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Università degli Studidi Palermo
DIPARTIMENTO DI ONCOLOGIA
SEZIONE DI ONCOLOGIA MEDICA(Dir. Prof. Nicola Gebbia)
Surrogate biomarkers in
evaluating response to
anti-angiogenic agents: focus on
SunitinibAntonio Russo
• New technologies allowing large-scale measurement of genomic (and other) biomarkers
• Many new (targeted) therapeutic options emerging
• Optimizing and individualizing therapy becoming increasingly urgent and theoretically feasible
• However, very few potential biological markers developed to the point of allowing reliable use in clinical practice
CANCER BIOMARKERS
PrognosticNatural history
regardless of treatment
Treatment decision
Predictive
CANCER BIOMARKERS
Ongoing treatment evaluation
Surrogate
DiagnosticScreening
RiskPredisposition
MARKER +
MARKER -
No treatment or
Standard, non targeted treatment
CANCER BIOMARKERS as PROGNOSTIC
Prognostic
Single trait or signature of traits that separates different risk classes with respect to an
outcome of interest
0 5
100
1 2 3 4
.
YEARS
LOW RISK
INT. RISK
HIGH RISK
80
100
20
RELA
PS
E-F
REE (
%)
40
60
PROGNOSTIC GOAL: “EARLY STAGE” CANCER
Under each curve → pts cured with local-regional therapy
Single trait or signature of traits that separates groups with a different
outcome in response to a particular (targeted) treatment
Predictive
CANCER BIOMARKERS as PREDICTIVE
No treatment
or Standard
Targeted
treatment
MARKER +
MARKER -
PREDICTIVE GOAL: “EARLY STAGE” CANCER
0 51 2 3 4
YEARS
RELA
PS
E-F
REE (
%)
LOW RISK
INT. RISK
HIGH RISK
100
20
60
40
80
SURGERY ALONE
SURGERY + CHEMOTHERAPY
Pts with residual micrometastases
SENSITIVE to adjuvant
CT/targeted therapy
ENDPOINTS OF CANCER CLINICAL TRIALS:
Why are surrogate biomarkers necessary in determining the activity of targeted therapies?
Tumor Response (WHO, RECIST)
Time To Progression (TTP)Progression-Free Survival (PFS)
Overall Survival (OS)
Quality of Life (QoL)
Determining the efficacy of treatment
Determining the activity of treatment
CANCER BIOMARKERS as SURROGATE ENDPOINT
The modulation of a trait or a signature of traits that reflects
the activity of a targeted therapy and is associated to
the clinical endpoint of interest
Surrogate Endpoint
Targeted therapy
Months
Response Lack of responseMark
er
lev.
1. EARLY surrogate measures of benefit
2.Association with the clinical outcome of
interest
3.Minimally/non-invasive, reproducible,
reliable, cost-effective
4.More rapidly informative and with smaller
sample sizes than traditional endpoints
FEATURES OF BIOMARKERS as SURROGATE ENDPOINT
PrognosticNatural history
regardless of treatment
Treatment decision
Predictive
Some Biomarkers
might have a Prognostic,
Predictive and Surrogate role
CANCER BIOMARKERS: summary
Ongoing treatment evaluation
Surrogate
Controlled studies are required to determine the contributions made by a particular marker
ACTIVITY
EFFICACY
Biomarkers in the development of antiangiogenic therapies
Confirmation of the biological antitumor activity
Definition of optimalbiological dose and schedule
Early identification ofresponders VS non responders
PRECLINICAL
PHASE I
Early detection ofpts developing resistance
PHASE II
PHASE III
PericyteEndothelial
cell
Capillary Vessel
Tumor and blood vessels
VEGF and angiogenesis
Adapted from Kerbel RS NEJM 2008
NEUROPILIN
ANG-2
VasostatinTHBS1/2
VasohibinEndostatin
The balance of angiogenesis
Anti-angiogenic
factors
Pro-angiogenic
factors
SDF-1 ANG-1
Leptin
PlGF
bFGFVEGF-B
PDGF
VEGF-D
VEGF-C
VEGF-A
PDGFbFGF
PlGF VasohibinVasostati
nbFGF
PlGF-APDGF
VEGF-AVEGF-A
↑ VEGF and other pro-angiogenic factors (PDGF,
bFGF) and “escape” from therapy
ANG-1
Vasohibin
Vasostatin
THBS1/2
SDF-1 PDGFbFGF
VEGF-A
VEGF-A
Response to anti-angiogenic therapy
and normalized vasculature
VEGF-AVEGF-A
VEGF-A
VasohibinVasostati
nVEGF-AVEGF-A
VEGF-AVEGF-A
Early tumors show ↑ expression of VEGF
and associated abnormal
vasculature
Tumor angiogenesis & anti-VEGF therapy
Review of Literature 2008
Molecular Marker
PtsGene
Alterations
Predicted Response
to treatment
VHL1 123Loss-of-function
mutationsGood response to
VEGF therapy
HIF-1/HIF-22 43High Expression
LevelGood response to
Sunitinib
Single Nucleotide Polymorphisms
(SNPs)3
42Various non
synonymous SNPsSignificant Sunitinib
toxicity
1. Choueiri TK et al. J Urol. 2008 Sep;180(3):860-52. Patel PH et al. ASCO 2008. Abstract 50083 Faber PW et al. ASCO 2008. Abstract 5009
VEGF-targeted therapies in RCC:are there PREDICTIVE BIOMARKERS?
