Chelala, C

Crnogorac-Jurcevic, T

Cutts, RJ

Gadaleta, E

Hahn, SA

Lemoine, NR

Nucleic Acids Research

Queen Mary Research Online

The Pancreatic Expression database: 2011 updateCutts, RJ; Gadaleta, E; Hahn, SA; Crnogorac-Jurcevic, T; Lemoine, NR; Chelala, C     © 2010 The Author(s)CC-BY-NC  For additional information about this publication click this link.http://qmro.qmul.ac.uk/xmlui/handle/123456789/18435   Information about this research object was correct at the time of download; we occasionallymake corrections to records, please therefore check the published record when citing. Formore information contact scholarlycommunications@qmul.ac.ukThe Pancreatic Expression database: 2011 updateRosalind J. Cutts1, Emanuela Gadaleta1, Stephan A. Hahn2, Tatjana Crnogorac-Jurcevic1, Nicholas R. Lemoine1 and Claude Chelala1,*1Centre for Molecular Oncology and Imaging, Institute of Cancer and CR-UK Centre, Barts and The LondonSchool of Medicine and Dentistry, Queen Mary University of London, Charterhouse Square, London EC1M6BQ, UK and 2Molecular GI-Onkologie (MGO), University of Bochum, 44780 Bochum, GermanyReceived August 9, 2010; Revised September 27, 2010; Accepted September 30, 2010ABSTRACTThe Pancreatic Expression database (PED, http://www.pancreasexpression.org) has establisheditself as the main repository for pancreatic-derived-omics data. For the past 3 years, its data contentand access have increased substantially. Here wedescribe several of its new and improved features,such as data content, which now includes over60 000 measurements derived from transcriptomics,proteomics, genomics and miRNA profiles fromvarious pancreas-centred reports on a broad rangeof specimen and experimental types. We also illus-trate the capabilities of its interface, which allowsintegrative queries that can combine PED datawith a growing number of biological resourcessuch as NCBI, Ensembl, UniProt and Reactome.Thus, PED is capable of retrieving and integratingdifferent types of -omics, annotations and clinicaldata. We also focus on the importance of datasharing and interoperability in the cancer field, andthe integration of PED into the International CancerGenome Consortium (ICGC) data portal.INTRODUCTIONPancreatic cancer is the fourth leading cause ofcancer-related death world-wide (1) with surgical interven-tion and radiotherapy having a minimal impact on 5-yearsurvival rates. As a result, patient survival rates haveremained relatively unchanged over the past 30 years.Because of the poor prognosis associated with pancreaticcancer, a multitude of studies have been dedicated toelucidating the pathogenesis of this malignancy (2).Augmented by advances in high-throughput technologies,this has resulted in a plethora of -omics data. Despite themagnitude of information available, the heterogeneity andisolation of public datasets prevents researchers fromeffectively mining, extracting and integrating relevantdata into their current research.The publicly available Pancreatic Expression database(PED) was developed to overcome these obstacles byenabling complex pancreatic datasets to be manipulated,mined and integrated with ease (3). Since its inception in2007, PED has undergone extensive improvements. Therange of clinical and -omics data types available forqueries has broadened substantially, facilitating the sys-tematic study of pancreatic cancer.Unlike many cancer databases specialised in providingsingle-type information, PED stores four different kindsof -omics data: transcriptomics, proteomics, miRNA andgenomics. These proﬁles are derived from a broad range ofspecimens from tissues and body ﬂuids of healthy peopleor patients, cell lines and mouse models as well as differenttreatments and drugs. This is key to providing a compre-hensive overview of the molecular changes in cancer.Another important feature of PED is that it allows forits data to be interrogated from major gene repositoriessuch as NCBI EntrezGene (4) and Ensembl GeneView (5),third-party software such as R statistical environment (6)and Cytoscape (7) or jointly with data from majorbiological resources such as the Reactome Pathwayproject (8), PRIDE (9) and UniProt (10). Increased func-tionality also allows for greater interoperability with inter-national cancer efforts, such as the International CancerGenome Consortium (ICGC, http://www.icgc.org), amajor international collaboration designed to identifythe key genetic mutations involved in up to 50 types ofcancer, which will enable the development of new andbetter ways of diagnosing, treating and preventingcancer (11).To the best of our knowledge, there are neither toolsnor databases that provide the same informationfor cancer research as our platform. By allowing for inte-gration and mining of published pancreatic cancerdata in the context of a wide range of annotations, PEDoffers more options than raw data repositories such as*To whom correspondence should be addressed. Tel: +44 20 7014 0474; Fax: +44 20 7014 0431; Email: c.chelala@qmul.ac.ukPublished online 18 October 2010 Nucleic Acids Research, 2011, Vol. 39, Database issue D1023–D1028doi:10.1093/nar/gkq937 The Author(s) 2010. Published by Oxford University