The Intratumoral Balance between Metabolic and Immunologic Gene Expression Is Associated with Anti–PD-1 Response in Patients with Renal Cell Carcinoma

In renal cell carcinoma (RCC) and some other tumor types, mAbs blocking the programmed death 1 (PD-1):PD-1 ligand (PD-L1, B7-H1) interaction, by targeting either PD-1 (e.g., nivolumab, pembrolizumab) or PD-L1 (e.g., MPDL3280A/atezolizumab, MEDI4736/durvalumab), can induce tumor regression (1–5). Approximately 15% to 30% of patients with advanced RCC experience durable objective tumor regressions following PD-1 pathway blockade (6–9). Attention is now focused on identifying biomarkers predicting response or resistance to anti–PD-1 treatment (10). We previously identified PD-L1 expression on the tumor cell surface as one factor associated with the clinical activity of nivolumab anti–PD-1 in solid tumors including RCC (1). This observation was supported by a subsequent study of nivolumab in RCC, showing a higher objective response rate and prolonged progression-free and overall survival in patients whose pretreatment tumor specimens were PD-L1⁺ (7), although this finding was not substantiated in a subsequent phase III trial of nivolumab (8). PD-L1 expression by tumor-infiltrating immune cells has been associated with an increased response rate and overall survival following atezolizumab anti–PD-L1 therapy in a nonrandomized trial in RCC (9).

Notably, a significant number of patients with PD-L1⁺ RCC do not respond to PD-1 pathway blockade, suggesting that additional intratumoral factors may influence treatment outcomes. The current study was undertaken to explore gene expression profiles characterizing PD-L1⁺ RCCs that did or did not respond to nivolumab anti–PD-1 therapy.

Detailed information is available online in Supplementary Materials and Methods.

Consenting patients with unresectable metastatic RCC received nivolumab anti–PD-1 monotherapy at the Johns Hopkins Kimmel Cancer Center (Baltimore, MD) on one of four clinical trials (NCT00441337, NCT00730639, NCT01354431, NCT01358721) approved by the Johns Hopkins Institutional Review Board. Patients were classified as responders (R) or nonresponders (NR) to anti–PD-1 therapy based on radiographic staging according to Response Evaluation Criteria in Solid Tumors (RECIST; ref. 11). Pretreatment tumor specimens were characterized for PD-L1 expression by IHC as described previously (refs. 1, 12; Supplementary Table S1).

Serial 5-μm-thick sections from PD-L1⁺ formalin-fixed, paraffin-embedded (FFPE) tumor specimens were stained for the expression of selected markers with specific mAbs as described previously (13). PD-L1⁺ tumor specimens had ≥5% of tumor cells showing cell surface staining with the murine anti-human PD-L1 mAb 5H1 (from Lieping Chen, Yale University, New Haven, CT). Comparisons of IHC results from PD-L1⁺ tumors derived from responding versus nonresponding patients were performed using The R Project for Statistical Computing (https://www.r-project.org/).

PD-L1⁺ tumor areas, identified with IHC on neighboring tissue sections, were either manually dissected by scraping with a scalpel, or were laser-capture microdissected from 5-μm FFPE tissue sections as described previously (refs. 12, 14; Supplementary Fig. S1). Total RNA was isolated from PD-L1⁺ tumor areas and the expression of selected genes was quantified using custom-made TaqMan Low-Density Array Micro Fluidic Cards per protocol (TLDA; Applied Biosystems), as described previously (14).

Global gene expression in pretreatment RCC specimens was measured by DASL (cDNA-mediated annealing, selection, extension, and ligation) assays arrayed on the Illumina Human HT-12 WG-DASL V4.0 R2 expression BeadChip, as per the manufacturer's specifications (Illumina) as previously described (14). Gene expression data are accessible through GEO Series accession number GSE67501 (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE67501).

Eight RCC cell lines (786-0, A498, ACHN, Caki I, RXF-393, TK-10, SN12C, and U0-31) from the NCI-60 panel were obtained in 2009 from the NCI-Frederick Cancer Center DCTD Tumor/Cell Repository (Frederick, MD). Characterization is provided at https://dtp.cancer.gov/discovery_development/nci-60/characterization.htm. They were cryopreserved and used within 6 months of continuous culture. RCC cell lines were assessed with multiplex qRT-PCR for selected gene expression.

The established melanoma cell lines 397-mel and 888-mel, generated from metastatic melanoma lesions in our laboratory and maintained as described previously (15, 16) were transfected with the empty vector pCMV6-Entry or with pCMV6-UGT1A6 encoding human UGT1A6 (RC215957; Origene Technologies) using Lipofectamine 2000 (Life Technologies). qRT-PCR and Western blotting revealed no UGT1A6 in these melanomas prior to transfection, and positive expression after transfection with the plasmid encoding UGT1A6, but not with the control vector. Tumor cell lysis was measured with a modified flow cytometry–based assay detecting the disappearance of CFSE-labeled transfectants expressing UGT1A6 or not, upon incubation with nonspecific LAK cells or specific T effector cells (17).

