# Patent

## Title
Polymorphism in CYP3A4 gene affecting drug metabolizing and uses thereof

## Abstract

A method for predicting a subject's risk factors for CYP3A4-related disorders includes detecting the allelic status of a SNP in a nucleic acid sample of the subject.

## Background
RELATED APPLICATIONS
This application claims priority to U.S. Provisional Patent Application 61/163,555 filed Mar. 26, 2009, the disclosure of which is incorporated herein by reference, in its entirety.


GOVERNMENT SUPPORT
This invention was made with government support under Grant Number NIH NIAID (1R21AI074399) awarded by the National Institutes of Health. The government has certain rights in the invention.


SEQUENCE LISTING
The instant application contains a Sequence Listing which has been submitted via EFS-Web and is hereby incorporated by reference in its entirety. The ASCII copy, created on Mar. 23, 2010, is named 604_50806_SEQLIST_OSURF-09032.txt and is 34,669 bytes in size.
BACKGROUND
Cytochrome P450 (CYP) enzymes metabolize endogenous and xenobiotic compounds. CYP3A4 belongs to the CYP3A subfamily and is the most abundant CYP enzyme. CYP3A4 is involved in metabolizing 45-60% of all currently used drugs (1), including several statins—cholesterol-lowering HMG-CoA reductase inhibitors. However, CYP3A4 activity shows wide inter-individual variation, influencing drug response and toxicity. While genetic factors are thought to be main contributors to inter-individual differences in CYP3A4 activity (2), currently known CYP3A4 polymorphisms cannot account for the observed variability.
Genetic variants in CYP3A4 that change the amino acid sequence are rare (<1%). A more common variant, CYP3A4*1B, in the 5′-flanking region, has been associated with drug response and diseases (3,4), but results are inconsistent (5-7), and its function remains controversial (3,8-10). Moreover, CYP3A4*1B is in linkage disequilibrium (LD) with the adjacent CYP3A5 (11), encoding a similar but usually less abundant CYP enzyme that could have accounted for any linked clinical phenotype (12).
Further suspected CYP3A4 polymorphisms include a TGT insertion (13), an enhancer region SNP (rs2737418) (14), and an intron7 SNP (rs4646437) (15). While reporter gene assays suggested an effect for the TGT insertion and for rs2737418, the in vivo significance of TGT remains unresolved (13), while results on CYP3A4 mRNA and enzyme activity were contradictory for rs2737418 (14). The intron7 SNP rs4646437 was found to be associated with CYP3A4 protein/enzyme activity, but only in livers from males (15). Therefore, the role of functional polymorphisms in CYP3A4 remains uncertain.
Single nucleotide polymorphisms (SNPs) are useful as biomarkers for predicting disease susceptibility or progression, or as a guide for individualized therapy, including drug therapy.
What are lacking are tools for predicting the likelihood that a particular patient will be responsive to a particular therapeutic agent, and in particular, identifying polymorphisms to which a CYP3A4 agent will be sensitive or resistant. Also lacking are tools for profiling genetic factors influencing sensitivity and resistance of patients to such therapeutic agents. Such tools, and the resulting gene expression profiles, would be predictive of treatment response of a patient to a particular drug, and would allow for increased predictability regarding efficacy, adverse drug reactions, chemosensitivity or chemoresistance of such patients to enable the design of optimal treatment regimens for patients, or to enable drug development in early clinical trials avoiding unexpected toxicities in poor metabolizers.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objects and advantages of the invention may be realized and attained as particularly pointed out in the appended claims.
SUMMARY
In a first aspect, there is provided herein a method for predicting a subject's response to CYP3A4-metabolized compounds, including, for example efficacy and/or adverse drug reactions. The method includes detecting the allelic status of one or more polymorphisms in a nucleic acid sample of the subject, wherein the polymorphism is one or more of: i) CYP3A4-associated SNP (rs35599367 [SEQ ID NO: 152]) located in intron6 (C>T); or, ii) a SNP in linkage disequilibrium therewith, wherein the allelic status of the polymorphism in the subject is predictive of the subject's risk for having or developing the CYP3A4-related disorder.
In another aspect, there is provided herein, a method of screening a subject for a prognostic biomarker of an CYP3A4-related disorder, comprising detecting the allelic status of one or more polymorphisms in a nucleic acid sample of the subject, wherein the polymorphism is one or more of: i) CYP3A4-associated SNP (rs35599367 [SEQ ID NO: 152]) located in intron6 (C>T); or, ii) a SNP in linkage disequilibrium therewith, wherein the allelic status of the polymorphism in the subject is predictive of the prognostic outcome of the CYP3A4-related disorder.
In certain embodiments, the method further includes the step of correlating the allelic status of the polymorphism in the subject with the allelic status of the polymorphism in a reference population to predict the subject's risk for having or developing the CYP3A4-related disorder.
In certain embodiment, the method further includes the step of correlating the allelic status of the polymorphism in the subject with the allelic status of the polymorphism in a reference population to predict whether the subject has a more or less severe phenotype of the CYP3A4-related disorder.
In certain embodiment, the method further includes the step of correlating the allelic status of the polymorphism in the subject with the allelic status of the polymorphism in a reference population to predict the prognostic outcome of the disorder in the subject.
In certain embodiment, the method further includes the step of correlating the allelic status of the polymorphism in the subject with the allelic status of the polymorphism in a reference population to predict the subject's response to treatment, dosage and/or toxicity.
In certain embodiment, the CYP3A4-related disorder comprises a metabolic-related disorder.
In certain embodiment, the CYP3A4-metabolized compounds comprise one or more pharmaceuticals metabolized in the liver, including statin drugs.
In certain embodiment, the CYP3A4-metabolized compounds comprise one or more of: CYP3A4 inhibitors or CYP3A4 enhancers.
In certain embodiment, the polymorphism comprises a CYP3A4-associated SNP (rs35599367 [SEQ ID NO: 152]) located in intron6 (C>T).
In certain embodiment, the polymorphism comprises rs35599367 [SEQ ID NO: 152], wherein the presence of the polymorphism in a subject is predictive of an increased risk for a CYP3A4-related disorder.
In certain embodiment, the presence of a minor allele of the polymorphism is predictive of lower levels of CYP3A4 in target tissue and is associated with a decreased CYP3A4 mRNA expression.
In another aspect, there is provided herein, a kit comprising an assay for detecting the allelic status of one or more polymorphisms in a nucleic acid sample of a subject, wherein the polymorphism is one or more of: i) a CYP3A4-associated SNP (rs35599367 [SEQ ID NO: 152]) located in intron6 (C>T); or, ii) a SNP in linkage disequilibrium therewith.
In certain embodiment, the kit further includes instructions for correlating the assay results with the subject's risk for having or developing a CYP3A4-related disorder.
In certain embodiment, the kit further includes instructions for correlating the assay results with the subject's prognostic outcome for the disorder.
In certain embodiment, the kit further includes instructions for correlating the assay results with the probability of success or failure of a particular drug treatment in the subject.
In another aspect, there is provided herein, a method for finding a functional polymorphism in a target gene implicated in an CYP3A4-related disorder, comprising: i) providing a sample of a target tissue expressing the target gene; ii) measuring the target gene's allelic mRNA expression imbalance (AEI) by: a) quantitatively measuring the relative amounts of mRNA generated from each of two alleles in a transcribed region of the target gene, and b) comparing the mRNA expression of one allele against the other allele to obtain an AEI ratio; and, iii) using the AEI ratio as a phenotype to scan the target gene for regions containing polymorphisms, wherein a significant association between the AEI ratio and the polymorphism indicates that the polymorphism is a functional polymorphism that can serve as a biomarker for the CYP3A4-related disorder.
In certain embodiment, the polymorphism resides in an intronic region.
In certain embodiment, the polymorphism is a SNP.
In certain embodiment, the biomarker affects gene transcription, mRNA processing, mRNA splicing, or a combination thereof.
In certain embodiment, the target gene is a CYP3A4 gene locus.
In another aspect, there is provided herein, a method for determining metabolism of a statin drug comprising screening for an intron6 SNP.
In another aspect, there is provided herein, a method for determining metabolism of a statin drug comprising screening for a CYP3A4-associated SNP (rs35599367 [SEQ ID NO: 152]) located in intron6 (C>T).
In another aspect, there is provided herein a biomarker for detecting variability in CYP3A4 comprising intron6 SNP.
In another aspect, there is provided herein a biomarker for detecting variability in CYP3A4 comprising a CYP3A4-associated SNP (rs35599367 [SEQ ID NO: 152]) located in intron6 (C>T).
In another aspect, there is provided herein, a biomarker for determining a dosing requirement of a CYP3A4-metabolizing therapeutic agent comprising SNP (rs35599367 [SEQ ID NO: 152]) located in intron6 (C>T).
In another aspect, there is provided herein a biomarker for determining a response of a CYP3A4-metabolizing therapeutic agent comprising SNP (rs35599367 [SEQ ID NO: 152]) located in intron6 (C>T).
In another aspect, there is provided herein a biomarker for determining toxicity of a CYP3A4-metabolizing therapeutic agent comprising SNP (rs35599367 [SEQ ID NO: 152]) located in intron6 (C>T). In certain embodiment, the therapeutic agent is a statin drug. In certain embodiment, the therapeutic agent is an anti-cancer drug. In certain embodiment, the therapeutic agent is a drug having a narrowly defined dosage regimen.
In another aspect, there is provided herein a method of clinical pharmacogenomic screening comprising: a) screening a sample for the presence of at least one or more biomarkers described herein, where the presence of the one or more biomarkers is indicative of a patient with altered metabolism; and b) including a reference control in a random or predetermined manner in the screening, wherein the reference control comprises DNA comprising a biomarker indicative of a patient with altered metabolism, wherein the detection of the presence of one or more biomarkers in one or more drug-metabolizing genes in the reference control verifies that the screening is effective to detect the same one or more biomarkers in one or more drug-metabolizing genes in the sample.
In another aspect, there is provided herein a method of personalized medical therapy, comprising: i) performing the method of screening described herein on samples from a target patient population to identify patients with a genetic profile comprising one or more mutations in the CYP3A4 or other gene associated with drug metabolism; and ii) treating patients identified in step i) as possessing a particular genetic profile with a therapy of interest particular to the identified genetic profile.
In certain embodiments, the genetic profile is indicative of a patient with altered metabolism.
In certain embodiments, the altered metabolism is selected from the group consisting of: poor metabolizer, intermediate metabolizer, extensive metabolizer, and ultra-rapid metabolizer.
In certain embodiments, the genetic profile is indicative of the effectiveness of the therapy of interest in the patient.
In certain embodiments, the genetic profile is indicative of a patient with a genetic disorder.
In certain embodiments, the genetic profile is indicative of a patient who should not be treated with a particular therapy.
In certain embodiments, the therapy of interest is used to treat a disease or disorder selected from the group consisting of: cancer, heart disease, neurological disorders, psychiatric disorders, autoimmune disorders, and metabolic disorders.
In certain embodiments, the one or more mutations comprises a mutation in CYP3A4 and wherein the therapy of interest comprises administration of at least one statin to the patient.
In another aspect, there is provided herein a method for identifying a cell that can be used to generate isolated genomic DNA suitable for use as a reference control, wherein the method comprises: a) prospectively screening a human volunteer for the presence of a human genomic DNA sequence comprising one or more polymorphisms in a nucleic acid sample of the volunteer associated with a genetic predisposition that determines a patient's predicted degree of response to a particular therapy; wherein the polymorphism is one or more of: i) CYP3A4-associated SNP (rs35599367 [SEQ ID NO: 152]) located in intron 6 (C>T); or, ii) a SNP in linkage disequilibrium therewith, wherein the allelic status of the polymorphism in the subject is predictive of the subject's risk for having or developing the CYP3A4-related disorder; b) isolating the cell from the volunteer that possesses the one or more mutations of interest; and c) subjecting DNA from the cell to a plurality of validated genomic screening assays.



