High Response Rate and Durability Driven by HLA Genetic Diversity in Patients with Kidney Cancer Treated with Lenvatinib and Pembrolizumab

This article is featured in Highlights of This Issue, p. 1439

Immune checkpoint blockade (ICB) therapy has improved the clinical outcomes of patients with cancer with various types of malignancies including melanoma, lung cancer, lymphoma, colorectal cancer, renal cell carcinoma (RCC), and others. Blockade of the programmed cell death-1 (PD-1) axis with anti-PD-1 or anti-PD-L1 (programmed cell death ligand 1) antibodies such as pembrolizumab, durvalumab, avelumab, or nivolumab can reinvigorate T cells and promote antitumor immunity. Cytotoxic T-cell recognition and targeting of neoantigens presented by MHC molecules facilitate the elimination of cancer cells that present them (1, 2). Anti-PD1/PD-L1 monotherapy has now been approved by the FDA for use in a variety of cancers; however, most patients do not benefit, and better immunotherapy-based treatments are needed. Features such as PD-L1 positivity, tumor mutational burden (TMB), microsatellite instability, and T-cell infiltration have various abilities to identify patients that respond to ICB treatment (3).

In patients with advanced RCC, anti-PD-1 monotherapy improved overall survival (OS) in a randomized phase III clinical trial of nivolumab versus everolimus (CheckMate 025; ref. 4). However, the response rate was only 25%, and most patients did not benefit from either treatment. To further improve outcomes, intense effort has been put into developing immunotherapy-based combination regimens built upon an anti-PD-1 backbone. One promising strategy is to combine multi-tyrosine kinase inhibitors (TKI) with ICB. For example, it has been shown that VEGF can directly inhibit T-cell activity, affect the maturation and differentiation of lymphocytes and antigen-presenting cells (APC), regulate angiogenesis, and modulate lymphocyte trafficking (5, 6). The combination of the multi-kinase inhibitor axitinib and anti-PD1/PD-L1 therapy has demonstrated improved progression-free survival (PFS) compared with TKI monotherapy in randomized phase III clinical trials in comparison with sunitinib (7, 8). However, the potential of this strategy is far from realized, and there are no effective biomarkers for most immunotherapy-based combination regimens. In fact, PD-L1 quantitation appears to lose predictive ability with combination immunotherapy regimens (9).

Lenvatinib is a multiple receptor TKI targeting VEGFR1–3, FGFR1–4, PDGFRα, Ret, and Kit (10). The combination of lenvatinib and everolimus is approved for the treatment of advanced RCC after progression on one VEGF-targeted treatment (11); lenvatinib is also being studied in combination with pembrolizumab in multiple malignancies. Combination regimens that block multiple tyrosine kinases as well as PD-1 are beginning to show exceptional promise (7, 8, 12). We describe the high clinical efficacy and its genomic correlates in patients with RCC treated with lenvatinib plus pembrolizumab.

The individuals included in this study consisted of patients who participated in a phase Ib/II, multicenter, open-label clinical trial (NCT02501096; ref. 12) designed to evaluate the efficacy and safety of lenvatinib/pembrolizumab for the treatment of several advanced stage tumors, including RCC. The trial included individuals with pathologic diagnosis of melanoma, non–small cell lung carcinoma, head and neck squamous cell carcinoma, RCC, urothelial carcinoma, and endometrial carcinoma. Individuals were treated with pembrolizumab [200 mg i.v. every 3 weeks for up to 35 doses (∼2 years)] and lenvatinib (oral). Lenvatinib dosages varied depending on the phase of the clinical trial, and consisted of a 3+3 dose de-escalation strategy starting at 24 mg daily (phase Ib) or 20 mg daily (phase II). Radiographic tumor assessments were performed using CT scans. Assessments were initially done every 6 weeks (first 24 weeks) and every 9 weeks thereafter. Tumor responses were evaluated by a specialized radiologist using the immune-related RECIST (irRECIST).

Baseline characteristics of individuals were assessed at the time of treatment start. PFS was calculated from the time of treatment start to the time of progression or death. For patients who developed objective responses [i.e., a partial or complete response (CR)], duration of response (DOR) was also calculated. DOR was defined as the time from initial response to progression or death. Individuals who did not die or progress, were considered censored at the time of last follow-up visit. The clinical data included in this study were reviewed by investigators and a database cutoff performed on March 29, 2019.

