Diagnostic markers of hypertension and methods of use thereof

• US 20070092888A1
• Filed: 05/15/2006
• Published: 04/26/2007
• Est. Priority Date: 09/23/2003
• Status: Abandoned Application

• Current Assignee: Albert Man, Cornelius Diamond, Troy Bremer
• Original Assignee: Albert Man, Cornelius Diamond, Troy Bremer
• Inventors: Man, Albert, Diamond, Cornelius, Bremer, Troy
• Application Number:
  US11/435,051
• Publication Number:
  US 20070092888A1
• Time in Patent Office: Days
• US Class Current: 435/6.11
• CPC Class Codes: C12Q 1/6883 for diseases caused by alterations of genetic material
• CPC Class Codes: C12Q 2600/106 Pharmacogenomics, i.e. genetic variability in individual responses to drugs and drug metabolism
• CPC Class Codes: C12Q 2600/156 Polymorphic or mutational markers
• CPC Class Codes: C12Q 2600/172 Haplotypes
• CPC Class Codes: G16B 40/00 ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
• CPC Class Codes: G16B 40/20 Supervised data analysis
• CPC Class Codes: Y02A 90/10 Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Abstract

The present invention relates to methods for the diagnosis and evaluation of cardiovascular illness, particularly hypertension treatment. In particular, patient test samples are analyzed for the presence and amount of members of a panel of markers comprising one or more specific markers for hypertension treatment and one or more non-specific markers for hypertension treatment. A variety of markers are disclosed for assembling a panel of markers for such diagnosis and evaluation. Algorithms for determining proper treatment are disclosed. A diagnostic kit for a panel of said markers is disclosed. In various aspects, the invention provides methods for the early detection and differentiation of hypertension treatment. Invention methods provide rapid, sensitive and specific assays that can greatly increase the number of patients that can receive beneficial treatment and therapy, reduce the costs associated with incorrect diagnosis, and provide important information about the prognosis of the patient.

Claims

1. A method of determining response to the pharmaceutical agent for hypertension, the method comprising:

correlating (i) a mutational burden at one or more nucleotide positions in the AGT, ACE, AGTR1, CACNA1C, GPB, EDN1, EDN2, alpha-adducin, haptoglobin, CYP2C9, RGS2, ADRA1a, 11betaHSD2, ADRA1b, ADRA2A, ADRAB1, ADRAB2, REN, APOA, APOB, CETP, LIPC, EDNRB, or ENOS gene(s) in a sample from the subject with (ii) the mutational burden at one or more corresponding nucleotide positions in a control sample with known response outcome, and therefrom identifying the probability of response to said pharmaceutical agent.

2. A method according to claim 1 wherein the mutational burden relates to a mutation in the CACNA1C gene at nucleotide position given by the RS # 2238032, 2239050, or 769087, further described by the genetic position descriptor chr12:

2092993-2092993, chr12;

2317675-2317675, or chr12;

2214905-2214905, respectively;

or combinations thereof, and the pharmaceutical agent is a calcium-channel blocker.

3. A method according to claim 2 wherein the mutational burden is comprised of at least one mutation in linkage disequilibrium with the genetic variants according to claim 2.

4. A method according to claim 1 wherein the mutational burden relates to a mutation in the AGTR1 gene at nucleotide position given by the RS # 5186 further described by the genetic position descriptor chr3:

149942686;

a mutation in the ADRB2 gene at nucleotide position given by the RS # 1042720 further described by the genetic position descriptor chr5;

148187826; and

mutations in the ADRB1 gene at nucleotide position given by the RS # 2183378 and RS # 2050393, further described by the genetic position descriptors chr10;

115798882 and chr10;

115804942, respectively;

or combinations thereof, and the pharmaceutical agent is a beta- blocker.

5. A method according to claim 4 wherein the mutational burden is comprised of at least one mutation in linkage disequilibrium with the genetic variants according to claim 4.

