BAMSE: Bayesian model selection for tumor phylogeny inference among multiple samples.

ABSTRACT:

Background

Intra-tumor heterogeneity is known to contribute to cancer complexity and drug resistance. Understanding the number of distinct subclones and the evolutionary relationships between them is scientifically and clinically very important and still a challenging problem.

Results

In this paper, we present BAMSE (BAyesian Model Selection for tumor Evolution), a new probabilistic method for inferring subclonal history and lineage tree reconstruction of heterogeneous tumor samples. BAMSE uses somatic mutation read counts as input and can leverage multiple tumor samples accurately and efficiently. In the first step, possible clusterings of mutations into subclones are scored and a user defined number are selected for further analysis. In the next step, for each of these can

SUBMITTER: Toosi H 

PROVIDER: S-EPMC6551234 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature