Framework for Identifying Common Aberrations in DNA Copy Number Data

  • Amir Ben-Dor
  • Doron Lipson
  • Anya Tsalenko
  • Mark Reimers
  • Lars O. Baumbusch
  • Michael T. Barrett
  • John N. Weinstein
  • Anne-Lise Børresen-Dale
  • Zohar Yakhini
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4453)


High-resolution array comparative genomic hybridization(aCGH) provides exon-level mapping of DNA aberrations in cells or tissues. Such aberrations are central to carcinogenesis and, in many cases, central to targeted therapy of the cancers. Some of the aberrations are sporadic, one-of-a-kind changes in particular tumor samples; others occur frequently and reflect common themes in cancer biology that have interpretable, causal ramifications. Hence, the difficult task of identifying and mapping common, overlapping genomic aberrations (including amplifications and deletions) across a sample set is an important one; it can provide insight for the discovery of oncogenes, tumor suppressors, and the mechanisms by which they drive cancer development.

In this paper we present an efficient computational framework for identification and statistical characterization of genomic aberrations that are common to multiple cancer samples in a CGH data set. We present and compare three different algorithmic approaches within the context of that framework. Finally, we apply our methods to two datasets – a collection of 20 breast cancer samples and a panel of 60 diverse human tumor cell lines (the NCI-60). Those analyses identified both known and novel common aberrations containing cancer-related genes. The potential impact of the analytical methods is well demonstrated by new insights into the patterns of deletion of CDKN2A (p16), a tumor suppressor gene crucial for the genesis of many types of cancer.


