Constraints-based analysis of gene expression data

• US 20050079508A1
• Filed: 10/10/2003
• Published: 04/14/2005
• Est. Priority Date: 10/10/2003
• Status: Abandoned Application

• Current Assignee: Regents of the University of California (University of California)
• Original Assignee: Regents of the University of California (University of California)
• Inventors: Slamon, Dennis, Dering, Judy, Wilson, Cindy A.
• Application Number:
  US10/683,535
• Publication Number:
  US 20050079508A1
• Time in Patent Office: Days
• US Class Current: 435/6
• CPC Class Codes: G16B 25/00 ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression
• CPC Class Codes: G16B 25/10 Gene or protein expression profiling; Expression-ratio estimation or normalisation
• 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

Abstract

In accordance with one aspect of the invention, a constraints-based method of analysis of gene expression data has been shown to provide a useful method for target identification in gene expression profiles. Such constraints-based methods of analysis provide useful analytical methods that focus on genes and related pathways that are likely to be important in disease progression, that is, to provide likely candidates that will serve as targets for various therapeutics.

Claims

1. A constraint-based method for identifying a genomic target of interest from a gene expression profiles, comprising:

obtaining tissue sample expression data sets;

selecting a working gene-expressionset, said gene-expression set having a plurality of members

defining subgroups of said tissue samples of said expression data sets, wherein said subgroups definition is a constrained definition; and

analyzing co-expression of said members gene set across said subgroups and identifying potential gene targets.

2. The method of claim 1, wherein said tissue sample expression data sets are comprised of expressions data sets from tumor samples.

3. The method of claim 1, wherein said tissue sample expression data sets are comprised of expression data sets from tissue from a mammal.

4. The method of claim 3, wherein said tissue sample expression data sets are comprised of expression data sets attained tissue from a least one of a human, mouse, primate, canine, pig, rat, and feline.

5. The method of claim 1, wherein said tissue sample expression data sets are comprised of expression data sets based upon tissue from and embryo.

6. The method of claim 2, wherein said tumor are breast cancer tumors.

7. The method of claim 2 wherein said tumors are cancer tumor of the digestive tract.

8. The method of claim 1, wherein said working gene expression set comprises at least one receptor tyrosine kinase.

9. The method of claim 1, wherein said working gene expression set comprises a receptor and a ligand.

10. The method of claim 1, further comprising a step of selecting known prognostic markers that are correlative with prognostic outcomes.

11. The method of claim 1, further comprising a step of binning said working gene-expression set.

12. A constraint-based method for analysis of gene expression profiles, comprising:

selecting a working gene set;

investigating expression patterns of said working gene set in a set of tissue samples;

defining cutting values to define categories of gene expression levels;

selecting constraints in order to bin said tissue samples into groups according to gene expression;

investigating the frequency of up-regulated and down-regulated genes across individual members and groups; and

forming at least one matrix which provides a basis for investigation of expression of members of said working gene set across said set of tissue samples, thereby providing at least one potential gene target.

13. The method of claim 12, wherein said investigation includes calculating an intensity ratio of a particular gene of said working gene set, said intensity ratio calculated by comparison of a particular gene expression intensity to a calculated average intensity.

14. The method of claim 13, wherein said intensity ratio is utilized to provide information utilized to construct at least a part of at least one matrix, said matrix being comprised of a plurality of calculated ratios.