Post on 09-Aug-2020
transcript
Modern Management of Advanced
Kidney Cancer
Primo N. Lara, Jr., MD
Professor of Medicine
University of California Davis
Comprehensive Cancer Center
Sacramento, CA USA
RCC is not a single disease
Clear cell
75%
Type
Incidence (%)
Hereditary mutations VHL
Papillary type 1
5%
MET
Papillary type 2
10%
FH
Chromophobe
5%
FLCN
Oncocytoma
5%
FLCN
Proximal Nephron Distal Nephron
FLCN= folliculin; BHD= Birt-Hogg-Dubé; FH = fumarate hydratase; MET = mesenchymal epithelial transition factor; VHL = von Hippel-Lindau.
Pfaffenroth . Expert Opin Biol Ther. 2008; Linehan, Semin Cancer Biol 2012
Sporadic mutations
VHL
(89%) MET
(13%)
TBD TBD TBD
Biology 1: Consequences of VHL Gene Disruption
Ubiquitin Ligase Complex Disrupted
X Chr 3
Hypoxia
Inducible
Factor (HIF)
Accumulation
HIF
VEGF, etc.
Angiogenesis
Also…
- Erythropoiesis
- Cell proliferation &
survival
- Glucose metabolism
- pH regulation
- Proteolysis
Tools are available to interrogate
biomarkers involved in the
complex VHL-HIF pathways
RTK IGFR-1
PI3K
HIF1a
S6K
AKT
HIF2a
4EBP1
VHL
PTEN
LKB1
p53
PIP2 PIP3
Glucose Amino
Acids
TSC1
TSC2
RHEB
PRAS40
GSK3a/b
RAF
MEK
RSK ERK1/2
RAS
MDM2
AMPK
PDK1 IRS-1
ATP
ATP
mTOR
Raptor
mTOR
Rictor
Vps34
eIF4E
eIF4B
Biology 2: Complexity of mTOR Signaling Pathways
Biology 3: RCC is a metabolic disease
Vander Heiden, Science 2009
Nature 2013
Key altered pathways
and metabolites
Worse survival correlates with
upregulation of :
• Pentose phosphate
pathway genes (G6PH,
PGLS, TALDO and TKT)
• Fatty acid synthesis genes
(ACC and FASN)
• PI(3)K pathway enhancing
genes (MIR21).
Better survival correlates with
upregulation of:
• AMPK complex genes
• Multiple Krebs cycle genes
• PI(3)K pathway inhibitors
(PTEN, TSC2).
Increased gene expression:
• Red shading = worse survival
• Blue shading = better survival
Gordan JD, et al. Cancer Cell. 2008;14:435-446 .
Biology 4: RCC has molecularly
distinct phenotypes
H2
(HIF2+ only)
H1H2
(HIF1+ and
HIF2+)
VHL wild type
Gerlinger, NEJM 2012
Biology 5: RCC is heterogeneous
“Intratumor heterogeneity can
lead to underestimation of the
tumor genomics landscape
portrayed from single tumor-
biopsy samples and may
present major challenges to
personalized-medicine and
biomarker development.”
Prognostic Factors in RCC:
Targeted Therapies Era
Heng DY, et al. J Clin Oncol 2009
●N = 645 patients with mRCC treated with VEGF-targeted therapy
– Sunitinib (61%); Sorafenib (31%); Bevacizumab (8%)
●Predictors for OS:
– Time from diagnosis
to treatment*
– Hemoglobin*
– Calcium*
– Performance status*
– Neutrophil count
– Platelet count
* Components of MSKCC
prognostic criteria
Risk Group Number of
Risk Factors
Median
Survival Time
Favorable Risk (n=133) 0 37 months
Intermediate Risk (n=292) 1-2 28.5 months
Poor Risk (n=139) >2 9.4 months
Favorable: 0 factors
(OS 37 months)
Intermediate: 1–2 factors
(OS 28 months)
Poor: 3–6 factors
(OS 9.4 months)
Timeline of RCC Drug Approvals (USA)
1987~
2004 2005 2006 2007 2008 2009 2010 2011 2012
High-dose IL-2: 1992
Sorafenib: Dec 2005
Sunitinib: Jan 2006
Temsirolimus: May 2007
Everolimus: May 2009
Bevacizumab: Aug 2009
Pazopanib: Oct 2009
Axitinib: Jan 2012
Metastatic RCC: Treatment Principles
● The best treatment is one that results in cure
– Not currently achievable in most patients
● In absence of cure, goals are palliative
– Disease control and prolongation of life are achievable with
current systemic agents
● Current standard of care:
– Risk stratification
– Sequential use of “targeted” agents, starting with VEGF-
directed therapy in good/intermediate risk
– New (presumably non-cross resistant) treatment initiated at
time of progression or unacceptable toxicity
Metastatic Clear Cell RCC: Current Algorithm
RISK CATEGORY
Initial Assessment Candidate for nephrectomy?
