Cancer renal

1. Cancer Treatment Reviews 122 (2024) 102667
Available online 10 December 2023
0305-7372/© 2023 Elsevier Ltd. All rights reserved.
Systematic or Meta-analysis Studies
Combination therapies in patients with favorable risk metastatic renal cell
Carcinoma: A Systematic Review and Meta-Analysis
Hatice Bolek a,b
, Emre Yekedüz c
, Yüksel Ürün a,b,*
a
Department of Medical Oncology, Ankara University School of Medicine, Ankara, Türkiye
b
Ankara University Cancer Research Institute, Ankara, Türkiye
c
Ankara Etlik City Hospital, Medical Oncology Clinic, Ankara, Türkiye
A R T I C L E I N F O
Keywords:
IMDC
Immunotherapy
Renal cell carcinoma
Tyrosine kinase inhibitors
A B S T R A C T
Introduction: Immunotherapy (IO)-based combination therapies have emerged as the standard of care for first-line
treatment of metastatic renal cell carcinoma (mRCC) among patients classified as intermediate and poor risk.
However, in the favorable risk group, the available data remains less compelling. This study aims to assess and
compare the effectiveness of IO-based combination therapies versus tyrosine kinase inhibitor (TKI) monotherapy
in patients with favorable risk group according to the International mRCC Database Consortium (IMDC).
Methods: Recent update data from phase-III RCTs of IO-based combinations approved by the Food and Drug
Administration were used. Studies that provided data on progression free survival (PFS) and overall survival (OS)
of IMDC favorable risk were included in the analysis.
Results: A cohort of 1,088 patients categorized within the IMDC favorable risk group was enrolled for analysis. In
comparison to sunitinib, the combination of immunotherapy (IO) and tyrosine kinase inhibitor (TKI) exhibited a
reduction in the risk of disease progression (HR = 0.67, 95 % CI: 0.55–0.82; p < 0.001). Conversely, the com­
bination of IO and IO displayed an elevated risk of disease progression (HR = 1.60, 95 % CI: 1.13–2.26; p =
0.008). However, neither the IO plus TKI (HR = 0.99, 95 % CI: 0.79–1.24; p = 0.92) nor IO plus IO (HR = 0.94,
95 % CI: 0.64–1.37; p = 0.75) combinations demonstrated a noteworthy improvement in overall survival (OS).
Notably, within the IO plus TKI subgroup, combination therapy yielded a higher objective response rate (ORR)
(OR = 0.40, 95 % CI: 0.28–0.57; p < 0.001). On the other hand, the IO plus IO combination displayed a lower
ORR than sunitinib (OR = 2.54, 95 % CI: 1.51–4.27; p < 0.001).
Conclusions: In the first-line treatment of IMDC favorable-risk mRCC, IO and TKI combinations show enhanced
progression-free survival and response rate without improving overall survival. This emphasizes the demand for
further exploration of combination therapies in this patient group.
Introduction
In recent years, there have been significant advancements in the
treatment of metastatic renal cell carcinoma (mRCC) with the intro­
duction of combination therapies involving immunotherapy (IO).
Particularly, the combination of nivolumab and ipilimumab has
demonstrated noteworthy improvements in overall survival (OS) when
compared to sunitinib in patients classified as intermediate- and poor-
risk according to the “International mRCC Database Consortium
(IMDC).” These findings highlight the promising potential of IO-based
combination therapies in the management of mRCC [1]. Furthermore,
findings from four phase-III randomized clinical trials (RCTs) have
established IO plus anti-vascular endothelial growth factor (VEGF)
tyrosine kinase inhibitor (TKI) combinations as the primary first-line
treatment approach for mRCC, regardless of IMDC risk categories. The
IMDC prognostic risk model, which considers six clinical and laboratory
parameters, was initially developed and validated during the era of anti-
VEGF TKI therapies [2,3]. Despite the absence of alternative IO-based
prognostic criteria, the IMDC risk score continues to be employed in
clinical trials for patient stratification. It has demonstrated both prog­
nostic and predictive value for patients receiving IO-based combinations
in the treatment of mRCC [4]. While the benefits of the IO-based com­
bination therapies are well established in patients with intermediate and
poor risk, their efficacy in the favorable risk group is still being
* Corresponding author at: Ankara University School of Medicine, Department of Medical Oncology, 06590 Ankara, Turkey.
