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

Faculty Disclosure

Dennis J. Slamon, MD, PhD, Speakers Bureau: Genentech/Roche, GSK, sanofi-aventis

Advisory Board: Novartis

Molecular Diversity of Human Cancers:

Biologic and Therapeutic Implications

HER2BRCA1

Paradigm Changes from Human Breast Cancers

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

THE PAST

The “One-Size-Fits-All” Approach to Cancer

Cell Type and Phenotype

K18

K14

TDLU

CALGB 9344: Overall Survival

99Henderson, et al. J Clin Oncol. 2003;21:976-83.

Sørlie et. al. PNAS 2003

Breast Cancer Subtypes are associated with disease outcome

CURRENT THERAPEUTIC BREAST CANCER SUBTYPES

60-65%

15-18%

20-25%

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

Can We Do Better?

The Hope - Clinical Translation of Biologically Relevant Molecular

Information Should Lead to More Effective and Less Toxic Therapeutic Approaches

CURRENT TRANSLATIONAL RESEARCH PROCESS

TRANS CLINICAL

TEAMS: Protocol

Development

BASIC SCIENCE LABORATORIES

BASIC SCIENCELABORATORIES

Hypothesis Generation

Tissue Specimens

Specimen/Sample

The HER2 AlterationThe HER2 Alteration

IHC

Southern

Northern

Western

Slamon et al. Science 1989

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

Target Validation - A

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

Target Validation - B

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

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

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

The HER2 AlterationThe HER2 Alteration

IHC

Southern

Northern

Western

Slamon et al. Science 1987,1989

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

%

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

Other Lessons Learned: What we are learning about already established

agents

The META-Analysis

How Did The Current Chapter Start ?

Attempts to explain the differential prognosis of HER2 positive breast

cancers

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

Pritchard, NEJM 354:2103, 2006

HER-2 neg MA-5 TRIAL HER-2 pos

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

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

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

The Topo IIa protein is a major target of the

anthracyclines

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

CURRENT TRANSLATIONAL RESEARCH PROCESS

TRANS CLINICAL

TEAMS: Protocol

Development

BASIC SCIENCE LABORATORIES

BASIC SCIENCELABORATORIES

Hypothesis Generation

Tissue Specimens

Specimen/Sample

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%)

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

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)

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

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

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)

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)

225 Ovarian Samples Clustered across 2830 Genes identifies three major subtypes

Normal POSTN ER

ARPR

POSTNTGFβICA125

ERNORMAL

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%)

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

ARPR

POSTNTGFβICA125

ERNORMAL

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

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