Biomarker for Angiogenesis Inhibitors

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


No biomarker identifiedNo biomarker identified

Pazopanib

Tumour type: RCC (one study)

Pazopanib

Tumour type: RCC (one study)


Vandetanib

Focus: VEGF, VEGFR, ICAM-1

Tumour type: NSCLC

Vandetanib

Focus: VEGF, VEGFR, ICAM-1

Tumour type: NSCLC


Sorafenib

Focus: VEGF, CECs


Sorafenib

Focus: VEGF, CECs



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


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


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


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


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


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


Placebo + 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


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


Placebo + 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

Documents

Biomarker for Angiogenesis Inhibitors