P - RV Mais

Biomarkers of Clinical Outcome to
VEGF-Targeted Therapy in Metastatic
Renal Cell Carcinoma
Brian I. Rini, M.D.
Department of Solid Tumor Oncology
Cleveland Clinic Taussig Cancer Institute
Glickman Urologic and Kidney Institute
Cleveland, Ohio USA
Potential Biomarkers in RCC
• Circulating biomarkers
– VEGF, sVEGFR, CAFs, LDH
• Tissue-based biomarkers
– VHL status, HIF, SNPs, RNA gene expression
• Clinical
Cli i l bi
biomarkers
k
– Hypertension, HFS, hypothyroidism, neutropenia
• Radiographic biomarkers
– DCE-MRI,
DCE MRI CE-CT
CE CT
Rini, Campbell and Escudier. Lancet . 373:1119-1132, 2009
VEGF, sVEGF-R2, and PlGF plasma levels fluctuate
d i treatment with
during
i h sunitinib
i i ib
C
PIGF Conce
entration
(pg/m
mL)
B
sV
VEGF Concentrration
(pg/mL)
VEGF Levels
(pg/mL)
A
3,000
2,500
2,000
1,500
1,000
500
0
16,000
14,000
12,000
10,000
8,000
6,000
4,000
2,000
0
1,000
800
600
400
200
0
D1
D28
(62)
(55)
Cycle 1
D1
D28
(53)
(51)
Cycle 2
D1
D28
(44)
(44)
Cycle 3
D1
D28
(40)
(40)
Cycle 4
D1
D28
(34)
(32)
Cycle 6
D1
D28
(23)
(19)
Cycle 8
D1
D28
(62)
(55)
Cycle 1
D1
D28
(53)
(51)
Cycle 2
D1
D28
(44)
(44)
Cycle 3
D1
D28
(40)
(40)
Cycle 4
D1
D28
(34)
(32)
Cycle 6
D1
D28
(23)
(19)
Cycle 8
D1
D28
(17)
(53)
Cycle 1
D1
D28
(13)
(46)
Cycle 2
D1
D28
(14)
(42)
Cycle 3
D1
D28
(12)
(38)
Cycle 4
D1
D28
(8)
(32)
Cycle 6
D1
D28
(8)
(19)
Cycle 8
PIGF = placenta growth factor
Motzer RJ, et al. J Clin Oncol 2006
Patients with a PR to sunitinib have greater
fluctuations in circulating VEGF/VEGFR
Mean change
M
h
PR patients
Mean change
M
h
non-PR patients
p value
VEGF (pg/mL)
7.91
3.94
0.0005
sVEGFR-2 (pg/mL)
–2.12
–1.79
0.0003
sVEGFR-3 (pg/mL)
–2.17
–1.89
0.010
Of note, sunitinib-induced dose-dependent and reversible increases in circulating plasma VEGF have
been observed in non tumor-bearing mice and in healthy humans; such non tumor-induced increases
in VEGF (and potentially VEGF-related proteins) may mask differences attributable to tumor-induced
protein changes in responding vs.
vs non-responding
non responding patients.
DePrimo SE, et al. J Transl Med 5:32 2007
Sorafenib phase III (TARGET): Biomarker analysis
Low baseline VEGF (≤131 pg/mL)
Sorafenib (n=180):
5.5 months
75
Placebo (n=176):
3.3 months
50
HR = 0.64
95% CI: 0.49–0.83
PFS (% patie
ents)
100
100
PFS (% patie
ents)
High baseline VEGF (>131 pg/mL)
HR = 0.48
95% CI: 0.38–0.62
25
0
0
2
4
6
8 10 12 14 16 18 20
Time (months)
Placebo (n=172):
2.7 months
50
25
0
Sorafenib (n=184):
5.5 months
75
0
2
4
6
8 10 12 14 16 18 20
Time (months)
VEGF levels are prognostic
(assoc. with MSKCC factors and PS)
Escudier B, et al. J Clin Oncol 27(20), 2009
Sorafenib phase III (TARGET): Biomarker analysis
Low baseline VEGF (≤131 pg/mL)
Sorafenib (n=180):
5.5 months
75
Placebo (n=176):
3.3 months
50
HR = 0.64
95% CI: 0.49–0.83
PFS (% patie
ents)
100
100
PFS (% patie
ents)
High baseline VEGF (>131 pg/mL)
HR = 0.48
95% CI: 0.38–0.62
25
0
0
2
4
6
8 10 12 14 16 18 20
Time (months)
Placebo (n=172):
2.7 months
50
25
0
Sorafenib (n=184):
5.5 months
75
0
2
4
6
8 10 12 14 16 18 20
Time (months)
VEGF llevels
l are NOT predictive
di ti for
f sorafenib
f ib PFS in
i RCC
(although lower HR in high VEGF pts, interaction p value > 0.05
sVEGFR3 in bevacizumab-refractory RCC patients
treated with sunitinib
Rini B. et al. J Clin Onc 26 (22), 2008
6 CAF signature (IL6, OPN, IL8, TIMP1, VEGF, HGF) is prognostic/predictive for
pazopanib
•
After adjusting for hemoglobin
<LLN and neutrophils >ULN, only
OPN and IL-6 in the placebo arm,
and OPN alone in the pazopanib
arm, were prognostic.
•
In a separate analysis,
analysis high IL-6
was predictive of improved PFS
with pazopanib compared with
placebo (P = 0.009).
•
Prognostic markers of shorter PFS
in placebo recipients were high IL6 (P < 0.0001),
0 0001) IL
IL-8
8 (P = 0.002),
0 002)
and OPN (P < 0.0001).
•
These analyses point to IL-6 as
potentially both adversely
prognostic in placebo-treated
patients and predictive of
pazopanib benefit in RCC.
RCC
Tran HT et al. The Lancet Oncology, Volume 13, Issue 8, 2012
Circulating IL6/HGF/E-selectin and relationship
to PFS in pazopanib-treated
pazopanib treated patients (retrospective
analysis of phase II/III data)
Median
PFS
S (wks)
(
)
N
Low/Low/HIGH
83.3
27
0.9000
High/High/Low
20.0
34
0 8000
0.8000
Mix
38 4
38.4
154
IL6/HGF/ESelectin
/ G / S
PFS prob
P
bability
1.0000
P-value
0.0016
IL6 = interleukin-6; HGF = hepatocyte growth factor
0.7000
0 6000
0.6000
0.5000
0.4000
0.3000
0.2000
0.1000
0
0
10 20 30 40 50 60 70 80 90 100 110 120 130
Weeks
Tran HT, et al. Lancet Oncol 2012;13:827–37
6 CAF Signature (IL6, OPN, IL8, TIMP1, VEGF, HGF) is prognostic and
predictive of OS benefit for pazopanib (phase III trial)
Pazopanib (high vs low)
Placebo (high vs low)
total Cox P = 0.5644
HR
0.680
0.628
0.714
95% CI
0.458 ‐ 1.01
0.431 ‐ 0.917
0.544 ‐ 0.938
P -value
<.0001 0.0001
Heymach et al. ESMO 2011
CAF “phenotypes” in a phase II trial of sorafenib+/IFN in RCC
Inflammatory
group
IL-1B, 2,3,4,5,
7,10,12,13,-1RA,
GCSF,
Hypoxiayp
regulated group
-VEGF
-OPN
-soluble CA9
-PDGF
Zurita, et al. Ann Oncol, 2012 (epub)
Evaluation of serum lactate dehydrogenase (LDH) as a predictive
biomarker for mTOR inhibition in patients with mRCC
•
LDH is regulated by the PI3K/AKT/mTOR pathway, and is associated with
tumor hypoxia/necrosis
•
Pretreatment LDH was assessed in 404 poor-risk mRCC pts treated with
temsirolimus or interferon alpha in a phase III trial
•
Among 264 subjects with normal LDH, OS was not improved with
temsirolimus as compared
p
to interferon therapy
py ((11.7 vs. 10.4 months, logg
rank p=0.514)
Among 140 subjects with elevated LDH, OS was significantly improved with
g
p<0.002))
p
temsirolimus ((6.9 vs. 4.2 months,, HR 0.51,, log-rank
•
•
The biological assumption is that LDH elevation connotes a greater activation
of mTOR pathway and thus enhanced effect with mTOR inhibition.
inhibition However,
However a
decline in LDH on therapy was only associated with improved outcome in the
IFN arm, not in the temsirolimus arm, refuting the hypothesis that
temsirolimus-induced
temsirolimus
induced reductions in baseline high LDH/mTOR
LDH/mTOR-driven
driven tumors
are responsible for the observed improved OS.
Kaplan-Meier estimates for overall survival distribution by treatment group
and lactate dehydrogenase (LDH) levels
LDH ≤ 1 × upper limit of normal
LDH > 1 × upper limit of normal
©2012 by American Society of Clinical Oncology
Armstrong A J et al. JCO 2012;30:3402-3407
Response to VEGF-targeted Therapy and
VHL Status
Factor
N
Response
122
ORR (%)
p Value
45/122 (37%)
VHL status
Mutated
M th l t d
Methylated
59
12
27 (46%)
2 (15%)
41% ORR
vs.
Wild Type
51
16 (31%)
p=0.34
31% ORR
Choueiri TK, et al. J Urol 2008
VHL gene status
Factor
N1
ORR (%)1
Overall response
123
45/123 (37)
VHL status
Mutated
Methylated
Wild type
60
12
51
27 (46)
2 (17)
16 (31)
Type of mutation
Frameshift
Inframe (d/i)
Nonsense
Splice
Missense
P-value1
31% ORR
vs
29
7
6
5
13
15 (54)2
4 (57)2
4 (67)2
1 (20)2
3 (23)2
p=0.04
52% ORR
Choueiri TK, et al. J Urol 2008
Pre-treatment HIF levels by Western analysis and
sunitinib
i i ib response in
i RCC patients
i
CR or PR SD or PD
HIF 2α
2α*
none
(<10%)
low
(10-50%)
(10
50%)
high
((>50%))
ORR**
(N=18)
(N=25)
2
13
15%
4
11
27%
12
1
92%
* Expression relative to cell line control
** p-value <0.0001 by Fisher’s exact test
Patel et al. ASCO 2008
Carbonic anhydrase IX is not a biomarker of efficacy in metastatic clear-cell
renal cell carcinoma patients receiving sorafenib or placebo: TARGET trial
Urologic Oncology: Seminars and Original Investigations, Volume 31, Issue 8, 2013, 1788 - 1793
Single Nucleotide
P l
Polymorphisms
hi
(SNPs)
(SNP )
Sunitinib Pathways
Absorption/Excretion
Metabolism
ABCB1
ABCG2
NR1I2
NR1I3
Metabolism
CYP3A4
Metabolites
Sunitinib
S nitinib
Sunitinib
Metabolism
CYP3A5
CYP3A
CYP1A1
CYP1A2
active (SU12662)
and inactive
VEGFR-1 VEGFR-2 VEGFR-3 PDGFR-α PDGFR-β FLT-3
(FLT-1)
(KDR)
(FLT-4)
Van der Veldt et al. Clin Cancer Res 2011
Multivariate analyses for PFS in 136 mRCC
patients with clear cell histology
Factors
Clinical
MSKCC risk factors
0
1-2
≥3
No. of metastatic sites
0
1-2
≥3
No
1 988
1.988
<0 001
<0.001
1.400
0.025
1.031
0.029
31
47
58
CYP3A5 6986A/G
GG vs.
AG + AA
117
11
NR1I3 haplotype
Other-other vs.
CAT-other + CAT-CAT
•
75
60
ABCB1 haplotype
Oth
Other-other
th vs.
TCG-other
P
33
81
22
Risk increase/year of age
Genetic
HR
0.032
1
0.266
‘Favorable’ genetic profile
0.017
Carriers (N = 95) vs. non-carriers:
median PFS:113.1 vs. 7.5 months (p=0.001)
median OS:1.758
19.9 vs. 12.3 months (p=0.009)
0.033
100
29
1
0.522
Van der Veldt et al. Clin Cancer Res 2011
Hypertension: VEGF/VEGFR pathway
in the regulation of vascular tone
VEGF
Vascular lumen
VEGFR-2
Sunitinib
TK
PI-3K
Endothelium
TK
P
P
Akt
eNOS
NO
Smooth
muscle cells
Inhibition
ET-1
ET
1
Vasodilatation
Vasoconstriction
HYPERTENSION
SNPs in VEGF-A and NOS3 are associated
with
i h grade
d 3 hypertension
h
i in
i sunitinibi i ib
p
treated RCC patients
Factors
VEGF-A haplotype
ACG-ACG + ACG-other vs.
other-other
NOS3
TT vs.
vs
CC + CT
No
OR
P
0.031
182
65
1
0.59
0.045
101
153
1
2.62
Association of SNPs with PFS to
P
Pazopanib
ib in
i RCC
G
Gene
P l
Polymorphism
hi
Reference
R
f
SNP ((rs))
Number
P l *
P-value*
IL-8
2767 A>T
rs1126647
0.009
IL-8
251 T>A
rs4073
0.01
HIF1α
1790 G>A
rs11549467
0.03
* Accounting for Sex, Eastern Cooperative Oncology Group performance status, time since initial
diagnosis, neutrophil count, and MSKCC risk score
Adapted from Hall et al. ASCO 2010 and Xu et al. JCO 2011
SNPs in IL8, FGFR2, VEGFR3, VEGFA, and NR1I2
associated with OS in pazopanib-treated RCC patients
Allele
frequency
Asians, %1
Allele
frequency
Blacks, %1
SNP
(NCBI)
P-value
Allele frequency
Caucasians, %1
IL8
rs1126647
0.003
39
32
6
IL8
rs4073
0.01
40
35
83
FGFR2
rs2981582
0.01
42
25
52
VEGFR3
rs307826
307826
0 04
0.04
7
0
0
VEGFA
rs1570360
0.05
25
17
3
NR1I2
rs3814055
0.03
41
27
27
Gene
 None of the SNPs associated with OS in p
patients who did not receive p
pazopanib
p
((N=37))
IL8 = interleukin-8; FGFR2 = fibroblast growth factor receptor 2