BRIEF DESCRIPTION OF THE DRAWINGS
The patent or application file may contain at least one drawing executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee.
The invention can be more fully understood from the following detailed description, the drawings and the Sequence Descriptions that form a part of this application. The Sequence Descriptions and Sequence Listing attached hereto comply with the rules governing nucleotide and/or amino acid sequence disclosures in patent applications as set forth in 37 CFR §§1.821-1.825. The Sequence Descriptions contain the three letter codes for amino acids as defined in 37 CFR §§1.821-1.825, which are incorporated herein by reference.
FIG. 1: Allelic mRNA/hnRNA expression ratios of CYP3A4 in human livers measured with a primer extension assay (SNaPshot) using multiple marker SNPs (FIG. 3) (FIG. 1A) or intron6 SNP rs35599367 [SEQ ID NO: 152] only (FIG. 1B). Allelic RNA ratios were normalized to gDNA ratios set at 1. Data represent the average of 2-3 measurement using single or multiple marker SNPs. An arrow indicates samples with AEI ratios significantly different from 1 (P<0.05). All allelic RNA ratios in Panel B are significantly different from 1 (ANOVA with Dunnett post-test, P<0.05).
FIGS. 2A-2B: Box plot of CYP3A4 mRNA levels (FIG. 2A) and enzyme activity (FIG. 2B) in human liver samples, grouped by intron6 SNP genotype.
FIG. 3: Location and rs number of marker SNPs used for AEI measurements. Intron6 SNP rs35599367 [SEQ ID NO: 152] is in red.
FIG. 4: Association between genotypes and allelic RNA expression imbalance (AEI). Only intron6 SNP rs35599367 [SEQ ID NO: 152] and much less strongly SNP rs2246709 associated with AEI with an adjusted P<0.05.
FIG. 5: LD plot for 13 polymorphisms in CYP3A4. Detailed SNP information is provided in Table 1. SNP6 is intron6 SNP rs35599367 [SEQ ID NO: 152], only partial LD with SNP 7 rs2246709.
FIGS. 6A-6D: Correlation between mRNA expression of four transcription factors and CYP3A4 in human livers.



DETAILED DESCRIPTION OF THE INVENTION
The present invention will now be described with occasional reference to the specific embodiments of the invention. This invention may, however, be embodied in different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
All publications, patent applications, patents, and other references mentioned herein are incorporated by reference in their entirety. The disclosure of all patents, patent applications (and any patents that issue thereon, as well as any corresponding published foreign patent applications), GenBank and other accession numbers and associated data, and publications mentioned throughout this description are hereby incorporated by reference herein. It is expressly not admitted, however, that any of the documents incorporated by reference herein teach or disclose the present invention.
The present invention may be understood more readily by reference to the following detailed description of the embodiments of the invention and the Examples included herein. However, before the present methods, compounds and compositions are disclosed and described, it is to be understood that this invention is not limited to specific methods, specific cell types, specific host cells or specific conditions, etc., as such may, of course, vary, and the numerous modifications and variations therein will be apparent to those skilled in the art. It is also to be understood that the terminology used herein is for the purpose of describing specific embodiments only and is not intended to be limiting.
In a broad aspect, the inventors herein found common polymorphisms in CYP3A4 by measuring allelic hnRNA/mRNA expression in human autopsy livers. A detectable allelic RNA expression imbalance (AEI) is a direct measure of cis-acting regulatory factors in CYP3A4 that affect RNA expression, processing, or turnover.
The results described herein demonstrate that an intron6 SNP of CYP3A4 fully accounts for the observed allelic mRNA expression pattern and correlates with CYP3A4 enzyme activity in human livers, while previously suggested polymorphisms had no effect.
Moreover, intron6 SNP was significantly associated with stable statin dosage taken for cholesterol control and with achievement of therapeutic LDL goal in a cohort of CAD patients.
EXAMPLE I
Methods:
Tissue samples. 133 liver autopsy/biopsy samples were obtained from The Cooperative Human Tissue Network Midwestern and Western Division, under a protocol approved by the Ohio State University Institutional Review Board (OSU IRB).
Patients. Subjects were participants in the Ohio State University Coronary Artery Disease Study, with patients presenting to the OSU Heart Center with symptomatic cardiovascular disease requiring PCI calls. 275 patients documented to be taking stable doses of a statin for lipid control were selected. Enrollment and trial conditions have been approved by the OSU IRB, with written informed consent obtained from each patient. The study population reflects demographics of the Columbus area and surrounding rural counties of Ohio.
DNA and RNA preparation. Preparation of genomic DNA, RNA and cDNA from tissues or blood samples were performed as described (16-18).
Quantitative analysis of allelic ratios in genomic DNA and RNA using SNaPshot. The detailed method has been published (16,17). Briefly, a fragment of DNA or RNA (after conversion to cDNA) surrounding a marker SNP was PCR amplified, followed by a primer extension assay (SNaPshot) that targets the polymorphic site. Seven marker SNPs located in either 3′UTR or intronic regions were used to measure allelic ratios of mature mRNA (3′UTR markers) or hnRNA (intronic markers) in 73 out of the 133 livers heterozygous for at least one marker SNP. Genomic DNA (gDNA) allelic ratios, normalized to 1, served as internal control; none of the subjects displayed gDNA copy number variants, detectable by a significant deviation from unity. Deviations of allelic RNA ratios from 1 (after normalization to DNA ratios), i.e., allelic expression imbalance (AEI), indicates the presence of cis-acting polymorphisms in CYP3A4 that affect mRNA expression levels.
Genotyping: Thirteen SNPs in CYP3A4 (including the 7 marker SNPs) were genotyped in gDNA from liver samples with a multiplex SNaPshot assay (19) or allele specific real-time PCR (20), as shown in Table 1.







