Published online before print June 3, 2009, doi: 10.1161/CIRCGENETICS.109.857839
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Original Articles |
CACNA1C Gene Polymorphisms, Cardiovascular Disease Outcomes, and Treatment Response
From the Department of Pharmacotherapy and Translational Research and Center for Pharmacogenomics (A.L.B., H.N., Y.G., J.M., T.Y.L., J.A.J.), University of Florida College of Pharmacy, Gainesville, Fla; Department of Pharmacology (D.W., W.S.), College of Medicine & Public Health, The Ohio State University, Columbus, Ohio; Department of Psychiatry (J.W., N.J.S.), University of California at San Diego, La Jolla, Calif; Division of Cardiology (R.M.C.D.H., C.J.P., J.A.J.), University of Florida College of Medicine, Mosville, Fla; The Scripps Translational Sciences Institute (N.J.S.), La Jolla, Calif; and Division of Endocrinology, Diabetes and Nutrition (A.L.B.), University of Maryland School of Medicine, Baltimore, Md. Dr. Beitelshees is now at the University of Maryland.
Correspondence to Dr. Julie A. Johnson, Department of Pharmacotherapy and Translational Research, University of Florida College of Pharmacy, P.O. Box 100486, Gainesville, FL. E-mail johnson{at}cop.ufl.edu
Received July 11, 2008; accepted May 21, 2009.
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Abstract |
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Background— The gene encoding the target of calcium channel blockers, the
1c-subunit of the L-type calcium channel (CACNA1C), has not been well characterized, and only small pharmacogenetic studies testing this gene have been published to date. Methods and Results— Resequencing of CACNA1C was performed followed by a nested case-control study of the INternational VErapamil SR/trandolapril STudy (INVEST) GENEtic Substudy (INVEST-GENES). Of 46 polymorphisms identified, 8 were assessed in the INVEST-GENES. Rs1051375 was found to have a significant interaction with treatment strategy (P=0.0001). Rs1051375 A/A genotype was associated with a 46% reduction in the primary outcome among those randomized to verapamil SR treatment, when compared with atenolol treatment (odds ratio 0.54 95% CI 0.32 to 0.92). In heterozygous A/G individuals, there was no difference in the occurrence of the primary outcome when randomized to verapamil SR versus atenolol treatment (odds ratio 1.47 95% CI 0.86 to 2.53), whereas homozygous G/G individuals had a greater than 4-fold increased risk of the primary outcome with verapamil treatment compared with those randomized to atenolol treatment (odds ratio 4.59 95% CI 1.67 to 12.67). We did not identify allelic expression imbalance or differences in mRNA expression in heart tissue by rs1051375 genotype.
Conclusions— Variation in CACNA1C is associated with treatment response among hypertensive patients with stable coronary artery disease. Our data suggest a genetically defined group of patients that benefit most from calcium channel blocker therapy, a group that benefits most from β-blocker therapy, and a third group in which calcium channel blocker and β-blocker therapy are equivalent.
Key Words: genetics • pharmacology • ion channels • calcium • pharmacogenetics
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Introduction |
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The
1c subunit is the major pore-forming subunit of the L-type calcium channel and is the binding site for all currently available calcium channel blockers (ie, dihydropyridines, phenylalkylamines, and benzothiazepines).1 The gene that encodes the 1c subunit of the L-type calcium channel, CACNA1C, is a large gene, nearly 300 kb in size, located on chromosome 12p13.3.2 It is made up of 44 invariant and 6 alternative exons with a coding region of over 8 kb.2 Although the NCBI dbSNP reports nearly 2000 single-nucleotide polymorphisms (SNPs) in CACNA1C, a relatively small proportion of those are validated. Furthermore, HapMap data suggest a very low degree of linkage disequilibrium (LD) in this region of the genome, making tagSNP approaches for association studies of this gene problematic.
