Using a 3D co-culture model, we identified significant sub-type-specific changes in gene expression, metabolic, and therapeutic sensitivity profiles of breast cancer cells in contact with cancer-associated fibroblasts (CAFs). CAF-induced gene expression signatures predicted clinical outcome and immune-related differences in the microenvironment. We found that fibroblasts strongly protect carcinoma cells from lapatinib, attributable to its reduced accumulation in carcinoma cells and an elevated apoptotic threshold. Fibroblasts from normal breast tissues and stromal cultures of brain metastases of breast cancer had similar effects as CAFs. Using synthetic lethality approaches, we identified molecular pathways whose inhibition sensitizes HER2+ breast cancer cells to lapatinib both in vitro and in vivo including JAK2/STAT3 and hyaluronic acid. Neoadjuvant lapatinib therapy in HER2+ breast tumors lead to a significant increase of phospho-STAT3+ cancer cells and a decrease in the spatial proximity of proliferating (Ki67+) cells to CAFs impacting therapeutic responses. Our studies identify CAF-induced physiologically and clinically relevant changes in cancer cells and offer novel approaches for overcoming microenvironment-mediated therapeutic resistance.

As it is the case with any OMICs technology, the value of proteomics data is defined by the degree of its functional interpretation in the context of phenotype. Functional analysis of proteomics profiles is inherently complex, as each of hundreds of detected proteins can belong to dozens of pathways, be connected in different context-specific groups by protein interactions and regulated by a variety of one-step and remote regulators. Knowledge-based approach deals with this complexity by creating a structured database of protein interactions, pathways and protein-disease associations from experimental literature and a set of statistical tools to compare the proteomics profiles with this rich source of accumulated knowledge. Here we describe the main methods of ontology enrichment, interactome topology and network analysis applied on a comprehensive, manually curated and semantically consistent knowledge source MetaBase and demonstrate several case studies in different disease areas.

BackgroundPsoriasis is complex inflammatory skin pathology of autoimmune origin. Several cell types are perturbed in this pathology, and underlying signaling events are complex and still poorly understood.ResultsIn order to gain insight into molecular machinery underlying the disease, we conducted a comprehensive meta-analysis of proteomics and transcriptomics of psoriatic lesions from independent studies. Network-based analysis revealed similarities in regulation at both proteomics and transcriptomics level. We identified a group of transcription factors responsible for overexpression of psoriasis genes and a number of previously unknown signaling pathways that may play a role in this process. We also evaluated functional synergy between transcriptomics and proteomics results.ConclusionsWe developed network-based methodology for integrative analysis of high throughput data sets of different types. Investigation of proteomics and transcriptomics data sets on psoriasis revealed versatility in regulatory machinery underlying pathology and showed complementarities between two levels of cellular organization.

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Activation of multipotent mesenchymal stromal cells (MSCs) is a central part of tissue response to damage. Platelet-derived growth factor (PDGF-BB), which is abundantly released in the damaged area, potently stimulates the proliferation and migration of MSCs. Recent evidence indicates that tissue injury is associated with the accumulation of senescent cells, including ones of MSC origin. Therefore, we hypothesized that PDGF-BB induces MSC senescence. To evaluate mechanisms of early activation of MSCs by PDGF-BB, we performed transcriptome profiling of human MSCs isolated from adipose tissue after exposure to PDGF-BB for 12 and 24 h. We demonstrated that PDGF-BB induced the expression of several genes encoding the components of senescence-associated secretory phenotype (SASP) in MSCs such as plasminogen activator inhibitor-1 (PAI-1), urokinase-type plasminogen activator and its receptor (uPA and uPAR), and some matrix metalloproteases. However, further experimental validation of transcriptomic data clearly indicated that PDGF-BB exerted mitogenic and pro-migratory effects on MSCs, and augmented their pro-angiogenic activity, but did not significantly stimulate MSC senescence.