Login to MyKarger

New to MyKarger? Click here to sign up.



Login with Facebook

Forgot your password?

Authors, Editors, Reviewers

For Manuscript Submission, Check or Review Login please go to Submission Websites List.

Submission Websites List

Institutional Login
(Shibboleth or Open Athens)

For the academic login, please select your country in the dropdown list. You will be redirected to verify your credentials.

Copy Number Variations: Technology of Detection and Analysis

Computational methods for identification of recurrent copy number alteration patterns by array CGH

Shah S.P.

Author affiliations

Department of Computer Science, University of British Columbia, and Department of Pathology (UBC), British Columbia Cancer Agency, Vancouver, B.C. (Canada)

Related Articles for ""

Cytogenet Genome Res 123:343–351 (2008)

Do you have an account?

Login Information





Contact Information











I have read the Karger Terms and Conditions and agree.



Login Information





Contact Information











I have read the Karger Terms and Conditions and agree.



To view the fulltext, please log in

To view the pdf, please log in

Buy

  • FullText & PDF
  • Unlimited re-access via MyKarger
  • Unrestricted printing, no saving restrictions for personal use
read more

CHF 38.00 *
EUR 35.00 *
USD 39.00 *

Select

KAB

Buy a Karger Article Bundle (KAB) and profit from a discount!

If you would like to redeem your KAB credit, please log in.


Save over 20% compared to the individual article price.
Learn more

Rent/Cloud

  • Rent for 48h to view
  • Buy Cloud Access for unlimited viewing via different devices
  • Synchronizing in the ReadCube Cloud
  • Printing and saving restrictions apply

Rental: USD 8.50
Cloud: USD 20.00


Select

Subscribe

  • Access to all articles of the subscribed year(s) guaranteed for 5 years
  • Unlimited re-access via Subscriber Login or MyKarger
  • Unrestricted printing, no saving restrictions for personal use
read more

Subcription rates


Select

* The final prices may differ from the prices shown due to specifics of VAT rules.

Article / Publication Details

First-Page Preview
Abstract of Copy Number Variations: Technology of Detection and Analysis

Accepted: August 20, 2008
Published online: March 11, 2009
Issue release date: March 2009

Number of Print Pages: 9
Number of Figures: 3
Number of Tables: 1

ISSN: 1424-8581 (Print)
eISSN: 1424-859X (Online)

For additional information: https://www.karger.com/CGR

Abstract

Recurrent DNA copy number alterations (CNA) are widely studied in diagnostic and cytogenetic cancer research. CNAs reveal locations that may alter gene dosage and thus expression of the genes contained within. Array comparative genomic hybridization has emerged as a popular high-throughput, genome-wide technique to interrogate tumor genomes for copy number alterations. When studying a group of tumors derived from a patient cohort, it is of great interest to detect the copy number alterations that are common across the population and thus assumed to be potential diagnostic markers and/or predictors of clinical outcome. In this paper, we review extant and available computational approaches for detecting such recurrent copy number alterations from array comparative genomic hybridization (aCGH) data. This is a challenging computational problem due to various sources of noise in the data that can obscure the recurrent copy number signals or induce false positives in their prediction. In this paper, we qualitatively evaluate methods designed to detect recurrent copy number alterations for aCGH data based on their analytical strengths and limitations, and discuss expected future directions in this important area of cancer research.

© 2009 S. Karger AG, Basel


References

  1. Aguirre AJ, Brennan C, Bailey G, Sinha R, Feng B, et al: High-resolution characterization of the pancreatic adenocarcinoma genome. Proc Natl Acad Sci USA 101:9067–9072 (2004).
  2. Andersson R, Bruder CE, Piotrowski A, Menzel U, Nord H, et al: A segmental maximum a posteriori approach to genome-wide copy number profiling. Bioinformatics 24:751–758 (2008).
  3. Balmain A, Gray J, Ponder B: The genetics and genomics of cancer. Nat Genet 33:238–244 (2003).
  4. Bejjani BA, Shaffer LG: Application of array-based comparative genomic hybridization to clinical diagnostics. J Mol Diagn 8:528–533 (2006).
  5. Ben-Dor A, Lipson D, Tsalenko A, Reimers M, Baumbusch LO, et al: Framework for identifying common aberrations in DNA copy number data, in Research in Computational Molecular Biology, Volume 4453 of Lecture Notes in Computer Science, pp 122–136 (Springer, Berlin 2007).
  6. Berger JA, Hautaniemi S, Mitra SK, Astola J: Jointly analyzing gene expression and copy number data in breast cancer using data reduction models. IEEE/ACM Trans Comput Biol Bioinform 3:2–16 (2006).
  7. Beroukhim R, Getz G, Nghiemphu L, Barretina J, Hsueh T, et al: Assessing the significance of chromosomal aberrations in cancer: methodology and application to glioma. Proc Natl Acad Sci USA 104:20007–20012 (2007).
  8. Broet P, Richardson S: Detection of gene copy number changes in CGH microarrays using a spatially correlated mixture model. Bioinformatics 22:911–918 (2006).
  9. Campbell PJ, Stephens PJ, Pleasance ED, O’Meara S, Li H, et al: Identification of somatically acquired rearrangements in cancer using genome-wide massively parallel paired-end sequencing. Nat Genet 40:722–729 (2008).
  10. Chin L, Gray J: Translating insights from the cancer genome into clinical practice. Nature 242:553–563 (2008).
    External Resources
  11. Chin SF, Teschendorff AE, Marioni JC, Wang Y, Barbosa-Morais NL, et al: High resolution aCGH and expression profiling identifies a novel genomic subtype of ER negative breast cancer. Genome Biol 8:R215 (2007).
  12. Coe BP, Lockwood WW, Girard L, Chari R, Macaulay C, et al: Differential disruption of cell cycle pathways in small cell and non-small cell lung cancer. Br J Cancer 94:1927–1935 (2006).
  13. Colella S, Yau C, Taylor JM, Mirza G, Butler H, et al: QuantiSNP: an Objective Bayes Hidden-Markov Model to detect and accurately map copy number variation using SNP genotyping data. Nucleic Acids Res 35:2013–2025 (2007).
  14. Collins FS, Barker AD: Mapping the cancer genome. Pinpointing the genes involved in cancer will help chart a new course across the complex landscape of human malignancies. Sci Am 296:50–57 (2007).
  15. de Leeuw RJ, Davies JJ, Rosenwald A, Bebb G, Gascoyne RD, et al: Comprehensive whole genome array CGH profiling of mantle cell lymphoma model genomes. Hum Mol Genet 13:1827–1837 (2004).
  16. Diskin SJ, Eck T, Greshock J, Mosse YP, Naylor T, et al: STAC: A method for testing the significance of DNA copy number aberrations across multiple array-CGH experiments. Genome Res 16:1149–1158 (2006).
  17. Engler DA, Mohapatra G, Louis DN, Betensky RA: A pseudolikelihood approach for simultaneous analysis of array comparative genomic hybridizations (aCGH). Biostatistics 7:399–421 (2006).
  18. Esteller M: Cancer epigenomics: DNA methylomes and histone-modification maps. Nat Rev Genet 8:286–298 (2007).
  19. Fridlyand J, Snijders A, Pinkel D, Albertson D, Jain A: Hidden Markov Models approach to the analysis of array CGH data. J Multivariate Anal 90:132–153 (2004).
    External Resources
  20. Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, et al: Bioconductor: open software development for computational biology and bioinformatics. Genome Biol 5:R80 (2004).
  21. Guan Y, Kuo WL, Stilwell JL, Takano H, Lapuk AV, et al: Amplification of PVT1 contributes to the pathophysiology of ovarian and breast cancer. Clin Cancer Res 13:5745–5755 (2007).
  22. Guha S, Li Y, Neuberg D: Bayesian hidden Markov modeling of array CGH data. J Am Stat Assoc 103:485–497 (2008).
  23. Hanahan D, Weinberg RA: The hallmarks of cancer. Cell 100:57–70 (2000).
  24. Heidenblad M, Lindgren D, Veltman JA, Jonson T, Mahlamaki EH, et al: Microarray analyses reveal strong influence of DNA copy number alterations on the transcriptional patterns in pancreatic cancer: implications for the interpretation of genomic amplifications. Oncogene 24:1794–1801 (2005).
  25. Hirsch FR, Varella-Garcia M, Bunn PA, Di Maria MV, Veve R, et al: Epidermal growth factor receptor in non-small-cell lung carcinomas: correlation between gene copy number and protein expression and impact on prognosis. J Clin Oncol 21:3798–3807 (2003).
  26. Hoglund M, Sehn L, Connors JM, Gascoyne RD, Siebert R, et al: Identification of cytogenetic subgroups and karyotypic pathways of clonal evolution in follicular lymphomas. Genes Chromosomes Cancer 39:195–204 (2004).
  27. Hosoya N, Sanada M, Nannya Y, Nakazaki K, Wang L, et al: Genomewide screening of DNA copy number changes in chronic myelogenous leukemia with the use of high resolution array-based comparative genomic hybridization. Genes Chromosomes Cancer 45:482–494 (2006).
  28. Hupe P, Stransky N, Thiery J, Radvanyi F, Barillot E: Analysis of array CGH data: from signal ratio to gain and loss of DNA regions. Bioinformatics 20:3413–3422 (2004).
  29. Iafrate AJ, Feuk L, Rivera MN, Listewnik ML, Donahoe PK, et al: Detection of large-scale variation in the human genome. Nat Genet 36:949–951 (2004).
  30. Idbaih A, Marie Y, Lucchesi C, Pierron G, Manie E, et al: BAC array CGH distinguishes mutually exclusive alterations that define clinicogenetic subtypes of gliomas. Int J Cancer 122:1778–1786 (2008).