RNA sequencing data from The Cancer Genome Atlas project (TCGA), including 444 clear cell RCC samples and 72 matched normal kidney samples, were used for in silico analysis (18). Data were analyzed using R/Bioconductor software.

In a prior study of archival melanoma specimens, we identified immune-related genes that were coordinately overexpressed in PD-L1⁺ compared with PD-L1⁻ tumors (14). They included genes associated with CD8⁺ T-cell activation (CD8A, IFNG, PRF1, CCL5), antigen presentation (CD163, TLR3, CXCL1, LYZ), and immunosuppression [PD1, CD274 (PD-L1), LAG3, IL10]. In the current study, multiplex qRT-PCR was used to characterize pretreatment PD-L1⁺ RCC specimens from 12 patients receiving anti–PD-1 (R = 4, NR = 8) for immune markers (Supplementary Table S1). Genes that were coordinately overexpressed in PD-L1⁺ melanomas were also expressed in PD-L1⁺ RCCs. However, none were significantly differentially expressed according to clinical outcomes after nivolumab therapy (data not shown). Similar results were obtained when IHC was used to examine protein expression for a more focused group of immune-related molecules in RCCs from 13 patients (R = 4, NR = 9), including PD-1, PD-L2, LAG-3, and TIM-3, and to characterize infiltrating immune cell subsets (FoxP3, CD4:CD8 ratio; Supplementary Fig. S2). No significant differences in expression of these molecules were observed between PD-L1⁺ RCCs from responders versus nonresponders. Thus, all PD-L1⁺ RCCs appeared to have an immune-reactive tumor microenvironment (TME) which, in itself, did not distinguish responders from nonresponders.

Because analysis of a selected panel of 60 immune-related genes did not reveal significant differences between PD-L1⁺ RCCs that were responsive or resistant to anti–PD-1 therapy, we next turned to whole genome expression profiling. Eleven available RCC specimens from among the original cohort (R = 4, NR = 7; Supplementary Table S1) were analyzed for the expression of 29,377 gene targets. We identified 234 probes corresponding to 223 genes that were differentially expressed between the two groups, based on a P of ≤ 0.01 and expression fold change magnitude ≥1.5. Among them, 116 probes corresponding to 113 genes were upregulated in tumors from responding patients, and 118 probes corresponding to 110 genes were upregulated in tumors from nonresponding patients (Fig. 1A; Supplementary Table S2). By inspection, genes upregulated in nonresponders appeared to be functionally related in metabolic pathways. In contrast, some genes that were upregulated in tumors from responding patients had immune functions. Thus, there appeared to be a functional dichotomy of gene expression profiles in PD-L1⁺ RCCs obtained from patients who responded or did not respond to anti–PD-1 therapy. To further explore these trends, a functional annotation analysis was performed with the DAVID Bioinformatics Resources 6.7 tool, based on 1,017 Illumina probes having differential expression in tumors from responding versus nonresponding patients with fold expression change ≥1.5 and P ≤ 0.05. A principal component analysis (PCA; ref. 19) of the entire set of 1,017 probes further revealed the segregation of gene expression profiles in RCCs from responding versus nonresponding patients (Fig. 1B). Among the 1,017 probes, 467 and 550 were found to be overexpressed in responders and nonresponders, respectively. Analysis of the 467 probes that were overexpressed in responders did not yield significant DAVID gene clusters. However, analysis of the 550 probes that were overexpressed in nonresponders yielded 23 pathways involving mitochondrial and other metabolic functions (DAVID Benjamini FDR ≤0.010; Table 1).

To further explore the results of global gene expression profiling, differential expression of 60 selected unique gene targets was assessed with multiplex qRT-PCR in 12 specimens (R = 4, NR = 8; Supplementary Table S3). By considering results obtained with each of four endogenous gene controls, 25 among the 60 queried genes were confirmed to be differentially expressed in the two groups of patients with divergent clinical outcomes (Table 2). These 25 genes were not differentially expressed in RCC specimens from primary versus metastatic sites (not shown). In particular, upregulation of genes corresponding to proteins associated with metabolic and solute transport functions was found in nonresponders (Fig. 2A). These proteins are known to have physiologic functions in normal renal epithelial cells. Among them, the UDP-glucuronosyltransferase UGT1A6 showed the greatest differential expression, being upregulated approximately 300-fold in nonresponders (P = 0.007 using GUSB endogenous control). Its family members UGT1A1 and UGT1A3 were also overexpressed in nonresponders. In addition, genes encoding proteins involved in solute transport, such as the potassium channel rectifier KCNJ16, the glucose-6-phosphate translocase SLC37A4, the human sodium-dependent ascorbic acid (vitamin C) transporter SLC23A1, and the myelin and lymphocyte–associated protein MAL, were also significantly upregulated in RCCs from nonresponding patients. In contrast, some genes associated with immune functions were upregulated in tumors from responding patients, including the chemokine CCL3, the plectin molecule (PLEC), the nuclear factor NFATC1, the transcription regulator BACH2, and the histone methyltransferase WHSC1 (Fig. 2A). Thus, qRT-PCR confirmed the dichotomous pattern of gene expression suggested by whole genome microarray in tumors from responding versus nonresponding patients.