Which prognostic group?
Favorable Intermediate Poor
INITIAL CHOICE
OF THERAPY HD IL2
(select pts) VEGF-targeted mTORi
SECOND LINE
THERAPY VEGF-targeted mTORi
?
Pazopanib vs. Sunitinib for First-line Treatment of
Clear Cell mRCC (COMPARZ)
Pazopanib 800 mg/day
Sunitinib 50 mg/day (Schedule
4/2*)
• Primary Endpoint: PFS (non-inferiority – upper bound of 95% CI for HR < 1.25)
N = 1100
Eligibility Criteria:
•Locally advanced
or mRCC with
clear cell histology
•No prior systemic
therapy for advanced
or mRCC
•Measurable disease
by RECIST
•KPS ≥70%
RANDOM I Z A T I ON
Phase 3 study
*Schedule 4/2 = 4 weeks on treatment/ 2 weeks off.
Available at: http://www.clinicaltrials.gov. NCT00720941.
COMPARZ: Intent to Treat Population
Efficacy Pazopanib (n=557) Sunitinib (n=553) HR
(95% CI); P Value
Median PFS, mos
(95% CI) Independent Review
8.4 (8.3, 10.9)
9.5 (8.3, 11.1)
1.047 (0.898, 1.220)
Interim OS, mos (95% CI)
28.4 (26.2, 35.6)
29.3 (25.3, 32.5)
0.908
(0.762, 1.082) P=0.275
Objective Response Rate (CR+ PR), %
31 (26.9, 34.5)
25 (21.2, 28.4)
P=0.032
Dose modifications Pazopanib
(n=554) Sunitinib (n=548)
Dose interruptions, %* 60 63
Dose reductions, % 44 51
Discontinuations due to AE, % 24 19
Motzer RJ, et al. Presented at ESMO. 2012 (abstr LBA8_PR). * Eisen ESMO 2012 (Discussant for abstr 2325).
COMPARZ: PFS and OS (Intent to Treat)
Pazopanib non-inferior to sunitinib
(PFS HR < 1.25)
Motzer, ESMO 2012
COMPARZ: Most Common Adverse Events
(Treatment-emergent)
17
Adverse Event a
Pazopanib (n = 554) % Sunitinib (n = 548) %
All Grs Gr 3/4 All Grs Gr 3/4
Any event b >99 59/15 >99 57/17
Diarrhea 63 9/0 57 7/<1
Fatigue 55 10/<1 63 17/<1
Hypertension 46 15/<1 41 15/<1
Nausea 45 2/0 46 2/0
Decreased appetite 37 1/0 37 3/0
ALT increased 31 10/2 18 2/<1
Hair color changes 30 0/0 10 <1/0
Hand-foot syndrome 29 6/0 50 11/<1
Taste Alteration 26 <1/0 36 0/0
Thrombocytopenia 10 2/<1 34 12/4
a AE ≥30% in either arm b 2% of patients in pazopanib arm and 3% of patients in sunitinib arm had grade 5 adverse events.