6. A method according to claim 1, wherein said correlating step comprising:

a) determining the sequence of one or more of the genes AGT, ACE, AGTR1, CACNA1C, GPB, EDN1, EDN2, alpha-adducin, haptoglobin, CYP2C9, RGS2, ADRA1a, 11betaHSD2, ADRA1b, ADRA2A, ADRAB1, ADRAB2, REN, APOA, APOB, CETP, LIPC, EDNRB, or ENOS from humans known to be responsive or non-responsive to anti- hypertension medications;

b) comparing said sequence to that of the corresponding wildtype AGT, ACE, AGTR1, CACNA1C, GPB, EDN1, EDN2, alpha-adducin, haptoglobin, CYP2C9, RGS2, ADRA1a, 11betaHSD2, ADRA1b, ADRA2A, ADRAB1, ADRAB2, REN, APOA, APOB, CETP, LIPC, EDNRB, or ENOS gene(s); and

c) training an algorithm to identify patterns of identifying mutations which correlate with the response or non-response to anti-hypertensive medications, respectively.

7. The method according to claim 6, where training said algorithm residing on a computer on characteristic mutations comprises the steps of obtaining numerous examples of (i) said SNP pattern genomic data, and (ii) historical clinical results corresponding to this genomic data;

constructing a algorithm suitable to map (i) said SNP pattern genomic data as inputs to the algorithm to (ii) the historical clinical results as outputs of the algorithm;

exercising the constructed algorithm to so map (i) the said SNP pattern genomic data as inputs to (ii) the historical clinical results as outputs; and

conducting an automated procedure to vary the mapping function, inputs to outputs, of the constructed and exercised algorithm in order that, by minimizing an error measure of the mapping function, a more optimal algorithm mapping architecture is realized;

wherein realization of the more optimal algorithm mapping architecture, also known as feature selection, means that any irrelevant inputs are effectively excised, meaning that the more optimally mapping algorithm will substantially ignore input alleles and/or said SNP pattern genomic data that is irrelevant to output clinical results; and

wherein realization of the more optimal algorithm mapping architecture, also known as feature selection, also means that any relevant inputs are effectively identified, making that the more optimally mapping algorithm will serve to identify, and use, those input alleles and/or SNP pattern genomic data that is relevant, in combination, to output clinical results.

8. The method according to claim 7, where the constructed algorithm is drawn from the group consisting essentially of:

linear or nonlinear regression algorithms;

linear or nonlinear classification algorithms;

ANOVA;

neural network algorithms;

genetic algorithms;

support vector machines algorithms;

hierarchical analysis or clustering algorithms;

hierarchical algorithms using decision trees;

kernel based machine algorithms such as kernel partial least squares algorithms, kernel matching pursuit algorithms, kernel fisher discriminate analysis algorithms, or kernel principal components analysis algorithms;

Bayesian probability function algorithms;

Markov Blanket algorithms;

a plurality of algorithms arranged in a committee network; and

forward floating search or backward floating search algorithms.

9. The method according to claim 7, where the feature selection process employs an algorithm drawn from the group consisting essentially of:

linear or nonlinear regression algorithms;

linear or nonlinear classification algorithms;

ANOVA;

neural network algorithms;

genetic algorithms;

support vector machines algorithms;

hierarchical analysis or clustering algorithms;

hierarchical algorithms using decision trees;

kernel based machine algorithms such as kernel partial least squares algorithms, kernel matching pursuit algorithms, kernel fisher discriminate analysis algorithms, or kernel principal components analysis algorithms;

Bayesian probability function algorithms;

Markov Blanket algorithms;

recursive feature elimination or entropy-based recursive feature elimination algorithms;

a plurality of algorithms arranged in a committee network; and

forward floating search or backward floating search algorithms.

10. The method according to claim 7, wherein a tree algorithm is trained to reproduce the performance of another machine-learning classifier or regressor by enumerating the input space of said classifier or regressor to form a plurality of training examples sufficient (1) to span the input space of said classifier or regressor and (2) train the tree to emulate the performance of said classifier or regressor.

11. The method according to claim 6 where the anti-hypertensive medication belongs to the class known as angiotensin converting enzyme inhibitors, calcium channel blockers, or beta-adrenergic receptor blockers.