E-mail address: yuksel.urun@ankara.edu.tr (Y. Ürün).
Contents lists available at ScienceDirect
Cancer Treatment Reviews
journal homepage: www.elsevier.com/locate/ctrv
https://doi.org/10.1016/j.ctrv.2023.102667
Received 22 August 2023; Received in revised form 28 November 2023; Accepted 30 November 2023

2. Cancer Treatment Reviews 122 (2024) 102667
2
determined. Recently published long-term follow-up analyses of com­
binations have shed light on this aspect.
In this study, we aimed to assess the effect of combination therapies
in mRCC patients with favorable IMDC risk.
Methods
This meta-analysis complied with Preferred Reporting Items for
Systematic Reviews and Meta-Analyses guidelines [5].
Study Cohort
We searched the MEDLINE database on August 01, 2023, using the
following keywords and boolean operators: ‘((‘renal cell carcinoma’ OR
‘kidney cancer’) AND (pembrolizumab OR nivolumab OR ipilimumab
OR atezolizumab OR avelumab))’. We also assessed the congress ab­
stracts to reach the latest data. The Inclusion criteria to select the
studies: (a) patients: advanced clear-cell RCC patients with favorable
IMDC risk; (b) intervention: IO-based combination therapies; (c)
comparator: sunitinib; (d) outcome: progression-free survival (PFS),
overall survival (OS), and response rates; (e) study design: phase-III
clinical trials. Pre-clinical studies, reviews, case reports, and articles
not in English were excluded from the study.
Data Extraction
According to the inclusion and exclusion criteria, two reviewers
assessed full-text articles of studies independently (H.B., E.Y.). The
following data were extracted from the articles and congress abstracts:
author names, publishing journals, the year of publication, the total
number of patients in each study, the number of male patients, median
age, the number of patients in each cancer treatment subtype, hazard
ratios (HRs) for PFS and OS, number of patients with objective response
rate (ORR) and complete remission (CR).
Assessment Quality of Included Studies
The quality of included studies was assessed independently by two
reviewers (H.B. and E.Y.) using the RevMan 5.3 meta-analysis software
(The Nordic Cochrane Centre, Copenhagen, Denmark) and in accor­
dance with the recommendations of the “Cochrane Handbook for Sys­
tematic Reviews of Interventions”. Sequence generation, allocation
concealment, blinding of participants and personnel, blinding of
outcome assessment, incomplete outcome data, and selective reporting
Fig. 1. PRISMA Diagram.
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Table 1
Baseline Characteristics of the Trials.
Trials Experimental
arm and control
arm
Number
of
patients
Median
age
(range)-
years
Male
sex
(%)
Number of
patients in
intermediate-
poor
risk (%)
Number of
patients in
favorable
risk
(%)
Previous
nephrectomy
(%)
Median
follow-up
time
(months)
Median
PFS for
favorable
risk
(months)
PFS for
favorable risk
HR (95 % CI)
Median OS
for
favorable
risk
(months)
OS for
favorable risk
HR (95 % CI)
ORR in
favorable
risk (%)
CR in
favorable
risk (%)
JADAD
Score
Keynote
426 5
Pembrolizumab +
Axitinib
432 62
(30–89)
308
(71.3)
294
(68.1)
138
(31.9)
357
(82.6)
67.2 20.7 0.76
(0.57–1.02)
60.3 1.10
(0.79–1.54)
66.8 13 2
Sunitinib 429 61
(26–90)
320
(74.6)
298
(69.5)
131
(30.5)
358
(83.4)
17.9 62.4 50.4 6.1
Javelin
Renal
101*6
Avelumab
+
Axitinib
442 62
(29–83)
316
(71.5)
343
(77.6)
94
(21.3)
352
(79.6)
34.1 20.7 0.71
(0.490–1.016)
NR 0.66
(0.356–1.223)
75.5 9.6 2
Sunitinib 444 61
(27–88)
344
(77.5)
347
(78.2)
96
(21.6)
355
(80.0)
33.6 13.8 NR 45.8 5.2
CheckMate
9ER 7
Nivolumab
+
Cabozantinib
323 62
(29–90)
249
(77.1)
249
(77.1)
74
(22.9)
222
(68.7)
44 21.4 0.75
(0.50–1.13)
NR 1.07
(0.63–1.79)
66.2 13.5 2
Sunitinib 328 61
(28–86)
232
(70.7)
256
(78.0)
72
(22.0)
233
(71.0)
13.9 47.6 44.4 11.1
Cleary 8
Pembrolizumab
+
Lenvatinib
355 64
(34–88)
255
(71.8)
243
(68.4)
110
(30.8)
262
(73.8)
49.8 28.6 0.50
(0.35–0.71)
NR
0.94
(0.52–1.58)
N/A N/A 2
Sunitinib 357 61
(29–82)
275
(77.0)
229
(64.1)
124
(34.7)
275
(77)
49.4 12.9 59.9 N/A N/A
CheckMate
214 1
Nivolumab
+
Ipilimumab
550 62
(26–85)
413
(75)
425
(77.3)
125
(22.7)
453
(82.4)
67.7 12.4 1.60
(1.13–2.26)
74.1 0.94
(0.65–1.37)
29.6 12.8 3
Sunitinib 546 62
(21–85)
395
(72)
422
(77.3)
124
(22.7)
437
(80)
28.9 68.4 51.6 6.5
H.