TABLE 1







Polymorphisms tested in liver samples.









SNP #
SNP ID
position












1
TGT ins
−11231


2
rs2737418 G > T
 −7310


3
rs2740574 A > G
 −392



(*1B)


4
rs2687105 A > T
intron 2


5
rs28988579 T > G
intron 4


6
rs35599367 C > T
intron 6


7
rs2246709 C > T
intron 7


8
rs4646437 C > T
Intron 7


9
rs2242480G > A
intron10


10
rs3735451A > G
intron 12


11
rs28988604C > T
3′UTR


12
rs28969391delT
3′UTR


13
rs28371763A > T
3′UTR









Seven SNPs in CYP3A4/3A5 were genotyped in gDNA from 275 patients, as shown in Table 2.







TABLE 2







Polymorphisms tested in 275 patients.











SNP ID
Gene
Position







TGT ins
CYP3A4
Enhancer, −11231



rs2740574
CYP3A4
Promoter, −392, *1B



rs35599367
CYP3A4
Intron 6



rs4987161
CYP3A4
Exon 7 (F189S)



rs28371759
CYP3A4
Exon 10 (L293P)



rs776746
CYP3A5
Intron 3, *3



rs41303343
CYP3A5
Exon 11, *7










PCR conditions and primer sequences are shown in Table 3, Table 4, Table 5.
Quantitative mRNA analysis of CYP3A4 and transcription factors PXR, RXRa, CAR, and HNF4a in human livers: mRNA levels were measured with real-time PCR (16,21) using gene-specific primers (22) (Table 3, Table 4, Table 5) and SYBR Green (Applied Biosystems), with GADPH mRNA as an internal control.
Table 3: Genotyping or AEI assays. Column labeled “Sequence of PCR primers and assay condition” discloses SEQ ID NOS 1-42, respectively, in order of appearance and column labeled “Primer extension primers and condition” discloses SEQ ID NOS 43-56, respectively, in order of appearance.







TABLE 3







Genotyping or AEI assays












Snap



Sequence of PCR 



shot



primers and assay
Primer extension


assay
SNP#
rs#
location
condition
primers and condition






 2
rs2737418
7.3 kb
F: GAACTTGCTGACCCTCTGCTTT
CATTCTCCTTTAACCT





upstream
[SEQ ID NO: 1]
GTTGACGA







[SEQ ID NO: 43]






R: TAGCAAGCCACAGACAGCA
60° C. extension






[SEQ ID NO: 2]







Choice Tag, 60° C. extension



 



 3
rs2740574
promoter
F: AGAGCCATGACAGGGAATAAGACT
AGGACAGCCATAGAG






AGA
ACAAGGGCA






[SEQ ID NO: 3]
[SEQ ID NO:44]






R: TGGGCTATGTGCATGGAGCTT
55° C. extension






[SEQ ID NO: 4]







JumpStart RedTag, 







60° C. extension



 



 5
rs28988579
intron 4
F: TTTCCTTTCCAATCTGTATGCC
AGTATAATGTTGAGT






[SEQ ID NO: 5]
AAATGTGGTGAAT







[SEQ ID NO: 45]






R: AAAGAACTGAAGGCTTCCCTC
55° C. extension






[SEQ ID NO: 6]







Choice Tag, 60° C. extension



 



 6
rs35599367
intron 6
F: CTGAAGCACAGTGCTTACCCAT
CCAGTGATGCAGCTG






[SEQ ID NO: 7]
GCCCTAC







[SEQ ID NO: 46]






R: GGTGCCAGTGATGCAGCT
60° C. extension






[SEQ ID NO: 8]







Choice Tag, 60° C. extension



 



 8
rs4646437
intron7
TTATGATTTGGGTTATTCTAGGAG
GGCAGGTCTATGCAT






AC
AAGGAGCACC






[SEQ ID NO: 9]
[SEQ ID NO: 47]






CCTGGAGCAATTCTAGTTTTCTCT







[SEQ ID NO: 10]







Choice Tag, 60° C. extension



 



11
rs28988604
3UTR
F: TGGTCATTGTAATCACTGTTGGC
(T15)CAAACTGCTAGG






[SEQ ID NO: 11]
ATTACAGGC







[SEQ ID NO: 48]






R: TTAAGTGTTCATTGCATCGAGAC
15 Ts were added at 5′ end






[SEQ ID NO: 12]
[SEQ ID NO: 49]






Choice Tag, 60° C. extension
60° C. extension


 



12
rs28969391
3′UTR
same as above
AAGCCTGGCCTACAT







GGT







[SEQ ID NO: 50]







60° C. extension


 



13
rs28371763
3UTR
same as above
(T4)GTGAGAGTGAGA







CTCAGTCTTAAAAA







[SEQ ID NO: 51]







4 Ts were added at 5′ end









Note: SNP 11-13 can be multiplexed

60° C. extension


 













 7
rs2246709
intron 7
F: TTTAGCTATCAGCCCCCTGT
(T11)CAACCACTAATC






[SEQ ID NO: 13]
AACTTTCTGC







[SEQ ID NO: 52]






R: TGAAGCCAGCAGAAGAAAGAA
11 Ts were added at 5′ end






[SEQ ID NO: 14]
[SEQ ID NO: 53]






JumpStart RedTag, 
55° C. extension






60° C. extension



 



 9
rs2242480
intron 10
F: GCTATGAAACCACGAGCAGTGT
(T18)CCTCCCTCCTTCT






[SEQ ID NO: 15]
CCATGTA







[SEQ ID NO: 54]






R: GGGAAGTGGTGAGGAGGC
18 Ts were added at 5′ end






[SEQ ID NO: 16]
[SEQ ID NO: 55]







55° C. extension


 



10
rs3735451
intron 12
F: ATAGATGATGAATGCTCTCACTG
TTTTTTTGCCCATTAC






TCC
TCCAT







[SEQ ID NO: 56]






[SEQ ID NO: 17]











Note: SNP 7, 9 and 10 

R: GGGATCTGCAACAGTTAAACAAG
55° C. extension


can be multiplexed

[SEQ ID NO: 18]



 












Allele
 1
TGT ins
11 kb
common F:



specific


upstream
GCTCATAGAATCCTGGGCAT



PCR



[SEQ ID NO: 19]



assay











TGTwtR:







CCTATCTAGCCATTAGAACCACA







TGT







[SEQ ID NO: 20]







TGTinsR:







CCTATCTAGCCATTAGAACCACA







TGTACA







[SEQ ID NO: 21]



 



 4
rs2687105
intron 2
snpF: GCTGCACCTTATGGGTGTGT







[SEQ ID NO: 22]







wtF: GCTGCACCTTATGGGTGTGA







[SEQ ID NO: 23]







common R:







ATTGACCACCACTGTCTCATCTC







[SEQ ID NO: 24]



 



 5
rs28988579
intron 4
wtF: GTATAATGTTGAGTAAATGTGGT







GAGTT







[SEQ ID NO: 25]







snpF: GTATAATGTTGAGTAAATGTGGT







GATTG







[SEQ ID NO: 26]







common R:







AAAGAACTGAAGGCTTCCCTC







[SEQ ID NO: 27]



 



 6
rs35599367
intron 6
wtF: GTGTCTCCATCACACCCTGC







[SEQ ID NO: 28]







snpF: GTGTCTCCATCACACCCCGT







[SEQ ID NO: 29]







common R:







GGTGTTATCAGGTGCCAGTG







[SEQ ID NO: 30]











Primers for real-time 

primer sequence



PCR to quantitate mRNA





 















CYP3A4
F: CTCTCATCCCAGACTTGGCCA







[SEQ ID NO: 31]







R: ACAGGCTGTTGACCATCATAAAAG







[SEQ ID NO: 32]



 





PXR
F: CAAGCGGAAGAAAAGTGAACG







[SEQ ID NO: 33]







R: CACAGATCTTTCCGGACCTG







[SEQ ID NO: 34]



 





RXR
F: GAGCGGCAGCGTGGCAAGG







[SEQ ID NO: 35]







R: GGCAAATGTTGGTGACAGGG







[SEQ ID NO: 36]



 





HNF4a
F: ACATGGACATGGCCGACTAC







[SEQ ID NO: 37]







R: CTCGAGGCACCGTAGTGTTT







[SEQ ID NO: 38]



 





CAR
F: CACATGGGCACCATGTTTGA







[SEQ ID NO: 39]







R: AAGGGCTGGTGATGGATGAA







[SEQ ID NO: 40]



 





GADPH
F: ACTCCTCCACCTTTGACGCT







[SEQ ID NO: 41]







R: GGTCCACCACCCTGTTGC







[SEQ ID NO: 42]









Table 4: Multiplex PCR and Snapshot assay for CYP3A4 and CYP3A5 SNP genotyping. Column labeled “PCR primer sequence” discloses SEQ ID NOS 57-68, respectively, in order of appearance and column labeled “Primer extension primers” discloses SEQ ID NOS 69-75, respectively, in order of appearance.