Clinical Perspective on p 362
Perhaps related to these issues, very few studies have been published evaluating genetic influences of any drug target candidate genes on calcium channel blocker response.3–5 The lack of pharmacogenetic data with calcium channel blockers is surprising given that amlodipine, verapamil, diltiazem, and nifedipine are all among the top 300 prescription drugs based on prescription numbers in 2005 (Rx List 2005). The studies published to date evaluating CACNA1C and calcium channel blocker response have been somewhat limited in that small numbers of individuals were evaluated (n=120 to 160) and that many different calcium channel blockers were all studied together.4,5
Because of the small number of coding region variants in the public domain databases in CACNA1C and because this gene is not in an extensively studied ENCODE region of HapMap, we undertook a resequencing effort for SNP discovery. We then performed a nested case-control study using the genetic substudy of the INternational VErapamil SR/Trandolapril STudy (INVEST-GENES) to evaluate the impact of selected SNPs on adverse outcomes and response to verapamil SR.
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Methods |
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Resequencing
Unrelated genomic DNA samples were purchased from the Coriell Cell Repository: 20 Native American samples (panels HD-17 and HD-18), 20 African American samples (first 20 samples from panel AA50), and 20 Caucasian samples (from the apparently healthy collection, sample numbers available on request).
Polymerase chain reaction (PCR) primers were designed using Mutation Discovery to create amplicons containing an exon (or portion of an exon) and at least 50 base pairs of intron upstream and downstream from the exons. Exon boundaries and numbering were defined according to Soldatov.2 PCR primers and conditions for the 54 amplicons are shown in Supplemental Table I.
For each amplicon, a reference sample that did not form a heteroduplex was chosen. Temperatures for denaturing high performance liquid chromatography (DHPLC) were chosen using Navigator software (Transgenomic, Omaha, Neb; Supplemental Table I). Each of the 60 samples was then pooled with the reference sample in a 2:1 ratio, denatured at 95°C, and allowed to slowly reanneal over 30 minutes to allow heteroduplex formation. Pooled samples were then run on DHPLC under partially denaturing conditions according the gradient calculated by the Navigator software. Reference samples and samples forming heteroduplexes were sent for direct sequencing to determine the nature and location of the variation present in the amplicon. Sequencing was performed in the forward and reverse directions using the same primers as those used for PCR amplification (Amersham, MegaBACE 1000; Supplemental Table I). In addition to the human samples, 1 chimpanzee sample and 1 gorilla sample were also purchased from the Coriell Cell Repository and sequenced to estimate the ancestral alleles of polymorphisms discovered.
Prediction of Functional Consequences of Polymorphisms and SNP Selection
PolyMAPr was run on all discovered polymorphisms to predict the functional consequences.6 Conserved noncoding regions were determined using VISTA browser (http://pipeline.lbl.gov/cgi-bin/gateway2).
SNPs to study for clinical association were chosen if they had a minor allele frequency 
0.10 and met one of the following criteria: (1) nonsynonymous in nature or putative functional significance based on in silico data (ie, located within putative transcription factor binding site, exonic splicing enhancer [ESE] region, or splice sites) or (2) located in conserved noncoding sequence. SNPs that met these criteria were then assessed for pairwise LD, and redundant SNPs were eliminated.
INVEST-GENES Clinical Cohort
INVEST was a randomized trial of 22 576 patients with hypertension and stable coronary artery disease. Patients were randomized to an atenolol- or verapamil-based treatment strategy with other antihypertensives added to achieve blood pressure control.7 INVEST-GENES has been described previously.3 Briefly, genetic samples were collected from 5979 INVEST patients residing in the mainland United States and Puerto Rico. All patients provided written informed consent for participation in the genetic substudy, and the study was approved by the University of Florida Institutional Review Board. Initial genotype data became available in August 2007, and final genotype data for CACNA1C became available on March 18, 2008. Using the 5979 patients with genetic samples as described previously, a nested case-control group consisting of all of the 258 patients who experienced the primary outcome (first occurrence of death, nonfatal myocardial infarction, or nonfatal stroke) and 774 age-, sex-, and race-frequency-matched controls from INVEST-GENES were assessed. The nested case-control study provides nearly the same statistical power as genotyping the entire cohort as only the number of controls is decreased. We have demonstrated previously with 4 other genes and 7 SNPs that this nested case-control approach yields similar results as genotyping the entire cohort of 5979 samples (ADRB1, ADRB2, KCNMB1, and ADD1).3,8,9
Genotyping
Genomic DNA was extracted from buccal cells collected in mouthwash samples according to standard protocols.10 Polymorphisms were genotyped by pyrosequencing (PSQ HS 96A) or TaqMan methods. The PSQ HS 96 genotyping platform (Biotage AB, Uppsala, Sweden) was used for the pyrosequencing assays for rs215976, SNP37, and rs2239128 (primer sequences available on request). PCRs were carried out using HotStar Taq mix, 10 pmmol each of forward and reverse primers, water, and 20 ng of genomic DNA. The Applied Biosystems 7900 HT SNP genotyping platform was used for the TaqMan assay. The PCR primers and probes for rs216008, rs1051375, rs10848683, rs2239050 and rs2238032 assays (IDs C__7499713_1_, C__2877394_1_, C__2877389_10, C__16173701_10, C__16171390_10, respectively) were purchased from Applied Biosystems (Foster City, Calif). Five-microliter reactions in a 384-well plate were prepared, and the assays were performed and analyzed according to the manufacturer’s recommendations. Haplotypes were predicted using Polymorphism and Haplotype Analysis Suite version 0.9.11 Analysis was performed using the most likely haplotype. The genotype and primary event data have been deposited in the Pharmacogenomics Knowledge Base (www.pharmgkb.org).
