Now you can visualize and explore the activity of a single QIAGEN IPA entity, such as an Upstream Regulator, Causal Network, Canonical Pathway, Disease or Function, across >60,000 OmicSoft Land analyses. The Activity Plot is a novel approach that helps you gain insights into an IPA entity by exploring its predicted biological activity across thousands of datasets that represent disease conditions, drug or other treatments, knockouts and much more in the Analysis Match database. Please note that the Activity Plot feature is included with Analysis Match licenses in IPA.
With this new capability, you can answer questions, such as “Which treatments are predicted to inhibit the epithelial to mesenchymal transition (EMT)? What disease states activate the ILK Signaling Pathway? In which cancer types is STAT3 activated as an upstream regulator?”
The figure below shows the predicted activity of the EMT function across >60,000 analyses in the Analysis Match database. EMT is significant in >3500 analyses (represented by the dots in the plot and indicated in the plot title). A subset of analyses with strong inhibition of EMT (z-score < –2) were selected and further filtered for those of the comparison type “Treatment vs. Control” (green dots in the image).
Many of the compounds that are predicted to inhibit EMT are kinase inhibitors, such as erlotinib, selumetinib, AZD8330, KIN001-043 and others. Selumetinib is a MEK inhibitor and a known inhibitor of EMT (PMID: 28179307). Interestingly, a top scoring analysis (that is not a compound) is an siRNA knockdown of the Q61R NRAS activating mutation, underscoring that an NRAS mutation can drive EMT but can be reversed by knocking down the expression of the mutated gene. Finally, the HDAC inhibitor pracinostat scored strongly and was recently shown to reverse EMT in a breast cancer cell line (PMID: 32109485). Data mining using the the IPA Activity Plot may help you discover novel inhibitors of EMT or other diseases and functions.
An Activity Plot for an upstream regulator is shown below. In the plot, 49 analyses for which SMAD4 is predicted to be activated as an upstream regulator are highlighted in green.
You can also run a quick computation to evaluate whether any particular metadata values are significantly enriched in the selected analyses compared to all of the unselected analyses. Notably, TGF, TGF beta and TGF beta1 (all synonyms of TGF-) were identified as metadata terms significantly enriched in the analyses that activate SMAD4. In each case, the cells or tissue had been exposed to TGF-, and, in each case, SMAD4 was predicted to be activated. This result confirms published research that has identified SMAD4 as a “central mediator” of TGF- signaling (PMID: 29483830)
To enable deeper exploration across ‘omics data for individual genes, expression correlation across genes and visualization of the expression details of Analysis Match datasets, this release brings a large expansion of the integration between QIAGEN IPA and QIAGEN Land Explorer. Now you can seamlessly jump from IPA into more granular sample- and gene-level details in Land Explorer, the web-based portal to OmicSoft’s massive Lands databases of curated disease ‘omics data (>500,000 samples). With this capability, you can navigate from a gene of interest in IPA to quickly discover its tissue or cell expression, the diseases and treatments that cause it to be up-or-down-regulated, the cancers in which it is frequently mutated, the effect of mutations on patient survival and much more.
You can easily answer questions, such as “Is ALAS2 expressed in a certain type of blood cell or cell line? In which types of viral infection is IRF7 upregulated? Is the expression of IRF7 and CXCL10 correlated, and, if so, in what tissues, cell types or disease conditions? In which cancer types is SMAD4 most often mutated, and how does that affect patient survival?”
Links to help you easily answer these questions have been added to IPA Gene Views, connecting you directly to the relevant visualization in Land Explorer. Note that accessing these links requires a Land Explorer license. However, as part of this IPA release you are automatically enrolled in a free 30-day trial of Land Explorer starting March 29th 2020.
Each OmicSoft link in a Gene View leads to a particular data visualization in Land Explorer. For example, the figure below shows the default Land Explorer view when clicking the HumanDisease link in the OmicSoft Differential Expression section for IRF7. Each dot in the visualization represents an analysis corresponding to an Analysis Match dataset, and the dot’s size and position correspond to the statistical significance of IRF7 and fold change in the analysis. Note that many of these comparisons involve treatments and other perturbations.
You can easily limit the results to datasets that are relevant to you by using filters to reveal that IRF7 has been observed to be up-regulated in several types of human infections, in particular, Dengue hemorrhagic fever and influenza.
Another option is to examine the expression of IRF7 in various hematopoietic cells by clicking the BluePrint link from the Gene View. IRF7 is expressed most abundantly in neutrophils.
You can also create visualizations in Land Explorer, such as survival plots, and gene–gene correlation plot, such as the correlation plot for IRF7 and CSF3 shown below. The two genes are clearly expressed in a similar manner; for example, they are both highly expressed in macrophages. In contrast, IRF7 is uniquely expressed in certain memory effector T cells, and CSF3 is present in a particular pancreatic cancer cell line that does not express IRF7.
The figure below is a survival plot for SMAD4 indicating that mutations in this gene reduce the duration of patient survival for pancreatic adenocarcinoma.
Land Explorer offers many more visualizations linked from IPA than can be shown here. Please visit this page for more information: https://digitalinsights.qiagen.com/products-overview/discovery-insights-portfolio/content-exploration-and-databases/qiagen-omicsoft-land-explorer/
Finally, IPA users with both Analysis Match and Land Explorer licenses can navigate to a volcano plot in Land Explorer for each underlying Analysis Match dataset with just one click from Project Search results or from the Analysis Match tab.
Now you can explore fusion-gene biology in cancer by adding known fusion genes to your networks and pathways. There are approximately 500 fusion genes available in IPA today, with Gene Views and interactive nodes that can participate in networks and pathways.
The image below displays a small network created by adding the BCR-ABL1 fusion gene to a blank pathway. A subset of other molecules, pathways and diseases were added to extend the network upstream and downstream of the fusion gene, and the Molecule Activity Predictor or MAP tool was used to simulate adding the drug imatinib to the system. The same types of relationships used for “standard” genes are used for fusion genes, as shown in the figure.
Fusion genes have an associated Gene View, such as the one for NPM1-ALK:
Several Canonical Pathways contain fusion genes; for example, Chronic Myeloid Leukemia Signaling is shown below.
Five new Canonical Signaling Pathways
Addition of Activity Patterns to 10 existing Canonical Signaling Pathways