4400
8551 214 553 554 676 677
Ex 1 Ex 2 Ex 3
5’UTR 3’UTR
1 14 54 63 204155 213163114
VHL (Von Hippel-Lindau) is a tumor suppressor gene located on chromosome 3p25
3 codifing exons VHL genetic abnormalities present in 60-90% of patients with sporadic RCC
• VHL mutated in 50-70% of patients with sporadic RCC• VHL silenced by hypermethylation in an additional 5-20%
VHL gene and RCC
Review of Literature 2008
Proximal nephron
Distal nephron
CLEAR CELL CARCINOMA (75%)
VHL mutation (70%)
Hypermetilation (20%)
Loss of Heterozygosity at 3p25
PAPILLARY CARCINOMA (15%)
Type 1
Type 2
c-Met mutation
FH mutation
Type 1
Type 2
ONCOCYTOMA (5%)
CHROMOPHOBE (5%)
Collecting duct, undifferentiated (rare)
RCC: histologic and molecular characteristics
Role of VHL in RCC progression
EGF
PDGF
VEGF
Endothelial or stromal cell
EGF
PDGFVEGF
EGF PDGF
VEGF
Tumor cell
RAF
MAPK
Erk
RAS
PI3K
AKT
mTOR
eIF-4E
VHL mut
HIF1-a
Degradation by proteasome
Ub
α βHIF
RAF
MAPK
Erk
RAS
PI3K
AKT
mTOR
eIF-4E
α βHIF
Paracrin function
Autocrin function Tumor
cell
RAFM
APK
Erk
RAS
PI3K
AKT
mTO
ReIF-4E
VEGF
VEGF
VEGF
VEGF
VEGF
Intracrin function
Targets of anti-VEGF therapies in RCC
EGF
PDGF
VEGF
Endothelial or stromal cell
EGF
PDGFVEGF
EGF PDGF
VEGF
Tumor cell
RAF
MAPK
Erk
RAS
PI3K
AKT
mTOR
eIF-4E
VHL mut
HIF1-a
Degradation by proteasome
Ub
α βHIF
RAF
MAPK
Erk
RAS
PI3K
AKT
mTOR
eIF-4E
α βHIF
Paracrin function
Autocrin function
TEMSIROLIMUS
BEVACIZUMAB
SUNITINIB
SORAFENIB
Location of VHL mutation
Exon 3
27%Ex
on 1
42%
Exon 2
32%
N=19
N=25
N=16
Type of mutation
Frameshift
48%
Missense
22%
Splice
8%
Non sense10%
N=5N=13
N=7
N=6
N=29
Characteristics of VHL mutations in 60/123 (49%) mRCC pts
Adapted from Choueiri TK et al. J Urol. 2008 Sep;180(3):860-5
FACTOR N* ORR (%) P-Value
Response 122 45/122 (37%)
VHL status
Mutated
Metilated
59
12
27 (46%)
2 (17%)
Wild Type 51 16 (31%)
41% ORR
31% ORR
vs p = n.s.
