Metastatic non–clear cell renal cell carcinoma treated with targeted therapy agents: Characterization of survival outcome and application of the International mRCC Database Consortium criteria




This study aimed to apply the International mRCC Database Consortium (IMDC) prognostic model in metastatic non–clear cell renal cell carcinoma (nccRCC). In addition, the survival outcome of metastatic nccRCC patients was characterized.


Data on 2215 patients (1963 with clear-cell RCC [ccRCC] and 252 with nccRCC) treated with first-line VEGF- and mTOR-targeted therapies were collected from the IMDC. Time to treatment failure (TTF) and overall survival (OS) were compared in groups with favorable, intermediate, and poor prognoses according to IMDC prognostic criteria


The median OS of the entire cohort was 20.9 months. nccRCC patients were younger (P < .0001) and more often presented with low hemoglobin (P = .014) and elevated neutrophils (P = .0001), but otherwise had clinicopathological features similar to those of ccRCC patients. OS (12.8 vs 22.3 months; P < .0001) and TTF (4.2 vs 7.8 months; P < .0001) were worse in nccRCC patients compared with ccRCC patients. The hazard ratio for death and TTF when adjusted for the prognostic factors was 1.41 (95% CI, 1.19-1.67; P < .0001) and 1.54 (95% CI, 1.33-1.79; P < .0001), respectively. The IMDC prognostic model reliably discriminated 3 risk groups to predict OS and TTF in nccRCC; the median OS of the favorable, intermediate, and poor prognosis groups was 31.4, 16.1, and 5.1 months, respectively (P < .0001), and the median TTF was 9.6, 4.9, and 2.1 months, respectively (P < .0001).


Although targeted agents have significantly improved the outcome of patients with nccRCC, for the majority survival is still inferior compared with patients with ccRCC. The IMDC prognostic model reliably predicts OS and TTF in nccRCC and ccRCC patients. Cancer 2013;119:2999—3006. © 2013 American Cancer Society.

Renal cell carcinoma (RCC) arises from the kidney parenchyma and is a complex aggregate of several malignant subtypes.[1] According to the World Health Organization classification system, the major subtypes are clear-cell RCC (ccRCC), papillary RCC (pRCC), chromophobe RCC (chRCC), unclassified RCC (unRCC), and RCC of the collecting duct.[2]

In ccRCC, the von Hippel Lindau (VHL) gene is inactivated in 80% to 90%, and as a consequence, the vascular endothelial growth factor (VEGF) and mammalian target of rapamycin (mTOR) pathways are deregulated.[1, 3] Agents that target members of these pathways have supplanted immunotherapies and have been internationally recognized as the standard-of-care therapy in metastatic RCC (mRCC).[4] Because the VEGF and mTOR pathways are particularly important for the biology of ccRCC, randomized phase 3 clinical trials were performed in patient cohorts that were exclusively or predominately composed of ccRCC patients.[5-9] To date, the 20% of patients with non–clear cell renal cell carcinoma (nccRCC) included in the temsirolimus phase 3 study are the largest cohort that has been investigated in a randomized phase 3 trial of targeted agents.[7] Therefore, the clinical outcomes of patients who have nccRCC and were treated with VEGF- and mTOR-targeted agents remain undefined.

The International mRCC Database Consortium (IMDC), or Heng model, has proven to be a useful prognostic tool in major clinical trials of novel targeted therapies.[10] This model has 6 independent predictors of poor survival: Karnofsky performance status (KPS) <80%, time from diagnosis to treatment interval <1 year, anemia, hypercalcemia, neutrophilia, and thrombocytosis. According to the number of poor prognostic factors, patients were segregated into favorable-, intermediate-, and poor- risk groups, with 0, 1-2, and ≥3 factors, respectively. The model was developed and externally validated without consideration of the histological RCC subtypes.[11, 12] It was assumed that the results were largely affected by the ccRCC subtype because it was the predominant histological subtype in the development and validation cohort.[11, 12] Therefore, it is unclear whether the IMDC prognostic model can be applied in nccRCC.

