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Maximum Parsimony Analysis of Gene Copy Number Changes

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Algorithms in Bioinformatics (WABI 2015)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 9289))

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Abstract

Evolution of cancer cells are characterized by large scale and rapid changes in the chromosomal landscape. The fluorescence in situ hybridization (FISH) technique provides a way to measure the copy numbers of preselected genes in a group of cells and has been found to be a reliable source of data to model the evolution of tumor cells. Chowdhury et al. [1, 2] recently develop a theoreticallly sound and scalable model for tumor progression driven by gains and losses in cell count patterns obtained by FISH probes. Their model aims to find the Rectilinear Steiner Minimum Tree (RSMT) that describes progression of FISH cell count patterns over its branches in a parsimonious manner. This model is found to effectively model tumor evolution and is also useful in tumor classification. However the RSMT problem is NP–complete and efficient heuristics are necessary to obtain useful solutions, especially for large datasets. In this paper we design a new algorithm for the RSMT problem, based on Maximum Parsimony phylogeny inference. Experimental results from both simulated and real tumor data show that our approach outperforms previous heuristics for the RSMT problem, thus obtaining better models for tumor evolution.

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Notes

  1. 1.

    We use the best result derived from the heuristic option in [1] and the option PLOIDY_LESS_HEURISTIC in [2] that also approximate RSMT under the case of gene copy number changes of single probes.

References

  1. Chowdhury, S.A., Shackney, S.E., Heselmeyer-Haddad, K., Ried, T., Schäffer, A.A., Schwartz, R.: Phylogenetic analysis of multiprobe fluorescence in situ hybridization data from tumor cell populations. Bioinformatics 29(13), 189–198 (2013)

    Article  Google Scholar 

  2. Chowdhury, S.A., Shackney, S.E., Heselmeyer-Haddad, K., Ried, T., Schäffer, A.A., Schwartz, R.: Algorithms to model single gene, single chromosome, and whole genome copy number changes jointly in tumor phylogenetics. PLoS Comput. Biol. 10(7), 1003740 (2014)

    Article  Google Scholar 

  3. Weinberg, R.: The Biology of Cancer. Garland Science, New York (2013)

    Google Scholar 

  4. Futreal, P.A., Coin, L., Marshall, M., Down, T., Hubbard, T., Wooster, R., Rahman, N., Stratton, M.R.: A census of human cancer genes. Nat. Rev. Cancer 4(3), 177–183 (2004)

    Article  Google Scholar 

  5. Swanton, C.: Intratumor heterogeneity: evolution through space and time. Cancer Res. 72(19), 4875–4882 (2012)

    Article  Google Scholar 

  6. Greaves, M., Maley, C.C.: Clonal evolution in cancer. Nature 481(7381), 306–313 (2012)

    Article  Google Scholar 

  7. Yates, L.R., Campbell, P.J.: Evolution of the cancer genome. Nat. Rev. Genet. 13(11), 795–806 (2012)

    Article  Google Scholar 

  8. Attolini, C.S.-O., Michor, F.: Evolutionary theory of cancer. Ann. N. Y. Acad. Sci. 1168(1), 23–51 (2009)

    Article  Google Scholar 

  9. Baudis, M.: Genomic imbalances in 5918 malignant epithelial tumors: an explorative meta-analysis of chromosomal cgh data. BMC Cancer 7(1), 226 (2007)

    Article  Google Scholar 

  10. Pleasance, E.D., Cheetham, R.K., Stephens, P.J., McBride, D.J., Humphray, S.J., Greenman, C.D., Varela, I., Lin, M.-L., Ordóñez, G.R., Bignell, G.R., et al.: A comprehensive catalogue of somatic mutations from a human cancer genome. Nature 463(7278), 191–196 (2009)

    Article  Google Scholar 

  11. Martins, F.C., De, S., Almendro, V., Gönen, M., Park, S.Y., Blum, J.L., Herlihy, W., Ethington, G., Schnitt, S.J., Tung, N., et al.: Evolutionary pathways in brca1-associated breast tumors. Cancer Discov. 2(6), 503–511 (2012)

