A Hidden Markov Model Approach for Prediction of Genomic Alterations from Gene Expression Profiling

  • Huimin Geng
  • Hesham H. Ali
  • Wing C. Chan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4983)


The mRNA transcript changes detected by Gene Expression Profiling (GEP) have been found to be correlated with corresponding DNA copy number variations detected by Comparative Genomic Hybridization (CGH). This correlation, together with the availability of genome-wide, high-density GEP arrays, supports that it is possible to predict genomic alterations from GEP data in tumors. In this paper, we proposed a hidden Markov model-based CGH predictor, HMM_CGH, which was trained in the light of the paired experimental GEP and CGH data on a sufficient number of cases, and then applied to new cases for the prediction of chromosomal gains and losses from their GEP data. The HMM_CGH predictor, taking advantage of the rich GEP data already available to derive genomic alterations, could enhance the detection of genetic abnormalities in tumors. The results from the analysis of lymphoid malignancies validated the model with 80% sensitivity, 90% specificity and 90% accuracy in predicting both gains and losses.


Gene Expression Profiling (GEP) Comparative Genomic Hybridization (CGH) Hidden Markov Model (HMM) Genomic Alterations 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Golub, T.R., Slonim, D.K., Tamayo, P., Huard, C., Gaasenbeek, M., et al.: Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science 286, 531–537 (1999)CrossRefGoogle Scholar
  2. 2.
    Ramaswamy, S., Golub, T.R.: DNA microarrays in clinical oncology. J. Clin. Oncol. 20, 1932–1941 (2002)Google Scholar
  3. 3.
    Fridlyand, J., Pinkel, S.A., Albertson, D., Jain, D.G.: AN: Hidden Markov Models Approach to the Analysis of Array CGH Data. J. Multivariate Anal. 90, 132–153 (2004)zbMATHCrossRefMathSciNetGoogle Scholar
  4. 4.
    Olshen, A., Venkatraman, E.: Change-point analysis of array-based comparative genomic hybridization data. In: Proceedings of Joint Statistical Meetings, pp. 2530–2535 (2002)Google Scholar
  5. 5.
    Snijders, A.M., Nowak, N., Segraves, R., Blackwood, S., Brown, N., et al.: Assembly of microarrays for genome-wide measurement of DNA copy number. Nat. Genet. 29, 263–264 (2001)CrossRefGoogle Scholar
  6. 6.
    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)zbMATHCrossRefGoogle Scholar
  7. 7.
    Orsetti, B., Nugoli, M., Cervera, N., Lasorsa, L., Chuchana, P., et al.: Genomic and expression profiling of chromosome 17 in breast cancer reveals complex patterns of alterations and novel candidate genes. Cancer Res. 64, 6453–6460 (2004)CrossRefGoogle Scholar
  8. 8.
    Clark, J., Edwards, S., John, M., Flohr, P., Gordon, T., et al.: Identification of amplified and expressed genes in breast cancer by comparative hybridization onto microarrays of randomly selected cDNA clones. Genes Chromosomes Cancer 34, 104–114 (2002)CrossRefGoogle Scholar
  9. 9.
    Kauraniemi, P., Barlund, M., Monni, O., Kallioniemi, A.: New amplified and highly expressed genes discovered in the ERBB2 amplicon in breast cancer by cDNA microarrays. Cancer Res. 61, 8235–8240 (2001)Google Scholar
  10. 10.
    Monni, O., Barlund, M., Mousses, S., Kononen, J., Sauter, G., et al.: Comprehensive copy number and gene expression profiling of the 17q23 amplicon in human breast cancer. PNAS 98, 5711–5716 (2001)CrossRefGoogle Scholar
  11. 11.
    Pollack, J.R., Sorlie, T., Perou, C.M., Rees, C.A., Jeffrey, S.S., Lonning, P.E., Tibshirani, R., Botstein, D., Borresen-Dale, A.L., Brown, P.O.: Microarray analysis reveals a major direct role of DNA copy number alteration in the transcriptional program of human breast tumors. PNAS 99, 12963–12968 (2002)CrossRefGoogle Scholar
  12. 12.
    Hyman, E., Kauraniemi, P., Hautaniemi, S., Wolf, M., Mousses, S., et al.: Impact of DNA amplification on gene expression patterns in breast cancer. Cancer Res. 62, 6240–6245 (2002)Google Scholar
  13. 13.
    Phillips, J.L., Hayward, S.W., Wang, Y., Vasselli, J., Pavlovich, C., et al.: The consequences of chromosomal aneuploidy on gene expression profiles in a cell line model for prostate carcinogenesis. Cancer Res. 61, 8143–8149 (2001)Google Scholar
  14. 14.
    Virtaneva, K., Wright, F.A., Tanner, S.M., Yuan, B., Lemon, W.J., et al.: Expression profiling reveals fundamental biological differences in acute myeloid leukemia with isolated trisomy 8 and normal cytogenetics. PNAS 98, 1124–1129 (2001)CrossRefGoogle Scholar
  15. 15.
    Varis, A., Wolf, M., Monni, O., Vakkari, M.L., Kokkola, A., et al.: Targets of gene amplification and overexpression at 17q in gastric cancer. Cancer Res. 62, 2625–2629 (2002)Google Scholar
  16. 16.
    Linn, S.C., West, R.B., Pollack, J.R., Zhu, S., Hernandez-Boussard, T., et al.: Gene expression patterns and gene copy number changes in dermatofibrosarcoma protuberans. Am J. Pathol. 163, 2383–2395 (2003)Google Scholar
  17. 17.
    Hughes, T.R., Roberts, C.J., Dai, H., Jones, A.R., Meyer, M.R., et al.: Widespread aneuploidy revealed by DNA microarray expression profiling. Nat. Genet. 25, 333–337 (2000)CrossRefGoogle Scholar
  18. 18.
    Bea, S., Zettl, A., Wright, G., Salaverria, I., Jehn, P., et al.: Diffuse large B-cell lymphoma subgroups have distinct genetic profiles that influence tumor biology and improve gene-expression-based survival prediction. Blood 106, 3183–3190 (2005)CrossRefGoogle Scholar
  19. 19.
    Salaverria, I., Zettl, A., Bea, S., Moreno, V., Valls, J., et al.: Specific Secondary Genetic Alterations in Mantle Cell Lymphoma Provide Prognostic Information Independent of the Gene Expression-Based Proliferation Signature. J. Clin. Oncol. 25, 1216–1222 (2007)CrossRefGoogle Scholar
  20. 20.
    Iqbal, J., DeLeeuw, R.J., Srivastava, G., Geng, H., Patel, K., et al.: High resolution genomic mapping and gene expression analysis of chromosomal aberrations in natural killer malignancies (submitted)Google Scholar
  21. 21.
    Finishing the euchromatic sequence of the human genome. Nature 431, 931–945 (2004)Google Scholar
  22. 22.
    Shi, L., Reid, L.H., Jones, W.D., Shippy, R., Warrington, J.A., et al.: The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements. Nature biotechnology 24, 1151–1161 (2006)CrossRefGoogle Scholar
  23. 23.
    Durbin, R., Eddy, S., Krogh, A., Mitchison, G.: Biological sequence analysis: Probabilistic models of proteins and necleic acids. Cambridge Unisersity Press, New York (1998)Google Scholar
  24. 24.
    Lymphoma/Leukemia Molecular Profiling Project (LLMPP),
  25. 25.
    Strategic Partnering to Evaluate Cancer Signatures (SPECS),
  26. 26.
    NCBI: Homo sapiens (human) genome view (2006),
  27. 27.
    Simon, R., Peng, A.: BRB-Array Tool,

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Huimin Geng
    • 1
    • 2
  • Hesham H. Ali
    • 1
  • Wing C. Chan
    • 2
  1. 1.Department of Computer ScienceUniversity of Nebraska at OmahaOmaha 
  2. 2.Department of Pathology and MicrobiologyUniversity of Nebraska Medical CenterOmaha 

Personalised recommendations