The protocol for this trial was pre-approved by our Institutional Review Board (IRB) and ethics committee and the study conducted in accordance with the principles of Good Clinical Practice and the Declaration of Helsinki. Written informed consent was obtained prior to any study-related activities. Individuals consented to their clinical records being reviewed and their blood and tumor tissue being profiled as part of the correlative research conducted in the context of the trial. Participants were allowed to withdraw their consent throughout the study period.

Of the patients who were enrolled in the clinical trial (n = 30), only a subset had sufficient tissue for biomarker analysis. Individuals who did not have tumor and germline (i.e., blood-derived) DNA of sufficient quality available were excluded from the analysis (n = 6). The final cohort of 24 individuals in this study included only one individual treated with the phase Ib dosing scheme. Blood samples were obtained as part of the research protocol during one of the follow-up visits and stored in EDTA-containing tubes frozen at −80°C. Formalin-fixed paraffin-embedded (FFPE) tumor samples were obtained from the pathology facilities where patients were initially diagnosed. Slides stained with hematoxylin and eosin (H&E) were procured and reviewed by a specialized genitourinary pathologist. On the basis of histologic review of 1–2 reference slides stained with H&E, 5–20 unstained slides per sample were macrodissected by scalpel (or scraped by razor) to collect tumor material.

IHC staining of PD-L1 molecules was performed in unstained slides obtained from the specimen using the PD-L1 IHC 22C3 pharmDx kit (Agilent). Semiquantitative H-scores were obtained by an experienced pathologist and a cutoff of 1% was used to determine positivity.

Paraffin was removed using heat treatment (90°C for 10′ in 480 μL PBS and 20 μL 10% Tween 20) and ice chill, followed by an incubation overnight in 400 μL of 1mol/L NaSCN for rehydration and impurity removal. Tissues were subsequently digested with 20 μL of Proteinase K (600 mAU/mL) in 360 μL of ATL buffer at 56°C. DNA from whole blood was extracted using the QIAamp DNA Blood Maxi Kit (Qiagen, catalog no. 51194), while DNA from FFPE material was isolated with the DNeasy Blood & Tissue Kit (QIAGEN, catalog no. 69504) according to the manufacturer's protocol. DNA was eluted in nuclease-free water.

Sample quantity was measured using the Quant-iT PicoGreen dsDNA Assay (Thermo Fisher Scientific, catalog no. P11496), while quality was assessed with TapeStation D1000 ScreenTape (Agilent, catalog no. 5067-5582). After PicoGreen quantification and quality control with Agilent 2100 Bioanalyzer Instrument, 144–500 ng of DNA were used to prepare libraries using the KAPA Hyper Prep Kit (Kapa Biosystems KK8504) with 8–12 cycles of PCR. After sample barcoding, 95–500 ng of library were captured by hybridization using the SureSelectXT Human All Exon V4 (Agilent, catalog no. 5190-4632) according to the manufacturer's protocol. PCR amplification of the post-capture libraries was carried out for eight cycles. Samples were then run on a HiSeq 4000 or HiSeq 2500 in Rapid Mode in a 100 bp/100 bp or 125 bp/125 bp paired-end run, using the HiSeq 3000/4000 SBS Kit or HiSeq Rapid SBS Kit v2 (Illumina). Normal and tumor samples were covered to an average of 118X and 176X, respectively.

Raw sequencing data in FASTQ format were aligned to the human reference genome (b37) using the Burrows–Wheeler aligner (BWA v.0.7.10). Local realignment was done using the Genome Analysis Toolkit (GATK v4.1.4.1; ref. 13) and duplicate reads removed using Picard v.2.13. For mutation calling, we used our integrated pipeline which includes four different variant calling tools: MuTect2 (part of GATKv4.1.4.1; ref. 13), Strelka2 v2.9.10 (14), Varscan v2.4.3 (15), and Platypus (16). Only mutations detected by at least two callers were included in the analysis. Additional filters were applied to obtain high-accuracy calls. These included a base coverage of at least 10 reads in the tumor, with at least five reads supporting the variant and allelic frequency below 2% in the matched blood sample. In addition, single-nucleotide variants identified at a frequency higher than 1% in dbSNP (17) or 1000Genomes project (18) were excluded. Dubious mutation calls were manually reviewed by investigators using the Integrative Genome Viewer software v2.4.10 for additional accuracy (19, 20; Supplementary Data S1).

