Phase III Trial of Adjuvant Sunitinib in Patients with High-Risk Renal Cell Carcinoma: Exploratory Pharmacogenomic Analysis

Because of the increased use of radiologic and ultrasound imaging techniques, incidental detection of renal cell carcinoma (RCC) occurs more frequently today, contributing to an increase in the number of patients diagnosed with stage I to III (localized or loco-regional) disease. Thus, optimizing the management of early-stage RCC is a key priority in clinical practice (1, 2). For patients with tumor, lymph nodes, metastasis (TNM) stage I and II RCC, surgery alone may be sufficient, with 5-year cancer-specific survival rates of 91% and 74% for stage I and II, respectively. However, a number of patients with stage III or IV nonmetastatic RCC (+/− regional lymph node involvement) will eventually relapse and progress to stage IV metastatic RCC (3). To try and identify those patients with the highest risk of relapse, there are several scoring systems in use, based on clinical and pathologic features (4–8). Based on risk stratification using the University of California Los Angeles Integrated Staging System (UISS), up to 15% of patients with non-metastatic RCC who undergo nephrectomy are considered at high risk (UISS III–IV) of recurrence and approximately 40% of these will relapse within 5 years (6, 9, 10).

Sunitinib is a multitargeted inhibitor of the VEGF signaling pathway and is approved for the treatment of metastatic RCC, and approved by the FDA as adjuvant therapy in patients at high risk for recurrent RCC post nephrectomy (11). In the randomized phase III S-TRAC trial, adjuvant sunitinib prolonged disease-free survival (DFS) versus placebo in patients with loco-regional RCC (≥T3 and/or N+) at high risk of recurrence following nephrectomy (based on blinded independent central review): HR 0.76; 95% confidence interval (CI), 0.59–0.98 (P = 0.03), and median DFS 6.8 years versus 5.6 years (12, 13).

The addition of a pharmacogenomic biomarker assay based on the understanding of host versus disease biology could potentially improve risk assessment and help to select patients who are most likely to benefit from adjuvant treatment. The most common genetic DNA sequence variations are SNPs (14). A number of SNPs have been reported as potential predictors of efficacy and/or toxicity in patients with advanced RCC treated with anti-angiogenic tyrosine kinase inhibitors (15–23). The impact of these SNPs on the efficacy and safety of tyrosine kinase inhibitors range from modulating pharmacokinetics (e.g., through increased drug clearance) through to pharmacodynamics (e.g., increased expression of target proteins), and/or activation of downstream signaling pathways (15–29). With regards to SNPs in VEGF and VEGF receptors (VEGFR)-1, 2, or 3, various studies and analyses have reported that certain SNPs are associated with poorer response rate, overall survival (OS), and/or progression-free survival (PFS) in patients with metastatic RCC treated with sunitinib (19, 20, 24–29).

Therefore, the objectives of these exploratory, hypothesis-generating analyses were to evaluate potential associations between a subset of SNPs in genes known to be involved in angiogenesis and clinical outcomes by using data from a subset of patients enrolled in the S-TRAC trial.

S-TRAC was a randomized, double-blind, phase III trial (ClinicalTrials.gov, NCT00375674) in patients with loco-regional RCC at high risk of tumor recurrence after nephrectomy (12, 13). Details on eligibility were previously reported (12, 13). Briefly, patients were ≥18 years old and had a histologic confirmation of clear-cell, loco-regional (≥T3 and/or N+) RCC. Other inclusion criteria were: Eastern Cooperative Oncology Group performance status 0–2 before nephrectomy; no prior systemic therapy for RCC; and lack of macroscopic residual or metastatic disease, determined by blinded independent central review.

