Expression of T-Cell Exhaustion Molecules and Human Endogenous Retroviruses as Predictive Biomarkers for Response to Nivolumab in Metastatic Clear Cell Renal Cell Carcinoma

Immune checkpoint inhibitors (ICI) have revolutionized the management of several cancer types, including advanced clear cell renal cell carcinoma (ccRCC). Nivolumab (nivo; an anti–PD-1 ICI) attained FDA approval as a second-line therapy in advanced ccRCC (1), whereas the combination of nivo and ipilimumab (an anti–CTLA-4 ICI) was FDA-approved as a first-line therapy in patients with advanced ccRCC based on the results of the CheckMate-214 trial (2). However, only a minority of patients achieve durable responses when treated with ICIs and predictive biomarkers of response are urgently needed to identify patients who are more likely to benefit from ICI therapy (3).

Results from mouse models show that CD8⁺PD-1⁺ cells expressing additional inhibitory receptors are more dysfunctional; for example, CD8⁺PD-1⁺TIM-3⁺ cells are more dysfunctional than CD8⁺PD-1⁺TIM-3⁻ cells (4, 5). Our group previously showed that levels (measured as either percentage or density) of CD8⁺ tumor-infiltrating cells (TIC) expressing PD-1 but not TIM-3 and LAG-3 (CD8⁺PD-1⁺TIM-3⁻LAG-3⁻ TIC) predict response to nivo in patients with metastatic ccRCC (mccRCC; ref. 6).

Data from different groups recently suggested that CD8⁺PD-1⁺TCF7⁺ progenitor or stem-like exhausted TIC are the major population that provides the proliferative burst after PD-1 ICI (7) and their presence is associated with response and better survival in ICI-treated patients with melanoma (8–10), whereas scarcity of MHC-II immune niches enriched in CD8⁺TCF7⁺ cells predicted progressive disease in a small cohort of ccRCC (11).

Human endogenous retrovirus (hERV) expression correlates with high levels of immune infiltration and increased cytolytic activity and immune checkpoint expression (PD-1, PD-L1, CTLA-4, CD80, BTLA, HVEM, LAG3) in ccRCC (12–14), and has been associated with response to ICI in two small ccRCC cohorts (12, 13). Of note, HERV-E/ERVE-4 (now referred to as ERVE-4) and hERV4700-derived epitopes elicit a tumor-restricted CD8⁺ T-cell–mediated immune response (13, 15, 16) and a phase 1 trial is currently evaluating the safety and efficacy of the infusion of ERVE-4 TCR-transduced CD8⁺/CD34⁺ enriched T cells (NCT03354390).

Here, we aimed to validate CD8⁺PD-1⁺TIM-3⁻LAG-3⁻ TIC as a biomarker of response to nivo and also investigate the predictive value of tumor cell (TC) PD-L1, CD8⁺TCF7⁺ TIC, and hERV expression in a randomized trial of nivo versus everolimus (evero) in advanced ccRCC (1).

We analyzed tumor tissue samples from patients enrolled in the CheckMate 025 clinical trial (CM-025; ref. 1). This was a randomized, multicenter, phase III study comparing nivo with evero in patients with advanced or mccRCC and measurable disease according to the Response Evaluation Criteria in Solid Tumors (RECIST version 1.1; ref. 17), who had received one or two previous anti-angiogenic therapy regimens. Archival formalin-fixed, paraffin-embedded (FFPE) tissue sections were collected by the study sponsor at time of enrollment and transferred to the investigators upon approval of a material transfer agreement. Written informed consent to study participation, tissue collection, and tissue analyses was provided by all patients before enrollment, according to the principles of the Declaration of Helsinki.

Objective response rate (ORR) was investigator assessed and defined as the proportion of randomized patients with a complete response (CR) or a partial response (PR) per RECIST 1.1. Durable response rate (DRR) was defined as the proportion of randomized patients with a CR or PR with responses being progression-free for ≥12 months. Progression-free survival (PFS) was defined as the time from randomization to the first documented tumor progression per RECIST 1.1 or death from any cause (patients were censored at date of last disease assessment), whereas overall survival (OS) was defined as the time from randomization to the date of death (patients alive were censored at the date of last contact).

