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Updated: Aug 28, 2025

A Practical Guide to Phylogenetics for Nonexperts

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

Reconstructing cancer phylogenies using Pairtree, a clone tree reconstruction algorithm.

Ethan Kulman¹, Jeff Wintersinger², Quaid Morris¹

¹Memorial Sloan Kettering Cancer Center, New York, NY, USA.

|September 21, 2022

Summary

This summary is machine-generated.

Pairtree reconstructs cancer evolution using somatic mutations. This algorithm builds clone trees to understand tumor development from multiple samples and subclonal populations.

Keywords:

Cancer Computer sciences Genetics

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Last Updated: Aug 28, 2025

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Published on: October 16, 2018

Area of Science:

Computational Biology
Cancer Genomics
Bioinformatics

Background:

Understanding cancer evolution is crucial for effective treatment.
Somatic mutations provide insights into tumor heterogeneity and clonal architecture.
Existing methods may face challenges in reconstructing complex cancer clone trees.

Purpose of the Study:

To introduce Pairtree, a novel algorithm for reconstructing cancer clone trees.
To provide a comprehensive protocol for using the Pairtree software package.
To enable detailed analysis of tumor evolutionary history using somatic mutation data.

Main Methods:

Utilizing somatic point mutations as the primary data source.
Implementing a step-by-step process for data preprocessing and mutation clustering.
Employing clone tree searching and visualization techniques within the Pairtree software.
Handling large datasets with up to 100 samples and numerous subclonal populations.

Main Results:

Successful reconstruction of cancer clone trees using the Pairtree algorithm.
Demonstration of Pairtree's capability to analyze complex tumor evolutionary histories.
Provision of a reproducible protocol for researchers in cancer genomics.

Conclusions:

Pairtree offers a robust method for inferring cancer evolutionary trajectories.
The Pairtree software package facilitates the analysis of subclonal architecture in cancer.
This approach aids in understanding cancer development and heterogeneity.