  31. Iehara T, Hosoi H, Akazawa K, Matsumoto Y, Yamamoto K, et al: MYCN gene amplification is a powerful prognostic factor even in infantile neuroblastoma detected by mass screening. Br J Cancer 94:1510–1515 (2006).
  32. Ishkanian A, Malloff C, Watson S, DeLeeuw R, Chi B, et al: A tiling resolution DNA microarray with complete coverage of the human genome. Nat Genet 36:299–303 (2004).
  33. Khojasteh M, Lam WL, Ward RK, MacAulay C: A stepwise framework for the normalization of array CGH data. BMC Bioinformatics 6:274 (2005).
  34. Klijn C, Holstege H, de Ridder J, Liu X, Reinders M, et al: Identification of cancer genes using a statistical framework for multiexperiment analysis of nondiscretized array CGH data. Nucleic Acids Res 36:e13 (2008).
  35. Krishnapuram B, Carin L, Figueiredo MA, Hartemink AJ: Sparse multinomial logistic regression: fast algorithms and generalization bounds. IEEE Trans Pattern Anal Mach Intell 27:957–968 (2005).
  36. Lai W, Johnson M, Kucherlapati R, Park P: Comparative analysis of algorithms for identifying amplifications and deletions in array CGH data. Bioinformatics 21:3763–3770 (2005).
  37. Lee H, Kong SW, Park PJ: Integrative analysis reveals the direct and indirect interactions between DNA copy number aberrations and gene expression changes. Bioinformatics 24:889–896 (2008).
  38. Lipson D, Ben-Dor A, Dehan E, Yakhini Z: Joint analysis of DNA copy numbers and gene expression levels, in WABI, Lecture Notes in Computer Science (LNCS), pp 135 (Springer, Berlin 2004).
  39. Lipson D, Aumann Y, Ben-Dor A, Linial N, Yakhini Z: Efficient calculation of interval scores for DNA copy number data analysis. J Comput Biol 13:215–228 (2006).
  40. Marioni JC, Thorne NP, Tavare S: BioHMM: a heterogeneous hidden Markov model for segmenting array CGH data. Bioinformatics 22:1144–1146 (2006).
  41. Marioni JC, Thorne NP, Valsesia A, Fitzgerald T, Redon R, et al: Breaking the waves: improved detection of copy number variation from microarray-based comparative genomic hybridization. Genome Biol 8:R228 (2007).
  42. Neuvial P, Hupe P, Brito I, Liva S, Manie E, et al: Spatial normalization of array-CGH data. BMC Bioinformatics 7:264 (2006).
  43. Olshen A, Venkatraman E, Lucito R, Wigler M: Circular binary segmentation for the analysis of array-based DNA copy number data. Biostatistics 5:557–572 (2004).
  44. Pinkel D, Albertson D: Array comparative genomic hybridization and its applications in cancer. Nat Genet 37 Suppl:11–17 (2005).
  45. Pique-Regi R, Monso-Varona J, Ortega A, Seeger RC, Triche TJ, Asgharzadeh S: Sparse representation and Bayesian detection of genome copy number alterations from microarray data. Bioinformatics 24:309–318 (2008).
  46. Pollack JR, Sorlie T, Perou CM, Rees CA, Jeffrey SS, et al: 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 (2002).
  47. Rapaport F, Barillot E, Vert JP: Classification of array CGH data using a fused SVM. Bioinformatics 24:i375–i382 (2008).
  48. Redon R, Ishikawa S, Fitch KR, Feuk L, Perry GH, et al: Global variation in copy number in the human genome. Nature 444:444–454 (2006).
  49. Rouveirol C, Stransky N, Hupe P, Rosa PL, Viara E, et al: Computation of recurrent minimal genomic alterations from array-CGH data. Bioinformatics 22:849–856 (2006).
  50. Rueda OM, Diaz-Uriarte R: Flexible and accurate detection of genomic copy-number changes from aCGH. PLoS Comput Biol 3:e122 (2007).
  51. Schwaenen C, Nessling M, Wessendorf S, Salvi T, Wrobel G, et al: Automated array-based genomic profiling in chronic lymphocytic leukemia: development of a clinical tool and discovery of recurrent genomic alterations. Proc Natl Acad Sci USA 101:1039–1044 (2004).
  52. Shah SP, Xuan X, DeLeeuw RJ, Khojasteh M, Lam WL, et al: Integrating copy number polymorphisms into array CGH analysis using a robust HMM. Bioinformatics 22:431–439 (2006).
  53. Shah SP, Lam WL, Ng RT, Murphy KP: Modeling recurrent DNA copy number alterations in array CGH data. Bioinformatics 23:450–458 (2007).
  54. Slamon DJ, Clark GM, Wong SG, Levin WJ, Ullrich A, McGuire WL: Human breast cancer: correlation of relapse and survival with amplification of the HER-2/neu oncogene. Science 235:177–182 (1987).
  55. Stjernqvist S, Ryden T, Skold M, Staaf J: Continuous-index hidden Markov modeling of array CGH copy number data. Bioinformatics 23:1006–1014 (2007).
  56. Tomlins SA, Rhodes DR, Perner S, Dhanasekaran SM, Mehra R, et al: Recurrent fusion of TMPRSS2 and ETS transcription factor genes in prostate cancer. Science 310:644–648 (2005).
  57. van de Wiel MA, Kim KI, Vosse SJ, van Wieringen WN, Wilting SM, Ylstra B: CGHcall: calling aberrations for array CGH tumor profiles. Bioinformatics 23:892–894 (2007).
  58. van Wieringen WN, van de Wiel MA: Nonparametric testing for DNA copy number induced differential mRNA gene expression. Biometrics 64:1–25 (2008).
  59. van Wieringen WN, Van De Wiel MA, Ylstra B: Weighted clustering of called array CGH data. Biostatistics 9:484–500 (2007).
  60. Venkatraman ES, Olshen AB: A faster circular binary segmentation algorithm for the analysis of array CGH data. Bioinformatics 23:657–663 (2007).
  61. Weinberg RA: The Biology of Cancer (Garland Science, Taylor and Francis Group, New York 2007).
  62. Willenbrock H, Fridlyand J: A comparison study: applying segmentation to array CGH data for downstream analyses. Bioinformatics 21:4084–4091 (2005).
  63. Wong KK, deLeeuw RJ, Dosanjh NS, Kimm LR, Cheng Z, et al: A comprehensive analysis of common copy-number variations in the human genome. Am J Hum Genet 80:91–104 (2007).
  64. Yaziji H, Goldstein LC, Barry TS, Werling R, Hwang H, et al: HER-2 testing in breast cancer using parallel tissue-based methods. JAMA 291:1972–1977 (2004).