12. The method according to claim 6 where the anti-hypertensive medication is the molecules captopril, benazepril, enalapril, enalaprilat, fosinopril, lisinopril, quinapril, ramipril, and trandolapril or others with similar molecular mechanisms;

the molecules nifedipine, verapamil, nicardipine,diltiazem, isradipine, amlodipine, nimodipine, felodipine, nisoldipine, bepridil or others with similar molecular mechanisms; and

the molecules atenolol, metoprolol, propranolol, timolol, nadolol, acebutolol, pindolol, sotalol, labetalol, oxprenolol or others with similar molecular mechanisms.

13. The method of claim 1 wherein at least one mutation is a silent mutation, missense mutation, or combination thereof.

14. A method according to claim 1, wherein said sample is selected from the group consisting of a blood sample, a serum sample, and a plasma sample.

15. A method according to any one of claims 1 wherein the presence of said mutation is detected by a technique that is selected from the group of techniques consisting of hybridization with oligonucleotide probes, a ligation reaction, a polymerase chain reaction and single nucleotide primer-guided extension assays, and variations thereof.

16. A method according to claim 1, wherein said correlating step comprises comparing said mutational burden to a second mutational burden measured in a second sample obtained from said patient, whereby, when said second mutational burden is of the type correlated by one or more of algorithm(s) drawn from the group consisting essentially of linear or nonlinear regression algorithms;

linear or nonlinear classification algorithms;

ANOVA;

neural network algorithms;

genetic algorithms;

support vector machines algorithms;

hierarchical analysis or clustering algorithms;

hierarchical algorithms using decision trees;

kernel based machine algorithms such as kernel partial least squares algorithms, kernel matching pursuit algorithms, kernel fisher discriminate analysis algorithms, or kernel principal components analysis algorithms;

Bayesian probability function algorithms;

Markov Blanket algorithms;

recursive feature elimination or entropy-based recursive feature elimination algorithms;

a plurality of algorithms arranged in a committee network; and

forward floating search or backward floating search algorithms than said second mutational burden, said patient is diagnosed as being responsive or resistant to anti-hypertensive therapy.

17. A method according to claim 16, wherein said second sample is obtained prior to treatment with an anti-hypertensive medication.

18. A method for detecting the presence or risk of developing hypertension in a human, said method comprising:

determining the presence in a biological sample from a human of a nucleic acid sequence having a mutational burden according to claim 1 at one or more nucleotide positions in a sequence region corresponding to a wildtype genomic DNA sequence, wherein the mutational burden correlates with the presence of or risk of developing hypertension.

19. A method for evaluating a compound for use in diagnosis or treatment of hypertension, said method comprising:

a) contacting a predetermined quantity of said compound with cultured cybrid cells or animal model having genomic DNA originating from a neuronal rho or human embryonic immortal kidney cell line and from tissue of a human having a disorder that is associated with severe hypertension and the mutational burden according to claim 2;

b) measuring a phenotypic trait in said cybrid cells or animal model that correlates with the presence of said mutational burden and that is not present in cultured cybrid cells or animal model having genomic DNA originating from a neuronal rho cell line and genomic DNA originating from tissue of a human free of a disorder that is associated with severe hypertension; and

c) correlating a change in the phenotypic trait with effectiveness of the compound.

20. A method according to claim 19 where the phenotypic trait is blockade of of at least one cascade in the renin-angiotensin-aldosterone biochemical pathway, calcium channel pathway, or beta-adrenergic pathway.

21. A method according to claim 19 where the correlating step is made in accordance with an algorithm drawn from the group consisting essentially of:

linear or nonlinear regression algorithms;

linear or nonlinear classification algorithms;

ANOVA;

neural network algorithms;

genetic algorithms;

support vector machines algorithms;

hierarchical analysis or clustering algorithms;

hierarchical algorithms using decision trees;

kernel based machine algorithms such as kernel partial least squares algorithms, kernel matching pursuit algorithms, kernel fisher discriminate analysis algorithms, or kernel principal components analysis algorithms;

Bayesian probability function algorithms;

Markov Blanket algorithms;

recursive feature elimination or entropy-based recursive feature elimination algorithms;

a plurality of algorithms arranged in a committee network; and

forward floating search or backward floating search algorithms.