TABLE 4







Multiplex PCR and Snapshot assay for CYP3A4 and CYP3A5 SNP genotyping













Final primer





conc in





snapshot


SNP
PCR primer sequence
Primer extension primers
reaction





TGT ins
F: GCCTGCATTTTATCTCTGTCTCGTGG
(T8)ATTCCCTATCTAGCCAT
20 nM



[SEQ ID NO: 57]
TAGAACCACA





[SEQ ID NO :69]




R: GAAGGTGGGAAACAGCCAGATCAGA





[SEQ ID NO: 58]




 


rs2740574
F: AGAGCCATGACAGGGAATAAGACTAGA
(T15)
50 nM



[SEQ ID NO: 59]
AGGACAGCCATAGAGACAA





GGGCA





[SEQ ID NO: 70]




R: TGGGCTATGTGCATGGAGCTT





[SEQ ID NO: 60]




 


rs35599367
F: CTGAAGCACAGTGCTTACCCAT
CCAGTGATGCAGCTGGCCCT
80 nM



[SEQ ID NO: 61]
AC





[SEQ ID NO: 71]




R: GCCACAACATAGTAAACGAAGAAGGGCA





[SEQ ID NO: 62]




 


rs4987161
same as above
(T20)
80 nM




GTTGAGAGAGTCGATGTTC





ACTCCA





[SEQ ID NO: 72]



 


rs28371759
F: CTGTGATGCCCTACATTGATCTGATTTA
(T16)TCTCCTTTCAGCTCTGT
60 nM



CCTA
CCGATC




[SEQ ID NO: 63]
[SEQ ID NO: 73]




R: CTGGGAAGTGGTGAGGAGGCATTTT





[SEQ ID NO:64]




 


rs776746
F: GTATGTACCACCCAGCTTAACGAATGCTC
TGTGGTCCAAACAGGGAAG
50 nM



[SEQ ID NO: 65]
AGATA





[SEQ ID NO: 74]




R: CACACAGGAGCCACCCAAGGC





[SEQ ID NO:66]




 


rs1303343
F: CCAATTCTGTTTCTTTCCTTCCAGGCA
(T2)CATCTGTACCACGGCAT
15 nM



[SEQ ID NO: 67]
CATAGGTA





[SEQ ID NO: 75]




R: AACCAGCCTGGGTCAGGGTGAG





[SEQ ID NO: 68]





Readymix Taq, 60° C. extension
55° C. extension




Final primer cont for rs2740574 and





rs35599367





is 300 nM, others are 150 nM





CYP3A4 PCR and sequencing primers





(ref sequence AF280107)









Table 5: Primers and sequencing primers. PCR primers disclosed as SEQ ID NOS 76-95, respectively, in order of appearance and Sequencing primers disclosed as SEQ ID NOS 96-151, respectively, in order of appearance.







TABLE 5







PCR Primers and Sequencing Primers









PCR




primers




Amplified

Sequencing primers


region
Sequence 5′-3′
Sequence 5′-3′





Promoter
F:
TTACAATAGCAATGACCTGGAACCA


1(49947 to
TTACAATAGCAATGACCTGGAACCAATCC
ATCC


51960)
[SEQ ID NO: 76]
[SEQ ID NO: 96]



R:
AATGGCAGGCACTGGAATT



GTGCAACCACAAACAATTAGGAACCTGT
[SEQ ID NO: 97]



[SEQ ID NO: 77]





GTGCAACCACAAACAATTAGGAACC




TGT




[SEQ ID NO: 98]


 


Promoter 2
F: AGCCCAGGAGGCAGCAGTTGC
AGCCCAGGAGGCAGCAGTTGC


(51740 to
[SEQ ID NO: 78]
[SEQ ID NO: 99]


53696)





R: GTCTTCCTAAAGGAGTGACTGTTTGCA
TATGAAGTGAAGGCCAGAAACGA



TTATCAT
[SEQ ID NO: 100]



[SEQ ID NO: 78]





ACATGAAAAACAAAGCAACTCCAAC




[SEQ ID NO: 101]


 


Promoter 3
F: TTGAGCCTGGGAGGCTGCG
TTGAGCCTGGGAGGCTGCG


(53408 to
[SEQ ID NO: 80]
[SEQ ID NO: 102]


55776)





R:
ATGCTGGTTGCTGGTTTATTCTA



TAGCTTCTTCATTCGGTCTCAGTCCACTT
[SEQ ID NO: 103]



[SEQ ID NO: 81]





GAGTTGGGCATGATGCCTTT




[SEQ ID NO: 104]


 


Promoter 4
F:
TCATTTTTGTAGAGCCTGAGGAGTGT


(55681 to
TCATTTTTGTAGAGCCTGAGGAGTGTCCA
CCA


59417)
[SEQ ID NO: 82]
[SEQ ID NO: 105]



R:
CAAGAATGCTACCGGCACAA



GATTTACCTGCCCTACAAACTTTAGGAG
[SEQ ID NO: 106]



GTGG




[SEQ ID NO: 83]





TGAATCCTGGCTCTGCTAAAGC




[SEQ ID NO: 107]




ACCACTGGGAGCTTAAGTAAAGGG




[SEQ ID NO: 108]




TCAAGTTTTCCCCTACTGAGAAGAAT




[SEQ ID NO: 109]


 


Promoter 5
F:
GTTACCTTCTGTGGAATTAAGTGGCA


(59230 to
GTTACCTTCTGTGGAATTAAGTGGCAGA
GAACT


62323)
ACT
[SEQ ID NO: 110]



[SEQ ID NO: 84]




R:
TTTTATCCCAGGGATTCCAG



GACAGAGTTTCACCATGTTAGCCAGGC
[SEQ ID NO: 111]



[SEQ ID NO: 85]





GAAATTCATCCCAACAAGCCACACC




[SEQ ID NO: 112]




GACAGAGTTTCACCATGTTAGCCAGGC




[SEQ ID NO: 113]


 


Promoter to
F: AGGGACCAGAGCCATGACAGGG
AAGATGTTCAGGCCGGGC


intron 3
[SEQ ID NO: 86]
[SEQ ID NO: 114]


(61296 to




68200)





R: TGACAAGAGCTTCATCCCAAGAGGC
GTAGTGGAGGCTTCTCACATGTCA



[SEQ ID NO: 87]
[SEQ ID NO: 115]




GCAGCCCAGGAGTCAGAAAC




[SEQ ID NO: 116]




GGATCCATTTATACACACCATGCTT




[SEQ ID NO: 117]




GATTCAGACCATATCACTGGCACT




[SEQ ID NO: 118]




TCATTGCCGTCAGAGTTACTGTTATTA




[SEQ ID NO: 119]




CCTGCTTCAATCCTCTCCGA




[SEQ ID NO: 120]




GTGAAAACTCTGAGCAAGTGTTGTA




ATT




[SEQ ID NO: 121]


 


Intron 2 to
2F2:
TTTCATTGGCTTCGACTGTTTTCATCC


intron 4
TTTCATTGGCTTCGACTGTTTTCATCC
[SEQ ID NO: 122]


(667947 to
[SEQ ID NO: 88]



73642)





2R2:
GGATCCCATGTGTCACCAGG



ACAGGATGAAGTGGACGTGGAACCTT
[SEQ ID NO: 123]



[SEQ ID NO: 89]





CCCGTGTCCATGTGTTCTCA




[SEQ ID NO: 124]




CTTCGTCAGATGGATAGATTGCAA




[SEQ ID NO: 125]




GTTCTTCCATTTGTTTGTGTCCTCT




[SEQ ID NO: 125]




TGCTGACTTGATCCTGGTGG




[SEQ ID NO: 127]




TCTTAGTTATTTCTTGTCTCCTGCTAGC




[SEQ ID NO: 128]




TCTTTGTAGGTCTCTAAGAACTTGCT




TTAT




[SEQ ID NO: 129]


 


Intron 3 to
3F2:
ATTATGTAAAGTCAGGATCAAAGTCT


intron 8
ATTATGTAAAGTCAGGATCAAAGTCTGG
GGCTTCC


(73332 to
CTTCC
[SEQ ID NO: 130]


