Identifying Tumors Expressing Predictive Markers: Lessons Learned From HER2
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Transcript of Identifying Tumors Expressing Predictive Markers: Lessons Learned From HER2
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Identifying Tumors Expressing Predictive
Markers: Lessons Learned From HER2
Dennis J. Slamon, MD, PhDTRIO Chairman
Chief, Division of Hematology/OncologyDavid Geffen School of Medicine at UCLA
Los Angeles, California
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Faculty Disclosure
Dennis J. Slamon, MD, PhD, Speakers Bureau: Genentech/Roche, GSK, sanofi-aventis
Advisory Board: Novartis
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Molecular Diversity of Human Cancers:
Biologic and Therapeutic Implications
HER2BRCA1
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Paradigm Changes from Human Breast Cancers
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Human Breast Cancer Is Highly Heterogeneous
Can we decipher new molecular genetic information for these complex and variable tumors and establish a new
classification with real therapeutic impact.
STAGE
In situ
invasive
Differentiation
Well-
Poorly-
Nuclear Grade
low
high
Margins
“pushing”infiltrating “single-file”
Lymph. infiltrate
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THE PAST
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The “One-Size-Fits-All” Approach to Cancer
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Cell Type and Phenotype
K18
K14
TDLU
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CALGB 9344: Overall Survival
99Henderson, et al. J Clin Oncol. 2003;21:976-83.
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Sørlie et. al. PNAS 2003
Breast Cancer Subtypes are associated with disease outcome
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CURRENT THERAPEUTIC BREAST CANCER SUBTYPES
60-65%
15-18%
20-25%
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Triple-Negative Breast Cancers: Some Potential Therapeutic Targets
Cell Cell CycleCycle
Transcriptional ControlTranscriptional Control
MAP Kinase PathwayMAP Kinase Pathway Akt PathwayAkt Pathway
EGFREGFR Tyrosine Tyrosine
KinaseKinase
MET MET tyrosine tyrosine kinasekinase
Cell DeathCell DeathAfter Cleator S et al. Lancet After Cleator S et al. Lancet
Oncol. 2006:8:235-244Oncol. 2006:8:235-244
DNA DNA Repair Repair
pathwayspathways
Anti-Anti-AngiogenesisAngiogenesis
CetuximabCetuximab MET mabMET mab
PARP inhibitors PARP inhibitors
BevacizumabBevacizumab
MAPK inhibitors; MAPK inhibitors; NOTCH inhibitorsNOTCH inhibitors
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Can We Do Better?
The Hope - Clinical Translation of Biologically Relevant Molecular
Information Should Lead to More Effective and Less Toxic Therapeutic Approaches
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CURRENT TRANSLATIONAL RESEARCH PROCESS
TRANS CLINICAL
TEAMS: Protocol
Development
BASIC SCIENCE LABORATORIES
BASIC SCIENCELABORATORIES
Hypothesis Generation
Tissue Specimens
Specimen/Sample
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The HER2 AlterationThe HER2 Alteration
IHC
Southern
Northern
Western
Slamon et al. Science 1989
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HER-2 OncogeneAmplification
HER-2 OncoproteinOverexpression
Shortened Survival
Median Survival from First Diagnosis
Breast Cancer
HER-2 overexpressing 3 yrsHER-2 normal 6 - 7 yrs
Slamon et al, Science 1987
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Target Validation - A
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Biologic Effects of HER-2/neu Amplification/Overexpression in Human Breast
Cancer Cells
DNA Synthesis
HER2- Breast
Cancer Cell Lines
HER2+
Breast Cancer Cell Lines
Cell Growth
Growth inSoft Agar
Tumorigenicity
MetastaticPotential
E2 Response, Tam Resist.
HER-2
Transfect
ion
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Target Validation - B
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Preclinical Impact of Trastuzumab on Tumor Growth
Pietras et al. Oncogene. 1998;17:2235.
