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Biomarker for Angiogenesis Inhibitors
Heinz-Josef Lenz
Associate Director, Clinical Research
Kathryn Balakrishnan Chair for Cancer Research
Co-Director, USC Center for Molecular Pathways and Drug Discovery
Co-Leader GI Oncology Program
USC/Norris Comprehensive Cancer Center
Significant research to date to Significant research to date to identify bevacizumab biomarkersidentify bevacizumab biomarkersaa
2010
2008
2007
2006
Pre-2006
2009
CECs
pVEGF-A
E-selectin, P-selectin, ICAM-1,VCAM-1, PDGF, bFGF, MMP-2/9
CECs, CTCs
sVEGFR-2
bFGF, HGF, PlGF,SDF-1, MCP-3
KRAS mutation
KRAS, BRAF, p53
D-dimer
CECs
sVEGF-A, Ang-1/2
Polymorphisms
CTCs
VEGF-A, VEGFR-1/2, MVDneuropilin, HER2, EGFR
CECs
VEGF-A, THBS, MVD
DCE-MRI
CECs
CECs
EGFR, VEGF-A, TS, Ki67, ERCC1, MSH2, MLH1
Collagen IV, VEGF-A
VEGFR-2, KDR, EGFR, CD31 (H&N)
CECs, MMP-2/9, VEGF-A, sVEGFR-2, IL-6/8, PlGF (HCC)
VEGF-A, bFGF, ICAM,E-selectin
VEGF-A, VCAM, ICAM,bFGF, E-selectin
DCE-MRI, PET
Polymorphisms
EGFR, KRAS
VEGF-A, VCAM, ICAM, bFGF, E-selectin
VEGF-A, CD31, factor VIII (BCL)
VEGF-A, VEGFR-2, CD31, CA9, HIF-2α (glioma)
VEGF-A, VEGFR-2, CA9,HIF-2α (astrocytoma)VEGF-A, VEGFR, VCAM (NHL)
VEGF-A, CEC, VCAM, bFGF (NHL)
VEGF-A, THBS, CD31, p53, (ovarian)
CA125 (ovarian)
CA19-9 (pancreas)
VEGF-A, sVEGFR-1, IL-6, CECs, PlGF (rectal)
CECs, FDG-PET (rectal)
MVD, CD34, CD31(solid tumours)
DCE-MRI, FDG-PET (solid tumours)
CD31, VEGF-A, Ki67, KRAS/BRAF (solid tumours)
CTCs, CECs, VEGFR-2 (solid tumours)
Primary paper Abstract
CD31-MVD (ovarian)Polymorphisms
VEGF-A, bFGF, E-selectin,VCAM-1, ICAM-1
VEGF-A, THBS, MVD
Polymorphisms (pancreas)
Polymorphisms (ovarian)
Polymorphisms
PolymorphismsSDF1α, PlGF, Ang 1/2, neuropilin-1, CXCR4, CXCL6 (rectal)
Ktrans, MVV, collagen IV (glioblastoma)
SDF-1α, bFGF, CECs(glioblastoma)
CECs, CTCs
CTCs
Polymorphisms
CTCs, CECs
pVEGF-A
aData based on search in PubMed with ‘biomarker’ in abstract, all ASCO abstracts, WCGIC ‘09, ESMO GI ’08, ESMO ‘09, SABCS ’08/’09/’10.
2011 VEGF-A, VEGF-B, MVDneuropilin, HER2, EGFR
CRCBreast NSCLC Other
Limited achievements Limited achievements with anti-angiogenic with anti-angiogenic
agentsagentsSunitinib
Focus: VEGF, VEGFR
Tumour type: Varied, predominantly single-arm studies
Sunitinib
Focus: VEGF, VEGFR
Tumour type: Varied, predominantly single-arm studies
No biomarker identifiedNo biomarker identified
Pazopanib
Tumour type: RCC (one study)
Pazopanib
Tumour type: RCC (one study)
No biomarker identifiedNo biomarker identified
Vandetanib
Focus: VEGF, VEGFR, ICAM-1
Tumour type: NSCLC
Vandetanib
Focus: VEGF, VEGFR, ICAM-1
Tumour type: NSCLC
No biomarker identifiedNo biomarker identified
Sorafenib
Focus: VEGF, CECs
Tumour type: Varied, predominantly single-arm studies
Sorafenib
Focus: VEGF, CECs
Tumour type: Varied, predominantly single-arm studies
No biomarker identifiedNo biomarker identified
Cediranib
Focus: Wide ranging
Tumour type: Glioblastoma (two single-arm studies)
Vascular normalisation index may correlate with OS1
Cediranib
Focus: Wide ranging
Tumour type: Glioblastoma (two single-arm studies)
Vascular normalisation index may correlate with OS1
No biomarker identifiedNo biomarker identified
Biomarker research for anti-angiogenic agents is challenging
Biomarker research for anti-angiogenic agents is challenging
1Sorensen et al. Cancer Res 2009
Ferrara & Kerbel. Nature 2005;438:967–74Reproduced with permission, Nature Publishing Group
CX
CR IL-1
R
Tumor associated angiogenesis
HIF1 NFkbARNTHIf1
NRP1V
EG
FR
Tumor cellDNA
EG
FR
VEGF
Endothelial cell
HypoxiaEGF
IL-8 IL-1 β
PA
R-
4 PA
R-
1
Endostatin
Platelet1-granules2-granules
Thrombin Thrombin
Biomarker Biomarker Tumor Tumor Microenvironment Microenvironment Host Host
Placebo + IFL
Bevacizumab + IFL
BiomarkerTotal
n nMedian
(months) nMedian
(months) HR (95% CI)
All patients 267 120 17.45 147 26.35 0.57 (0.39–0.85)
KRAS mutation statusMutantWild type
78152
3467
13.617.64
4485
19.9127.7
0.690.58
(0.37–1.31)(0.34–0.99)
BRAF mutation statusMutantWild type
10217
397
7.6517.45
7120
15.9326.35
0.110.53
(0.01–1.06)(0.34–0.82)
KRAS mutation statusEither mutantBoth wild type
88125
3757
13.621.72
5168
19.9127.7
0.670.57
(0.37–1.20)(0.31–1.06)
p53 mutation statusMutantWild type
13966
6331
21.7216.36
7635
27.7NR
0.540.67
(0.30–0.95)(0.32–1.42)
p53 overexpressionPositiveNegative
19175
9228
17.4516.26
9947
26.3525.07
0.700.32
(0.45–1.10)(0.15–0.70)
Bevacizumab OS effect independent of tumor mutations
Ince et al. JNCI 2005
0.2 0.5 1 2 5
HR (95% CI)
Bevacizumab + chemobetter
Chemo alone better
AVF2107g mCRC
Biomarker focus
Definition RationaleKey
Marker
Predictive
EfficacyPredict response to
bevacizumabWill help to select patients
who benefit from bevacizumab
VEGF-A, VEGFRs and
neuropilin
DurationPredict duration of
response to bevacizumab
Can be used to adapt therapy in anticipation of non-
response
PlGF, bFGF, ICAM
SafetyPredict probability of side effects with
bevacizumab
Well-defined and manageable safety profile. Bevacizumab has been used to treat >800
000 patients
Polymorphisms of VEGF-A,
eNOS, WNK-1 genes
PrognosticPredict outcome irrespective of
therapy
Can help determine aggressiveness of tumour and likelihood of early progression
VEGF-A
Bevacizumab dose Marker Events HR (95% CI)
7.5 mg/kg VEGF-A Low
High
127
128
0.96
0.52
(0.62–1.48)
(0.33–0.81)
VEGFR-2 Low
High
133
122
1.10
0.46
(0.73–1.67)
(0.28–0.74)
15 mg/kg VEGF-A Low
High
139
126
0.86
0.49
(0.56–1.32)
(0.31–0.76)
VEGFR-2 Low
High
134
131
0.75
0.54
(0.49–1.16)
(0.35–0.85)
Plasma VEGF-A and VEGFR-2 levelsa: PFS
• In this study, data suggest that high levels of plasma VEGF-A and VEGFR-2 are associated with PFS
• In this study, data suggest that high levels of plasma VEGF-A and VEGFR-2 are associated with PFS
aLevels measured using novel ELISA assaybLikelihood ratio test. Multiple logistics regression, factors included: trial treatment, biomarker level, binary stratification factors (ER/PgR status, measurable disease at baseline, prior adjuvant taxane therapy), interaction term of treatment by biomarker level
aLevels measured using novel ELISA assaybLikelihood ratio test. Multiple logistics regression, factors included: trial treatment, biomarker level, binary stratification factors (ER/PgR status, measurable disease at baseline, prior adjuvant taxane therapy), interaction term of treatment by biomarker level
Bev + chemobetter
0.2 0.5 1 2 5
Interaction p-valueb
Interaction p-valueb
0.01360.0136
0.03420.0342
0.08080.0808
0.25450.2545
HR (95% CI)
Chemo alone better
Miles et al. SABCS 2010
AVADOmBC
1.0
0.8
0.6
0.4
0.2
0
1.0
0.8
0.6
0.4
0.2
0
Pro
babi
lity
Pro
babi
lity
0 6 12 18 240 6 12 18 24
Low VEGF-ALow VEGF-APlacebo
Bev 15 mg/kg
Bev 7.5 mg/kg
Placebo
Bev 15 mg/kg
Bev 7.5 mg/kg
Time (months)Time (months)
1.0
0.8
0.6
0.4
0.2
0
1.0
0.8
0.6
0.4
0.2
00 6 12 18 240 6 12 18 24
High VEGF-AHigh VEGF-APlacebo
Bev 15 mg/kg
Bev 7.5 mg/kg
Placebo
Bev 15 mg/kg
Bev 7.5 mg/kg
Time (months)Time (months)
1.0
0.8
0.6
0.4
0.2
0
1.0
0.8
0.6
0.4
0.2
0
Pro
babi
lity
Pro
babi
lity
0 6 12 18 240 6 12 18 24
Placebo
Bev 15 mg/kg
Bev 7.5 mg/kg
Placebo
Bev 15 mg/kg
Bev 7.5 mg/kg
Time (months)Time (months)
Low VEGFR-2Low VEGFR-2 1.0
0.8
0.6
0.4
0.2
0
1.0
0.8
0.6
0.4
0.2
00 6 12 18 240 6 12 18 24
Placebo
Bev 15 mg/kg
Bev 7.5 mg/kg
Placebo
Bev 15 mg/kg
Bev 7.5 mg/kg
Time (months)Time (months)
High VEGFR-2High VEGFR-2
Miles et al. SABCS 2010
AVADOmBC
• In this study, data suggest that high levels of plasma VEGF-A and VEGFR-2 may be associated with PFS
• In this study, data suggest that high levels of plasma VEGF-A and VEGFR-2 may be associated with PFS
Plasma VEGF-A and VEGFR-2 levels: PFS
• In this study, data suggest that low levels of ICAM-1 are associated with improved PFS• In this study, data suggest that low levels of ICAM-1 are associated with improved PFS
Plasma ICAM-1 levels: Plasma ICAM-1 levels: PFS and OSPFS and OS
High baseline ICAM-1Low baseline ICAM-1
PC
Bev + PC
(p=0.99)
Time (months)
PC
Bev + PC
(p=0.0018)
1.0
0.8
0.6
0.4
0.2
0
Prob
abili
ty