1. Frequency data: HapMap (http://www.ncbi.nlm.nih.gov)
Sternberg CN, et al. ASCO GU 2011
Time to Progression in mRCC Patients
Receiving Sunitinib Based on VEGFR3 SNPs
Donas JG et al. Lancet Oncology 12(12):1143-50, 2011
Frequency of hyypertension (%)
VEGF SNP-634 predicts incidence of hypertension
in sunitinib-treated mRCC patients
100
90
80
70
60
50
40
30
20
10
0
p=0
p
0.03
03
94%
81%
G/G (n=36)
C/G (n=21)
67%
C/C (n=6)
Genotype (# of pts)
Kim JJ, et al. Cancer 2011
% Time
e DBP>9
90 and/orr SBP>15
50
VEGF SNP -634 is Associated with the Duration of
HTN in
i Sunitinib-treated
S iti ib t t d mRCC
RCC P
Patients
ti t
100
90
80
70
60
50
40
30
20
10
0
*
*
*
*
**
*
*
*
p = 0.007
*
*
**
27.2
%
**
**
*
G/G (n=36)
*
*
*
10.2%
C/G (n=21)
Genotype (# of pts)
8.9%
C/C (n=6)
Kim JJ, et al. Cancer 2011
VEGFR2 SNPs 889 and 1416 is Associated with
Overall Survival in mRCC Patients
●
●
VEGFR SNPs, 889 and 1416 were marginally associated with overall survival
(p=0.13, 0.17)
Combining 889 and 1416 suggests possible impact on overall survival
The combined genotype remained statistically significant after adjusting for
prognostic factors (p
(p=0
0.03
03 for each factor)
P=0.02
% Surviva
al
●
889 G/A and 1416 A/A (n=4)
889 G/G or 1416 A/T, T/T, but not both (n=35)
899 G/G and 1416 A/T or T/T (n=14)
Months
Kim JJ, et al. Cancer 2011
Prrogressio
on-Free S
Survival (probabiility)
AXIS: Progression-free
g
Survival
mPFS, mo
1.0
Axitinib
A
iti ib
Sorafenib
0.9
0.8
07
0.7
95% CI
6.7
6
7
4.7
6.3–8.6
6
3 86
4.6–5.6
P<0.0001 ((log-rank)
g
)
Stratified HR 0.665
(95% CI 0.544–0.812)
0.6
0.5
0.4
0.3
02
0.2
0.1
0.0
0
2
4
6
8
10
12
14
16
18
20
20
6
10
3
1
1
0
0
Time (months)
Subjects at risk, n
Axitinib 361
Sorafenib 362
256
224
202
157
145
100
96
51
64
28
38
12
Rini B. et al. Lancet. 378(9807):1931-9, 2011
Prog
gression
n-Free Su
urvival (p
probability)
PFS
S for
o VEGF-A/rs699947
G
/ s6999 A/A
/ Subgroup
Subg oup
mPFS, wks 95% CI
mPFS
Axitinib
52.0
29.1–NR
12.7–31.1
Sorafenib
21.0
10
1.0
0.9
0.8
HR = 0.384 (95% CI: 0.195–0.75
Unadjusted P=0.005
Adjusted P=0.070
0.7
0.6
05
0.5
0.4
0.3
0.2
0.1
00
0.0
0
10
20
30
40
50
60
70
80
90
4
0
1
0
1
0
100
Time (weeks)
Patients
P
ti t att risk,
i k n
Axitinib 35
Sorafenib 27
28
20
26
15
16
5
11
2
10
1
8
1
Escudier et al. ESMO 2011
Prog
gression--Free Su
urvival (p
probabiliity)
PFS
S for
o VEGF-A/rs699947
G
/ s6999 C/C Subgroup
Subg oup
mPFS, wks 95% CI
mPFS
Axitinib
28.3
15.9–36.1
Sorafenib 42.9
20.1–47.7
1.0
0.9
08
0.8
HR = 2.02 (95% CI: 1.03–4.0)
Unadjusted P=0.040
Adjusted P=0.426
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.0
0
10
20
30
40
50
60
70
80
90
0
1
0
0
0
Time (weeks)
Patients at risk, n
Axitinib 30
Sorafenib 34
19
24
15
18
7
11
3
10
1
6
0
5
100
 UGT1A1*28 ((Gilbert’s))
associated with total bilirubin
elevation
– 84% of patients with TBili
≥1.5×ULN were *28 allele
carriers
Cumulative Inc
cidence of TBL ≥ 15 × ULN
PGx and Pazopanib Hepatic Events in RCC
UGT 1A1and Bilirubin
0.5
0.4
TA7/TA7, n = 37
TA6/TA7, n = 113
TA6/TA6, n = 86
0.3
0.2
0.1
0.0
0
20
40
60
80
100
120
 HFE hemochromatosis gene
SNPs were associated with
ALT elevation
– 17% of patients with ALT ≥3 ×
ULN carried the TT HFE
genotype
– Clinical outcome similar to
other p
pts with ALT elevation
Cumulative Incidence of ALT ≥ 3.0 × U
ULN
Time, weeks
HFE and ALT
0.7
0.6
0.5
TT, n = 12
GT, n = 82
GG, n = 148
0.4
0.3
0.2
0.1
0.0
0
20
40
60
80
100
120
Time, weeks
Xu et al, BJC (2010); Xu et al, J Hepatol (2011).
