Diagnostic markers of mood disorders and methods of use thereof

• US 20050176057A1
• Filed: 03/14/2005
• Published: 08/11/2005
• Est. Priority Date: 09/26/2003
• Status: Abandoned Application

• Current Assignee: Cornelius Diamond, Troy Bremer
• Original Assignee: Cornelius Diamond, Troy Bremer
• Inventors: Diamond, Cornelius, Bremer, Troy
• Application Number:
  US11/080,218
• Publication Number:
  US 20050176057A1
• Time in Patent Office: Days
• US Class Current: 435/6
• CPC Class Codes: A61K 31/00 Medicinal preparations containing organic active ingredients
• CPC Class Codes: A61K 31/19 Carboxylic acids, e.g. valproic acid salicylic acid A61K31/60
• CPC Class Codes: A61K 31/53 having six-membered rings with three nitrogens as the only ring hetero atoms, e.g. chlorazanil, melamine melarsoprol A61K31/555 ; with four nitrogen atoms A61K31/495
• CPC Class Codes: A61K 31/551 having two nitrogen atoms, e.g. dilazep
• 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 2600/136 Screening for pharmacological compounds
• CPC Class Codes: C12Q 2600/156 Polymorphic or mutational markers
• CPC Class Codes: C12Q 2600/158 Expression markers
• CPC Class Codes: C12Q 2600/172 Haplotypes
• CPC Class Codes: G01N 2800/304 Mood disorders, e.g. bipolar, depression
• CPC Class Codes: G01N 33/5082 Supracellular entities, e.g. tissue, organisms
• CPC Class Codes: G16B 20/00 ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
• CPC Class Codes: G16B 20/20 Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
• CPC Class Codes: G16B 20/50 Mutagenesis
• CPC Class Codes: G16B 30/00 ICT specially adapted for sequence analysis involving nucleotides or amino acids
• CPC Class Codes: G16B 30/10 Sequence alignment; Homology search
• 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: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• 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, evaluation, and treatment of mood disorders, particularly bipolar disorder. In particular, patient test samples are analyzed for the presence and amount of members of a panel of biallelic markers comprising one or more specific markers for bipolar treatment and one or more non-specific markers for bipolar 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 mood disorders or bipolar treatment. Methods for screening therapeutic compounds for mood disorders are disclosed. The invention (1) gives methods providing rapid, sensitive and specific assays that can greatly increase the number of patients that can receive beneficial treatment and therapy, thereby reducing the costs associated with incorrect diagnosis, and (2) provides methods for improved therapies.

Claims

1. A method of determining a probability of response of a subject of known clinical history to a pharmaceutical agent for a mood disorder, the method comprising:

correlating (i) a mutational burden at one or more nucleotide positions in genes drawn from the group consisting essentially of ADRBK2, BNDF, GSK3B, GRK3, IMPA1, IMPA2, INPP1, MARCKS, NTRK2 and/or NR1I2 within a sample taken from the subject of known clinical history with (ii) a mutational burden at one or more corresponding nucleotide positions in a control sample having known clinical history and response outcomes to the pharmaceutical agent; and

determining from the correlating the probability of response of the subject to the pharmaceutical agent.

2. The method according to claim 1 wherein the mutational burden consists essentially of a mutation in

the BNDF gene at nucleotide position given by the RS# and genetic position 2049045 and chr11:

27650817,

the IMPA2 gene at nucleotide position given by the RS# and genetic position 971362 and chr18;

11970618,

the IMPA2 gene at nucleotide position given by the RS# and genetic position 971363 and chr18;

11970460,

the INPP1 gene at nucleotide position given by the RS# and genetic position 972691 and chr2;

191053261,

the INPP1 gene at nucleotide position given by the RS# and genetic position 2016037 and chr2;

191042763,

the NTRK2 gene at nucleotide position given by the RS# and genetic position 1619120 and chr9;

84531750,

the NTRK2 gene at nucleotide position given by the RS# and genetic position 1565445 and chr9;

84846625,

mutations in linkage disequilibrium with any of the aforementioned nucleotides,

or combinations thereof.