0 10 20 30 40 50
Time (months)
1.0
0.8
0.6
0.4
0.2
0
HR 2.14 (95% CI 1.31–3.48)*
HR 1.00 (95% CI 0.62–1.60)*
0 10 20 30 40 50
PFS
Low baseline ICAM-1 High baseline ICAM-1
PC Bev + PC
(p=0.19)
PCBev + PC
(p=0.66)
Time (months)
1.0
0.8
0.6
0.4
0.2
0
Prob
abili
ty
1.0
0.8
0.6
0.4
0.2
0
0 10 20 30 40 50
Time (months)
HR 1.39 (95% CI 0.84–2.30)
HR 0.90 (95% CI 0.56–1.44)
0 10 20 30 40 50
OS
Dowlati et al. ASCO 2006
E4599NSCLC
HR shown as PC/Bev + PC. *Cox model treatment interaction tests p≤0.05
Plasma ICAM-1 and Plasma ICAM-1 and bFGF levels: PFSbFGF levels: PFS
High baseline ICAM-1High baseline ICAM-11.0
0.8
0.6
0.4
0.2
0
1.0
0.8
0.6
0.4
0.2
0
0 6 12 18 24 300 6 12 18 24 30
Bevacizumab 15 mg/kg + CG
Placebo + CG
Bevacizumab15 mg/kg + CG
Placebo + CG
Bevacizumab15 mg/kg + CG
1.0
0.8
0.6
0.4
0.2
0
1.0
0.8
0.6
0.4
0.2
0
Pro
babi
lity
Pro
babi
lity
0 6 12 18 24 300 6 12 18 24 30
Low baseline ICAM-1Low baseline ICAM-1
Placebo + CG
Bevacizumab15 mg/kg + CG
Placebo + CG
Bevacizumab15 mg/kg + CG
Leighl et al. ECCO-ESMO 2009
HR 0.64*(95% CI 0.43–0.96)
HR 1.04* (95% CI 0.69–1.56)
Low baseline bFGFLow baseline bFGF1.0
0.8
0.6
0.4
0.2
0
1.0
0.8
0.6
0.4
0.2
0
Pro
babi
lity
Pro
babi
lity
0 6 12 18 24 300 6 12 18 24 30Time (months)Time (months)
Placebo + CG
Bevacizumab7.5 mg/kg + CG
Placebo + CG
Bevacizumab7.5 mg/kg + CG
High baseline bFGFHigh baseline bFGF1.0
0.8
0.6
0.4
0.2
0
1.0
0.8
0.6
0.4
0.2
0
0 6 12 18 24 300 6 12 18 24 30Time (months)Time (months)
Bevacizumab 7.5 mg/kg + CG
Placebo + CG
Bevacizumab7.5 mg/kg + CG
Placebo + CG
Bevacizumab7.5 mg/kg + CG
HR 0.74* (95% CI 0.50–1.09)
HR 0.47* (95% CI 0.31–0.71)
*Cox regression analysis treatment interaction p<0.15
AVAiLNSCLC
Time (months)Time (months)Time (months)Time (months)
• In this study, data suggest that low levels of ICAM-1 (bev 15 mg/kg) and high levels of bFGF (bev 7.5 mg/kg) are associated with improved PFS
• In this study, data suggest that low levels of ICAM-1 (bev 15 mg/kg) and high levels of bFGF (bev 7.5 mg/kg) are associated with improved PFS
CX
CR IL-1
R
Tumor associated angiogenesis
HIF1 NFkbARNTHIf1
NRP1V
EG
FR
Tumor cellDNA
EG
FR
VEGF
Endothelial cell
HypoxiaEGF
IL-8 IL-1 β
PA
R-
4 PA
R-
1
Endostatin
Platelet1-granules2-granules
Thrombin Thrombin
0
10
20
30
40
50
60
A/A A/T T/T
%
Response to Bevacizumab+ Cyclophosphamide by IL-8 polymorphism
(n=13) (n=25) (n=14)
Schultheiss et al Clin Cancer Res 2008
Il-8 251 Polymorphism predicted Response to BEV/low dose
cyclophosphomide in ovarian cancer
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
0 3 6 9 12 15 18
Months Since Start of Bevacizumab+ Cyclophosphamide Treatment
Estim
ate
d
Pro
ba
bility
of
Be
ing
Pro
gre
ssio
n-F
ree
VEGF 936C/C (n=38)
VEGF 936 C/T, T/T (n=14)
Log-rank P value = 0.039
Schultheiss Clin Cancer Res 2008
VEGF 936 associated with PFS in metastatic ovarian cancer
VEGF SNPs: OSVEGF-2578 VEGF-1154
AA vs CA + CCb HR 0.58
(98.3% CI 0.36–0.93)
(p=0.023)
aAA in experimental arm vs all genotypes in control arm. bIn experimental arm only.