Time on sunitinib treatment until dose reduction for toxicity in patients with the rs776746 variant in CYP3A5
patients with the rs776746 variant in CYP3A5
Donas JG et al. Lancet Oncology 12(12):1143-50, 2011
0.8
0.6
0.0
SPP1
IL6
IL_8
MMP14
LAMB1
LOX
ENO2
CA12
CCR7
HLA_DPB1
CXCL9
CXCL10
CD8A
CCL5
LMNB1
CCNB1
PTTG1
TPX2
BUB1
ANGPTL3
TUBB2A
KIT
PDGFA
ITGB5
ITGB1
RAF1
MTOR
PRKCH
CASP10
VCAM1
SGK1
MAP2K3
KRAS
UGCG
EGR1
CYR61
PDGFC
NUDT6
HIF1AN
CEACAM1
KL
CX3CL1
VEGFA
TSPAN7
SNRK
RGS5
KDR
FLT1
PDGFD
EDNRB
PPAP2B
EMCN
C13orf15
AQP1
SDPR
APOLD1
ADD1
NOS3
ID1
PDGFB
PDGFRb
JAG1
FLT4
ICAM2
TGFBR2
TEK
TIMP3
PTPRB
SHANK3
LDB2
EPAS1
A2M
Lin
nkage Dista
ance
RNA‐based Gene Expression Analysis
Tree Diagram for 72 Variables
W i ht d pair-group
Weighted
i
average
1-Pearson r
1.2
1.0
Immune
Response
Cell
Cycle
ECM /
Cell Adhesion
Angiogenesis
0.4
0.2
35
Rini et al. ASCO 2010
Clinical Biomarkers
HTN incidence with VEGF-targeted Therapy
Drug
Disease
N
Incidence of HTN
All grade
Sorafenib1
3000+
23%
(range, 16-32%)
≥ CTC Grade 3
5.7%
(2.5-12.6%)
Sunitinib2
Multiple
solid tumors
4600
22%
(19-25%)
6.8%
(5.3-8.8%)
Axitinib3
Multiple
solid
lid tumors
230
55%
17%
9-16%
1 850
1,850
25%
(21-30%)
Bevacizumab4
1
Multiple
p
solid tumors
Multiple
solid tumors
Comments
No difference
between RCC and
non-RCC for HTN
incidence
Higher HTN
incidence for RCC
(vs.. non-RCC) and
continuous (vs.
(
intermittent) dosing
Wu S. et al. Lancet Onc 2008; 9:117-23; 2 Zhu et al. Acta Onc 2009; 48:9-17; 3 Rini et al. CCR 17:3841-3849; 17 4 Zhu et al. Am J Kidney Dis. 2007 49:186-93
Postulated Mechanisms of HTN
Alteration in
glomerular integrity
d/t VEGF inhibition
Decreased GFR
Sodium/water
retention
VEGF depletion
reduces NO
synthetase and
causes
vasoconstriction
The role
Th
l off other
th
vasoactive
proteins (PG,
thrombo ane ET
thromboxane,
ET1) not clear
Humphreys B D, Atkins M B. Clin Cancer Res 2009;15:5947-5949
Axitinib: OS in patients with or without dBP
≥ 90 mmHg:
H six
i phase
h
II studies
t di combined
bi d
Rini B I et al. Clin Cancer
Res
Rini et
al.2011;17:3841-3849
JCO (submitted)
Landmark analysis (8 weeks) of overall survival in metastatic RCC patients
treated with axitinib with and without dBP ≥90 mmHg
Rini B I et al. Clin Cancer Res 2011;17:3841-3849
• Multivariate analyses, including dBP, baseline PS, dose level, and use of antihypertensive medication revealed that dBP
was an independent predictor of OS, HR of 0.676 (95% CI 0.470–0.972; P = 0.036) in favor of the ≥ 90 mmHg group.
For the 2 RCC studies,
studies median PFS was significantly longer for patients in the ≥90 mmHg group compared with the <90
•For
group [16.5 vs. 6.4 months; HR (95% CI) = 0.53 (0.31, 0.90); P = 0.019]
•The greatest tumor response difference by dBP group was observed in patients with cytokine-refractory mRCC (65.6%
vs. 10.5%, ≥90 vs. <90 groups, respectively; P < 0.001).
