Microarray analysis as a

prognostic and predictive tool:

are we ready?

Enzo Medico

Laboratory of Functional Oncogenomics

Institute for Cancer Research and Treatment

University of Torino

enzo.medico@ircc.it

Topics

• Platforms for gene expression profiling

• Breast cancer signatures

• From cell-based models to cancer classifiers

AFFYMETRIX GeneChip

45,000 gènes !

AFFYMETRIX GeneChip

The Probe Sets

Hybridization on the chip

Signal detection

Genomic raw data

Gene expression profilingby spotted microarrays

Oligonucleotides or cDNAs

Roboticprinting

Gene expression profilingby spotted/dual colour microarrays

RNAextraction,

cDNA labelling

Hybridization

“Reference” RNA sample (pool)

“Test” sample (tumour specimen)

Different platforms generatedifferent data types

Two-colour One-colour

Paired samples Independent samples

Ref vs Sample – 1

Ref vs Sample - 2

Ref – 1

Ref - 2

Sample – 1

Sample - 2vs

Topics

• Platforms for gene expression profiling

• Breast cancer signatures

• From cell-based models to cancer classifiers

61 years IDC PostmenopausalN -pT = 0.9 cm Grade 2 ER et PgR -HER2 -

FA(E)C x 6

%

4848

2525

151544

88

Choices of 40 experts worldwide

0

10

20

30

40

50

AUCUN CMFx6 ACx4 TAM AUTRE

SHOULD ONE TREAT A SMALL (<1CM) ENDOCRINE UNRESPONSIVE TUMOR ?

WHO CAN BE SPARED

THERAPY?

WHICH THERAPY WILL

WORK BEST?

Prognostic factors neededPredictive factors needed

THERAPY DECISION-MAKING FOR EARLY BREAST CANCER

Clinical Outcome

ER- ER+

PNAS vol 98, no 19, 10869-10874, 2001

The “Intrinsic” Breast Cancer Signatures

PNAS vol 100, no 18, 10393-10398, 2003

ER- ER+

grade

Clinical Outcome

Confirmatory Study

Amsterdam’s Signature

312 patients

70 genes

Rotterdam’s Signature

286 patients

76 genes

Discovery of «poor prognosis signatures» for distant relapses

78 tumor samples

70-gene poor prognosis signature

Marc J Van de Vijver et al., NEJM, 347, 25, 2002

High Risk Low Risk

70-gene expression signature outperforms clinicopathological criteria

Lancet, 2005, 365, 671-679

286 tumor samples

G3

G2

G1

Histologic Grade

GG1

GG2

GG3

Genomic Grade

Sotiriou et al., JNCI 2006

• Poor inter observer reproducibility• G2: difficult treatment decision making, under- or over-treatment likely

• Findings consistent across multiple data sets and microarray platforms• More objective assessment• Easier treatment decision-making• High proportion of genes involved in cell proliferation !

Identify genes correlated with

grade 1 vs grade 3

Grade 1 Grade 3

Grade 1 Grade 2 Grade 3

Analyze on validation set (n = 125)

Definition and validation of the Genomic Grade

Sorlie et al. PNAS 2001

Sotiriou et al. PNAS 2003

Van de Vijver et al. NEJM 2002Central Pathology Review!

Consistent Distribution of GG in Different Populations and Microarrays Platforms

GENE EXPRESSION SIGNATURE=

POWERFUL PROGNOSTIC TOOL

Highest priority=

Transfer from bench to bedside

HOW ?HOW ?

Validation study…

TRANSLATING TRANSLATING MOLECULAR MOLECULAR KNOWLEDGEKNOWLEDGEINTO EARLY INTO EARLY

BREAST CANCER BREAST CANCER MANAGEMENTMANAGEMENT

THERAPY DECISION-MAKING FOR EARLY BREAST CANCER

WHO CAN BE SPARED

THERAPY?

WHICH THERAPY WILL

WORK BEST?

