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Exact and Efficient Inference of Tumor Phylogenies via Novel Pruning Techniques

Authors Juan Luque , Jacob Gilbert, Arjun Subramanian, Aravind Srinivasan , Salem Malikic , S. Cenk Sahinalp

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Subject Classification

ACM Subject Classification
  • Theory of computation → Design and analysis of algorithms
Keywords
  • Branch and Bound
  • Vertex Cover
  • Linear Programming
  • Tumor Evolution
  • Single-Cell Sequencing

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Abstract

Reconstructing the evolutionary history of tumors using single-cell sequencing (SCS) data presents significant computational challenges. Existing approaches are either computationally intractable for emerging large-scale datasets or rely on heuristics that lack optimality guarantees. In this work, we propose a novel, time-efficient algorithm that constructs the phylogenetic tree of tumor evolution with a provable guarantee of optimality.
Our main result is a branch-and-bound algorithm that reconstructs the most likely tumor evolutionary history up to two orders of magnitude faster than the previous best algorithm. To achieve this, we use efficient and well-known 2-approximation algorithms for the Vertex Cover problem to prune the branch-and-bound tree effectively. Unlike previous works' polynomial-time branch-and-bound bounding strategies, our bounding algorithm provides strong worst-case theoretical guarantees, leading to faster reconstruction of the tumor evolution.

Cite As Get BibTex

Juan Luque, Jacob Gilbert, Arjun Subramanian, Aravind Srinivasan, Salem Malikic, and S. Cenk Sahinalp. Exact and Efficient Inference of Tumor Phylogenies via Novel Pruning Techniques. In 26th International Conference on Algorithms for Bioinformatics (WABI 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 390, pp. 10:1-10:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026) https://doi.org/10.4230/LIPIcs.WABI.2026.10

Author Details

Juan Luque
  • Department of Computer Science, University of Maryland, College Park, MD, USA
Jacob Gilbert
  • Cancer Data Science Laboratory, National Cancer Institute, Bethesda, MD, USA
Arjun Subramanian
  • The Pingry School, Basking Ridge, NJ, USA
Aravind Srinivasan
  • Department of Computer Science, University of Maryland, College Park, MD, USA
Salem Malikic
  • Cancer Data Science Laboratory, National Cancer Institute, Bethesda, MD, USA
S. Cenk Sahinalp
  • Cancer Data Science Laboratory, National Cancer Institute, Bethesda, MD 20894, USA

Funding

S.C.S., S.M. and J.G. were supported by the Intramural Research Program of the National Cancer Institute (NCI), National Institutes of Health.

Supplementary Materials

References

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