Bolek
et
al.

4. Cancer Treatment Reviews 122 (2024) 102667
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were evaluated. The Jadad score was also computed for each study [6].
Statistical analysis
The meta-analysis was performed using the generic inverse-variance
method with a random-effects model to calculate PFS and OS risk. The
calculated effect size was the HR and its 95 % Confidence Interval (CI).
Additionally, the Mantel-Haenszel method with a random-effects model
was used to compare response rates. The calculated effect size was the
odds ratio (OR) and its 95 % CI. All analyses were done using the Review
Manager software, version 5.3 (The Nordic Cochrane Center, The
Cochrane Collaboration, Copenhagen, Denmark). The thresholds for
statistical significance for overall effect tests were 0.05, and that for the
heterogeneity tests was 0.10. The I2
coefficient was also used to quantify
the degree of heterogeneity between the studies.
Results
After doing a search based on the criteria described previously, a
total of 3092 articles were identified, and 1119 articles were evaluated
after the duplicates were removed. After excluding reviews, pre-clinical
studies, case reports, letter and commentaries, and phase I or II trials, we
assessed 10 full-text articles. Finally, 5 phase-III RCTs (CheckMate 214,
CheckMate 9 ER, CLEAR, Javelin Renal 101 and Keynote 426) were
included in the final analysis. The PRISMA diagram of search results is
shown in Fig. 1. The risk of bias of included studies is summarized by the
Jadad score and Cochrane Risk Bias Tool in Table 1 and in Fig. 2,
respectively.
Baseline Characteristics
A total of 1,088 patients (541 patients in experimental arms) with
IMDC favorable risk group were included in this meta-analysis [1,7–10].
The experimental arm of four studies were IO and TKI combinations,
while the experimental arm of one study was IO and IO combination.
All studies included in the final analysis used sunitinib in the control
arm. The baseline characteristics of the included trials are shown in
Table 1.
Survival Outcomes
Our meta-analysis showed that (Fig. 3) IO plus TKI was associated
with a 33 % risk reduction in disease progression compared to sunitinib
(HR = 0.67, 95 % CI:0.55–0.82; p < 0.001). Conversely, IO plus IO
combination had an increased risk for disease progression (HR = 1.60,
95 % CI:1.13–2.26; p = 0.008). But there was no OS benefit in the IO
plus TKI (HR = 0.99, 95 % CI:0.79–1.24; p = 0.92) and IO plus IO (HR =
0.94, 95 % CI: 0.64–1.37; p = 0.75) subgroups.
Response Rates
Data for response rates were available in four trials [1,7–9]. A clear
benefit in terms of ORR and CR was observed with the combination of
IO-TKI (Odds Ratio (OR) = 0.40, 95 % CI:0.28–0.57; p < 0.001 for ORR
and OR = 0.55, 95 % CI:0.31–0.98; p = 0.04 for CR). However, there was
no significant difference in CR when comparing IO-IO and sunitinib (OR
= 0.47, 95 % CI:0.19–1.14; p = 0.10) alone, and IO-IO was even asso­
ciated with worse ORR (OR = 2.54, 95 % CI:1.51–4.27; p < 0.001).
