Gene Finding in Clinical Trial Populations Tom Price SGDP 18 th Feb 2009
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Transcript of Gene Finding in Clinical Trial Populations Tom Price SGDP 18 th Feb 2009
Gene Finding in Clinical Trial Populations
Tom Price
SGDP 18th Feb 2009http://sgdp.iop.kcl.ac.uk/tprice/
Translational Value of Pharmacogenetics
Genetic studies can help:• Identify drug targets• Decrease attrition in development of new drugs• Increase safety by predicting adverse events
• Improve treatment by predicting efficacy
Roses AD (2008). Nat Rev Drug Discov. 7(10):807-1.
Problems in Pharmacogenetics
• Questionable value of many diagnostics– Sensitivity/specificity– Clinical relevance
• Misaligned goals of academe / industry– Marketing function of pharma RCTs – Unpublished studies
• Expense
The Good, the Banned, the Ugly
• According to FDA estimates, Vioxx (rofecoxib) caused more than 27,000 excess cases of myocardial infarction and sudden cardiac death before it was withdrawn from the market
Do me-too drugs have a similar risk profile?
Are there subgroups for whom this drug is safe?
COX Isoforms & Prostaglandins
Maree & Fitzgerald Thromb Haemost 2004, 92:1175–81
NSAIDsCoxibsNSAIDs
COX Inhibitors
• NSAIDs (nonsteroidal anti-inflammatories)– e.g. aspirin, ibuprofen– The world's most prescribed drugs– Inhibit both COX-1 and COX-2
• Gastrointestinal side effects
• Coxibs– e.g. rofecoxib (Vioxx), celecoxib– Selectively inhibit COX-2
• Easier on stomach• Elevated cardiovascular risk vs placebo
Side Effects of COX Inhibition
Grosser, Fries & FitzGerald J Clin Invest 2006, 116:4–15
Should We Ban All Coxibs?
• Coxibs have similar biochemical effects – are they all as dangerous as Vioxx?
• Alternatives:– Off-target effects specific to Vioxx– Interindividual variability– Subgroups account for cardiovascular hazard
• Crossover trial of 2 anti-inflammatory drugs
• 50 healthy volunteers received single doses of celecoxib, rofecoxib or placebo in random order with a 7 day washout
• Drug effects measured using in vivo and ex vivo assays of COX-1 and COX-2 enzymatic activity
• 5 individuals underwent this protocol 5 times to study intraindividual variability of response
Biomarkers of Drug Action
PGI2 biosynthesis in vivo2,3-dinor-6keto PGF1αUrine
COX-2 activity ex vivoPGE2Plasma
TxA2 biosynthesis in vivo11-dehydro TxB2Urine
COX-1 activity ex vivoTxB2Serum
MeasureBiomarkerTissue
Drug Responses
Measurements obtained at baseline and 4 hours after dosing
Attained COX-2 Selectivity
Ratio of ex vivo COX-2 to COX-1 inhibition 4h post dose
Variability in Drug Response(COX-2 ex vivo assay)
Within subjectsBetween subjects
Drug Availability
Plasma concentration in blood drawn 4 hours after dosing
Biomarkers of Drug Efficacy
COX inhibition in vivo COX inhibition ex vivo
Conclusions
• Rofecoxib and celecoxib attain similar selectivity for COX-2 ex vivo and in vivo– We have no reason to believe that CV toxicity
is not a class effect of COX-2 inhibitors
• Genetic factors are likely to explain some interindividual variability in drug response– Can we exploit this to predict CV toxicity?
MEDAL Program
• Comparative trial of cardiovascular safety of 2 COX inhibitors – Etoricoxib (Vioxx clone approved in UK)– Diclofenac (NSAID)
• Over 34,000 arthritis patients enrolled
• Average 18 month follow up
Cannon et al. Lancet 2006, 368: 1771-81
MEDAL Results
• No difference between drugs in primary endpoint (heart attack or stroke)
• Some improvement in minor GI symptoms for etoricoxib vs diclofenac
• Blood pressure increase in etoricoxib vs diclofenac
• FDA rejected approval by 19:1
Cannon et al. Lancet 2006, 368: 1771-81
MEDAL Pharmacogenetic Study
• Over 6,000 subjects genotyped on 50K and 38K custom SNP arrays
• Genotyping funded by Rosetta (Merck)
• Intention to investigate gene x drug interactions in blood pressure & gastro side effects
IBC Chip
Brendan Keating
(U Penn)
Stacey Gabriel
(Broad Institute)
• Illumina custom SNP genotyping array for cardiovascular / metabolic / inflammatory disease
• 50K SNPs covering ~2,100 genes
• Dense ‘cosmopolitan’ tagging plus functional variants
• Many resequenced genes
• 200,000 chips manufactured and sold
Merck Chip
• Quickchip V1.5– Illumina custom 38K SNP array– eSNPs (liver, brain, blood, adipose)– All GWAS SNPs– OA/RA genes (WTCCC, franchise nominated)– HTN genes (Current targets)– Network derived (MM)
Sample
Data Analysis
• Genotype data available for 50K chip• Analyzed using PLINK • Genome-wide significance was assessed
as about 10-6 ≈ α of 0.05 after correction for about 50,000 SNPs
• Interesting results followed up by permutation analysis
Preliminary Analysis
• QC removed around 1,600 individuals (mainly non-whites) and 8,000 SNPs (mainly those with low frequency, MAF < 0.2%, in whites).
• The final sample included 4,441 unrelated ethnically white individuals genotyped on 33,661 SNPs, including ancestry informative markers.
QC
Individuals
Total number in PED file 6143
X chromosome genotype data inconsistent with sex
-81
Missing > 1% of genotypes -2
Not ethnic white -1531
Extreme heterozygosity -2
Remove all but one of groups of relatives -57
Remove outliers from EIGENSTRAT -29
Final 6086
SNPs
Total number in PED file 45707
Control SNPs -114
Monomorphic -441
MAF < 0.2% (total sample) -4415
Missing > 1% (MAF > 0.05), Missing > 0.2% (MAF <= 0.05)
-1055
Missing > 1% (ethnic whites) -18
MAF < 0.2% (ethnic whites) -5275
HWE p < 0.001 -435
MAF < 0.2% (after removing EIGENSTRAT outlying individuals)
-277
Final 34216
PhenotypesBaseline Traits• History of hypertension (HBHX)• Antihypertensive treatment at baseline (ANTIHYPE)• Baseline systolic blood pressure (SBP)• Baseline diastolic blood pressure (DBP)• BMI (BMI)• History of diabetes (DMHX)• History of dyslipidaemia (DLHX)
Drug-induced changes in blood pressure• Change in systolic blood pressure over baseline (SC)• Change in diastolic blood pressure over baseline (DC)• Interaction between drug and change in systolic blood pressure over baseline (SCX)• Interaction between drug and change in diastolic blood pressure over baseline (DCX)
Covariates
• First 2 principal components of genotype data from EIGENSTRAT (after removing high LD regions and outlier individuals)
• Age• Sex• Region (US/non US)• RA/OA• Plus (for change in BP over baseline)
– Smoking status– Time since baseline– Square of time since baseline
Eigenstrat
Blue = US
Green = Europe
Eigenstrat
• Interestingly the first 2 PCs of the genotype data, which presumably information on geographic origin within European populations, correlated significantly with baseline systolic and BMI but not baseline diastolic or antihypertensive medication.
