NetStrat

NetStrat integrates copy number alteration (CNA) and gene expression data using network modeling to identify cis- and trans-associated genes and subtype-specific drivers in breast cancer.

Key Features:

• Integration of Data Types: Combines CNA data with gene expression profiles to assess how CNAs affect gene regulation in breast cancer.
• Identification of Associated Genes: Distinguishes cis-associated genes influenced by proximal CNAs and trans-associated genes affected by distal loci.
• Subtype Classification: Reconstructs ten distinct breast cancer subtypes from the METABRIC cohort driven by unique sets of cis- and trans-associated genes.
• Validation through Subtyping: Uses subtype-specific gene associations and external experimental results to validate identified drivers.

Scientific Applications:

• Recovery of known and novel drivers: Recovers established genes CCND1, ERRB2, MDM2, and ZNF703 and highlights novel candidates such as BRF2 and SF3B3.
• Therapeutic target identification: Supports identification of targets exemplified by siRNA knockdown showing BRF2 as a viable target in estrogen receptor-negative/HER2-enriched (ER-/HER2+) breast cancers.
• Discovery of trans-associated modules: Identifies trans-associated gene modules involved in immune response (CD2, CD19), mitotic/cell-cycle regulation (AURKB, MELK), and DNA-damage response (RFC4).
• Biomarker and functional validation: Links RFC4 to reduced cell proliferation upon knockdown in ER-negative breast cancer lines, suggesting a biomarker for aggressive tumors.

Methodology:

Integrates CNA and gene expression profiles to construct network models that reveal cis- and trans-associations between CNAs and genes.