The RCC TME is a complex milieu containing many different cell types. To understand whether metabolic genes that were overexpressed in tumor specimens from nonresponding patients were specifically associated with renal carcinoma cells, we evaluated their expression in 8 established kidney cancer cell lines using qRT-PCR. Results showed that cultured renal carcinoma cells expressed the metabolic genes of interest (Supplementary Fig. S3).

UGT1A6 is involved in the chemical “defensome” and detoxifies exogenous and stress-related lipids (20). In whole genome expression profiling and qRT-PCR assessment, it was the most highly overexpressed gene associated with nonresponse to anti–PD-1 (∼8-fold and ∼300-fold, P ≤ 0.005 with multiple probes and P = 0.007, respectively). Therefore, the expression of UGT1A6 in RCC was further explored at the protein level with IHC in the same 12 specimens as those used for gene expression profiling by qRT-PCR. UGT1A6 protein expression varied widely among the specimens and was associated with clinical outcomes following anti–PD-1 therapy (P = 0.036, one-sided Wilcoxon rank-sum test; Fig. 2B).

To examine whether overexpression of UGT1A6 protein might protect tumor cells from immune cell killing, we tested the ability of non-MHC–restricted LAK cells and MHC I–restricted T cells (397 TILs or 888 TILs) to lyse 397-mel or 888-mel cells transfected with a plasmid encoding UGT1A6. No substantial difference in lysis of UGT1A6-positive or -negative melanoma cells was observed (not shown).

To explore whether the observed overexpression of genes such as UGT1A6 is specifically associated with adverse outcomes to anti–PD-1 therapy, or generally associated with poor prognosis in patients with RCC, an in silico analysis was performed on RNA expression data obtained from 444 primary clear cell RCC specimens in The Cancer Genome Atlas project (TCGA; ref. 18). Kaplan–Meier curves were generated using the median expression value for UGT1A6 to segregate samples into high or low expressers. There was no significant correlation between the level of UGT1A6 mRNA expression in primary kidney cancers and overall survival in the entire cohort of 444 patients, nor with survival in a subset of 71 patients with stage IV metastatic disease (similar to patients in our study; Supplementary Fig. S4A). Furthermore, UGT1A6 expression levels did not correlate with RCC clinical stage in an analysis of TCGA data (Supplementary Fig. S4B). These findings suggest that the association of UGT1A6 expression with clinical outcomes following anti–PD-1 therapy is specifically relevant in the context of this treatment.

Expression of the immunosuppressive ligand PD-L1, detected in pretreatment tumor biopsies with IHC, has been associated with favorable clinical outcomes following PD-1– and PD-L1–blocking therapies in some studies in several types of cancer (1, 7, 9, 21). This can be understood by viewing PD-L1 as a surrogate marker for an immune-reactive TME, as inflammatory cytokines such as IFNγ are major drivers of PD-L1 expression on tumor and stromal cells. In this model, blocking the PD-1/PD-L1 interaction unleashes an immune response that is already properly trained and poised to attack cancer cells, but held in check by this immunosuppressive pathway. Despite the therapeutic impact of PD-1 blockade in many patients with certain cancer types (22), the majority of patients with PD-L1⁺ tumors do not respond to anti–PD-1/PD-L1 drugs, suggesting the involvement of additional factors in the TME contributing to treatment resistance. The current study attempts to identify such factors by exploring the gene expression landscape of PD-L1⁺ RCCs derived from patients with divergent clinical outcomes after anti–PD-1 therapy, and identifies groups of metabolic and immunologic factors associated with adverse or favorable clinical outcomes, respectively.