Patients were randomly assigned (1:1) to receive sunitinib 50 mg/day or placebo on a 4-weeks-on/2-weeks-off schedule for 1 year or until disease recurrence, diagnosis of secondary malignancy, unacceptable side effect, or consent withdrawal (12, 13). The trial was approved by the independent review board or ethics committee at each center and was conducted in accordance with the Declaration of Helsinki, the International Conference on Harmonization Good Clinical Practice guidelines and applicable local regulatory requirements and laws. All patients provided written informed consent.

Anonymized blood samples were prospectively collected from patients in the S-TRAC trial who consented to the molecular profiling substudy. Samples were genotyped for 10 SNPs and one insertion/deletion mutation, some of which have been previously hypothesized to predict response to sunitinib in treatment of metastatic RCC (Table 1; refs. 19, 24–26).

Following sample de-identification and DNA extraction, DNA amplification was carried out using PCR. Commercially available TaqMan assays (Thermo Fisher Scientific) were used and analyzed on an Applied Biosystems QuantStudio 12K Flex Real-Time PCR System (Thermo Fisher Scientific; dba: Life Technologies), and was performed at Pfizer Clinical Pharmacogenomics Laboratory (Pfizer Inc.). The SNP SH3GL2 rs10963287 was determined using a laboratory developed TaqMan assay. The region immediately surrounding SNP rs10963287 contains a short interspersed nuclear elements [SINE] region; therefore, the assay to determine the genotype of rs10963287 involved amplifying a region encompassing the SNP. The PCR product from this initial reaction was used as the input template for TaqMan analysis. For coiled-coil domain-containing protein 26 (CCDC26), the three nucleotide small insertion and deletion (INDEL) rs60315789 was detected by amplifying a region around the insertion, sizing the fragment using an ABI3730xl DNA analyzer, and then analyzing the data using the GeneMapper 5.0 software (Thermo Fisher Scientific).

The pharmacogenomics population comprised all patients who received at least one dose of study medication (i.e., treatment assignments designated according to actual study treatment received) and who had at least one genotype result. The primary endpoint in S-TRAC was DFS, which was defined as time from randomization until recurrence or secondary malignancy, as assessed by blinded independent central review, or death. OS was assessed as a secondary endpoint.

Fisher exact test was used to compare baseline demographics and characteristics between treatment groups. Two-sided 95% CIs for allele frequency and genotype frequency were determined by exact method using the F distribution. All SNPs were examined for deviation from Hardy–Weinberg Equilibrium; P values were calculated using Pearson's Chi-squared test based on 10,000 replicates. Linkage disequilibrium analysis was also performed for SNP pairs. D′, r², and P values were calculated for each pair of SNPs within a given gene or on the same chromosome.

In exploratory analyses, DFS and OS between sunitinib and placebo groups were compared for each genotype with Kaplan–Meier estimates and an unstratified log-rank test. Estimated HR and its two-sided 95% CI were provided using Cox proportional hazards models. Genotype subgroups were also compared within each treatment group and for the combined sunitinib and placebo treatment groups (“combined group”) using the same statistical approach, where applicable. P values were unadjusted for multiplicity. The log-rank P value and HR statistic were produced only when there were ≥10 events in both comparison groups. The data cut-off date for these analyses was April 7, 2016 (per the primary S-TRAC analyses; ref. 12).

Of 615 patients enrolled in S-TRAC, 610 received treatment (n = 306 and n = 304, sunitinib and placebo, respectively; ref. 12). Of these patients, 286 (46.9%; n = 142 and 144, sunitinib and placebo, respectively) consented to the pharmacogenomic analysis and provided a blood sample that was successfully genotyped. Because of ethnic or religious reasons, or regulations imposed by local health authorities, 324 patients did not consent or provide a blood sample that could be successfully genotyped. Demographics and baseline clinical characteristics of genotyped subgroups treated with sunitinib or placebo were generally comparable (Table 2). However, there were differences between 286 patients who were genotyped and the 324 who were not genotyped; a greater number of genotyped patients were older, white, and were categorized in the UISS high-risk group (Table 2).