This study aimed to characterize the applicability of the IMDC prognostic model and the survival outcome of patients with nccRCC who were treated with first-line VEGF and mTOR inhibitors. For this purpose, we assessed the time to treatment failure (TTF) and overall survival (OS) in ccRCC and nccRCC patients. We applied the IMDC prognostic model to the nccRCC patients and evaluated its discriminatory ability. This study was performed as a large retrospective analysis by the IMDC, a worldwide collaboration of academic centers.


Study Populations

The International mRCC Database Consortiums includes 20 academic centers from Canada, the United States, Japan, South Korea, Singapore, and Denmark. Data were collected from August 15, 2008, until October, 10, 2012. At the time of analysis, the database covered data of 2370 patients who had received first-line targeted therapy between 2003 and 2012. Patients were excluded from analysis because of unknown histological subtypes (n = 153) and unknown treatment initiation date (n = 2).

All centers obtained local institutional review board approval before including data in this large retrospective study. Baseline patient characteristics included demographic, clinicopathological, and laboratory data as described in the development study of the IMDC risk model.[11] Survival data were retrospectively collected from medical chart reviews and publically available records. Uniform data templates were used to ensure consistent data collection at each institution. Patients may have been treated in part in clinical trials or with the standard of care according to national cancer guidelines.

Statistical Analyses

The primary objective of this study was to prove the applicability of the IMDC prognostic model separately in nccRCC. The secondary objective was to characterize clinical outcomes in terms of TTF and OS of nccRCC compared with ccRCC in patients treated with targeted therapies. OS was defined as the period between targeted therapy initiation and date of death, or it was censored on the day of the last follow-up visit. TTF was defined as the period between treatment initiation and progression, drug cessation, or death, or it was censored at the last follow-up visit. Progression was determined according to clinical criteria that made continuation of treatment impossible or radiographic criteria using the Response Evaluation Criteria in Solid Tumors.

Patient and tumor characteristics of ccRCC and nccRCC patients were compared using the chi-square test. OS and TTF were estimated with the Kaplan-Meier method, and differences between histological groups were examined with the log-rank test or the Wald chi-square test from the Cox regression adjusted for the IMDC risk factors.

We applied the IMDC model (presence/absence of the 6 predetermined prognostic factors to determine the favorable-, intermediate-, and poor-risk groups)[11, 12] to nccRCC patients using Cox regression. Concordance indices (c-index) were computed to test the predictive accuracy of the IMDC prognostic model; a c-index of 0.5 indicates no predictive accuracy, and an index of 1 shows perfect predictive accuracy.[13]

The statistical analyses were performed using SAS version 9 (SAS Institute, Cary, NC), and P < .05 (2 sided) was considered statistically significant.


Characterization of the Clinical Outcome

The study cohort was composed of 2215 patients. Overall, 1963 patients (88.6%) had ccRCC, and 252 (11.4%) had nccRCC. Tumors with a clear-cell component/mixed ccRCC and nccRCCs (n = 21) were considered ccRCC based on the study design of previously reported phase 3 randomized clinical trials in mRCC.[10, 14] Tumors with nccRCC histology included papillary RCC (n = 151, 59.9%), chromophobe RCC (n = 37, 14.7%), collecting duct RCC (n = 7, 2.8%), unclassified RCC (n = 34, 13.5%), and RCC with Xp11 translocation (n = 4, 1.6%). Nineteen patients were coded as nccRCC by the study center without further information on the exact nccRCC subtype. We did not analyze pRCC separately in groups of type I and type II because our database does not contain this information. The comparison of patient characteristics revealed that nccRCC patients were younger (P < .0001) and had more baseline anemia (P = .014) and neutrophilia (P = .0001). Otherwise, ccRCC and nccRCC patients had similar clinicopathological characteristics (Table 1).

