Abstract
Tumors exhibit extensive intra-tumor heterogeneity, the presence of groups of cellular populations with distinct sets of somatic mutations. This heterogeneity is the result of an evolutionary process, described by a phylogenetic tree. The problem of reconstructing a phylogenetic tree T given bulk sequencing data from a tumor is more complicated than the classic phylogeny inference problem. Rather than observing the leaves of T directly, we are given mutation frequencies that are the result of mixtures of the leaves of T. The majority of current tumor phylogeny inference methods employ the perfect phylogeny evolutionary model. In this work, we show that the underlying Perfect Phylogeny Mixture combinatorial problem typically has multiple solutions. We provide a polynomial-time computable upper bound on the number of solutions. We use simulations to identify factors that contribute to and counteract non-uniqueness of solutions. In addition, we study the sampling performance of current methods, identifying significant biases.
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Notes
- 1.
We expect the counting problem #PPM to be #P-complete, as to date no NP-complete problem has been found whose counting version is not NP-complete [14]. To prove that #PPM is #P-complete, we need to give a parsimonious reduction from a known #P-complete problem to #PPM.
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Acknowledgements
This research is part of the Blue Waters sustained-petascale computing project, which is supported by the National Science Foundation (awards OCI-0725070 and ACI-1238993) and the state of Illinois. Blue Waters is a joint effort of the University of Illinois at Urbana-Champaign and its National Center for Supercomputing Applications. The authors thank the anonymous referees for insightful comments that have improved the manuscript.
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Pradhan, D., El-Kebir, M. (2018). On the Non-uniqueness of Solutions to the Perfect Phylogeny Mixture Problem. In: Blanchette, M., Ouangraoua, A. (eds) Comparative Genomics. RECOMB-CG 2018. Lecture Notes in Computer Science(), vol 11183. Springer, Cham. https://doi.org/10.1007/978-3-030-00834-5_16
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DOI: https://doi.org/10.1007/978-3-030-00834-5_16
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