Functional Assessment of Selected Single-Nucleotide Polymorphisms
To test whether rs1051375 changes CANA1C mRNA levels via RNA processing or splicing, we measured allelic RNA ratio with rs1051375 as a marker in heterozygous human ventricular heart tissues, as described previously.12 A segment of DNA or cDNA surrounding the marker SNP was amplified by PCR using primer sets to amplify different splice variants. Primer sequences are as follows: E44F, TCGTCCACCGGCTCCA; E44R, CTGAGCTTCCACGCCACCT; E40F, GGCCCTGAGGATCAAAACAG; E46R, CACTTCATAGGTCTCATCCTGAGAC. PCR products were then subjected to a primer extension assay using extension primer (CCGGCTACCCCAGCAC). Allelic mRNA ratios were normalized by DNA ratios. If rs1051375 affects mRNA splicing, we would expect to observe differences in RNA ratios after amplification with different primer sets. Total CACNA1C mRNA in human ventricular tissues was measured using real-time PCR as described previously.12
Statistical Analysis
Baseline characteristics were compared by genotype using 
2 or ANOVA, as appropriate. Hardy-Weinberg equilibrium was calculated separately by race/ethnicity using a 2 test with 1 degree of freedom. All statistical analyses were conducted using SAS version 9.1 (Cary, NC) or SPSS version 11.5 (Chicago, Ill). Analyses by treatment strategy were based on patients receiving at least 1 dose of the randomized drug in their assigned strategy. A 2-sided P<0.006 (0.05/8 SNPs assessed) was considered significant for all analyses. As a method of further ensuring that we did not have excess type I errors given that there were actually >8 tests performed (eg, by race, by genotype, and individual components of the primary outcome), we also performed the false discovery rate according to the method of Benjamini and Hochberg13 to adjust for multiple comparisons. Unadjusted and adjusted odds ratios (ORs) and 95% CIs for occurrence of the primary outcome were calculated using logistic regression model for the case-control group. The model contained the following covariates: age, sex, race/ethnicity, genomic ancestry proportions (see below), body mass index, smoking, INVEST treatment strategy, previous myocardial infarction, previous stroke, heart failure, diabetes, peripheral vascular disease, renal insufficiency, and baseline systolic and diastolic blood pressure. Additionally, we included genotype/haplotype and the interaction term between genotype/haplotype and treatment strategy. The modeling was also conducted separately by genotype/haplotype and by race/ethnicity. Genotype and haplotypes were entered in models according to additive models of inheritance.
Considering our case-control group, we had 80% power to detect an OR of 1.83 for the primary outcome with an of 0.006 and minor allele frequency of 0.10.
