Advertisement

Towards Recovering Allele-Specific Cancer Genome Graphs

  • Ashok Rajaraman
  • Jian Ma
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10229)

Abstract

Integrated analysis of structural variants (SVs) and copy number alterations (CNAs) in aneuploid cancer genomes is key to understanding the tumor genome complexity. A recently developed new algorithm Weaver can estimate, for the first time, allele-specific copy number of SVs and their interconnectivity in aneuploid cancer genomes. However, one major limitation is that not all SVs identified by Weaver are phased. In this paper, we develop a general convex programming framework that predicts the interconnectivity of unphased SVs with possibly noisy allele-specific copy number estimations as input. We demonstrated through applications to both simulated data and the HeLa whole-genome sequencing data that our method is robust to the noise in the input copy numbers and can predict SV phasings with high specificity. We found that our method can make consistent predictions with Weaver even if a large proportion of the input variants are unphased. We also applied our method to TCGA ovarian cancer whole-genome sequencing samples to phase unphased SVs obtained by Weaver. Our work provides an important new algorithmic framework for recovering more complete allele-specific cancer genome graphs.

Keywords

Integer Linear Program Cancer Genome Tumor Genome Region Extremity Somatic Copy Number Alteration 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

The authors would like to thank anonymous reviewers for suggestions that improved the paper. The authors would also like to thank the TCGA Research Network for making the data publicly available. This work is supported in part by National Institutes of Health Grants CA182360, HG007352, and DK107965 (to J.M.), and National Science Foundation Grants 1054309 and 1262575 (to J.M.).

References

  1. 1.
    Adey, A., Burton, J.N., Kitzman, J.O., Hiatt, J.B., Lewis, A.P., Martin, B.K., Qiu, R., Lee, C., Shendure, J.: The haplotype-resolved genome and epigenome of the aneuploid HeLa cancer cell line. Nature 500(7461), 207–211 (2013)CrossRefGoogle Scholar
  2. 2.
    Beroukhim, R., Mermel, C.H., Porter, D., Wei, G., Raychaudhuri, S., Donovan, J., Barretina, J., Boehm, J.S., Dobson, J., Urashima, M., et al.: The landscape of somatic copy-number alteration across human cancers. Nature 463(7283), 899–905 (2010)CrossRefGoogle Scholar
  3. 3.
    Carter, S.L., Cibulskis, K., Helman, E., McKenna, A., Shen, H., Zack, T., Laird, P.W., Onofrio, R.C., Winckler, W., Weir, B.A., et al.: Absolute quantification of somatic DNA alterations in human cancer. Nat. Biotechnol. 30(5), 413–421 (2012)CrossRefGoogle Scholar
  4. 4.
    Diamond, S., Boyd, S.: CVXPY: a python-embedded modeling language for convex optimization. J. Mach. Learn. Res. 17(83), 1–5 (2016)MathSciNetzbMATHGoogle Scholar
  5. 5.
    Dzamba, M., Ramani, A.K., Buczkowicz, P., Jiang, Y., Yu, M., Hawkins, C., Brudno, M.: Identification of complex genomic rearrangements in cancers using CouGaR. Genome Res. 27(1), 107–117 (2017)CrossRefGoogle Scholar
  6. 6.
    Eid, J., Fehr, A., Gray, J., Luong, K., Lyle, J., Otto, G., Peluso, P., Rank, D., Baybayan, P., Bettman, B., et al.: Real-time DNA sequencing from single polymerase molecules. Science 323(5910), 133–138 (2009)CrossRefGoogle Scholar
  7. 7.
    Gordon, D.J., Resio, B., Pellman, D.: Causes and consequences of aneuploidy in cancer. Nat. Rev. Genet. 13(3), 189–203 (2012)Google Scholar
  8. 8.
    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)CrossRefGoogle Scholar
  9. 9.
    Gupta, A., Place, M., Goldstein, S., Sarkar, D., Zhou, S., Potamousis, K., Kim, J., Flanagan, C., Li, Y., Newton, M.A., et al.: Single-molecule analysis reveals widespread structural variation in multiple myeloma. Proc. Nat. Acad. Sci. 112(25), 7689–7694 (2015)CrossRefGoogle Scholar
  10. 10.
    Gurobi Optimization Inc.: Gurobi optimizer reference manual (2015)Google Scholar
  11. 11.
    Karp, R.M.: Reducibility among combinatorial problems. In: Miller, R.E., Thatcher, J.W., Bohlinger, J.D. (eds.) Complexity of Computer Computations, pp. 85–103. Springer, New York (1972)CrossRefGoogle Scholar
  12. 12.
    Kimura, M.: The number of heterozygous nucleotide sites maintained in a finite population due to steady flux of mutations. Genetics 61(4), 893 (1969)Google Scholar
  13. 13.
    Li, Y., Zhou, S., Schwartz, D.C., Ma, J.: Allele-specific quantification of structural variations in cancer genomes. Cell Syst. 3(1), 21–34 (2016)CrossRefGoogle Scholar
  14. 14.
    Ma, J., Ratan, A., Raney, B.J., Suh, B.B., Miller, W., Haussler, D.: The infinite sites model of genome evolution. Proc. Nat. Acad. Sci. 105(38), 14254–14261 (2008)CrossRefGoogle Scholar
  15. 15.
    Medvedev, P., Fiume, M., Dzamba, M., Smith, T., Brudno, M.: Detecting copy number variation with mated short reads. Genome Res. 20(11), 1613–1622 (2010)CrossRefGoogle Scholar
  16. 16.
    Medvedev, P., Stanciu, M., Brudno, M.: Computational methods for discovering structural variation with next-generation sequencing. Nat. Methods 6, S13–S20 (2009)CrossRefGoogle Scholar
  17. 17.
    Oesper, L., Ritz, A., Aerni, S.J., Drebin, R., Raphael, B.J.: Reconstructing cancer genomes from paired-end sequencing data. BMC Bioinform. 13(6), S10 (2012)CrossRefGoogle Scholar
  18. 18.
    Van Loo, P., Nordgard, S.H., Lingjærde, O.C., Russnes, H.G., Rye, I.H., Sun, W., Weigman, V.J., Marynen, P., Zetterberg, A., Naume, B., et al.: Allele-specific copy number analysis of tumors. Proc. Nat. Acad. Sci. 107(39), 16910–16915 (2010)CrossRefGoogle Scholar
  19. 19.
    Wang, J., Mullighan, C.G., Easton, J., Roberts, S., Heatley, S.L., Ma, J., Rusch, M.C., Chen, K., Harris, C.C., Ding, L., et al.: Crest maps somatic structural variation in cancer genomes with base-pair resolution. Nat. Methods 8(8), 652–654 (2011)CrossRefGoogle Scholar
  20. 20.
    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)CrossRefGoogle Scholar
  21. 21.
    Zerbino, D.R., Ballinger, T., Paten, B., Hickey, G., Haussler, D.: Representing and decomposing genomic structural variants as balanced integer flows on sequence graphs. BMC Bioinform. 17(1), 400 (2016)CrossRefGoogle Scholar
  22. 22.
    Zheng, G.X., Lau, B.T., Schnall-Levin, M., Jarosz, M., Bell, J.M., Hindson, C.M., Kyriazopoulou-Panagiotopoulou, S., Masquelier, D.A., Merrill, L., Terry, J.M., et al.: Haplotyping germline and cancer genomes with high-throughput linked-read sequencing. Nat. Biotechnol. 34(3), 303–311 (2016)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Computational Biology Department, School of Computer ScienceCarnegie Mellon UniversityPittsburghUSA

Personalised recommendations