Are pharmacogenomic studies useful for
developing predictors of drug response?
Benjamin Haibe-Kains
Director, Bioinformatics and Computational
Genomics Laboratory
Scientific Advisor, Bioinformatics Core Facility
Genomic predictive biomarkers
• Predicting therapeutic response of patients based on their
genomic profiles
D
Non-Responders
E
C
Treat with
alternative drugs
Genomic data
Treat with
conventional drugs
A
Responders
B
Benjamin Haibe-Kains
QBBMM Conference
2013-09-20
Therapeutic strategies in cancer
Adapted from Luo et al. Cell, 2009
Benjamin Haibe-Kains
QBBMM Conference
2013-09-20
Anticancer therapies
• Many drug compounds have been designed and many
others are under development
• Success stories enabled to develop relevant therapeutic
strategies and bring them to the clinic
• But the number of new (targeted) drugs being approved is
dramatically slowing down
• Need for companion tests to identify patients who are likely
to respond to targeted therapies
Benjamin Haibe-Kains
QBBMM Conference
2013-09-20
Drug screening in preclinical models
• It is not sustainable to test thousands of compounds (and
their combinations) in clinical trials
• One needs a different approach to screen the therapeutic
potential of new compounds
• Cancer cell lines can be used as preclinical models:
Cheap and high-throughput
Simple models to investigate drugs’ mechanisms of
action
 Enable to build genomic predictors of drug response
Benjamin Haibe-Kains
QBBMM Conference
2013-09-20
Current studies
• Most studies investigated isolated, small pharmacogenomic
datasets
• Very few have been validated in independent experiments
and in clinical samples
• Some are sadly famous: Anil Potti’s scandal at Duke
University [forensic Bioinformatics by Baggerly and
Coombes]
The solution may lie in analyzing large collections of
cell lines from multiple datasets
Benjamin Haibe-Kains
QBBMM Conference
2013-09-20
association with clinical variablesin our original dataset
eviously published geneexpression datasetscollected on
r of diverse microarray platforms (Table 1). We
d and scaled data from each study and assigned an,
Pharmacogenomic data
(A)
had a ha
value= 0.2
similar, an
grade, late
Cell lines
Activity area
Amax
0
Drugs
–0.5
–1
(B)
d
1.0
e true positive rate
Gene expression
profiles
IC50
Sensitivity
(growth inhibition)
0.8
Cell lines
0.6
Resistant vs. sensitive cell lines
Drugs
Benjamin Haibe-Kains
EC 50
0.4
QBBMM Conference
2013-09-20
Large pharmacogenomic datasets
• Large-scale studies have been recently published in Nature
• The Cancer Genome Project (CGP) initiated by the Sanger
Institute
• 138 drugs
• 727 cancer cell lines
• The Cancer Cell Line Encyclopedia (CCLE) initiated by
Novartis/Broad Institute
• 24 drugs
• 1036 cancer cell lines
Benjamin Haibe-Kains
QBBMM Conference
2013-09-20
CGP
CCLE
• Drugs: 15 drugs have been investigated both in CGP and
CCLE
Paclitaxel
PD-0325901,
AZD6244
• Cell lines:
471
Microtubules depolymerization inhibitor
Mitogen-activated
kinaseCGP
kinase
cancer cell lines
in common protein
between
(MEK) inhibitor
and CCLE
AZD0530 (Saracatinib)
Proto-oncogene tyrosine-protein Src inhibitor
Nutlin-3
Ubiquitin-protein ligase MDM2 inhibitor
CCLE
BCR-ABL fusion protein inhibitor
CGP
Nilotinib
• Gene expression: ~12,000 genes were commonly
17-AAG (Tanespamycin)
Heat shock protein (Hsp90) inhibitor
assessed using Affymetrix HG-U133A and Plus2 chips
PD-0332991
PLX4720, Sorafenib
256
CDK4/6-Cyclin D inhibitor
471
565
RAF kinase inhibitors
Crizotinib,
TAE684
• Mutation:
ALK kinase
68 genes were screened
forinhibitors
mutations in both
Erlotinib,
Lapatinib
EGFR/HER2 kinase inhibitors
CGP
and CCLE
PHA-665752
Benjamin Haibe-Kains
Proto-oncogene c-MET kinase inhibitor
QBBMM Conference
2013-09-20
Genomic predictors of drug response
• We used CGP data to train genomic predictors of drug
response for the 15 drugs
• Gene expressions as input and IC50 as output
• We implemented five linear modeling approaches to build
genomic predictors:
• SINGLEGENE
• RANKENSEMBLE
• RANKMULTIV
• MRMR
• ELASTICNET
Benjamin Haibe-Kains
QBBMM Conference
2013-09-20
Validation framework
Benjamin Haibe-Kains
QBBMM Conference
2013-09-20
Genomic predictors of drug sensitivity (IC50)
CGP in 10-fold cross-validations
Benjamin Haibe-Kains
QBBMM Conference
2013-09-20
Genomic predictors of drug sensitivity (IC50)
Trained on CGP, tested on CCLE
Common cell lines
Benjamin Haibe-Kains
QBBMM Conference
2013-09-20
Genomic predictors of drug sensitivity (IC50)
Trained on CGP, tested on CCLE
New cell lines
Benjamin Haibe-Kains
QBBMM Conference
2013-09-20
Consistency between CGP and CCLE
• Given the poor performance of our predictors we decided to
explore consistency between CGP and CCLE
• Different cell viability assays:
• CGP: Cell Titer 96 Aqueous One Solution Cell (Promega)
 amount of nucleic acids
• CCLE: Cell Titer Glo luminescence assay (Promega)
 metabolic activity via ATP generation
• Differences in experimental protocols including
• range of drug concentrations tested
• estimator for summarizing the drug dose-response curve
• Different technologies for measuring genomic profiles (gene
expressions and mutations)
Benjamin Haibe-Kains
QBBMM Conference
2013-09-20
Consistency measure
• Spearman correlation at different levels
• Genomic data (gene expression)
• Drug sensitivity (IC50 and AUC)
• Gene-drug associations
0
0.6
0.5
0.7
0.8
1
Correlation
poor
fair
moderate substantial
good
• Cohen’s Kappa coefficient for mutations
Benjamin Haibe-Kains
QBBMM Conference
2013-09-20
Consistency of gene expression profiles
Gene expression profiles across cell lines
Replicates in CGP + CCLE vs. CGP
1.0
0.8
Rs
0.6
0.4
0.2
0.0
Replicates
Identical
Different
Cell lines
Benjamin Haibe-Kains
QBBMM Conference
2013-09-20
Consistency of mutational profiles
1.0
Missense mutation profiles across cell lines
CCLE vs. CGP
0.4
−0.2
0.0
0.2
Kappa
0.6
0.8
Wilcoxon test p−value=4.0E−122
Identical
Different
Cell lines
Benjamin Haibe-Kains
QBBMM Conference
2013-09-20
Consistency of drug sensitivity (IC50)
ERLOTINIB
LAPATINIB
PHA_665752
CRIZOTINIB
TAE684
−log10 IC50 (CCLE)
7.5
7.0
7.0
6.5
6.5
8.0
7.0
7.5
6.5
7.0
6.5
6.0
6.0
5.5
5.5
6.0
6.5
6.0
6.0
5.5
5.5
5.5
5.0
4
5
6
7
4
5
NILOTINIB
6
7
4
AZD0530
5
6
7
4
SORAFENIB
5
6
7
3
4
5
PD_0332991
6
7
8
9
PLX4720
6.4
8.0
−log10 IC50 (CCLE)