Allele-specific copy-number analysis was performed using FACETS v0.5.6 (21). This tool provides integer copy number (CN) values for each tumor allele in addition to conventional outputs. Genomic segments were defined as altered whether they had a CN status different than 2:1 (i.e., two total copies, one per allele). Finally, the fraction of non-diploid genome was calculated by normalizing the length of all the CN-altered segments by the total genome length.

As described previously, HLA-I genotyping of blood-derived DNA was performed using whole-exome sequencing (WES) data. Polysolver, a previously validated tool, was used with default parameters settings to identify the individuals' HLA class-I alleles (21–23). Patients with two different alleles at a specific locus (i.e., HLA-A, HLA-B, and HLA-C) were considered heterozygous at that locus, and individuals who were found to have six different HLA class-I alleles were considered fully heterozygous at HLA-I.

HLA evolutionary divergence (HED) was calculated as described by Pierini and Lenz (24). This approach involves sequence extraction of exons 2 and 3 of each HLA class I allele (i.e., where the peptide-binding domains are located, obtained from the Ensembl database (25)) and retrieval of the corresponding protein sequences from the ImMunoGeneTics/HLA database (26). After alignment of the two HLA alleles, the Grantham distance metric (27) is calculated for each pair of corresponding amino acids. The value represents a pairwise distance based on their physiochemical properties (i.e., composition, polarity, and volume). The sum of amino-acidic distances is then normalized by the length of the sequence. The resulting value (i.e., HED) represents a measure of functional divergence between the two HLA alleles. Finally, the values for each of the three HLA-I loci are averaged and reported as “Mean HED” (Supplementary Data S2).

Data from the MSK-IMPACT cohort were obtained for validation purposes (28, 29). After IRB approval, the medical records of patients who received a PD-1/PD-L1 inhibitor containing regimen between 2015 and 2018 were reviewed. Only individuals who had histologic evidence of clear cell components in their RCC tumor specimens were included. Patients who were exclusively treated with nivolumab monotherapy were not included in the analysis. Data on therapy administration, life status, and previous treatments were analyzed. OS in this cohort was calculated from the time of ICB therapy initiation to the time of death or last follow-up. MSK-IMPACT is a targeted next-generation sequencing assay performed at a Clinical Laboratory Improvement Amendments–certified laboratory and approved by the FDA for the study of solid tumors. It involves the profiling of 468 cancer-associated genes as well as a SNP assay for copy-number analysis and computational assessment of microsatellite instability (30). As described previously (31), we obtained the base sequences corresponding to the HLA class I loci from the BAM files derived from germline DNA sequencing. Only individuals whose normal DNA samples had sufficient coverage at the HLA-class I loci were included in the analysis.

For survival analysis, the Kaplan–Meier (KM) method was used to calculate estimates and log-rank tests were used to assess between-group differences. Multivariable Cox proportional hazards regression models were used to assess the relationship between multiple covariates of interest and time-to-event outcomes. Results were reported as point estimates along with its 95% confidence intervals (CI) and P values. To assess potential dose–response relationships between mean HED and time-to-event outcomes, we tested all possible mean HED cutoffs and the HR estimates were reported. Furthermore, to evaluate the effect on outcomes along the mean HED spectrum, we constructed subpopulation treatment effect pattern plots (STEPP; refs. 32, 33). An R implementation of this nonparametric approach [“stepp” package (34)] was used to estimate the 2-year event-free probabilities across the range of mean HED values observed in both cohorts (parameters r2 = 20, r1 = 10, total n = 65). Shapiro–Wilk tests were used to assess the mean HED distributions in both cohorts, and Spearman rank correlation test was used to assess the relationship between mean HED and maximum tumor shrinkage. All analyses were performed in the R platform v.3.5.0 (35).

All WES data produced in this study are deposited on the Sequence Read Archive (SRA) under the accession number PRJNA610643. All the data from the validation set can be explored at http://cbioportal.org. Any other relevant data are available from the corresponding authors upon reasonable request.