To control for the potential of population stratification in our racially and ethnically diverse population, we used a total panel including up to 87 ancestry informative markers (mean 68±24 markers per subject), selected to show large allele frequency differences across 3 parental populations (West Africans, Indigenous Americans, and Europeans) selected from a large panel of >10 000 SNPs.14 Maximum likelihood was then used to estimate each patient’s individual genomic ancestry proportions on these 3 axes, and these terms were included in statistical models in addition to the race/ethnicity term. To ensure accurate ancestry proportion estimates, at least 30 ancestry informative markers had to be genotyped successfully in each individual to be included in analyses.
In addition to controlling for potential confounding by population stratification through inclusion of race/ethnicity and ancestry estimates in the models, we also analyzed each racial/ethnic group separately. Although we do not have sufficient power in each group, we sought to determine whether the direction of the association was similar in each group. When similar, we then pooled the data still adjusting for race and ancestry.
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Results |
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SNP Discovery and In Silico Modeling
On screening with DHPLC, 8 of the 54 amplicons contained no variation based on the DHPLC chromatograms. For those 8 amplicons, only the reference sequence was sent for sequencing. All other amplicons either clearly contained samples with heteroduplex formation (30 amplicons) or were associated with some degree of ambiguity (16 amplicons) and selected samples were sent for direct sequencing.
A total of 46 polymorphisms were identified, 44 SNPs and 2 insertion/deletion polymorphisms (Supplemental Table II). The linkage disequilibrium plots for the discovered SNPs in each population are shown in Figure 1. Thirty-one were located in introns, 11 were synonymous, and 4 were nonsynonymous. Of the 46 polymorphisms, 18 (39%) were novel, not found in dbSNP. Of the novel polymorphisms, 11 were intronic, 5 synonymous, and 2 nonsynonymous (Supplemental Table II).
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The estimated minor allele frequencies of the discovered polymorphisms range from being polymorphic only in a single population to 50% (Supplemental Table II). Figure 2 depicts all common SNPs (>5% overall minor allele frequency) mapped to the gene structure with degree of conservation with mouse shown. None of the discovered polymorphisms occurred in conserved noncoding regions (Figure 2), although this finding may have been biased by our resequencing strategy that focused more heavily on coding regions of the gene.
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In silico functional analysis of the discovered exonic SNPs in CACNA1C revealed potential ESE motifs in Thr1149Thr (novel, SNP23) and Thr1396Thr (novel, SNP27) for SF2/ASF and SRp40. Leu1058Leu (novel, SNP20) contains a potential ESE for SRp55. A potential ESE motif for SF2/ASF was identified in Asp812Asp (rs215976) and Asp1543Asp (rs41276710). Asn1812Asn (novel, SNP37) and Phe1282Phe (rs216008) contain potential ESE motifs for SRp40. Arg2056Gln (novel, SNP45) contains a potential ESE for SC35 and Thr1835Thr (rs1051375) for SC35 and SF2/ASF. The nonsynonymous SNP, Pro1868Leu (rs10848683), was predicted to be possibly damaging. Both P1868L and M1869V (rs10774053) are located in alternate exon 45, which is not designated as an exon in the NCBI dbSNP.
Association Studies
Based on the resequencing data, subsequent in silico studies, and LD analysis, we identified 5 SNPs to genotype in the INVEST-GENES case-control set (rs215976, rs216008, novel SNP37, rs1051375, and rs10848683). Additionally, we genotyped the 3 SNPs identified by Bremer et al4 as being associated with calcium antagonist response (rs2239050, rs2238032, and rs2239128) for a total of 8 SNPs.
Genotyping was complete for 1010 (98%) for rs2238032, 972 (94%) for rs2239050, 1017 (99%) for rs215976, 961 (93%) for rs216008, 952 (92%) for rs2239128, 977 (95%) for SNP37, 975 (94%) for rs1051375, and 1002 (97%) for rs10848683. Genotype frequencies are shown in Table 1. All genotype frequencies were consistent with those predicted by Hardy-Weinberg equilibrium, with the exception of rs2239050, which was out of Hardy-Weinberg equilibrium in Hispanic individuals (P=0.035). When genotype frequencies for rs2239050 were calculated among only those with >75% European ancestry, they were consistent with those predicted by Hardy-Weinberg equilibrium, suggesting that this SNP may have been out of equilibrium because of admixture among the Hispanic individuals. The pairwise LD plots for each of the populations are shown in Supplemental Figure I. Baseline characteristics for cases and controls are shown in Table 2. As outlined in the Methods section, any differences in baseline characteristics between cases and controls were included as covariates in the analysis. Smoking was less common, body mass index was greater, and angina history was more prevalent in rs1051375 variant homozygote G/G than in A/G or A/A individuals and the minor allele frequency was lower in whites than other racial/ethnic groups. All characteristics were similar by other genotype groups with the exception of race (rs2238032, rs2239050, rs2239128, and rs10848683), body mass index (rs2239050), baseline diastolic blood pressure (rs10848683, rs2239128, rs2238032, and rs2239050), and history of arrhythmia (SNP37) (data not shown). The differences in body mass index and diastolic blood pressure by genotype at baseline seemed to be caused by racial differences in allele frequencies because they were no longer significant when compared in whites only.