Motzer R, et al. ESMO 2012 (LBA8_PR) Taken directly from Motzer. ESMO 2012 oral abstract presentation
Instrument Domain Description Treatment difference :
mean change from baseline 2
P -value
FACIT-F Fatigue/Total score 2.32 <0.001
FKSI-19
Kidney Symptom Index/Total score 1.41 0.018
Physical 0.78 0.027
Emotional 0.05 0.409
Treatment Side Effects 0.31 0.033
Functional Well Being 0.31 0.098
Cancer Treatment Satisfaction Questionnaire (CTSQ)
Expectations of Therapy 1.41 0.284
Feelings about Side Effects 8.50 <0.001
Satisfaction with Therapy 3.21 <0.001
Supplementary Quality of Life Questionnaire (SQLQ)
Worst mouth/throat soreness 0.505 <0.0001
Worst foot soreness 0.204 0.0016
Worst hand soreness 0.267 0.0008
Limitations due to mouth/throat soreness 0.94 <0.001
Limitations due to foot soreness 0.65 0.014
1Pre-specified analysis. HRQoL changes in mean scores over time were analyzed with a repeated measures analysis of covariance (ANCOVA). 2Yellow Font: favors pazopanib. Blue Font: favors sunitinib. P-value <0.05 is statistically significant
Motzer R, et al. ESMO 2012 (LBA8_PR) Taken directly from Motzer. ESMO 2012 oral abstract presentation
COMPARZ: Quality of Life Results (First 6 months1)
18
COMPARZ: Per-protocol analysis of PFS
Pazopanib Sunitinib HR (95% CI)
Median PFS (IRC
PP)
Months (95%CI)
8.4 (8.3-10.9) (n=501)
10.2 (8.3-11.1) (n=494)
1.069 (0.910-1.255)
VEG108844: http://www.gsk-clinicalstudyregister.com/result_detail.jsp;jsessionid=0E2869B9C6A1942B31984ACDE6CE84D7?
protocolId=108844&studyId=A1C548E6-186D-405F-B1D6-853A51D9376B&compound=pazopanib
Does not meet protocol defined non-inferiority criteria
(HR upper bound of 95% CI <1.25)
COMPARZ Trial: Insights
Median PFS lower than original registration trials
– Likely due to lower % of good risk patients
Median OS higher than original registration trials
– Likely due to subsequent lines of active therapy
Sunitinib and pazopanib: comparable efficacy (PFS, OS)
– Per-protocol analysis did not meet non-inferiority criterion
Several HRQoL domains favored pazopanib
– Confounded by timing of QOL assessment (day 28, time of worst toxicity for sunitinib)
Sunitinib Pazopanib
NEJM 2007 COMPARZ JCO 2010 COMPARZ
PFS (months) 11 9.5 11.1 8.4
OS (months) 26.4* 29.3 22.9** 28.4
Good risk 38% 27% 39% 27%
Intermediate
risk
56% 59% 55% 58%
* Crossover patients censored; **from GSK website
Efficacy Scorecard: First-line RCC
1. Motzer RJ, et al. J Clin Oncol. 2009;27:3584–3590. 2. Escudier B, et al. J Clin Oncol. 2009;27(Suppl. 15S):5020 (Abstract); 3. Rini B, et al. J Clin Oncol. 2009;27(Suppl. 15S):LBA5019
(Abstract); 4. Escudier B, et al. J Clin Oncol. 2009;27:1280–1289. 5. Sternberg CN, et al. J Clin Oncol. 2010;28:1061–1068. 6. Hudes G, et al. N Eng J Med. 2007;356:2271–2281.
n ORR (%)
Median PFS (months)
Median OS (months)
Sunitinib vs. IFN-α1 750 47 vs. 12* 11 vs. 5* 26.4 vs. 21.8
Bevacizumab + IFN-α vs. IFN-α2, 3
649 31 vs. 12 10.4 vs. 5.5 23.3 vs. 21.3
732 25.5 vs. 13.1 8.4 vs. 4.9 18.3 vs. 17.4
Sorafenib vs. IFN-α4 189 5.2 vs. 8.7 5.7 vs. 5.6* NA
Pazopanib vs. placebo5 233 30 vs. 3 11.1 vs. 2.8 22.9 vs. 20.5
Pazopanib vs. Sunitinib 1,110 31 vs. 25 8.4 vs. 9.5 28.4 vs. 29.3
Tivozanib vs. Sorafenib 362 33 vs. 23 12.7 vs. 9.1 28.8 vs. 29.3
Temsirolimus vs. IFN-α6 (poor risk) 626 8.6 vs. 4.8 5.5 vs. 3.1* 10.9 vs. 7.3
*Independent assessment. ORR = overall response rate; PFS = progression-free survival; OS = overall survival; NA= not available
How does one choose?
In frontline RCC, efficacy is similar
across all VEGF inhibitors
– Poor risk: mTORi preferred
Therapy must be individualized
– Patient and tumor characteristics
– Preferences
– Reimbursement status
– Office workflow, nursing support
– Physician experience
Principle: “Choose one and use it
well!”
… and what about combinations?
