STING, also known as the Stimulator of interferon genes, has been gaining attention lately for its potential as a target for autoimmune, cancer, and anti-viral treatments. In March of 2015 it was announced that Novartis and Aduro Biotech would codevelop a STING modulating compound and by May 2016 the first patients were being dosed in efficacy clinical trials1. Recent research has highlighted the importance of STING’s role in regulating type I interferon pathways, as well as participating in inflammatory responses. Combining the knowledge from this growing body of published research into the context of an interaction pathway can be a powerful tool for analyzing data and sharing knowledge. In these webinar, we will be using a combination of network building and the Pathway Map Creator in MetaCore to answer these questions:
• How are neighboring biological interactors around STING related to each other?
• How do I build a custom pathway map detailing the interaction network I’ve built? • How do I overlay datasets or run enrichment analysis with these novel pathway maps?
Quite often in drug development, a therapy will face a number of challenges including lack of efficacy, resistance, or adverse events that restrict its broader usage in treating patients. Pathway analysis of omics data can address these challenges by reconstructing the molecular mechanisms of the therapeutic signaling cascades. Glucocorticoids are a useful class of steroids that bind to the glucocorticoid receptor and have a number of useful effects such as suppressing immune and inflammatory responses. They are currently being used to suppress an overactive immune system or inhibit excessive proliferation of lymphocytes in blood cancers. Gamma-secretase inhibitors (GSI) are effective for inhibiting NOTCH signaling, which is often overexpressed in a number of cancers and plays a major role in cell survival, differentiation, and tumorigenesis. Both of these therapies are being used or tested against T-cell acute lymphoblastic leukemia and face a number of challenges including relapse with resistance for glucocorticoids and lack of efficacy as well as toxicity for GSI. Could combining these two therapies help mitigate their individual weaknesses and improve efficacy?
I will be using MetaCore to perform pathway analysis using transcriptomic data to better understand the mechanisms behind how this combination therapy enhances the efficacy of these treatments. For this training session, we will be using the results reported in the Gene Expression Omnibus (GEO) dataset GSE33562. In this dataset, the CUTLL1 cell line was treated with PF-03084014, dexamethasone, the combination of PF-0308014, or the vehicle control to see how the cells responded to the treatment. Using this data to compare how the mono treatments compare against the combination treatment, we will seek to answer these questions:
• How is the expression signature for the combination therapy unique to the two mono therapies?
• What transcription factors are overconnected with differentially expressed genes from the combination therapy?
• How could gamma-secretase inhibitors impact glucocorticoid treatments?
Sepsis and community acquired pneumonia are major concerns in the health care industry as leading to organ failure or death in patients. According to the CDC, there are over 1 million cases of sepsis and 258,000 reported deaths from it each year. For pneumonia, over 1.7 million hospitalizations have been reported with 53,000 deaths each year in the United States. Many patients who develop pneumonia could lead to sepsis. Renal failure is just one type of organ failure that can occur in patients with sepsis, and can lead to lifelong dependence on dialysis even after surviving the infection. Early detection of these conditions and their associated complications through the use of biomarkers can help save lives and prevent lifelong disabilities.
I will be using MetaCore to perform pathway analysis using metabolomic, proteomic, and transcriptomic data to uncover biological relationships between them. For this training session, we will be using the results reported here2 based on the CAPSOD study data. Using data collected from plasma of patients with different levels of renal function, we will seek to answer these questions:
• What metabolic relationships can we find between the metabolomic and RNA-seq data?
• What changes in the metabolites and proteins concentrations measured from patient’s plasma
could be biomarkers for disrupted processes?
In this webinar, we will discuss using MetaCore to analyze the impact of RN486 (a BtK inhibitor) to attenuate the signaling from Toll-like Receptors (TLR). Pathway analysis and interactome analysis will be used to better understand the impact of TLR signaling in cellular processes and diseases. For this training session, we will analyze transcriptomic data of gardiquimod, ODN 2216, and RN486 treatments on plasmacytoid dendritic cells (pDC) collected from healthy patients and available in the GEO Series entry GSE41825 as the input datasets, to answer these questions:
• What can we learn about BtK and its role in immune response signaling?
• What pathways and diseases are potentially related to the activation of TLR9 and TLR7? How effective is BtK signaling in the attenuation of these signaling pathways?
• What transcription factors are significantly related to the changes in gene expression due to TLR signaling?
In this webinar, we will discuss using MetaCore’s Key Pathway Advisor and its latest enhancements to predict drug targets and biomarkers from patients with follicular lymphoma. Using the causal reasoning algorithm to identify potential key regulatory hubs, we will associate the results with curated data on known drug targets and biomarkers. For this training session, we will analyze transcriptomic data of CD8+ T-cells collected from the tonsils and available in the GEO Series entry GSE27928 as the input datasets, to answer these questions:
• What pathways show synergistic behavior with the list of DEGs and key hubs identified through the causal reasoning algorithm?
• What drug targets associated with follicular lymphoma and similar diseases are also identified as a key regulatory hub? Could drugs being tested in similar diseases be repurposed for follicular lymphoma?
• What curated genes associated with follicular lymphoma and similar diseases are linked to the results from the causal reasoning analysis? Might genes associated with similar diseases find a use in follicular lymphoma?