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In INVEST-GENES, the crossover rate to β-blocker use in the calcium channel blocker arm was 0%, and the crossover to calcium channel blocker use in the β-blocker arm was 0.03%. As allowed per the INVEST protocol for refractory angina, calcium channel blocker use at any time in the β-blocker strategy was 18.73%, and the β-blocker use at any time in the calcium channel blocker strategy was 12.44%.
Main Effects of CACNA1C SNPs on Outcomes
None of the 8 SNPs genotyped were associated with a main effect on outcomes with a P<0.006. Two SNPs exhibited trends relative to the composite primary outcome with p values <0.05 but >0.006, rs2238032 (OR 0.41 95% CI 0.21 to 0.83, P=0.01), and rs10848683 (OR 0.74 95% CI 0.56 to 0.98, P=0.03). The main effect of both SNPs remained consistent with ancestry informative markers included in the model.
TreatmentxGenotype Interactions
One SNP, rs1051375 (P=0.0001), was identified with a significant interaction with treatment strategy (Table 3). This SNP remained significant with false discovery rate adjustment (P=0.01). rs10848683 trended toward a significant interaction with treatment strategy (P=0.10) and rs2238032, which had a modest main effect, had no evidence for a pharmacogenetic effect on outcomes (P=0.95). rs1051375 and rs10848683 are in significant LD with D'=0.87 and r2=0.36 in whites. We performed analyses stratified by genotype for rs1051375 and found that A/A individuals randomized to verapamil SR were less likely to experience a primary outcome than those randomized to atenolol (Table 3 and Figure 3). G/G individuals randomized to verapamil SR were more likely to experience the primary outcome than those randomized to atenolol (Table 3 and Figure 3). No difference in the occurrence of the primary outcome was noted between treatment groups in heterozygous individuals (Table 3 and Figure 3). When whites, our largest racial group, were analyzed alone, the effects of rs1051375 remained consistent (Table 3). rs1051375 interactions were consistent across all components of the primary outcome (P=0.01 for death, P=0.01 for myocardial infarction, and P=0.06 for stroke).
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In post hoc analysis, we also assessed average treatment blood pressure and the number of antihypertensive drugs required by rs1051375 genotype, given that the INVEST study design called for the addition of additional drugs to achieve blood pressure goals. The average treatment blood pressure was 135.7/76.5±11.5/7.2 mm Hg in A/As, 134.5/76.6±11.0/6.9 mm Hg in A/Gs, and 138.3/78.2±12.2/7.0 mm Hg in G/Gs (overall ANOVA P=0.001 for systolic blood pressure and P=0.024 for diastolic blood pressure). This trend for a higher treatment blood pressure in G/G individuals was similar in the atenolol- and verapamil SR-based treatment groups. Consistent with worse outcomes among rs1051375 A/A patients in the atenolol strategy, significantly more A/A patients randomized to the atenolol strategy (41%) required
4 drugs (including nonstudy drugs) for blood pressure control than those randomized to the verapamil SR strategy (24%), P=0.0005.
Haplotype Analysis
We conducted haplotype analyses in 2 ways. First, we imputed haplotypes across all 8 SNPs genotyped in CACNA1C. Second, because of the relatively low degree of LD across the gene and the fact that a block was evident in whites across SNP37, rs1051375, and rs10848683, we assigned haplotypes for just those 3 SNPs. The haplotype analysis results were largely consistent with the single SNP analyses, whereby individuals with haplotypes containing the rs1051375 A allele had improved outcomes with verapamil SR randomization and those with haplotypes constraining rs1051375 G had worse outcomes with verapamil SR than with atenolol (data not shown).