3
7.0
6.0
6.5
7.0
6.0
6.0
6.0
5.8
6.0
6.5
6.5
6.2
6.5
7.5
5.5
5.6
5.5
5.4
5.5
5.5
5.2
5.0
3
4
5
6
7
8
3
4
−log10 IC50 (CCLE)
PD_0325901
5
6
3
4
AZD6244
8.5
8.0
8.0
7.5
5
6
7
8
NUTLIN_3
7.5
7.0
7.0
7.0
6.5
6.5
6.5
5
6
7
3
8.5
8.5
8.0
8.0
7.5
7.5
7.0
7.0
6.5
6.5
6.0
6.0
6.0
5.5
5.5
5.5
5.5
5.5
5.0
5.0
5.0
5.0
5
6
7
8
−log10 IC50 (CGP)
Benjamin Haibe-Kains
9
3
4
5
6
7
−log10 IC50 (CGP)
8
3.0
3.5
4.0
4.5
5.0
5.5
6.0
−log10 IC50 (CGP)
QBBMM Conference
5
6
7
PACLITAXEL
6.0
6.0
4
17_AAG
8.0
7.5
4
5.0
4
5
6
7
8
−log10 IC50 (CGP)
9
5
6
7
8
9
−log10 IC50 (CGP)
2013-09-20
Consistency of drug sensitivity (AUC)
AUC (CCLE)
ERLOTINIB
LAPATINIB
0.5
0.5
0.4
0.4
0.3
0.3
CRIZOTINIB
TAE684
0.5
0.7
0.6
0.4
0.3
0.5
0.3
0.4
0.2
0.3
0.2
0.2
0.2
0.1
0.1
0.0
0.0
0.0
0.1
0.2
0.3
0.4
0.5
0.2
0.1
0.1
0.0
0.0
0.1
NILOTINIB
0.2
0.3
0.4
0.5
0.6
0.1
0.0
0.0
AZD0530
0.8
AUC (CCLE)
PHA_665752
0.4
0.1
0.2
0.3
0.4
0.0
0.0
0.1
SORAFENIB
0.2
0.3
0.4
0.5
PD_0332991
PLX4720
0.5
0.30
0.5
0.6
0.4
0.25
0.4
0.5
0.20
0.3
0.4
0.4
0.3
0.15
0.2
0.2
0.0
0.0
0.2
0.4
0.6
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7
0.3
0.2
0.10
0.2
0.1
0.05
0.1
0.1
0.0
0.00
0.0
0.0
0.8
0.0
0.1
PD_0325901
0.2
0.3
0.4
0.5
0.00
0.10
AZD6244
0.20
0.30
0.0
0.1
NUTLIN_3
0.2
0.3
0.4
0.5
0.0
0.1
17_AAG
0.8
0.4
0.8
0.6
0.3
0.6
0.4
0.2
0.4
0.2
0.1
0.2
0.2
0.3
0.4
0.5
0.6
PACLITAXEL
1.0
AUC (CCLE)
0.8
0.8
0.6
0.6
0.4
0.4
0.2
0.0
0.0
0.0
0.2
0.4
0.6
AUC (CGP)
Benjamin Haibe-Kains
0.8
0.0
0.0
0.2
0.4
AUC (CGP)
0.6
0.8
0.2
0.0
0.0
0.1
0.2
0.3
0.4
AUC (CGP)
QBBMM Conference
0.0
0.0
0.2
0.4
AUC (CGP)
0.6
0.8
0.0
0.2
0.4
0.6
0.8
1.0
AUC (CGP)
2013-09-20
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0.0
0.1
0.2
0.3
Rs
0.4
0.5
0.6
0.7
Consistency of drug sensitivity
Moderate
Benjamin Haibe-Kains
Drug sensitivity measures
IC50
AUC
Fair
Poor
QBBMM Conference
2013-09-20
GSK Cancer Cell Line Genomic Profiling Data
• In 2010, GlaxoSmithKline tested
• 19 compounds
• on 311 cancer cell lines
• 194 cell lines in common with CGP and CCLE
• 2 drugs in common, Lapatinib and Paclitaxel
• CCLE and GSK used the same pharmacological assay
(Cell Titer Glo luminescence assay, Promega)
Benjamin Haibe-Kains
QBBMM Conference
2013-09-20
Comparison with GSK for Lapatinib
LAPATINIB
CCLE vs. GSK
LAPATINIB
CGP vs. GSK
R=0.24, p=6.0E−02, n=43
R=0.506, p=9.6E−08, n=94
●
7
6.5
●
5
●
●
●
●
●
●
●
●
●
4
−log10 IC50 (CCLE)
−log10 IC50 (CGP)
6
●
●
●
●
●
●
●
●
●●
●
●
●●
●
6.0
5.5
●
●
●
●●
●
●
●●
● ● ●
●
●
●
5
6
7
8
9
5
7
8
9
−log10 IC50 (GSK)
−log10 IC50 (GSK)
Benjamin Haibe-Kains
6
QBBMM Conference
2013-09-20
Comparison with GSK for Paclitaxel
PACLITAXEL
CCLE vs. GSK
PACLITAXEL
CGP vs. GSK
9
R=0.232, p=6.5E−02, n=44
R=0.398, p=3.2E−05, n=95
●
8.5
●
●
●
8
●
8.0
●
●
●●
●
●
●
●
● ●
●
●
●
●
7.5
●
−log10 IC50 (CCLE)
−log10 IC50 (CGP)
●
●
●
7
●
●
●
●
●
●
●
●
●
●
●
6
●
7.0
6.5
●
●
●
●
6.0
●
●
5
5.5
●
●
●
5
6
7
8
5.0
9
5
7
8
9
−log10 IC50 (GSK)
−log10 IC50 (GSK)
Benjamin Haibe-Kains
6
QBBMM Conference
2013-09-20
Replicates in CGP
Same assay, same protocol
CAMPTOTHECIN
10
8
6
−log10 IC50 (MGH)
12
Rs=0.575, p=1.5E−23, n=252
5
6
7
8
9
10
−log10 IC50 (WTSI)
Benjamin Haibe-Kains
QBBMM Conference
2013-09-20
Consistency of gene-drug associations
Model for gene-drug association:
Y = b0 + biGi + btT
Significant gene-drug associations
where
Y = drug sensitivity
FDR < 20%
Gi = gene expression of gene i
Moderate
T = tissue type
Fair
Poor
Benjamin Haibe-Kains
QBBMM Conference
2013-09-20
Source of inconsistencies
• To identify the most likely source of inconsistencies we
intermixed the gene expressions and drug sensitivity
measures between studies
•
•
•
•
•
Original = [CGPg+CGPd] vs. [CCLEg+CCLEd]
GeneCGP.fixed = [CGPg+CGPd] vs. [CGPg+CCLEd]
GeneCCLE.fixed = [CCLEg+CGPd] vs. [CCLEg+CCLEd]
DrugCGP.fixed = [CGPg+CGPd] vs. [CCLEg+ CGPd]
DrugCCLE.fixed = [CGPg+CCLEd] vs. [CCLEg+CCLEd]
Benjamin Haibe-Kains
QBBMM Conference
2013-09-20
Source of inconsistencies
Benjamin Haibe-Kains
QBBMM Conference
2013-09-20
Take home messages
• Gene expressions used to be noisy but years of
standardization enabled reproducible measurements
• Some more work needed to make variant calling more
consistent but we will get there
• Drug phenotypes appear to be quite noisy though
• This prevents us to characterize drugs’ mechanism of action
and to build robust genomic predictors of drug response
• Needs for standardization in terms of pharmacological
assay and experimental protocol
• New protocols may be needed (combination of assays +
more controls)
Benjamin Haibe-Kains
QBBMM Conference
2013-09-20
Acknowledgements
•
•
•
•
Nehme Hachem
Rachad El-Badrawi
Simon Papillon-Cavanagh
Nicolas de Jay
• Hugo Aerts
• John Quackenbush
• Jacques Archambault
• Nicolai Juul Birkbak
• Andrew Beck
• Andrew Jin
Thank you for your attention!
One more thing …
• Frank Emmert-Streib (Queen’s University, Ireland) and I are
editing a Special Issue on Network Inference
Deadline: Sept 15
• Your contributions are welcome!
Benjamin Haibe-Kains
QBBMM Conference
2013-09-20
Appendix
Modeling techniques
• We implemented five linear models to build genomic
predictors:
• SINGLEGENE: Univariate linear regression model with
the gene the most correlated to sensitivity [-log10(IC50)]
• RANKENSEMBLE: Average of the predictions of the top
30 models
• RANKMULTIV: Multivariate model with the top 30 genes
• MRMR: Multivariate model with the 30 genes most
correlated and less redundant
• ELASTICNET: Regularized multivariate model (L1/L2
penalization)
Benjamin Haibe-Kains
QBBMM Conference
2013-09-20
Consistency of gene expression profiles
by tissue types
Correlations of gene expression profiles across tissue types
CCLE vs. CGP
Kruskal−Wallis test p−value=1.3E−10
1.0
0.8
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di
sm
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er
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pe
up
QBBMM Conference
2013-09-20
Consistency of drug sensitivity
by tissue types
(IC50) acr
oss tissue types
Correlations of drug sensitivity (AUC)
across
CCLE vs. CGP
p−value=1.9E−01
Kruskal−Wallis test p−value=2.4E−02
1.0
IC50
AUC
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−0.5
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Benjamin Haibe-Kains
lu
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ng
so
ph
ag
us