79586)
[SEQ ID NO: 90]




3R2:




TCCCTTCTGAGAATATGGCTCCTTGAAG
TGGTGCCATGTTTGAAAGTTCTT



[SEQ ID NO: 91]
[SEQ ID NO: 131]




ATGTGCTTTGATTTTGTGTGTTGAT




[SEQ ID NO: 132]




CCACGTGGATAATTTGCATGTAA




[SEQ ID NO: 133]




TTGAAAGAGTAAGTAGAAGCGCAGC




[SEQ ID NO: 134]




TGAGCAGGGAGTATAGAGAATAAGGAT




[SEQ ID NO: 135]




TCAATCTAGAGACCTCATACATTTTT




AGCT




[SEQ ID NO: 136]




GAGAAAACTAGAATTGCTCCAGGTAAA




[SEQ ID NO: 137]


 


Intron 8 to
4F:
AAATGAAAGTCCCTATCAGGCCACCTG


intron 11
AAATGAAAGTCCCTATCAGGCCACCTG
[SEQ ID NO: 138]


(79395 to
[SEQ ID NO: 92]



84596)





4R:
TTGCAGCTACCTAATACATCTAACATCC



TCTTTCTCCCCCACACCTCCATAGAATA
[SEQ ID NO: 139]



[SEQ ID NO: 93]





GCTAGTGCCATTGAAGATCAATTTTA




[SEQ ID NO: 140]




AATATTGGTCCCTTAAGTTCCCTCA




[SEQ ID NO: 141]




ACATGGAGAAGGAGGGAGGAG




[SEQ ID NO: 142]




GCTGACTCAGCTCTCCCCAC




[SEQ ID NO: 143]




ACCAGTATGAGTTAGTCTCTGGAGCTC




[SEQ ID NO: 144]


 


Intron 11 to
5F:
CATCCACAATTCCAACAACTTACGAT


intron 13
CATCCACAATTCCAACAACTTACGATGA
GAAG


(84264 to
AG
[SEQ ID NO: 145]


89410)
[SEQ ID NO: 94]




5R:
ATTTCAATGACCAGCCCACAA



CTATTTAGGCTCTGGCTGCTCTTGCAA
[SEQ ID NO: 146]



[SEQ ID NO: 95]





CATTGGAATCACCAGGGAGC




[SEQ ID NO: 147]




TTACTTCTCTGCTCTGTTATTGGATAC




TG




[SEQ ID NO: 148]




CTCCCTGGCAATTTTCTTGC




[SEQ ID NO: 149]




TTACAAAGCATTATTGTCATTACTGC




AT




[SEQ ID NO: 150]




CTGTGACTTTGCCCATTGTTTAGA




[SEQ ID NO: 151]









Sequencing CYP3A4: The region from  ̃10,000 bp upstream of transcription start site to the last exon (from 50013 to 89410 in AF280107, total length 39,397 bp) was sequenced in two liver samples that showed allele-specific RNA expression. PCR and sequencing primers are shown in Table 10.
CYP3A4 enzymatic activity assay: CYP3A4 activities were quantified from liver microsomes with testosterone as a probe as described (23).
Data analysis: Association between discrete variables and genotypes were analyzed with Helix-Tree software (Golden Helix, Bozeman Mont.). LD plots were generated using Haploview. Multiple linear regression analysis was used for testing genotype effects on RNA expression, enzyme activity, and statin dose requirement using SPSS or Minitab software.
Results:
Scanning for cis-acting CYP3A4 polymorphisms that affect mRNA levels:
Allelic mRNA expression of CYP3A4 was measured in human livers using 3 frequent 3′ UTR marker SNPs (FIG. 3). Because of high CYP3A4 expression, 4 intronic SNPs also served to measure allelic expression of CYP3A4 hnRNA (24). Among 133 liver samples screened, 73 were heterozygous for at least one of the seven marker SNPs and therefore suitable for AEI measurement. Of the 73 samples, 7 were identified showing strong allelic expression imbalance (AEI), with the main allele expressed less than the minor allele (0.2 to 0.5 times), observed with both intronic and exonic marker SNPs (FIG. 1A). Therefore, a cis-acting polymorphism(s) in CYP3A4 affects both mRNA and hnRNA levels equally.
To search for responsible polymorphism(s), 13 CYP3A4 polymorphisms (Table 1) were genotyped and the association between allelic RNA ratios and genotype tested.
A single SNP (rs35599367 [SEQ ID NO: 152]) located in intron6 (C>T) showed highly significant association with AEI (adjusted p value 9.12×)10−10 (FIG. 4), while another SNP rs2246709 also scored with moderate significance (P=0.034), a likely result of partial linkage disequilibrium (LD) with the intron6 SNP (FIG. 5).
Other SNPs including previously identified promoter SNP rs2740574, TGT insertion, rs2737418, and rs4646437 did not show significant association (P>0.05). These results indicated that intron6 SNP is functional or in highly LD with a functional SNP.
To test this further, intron6 SNP was used as a marker, and AEI was measured in 10 heterozygous samples, including 3 heterozygous only for intron6 SNP. All 10 samples showed AEI with allelic ratios (minor T allele/major C allele) ranging from 0.16 to 0.62 (FIG. 1B), with no differences between males and females (P=0.314). Therefore, the minor T allele of intron6 SNP is linked to reduced mRNA/hnRNA levels (the inverse allelic mRNA ratio of major C/minor T ranges from 1.6 to 6.25). The minor T allele is exclusively linked to the main CYP3A4 haplotype, except for low LD with rs2246709 (Table 6; LD plot FIG. 5).
Table 6: Haplotype structure and estimated frequency of 13 SNPs tested in livers. Detailed SNP information is provided in Table 1. In haplotype 5, the minor T allele of intron6 SNP (#6 from the left) is exclusively linked to the major haplotype 1 (in bold), whereas it has no detectable LD with any of the other SNPs.









TABLE 6





Haplotype

EM Frequency

















1
D, G, A, A, T, C, T, C, G, A, C, T, A
0.408


2
D, G, A, A, T, C, C, C, G, A, C, T, A
0.208


3
D, G, G, T, T, C, T, T, A, G, C, G, A
0.072


4
D, G, A, A, T, C, T, T, A, G, C, G, A
0.048


5
D, G, A, A, T, T, C, C, G, A, C, T, A
0.044


6
D, G, A, A, T, C, C, T, A, G, C, G, A
0.032


7
I, G, A, A, G, C, T, C, G, A, C, T, A
0.027


8
D, G, A, A, T, C, T, T, G, A, C, T, T
0.026


9
D, T, G, T, T, C, C, T, A, G, C, G, A
0.026


10
D, G, A, A, T, C, T, C, G, G, T, G, A
0.014









Sequencing of the entire CYP3A4 locus in two AEI-positive samples did not implicate any other polymorphisms that would have to be heterozygous in both samples, indicating that intron6 SNP is functional.
Intron6 SNP associates with decreased CYP3A4 mRNA level and enzyme activity in human livers:
Total CYP3A4 mRNA levels were measured in 93 liver samples. While mRNA levels did not differ between Caucasians and African Americans, females had −1.3 fold higher levels than males (95% CL 1.00-1.68, two-sided P=0.042) as reported (25). Livers with the main CC genotype of intron6 SNP had 1.71-fold (95% confidence interval (CI) 1.06-2.76) higher levels than CT/TT carriers (t test, two-sided p=0.028), with no interactions between genotypes and sex. To test the effect of CYP3A4 transcription factors (26-29), mRNA levels were also measured for pregnane X receptor (PXR, NR112), constitutive androstane receptor (CAR, NR113), retinoid receptor (RXRa), and hepatocyte nuclear factor (HNF4α1A). CYP3A4 mRNA expression positively correlated with all four transcription factors, as reported (26-29) (FIGS. 6A-6D).
After adjusting for age and transcription factors, intron6 SNP remained significantly linked to CYP3A4 expression (1.67-fold CC over CT/TT (95%, CI 1.11-2.46, p=0.014) (FIG. 2A), showing that the genotype effect is independent of the transcription factors.
CYP3A4 enzyme activity (testosterone 6β-hydroxylation) in 23 livers was 2.46 fold higher for intron6 SNP CC than CT carriers, after adjusting for age (<15 child, >15 adult), sex, and use of inducers (phenobarbital, carbamazepine, nifedipine and dexamethasone) (two-sided p=0.037, 95% CI 1.07-5.62) (FIG. 2B).
Consistent with allelic mRNA expression, these results demonstrate that intron6 SNP decreases CYP3A4 mRNA and protein levels in vivo. In contrast, CYP3A4*1B, TGT insertion, rs2737418, and rs4646437 had no effect on total CYP3A4 mRNA level or enzyme activity (P>0.05).
Intron6 SNP (C>T) associates with statin dose requirement and lipid control outcome:
The in vivo effect of intron6 SNP was assessed as the dosage of CYP3A4-metabolized statins required for reaching a pre-determined LDL goal. Intron6 SNP was genotyped in 275 patients on stable doses of atorvastatin, lovastatin, and simvastatin, or the non-CYP3A4 substrates rosuvastatin and pravastatin. Additional SNPs in CYP3A4 (*1B rs2740574, TGT insertion, *17 rs4987161, and *18 rs28371759) and CYP3A5 (*3 rs776746 and *5 rs41303343) (Table 5) were also genotyped. Three SNPs deviated from Hardy-Weinberg equilibrium (HWE), because of different allele frequency in Caucasian and African American populations, in which all SNPs followed HWE when analyzed separately (Table 7).







