LUNG CANCER OMICS

VLADIMIR LAZAR MD, PhD

Director of IGR’s Genomic Centre and Integrated biology platform

vladimir.lazar@igr.fr

2nd ,Quebec conference on Therapeutic Resistance in Cancer Montreal, November 6th, 2010

02468

101214161820

Ove

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rviv

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ian

)

70’s 80s 90s

BSC2–4 m.

Cisplatin‘old

fashion’6–8 m.

Platinum +

3rd gen.8–10

m.

2000

Platinum +

3rd gen.8–10

m.

Docetax.2 m.

Pemetrex2 m.

Pemetrex2 m.

Erlotinib2 m.

2005

Platinum +

3rd gen.8–10

m.

Erlotinib2 m.

2007-8 2013

Pemetrex.> 2 m.

Erlotinib>2 m.

Bev./Cet2 m.

Cetux.or bev. >2 m.

Platinum +

3rd gen.8–10

m.

Platinum +

3rd gen.>10 m.

CO

ST

-EF

FE

CT

IVE

ME

SS

Cost in metastatic NSCLCLung cancer overview

1. 170 000 cases in USA

2. 380 000 cases in Europe

3. At diagnosis 70% are metastatic

4. Overal survival at 5 Years <15%

Goals of tailoring therapy according to predictive markers

Other treatments

Gandara R, et al. J Clin Oncol, 2007: Abst 7500

Responderswith standard therapy

Non responders Toxicity

Patients withsame diagnosis

Improve therapyImprove therapy

Tumor 10%

GeneticVariability 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

Classic Strategy for biopsies collection / analysis

•1 biopsy per patient, before treatment•Cohort responders non responders•Corelate data with end point and

30

Sotiriou NEJM 2009

Drug effect on Tumor 85%

noise

• Avoid inter-individual variability (same patient, same genetic background, same tumour

type…)

• Advantage dual-fluorescence labeling (direct comparison)

• Preliminary studies 5 couples of biopsies analyzed in duplicate & dye-swap.

– SD of log of l’exp° « before » et « 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=5Tumor versus normal = individual studies

IGR sequential Biopsies program« 2 biopsies , before/after treatment »Tumoral versus normal tissue

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Expression AFTER

P= 0.0012

ResponseNo response

100

1000

10000

100000

1000000

short name=CD69

Expression BEFORE

P= 0.8654

short name=CD69

100

1000

10000

100000

1000000

ResponseNo response

P=7.5E-12

Exp

ress

ion

Rat

io

0.1

1.0

10.0

Ratio of expression BEFORE/AFTER

ResponseNo response

Example IGR’s Team project

Mantle cell lymphoma

Sequential Biopsies

Proteasome inhibitor

32

SOP pain, anxiety and risk management

Radiologie-Interventionnelle team (Dr T De BAERE)

biopsies18 gauges

Control radio/echo

1. SNAP FROZEN VS RNA LATTER2. HISTOLOGY CONTROL3. RISK OF MIRROR ADJACENT BIOPSY4. Variable % of tumoralcells5. Need suplementary QC

FNA18 gauges23 gauges

1. HISTOLOGIC control ON CYTO2. Lysis buffer (DNA, RNA

Proteins)3. Highcontent in tumoral cells

in breast tumors,4. metastatic lymph

nodes,lymphomas5. Possible cell suspension

ANOVA Repondeur /NRepondeur (4 5 8 / 1 3 7) 114 genes, p10-10

P4B + P4TProfils similaires, à la dynamique près

(amplitude supérieure pour P4B).

ANOVA Repondeur /NRepondeur (4 8 / 3 7) 474 genes, p10-10

How does classyfy the signature the non evaluable patient?

6th PCRDCoordinator Pr Johan Hanson Karolinska InstitutedIGR – Genomic workpackageChemores : the first fully integrated Omics project in Lung

cancerperformed with dual biopsies strategies T versus normal tissue

Comparisons

1 Surgery Chemo Relapse +

2 Surgery Chemo Relapse -

3 Surgery No Chemo Relapse +

4 Surgery No Chemo Relapse -

• Groups:

• To compare• Group 1 vs 2 (prognostic + predictive)• Group 3 vs 4 (prognostic)• Interaction: (1-2) vs (3-4) = predictive biomarkers

Each patient TUMOR VS normal Tissue,( certified by histology control>85%, unique quality)

• To compare• Tumor versus normal in ADK and SCC (early diagnosis)• Individualized estimation of resistance and of sensitivity

Lung cancer overview

1. 170 000 cases in USA2. 380 000 cases in Europe3. At diagnosis 70% are metastatic4. Overal survival at 5 Years <15%

1.Early diagnosis ( compare T vs normal lung tissues)1. Serum biomarkers –target secreted proteins2. Enhancing sensitivity of imaging –target receptors

2.Predict efficacy of treatments1. Populational studies (dissociate prognostic and

predictive biomarkers2. Individualized selection of treatment

3.Switch to integrative medicine (P4 medicine)

Molecular data

• DNA– CGH (comparative genomic hybridization): measures copy

number. Agilent 250K array– Methylation array: measures gene silencing. (Tumor-suppressor

genes are often silenced.)

