Method of providing a treatment to a subject based on a dynamic pathway map

Patent metadata

Field	Value
Publication number	US-10770169-B2
Application number	US-201113068002-A
Country	US
Kind code	B2
Filing date	Apr 29, 2011
Priority date	Apr 29, 2010
Publication date	Sep 8, 2020
Grant date	Sep 8, 2020

Abstract

The present invention relates to methods for evaluating the probability that a patient's diagnosis may be treated with a particular clinical regimen or therapy.

First claim

What is claimed is:

1. A method of generating a dynamic pathway map (DPM), comprising:
accessing a pathway element database storing at least one pathway including a plurality of pathway elements comprising genes, RNA, and proteins;
associating a first pathway element of the pathway element database with at least one known attribute corresponding to a gene, an RNA, or a protein by connecting the first pathway element to the at least one known attribute using at least one edge in a factor graph;
associating a second pathway element of the pathway element database with at least one assumed attribute corresponding to a gene, an RNA, or a protein by connecting the second pathway element to the at least one assumed attribute using at least one edge in the factor graph;
cross-correlating the first pathway element, the second pathway element, and additional pathway elements for the at least one pathway using the edges in the factor graph;
assigning, based on the cross-correlating, an influence level of the first and second pathway elements relative to activity or expression of each other or the additional pathway elements for the at least one pathway using the at least one known attribute and the at least one assumed attribute, wherein the influence level corresponds to an edge of the factor graph;
converting each pathway of the at least one pathway, and the influence level of the first and second pathway elements for the at least one pathway, to a probabilistic pathway model having each interaction in each pathway represented as an edge in the factor graph of the probabilistic pathway model, with the influence level of the first and second pathway elements comprising one of a negative value or a positive value, the negative value and the positive value correlated to the corresponding edge of the factor graph associated with the at least one pathway;
storing the probabilistic pathway model, additional pathway models, and a plurality of measured attributes for a plurality of elements based on a genome-scale assay of a biological sample, each instance model having edges between pathway elements comprising genes, RNA, and proteins, with the edges having influence levels;
deriving the DPM from expectation maximization of the probabilistic pathway model and the plurality of measured attributes for the plurality of elements based on the genome-scale assay of the biological sample, as well as the additional pathway models, to determine influence levels across the pathway models, the DPM inferring probabilistic reference pathway activity information for pathway elements of a particular pathway; and
identifying that an activity or an expression of a particular pathway element of the particular pathway is altered for the biological sample compared to a control based on pathway inferences in the DPM, wherein at least one method operation is executed by a processor, and wherein the biological sample was obtained from a subject; and
providing a treatment to the subject based on the activity or the expression of the pathway element of the particular pathway being altered compared to the control, wherein the treatment is administration of a pharmaceutical.

2. The method of claim 1 wherein the pathway is a regulatory pathway network.

3. The method of claim 2 wherein the regulatory pathway network is selected from the group consisting of an apoptosis pathway network, a homeostasis pathway network, a metabolic pathway network, a replication pathway network, and an immune response pathway network.

4. The method of claim 1 wherein the pathway is selected from the group consisting of a signaling pathway network and within a network of distinct pathway networks.

5. The method of claim 4 wherein the signaling pathway network is selected from the group consisting of a calcium/calmodulin dependent signaling pathway network, a cytokine mediated signaling pathway network, a chemokine mediated signaling pathway network, a growth factor signaling pathway network, a hormone signaling pathway network, a MAP kinase signaling pathway network, a phosphatase mediated signaling pathway network, a Ras superfamily mediated signaling pathway network, and a transcription factor mediated signaling pathway network.

6. The method of claim 1 wherein the particular pathway element is associated with a protein, wherein the protein is selected from the group consisting of a receptor, a hormone binding protein, a kinase, a transcription factor, a methylase, a histone acetylase, and a histone deacetylase.

7. The method of claim 1 wherein the particular pathway element is a nucleic acid.

8. The method of claim 7 wherein the nucleic acid is selected from the group consisting of a protein coding sequence, a genomic regulatory sequence, a regulatory RNA, and a trans-activating sequence.

9. The method of claim 1 wherein the probabilistic reference pathway activity information is specific with respect to a normal tissue, a diseased tissue, an ageing tissue, or a recovering tissue.

10. The method of claim 1 wherein the known attribute is selected from the group consisting of a compound attribute, a class attribute, a gene copy number, a transcription level, a translation level, and a protein activity.

11. The method of claim 1 wherein the assumed attribute is selected from the group consisting of a compound attribute, a class attribute, a gene copy number, a transcription level, a translation level, and a protein activity.

12. The method of claim 1 wherein the measured attributes are selected from the group consisting of a mutation, a differential genetic sequence object, a gene copy number, a transcription level, a translation level, a protein activity, and a protein interaction.

13. The method of claim 1 , further comprising:
measuring the plurality of measured attributes in the biological sample, wherein the measuring includes:
sequencing DNA in the biological sample;
measuring, with a microarray, gene expression of RNA in the biological sample; and
measuring protein levels using ELISA.

14. The method of claim 13 , wherein the biological sample is blood.

15. The method of claim 13 , wherein the biological sample includes blood and tumor tissue.

16. The method of claim 1 , wherein the pharmaceutical includes a candidate molecule.

Assignees

• Vaske Charles J
• Benz Stephen C
• Stuart Joshua M
• Haussler David
• Univ California

Inventors

• Vaske Charles J
• Benz Stephen C
• Stuart Joshua M
• Haussler David

Classifications

• G16B40/30

  Unsupervised data analysis · CPC title

• G16B5/30 Primary

  Dynamic-time models · CPC title

• G16B25/10

  Gene or protein expression profiling; Expression-ratio estimation or normalisation · CPC title

• G16B5/00 Primary

  ICT specially adapted for modelling or simulations in systems biology, e.g. gene-regulatory networks, protein interaction networks or metabolic networks · CPC title

• G16B45/00

  ICT specially adapted for bioinformatics-related data visualisation, e.g. displaying of maps or networks · CPC title