lung cancer omics vladimir lazar md, phd director of igr’s genomic centre and integrated biology...
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LUNG CANCER OMICS
VLADIMIR LAZAR MD, PhD
Director of IGR’s Genomic Centre and Integrated biology platform
2nd ,Quebec conference on Therapeutic Resistance in Cancer Montreal, November 6th, 2010
02468
101214161820
Ove
rall
su
rviv
al (
med
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
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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 !!
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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”