Our study was performed in the context of a phase Ib/II, single-arm, multi-institutional clinical trial where patients with advanced stage RCC received combination therapy with lenvatinib and pembrolizumab. The baseline characteristics and a summary of the clinical outcomes for patients with tissue available for genomic analysis (N = 24) are shown in Table 1. The median age of the cohort was 60 years [interquartile range (IQR), 56–66]. When stratifying patients according to the International Metastatic Database Consortium (IMDC) criteria, 54% of individuals were found to have favorable risk and 46% intermediate risk (36). Twenty-three patients were treated at the recommended phase II dose (lenvatinib 20 mg daily + pembrolizumab 200 mg every 3 weeks), and only one individual received treatment at the initial phase Ib starting dose (lenvatinib 24 mg daily + pembrolizumab 200 mg every 3 weeks). Notably, in this group of patients, at a median follow-up of 32.3 months, the objective response rate was 79.2% with a disease control rate of 96% and a median PFS of 21.9 months (95% CI, 11.7–26.1; Fig. 1A and B). Notably, the median time to first response was 1.4 months (IQR, 1.3–3.4). Furthermore, a high proportion of patients achieved significant tumor shrinkage (Fig. 1B). Representative radiographic responses (Fig. 1C), as well as the timing of responses and clinical course of patients are shown (Fig. 1D). These results highlight the clinical promise of the lenvatinib plus pembrolizumab combination. To our knowledge, the response rate we observe with this treatment is among the highest seen in any therapy for RCC (7, 8, 37). Taken together, our data from 24 trial subjects suggest that HLA-I diversity is associated with prolonged progression-free intervals as well as treatment durability following therapy with combined multi-kinase and PD1 blockade.

To assess potential genomic correlates of treatment response and durability, we performed WES on tumor and matched normal DNA (n = 24, see Materials and Methods). Tumor from each individual was obtained before therapy was initiated. We also determined PD-L1 staining status of the tumors (Fig. 2 and Materials and Methods). Somatic mutations, TMB, arm-level copy-number alterations, the fraction of genome bearing CN aberrations (FCNA), HLA-I zygosity, and other metrics were determined using well-validated computational pipelines (Fig. 2; refs. 13, 23, 31, 38). The overall spectrum of somatic mutations was consistent with previously reported results from The Cancer Genome Atlas Consortium (TCGA) and other landmark genomic studies on RCC (39–41). As expected from the histologic subtype of these tumors (23/24 clear cell RCC), there was a high prevalence of specific driver mutations in VHL (75%), PBRM1 (42%), and BAP1 (29%; Fig. 2). The number of exonic, nonsynonymous somatic mutations identified was also consistent with previous reports, with a median mutation count of 48.5 (IQR, 40–67) and median TMB of 1.31 mutations per DNA megabase (mut/Mb). As expected in this tumor type, we also observed a relatively high insertion and deletion (indel) occurrence rate (median 13.7%), which is consistent with results from TCGA and others where a relatively low TMB and higher indel proportion (around 9%–12%) have been reported for RCC when compared with other cancer types (42). Nearly all tumors displayed genomic losses involving cytoband 3p21 (92%). Other common events identified in these tumors were 14q loss (58%), 5q gain (54%), and 9p loss (42%). These results are in line with previous studies in clear cell RCC that have demonstrated a near-ubiquitous presence of biallelic VHL inactivation through a combination of mutations and 3p21 losses (39–41), recurrent 5q gains (39, 41), and an enrichment of 14q/9p losses in metastases (usually involving the CDKN2A/2B loci; ref. 43). We also calculated the FCNA in each tumor sample (44). The median FCNA was found to be 0.17 (IQR, 0.24–0.35), this result is also consistent with previous reports (39, 45, 46). In summary, the mutational and copy-number landscape of the RCC tumors obtained from our trial patients have genomic characteristics that are typical of this tumor type.

There is no actionable biomarker that is currently approved for use in patients with RCC treated with immune checkpoint therapy or ICB-based combinations. We, therefore, examined the associations between clinical outcomes and different molecular correlates. Several groups of investigators have observed that HLA genotype can affect ICB response (31, 47). Reduced MHC function can lead to reduced antigen presentation, resulting in insufficient T cell–dependent tumor cell killing (48–52). Four patients in the study cohort were found to be homozygous at any HLA-I gene and these individuals showed worse outcomes (Fig. 2). Although a trend was observed in survival analysis when comparing homozygous to fully heterozygous individuals [median, 22 vs. 9.4 months; log-rank, P = 0.2; HR 0.46 (0.13–1.64)], our power to detect any meaningful associations was limited. Strikingly, however, HLA-class I evolutionary diversity (HED) was associated with longer progression-free intervals and greater durability of clinical response (Fig. 3). Our group has recently demonstrated the value of quantifying the overall evolutionary divergence at the HLA class-I alleles (ref. 24; i.e., mean HED) in patients treated with ICB therapy (53). HED is a measure that represents the molecular diversity of a patient's HLA-I alleles. Greater HLA diversity is associated with the ability to present a wider antigen repertoire (24), higher T-cell clonal activation status following ICB exposure (2), and improved response to ICIs (31, 47, 53). In our cohort, the distribution of mean HED was found to be nonnormal (Shapiro–Wilk, P = 0.01) with a median mean HED of 6.9 (range, 0.69–9.0).

