Purpose: Adjuvant sunitinib prolonged disease-free survival (DFS; HR, 0.76) in patients with locoregional high-risk renal cell carcinoma (RCC) in the S-TRAC trial (ClinicalTrials.gov NCT00375674). The 16-gene Recurrence Score (RS) assay was previously developed and validated to estimate risk for disease recurrence in patients with RCC after nephrectomy. This analysis further validated the prognostic value of RS assay in patients from S-TRAC and explored the association of RS results with prediction of sunitinib benefit.

Patients and Methods: The analysis was prospectively designed with prespecified genes, algorithm, endpoints, and analytical methods. Primary RCC was available from 212 patients with informed consent; primary analysis focused on patients with T3 RCC. Gene expression was quantitated by RT-PCR. Time to recurrence (TTR), DFS, and renal cancer–specific survival (RCSS) were analyzed using Cox proportional hazards regression.

Results: Baseline characteristics were similar between patients with and those without RS results, and between the sunitinib and placebo arms among patients with RS results. RS results predicted TTR, DFS, and RCSS in both arms, with the strongest results observed in the placebo arm. When high versus low RS groups were compared, HR for recurrence was 9.18 [95% confidence interval (CI), 2.15–39.24; P < 0.001) in the placebo arm; interaction of RS results with treatment was not significant.

Conclusions: The strong prognostic performance of the 16-gene RS assay was confirmed in S-TRAC, and the RS assay is now supported by level IB evidence. RS results may help identify patients at high risk for recurrence who may derive higher absolute benefit from adjuvant therapy. Clin Cancer Res; 24(18); 4407–15. ©2018 AACR.

Translational Relevance

The 16-gene Recurrence Score (RS) assay was developed and validated previously to predict risk of disease recurrence in patients with stages I–III renal cell carcinoma (RCC) after nephrectomy. This study provides an additional validation for the 16-gene RS assay using data from the phase III adjuvant sunitinib (S-TRAC) trial in high-risk stage III RCC. The results showed that the RS is a strong prognosticator of recurrence risk and provides independent prognostic information for time to recurrence and renal cancer–specific survival beyond currently used clinicopathologic parameters in stage III RCC patients randomly assigned to placebo or sunitinib adjuvant therapy. The RS assay is validated as a predictor of clinical outcome in stages I–III RCC and supported by level IB evidence. However, additional studies are needed to determine whether RS can predict adjuvant treatment benefit.

Patients with stage I–III renal cell carcinoma (RCC), the major subtype of kidney cancer, commonly undergo radical or partial nephrectomy with curative intent, but approximately 30% of these patients will relapse (1). Assessment of recurrence risk for patients with localized RCC is currently based on clinical and pathological features, and there are several scoring systems in use (2–4).

Treatment of RCC in the metastatic setting has evolved rapidly in the past decade, with the introduction of multitargeted kinase inhibitors, including sunitinib, sorafenib, pazopanib, axitinib, cabozantinib, lenvatinib, temsirolimus, and everolimus, as well as the immune checkpoint inhibitor nivolumab (5). The efficacy of antiangiogenic tyrosine kinase inhibitors in the metastatic setting led to the initiation of adjuvant trials with these agents. Three such trials have been reported (6–8), and thus far, only one, the Sunitinib as Adjuvant Treatment for Patients at High Risk of Recurrence of Renal Cell Carcinoma Following Nephrectomy (S-TRAC) trial (6, 9), demonstrated a statistically significant result. In the S-TRAC trial, 1-year adjuvant sunitinib therapy (50 mg daily on a 4-weeks-on, 2-weeks-off [4/2] schedule) prolonged disease-free survival (DFS) versus placebo [median 6.8 vs. 5.6 years; HR, 0.76; 95% confidence interval (CI), 0.59–0.98; P = 0.03) in patients with locoregional, high-risk RCC following nephrectomy. Reasons for different outcomes in S-TRAC compared with other reported studies are not clear, but likely multifactorial, including higher-risk patient population and/or higher drug exposure (8, 9). The potential reasons contributing to the positive outcome in S-TRAC compared with the other two reported trials are discussed extensively in recently published review articles (9, 10) and commentary (11). On the basis of the results from the trial, the FDA recently approved sunitinib for adjuvant treatment of adult patients at high risk for recurrent RCC following nephrectomy (11).

Multigene assays have been shown to provide prognostic, and sometimes predictive, information beyond traditional parameters relevant for selection of adjuvant therapy in several tumor types (12–16). Those that have been extensively validated are now included in treatment guidelines for some of these tumors, such as breast cancer (17). A 16-gene RCC signature assay, consisting of 11 cancer-specific and 5 reference genes, was developed using archived, formalin-fixed, paraffin-embedded (FFPE) tumor tissue from patients with stage I–III RCC who underwent nephrectomy at Cleveland Clinic (Cleveland, OH) between 1985 and 2003, and validated using archived FFPE tumor tissue from an independent cohort of patients with stage I–III RCC treated with nephrectomy from 1995 to 2007 at French hospitals, as previously described (14). The continuous Recurrence Score (RS) result was significantly associated with recurrence-free interval (HR, 3.91 for a 25-unit increase in RS; 95% CI, 2.63–5.79; P < 0.001), and in multivariable analyses, stratified by stage and adjusting for significant clinical covariates (tumor size and Fuhrman grade), the RS results remained a strong significant predictor of recurrence (HR, 3.37; P < 0.0001; ref. 14).

The current guidelines stipulate that for assays validated using prospectively designed studies with archived tissue, adequate follow-up, and prespecified analysis methods, the results must be validated in at least one or more independent clinical validation studies in order to show consistency in performance and obtain level IB evidence (18). The current study represents the second validation of the RS assay in RCC. Compared with the first validation based on an observational cohort of untreated stage I, II, and III RCC patients, the current analysis was restricted to high-risk stage III (T3) patients randomized to placebo or sunitinib treatment in the S-TRAC trial supporting the adjuvant approval for sunitinib. The primary objectives of the study were to validate the prognostic ability of the RS assay to differentiate recurrence risk in untreated patients with locoregional, high-risk T3 RCC from the S-TRAC trial, and evaluate the potential association between RS result and benefit from sunitinib treatment. Secondary objectives included assessing the relationship of RS result with DFS and renal cancer-specific survival (RCSS), and evaluating the relationship of RS result with outcome after adjustment for clinical and pathological factors.

Study design and participants

Eligibility criteria for patients enrolled in the S-TRAC trial (ClinicalTrials.gov identifier NCT00375674) have been described in detail elsewhere (6). In brief, patients were included if they had high-risk, locoregional RCC (≥T3 and/or N+) as per the University of California Los Angeles Integrated Staging System (UISS) with predominantly clear cell component, an Eastern Cooperative Oncology Group performance status (ECOG PS) ≤2, a nephrectomy, and received the first treatment dose within 3 to 12 weeks after nephrectomy. Patients were excluded if they had previous systemic therapy for renal cell carcinoma or antiangiogenic therapy.

As reported previously (6), patients received, in a blind fashion, either 50-mg sunitinib once daily or placebo, on a 4/2 schedule for nine cycles (approximately 1 year) or until disease recurrence, occurrence of a secondary cancer, significant toxicity, or withdrawal of consent. Patients were followed for disease recurrence or the occurrence of a secondary malignancy from initiation of study treatment every 12 weeks during the first 3 years and every 6 months thereafter until disease recurrence, metastasis, or the time of final analysis, whichever occurred first. Tumor assessments included computed tomography or magnetic resonance imaging of chest, abdomen, pelvis, and other sites. This trial was conducted in accordance with International Conference on Harmonisation Good Clinical Practice guidelines and applicable local regulatory requirements. The study protocol and informed consent form were approved by the independent review board/ethics committee at each center. All patients provided written informed consent prior to study initiation. Patients in high-risk T3 RCC from the S-TRAC trial who consented for use of tissue in the pharmacogenomic analysis and had FFPE tumor tissue were eligible for this prespecified analysis.

Pathology and sample preparation

Anonymized, archival, FFPE tumor tissue samples obtained from nephrectomy or tumor biopsy specimens were used for biomarker analysis. Presence of RCC tumor type and non-tumor elements on the guide hematoxylin and eosin slide was identified by a pathologist from Genomic Health Inc. Non-tumor elements were removed by manual micro-dissection before transfer of tumor tissue to the extraction tube. Six 5-μm sections were used to procure tumor for analysis.

Gene expression analysis

FFPE tumor RNA was extracted as described previously (16) and quantitated using the RiboGreen method (Thermo Fisher Scientific). The absence of genomic DNA was confirmed by an ATCB TaqMan assay (Thermo Fisher Scientific; ref. 19). Before reverse transcription–polymerase chain reaction (RT-PCR) analysis, RNA was reverse transcribed to 16 genes. Expression analysis was performed using the Roche LightCycler 480 Real-Time Instrument (Roche Diagnostics) by measuring expression of prespecified 11 cancer genes and 5 reference genes (14) and reporting crossing point (Cp). RT-PCR analysis was carried out blinded to the clinical data.

Calculation of the RS was performed using the previously published genes, gene groups, and algorithm (Supplementary Fig. S1; ref. 14).

Outcomes

The primary endpoint was time to recurrence (TTR), defined as time from the date of randomization to first disease recurrence, whether local or distant recurrence. Diagnosis of recurrence was based on blinded independent central review of imaging and/or histological findings. Death due to RCC was considered an event at the time of death in patients not previously diagnosed with recurrence. Second primary cancers were not considered to be events. Losses to follow-up and deaths without recurrence were censored.

Secondary endpoints included DFS and RCSS. DFS was defined as time from the date of randomization to the first date of recurrence, or occurrence of a secondary malignancy, or death from any cause. RCSS was defined as time from the date of randomization to death due to RCC, or death preceded by recurrence. Other deaths were censored at the time of death.

For patients receiving further antitumor therapy before disease recurrence or death due to RCC, their times for TTR and DFS endpoints were censored at the date of last disease assessment before this antitumor therapy. For those without an event, their times were censored at the date of last disease assessments.

Statistical analysis

The study was prospectively designed with prespecified genes, algorithm, endpoints, and analytical methods, and the statistical analysis plan was completed, signed, and locked before the initiation of analyses. Two primary objectives were assessed using conditional fixed sequential testing. The first primary objective (prognosis) evaluated association between TTR and continuous RS results in patients with locoregional, high-risk RCC randomized to the placebo arm using univariate Cox proportional hazards regression. The second primary objective (prediction) examined association between the continuous RS results and the benefit from sunitinib with respect to TTR in the patients randomized to sunitinib or placebo. Multivariable Cox proportional hazards regression with RS result, treatment, and an interaction of RS result with treatment was used. The two primary objectives were tested in sequence; the primary analyses only proceeded to the second hypothesis if the null hypothesis of the first primary objective was rejected at the 0.05 significance level. Assessment of functional form was carried out by evaluating models with polynomial (quadratic and cubic) terms and smoothing splines. No evidence of nonlinearity was detected. Assessment of proportional hazards was performed by examining the interaction of the RS result with time and the relationship between scaled Schoenfeld residuals and time. Nonproportional hazards were identified in the placebo arm (P < 0.01; consistent with previous studies in untreated patients) but not in the sunitinib arm (P > 0.20). Piecewise constant models with time-dependent RS effect in the placebo arm were considered. The HR for RS assay was reported per 25 units, consistent with previous studies (14). Kaplan–Meier analyses were used to evaluate the relationship between prespecified RS risk groups (i.e., RS <32 as low risk; RS 32–44 as intermediate risk; RS ≥45 as high risk; ref. 14) and outcome by treatment arm. Multivariable Cox models were implemented to evaluate association of RS result with outcome adjusting for clinical and pathological covariates. Association of individual genes and gene groups with TTR was evaluated in each treatment arm. The analyses were repeated in all patients, using models stratified by stage. SAS versions 9.2 and 9.4 (SAS Institute) were used to analyze the data.

Primary RCC tumors were available from 221 (36%) of 615 patients enrolled in the S-TRAC trial with informed consent for pharmacogenomic analyses. Nine patients were not evaluable due to lack of tumor (n = 2), not confirmed clear cell RCC (n = 6), or insufficient RNA yield (n = 1). Therefore, a total of 212 patients comprised the gene signature evaluable population. The primary analysis focused only on patients with T3 RCC (n = 193) within the gene signature evaluable population given that this subpopulation comprised the majority of available samples in the dataset (Table 1).

Table 1.

Patient baseline characteristics in the gene signature cohort versus all other S-TRAC patients

Gene signature cohort
PlaceboSunitinibTotalOther S-TRAC patients
Characteristic(n = 101)(n = 111)(n = 212)(n = 403)Pa
Mean age, y (SD) 58.7 (10.9) 56.1 (11.3) 57.3 (11.2) 57.4 (10.3) 0.973 
Age ≥65 years, n (%) 35 (35) 29 (26) 64 (30) 94 (23) 0.066 
Male, n (%) 78 (77) 82 (74) 160 (75) 291 (72) 0.443 
Race, n (%) 
 White 91 (90) 94 (85) 185 (87) 332 (82) 0.234 
 Black 1 (1) 1 (1) 2 (1) 2 (<1)  
 Asian 6 (6) 13 (12) 19 (9) 57 (14)  
 Other 3 (3) 3 (3) 6 (3) 12 (3)  
ECOG PS 0, n (%) 75 (74) 81 (73) 156 (74) 292 (73) 0.631 
Staging classification, n (%) 
 T3 lowb 36 (36) 33 (30) 69 (33) 158 (39) 0.359 
 T3 highc 54 (54) 70 (63) 124 (58) 207 (51)  
 T4 N0 or NXd 1 (1) 2 (2) 3 (1) 5 (1)  
 Any T, N1–2d 10 (10) 6 (5) 16 (8) 33 (8)  
Gene signature cohort
PlaceboSunitinibTotalOther S-TRAC patients
Characteristic(n = 101)(n = 111)(n = 212)(n = 403)Pa
Mean age, y (SD) 58.7 (10.9) 56.1 (11.3) 57.3 (11.2) 57.4 (10.3) 0.973 
Age ≥65 years, n (%) 35 (35) 29 (26) 64 (30) 94 (23) 0.066 
Male, n (%) 78 (77) 82 (74) 160 (75) 291 (72) 0.443 
Race, n (%) 
 White 91 (90) 94 (85) 185 (87) 332 (82) 0.234 
 Black 1 (1) 1 (1) 2 (1) 2 (<1)  
 Asian 6 (6) 13 (12) 19 (9) 57 (14)  
 Other 3 (3) 3 (3) 6 (3) 12 (3)  
ECOG PS 0, n (%) 75 (74) 81 (73) 156 (74) 292 (73) 0.631 
Staging classification, n (%) 
 T3 lowb 36 (36) 33 (30) 69 (33) 158 (39) 0.359 
 T3 highc 54 (54) 70 (63) 124 (58) 207 (51)  
 T4 N0 or NXd 1 (1) 2 (2) 3 (1) 5 (1)  
 Any T, N1–2d 10 (10) 6 (5) 16 (8) 33 (8)  

Abbreviations: ECOG PS, Eastern Cooperative Oncology Group performance status; SD, standard deviation.

aTotal patients in gene signature cohort (n = 212) versus all other S-TRAC patients (n = 403).

bN0 or NX, any Fuhrman grade and ECOG PS 0 or Fuhrman grade 1 and ECOG PS ≥1.

cN0 or NX, Fuhrman grade ≥2, ECOG PS ≥1.

dAny Fuhrman grade, any ECOG PS.

