towards new classifications and individualised treatment...

Milano January 21 2009

Towards new classifications Towards new classifications and individualised treatment and individualised treatment

for cancer patients for cancer patients ProfessorProfessor Thomas TURSZThomas TURSZ

General General DirectorDirector

Institut Gustave RoussyVillejuif – Paris - France

Workshop Chemores

Reprise en titre courant du sujet de la présentation

Just Imagine…


What would have happened if imatinib / Glivec® or Gleevec®

had been discovered 25 years ago ?


Imatinib Mesylate: BackgroundA selective tyrosine kinase inhibitor of:

KITBcr-AblPDGFR-A/B

First used in Ph+ CML

Druker et al. Nat Med. 1996;2:561.

Class: Phenylaminopyrimidines

C29 H31 N7 O•CH4 SO3

Molecular weight = 589.7


Imatinib / Glivec® or Gleevec®

would never have passed through preclinical screenings and,

even if it had done so, it would have been “killed”

in conventional Phase I clinical trials


Genome Analysis and Cancer Treatment

The process of drug development will completely change by 2010, based upon:

New targets for new drugsNew tumors classificationNew predictive factorsNew criteria for responses


NucleusNucleus

Cellular Biology in 1975

Cell

??

Cytoplasm


α-cat

SOSGRB2

β-cat

Extracellular E-cad

HGF

GDNFMET EGFR

PTEN

NF2

??

?? ?? ??

E2FDP

P105P105--RBRBP

p16p16

CYC D1

CDK4CDK4

S-Phase genes

Tcf-4

β-cat

WT1WT1

p53p53 P21 Gene ? Other targets ?

MLH1MLH1PMS2PMS2

MSH2MSH2GTBP (MSH3)

BRDCA2BRDCA2

BRCA1BRCA1Rad51

Nucleus

?

ATMATM?

PI-3K

MAPKsCytoplasm

DNA mismatch

??

Hh

?

PTCH

SMO

Repair ?

RET

ELG-C

RNA Pol II

ELGELG--BBVHLVHL

β-cat

? Target genes ?

? Target genes ?

EGF

GTPGTP

APC

MEN1NF1

RAS

RAF

Mdm-2

ELG-A

××

? Target genes ?


The Oncogene Era: 1970-1990EGFR, HRR2EGFR, HRR2cc--Kit, FLT3Kit, FLT3

KK--RASRAS

BB--RAFRAF

PI3KPI3K

PTEN lossPTEN loss

p53p53

MycMyc


Multiplication of new potential targets

Less than 5 % of European patients are entered in clinical trialsThe duration between a drug entry in clinical research and entry on the market is ~ 5 yearsTumor responses assessed by clinical/radiology size: poor criteriaTargeted drugs to be more efficient in combination.


A need to change the methodology of clinical trials

1. New methodologies2. Go fast to combinations (including targeted drugs +

radiotherapy: the BIRTH Project)3. New classification of tumors:

– Better definition of targets– Drug allocation to potential responders

4. New criteria for response:– Functional imaging– Proteomics– New cellular / genomic biomarkers obtained

by sequential fine needle tumor aspiration


Dissecting Human Tumors with Genomics

MELANOMA (and metastases)

Breast Cancer


Cutaneous Malignant Melanoma

Incidence and Incidence and mortalitymortality

increasedincreased

regularlyregularlyin the last 50 in the last 50 yearsyears

8th more 8th more frequentfrequent type of cancer in the world type of cancer in the world (7300 new cases per (7300 new cases per yearyear

in France)in France)

No efficient No efficient treatmenttreatment for for metastaticmetastatic MMMM((lessless

thanthan

10% 10% survivalsurvival

atat

5 5 yearsyears))


The study

Gene expression profilesAgilent 44K oligonucleotide array

(27 stage I, 38 stage II, 15 stage III, 3 stage IV)

69 primary melanomas83 primary melanomas

DMFS Breslow

Is there any modification of gene expression associated

with BRAF mutation ?