We observed a statistically significant association between high mean HED and better outcomes in patients treated with lenvatinib plus pembrolizumab. More specifically, when using the top quartile to stratify patients, PFS was found to be longer in patients with high mean HED [median, not reached vs. 17.7 months; log-rank, P = 0.03; HR 0.23 (0.05–1)]. Importantly, mean HED was also associated with greater duration of treatment response [median not reached vs. 10.3 months, log-rank P = 0.045, HR: 0.23 (0.05–1.09)] (Fig. 3B). In a multivariable Cox proportional hazards model, the association between mean HED and improved outcomes was found to be significant [HR 0.08 (0.01–0.54), P = 0.009] even after adjusting for potential confounders such as the number of previous therapy lines, IMDC risk group, and TMB. Similar results were observed with regards to response durability (Fig. 3C). Notably, there was a trend between the degree of HED diversity and PFS; with higher mean HED linked to longer PFS and tumor shrinkage in a “dose-dependent fashion” (Fig. 4A and B). When assessing different HED thresholds, we observed that increasing HED was associated with longer PFS (Fig. 4C and D). This relationship has been previously described for HED (53) as well as TMB (38, 47), and different optimal thresholds may be needed to predict response to ICB therapy in different disease and therapeutic contexts. Despite the fact that immunotherapy combinations are quickly becoming standard of care, no biomarkers currently exist for immunotherapy-based combination regimens. Our results, from this biomarker correlative analysis of clinical trial data, demonstrate that HLA-I evolutionary divergence is associated with clinical benefit in patients with RCC treated with combined ICB/multi-kinase inhibitor therapy.

We sought to validate these findings in a separate cohort of patients with RCC treated with ICB. We identified 41 patients with metastatic ccRCC that were treated with ICB combination therapy and had their tumors analyzed using MSK-IMPACT (28–30). This is a targeted next-generation sequencing assay approved by the FDA for the study of solid tumors (see Materials and Methods). We explored the relationship between HLA diversity and clinical benefit in this set of patients and observed that individuals with high mean HED (stratified by the median) who were treated with ICB therapy showed improved OS when compared with the rest (median, not reached vs. 35.4 months; log-rank, P = 0.03; Fig. 5A and B). The distribution of HED in this cohort did not reach the threshold for nonnormality on statistical testing (Shapiro–Wilk, P = 0.5; Fig. 5C). To confirm that the dose–response association followed the same trend in both groups of patients, we used the STEPP approach to assess the 2-year event-free probability along the mean HED range. In both cohorts, higher mean HED values were found to be associated with improved outcomes. However, the threshold at which these benefits were observed differed between the two sets of patients (Figs. 4D and 2D). Taken together, these data suggest that the overall degree of HLA diversity, which shapes the size and diversity of an individual's immunopeptidome, is associated with the depth and persistence of clinical benefit to ICB therapy in RCC.

Finally, to investigate the independence of the predictive ability of mean HED from other known prognostic factors and previously reported molecular correlates of response (not yet validated in RCC; ref. 3), we developed a multivariable Cox model including HED as well as clinical and genomic covariates (Fig. 6A–C). TMB, defined as the number of nonsynonymous exonic mutations per DNA megabase, has been widely validated as a measure that is associated with improved outcomes after treatment with ICIs in many cancer types (38, 47, 54). We did not observe any association between TMB and outcomes in any of the cohorts analyzed [stratified by the median, log-rank, P = 0.14 for the lenvatinib/pembrolizumab (PFS; Fig. 6A) and P = 0.72 for the MSK-IMPACT cohort (OS, not shown)], nor did we find any associations with PD-L1 status and clinical benefit in the lenvatinib/pembrolizumab cohort (log-rank, P = 0.3, not shown). Another genomic measure that has been shown to be associated with immune evasion and worse survival after immunotherapy is the overall burden of copy-number alterations in the genome (FCNA; refs. 45, 55). We did not observe an association between FCNA (stratified by the median) and response to ICB therapy in these patients (Fig. 6B). Additional features, such as somatic PBRM1 loss-of-function mutations, have shown mixed results in studies evaluating their ability to predict outcomes after ICB therapy (48, 56, 57). In multivariate analysis, loss-of-function mutations in PBRM1 were not an independent predictor of response (Fig. 6C).