(Fig. 4).
Discussion
The first-line treatment for mRCC underwent a major shift after the
advent of IO-based combination treatments. Combination therapies
becomes standard of care in these patients. Risk stratification has
become an integral component of the clinical and therapeutic decision-
making process in patients with mRCC. While the efficacy of IO plus IO
and IO plus TKI combinations has been clearly demonstrated in patients
with IMDC intermediate and poor risk, their effectiveness in patients
with IMDC favorable risk according to IMDC criteria remains uncertain.
The meta-analysis demonstrated that the PFS advantage observed in
patients with favorable risk was not reflected in OS [11–14]. Moreover,
the updated analysis of the studies evaluating the IO-based combina­
tions did not demonstrate OS improvement in the favorable risk group.
We conducted a meta-analysis using updated data from phase III RCTs of
Food and Drug Administration-approved combinations to investigate
the efficacy of combination therapy in the IMDC favorable-risk group.
Combinations of IO plus TKIs showed a higher ORR in the favorable
risk group than sunitinib in Keynote 426 (66.8 % vs. 50.4 %), Javelin
Renal 101 (75.5 % vs. 45.8 %), and Checkmate 9 ER (66.2 % vs. 44.4 %)
trials. This meta-analysis also demonstrated a clear benefit of IO plus TKI
combination therapy in terms of ORR and CR. IO plus TKI combinations
have been associated with a 33 % risk reduction in disease progression.
Therefore, combining IO and TKI may be a reliable choice in patients
with a high tumor burden and disease-related symptoms requiring early
disease control. Considering the side effects and financial toxicity, TKI
monotherapy may be an option for first-line therapy in patients without
disease-related symptoms, low disease burden, or a long interval be­
tween nephrectomy and metastasis. Furthermore, a recent analysis from
the IMDC re-defined favorable risk into two groups as very favorable and
favorable risk [15,16]. Patients with very-favorable risk mRCC have a
longer duration of initiating systemic treatment for metastatic disease
Fig. 2. Risk of Bias Assessment.
H. Bolek et al.

5. Cancer Treatment Reviews 122 (2024) 102667
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after diagnosis, do not have liver, bone, and brain metastasis, and have a
better Karnofsky performance status than those with favorable risk
[15,16]. Patients with IMDC favorable risk are heterogeneous in terms
of biological and clinical behavior; because of this reason, a patient-
based approach is essential.
VEGF plays a crucial role in tumor progression by promoting the
formation of new blood vessels and creating an immune-suppressive
environment. Inhibition of the VEGF pathway and reversal of the
immune-suppressive environment through the combined use of anti-
VEGF-TKIs and immune checkpoint inhibitors have been shown to
prolong the survival of patients with mRCC. However, the efficacy of
this dual blockade strategy in patients with favorable risk mRCC needs
further investigation. Patients with favorable risk mRCC have been
found to exhibit higher expression levels of angiogenic genes and spe­
cific targets for TKIs [17]. In IMmotion 150 study, higher angiogenesis
gene signature was associated with a better response to sunitinib mon­
otherapy (ORR was 46 % in AngioHigh
versus 9 % in AngioLow
) [18]. This
data further supported by biomarker analysis of Immotion 151 and
Javelin Renal 101, high angiogenic score associated with longer PFS in
sunitinib monotherapy arm [19]. As a result, the effectiveness of anti-
angiogenic therapy in these patients may be more pronounced
compared to other risk groups. Establishing the therapeutic benefit of
anti-angiogenic therapy in patients with favorable risk mRCC would
provide valuable insights for personalized treatment approaches in this
specific patient population. The findings from the Checkmate 214 study
revealed that patients with favorable risk mRCC who were treated with
sunitinib exhibited a higher ORR and longer PFS compared to those
treated with IO-IO combination. These results highlight the significant
role of anti-angiogenic therapy, such as sunitinib, in the management of
mRCC within the favorable risk group. These findings support the notion
that targeting angiogenesis plays an essential role in achieving favorable
treatment outcomes in patients with favorable risk mRCC. Under­
standing the impact of anti-angiogenic therapy in this context provides
valuable insights into the optimal treatment strategies for this specific
patient population. Further investigation and validation of these results
would help establish the clinical significance of anti-angiogenic therapy
in the management of favorable risk mRCC.