CGH cancer microarray data analysis common aberrations breast cancer NCI-60 


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  1. 1.
    Kauraniemi, P., Hautaniemi, S., Autio, R., Astola, J., Monni, O., Elkahloun, A., Kallioniemi, A.: Effects of Herceptin treatment on global gene expression patterns in HER2-amplified and nonamplified breast cancer cell lines. Oncogene 23(4), 1010–1013 (2004)CrossRefGoogle Scholar
  2. 2.
    Binh, M., Sastre-Garau, X., Guillou, L., de Pinieux, G., Terrier, P., Lagace, R., Aurias, A., Hostein, I., Coindre, J.: MDM2 and CDK4 immunostainings are useful adjuncts in diagnosing well-differentiated and dedifferentiated liposarcoma subtypes: A comparative analysis of 559 soft tissue neoplasms with genetic data. American Journal of Surgical Pathology 29(10), 1340–1347 (2005)CrossRefGoogle Scholar
  3. 3.
    Balsara, B., Testa, J.: Chromosomal imbalances in human lung cancer. Oncogene 21(45), 6877–6883 (2002)CrossRefGoogle Scholar
  4. 4.
    Kallioniemi, O., Kallioniemi, A., Sudar, D., Rutovitz, D., Gray, J., Waldman, F., Pinkel, D.: Comparative genomic hybridization: a rapid new method for detecting and mapping DNA amplification in tumors. Semin. Cancer Biol. 4(1), 41–46 (1993)Google Scholar
  5. 5.
    Mertens, F., Johansson, B., Hoglund, M., Mitelman, F.: Chromosomal imbalance maps of malignant solid tumors: a cytogenetic survey of 3185 neoplasms. Cancer Research 57(13), 2765–2780 (1997)Google Scholar
  6. 6.
    Barrett, M., Scheffer, A., Ben-Dor, A., Sampas, N., Lipson, D., Kincaid, R., Tsang, P., Curry, B., Baird, K., Meltzer, P., Yakhini, Z., Bruhn, L., Laderman, S.: Comparative genomic hybridization using oligonucleotide microarrays and total genomic DNA. PNAS 101(51), 17765–17770 (2004)CrossRefGoogle Scholar
  7. 7.
    Bignell, G., Huang, J., Greshock, J., Watt, S., Butler, A., West, S., Grigorova, M., Jones, K., Wei, W., Stratton, M., Futreal, P., Weber, B., Shapero, M., Wooster, R.: High-resolution analysis of DNA copy number using oligonucleotide microarrays. Genome Research 14(2), 287–295 (2004)CrossRefGoogle Scholar
  8. 8.
    Brennan, C., Zhang, Y., Leo, C., Feng, B., Cauwels, C., Aguirre, A., Kim, M., Protopopov, A., Chin, L.: High-resolution global profiling of genomic alterations with long oligonucleotide microarray. Cancer Research 64(14), 4744–4748 (2004)CrossRefGoogle Scholar
  9. 9.
    Hedenfalk, I., Ringner, M., Ben-Dor, A., Yakhini, Z., Chen, Y., Chebil, G., Ach, R., Loman, N., Olsson, H., Meltzer, P., Borg, A., Trent, J.: Molecular classification of familial non-BRCA1/BRCA2 breast cancer. PNAS 100(5), 2532–2537 (2003)CrossRefGoogle Scholar
  10. 10.
    Pinkel, D., Segraves, R., Sudar, D., Clark, S., Poole, I., Kowbel, D., Collins, C., Kuo, W., Chen, C., Zhai, Y., Dairkee, S., Ljung, B., Gray, J., Albertson, D.: High resolution analysis of DNA copy number variation using comparative genomic hybridization to microarrays. Nature Genetics 20(2), 207–211 (1998)CrossRefGoogle Scholar
  11. 11.
    Pollack, J., Perou, C., Alizadeh, A., Eisen, M., Pergamenschikov, A., Williams, C., Jeffrey, S., Botstein, D., Brown, P.: Genome-wide analysis of DNA copy-number changes using cDNA microarrays. Nature Genetics 23(1), 41–46 (1999)CrossRefGoogle Scholar
  12. 12.
    Sebat, J., Lakshmi, B., Troge, J., Alexander, J., Young, J., Lundin, P., Maner, S., Massa, H., Walker, M., Chi, M., Navin, N., Lucito, R., Healy, J., Hicks, J., Ye, K., Reiner, A., Gilliam, T., Trask, B., Patterson, N., Zetterberg, A., Wigler, M.: Large-scale copy number polymorphism in the human genome. Science 305(5683), 525–528 (2004)CrossRefGoogle Scholar
  13. 13.
    Fridlyand, J., Snijders, A., Pinkel, D., Albertson, D., Jain, A.: Hidden markov models approach to the analysis of array cgh data. Journal of Multivariate Analysis 90, 132–153 (2004)MATHCrossRefMathSciNetGoogle Scholar
  14. 14.
    Lai, W., Johnson, M., Kucherlapati, R., Park, P.: Comparative analysis of algorithms for identifying amplifications and deletions in array CGH data. Bioinformatics 21(19), 3763–3770 (2005)CrossRefGoogle Scholar
  15. 15.
    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)MATHCrossRefGoogle Scholar
  16. 16.
    Wang, P., Kim, Y., Pollack, J., Narasimhan, B., Tibshirani, R.: A method for calling gains and losses in array CGH data. Biostatistics 6, 45–58 (2005)MATHCrossRefGoogle Scholar
  17. 17.
    Willenbrock, H., Fridlyand, J.: A comparison study: applying segmentation to array CGH data for downstream analyses. Bioinformatics 21(22), 4084–4091 (2005)CrossRefGoogle Scholar
  18. 18.
    Diskin, S., Eck, T., Greshock, J., Mosse, Y., Naylor, T., Stoeckert, C., Weber, B., Maris, J., Grant, G.: STAC: a method for testing the significance of DNA copy number aberrations across multiple array-CGH experiments. Genome Research 16, 1149–1158 (2006)CrossRefGoogle Scholar