History of Hypertension Results• Genome-wide hit for common SNP rs179998 (C−344T)
in promoter region of CYP11B2 (Aldosterone synthase)• LD with rs179998 accounts for subthreshold associations
with other SNPs in CYP11B2
History of Hypertension
CHR SNP UNADJ GC BONF HOLM SIDAK_SS SIDAK_SD FDR_BH FDR_BY 8 rs1799998 1.464e-06 1.464e-06 0.04928 0.04928 0.04809 0.04809 0.04928 0.5422 8 rs3802228 2.96e-05 2.96e-05 0.9963 0.9963 0.6308 0.6307 0.3749 1 8 rs11250163 3.904e-05 3.904e-05 1 1 0.7313 0.7313 0.3749 1 8 rs6433 4.455e-05 4.455e-05 1 1 0.7768 0.7767 0.3749 1 19 rs2230204 9.032e-05 9.032e-05 1 1 0.9522 0.9522 0.5778 1 20 rs6083780 0.0001268 0.0001268 1 1 0.986 0.986 0.5778 1 1 rs1200132 0.0001322 0.0001322 1 1 0.9883 0.9883 0.5778 1 8 rs6410 0.0001373 0.0001373 1 1 0.9902 0.9902 0.5778 1 16 rs7185735 0.0002862 0.0002862 1 1 0.9999 0.9999 0.6259 1
History of Hypertension Results
Genome wide significant hit on rs179998 5’ of CYP11B2
Aldosterone synthase CYP11B2
• Meta-analysis of 19 studies suggests that rs179998 may be associated with essential hypertension– CC homozygotes had a 17% lower risk than TT homozygotes
under a fixed effects model (OR 0.83; CI 0.76–0.91; p < 0.001) but not under a random effects model (OR 0.89; CI 0.76–1.04; p = .13)
– Heterogeneity between studies would suggest a random effects model is appropriate
– HTN defined as SBP>140 or DBP>90 or antihypertensive Rx
– No effect of rs179998 on SBP or DBP in untreated patients– Sookoian et al. J. Hypertens. 2007, 25:5-13
– Staessen et al. J. Hypertens. 2007, 25:37-39
Antihypertensive Use Results• Antihypertensive use at baseline correlates highly with
history of hypertension• There is some evidence of association with rs179998 but
below the threshold for genome-wide significance
Antihypertensive use at baseline
CHR SNP UNADJ GC BONF HOLM SIDAK_SS SIDAK_SD FDR_BH FDR_BY 21 rs2073362 3.989e-05 3.989e-05 1 1 0.7389 0.7389 0.4605 1 21 rs4986956 4.033e-05 4.033e-05 1 1 0.7427 0.7427 0.4605 1 8 rs1799998 4.104e-05 4.104e-05 1 1 0.7488 0.7488 0.4605 1 5 rs1498928 0.0001537 0.0001537 1 1 0.9943 0.9943 1 1 13 rs532625 0.0003381 0.0003381 1 1 1 1 1 1 1 rs4072431 0.0003654 0.0003654 1 1 1 1 1 1 19 rs2230204 0.0004178 0.0004178 1 1 1 1 1 1 14 rs12896130 0.0004234 0.0004234 1 1 1 1 1 1 2 rs2059693 0.0004714 0.0004714 1 1 1 1 1 1
Blood Pressure Results
Nothing much came up for any of the blood pressure phenotypes
Blood Pressure Results
• Nothing much came up for any of the measured blood pressure phenotypes - possibly because antihypertensive use was included as a covariate, so genetic influences on liability to antihypertensive use were already excluded
• Among the nonsignificant top hits were FTO on chromosome 16 with baseline systolic BP
Baseline Systolic
CHR SNP UNADJ GC BONF HOLM SIDAK_SS SIDAK_SD FDR_BH FDR_BY 16 rs12324955 1.991e-05 1.991e-05 0.6703 0.6703 0.4885 0.4885 0.6703 1 3 rs3774061 5.446e-05 5.446e-05 1 1 0.8401 0.8401 0.7331 1 12 rs11172124 6.948e-05 6.948e-05 1 1 0.9035 0.9035 0.7331 1 16 rs6499656 8.712e-05 8.712e-05 1 1 0.9467 0.9467 0.7331 1 1 rs300267 0.0001123 0.0001123 1 1 0.9772 0.9772 0.7562 1 2 rs4675278 0.0001524 0.0001524 1 1 0.9941 0.9941 0.7891 1 2 rs10200844 0.0002173 0.0002173 1 1 0.9993 0.9993 0.7891 1 6 rs2295591 0.0002566 0.0002566 1 1 0.9998 0.9998 0.7891 1 4 rs2069763 0.0003063 0.0003063 1 1 1 1 0.7891 1
BMI Results• A low frequency (1%) SNP rs3781637 in intron 1 of MTNR1B melatonin receptor
1B was associated with BMI at genome-wide significant level• Statistical significance was confirmed by permutation analysis (p = 0.03)• There is also a cluster of SNPs associated in the range p=10-4 - 10-5 in the NRG1
neuregulin1 gene locus on chromosome 8. There was also a hit on NRG1 with a similar p value for history of hypertension.