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ar
y
pa
nc
re
as
pl
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pr
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sa
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an
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ia
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ry
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−1.0
QBBMM Conference
2013-09-20
Consistency of mutation-drug associations
b0 bi Mi + btT
Model for gene-drug association:
Mutation−drug associations
Y = (all)+
Y = drug sensitivity
Mi = presence ofIC50
mutation in gene i
0.7
where
AUC
0.4
*
*
*
ER
0.0
LO
LA TI
P NI
PH AT B
A INIB
C 66
R 57
IZ
O 52
TI
N
TA IB
N E6
IL
O 84
T
A INI
SO ZD B
R 053
A
PD FE 0
03 N I B
32
P L 99
PD X4 1
03 720
2
A 590
ZD 1
6
N 24
U
TL 4
I
PA 17 N3
C AA
LI
TA G
XE
L
0.1
0.2
0.3
Rs
0.5
0.6
T = tissue type
Benjamin Haibe-Kains
QBBMM Conference
2013-09-20
LO
LA TIN
IB
P
PH ATI
A NIB
C 665
R
IZ 75
O 2
TI
N
TA IB
N E6
IL
O 84
T
A INI
SO ZD B
R 053
A
0
PD FE
03 NIB
32
P L 99
1
PD X4
03 720
25
A 90
ZD 1
6
N 24
U
TL 4
IN
PA 17 3
C AA
LI
TA G
XE
L
ER
0.0
0.1
0.2
Kappa
0.3
0.4
0.5
Consistency of drug sensitivity calling
Benjamin Haibe-Kains
Drug sensitivity calling
IC50 calls
AUC calls
*
*
*
*
*
*
*
*
*
*
QBBMM Conference
* *
*
*
*
*
* *
* *
2013-09-20
N
R
IL
N
4
IB
E6
8
O
TI
TA
IZ
PA
C
1
0
1
XE
L
AG
3
44
IN
A
TL
17
U
LI
TA
N
90
62
25
ZD
03
72
99
2
4
6
−log10(IC50)
8
10
A
PA
C
LI
TA
3
AG
N
44
XE
L
A
TL
I
17
U
62
1
20
90
47
25
ZD
N
A
03
30
IB
N
IB
32
99
1
PL
X
PD
05
FE
03
R
PD
SO
ZD
IL
O
TI
N
A
N
ER
L
LA OT
PA IN
TI I B
N
IB
PH
A
C 6
R 65
IZ 7
O 5
TI 2
N
TA IB
E6
84
Drugs sensitivity (IC50)
CGP
A
PD
32
X4
03
PL
PD
O
TI
N
S O AZ I B
R D 05
A 3
FE 0
N
IB
C
ER
L
L O
PHAP TIN
A AT IB
66 I N
57 IB
52
0
0.0
0.2
AUC
0.4
0.6
0.8
Drug sensitivity in CGP
Drugs sensitivity (AUC)
CGP
IC50
AUC
PA
N
O
B
LI
N
N
AG
A
A
XE
L
ST
AT
A
TA
O
17
O
TE
C
IN
IN
1
65
84
IB
90
C
25
PO
TE
PA
C
N
IR
TO
03
F2
E6
A
TA
1
44
N
62
ET
A
ZD
30
58
54
05
I2
EW
R
D
A
A
ZD
TK
2
1
IB
24
99
IZ
O
TI
N
W
32
IB
IB
IB
IB
0.2
0.4
AUC
0.6
0.8
1.0
C
PA
N
B
N
N
AG
A
A
65
84
8
1
XE
L
ST
AT
A
LI
TA
O
17
C
C
F2
O
TE
IN
IN
O
A
PO
TE
PA
C
N
IR
TO
R
I2
5
54
1
IB
90
E6
TK
EW
TA
A
IB
44
N
25
ET
A
03
D
PD
VA
IB
IB
30
N
62
O
TI
ZD
IZ
A
R
05
N
ZD
N
PA
TI
A
LA
O
TI
IB
20
FE
N
X4
7
1
3
2
8
52
99
57
32
IN
24
TL
66
A
IL
R
N
SO
A
03
PL
PH
PD
U
W
IB
45
N
85
LB
L6
LO
TI
N
ER
Drugs sensitivity (AUC)
CCLE
PD
VA
03
A
R
N
PA
TI
N
LB
PD
C
FE
52
IL
O
TI
N
LA
N
A
57
LO
TI
N
R
3
20
58
N
47
54
TL
I
66
U
A
ER
SO
PH
N
PL
X
L6
8
0.0
5.0
5.5
6.0
7.0
−log10(IC50)
6.5
7.5
8.0
8.5
Drug sensitivity in CCLE
Drugs sensitivity (IC50)
CCLE
IC50
AUC
66
5
75
2
72
0
ER
TL
IN
LO 3
TI
LA NIB
PA
TI
N
IB
A
ZD
05
C
30
R
IZ
O
TI
N
IB
N
IL
O
TI
SO
N
IB
R
A
FE
N
IB
A
ZD
62
PD
44
03
32
99
1
TA
E6
PD
84
03
25
90
1
17
A
PA
AG
C
LI
TA
XE
L
N
U
PH
A
PL
X4
3
4
5
6
7
8
9
IC50 in CGP and CCLE
CGP
CCLE
A
66
57
ER
5
LO 2
TI
N
IB
N
IL
O
TI
LA NIB
PA
TI
N
C
IB
R
IZ
O
TI
N
IB
A
ZD
05
30
N
U
TL
IN
3
PL
X4
SO
72
0
R
A
FE
N
IB
A
ZD
62
PD
44
03
25
90
PD
1
03
32
99
1
TA
E6
PA
84
C
LI
TA
XE
L
17
A
AG
PH
0.0
0.2
0.4
0.6
0.8
1.0
AUC in CGP and CCLE
CGP
CCLE