Table 1. Patient Characteristics at Initiation of Targeted Therapy
 ccRCC (n  =  1963)nccRCC (n  =  252)P
n/Total n%n/Total n%
  1. Abbreviations: ccRCC, clear-cell renal cell carcinoma; nccRCC, non–clear cell renal cell carcinoma; KPS, Karnofski performance status; Hb, hemoglobin; LDH, lactate dehydrogenase; Dx, diagnosis; ULN, upper limit of normal; LLN, lower limit of normal.

Age at therapy initiation ≥601066/1963(54)102/252(40)< .0001
KPS (<80)435/1881(23)53/233(23)NS
Male sex1452/1954(74)182/252(72)NS
Number of metastases >11436/1959(73)182/251(73)NS
Sarcomatoid pathology171/1816(9)22/228(10)NS
Prior nephrectomy1593/1961(81)197/252(78)NS
Prior immunotherapy492/1963(25)54/252(21)NS
Dx to TKI therapy <1 y1016/1960(52)143/251(57)NS
Dx to metastasis <1 y1319/1943(68)183/250(73)NS
Low Hb1069/1845(58)152/229(66).014
High LDH (>1.5 ULN)234/1364(17)34/145(23)NS (.06)
Neutrophilia (>ULN)233/1775(13)51/226(23).0001
Thrombophilia (>ULN)326/1839(17)48/229(21)NS

In the whole cohort, the median overall survival time after targeted therapy initiation was 20.9 months (95% CI, 19.7-22.6 months), with 798 patients (36%) remaining alive at the time of analysis. The median follow-up in living patients was 22.3 months (IQR, 10.8-38.4 months). The first-line targeted therapies had been stopped in 1898 of 2215 patients (86%) at the time of analysis. The median time on the first-line targeted therapies was 7.2 months (range, 0+-91+ months).

Comparing nccRCC with ccRCC in Treatment Outcomes

Response and Time to Treatment Failure

Patients with nccRCC were significantly more often treated with mTOR therapies in first- (6.8% vs 1.5%, P < .0001) and second-line (44.7% vs 30.9%, P = .011) therapy. Treatment response data to first-line therapy were available for 1801 of 2215 patients (81%). In ccRCC patients, the best response rates to first-line therapies were 1.1% complete response (CR), 26.8% partial response (PR), 50.2% stable disease (SD), and 21.9% progressive disease (PD), whereas it was 1.0% CR, 14.9% PR, 50.0% SD, and 34.1% PD in nccRCC patients (P < .0001). For first-line therapy, the median TTF was 7.8 months (95% CI, 7.2-8.1 months) and 4.2 months (95% CI, 3.7-5.2 months) in ccRCC and nccRCC, respectively (HR, 1.57; 95% CI, 1.37-1.80; P < .0001; Table 2, Fig. 1A). Second-line therapy achieved best response rates of 0.4% CR, 11.3% PR, 49.6% SD, and 38.7% PD in ccRCC patients and 8.5% PR, 45.1% SD, and 46.3% PD in nccRCC patients (P = .526). The detailed best response rates of each subtype are shown in Table 3. The median TTF on the second-line therapy for ccRCC and nccRCC was 3.9 months (95% CI, 3.4-4.4 months) and 2.8 months (95% CI, 2.3-3.7 months), respectively (P = .142; Table 2, Fig. 1B). Both groups had a similar median TTF for the third-line therapy: 4.0 months (95% CI, 3.3-4.6 months) in ccRCC and 3.4 months (95% CI, 2.4-7.1 months) in nccRCC (P = .544; Table 2). All treatment sequences are displayed in detail in Table 2.

Figure 1.

(A) Patients with nccRCC had a significantly worse time to treatment failure in first-line targeted therapy (P < .0001). (B) In second-line therapy, the time to treatment failure was similar in ccRCC and nccRCC. (C) nccRCC patients had significantly worse overall survival than ccRCC patients (22.3 vs 12.8 months; P < .0001). (D) Metastatic chRCC patients currently have the best OS among the 4 major RCC subtypes and unRCC patients the worst OS (median OS, 27.1 months in chRCC, 22.3 months in ccRCC, 14.0 months in pRCC, and 10.1 months in unRCC).