Tissue slides previously stained with Dako PD-L1 immunohistochemical (IHC) assay (1) were acquired by the study sponsor at ×20 magnification using the Aperio AT2 Scanner (Leica Biosystems), exported as .svs files and transferred to the investigators.

The percentage of PD-L1–positive TCs was independently scored by two pathologists (M. Ficial and M. Sant'Angelo) using a cutoff value of ≥1% for membranous staining. All cases considered positive by at least one pathologist were independently reviewed by an expert GU pathologist (S. Signoretti). An agreement on the percentage of positive TC was found for discordant cases. All pathologists were blinded to the patient clinical outcomes.

A 6-plex immunofluorescence (IF) assay was optimized on a Bond RX Autostainer (Leica Biosystems) using the Opal multiplex IHC system (PerkinElmer/Akoya Biosciences Cat# NEL871001KT) and the BOND Polymer Refine Detection Kit (Leica Biosystems Cat# DS9800; see Supplementary Materials and Methods and Supplementary Table S1; DOI: dx.doi.org/10.17504/protocols.io.bjbzkip6).

Multiplex IF slides were acquired as whole-slide multispectral images at ×10 magnification using Vectra 3.0 (PerkinElmer) and five or more intra-tumoral CD8⁺T cell–enriched regions of interest (ROI; area of each ROI = 669 × 500 μm) were identified for each case and scanned at ×20 magnification. ROI multispectral images were imported into InForm v2.2.0 (PerkinElmer, RRID:SCR_019161) and deconvoluted using a multispectral library. Image analysis was performed using HALO v2.1.1637.18 (Indica Laboratories, RRID:SCR_018350). Details of the image acquisition and image analysis workflow are provided in the Supplementary Materials and Methods.

RNA sequencing (RNA-seq) and whole-exome sequencing (WES) data were obtained as previously described (18). Among these, 71 patients had both IF and RNA-seq data and 117 patients had both IF and WES data available (see Supplementary Materials and Methods).

RNA was extracted from micro-dissected tumor-enriched areas from 4-μm–thick unstained FFPE slides using the AllPrep DNA/RNA FFPE Mini Kit (Qiagen Cat# 80234). RNA concentration was assessed using the NanoDrop ND-1000 (Thermo Fisher Scientific).

Quantitative real-time PCR was performed using reverse transcribed RNA extracted from FFPE tumor tissues to assess levels of pan-ERVE-4 and hERV4700-ENV and the reference genes 18S and HPRT1 (see Supplementary Materials and Methods). A list of forward primers, reverse primers, and probes used for the assay is provided in Supplementary Table S2.

All statistical analyses are described in the Supplementary Materials and Methods. Unless indicated otherwise, 1-sided P values are presented and values ≤0.05 were considered statistically significant (see Supplementary Materials and Methods for methodology details).

Expression of PD-1, TIM-3, LAG-3, and TCF7 on CD8⁺ TIC was studied by multiparametric IF in 304 (nivo = 145, evero = 159) patients (Supplementary Fig. S1) and evaluable IF data were obtained in 223 patients (nivo = 116, evero = 107; see Supplementary Fig. S2A for CONSORT diagram). Among the 304 available tissue samples, 191 (62.9%) were from primary tumors and 91 (29.9%) were from metastatic sites; in 2 cases (0.7%) information about the sample type was not available. The majority of patients (62.9%) had received only one line of prior therapies and the median time from tissue collection to enrollment in the trial was 23.2 months. Detailed clinical information for patients with and without available tissue is provided in Supplementary Table S3.

There was no imbalance among the patients with any available tissue (n = 304) and those without (n = 499) with regards to gender, age, treatment arm, study crossover, clinical outcomes, clinical scores, number of prior therapies, and time from tissue collection to randomization (Supplementary Table S3). However, the group of patients with available tissue was enriched for metastatic lesions as compared with patients without available tissue (29.9% vs. 21.2%, P-value 0.018). Among the subset of patients with IF data (n = 223), patients in the nivo arm (n = 116) showed higher ORR (25% vs. 1.9%, P < 0.001), higher DRR (18.1% vs. 0.9%, P < 0.001), longer median PFS (4.2 vs. 3.9 months, P = 0.030), and longer median OS (24.0 vs. 16.2 months, P < 0.001) than patients in the evero arm (n = 107).

We first used IF data to validate our previous finding that levels of CD8⁺PD-1⁺TIM-3⁻LAG-3⁻ TIC are a determinant of response to nivo in ccRCC (6). In the nivo arm, both density and percentage of CD8⁺PD-1⁺TIM-3⁻LAG-3⁻ TIC (as continuous variables) were linearly associated with higher ORR (OR = 1.43, P = 0.028 and OR = 9.22, P = 0.020, respectively). However, only density of CD8⁺PD-1⁺TIM-3⁻LAG-3⁻ TIC was associated with higher DRR (OR = 1.54, P = 0.023) and tended to be associated with longer PFS (HR = 0.87, P = 0.058) in the nivo arm. In the evero arm, there was no association between the tested biomarkers and any clinical outcomes (Table 1). Similar results were obtained when controlling for MSKCC and IMDC groups using stratified tests (Supplementary Table S4). Of note, a significant interaction between treatment and density of CD8⁺PD-1⁺TIM-3⁻LAG-3⁻ TIC was seen for both PFS and OS (2-sided P = 0.024 and 2-sided P = 0.079, respectively; significance determined as 2-sided P < 0.15; Supplementary Table S5). The interaction was such that the higher the density of CD8⁺PD-1⁺TIM-3⁻LAG-3⁻ TIC, the longer the PFS and OS in the nivo arm but not in the evero arm (Supplementary Fig. S3).

To further explore the value of CD8⁺PD-1⁺TIM-3⁻LAG-3⁻ TIC as a predictive biomarker, we optimized a cutoff value by maximizing sensitivity and specificity (Youden's Index approach; ref. 19) for ORR in the nivo arm. At the optimized cutoff value, nivo-treated patients with high density of CD8⁺PD-1⁺TIM-3⁻LAG-3⁻ TIC (24/116, 20.7%) had higher ORR (45.8% vs. 19.6%, P = 0.011) and DRR (33.3% vs. 14.1%, P = 0.035) and longer median PFS (9.6 vs. 3.7 months, P = 0.032) than patients with low IF biomarker (Fig. 1A; Supplementary Table S6). High density of CD8⁺PD-1⁺TIM-3⁻LAG-3⁻ TIC showed no association with PFS in the evero arm (Fig. 1A) and no difference in OS was observed between patients with high and low IF biomarker in either arm (Fig. 1B).

Because TCF7 expression on either CD8⁺ or CD8⁺PD-1⁺ TIC has been associated with response to anti–PD-1 in patients with melanoma (8–10), we tested whether the expression of TCF7 (alone and in combination with immune checkpoints) on CD8⁺ TIC could help predict responses to nivo in patients with ccRCC. In our cohort, the percentage of CD8⁺ cells expressing TCF7 ranged from 0.00% to 51.01% (mean: 6.61% ± 7.66%; median: 4.17%). Among CD8⁺PD-1⁺ cells, TCF7 expression was more frequent in TIM-3–negative cells than in TIM-3–positive cells (P < 0.001) but did not significantly vary depending on LAG-3 expression. However, the density of CD8⁺PD-1⁺TCF7⁺TIM-3⁻LAG-3⁻ cells was higher than the density of CD8⁺PD-1⁺TCF7⁺ cells expressing either TIM-3 or LAG-3, indicating that the majority of CD8⁺PD-1⁺ cells expressing TCF7 were negative for both TIM-3 and LAG-3 (Supplementary Fig. S4).

In our analyses, density/percentage of CD8⁺TCF7⁺, CD8⁺PD-1⁺TCF7⁺ or CD8⁺PD-1⁺TCF7⁺TIM-3⁻LAG-3⁻ TIC (measured as continuous variables) were not associated with any clinical outcomes in either the nivo or the evero arm (Supplementary Tables S7 and S8). However, there was a significant interaction between density of CD8⁺TCF7⁺, CD8⁺PD-1⁺TCF7⁺ or CD8⁺PD-1⁺TCF7⁺TIM-3⁻LAG-3⁻ TIC and treatment arm with respect to PFS (P < 0.15; Supplementary Table S9).

As our data show that tumors with high density of CD8⁺PD-1⁺TIM-3⁻LAG-3⁻ TIC are more likely to respond to anti–PD-1 therapy, we sought to gain insights into the biology of these tumors by analyzing genomic and transcriptomic data available from 117 (nivo = 61; evero = 56) and 71 (nivo = 32; evero = 39) patients, respectively.

None of the most frequently mutated genes and copy-number variations (CNV) showed an association with density of CD8⁺PD-1⁺TIM-3⁻LAG-3⁻ TIC (Supplementary Tables S10 and S11).

By single sample gene set enrichment analysis (ssGSEA; ref. 20) using the 50 Hallmark genes sets from the Molecular Signatures Database (21), multiple inflammatory pathways were enriched in the high CD8⁺PD-1⁺TIM-3⁻LAG-3⁻ TIC density tumors as compared with the low-density tumors (FDR q < 0.1; Fig. 1C). Consistent with this observation, immune-related gene signature scores (GSS) such as tumor inflammation score (TIS; ref. 22), IMmotion150_Teff (23), Cytolytic Activity (14), and JAVELIN (24) were also enriched in the high biomarker tumors (FDR q < 0.05; Fig. 1D). Transcriptomic inference of tumor-infiltrating immune populations (using CIBERSORTx; ref. 25) showed increased CD8⁺ T cells, activated NK cells, follicular helper T cells and plasma cells in the high CD8⁺PD-1⁺TIM-3⁻LAG-3⁻ TIC density tumors (FDR q < 0.05), while resting mast cells and resting CD4⁺ memory T cells were enriched in the low CD8⁺PD-1⁺TIM-3⁻LAG-3⁻ TIC density cases (Fig. 1E).

Our previous study suggested that quantification of CD8⁺PD-1⁺TIM-3⁻LAG-3⁻ TIC combined with TC PD-L1 expression can improve prediction of response to anti–PD-1 therapy in patients with ccRCC (6). TC PD-L1 analysis performed by our group in cases with IF data showed that patients with TC PD-L1 ≥ 1% had higher ORR (P = 0.04) and tended to have higher DRR (P = 0.14) and longer PFS (P = 0.07) in the nivo arm but not in the evero arm (Supplementary Tables S12 and S13; Supplementary Fig. S5).

Similar to previous observations (6), when a maximum sensitivity cutoff value was defined for the IF biomarker, combination with TC PD-L1 status further separated clinical outcomes in the nivo-treated patients with high CD8⁺PD-1⁺TIM-3⁻LAG-3⁻ TIC levels (92/111, 82.8%). In a combination of TC PD-L1 and CD8⁺PD-1⁺TIM-3⁻LAG-3⁻ TIC density at the maximum sensitivity cutoff, we identified three groups of patients in the nivo arm with different clinical outcomes with regards to ORR (trend test P value = 0.027), DRR (trend test P value = 0.045) and PFS (trend test P value = 0.019). Patients with both positive TC PD-L1 expression and high CD8⁺PD-1⁺TIM-3⁻LAG-3⁻ TIC density had the most favorable outcomes, patients with negative TC PD-L1 expression and high CD8⁺PD-1⁺TIM-3⁻LAG-3⁻ TIC density had intermediate outcomes, and patients with negative/low levels for both markers had the poorest outcomes. Separation of PFS was not seen in the evero arm (Table 2 and Fig. 2). Similar results were obtained when controlling for MSKCC and IMDC groups using stratified tests (Supplementary Table S14).

On the basis of the published literature (12–14), we hypothesized that tumors expressing high levels of hERVs might have increased levels of CD8⁺PD-1⁺TIM-3⁻LAG-3⁻ TIC and would display better outcome on nivo.

ERVE-4 and hERV4700-ENV mRNA expression data were obtained for 224 patients (nivo = 112; evero = 112; Supplementary Fig. S2B for CONSORT diagram). ERVE-4 and hERV4700-ENVwere expressed in 51% and 40% of patients, respectively. The observed ERVE-4 expression rate was in line with previously published data (26). No data about the expression rate of hERV4700-ENV were found in the literature. When we assessed the association between CD8⁺PD-1⁺TIM-3⁻LAG-3⁻ TIC density and levels of ERVE-4 and/or hERV4700-ENV dichotomized using an optimized cutoff (Youden's Index approach; ref. 19), no difference in the distribution of CD8⁺PD-1⁺TIM-3⁻LAG-3⁻ TIC density was observed between hERV-positive and hERV-negative cases (Supplementary Fig. S6).

