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

Authors Yuanyuan Qi , Mohammed El-Kebir

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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.

Cite AsGet 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

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.

Subject Classification

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

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Supplementary Materials

References

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