3. The method according to claim 1 wherein the mutational burden consists essentially of a mutation in:

the BNDF gene at nucleotide position given by the RS# and genetic position 2049045 and chr11;

27650817,

the IMPA2 gene at nucleotide position given by the RS# and genetic position 971362 and chr18;

11970618,

the IMPA2 gene at nucleotide position given by the RS# and genetic position 971363 and chr18;

11970460,

the INPP1 gene at nucleotide position given by the RS# and genetic position 972691 and chr2;

191053261,

the INPP1 gene at nucleotide position given by the RS# and genetic position 2016037 and chr2;

191042763,

the NTRK2 gene at nucleotide position given by the RS# and genetic position 1619120 and chr9;

84531750,

the NTRK2 gene at nucleotide position given by the RS# and genetic position 1565445 and chr9;

84846625,

the NTRK2 gene at nucleotide position given by the RS# and genetic position 1387923 and chr9;

84870190, and

mutations in linkage disequilibrium with any of the aforementioned nucleotides,

or combinations thereof.

4. The method according to claim 1 wherein the clinical history of the subject comprises:

a clinical history including at least one of previous experience of suicidal ideation, post-traumatic stress disorder, panic disorder, rapid cycling, or disphoria.

5. The method according to claim 1, wherein the correlating comprises:

predetermining a sequence of one or more of the genes BDNF, IMPA2, INPP1, or NTRK2 from humans known to be responsive or non-responsive to mood disorder medications,

comparing the predetermined sequence to that of a corresponding wildtype sequence of the BDNF, IMPA2, INPP1, or NTRK2 gene(s); and

training a computer algorithm consisting of executable computer code residing on a memory medium to identify mutations in the subjects which correlate with the response or non-response to mood disorder medications;

wherein the computer algorithm selects only subject mutations that show discriminating power, respectively.

6. The method according to claim 1, wherein the pharmaceutical agent consists essentially of:

a medication for bipolar disorder.

7. The method according to claim 5 wherein the mutational burden consists essentially of:

the BNDF gene at nucleotide position given by the RS# and genetic position 2049045 and chr11;

27650817,

the IMPA2 gene at nucleotide position given by the RS# and genetic position 971362 and chr18;

11970618,

the IMPA2 gene at nucleotide position given by the RS# and genetic position 971363 and chr18;

11970460,

the INPP1 gene at nucleotide position given by the RS# and genetic position 972691 and chr2;

191053261,

the INPP1 gene at nucleotide position given by the RS# and genetic position 2016037 and chr2;

191042763,

the NTRK2 gene at nucleotide position given by the RS# and genetic position 1619120 and chr9;

84531750,

the NTRK2 gene at nucleotide position given by the RS# and genetic position 1565445 and chr9;

84846625,

the NTRK2 gene at nucleotide position given by the RS# and genetic position 1387923 and chr9;

84870190, and

mutations in linkage disequilibrium with any of the aforementioned nucleotides,

or combinations thereof;

and wherein the training of the computer algorithm on the mutational burden comprises the steps of

obtaining numerous examples of (i) mutational genomic data, and (ii) historical clinical history corresponding to the mutational genomic data;

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

exercising the constructed algorithm to so map (i) the mutational genomic data as inputs to (ii) the historical clinical results as outputs, receiving as output values from the computer-based constructed algorithm which output values concur with the probability of occurrence of the clinical results, and transmitting the outputs to an output value receiver connected to a display; and

conducting an automated procedure to vary a 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 mutational 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 mutational genomic data that is relevant, in combination, to output clinical results that would result in a clinical detection of disease, disease diagnosis, disease prognosis, or treatment outcome or a combination of any two, three or four of these actions.

8. The method according to claim 7 wherein the constructing is of 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 including kernel partial least squares algorithms,

kernel matching pursuit algorithms,

kernel fisher discriminate analysis algorithms,

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, wherein the realization of the more optimal feature mapping architecture, also known as feature selection, 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 including kernel partial least squares algorithms,

kernel matching pursuit algorithms,

kernel fisher discriminate analysis algorithms,

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.

10. The method according to claim 7 wherein a tree algorithm, including a CART or a MARS 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 1 wherein the pharmaceutical agent for which response is determined consists essentially of

lithium.

12. The method according to claim 1 where the pharmaceutical agent for which response is determined is drawn from the group of mood disorder medications consisting essentially of

molecular depakote,

olanzapine, and

lamotrigine.

13. The method of claim 1 wherein the at least one mutation in the mutational burden is from a group of mutations consisting essentially of

a silent mutation.

missense mutation,

or combination thereof.

14. The method according to claim 1 wherein the subject sample is selected from the group consisting of

a blood sample,

a serum sample,

a urine sample,

a tissue sample,

a saliva sample, and

a plasma sample.

15. The method according to claim 1 exercised on a mutation detected by a detection technique selected from the group consisting essentially of

hybridization with oligonucleotide probes,

a ligation reaction,

a polymerase chain reaction and single nucleotide primer-guided extension assays, and

variations thereof.

16. The method according to claim 1 wherein said correlating comprises:

comparing said mutational burden to a second mutational burden measured in a second sample obtained from another, second, subject;

wherein, when the second mutational burden of the second subject is of the type correlated with the mutational burden, then the second subject is diagnosed as being responsive or resistant to mood disorder medication.

17. The method according to claim 16 wherein the correlating is in accordance with an algorithm drawn from the group consisting essentially of

linear or nonlinear regression algorithms,

llinear 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 including kernel partial least squares algorithms,

kernel matching pursuit algorithms,

kernel fisher discriminate analysis algorithms,

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.

18. The method according to claim 16 that, after the determining step, comprises:

obtaining a second sample for the subject prior to any treatment with a mood disorder medication.

19. A method for detecting the presence of, or the risk of developing, post-traumatic stress disorder in a human, said method comprising:

determining in a biological sample from the human the presence of a nucleic acid sequence having a mutational burden relating to a mutation in genes of the group consisting essentially of

the BNDF gene at nucleotide position given by the RS# and genetic position 2049045 and chr11;

27650817.

the IMPA2 gene at nucleotide position given by the RS# and genetic position 971362 and chr18;

11970618,

the IMPA2 gene at nucleotide position given by the RS# and genetic position 971363 and chr18;

11970460,

the INPP1 gene at nucleotide position given by the RS# and genetic position 972691 and chr2;

191053261,

the INPP1 gene at nucleotide position given by the RS# and genetic position 2016037 and chr2;

191042763,

the NTRK2 gene at nucleotide position given by the RS# and genetic position 1619120 and chr9;

84531750,

the NTRK2 gene at nucleotide position given by the RS# and genetic position 1565445 and chr9;

84846625,

the NTRK2 gene at nucleotide position given by the RS# and genetic position 1387923 and chr9;

84870190, and

mutations in linkage disequilibrium with any of the aforementioned nucleotides,

or combinations thereof; and

finding one or more nucleotide positions in a sequence region corresponding to a wildtype genomic DNA sequence; and

comparing the determined nucleic acid sequence having the mutational burden to the wildtype genomic DNA sequence to detect the presence of, or the risk of developing, post-traumatic stress disorder in the human.