Patients with previously untreated
advanced RCC (N=791)
Stratification factors:
• MSKCC risk group • Nephrectomy status
R
A
N
D
O
M
I
Z
E
TEM + BEV TEM: 25 mg IV weekly†
BEV: 10 mg/kg IV every 2 wk
(n=400)
1:1
IFN + BEV IFN: 9 MU SC 3 x wk†
BEV: 10 mg/kg IV every 2 wk
(n=391)
INTORACT* Study Design
BEV, bevacizumab; IFN, interferon alfa; IRC, independent review committee; IV, intravenous; MSKCC, Memorial Sloan-Kettering Cancer Center; PFS, progression-free survival; RCC, renal cell carcinoma; SC, subcutaneously; TEM, temsirolimus.
*ClinicalTrials.gov Identifier: NCT00631371 †Dose reductions were allowed for TEM and IFN, but not for BEV
April 2008–October 2012
Treat until PD, unacceptable
toxicity, or discontinuation for any other
reason
Characteristic
TEM + BEV (n=400)
IFN + BEV (n=391)
MSKCC risk factors,1 %
0 (good)
1–2 (intermediate)
3 (poor)
28
65
8
27
65
8
MSKCC Prognostic Risk Groups
1. Motzer RJ, et al. J Clin Oncol 2004;22:454-463.
BEV, bevacizumab; IFN, interferon alfa; MSKCC, Memorial Sloan-Kettering Cancer Center; TEM, temsirolimus.
0
1.0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.0
2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42
TEM + BEV
IFN + BEV
1-sided P=0.759 (log-rank)
Stratified HR: 1.07
(95% CI: 0.89, 1.28)
Median PFS,
months 95% CI
9.1
9.3
8.1, 10.2
9.0, 11.2
Progression-Free Survival (IRC Assessment)
BEV, bevacizumab; CI, confidence interval; HR, hazard ratio; IFN, interferon alfa; IRC, Independent Review
Committee; PFS, progression-free survival; TEM, temsirolimus.
400 316 256 208 161 120 95 76 59 48 36 31 26 21 14 9 4 3 2 1 1
391 280 230 196 167 138 114 92 78 68 60 42 32 26 22 16 12 9 6 2 2
TEM + BEV
IFN + BEV
Patients at risk, n Time (months)
Pro
ba
bil
ity o
f P
FS
PFS by Stratification Factors
Median PFS, mo (95% CI)
TEM + BEV (n=400)
IFN + BEV (n=391)
Nephrectomy
No
Yes
9.2 (7.2, 11.1)
9.1 (8.1, 10.4)
6.8 (2.4, 7.5)
10.9 (9.0, 12.7)
MSKCC status
Good
Intermediate
Poor
11.0 (9.0, 14.5)
9.2 (8.1, 10.9)
4.0 (3.4, 7.2)
11.2 (10.7, 14.9)
9.1 (7.3, 12.7)
2.1 (1.8, 5.4)
BEV, bevacizumab; CI, confidence interval; IFN, interferon alfa; MSKCC, Memorial Sloan-Kettering Cancer Center; PFS, progression-free survival; TEM, temsirolimus.
Is There an Optimal Sequence of Drugs?
• Current therapeutic options are not considered
curative in late-stage disease
• Sequencing of agents is presently a community
standard of care
• There are no phase III data showing one specific
sequence is superior to another
– Data from phase II RECORD-3 suggest that mTOR ->
TKI is inferior to TKI -> mTOR
Sunitinib
50 mg/day**
RECORD-3: Study design
S
C
R
E
E
N
R
A
N
D
O
M
I
Z
E
*
Everolimus
10 mg/day
Sunitinib
50 mg/day**
Everolimus
10 mg/day
Study endpoints
Primary
•PFS 1st line
Secondary
•Combined PFS
•ORR 1st line
•OS
•Safety
1:1 Cross-over upon
progression
N=471 First line Second line
*stratified by MSKCC prognostic factors. **4 weeks on, 2 weeks off.
