Tumor phylogeny inference using tree-constrained importance sampling.

ABSTRACT:

Motivation

A tumor arises from an evolutionary process that can be modeled as a phylogenetic tree. However, reconstructing this tree is challenging as most cancer sequencing uses bulk tumor tissue containing heterogeneous mixtures of cells.

Results

We introduce P robabilistic A lgorithm for S omatic Tr ee I nference (PASTRI), a new algorithm for bulk-tumor sequencing data that clusters somatic mutations into clones and infers a phylogenetic tree that describes the evolutionary history of the tumor. PASTRI uses an importance sampling algorithm that combines a probabilistic model of DNA sequencing data with a enumeration algorithm based on the combinatorial constraints defined by the underlying phylogenetic tree. As a result, tree inference is fast, accurate and robust to nois

SUBMITTER: Satas G 

PROVIDER: S-EPMC5870673 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature