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Distance measures for tumor evolutionary trees.

Zach DiNardo1, Kiran Tomlinson1,2, Anna Ritz3

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

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|November 22, 2019
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Summary
This summary is machine-generated.

New distance measures, Common Ancestor Set distance (CASet) and Distinctly Inherited Set Comparison distance (DISC), accurately differentiate tumor evolutionary trees by accounting for subclonal mutation patterns.

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Area of Science:

  • Computational Biology
  • Cancer Research
  • Evolutionary Biology

Background:

  • Inferring tumor evolutionary trees is crucial for understanding cancer development.
  • Existing methods for comparing tumor trees lack resolution and do not fully capture mutation inheritance patterns.
  • There is a need for robust quantitative measures to benchmark tree inference and analyze inheritance across patients.

Purpose of the Study:

  • To introduce novel distance measures for comparing tumor evolutionary trees.
  • To address limitations of existing measures in accounting for subclonal mutation inheritance.
  • To provide tools for more accurate delineation of tumor evolutionary histories.

Main Methods:

  • Developed two new distance measures: Common Ancestor Set distance (CASet) and Distinctly Inherited Set Comparison distance (DISC).
  • Designed these measures to specifically account for subclonal mutation inheritance patterns in tumor evolution.
  • Applied CASet and DISC to simulated datasets and real breast cancer data.

Main Results:

  • CASet and DISC demonstrate higher resolution in differentiating tumor evolutionary trees compared to existing methods.
  • The novel measures provide a more nuanced analysis of subclonal mutation inheritance.
  • Successful application to both simulated and empirical breast cancer datasets.

Conclusions:

  • CASet and DISC offer improved accuracy and resolution for comparing tumor evolutionary trees.
  • These measures enhance the ability to study cancer evolution and inheritance patterns.
  • The developed tools facilitate benchmarking of tree inference algorithms and clinical applications.