Motzer RJ et al. ASCO 2013 Abstract 4504
Primary endpoint: First-line PFS
0 3 6 9 12 15 18 21 24 27 30 33
0
10
20
30
40
50
60
70
80
90
100
Cu
mu
lati
ve e
ven
t-fr
ee p
rob
ab
ilit
y (
%)
Time (months)
Everolimus
Sunitinib
164 238 118 88 68 44 31 23 12 5 0 0
181 233 145 108 84 55 42 28 15 9 3 0
Number of patients still at risk
Everolimus (events/N=182/238)
Sunitinib (events/N=158/233)
K-M Median PFS (mo)
Everolimus Suntinib
7.85 10.71
Hazard ratio=1.43
Two-sided 95% CI [1.15, 1.77]
Motzer RJ et al. ASCO 2013 Abstract 4504
RECORD-3: Secondary endpoints
Motzer RJ et al. ASCO 2013 Abstract 4504
SUN EVE HR (2-sided 95% CI)
ORR (%) 26.6 8.0
CR (%) 1.3 0.4
PR (%) 25.3 7.6
SD (%) 51.9 57.6
First PFS favorable risk (months) 13.40 11.07 1.20 (0.83,1.75)
First PFS poor risk (months) 2.99 2.63 1.73 (0.96, 3.12)
First PFS clear cell (months) 10.84 8.08 1.39 (1.10, 1.75)
First PFS non clear cell (months) 7.23 5.09 1.54 (0.86, 2.75)
SUNEVE EVESUN HR (2-sided 95% CI)
Crossed to 2nd line 42.9% 45.4%
Combined PFS (months) 25.79 21.13 1.28 (0.94, 1.73)
OS (months) 32.03 22.41 1.24 (0.94, 1.64)
Dovitinib: FGF Inhibitor
Fibroblast Growth Factor
(FGF) pathway is a potential
resistance mechanism to
VEGF-targeted therapies
Dovitinib (TKI258) is an oral
tyrosine kinase inhibitor that
targets FGFR, VEGFR,
PDGFR, and other kinases
Semrad, CLC 2012
DOVE Trial: Study Design
Key Eligibility Criteria
• Metastatic RCC with clear cell
component
• 1 prior VEGF-targeted therapy
and 1 prior mTOR inhibitor
• Other anticancer therapies
permitted (cytokines)
• Progressive disease within 6
months of last targeted
therapy
• Measurable disease
Sorafenib
400 mg
twice daily
Dovitinib
500 mg/day
5 days on/2 days off
R
A
N
D
O
M
I
Z
A
T
I
O
N
1:1
Stratification
MSKCC risk group: favorable, intermediate, poor
Geographic region: Japan, Asia Pacific,
Europe/Middle East, Americas
Motzer, ESMO 2013
• Primary Endpoint: PFS (central review)
• 33% risk reduction (hazard ratio = 0.67)
• 550 patients randomized to observe the 411 events
needed for 96% powera
Progression-Free Survival (Central)
100
80
60
40
20
0
0 3 6 9 12 15 18 21
Months
Pro
ba
bil
ity (
%)
eve
nt-
free
284 148 41 20 3 1 1 0 Dovitinib
286 152 42 12 2 1 0 0 Sorafenib
n/N Median, months
(95% CI)
Hazard Ratio
(95% CI)
Dovitinib 209/284 3.7 (3.5-3.9) 0.86 (0.72-1.04)
P = .063a Sorafenib 231/286 3.6 (3.5-3.7)
Patients at risk
a1-sided based on
stratified log-rank test
Interim Overall Survival
100
80
60
40
20
0
0 3 6 9 12 15 18 21
Months
Pro
ba
bil
ity (
%)
eve
nt-
free
284 246 165 102 51 23 9 0 Dovitinib
286 242 160 95 52 22 7 0 Sorafenib
n/N Median, months
(95% CI)
Hazard Ratio
(95% CI)
Dovitinib 130/284 11.1 (9.5-13.4) 0.96 (0.75-1.22)
Sorafenib 135/286 11.0 (8.6-13.5)
Patients at risk
Maximum % Change in Target Lesions and Objective Response Rate (Central)
100
75
50
25
–25
Be
st
% c
ha
ng
e f
rom
ba
se
lin
e
(me
as
ura
ble
le
sio
ns
)
0
– 75
– 50
– 100
Best response
Dovitinib n = 284
PR, % 4
SD, % 52
PD, % 29
UNK/other, % 15
Best response
Sorafenib n = 286
PR, % 4
SD, % 52
PD, % 31
UNK/other, % 13
Future Directions
1. Biology-based drug development
2. Predictive biomarkers to select therapy
3. Revisiting immunotherapy
“Drugging” driver mutations in clear cell RCC
Gerlinger, NEJM 2012; Varela, Nature 2011