– Full sequencing of candidate genes (1,000 genes)

• RNA– Exon expression array. Agilent 244K array. Average 8

exons/gene.– microRNA. Affects mRNA-protein translation. Agilent

array~800miRNA.

• Protein– LC/MS method

Clinical data

• N=123 patients

• table(Relapse, Adj.chemo) Adj.chemoRelapse 0 1 0 39 36 1 22 26

• Pilot data: 4 subjects/group

• Cisplatin + vinorelbin regimen

Analyses of gene-expression data244 k exon array

ANOVA 1 vs 2

ANOVA 3 vs 4

P18_CHE_an34_244F_c2d_397.xls

Probe Gene Symbol Gene Name logFC P.Val

A_23_P23485 - - -5.330153169 7.57E-05

A_23_P393620 TFPI2 tissue factor pathway inhibitor 2 -5.709986801 0.000119958

A_23_P139912 IGFBP6 insulin-like growth factor binding protein 6 -4.326742547 0.000193949

A_24_P13024 SLC16A12solute carrier family 16, member 12 (monocarboxylic acid

transporter 12) -5.271060997 0.000211761

A_32_P60065 F2RL2 coagulation factor II (thrombin) receptor-like 2 4.810036099 0.00026494

A_24_P95070 - - -4.96365972 0.000435701

A_23_P257129 PAEP progestagen-associated endometrial protein -5.165355562 0.000456532

A_23_P33093 ST6GALNAC5ST6 (alpha-N-acetyl-neuraminyl-2,3-beta-galactosyl-1,3)-N-

acetylgalactosaminide alpha-2,6-sialyltransferase 5 -4.125135877 0.000500994

A_23_P38732 CDH2 cadherin 2, type 1, N-cadherin (neuronal) -4.319612403 0.000539076

A_23_P201687 - - 4.813030126 0.001212533

A_23_P170888 DPP6 dipeptidyl-peptidase 6 -3.426572884 0.00127277

A_23_P122068 C1QTNF3 C1q and tumor necrosis factor related protein 3 -3.907437966 0.001292852

A_23_P64808 HOXC13 homeobox C13 5.037726942 0.001663286

A_23_P87700 MFAP5 microfibrillar associated protein 5 -4.927060503 0.001734624

A_24_P912799 - - -4.678436159 0.001837989

A_23_P41476 SHISA3 shisa homolog 3 (Xenopus laevis) -7.063715193 0.001870976

A_23_P122067 C1QTNF3 C1q and tumor necrosis factor related protein 3 -3.839851658 0.001951797

A_32_P188860 IL17RD interleukin 17 receptor D -3.784957052 0.002018497

A_32_P59308 - - 4.551095303 0.002028253

A_32_P183598 LOC645904 similar to MAD1 mitotic arrest deficient-like 1 (yeast) 3.294744404 0.00216031

Interaction Chemo/Relapse

Analyses of CGH data

0.0e+00 5.0e+07 1.0e+08 1.5e+08

-10

-50

5

Position

t-st

at

Chromosome 5

p15.

33

p15.

32

p15.

31

p15.

2

p15.

1

p14.

3

p14.

2

p14.

1

p13.

3

p13.

2

p13.

1

p12

p11

q11.

1

q11.

2

q12.

1q1

2.2

q12.

3

q13.

1

q13.

2

q13.

3

q14.

1

q14.

2

q14.

3

q15

q21.

1

q21.

2

q21.

3

q22.

1q2

2.2

q22.

3

q23.

1

q23.

2

q23.

3

q31.

1

q31.

2

q31.

3

q32

q33.

1

q33.

2

q33.

3

q34

q35.

1

q35.

2

q35.

3

FSTSKIV2L2PARP8GPX8

PDE4DGZMAESM1GZMKMIER3

SNX18ITGA1DDX4

NDUFS4ANKRD55

PLK2GPBP1CDC20BC5orf29

IL31RAC5orf35RAB3CIL6STPELO

PPAP2AITGA2DHX29ISL1

HSPB3CCNO

MOCS2ARL15

ACTBL2MAP3K1SLC38A9

t-stat=1.5

0.0e+00 5.0e+07 1.0e+08 1.5e+08

-10

-50

5

Position

t-st

at

Chromosome 5

t-stat=1.5

p15.

33

p15.

32

p15.

31

p15.

2

p15.

1

p14.

3

p14.

2

p14.

1

p13.

3

p13.

2

p13.

1

p12

p11

q11.

1

q11.

2

q12.

1q1

2.2

q12.

3

q13.

1

q13.

2

q13.

3

q14.

1

q14.

2

q14.

3

q15

q21.

1

q21.

2

q21.

3

q22.

1q2

2.2

q22.

3

q23.

1

q23.

2

q23.

3

q31.

1

q31.

2

q31.

3

q32

q33.

1

q33.

2

q33.

3

q34

q35.

1

q35.

2

q35.

