Abstract
BackgroundGenes do not act in isolation but instead as part of complex regulatory networks. To understand how breast tumors adapt to the presence of the drug letrozole, at the molecular level, it is necessary to consider how the expression levels of genes in these networks change relative to one another.MethodsUsing transcriptomic data generated from sequential tumor biopsy samples, taken at diagnosis, following 10-14 days and following 90 days of letrozole treatment, and a pairwise partial correlation statistic, we build temporal gene coexpression networks. We characterize the structure of each network and identify genes that hold prominent positions for maintaining network integrity and controlling information-flow.ResultsLetrozole treatment leads to extensive rewiring of the breast tumor coexpression network. Approximately 20% of gene-gene relationships are conserved over time in the presence of letrozole while 80% of relationships are condition dependent. The positions of influence within the networks are transiently held with few genes stably maintaining high centrality scores across the three time points.ConclusionsGenes integral for maintaining network integrity and controlling information flow are dynamically changing as the breast tumor coexpression network adapts to perturbation by the drug letrozole.
Highlights
Genes do not act in isolation but instead as part of complex regulatory networks
We look at how the gene coexpression networks from ER+ breast tumor samples are rewired in response to letrozole treatment over time
Biological process annotation through the Gene Ontology shows the upregulated genes are enriched for cell migration (p = 2.2E-7), positive regulation of gene transcription (p = 1.3E-6), polysaccharide binding (p = 1.0E-6), cell morphogenesis involved in differentiation (p = 2.9E-5), ovulation cycle (p = 1.8E-3) and blood coagulation (p = 2.4E-3)
Summary
Genes do not act in isolation but instead as part of complex regulatory networks. To understand how breast tumors adapt to the presence of the drug letrozole, at the molecular level, it is necessary to consider how the expression levels of genes in these networks change relative to one another. Gene signatures are derived from genome-wide expression profiles that capture the global state of gene transcription at a given moment in time. The utility of these genomewide measures largely depends on the computational methods used to transform the data into an interpretable form. Genes that are being coregulated will have correlated expression values and a tendency to function as part of the same or related regulatory processes [4]. By focusing on these coexpression relationships in breast cancer we can maximize the amount of information gained from genomic data
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