Biology-based drug development
• Restoring tumor
suppressor
function is a drug
development
challenge
• Oncogenes easily
“druggable” but not
common in RCC
Vander Heiden, Science 2009
Biology-based drug development
Serum lactate dehydrogenase (LDH) as a predictive
biomarker for mTOR inhibition in patients with mRCC
Predictive biomarkers to select therapy
0 6 12 18 24 30
Time (months)
0.0
0.2
0.4
0.6
0.8
1.0
Su
rviv
al
Pro
bab
ilit
y
LDH <1 ULN: 11.15 mo LDH >1 ULN: 5.63 mo
Time (months)
0.0
0.2
0.4
0.6
0.8
1.0
Su
rviv
al
Pro
bab
ilit
y
6 12 18 24 30 0
Log rank p-value = 0.5138
Median Survival Time:
Temsirolimus: 11.74 mo
IFN-α: 10.36 mo
HR 0.90 (95% Cl 0.61-1.22)
NORMAL LDH (<1x ULX)
Time (months)
0.0
0.2
0.4
0.6
0.8
1.0
Su
rviv
al
Pro
bab
ilit
y
6 12 18 24 30 0
Log rank p-value = 0.0017
ELEVATED LDH (≥1x ULX) Median Survival Time:
Temsirolimus: 6.88 mo
IFN-α: 4.18 mo
HR 0.56 (95% Cl 0.38-0.81)
Median Survival Time:
HR 1.97(95% CI 1.54-2.47)
Log rank p-value < 0.0001
OS in patients with normal pretreatment serum LDH (n=140)
OS in patients with elevated pretreatment serum LDH (n=264)
Armstrong AJ, et al. JCO 2012
IL-6 is both prognostic and predictive: VEGFR-TKI therapy
Tran & Heymach; Lancet Oncol 2012
Predictive biomarkers to select therapy
Randomize
Temsirolimus
Pazopanib
PROGRESS ION
Newly diagnosed metastatic
ccRCC
Poor Risk
(Heng criteria)
Stratification
LDH (local)
Mandatory
Plasma (for biomarkers)
PBMCs
FFPE tumor tissue
(patient must agree to
release)
Phase III study 562 patients
•Co-Primary Endpoints: Validate LDH and IL-6 as predictive plasma markers for PFS
PFS (TKI vs mTOR)
•IL-6, LDH are candidate biomarkers, but others to be tested as well.
•Biomarkers are integral to the study, but not measured pre-randomization
•Other biomarkers: Other Plasma CAFs
SNPs (PBMCS)
Tissue based Markers
Proposed Intergroup Trial in Poor Risk RCC
Interim analyses •Evaluate for PFS in TKI vs mTOR
N=562
NEJM 2012
Revisiting Immunotherapy
PDL1 expressed in ~20% of RCC
Revisiting Immunotherapy
BMS-936558
Topalian, NEJM 2012
Targeting PD1
Revisiting Immunotherapy
Cho, ASCO 2013
• MPDL3280A: Engineered Anti-PDL1 Antibody
• Phase 1A trial in solid tumors (RCC = 55 pts)
• No MTD, DLT, deaths
• Most AEs: Grade 1 or 2
RCC
(n=47 evaluable)
RECIST SD > 24
weeks
24 week PFS
All 13% 32% 53%
Clear cell 13% 35% 57%
Non-clear cell 17% 0 20%
PDL1 + PDL1 - All
20% (2/10) 10% (2/21) 13% (6/47)
RECIST RR by PD-L1 expression
Targeting PD-L1
Metastatic Clear Cell RCC: Future Algorithm
BIOLOGIC
PROFILE
Initial Assessment:
MOLECULAR PROFILING OF
TUMOR AND HOST
(+ CLINICAL FEATURES)
Sensitive to
VEGF-
targeted
therapy
Sensitive to
mTORi Sensitive to
immunotherapy
INITIAL CHOICE
OF THERAPY Immunotherapy VEGF-targeted mTORi
SUBSEQUENT
THERAPY Based on predicted resistance patterns
from molecular profile
Primary resistance
to known agents
Clinical trial
Tumor: VHL, HIF,
mTOR, AKT, s6k,
MET, FGF, PD-L1,
etc.
Serum markers:
IL6, LDH
Conclusions
● Frontline mRCC treatment is guided by
prognostic (rather than predictive) groups
● VEGFR-targeted therapy is standard for
good/intermediate risk patients in frontline
setting
● Targeting molecular drivers will likely result in
better outcomes
● Need to address biomarker development,
intratumoral heterogeneity, and drug resistance