• FTO and MCR4, the most consistently replicated associations with BMI, do not feature in the top hits
BMI
CHR SNP UNADJ* GC* BONF* HOLM* SIDAK_SS* SIDAK_SD* FDR_BH* FDR_BY* 11 rs3781637 2.242e-08 2.242e-08 0.0007548 0.0007548 0.0007545 0.0007545 0.0007548 0.008304 8 rs12675298 2.151e-05 2.151e-05 0.7239 0.7239 0.5151 0.5151 0.2076 1 8 rs2881544 3.524e-05 3.524e-05 1 1 0.6946 0.6946 0.2076 1 10 rs196335 3.697e-05 3.697e-05 1 1 0.7119 0.7119 0.2076 1 8 rs989465 4.09e-05 4.09e-05 1 1 0.7476 0.7475 0.2076 1 8 rs1383961 4.436e-05 4.436e-05 1 1 0.7753 0.7753 0.2076 1 6 rs3734681 4.637e-05 4.637e-05 1 1 0.7901 0.79 0.2076 1 8 rs1979565 4.935e-05 4.935e-05 1 1 0.8101 0.81 0.2076 1 12 rs3213900 5.868e-05 5.868e-05 1 1 0.8613 0.8612 0.2195 1
*Test statistics are approximate since BMI is not normally distributed
BMI Results
Genome wide significant hit on Chromosome 11
MTNR1B
• Meta analyses have not previously identified SNPs in this gene to be associated with BMI.
• Murine KOs for the homologous gene GPR50 are resistant to diet-induced obesity (PMID: 17957037).
• The MAGIC consortium has reported that MTNR1B genotype is associated with fasting glucose levels and diabetes susceptibility (Prokopenko et al., poster AHSG 2008).
Diabetes Results
• MTNR1B has been associated with type II diabetes, but no association was found with history of diabetes in this sample
• The top 2 hits were in GLUT5 and CETP
History of diabetes
CHR SNP UNADJ GC BONF HOLM SIDAK_SS SIDAK_SD FDR_BH FDR_BY 2 rs2018414 2.015e-05 2.015e-05 0.6784 0.6784 0.4926 0.4926 0.3436 1 16 rs9923854 2.042e-05 2.042e-05 0.6873 0.6872 0.4971 0.497 0.3436 1 1 rs12145292 3.372e-05 3.372e-05 1 1 0.6786 0.6786 0.3783 1 17 rs9892909 6.299e-05 6.299e-05 1 1 0.88 0.88 0.5301 1 5 rs2895795 0.0001022 0.0001022 1 1 0.9679 0.9679 0.5809 1 10 rs7903146 0.0001046 0.0001046 1 1 0.9704 0.9704 0.5809 1 5 rs1042718 0.0001656 0.0001656 1 1 0.9962 0.9962 0.5809 1 2 rs315921 0.0001699 0.0001699 1 1 0.9967 0.9967 0.5809 1 6 rs1057293 0.0002449 0.0002449 1 1 0.9997 0.9997 0.5809 1
Further possibilities
• Gene x drug effects on GI side-effects– Summer 2009
• Case-only estimation of epistasis in osteo-/rheumatoid arthritis
Case Control Study
G- G+
E- a b
E+ c d
Cases Controls
G- G+
E- e f
E+ g h
ORGXE = a d f g / b c e h
Var (ln ORGXE) = 1/a + 1/b + 1/c + 1/d + 1/e + 1/f + 1/g + 1/h
Case Only Study
G- G+
E- a b
E+ c d
Cases
ORGXE = a d / b c
Var (ln ORGXE) = 1/a + 1/b + 1/c + 1/d
Assuming that G and E are uncorrelated
Case Only Study nested in RCT
G- G+
Drug A
a b
Drug B
c dSide effect cases
Drug exposure is randomized →
Drug exposure and genotype are uncorrelated
Conclusions
• Genetic epidemiology studies can make use of RCT populations
• RCT samples of convenience can have disadvantages e.g.– Inappropriate size – Unrepresentative populations– Nonrandom recruitment into PhGx / dropout
• Some innovative research designs are possible