22. A method for diagnosing treatment-resistant hypertension, said method comprising:

determining the presence in a biological sample from a human of a nucleic acid sequence having a mutational burden according to claim 2 at one or more nucleotide positions in a sequence region corresponding to a wildtype genomic DNA sequence, wherein the mutational burden correlates with the lack of response to a hypertension medication.

23. A method according to claim 22 where the correlating step is made in accordance with an algorithm drawn from the group consisting essentially of:

linear or nonlinear regression algorithms;

linear or nonlinear classification algorithms;

ANOVA;

neural network algorithms;

genetic algorithms;

support vector machines algorithms;

hierarchical analysis or clustering algorithms;

hierarchical algorithms using decision trees;

kernel based machine algorithms such as kernel partial least squares algorithms, kernel matching pursuit algorithms, kernel fisher discriminate analysis algorithms, or kernel principal components analysis algorithms;

Bayesian probability function algorithms;

Markov Blanket algorithms;

recursive feature elimination or entropy-based recursive feature elimination algorithms;

a plurality of algorithms arranged in a committee network; and

forward floating search or backward floating search algorithms.

24. A method according to claim 22, wherein said specific marker for treatment-resistant hypertension is selected from the group of genes consisting of AGT, ACE, AGTR1, CACNA1C, GPB, EDN1, EDN2, ALPHA-ADDUCIN, HAPTOGLOBIN, CYP2C9, RGS2, ADRA1A, 11BETAHSD2, ADRA1B, ADRA2A, ADRAB1, ADRAB2, REN, APOA, APOB, CETP, LIPC, EDNRB, OR ENOS.

25. A therapeutic composition comprising antisense or small interfering RNA sequences which are specific to mutant genes according to claim 1 or mutant messenger RNA transcribed therefrom, said antisense or small interfering RNA sequences adapted to bind to and inhibit transcription or translation of said target genes according to claim 1 without preventing transcription or translation of wild-type genes of the same type.

26. The therapeutic composition of claim 25, wherein hypertension is treated and wherein said mutant genes are selected from the group:

AGT, ACE, AGTR1, CACNAC1, GPB, EDN1, EDN2, ALPHA-ADDUCIN, HAPTOGLOBIN, CYP2C9, RGS2, ADRA1A, 11BETAHSD2, ADRA1B, ADRA2A, ADRAB1, ADRAB2, REN, APOA, APOB, CETP, LIPC, EDNRB, OR ENOS.

27. A kit comprising devices and reagents and a computer algorithm for measuring one or more mutational burdens of a patient and determining the diagnosis or prognosis in that patient for hypertension.

28. The method of claim 27 when the mutational burden is a mutation in the CACNA1C gene at nucleotide position given by the RS # 2238032, 2239050, or 769087, further described by the genetic position descriptor chr12:

2092993-2092993, chr12;

2317675-2317675, or chr12;

2214905-2214905, respectively;

a mutation in the AGTR1 gene at nucleotide position given by the RS # 5186 further described by the genetic position descriptor chr3;

149942686;

a mutation in the ADRB2 gene at nucleotide position given by the RS # 1042720 further described by the genetic position descriptor chr5;

148187826; and

mutations in the ADRB1 gene at nucleotide position given by the RS # 2183378 and RS # 2050393, further described by the genetic position descriptors chr10;

115798882 and chr10;

115804942, respectively;

a mutation in linkage disequilibrium with the above genetic variants;

or combinations thereof.

29. The method of claim 27 when the determination of diagnostic or prognostic outcome is made in accordance with an algorithm drawn from the group consisting essentially of:

linear or nonlinear regression algorithms;

linear or nonlinear classification algorithms;

ANOVA;

neural network algorithms;

genetic algorithms;

support vector machines algorithms;

hierarchical analysis or clustering algorithms;

hierarchical algorithms using decision trees;

kernel based machine algorithms such as kernel partial least squares algorithms, kernel matching pursuit algorithms, kernel fisher discriminate analysis algorithms, or kernel principal components analysis algorithms;

Bayesian probability function algorithms;

Markov Blanket algorithms;

recursive feature elimination or entropy-based recursive feature elimination algorithms;

a plurality of algorithms arranged in a committee network; and

forward floating search or backward floating search algorithms.