Tu
mor
vol
ume
(mm
3 )
Treatment day
500
1000
1500
2000
0 10 20 30 40 50 60 700
ControlTrastuzumab
Trastuzumabwithdrawn
Effect of Trastuzumab Treatment on HER2+ Breast Cancer Xenografts
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Trastuzumab in Combination with Chemotherapy
Primary
– Time to disease progression (REC)
– Safety
Secondary
– Overall response rates
– Durations of response
– Time to treatment failure
– 1-year survival
– Quality of life
Objective - Combination Compared to Chemotherapy Alone
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Summary: Phase III Clinical Trial Comparing Best Available Chemotherapy to
Chemotherapy+Trastuzumab
Enrolled 469 pts RR Resp Duration TTP
H +CT 235 pts 49% (^53%) 9.3M (^59%) 7.6M (^68%)
CT 234 pts 32% 5.9 M 4.6M
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The HER2 AlterationThe HER2 Alteration
IHC
Southern
Northern
Western
Slamon et al. Science 1987,1989
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0 1 2 3 4 550
60
70
80
90
100
0 1 2 3 4 5
50
60
70
80
90
100
Disease-Free Survival
B-31 N9831
ACTH 864 83
ACT 872 171 ACT 807 90ACTH 808 51
N Events N Events
HR=0.45, 2P=1x10-9 HR=0.55, 2P=0.0005
ACACTHTH ACACTHTH
ACTACT
74%
87%85%
66%
78%
87% 86%
68%
Years From Randomization
%
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Lessons from the HER2 Story
1.) Target Identification
2.) Target Validation
3.) Preclinical Confirmation
4.) Determintion of Potential Usage Preclinically
5.) Clinical Translation - Proof of Concept
6.) Clinical Optimization
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Other Lessons Learned: What we are learning about already established
agents
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The META-Analysis
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How Did The Current Chapter Start ?
Attempts to explain the differential prognosis of HER2 positive breast
cancers
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The HER-2 Gene: encodes a 185kd protein that is a member of the type I receptor tyrosine kinase family which also contains EGFR, HER-3 and HER-4
Functions When Altered:1.) Growth and proliferation - increased
2.) Differentiation - decreased
3.) Cell survival - increased
4.) Motility - increased
5.) Neoangiogenesis - increased
6.) Reduced dependency on estrogen and insensitivity to hormonal blockade
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Pritchard, NEJM 354:2103, 2006
HER-2 neg MA-5 TRIAL HER-2 pos
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Disease Free SurvivalDisease Free Survival
Test for interaction chi2 = 13.7 p < 0.001
non anthra better
0.34 - 0.800.71 - 1.17
0.520.91NCIC MA-5
0.61 - 0.830.90 - 1.11
0.53 - 1.06 0.60 - 1.05
0.46 - 1.490.91 - 1.64
0.65 - 1.080.86 - 1.20
0.44 - 0.820.75 - 1.23
0.711.00
Overall
0.750.79DBCCG-89-D
0.831.22Milan
0.34 - 1.270.93 - 1.97
0.651.35Brussels
NSABP B15
0.600.96
NSABP B11
0.841.02
heterogeneity c25 = 5.3, p = 0.38heterogeneity c25 = 7.6, p = 0.18
Study HR 95% CI anthra better
0.6 1 2 50.4
p < 0.0001
p = 1.0
0.9
HER2 positive HER2 negative
A. Gennari, JNCI 2007
0.82 - 0.980.90Total p = 0.01
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Overall SurvivalOverall Survival
heterogeneity c25 = 5.2, p = 0.39heterogeneity c25 = 5.5, p = 0.36
Test for interaction chi2 = 12.0, p < 0.001
Study HR 95% CI0.47 - 0.92
0.69 -
1.18 0.66 0.90 NSABP B11
0.63 - 1.06
0.88 - 1.30
0.821.07 NSABP B15
0.27 - 2.69
0.85 - 3.15
0.85 1.64 GUN 3
0.32 - 1.16
0.89 - 1.79
0.611.26 Milan
0.50 - 1.05
0.59 - 1.13
0.730.82DBCG-89-D
0.42 - 1.01
0.80 - 1.40
0.651.06 NCIC MA-5
0.62 - 0.85
0.92 - 1.16
0.73 1.03
Overall
HER2 positive HER2 negative
non anthra betteranthra better
0.6 1 2 50.4
p < 0.0001
p = 0.86
0.9
A. Gennari, JNCI 2007
0.83 - 1.000.91Total p = 0.056
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The Topoisomerase IIa Gene: encodes an enzyme which is critical
in DNA replication and function
including RNA transcription
Functions:1.) Resolves topological problems in DNA
2.) Is critical in RNA transcription from DNA
3.) Makes transient protein-bridged DNA breaks on one or both DNA strands during replication
4. Plays critical roles in segregation, condensation and superhelicity
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The Topo IIa protein is a major target of the
anthracyclines
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Can We Do Even Better?