Prognostic factors neededPredictive factors needed

Topics

• Platforms for gene expression profiling

• Breast cancer signatures

• From cell-based models to cancer classifiers

The Invasive Growth biological program

ProliferationDifferentiation,

cell polarity, tubulogenesis

Scattering and

migration

Survival and protection against apoptosis

MET

SHHSMO

PTCH1GLI

AFPCK19

ALB-ATTO

MLP-29 liver stem/progenitor cells activate the invasive growth program in response to HGF

Hedgehogpathway

Liver lineage

Liver differentiation

-3 +3MLP29 / liver log2 ratio

CTRL

HGF 6h

HGF 16h

Day 1

Day 2

Day 4

Ind

uc

ed

at

1h

HGF/CTRL

1h 6h 24h

EGF/CTRL

1h 6h 24h

Ind

uc

ed

at

6h

Ind

uc

ed

at

24

h

1

2

3

4

5

Su

pp

res

se

da

t 2

4h

HGF/CTRL

1h 6h 24h

EGF/CTRL

1h 6h 24h

10

11

12

13

14

15

Su

pp

r.a

t 1

hS

up

pre

ss

ed

at

6h

Su

pp

res

se

da

t 2

4h

HGF/CTRL

1h 6h 24h

EGF/CTRL

1h 6h 24h

6

7

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9

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12

The Invasive Growth Transcriptional Program

Total NKI Breast cancer Dataset (311 samples -

Agilent)

Rotterdam Breast cancer Dataset (286 samples -

Affymetrix)

Statistical analysis

Kaplan-MeierCOX proportional

hazard

IG genes ranked by their individual

performance(SNR over 1000

bootstraps)

Number of genes in the classifier optimized and

definition of the nearest mean

classifier (NMC)

Classifier construction and in silico validation

using breast cancer microarray datasets

The Nearest Mean Classifier

AVG

ZZ3Z2Z1

AVG

YY3Y2Y1

AVG

XX3X2X1

Class

ASample

3Sample

2Sample

1

AVG

XX6X5X4

AVG

ZZ6Z5Z4

AVG

YY6Y5Y4

Class

BSample

6Sample

5Sample

4

Gene Z

GeneY

GeneX

Training Group A Training Group B

The Nearest Mean Classifier

AVG

ZZ3Z2Z1

AVG

YY3Y2Y1

AVG

XX3X2X1

Good Progn Class

Sample 3

Sample 2

Sample 1

AVG

XX6X5X4

AVG

ZZ6Z5Z4

AVG

YY6Y5Y4

Poor Prog Class

Sample 6

Sample 5

Sample 4

Gene Z

Gene Y

Gene X

Zs

Ys

Xs

Test sample

GeneZ

GeneY

GeneX

Group A Group B

Pearson correlation -> classification

Invasive growth genes classify breast cancersamples by their metastatic propensity

Variables in the Equation

1,052 ,414 6,457 1 ,011 2,863 1,272 6,444

2,288 ,610 14,091 1 ,000 9,858 2,985 32,560

IRCC60

NKI70

B SE Wald df Sig. Exp(B) Lower Upper

95,0% CI for Exp(B)

Validation on the Rotterdam dataset(286 breast samples, Wang et all., Lancet, 2005)

Variables in the Equation

,848 ,260 10,620 1 ,001 2,334 1,402 3,885

,440 ,293 2,260 1 ,133 1,553 ,875 2,759

IG 49 genes

NKI 49 genes

B SE Wald df Sig. Exp(B) Lower Upper

95,0% CI for Exp(B)

Cox’s proportional hazards model

Overall Comparisons

18,103 1 ,000Log Rank (Mantel-Cox)Chi-Square df Sig.

Test of equality of survival distributions for the different levels ofIRCC-49 AVG boot on 311 samples.

Case Processing Summary

115 22 93 80,9%

171 73 98 57,3%

286 95 191 66,8%

IRCC490

1

Overall

Total N N of Events N Percent

Censored

IG 49 genes

Cum

ula

tive S

urv

ival

Time to relapse or last follow-up (months)

Case Processing Summary

80 16 64 80,0%

206 79 127 61,7%

286 95 191 66,8%

NKI490

1

Overall

Total N N of Events N Percent

Censored

Overall Comparisons

8,966 1 ,003Log Rank (Mantel-Cox)Chi-Square df Sig.

Test of equality of survival distributions for the different levels ofNKI-0.15.

NKI 49 genes

Cum

ula

tive S

urv

ival

Time to relapse or last follow-up (months)

Poor prognosis

Good prognosis

Legend:

0 = Good prognosis samples

1 = Poor prognosis samples

Breast cancer expression profiling: towards an integrated approach to personalized therapy

Acknowledgments

IRCC Laboratory of Functional Oncogenomics

Tommaso RenzulliClaudio Isella

Daniela CantarellaBarbara MartinoglioRoberta Porporato

IRCC Gynaecological Oncology

Daniela CiminoLuca Fuso

Prof. Michele De BortoliProf. Piero Sismondi