Functional Assessment
When using rs1051375 as a marker to measure allelic mRNA expression in heart tissues, we did not identify allelic expression imbalance in CACNA1C (Figure 4), indicating this SNP does not affect mRNA expression. From exon 40 (E40) to exon 46 (E46), 6 splice variants have been reported, E40-E41, E40-E41+57nt, E44-E46, E44-E45*-E46, E44-E45-E46, and E44-E45*-187nt-E46, but the major splice variants E40-E41 and E44-E46 comprise >95% of total transcripts. If rs1051375 was to affect the splicing in this locus, and the splice variants are unstable or undergo nonsense mediated decay, we would see allelic expression imbalance when using primers spanning different exons, like E40F/E44R and E44F/E46R. However, this result failed to identify allelic expression imbalance after PCR amplification using different primers (Figure 4), suggesting that rs1051375 does not affect mRNA splicing in this locus in ventricular tissue. We also measured CACNA1C mRNA expression in ventricular tissue by rs1051375 genotype and did not find differences (Figure 5).
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Discussion |
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We performed an extensive SNP discovery effort of the CACNA1C coding region and intron/exon junctions and a clinical association study using the INVEST-GENES to assess the impact of CACNA1C genetic variation on outcomes and treatment response. Of the 8 SNPs tested in the genetic association study, we identified 1 SNP with a significant interaction with treatment strategy. The effect of this interaction was such that individuals homozygous for the major allele (A/A) randomized to verapamil SR-based treatment regimens had a 45% reduced risk of the primary outcome, when compared with A/A individuals randomized to atenolol-based regimens. In contrast, individuals homozygous for the minor allele (G/G) randomized to verapamil SR-based treatment had a 4.5-fold increase in the primary outcome, when compared with G/G individuals randomized to atenolol-based treatment. These findings suggest that individuals with rs1051375 A/A would benefit from treatment with a calcium channel blocker, those with the G/G genotype would benefit from treatment with a β-blocker, and in those with the heterozygous genotype it would not matter which treatment was chosen.
Given the study design of INVEST, it is difficult to determine whether the differences in treatment outcomes observed are caused by differences in blood pressure response. In our post hoc analysis, we observed overall average treatment blood pressures that were higher among those with the G/G genotype. Additionally, patients with the A/A genotype who were randomized to the atenolol treatment strategy were more likely to require 4 or more drugs for blood pressure control than A/A patients randomized to the verapamil SR treatment strategy, suggesting that blood pressure response differences may be playing a role in differences in treatment outcomes.
Although the mechanism of the SNP*treatment interaction is unclear at this time, one explanation is that variants in CACNA1C result in reduced function of the L-type calcium channel. If this were the case, individuals with these genotypes might gain more benefit from a treatment approach involving a mechanism of action not dependent on the L-type calcium channel (eg, β-blocker instead of calcium channel blocker). In contrast, the major alleles might have greater L-type calcium channel function and thus benefit more from treatment with calcium channel blockade. Finally, it is possible that variation in CACNA1C might directly influence treatment response through interactions between calcium signaling and β-adrenergic signaling pathways because protein kinase A activation via the β1-adrenergic receptor results in activation of the L-type calcium channel.
Two of the SNPs we found to be associated with outcomes or treatment response, rs1051375 and rs10848683, are in a fair degree of LD (D'=0.84 and r2=0.36 in whites). Therefore, it is unclear whether either or both of these SNPs are functional, or both might be tagSNPs for the actual functional SNP. The results of our haplotype analysis and the more significant probability values in individual SNP analysis suggest that rs1051375 is the more likely causative SNP of the 2 or in stronger LD with the causative SNP. Rs1051375 is synonymous, Thr1835Thr, so the functional relevance of this SNP is not immediately clear. It was selected for analysis because it is located at putative ESE sites for SC35 and SF2/ASF. In addition, other synonymous SNPs have recently been identified as having functional importance in the ABCB1 gene.15 Whether one or both of these SNPs are functional or whether they are linked to another functional SNP needs to be elucidated.