RCC has been characterized as a metabolic disease, with the signature upregulation of factors adapting to hypoxia and functioning to meet the bioenergetic demands of cellular proliferation (23). We here describe a likely metabolic shift in RCCs resistant to anti–PD-1 therapy, with overexpression of molecules associated with glucuronidation and the transport of solutes and nutrients such as glucose. Among them, UGT1A6, whose principal role is to promote cellular clearance of toxins and exogenous lipophilic chemicals (18), was the single most highly overexpressed molecule associated with anti–PD-1 treatment resistance, and other UGT1A family members and solute carriers constituting the chemical “defensome” were also upregulated. This gene expression profile may simply reflect an activated cell phenotype; however, one might hypothesize that the heightened clearance of toxins from tumor cells by molecules typified by UGT1A6 may render them more fit to outpace the immune system. Furthermore, the overexpression of metabolic genes in RCCs resistant to anti–PD-1 therapy may reflect tumor-imposed metabolic effects that can restrict the responsiveness of tumor-specific infiltrating T cells by competing for vital nutrients such as glucose within the TME (24).

Despite the limitations inherent to this retrospective study of a small number of specimens, the significantly differential gene expression profiles described here provide a basis for future exploration in larger RCC cohorts, potentially including PD-L1⁺ and (−) tumors. The general approach to identifying biomarkers of clinical response to PD-1–targeted therapies has so far focused on immunologic factors in the TME. However, a deeper level of investigation may be warranted for individual tumor types, and intersections of tumor cell–intrinsic factors with immunologic factors may be particularly revealing. For instance, a recent study unexpectedly found overexpression of β-catenin to be associated with decreased infiltration of tumor-specific T cells in melanoma (25). A greater knowledge of such tumor-intrinsic mechanistic markers may reveal new therapeutic targets for combination treatment regimens based on PD-1 pathway blockade, and useful markers for selecting patients most likely to respond to these therapies.

R.A. Anders reports receiving commercial research support from Five Prime Diagnostics and Bristol-Myers Squibb and is a consultant/advisory board member for Adapative Biotech. C.G. Drake reports receiving commercial research support from Bristol-Myers Squibb, Janssen, and Aduro Biotech; has ownership interest (including patents) in Potenza and Compugen; and is a consultant/advisory board member for Compugen, Dendreon, Roche/Genentech, Merck, Eli Lilly, and AstraZeneca/Medimmune. D.M. Pardoll reports receiving commercial research support and has an ownership interest (including patents) in Bristol-Myers Squibb. J.M. Taube reports receiving commercial research support from Bristol-Myers Squibb and is a consultant/advisory board member for Bristol-Myers Squibb, Merck, and AstraZeneca. S.L. Topalian has served as a consultant/advisory board member for Five Prime Therapeutics, MedImmune, Merck, and Pfizer, and has an ownership interest in Bristol-Myers Squibb, Potenza Therapeutics, and Five Prime Therapeutics. No potential conflicts of interest were disclosed by the other authors.

Conception and design: R.A. Anders, J.M. Taube, S.L. Topalian

Development of methodology: M.L. Ascierto, H. Xu, J. Fan, J.M. Taube, S.L. Topalian

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): T.L. McMiller, R.A. Anders, G.J. Netto, C. Cheadle, J.M. Taube

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): M.L. Ascierto, A.E. Berger, L. Danilova, R.A. Anders, T.S. Pritchard, L. Cope, C.G. Drake, J.M. Taube, S.L. Topalian

Writing, review, and/or revision of the manuscript: M.L. Ascierto, T.L. McMiller, A.E. Berger, L. Danilova, G.J. Netto, T.S. Pritchard, L. Cope, C.G. Drake, D.M. Pardoll, J.M. Taube, S.L. Topalian

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): M.L. Ascierto

Study supervision: S.L. Topalian

Other (developed the IHC protocol and performed relevant IHC staining): H. Xu

The authors would like to thank Dr. Hans Hammers, Dr. Jenny Kim, Ting Wang and Yelena Millman (Johns Hopkins University) for providing clinical response data; Dr. Lieping Chen (Yale University) for providing the anti–PD-L1 mAb 5H1; Dr. Shuming Chen, George A. Crabill, and Matthew Presby (Johns Hopkins University) for helpful discussions and technical assistance; Jessica Esandrio for administrative assistance; and Dr. Mark Ratain (University of Chicago) and Dr. David Feltquate (Bristol-Myers Squibb) for helpful discussions.

This study was supported by research grants from Bristol-Myers Squibb (to S.L. Topalian); the National Cancer Institute, NIH (R01 CA142779; to J.M. Taube, D.M. Pardoll, and S.L. Topalian; and P30 CA0006973, all authors); the Bloomberg∼Kimmel Institute for Cancer Immunotherapy at Johns Hopkins (to R.A. Anders, C.G. Drake, D.M. Pardoll, J.M. Taube, and S.L Topalian); and Stand Up To Cancer—Cancer Research Institute Cancer Immunology Translational Cancer Research Grant SU2C-AACR-DT1012 (to R.A. Anders, J.M. Taube, D.M. Pardoll, and S.L. Topalian). Stand Up To Cancer is a program of the Entertainment Industry Foundation administered by the American Association for Cancer Research.

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