No abstract

The tumor microenvironment is composed of many immune cell subpopulations and is an important factor in the malignant progression of neoplasms, particularly breast cancer (BC). However, the cytokine networks that coordinate various regulatory events within the BC interstitium remain largely uncharacterized. Moreover, the data obtained regarding the origin of cytokine secretions, the levels of secretion associated with tumor development, and the possible clinical relevance of cytokines remain controversial. Therefore, we profiled 27 cytokines in 78 breast tumor interstitial fluid (TIF) samples, 43 normal interstitial fluid (NIF) samples, and 25 matched serum samples obtained from BC patients with Luminex xMAP multiplex technology. Eleven cytokines exhibited significantly higher levels in the TIF samples compared with the NIF samples: interleukin (IL)-7, IL-10, fibroblast growth factor-2, IL-13, interferon (IFN)γ-inducible protein (IP-10), IL-1 receptor antagonist (IL-1RA), platelet-derived growth factor (PDGF)-β, IL-1β, chemokine ligand 5 (RANTES), vascular endothelial growth factor, and IL-12. An immunohistochemical analysis further demonstrated that IL-1RA, IP-10, IL-10, PDGF-β, RANTES, and VEGF are widely expressed by both cancer cells and tumor-infiltrating lymphocytes (TILs), whereas IP-10 and RANTES were preferentially abundant in triple-negative breast cancers (TNBCs) compared to Luminal A subtype cancers. The latter observation corresponds with the high level of TILs in the TNBC samples. IL-1β, IL-7, IL-10, and PDGFβ also exhibited a correlation between the TIF samples and matched sera. In a survival analysis, high levels of IL-5, a hallmark T2 cytokine, in the TIF samples were associated with a worse prognosis. These findings have important implications for BC immunotherapy research.

BackgroundOver the last decade network enrichment analysis has become popular in computational systems biology to elucidate aberrant network modules. Traditionally, these approaches focus on combining gene expression data with protein-protein interaction (PPI) networks. Nowadays, the so-called omics technologies allow for inclusion of many more data sets, e.g. protein phosphorylation or epigenetic modifications. This creates a need for analysis methods that can combine these various sources of data to obtain a systems-level view on aberrant biological networks.ResultsWe present a new release of KeyPathwayMiner (version 4.0) that is not limited to analyses of single omics data sets, e.g. gene expression, but is able to directly combine several different omics data types. Version 4.0 can further integrate existing knowledge by adding a search bias towards sub-networks that contain (avoid) genes provided in a positive (negative) list. Finally the new release now also provides a set of novel visualization features and has been implemented as an app for the standard bioinformatics network analysis tool: Cytoscape.ConclusionWith KeyPathwayMiner 4.0, we publish a Cytoscape app for multi-omics based sub-network extraction. It is available in Cytoscape’s app store http://apps.cytoscape.org/apps/keypathwayminer or via http://keypathwayminer.mpi-inf.mpg.de.