AA vs GA vs GGb HR 0.62
(98.3% CI 0.46–0.83)(p=0.001)
Schneider et al. JCO 2008
1.0
0.8
0.6
0.4
0.2
0
1.0
0.8
0.6
0.4
0.2
0
1.0
0.8
0.6
0.4
0.2
0
1.0
0.8
0.6
0.4
0.2
0
OS
pro
babi
lity
OS
pro
babi
lity
0 10 20 30 40 50 60 700 10 20 30 40 50 60 70Time (months)Time (months) Time (months)Time (months)
AACACCBev+PacPac
AACACCBev+PacPac
AAGAGGBev+PacPac
AAGAGGBev+PacPac
0 10 20 30 40 50 60 700 10 20 30 40 50 60 70
p=0.035a p=0.047a
E2100mBC
• In this study, data suggest that AA genotypes of two VEGF SNPs are associated with improved OS
• Interpretation limited:
– Information on genotypes from control patients not reported – DNA samples originate from tumour tissue rather than blood
• To date these SNPs have not been confirmed in other indications (E4599, AViTA)
• In this study, data suggest that AA genotypes of two VEGF SNPs are associated with improved OS
• Interpretation limited:
– Information on genotypes from control patients not reported – DNA samples originate from tumour tissue rather than blood
• To date these SNPs have not been confirmed in other indications (E4599, AViTA)
VEGFR-1 SNP: OSVEGFR-1 SNP: OSAViTAmPaC
Bevacizumab-treated patients
Median OS, days
HR vs AA genotype (95% CI)
Wald test
AA genotype (n=40)
309
AC genotype (n=28)
171 2.00 (3.36–1.19) p=0.0091
CC genotype (n=9) 144 4.72 (10.68–2.08) p=0.0002
1.0
0.8
0.6
0.4
0.2
0
1.0
0.8
0.6
0.4
0.2
0
OS
pro
babi
lity
OS
pro
babi
lity
0 200 400 600 8000 200 400 600 800Time on study (days)Time on study (days)
Overall placeboOverall bevacizumabOverall placeboOverall bevacizumab
Roche data on file; Lambrechts et al. ECCO-ESMO 2009
• In this study, data suggest that rs9582036-A allele is associated with improved OS (shown) and PFS (not shown)
Germline Polymorphisms (Il-8, VEGF, ICAM) associated with Response in patients
enrolled in E4599
(multivariate analyses)
PCB selected (44%)PCB unselected (16%)
PC selected (10%)PC unselected (13%)
Fisher’s test p=0.01.
HR=0.4 (0.25-0/65 95% CI)
p=0.002
PFS in Patients selected for BEV using (VEGF/IL8/ICAM) in Multivariate Analysis.
HR=0.39 (CI95% 0.25-0.63)
P=0.0001
Overall Survival (OS) by selected SNP profile in addition to fitting a multivariable model in PCB arm
Tumor VEGF-A and Tumor VEGF-A and neuropilin expression: PFS neuropilin expression: PFS
Foernzler et al. ASCO GI 2010
All
First tertile
Second tertile
Third tertile
First tertile
Third tertile
First tertile
Second tertile
Third tertile
First tertile
Second tertile
Third tertile
First tertile
Second tertile
Third tertile
Category Subgroup
0.2 0.4 0.6 1 2 3 4 5 60.2 0.4 0.6 1 2 3 4 5 6
All
VEGF-A
HER2
EGFR
Neuropilin
VEGFR-1
247 0.70 (0.49–1.00)
80 0.91 (0.49–1.69)
83 0.74 (0.39–1.40)
78 0.57 (0.29–1.10)
158 0.60 (0.38–0.95)
77 0.90 (0.49–1.64)
88 0.64 (0.34–1.19)
73 0.67 (0.33–1.38)
79 0.72 (0.40–1.30)
81 0.46 (0.22–0.93)
84 0.61 (0.31–1.21)
79 0.99 (0.55–1.77)
76 0.73 (0.37–1.42)
79 0.61 (0.30–1.25)
75 0.73 (0.38–1.40)
n HR (95% CI)
Patients with higherlevels of VEGF-A show increased benefit
Patients with lower levels of neuropilin show increased benefit
HR (95% CI)
NO16966mCRC
• In this study, data suggest that high tumour VEGF-A and low neuropilin expression are associated with improved PFS
Placebo biomarker
> median
Placebo biomarker
> median
Bevacizumab biomarker > medianBevacizumab biomarker > median