Press.This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.Figure 1. Integrated view of genomic and transcriptomic changes in pancreatic cancer. This ﬁgure integrates the results from both genome-wideDNA copy number and genome-wide gene expression proﬁling. In a few seconds, this allows for visual inspection of copy number-driven expressionchanges. Here the query is to ﬁnd genes differentially up-regulated in PDAC versus normal using microdissected ductal cells (A) and then combinethis information with data on genes that are also associated with genomic variations in PDAC samples by combining with results for copy numberchanges on high level ampliﬁcations (B). Pick attributes for display (C). A summary of the ﬁrst 20 results is shown in (D).D1024 Nucleic Acids Research, 2011, Vol. 39, Database issueGene Expression Omnibus (GEO) (12) or ArrayExpress(13). One of the main strengths of our system is thepossibility of setting many ﬁlters to ask very speciﬁcpancreatic cancer-related questions and obtain a focusedannotated data output. Here we outline details of theimproved data content, query interfaces andinteroperability.IMPROVED DATA CONTENTThe database contains 56 015 differential or expressionmeasurements and 6363 DNA copy number alterations.These data values are extracted from over 59 publishedstudies and include proﬁles from a large number ofspecimen types, such as pancreatic tissues and variousbody ﬂuids obtained from healthy subjects and patientswith benign, pre-malignant and malignant diseases. Inaddition, studies using pancreatic cancer cell lines andmurine models have also been incorporated(Supplementary Table S1). Where applicable, informationon the different treatment conditions applied is providedto users.The collected samples were proﬁled on a wide rangeof transcriptomics, proteomics, miRNAs and gen-omics platforms (Supplementary Table S2). To date,PED describes pancreatic-related regulation events in8229 genes/proteins, 27 327 transcripts and 279 miRNAas well as 2771 gains, 1073 losses, 347 homozygousdeletions, 1297 high-level ampliﬁcations and 875 lossof heterozygosity events occurring in distinct genomicareas.The data collection pipeline from the original data-base was enhanced to cover not only transcriptomicsbut also genomics, proteomics and miRNA information.Data from the primary literature were manually curated,reviewed for accuracy and consistency, and loadedinto a relational database. The Ensembl annotationsoriginally used for mapping probe identiﬁers and genenames to standard values (version 48) have beenupdated to a more current version (version 56). Thisensures that our database remains up-to-date withongoing improvements to annotation and microarrayprobe set mappings and helps avoid data integrityerrors. The data collection process was expanded to en-compass new data types such as DNA copy numberchanges. Here, chromosomal coordinates for each copynumber variation identiﬁed from papers were mapped togenome release GRCh37 with conversions betweengenome versions carried out using the liftover tool fromUCSC (14).We imported the available Ensembl human gene anno-tations (5) for genes, proteins, SNP information, se-quences, gene structure and multi-species data, enablingthe integration and annotation of heterogeneous pancre-atic data (Supplementary Table S3).Figure 2. Cross-linking pathways information from reactome. Results from the previous query (shown in Figure 1) can be further mined and mergedwith data from Reactome. Here we select Reactome pathways as a second dataset to combine in queries and then restrict obtained information to‘Homo sapiens’ data (A). Pick attributes and display a summary of the ﬁrst 20 results (B). Query results return pathway name and stable ID with ahyper-link to the Reactome website allowing to instantly extract data (C).Nucleic Acids Research, 2011, Vol. 39, Database issue D1025IMPROVED QUERY CAPABILITIESWhile the original PED system included tools for queryingacross different tumour stages of pancreatic cancer anddifferent pancreatic disease types in a simple integratedway (3), the interface has been greatly improved toprovide the user access to the expanded data content. Byincluding new platforms, comparisons and data types, it ispossible to combine information and, therefore, toperform more complex queries than by viewing expressiondata alone. For example, it is possible to highlight theimpact of copy number aberrations on gene expressionpatterns to ﬁlter out genes whose expression levels arenot consistent with their DNA copy number status andpoint to the subset of candidate oncogenes or tumour sup-pressor genes showing copy number-driven expressionchanges (Figure 1). There are now options available forproteomics, transcriptomics and miRNA proﬁling,allowing these data types to be queried in isolation orcombined to look for genes consistently identiﬁed acrossdifferent data technologies or to extract speciﬁc data suchas microRNAs deregulated in the different stages of pan-creatic cancer.The new interface incorporates the full functionalityand data from Ensembl Mart 56 including richer datacontent and more advanced querying capabilities andalso makes better use of