Genomics and Personalized Medicine:
Smoking Cessation Treatment
Li-Shiun Chen, MD, MPH, ScD
Washington University School of Medicine
Apr 18, 2013
Genomics Informs Smoking Cessation Treatment
I. What do we know about genetics of nicotine
dependence?
II. Are genes important for smoking cessation?
Cessation success
Response to pharmacotherapy
III. Are these genetic associations real and
useful?
Genomics can lead to personalized medicine
Risks
Benefits
Cardiovascular side effect (NRT, varenicline)
Seizure, MAO-I (bupropion)
Perinatal safety?
Medication Cost
Efficacy of cessation medication
Combination vs. monotherapy
E D. Green et al. Nature 2011
Chromosome 15q25 Is Important for Smoking
CHRNA5-A3-B4
The Tobacco and Genetics Consortium (2010) Nature Genetics
Genetics of nicotine dependence
• Heritability 56%-71%
• Specific genetic risks identified
– CHRNA5-CHRNA3-CHRNB4 gene cluster
• Association -> Function
– amino acid change in nicotinic receptor (rs16969968)
– CHRNA5 mRNA expression in brain/lung (rs588765)
• Are genes important for nicotine dependence
also relevant for smoking cessation?
Does CHRNA5 Predict
Smoking Cessation Success?
Predicting nicotine dependence
Altered nicotinic receptor function
Divided evidence with cessation
CHRNA5 predicts cessation success and
response to medication
Study Design
U Wisconsin - TTURC
• N=1073, European Ancestry
• Pharmacotherapy arms
(NRT, bupropion, combo)
and 1 placebo arm
• Cessation
Abstinence at 60 days
Time to relapse over 60 days
CHRNA5-A3-B4 Haplotypes
• Rs16969968
Non-synonymous coding,
Amino acid change in CHRNA5
• Rs680244
CHRNA5 mRNA levels in brain
and lung
• Combination of 2 variants
– H1 (GC, 20.8%) Low smoking quantity
– H2 (GT, 43.7%)
– H3 (AC, 35.5%) High smoking quantity
CHRNA5 haplotypes predict cessation and response to medication
1.6
1.4
1.2
reference
1.00
OR
(Abstinence)
1.0
1.13
1.11
0.98
Placebo
0.8
Treatment
0.62
0.6
0.37
0.4
0.2
0.0
H1
N=1,073
Haplotypes (rs16969968, rs680244):
H1=GC(20.8%)
H2=GT(43.7%)
H3=AC(35.5%)
H2
Haplotypes
H3
Chen et al, Am J Psychiatry 2012
CHRNA5 Haplotypes predict abstinence in individuals
receiving placebo medication
1.6
1.4
1.2
reference
1.00
OR
(Abstinence)
1.0
1.13
1.11
0.98
Placebo
0.8
Treatment
0.62
0.6
0.37
0.4
0.2
0.0
H1
H2
Haplotypes
H3
Chen et al, Am J Psychiatry 2012
CHRNA5 Haplotypes does not predict abstinence in
individuals receiving active medication
1.6
1.4
1.2
reference
1.00
OR
(Abstinence)
1.0
1.13
1.11
0.98
Placebo
0.8
Treatment
0.62
0.6
0.37
0.4
0.2
0.0
H1
H2
Haplotypes
H3
Chen et al, Am J Psychiatry 2012
Smokers with the high risk haplotypes are 3 times more
likely to respond to pharmacotherapy
1.6
1.4
1.2
reference
1.00
OR
(Abstinence)
1.0
1.13
1.11
0.98
Placebo
0.8
Treatment
0.62
0.6
0.37
0.4
0.2
0.0
H1
H2
Haplotypes
H3
Chen et al, Am J Psychiatry 2012
Smokers with the low risk haplotypes do not benefit
from pharmacotherapy
1.6
1.4
1.2
reference
1.00
OR
(Abstinence)
1.0
1.13
1.11
0.98
Placebo
0.8
Treatment
0.62
0.6
0.37
0.4
0.2
0.0
H1
H2
Haplotypes
H3
Chen et al, Am J Psychiatry 2012
A Significant Genotype by Treatment Interaction
1.6
1.4
1.2
reference
1.00
OR
(Abstinence)
1.0
1.13
1.11
0.98
Placebo
0.8
Treatment
0.62
0.6
0.37
0.4
0.2
0.0
H1
H2
Haplotypes
H3
The interaction of haplotypes and treatment is significant (X2=8.97, df=2, p=0.011).
Chen et al, Am J Psychiatry 2012
Number Needed to Treat (NNT) Varies with Haplotypes
NNT: # of patients to treat for 1 to benefit
1.0
0.9
0.8
NNT=7
0.7
0.6
H1
Abstinence0.5
>1000
H2
0.4
H3
0.3
4
0.2
0.1
0.0
Placebo
H1=GC(20.8%)
H2=GT(43.7%)
H3=AC(35.5%)
Treatment
Chen et al, Am J Psychiatry 2012
Genetics can predict prognosis & inform treatment
• Smokers with the low risk haplotype (H1/GC)
– quit more successfully without medication
– do not benefit from medication
• Smokers with the high risk haplotype (H3/AC)
– have more difficulty quitting without medication
– benefit from medication
Does CYP2A6 Predict
Smoking Cessation Success?
Predicts smoking quantity
Encodes the primary nicotine metabolism enzyme
Fast metabolizers have more withdrawal
CYP2A6 predicts response to medication
Faster metabolism (n=501)
Slower metabolism (n=208)
Placebo
Active Treatment
A significant interaction (wald=7.15, df=1, p=0.0075)
Chen, Bloom, et al, Under review
Medication effect (NRT, Not bupropion) differs by metabolism
Faster metabolism
Slower metabolism
Nicotine
Replacement
Therapy
Buproprion
Placebo
Active Treatment
Time to relapse over 90 days
A significant interaction between NRT and CYP2A6 (wald=4.84, df=1, p=0.028).
No interaction between bupropion and CYP2A6 (wald=0.036, df=1, p=0.85).
NRT
Bupropion
Placebo
Faster metab olism
363
157
58
Slower metabolism
149
96
21
Combine CHRNA5 and CYP2A6
Independent
Additive
Nicotine replacement therapy (NRT) vs. placebo effect varies with the
combined effects of CYP2A6 and CHRNA5
90.0%
80.0%
70.0%
60.0%
Abstinence
50.0%
placebo
40.0%
NRT
30.0%
20.0%
10.0%
0.0%
CYP2A6:
CHRNA5:
Placebo
Medication
Low risk
Low risk
n=6
n=50
Low risk
High risk
n=14
n=90
High risk
Low risk
n=23
n=134
High risk
High risk
n=33
n=221
NNT
>1000
16.6
3.7
2.6
A significant interaction (wald=7.44, df=1, p=0.0064)
Chen, Bloom, et al, Under review
Are these results real and useful?
Validation in different samples (PNAT)
Validation in special populations (myocardial infarction)
Validation in natural cessation in observational studies
Replication by PNAT Consortium
CHRNA5 decreases abstinence with PLACEBO but not with NRT
Less likely to quit
N=2,633; 8 RCTs
PNAT, Bergen et al, 2013, Pharmacogenetics and genomics
Replication in Smokers Hospitalized with Myocardial Infarction,
CHRNA5 predicts quitting
Cessation before Admission
100%
90%
80%
70%
% Abstinence 60%
50%
40%
30%
20%
10%
0%
Cessation at 1 Year
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
GG
GA
AA
GG
CHRNA5 (rs16969968)
Predictors
Age
Sex
Genotype (rs16969968)
N=1,450; TRIUMPH Consortium
GA
AA
CHRNA5 (rs16969968)
Having Quit Smoking
at Baseline Admission for MI
OR
95% C.I.
P
1.10
(1.08-1.11)
<0.0001
0.59
(0.45-0.77)
0.0001
0.81
(0.68-0.97)
0.0201
Abstinence
at 1 Year Follow-up after Admission
OR
95% C.I.
