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 • What do we know about genetics of nicotine dependence? • Are genes important for smoking cessation? Cessation success Response to pharmacotherapy • Are these genetic associations real and useful?
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 E D. Green et al. Nature2011
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
Study Design U Wisconsin - TTURC CHRNA5-A3-B4Haplotypes 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%) H2 (GT, 43.7%) H3 (AC, 35.5%) • N=1073, European Ancestry • Pharmacotherapy arms (NRT, bupropion, combo) and 1 placebo arm • Cessation Abstinence at 60 days Time to relapse over 60 days Low smoking quantity High smoking quantity
CHRNA5haplotypes predict cessation and response to medication OR (Abstinence) Haplotypes N=1,073 Haplotypes (rs16969968, rs680244): H1=GC(20.8%) H2=GT(43.7%) H3=AC(35.5%) Chen et al, Am J Psychiatry 2012
CHRNA5Haplotypes predict abstinence in individuals receiving placebo medication OR (Abstinence) Haplotypes Chen et al, Am J Psychiatry 2012
CHRNA5Haplotypes does not predict abstinence in individuals receiving active medication OR (Abstinence) Haplotypes Chen et al, Am J Psychiatry 2012
Smokers with the high risk haplotypes are 3 times more likely to respond to pharmacotherapy OR (Abstinence) Haplotypes Chen et al, Am J Psychiatry 2012
Smokers with the low risk haplotypes do not benefit from pharmacotherapy OR (Abstinence) Haplotypes Chen et al, Am J Psychiatry 2012
A Significant Genotype by Treatment Interaction OR (Abstinence) Haplotypes 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 NNT=7 >1000 4 Abstinence H1=GC(20.8%) H2=GT(43.7%) H3=AC(35.5%) 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 Chen, Bloom, et al, Under review A significant interaction (wald=7.15, df=1, p=0.0075)
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).
Combine CHRNA5 and CYP2A6 Independent Additive
Nicotine replacement therapy (NRT) vs. placeboeffect varies with the combined effects of CYP2A6 and CHRNA5 Abstinence Chen, Bloom, et al, Under review A significant interaction (wald=7.44, df=1, p=0.0064)
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 ConsortiumCHRNA5decreases abstinence with PLACEBO but not with NRT Less likely to quit PNAT, Bergen et al, 2013, Pharmacogenetics and genomics N=2,633; 8 RCTs
Replication in Smokers Hospitalized with Myocardial Infarction, • CHRNA5predicts quitting Cessation before Admission Cessation at 1 Year % Abstinence CHRNA5 (rs16969968) CHRNA5 (rs16969968) Chen et al, Under review N=1,450; TRIUMPH Consortium
Replication in NCI/GAMEON meta-analysisCHRNA5rs16969968 (A) delays age of quitting smoking Cox regression models adjusted for age, sex, and lung cancer status for lung cancer /ILCCO studies
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
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
Acknowledgement • Special acknowledgement to • Cross-Population Meta-Analyses International Consortium of Smoking, PHASE I
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 N=20,000 N=39,000 N=50,000 N=109,000
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
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.
Fast Metabolizers benefit from NRT Cigarettes per day (CPD) Fast metabolizers (n=409) Post-quit Treatment Weeks Cigarettes per day (CPD) Slow metabolizers (n=145) Post-quit Treatment Weeks
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) Cig per day Testing G Time Cig per day Testing G *E Time 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) Testing G Testing G *E Cig per day Smoking Pregnant Women Time Time Cessation Trial Placebo CO level Time Time 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)
Genetic Effects (main G and G*E) in the placebo group can be neutralized by medication Testing G Testing G *E CO level Placebo N=104 Time Time CO level Treated N=765 Time Time 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)
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. HaplotypeH1 (GC) RH=0.83, p=0.36 b. HaplotypeH2 (GT) RH=0.48, p=2.7*10-8 c. HaplotypeH3 (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 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.