Front line Axitinib Htn / dose escalation study
Front-line
Randomization
Criteria:
• sBP < 150 mmHg
Lead-in
• dBP < 90 mmHg
Axitinib 5mg BID
(4 weeks)
• no grade 3 or 4
axitinib-related AE
• ≤ 2 htn meds
no
Arm C
Axitinib 5mg BID
or at reduced dose
yyes
R
A
N
D
O
M
I
Z
E
1:1
Arm A
Axitinib 5 mg BID
+ axitinib dose
escalation
Arm B
Axitinib 5 mg BID
+ placebo dose
escalation
Sunitinib: Clinical outcome by hypertension status
in RCC patients
Max SBP
≥140 mmHg
(n=442)
Max SBP
<140 mmHg
(n=92)
P-value
242 (54.8%)
(54 8%)
8 (8
(8.7%)
7%)
<0 001
<0.001
Progression-free survival, months
12.5
2.5
<0.001
Overall survival,, months
30.9
7.2
<0.001
Max DBP
≥90 mmHg
(n=363)
Max DBP
<90 mmHg
(n=171)
P-value
208 (57.3%)
42 (24.6%)
<0.001
Progression-free survival, months
13.4
5.3
<0.001
Overall survival, months
32.2
14.9
<0.001
Objective response rate
rate, n (%)
Objective response rate, n (%)
SBP = systolic blood pressure; DBP = diastolic blood pressure
Rini B, et al. J Natl Cancer Inst 2011
Median OS by hypertension status as defined by
maximum SBP ≥140 mmHg on sunitinib
With hypertension (n=442)
Median OS, 30.9 months
Probability of ove
erall survival
1.0
0.9
0.8
Without hypertension (n
(n=92)
92)
Median OS, 7.2 months
0.7
p<0.0001
06
0.6
0.5
0.4
0.3
0.2
0.1
0
0
5
10
15
20
25
Time (months)
30
35
40
Rini B, et al. J Natl Cancer Inst 2011
Kaplan–Meier estimates of OS by HTN status experienced by the end of cycle 2 (12 weeks)
experienced by the end of cycle 2 (12 weeks) Probab
bility of o
overall survival
Median OS by management of hypertension and hypertension
status as defined by maximum SBP ≥140 mmHg on sunitinib
1.0
0.9
0.8
0.7
0.6
05
0.5
0.4
0.3
0.2
0.1
00
0.0
Dose reduction only
Anti-hypertension drug only
Both
Neither
No hypertension
0
5
10
15
20
25
30
35
40
45
50
Time (months)
Rini B, et al. J Natl Cancer Inst 2011
Hypertension as a biomarker in
VEGF-targeted therapy
Study
Disease (N)
Anti-VEGF
agent
Hypertension definition
Results
Rini et al.1
RCC
(n=544)
Sunitinib
SBP >140 mmHg and
DBP ≥90 mmHg
OS: 30.9 vs 7.2 months
(p<0.0001)
PFS: 12.5 vs 2.5 months
( 0 0001)
(p<0.0001)
ORR: 55% vs. 9% (p<0.0001)
Rini et al.2
RCC
((n=366))
Bevacizumab
((+IFN))
≥CTC Grade 2
OS: 41.6 vs 16.2 months
(p<0.0001))
(p
PFS: 13.2 vs 8.0 months
(p=0.0009)
ORR: 13% vs. 9% (p=ns)
Escudier et al.
al 3
RCC
(n=337)
Bevacizumab
(+IFN)
≥CTC Grade 2
PFS: 10
10.2
2 vs 8
8.4
4 months (p=ns)
Schneider et al.4
Breast Ca
(n=345)
Bevacizumab
(+chemo)
≥CTC Grade 3
OS: 38.7 vs 25.3 months
(p=0.002)
Dahlberg et al.5
NSC Lung Ca
(n=741)
Bevacizumab
(+chemo)
>150/100 mmHg, OR
>20 mmHg increase
vs. baseline by end of C#1
OS: 15.9 vs 11.5 months
(p=0.0002)
PFS: 7.0 vs 5.5 months
(p
(p<0.0001)
)
1Rini
3Escudier
B, et al. ASCO 2008;
4Schneider
B, et al. J Natl Cancer Inst 2011; 2 Rini B, et al. JCO 2010
BP, et al. J Clin Oncol 2008 5Dahlberg SE, et al. J Clin Oncol 2010
Hypotheses
• Host effect: The effect of VEGF agents on normal
vasculature (HTN) is reflective of tumor-associated
vasculature susceptibility to VEGF depletion and
thus anti-tumor effect.
• Mediators of HTN or HTN itself ‘causes’ the antit
tumor
effect.
ff t
• Genetic susceptibility to HTN by these agents
((SNPs)) are the same as those identifying
y g
susceptibility to anti-VEGF agents.
Landmark analysis of clinical outcome by neutropenia and
thrombocytopenia severity grade at the end of 6 and 12 weeks of
sunitinib treatment
Efficacy endpoint
Median time to
progression/survival
event, months (n)
Median time to
progression/survival
event, months (n)
Neutropenia
Thrombocytopenia*
Grade ≥2
Grade <2
P
Grade >1
Grade ≤1
P
TTP
9.7 (133)
9.6 (522)
0.782
12.4 (47)
9.4 (608)
0.172
PFS
9.7 (133)
9.4 (527)
0.615
12.4 (47)
9.4 (613)
0.224
OS
26.5 (148)
21.9 (612)
0.041
31.1 (51)
21.9 (709)
0.187
TTP
11.0 (211)
8.2 (375)
0.009
11.1 (66)
9.1 (520)
0.345
PFS
11.0 (212)
8.0 (377)
0.005
11.1 (67)
8.5 (522)
0.395
OS
29.7 (250)
19.0 (485)
0.001
28.2 (74)
21.0 (661)
0.460
6-week landmark
12-week landmark
* Thrombocytopenia > grade 1 significant for efficacy endpoints in K-M analysis when
restricted to 50mg 4/2 schedule
Donskov F et al. ESMO 2011
Hypothyroidism as a surrogate marker
of treatment outcome in patients with mRCC
 Initial data have indicated that hypothyroidism may serve as a predictive
marker of treatment outcome1,2
 Further prospective studies are required to validate the potential correlation
