Physiogenomic method for predicting clinical outcomes of treatments in patients

• US 20060234262A1
• Filed: 03/08/2006
• Published: 10/19/2006
• Est. Priority Date: 12/14/2004
• Status: Active Grant

• Current Assignee: Genomas, Inc. (Rennova Health, Inc.)
• Original Assignee: Genomas, Inc. (Rennova Health, Inc.)
• Inventors: Windemuth, Andreas, Ruano, Gualberto
• Granted Patent: US 7,747,392 B2
• Time in Patent Office: Days
• US Class Current: 435/6
• 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 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 20/40 Population genetics; Linkage disequilibrium
• 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: G16H 20/30 relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders
• 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

A physiogenomic-based method for predicting the outcome of treatment regimens in human patients based upon association screening to identify genetic markers and related physiological characteristics that influence the disease status of a patient, the progression to disease and response to the treatment. By repeating the analysis quantitatively for each of multiple treatment regimens, a profile can be created for each patient to determine which of several treatment regimens are best suited to the patient'"'"'s clinical needs.

Claims

1. A physiogenomics method for predicting an individual'"'"'s response to the environment comprising:

(a) selecting a plurality of genetic markers based on an analysis of the entire human genome or a fraction thereof;

(b) identifying significant covariates among demographic data and the other phenotypes by principal component analysis;

(c) performing for each selected genetic marker an unadjusted association test using genetic data

(d) using permutation testing to obtain a non-parametric and marker complexity probability value for identifying significant genetic markers;

and

(e) constructing a physiogenomic model by linear regression analyses and model parameterization for the dependence of said patient'"'"'s response to treatment with respect to said genetic markers; and

(f) identifying one or more genes not associated with a particular outcome in said patient to serve as a physiogenomic control.

2. The method according to claim 1, wherein the step of selecting a plurality of genetic markers comprises DNA screening using a fixed array.

3. The method according to claim 1, wherein the step of selecting a plurality of genetic markers comprises DNA screening using a liquid array.

4. The method according to claim 1, wherein the step of selecting a plurality of genetic markers comprises DNA screening using a random array.

5. The method according to claim 4, wherein the random array is a microbead array comprising microbeads with an average diameter less than or equal to 2 microns.

6. The method according to claim 1, wherein said phenotypes are endophenotypes.

7. The method according to claims 1-5, wherein said covariates are determined by generating a covariance matrix for all markers and selecting each significantly correlated marker for use as a covariate in the association test for each marker, and wherein serological and baseline outcomes are tested by linear regression.

8. The method according to claims 1-5, wherein said permutation testing comprises selecting an original data set;

creating a plurality of permuted data sets that differ from the original data set by having a response variable permuted at random with respect to a marker;

performing the same tests on the original data set and the plurality of permuted data sets to obtain a non-parametric estimate of the null distribution of the test statistics, and

determining the false positive rate of a marker by determining where in the distribution of test results corresponding to the permuted data sets lies the test result corresponding to the original data set.

9. The method of claim 8, wherein the number of data sets in the plurality of data sets is at least 1000, and wherein a marker is selected for model building when test results corresponding to the original data set are in the top 50.

10. The method of claims 1-5, wherein said linear regression model in the construction of said physiogenomic model has the form

11. A method for treating an individual suffering from a disease or disorder, said method comprising the steps of

preparing a physiogenomics database, said physiogenomics database comprising a plurality of ensembles of genetic markers obtained from a plurality of people, wherein each ensemble of a genetic markers is associated with a physiological response to a treatment;

obtaining genotype and phenotype data of the individual; and

comparing the genotype data of the individual with said plurality of ensembles of genetic markers in said physiogenomics database;

calculating a predicted physiological response of the individual to each treatment associated with each ensemble of genetic markers, and recommending a treatment regimen based on said calculation.

12. The method of claim 11, further comprising ranking the predicted physiological response of the individual as percentile against the physiological responses of the people whose ensembles of genetic markers constitute the database.

13. The method of claim 11, wherein the step of preparing a physiogenomics database further comprises

selecting a plurality of ensembles of genetic markers based on an analysis of the entire human genome or a fraction thereof;

identifying significant covariates among demographic data and the other phenotypes by principal component analysis;

performing for each selected genetic marker an unadjusted association test using genetic data

using permutation testing to obtain a non-parametric and marker complexity probability (“

p”

) value for identifying significant markers, wherein the significance is shown by p<

0.05; and

constructing a physiogenomic model by linear regression analyses and model parameterization for the dependence of said patient'"'"'s response to treatment with respect to said markers, wherein said physiogenomic model has p<

0.05; and

identifying one or more genes not associated with a particular outcome in said patient to serve as a physiogenomic control.

14. An assay for constructing a physiogenomic database, wherein said assay comprises a plurality of subpopulations of oligonucleotide probes, wherein all of the oligonucleotide probes in each subpopulation are capable of hybridizing with an oligonucleotide selected from the group consisting of SEQ ID NOs 1-384,

a plurality of binding sites, wherein each binding site binds one of the subpopulations of oligonucleotides.

15. The assay according to claim 14, wherein each binding site of the plurality of binding sites is on a separate bead.

16. The assay according to claim 15, wherein the bead has an average diameter less than or equal to 2 microns.

17. The assay according to claim 16, wherein said beads are fixed on a surface of a solid support.

18. The assay according to claim 14, wherein each of said subpopulations is attached on a discrete area on the surface of a solid support.

19. The assay according to claim 14, wherein said plurality of subpopulations of oligonucleotide probes comprises oligonucleotide probes capable of hybridizing with at least about 10 oligonucleotides selected from the group of oligonucleotides consisting of SEQ ID NOs 1-384.

20. The assay according to claim 14, wherein said plurality of subpopulations of oligonucleotide probes comprises oligonucleotide probes capable of hybridizing with at least about 20 oligonucleotides selected from the group of oligonucleotides consisting of SEQ ID NOs 1-384.

21. The assay according to claim 14, wherein said plurality of subpopulations of oligonucleotide probes comprises oligonucleotide probes capable of hybridizing with at least about 50 oligonucleotides selected from the group of oligonucleotides consisting of SEQ ID NOs 1-384.

22. The assay according to claim 14, wherein said plurality of subpopulations of oligonucleotide probes comprises oligonucleotide probes capable of hybridizing with at least about 100 oligonucleotides selected from the group of oligonucleotides consisting of SEQ ID NOs 1-384.

23. The assay according to claim 14, wherein said plurality of subpopulations of oligonucleotide probes comprises oligonucleotide probes capable of hybridizing with at least about 200 oligonucleotides selected from the group of oligonucleotides consisting of SEQ ID NOs 1-384.

24. The assay according to claim 14, wherein said plurality of subpopulations of oligonucleotide probes comprises oligonucleotide probes capable of hybridizing with at least about 300 oligonucleotides selected from the group of oligonucleotides consisting of SEQ ID NOs 1-384.