Combination therapy with PD-1 axis/multi-kinase blockade constitutes one of the most promising treatment strategies for RCC and other cancers with the regulatory approval of multiple PD-1 axis/multi-kinase regimens as first line therapy (7, 8). Previous studies have reported that some of the ligands of these kinases, such as VEGF, can directly inhibit T-lymphocyte maturation and activity, and lead to dedifferentiation of lymphocytes and APCs (5, 6). Furthermore, clear cell RCC tumors have been shown to display a high degree of angiogenesis signaling mediated to a significant extent by increased VEGF pathway activity (58). Extrapolation of these findings to the systemic therapy setting provides a biological basis for the additional antitumor efficacy demonstrated by the lenvatinib/pembrolizumab combination, and a working model to investigate biomarkers of response to these ICB-based therapies.

Genomic analyses were undertaken in our cohort to explore the significance of emerging biomarkers of response to ICB-based therapies in this context, and our findings were largely consistent with previous reports. Consistent with previous studies, our results failed to demonstrate a significant association between TMB and response to ICB-based combination therapy in this cohort. Although TMB has proven to be a useful predictor of response to ICB therapy in many different solid tumors (38, 47, 54), these findings have not been recapitulated in RCC (38, 57, 59) and the biological basis underlying these observations remains to be elucidated.

Antigen recognition by CD8⁺ T lymphocytes has become a topic of particular interest due to its well-established role in the development of adaptive immune responses (60). T-cell dynamics have not only been directly implicated in response to ICB therapy, but T-cell receptor (TCR) repertoires have been shown to vary by therapeutic context (2, 31, 61–63). Sequencing platforms now allow for the assessment of antigen specificity and clonal heterogeneity of CD8⁺ T cells in the tumor microenvironment at useful resolution (60). Although the utility of TCR repertoire features in predicting response to ICB-based therapies is far from clear (61–70), clonal T-cell populations have been shown to develop in ICB responders and their presence has been associated with tumor neoantigen depletion (2). Notably, our group has previously shown TCR clonality to be directly associated with HLA class I genetic variability and evolutionary divergence, features that were in turn predictive of ICB therapy response in a cohort of patient with advanced melanoma (31, 53). Although not explored as part of this study due to technical limitations, research efforts aimed at further elucidating these findings should consider assessing the relationship between HED and TCR repertoire composition in different contexts.

The role of loss-of-function mutations in the PBRM1 gene, a recurrent molecular feature in clear cell RCC, was explored. Truncating PBRM1 variants have been reported in association with a distinct nonimmunogenic tumor phenotype leading to ICB resistance (71), as well as with IFNγ-associated expression programs that promote tumor clearance after ICB therapy (56, 72). Contradicting evidence also exists on the role of PBRM1 mutations in the immunotherapy context (56, 57, 59), and studies have yet to provide conclusive evidence about their utility in this setting. Similar to previous reports, our results did not support a significant association between PBRM1 loss-of-function mutations and clinical benefit after treatment with lenvatinib/pembrolizumab.

Taken together, our data suggest that HLA-I diversity is associated with prolonged progression-free intervals as well as treatment durability following therapy with combined multi-kinase and PD1 blockade. Questions still remain about the optimal HED thresholds required to predict response to ICB, and it is likely that, much like TMB, optimization to each therapeutic context will be needed in the future.