Article / Publication Details

First-Page Preview
Abstract of Copy Number Variations: Technology of Detection and Analysis

Accepted: August 20, 2008
Published online: March 11, 2009
Issue release date: March 2009

Number of Print Pages: 9
Number of Figures: 3
Number of Tables: 1

ISSN: 1424-8581 (Print)
eISSN: 1424-859X (Online)

For additional information: https://www.karger.com/CGR


Copyright / Drug Dosage / Disclaimer

Copyright: All rights reserved. No part of this publication may be translated into other languages, reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording, microcopying, or by any information storage and retrieval system, without permission in writing from the publisher.
Drug Dosage: The authors and the publisher have exerted every effort to ensure that drug selection and dosage set forth in this text are in accord with current recommendations and practice at the time of publication. However, in view of ongoing research, changes in government regulations, and the constant flow of information relating to drug therapy and drug reactions, the reader is urged to check the package insert for each drug for any changes in indications and dosage and for added warnings and precautions. This is particularly important when the recommended agent is a new and/or infrequently employed drug.
Disclaimer: The statements, opinions and data contained in this publication are solely those of the individual authors and contributors and not of the publishers and the editor(s). The appearance of advertisements or/and product references in the publication is not a warranty, endorsement, or approval of the products or services advertised or of their effectiveness, quality or safety. The publisher and the editor(s) disclaim responsibility for any injury to persons or property resulting from any ideas, methods, instructions or products referred to in the content or advertisements.