One challenge facing biologists is to tease out useful information from massive data sets for further analysis. A pathway-based analysis may shed light by projecting candidate genes onto protein ...functional relationship networks. We are building such a pathway-based analysis system.
We have constructed a protein functional interaction network by extending curated pathways with non-curated sources of information, including protein-protein interactions, gene coexpression, protein domain interaction, Gene Ontology (GO) annotations and text-mined protein interactions, which cover close to 50% of the human proteome. By applying this network to two glioblastoma multiforme (GBM) data sets and projecting cancer candidate genes onto the network, we found that the majority of GBM candidate genes form a cluster and are closer than expected by chance, and the majority of GBM samples have sequence-altered genes in two network modules, one mainly comprising genes whose products are localized in the cytoplasm and plasma membrane, and another comprising gene products in the nucleus. Both modules are highly enriched in known oncogenes, tumor suppressors and genes involved in signal transduction. Similar network patterns were also found in breast, colorectal and pancreatic cancers.
We have built a highly reliable functional interaction network upon expert-curated pathways and applied this network to the analysis of two genome-wide GBM and several other cancer data sets. The network patterns revealed from our results suggest common mechanisms in the cancer biology. Our system should provide a foundation for a network or pathway-based analysis platform for cancer and other diseases.
Abstract
The Reactome Knowledgebase (https://reactome.org) provides molecular details of signal transduction, transport, DNA replication, metabolism and other cellular processes as an ordered network ...of molecular transformations in a single consistent data model, an extended version of a classic metabolic map. Reactome functions both as an archive of biological processes and as a tool for discovering functional relationships in data such as gene expression profiles or somatic mutation catalogs from tumor cells. To extend our ability to annotate human disease processes, we have implemented a new drug class and have used it initially to annotate drugs relevant to cardiovascular disease. Our annotation model depends on external domain experts to identify new areas for annotation and to review new content. New web pages facilitate recruitment of community experts and allow those who have contributed to Reactome to identify their contributions and link them to their ORCID records. To improve visualization of our content, we have implemented a new tool to automatically lay out the components of individual reactions with multiple options for downloading the reaction diagrams and associated data, and a new display of our event hierarchy that will facilitate visual interpretation of pathway analysis results.
Abstract
The Reactome Knowledgebase (https://reactome.org), an Elixir core resource, provides manually curated molecular details across a broad range of physiological and pathological biological ...processes in humans, including both hereditary and acquired disease processes. The processes are annotated as an ordered network of molecular transformations in a single consistent data model. Reactome thus functions both as a digital archive of manually curated human biological processes and as a tool for discovering functional relationships in data such as gene expression profiles or somatic mutation catalogs from tumor cells. Recent curation work has expanded our annotations of normal and disease-associated signaling processes and of the drugs that target them, in particular infections caused by the SARS-CoV-1 and SARS-CoV-2 coronaviruses and the host response to infection. New tools support better simultaneous analysis of high-throughput data from multiple sources and the placement of understudied (‘dark’) proteins from analyzed datasets in the context of Reactome’s manually curated pathways.
High-throughput experiments are routinely performed in modern biological studies. However, extracting meaningful results from massive experimental data sets is a challenging task for biologists. ...Projecting data onto pathway and network contexts is a powerful way to unravel patterns embedded in seemingly scattered large data sets and assist knowledge discovery related to cancer and other complex diseases. We have developed a Cytoscape app called "ReactomeFIViz", which utilizes a highly reliable gene functional interaction network combined with human curated pathways derived from Reactome and other pathway databases. This app provides a suite of features to assist biologists in performing pathway- and network-based data analysis in a biologically intuitive and user-friendly way. Biologists can use this app to uncover network and pathway patterns related to their studies, search for gene signatures from gene expression data sets, reveal pathways significantly enriched by genes in a list, and integrate multiple genomic data types into a pathway context using probabilistic graphical models. We believe our app will give researchers substantial power to analyze intrinsically noisy high-throughput experimental data to find biologically relevant information.
Reactome is a free, open-source, open-data, curated and peer-reviewed knowledgebase of biomolecular pathways. One of its main priorities is to provide easy and efficient access to its high quality ...curated data. At present, biological pathway databases typically store their contents in relational databases. This limits access efficiency because there are performance issues associated with queries traversing highly interconnected data. The same data in a graph database can be queried more efficiently. Here we present the rationale behind the adoption of a graph database (Neo4j) as well as the new ContentService (REST API) that provides access to these data. The Neo4j graph database and its query language, Cypher, provide efficient access to the complex Reactome data model, facilitating easy traversal and knowledge discovery. The adoption of this technology greatly improved query efficiency, reducing the average query time by 93%. The web service built on top of the graph database provides programmatic access to Reactome data by object oriented queries, but also supports more complex queries that take advantage of the new underlying graph-based data storage. By adopting graph database technology we are providing a high performance pathway data resource to the community. The Reactome graph database use case shows the power of NoSQL database engines for complex biological data types.
Discovering robust prognostic gene signatures as biomarkers using genomics data can be challenging. We have developed a simple but efficient method for discovering prognostic biomarkers in cancer ...gene expression data sets using modules derived from a highly reliable gene functional interaction network. When applied to breast cancer, we discover a novel 31-gene signature associated with patient survival. The signature replicates across 5 independent gene expression studies, and outperforms 48 published gene signatures. When applied to ovarian cancer, the algorithm identifies a 75-gene signature associated with patient survival. A Cytoscape plugin implementation of the signature discovery method is available at http://wiki.reactome.org/index.php/Reactome_FI_Cytoscape_Plugin
We screened 124 genes that are amplified in human hepatocellular carcinoma (HCC) using a mouse hepatoblast model and identified 18 tumor-promoting genes, including
CCND1 and its neighbor on 11q13.3,
...FGF19. Although it is widely assumed that
CCND1 is the main driving oncogene of this common amplicon (15% frequency in HCC), both forward-transformation assays and RNAi-mediated inhibition in human HCC cells established that
FGF19 is an equally important driver gene in HCC. Furthermore, clonal growth and tumorigenicity of HCC cells harboring the 11q13.3 amplicon were selectively inhibited by RNAi-mediated knockdown of
CCND1 or
FGF19, as well as by an anti-FGF19 antibody. These results show that 11q13.3 amplification could be an effective biomarker for patients most likely to respond to anti-FGF19 therapy.
► An oncogenomic-based screen identifies 18 amplified tumor-promoting genes in HCC ► In HCC,
FGF19 is a codriver gene, along with
CCND1, of the 11q13.3 amplicon ► Amplification of 11q13.3 in human HCC cells predicts response to anti-FGF19 treatment
Reactome describes biological pathways as chemical reactions that closely mirror the actual physical interactions that occur in the cell. Recent extensions of our data model accommodate the ...annotation of cancer and other disease processes. First, we have extended our class of protein modifications to accommodate annotation of changes in amino acid sequence and the formation of fusion proteins to describe the proteins involved in disease processes. Second, we have added a disease attribute to reaction, pathway, and physical entity classes that uses disease ontology terms. To support the graphical representation of "cancer" pathways, we have adapted our Pathway Browser to display disease variants and events in a way that allows comparison with the wild type pathway, and shows connections between perturbations in cancer and other biological pathways. The curation of pathways associated with cancer, coupled with our efforts to create other disease-specific pathways, will interoperate with our existing pathway and network analysis tools. Using the Epidermal Growth Factor Receptor (EGFR) signaling pathway as an example, we show how Reactome annotates and presents the altered biological behavior of EGFR variants due to their altered kinase and ligand-binding properties, and the mode of action and specificity of anti-cancer therapeutics.
Different human responses to the same vaccine were frequently observed. For example, independent studies identified overlapping but different transcriptomic gene expression profiles in Yellow Fever ...vaccine 17D (YF-17D) immunized human subjects. Different experimental and analysis conditions were likely contributed to the observed differences. To investigate this issue, we developed a Vaccine Investigation Ontology (VIO), and applied VIO to classify the different variables and relations among these variables systematically. We then evaluated whether the ontological VIO modeling and VIO-based statistical analysis would contribute to the enhanced vaccine investigation studies and a better understanding of vaccine response mechanisms.
Our VIO modeling identified many variables related to data processing and analysis such as normalization method, cut-off criteria, software settings including software version. The datasets from two previous studies on human responses to YF-17D vaccine, reported by Gaucher et al. (2008) and Querec et al. (2009), were re-analyzed. We first applied the same LIMMA statistical method to re-analyze the Gaucher data set and identified a big difference in terms of significantly differentiated gene lists compared to the original study. The different results were likely due to the LIMMA version and software package differences. Our second study re-analyzed both Gaucher and Querec data sets but with the same data processing and analysis pipeline. Significant differences in differential gene lists were also identified. In both studies, we found that Gene Ontology (GO) enrichment results had more overlapping than the gene lists and enriched pathway lists. The visualization of the identified GO hierarchical structures among the enriched GO terms and their associated ancestor terms using GOfox allowed us to find more associations among enriched but often different GO terms, demonstrating the usage of GO hierarchical relations enhance data analysis.
The ontology-based analysis framework supports standardized representation, integration, and analysis of heterogeneous data of host responses to vaccines. Our study also showed that differences in specific variables might explain different results drawn from similar studies.
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