Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Distance Measures for Tumor Evolutionary Trees

Zach DiNardo, Kiran Tomlinson, Anna Ritz, Layla Oesper
doi: https://doi.org/10.1101/591107
Zach DiNardo
1Department of Computer Science, Carleton College, Northfield, MN, USA.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Kiran Tomlinson
1Department of Computer Science, Carleton College, Northfield, MN, USA.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Anna Ritz
2Department of Biology, Reed College, Portland, OR, USA.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Layla Oesper
1Department of Computer Science, Carleton College, Northfield, MN, USA.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: loesper@carleton.edu
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

Abstract

In recent years, there has been increased interest in studying cancer by using algorithmic methods to infer the evolutionary tree underlying a tumor’s developmental history. Quantitative measures that compare such trees are then vital to benchmarking these algorithmic tree inference methods, understanding the structure of the space of possible trees for a given dataset, and clustering together similar trees in order to evaluate inheritance patterns. However, few appropriate distance measures exist, and those that do exist have low resolution for differentiating trees or do not fully account for the complex relationship between tree topology and how the mutations that label that topology are inherited. Here we present two novel distance measures, Common Ancestor Set distance (CASet) and Distinctly Inherited Set Comparison distance (DISC), that are specifically designed to account for the subclonal mutation inheritance patterns characteristic of tumor evolutionary trees. We apply CASet and DISC to two simulated and two breast cancer datasets and show that our distance measures allow for more nuanced and accurate delineation between tumor evolutionary trees than existing distance measures. Implementations of CASet and DISC are available at: https://bitbucket.org/oesperlab/stereodist.

Footnotes

  • ↵* Joint first author

  • This project is supported by NSF award IIS-1657380, Elledge, Eugster, and Class of’49 Fellowships from Carleton College

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
Back to top
PreviousNext
Posted March 28, 2019.
Download PDF
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Distance Measures for Tumor Evolutionary Trees
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Distance Measures for Tumor Evolutionary Trees
Zach DiNardo, Kiran Tomlinson, Anna Ritz, Layla Oesper
bioRxiv 591107; doi: https://doi.org/10.1101/591107
Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools
Distance Measures for Tumor Evolutionary Trees
Zach DiNardo, Kiran Tomlinson, Anna Ritz, Layla Oesper
bioRxiv 591107; doi: https://doi.org/10.1101/591107

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Bioinformatics
Subject Areas
All Articles
  • Animal Behavior and Cognition (4229)
  • Biochemistry (9118)
  • Bioengineering (6753)
  • Bioinformatics (23949)
  • Biophysics (12103)
  • Cancer Biology (9498)
  • Cell Biology (13746)
  • Clinical Trials (138)
  • Developmental Biology (7618)
  • Ecology (11666)
  • Epidemiology (2066)
  • Evolutionary Biology (15479)
  • Genetics (10621)
  • Genomics (14298)
  • Immunology (9468)
  • Microbiology (22808)
  • Molecular Biology (9083)
  • Neuroscience (48898)
  • Paleontology (355)
  • Pathology (1479)
  • Pharmacology and Toxicology (2566)
  • Physiology (3827)
  • Plant Biology (8319)
  • Scientific Communication and Education (1467)
  • Synthetic Biology (2294)
  • Systems Biology (6172)
  • Zoology (1297)