There were no unexpected findings in allele and genotype frequencies for SNPs (Supplementary Tables S1 and S2). For most SNPs, there were no deviations in genotype frequencies from the Hardy–Weinberg Equilibrium; however, two SNPs showed deviation from Hardy–Weinberg Equilibrium: VEGFR2 rs1870377 (P = 0.017) and VEGFR3 rs6877011 (P < 0.001). High linkage disequilibrium was detected between the following SNPs: VEGFR2 rs1870377 and rs2071559 on chromosome 4 (D′ = 0.250; r² = 0.015; P = 0.036); VEGFA rs699947 and rs833061 on chromosome 6 (D′ = 1.000; r² = 0.979; P < 0.001); and VEGFR1 rs9582036 and rs9554320 on chromosome 13 (D′ = 0.977; r² = 0.584; P < 0.001).

The following genotypes were associated with a longer DFS in the sunitinib versus placebo group (Table 3): C/C genotype for VEGFR1 rs9554320 (HR 0.44; 95% CI, 0.21–0.91; P = 0.023; Fig. 1A); T/T genotype for VEGFR2 rs2071559 (HR 0.46; 95% CI, 0.23–0.90; P = 0.020; Fig. 1B); T/T genotype for eNOS rs2070744 (HR 0.53; 95% CI, 0.30–0.94; P = 0.028; Fig. 1C).

A trend for a longer DFS in the sunitinib versus placebo group was observed for VEGFR1 rs9582036 A/A (HR 0.56; 95% CI, 0.30–1.02; P = 0.054; Table 3). None of the genotypes for other tested SNPs showed differences in DFS between sunitinib and placebo (Table 3).

The following genotype subgroups were associated with a shorter DFS (Supplementary Table S3). The heterozygous genotype C/A of VEGFR1 rs9582036 compared with the homozygous genotype C/C in the sunitinib (P < 0.001), placebo (P = 0.027), and combined (P < 0.001) groups; homozygous genotype A/A compared with genotype C/C only in the sunitinib group (P = 0.022; Fig. 2A and B); heterozygous genotype A/C of VEGFR1 rs9554320 compared with the homozygous genotype A/A in the placebo (P = 0.038) and combined (P = 0.006) groups, with a trend in the sunitinib group (P = 0.051); and A/A genotype of VEGFR2 rs1870377 compared with genotype T/T in the combined (P = 0.018) but not the placebo (P = 0.198) groups. No statistical test was performed in the sunitinib group because only seven patients had the A/A genotype.

None of the genotype subgroups for the other SNPs analyzed showed differences in DFS within each treatment group or when the treatments groups were combined.

Correlation between the genotypes and OS was assessed by comparing sunitinib versus placebo within each SNP genotype subgroup. There were no other significant associations between SNP genotype and OS for sunitinib versus placebo; however, OS data were not mature at the time of data cut-off (Supplementary Table S4).

Correlations between OS and the SNP genotype subgroups were also carried out within each treatment group and by combining the two treatment groups. The genotype “−/−” (i.e., no insertion) of CCDC26 rs60315789 showed a trend toward longer OS versus the heterozygous −/TAT genotype in the sunitinib (HR 3.01; 95% CI, 0.88–10.31; P = 0.089) and placebo (HR 2.01; 95% CI, 0.74–5.44; P = 0.122) groups. A significant association was observed when the treatment groups were combined (HR 2.39; 95% CI, 1.11–5.16; P = 0.022). No other associations were reported for OS and genotypes within each treatment group or in the combined group.

