Dataset Information

MIPUP: minimum perfect unmixed phylogenies for multi-sampled tumors via branchings and ILP.

ABSTRACT:

Motivation

Discovering the evolution of a tumor may help identify driver mutations and provide a more comprehensive view on the history of the tumor. Recent studies have tackled this problem using multiple samples sequenced from a tumor, and due to clinical implications, this has attracted great interest. However, such samples usually mix several distinct tumor subclones, which confounds the discovery of the tumor phylogeny.

Results

We study a natural problem formulation requiring to decompose the tumor samples into several subclones with the objective of forming a minimum perfect phylogeny. We propose an Integer Linear Programming formulation for it, and implement it into a method called MIPUP. We tested the ability of MIPUP and of four popular tools LICHeE, AncesTree, CITUP, Treeomics to reconstruct the tumor phylogeny. On simulated data, MIPUP shows up to a 34% improvement under the ancestor-descendant relations metric. On four real datasets, MIPUP's reconstructions proved to be generally more faithful than those of LICHeE.

Availability and implementation

MIPUP is available at https://github.com/zhero9/MIPUP as open source.

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Husic E

PROVIDER: S-EPMC6394401 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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Publications

MIPUP: minimum perfect unmixed phylogenies for multi-sampled tumors via branchings and ILP.

Husić Edin E Li Xinyue X Hujdurović Ademir A Mehine Miika M Rizzi Romeo R Mäkinen Veli V Milanič Martin M Tomescu Alexandru I AI

Bioinformatics (Oxford, England) 20190301 5

<h4>Motivation</h4>Discovering the evolution of a tumor may help identify driver mutations and provide a more comprehensive view on the history of the tumor. Recent studies have tackled this problem using multiple samples sequenced from a tumor, and due to clinical implications, this has attracted great interest. However, such samples usually mix several distinct tumor subclones, which confounds the discovery of the tumor phylogeny.<h4>Results</h4>We study a natural problem formulation requiring ...[more]

PMID: 30101335

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Dataset Information

MIPUP: minimum perfect unmixed phylogenies for multi-sampled tumors via branchings and ILP.

Motivation

Results

Availability and implementation

Supplementary information

Publications

MIPUP: minimum perfect unmixed phylogenies for multi-sampled tumors via branchings and ILP.

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