Patients in the gene signature evaluable population had similar baseline characteristics to the S-TRAC patients not included in the biomarkers study (Table 1). Patient baseline characteristics were also similar between the sunitinib and placebo arms in the gene signature evaluable population (Table 1) as well as in T3 patients (Supplementary Table S1). More than 50% of patients were in the high-risk group, with Fuhrman grade ≥2 and/or ECOG PS ≥1 in both treatment arms. The effect of sunitinib treatment on DFS in the gene signature cohort was similar to that in the entire trial (HR, 0.73; 95% CI, 0.47–1.13, in 212 gene signature evaluable patients and HR, 0.78; 95% CI, 0.48–1.24 in 193 T3 patients, vs. HR, 0.76; 95% CI, 0.59–0.98, in 615 patients).

Association of RS with TTR, DFS, and RCSS

Among all T3 patients in the gene signature cohort, RS results ranged from 15 to 80 with a median of 43.0 (mean ± standard deviation: 44.6 ± 14.1) and had similar distribution across the treatment arms (Supplementary Fig. S2). Median RS in the placebo arm was 42.1 (mean ± standard deviation: 44.9 ± 14.8) compared with 43.2 (mean ± standard deviation: 44.3 ± 13.5) in the sunitinib arm (Supplementary Table S1).

In the prespecified primary analysis, continuous RS result was significantly associated with TTR in the placebo arm (HR per 25 RS units = 4.24; P < 0.001; Table 2). RS quantified a wide range of 5-year recurrence risks ranging from approximately 10% to >90% in this high-risk population (Fig. 1). To account for nonproportional hazards, association of RS result with TTR was modeled during the first year following surgery and then after 1 year. Consistent with previous findings, the effect appeared to be stronger early on (HR per 25 RS units = 13.5 in the first year; HR per 25 RS units = 1.78 after 1 year).

Table 2.

Univariate and multivariable Cox regression models by treatment arms in locoregional, high-risk patients

Placebo (n = 90)Sunitinib (n = 103)
CharacteristicsEndpointHR (95% CI)PHR (95% CI)PInteraction P Value
Univariate model 
 Recurrence Scorea TTR 4.24 (2.31–7.80) <0.001 2.53 (1.29–4.97) 0.008 0.192 
 Recurrence Scorea DFS 3.75 (2.13–6.60) <0.001 2.31 (1.20–4.43) 0.014 0.220 
 Recurrence Scorea RCSS 7.21 (2.85–18.2) <0.001 3.33 (1.48–7.49) 0.005 0.213 
 Age (≥65 vs. <65 years) TTR 1.29 (0.64–2.63) 0.485 0.75 (0.29–1.97) 0.554 0.343 
 Sex (male vs. female) TTR 0.74 (0.33–1.63) 0.464 1.08 (0.46–2.53) 0.849 0.491 
 Race (nonwhite vs. white) TTR 0.80 (0.24–2.62) 0.703 0.67 (0.23–1.92) 0.431 0.851 
 ECOG PS (1 vs. 0) TTR 0.50 (0.18–1.43) 0.158 2.10 (0.99–4.41) 0.062 0.023 
 T3 (high- vs. low-risk group)b TTR 1.42 (0.69–2.93) 0.335 1.55 (0.66–3.61) 0.292 0.885 
Multivariable model 
 Recurrence Scorea TTR 4.30 (2.32–7.96) <0.001 2.55 (1.29–5.05) 0.009 NA 
 T3 (high- vs. low-risk group)b TTR 1.44 (0.70–2.97) 0.317 1.52 (0.65–3.54) 0.318 NA 
Placebo (n = 90)Sunitinib (n = 103)
CharacteristicsEndpointHR (95% CI)PHR (95% CI)PInteraction P Value
Univariate model 
 Recurrence Scorea TTR 4.24 (2.31–7.80) <0.001 2.53 (1.29–4.97) 0.008 0.192 
 Recurrence Scorea DFS 3.75 (2.13–6.60) <0.001 2.31 (1.20–4.43) 0.014 0.220 
 Recurrence Scorea RCSS 7.21 (2.85–18.2) <0.001 3.33 (1.48–7.49) 0.005 0.213 
 Age (≥65 vs. <65 years) TTR 1.29 (0.64–2.63) 0.485 0.75 (0.29–1.97) 0.554 0.343 
 Sex (male vs. female) TTR 0.74 (0.33–1.63) 0.464 1.08 (0.46–2.53) 0.849 0.491 
 Race (nonwhite vs. white) TTR 0.80 (0.24–2.62) 0.703 0.67 (0.23–1.92) 0.431 0.851 
 ECOG PS (1 vs. 0) TTR 0.50 (0.18–1.43) 0.158 2.10 (0.99–4.41) 0.062 0.023 
 T3 (high- vs. low-risk group)b TTR 1.42 (0.69–2.93) 0.335 1.55 (0.66–3.61) 0.292 0.885 
Multivariable model 
 Recurrence Scorea TTR 4.30 (2.32–7.96) <0.001 2.55 (1.29–5.05) 0.009 NA 
 T3 (high- vs. low-risk group)b TTR 1.44 (0.70–2.97) 0.317 1.52 (0.65–3.54) 0.318 NA 

Abbreviations: CI, confidence interval; ECOG PS, Eastern Cooperative Oncology Group performance status; NA, not applicable.

aModel with continuous RS result, HR reported per 25-unit change.

bT3 low-risk includes any Fuhrman grade and ECOG PS 0 or Fuhrman grade 1 and ECOG PS ≥1; T3 high-risk includes Fuhrman grade ≥2 and ECOG PS ≥1.

Figure 1.

Five-year risk of recurrence according to RS results in patients with high-risk renal cell carcinoma after nephrectomy. Blue solid and dotted lines represent mean and 95% confidence interval (CI), respectively, risk of recurrence in the placebo arm; red solid and dotted lines represent mean and 95% CI, respectively, risk of recurrence in the sunitinib arm.

Figure 1.

Five-year risk of recurrence according to RS results in patients with high-risk renal cell carcinoma after nephrectomy. Blue solid and dotted lines represent mean and 95% confidence interval (CI), respectively, risk of recurrence in the placebo arm; red solid and dotted lines represent mean and 95% CI, respectively, risk of recurrence in the sunitinib arm.

Close modal

Continuous RS result was also significantly associated with TTR in the sunitinib arm (HR per 25 RS units = 2.53; P = 0.008; Table 2). The interaction of RS result and treatment was not statistically significant (P = 0.192) in the prespecified analysis, but the interaction test had low power (<40% power to detect a standardized interaction HR of 1.5 at 0.05 level) due to a relatively small number of recurrences (n = 63). When relationship between RS result and treatment was examined in a model with time-dependent RS effect in the placebo arm, the recurrence risk estimates at 5 years were consistent with those from the prespecified model.

Prespecified RS cutoff points of 32 and 44 identified 18.7% of T3 patients as low risk, 34.7% as intermediate risk, and 46.6% as high risk. When high versus low RS groups were compared, the HR for recurrence was 9.18 (95% CI, 2.15–39.24; P < 0.001) in the placebo arm and 1.86 (95% CI, 0.68–5.06; P = 0.201) in the sunitinib arm. Five-year recurrence risk estimates in the low and high RS groups were 11% and 59%, respectively, in the placebo arm and 31% and 43%, respectively, in the sunitinib arm (Fig. 2A and B). Relatively few recurrences were observed in the low RS group (two in the placebo arm and five in the sunitinib arm).

Figure 2.

Kaplan–Meier estimates for TTR by RS risk group. A, Placebo arm; B, Sunitinib arm. *, On the basis of Cox proportional hazards regression model with indicators for high and intermediate RS risk group. Separate models were fit for each treatment arm. CI, confidence interval.

Figure 2.

Kaplan–Meier estimates for TTR by RS risk group. A, Placebo arm; B, Sunitinib arm. *, On the basis of Cox proportional hazards regression model with indicators for high and intermediate RS risk group. Separate models were fit for each treatment arm. CI, confidence interval.

Close modal

Continuous RS result was also significantly associated with DFS and RCSS in both the placebo and sunitinib arms (Table 2). With a small number of events, interaction of RS result and treatment was not significant for either DFS (70 events) or RCSS (32 events). Five-year risk of DFS events was 17% and 62%, respectively, in the low and high RS groups in the placebo arm and 31% and 43%, respectively, in the sunitinib arm (Fig. 3A and B). No RCSS events were observed in the low RS group in the placebo arm, whereas the average 5-year recurrence risk in the high RS group was 27%. In the sunitinib arm, 5-year risk of RCSS events was 6% and 22%, respectively, in the low versus high RS group (Supplementary Fig. S3A and S3B).

Figure 3.

Kaplan–Meier estimates for DFS by RS risk group. A, Placebo arm; B, Sunitinib arm. *, On the basis of Cox proportional hazards regression model with indicators for high and intermediate RS risk group. Separate models were fit for each treatment arm. CI, confidence interval.

Figure 3.

Kaplan–Meier estimates for DFS by RS risk group. A, Placebo arm; B, Sunitinib arm. *, On the basis of Cox proportional hazards regression model with indicators for high and intermediate RS risk group. Separate models were fit for each treatment arm. CI, confidence interval.

Close modal

Contribution of RS beyond conventional prognostic factors

Age, sex, race, and staging classification were not associated with TTR (Table 2). Higher ECOG PS was associated with higher risk of recurrence in the sunitinib arm but not in the placebo arm (Table 2). In a multivariable model adjusted for risk group defined on the basis of Fuhrman grade and ECOG PS, RS results were significantly associated with TTR in each treatment arm (Table 2).

