial (79). In contemporary cohorts of patients with metastatic RCC, nearly 50% of patients will not receive a second line treatment due to either disease progression or declining performance status (80). In real-world data sets, it is estimated that >80% of patients do not receive any second-line treatment (80, 81). As a consequence, maximizing the efficacy of first-line therapy is of utmost importance.

There are now three FDA-approved combination immunotherapies for the treatment of first-line kidney cancer, but only anti-PD-1-based combinations to date have illustrated an OS benefit. Blockade of PD-1 permits direct reprogramming of T cells, whereas anti-PD-L1 exerts those effect in an indirect fashion and permits binding between PD-1 and PD-L2. Although in contemporary models, the activity of anti-PD-1 + VEGF TKI appears to be additive, the remarkable gains in ORR, PFS, and OS will likely necessitate that anti-PD-1 therapeutics remain the backbone of first-line treatment for renal cell carcinoma. For the foreseeable future, the selection of first-line treatment will be guided by side-effect profile, risk group, and patient preference, whereas the next generation of first-line therapies may require clinically validated biomarkers to select the appropriate treatment regimen.

D.H. Aggen is a paid consultant for Boehringer Ingelheim. C.G. Drake is a paid consultant for AstraZeneca, Bristol-Myers Squibb, Roche/Genentech, Merck, Novartis, and Pfizer, and reports receiving speakers bureau honoraria from Bristol-Myers Squibb. B.I. Rini is a paid consultant for Pfizer, Merck, and Bristol-Myers Squibb, and reports receiving commercial research grants from Pfizer, Merck, Roche, Bristol-Myers Squibb, and AstraZeneca. No other potential conflicts of interest were disclosed.

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