*one pts with inadeguate follow-up
VHL STATUS and RESPONSE to VEGF-targeted therapies
Adapted from Choueiri TK et al. J Urol. 2008 Sep;180(3):860-5
FACTOR N* ORR P-value
“Loss Of Function” (LOF) mutations
(frameshift, non sense, splice and
inframe deletions/insertions)
4624/46 (52%)
vs
Wild type 51 16/51 (31%)
Types of VHL mutations and response to VEGF-targeted therapies
0.04
*one pts with inadeguate follow-up
Adapted from Choueiri TK et al. J Urol. 2008 Sep;180(3):860-5
Missense mutations were excluded because usually they don’t result in a LOF of the VHL protein
VHL Status Sunitinib Sorafenib Bevacizumab
Mutated 18/32 (56%) 2/10 (20%) 4/9 (44%)
Methylated 2/6 (33%) 0/2 (0%) 0/3(0%)
Wild-type 13/25 (52%) 0/16 (0%) 0/5 (0%)
ORR by specific VEGF-targeted therapies
in relation to VHL status
Adapted from Choueiri TK et al. J Urol. 2008 Sep;180(3):860-5
Adapted from Choueiri TK et al. J Urol. 2008 Sep;180(3):860-5
PFS in relation to VHL status
First large study testing the impact of VHL mutation
and promoter hypermethilation on outcome of VEGF-
targeted agents in advanced RCC
No significant difference in ORR
Subset analysis generated a hypothesis that certain
types of VHL mutations (e.g. “LOF” mutations) may
identify a population of pts with a greater ORR to VEGF-
targeted agents
VHL status and response to VEGF-targeted therapies
CONCLUSIONS
Adapted from Choueiri TK et al. J Urol. 2008 Sep;180(3):860-5
Molecular Marker
PtsGene
Alterations
Predicted Response
to treatment
VHL1 123Loss-of-function
mutationsGood response to
VEGF therapy
HIF-1/HIF-22 43High Expression
LevelGood response to
Sunitinib
Single Nucleotide Polymorphisms
(SNPs)3
42Various non
synonymous SNPsSignificant Sunitinib
toxicity
1. Choueiri TK et al. J Urol. 2008 Sep;180(3):860-52. Patel PH et al. ASCO 2008. Abstract 50083 Faber PW et al. ASCO 2008. Abstract 5009
VEGF-targeted therapies in RCC:are there PREDICTIVE BIOMARKERS?
43 pts with metastatic clear cell RCC
Sunitinib treatment
Pretreatment snap-frozen RCC tumor available
Treatment outcome assessed
HIF-1α
HIF-2α
α-tubulin
Patients 1 2 3 4 5 6 7 8 9 10 11 12
HIF-α levels and response to Sunitinib
Adapted from Patel PH et al. ASCO 2008. Abstract 5008
Resp
on
se R
ate
(%
)
2/15
4/15
12/13
High(> 50%)
Low(10-50%)
None(< 10%)
p-value 0.0001
Pre-treatment HIF levels by Western analysis and Sunitinib response in RCC patiens
15%27%
92%
HIF 2α
Adapted from Patel PH et al. ASCO 2008. Abstract 5008
Detectable HIF-2
No Detectable HIF-2
p = 0.0085
PFS to SUNITINIB according to HIF-2
Adapted from Patel PH et al. ASCO 2008. Abstract 5008
Molecular Marker
PtsGene
Alterations
Predicted Response
to treatment
VHL1 123Loss-of-function
mutationsGood response to
VEGF therapy
HIF-1/HIF-22 43High Expression
LevelGood response to
Sunitinib
Single Nucleotide Polymorphisms
(SNPs)3
42Various non
synonymous SNPsSignificant Sunitinib
toxicity
1. Choueiri TK et al. J Urol. 2008 Sep;180(3):860-52. Patel PH et al. ASCO 2008. Abstract 5008
3. Faber PW et al. ASCO 2008. Abstract 5009
VEGF-targeted therapies in RCC:are there PREDICTIVE BIOMARKERS?