P
1.06
(1.05-1.08)
<0.0001
0.67
(0.48-0.44)
0.0197
0.77
(0.62-0.96)
0.0199
Chen et al, Under review
Replication in NCI/GAMEON meta-analysis
CHRNA5 rs16969968 (A) delays age of quitting smoking
Cox regression models adjusted for age, sex, and lung cancer status for lung cancer /ILCCO studies
26
CHRNA5 rs16969968 delays quitting by 2-4 years
(age 41->45 at first quartile, 54->56 at median)
Proportion Having Quit
rs16969968 genotype
+ AA
+ GA
+ GG
Age of Quitting Smoking
AGE at Cessation
27
Quit early, live longer
Jha et al, 2013, NEJM
Quit delay is
clinically significant
Quit by 40
• Both smoking quantity
and quit age affect risk
• Quit by 40 avoided
nearly all the excess risk
• Quit age delay of 2-4
years
Genetic Effect
Genetic Effect
Ongoing International Collaboration
on Smoking Research
Acknowledgement
•
Cross-Population Meta-Analyses International Consortium of Smoking, PHASE I
Washington U Nancy
Robert
Alison
Sarah
Thomas
John
Linus
Jen
Hong
Laura
MD Anderson Chris
Margaret
Sanjay
Younghun
MSTF
Ming
Jennie
Thomas
WSU
Ann
Angie
UM
Nicole
Stephen
Braxton
Yu-Ching
MGS
Alan R.
Jubao
Jianxin
Douglas F.
Pablo V.
Sharon
WHI
Andrew
Sean
Charles
Helena
Saccone
GENOA
Culverhouse
Goate
Hartz
Przybeck
HyperGen
Rice
SchwantesAn
UCSF
Wang
Xian
Bierut
Amos
Nanjing/Beijing, China
Spitz
Shete
Han
Li
Ma
Korea
Payne
Schwartz
Wenzlaff
Dueker
Japan
Kittner
Mitchell
Taiwan
Cheng
Sanders
Duan
Shi
GenSalt, China
Levinson
Gejman
Kardia
Bergen
David
ARIC
Eaton
Furberg
Thomas
Jennifer
Yan
Steve
DC
Yun Ju
Mosley
Smith
Sun
Hunt
Rao
Sung
John
Helen
Paige
Margaret
Jin
Hongbing
Zhibin
Dongxin
Chen
Dankyu
Taesung
Young Jin
Yoon Shin
Taskashi
Jun
Chien-Hsiun
Jer-Yuarn
Ying Ting
Fuu-Jen
Treva
Jiang
Dongfeng
Hongyan
Jiang
investigators
Wiencke
Hansen
Bracci
Wrensch
Guangfu
Shen
Hu
Lin
Wu
Yoon
Park
Kim
Cho
Kohno
Yokota
Chen
Wu
Chen
Tsai
Rice
He
Gu
Huang
He
• Special acknowledgement to
COGEND
CTRC KL2
NIDA
Louis Fox
Sherri Fisher
Hilary Davidson
collaborators and staff
KL2 RR024994
P01 CA89392
International Cross-Population Consortium
CHRNA5 rs16969968 is consistently associated with heavy
smoking across three populations (Phase I Finding)
Bin A rs16969968*
European
ancestry
Sub-bin A-AS1: rs16969968*
Asian
ancestry
Sub-bin A-AA1: rs16969968
African
American
ancestry
Chen et al. 2012, Genetic Epidemiology
PHASE II: Meta-Analysis with Imputed Data
Cross-Population Meta-Analyses International Consortium
Smoking and Chromosome 15q25
European ancestry
COGEND
MD Anderson
MSTF
WSU
GEOS
MGS
GENOA
HyperGEN
ARIC
Marchini Oxford samples
WTCCC-CAD
QIMR
UK
UK lung cancer
Northern Finland Birth Cohort
Germany
Finnish Study
NAG
Young Finns Study
SHIP
NFBC66
Croatian Cohorts
Dental Study
COGA
CADD
NYSFS
Sardinia
Netherland Twin Registry (NTR)
SMOFAM
Yale study
Total- European ancestry
N=50,000
Asian ancestry
African American ancestry
Nanjing
COGEND
Beijing
MD Anderson
KARE (Korea)
MSTF
Tokyo
WSU
SC (Taiwan)
UCSF
T2D (Taiwan)
GEOS
GenSalt (China)
AGEN-Chen Peng/Singapore (Malay,
Indian, Chinese)
MGS
AGEN-Ying Wu CLHNS China
ARIC
AGEN-Jaeseong Korea
WHI
AGEN-Huaixing China
MESA
AGEN-Xiao-Ou, China
CARDIA, CFS, JHS
Wuhan study
Dental Study
PROMIS Pakistani
COGA
