Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Original Article
  • Published:

ACTuDB, a new database for the integrated analysis of array-CGH and clinical data for tumors

Oncogene volume 26, pages 6641–6652 (2007)Cite this article

Abstract

In recent years, an increasing number of projects have investigated tumor genome structure, using microarray-based techniques like array comparative genomic hybridization (array-CGH) or single nucleotide polymorphism (SNP) arrays. The forthcoming studies have to integrate these former results and compare their findings to the existing sets of copy number data for validation. These sets also form the basis from which many comparative retrospective analyses can be carried out. Nevertheless, exploitation of this mass of data relies on a homogeneous preparation of copy number data, which will make it possible to compare them together, and their integration into a unified bioinformatics environment with ad hoc analysis tools and interfaces. To our knowledge, no such data integration has been proposed yet. Therefore the biologists and clinicians involved in cancer research urgently need such an integrative tool, which motivated us to undertake the construction of a database for array-CGH and other DNA copy number data for tumors (ACTuDB). When available, the associated clinical, transcriptome and loss of heterozygosity data were also integrated into ACTuDB. ACTuDB contains currently about 1500 genomic profiles for tumors and cell lines for the bladder, brain, breast, colon, liver, lymphoma, neuroblastoma, mouth and pancreas, together with data for replication timing experiments. The CGH array data were processed, using ad hoc algorithms (probe mapping, breakpoint detection, gain or loss status assignment and visualization) developed at Institut Curie. The database is available from http://bioinfo.curie.fr/actudb/ and can be browsed with a user-friendly interface. This database will be a useful resource for the genomic profiling of tumors, a field of highly active research. We invite research groups involved in tumor genome profiling to submit their data to ACTuDB.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8

Similar content being viewed by others

References

  • Albertson DG, Collins C, McCormick F, Gray JW . (2003). Chromosome aberrations in solid tumors. Nat Genet 34: 369–376.

    Article  CAS  Google Scholar 

  • Baudis M . (2006). Online database and bioinformatics toolbox to support data mining in cancer cytogenetics. BioTechniques 40: 269–271.

    Article  CAS  Google Scholar 

  • Blaveri E, Brewer JL, Roydasgupta R, Fridlyand J, DeVries S, Koppie T et al. (2005). Bladder cancer stage and outcome by array-based comparative genomic hybridization. Clin Cancer Res 11: 7012–7022.

    Article  CAS  Google Scholar 

  • Bredel M, Bredel C, Juric D, Harsh GR, Vogel H, Recht LD et al. (2005). High-resolution genome-wide mapping of genetic alterations in human glial brain tumors. Cancer Res 65: 4088–4096.

    Article  CAS  Google Scholar 

  • Calin GA, Croce CM . (2006). MicroRNA-cancer connection: the beginning of a new tale. Cancer Res 66: 7390–7394.

    Article  CAS  Google Scholar 

  • Chen X, Cheung ST, So S, Fan ST, Barry C, Higgins J et al. (2002). Gene expression patterns in human liver cancers. Mol Biol Cell 13: 1929–1939.

    Article  CAS  Google Scholar 

  • de Leeuw RJ, Davies JJ, Rosenwald A, Bebb G, Gascoyne RD, Dyer MJS et al. (2004). Comprehensive whole genome array CGH profiling of mantle cell lymphoma model genomes. Hum Mol Genet 13: 1827–1837.

    Article  CAS  Google Scholar 

  • Douglas EJ, Fiegler H, Rowan A, Halford S, Bicknell DC, Bodmer W et al. (2004). Array comparative genomic hybridization analysis of colorectal cancer cell lines and primary carcinomas. Cancer Res 64: 4817–4825.

    Article  CAS  Google Scholar 

  • Fridlyand J, Snijders AM, Ylstra B, Li H, Olshen A, Segraves R et al. (2006). Breast tumor copy number aberration phenotypes and genomic instability. BMC Cancer 6: 96.

    Article  Google Scholar 

  • Griffiths-Jones S . (2006). miRBase: the microRNA sequence database. Methods Mol Biol 342: 129–138.

    CAS  Google Scholar 

  • Gysin S, Rickert P, Kastury K, McMahon M . (2005). Analysis of genomic DNA alterations and mRNA expression patterns in a panel of human pancreatic cancer cell lines. Genes Chromosomes Cancer 44: 37–51.

    Article  CAS  Google Scholar 

  • Hupé P, Stransky N, Thiery JP, Radvanyi F, Barillot E . (2004). Analysis of array CGH data: from signal ratio to gain and loss of DNA regions. Bioinformatics 20: 3413–3422.

    Article  Google Scholar 

  • Hurst CD, Fiegler H, Carr P, Williams S, Carter NP, Knowles MA . (2004). High-resolution analysis of genomic copy number alterations in bladder cancer by microarray-based comparative genomic hybridization. Oncogene 23: 2250–2263.

    Article  CAS  Google Scholar 

  • Iafrate AJ, Feuk L, Rivera MN, Listewnik ML, Donahoe PK, Qi Y et al. (2004). Detection of large-scale variation in the human genome. Nat Genet 36: 949–951.

    Article  CAS  Google Scholar 

  • Janoueix-Lerosey I, Hupé P, Maciorowski Z, La Rosa P, Schleiermacher G et al. (2005). Preferential occurrence of chromosome breakpoints within early replicating regions in neuroblastoma. Cell Cycle 4: 1842–1846.

    Article  CAS  Google Scholar 

  • Karolchik D, Baertsch R, Diekhans M, Furey TS, Hinrichs A, Lu YT et al. (2003). The UCSC genome browser database. Nucleic Acids Res 31: 51–54.

    Article  CAS  Google Scholar 

  • Kent WJ . (2002). BLAT – The BLAST-like alignment tool. Genome Res 12: 656–664.

    Article  CAS  Google Scholar 

  • Kotliarov Y, Steed ME, Christopher N, Walling J, Su Q, Center A et al. (2006). High-resolution global genomic survey of 178 gliomas reveals novel regions of copy number alteration and allelic imbalances. Cancer Res 66: 9428–9436.

    Article  CAS  Google Scholar 

  • La Rosa P, Viara E, Hupé P, Pierron G, Liva S, Neuvial P et al. (2006). VAMP: Visualization and analysis of array-CGH, transcriptome and other molecular profiles. Bioinformatics 22: 2066–2073.

    Article  CAS  Google Scholar 

  • Lee RC, Feinbaum RL, Ambros V . (1993). The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14. Cell 75: 843–854.

    Article  CAS  Google Scholar 

  • Lu J, Getz G, Miska EA, Alvarez-Saavedra E, Lamb J, Peck D et al. (2005). MicroRNA expression profiles classify human cancers. Nature 435: 834–838.

    Article  CAS  Google Scholar 

  • Mosse YP, Greshock J, Margolin A, Naylor T, Cole K, Khazi D et al. (2005). High-resolution detection and mapping of genomic DNA alterations in neuroblastoma. Genes Chromosomes Cancer 43: 390–403.

    Article  CAS  Google Scholar 

  • Nakao K, Mehta KR, Fridlyand J, Moore DH, Jain AN, Lafuente A et al. (2004). High-resolution analysis of DNA copy number alterations in colorectal cancer by array-based comparative genomic hybridization. Carcinogenesis 25: 1345–1357.

    Article  CAS  Google Scholar 

  • Patil MA, Gutgemann I, Zhang J, Ho C, Cheung S-T, Ginzinger D et al. (2005). Array-based comparative genomic hybridization reveals recurrent chromosomal aberrations and Jab1 as a potential target for 8q gain in hepatocellular carcinoma. Carcinogenesis 26: 2050–2057.