between hypothyroidism and improved clinical outcome
 In a prospective, explorative study1 investigating the impact of hypothyroidism
on the outcome of patients during sunitinib or sorafenib treatment (n=87):
 Patients who had subclinical hypothyroidism during treatment had a significantly
greater chance of responding to treatment1
 An increase in TSH within 2 months of starting treatment was associated
with significantly longer survival compared with the survival of patients
without hypothyroidism1
 The
Th d
development
l
t off h
hypothyroidism
th idi
within
ithi 2 months
th off ttreatment
t
t was
1
an independent predictor of survival
TSH = thyroid-stimulating hormone
1. Schmidinger M, et al. Cancer 2010
2. Wolter P, et al. ASCO 2008
Hypothyroidism
y
y
as a Surrogate
g
Marker of
Treatment Outcome in Patients with mRCC
 Data indicating
g that hypothyroidism
yp y
may
y serve as a p
predictive marker
of treatment outcome are supported by findings from Wolter
 Thyroid function was evaluated prospectively in patients (n=53) with
mRCC who received sunitinib 50 mg (Schedule 4/2)
 40/53 patients were evaluated for thyroid function
TFT
Median PFS
Median OS
No TFT abnormality
(n=12)
3.6 months
(95% CI: 2.3−6.0)
6.6 months
(95% CI: 3.3−7.9)
TFT abnormality (n=28)
10.3 months
(95% CI: 5.0−18.4)
18.2 months
(95% CI:7.5−22.3)
TFT = thyroid function test; OS = overall survival;
PFS = progression-free survival
Wolter P, et al. J Clin Oncol 26: 2008 (Suppl; abstr 5126)
Combined toxicity
y analysis
y
● Prior retrospective analyses of pooled data from five clinical
mRCC trials have separately
p
y identified the following
g treatmentassociated AEs as potential biomarkers of sunitinib efficacy:
–
hypertension1*
–
hand–foot syndrome2
–
neutropenia3
–
thrombocytopenia3
–
asthenia/fatigue4
● AEs were chosen for study if they were common, manageable,
readily and systematically measurable, and potentially
reflective
fl ti off intended
i t d d target
t
t inhibition
i hibiti with
ith sunitinib
iti ib
● We assessed the relative strength and independence of each
biomarker in a combined analysis using the same database
*This efficacy biomarker analysis included three trials, excluding
two trials that used continuous daily dosing (CDD), rather than
the approved Schedule 4/2
51
1. Rini BI, et al. J Natl Cancer Inst 2011;103:763–773; 2. Michaelson MD, et al. J Clin Oncol 2011;29(suppl 7; abstr 320);
3. Donskov F, et al. Eur J Cancer 2011;47:S136(abstr 1141); 4. Davis MP, et al. Eur J Cancer 2011:47:S135(abstr 1139).
Sunitinib-associated Hypertension
yp
((HTN)) Has Been
Associated with Improved Clinical Outcomes
With HTN (n=442)
Median OS, 30.9 months
(95% CI: 27.9–33.7)
Proba
ability of OS
S
1.0
Without (n=92)
Median OS, 7.2 months
(95% CI: 5.6–10.7)
08
0.8
0.6
P<0.0001
04
0.4
0.2
0
0
No. of patients
at risk
With HTN
442
Without HTN
92
●
52
5
10
15
20
25
30
35
40
106
3
29
1
45
50
Time (months)
418
55
377
38
308
21
257
15
224
7
190
5
HTN-associated complications were investigated by expanding the safety analysis with
4 373 patients from an expanded access trial
4,373
– AE rates were similar for patients with and without SBP-defined HTN; however, patients
with HTN had somewhat more renal AEs (5% vs. 3%; P=0.013)
Rini BI, et al. J Natl Cancer Inst 2011;103:763–773.
Sunitinib-associated Hand–foot Syndrome (HFS) Has
Been Associated with Improved Clinical Outcomes
1.0
With HFS (n=179)
Median OS, 38.2 months
Without HFS (n=591)
Median OS, 18.9 months
P<0.0001
Pro
obability o
of OS
0.8
06
0.6
0.4
0.2
0
0
53
5
10
15
20
25
30
35
Ti
Time
(months)
(
th )
40
45
50
55
60
Michaelson MD, et al. Poster presented at ASCO GU, Orlando, Florida, USA, February 17–19, 2011.
Sunitinib-associated Myelosuppression Has Been
Associated with Improved Clinical Outcomes
1.0
95% CI
Grade ≥2
366
35.6
31.4–39.5
Grade <2
404
15.8
13.3–17.7
N t
Neutropenia
i
0.9
0.8
Probab
bility of OS
S
n
Median OS,
months
th
07
0.7
P<0 001
P<0.001
0.6
0.5
0.4
0.3
02
0.2
0.1
0
0
5
10
15
20
25
30
35
40
45
50
55
60
Time (months)
●
54
Neutropenia- and thrombocytopenia-related AEs were investigated by expanding the safety
analysis with 4,388 patients from an expanded access trial
– Related AEs were more frequent with neutropenia grade ≥2 and thrombocytopenia
grade >1 (P<0.001)
Donskov F, et al. Poster presented at ECCO-ESMO, Stockholm, Sweden, September 23–27, 2011.