The Hope - Further Clinical Translation of Biologically
Relevant Molecular Information Should Lead to Even More
Effective and Less Toxic Therapeutic Approaches
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CURRENT TRANSLATIONAL RESEARCH PROCESS
TRANS CLINICAL
TEAMS: Protocol
Development
BASIC SCIENCE LABORATORIES
BASIC SCIENCELABORATORIES
Hypothesis Generation
Tissue Specimens
Specimen/Sample
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Clinical Outcome in Primary Papillary Serous Carcinoma
Primary Papillary Serous
Complete Censored
0 365 730 1095 1460 1825
Survival Time
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Cu
mu
lativ
e P
rop
ort
ion
Su
rviv
ing
Overall Survival
≈ 20% mortality within 2 years
≈ 40% mortality within 3 years
Disease Free Survival
≈ 60% recur within 2 years
≈ 75% recur within 3 years
uncensored: 83 ( 83.00%) censored: 17 ( 17.00%)
uncensored: 57 ( 55.34%) censored: 46 ( 44.66%)
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Goals
Identify molecular subtypes of ovarian tumors that may have clinical and biological relevance for disease
initiation and progression
Utilize these data to generate and test therapeutic hypotheses
Build on the work done in other programs
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Cedars-Sinai/UCLA Ovarian Cohort
225 ovarian samples have been received from Dr. Beth Karlan of Cedar Sinai, profiled and imported into Rosetta analysis software
– Samples collected between 1989 and 2005
– RNA quality measured using Agilent BioAnalyzer– RNA Integrity Number (RIN) average = 9.16
All samples were profiled using Agilent Human 1A V2 chip– Reference is an equal mixture of the first 106 ovarian samples
profiled
Detailed clinical outcome is available on 90% of the samples
UCLA has completed FISH analysis and/or Northerns for a number of genes including HER2, EGFR, Periostin (POSTN, PN)
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UCLA/Cedar Sinai Ovarian Tumor Study: Papillary Serous
Characteristic No. of patients (%)
(N=132)
Age
< 50 yr 31 (23.5)
≥ 50 yr 98 (74.2)
Missing 3 (2.3)
Stage
I 4 (3)
II 5 (3.8)
III 95 (72.0)
IV 21 (15.9)
Locally advanced 1 (.75)
Missing 6 (4.5)
Characteristic No. of patients (%)
(N=132)
Recurrence
≤ 12 months 55 (41.7)
> 12 months 46 (34.8)
Progressive/Refractory 4 (3.1)
NED 17 (12.8)
Missing 5 (3.9)
Tissue Status
Primary 106 (80.3)
Recurrence 20 (15.2)
Interval 1 (0.75)
Missing 5 (3.8)
NED: No evidence of disease
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Hierarchical Cluster of Ovarian Samples across 6165 Genes
Normal samples (n=14) show a very similar pattern of gene expression
Unsupervised clustering does not group remaining
samples into clear subtypes
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Refine Analysis to Discover Ovarian Subtypes
Unsupervised hierarchical clustering clearly defines only a normal & “normal-like” subtype
Clinical outcome does not define subgroups
– ANOVA based on overall survival finds 0 differentially expressed genes (DEG)
Consider other markers to distinguish ovarian subgroups
– Periostin (POSTN, PN) & TGFβ Induced (TGFβI)
– Hormone receptor markers: AR, PGR, ER
