Physiogenomic method for predicting clinical outcomes of treatments in patients

• US 20060278241A1
• Filed: 12/14/2004
• Published: 12/14/2006
• Est. Priority Date: 12/14/2004
• Status: Abandoned Application

• Current Assignee: Genomas, Inc. (Rennova Health, Inc.)
• Original Assignee: Genomas, Inc. (Rennova Health, Inc.)
• Inventors: Ruano, Gualberto
• Application Number:
  US11/010,716
• Publication Number:
  US 20060278241A1
• Time in Patent Office: Days
• US Class Current: 128/898
• 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 10/20 for electronic clinical trials or questionnaires
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems

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 can be used to determine which of several treatment regimens are best suited to the patient'"'"'s clinical needs.

Claims

1. A physiogenomics method for predicting whether or not a particular treatment regimen will produce a beneficial effect on a patient, comprising, in the first stage, conducting association screening to identify genetic markers and physiological characteristics that have an influence on the disease status of said patient or the response to treatment, wherein said association screening is carried out by the steps of:

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

(b) performing for each selected genetic marker an unadjusted association test using genetic data, and linear regression for phenotypes reflective of the disease and baseline states of the patient;

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

p”

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

0.05; and

,

(d) constructing a validated physiogenomic model by linear regression analyses and model parameterization for the dependence of said patient'"'"'s response to treatment on the markers, wherein a valid model is one with a p<

0.05;

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

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

3. The method of claim 1, wherein said permutation testing correction is conducted by performing the same tests on a large number of data sets that differ from the original by having the response variate permutated at random with respect to the marker, thereby providing a nonparametric estimate of the null distribution of the test statistics, whereby the unpermutated test result in the distribution of permutated test results provides a nonparametric and statistically rigorous estimate of the false positive rate for the marker.

4. The method of claim 3, wherein the number of data sets is 1000, and wherein a marker is selected for model building when the original test ranks in the top 50.

5. The method of claim 1, wherein said linear regression model in the construction of said physiogenomic model has the form of:

$R=R_0+\underset{i}{\sum <br><br>}<br><br>{\mathrm{\alpha <br><br>}}_i<br><br>M_i+\underset{i}{\sum <br><br>}<br><br>{\mathrm{\beta <br><br>}}_i<br><br>D_i+\mathrm{\varepsilon <br><br>}$

where R is the respective phenotype variable, Mi represents the marker variables, Di are demographic covariates, and ε

is the residual unexplained variation, and wherein the model parameters that are to be estimated from the data are Ro, α

_i and β

_i.

6. The method of claim 1, wherein said model parameterization is carried out by the maximum likelihood method to obtain optimal estimates of parameters.

7. The method of claim 1, further comprising model refinement by, in the first linear regression model, considering in the first phase a set of simplified models obtained by eliminating each variable in turn and re-optimizing the likelihood function, wherein the ratio between the two maximum likelihoods of the original vs the simplified model provides a significance measure for the contribution of each variable and, in the second phase probabilistic network model, removing dependency links instead of variables.

8. The method of claim 1, wherein said model validation is conducted by cross-validation, wherein said cross-validization comprises the steps of:

(a) validating the model by reparameterization using all data except that from one patient;

(b) calculating the likelihood of the outcome for this patient from the outcome distribution from the model;

(c) repeating the procedure for each patient;

(d) calculating the product of all likelihoods;

(e) comparing the resulting likelihood with the likelihood of the data from the null model, said null model consiting of no markers and a predicted distribution equal to general distribution; and

(f) determining the probability value, wherein if p<

0.05 the model is a significant improvement over the null model.

9. The method of claim 1, further comprising the development of the physiotype for a patient with a medical condition, said physiotype consisting of a quantitative profile, said quantitive profile being constructed by combining said physiogenomic information with the patient'"'"'s clinical and physiological status for each of one or more clinically suitable treatment regimens and assigning a score to each said treatment regimen, and employing said quantitive profile to predict which of said treatment regimen(s) is/are best suited for said patient'"'"'s medical condition.

10. The method of claim 9, wherein said clinically suitable treatment regimens are selected from the group consisting of drugs, diet and exercise.

11. A printed form, produced from the results of the method of claim 9, for compiling in portable form a patient'"'"'s physiogenomic treatment profile.