Skip to main content
SpringerLink
Log in

Tumor Copy Number Deconvolution Integrating Bulk and Single-Cell Sequencing Data

  • Conference paper
  • First Online:
Research in Computational Molecular Biology (RECOMB 2019)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 11467))

Abstract

Characterizing intratumor heterogeneity (ITH) is crucial to understanding cancer development, but it is hampered by limits of available data sources. Bulk DNA sequencing is the most common technology to assess ITH, but mixes many genetically distinct cells in each sample, which must then be computationally deconvolved. Single-cell sequencing (SCS) is a promising alternative, but its limitations—e.g., high noise, difficulty scaling to large populations, technical artifacts, and large data sets—have so far made it impractical for studying cohorts of sufficient size to identify statistically robust features of tumor evolution. We have developed strategies for deconvolution and tumor phylogenetics combining limited amounts of bulk and single-cell data to gain some advantages of single-cell resolution with much lower cost, with specific focus on deconvolving genomic copy number data. We developed a mixed membership model for clonal deconvolution via non-negative matrix factorization (NMF) balancing deconvolution quality with similarity to single-cell samples via an associated efficient coordinate descent algorithm. We then improve on that algorithm by integrating deconvolution with clonal phylogeny inference, using a mixed integer linear programming (MILP) model to incorporate a minimum evolution phylogenetic tree cost in the problem objective. We demonstrate the effectiveness of these methods on semi-simulated data of known ground truth, showing improved deconvolution accuracy relative to bulk data alone.

Supplementary material is available at bioRxiv (https://doi.org/10.1101/519892/).

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 59.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 74.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Barber, L.J., Davies, M.N., Gerlinger, M.: Dissecting cancer evolution at the macro-heterogeneity and micro-heterogeneity scale. Curr. Opin. Genet. Dev. 30, 1–6 (2015)

    Article  Google Scholar 

  2. Baslan, T., et al.: Genome-wide copy number analysis of single cells. Nat. Protoc. 7(6), 1024 (2012)

    Article  Google Scholar 

  3. Berry, M.W., Browne, M., Langville, A.N., Pauca, V.P., Plemmons, R.: Algorithms and applications for approximate nonnegative matrix factorization. Comput. Stat. Data Anal. 52(1), 155–173 (2007)

    Article  MathSciNet  Google Scholar 

  4. Chowdhury, S.A., et al.: Inferring models of multiscale copy number evolution for single-tumor phylogenetics. Bioinformatics 31(12), i258–i267 (2015)

    Article  Google Scholar 

  5. Chowdhury, S., Shackney, S., Heselmeyer-Haddad, K., Ried, T., Schäffer, 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), e1003740 (2014)

    Article  Google Scholar 

  6. Coyne, G.O., Takebe, N., Chen, A.P.: Defining precision: the precision medicine initiative trials NCI-IMPACT and NCI-MATCH. Curr. Probl. Cancer 41, 182–193 (2017)

    Article  Google Scholar 

  7. Deshwar, A.G., Vembu, S., Yung, C.K., Yang, G.H., Stein, L., Morris, Q.: PhyloWGS: reconstructing subclonal composition and evolution from whole-genome sequencing of tumors. Genome Biol. 16, 35 (2015)

    Article  Google Scholar 

  8. Dexter, D.L., Leith, J.T.: Tumor heterogeneity and drug resistance. J. Clin. Oncol. 4(2), 244–257 (1986)

    Article  Google Scholar 

  9. Eaton, J., Wang, J., Schwartz, R.: Deconvolution and phylogeny inference of structural variations in tumor genomic samples. Bioinformatics 34, i357–i365 (2018)

    Article  Google Scholar 

  10. El-Kebir, M., et al.: Complexity and algorithms for copy-number evolution problems. Algorithms Mol. Biol. 12(1), 13 (2017)

    Article  Google Scholar 

  11. El-Kebir, M., Satas, G., Oesper, L., Raphael, B.J.: Inferring the mutational history of a tumor using multi-state perfect phylogeny mixtures. Cell Syst. 3(1), 43–53 (2016)

    Article  Google Scholar 

  12. Fisher, R., Pusztai, L., Swanton, C.: Cancer heterogeneity: implications for targeted therapeutics. Br. J. Cancer 108(3), 479–485 (2013)

    Article  Google Scholar 

  13. Heselmeyer-Haddad, K., et al.: Single-cell genetic analysis of ductal carcinoma in situ and invasive breast cancer reveals enormous tumor heterogeneity yet conserved genomic imbalances and gain of MYC during progression. Am. J. Pathol. 181(5), 1807–1822 (2012)

    Article  Google Scholar 

  14. Hou, Y., et al.: Single-cell exome sequencing and monoclonal evolution of a JAK-2 negative myeloproliferative neoplasm. Cell 148(5), 873–885 (2012)

