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Transcript of Genomics and Personalized Medicine: Smoking Cessation Treatment Li-Shiun Chen, MD, MPH, ScD...
Genomics and Personalized Medicine: Smoking Cessation Treatment
Li-Shiun Chen, MD, MPH, ScDWashington 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 successResponse to pharmacotherapy
III. Are these genetic associations real and useful?
E D. Green et al. Nature 2011
Genomics can lead to personalized medicine
Risks
Cardiovascular side effect (NRT, varenicline)Seizure, MAO-I (bupropion)
Perinatal safety? Medication Cost
Benefits
Efficacy of cessation medicationCombination vs. monotherapy
The Tobacco and Genetics Consortium (2010) Nature Genetics
Chromosome 15q25 Is Important for Smoking
CHRNA5-A3-B4
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 dependenceAltered 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
• CessationAbstinence at 60 daysTime to relapse over 60 days
CHRNA5-A3-B4 Haplotypes• Rs16969968
Non-synonymous coding, Amino acid change in CHRNA5
• Rs680244CHRNA5 mRNA levels in brain and lung
• Combination of 2 variants– H1 (GC, 20.8%)– H2 (GT, 43.7%)– H3 (AC, 35.5%)
Low smoking quantity
High smoking quantity
H1 H2 H30.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.00
0.62
0.37
0.98
1.11 1.13
PlaceboTreatment
reference
OR (Abstinence)
Haplotypes
CHRNA5 haplotypes predict cessation and response to medication
N=1,073Haplotypes (rs16969968, rs680244): H1=GC(20.8%)H2=GT(43.7%)H3=AC(35.5%) Chen et al, Am J Psychiatry 2012
H1 H2 H30.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.00
0.62
0.37
0.98
1.11 1.13
PlaceboTreatment
reference
OR (Abstinence)
Haplotypes
CHRNA5 Haplotypes predict abstinence in individuals receiving placebo medication
Chen et al, Am J Psychiatry 2012
H1 H2 H30.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.00
0.62
0.37
0.98
1.11 1.13
PlaceboTreatment
reference
OR (Abstinence)
Haplotypes
CHRNA5 Haplotypes does not predict abstinence in individuals receiving active medication
Chen et al, Am J Psychiatry 2012
H1 H2 H30.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.00
0.62
0.37
0.98
1.11 1.13
PlaceboTreatment
reference
OR (Abstinence)
Haplotypes
Smokers with the high risk haplotypes are 3 times more likely to respond to pharmacotherapy
Chen et al, Am J Psychiatry 2012
H1 H2 H30.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.00
0.62
0.37
0.98
1.11 1.13
PlaceboTreatment
reference
OR (Abstinence)
Haplotypes
Smokers with the low risk haplotypes do not benefit from pharmacotherapy
Chen et al, Am J Psychiatry 2012
H1 H2 H30.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.00
0.62
0.37
0.98
1.11 1.13
PlaceboTreatment
reference
OR (Abstinence)
Haplotypes
A Significant Genotype by Treatment Interaction
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 HaplotypesNNT: # of patients to treat for 1 to benefit
Placebo Treatment0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
H1H2H3
Abstinence
Chen et al, Am J Psychiatry 2012
NNT=7
>1000
4
H1=GC(20.8%)H2=GT(43.7%)H3=AC(35.5%)
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 quantityEncodes the primary nicotine metabolism enzyme
Fast metabolizers have more withdrawal
CYP2A6 predicts response to medication
Faster metabolism (n=501) Slower metabolism (n=208)
PlaceboActive 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
PlaceboActive 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).
Faster metab olism Slower metabolismNRT 363 149Bupropion 157 96Placebo 58 21
Combine CHRNA5 and CYP2A6
IndependentAdditive
Abstinence
Nicotine replacement therapy (NRT) vs. placebo effect varies with the combined effects of CYP2A6 and CHRNA5
A significant interaction (wald=7.44, df=1, p=0.0064)
CYP2A6: Low risk Low risk High risk High riskCHRNA5: Low risk High risk Low risk High riskPlacebo n=6 n=14 n=23 n=33
Medication n=50 n=90 n=134 n=221
NNT >1000 16.6 3.7 2.6
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
90.0%
placeboNRT
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 ConsortiumCHRNA5 decreases abstinence with PLACEBO but not with NRT
PNAT, Bergen et al, 2013, Pharmacogenetics and genomicsN=2,633; 8 RCTs
Less likely to quit
GG GA AA0%
10%20%30%40%50%60%70%80%90%
100%
GG GA AA0%
10%20%30%40%50%60%70%80%90%
100%
% Abstinence
CHRNA5 (rs16969968)CHRNA5 (rs16969968)
Having Quit Smoking at Baseline Admission for MI
Predictors OR 95% C.I. P
Age 1.10 (1.08-1.11) <0.0001
Sex 0.59 (0.45-0.77) 0.0001
Genotype (rs16969968) 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
Cessation before Admission Cessation at 1 Year
Replication in Smokers Hospitalized with Myocardial Infarction,CHRNA5 predicts quitting
N=1,450; TRIUMPH Consortium Chen et al, Under review
26
Replication in NCI/GAMEON meta-analysisCHRNA5 rs16969968 (A) delays age of quitting smoking
Cox regression models adjusted for age, sex, and lung cancer status for lung cancer /ILCCO studies
27
CHRNA5 rs16969968 delays quitting by 2-4 years (age 41->45 at first quartile, 54->56 at median)
Age of Quitting Smoking
Prop
ortio
n H
avin
g Q
uit
rs16969968 genotype+ AA+ GA+ GG
AGE at Cessation
Quit early, live longer
Jha et al, 2013, NEJM
Quit delay is clinically significant
• Both smoking quantity and quit age affect risk
• Quit by 40 avoided nearly all the excess risk
• Quit age delay of 2-4 years
Quit by 40
Genetic EffectGenetic Effect
Ongoing International Collaboration on Smoking Research
Acknowledgement• Cross-Population Meta-Analyses International Consortium of Smoking, PHASE I
Washington U Nancy Saccone GENOA Thomas MosleyRobert Culverhouse Jennifer SmithAlison Goate Yan SunSarah Hartz Steve HuntThomas Przybeck HyperGen DC RaoJohn Rice Yun Ju Sung
LinusSchwantes-An UCSF John Wiencke
Jen Wang Helen HansenHong Xian Paige BracciLaura Bierut Margaret Wrensch
MD Anderson Chris Amos Nanjing/Beijing, China Jin GuangfuMargaret Spitz Hongbing ShenSanjay Shete Zhibin HuYounghun Han Dongxin Lin
MSTF Ming Li Chen WuJennie Ma Korea Dankyu YoonThomas Payne Taesung Park
WSU Ann Schwartz Young Jin KimAngie Wenzlaff Yoon Shin Cho
UM Nicole Dueker Japan Taskashi KohnoStephen Kittner Jun YokotaBraxton Mitchell Taiwan Chien-Hsiun ChenYu-Ching Cheng Jer-Yuarn Wu
MGS Alan R. Sanders Ying Ting ChenJubao Duan Fuu-Jen TsaiJianxin Shi GenSalt, China Treva RiceDouglas F. Levinson Jiang HePablo V. Gejman Dongfeng GuSharon Kardia Hongyan Huang
WHI Andrew Bergen Jiang HeSean David ARIC investigatorsCharles EatonHelena Furberg
• Special acknowledgement to
COGEND Louis FoxSherri FisherHilary Davidsoncollaborators and staff
CTRC KL2NIDA
KL2 RR024994P01 CA89392
International Cross-Population ConsortiumCHRNA5 rs16969968 is consistently associated with heavy
smoking across three populations (Phase I Finding)
European ancestry
Asian ancestry
African American ancestry
Sub-bin A-AS1: rs16969968*
Sub-bin A-AA1: rs16969968
Bin A rs16969968*
Chen et al. 2012, Genetic Epidemiology
N=109,000N=50,000
N=39,000 N=20,000
PHASE II: Meta-Analysis with Imputed DataCross-Population Meta-Analyses International Consortium
Smoking and Chromosome 15q25 European ancestryCOGENDMD AndersonMSTFWSUGEOSMGSGENOAHyperGENARICMarchini Oxford samplesWTCCC-CADQIMRUKUK lung cancerNorthern Finland Birth CohortGermanyFinnish StudyNAGYoung Finns StudySHIPNFBC66Croatian CohortsDental StudyCOGACADDNYSFSSardiniaNetherland Twin Registry (NTR)SMOFAMYale studyTotal- European ancestry
Asian ancestryNanjing
Beijing
KARE (Korea)
Tokyo
SC (Taiwan)
T2D (Taiwan)
GenSalt (China)AGEN-Chen Peng/Singapore (Malay, Indian, Chinese)
AGEN-Ying Wu CLHNS China
AGEN-Jaeseong Korea
AGEN-Huaixing China
AGEN-Xiao-Ou, China
Wuhan study
PROMIS Pakistani
ABNET's study
Total-Asian ancestry
African American ancestryCOGEND
MD Anderson
MSTF
WSU
UCSF
GEOS
MGS
GENOA
HyperGEN
ARIC
WHI
MESA
CARDIA, CFS, JHS
Dental Study
COGA
Total- African American ancestry
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
Washington U Laura Bierut Rich Grucza Sarah HartzIn St. Louis Alison Goate Joseph Bloom Jen Wang
Nancy Saccone Rob Culverhouse John RiceRobert Carney Sharon Cresci Richard Bach
U Wisconsin Timothy Baker Megan Piper Steven SmithU Utah Dale Cannon Robert WeissHarvard U Pete Kraft Nancy RigottiDarmouth Christopher AmosRTI Eric JohnsonMichigan State U Naomi BreslauU Minnesota Dorothy HatsukamiU Bristol Marcus MunafoCross-population Consortium on Genetics of Smoking
Acknowledgement
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)
1 2 3 4 5 6 7 80
2
4
6
8
10
placebo
lozenge
patch
patch+lozenge
Ciga
rett
es p
er d
ay (C
PD)
Post-quit Treatment Weeks
Post-quit Treatment Weeks
Fast metabolizers (n=409)
Slow metabolizers (n=145)
Fast Metabolizers benefit from NRT
1 2 3 4 5 6 7 80
2
4
6
8
10
placebolozengepatchpatch+lozenge
Ciga
rett
es p
er d
ay (C
PD)
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?