ERVE-4 expression as a continuous measure was associated with DRR (OR = 4.05, P value = 0.042) and PFS in the nivo arm (HR = 0.42, P value = 0.020) and showed an interaction with treatment with regard to PFS (2-sided P = 0.080, P < 0.15). The interaction was such that the higher the ERVE-4 expression score, the better the PFS in the nivo arm but not in the evero arm. No association of continuous ERVE-4 with ORR and OS was observed (Table 3). Similar results were obtained when controlling for MSKCC and IMDC groups using stratified tests (Supplementary Table S15). At the optimized cutoff value, nivo-treated patients expressing ERVE-4 had higher DRR (P = 0.042) and a significantly longer PFS (5.3 vs. 3.6 months, P = 0.017) than patients not expressing ERVE-4 (Supplementary Table S16; Fig. 3A). No difference in ORR and OS was observed between ERVE-4–positive and ERVE-4–negative nivo-treated patients (Supplementary Table S17; Fig. 3B). In the evero arm, ERVE-4 status was not associated with clinical outcomes. No difference in gene mutations and CNVs was detected according to ERVE-4 status (Supplementary Tables S18 and S19). By RNA-seq data analysis, ERVE-4 (X2256_chr6.89371970.89380600) was the only differentially expressed ERV transcript between ERVE-4–positive and ERVE-4–negative cases (FDR q < 0.25, Fig. 3C). TIS (22), IMmotion150_Teff (23), Cytolytic Activity (14), and JAVELIN (24) GSS were upregulated in ERVE-4–positive tumors (FDR q < 0.25; Fig. 3D). ssGSEA (ref. 20; Hallmark gene sets) and CIBERSORTx (25) analyses showed no significant difference between ERVE-4–positive and ERVE-4–negative cases (Supplementary Fig. S7).

hERV4700-ENV as a continuous measure showed both an association with OS in the nivo arm (HR = 0.59, P = 0.050) and an interaction with treatment with regard to OS (2-sided P = 0.113, P < 0.15). The interaction was such that the higher the hERV4700-ENV expression score, the better the OS in the nivo-treated patients but not in the evero-treated ones. Continuous hERV4700-ENV showed no association with ORR, DRR, and PFS in either arm (Table 3). Similar results were obtained when controlling for MSKCC and IMDC groups using stratified tests (Supplementary Table S15). When split at the optimized cutoff (Youden's Index; ref. 19), hERV4700-ENV–positive patients showed a trend toward better PFS in both arms (P = 0.096 and P = 0.054 in the nivo and evero arm, respectively; Supplementary Tables S16 and S17; Supplementary Fig. S8).

There are currently no approved predictive biomarkers that can guide treatment decision for patients with metastatic RCC. A major challenge in the development of clinical biomarkers is that promising results obtained in individual studies are not reproduced by investigations conducted in independent patient cohorts. Our previous study of a phase 2 trial of nivo in mccRCC (6) suggested that levels (measured as either percentage or density) of CD8⁺ TIC expressing the therapy target PD-1 but negative for other exhaustion molecules (TIM-3 and LAG-3) are associated with response to anti–PD-1 treatment. Here, we independently confirmed these findings by analyzing tumor specimens from the randomized CM-025 trial and demonstrated that the predictive value of our IF biomarker is specific to patients treated with anti–PD-1 therapy. High levels of CD8⁺PD-1⁺TIM-3⁻LAG-3⁻ TIC likely identify inflamed tumors harboring mildly exhausted tumor-specific T cells, which can be effectively re-invigorated by anti–PD-1 monotherapy. In line with this hypothesis, we found that tumors with high CD8⁺PD-1⁺TIM-3⁻LAG3⁻ TIC were enriched for several inflammatory pathways, immune-related gene signature scores, and immune cell populations such as CD8⁺ T cells, NK-activated T cells, plasma cells, and helper T cells. Of note, we showed that baseline CD8⁺ cell infiltration and immune-related gene signature scores were not associated with improved response or survival in a large cohort of mccRCC treated with anti–PD-1 monotherapy (18), indicating that more detailed analysis of CD8 subsets is necessary for predictive biomarker development.