C.-H. Lee reports grants from Eisai during the conduct of the study; grants and personal fees from Merck, Pfizer, Bristol-Myers Squibb, and Exelixis; grants from Eli Lilly; grants and non-financial support from Calithera; personal fees from Amgen, EMD Serono, and grants, personal fees, and non-financial support from Eisai outside the submitted work. D. Chowell reports a patent for TMB for immunotherapy response with royalties paid. V. Makarov reports a patent for EP3090066A2, Determinants of cancer response to immunotherapy issued and with royalties paid. C. Valero reports grants from Fundación Alfonso Martín Escudero outside the submitted work. D. Hoen reports grants from AstraZeneca during the conduct of the study. L. Morris reports grants from AstraZeneca outside the submitted work. R. Kotecha reports grants from Pfizer and Takeda outside the submitted work. M.H. Voss reports other from Merck and Eisai during the conduct of the study; personal fees from Pfizer, Merck, Aveo, Exelixis, Calitehrap, Eisai, Corvus, and Chengdu outside the submitted work. M.I. Carlo reports grants from Eisai during the conduct of the study. D. Feldman reports grants from NIH/NCI Cancer Center Support Grant P30 CA008748 during the conduct of the study; other support from Novartis, Astellas, Decibel, and Seattle Genetics outside the submitted work; in addition, D. Feldman has a patent for UpToDate with royalties paid. J. Matsui reports other support from Eisai Inc. outside the submitted work. Y. Funahashi reports personal fees from Eisai Co., Ltd. during the conduct of the study; in addition, Y. Funahashi has a patent for WO02/032872 issued. K. Nomoto reports personal fees from Eisai Inc. during the conduct of the study; personal fees from Eisai Inc. outside the submitted work. R.J. Motzer reports grants and personal fees from Easai and Merck during the conduct of the study; grants and personal fees from Pfizer, Novartis, Exelixis, Genentech, Roche; personal fees from Incyte, AstraZeneca, EMD Serome, and Aveo; grants from Bristol-Myers Squibb outside the submitted work. T.A. Chan reports other from Gritstone Oncology; grants from Bristol-Myers Squibb, AstraZeneca, Pfizer; grants and personal fees from Illumina, Nysnobio during the conduct of the study; in addition, T.A. Chan has a patent for TMB licensed to PGDX. No disclosures were reported by the other authors.

C.-H. Lee: Conceptualization, resources, supervision, writing–original draft, project administration, writing–review and editing. R.G. DiNatale: Data curation, formal analysis, writing–original draft, writing–review and editing. D. Chowell: Data curation, software, formal analysis, writing–original draft, writing–review and editing. C. Krishna: Data curation, software, formal analysis, writing–review and editing. V. Makarov: Data curation, software, formal analysis, writing–review and editing. C. Valero: Data curation, writing–review and editing. L. Vuong: Data curation, writing–review and editing. M. Lee: Data curation, writing–review and editing. K. Weiss: Data curation, writing–review and editing. D. Hoen: Data curation, writing–review and editing. L. Morris: Data curation, formal analysis, writing–review and editing. E. Reznik: Formal analysis, supervision, writing–review and editing. S. Murray: Data curation, writing–review and editing. R. Kotecha: Data curation, investigation, writing–review and editing. M.H. Voss: Data curation, investigation, writing–review and editing. M.I. Carlo: Data curation, investigation, writing–review and editing. D. Feldman: Data curation, investigation, writing–review and editing. P. Sachdev: Resources, project administration, writing–review and editing. Y. Adachi: Resources, writing–review and editing. Y. Minoshima: Resources, writing–review and editing. J. Matsui: Resources, writing–review and editing. Y. Funahashi: Resources, project administration, writing–review and editing. K. Nomoto: Project administration, writing–review and editing. A. Hakimi: Supervision, writing–review and editing. R.J. Motzer: Conceptualization, investigation, writing–review and editing. T.A. Chan: Conceptualization, resources, supervision, writing–original draft, project administration, writing–review and editing.

We thank the Chan lab members for helpful discussions. We thank the staff and physicians of the MSK Department of Medicine Kidney Program and the Urology Service for helpful suggestions. We acknowledge the use of the Integrated Genomics Operation Core, funded by the NCI Cancer Center Support Grant (CCSG, P30 CA08748), Cycle for Survival, and the Marie-Josée and Henry R. Kravis Center for Molecular Oncology. This work was in part supported by grants NIH R35 CA232097 and DOD KC180165 (T.A. Chan). This work was also supported by the Mellnikoff Fund (R.J. Motzer and T.A. Chan) and the Weiss Family Fund (A.A. Hakimi, R.J. Motzer). C. Valero acknowledges grant funding from “Fundación Alfonso Martín Escudero.” We thank Rodolfo Perini of Merck pharmaceuticals for his support.

Study 111 (NCT02501096) was funded by Eisai Inc., Woodcliff Lake, NJ, and Merck Sharp & Dohme Corp., a subsidiary of Merck & Co., Inc., Kenilworth, NJ. Patients treated at Memorial Sloan Kettering Cancer Center were supported in part by a Memorial Sloan Kettering Cancer Center Support Grant/Core Grant (P30 CA008748).

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