In these exploratory analyses, correlations between 11 SNPs (including specific SNPs in VEGFA, VEGFR1, and VEGFR3) and DFS and OS were assessed in patients with locoregional RCC. Three of the 11 SNPs demonstrated improved DFS with sunitinib treatment over placebo with HRs between 0.44 and 0.56, compared with 0.76 for the overall population. This improvement in HR supports a predictive value for this biomarker-defined subset of patients receiving sunitinib treatment. In particular, the genotypes C/C for VEGFR1 rs9554320, T/T for VEGFR2 rs2071559, and T/T for eNOS rs2070744 were associated with a longer DFS with sunitinib versus placebo treatment. In addition, some SNPs demonstrated a potential trend toward prognostic value across treatment groups. The genotypes homozygous C/C of VEGFR1 rs9582036 and A/A of VEGFR1 rs9554320 showed trends toward longer DFS versus the heterozygous and other homozygous genotypes in the sunitinib, placebo, and combined treatment groups. For the VEGFR2 rs1870377 SNP, the only significant association was observed when both treatment groups were combined; the A/A genotype showed an association with shorter DFS versus the T/T genotype only. Despite differences in baseline demographics, the effect of sunitinib treatment on DFS in the pharmacogenomic analysis cohort was similar to that overall S-TRAC population (HR 0.74; 95% CI, 0.51–1.07; P = 0.107), with median DFS of 7.07 and 5.56 years for sunitinib and placebo, respectively. With a limited number of OS events, there were no significant associations between SNP genotype and OS for sunitinib versus placebo. Still, for OS and genotypes within each treatment group and in the combined group, the genotype “−/−” (i.e., no insertion) of CCDC26 rs60315789 showed a trend towards longer OS versus the heterozygous −/TAT genotype in the sunitinib and placebo groups. A significant association was observed when the treatment groups were combined.

In the Renal EFFECT study, patients with metastatic RCC demonstrated marginally significant interactions between two sunitinib treatment arms and VEGFR3 rs448012 for time-to-tumor progression, PFS, and OS; however, the study could not identify which treatment group had a significant association between genotype and the clinical outcome (28). The current analyses of patients in the adjuvant setting did not demonstrate any associations between efficacy and VEGFR3 with sunitinib versus placebo, although longer DFS was seen for sunitinib versus placebo treatment in patients with C/C for VEGFR1 rs9554320, T/T for VEGFR2 rs2071559, and T/T for eNOS rs2070744. Genetic polymorphism in eNOS rs2070744 has been previously identified as independently predicting a rise in blood pressure and/or development of severe hypertension, but not as a predictor of clinical outcome in sunitinib-treated patients with metastatic RCC (19, 30). A recent study has highlighted that endothelial nitric oxide synthase (eNOS) SNPs may have a role as predictive biomarkers for bevacizumab efficacy and toxicity in patients with metastatic colorectal carcinoma (31). Therefore, it will be necessary to explore the impact of eNOS, VEGFR1, VEGFR2, and VEGFR3 SNPs on clinical outcomes in different treatment arms in patients with RCC or other tumor types.

In the current analyses, some SNPs in VEGFR1, VEGFR2, and CCDC26 demonstrated potential for prognostic value in DFS or OS outcomes across treatment groups in the adjuvant setting. To date, certain VEGFR1 SNPs have been proposed as candidate prognostic biomarkers in patients with metastatic RCC (24, 25). In patients treated with first-line sunitinib, poorer response rate, PFS, and OS were observed in those with VEGFR1 rs9554320 A/A and rs9582036 C/C genotypes compared with more favorable genotypes (19, 24, 25). Dornbusch and colleagues also reported that poorer OS was observed in those with VEGFR1 rs9582036 C/C genotypes versus A/A and A/C genotypes in patients with metastatic RCC treated with first-line sunitinib (26). However, they did not report any associations between VEGFR1 rs9582036 and PFS or VEGFR1 rs9554320 and PFS or OS (26).

Furthermore, SNPs in the VEGFA (rs833061, rs699947, rs2010963, and rs3025039), VEGFR2 (rs2305948), and VEGFR3 (rs6877011, rs307826, and rs307821) genes have been associated with varying clinical outcomes in patients with metastatic RCC treated with sunitinib (19, 20, 27, 29). For example, significantly longer PFS and OS were observed in patients with the C/C or C/T genotype of VEGFA rs833061 compared with the T/T genotype; similar results were observed in patients with the A/A or A/C versus the C/C genotype of VEGFA rs699947 (29). In patients with metastatic RCC treated with axitinib, certain SNPs in the VEGFA gene were associated with improved OS (19, 32). In contrast, in the Renal EFFECT study, Motzer and colleagues could not identify associations of certain SNPs (VEGFA rs699947, VEGFA rs1570360, VEGFR3 rs448012, VEGFR3 rs307821, and VEGFR3 rs307826) with efficacy (PFS, OS, objective response, or time-to-tumor progression; refs. 19, 33). The contrasting observations for the impact of VEGF and VEGFR SNPs on sunitinib efficacy in RCC highlight the need to confirm exploratory studies and the differences reported. Additionally, some of the observations in the adjuvant setting appear to be divergent from the observations reported in the metastatic setting. These analyses were exploratory and were limited by small sample sizes in some subgroup analyses; therefore, further validation in independent adjuvant studies are needed to confirm if these SNPs can predict those patients who are most likely to derive benefit from adjuvant sunitinib treatment. Investigation into the association of SNPs and OS in the adjuvant setting would be of value; however, conservative estimates suggest that a suitably powered trial would require approximately 1,650 patients with a follow-up of 18.5 years to demonstrate a substantial impact on OS (13). Patients in the S-TRAC trial were followed-up for an additional year to further assess OS; however, only 16 additional events in the intent-to-treat population occurred between the data cut-off date and the final OS follow-up and data remained immature. Further analyses would therefore not be expected to provide additional insight. In lieu of mature OS data and given the clinical need, DFS can be considered a suitable endpoint, especially where survival may be prolonged. The importance of DFS as an endpoint is recognized by the FDA and formed the primary basis for the approval of other adjuvant therapies including colon cancer and hormonal and cytotoxic breast cancer therapies (34). Finally, additional analyses in the adjuvant setting should also address whether the frequency and outcomes associated with the identified SNPs are consistent across a more diverse range of ethnic groups.

Correlations between SNPs in VEGFR1, VEGFR2, and eNOS genes and improved DFS in the sunitinib compared with placebo groups support the hypothesis that germline SNPs could have potential predictive value to identify patients who might derive more benefit from adjuvant sunitinib. Additionally, SNPs in VEGFR1, VEGFR2, and CCDC26 demonstrated a potential trend toward prognostic value for either DFS or OS across treatment groups. Further independent validation studies are necessary to confirm the potential predictive and prognostic value of these SNPs in the adjuvant setting.

D.J. George receives other commercial research support from Acerta Pharmaceuticals, Astellas, Bayer H/C Pharmaceuticals, Bristol-Meyers Squibb, Calithera, Dendreon, Exelixis, Innocrin, Janssen Pharmaceuticals, Novartis, Pfizer, and Sanofi, speakers bureau honoraria from Bayer H/C Pharmaceuticals, Sanofi, and Exelixis, is a consultant/advisory board member for Astellas, Astrazeneca, Bayer H/C Pharmaceuticals, Bristol-Meyers Squibb, Capio Biosciences, Exelixis, Genentech, Innocrin, Janssen Pharmaceuticals, Merck Sharp & Dohme, Myovant Sciences, Pfizer, and Sanofi, receives other honorarium from EMD Serono, Michael J. Hennessey Associates, OncLive, Pfizer, and UroToday, is an independent contractor for Axess Oncology, is on the steering committees of NCI and Pfizer, and is on the independent data monitoring committees of Acceleron Pharmaceuticals and Janssen Pharmaceuticals. J.-F. Martini holds ownership interest (including patents) in Pfizer. M. Staehler reports receiving commercial research grants from Pfizer, Roche, Bristol-Myers Squibb, EISAI, GlaxoSmithKline, Bayer, Exelixis, and Novartis, speakers bureau honoraria from Pfizer, Ipsen, Novartis, EUSAPharm, and EISAI, and is a consultant/advisory board member for Pfizer, Ipsen, Novartis, EUSAPharm, and EISAI. R.J. Motzer reports receiving commercial research grants from Pfizer, Eisai, Exelixis, Bristol-Myers Squibb, Genentech/Roche, and Novartis, and is a consultant/advisory board member for Pfizer, Merck, Genentech, Exelixis, Eisai, and Novartis. A. Magheli is a consultant/advisory board member for Ipsen Pharma, and reports receiving speakers bureau honoraria from Pfizer, Astellas, Janssen, and Ipsen Pharma. F. Donskov reports receiving commercial research grants from Novartis, Pfizer, and Ipsen. B. Escudier is a consultant/advisory board member for Pfizer, Novartis, Bristol-Myers Squibb, Exelixis, and Roche, and reports receiving speakers bureau honoraria from Pfizer and Novartis. S. Li, M.Casey, O. Valota, and M.J. Lechuga hold ownership interest (including patents) in Pfizer. B. Laguerre receives honoraria from Pfizer. A.J. Pantuck and A. Patel are consultant/advisory board members for Pfizer. H.S. Pandha is a consultant/advisory board member for Ipsen and Eisai. A. Ravaud is a consultant/advisory board member for Pfizer, Novartis, Bristol-Myers Squibb, AstraZeneca, Roche, and Ipsen, and received travel support from Pfizer, Novartis, Bristol-Myers Squibb, AstraZeneca, Roche, Ipsen, and Merck Sharp & Dohme. No potential conflicts of interest were disclosed by the other authors.

Conception and design: D.J. George, J.-F. Martini, R.J. Motzer, B. Escudier, A.J. Pantuck, H.S. Pandha, A. Patel, M. Lechuga, A. Ravaud

Development of methodology: J.-F. Martini, B. Escudier, A. Patel, A. Ravaud

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): M. Staehler, R.J. Motzer, A. Magheli, F. Donskov, B. Laguerre, A.J. Pantuck, M. Lechuga, A. Ravaud

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): D.J. George, J.-F. Martini, M. Staehler, R.J. Motzer, A. Magheli, F. Donskov, B. Escudier, S. Li, M. Casey, B. Laguerre, A.J. Pantuck, H.S. Pandha, A. Patel, M. Lechuga

Writing, review, and/or revision of the manuscript: D.J. George, J.-F. Martini, M. Staehler, R.J. Motzer, A. Magheli, F. Donskov, B. Escudier, S. Li, M. Casey, O. Valota, B. Laguerre, A.J. Pantuck, H.S. Pandha, A. Patel, M. Lechuga, A. Ravaud

Study supervision: D.J. George, J.-F. Martini, R.J. Motzer, A. Magheli, A.J. Pantuck, A. Patel, M. Lechuga, A. Ravaud

Other (Trial Global Steering Committee Member): D.J. George, M. Staehler, R.J. Motzer, A.J. Pantuck, H.S. Pandha, A. Patel, A. Ravaud

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The authors thank Patients in the S-TRAC trial who provided blood samples for analytical purposes. They acknowledge the following investigators who were also involved in the study: Yen-Hwa Chang, Jan Breza, Jean-Jacques Patard, and Giacomo Carteni. They also acknowledge the contribution of the following individuals at Pfizer: Linda Wood, Jean-Claude Marshall for their assistance with laboratory study execution and review of the article, and Cheyenne Bolanos and Lea Harty for biospecimens reconciliation and informed consent review. Medical writing support was provided by Anne Marie McGonigal, PhD, of Engage Scientific Solutions and was funded by Pfizer Inc.

This study was sponsored by Pfizer. Patients treated at Memorial Sloan Kettering Cancer Center were supported in part by Memorial Sloan Kettering Cancer Center Support Grant/Core Grant (P30 CA008748).

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