Copy number alterations (CNAs) are thought to account for 85% of the variation in gene expression observed among breast tumours. The expression of cis-associated genes is impacted by CNAs occurring at proximal loci of these genes, whereas the expression of trans-associated genes is impacted by CNAs occurring at distal loci. While a majority of these CNA-driven genes responsible for breast tumourigenesis are cis-associated, trans-associated genes are thought to further abet the development of cancer and influence disease outcomes in patients. Here we present a network-based approach that integrates copy-number and expression profiles to identify putative cis- and trans-associated genes in breast cancer pathogenesis. We validate these cis- and trans-associated genes by employing them to subtype a large cohort of breast tumours obtained from the METABRIC consortium, and demonstrate that these genes accurately reconstruct the ten subtypes of breast cancer. We observe that individual breast cancer subtypes are driven by distinct sets of cis- and trans-associated genes. Among the cis-associated genes, we recover several known drivers of breast cancer (e.g. CCND1, ERRB2, MDM2 and ZNF703) and some novel putative drivers (e.g. BRF2 and SF3B3). siRNA-mediated knockdown of BRF2 across a panel of breast cancer cell lines showed significant reduction specifically in cell proliferation in HER2+ lines, thereby indicating that BRF2 could be a context-dependent oncogene and potentially targetable in these lines. Among the trans-associated genes, we identify modules of immune-response (CD2, CD19, CD38 and CD79B), mitotic/cell-cycle kinases (e.g. AURKB, MELK, PLK1 and TTK), and DNA-damage response genes (e.g. RFC4 and FEN1). siRNA-mediated knockdown of RFC4 significantly reduced cell proliferation in estrogen receptor-negative normal breast and cancer lines, thereby indicating that RFC4 is essential for cancer cell survival but could also be a useful biomarker for stratification of aggressive (ER-negative) breast tumours. Availability: http://bioinformatics.org.au/tools-data/ under NetStrat.