    Article  Google Scholar 

  12. Navin, N., Krasnitz, A., Rodgers, L., Cook, K., Meth, J., Kendall, J., Riggs, M., Eberling, Y., Troge, J., Grubor, V., et al.: Inferring tumor progression from genomic heterogeneity. Genome Res. 20(1), 68–80 (2010)

    Article  Google Scholar 

  13. Cheng, Y.-K., Beroukhim, R., Levine, R.L., Mellinghoff, I.K., Holland, E.C., Michor, F.: A mathematical methodology for determining the temporal order of pathway alterations arising during gliomagenesis. PLoS Comput. Biol. 8(1), 1002337 (2012)

    Article  Google Scholar 

  14. Shlien, A., Malkin, D.: Copy number variations and cancer. Genome Med 1(6), 62 (2009)

    Article  Google Scholar 

  15. Zack, T.I., Schumacher, S.E., Carter, S.L., Cherniack, A.D., Saksena, G., Tabak, B., Lawrence, M.S., Zhang, C.-Z., Wala, J., Mermel, C.H., et al.: Pan-cancer patterns of somatic copy number alteration. Nat. Genet. 45(10), 1134–1140 (2013)

    Article  Google Scholar 

  16. De Bont, R., Van Larebeke, N.: Endogenous dna damage in humans: a review of quantitative data. Mutagenesis 19(3), 169–185 (2004)

    Article  Google Scholar 

  17. Schärer, O.D.: Dna interstrand crosslinks: natural and drug-induced dna adducts that induce unique cellular responses. ChemBioChem 6(1), 27–32 (2005)

    Article  Google Scholar 

  18. Sung, J.-S., Demple, B.: Roles of base excision repair subpathways in correcting oxidized abasic sites in dna. Febs J. 273(8), 1620–1629 (2006)

    Article  Google Scholar 

  19. Caldecott, K.W.: Single-strand break repair and genetic disease. Nat. Rev. Genet. 9(8), 619–631 (2008)

    Google Scholar 

  20. Cleaver, J.E., Lam, E.T., Revet, I.: Disorders of nucleotide excision repair: the genetic and molecular basis of heterogeneity. Nat. Rev. Genet. 10(11), 756–768 (2009)

    Article  Google Scholar 

  21. Sale, J.E., Lehmann, A.R., Woodgate, R.: Y-family dna polymerases and their role in tolerance of cellular dna damage. Nat. Rev. Mo. Cell Biol. 13(3), 141–152 (2012)

    Article  Google Scholar 

  22. Chapman, J.R., Taylor, M.R., Boulton, S.J.: Playing the end game: Dna double-strand break repair pathway choice. Mo. Cell 47(4), 497–510 (2012)

    Article  Google Scholar 

  23. Wolters, S., Ermolaeva, M.A., Bickel, J.S., Fingerhut, J.M., Khanikar, J., Chan, R.C., Schumacher, B.: Loss of caenorhabditis elegans brca1 promotes genome stability during replication in smc-5 mutants. Genetics 196(4), 985–999 (2014)

    Article  Google Scholar 

  24. Pennington, G., Smith, C.A., Shackney, S., Schwartz, R.: Reconstructing tumor phylogenies from heterogeneous single-cell data. J. Bioinf. Comput. Biol. 5(02a), 407–427 (2007)

    Article  Google Scholar 

  25. Xu, X., Hou, Y., Yin, X., Bao, L., Tang, A., Song, L., Li, F., Tsang, S., Wu, K., Wu, H., et al.: Single-cell exome sequencing reveals single-nucleotide mutation characteristics of a kidney tumor. Cell 148(5), 886–895 (2012)

    Article  Google Scholar 

  26. Von Heydebreck, A., Gunawan, B., Füzesi, L.: Maximum likelihood estimation of oncogenetic tree models. Biostatistics 5(4), 545–556 (2004)

    Article  MATH  Google Scholar 

  27. Greenman, C.D., Pleasance, E.D., Newman, S., Yang, F., Fu, B., Nik-Zainal, S., Jones, D., Lau, K.W., Carter, N., Edwards, P.A., et al.: Estimation of rearrangement phylogeny for cancer genomes. Genome Res. 22(2), 346–361 (2012)

