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Phyolin: Identifying a Linear Perfect Phylogeny in Single-Cell DNA Sequencing Data of Tumors

Authors Leah L. Weber, Mohammed El-Kebir

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Author Details

Leah L. Weber
  • Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL
Mohammed El-Kebir
  • Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL

Funding

  • El-Kebir, Mohammed: This work was supported by the National Science Foundation under award number CCF 1850502.

Acknowledgements

This work was a project in the course CS598MEB (Computational Cancer Genomics, Spring 2020) at UIUC. We thank the students in this course for their valuable feedback.

Cite AsGet BibTex

Leah L. Weber and Mohammed El-Kebir. Phyolin: Identifying a Linear Perfect Phylogeny in Single-Cell DNA Sequencing Data of Tumors. In 20th International Workshop on Algorithms in Bioinformatics (WABI 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 172, pp. 5:1-5:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)
https://doi.org/10.4230/LIPIcs.WABI.2020.5

Abstract

Cancer arises from an evolutionary process where somatic mutations occur and eventually give rise to clonal expansions. Modeling this evolutionary process as a phylogeny is useful for treatment decision-making as well as understanding evolutionary patterns across patients and cancer types. However, cancer phylogeny inference from single-cell DNA sequencing data of tumors is challenging due to limitations with sequencing technology and the complexity of the resulting problem. Therefore, as a first step some value might be obtained from correctly classifying the evolutionary process as either linear or branched. The biological implications of these two high-level patterns are different and understanding what cancer types and which patients have each of these trajectories could provide useful insight for both clinicians and researchers. Here, we introduce the Linear Perfect Phylogeny Flipping Problem as a means of testing a null model that the tree topology is linear and show that it is NP-hard. We develop Phyolin and, through both in silico experiments and real data application, show that it is an accurate, easy to use and a reasonably fast method for classifying an evolutionary trajectory as linear or branched.

Subject Classification

ACM Subject Classification
  • Applied computing → Molecular evolution
Keywords
  • Constraint programming
  • intra-tumor heterogeneity
  • combinatorial optimization

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