Table 2. Therapy Sequences and TTF in ccRCC and nccRCC
 First-Line TherapySecond-Line TherapyThird-Line Therapy
  1. a

    Including ABT-869, AG117+taxotere, ARQ-197, BMS 93655, BMS-275183, CRLX101, doxorubicin, GSK1363089, gemcitabine/cisplatin, IMCL1121, gemcitabine/xeloda, MCL1121B, Imclone, MLN trial, Perifosine, Revlamid (CC5013), sirolimus, temsirolimus-bevacizumab, XL-880, XL-999, xeloda.

  2. b

    Adjusted for IMDC risk group criteria

Total number196325289411433635
Type of therapy, n (%)
Anti-VEGF Therapy1934 (98.5)235 (93.2)570 (63.8)57 (50.0)169 (50.3)20 (57.1)
Sunitinib1417 (72)181 (72)224 (25)20 (18)59 (18)7 (20)
Sorafenib388 (20)45 (18)273 (31)27 (24)58 (17)7 (20)
Axitinib3 (0.2)11 (1)3 (3)7 (2) 
Bevacizumab87 (4)6 (2)34 (4)3 (3)22 (7)2 (6)
Pazopanib34 (2)1 (0.4)25 (3)3 (3)22 (7)4 (11)
Tivozanib5 (0.3)2 (1)3 (0.3)1 (1)1 (0) 
mTOR inhibitors29 (1.5)17 (6.8)276 (30.9)51 (44.7)133 (39.6)12 34.3)
Temsirolimus24 (1)15 (6)104 (12)30 (26)64 (19)7 (20)
Everolimus5 (0.3)2 (1)172 (19)21 (18)69 (21)5 (14)
Other therapya  48 (5.4)6 (5.3)34 (10.1)3 (8.6)
No. of failures/total1666/1937232/252755/88198/114271/31726/33
Median, months7.8 (7.2–8.1)4.2 (3.7–5.2)3.9 (3.4–4.4)2.8 (2.3–3.7)4.0 (3.3–4.6)3.4 (2.4–7.1)
HR 1.57 (1.37–1.80) 95% CI: 0.95–1.45 0.88 (0.59–1.32)
P < .0001 0.142 0.544
Multivariable HRb 1.54 (1.33–1.79)    
Adjusted Pb < .0001    
Table 3. Best Response Rates and Overall Survival (OS) Between ccRCC and nccRCC
 General ComparisonnccRCC Subtypes Compared With ccRCC
  1. Abbreviations: ccRCC, clear-cell renal cell carcinoma; nccRCC, non–clear cell renal cell carcinoma; HR, hazard ratio; BR, best response; CR, complete response; PR, partial response; SD, stable disease; PD, progressive disease.

  2. a

    Adjusted for the IMDC risk group criteria (time from diagnosis to treatment, Karnofsky performance status <80, hemoglobin < upper limit of normal, neutrophilia, thrombocytosis, hypercalcemia).

BR first-line CR/PR/SD/PD (%)17/428/803/350 (1.1/26.8/50.2/21.9)2/30/101/69 (1.0/14.9/50.0/34.2)0/17/63 /41 (0/14.0/52.1/33.9)1/6/16/9 (3.1/18.8/50.0/28.1)1/6/9/9 (4.0/24.0/36.0/36.0)0/1/13/10 (0/4.2/54.2/41.7)
BR second-line CR/PR/SD/PD (%)3/76/334/261 (0.4/11.3/49.6/38.7)0/7/37/38 (0/8.5/45.1/46.3)0/2/22/20 (0/4.5/50.0/45.5)0/2/5/7 (0/14.3/35.7/50.0)0/1/4/5 (0/10/40/50)0/2/6/6 (0/14.3/42.9/42.9)
OS analyses, n1963252151373430
No. of deaths1240177105212427
Median (95% CI)22.3 (20.7–23.5)12.8 (11.0–16.1)14.0 (10.9–17.1)27.1 (12.6–75.3)10.1 (5.1–13.2)11.3 (9.6–19.4)
Unadjusted HR (95% CI)Reference1.44 (1.23–1.69)1.48 (1.21–1.81)0.98 (0.64–1.51)1.71 (1.14–2.56)1.67 (1.14–2.45)
Unadjusted P < .0001.0001.923.010.008
Adjusted HRa (95% CI)Reference1.41 (1.19–1.67)1.57 (1.27–1.94)0.89 (0.55–1.45)1.51 (0.98–2.31)1.30 (0.84–2.00)
Adjusted Pa < .0001< .0001.646.060.243