30. The method of claim 27 when the prognostic outcome is that of response to ACE anti-hypertension medication, a calcium channel blocker anti-hypertension medication, or a beta-blocker anti-hypertension medication.

31. The method of claim 30 the determination of diagnostic or prognostic outcome is made in accordance with an algorithm drawn from the group consisting essentially of:

linear or nonlinear regression algorithms;

linear or nonlinear classification algorithms;

ANOVA;

neural network algorithms;

genetic algorithms;

support vector machines algorithms;

hierarchical analysis or clustering algorithms;

hierarchical algorithms using decision trees;

kernel based machine algorithms such as kernel partial least squares algorithms, kernel matching pursuit algorithms, kernel fisher discriminate analysis algorithms, or kernel principal components analysis algorithms;

Bayesian probability function algorithms;

Markov Blanket algorithms;

recursive feature elimination or entropy-based recursive feature elimination algorithms;

a plurality of algorithms arranged in a committee network; and

forward floating search or backward floating search algorithms.

32. The method of claim 27 when the diagnostic outcome is that of treatment-resistant hypertension.

33. The method of claim 32 when the determination of diagnostic or prognostic outcome is made in accordance with an algorithm drawn from the group consisting essentially of:

linear or nonlinear regression algorithms;

linear or nonlinear classification algorithms;

ANOVA;

neural network algorithms;

genetic algorithms;

support vector machines algorithms;

hierarchical analysis or clustering algorithms;

hierarchical algorithms using decision trees;

kernel based machine algorithms such as kernel partial least squares algorithms, kernel matching pursuit algorithms, kernel fisher discriminate analysis algorithms, or kernel principal components analysis algorithms;

Bayesian probability function algorithms;

Markov Blanket algorithms;

recursive feature elimination or entropy-based recursive feature elimination algorithms;

a plurality of algorithms arranged in a committee network; and

forward floating search or backward floating search algorithms.

34. The method of claim 27 when the prognostic outcome is that of response to the molecules captopril, benazepril, enalapril, enalaprilat, fosinopril, lisinopril, quinapril, ramipril, and trandolapril or others with similar molecular mechanisms;

the molecules nifedipine, verapamil, nicardipine,diltiazem, isradipine, amlodipine, nimodipine, felodipine, nisoldipine, bepridil or others with similar molecular mechanisms; and

the molecules atenolol, metoprolol, propranolol, timolol, nadolol, acebutolol, pindolol, sotalol, labetalol, oxprenolol or others with similar molecular mechanisms.

35. The method of claim 34 when the determination of diagnostic or prognostic outcome is made in accordance with an algorithm drawn from the group consisting essentially of:

linear or nonlinear regression algorithms;

linear or nonlinear classification algorithms;

ANOVA;

neural network algorithms;

genetic algorithms;

support vector machines algorithms;

hierarchical analysis or clustering algorithms;

hierarchical algorithms using decision trees;

kernel based machine algorithms such as kernel partial least squares algorithms, kernel matching pursuit algorithms, kernel fisher discriminate analysis algorithms, or kernel principal components analysis algorithms;

Bayesian probability function algorithms;

Markov Blanket algorithms;

recursive feature elimination or entropy-based recursive feature elimination algorithms;

a plurality of algorithms arranged in a committee network; and

forward floating search or backward floating search algorithms.

36. The method of claim 27 when the diagnostic outcome is that of determining risk of hypertension.

37. The method of claim 36 when the determination of diagnostic or prognostic outcome is made in accordance with an algorithm drawn from the group consisting essentially of:

linear or nonlinear regression algorithms;

linear or nonlinear classification algorithms;

ANOVA;

neural network algorithms;

genetic algorithms;

support vector machines algorithms;

hierarchical analysis or clustering algorithms;

hierarchical algorithms using decision trees;

kernel based machine algorithms such as kernel partial least squares algorithms, kernel matching pursuit algorithms, kernel fisher discriminate analysis algorithms, or kernel principal components analysis algorithms;

Bayesian probability function algorithms;

Markov Blanket algorithms;

recursive feature elimination or entropy-based recursive feature elimination algorithms;

a plurality of algorithms arranged in a committee network; and

forward floating search or backward floating search algorithms.