Here, we also validated that combination of CD8⁺ TIC immunophenotyping with TC PD-L1 status stratifies patients with mccRCC in three groups with significantly different outcome on nivo but not on the control treatment (evero). Combining CD8⁺ TIC immunophenotyping with TC PD-L1 status is particularly effective in identifying subsets of patients with high likelihood (ORR, 50% and DRR, 33%) of experiencing durable responses to anti–PD-1 and could help to select patients who can be treated with PD-1 monotherapy.

Although analyses of melanoma cohorts recently demonstrated that CD8⁺TCF7⁺ progenitor/stem-like exhausted TIC can be frequently detected in this tumor type and might represent a determinant of response in ICI-treated patients (8–10), our data show that in mccRCC TCF7 expression on CD8⁺ TIC is relatively rare (mean 6.61% ± 7.66%). Although we confirmed that the majority of CD8⁺TCF7⁺ cells lacked the expression of exhaustion markers (TIM-3 and LAG-3; ref. 7, 8, 10, 11), we did not detect any definitive association between TCF7 expression of CD8⁺ TIC and outcome on anti–PD-1 therapy in our mccRCC cohort. Overall, these findings suggest that relative to melanoma, mccRCC displays a more terminally exhausted phenotype characterized by depletion of progenitor exhausted CD8⁺ T cells that were critical for response to immunotherapy in melanoma. Our results may provide biological insight as to why, compared with melanoma, response rates to ICI treatment are generally lower in mccRCC and why the development of adoptive cell therapies with tumor-infiltrating lymphocytes has been largely unsuccessful in this disease.

We also showed for the first time that ERVE-4 expression, both as a continuous and as a binary variable, was associated with increased DRR and longer PFS in nivo-treated (but not in evero-treated) patients. In comparison with other tumor types, ccRCC has abundant CD8⁺ T-cell infiltration but a modest mutation burden and tumor-specific antigens recognized by immune cells remain largely unknown. Expression of ERVE-4 is particularly high in ccRCC likely due to its direct regulation by HIF-2α (upregulated by VHL inactivation) in concert with hypomethylation of proviral long terminal repeat (26), and there is evidence that antigens derived from this provirus can be immunogenic and stimulate kidney cancer cell killing by cytotoxic T cells in vitro and in vivo (13–16). Our findings further support the role of ERVE-4 as a kidney cancer antigen and therapeutic target for immunotherapy. Future large-scale analysis of the hERV landscape in mccRCC might help identify other immunogenic hERVs that could be helpful in enhancing the predictive value of ERVE-4 for ICI therapy in this tumor type. Moreover, as ERVE-4 expression is modulated by epigenetic events (26), our data support the potential of combining DNA methylation inhibitors with immunotherapy agents in ccRCC (27).

This study has several limitations. As only one tumor sample was analyzed per patient, our study did not account for intratumor heterogeneity. However, it needs to be recognized that the same limitation applies when analyzing marker expression in the clinical setting. Moreover, in most cases the samples analyzed derived from primary tumors obtained before initial systemic therapy and might therefore not entirely reflect the biology of the VEGFR TKI-resistant/modulated metastatic lesions that were the target of the systemic therapy. Second-line IO cohorts could also be relatively depleted for patients with aggressive tumor types, such as sarcomatoid. By focusing on CD8⁺ TIC immunophenotyping, our study did not explore additional immune cell populations that might play an important role in determining response and resistance to ICI, such as B cells and myeloid cells (28–32). In addition, due to the low response rate in the evero arm, ORR and DRR were analyzed in the nivo arm only limiting comparison between the two treatment arms with PFS and OS. Finally, the retrospective nature of the study represents a further limitation by itself.

In conclusion, here we provided evidence that high levels of CD8⁺PD1⁺TIM3⁻LAG3⁻TIC and ERVE-4 expression predict response to nivo (but not to control evero) in patients with mccRCC. Combination of CD8⁺PD1⁺TIM3⁻LAG3⁻TIC with TC PD-L1 status further improves its predictive value, confirming our previous findings (6).