3

FSTGZMAESM1GZMKSNX18ITGA1

NDUFS4PELOITGA2HSPB3MOCS2ARL15

Symbol Name

FST follistatin

GZMA granzyme A (granzyme 1, cytotoxic T-lymphocyte-associated serine esterase 3)

ESM1 endothelial cell-specific molecule 1

GZMK granzyme K (granzyme 3; tryptase II)

SNX18 sorting nexin 18

ITGA1 integrin, alpha 1

NDUFS4 NADH dehydrogenase (ubiquinone) Fe-S protein 4, 18kDa (NADH-coenzyme Q reducta

PELO pelota homolog (Drosophila)

ITGA2 integrin, alpha 2 (CD49B, alpha 2 subunit of VLA-2 receptor)

HSPB3 heat shock 27kDa protein 3

MOCS2 molybdenum cofactor synthesis 2

ARL15 ADP-ribosylation factor-like 15

Analysis microRNA data

ANOVA 2 groups 1 vs 2

Path analysisOnly tumors - Interaction

PATHWAY_ID Pathway Name P-values

151 Antigen processing and presentation 0.00033

44 Benzoate degradation via CoA ligation 0.00038

92 Apoptosis 0.00152

81 Metabolism of xenobiotics by cytochrome P450 0.00170

60 Tight junction 0.00298

48 Citrate cycle (TCA cycle) 0.00748

186 Glutathione metabolism 0.00936

102 Arachidonic acid metabolism 0.01051

96 Galactose metabolism 0.01293

136 Nicotinate and nicotinamide metabolism 0.02578

50 Glyoxylate and dicarboxylate metabolism 0.03323

76 Calcium signaling pathway 0.03376

51 Limonene and pinene degradation 0.04662

178 D-Arginine and D-ornithine metabolism 0.04886

Building of algorithme relies on 3 steps

Complet genome profiling of the Tumor (metastasis) as compared to the original histological normal tissue

NormalNormal TumorTumor Cancer is a clonal diseaseCancer is a clonal disease Cancer is a polygenic Cancer is a polygenic diseasedisease Drivers are mutations Drivers are mutations

9

Identifiction of all genes altered byThe drugs, or interacting with drugs

Understanding of the interaction drug-gene ( genes of resistance, targets, genes of sensitisation,

Second step

10

11-01-10Baseline

10-03-10After 2 cycles

11-01-10Baseline

10-03-10After 2 cycles

11-01-10Baseline

10-03-10After 2 cycles

11-01-10Baseline

10-03-10After 2 cycles

Male Caucasian,58Y, 2003, NSCLC, cT4,N0,M1

• 9 therapeutic linesCisplatin Gemzar TaxotereNavelbineTaxolCarboplatinMediastinal RadiotherapyIRESSA AlimtaTarceva(HKI 272 (included in clinical trial) (pan Her Inhibitor)

2

Adrenal node (C2) = 26 mm

START HKI2724

21/11/08 : Progression Disease

DECISION TO STOP HKI 272

Adrenal node (C2) = 58 Disease ProgressionNew sublclavious metastasis

7

• 9 therapeutic linesCisplatin (108)

Gemzar (70)Taxotere (77)Navelbine (50)Taxol (82)Carboplatin (66)Mediastinal Radiotherapy

IRESSA (66)Alimta (73)Tarceva(HKI 272 (included in clinical trial) (pan Her Inhibitor)

2

12

23/12/08 =

• STOP HKI 272

• START Lapatinib +Xeloda + Thiothepa (introduced sequentially during 1 month)

13

01/02/10: Still on Lapatinib, Xeloda,Thiotepa

Adrenal node (C2) = 62 mm Stable Disease !!

16

13

45 yearsRhabdomyosarcoma5 metastases

13

13

A

B

Sample

Bioinformatics

Hybridization

oligo Microarray

Genomics platform based on Agilent technology

Resolver™

AAAAA

AAAAAAAAAA

AAAAA

AAAAAAAAAA

AAAAA

RNA isolation T7 RNA amplification & Labeling

Q-PCR validations

DNA isolation &fragmentation

Klenow Labeling

ScanningQC

QC

QC

QC

QC

Study design, Standard operating procedures, Quality control

Bioinformatics, biological interpretation of data

Gene expressionmicroRNA

CGHChIPmethylation

• Worldwide Innovative Networking

Only 50 % curedLate diagnosisTherapeutic failure

GAP

WIN

Accelerate integration of ground-breaking personalized cancer medicine discoveries into clinical practice and to significantly improve clinical outcome and quality of life

WIN GOALS

1. Early Diagnosis2. Predict efficacy of Individualized

Treatments3. Innovative drug associations

Focus1. Validation of new tools, biomarkers,

technologies in America Europe Asia and Middle East patients

2. Innovative clinical trials conducted worldwide

3. An operational structure; The WIN consortium

4. Dissemination of knowledge: The WIN symposium

5. WIN database

Strategy

1. First concrete results in 3-5 years (kit early diagnosis)

2. Critic mass in relationship with Pharma and Regulatories

3. Generate incoming revenues

Expectedresults

www.winconsortium.org

George Bernard Shaw

You see things that happen and ask ”WHY”. I dream about things that did not happen and ask ”Why Not”