ABNET's study
Total- African American ancestry
Total-Asian ancestry
N=39,000
N=109,000
GENOA
HyperGEN
N=20,000
Conclusion on Personalized Medicine
• It matters
– Minimize medication risk and cost
– Target high risk patients
– Optimize treatment matching for improved effectiveness
• It works
– Addiction/Smoke/Onco chip
Acknowledgement
Washington U
In St. Louis
Laura Bierut
Rich Grucza
Sarah Hartz
Alison Goate
Joseph Bloom Jen Wang
Nancy Saccone Rob Culverhouse John Rice
Robert Carney Sharon Cresci Richard Bach
U Wisconsin
Timothy Baker Megan Piper Steven Smith
U Utah
Dale Cannon
Robert Weiss
Harvard U
Pete Kraft
Nancy Rigotti
Darmouth
Christopher Amos
RTI
Eric Johnson
Michigan State U
Naomi Breslau
U Minnesota
Dorothy Hatsukami
U Bristol
Marcus Munafo
Cross-population Consortium on Genetics of Smoking
chenli@psychiatry.wustl.edu
Extra Slides
Smoking Cessation and Psychiatric
Disorders
• Patients with
psychopathology are
less likely to quit
• Quitting failure->
decreased mental
health
• Patients with anxiety
have decreased
response to treatment
• Introducing genetics:
– Hypothesis: Negative
affect decrease cessation
in subjects with high
genetic risk.
Smoking Cessation Trial (TTURC)
Fast Metabolizers benefit from NRT
Fast metabolizers
(n=409)
Cigarettes per day (CPD)
10
8
placebo
6
lozenge
4
patch
patch+lozenge
2
0
1
2
3
4
5
6
7
8
Post-quit Treatment Weeks
Slow metabolizers
(n=145)
Cigarettes per day (CPD)
10
8
placebo
6
lozenge
patch
4
patch+lozenge
2
0
1
2
3
4
5
6
Post-quit Treatment Weeks
7
8
What is new
• PNAT
– Patch: slow metabolizers quit
better
– Spray: no difference
– Placebo: slow metabolizers quit
better
– Bupropion: no difference
• We confirm placebo and
bupropion
• New
– PNAT: It was unknown if NRT vs
placebo differ by NMR
– we find NRT vs placebo effect
differ with CYP2A6 (like their spray
substracting placebo effect if it
exists)
– Combo is better than mono
Genes, Environment, and Clinical
Prediction
We know genetic (G) risk is modified by treatment
Is environmental (E) risk modified by G?
Does treatment alter G by E risks?
Smoking
Pregnant
Women
Partner Smoking:
Partner Smoking Is Worse in Individuals with CHRNA5 Risk (G*E)
3
Testing G
Cig per day
2
2
GG
GA
1
AA
1
0
Testing G *E
Cig per day
CPD0
CPD1
Time
CPD2
3
GG/no partner smoking
2
GG/partner smoking
2
GA/no partner smoking
1
GA/partner smoking
1
AA/no partner smoking
0
CPD0
CPD1
Time
CPD2
AA/partner smoking
Interaction of rs16969968 and partner smoking on quitting (decrease of smoking quantity over time) is significant
(n=869, t=2.60, p=0.017 in ALSPAC, and n=104, t=2.97, p=0.0033 in TTURC)
Partner Smoking:
Environmental Effect Is Stronger in Individuals with CHRNA5 Risk Alleles (G*E)
Smoking
Pregnant
Women
Cig per day
Testing G
Testing G *E
3
3
2
2
2
GG2
1
GA
1
AA
1
0
0
1
CPD0
CPD1
Time
GA/no partner
smoking
GA/partner smoking
AA/no partner
smoking
CPD0
CPD1
Time
CPD2
35
AA/partner smoking
30
GG/partner
smoking
25
25
GG/no partner
smoking
20
GG20
15
GA15
10
AA
10
5
5
0
0
30
CO level
GG/partner smoking
CPD2
35
Cessation
Trial
Placebo
GG/no partner
smoking
GA/partner smoking
GA/no partner
smoking
AA/partner smoking
CO1
CO2
CO3
CO4 CO5
Time
CO6
CO7
Time