Overall Survival Outcomes

The entire nccRCC cohort had a significantly shorter OS than the ccRCC cohort; median OS was 22.3 months (95% CI, 20.7-23.5 months) and 12.8 months (95% CI, 11.0-16.1 months) in ccRCC and nccRCC, respectively (P < .0001; Fig. 1C). After adjustment for the IMDC risk group criteria, patients with nccRCC tumors had a HR for death of 1.41 (95% CI, 1.19-1.67; P < .0001; Table 3). In subgroup analyses, median OS was 27.1 months (95% CI, 12.6-75.3 months), 14.0 months (95% CI, 10.9-17.1 months), and 10.1 months (95% CI, 5.1-13.2 months) in chRCC, pRCC, and unRCC, respectively (Table 3, Fig. 1D). When pRCC was compared with ccRCC, the adjusted HR for death was 1.57 (95% CI, 1.27-1.94; P < .0001). Likewise, unRCC subtype had a higher risk for death (HR, 1.71; 95% CI, 1.14-2.56; adjusted HR, 1.51; 95% CI, 0.98-2.31). By contrast, the chRCC subtype had comparable survival outcome with ccRCC in both univariate and multivariable analysis (adjusted HR, 0.89; 95% CI, 0.55-1.45; P = .646; Table 3).

IMDC Risk Model for nccRCC

We next investigated the applicability of the IMDC risk model as a prognostic tool for TTF and OS in nccRCC. According to the 6 predefined IMDC risk criteria, 29 (13%), 127 (57%), and 66 (30%) nccRCC patients and 337 (19%), 972 (55%), and 463 (26%) ccRCC patients were assigned to favorable, intermediate, and poor prognosis groups (P = .08). Among the nccRCC patients, the median TTF of the first-line treatment in the 3 risk groups was 9.6 months (95% CI, 3.9-16.2 months), 4.9 months (95% CI, 3.9-5.7 months), and 2.1 months (95% CI, 1.3-2.9 months), see Figure 2A. The HRs were 1.57 (95% CI, 1.02-2.42) and 3.10 (95% CI, 1.94-4.95) in the intermediate and poor prognosis groups, respectively, compared with favorable-risk patients (P < .0001). The estimated median OS of the 3 IMDC risk groups was 31.4 months (95% CI, 14.2-78.3 months), 16.1 months (95% CI, 12.5-18.7 months), and 5.1 months (95% CI, 2.7-7.1 months); see Figure 2B. The intermediate-risk group had an increased HR for death of 1.97 (95% CI, 1.13-3.42), and those with a poor prognosis had an HR of 5.69 (95% CI, 3.20-10.1; P < .0001). The c-indices for OS with the 3 groups were 0.66 (95% CI, 0.63-0.70) for the IMDC model and 0.64 (95% CI, 0.60-0.68) for the Memorial Sloan-Kettering Cancer Center (MSKCC) risk criteria.

Figure 2.

Application of the IMDC risk criteria in the nccRCC-segregated 3 risk groups with favorable, intermediate, and poor risk profiles in prognostication of (A) time to treatment failure in the first line targeted therapy (median TTF: favorable, 9.6 months; intermediate, 4.9 months; poor, 2.1 months; P < .0001) and (B) overall survival (median OS: favorable, 31.4 months; intermediate, 16.1 months; poor, 5.1 months; P < .0001).

We then correlated each of the individual 6 IMDC risk criteria with OS outcome in nccRCC. There was a significant association of all prognostic factors with OS in univariable analyses (HR range, 1.5-3.2; P < .01; Table 4B). Given the limited numbers of patients and low prevalence of some laboratory risk factors, we did not have sufficient power to test all 6 risk factors in the multivariable model. However, the model yielded a c-index of 0.70 (95% CI, 0.66-0.74) when using the 6 risk factors individually instead of collapsing them into 3 risk groups, suggesting good discriminatory ability.

Table 4. IMDC Risk Group Criteria for OS in nccRCC
 Univariate AnalysisMultivariable Modela
ParameterHR (95% CI)PHR (95% CI)P
  1. Abbreviations: Hb, hemoglobin; KPS, Karnofsky performance status; Dx, diagnosis; ULN, upper limit of normal; LLN, lower limit of normal.

  2. a

    c-Index of 0.70 (95% CI, 0.66–0.74).

Hb low (<LLN)1.72 (1.23–2.40).0021.48 (1.03–2.14).034
Neutrophilia (>ULN)3.23 (2.24–4.65)< .00012.43 (1.58–3.74)< .0001
KPS <803.15 (2.23–4.45)< .00012.02 (1.35–3.02).0006
Thrombophilia (>ULN)2.56 (1.80–3.63)< .00011.13 (0.72–1.79).592
Hypercalcemia (>ULN)2.13 (1.24–3.65).0061.42 (0.78–2.60).256
Dx to treatment initiation <1 y1.53 (1.12–2.07).0071.22 (0.87–1.71).251


In the current study, we report 2 findings: 1) the IMDC risk model reliably prognosticates clinical outcome in nccRCC, and 2) in the targeted-therapy era, the majority of nccRCC patients still have inferior clinical outcomes compared with patients with ccRCC.

The introduction of agents targeting the VEGF and mTOR pathways in clinical practice has revolutionized the treatment of mRCC. Median overall survival time has doubled when compared with historical control treatment with immunotherapy.[12] The improvement of the survival rates is particularly true for ccRCC, which represents the majority of mRCC. Less is known about the survival outcomes of nccRCC, which accounts for only 10%-15% of RCC.[1, 2] Patients with metastatic nccRCC have a worse response to immunotherapies than do those with ccRCC.[15] Motzer et al described a median OS of 9.4 months in a cohort treated with several kinds of cytokines.[16] An early retrospective study of the targeted-therapy era described a median OS of 19.4 months in a cohort of pRCC and chRCC patients, and this was significantly longer than the OS outcome of 13.4 months in the sunitinib expanded-access program.[17, 18] The OS was very heterogeneous, with 25.6 and 16.8 months in 2 recent sunitinib phase 2 trials and 14.0 months in a phase 2 everolimus study.[19-21] In the 2 studies demonstrating the longest OS (19.4 and 25.6 months), the rate of death events (∼40%) was very low, and thus both studies may have overestimated the median OS.[17, 19] In our study, 70% of nccRCC patients had died at the time of analysis, and the majority of nccRCC patients (93.2%) were treated with an anti-VEGF therapy, with sunitinib the drug used most often. This may explain the shorter median OS compared with the smaller studies and the high concordance with the sunitinib open-access trial.[18] Moreover, the sunitinib open-access program and the current study are the largest cohorts to investigate survival outcomes in nccRCC. Other studies that revealed better survival outcomes of nccRCC may have been biased by their small sample sizes.