TABLE 7







SNPs tested in 275 patients.













Black and



All Patients
White
others



n = 275
n = 240
n = 35















SNP
Alleles
Location
MAF
HWE P
MAF
HWE P
MAF
HWE P


















TGT
TGT
CYP3A4
0.016
0.78 
0.017
0.79
0.018
0.92



Del/Ins
enhancer


rs2740574
A > G
CYP3A4
0.105
6.06E−07
0.054
0.71
0.350
0.77




promoter


rs35599367
C > T
CYP3A4
0.047
0.061
0.052
0.08
0
NA




intron 6


rs4987161
T > C
CYP3A4
0
NA
0
NA
0
NA




exon 7


rs28371759
T > C
CYP3A4
0
NA
0
NA
0
NA




exon 10


rs776746
G > A
CYP3A5
0.138
2.81E−08
0.088
0.09
0.376
0.28




intron 3


rs1303343
T Del/Ins
CYP3A5
0.012
4.53E−06
0.008
0.95
0.055
0.76




exon 11









The allele frequency of intron6 SNP is  ̃5%, consistent with that reported in NCBI database. Absence of CYP3A4 SNPs *17 and *18 is consistent with reported low allele frequency.
A subset of 237 patients were on CYP3A4-metabolized statins (atorvastatin, lovastatin, simvastatin) with doses ranging from 5 mg to 80 mg. The statins were either evaluated together, or each separately to account for potency differences. Patients on all statins were divided into low-dose (<20 mg, mean 16±5 mg) and high-dose (>40 mg, mean 53±19 mg) (P<0.001) groups. Baseline characteristics and lipid levels did not differ between low-and high-dose groups, except for maximum triglyceride and stable HDL levels (P<0.05) (Table 8). Table 8 shows the baseline characteristics of patients on CYP3A4-metabolized statins (atorvastatin, lovastatin and simvastatin).







TABLE 8







Baseline characteristics of patients on


CYP3A4-metabolized statins (atorvastatin, lovastatin and simvastatin).












All patient
Low dose
High dose




n = 237
n = 121
n = 116
P value















Dose (mg)
34 ± 23
16 ± 5 
53 ± 19
<0.001


Age
62 ± 11
63 ± 13
62 ± 10
0.444


Maximum
105 ± 49 
102 ± 52 
108 ± 45 
0.362


LDL (mg/dl)


Stable LDL
82 ± 33
85 ± 39
80 ± 24
0.297


(mg/dl)


Minimum
33 ± 10
34 ± 10
32 ± 9 
0.141


HDL (mg/dl)


Stable HDL
35 ± 10
36 ± 10
33 ± 9 
0.042*


(mg/dl)


Max
256 ± 430
196 ± 159
318 ± 590
0.043*


Triglyceride


(mg/dl)


Stable
156 ± 106
145 ± 108
166 ± 104
0.159


Triglyceride


(mg/dl)


Max total
185 ± 79 
175 ± 65 
194 ± 91 
0.085


Cholesterol


(mg/dl)


Stable
148 ± 41 
149 ± 47 
147 ± 35 
0.650


Cholesterol


(mg/dl)


Male
156/232 (67%)
74/120 (62%)
82/112 (73%)
0.070


White
204/228 (89%)
108/120 (90%)
97/108 (89%)
0.607









The minor intron6 SNP T allele was significantly associated with a lower stable statin dose (odd ratios=3.80, P=0.005). None of the other SNPs were significant. Table 9 shows the association of CYP3A4/3A5 SNP genotypes with statins dose requirement and cholesterol control outcome. *P<0.05, **P<0.01.







TABLE 9







Association of CYP3A4/3A5 SNP genotypes with statins dose


requirement and cholesterol control outcome.










Low dose vs high dose
Goal vs not goal












Minor allele odd

Minor allele odd




ratio

ratio


SNP
(95% CL)
P value
(95% CL)
P value














TGT
0.31 (0.06~1.54)
0.165
1.24 (0.24~6.50)
0.794


rs2740574
1.30 (0.74~2.32)
0.388
0.58 (0.32~1.05)
0.083


(*1B)


rs35599367
3.80 (1.38~10.35)
0.005**
3.49 (1.02~11.96)
0.039*


(intron 6)


rs776746
0.96 (0.57~1.63)
0.902
0.65 (0.37~1.14)
0.139


rs41303343
0.94 (0.18~4.71)
0.942
0.24 (0.04~1.34)
0.097





*P < 0.05,


**P < 0.01.






Multiple linear regression analysis shows that the stable statin dose for intron6 T allele carriers was only 0.27 that for non T-carriers (P=0.019), after adjusting for maximum lipid levels (Table 10). Similar results were obtained when analyzing patients on atorvastain and simvastatin separately (Table 10). Therefore, intron6 SNP is linked to reduced statin dose requirement.







TABLE 10







CYP3A4 intron6 SNP genotypes and stable statin doses.