BRAF

Prognosis signature & gene discovery

Genotyping

NRAS


EORTC


Molecular investigation of tumors with known metastatic statusWithout metastasis (M-) Metastasis (M+)


Differences in gene expression for the replication pathway between M+ and M- melanoma

Replication and DNA polymerasecond1 - cond0

-0,2 -0,15 -0,1 -0,05 0 0,05 0,1 0,15 0,2 0,25 0,3 0,35

CENPFPTTG1RFC4PTTG2CDC6GMNNMCM6PCNAPRIM2ACDC45LMCM4SMC2L1RPA3DEKMCM3BRCA1MSH2RFC5RFC2MCM7POLE4HBXIPRAD17CHAF1ABLMMCM2TFAMORC6LRRM2CTBP2POLQORC4LTOM1L2PPIANFIA

30 genes overexpressed and 5 genes underexpressed in

M+M-

35/138 genes in the pathway (on the chip)30 genes overexpressed and 5 genes underexpressed in M+/M- among 11,000 genes

Z score : 10 ; P=10-14

Log (Fold change)


M+

M-Genome-widegene expressionanalysis

DNARepair Recombination

Replication

Stress Response Pathways

PrimaryMelanoma(83)

T = 0Surgery

T > 4 yearsDistant metastases

Clusterization

The transcriptome of primary melanoma is IDENTICAL to the one found in metastasis for the same patient


Which genes ? Which pathways ?Over-expressed in M+ and under-expressed in M-

- MCM Genes involved in initiation of DNA-replication

other genes involved in transcription, ATP-binding,

signal transduction, oncogenesis, cell-cycle

regulation, protein-serine/-threonine kinase

activity…

Under-expressed in M+ and over-expressed in M-

- Genes involved in Wnt-binding, hydrolase activity,

Ca++-binding, proteolysis…


IHC FOR KLK7 (a), MCM3 (b), MCM6 (c) and karyopherin alpha-2 (d)

AS2096

J. Natl. Cancer Inst.,98, 2006, 472- 482


Patients at risk

<=average 68 56 41 27 18>average 108 84 61 45 35

OS

0.0

0.2

0.4

0.6

0.8

1.0

YEARS0 2 4 6 8

p = 0.0016

Patients at risk

<=average 56 47 36 28 21>average 120 93 66 44 32

OS

0.0

0.2

0.4

0.6

0.8

1.0

YEARS0 2 4 6 8

P= 0.0038

MCM4 MCM6

J. Natl. Cancer Inst.,98, 2006, 472-482


Dissecting Human Tumors with Genomics

BREAST CANCER


GENOMICS and PROGNOSTIC EVALUATION OF EARLY BREAST CANCER TUMOURS:

Gene-expression profile

Van de Vivjer et al. N Engl J Med, Vol 347 (25),Dec. 2002

Retrospective evaluation of the prognostic signature

295 N+ and N- patients from a single institution (NKI)

Median follow-up: 7 years

In both groups, the 70-gene signature is a predictor of outcome (10-year survival)


TRANSLATINGTRANSLATINGMOLECULAR MOLECULAR KNOWLEDGEKNOWLEDGEINTO EARLY INTO EARLY BREAST CANCER BREAST CANCER MANAGEMENTMANAGEMENT

Coordinator: Dr. Martine PICCARTInstitut Jules Bordet, Belgium

Validation set: 313 ptsJNCI, 2006

Institut Gustave Roussy, France: 96Centre René Huguenin, France:60Karolinska Institute Sweden: 73Guy’s Hospital, UK: 52John Radcliffe, UK: 10

