Related Experiment Video
Updated: Apr 12, 2026
07:41
Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
Published on: May 17, 2019
9.7K
Pathway-based personalized analysis of breast cancer expression data
Anna Livshits1, Anna Git2, Garold Fuks1
1Department of Physics of Complex Systems, Weizmann Institute of Science, Rehovot 76100, Israel.
Molecular Oncology
|May 13, 2015
Summary
Pathifier analysis reveals new breast cancer subtypes by examining pathway deregulation. This coarse-grained approach identifies distinct patient groups, improving prognostic accuracy and revealing novel therapeutic targets.
Area of Science:
- Genomics
- Bioinformatics
- Cancer Biology
Background:
- Traditional cancer data analysis relies on single-gene properties.
- Pathifier offers a novel
- coarse-grained
- approach using pathway-based variables.
Purpose of the Study:
- To apply Pathifier to a large breast cancer dataset.
- To characterize tumors using pathway deregulation scores (PDS).
- To identify novel tumor subtypes and prognostic signatures.
Main Methods:
- Utilized Pathifier on 2000 breast cancer samples and 144 normal tissues.
- Calculated Pathway Deregulation Scores (PDS) for hundreds of pathways.
- Stratified samples based on PDS profiles for robust analysis.
Main Results:
- Identified nine distinct tumor subtypes, including a new subclass with high PKA pathway deregulation.
- Discovered two basal tumor subclasses with differential immune system pathway deregulation.
- Observed higher Tumor Infiltrating Lymphocytes and better prognosis in basal subtypes with high immune pathway deregulation.
Conclusions:
- Pathway deregulation analysis provides robust and novel insights into breast cancer.
- Coarse-grained variables are effective for identifying clinically relevant subgroups.
- Separate analysis of patient subgroups is crucial for reliable prognostic signatures in breast cancer.
