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

Hosein Toosi,Iman Hajirasouliha,Ali Moeini

doi:10.1186/s12859-019-2824-3

Hosein Toosi, Iman Hajirasouliha + Show 1 more

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https://doi.org/10.1186/s12859-019-2824-3

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Journal: BMC bioinformatics	Publication Date: Jun 1, 2019
Citations: 9	License type: open-access

Affiliation: University of Tehran, Cornell University

Abstract

BackgroundIntra-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.ResultsIn 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 candidates, a list of trees describing the evolutionary relationships between the subclones is generated. These trees are sorted by their posterior probability. The posterior probability is calculated using a Bayesian model that integrates prior belief about the number of subclones, the composition of the tumor and the process of subclonal evolution. BAMSE also takes the sequencing error into account. We benchmarked BAMSE against state of the art software using simulated datasets.ConclusionsIn this work we developed a flexible and fast software to reconstruct the history of a tumor’s subclonal evolution using somatic mutation read counts across multiple samples. BAMSE software is implemented in Python and is available open source under GNU GLPv3 at https://github.com/HoseinT/BAMSE.

Highlights

Intra-tumor heterogeneity is known to contribute to cancer complexity and drug resistance
In his seminal paper, Peter Nowell [1] proposed the clonal evolution theorem. He hypothesized that single tumors consist of subclones with distinct genetic makeup, all descending from an initiating cancerous founder cell. These subclones are subject to Darwinian evolution in their environment. i.e. they may expand with rapid cell divisions, new subclones appear as mutations accumulate in earlier ones and subclones may vanish as a result of competition with each other
Four measures were used for the purpose of comparison: Algorithm 1: Algorithm for limiting the number of searched trees Data: mean variant allele fraction (VAF) for each cluster Ek,m, δ Result: Set of all trees T such that the Infinite Site Assumption (ISA) violation is less than δ along with UT, K × M matrix containing mean cellular fraction for subclones across samples

Summary

Introduction

Intra-tumor heterogeneity is known to contribute to cancer complexity and drug resistance. In his seminal paper, Peter Nowell [1] proposed the clonal evolution theorem. Using standard short-read sequencing, the number of reads supporting the reference allele and the number of those supporting the variant allele at each somatic variant locus, gives us an estimate on the variant allele fraction (VAF) of each somatic SNV Given these measurements (i.e. the variant allele fraction of each somatic SNV in each of the given samples), the intra-tumor heterogeneity deconvolution problem is to infer the normal contamination, number of subclones and mutations falling in each of them, and reconstructing the tumor phylogeny relating these subclones

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Conclusion