Complexity and algorithms for finding a perfect phylogeny from mixed tumor samples

TL;DR

This paper investigates the computational complexity of finding perfect phylogenies from mixed tumor samples, disproves previous claims of polynomial solvability, proves NP-hardness, and offers heuristic and exact algorithms for specific cases.

Contribution

It corrects prior misconceptions by establishing the NP-hardness of the problem and introduces algorithms for practical and special cases.

Findings

Proves the problem is NP-hard, correcting previous claims.

Establishes NP-completeness of a related variant.

Provides heuristic and polynomial-time algorithms for specific instances.

Abstract

Recently, Hajirasouliha and Raphael (WABI 2014) proposed a model for deconvoluting mixed tumor samples measured from a collection of high-throughput sequencing reads. This is related to understanding tumor evolution and critical cancer mutations. In short, their formulation asks to split each row of a binary matrix so that the resulting matrix corresponds to a perfect phylogeny and has the minimum number of rows among all matrices with this property. In this paper we disprove several claims about this problem, including an NP-hardness proof of it. However, we show that the problem is indeed NP-hard, by providing a different proof. We also prove NP-completeness of a variant of this problem proposed in the same paper. On the positive side, we propose an efficient (though not necessarily optimal) heuristic algorithm based on coloring co-comparability graphs, and a polynomial time algorithm for solving the problem optimally on matrix instances in which no column is contained in both columns of a pair of conflicting columns. Implementations of these algorithms are freely available at https://github.com/alexandrutomescu/MixedPerfectPhylogeny