– CA125 (MUC16)
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Refine Analysis to Discover Ovarian Subtypes
Unsupervised hierarchical clustering clearly defines only a normal & “normal-like” subtype
Clinical outcome does not define subgroups
– ANOVA based on overall survival finds 0 differentially expressed genes (DEG)
Consider other markers to distinguish ovarian subgroups
– Periostin (POSTN, PN) & TGFβ Induced (TGFβI)
– Hormone receptor markers: AR, PGR, ER
– CA125 (MUC16)
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225 Ovarian Samples Clustered across 2830 Genes identifies three major subtypes
Normal POSTN ER
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ARPR
POSTNTGFβICA125
ERNORMAL
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Clinical Outcome in Primary Papillary Serous Carcinoma
Primary Papillary Serous
Complete Censored
0 365 730 1095 1460 1825
Survival Time
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Cu
mu
lativ
e P
rop
ort
ion
Su
rviv
ing
Overall Survival
≈ 20% mortality within 2 years
≈ 40% mortality within 3 years
Disease Free Survival
≈ 60% recur within 2 years
≈ 75% recur within 3 years
uncensored: 83 ( 83.00%) censored: 17 ( 17.00%)
uncensored: 57 ( 55.34%) censored: 46 ( 44.66%)
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POSTN Signature Related to Clinical Outcome in Primary Ovarian
SamplesOverall SurvivalDisease Free Survival
Prim ary Ova rian Sam p les : POSTN in Prim ary Ovarian Sam p les
C om p le te C ens o red
Group 0 . Group 1 .
0 365 730 1095 1460 1825
Tim e
0 .0
0 .1
0 .2
0 .3
0 .4
0 .5
0 .6
0 .7
0 .8
0 .9
1 .0
Probability of Remaining Recurrence Free
p =0 .03
n=112
n=29
Overa ll Su rviva l: POSTN in Prim ary Ova rian Sam p les
C om p le te C ens o red
Group 0 . Group 1 .
0 365 730 1095 1460 1825
Tim e
-0 .1
0 .0
0 .1
0 .2
0 .3
0 .4
0 .5
0 .6
0 .7
0 .8
0 .9
1 .0
Cumulative Proportion Surviving
p =0 .008
n=113
n=31
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ARPR
POSTNTGFβICA125
ERNORMAL
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POSTN Signature Related to Clinical Outcome in Primary Ovarian Samples
Overall SurvivalDisease Free Survival
Prim ary Ova rian Sam p les : POSTN in Prim ary Ovarian Sam p les
C om p le te C ens o red
Group 0 . Group 1 .
0 365 730 1095 1460 1825
Tim e
0 .0
0 .1
0 .2
0 .3
0 .4
0 .5
0 .6
0 .7
0 .8
0 .9
1 .0
Probability of Remaining Recurrence Free
p =0 .03
n=112
n=29
Overa ll Su rviva l: POSTN in Prim ary Ova rian Sam p les
C om p le te C ens o red
Group 0 . Group 1 .
0 365 730 1095 1460 1825
Tim e
-0 .1
0 .0
0 .1
0 .2
0 .3
0 .4
0 .5
0 .6
0 .7
0 .8
0 .9
1 .0
Cumulative Proportion Surviving
p =0 .008
n=113
n=31
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Challenges to new and/or combined use of targeted therapeutics
Identifying the appropriate patient population
Do we simply integrate new targeted therapies with established regimens? Advantages/Problems
Is broader target specificity better than more narrow targeting?
What are the most rational targeted combinations to test clinically?
Can we determine the best likely combinations pre-clinically before going into the clinic? Challenges - predictive value of models