NKI 70 gene signature Validated

First clinical trial MINDACT

Signature70 genes

NKI

EORTC


N=6000ER absent

Evaluate Clinical-Pathological risk and 70-gene signature risk

Clinical-pathological and 70-gene both

HIGH risk

Discordant

Clin-Path HIGH70-gene LOW

80%N=1680

Clin-Path LOW70-gene HIGH

20%N=420

Clinical-pathological and 70-gene both

LOW risk

Use Clin-Path risk to decide Chemo or not

Clin-Path High70-gene Low: CTx

80%840

ClinClin--PathPath LowLow7070--genegene High: no High: no CTxCTx

20%20%210210

ClinClin--PathPath HighHigh7070--genegene LowLow: no : no CtxCtx

80%80%840840

Clin-Path Low70-gene High: Ctx

20%210

Use 70-gene risk to decide Chemo or not

55%55% 35%35% 10%10%

R1

Chemotherapy4350 patients

N=3300N=3300 N=600N=600

R2

AnthracyclineAnthracycline --basedbased

TaxaneTaxane CapecitabineCapecitabine--basedbased

Endocrine therapy 600 patients

R32yrs Tam → 5yrs Letrozole

7yrs Letrozole

MIND A C T

NODE

N E G


Further exploratory questions to be adressed

BREAST CANCER:a heterogenous and a complex

disease,more than 2 entities?

Local relapse vs distant metastasisPre-malignant vs malignant lesionsEarly metastasis vs late metastasis


Early vs late metastasis

Untreated node negative breast tumours2 institutions (IGR, CRH)

NRNo Relapse > 10 years

n = 60 patients M2Late Metastases

> 5 years

n = 18 patients

M1Early Metastases

< 5 years

n = 29 patients


Gene predictor 1: Late (M2) vs Early (M1) metastases

141 genes selected at p<0.001 Prediction accuracy: 71 % (permutation p-value p=0.04)

M2 M1M2

.


Impact of therapeutic strategies and

biological determinants of response


T Le Chevalier, JP Pignon, A Dunant, R Arriagada (IGR), Bengt Bergman,(Göteborg),J Vansteenkiste (Leuven)

1867 patientsby 148 centers in 33 countries

4.1% absolute 5-year benefit in OS

7 deaths of CT-induced toxic effects(0.8%)

Essai IALT


IALT Bio Project

Step 1Tissue array


IALT Bio: step 2

3’

5’3’

5’

3’

5’3’

5’

Role of ERCC1*: excision repair

cross-complementation


ERCC1 negative

ERCC1 positive

ResultsResultsResults

761 patientsOut of 1867

N=33544%

N=42656%


Effect

of adjuvant chemotherapy

on overall survival

in all patients (N=761)

EffectEffect

of adjuvant of adjuvant chemotherapychemotherapy

on on overalloverall survivalsurvival

in all patients (N=761)in all patients (N=761)

389 341 282 206 143 81372 312 247 187 128 68

0%

20%

40%

60%

80%

100%

0 1 2 3 4 5YearsNo at riskChemotherapy

Control

Ove

rall

Sur

viva

l

Chemotherapy (197 deaths)

Control (193 deaths)

389 341 282 206 143 81372 312 247 187 128 68

0%

20%

40%

60%

80%

100%


Control

Ove

rall

Sur

viva

l



Adjusted HR = 0.84, 95%CI [0.68Adjusted HR = 0.84, 95%CI [0.68--1.03], p = 0.091.03], p = 0.09


4781121161194224355991120163202

0%

20%

40%

60%

80%

100%


Control



Ove

rall

Sur

viva

l

Adjusted HR=0.65, 95%CI [0.50Adjusted HR=0.65, 95%CI [0.50--0.86], p = 0.0020.86], p = 0.002

Effect


on overall survival

in pts with

ERCC1 negative

tumor

EffectEffect



in pts in pts withwith

ERCC1 ERCC1 negativenegative

tumortumor


Adjusted HR=1.14, 95%CI [0.84Adjusted HR=1.14, 95%CI [0.84--1.55], P = 0.401.55], P = 0.40

Effect


on overall survival

in pts with

ERCC1 positive tumor

EffectEffect



in pts in pts withwith

ERCC1 positive ERCC1 positive tumortumor

346285121147165336996127149170

0%

20%

40%

60%

80%

100%


Control

Ove

rall

Sur

viva

l

Control (80 deaths)



What are the needs of modern oncology?

new disease classificationhistology completed with other criteria based on genomics, proteomics

Identification and validation of new prognostic criteria

Identification and validation of markers able to predict individual responses to treatment

new intelligent drugs and designs for delivery (in clinical trials and standard care)