In summary, the combination of IO and TKI demonstrates improved
progression-free survival (PFS) but not overall survival (OS) in the first-
line treatment of patients with IMDC favorable-risk mRCC. It is impor­
tant to note that the IMDC prognostic risk model used in clinical practice
is based solely on clinical and laboratory variables, which may not fully
capture the molecular and biological heterogeneity within patients
classified in the same risk group. Although the IMDC risk score remains a
reliable tool for risk stratification in mRCC, further genomic and mo­
lecular investigations are necessary to identify specific patient sub­
groups that would benefit the most from combination therapies or
potentially require less intensive treatment approaches. The integration
of genomic and molecular information into risk stratification algorithms
could enhance our ability to personalize treatment decisions.
Fig. 3. Forest plot estimating progression free survival (A) and overall survival (B) in comparison of TKI combined treatment versus sunitinib in the favorable-
risk group.
H. Bolek et al.

6. Cancer Treatment Reviews 122 (2024) 102667
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However, it is important to acknowledge certain limitations in our
study, primarily the absence of individual-level patient data. This limi­
tation prevents us from conducting more detailed subgroup analyses and
personalized treatment recommendations based on specific patient
characteristics. The lack of reliable biomarkers in our current dataset
also hampers our ability to confidently differentiate between patients
who would derive substantial benefit from combination therapy and
those who might be better suited for less intensive treatment options. As
such, while our findings shed light on the efficacy of IO and TKI com­
binations in IMDC favorable-risk mRCC, the absence of individual-level
data and comprehensive biomarker insights restricts the depth of our
conclusions. Further research efforts should aim to incorporate indi­
vidual patient-level data and explore robust molecular biomarkers that
can enhance our ability to tailor treatment strategies and optimize
outcomes for this specific subgroup of patients.
Certainly, it would not be incorrect to say that some patients in the
favorable risk group also derive additional benefit from combination
therapy. However, it is important to acknowledge that for certain pa­
tients, combination therapy may be overtreatment, and single-agent
sunitinib or even active surveillance could be sufficient. Personalized
decision-making is crucial, taking into consideration individual patient
factors such as tumor burden, comorbidities, and treatment tolerability.
Identifying reliable biomarkers that can accurately predict treatment
response and guide therapy selection remains an unmet need in this
context. Moving forward, further research efforts are warranted to
explore the molecular landscape of mRCC and identify predictive bio­
markers that can help distinguish those patients who would benefit most
from combination therapy versus those who may be better suited for less
intensive treatment options.
In conclusion, while some patients in the favorable risk group may
derive additional benefits from combination therapy, the decision to
pursue such treatment should be made on an individual basis, consid­
ering both clinical and molecular factors. The absence of reliable bio­
markers currently hampers our ability to make this selection in a more
informed manner. Future studies focusing on the identification of robust
biomarkers are necessary to guide treatment decisions and optimize
outcomes for patients in this subgroup.
Declaration of conflicting interest
Yüksel Ürün declared research funding (Institutional and personal)
from Turkish Oncology Group. Yüksel Ürün has served on the advisory
board for Abdi-İbrahim, Astellas, AstraZeneca, Bristol Myers-Squibb,
Eczacıbası, Gilead, Janssen, Merck, Novartis, Pfizer, Roche. Yüksel
Ürün received honoraria or has served as a consultant for Abdi-İbrahim,
Astellas, Bristol Myers-Squibb, Eczacıbasi, Janssen, Merck, Novartis,
Fig. 4. Forest plot estimating objective response rate (A) and complete remission rate (B) in comparison of TKI combined treatment versus sunitinib in the favorable-
risk group.
H. Bolek et al.

7. Cancer Treatment Reviews 122 (2024) 102667
7
Pfizer, Roche.
CRediT authorship contribution statement
Hatice Bolek: Investigation, Writing – original draft, Visualization.
Emre Yekedüz: Methodology, Formal analysis, Writing – review &
editing. Yüksel Ürün: Conceptualization, Methodology, Writing – re­
view & editing, Supervision.
Declaration of competing interest
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influence
the work reported in this paper.
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