Individual genes and gene groups

Association of individual genes and gene groups with risk of recurrence was examined in each treatment arm (Fig. 4). Lower expression of immune response and vascular normalization genes was associated with higher risk of recurrence, with the strongest effects observed in the placebo arm (Fig. 4). An interaction of continuous gene expression and treatment was nominally significant for nitric oxide synthase 3 and immune response gene group at the 0.05 level (no adjustment for multiple comparisons was applied).

Figure 4.

Forest plot for individual genes and gene groups by treatment arm; *, Interaction P Value. APOLD1, apolipoprotein L domain containing 1; CCL5, C-C motif chemokine ligand 5; CEACAM1, carcinoembryonic antigen related cell adhesion molecule 1; CI, confidence interval; CX3CL1, C-X3-C motif chemokine ligand 1; EDNRB, endothelin receptor type B; EIF4EBP1, eukaryotic translation initiation factor 4E-binding protein 1; GPX1, glutathione peroxidase 1; IL6, interleukin 6; LMNB1, lamin B1; NOS3, nitric oxide synthase 3; PPAP2B (PLPP3), phosphatidic acid phosphatase type 2B; Std, standardized; TUBB2A, tubulin beta 2A class IIa.

Figure 4.

Forest plot for individual genes and gene groups by treatment arm; *, Interaction P Value. APOLD1, apolipoprotein L domain containing 1; CCL5, C-C motif chemokine ligand 5; CEACAM1, carcinoembryonic antigen related cell adhesion molecule 1; CI, confidence interval; CX3CL1, C-X3-C motif chemokine ligand 1; EDNRB, endothelin receptor type B; EIF4EBP1, eukaryotic translation initiation factor 4E-binding protein 1; GPX1, glutathione peroxidase 1; IL6, interleukin 6; LMNB1, lamin B1; NOS3, nitric oxide synthase 3; PPAP2B (PLPP3), phosphatidic acid phosphatase type 2B; Std, standardized; TUBB2A, tubulin beta 2A class IIa.

Close modal

This study represents the second validation of the 16-gene assay in patients with stage III RCC. The RS assay was able to identify patients with low and high risk of recurrence, and it provided independent prognostic information beyond the currently used risk classification parameters of tumor, node, metastasis (TNM) staging, Fuhrman grade, and ECOG PS in multivariable analyses. The RS result was prognostic for TTR, DFS, and RCSS. The performance of the RS result in the placebo arm was similar to that presented in the first validation study (14), with a HR for a 25-unit increase in RS result of 4.24 versus 3.91 for TTR, and 7.21 versus 5.55 for RCSS. However, there are notable differences between this study and the first validation study. First, the inclusion criteria in the current study was restricted to stage III high-risk patients (i.e., ≥T3, N0, or NX), whereas the first validation study included patients with stages I, II, or III (i.e., a majority of patients were representative of the low-risk population). In addition, the current analysis was performed on the subset of patients with available tumor tissue and consent from a large randomized controlled prospective trial, whereas the first study was an observational cohort study. Despite the differences between the two validation studies, the performance of the score was very consistent, and the assay has now been validated in more than 830 patients across stages I–III RCC.

The potential clinical utility of the RS assay is relevant in the context of the recent FDA approval of sunitinib adjuvant therapy in patients at high risk for recurrence postnephrectomy. In the placebo arm of the present study, 47% of patients were classified as high risk by the RS with a 5-year recurrence risk of 59% (95% CI, 43%–75%), whereas 22% of patients were classified as low risk by the RS with a 5-year recurrence risk of 11% (95% CI, 3%–38%). These data underscore that a certain percentage of patients considered high risk by clinicopathologic parameters are genomically low risk per the RS. From a clinical perspective, a substantial proportion of patients with high RS had recurrences in the first 2 years after nephrectomy, suggesting that a closer follow-up over a more extended period may be indicated for these patients.

The previous validation study also included 398 stage I patients and 15% of these were classified as high risk with a 5-year recurrence risk of 23% (95% CI, 13%–39%). Thus, a subset of patients considered low risk by traditional tumor staging have a risk of recurrence that is in parity with a subgroup of stage III patients. The RS assay can thus identify patients at low and high risk of recurrence across stages I, II, and III resected RCC.

An interaction of RS result and sunitinib treatment was not statistically significant in this study (P = 0.19), although there appeared to be some differences in the risk estimates between the two treatment arms (Fig. 1). The study was underpowered for the test of interaction, and additional studies are warranted to determine if RS result predicts differential benefit from sunitinib. Similarly, the study was underpowered to study interactions of individual genes and gene groups. The immune response group and one of vascular normalization genes reached nominal statistical significance although no adjustment for multiple comparison was made. These findings appear to be consistent with sunitinib having both antiangiogenic and possibly immunomodulatory effects (20, 21) but would warrant further research.

The current study has limitations. The proportion of patients with available archived tumor tissue and consent for pharmacogenomic analyses was <50%; however, patient baseline characteristics and risk reduction associated with sunitinib for the subgroup tested with the assay were similar to those for the S-TRAC patients without pharmacogenomic assay results, indicating that there was no apparent selection bias. In addition, although the number of patients and events in each treatment arm were sufficient to evaluate prognostic performance of the assay (power >95%), the power to test for interaction was low (<40%).