Although we have not yet discovered the mechanisms underlying our observed associations, we have made substantial progress toward eliminating several possible functional mechanisms. Based on our work presented here and published previously, we can now exclude differences in expression levels in the myocardium by genotype and that rs1051375 alters splicing in myocardial tissue as potential explanations for the functional basis.12 It is still possible, however, that expression or splicing differences in vasculature smooth muscle may exist.
We were unable to replicate the findings of Bremer et al in which rs2239050, rs2238032, and rs2239128 were found to be associated with calcium channel blocker antihypertensive response. However, rs2238032 was associated with a main effect on outcomes in our study, although it did not meet our predefined level for significance after adjustment for multiple comparisons. An additional study published during the revision of this manuscript found 1 SNP in CACNA1C, although it was not 1 of the SNPs we assessed, and 2 SNPs in CACNA1D to be associated with response to dihydropyridine calcium channel blockers among 161 Japanese individuals.5
One of the major strengths of our study is the fact that our population came from a randomized clinical trial. This study design greatly reduces possible biases that can be introduced into observational studies where many factors influence treatment decisions. Additionally, all end points in the clinical trial were adjudicated by a blinded end points committee which also strengthens our phenotype.
Limitations
Our study has some limitations that should be addressed. First, although we have eliminated some possible functional mechanisms, we do not know the functional mechanism underlying the association we observed between CACNA1C variants and cardiovascular outcomes. These mechanistic studies will be important to understand how we might use this information in the future for genotype-guided treatment decisions. A second limitation of our study is that we do not have a replication cohort for our findings. Unfortunately, it is very challenging to find replication cohorts for pharmacogenetic studies with detailed drug phenotype data and similar patient populations, particularly when the phenotype is adverse cardiovascular outcomes. However, we are working toward replicating these findings in other cardiovascular outcomes studies.
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Conclusions |
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We have identified a SNP in CACNA1C, the binding site for calcium channel blockers, with a significant interaction with treatment strategy in a group of hypertensive patients with CAD. Individuals homozygous for the major allele had a reduction in the occurrence of death, nonfatal myocardial infarction, or nonfatal stroke when treated with a calcium channel blocker-based treatment regimen compared with those treated with a β-blocker-based treatment regimen. In contrast, individuals homozygous for the minor allele had a reduction in adverse outcomes when treated with β-blocker-based regimens. If validated, these findings might be used in the future to help guide choice of therapy in the treatment of hypertension. Of great interest, our findings suggest the potential of targeting an individual’s underlying molecular mechanism of disease to improve clinical outcomes.
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Acknowledgments |
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Sources of Funding
This study was supported by NIH grants HL074730, GM074492, and RR017568 and grants from the University of Florida Opportunity Fund and Abbott Laboratories. A.L.B. is supported by K23HL91120.
Disclosures
A.L.B., Y.G., R.M.C.D., C.J.P., N.J.S., and J.A.J. report receiving research grants from NIH; C.J.P. reports receiving research grants from Baxter, Bioheart, Cardium, Pfizer, Viron, Abbott, and Berlex Lab/Bayer HealthCare; R.M.C.D. reports receiving honoraria from the Preventive Cardiovascular Nurses Association and the American College of Cardiology; C.J.P. reports serving as a consultant for Abbott as DSMB Chair, Forest Laboratories, Novartis/Cleveland Clinic DSMB Chair, Pfizer, CV Therapeutics, NicOx, Angiobalst DSMB member, Indigo, Boerhinger Ingleheim, DCRI/The Medicines Company-Interim Analysis Committee Research, Reliant Pharmaceuticals, Schering-Plough, and Sanofi Aventis; and C.J.P. reports receiving unrestricted educational grants from Astra Zeneca, Boehringer Ingelheim, CV Therapeutics, Pfizer, Sanofi Aventis, Schering Plough, Daiichi-Sankyo, Merck, Novartis, The Medicines Company, GSK, and Reliant Pharmaceuticals.
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References |
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The online-only Data Supplement is available at http://circgenetics.ahajournals.org/cgi/content/full/CIRCGENETICS.108.857839/DC1.
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