Dose ratio






(T carrier/
95% confidence


Statins
N
non-Tcarrier)
interval
P value














CYP3A4 substrates
237
0.27
0.19~0.66
0.019*


Atorvastatin
143
0.22
0.14~0.54
0.024*


Simvastatin
85
0.6
0.37~0.97
0.042*





*P < 0.05






For testing the association of intron6 SNP with cholesterol control outcome, 198 patients with available LDL data were divided into those who reached the cholesterol goal (LDL cholesterol<70 mg/dl; or 71-99 mg/dl with low/moderate risk; or 100-129 mg/dl with low risk) and those not at goal (>130 mg/dl; or 100-129 mg/dl with moderate/high risk; or 71-99 mg/dl with high risk) according to Adult Treatment Panel III Guidelines (30). Individuals bearing the T allele of intron6 SNP had a 3.5 fold higher chance of reaching goal cholesterol control than non T-carriers (P=0.039) (Table 2). In contrast, other SNPs did not show such associations.
Discussion of EXAMPLE I:
Example I shows that intron6 SNP rs35599367 [SEQ ID NO: 152] is significantly linked to reduced CYP3A4 mRNA expression and enzyme activity in human livers, and moreover, fully accounts for differences in allelic mRNA expression.
Since intron6 SNP is embedded in the main haplotype of CYP3A4, lacking substantial LD to any other SNPs, it had escaped detection by association studies using haplotype tag SNPs (14,15). None of the previously reported CYP3A4 SNPs, including promoter *1B, enhancer TGT insertion, enhancer rs2737418, and intron7 SNP rs4646437 had detectable effects on allelic mRNA expression, mRNA, and enzyme activity, arguing against a contribution of these SNPs to CYP3A4 variability.
The allele frequency of intron6 SNP in the examined cohort was  ̃5%, resulting in  ̃10% heterozygocity. Previously reported allele frequencies (35) were 0.043, 0.043, and 0.083, for African Americans, Chinese, and Caucasians, respectively, indicating that intron6 SNP allele frequency ranges from 4 to 8% in various ethnic groups.
A common molecular mechanism for an intronic SNP to alter mRNA levels is to affect RNA expression, elongation, splicing, or maturation. Since the allelic ratios were similar for mRNA and hnRNA in livers heterozygous for both exonic and intronic marker SNPs, splicing and mRNA turnover can be ruled out as main mechanisms. Moreover, CYP3A4 mRNA and hnRNA levels were shown to vary in parallel in human livers (36), arguing for an early event in expression and processing. For example, intron6 SNP could affect the folding of nascent RNA and hence elongation.
In silico RNA folding analysis shows that intron6 SNP promotes a loop-to-stem structural change (not shown), possibly impeding the binding of regulatory proteins.
Consistent with reduced expression of the minor allele, intron6 SNP was significantly associated with reduced stable dose requirements of statin drugs that are mainly metabolized by CYP3A4 (atorvastatin, lovastatin and simvastatin). Since statin doses are titrated to reach a desired LDL, this can be achieved at lower doses in carriers of the intron6 SNP T allele conveying reduced metabolism. In support of the inventors' belief, pharmacokinetics studies showed that inhibition of CYP3A4 activity drastically increased plasma concentrations of simvastatin and lovastatin (31,32), suggesting CYP3A4 activity is a major determinant of serum concentration of CYP3A4 metabolized statins.
The finding that patients carrying the intron6 T allele are also more likely to reach LDL goal may be related to insufficient dosage titration in subjects with normal CYP3A4 metabolism, or with lower fluctuations in statin levels between doses in T allele carriers. The previously identified CYP3A4 promoter SNP (*1B, −392 A>G) did not associate with lipid-lowering efficacy and safety of simvastin treatment (33), consistent with the results here showing this promoter SNP has no effect on hepatic mRNA expression. Further, the current results indicate that SNPs in CYP3A5 do not affect statin dose or treatment outcomes, consistent with reports that CYP3A5 does not play a major role in statin metabolism (34). Therefore, intron6 SNP is the only CYP3A polymorphism shown to affect statin therapy.
Even though CYP3A4 activity shows considerable inter-individual variability, new drugs are often targeted for metabolism by CYP3A4, to avoid problems arising from null mutations in other drug metabolizing CYP enzymes, such as CYP2D6.
The results presented here show that a portion of the variability in CYP3A4 can be accounted for by intron6 SNP. The clinical relevance of this finding is demonstrated by the impact of intron6 SNP on the titrated dose of two statin drugs that depend on CYP3A4 for their elimination. Because CYP3A4 is involved in the metabolism of approximately half of all clinically used drugs, the intron6 SNP is now believed by the inventors herein to affect dosing requirements, response, and toxicity of numerous drugs, including anticancer agents with narrowly defined dosage regimens.
Therefore, CYP3A4 intron6 SNP is a valuable biomarker in clinical practice, and in drug discovery and development.
EXAMPLE II
The DNA isolated from cells can be used as positive human genomic reference controls (i.e., they have mutations present) or negative controls (i.e., they represent the normal or wild-type), in particular, for human CYP3A4 genes. This can ensure accurate and reliable clinical diagnostic testing for these genes.
The reference controls can be used, for example, in genotyping assays performed during clinical trials. Where the reference controls include a genetic variation typical of a patient who does not respond to therapy, the use of reference controls helps ensure that the genotyping assay used performs reliably such that non-responders are properly identified and data regarding the ineffectiveness of the investigative therapy for non-responders is properly identified. Similarly, where the reference controls include a genetic variation typical of a patient who metabolizes drugs at a different rate than normal patients (i.e., patients with mutant cytochrome P450 genes), the use of reference controls helps ensure the validity of the genetic variation so that these patients are properly identified and properly dosed and adverse drug reactions or ineffective therapies are avoided.
The reference controls can also be used in patient care. As with their use in clinical trials, it is essential that effective therapy is identified in a time-sensitive manner, so that the patient's condition is not worsened before appropriate therapy is initiated. It is also essential that appropriate dosing regimens are selected.
Accordingly, one aspect of the present invention relates to a method of testing a plurality of patients for their genetic predisposition to respond to a particular therapy. In this aspect, one or more reference controls are tested as “samples”—with known expected results. These controls can include a genetic variant associated with patients who show a predisposition to not respond to the therapy (positive controls) or can include a normal/wild type variant (negative controls), associated with patients who show a predisposition to respond to the therapy.
Thus, the reference controls can be used by testing laboratories to ensure that their diagnostics assays are performing correctly and identify the genetic variations that convey resistance to drug therapy or reduced metabolic state. To ensure that non-responders are properly identified, testing laboratories can include reference controls in each assay to determine the validity of the assay, and hence, patient results. The reference controls can be used at random, or at pre-determined intervals. In the same respect, testing laboratories can use the reference controls as panels to evaluate the accuracy of their laboratory staff.
Accordingly, another aspect of the present invention relates to a method of testing a plurality of patients for their genetic predisposition to show rapid or slowed metabolism, so that proper dosing regimens can be set. In this aspect, one or more samples that are tested are reference controls that include a genetic variant associated with patients having a predisposition to be “rapid” or “slow” metabolizers.
These polymorphisms are expressed in a number of phenotypes in the population such as, for example, a “poor” metabolizer, an “intermediate” metabolizer, an “extensive” metabolizer, and an “ultra-rapid” metabolizer. In certain situations, the “extensive” metabolizers can have at least one, and no more than two, normal functional alleles; the “intermediate” metabolizers can possess one reduced activity allele and one null allele; and, the “poor” metabolizers can carry two mutant alleles which result in complete loss of enzyme activity. In certain situations, the ultra-rapid metabolizers can carry multiple copies of functional alleles, and thus produce excess enzymatic activity. Thus, when certain drugs are administered, a “poor” metabolizer may not obtain a significant benefit from the drugs, but rather, experience exaggerated drug response and side effects when they receive standard doses. That is, if a metabolite is the active therapeutic moiety, “poor” metabolizer may show no therapeutic response. On the other hand, an “ultra-rapid” metabolizer may fail to respond to standard doses.
Various methods for pre-screening samples may be used. For example, biological samples can be pre-screened to ensure that they have the mutation of interest. Patient populations can be pre-screened, based on a variety of factors, to minimize the sample size needed to identify individuals that include the mutation. After performing an initial genomic screening on the samples to identify one or more samples which include the mutation of interest, the patients with these mutations can optionally be recalled to obtain additional biological material. This material can optionally be thoroughly sequenced to confirm the presence of the mutation of interest. The biological material can be immortalized, so it can provide a steady, on-demand source of the reference controls, or, alternatively, the cells themselves can be the reference controls. Various types of biological samples can be used, such as, but not limited to human genomic DNA present in any nucleic acid-containing sample of tissues, bodily fluids (for example, blood, serum, plasma, saliva, urine, tears, semen, vaginal secretions, lymph fluid, cerebrospinal fluid or mucosa secretions), individual cells or extracts of the such sources that contain the nucleic acid of the same, and subcellular structures such as mitochondria or chloroplasts, using protocols well established within the art.
In certain embodiments, the nucleic acid has been obtained from a human to be pre-screened for the presence of one or more genetic sequences that can be diagnostic for, or predispose the subject to, a medical condition or disease. As an alternative to prescreening patient populations, one can screen cell cultures commonly available for the genetic sequence of interest, and then expand and preserve the cell clones for future continuous supply
There are a number of known genomic assay methods for which the reference control can be used in testing where most involve hybridizing a primer with a DNA sample that may or may not include a SNP of interest. A diagnostic primer and/or probe can be tagged to permit rapid identification. Once hybridization has occurred, the DNA can be amplified, and the tagged primer and/or probe are detected. The validated primers can be used to confirm the validity of reference controls. Once the reference controls are validated, they can be used in commercially available assays as a reference control, and can be used to validate primers that are designed for use in these or other assays to determine the presence or absence of a particular mutation. Thus, while exemplary assay methods are described herein, the invention is not so limited.
While the invention has been described with reference to various and preferred embodiments, it should be understood by those skilled in the art that various changes may be made and equivalents may be substituted for elements thereof without departing from the essential scope of the invention. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the invention without departing from the essential scope thereof. Therefore, it is intended that the invention not be limited to the particular embodiment disclosed herein contemplated for carrying out this invention, but that the invention will include all embodiments falling within the scope of the claims.
The citation of any reference herein is not an admission that such reference is available as prior art to the instant invention. Any publications mentioned in this specification are herein incorporated by reference. Any discussion of documents, acts, materials, devices, articles or the like which has been included in the present specification is solely for the purpose of providing a context for the present invention. It is not to be taken as an admission that any or all of these matters form part of the prior art base or were common general knowledge in the field relevant to the present invention as it existed before the priority date of each claim of this application.
References