    Article  CAS  Google Scholar 

  • Pinkel D, Albertson DG . (2005). Array comparative genomic hybridization and its applications in cancer. Nat Genet 37(Suppl): 11–17.

    Article  Google Scholar 

  • Pollack JR, Sorlie T, Perou CM, Rees CA, Jeffrey SS, Lonning PE et al. (2002). Microarray analysis reveals a major direct role of DNA copy number alteration in the transcriptional program of human breast tumors. Proc Natl Acad Sci USA 99: 12963–12968.

    Article  CAS  Google Scholar 

  • Rouveirol C, Stransky N, Hupé P, La Rosa P, Viara E, Barillot E et al. (2006). Computation of recurrent minimal genomic alterations from CGH data. Bioinformatics 22: 849–856.

    Article  CAS  Google Scholar 

  • Snijders AM, Nowak N, Segraves R, Blackwood S, Brown N, Conroy J et al. (2001). Assembly of microarrays for genome-wide measurement of DNA copy number. Nat Genet 29: 263–264.

    Article  CAS  Google Scholar 

  • Snijders AM, Schmidt BL, Fridlyand J, Dekker N, Pinkel D, Jordan RCK et al. (2005). Rare amplicons implicate frequent deregulation of cell fate specification pathways in oral squamous cell carcinoma. Oncogene 24: 4232–4242.

    Article  CAS  Google Scholar 

  • Stransky N, Vallot C, Reyal F, Bernard-Pierrot I, de Medina SGD, Segraves R et al. (2006). Regional copy number-independent deregulation of transcription in cancer. Nat Genet 38: 1386–1396.

    Article  CAS  Google Scholar 

  • Veltman JA, Fridlyand J, Pejavar S, Olshen AB, Korkola JE, DeVries S et al. (2003). Array-based comparative genomic hybridization for genome-wide screening of DNA copy number in bladder tumors. Cancer Res 63: 2872–2880.

    CAS  Google Scholar 

  • Woodfine K, Fiegler H, Beare DM, Collins JE, McCann OT, Young BD et al. (2004). Replication timing of the human genome. Hum Mol Genet 13: 191–202.

    Article  CAS  Google Scholar 

  • Ylstra B, van den Ijssel P, Carvalho B, Brakenhoff RH, Meijer GA . (2006). BAC to the future! Or oligonucleotides: a perspective for micro array comparative genomic hybridization (array CGH). Nucleic Acids Res 34: 445–450.

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We thank Olivier Delattre and Alain Aurias (Institut Curie, INSERM U509), who made information from the Institut Curie clone database available within ACTuDB. We thank our colleagues at Institut Curie for their help in setting up ACTuDB: Stéphane Tsacas, Jean-Gabriel Dick and Fraņcois-David Collin (Institut Curie) for system, network and database administration. This work was supported partly by the EC contract ESBIC-D (LSHG-CT-2005-518192).

Author information

Author notes

  1. P Hupé and P La Rosa: These authors contributed equally to this work.

Authors and Affiliations

  1. Institut Curie, Service Bioinformatique, Paris, France

    P Hupé, P La Rosa, S Liva, S Lair, N Servant & E Barillot

  2. Institut Curie, CNRS UMR 144, Paris, France

    P Hupé

Authors
  1. P Hupé
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. P La Rosa
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. S Liva
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. S Lair
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. N Servant
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. E Barillot
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to P Hupé.

Additional information

Supplementary Information accompanies the paper on the Oncogene website (http://www.nature.com/onc).

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Cite this article

Hupé, P., La Rosa, P., Liva, S. et al. ACTuDB, a new database for the integrated analysis of array-CGH and clinical data for tumors. Oncogene 26, 6641–6652 (2007). https://doi.org/10.1038/sj.onc.1210488

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/sj.onc.1210488

Keywords

This article is cited by

Search

Advanced search

Quick links