  19. 19.
    Rouveirol, C., Stransky, N., Hupe, P., Rosa, P.L., Viara, E., Barillot, E., Radvanyi, F.: Computation of reccurant minimla genomic alterations from array-cgh data. Bioinformatics, 849–856 (2006)Google Scholar
  20. 20.
    Lipson, D., Aumann, Y., Ben-Dor, A., Linial, N., Yakhini, Z.: Efficient calculation of interval scores for DNA copy number data analysis. Journal of Computational Biology 13(2), 215–228 (2006)CrossRefMathSciNetGoogle Scholar
  21. 21.
    Lingjarde, O.C., Baumbusch, L.O., Liestol, K., Glad, I.K., Borresen-Dale, A.L.: Cgh-explorer: a program for analysis of array-cgh data. Bioinformatics 21(6), 821–822 (2005)CrossRefGoogle Scholar
  22. 22.
    Wiedswang, G., Borgen, E., Kvalheim, R.K.G., Nesland, J., Qvist, H., Schlichting, E., Sauer, T., Janbu, J., Harbitz, T., Naume, B.: Detection of isolated tumor cells in bone marrow is an independent prognostic factor in breast cancer. Journal of Clinical Oncology 21, 3469–3478 (2003)CrossRefGoogle Scholar
  23. 23.
    Sorlie, T., Perou, C., Tibshirani, R., Aas, T., Geisler, S., Johnsen, H., Hastie, T., Eisen, M., van de Rijn, M., Jeffrey, S., Thorsen, T., Quist, H., Matese, J., Brown, P., Botstein, D., Lonning, P.E., Borresen-Dale, A.: Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. PNAS 98(10), 10869–10874 (2001)CrossRefGoogle Scholar
  24. 24.
    Sorlie, T., Wang, Y., Xiao, C., Johnsen, H., Naume, B., Samaha, R., Borresen-Dale, A.L.: Distinct molecular mechanisms underlying clinically relevant subtypes of breast cancer: Gene expression analyses across three different platforms. BMC Genomics 7, 127 (2006)CrossRefGoogle Scholar
  25. 25.
    Weinstein, J., Myers, T., O’Connor, P., Friend, S., Fornace, A.J., Kohn, K., Fojo, T., Bates, S., Rubinstein, L., Anderson, N., Buolamwini, J., van Osdol, W., Monks, A., Scudiero, D., Sausville, E., Zaharevitz, D., Bunow, B., Viswanadhan, V., Johnson, G., Wittes, R., Paull, K.: An information-intensive approach to the molecular pharmacology of cancer. Science 275(10), 343–349 (1997)CrossRefGoogle Scholar
  26. 26.
    Monks, A., Scudiero, D., Skehan, P., Shoemaker, R., Paull, K., Vistica, D., Hose, C., Langley, J., Cronise, P., et al.: A.V.W.: Feasibility of a high-flux anticancer drug screen using a diverse panel of cultured human tumor cell lines. Journal of the National Cancer Institute 83, 757–766 (1991)CrossRefGoogle Scholar
  27. 27.
    Shoemaker, R., Monks, A., Alley, M., Scudiero, D., Fine, D., McLemore, T., Abbott, B., Paull, K., Mayo, J., Boyd, M.: Development of human tumor cell line panels for use in disease-oriented drug screening. Progress in Clinical and Biological Research 276, 265–286 (1988)Google Scholar
  28. 28.
    Nishizuka, S., Charboneau, L., Young, L., Major, S., Reinhold, W., Waltham, M., Kouros-Mehr, H., Bussey, K., Lee, J., Espina, V., Munson, P., Petricoin 3rd, E., Liotta, L., Weinstein, J.: Proteomic profiling of the nci-60 cancer cell lines using new high-density reverse-phase lysate microarrays. PNAS 100, 14229–14234 (2003)CrossRefGoogle Scholar
  29. 29.
    Paull, K., Shoemaker, R., Hodes, L., Monks, A., Scudiero, D., Rubinstein, L., Plowman, J., Boyd, M.: Display and analysis of patterns of differential activity of drugs against human tumor cell lines: development of mean graph and compare algorithm. Journal of the National Cancer Institute 81, 1088–1092 (1989)CrossRefGoogle Scholar
  30. 30.
    Shi, L., Fan, Y., Lee, J., Waltham, M., Andrews, D.T., Scherf, U., Paull, K., Weinstein, J.: Mining and visualizing large anticancer drug discovery databases. Journal of Chemical Information and Computer Sciences 40, 367–379 (2000)CrossRefGoogle Scholar
  31. 31.
    Staunton, J., Slonim, D., Coller, H., Tamayo, P., Angelo, M., Park, J., Scherf, U., Lee, J., Reinhold, W., Weinstein, J., Mesirov, J., Lander, E., Golub, T.: Chemosensitivity prediction by transcriptional profiling. PNAS 98, 10787–10792 (2001)CrossRefGoogle Scholar
  32. 32.
    Kitao, H., Yamamoto, K., Matsushita, N., Ohzeki, M., Ishiai, M., Takata, M.: Functional interplay between brca2/fancd1 and fancc in dna repair. Journal of Biological Chemistry 281(30), 21312–21320 (2006)CrossRefGoogle Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Amir Ben-Dor
    • 1
  • Doron Lipson
    • 2
  • Anya Tsalenko
    • 1
  • Mark Reimers
    • 3
  • Lars O. Baumbusch
    • 4
  • Michael T. Barrett
    • 1
    • 5
  • John N. Weinstein
    • 3
  • Anne-Lise Børresen-Dale
    • 4
  • Zohar Yakhini
    • 1
    • 2
  1. 1.Agilent Laboratories, Santa-Clara, CA 
  2. 2.Computer Science Dept., Technion, Haifa 
  3. 3.National Cancer Institute, Bethesda, MD 
  4. 4.Department of Genetics, Institute for Cancer Research, Rikshospitalet-Radiumhospitalet Medical Center 
  5. 5.Translational Genomics Research Institute, Phoenix, AZ 

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