In our study, nccRCC patients were more often treated with mTOR-targeted therapies than were ccRCC patients. Dutcher et al showed that nccRCC and ccRCC patients who were treated with temsirolimus had comparable OS and progression-free survival.[22] Our study found that nccRCC patients had a significantly inferior survival outcome compared with ccRCC patients. Some studies restricted the nccRCC population to certain subtypes,[17, 19] whereas others included a wider range of nccRCC subtypes.[20, 21] Similar to the sunitinib open-access trial, our study cohort included patients without restriction by nccRCC subtype.[18] However, the sunitinib open-access program described survival outcome not separately by nccRCC subtype.[18] We found that patients with chRCC had the best OS and those with pRCC and unRCC the worst OS. These findings confirm the results of smaller studies.[11, 23-25] In chRCC, preclinical studies have demonstrated that mTOR is activated by inactivation of its negative regulatory protein folliculin.[26] Higher mTOR activity is related to higher hypoxia-inducible factor-α production.[27] Both pathways are major targets of the drugs used in our patients. Recently, a phase 2 trial with foretinib, a dual inhibitor of VEGFR2 and MET, demonstrated an overall response rate of 13.5% (all PR) and a PFS of 9.3 months, with an intriguing 50% response in pRCC patients with a germ-line MET mutation.[28] In the future, MET inhibitors may improve the survival outcome of pRCCs patients, possibly in a selected population. However, new drugs that better consider the unique biological properties of each nccRCC subtype are needed.

To the best of our knowledge, there is actually no other modern prognostic model that has been assessed exclusively in advanced nccRCC. Here, we have demonstrated that the IMDC risk criteria reliably segregated nccRCC into 3 risk groups similar to our previous studies.[11, 12] Moreover, the accuracy in prognosticating the OS was slightly higher than with the MSKCC risk model, which was one of the most often used prognostic models in the past.[29]

The current study has several limitations that merit discussion. First, the evaluation of TTF and OS was done at each center without an independent radiological assessment. Our results are based on a retrospective investigation of a highly heterogeneous study population. We had to exclude 7% of the patients because of an unknown histology. There was no central pathology review, and therefore, some tumors may have been misclassified, but this may better reflect daily clinical practice, in which dedicated kidney cancer pathologists may not be available, and therefore results may be more generalizable.


The IMDC risk model is a reliable prognostication tool that can be employed to prognosticate OS in nccRCC for patient counseling and clinical trials design. The survival outcome for the majority of nccRCC patients remains lower than their clear-cell counterparts. However, chRCC patients have a survival outcome that is comparable to ccRCC patients.


The authors made no disclosures.


Nils Kroeger, Wanling Xie, Sandy Srinivas, Sumanta K. Pal, Takeshi Yuasa, Neeraj Agarwal, and Min-Han Tan made no disclosures. Jae-Lyn Lee has received honoraria from Novartis, Bayer, and Pfizer; has received research funding from Bayer. Georg A. Bjarnason has been a consultant and played an advisory role at Pfizer and received honoraria and research funding from Pfizer. Jennifer J. Knox has been a consultant and played an advisory role at Aveo and has received research funding from Pfizer. Mary J. MacKenzie has an advisory role at Novartis and Pfizer and has received research funding from both. Lori Wood has an advisory role at Pfizer and Novartis and has received research funding from Pfizer, Novartis, and GlaxoSmithKline. Ulka N. Vaishamayan has received honoraria and research funding from Pfizer, Novartis, and GlaxoSmithKline. Sun-Young Rha has an advisory role at Novartis, Pfizer, and GlaxoSmithKline and has received research funding from Novartis and Bayer Korea. Frede Donskov has received research funding from Novartis. Christian K. Kollmannsberger has an advisory role at Pfizer, Novartis, and GlaxoSmithKline and has received honoraria and research funding from Pfizer, Novartis, and GlaxoSmithKline. Scott A. North is a consultant and has an advisory role at Novartis, Bayer, GlaxoSmithKline, and Pfizer. Brian I. Rini has an advisory role at Pfizer, GlaxoSmithKline, Aveo, Bayer, and Onyx and has received research funding from GlaxoSmithKline and Pfizer. Toni K. Choueiri has received research funding from Pfizer and has an advisory role at Aveo, Pfizer, Novartis, GlaxoSmithKline, Genentech, Bayer, and Onyx. Daniel Y.C. Heng has an advisory role at Aveo, Pfizer, Novartis, and Bayer.