Sunitinib-associated Asthenia/Fatigue (A/F) Has Been
Associated with Improved Clinical Outcomes
1.0
With A/F (n=583)
Median OS,
OS 26.2
26 2 months
Prrobability of OS
08
0.8
Without A/F (n=187)
Median OS, 15.0 months
P<0.001
0.6
0.4
0.2
0
0
5
10
15
20
25
30
35
40
45
50
55
60
Time (months)
55
Davis MP, et al. Poster presented at ECCO-ESMO, Stockholm, Sweden, September 23–27, 2011; Pfizer data on file.
Final multivariate models of associations between adverse events and
survival endpoints for mRCC patients receiving sunitinib on schedule 4/2
Adverse event
at any time point
Adverse event
by the 12
12-week
week landmark
HR
95% CI
P value*
HR
95% CI
P value*
Neutropenia
p
PFS
OS
0.77
0.65
0.61–0.97
0.50–0.85
0.0276
0.0014
0.72
0.71
0.56–0.93
0.55–0.93
0.0130
0.0122
Hypertension
PFS
OS
0.37
0.36
0.27–0.52
0.27–0.50
<0.0001
<0.0001
0.81
0.68
0.61–1.07
0.53–0.88
0.1305
0.0036
Hand-foot syndrome
PFS
OS
0.90
0 70
0.70
0.70–1.15
0 52 0 93
0.52–0.93
0.3986
0 0152
0.0152
0.83
0 64
0.64
0.59–1.16
0 44 0 94
0.44–0.94
0.2651
0 0218
0.0218
Asthenia/fatigue
PFS
OS
0.56
0 82
0.82
0.42–0.74
0 61–1 10
0.61–1.10
<0.0001
0 1882
0.1882
1.01
0 99
0.99
0.78–1.30
0 78–1 27
0.78–1.27
0.9555
0 9586
0.9586
Thrombocytopenia
PFS
OS
0.83
0.96
0.63–1.10
0.70–1.33
0.1971
0.8271
1.05
1.07
0.73–1.51
0.74–1.53
0.7905
0.7233
Subgroup Analysis
• According to the results of an 8-group analysis based on
a combined baseline neutrophil count, nadir neutropenia
grade and hypertension status during treatment
treatment, the
following subgroup of patients had the best clinical
outcome with sunitinib
• Patients
a e s with normal
o a neutrophil
eu op cou
counts
sa
at base
baseline
e
(≤ULN) who subsequently experienced both ontreatment neutropenia grade >1 and hypertension (SBP
≥ 140 mmHG)
• In this subgroup, median OS was 38.4 months
Radiographic Biomarkers
Pretreatment DCE-MRI as predictor of outcome
on sorafenib (n=15)
Pe
ercent surrvival
Progression-free
g
survival
110
100
90
80
70
60
50
40
30
20
10
0
0
Baseline Ktr >3
Baseline Ktr <3
p=0 003
p=0.003
100
200
300
Time
400
500
600
* Ktrans change was not predictive
Flaherty KT, et al. Cancer Biol Ther 2008
MASS Criteria
Response
p
Category
g y
MASS Criteria Description
p
No new lesions and either of the following:
Favorable response
Indeterminate
response
1.
Decrease in tumor size of ≥ 20%
2.
One or more predominantly solid enhancing lesions with
marked central necrosis or marked decreased attenuation
(≥ 40 HU)
Does not fit criteria for favorable response or unfavorable response
Either of the following:
Unfavorable
response
1
1.
IIncrease in
i tumor
t
size
i off ≥ 20% iin th
the absence
b
off marked
k d
central necrosis or marked decreased attenuation
2.
New metastases, marked central fill-in,
fill in, or new
enhancement of a previously homogeneously hypoattenuating non-enhancing mass
Smith A. et al. AJR 2010
Central fill-in or new enhancement signals
eventual radiographic PD
New Metastasis Group Never Progresses Group
(N = 6 patients)
(N = 21 patients)
Patients with central fill-in or
change from homogeneously
low density to enhancing
83%
.
Pre-PD = 236d
14%
PD=
343d
Central fill in
(P < 0.029)
Multivariable analysis of CT parameters associated with
subsequent surgical resectability after presurgical sunitinib
F t
Factor
MASS criteria (morphology,
attenuation, size, structure)
(Favorable vs indeterminate
or unfavorable)
Baseline tumor margins
(well-defined vs poorly defined)
Odd ratio
Odds
ti
(95% CI)
P l
P-value
10.10
(1.5–66.7)
0.02
8.99
(1.2–69.3)
0.04
Salem R, et al. ASCO GU 2011
Conclusions
• Circulating VEGF pathway proteins fluctuate
with VEGF-inhibiting therapy
– Baseline VEGF levels are prognostic
– Post-treatment changes of these markers may be
predictive, but this is not yet useful in clinical practice
• VHL,, HIF status or HIF-dependent
p
markers
have not differentiated responders
• SNPs are extremely promising for prediction of
efficacyy and toxicity
y but need prospective
p p
validation and are not currently clinically useful
Conclusions
• H
Hypertension
t
i and
d neutropenia
t
i are easily
il
measured, robust biomarkers of clinical outcome
after VEGF inhibitor therapy