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Comparative Lesions Analysis Through a Targeted Sequencing Approach

Comparative Lesions Analysis Through a Targeted Sequencing Approach

Published on: November 5, 2019

A weighted distance-based approach for deriving consensus tumor evolutionary trees.

Ziyun Guang¹, Matthew Smith-Erb¹, Layla Oesper¹

¹Department of Computer Science, Carleton College, Northfield, MN 55057, USA.

Bioinformatics (Oxford, England)

|June 30, 2023

Summary

This summary is machine-generated.

We developed TuELiP, an algorithm to create a consensus tumor evolutionary tree from multiple inferred trees. This method improves accuracy by incorporating confidence weights, outperforming existing approaches on simulated and real cancer data.

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Area of Science:

Computational biology
Cancer genomics
Phylogenetics

Background:

Somatic mutations in tumors form evolutionary trees, but these are inferred, not directly observed.
Existing algorithms can yield conflicting tumor trees for the same patient.
Combining multiple inferred tumor trees into a consensus tree is crucial for accurate evolutionary history reconstruction.

Purpose of the Study:

To introduce the Weighted m-Tumor Tree Consensus Problem (W-m-TTCP) for creating consensus tumor trees.
To present TuELiP, an algorithm solving the W-m-TTCP using integer linear programming.
To enable differential weighting of input tumor trees for improved consensus.

Main Methods:

Integer linear programming formulation for the Weighted m-Tumor Tree Consensus Problem.
Development of the TuELiP algorithm to solve the W-m-TTCP.
Evaluation using simulated datasets and a Triple-Negative Breast Cancer dataset.

Main Results:

TuELiP demonstrated superior performance in identifying the true evolutionary tree compared to two existing methods on simulated data.
Incorporating confidence weights into the consensus process led to more accurate tree inference.
Analysis of a Triple-Negative Breast Cancer dataset highlighted the significant impact of confidence weights on the resulting consensus tree.

Conclusions:

TuELiP provides an effective method for constructing weighted consensus tumor evolutionary trees.
The use of confidence weights enhances the accuracy of phylogenetic inference in cancer genomics.
The TuELiP algorithm and associated data are publicly available for further research.