the BioMart interface capabilities(15) for cross-linking different datasets that have datatypes in common; for example by combining PED datawith Reactome pathway data of human reactions andpathways (8) (Figure 2). This integration allows research-ers to quickly identify both pancreatic cancer genes andthe pathways they control.IMPROVED DATA ACCESSPED is freely accessible through a BioMart web-based query interface at http://www.pancreasexpression.org. PED is also a DAS server (16) providing DASannotations for the wider community, so it can beused in other resources or browsers such as EnsemblGeneView using the GeneDAS protocol. Access is alsopossible via web services through third party softwaretools that have been made compatible with BioMartresources such as Bioconductor (http://www.bioconductor.org) (6,17), Galaxy (18) and Cytoscape (7).Interoperability with the ICGC is also possible throughthis web services layer and PED is now availablethrough the ICGC data portal (http://dcc.icgc.org)(Figure 3) allowing researchers to conduct combinedqueries on ICGC experimental data alongside PED litera-ture data. This further increases the ways in which thedatabase can be accessed and its exposure to a variety ofdisciplines and interests in the scientiﬁc community. Thedatabase is also accessible as a Linkout resource fromNCBI EntrezGene (4). This allows EntrezGene usersto be alerted to pancreatic expression data by thepresence of a data link for relevant genes that are in thedatabase.DISCUSSIONWe have described how PED has evolved from its originalrole as a repository for cancer transcriptomics data into acomprehensive resource capable of providing a quickoverview of molecular changes at the transcriptome,proteome, genome and/or miRNA level. Consequently,there has been a huge growth in its -omics, specimen/clinical and annotation data content.We believe that interoperability is a key factor in theutility and productive use of any current and future cancerdatabases. This is essential to ensure the sustainability ofany cancer database and facilitate its integration withmajor international efforts in cancer research such as theICGC. This will also allow the design and implementationof more sophisticated analysis portals. The cancerresearch community needs open source, fully interoper-able resources allowing information connectivity anddata sharing. Only these types of resource can ensurethat cancer data generated across different organisationsare shared, thereby maximising the impact of cancerresearch. By using the BioMart technology for its datamanagement system, PED is fully interoperable with theICGC. This ensures that PED is integrated with TheCancer Genome Atlas (TCGA, http://cgap.nci.nih.gov)data available through the ICGC data portal. TheBioMart web service layer also allows PED to beintegrated with several other data sources that also usethe BioMart technology such as Reactome, PRIDE,UniProt and Ensembl. Moreover, PED is a Linkoutresource integrated with NCBI.Our database ﬁlls the urgent requirement of the pancre-atic cancer community for resources capable of integratingthe overﬂowing inﬂux of data generated by novelhigh-throughput technologies.The architectural ﬂexibility of PED is easily extendableto other disease types, with this model being used to createa similar resource for malignant (breast cancer) andnon-malignant (neurodegenerative) diseases. Reuse of asimilar database design will facilitate complex querycapabilities across multiple diseases and data types.SUPPLEMENTARY DATASupplementary Data are available at NAR Online.ACKNOWLEDGEMENTSThe authors thank our colleagues who have suggested orcontributed data for current or previous versions of thedatabase. They also would like to thank the swift andhelpful advice from the ICGC DCC and BioMart team.FUNDINGCancer Research UK (programme grant C355/A6253)and FW6 EU project MolDiag-Paca. R.C. is funded byBreast Cancer Campaign. Funding for open access charge:Cancer Research UK.Conﬂict of interest statement. None declared.D1026 Nucleic Acids Research, 2011, Vol. 39, Database issueFigure 3. Interoperability with the ICGC experimental data. Access available from http://dcc.icgc.org. The ﬁgure shows the ICGC data portal reportfor the TP53 gene including gene information; ICGC experimental sequencing results obtained from the participating centres; and PED data. In thePED Report section, a heatmap represents, visually, the level of de-regulation as extracted from the original publication and stored in PED. If youhold your mouse over the coloured box, it will display the fold change value (when available).Nucleic Acids Research, 2011, Vol. 39, Database issue D1027REFERENCES1. Hariharan,D., Saied,A. and Kocher,H.M. (2008) Analysis ofmortality rates for pancreatic cancer across the world. HPB, 10,58–62.2. Goonetilleke,K.S. and Siriwardena,A.K. (2008) Current status ofgene expression proﬁling of pancreatic cancer. 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The Pancreatic Expression database: 2011 update

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The Pancreatic Expression database: 2011 update

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