    1. Danielson, P. B. (2002) Curr Drug Metab 3, 561-597.
    2. Ozdemir, V., Kalow, W., Tang, B. K., Paterson, A. D., Walker, S. E., Endrenyi, L., and Kashuba, A. D. (2000) Pharmacogenetics 10, 373-388.
    3. Rebbeck, T. R., Jaffe, J. M., Walker, A. H., Wein, A. J., and Malkowicz, S. B. (1998) J Natl Cancer Inst 90, 1225-1229.
    4. Westlind, A., Lofberg, L., Tindberg, N., Andersson, T. B., and Ingelman-Sundberg, M. (1999) Biochem Biophys Res Commun 259, 201-205.
    5. Wojnowski, L., and Kamdem, L. K. (2006) Expert Opin Drug Metab Toxicol 2, 171-182.
    6. Lamba, J. K., Lin, Y. S., Thummel, K., Daly, A., Watkins, P. B., Strom, S., Zhang, J., and Schuetz, E. G. (2002) Pharmacogenetics 12, 121-132.
    7. Garcia-Martin, E., Martinez, C., Pizarro, R. M., Garcia-Gamito, F. J., Gullsten, H., Raunio, H., and Agundez, J. A. (2002) Clin Pharmacol Ther 71, 196-204.
    8. Amirimani, B., Walker, A. H., Weber, B. L., and Rebbeck, T. R. (1999) J Natl Cancer Inst 91, 1588-1590.
    9. Spurdle, A. B., Goodwin, B., Hodgson, E., Hopper, J. L., Chen, X., Purdie, D. M., McCredie, M. R., Giles, G. G., Chenevix-Trench, G., and Liddle, C. (2002) Pharmacogenetics 12, 355-366.
    10. Ball, S. E., Scatina, J., Kao, J., Ferron, G. M., Fruncillo, R., Mayer, P., Weinryb, I., Guida, M., Hopkins, P. J., Warner, N., and Hall, J. (1999) Clin Pharmacol Ther 66, 288-294.
    11. Zeigler-Johnson, C., Friebel, T., Walker, A. H., Wang, Y., Spangler, E., Panossian, S., Patacsil, M., Aplenc, R., Wein, A. J., Malkowicz, S. B., and Rebbeck, T. R. (2004) Cancer Res 64, 8461-8467.
    12. Kuehl, P., Zhang, J., Lin, Y., Lamba, J., Assem, M., Schuetz, J., Watkins, P. B., Daly, A., Wrighton, S. A., Hall, S. D., Maurel, P., Relling, M., Brimer, C., Yasuda, K., Venkataramanan, R., Strom, S., Thummel, K., Boguski, M. S., and Schuetz, E. (2001) Nat Genet 27, 383-391.
    13. Matsumura, K., Saito, T., Takahashi, Y., Ozeki, T., Kiyotani, K., Fujieda, M., Yamazaki, H., Kunitoh, H., and Kamataki, T. (2004) Mol Pharmacol 65, 326-334.
    14. Perera, M. A., Thirumaran, R. K., Cox, N. J., Hanauer, S., Das, S., Brimer-Cline, C., Lamba, V., Schuetz, E. G., Ratain, M. J., and Di Rienzo, A. (2008) Pharmacogenomics J. 
    15. Schirmer, M., Rosenberger, A., Klein, K., Kulle, B., Toliat, M. R., Nurnberg, P., Zanger, U. M., and Wojnowski, L. (2007) Pharmacogenomics 8, 443-453.
    16. Wang, D., Johnson, A. D., Papp, A. C., Kroetz, D. L., and Sadee, W. (2005) Pharmacogenet Genomics 15, 693-704.
    17. Pinsonneault, J., Nielsen, C. U., and Sadee, W. (2004) J Pharmacol Exp Ther 311, 1088-1096.
    18. Miller, S. A., Dykes, D. D., and Polesky, H. F. (1988) Nucleic Acids Res 16, 1215
    19. Dai, Z., Papp, A. C., Wang, D., Hampel, H., and Sadee, W. (2008) BMC Med Genomics 1, 24.
    20. Papp, A. C., Pinsonneault, J. K., Cooke, G., and Sadee, W. (2003) Biotechniques 34, 1068-1072.
    21. Wang, D., Papp, A. C., Binkley, P. F., Johnson, J. A., and Sadee, W. (2006) Pharmacogenet Genomics 16, 735-745.
    22. Leeder, J. S., Gaedigk, R., Marcucci, K. A., Gaedigk, A., Vyhlidal, C. A., Schindel, B. P., and Pearce, R. E. (2005) J Pharmacol Exp Ther 314, 626-635.
    23. Kolwankar, D., Vuppalanchi, R., Ethell, B., Jones, D. R., Wrighton, S. A., Hall, S. D., and Chalasani, N. (2007) Clin Gastroenterol Hepatol 5, 388-393.
    24. Hirota, T., Ieiri, I., Takane, H., Maegawa, S., Hosokawa, M., Kobayashi, K., Chiba, K., Nanba, E., Oshimura, M., Sato, T., Higuchi, S., and Otsubo, K. (2004) Hum Mol Genet 13, 2959-2969.
    25. Wolbold, R., Klein, K., Burk, O., Nussler, A. K., Neuhaus, P., Eichelbaum, M., Schwab, M., and Zanger, U. M. (2003) Hepatology 38, 978-988.
    26. Vyhlidal, C. A., Gaedigk, R., and Leeder, J. S. (2006) Drug Metab Dispos 34, 131-137.
    27. Goodwin, B., Redinbo, M. R., and Kliewer, S. A. (2002) Annu Rev Pharmacol Toxicol 42, 1-23.
    28. He, P., Court, M. H., Greenblatt, D. J., and von Moltke, L. L. (2006) J Clin Pharmacol 46, 1356-1369.
    29. Lamba, J., Lamba, V., and Schuetz, E. (2005) Curr Drug Metab 6, 369-383.
    30. Grundy, S. M., Cleeman, J. I., Merz, C. N., Brewer, H. B., Jr., Clark, L. T., Hunninghake, D. B., Pasternak, R. C., Smith, S. C., Jr., and Stone, N. J. (2004) J Am Coll Cardiol 44, 720-732.
    31. Neuvonen, P. J., Kantola, T., and Kivisto, K. T. (1998) Clin Pharmacol Ther 63, 332-341.
    32. Jalava, K. M., Olkkola, K. T., and Neuvonen, P. J. (1997) Clin Pharmacol Ther 61, 410-415.
    33. Fiegenbaum, M., da Silveira, F. R., Van der Sand, C. R., Van der Sand, L. C., Ferreira, M. E., Pires, R. C., and Hutz, M. H. (2005) Clin Pharmacol Ther 78, 551-558.
    34. Park, J. E., Kim, K. B., Bae, S. K., Moon, B. S., Liu, K. H., and Shin, J. G. (2008) Xenobiotica 38, 1240-1251.
    35. Thompson, E. E., Kuttab-Boulos, H., Witonsky, D., Yang, L., Roe, B. A., and Di Rienzo, A. (2004) Am J Hum Genet 75, 1059-1069.
    36. Rodriguez-Antona, C., Sayi, J. G., Gustafsson, L. L., Bertilsson, L., and Ingelman-Sundberg, M. (2005) Biochem Biophys Res Commun 338, 299-305.

## Claims

1. A method of administering a statin dose to a human, comprising:
obtaining a nucleic acid-containing test sample from a human, wherein the human is in need of statin therapy;
conducting at least one genotyping assay of the sample so as to obtain at least the genotype data at position rs35599367 of CYP3A4 in the sample, wherein the at least one genotyping assay comprises hybridizing a tagged primer to DNA in the nucleic acid-containing test sample, amplifying the hybridized DNA and detecting the tagged primer;
detecting a T allele at position rs35599367 of CYP3A4; and
administering a statin dose to the human comprising a TT or TC genotype at position rs35599367 of CYP3A4 of less than 20 mg per day wherein the statin administered is a statin that depends on CYP3A4 for elimination.

2. The method of claim 1, which further comprises a step of predicting cholesterol control outcome.

3. The method of claim 1, which further comprises identifying at least one additional biomarker of statin metabolism.

4. The method of claim 1, wherein said nucleic acid is a nucleic acid extract from a biological sample from said human.

5. The method of claim 4, in which said sample is blood, saliva, or buccal cells.

6. A method of administering a statin dose to a human, comprising:
obtaining a nucleic acid-containing test sample from a human, wherein the human is in need of statin therapy;
conducting at least one genotyping assay of the sample so as to obtain at least the genotype data at position rs35599367 of CYP3A4 in the sample, wherein the at least one genotyping assay comprises hybridizing a tagged primer to DNA in the nucleic acid-containing test sample, amplifying the hybridized DNA, and detecting the tagged primer;
detecting a C allele at position rs35599367 of CYP3A4; and
administering a statin dose to the human comprising a CC genotype at position rs35599367 of CYP3A4 of more than 40 mg per day, wherein the statin administered is a statin that depends on CYP3A4 for elimination.

7. The method of claim 6, which further comprises a step of predicting cholesterol control outcome.

8. The method of claim 6, which further comprises identifying at least one additional biomarker of statin metabolism.

9. The method of claim 6, wherein said nucleic acid is a nucleic acid extract from a biological sample from said human.

10. The method of claim 9, in which said sample is blood, saliva, or buccal cells.

11. The method of claim 1, wherein one or more statins are given, and the one or more statins is selected from the group consisting of atorvastatin, lovastatin, and simvastatin.

12. The method of claim 6, wherein one or more statins are given, and the one or more statins is selected from the group consisting of atorvastatin, lovastatin, and simvastatin.