DNA sequence variation of a single nucleotide in the
coding region change in the aminoacid sequence
Arg – Pro - Phe
CGC – CCA - TTC
Arg – Thr - Phe
CGC – ACA - TTC
Non-Synonimous Single Nucleotide Polymorphism (nsSNP) and Sunitinib Toxicity
Adapted from Faber PW et al. ASCO 2008. Abstract 5009
nsSNPs can affect protein structure/function and thus impact drug metabolism and toxic effects
23 nsSNPs were associated in 19/42 mRCC pts with Sunitinib-related G3-4 toxicities
9/23 genes were integrated in a network involving cytokines, transcription regulators and apoptosis regulators
No ≠ in the 3 nsSNPs for cytochrome P450 involved in Sunitinib metabolism
nsSNPs and Sunitinib Toxicity: RESULTS
Adapted from Faber PW et al. ASCO 2008. Abstract 5009
VEGF-targeted therapies in RCC:are there SURROGATE BIOMARKERS?
MARKERS METHODS
INVASIVE MicroVessel Density (MVD) IHC (e.g. CD31) on tumor biopsy samples
MINIMALLY INVASIVE
Circulating endothelial GF
(e.g. VEGF, PlGF, VEGFR2)Plasma protein levels by ELISA
CECs & CEPs Blood concentration by Flow cytometry
NON INVASIVE Functional ImagingDCE-MRI
PET
VEGF-targeted therapies in RCC:MICROVESSEL DENSITY as SURROGATE
BIOMARKER
LIMITATIONS
• invasive (tumor biopsies)• difficult to standardize• tumor tissue heterogeneity• reader-dependent variability
LOW MVD
MOD. MVD
HIGH MVD
VEGFR2 levels
Adapted from Motzer RJ et al. J Clin Oncol. 2006
VEGF, PlGF & VEGFR2 levels during Sunitinib as SURROGATE BIOMARKERS
PlGF
VEGF VEGFR2
• Circulating levels of VEGF, PlGF and VEGFR2 in 63 mRCC pts receiving Sunitinib
• Biomarkers levels evaulation: Day 1 & 28 of each cycle of treatment
• RESULTS: significant differences between baseline and D28 biomarker levels through cycle 8 (p < 0.002)
CECs & CEPs as SURROGATE BIOMARKERS
• Circulating endothelial cells (CECs) represent 1/1000-100000 of blood circulating cells in healthy people
• The majority (~95%) of mature CECs are terminally differentiated and frequently apoptotic cells (“anucleated carcasses”)
• Only a putative sub-population (<5%) of “stem-like” precursors (CEPs) might stimulate angiogenesis and contribute to tumor vasculogenesis
CD31
CD146
CD105
VEGFR2
CD133
CD144
VEGFR3VEGFR1
• CD45 is used to exclude haematopoietic cells from analysis
• All CECs express CD31
• Sub-populations express endothelial markers e.g. CD146, CD105, VEGFR2, CD144
• Only CEPs express CD133 marker (also haematopoietic stem cells express CD133)
CECs & CEPs as SURROGATE BIOMARKERS
CECs & CEPs as SURROGATE BIOMARKERS
CEPs = CD45-, CD31+, CD133+
CECs = CD45-, CD31+, CD117+
APOPTOTIC CECs = CD45-, CD31+, CD146+ & PI,
7AAD
Tumor
BM
Angiogenesis ONBONE MARROW
CEP ↓
Healthy
BM
Anti-VEGF therapies
Angiogenesis OFF
CEP ↓
CEP ↑
New perspectives:MicroRNAs as SURROGATE BIOMARKERS
• MicroRNAs are a group of non-coding regulatory RNAs (20-25 nucleotides)
• They regulate proliferation, differentiation, apoptosis, cell metabolism and are involved in tumorigenesis
• They can regulate gene expression through translational repression or mRNA degradation of target genes
New perspectives:MicroRNAs as SURROGATE BIOMARKERS
Hypoxia
Mir-20b
VEGF
CECs/CEPs
20b
?20b
20b
20b
Hua Z et al. PLoS ONE. 2006
VEGF-targeted therapies in RCC:Functional imaging as SURROGATE
BIOMARKER
• Based on contrast-enhancement of vascular and tumoral structures
• NON INVASIVE
• Included in many antiangiogenesis studies
• No standard protocols available
CONCLUSIONS
o Objective Response Rate may not be a realistic estimate of activity of new targeted therapies
o Predictive biomarkers related to angiogenesis might be useful to predict therapy outcome
o Surrogate biomarkers of anti-angiogenic drugs activity are requested to monitor response and detect resistance during treatment
o No biomarkers of angiogenesis or antiangiogenic activity available for routine clinical use
o Non-standardized protocols are available