Placebo biomarker ≤ medianPlacebo biomarker ≤ median
Tumor VEGF-A and neuropilin expression: PFS
Foernzler et al. ASCO GI 2010
PF
S p
roba
bili
tyP
FS
pro
babi
lity
Time (days)Time (days) Time (days)Time (days)
Bevacizumab biomarker ≤ medianBevacizumab biomarker ≤ median
NeuropilinNeuropilin VEGF-AVEGF-A
0 100 200 300 400 500 600 700 800 9000 100 200 300 400 500 600 700 800 900
1.0
0.8
0.6
0.4
0.2
0
1.0
0.8
0.6
0.4
0.2
0
1.0
0.8
0.6
0.4
0.2
0
1.0
0.8
0.6
0.4
0.2
00 100 200 300 400 500 600 700 800 9000 100 200 300 400 500 600 700 800 900
• In this study, data suggest that high tumour VEGF-A and low neuropilin expression are associated with improved PFS
NO16966mCRC
Tumor marker expression: Tumor marker expression: PFSPFS
Cox regression analysis of PFS for each biomarker subgroup after adjustment for stratification factor and interaction between treatment and the biomarker variable. P-values for HR were constructed on the basis of Wald tests and then adjusted for the FDR
Biomarker No. of patients HR (95% CI) p-value: Wald/FDR
VEGF-CLowHigh
7387
0.59 (0.35–1.01)1.11 (0.64–1.91)
0.05/0.390.71/0.86
Neuropilin-1 (endothelial)LowHigh
9566
0.61 (0.36–1.03)1.34 (0.75–2.40)
0.07/0.390.32/0.70
DII4LowHigh
33126
0.31 (0.13–0.77)1.05 (0.69–1.60)
0.01/0.220.82/0.86
TPLowHigh
46127
0.50 (0.24–1.06)1.30 (0.83–2.03)
0.07/0.390.25/0.70
Jubb et al. Clin Cancer Res 2011
AVF2119gmB
C
• In this study, data suggest that expression of different tumour markers is associated with improved PFS
VEGFR1 mRNA predicts response to FOLFOX/PTK
VEGFR1(n=42)
<3.85
Group 1
(10%)
≥ 3.85
10
1Group 2
(61%)32
20
Confirm 1
Response(n=93)
Multivariate Analysis:
- Serum LDH
- Age
- Gender
- Performance Status
High VEGFR2 associated with poor OS in CONFIRM1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
0 6 12 18 24 30 36 42 48
Months since randomization
Est
imat
ed P
rob
abili
ty o
f S
urv
ival
Adjusted P value = 0.012
VEGFR2
(n=38)
VEGFR2 (n=45)
VEGFR2 >1.76
35.8 mo v 20 mo
VEGFR2 CONFIRM1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
0 5 10 15 20 25 30 35 40
Months since randomization
Est
ima
ted
P
rob
ab
ility
o
f P
rog
ress
ion
-Fre
e S
urv
iva
lP for interaction between treatment and
VEGFR2 expression = 0.001
VEGFR2 <2.98 (n=34)with PTK/ZK
VEGFR2 > 2.98 (n=8)with PTK/ZK
VEGFR2 <2.98 (n=34)w/o PTK/ZK
CONFIRM 1
VEGFR2 > 2.98 (n=7) w/o PTK/ZK
Gimminger in press Pharmacogenomics 2011
Or how do we find our perfect Partner?
• Identification of Predictive Biomarker is critical to develop Anti-VEGF therapies
• VEGFA, VEGFR1,VEGFR2, Neuropilin expression levels (plasma/tumors) are promising markers which need validation
• VEGF independent pathways may play a critical role in efficacy of anti-VEGF therapies (ICAM, IL8)
• Collaborations between Industry, Cooperative Groups and Academics are essential to successful develop clinically useful biomarkers
• Interaction between EGFR and VEGF pathways and drug resistance
Conclusions
CollaborationsMedical Oncology: Syma Iqbal, Anthony El-Khoueiry
Danenberg Lab: Peter Danenberg, Peter Grimminger
ResponseGenetics: Kathy Danenberg
Lenz Lab: Zhang Wu Anne Schultheiss Mizutomo Azuma Georg Lurje Alexandra Pohl Fumio Nagashima
Thomas Winder, Pierre Bohanes,
Yan Ning
Statistics: Susan Groshen, Dongyun Yang
Stem Cell Institute Michael Kahn
Thank you Stefan Scherer (Genentech) for sharing Slides on Bev Biomarkers