    Article  Google Scholar 

  15. Jahn, K., Kuipers, J., Beerenwinkel, N.: Tree inference for single-cell data. Genome Biol. 17(1), 86 (2016)

    Article  Google Scholar 

  16. Jiang, Y., Qiu, Y., Minn, A.J., Zhang, N.R.: Assessing intratumor heterogeneity and tracking longitudinal and spatial clonal evolutionary history by next-generation sequencing. Proc. Natl. Acad. Sci. 113(37), E5528–E5537 (2016)

    Article  Google Scholar 

  17. Kuipers, J., Jahn, K., Beerenwinkel, N.: Advances in understanding tumour evolution through single-cell sequencing. Biochimica et Biophysica Acta (BBA)-Rev. Cancer 1867(2), 127–138 (2017)

    Google Scholar 

  18. Lee, D.D., Seung, H.S.: Algorithms for non-negative matrix factorization. In: Advances in Neural Information Processing Systems, pp. 556–562 (2001)

    Google Scholar 

  19. Lei, H., Ma, F., Chapman, A., Lu, S., Xie, X.S.: Single-cell whole-genome amplification and sequencing: methodology and applications. Ann. Rev. Genomics Hum. Genet. 16, 79–102 (2015)

    Article  Google Scholar 

  20. Loeb, L.A.: A mutator phenotype in cancer. Cancer Res. 61(8), 3230–3239 (2001)

    Google Scholar 

  21. Macintyre, G., et al.: Copy number signatures and mutational processes in ovarian carcinoma. Nat. Genet. 50(9), 1262–1270 (2018)

    Article  Google Scholar 

  22. Malikic, S., et al.: PhISCS-a combinatorial approach for sub-perfect tumor phylogeny reconstruction via integrative use of single cell and bulk sequencing data. bioRxiv p. 376996 (2018)

    Google Scholar 

  23. Malikic, S., Jahn, K., Kuipers, J., Sahinalp, C., Beerenwinkel, N.: Integrative inference of subclonal tumour evolution from single-cell and bulk sequencing data. bioRxiv p. 234914 (2017)

    Google Scholar 

  24. Marusyk, A., Polyak, K.: Tumor heterogeneity: causes and consequences. Biochimica et Biophysica Acta (BBA)-Rev. Cancer 1805(1), 105–117 (2010)

    Google Scholar 

  25. McGranahan, N., et al.: Allele-specific HLA loss and immune escape in lung cancer evolution. Cell 171(6), 1259–1271 (2017)

    Article  Google Scholar 

  26. Navin, N., et al.: Tumour evolution inferred by single-cell sequencing. Nature 472(7341), 90–94 (2011)

    Article  Google Scholar 

  27. Nowell, P.C.: The clonal evolution of tumor cell populations. Science 194(4260), 23–28 (1976)

    Article  Google Scholar 

  28. Ortega, M.A., et al.: Using single-cell multiple omics approaches to resolve tumor heterogeneity. Clin. Transl. Med. 6, 46 (2017)

    Article  Google Scholar 

  29. Ross, E.M., Markowetz, F.: OncoNEM: inferring tumor evolution from single-cell sequencing data. Genome Biol. 17(1), 69 (2016)

    Article  Google Scholar 

  30. Schwartz, R., Schäffer, A.A.: The evolution of tumour phylogenetics: principles and practice. Nat. Rev. Genet. 18(4), 213–229 (2017)

    Article  Google Scholar 

  31. Schwartz, R., Shackney, S.E.: Applying unmixing to gene expression data for tumor phylogeny inference. BMC Bioinform. 11(1), 42 (2010)

    Article  Google Scholar 

  32. Schwarz, R.F., et al.: Spatial and temporal heterogeneity in high-grade serous ovarian cancer: a phylogenetic analysis. PLoS Med. 12(2), e1001789 (2015)

    Article  Google Scholar 

  33. Shackleton, M., Quintana, E., Fearon, E.R., Morrison, S.J.: Heterogeneity in cancer: cancer stem cells versus clonal evolution. Cell 138(5), 822–829 (2009)

    Article  Google Scholar 

  34. Siegel, R.L., et al.: Colorectal cancer statistics, 2017. CA: Cancer J. Clin. 67(3), 177–193 (2017)

    Google Scholar 

  35. Sridhar, S., Lam, F., Blelloch, G.E., Ravi, R., Schwartz, R.: Efficiently finding the most parsimonious phylogenetic tree via linear programming. In: Măndoiu, I., Zelikovsky, A. (eds.) ISBRA 2007. LNCS, vol. 4463, pp. 37–48. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-72031-7_4

