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Genomics and Personalized Medicine: Smoking Cessation Treatment Li-Shiun Chen, MD, MPH, ScD Washington University School of Medicine Apr 18, 2013.

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Presentation on theme: "Genomics and Personalized Medicine: Smoking Cessation Treatment Li-Shiun Chen, MD, MPH, ScD Washington University School of Medicine Apr 18, 2013."— Presentation transcript:

1 Genomics and Personalized Medicine: Smoking Cessation Treatment Li-Shiun Chen, MD, MPH, ScD Washington University School of Medicine Apr 18, 2013

2 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?

3 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 medication Combination vs. monotherapy

4 The Tobacco and Genetics Consortium (2010) Nature Genetics Chromosome 15q25 Is Important for Smoking CHRNA5-A3-B4

5 Genetics of nicotine dependence Heritability 56%-71% Specific genetic risks identified – CHRNA5-CHRNA3-CHRNB4 gene cluster Association -> Function – amino acid change in nicotinic receptor (rs ) – CHRNA5 mRNA expression in brain/lung (rs588765) Are genes important for nicotine dependence also relevant for smoking cessation?

6 Does CHRNA5 Predict Smoking Cessation Success? Predicting nicotine dependence Altered nicotinic receptor function Divided evidence with cessation

7 CHRNA5 predicts cessation success and response to medication

8 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 Rs Non-synonymous coding, Amino acid change in CHRNA5 Rs CHRNA5 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

9 OR (Abstinence) Haplotypes CHRNA5 haplotypes predict cessation and response to medication N=1,073 Haplotypes (rs , rs680244): H1=GC(20.8%) H2=GT(43.7%) H3=AC(35.5%) Chen et al, Am J Psychiatry 2012

10 OR (Abstinence) Haplotypes CHRNA5 Haplotypes predict abstinence in individuals receiving placebo medication Chen et al, Am J Psychiatry 2012

11 OR (Abstinence) Haplotypes CHRNA5 Haplotypes does not predict abstinence in individuals receiving active medication Chen et al, Am J Psychiatry 2012

12 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

13 OR (Abstinence) Haplotypes Smokers with the low risk haplotypes do not benefit from pharmacotherapy Chen et al, Am J Psychiatry 2012

14 OR (Abstinence) Haplotypes A Significant Genotype by Treatment Interaction The interaction of haplotypes and treatment is significant (X 2 =8.97, df=2, p=0.011). Chen et al, Am J Psychiatry 2012

15 Number Needed to Treat (NNT) Varies with Haplotypes NNT: # of patients to treat for 1 to benefit Abstinence Chen et al, Am J Psychiatry 2012 NNT=7 > H1=GC(20.8%) H2=GT(43.7%) H3=AC(35.5%)

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17 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

18 Does CYP2A6 Predict Smoking Cessation Success? Predicts smoking quantity Encodes the primary nicotine metabolism enzyme Fast metabolizers have more withdrawal

19 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

20 Medication effect (NRT, Not bupropion) differs by metabolism Faster metabolismSlower 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). Faster metab olismSlower metabolism NRT Bupropion15796 Placebo5821

21 Combine CHRNA5 and CYP2A6 Independent Additive

22 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 High risk CHRNA5:Low riskHigh riskLow riskHigh risk Placebon=6n=14n=23n=33 Medicationn=50n=90n=134n=221 NNT> Chen, Bloom, et al, Under review

23 Are these results real and useful? Validation in different samples (PNAT) Validation in special populations (myocardial infarction) Validation in natural cessation in observational studies

24 Replication by PNAT Consortium CHRNA5 decreases abstinence with PLACEBO but not with NRT PNAT, Bergen et al, 2013, Pharmacogenetics and genomics N=2,633; 8 RCTs Less likely to quit

25 % Abstinence CHRNA5 (rs ) Having Quit Smoking at Baseline Admission for MI Predictors OR95% C.I.P Age1.10( )< Sex0.59( ) Genotype (rs )0.81( ) Abstinence at 1 Year Follow-up after Admission OR95% C.I.P 1.06( )< ( ) ( ) Cessation before AdmissionCessation 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-analysis CHRNA5 rs (A) delays age of quitting smoking Cox regression models adjusted for age, sex, and lung cancer status for lung cancer /ILCCO studies 26

27 CHRNA5 rs delays quitting by 2-4 years (age 41->45 at first quartile, 54->56 at median) Age of Quitting Smoking 27 Proportion Having Quit rs genotype + AA + GA + GG AGE at Cessation