J.-C. Pignon reports personal fees from Celgene–Bristol Myers Squibb and EMD Serono outside the submitted work. D.A. Braun reports personal fees from LM Education and Exchange Services, non-financial support from Bristol-Myers Squibb, and personal fees from Schlesinger outside the submitted work. M. Wind-Rotolo reports other from Bristol Myers Squibb during the conduct of the study and outside the submitted work. G.J. Freeman reports grants from National Cancer Institute and Department of Defense during the conduct of the study, as well as personal fees from Roche, Bristol-Myers Squibb, Xios, Origimed, Triursus, iTeos, NextPoint, IgM, Jubilant, GV20, and Trillium outside the submitted work; Dr. Freeman also reports a patent for PD-L1/PD-1 pathway issued, licensed, and with royalties paid from Roche, Merck MSD, Bristol Myers Squibb, Merck KGA, Boehringer Ingelheim, AstraZeneca, Dako, Mayo Clinic, and Novartis, as well as equity in Nextpoint, Triursus, Xios, iTeos, IgM, and GV20. A.H. Sharpe reports grants from NIH P50-CA101942 during the conduct of the study. Dr. Sharpe also reports personal fees from Surface Oncology, Sqz Biote, and Selecta; other from Monopteros; personal fees from Elstar and Elpiscience; and grants from Novartis, Merck, Roche, Ipsen, UCB, and Quark Ventures outside the submitted work. Dr. Sharpe also reports patent 7,432,059 with royalties paid, 7,722,868 with royalties paid, 8,652,465 licensed to Roche, 9,457,080 licensed to Roche, 9,683,048 licensed to Novartis, 9,815,898 licensed to Novartis, 9,845,356 licensed to Novartis, 10,202,454 licensed to Novartis, 10,457,733 licensed to Novartis, 9,580,684 issued, 9,988,452 issued, and 10,370,446 issued, as well as scientific advisory boards for the Massachusetts General Cancer Center, Program in Cellular and Molecular Medicine at Boston Children's Hospital, and the Human Oncology and Pathogenesis Program at Memorial Sloan Kettering Cancer Center. F.S. Hodi reports grants and personal fees from Bristol-Myers Squibb; grants from Novartis; and personal fees from Merck, EMD Serono, Sanofi, Surface, Compass Therapeutics, Apricity, Aduro, Pionyr, Torque, Rheos, Bicara, Psioxus Therapeutics, Pieris Pharmaceutical, Eisai, Checkpoint Therapeutics, Idera, Takeda, Genentech, and Bioentre outside the submitted work. Dr. Hodi also reports a patent for Methods for Treating MICA Related Disorders pending, licensed, and with royalties paid; for Tumor Antigens and Uses Thereof pending; for Angiopoietin-2 Biomarker pending; for Compositions and Methods for Identification, Assessment, Prevention, and Treatment of Melanoma using PD-1 Isoforms issued; for Therapeutic Peptides issued; for Vaccine Compositions and Methods for Restoring NKG2D Pathway Function Against Cancers pending, licensed, and with royalties paid; for Antibodies That Bind to MHC Class I Polypeptide-Related Sequence A pending; and for Anti-galaectin Antibody Biomarkers Predictive of Anti-immune Checkpoint and Anti-angiogenesis Responses pending. R.J. Motzer reports grants and other from Bristol Myers Squibb during the conduct of the study. Dr. Motzer also reports grants and personal fees from Pfizer, Novartis, Eisai, and Exelixis; personal fees from Merck; grants and personal fees from Genentech/Roche; and personal fees from Lilly, Incyte, and EMD Serono Research and Development Institute outside the submitted work. C.J. Wu reports other from BionTech outside the submitted work. M.B. Atkins reports grants and personal fees from BMS and Merck; personal fees from Novartis, Genentech/Roche, Pfizer, Eisai, Exelixis, AstraZeneca, Agenus, Adagene, Aveo, Array, Pyxis Oncology, Leads BioPharma, and Werewolf; and personal fees and other from Elpis, TRV, PACT, and Iovance outside the submitted work. Dr. Atkins also reports ownership of Pyxis Oncology, Werewolf, and Elpis stock options. D.F. McDermott reports personal fees from Merck and BMA outside the submitted work. S.A. Shukla reports grants from NCI during the conduct of the study. Dr. Shukla also reports non-financial support from Bristol-Myers Squibb, personal fees from Neon Therapeutics, and other from Agenus, Agios Pharmaceuticals, Breakbio Corp, Bristol-Myers Squibb, and Lumos Pharma outside the submitted work. T.K. Choueiri reports grants, personal fees, non-financial support, and other from BMS, Merck, Roche, EMD, Pfizer, Exelixis, Novartis, and AstraZeneca during the conduct of the study, as well as grants, personal fees, non-financial support, and other from BMS, Merck, Pfizer, Roche, Exelixis, Pfizer, Novartis, and AstraZeneca outside the submitted work. S. Signoretti reports grants from Bristol Myers Squibb and personal fees from Merck during the conduct of the study. Dr. Signoretti also reports grants from Exelixis, grants and personal fees from AstraZeneca, grants from Novartis, and personal fees from CRISPR Therapeutics AG, AACR, and NCI outside the submitted work; Dr. Signoretti also reports a patent for Biogenex with royalties paid. No disclosures were reported by the other authors.