Adjuvant studies assessing the efficacy of sunitinib, sorafenib, pazopanib, axitinib, or everolimus have readout or are ongoing, and new trials with agents targeting the immune system, including programmed cell death 1 (PD-1) and programmed cell death ligand 1 (PD-L1) inhibitors, have been initiated. Inclusion of the RS result as a risk-stratifier in future RCC adjuvant trials could improve identification of patients with a higher biological risk of recurrence and potentially greater absolute benefit from treatment.

The validated RS assay may help patients and physicians make more informed adjuvant treatment decisions by providing strong prognostic information beyond traditional clinicopathologic parameters. Future studies with sufficient power to detect an interaction of RS result and treatment are needed to determine whether RS result might predict differential benefit from adjuvant therapy.

B.I. Rini reports receiving commercial research grants from and is a consultant/advisory board member for Pfizer. B. Escudier has served on advisory boards for Bristol-Myers Squibb, Novartis, Pfizer Inc., Eisai, EUSA Pharma, Ipsen, Acceleron, Roche, and Exelixis; has served as a consultant for Bristol-Myers Squibb, Novartis, and Ipsen; and has received research funding from Bristol-Myers Squibb and Novartis, and honoraria from Pfizer Inc., Novartis, Bristol-Myers Squibb, Roche, Exelixis, Ipsen, Acceleron, and Bayer. J.-F. Martini, X. Lin, and O. Valota are employees of and own stock in Pfizer Inc. A. Magheli has received honoraria from Janssen, Bayer, Astellas, and Pfizer Inc. C. Svedman, D. Knezevic, A.D. Goddard, and P.G. Febbo hold ownership interest (including patents) in Genomic Health. M. Staehler reports receiving commercial research grants from Novartis and Pfizer and has received speakers bureau honoraria from and is a consultant/advisory board member for Eisai, EusaPharma, Ipsen, Novartis, and Pfizer. R.J. Motzer is a consultant/advisory board member for Eisai, Exelixis, Genentech/Roche, Merck, Novartis, and Pfizer. A. Ravaud is a consultant/advisory board member for Astra Zeneca, Bristol-Myers Squibb, Ipsen, Novartis, and Pfizer. No potential conflicts of interest were disclosed by the other authors.

Conception and design: B.I. Rini, B. Escudier, J.-F. Martini, C. Svedman, M. Lopatin, D. Knezevic, A.D. Goddard, P.G. Febbo, X. Lin, R.J. Motzer, A. Ravaud

Development of methodology: B.I. Rini, B. Escudier, J.-F. Martini, C. Svedman, M. Lopatin, D. Knezevic, A.D. Goddard, X. Lin, R.J. Motzer, A. Ravaud

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): B.I. Rini, B. Escudier, A. Magheli, D. Knezevic, M. Staehler, R.J. Motzer

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): B.I. Rini, B. Escudier, J.-F. Martini, C. Svedman, M. Lopatin, D. Knezevic, P.G. Febbo, R. Li, X. Lin, M. Staehler

Writing, review, and/or revision of the manuscript: B.I. Rini, B. Escudier, J.-F. Martini, A. Magheli, C. Svedman, M. Lopatin, D. Knezevic, A.D. Goddard, P.G. Febbo, R. Li, X. Lin, O. Valota, M. Staehler, R.J. Motzer, A. Ravaud

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): D. Knezevic

Study supervision: B.I. Rini, B. Escudier, J.-F. Martini, A. Magheli, D. Knezevic, A. Ravaud

Other (PI of STRAC study): A. Ravaud

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). The authors thank patients in the S-TRAC trial who provided tissue samples for analytical purposes. They acknowledge the contribution of Patricia English, who was employed by Pfizer at the time of study, for her input on study design, and of the following individuals at Genomic Health Inc: Jenny Wu for her assistance with laboratory study execution, Michael Bonham for pathology review, and Steve Shak for input on study design and review of the article. Editorial support was provided by Mariko Nagashima, PhD, of Engage Scientific Solutions, and was funded by Pfizer.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