    Article  Google Scholar 

  28. Gerstung, M., Baudis, M., Moch, H., Beerenwinkel, N.: Quantifying cancer progression with conjunctive bayesian networks. Bioinformatics 25(21), 2809–2815 (2009)

    Article  Google Scholar 

  29. Langer-Safer, P.R., Levine, M., Ward, D.C.: Immunological method for mapping genes on drosophila polytene chromosomes. Proc. Natl. Acad. Sci. 79(14), 4381–4385 (1982)

    Article  Google Scholar 

  30. Zhou, J., Lin, Y., Hoskins, W., Tang, J.: An iterative approach for phylogenetic analysis of tumor progression using FISH copy number. In: Harrison, R., Li, Y., Mandoiu, I. (eds.) ISBRA 2015. LNCS, vol. 9096, pp. 402–412. Springer, Heidelberg (2015)

    Google Scholar 

  31. Wangsa, D., Heselmeyer-Haddad, K., Ried, P., Eriksson, E., Schäffer, A.A., Morrison,L.E., Luo, J., Auer, G., Munck-Wikland, E., Ried, T., et al.: Fluorescence insitu hybridization markers for prediction of cervical lymph node metastases. Am. J. Pathol. 175(6), 2637–2645 (2009)

    Google Scholar 

  32. Goloboff, P.A., Farris, J.S., Nixon, K.C.: TNT, a free program for phylogenetic analysis. Cladistics 24(5), 774–786 (2008)

    Article  Google Scholar 

  33. Goloboff, P.A., Mattoni, C.I., Quinteros, A.S.: Continuous characters analyzed as such. Cladistics 22(6), 589–601 (2006)

    Article  Google Scholar 

  34. Garey, M.R., Johnson, D.S.: The rectilinear steiner tree problem is np-complete. SIAM J. Appl. Math. 32(4), 826–834 (1977)

    Article  MathSciNet  MATH  Google Scholar 

  35. Day, W.H.: Computational complexity of inferring phylogenies from dissimilarity matrices. Bull. Math. Biol. 49(4), 461–467 (1987)

    Article  MathSciNet  MATH  Google Scholar 

  36. Swofford, D.L., Maddison, W.P.: Reconstructing ancestral character states under wagner parsimony. Math. Biosci. 87(2), 199–229 (1987)

    Article  MathSciNet  MATH  Google Scholar 

  37. Giribet, G.: Efficient tree searches with available algorithms. Evolutionary bioinformaticsonline 3, 341 (2007)

    Google Scholar 

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Acknowledgements

We thank Lingxi Zhou, Bin Feng,and Yan Zhang for helpful comments. JZ, WH and, JT were funded by NSF IIS 1161586 and an internal grant from Tianjin University, China. YL was supported by a fellowship of the Swiss National Science Foundation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Authors and Affiliations

  1. Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin University, Tianjin, 300072, China

    Jun Zhou & Jijun Tang

  2. University of South Carolina, Columbia, SC, 29205, USA

    Jun Zhou, William Hoskins & Jijun Tang

  3. University of California, San Diego, La Jolla, CA, 92093, USA

    Yu Lin

  4. Xerox Research Centre India (XRCI), Bangalore, India

    Vaibhav Rajan

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  1. Jun Zhou
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  2. Yu Lin
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  3. Vaibhav Rajan
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Correspondence to Jijun Tang .

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Editors and Affiliations

  1. University of Maryland, College Park, Maryland, USA

    Mihai Pop

  2. University of Lille, Lille, France

    Hélène Touzet

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Zhou, J., Lin, Y., Rajan, V., Hoskins, W., Tang, J. (2015). Maximum Parsimony Analysis of Gene Copy Number Changes. In: Pop, M., Touzet, H. (eds) Algorithms in Bioinformatics. WABI 2015. Lecture Notes in Computer Science(), vol 9289. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-48221-6_8

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  • DOI: https://doi.org/10.1007/978-3-662-48221-6_8

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