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

chenli@psychiatry.wustl.edu

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