M. Ficial: Conceptualization, data curation, formal analysis, validation, investigation, visualization, methodology, writing-original draft. O.A Jegede: Data curation, formal analysis, methodology, writing-review and editing. M. Sant'Angelo: Data curation, formal analysis, validation, writing-review and editing. Y. Hou: Data curation, formal analysis, methodology, writing-review and editing. A. Flaifel: Conceptualization, investigation, methodology, writing-review and editing. J.-C. Pignon: Conceptualization, data curation, investigation, methodology, writing-review and editing. D.A. Braun: Conceptualization, data curation, formal analysis, funding acquisition, investigation, methodology, writing-review and editing. M. Wind-Rotolo: Resources, funding acquisition, writing-review and editing. M.A. Sticco-Ivins: Investigation, methodology, writing-review and editing. P.J. Catalano: Formal analysis, supervision, writing-review and editing. G.J. Freeman: Conceptualization, resources, supervision, writing-review and editing. A.H. Sharpe: Conceptualization, supervision, writing-review and editing. F.S. Hodi: Resources, writing-review and editing. R.J. Motzer: Resources, supervision, funding acquisition, writing-review and editing. C.J. Wu: Conceptualization, resources, supervision, funding acquisition, writing-review and editing. M.B. Atkins: Conceptualization, supervision, funding acquisition, writing-review and editing. D.F. McDermott: Conceptualization, resources, supervision, funding acquisition, project administration, writing-review and editing. S.A. Shukla: Conceptualization, data curation, formal analysis, supervision, methodology, writing-review and editing. T.K. Choueiri: Conceptualization, resources, supervision, funding acquisition, project administration, writing-review and editing. S. Signoretti: Conceptualization, resources, supervision, funding acquisition, writing-original draft, project administration, writing-review and editing.

This work was supported by Dana-Farber/Harvard Cancer Center Kidney Cancer SPORE P50-CA101942-12 (to S. Signoretti, T.K. Choueiri, and D.F. McDermott) and program P30-CA06516 (to S. Signoretti, T.K. Choueiri, and D.F. McDermott), DOD CDMRP W81XWH-18–1-0480 (to S. Signoretti and T.K. Choueiri), and Bristol-Myers Squibb (to S. Signoretti, T.K. Choueiri, and D.F. McDermott). D.A. Braun is supported by the DF/HCC Kidney Cancer SPORE Career Enhancement Program (P50CA101942–15), DOD CDMRP (KC170216, KC190130), and the DOD Academy of Kidney Cancer Investigators (KC190128). C.J. Wu is a scholar of the Leukemia and Lymphoma Society. S.A. Shukla acknowledges support by the NCI (R50RCA211482). T.K. Choueiri is supported in part by the Kohlberg Chair at Harvard Medical School and the Trust Family, Michael Brigham, and Loker Pinard Funds for Kidney Cancer Research at DFCI. Patients treated at Memorial Sloan Kettering Cancer Center were supported in part by Memorial Sloan Kettering Cancer Center support grant/core grant (P30 CA008784).

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