CO1 CO2 CO3 CO4 CO5 CO6 CO7
Interaction of rs16969968 and partner smoking on quitting (decrease of smoking quantity over time) is significant
(n=869, t=2.60, p=0.017 in ALSPAC, and n=104, t=2.97, p=0.0033 in TTURC)
AA/no partner
smoking
Genetic Effects (main G and G*E) in the placebo group
can be neutralized by medication
Testing G
35
35
CO level
30
25
Placebo
N=104
30
GG/partner
smoking
25
GG/no partner
smoking
20
GG
20
GA/partner smoking
15
GA
15
GA/no partner
smoking
AA
10
10
AA/partner smoking
5
5
0
0
CO1
CO2
CO3
CO4 CO5
Time
CO6
CO7
CO1 CO2 CO3 CO4 CO5 CO6 CO7
35
35
CO level
30
Treated
N=765
Testing G *E
25
AA/no partner
smoking
Time
30
GG/partner
smoking
25
GG/no partner
smoking
20
GG
20
GA/partner smoking
15
GA
15
GA/no partner
smoking
AA
10
10
AA/partner smoking
5
5
0
0
CO1
CO2
CO3
CO4
Time
CO5
CO6
CO7
Medication neutralizes the G effect (n=869, t=2.60, p=0.0093)
Medication neutralizes the G*E effect (n=869, t=3.59, p=0.00034)
CO1 CO2 CO3 CO4 CO5 CO6 CO7
Time
AA/no partner
smoking
Combination of G and E informs
who will benefit from treatment
• Most cessation is unassisted
– during pregnancy or post-MI
• In unassisted cessation, there is a G*E interaction
on quitting
– accentuated E effect with risk G, or
– expression of G effect with risk E
• Medication neutralizes both the main effect of G
and G*E
Future Goals
• Generalize to diverse populations
• Design mechanism-specific treatments
• Develop treatment algorithm incorporating
multiple G, E, and other predictors
• Conduct cost benefit analysis of random vs.
genotype-based treatment
Response to Treatment Differs by Haplotype
a. Haplotype H1 (GC)
RH=0.83, p=0.36
b. Haplotype H2 (GT)
RH=0.48, p=2.7*10-8
c. Haplotype H3 (AC)
RH=0.48, p=9.7*10-7
Placebo
Active Treatment
Chen et al, Am J Psychiatry 2012
The CHRNA5 genetic effect does not differ
by type of pharmacotherapy
1.0
0.9
0.8
0.7
0.6
Abstinence
0.5
H1
0.4
H2
H3
0.3
0.2
0.1
0.0
Placebo
Buproprion
only
NRT only
Combined
No difference in haplotypic risks on cessation across medication groups
(wald=1.16, df=3, p=0.88)
Chen et al, Am J Psychiatry 2012
Fast metabolizers on placebo treatment have a significantly faster escalation
into heavy smoking over time
Cigarettes per day (CPD)
10
8
Fast metabolizer on
placebo (n=72)
6
Slow metabolizer on
placebo (n=27)
4
Fast metabolizer on active
medication (n=521)
Slow metablizer on
medication (n=224)
2
0
wk 1
wk 2
wk 3
wk 4
wk 5
wk 6
Post-quit Treatment Weeks
A significant interaction t=3.13, df=1, p=0.0020.
wk 7
wk 8
Phase II goals
• Genotyped data -> imputed data
– Because some variants were not genotyped
– Can impute insertions and deletions
• Expanded smoking behavior phenotypes
– Heavy smoking phenotype
– Age of quitting
• Scientific questions
– Refinement of association signals
– Identify additional new loci
– Identify consistent (or unique), and biologically significant
associations
51
CHRNA5 rs16969968 delays smoking cessation
Proportion Having Quit
rs16969968 genotype
+ AA
+ GA
+ GG
Age of Quitting Smoking
AGE at Cessation
52
Smoking quantity and age of quitting are both important
for risk of lung cancer and COPD
Lung Cancer Risk
COPD Risk
Thun et al, 2013, NEJM