    Chapter  Google Scholar 

  36. Subramanian, A., Schwartz, R.: Reference-free inference of tumor phylogenies from single-cell sequencing data. BMC Genomics 16(11), S7 (2015)

    Article  Google Scholar 

  37. Thurau, C., Kersting, K., Bauckhage, C.: Convex non-negative matrix factorization in the wild. In: 2009 Ninth IEEE International Conference on Data Mining, pp. 523–532, December 2009. https://doi.org/10.1109/ICDM.2009.55

  38. Tolliver, D., Tsourakakis, C., Subramanian, A., Shackney, S., Schwartz, R.: Robust unmixing of tumor states in array comparative genomic hybridization data. Bioinformatics 26(12), i106–i114 (2010)

    Article  Google Scholar 

  39. Wang, Y., et al.: Clonal evolution in breast cancer revealed by single nucleus genome sequencing. Nature 512(7513), 155–160 (2014)

    Article  Google Scholar 

  40. Wang, Y., Zhang, Y.: Nonnegative matrix factorization: a comprehensive review. IEEE Trans. Knowl. Data Eng. 25(6), 1336–1353 (2013)

    Article  Google Scholar 

  41. Williams, M.J., Werner, B., Barnes, C.P., Graham, T.A., Sottoriva, A.: Identification of neutral tumor evolution across cancer types. Nat. Genet. 48(3), 238–244 (2016)

    Article  Google Scholar 

  42. Wu, K., et al.: Diverse evolutionary dynamics in glioblastoma inference by multi-region and single-cell sequencing. J. Clin. Oncol. 34(15\_suppl), 11580 (2016)

    Google Scholar 

  43. Zaccaria, S., El-Kebir, M., Klau, G.W., Raphael, B.J.: Phylogenetic copy-number factorization of multiple tumor samples. J. Comput. Biol. 25(7), 689–708 (2018)

    Article  MathSciNet  Google Scholar 

  44. Zack, T.I., et al.: Pan-cancer patterns of somatic copy number alteration. Nat. Genet. 45(10), 1134–1140 (2013)

    Article  Google Scholar 

  45. Zafar, H., Tzen, A., Navin, N., Chen, K., Nakhleh, L.: SiFit: inferring tumor trees from single-cell sequencing data under finite-sites models. Genome Biol. 18(1), 178 (2017)

    Article  Google Scholar 

  46. Zahn, H., et al.: Scalable whole-genome single-cell library preparation without preamplification. Nat. Methods 14(2), 167 (2017)

    Article  Google Scholar 

Download references

Acknowledgements

This research was supported in part by the Intramural Research Program of the National Institutes of Health, National Library of Medicine and both Center for Cancer Research and Division of Cancer Epidemiology and Genetics within the National Cancer Institute. This research was supported in part by the Exploration Program of the Shenzhen Science and Technology Innovation Committee [JCYJ20170303151334808]. Portions of this work have been funded by U.S. N.I.H. award R21CA216452 and Pennsylvania Dept. of Health award 4100070287. The Pennsylvania Department of Health specifically disclaims responsibility for any analyses, interpretations or conclusions.

Author information

Authors and Affiliations

  1. Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA

    Haoyun Lei & Russell Schwartz

  2. Department of Mathematics, Rose-Hulman Institute of Technology, Terre Haute, IN, USA

    Bochuan Lyu

  3. National Center for Biotechnology Information, U.S. National Institutes of Health, Bethesda, MD, USA

    E. Michael Gertz & Alejandro A. Schäffer

  4. Cancer Data Science Laboratory, National Cancer Institute, U.S. National Institutes of Health, Bethesda, MD, USA

    E. Michael Gertz & Alejandro A. Schäffer

  5. BGI-Shenzhen, Shenzhen, China

    Xulian Shi, Kui Wu, Guibo Li, Liqin Xu & Yong Hou

  6. Laboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, U.S. National Institutes of Health, Gaithersburg, MD, USA

    Michael Dean

  7. Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA, USA

    Russell Schwartz

Authors
  1. Haoyun Lei
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bochuan Lyu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. E. Michael Gertz
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Alejandro A. Schäffer
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xulian Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Kui Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Guibo Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Liqin Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Yong Hou
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Michael Dean
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Russell Schwartz
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Russell Schwartz .

Editor information

Editors and Affiliations

  1. Tufts University, Cambridge, MA, USA

    Lenore J. Cowen

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Lei, H. et al. (2019). Tumor Copy Number Deconvolution Integrating Bulk and Single-Cell Sequencing Data. In: Cowen, L. (eds) Research in Computational Molecular Biology. RECOMB 2019. Lecture Notes in Computer Science(), vol 11467. Springer, Cham. https://doi.org/10.1007/978-3-030-17083-7_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-17083-7_11

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-17082-0

  • Online ISBN: 978-3-030-17083-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation