5 research outputs found
Understanding the functional impact of copy number alterations in breast cancer using a network modeling approach
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).Comment: 23 pages, 2 tables, 7 figure
Methods for protein complex prediction and their contributions towards understanding the organization, function and dynamics of complexes
Complexes of physically interacting proteins constitute fundamental
functional units responsible for driving biological processes within cells. A
faithful reconstruction of the entire set of complexes is therefore essential
to understand the functional organization of cells. In this review, we discuss
the key contributions of computational methods developed till date
(approximately between 2003 and 2015) for identifying complexes from the
network of interacting proteins (PPI network). We evaluate in depth the
performance of these methods on PPI datasets from yeast, and highlight
challenges faced by these methods, in particular detection of sparse and small
or sub- complexes and discerning of overlapping complexes. We describe methods
for integrating diverse information including expression profiles and 3D
structures of proteins with PPI networks to understand the dynamics of complex
formation, for instance, of time-based assembly of complex subunits and
formation of fuzzy complexes from intrinsically disordered proteins. Finally,
we discuss methods for identifying dysfunctional complexes in human diseases,
an application that is proving invaluable to understand disease mechanisms and
to discover novel therapeutic targets. We hope this review aptly commemorates a
decade of research on computational prediction of complexes and constitutes a
valuable reference for further advancements in this exciting area.Comment: 1 Tabl
Employing functional interactions for characterisation and detection of sparse complexes from yeast PPI networks
10.1504/IJBRA.2012.048962International Journal of Bioinformatics Research and Applications83-4286-30