Other research programs

Genomics profiling as predictor of cancer treatment effect

Example : Sorafenib or Nexavar® (B-raf inhibitor)


SORAFENIB : kidney cancer PFS Benefit*

Prop

ortio

n of

pat

ient

s pr

ogre

ssio

n fr

ee

Time from randomization (weeks)*Independently assessed

0

0.25

0.50

0.75

1.00

0 6 12 18 24 36 48 60 66

SorafenibPlaceboCensored observation

Median PFSSorafenib = 24 weeksPlacebo = 12 weeksHazard ratio (S/P) = 0.44p-value <0.000001

54


Tumor10%

GeneticVariabilit

90%

noiseHistologicpreparation

Noise linked to the wide interindividual variability

(genetic background, sexe, organ, tumor type….)

need of large sample size, >>100(e.g MINDACT Clinical trial >6,000 patients)

⇒Not compatible with limited number of patient.

List of gene obtained instable, not able to predict clinical benefit.

Michiels S, et al. Lancet. 2005 - Prediction of cancer outcome with microarrays: a multiple random validation strategy. Michiels S, et al. Br. J. Cancer 2007 - Interpretation of Microarray Data

« usual » Strategy for biopsies collection / analysis

« 1 biopsy per patient, before treatment »


Drugeffect

on Tumor

85%

noise

Avoid inter-individual variability(same patient, same genetic background, same tumour type…)

Advantage dual-fluorescence labeling (direct comparison)

Preliminary studies5 couples of biopsies analyzed in duplicate & dye-swap.

SD of log of l’exp° « before » and « after » (SD1= 1,6)SD of log of l’exp° « before/after »(SD2=0,4)

=>sample size needed to detect the same difference with « t-test »« usual » Strategy n= 86« Sequential Biopsies » Strategy n=5

IGR sequential Biopsies program« 2 biopsies , before/after (D15) »


PK SNP

A BClinical follow up

Evaluation ofResponse status &

Toxicity

Biology monitoringPerformed on blood

Paired Tumoral biopsies(before and after treatment)

Patient selection Sorafenib

Total RNA DNA ProteinsSpecific investigations

From strictly the same cells

Geneexpression

miRNA

CGH

mutationsBraf, others?

Phosphorylation

responders

Nonresponders

Define correlations

PK SNP

A BClinical follow up

Evaluation ofResponse status &

Toxicity

Biology monitoringPerformed on blood

Paired Tumoral biopsies(before and after treatment)

Patient selection Sorafenib

Total RNA DNA ProteinsSpecific investigations

From strictly the same cells

Geneexpression

miRNA

CGH

mutationsBraf, others?

Phosphorylation

responders

Nonresponders

Define correlations

chemotherapy

mutations

CGH

TumorImaging

Quantitativeproteomics

Clinicalbenefit

No Clinicalbenefit

Serum after14 days

Serumbaseline

Phosphorylation

Integrated Biology Strategy

CorrelationTo clinicalresponse


Lazar et al : AACR-NCI-EORTC, 2007

Example IGR’s Team project


Bioinformatics &Statistics

R&D

Strategicpartnerships

Functional & StructuralGenomics

IGR projects

IGR projects

______________ GLP

Facility

qualitycontrol

PROTEOMICSLIPIDOMICSMETABOLOMICS

Training centerCertifiedservices

provider

Agilent technology

Cell biologyvalidation

Access to System Biology

IGR Integrated biology platform

Biological resources center


Collaborators

IGR :UGF V Lazar, T Robert, C Richon, V RouxIGR Bioinformatics S Koscielny, P Dessen B JobClinical studies:

Early clinical trials JC Soria, JP ArmandMelanoma A Spatz, C RobertBreast F Andre, S Delaloge, M SaghatchianLung cancer T Le Chevalier, B Besse

Partners CHEMORES Consortiuom ( IGR Coordinator V LazarECCCA (Descartes) NKI-KI-IGR Consortium IGR Coordinator R Arriagada)MDACC Houston –IGR Tween Institution Programm IGR Coordinator F Andre)

towards new classifications and individualised treatment...

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