28 Quit early, live longer Jha et al, 2013, NEJM

29 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 Effect

30 Ongoing International Collaboration on Smoking Research

31 Acknowledgement Cross-Population Meta-Analyses International Consortium of Smoking, PHASE I Washington UNancySacconeGENOAThomasMosley RobertCulverhouseJenniferSmith AlisonGoateYanSun SarahHartzSteveHunt ThomasPrzybeckHyperGenDCRao JohnRiceYun JuSung Linus Schwantes- AnUCSFJohnWiencke JenWangHelenHansen HongXianPaigeBracci LauraBierutMargaretWrensch MD AndersonChrisAmosNanjing/Beijing, ChinaJinGuangfu MargaretSpitzHongbingShen SanjaySheteZhibinHu YounghunHanDongxinLin MSTFMingLiChenWu JennieMaKoreaDankyuYoon ThomasPayneTaesungPark WSUAnnSchwartzYoung JinKim AngieWenzlaffYoon ShinCho UMNicoleDuekerJapanTaskashiKohno StephenKittnerJunYokota BraxtonMitchellTaiwanChien-HsiunChen Yu-ChingChengJer-YuarnWu MGSAlan R.SandersYing TingChen JubaoDuanFuu-JenTsai JianxinShiGenSalt, ChinaTrevaRice Douglas F.LevinsonJiangHe Pablo V.GejmanDongfengGu SharonKardiaHongyanHuang WHIAndrewBergenJiangHe SeanDavidARICinvestigators CharlesEaton HelenaFurberg Special acknowledgement to COGENDLouis Fox Sherri Fisher Hilary Davidson collaborators and staff CTRC KL2 NIDA KL2 RR P01 CA89392

32 International Cross-Population Consortium CHRNA5 rs is consistently associated with heavy smoking across three populations (Phase I Finding) European ancestry Asian ancestry African American ancestry Sub-bin A-AS1: rs * Sub-bin A-AA1: rs Bin A rs * Chen et al. 2012, Genetic Epidemiology

33 N=109,000 N=50,000 N=39,000 N=20,000 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 Asian ancestry Nanjing 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 ancestry COGEND MD Anderson MSTF WSU UCSF GEOS MGS GENOA HyperGEN ARIC WHI MESA CARDIA, CFS, JHS Dental Study COGA Total- African American ancestry

34 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

35 Washington ULaura BierutRich GruczaSarah Hartz In St. LouisAlison GoateJoseph BloomJen Wang Nancy SacconeRob Culverhouse John Rice Robert CarneySharon CresciRichard Bach U WisconsinTimothy BakerMegan PiperSteven Smith U UtahDale CannonRobert Weiss Harvard UPete KraftNancy Rigotti DarmouthChristopher Amos RTIEric Johnson Michigan State UNaomi Breslau U MinnesotaDorothy Hatsukami U BristolMarcus Munafo Cross-population Consortium on Genetics of Smoking Acknowledgement

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37 Extra Slides

38 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.

39 Smoking Cessation Trial (TTURC)

40 Cigarettes per day (CPD) Post-quit Treatment Weeks Fast metabolizers (n=409) Slow metabolizers (n=145) Fast Metabolizers benefit from NRT Cigarettes per day (CPD)

41 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

42 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?

43 Partner Smoking: Partner Smoking Is Worse in Individuals with CHRNA5 Risk (G*E) Smoking Pregnant Women Interaction of rs 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= in TTURC) Cig per day Time Testing G Testing G *E Cig per day

44 Partner Smoking: Environmental Effect Is Stronger in Individuals with CHRNA5 Risk Alleles (G*E) Smoking Pregnant Women Cessation Trial Placebo Interaction of rs 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= in TTURC) Cig per day CO level Time Testing GTesting G *E

45 Genetic Effects (main G and G*E) in the placebo group can be neutralized by medication Placebo N=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= ) Time CO level Testing GTesting G *E

46 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

47 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

48 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 Placebo Active Treatment Response to Treatment Differs by Haplotype Chen et al, Am J Psychiatry 2012

49 The CHRNA5 genetic effect does not differ by type of pharmacotherapy 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

50 Fast metabolizers on placebo treatment have a significantly faster escalation into heavy smoking over time A significant interaction t=3.13, df=1, p=

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 51

52 CHRNA5 rs delays smoking cessation Age of Quitting Smoking 52 Proportion Having Quit rs genotype + AA + GA + GG AGE at Cessation

53 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


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