Document Open Access Logo

Sapling: Inferring and Summarizing Tumor Phylogenies from Bulk Data Using Backbone Trees

Authors Yuanyuan Qi , Mohammed El-Kebir

PDF

File

Thumbnail PDF
PDF
LIPIcs.WABI.2024.7.pdf
  • Filesize: 11.55 MB
  • 19 pages

Document Identifiers

Subject Classification

ACM Subject Classification
  • Applied computing → Computational biology
Keywords
  • Cancer
  • intra-tumor heterogeneity
  • consensus
  • maximum agreement

Metrics

  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    0
    PDF Downloads
    0
    Metadata Views

Abstract

Cancer phylogenies are key to understanding tumor evolution. There exist many important downstream analyses that take as input a single or a small number of trees. However, due to uncertainty, one typically infers many, equally-plausible phylogenies from bulk DNA sequencing data of tumors. We introduce Sapling, a heuristic method to solve the Backbone Tree Inference from Reads problem, which seeks a small set of backbone trees on a smaller subset of mutations that collectively summarize the entire solution space. Sapling also includes a greedy algorithm to solve the Backbone Tree Expansion from Reads problem, which aims to expand an inferred backbone tree into a full tree. We prove that both problems are NP-hard. On simulated and real data, we demonstrate that Sapling is capable of inferring high-quality backbone trees that adequately summarize the solution space and that can be expanded into full trees.

Cite As Get BibTex

Yuanyuan Qi and Mohammed El-Kebir. Sapling: Inferring and Summarizing Tumor Phylogenies from Bulk Data Using Backbone Trees. In 24th International Workshop on Algorithms in Bioinformatics (WABI 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 312, pp. 7:1-7:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024) https://doi.org/10.4230/LIPIcs.WABI.2024.7

Author Details

Yuanyuan Qi
  • Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA
Mohammed El-Kebir
  • Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA
  • Cancer Center at Illinois, University of Illinois at Urbana-Champaign, Urbana, IL, USA

Funding

  • El-Kebir, Mohammed: National Science Foundation award number CCF 2046488.

Supplementary Materials

References

  1. Aguse et al. Summarizing the solution space in tumor phylogeny inference by multiple consensus trees. Bioinformatics, 35(14):i408-i416, 2019. Google Scholar
  2. Giulio Caravagna, Ylenia Giarratano, Daniele Ramazzotti, Ian Tomlinson, Trevor A Graham, Guido Sanguinetti, and Andrea Sottoriva. Detecting repeated cancer evolution from multi-region tumor sequencing data. Nature methods, 15(9):707-714, 2018. Google Scholar
  3. Cayley. A theorem on trees. Quart. J. Math., 23:376-378, 1878. Google Scholar
  4. Christensen et al. Detecting evolutionary patterns of cancers using consensus trees. Bioinformatics, 36(Supplement_2):i684-i691, 2020. Google Scholar
  5. Deshwar et al. PhyloWGS: reconstructing subclonal composition and evolution from whole-genome sequencing of tumors. Genome biology, 16:1-20, 2015. Google Scholar
  6. DiNardo et al. Distance measures for tumor evolutionary trees. Bioinformatics, 36(7):2090-2097, 2020. Google Scholar
  7. El-Kebir et al. Reconstruction of clonal trees and tumor composition from multi-sample sequencing data. Bioinformatics, 31(12):i62-i70, June 2015. URL: https://doi.org/10.1093/bioinformatics/btv261.
  8. El-Kebir et al. Inferring the mutational history of a tumor using multi-state perfect phylogeny mixtures. Cell systems, 3(1):43-53, 2016. Google Scholar
  9. Fu and Schwartz. ConTreeDP: A consensus method of tumor trees based on maximum directed partition support problem. In 2021 BIBM, pages 125-130. IEEE, 2021. Google Scholar
  10. Govek et al. A Consensus Approach to Infer Tumor Evolutionary Histories. BCB, pages 63-72, 2018. Google Scholar
  11. Govek et al. GraphyC: Using consensus to infer tumor evolution. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 19(1):465-478, 2020. Google Scholar
  12. Guang et al. A weighted distance-based approach for deriving consensus tumor evolutionary trees. Bioinformatics, 39(Supplement 1):i204-i212, June 2023. URL: https://doi.org/10.1093/bioinformatics/btad230.
  13. Hodzic et al. Identification of conserved evolutionary trajectories in tumors. Bioinformatics, 36(Supplement_1):i427-i435, 2020. Google Scholar
  14. Ivanovic and El-Kebir. Modeling and predicting cancer clonal evolution with reinforcement learning. Genome Research, 33(7):1078-1088, 2023. Google Scholar
  15. Jamal-Hanjani et al. Tracking the evolution of non-small-cell lung cancer. New England Journal of Medicine, 376(22):2109-2121, 2017. Google Scholar
  16. Karpov et al. A multi-labeled tree dissimilarity measure for comparing “clonal trees” of tumor progression. Algorithms for Molecular Biology, 14(1):1-18, 2019. Google Scholar
  17. Khakabimamaghani et al. Collaborative intra-tumor heterogeneity detection. Bioinformatics, 35:i379-i388, 2019. Google Scholar
  18. Kulman et al. Reconstructing cancer phylogenies using pairtree, a clone tree reconstruction algorithm. STAR protocols, 3(4):101706, 2022. Google Scholar
  19. Ethan Kulman, Rui Kuang, and Quaid Morris. Orchard: building large cancer phylogenies using stochastic combinatorial search. arXiv preprint arXiv:2311.12917, 2023. Google Scholar
  20. Lohr et al. Widespread genetic heterogeneity in multiple myeloma: Implications for targeted therapy. Cancer Cell, 25(1):91-101, January 2014. URL: https://doi.org/10.1016/j.ccr.2013.12.015.
  21. Malikic et al. Clonality inference in multiple tumor samples using phylogeny. Bioinformatics, 31(9):1349-1356, 2015. Google Scholar
  22. Nemirovski. Interior point polynomial time methods in convex programming. Lecture notes, 42(16):3215-3224, 2004. Google Scholar
  23. Nowell. The clonal evolution of tumor cell populations. Science, 194(4260):23-8, October 1976. Google Scholar
  24. Prufer. Neuer beweis eines satzes uber per mutationen. Archiv der Mathematik und Physik, 27:742-744, 1918. Google Scholar
  25. Qi et al. Implications of non-uniqueness in phylogenetic deconvolution of bulk DNA samples of tumors. Algorithms for Molecular Biology, 14(1):19, December 2019. URL: https://doi.org/10.1186/s13015-019-0155-6.
  26. Schwartz and Schäffer. The evolution of tumour phylogenetics: principles and practice. Nature Reviews Genetics, 18(4):213-229, April 2017. URL: https://doi.org/10.1038/nrg.2016.170.
  27. Sundermann et al. Reconstructing tumor evolutionary histories and clone trees in polynomial-time with submarine. PLoS CB, 17(1):e1008400, 2021. Google Scholar
  28. Warnow. Computational phylogenetics: an introduction to designing methods for phylogeny estimation. Cambridge University Press, 2017. Google Scholar
  29. Wintersinger et al. Reconstructing complex cancer evolutionary histories from multiple bulk DNA samples using pairtree. Blood Cancer Discovery, 3(3):208-219, 2022. Google Scholar
Any Issues?
X

Feedback on the Current Page

CAPTCHA

Thanks for your feedback!

Feedback submitted to Dagstuhl Publishing

Could not send message

Please try again later or send an E-mail
© 2023-2025 Schloss Dagstuhl – LZI GmbH About DROPS Imprint Privacy Contact