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. 2015 Jul 20;4(7):e160.
doi: 10.1038/oncsis.2015.19.

Atlas of Cancer Signalling Network: A Systems Biology Resource for Integrative Analysis of Cancer Data With Google Maps

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Free PMC article

Atlas of Cancer Signalling Network: A Systems Biology Resource for Integrative Analysis of Cancer Data With Google Maps

I Kuperstein et al. Oncogenesis. .
Free PMC article

Abstract

Cancerogenesis is driven by mutations leading to aberrant functioning of a complex network of molecular interactions and simultaneously affecting multiple cellular functions. Therefore, the successful application of bioinformatics and systems biology methods for analysis of high-throughput data in cancer research heavily depends on availability of global and detailed reconstructions of signalling networks amenable for computational analysis. We present here the Atlas of Cancer Signalling Network (ACSN), an interactive and comprehensive map of molecular mechanisms implicated in cancer. The resource includes tools for map navigation, visualization and analysis of molecular data in the context of signalling network maps. Constructing and updating ACSN involves careful manual curation of molecular biology literature and participation of experts in the corresponding fields. The cancer-oriented content of ACSN is completely original and covers major mechanisms involved in cancer progression, including DNA repair, cell survival, apoptosis, cell cycle, EMT and cell motility. Cell signalling mechanisms are depicted in detail, together creating a seamless 'geographic-like' map of molecular interactions frequently deregulated in cancer. The map is browsable using NaviCell web interface using the Google Maps engine and semantic zooming principle. The associated web-blog provides a forum for commenting and curating the ACSN content. ACSN allows uploading heterogeneous omics data from users on top of the maps for visualization and performing functional analyses. We suggest several scenarios for ACSN application in cancer research, particularly for visualizing high-throughput data, starting from small interfering RNA-based screening results or mutation frequencies to innovative ways of exploring transcriptomes and phosphoproteomes. Integration and analysis of these data in the context of ACSN may help interpret their biological significance and formulate mechanistic hypotheses. ACSN may also support patient stratification, prediction of treatment response and resistance to cancer drugs, as well as design of novel treatment strategies.

Figures

Figure 1
Figure 1
Structure and content of ACSN resource. The scheme demonstrates the concept of ACSN construction starting from the cancer hallmarks: collecting information about molecular mechanisms underlying those hallmarks from scientific publications and manually depicting them in the global map of ACSN and further supporting by consulting the information from the external pathway databases. ACSN is hierarchically organized into three levels: the seamless global map divided into the interconnected biological process maps that are further decomposed into interconnected module maps. ACSN can be exploited through web-based NaviCell interface allowing map navigation using Google Maps engine, map commenting via associated blog system and user omics data visualization and analysis.
Figure 2
Figure 2
ACSN browsing and data analysis features. ACSN interface. (a) The NaviCell-powered Google Maps-based ACSN interface includes map window, selection panel, data analysis panel and upper panel. Such elements as zoom bar, markers and callout window (in panel b) are part of the Google Maps engine. Querying ACSN is possible via search window or by checking on the entity in the list of entities in the selection panel that will drop markers all over the map (for example, MYC). (b) Zoom in at a fragment of ACSN global map. Markers are preserved through all zoom levels. Clicking on a marker opens a callout window containing three sections: ‘Identifiers' with links to external databases; ‘Maps_Modules' with links to ACSN maps and modules where the entity (for example, MYC) is found, ‘References' with links to PubMed, and comments from the map managers. Clicking on the ‘globe' icon opens the corresponding map, clicking on the ‘book' icon opens the blog with corresponding post with detailed information about the entity. (c) Zoom in at a fragment of a module. The zoom of the WNT-non-canonical module, part of the survival map, shows the most detailed level of the molecular mechanism representation.
Figure 3
Figure 3
Visualization of siRNA screen hits increasing cell sensitivity to drugs. Visualization of siRNA screen hits increasing cell sensitivity to (a) Cisplatin and (c) Gemcitabine. Mapping of gene list in ACSN demonstrates coverage across ACSN by the corresponding entities represented by all their molecular modifications as genes, proteins, RNA, complexes and entities with posttranslational modifications. Visualization on the top level zoom shows implication of functional modules. Zooming in to detailed level shows the involved molecular components. (b) Molecular modifications of MOMP functional module in Apoptosis map involved in sensitization to Cisplatin. (d) Molecular modifications of EMT regulators functional module in EMT and cell motility map involved in sensitization for Gemcitabine.
Figure 4
Figure 4
Visualization of ACSN maps coverage by frequently mutated diver genes in breast and lung cancers. Visualization of mutation frequencies in (a) oncogenes and (b) TSGs in breast and lung cancers. The frequency of a gene mutation is estimated as a percentage of samples carrying this mutation over the number of sample tested in each data set from COSMIC database. The size of the glyphs corresponds to the mutation frequency in the studied data set, varying from high (30–5%), medium (5–1%) to low (1–0.5%).
Figure 5
Figure 5
BC gene expression data integration and analysis using ACSN. The mRNA expression data from TCGA collection has been used for evaluation of functional modules activities and ACSN colouring for (a) Basal-like, (b) Her2-positive, (c) Luminal B and (d) Luminal A BC types. The four BC subtypes are characterized by different patterns of module activities. Expression values of individual genes for four subtypes of BC are visualized using heat map in NaviCell web-tool. (e) Expression levels of genes involved in the same complex for four subtypes of BC. (f) Expression levels across member of the gene family for four subtypes of BC. Red colour reflects high expression and green colour shows low expression of the corresponding gene.
Figure 6
Figure 6
Visualization of phosphoproteomic data using ACSN. The data on phosphorylated forms of proteins in lung cancer samples from the PosphoSitePlus database is mapped onto ACSN maps. The intensity of red colour corresponds to the observed frequency of the phosphorylation event among the tumour samples (pale pink—low to dark red—high frequency). Two regions with relatively high density of phosphorylated protein forms are shown in (a) fragment of MAPK module map and (b) fragment of cell–matrix adhesion module.

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