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Gene Finding in Clinical Trial Populations

Tom Price SGDP 18 th Feb 2009 http://sgdp.iop.kcl.ac.uk/tprice/. Gene Finding in Clinical Trial Populations. Translational Value of Pharmacogenetics. Genetic studies can help: Identify drug targets Decrease attrition in development of new drugs Increase safety by predicting adverse events

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Gene Finding in Clinical Trial Populations

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  1. Tom Price SGDP 18th Feb 2009 http://sgdp.iop.kcl.ac.uk/tprice/ Gene Finding in Clinical Trial Populations

  2. 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.

  3. 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• Clinical relevance • Misaligned goals of academe / industry • Marketing function of pharma RCTs • Unpublished studies • Expense

  4. 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Vioxx (rofecoxib) caused more than 27,000 excess cases of myocardial infarction and sudden cardiac death before it was withdrawn from the market Do me-too drugs have a similar risk profile?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?

  5. NSAIDs Coxibs NSAIDs COX Isoforms & Prostaglandins Maree & Fitzgerald Thromb Haemost 2004, 92:1175–81

  6. 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

  7. Side Effects of COX Inhibition Grosser, Fries & FitzGerald J Clin Invest 2006, 116:4–15

  8. 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

  9. 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

  10. Tissue Biomarker Measure Plasma PGE2 COX-2 activity ex vivo Serum TxB2 COX-1 activity ex vivo Urine 2,3-dinor-6keto PGF1α PGI2 biosynthesis in vivo Urine 11-dehydro TxB2 TxA2 biosynthesis in vivo Biomarkers of Drug Action

  11. Drug Responses Measurements obtained at baseline and 4 hours after dosing

  12. Attained COX-2 Selectivity Ratio of ex vivo COX-2 to COX-1 inhibition 4h post dose

  13. Variability in Drug Response(COX-2 ex vivo assay)‏ Between subjects Within subjects

  14. Drug Availability Plasma concentration in blood drawn 4 hours after dosing

  15. Biomarkers of Drug Efficacy COX inhibition in vivo COX inhibition ex vivo

  16. 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?

  17. 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

  18. 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

  19. 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

  20. Stacey Gabriel (Broad Institute)‏ IBC Chip Brendan Keating (U Penn)‏

  21. 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

  22. 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)

  23. Sample

  24. 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

  25. 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.

  26. QC

  27. Phenotypes Baseline 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)

  28. 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

  29. Eigenstrat Green = Europe Blue = US

  30. 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.

  31. 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

  32. History of Hypertension Results Genome wide significant hit on rs179998 5’ of CYP11B2

  33. 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

  34. 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

  35. Blood Pressure Results Nothing much came up for any of the blood pressure phenotypes

  36. 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

  37. 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

  38. BMI Results Genome wide significant hit on Chromosome 11

  39. 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).

  40. 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

  41. Further possibilities • Gene x drug effects on GI side-effects • Summer 2009 • Case-only estimation of epistasis in osteo-/rheumatoid arthritis

  42. Case Control Study Cases Controls 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

  43. Case Only Study Cases ORGXE = a d / b c Var (ln ORGXE) = 1/a + 1/b + 1/c + 1/d Assuming that G and E are uncorrelated

  44. Case Only Study nested in RCT Side effect cases Drug exposure is randomized → Drug exposure and genotype are uncorrelated

  45. 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

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