Partner Smoking: Partner Smoking Is Worse in Individuals with CHRNA5 Risk (G*E)
CPD0 CPD1 CPD20
1
2
3GG/no partner smoking
GG/partner smoking
GA/no partner smoking
GA/partner smoking
AA/no partner smoking
AA/partner smoking
Smoking Pregnant Women
CPD0 CPD1 CPD20
1
2
3
GGGAAA
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)
Cig
per d
ay
Time
Time
Testing G
Testing G *E
Cig
per d
ay
Partner Smoking: Environmental Effect Is Stronger in Individuals with CHRNA5 Risk Alleles (G*E)
CO1 CO2 CO3 CO4 CO5 CO6 CO70
5
10
15
20
25
30
35
GGGAAA
CO1 CO2 CO3 CO4 CO5 CO6 CO70
5
10
15
20
25
30
35
GG/partner smokingGG/no partner smokingGA/partner smokingGA/no partner smokingAA/partner smokingAA/no partner smoking
CPD0 CPD1 CPD20
1
2
3
GG/no partner smokingGG/partner smokingGA/no partner smokingGA/partner smokingAA/no partner smokingAA/partner smoking
Smoking Pregnant Women
Cessation Trial Placebo
CPD0 CPD1 CPD20
1
2
3
GGGAAA
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)
Cig
per d
ay C
O le
vel
Time Time
Time Time
Testing G Testing G *E
Genetic Effects (main G and G*E) in the placebo group can be neutralized by medication
CO1 CO2 CO3 CO4 CO5 CO6 CO70
5
10
15
20
25
30
35
GGGAAA
CO1 CO2 CO3 CO4 CO5 CO6 CO70
5
10
15
20
25
30
35
GGGAAA
CO1 CO2 CO3 CO4 CO5 CO6 CO70
5
10
15
20
25
30
35
GG/partner smokingGG/no partner smokingGA/partner smokingGA/no partner smokingAA/partner smokingAA/no partner smoking
PlaceboN=104
Treated N=765
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 CO70
5
10
15
20
25
30
35
GG/partner smokingGG/no partner smokingGA/partner smokingGA/no partner smokingAA/partner smokingAA/no partner smoking
Time Time
Time Time
CO
leve
l C
O le
vel
Testing G Testing G *E
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
c. Haplotype H3 (AC)RH=0.48, p=9.7*10-7
b. Haplotype H2 (GT)RH=0.48, p=2.7*10-8
a. Haplotype H1 (GC)RH=0.83, p=0.36
PlaceboActive Treatment
Response to Treatment Differs by Haplotype
Chen et al, Am J Psychiatry 2012
The CHRNA5 genetic effect does not differ by type of pharmacotherapy
Placebo Buproprion only
NRT only Combined0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
H1H2H3
Abstinence
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
A significant interaction t=3.13, df=1, p=0.0020.
wk 1 wk 2 wk 3 wk 4 wk 5 wk 6 wk 7 wk 80
2
4
6
8
10
Fast metabolizer on placebo (n=72)Slow metabolizer on placebo (n=27)Fast metabolizer on active medication (n=521)Slow metablizer on medica-tion (n=224)
Post-quit Treatment Weeks
Ciga
rette
s pe
r day
(CPD
)
51
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
52
CHRNA5 rs16969968 delays smoking cessation
Age of Quitting Smoking
Prop
ortio
n H
avin
g Q
uit
rs16969968 genotype+ AA+ GA+ GG
AGE at Cessation
Smoking quantity and age of quitting are both important for risk of lung cancer and COPD
Thun et al, 2013, NEJM
Lung Cancer Risk
COPD Risk