20. A method for evaluating a compound for use in diagnosis or treatment of bipolar disorder, the method comprising:

contacting a predetermined quantity of the 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 both

(1) a disorder that is associated with bipolar disorder and,

(2) a mutational burden relating to a mutation in genes of the group consisting essentially of

the BNDF gene at nucleotide position given by the RS# and genetic position 2049045 and chr11;

27650817.

the IMPA2 gene at nucleotide position given by the RS# and genetic position 971362 and chr18;

11970618,

the IMPA2 gene at nucleotide position given by the RS# and genetic position 971363 and chr18;

11970460,

the INPP1 gene at nucleotide position given by the RS# and genetic position 972691 and chr2;

191053261,

the INPP1 gene at nucleotide position given by the RS# and genetic position 2016037 and chr2;

191042763,

the NTRK2 gene at nucleotide position given by the RS# and genetic position 1619120 and chr9;

84531750,

the NTRK2 gene at nucleotide position given by the RS# and genetic position 1565445 and chr9;

84846625,

the NTRK2 gene at nucleotide position given by the RS# and genetic position 1387923 and chr9;

84870190, and

mutations in linkage disequilibrium with any of the aforementioned nucleotides;

or combinations thereof;

measuring a phenotypic trait in the cybrid cells or animal model that correlates with the presence of said mutational burden and that is not present in cultured cybrid cells or an 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 bipolar disorder; and

correlating any change in the phenotypic trait with the effectiveness of the compound.

21. The method according to claim 20 wherein the phenotypic trait comprises:

a blockade of at least one cascade in the Inositol, Serotonergic, Dopaminergic, or Noradrenergic biochemical pathways.

22. The method according to claim 20 wherein the correlating is 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 including kernel partial least squares algorithms,

kernel matching pursuit algorithms,

kernel fisher discriminate analysis algorithms,

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.

23. A method for diagnosing bipolar disorder, said method comprising:

determining, in a nucleic acid sequence of a biological sample from a human a mutational burden according to a mutation in genes of the group consisting essentially of

the BNDF gene at nucleotide position given by the RS# and genetic position 2049045 and chr11;

27650817.

the IMPA2 gene at nucleotide position given by the RS# and genetic position 971362 and chr18;

11970618,

the IMPA2 gene at nucleotide position given by the RS# and genetic position 971363 and chr18;

11970460,

the INPP1 gene at nucleotide position given by the RS# and genetic position 972691 and chr2;

191053261,

the INPP1 gene at nucleotide position given by the RS# and genetic position 2016037 and chr2;

191042763,

the NTRK2 gene at nucleotide position given by the RS# and genetic position 1619120 and chr9;

84531750,

the NTRK2 gene at nucleotide position given by the RS# and genetic position 1565445 and chr9;

84846625,

the NTRK2 gene at nucleotide position given by the RS# and genetic position 1387923 and chr9;

84870190, and

mutations in linkage disequilibrium with any of the aforementioned nucleotides.

or combinations thereof,

one or more nucleotide positions in a sequence region corresponding to a wildtype genomic DNA sequence; and

correlating any determined mutational burden as a diagnosis of bipolar disorder.

24. The method according to claim 23 wherein the correlating is 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 including kernel partial least squares algorithms,

kernel matching pursuit algorithms,

kernel fisher discriminate analysis algorithms,

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.

25. A method according to claim 23 wherein the diagnosis of bipolar disorder is from the determining of mutations occurring within the group of genes consisting essentially of ADRBK2, BNDF, GSK3B, GRK3, IMPA1, IMPA2, INPP1, MARCKS, NTRK2 and/or NR1I2.

26. The method according to claim 23 wherein the type of bipolar disorder diagnosed is treatment-resistant bipolar disorder.

27. The method according to claim 23 wherein the type of bipolar disorder diagnosed is rapid-cycling bipolar disorder.

28. A therapeutic composition comprising:

antisense or small interfering RNA sequences that are specific to mutant genes drawn from the group of genes consisting essentially of

the BNDF gene at nucleotide position given by the RS# and genetic position 2049045 and chr21;

27650817.

the IMPA2 gene at nucleotide position given by the RS# and genetic position 971362 and chr18;

11970618,

the IMPA2 gene at nucleotide position given by the RS# and genetic position 971363 and chr18;

11970460,

the INPP1 gene at nucleotide position given by the RS# and genetic position 972691 and chr2;

191053261,

the INPP1 gene at nucleotide position given by the RS# and genetic position 2016037 and chr2;

191042763,

the NTRK2 gene at nucleotide position given by the RS# and genetic position 1619120 and chr9;

84531750,

the NTRK2 gene at nucleotide position given by the RS# and genetic position 1565445 and chr9;

84846625,

the NTRK2 gene at nucleotide position given by the RS# and genetic position 1387923 and chr9;

84870190, and

mutations in linkage disequilibrium with any of the aforementioned nucleotides.

or combinations thereof,

or mutant messenger RNA transcribed therefrom;

wherein the antisense or small interfering RNA sequences are adapted to bind to and inhibit transcription or translation of target genes according to the genes having mutational burden without preventing transcription or translation of wild-type genes of the same type.

29. The therapeutic composition of claim 28

wherein the diagnosed bipolar disorder is treated by therapy directed genes selected from the group consisting of:

ADRBK2, BNDF, GSK3B, GRK3, IMPA1, IMPA2, INPP1, MARCKS, NTRK2 and/or NR1I2.

30. A kit comprising

devices and reagents for measuring mutational burden in the genes of a patient;

and

a computer algorithm consisting of executable computer code residing on a memory medium that, in response to the measured mutational burdens of the patient'"'"'s genes, determines in and for that patient a diagnosis for mood disorders, or for treatment outcome should the patient be given medication for mood disorders.

31. The kit according to claim 30 for determining patient diagnosis for mood disorders, or for treatment outcome for a medication for mood disorders, wherein the devices and reagents comprise:

a device having reagents at each of a plurality of discrete locations, each reagent and corresponding location configured and arranged to immobilize for detection one of said plurality of subject-derived markers, the device supporting the analysis of mutational content of one or more of the genes drawn from the group consisting of ADRBK2, BNDF, GSK3B, GRK3, IMPA1, IMPA2, INPP1, MARCKS, NTRK2 AND/OR NR1I2;

wherein the computer algorithm is calculating, in consideration of the analyzed mutational burden and additional clinical information, a probability of response to mood disorder medication for the patient, or a diagnosis of a mood disorder of the patient.

32. The kit according to claim 31 when the mutational burden consists essentially of:

a mutation in the BNDF gene at nucleotide position given by the RS# and genetic position 2049045 and chr11;

27650817;

in the IMPA2 gene at nucleotide position given by the RS# and genetic position 971362 and chr18;

11970618;

in the IMPA2 gene at nucleotide position given by the RS# and genetic position 971363 and chr18;

11970460;

in the INPP1 gene at nucleotide position given by the RS# and genetic position 972691 and chr2;

191053261;

in the INPP1 gene at nucleotide position given by the RS# and genetic position 2016037 and chr2;

191042763;

in the NTRK2 gene at nucleotide position given by the RS# and genetic position 1619120 and chr9;

84531750;

in the NTRK2 gene at nucleotide position given by the RS# and genetic position 1565445 and chr9;

84846625;

in the NTRK2 gene at nucleotide position given by the RS# and genetic position 1387923 and chr9;

84870190;

mutations in linkage disequilibrium with any of the aforementioned nucleotides; and

/or combinations thereof.

33. The kit according to claim 31 when the calculation of diagnostic or probability of response to mood disorder medication is made by a computer 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 kit according to claim 33 wherein the mood disorder medication is drawn from the group consisting essentially of

depakote,

olanzapine, and

lamotrigine

35. The kit according to claim 31 when the determined patient diagnosis comprises:

risk of developing bipolar disorder;

post-traumatic stress syndrome;

panic disorder; and

/or

suicidal ideation.

36. The kit according to claim 31 wherein the device comprises:

an assay selected from the group consisting of a hybridization assay, a sequencing assay, a microsequencing assay and a an enzyme-based mismatch detection assay.