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Reconstructing Complex Cancer Evolutionary Histories from Multiple Bulk DNA Samples Using Pairtree.

Jeff A Wintersinger1,2,3,4, Stephanie M Dobson5,6, Ethan Kulman7

  • 1Department of Computer Science, University of Toronto, Toronto, Ontario, Canada.

Blood Cancer Discovery
|March 5, 2022
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Summary
This summary is machine-generated.

Pairtree is a new computational method that builds more accurate cancer clone trees using DNA sequencing data. This tool enhances understanding of cancer evolution and can inform treatment strategies.

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Area of Science:

  • Cancer genomics
  • Computational biology
  • Evolutionary biology

Background:

  • Cancers comprise genetically diverse malignant cell subpopulations.
  • Clone trees, derived from DNA sequencing, map cancer evolutionary relationships.
  • Accurate clone trees are crucial for understanding disease progression and guiding therapy.

Purpose of the Study:

  • To introduce Pairtree, a novel computational method for constructing cancer clone trees.
  • To improve the accuracy and detail of clone tree reconstructions from DNA sequencing data.
  • To provide a more robust tool for analyzing cancer subclonal architecture and evolution.

Main Methods:

  • Pairtree utilizes variant allele frequency data from one or more bulk cancer samples.
  • It constructs a Pairs Tensor to capture pairwise subpopulation evolutionary relationships.
  • The method constrains clone trees and infers violations of the infinite sites assumption.

Main Results:

  • Pairtree accurately builds detailed clone trees from up to 100 cancer samples.
  • The method effectively handles cancers with 30 or more subclonal populations.
  • On B-progenitor acute lymphoblastic leukemias, Pairtree matched or surpassed expert reconstructions.

Conclusions:

  • Pairtree offers a significant advancement in the accuracy and resolution of cancer clone tree construction.
  • This method enhances the ability to study cancer evolution and identify key developmental junctures.
  • Pairtree has the potential to refine our understanding of cancer heterogeneity and inform clinical decision-making.