1.
Rini
BI
,
Campbell
SC
,
Escudier
B
. 
Renal cell carcinoma
.
Lancet
2009
;
373
:
1119
32
.
2.
Leibovich
BC
,
Blute
ML
,
Cheville
JC
,
Lohse
CM
,
Frank
I
,
Kwon
ED
, et al
Prediction of progression after radical nephrectomy for patients with clear cell renal cell carcinoma: a stratification tool for prospective clinical trials
.
Cancer
2003
;
97
:
1663
71
.
3.
Zisman
A
,
Pantuck
AJ
,
Dorey
F
,
Said
JW
,
Shvarts
O
,
Quintana
D
, et al
Improved prognostication of renal cell carcinoma using an integrated staging system
.
J Clin Oncol
2001
;
19
:
1649
57
.
4.
Sorbellini
M
,
Kattan
MW
,
Snyder
ME
,
Reuter
V
,
Motzer
R
,
Goetzl
M
, et al
A postoperative prognostic nomogram predicting recurrence for patients with conventional clear cell renal cell carcinoma
.
J Urol
2005
;
173
:
48
51
.
5.
Choueiri
TK
,
Motzer
RJ
. 
Systemic therapy for metastatic renal-cell carcinoma
.
N Engl J Med
2017
;
376
:
354
66
.
6.
Ravaud
A
,
Motzer
RJ
,
Pandha
HS
,
George
DJ
,
Pantuck
AJ
,
Patel
A
, et al
Adjuvant sunitinib in high-risk renal-cell carcinoma after nephrectomy
.
N Engl J Med
2016
;
375
:
2246
54
.
7.
Haas
NB
,
Manola
J
,
Uzzo
RG
,
Flaherty
KT
,
Wood
CG
,
Kane
C
, et al
Adjuvant sunitinib or sorafenib for high-risk, non-metastatic renal-cell carcinoma (ECOG-ACRIN E2805): a double-blind, placebo-controlled, randomised, phase 3 trial
.
Lancet
2016
;
387
:
2008
16
.
8.
Motzer
RJ
,
Haas
NB
,
Donskov
F
,
Gross-Goupil
M
,
Varlamov
S
,
Kopyltsov
E
, et al
Randomized phase III trial of adjuvant pazopanib versus placebo after nephrectomy in patients with locally advanced renal cell carcinoma (RCC)(PROTECT)
.
J Clin Oncol
35
:
15s
; 
2017
_(
suppl;abstr 4507)
.
9.
Figlin
RA
,
Leibovich
BC
,
Stewart
GD
,
Negrier
S
. 
Adjuvant therapy in renal cell carcinoma: does higher risk for recurrence improve the chance for success?
Ann Oncol
2018
;
29
:
324
31
.
10.
Lenis
AT
,
Donin
NM
,
Johnson
DC
,
Faiena
I
,
Salmasi
A
,
Drakaki
A
, et al
Adjuvant therapy for high risk localized kidney cancer: emerging evidence and future clinical trials
.
J Urol
2018
;
199
:
43
52
.
11.
Salmasi
A
,
Faiena
I
,
Drakaki
A
,
Pantuck
AJ
. 
Re: adjuvant sunitinib in high-risk renal-cell carcinoma after nephrectomy
.
Eur Urol
2018
;
74
:
119
21
.
12.
Paik
S
,
Shak
S
,
Tang
G
,
Kim
C
,
Baker
J
,
Cronin
M
, et al
A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer
.
N Engl J Med
2004
;
351
:
2817
26
.
13.
Reimers
MS
,
Kuppen
PJ
,
Lee
M
,
Lopatin
M
,
Tezcan
H
,
Putter
H
, et al
Validation of the 12-gene colon cancer recurrence score as a predictor of recurrence risk in stage II and III rectal cancer patients
.
J Natl Cancer Inst
2014
;
106
:
pii:
dju269
.
14.
Rini
B
,
Goddard
A
,
Knezevic
D
,
Maddala
T
,
Zhou
M
,
Aydin
H
, et al
A 16-gene assay to predict recurrence after surgery in localised renal cell carcinoma: development and validation studies
.
Lancet Oncol
2015
;
16
:
676
85
.
15.
O'Connell
MJ
,
Lavery
I
,
Yothers
G
,
Paik
S
,
Clark-Langone
KM
,
Lopatin
M
, et al
Relationship between tumor gene expression and recurrence in four independent studies of patients with stage II/III colon cancer treated with surgery alone or surgery plus adjuvant fluorouracil plus leucovorin
.
J Clin Oncol
2010
;
28
:
3937
44
.
16.
Klein
EA
,
Cooperberg
MR
,
Magi-Galluzzi
C
,
Simko
JP
,
Falzarano
SM
,
Maddala
T
, et al
A 17-gene assay to predict prostate cancer aggressiveness in the context of Gleason grade heterogeneity, tumor multifocality, and biopsy undersampling
.
Eur Urol
2014
;
66
:
550
60
.
17.
National Comprehensive Cancer Network
. 
NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines): Breast Cancer Version 2.2017-April 6, 2017
;
(
2017
). Available from
: https://www.nccn.org/professionals/physician_gls/pdf/breast.pdf.
18.
Simon
RM
,
Paik
S
,
Hayes
DF
. 
Use of archived specimens in evaluation of prognostic and predictive biomarkers
.
J Natl Cancer Inst
2009
;
101
:
1446
52
.
19.
Cronin
M
,
Pho
M
,
Dutta
D
,
Stephans
JC
,
Shak
S
,
Kiefer
MC
, et al
Measurement of gene expression in archival paraffin-embedded tissues: development and performance of a 92-gene reverse transcriptase-polymerase chain reaction assay
.
Am J Pathol
2004
;
164
:
35
42
.
20.
George
DJ
,
Martini
JF
,
Staehler
M
,
Motzer
RJ
,
Magheli
A
,
Escudier
B
, et al
Immune biomarkers predictive for disease-free survival with adjuvant sunitinib in high-risk locoregional renal cell carcinoma: from randomized phase III S-TRAC study
.
Clin Cancer Res
2018
;
24
:
1554
61
.
21.
Kwilas
AR
,
Donahue
RN
,
Tsang
KY
,
Hodge
JW
. 
Immune consequences of tyrosine kinase inhibitors that synergize with cancer immunotherapy
.
Cancer Cell Microenviron
2015
;
2
:
pii:
e677
.