Weighted gene coexpression network analysis (WGCNA) is a powerful 'guilt-by-association'-based method to extract coexpressed groups of genes from large heterogeneous messenger RNA expression data sets. We have utilized WGCNA to identify 11 coregulated gene clusters across 2342 breast cancer samples from 13 microarray-based gene expression studies. A number of these transcriptional modules were found to be correlated to clinicopathological variables (e.g. tumor grade), survival endpoints for breast cancer as a whole (disease-free survival, distant disease-free survival and overall survival) and also its molecular subtypes (luminal A, luminal B, HER2+ and basal-like). Examples of findings arising from this work include the identification of a cluster of proliferation-related genes that when upregulated correlated to increased tumor grade and were associated with poor survival in general. The prognostic potential of novel genes, for example, ubiquitin-conjugating enzyme E2S (UBE2S) within this group was confirmed in an independent data set. In addition, gene clusters were also associated with survival for breast cancer molecular subtypes including a cluster of genes that was found to correlate with prognosis exclusively for basal-like breast cancer. The upregulation of several single genes within this coexpression cluster, for example, the potassium channel, subfamily K, member 5 (KCNK5) was associated with poor outcome for the basal-like molecular subtype. We have developed an online database to allow user-friendly access to the coexpression patterns and the survival analysis outputs uncovered in this study (available at http://glados.ucd.ie/Coexpression/).

The majority of anticancer drugs are substrates for the ABC transporter and SLC transporter families. Together, these proteins have the ability to control the influx and the efflux of structurally unrelated chemotherapeutic drugs, thereby modulating the intracellular drug concentration. These interactions have important clinical implications for chemotherapy because ultimately they determine therapeutic efficacy, disease progression/relapse and the success or failure of patient treatment.

Multiple sclerosis (MS) is characterized by central nervous system (CNS) inflammation, demyelination, and axonal degeneration. CXCL10 (IP-10), a chemokine for CXCR3+ T cells, is known to regulate T cell differentiation and migration in the periphery, but effects of CXCL10 produced endogenously in the CNS on immune cell trafficking are unknown. We created floxed cxcl10 mice and crossed them with mice carrying an astrocyte-specific Cre transgene (mGFAPcre) to ablate astroglial CXCL10 synthesis. These mice, and littermate controls, were immunized with myelin oligodendrocyte glycoprotein peptide 35-55 (MOG peptide) to induce experimental autoimmune encephalomyelitis (EAE). In comparison to the control mice, spinal cord CXCL10 mRNA and protein were sharply diminished in the mGFAPcre/CXCL10fl/fl EAE mice, confirming that astroglia are chiefly responsible for EAE-induced CNS CXCL10 synthesis. Astroglial CXCL10 deletion did not significantly alter the overall composition of CD4+ lymphocytes and CD11b+ cells in the acutely inflamed CNS, but did diminish accumulation of CD4+ lymphocytes in the spinal cord perivascular spaces. Furthermore, IBA1+ microglia/macrophage accumulation within the lesions was not affected by CXCL10 deletion. Clinical deficits were milder and acute demyelination was substantially reduced in the astroglial CXCL10-deleted EAE mice, but long-term axon loss was equally severe in the two groups. We concluded that astroglial CXCL10 enhances spinal cord perivascular CD4+ lymphocyte accumulation and acute spinal cord demyelination in MOG peptide EAE, but does not play an important role in progressive axon loss in this MS model.