Functional characterization of breast cancer using pathway profiles

Area of Science:

• Bioinformatics
• Genomics
• Cancer Research

Background:

• Traditional gene signatures for disease characterization lack reproducibility across patient cohorts.
• Perturbed cellular functions, rather than specific genes, offer a more robust disease characterization method.

Purpose of the Study:

• To develop and evaluate a profile-based approach for disease characterization using perturbed cellular functions.
• To assess the reproducibility, accuracy, and resolution of pathway profiles for breast cancer subtypes.

Main Methods:

• A profile-based approach was developed, utilizing binary vectors to represent perturbed cellular functions based on gene expression enrichment analysis.
• Pathway profiles were constructed for breast cancer and its subtypes using The Cancer Genome Atlas (TCGA) and European Genome-phenome Archive (EGA) datasets (microarray and RNA-Seq).

Main Results:

• Pathway profiles demonstrated good reproducibility (68% between datasets, 67% between technologies).
• 74% of identified pathways are known to be cancer-associated.
• Distinct and common pathway enrichments were observed across breast cancer subtypes, with varying degrees of similarity between subtypes and other diseases.

Conclusions:

• Pathway profiles effectively characterize common and distinct functional aspects of breast cancer subtypes.
• The approach offers acceptable reproducibility, high accuracy, and reasonable resolution for disease characterization.