License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.WABI.2021.9
URN: urn:nbn:de:0030-drops-143624
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2021/14362/
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Sashittal, Palash ; Zaccaria, Simone ; El-Kebir, Mohammed

Parsimonious Clone Tree Reconciliation in Cancer

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LIPIcs-WABI-2021-9.pdf (2 MB)

Abstract

Every tumor is composed of heterogeneous clones, each corresponding to a distinct subpopulation of cells that accumulated different types of somatic mutations, ranging from single-nucleotide variants (SNVs) to copy-number aberrations (CNAs). As the analysis of this intra-tumor heterogeneity has important clinical applications, several computational methods have been introduced to identify clones from DNA sequencing data. However, due to technological and methodological limitations, current analyses are restricted to identifying tumor clones only based on either SNVs or CNAs, preventing a comprehensive characterization of a tumor’s clonal composition. To overcome these challenges, we formulate the identification of clones in terms of both SNVs and CNAs as a reconciliation problem while accounting for uncertainty in the input SNV and CNA proportions. We thus characterize the computational complexity of this problem and we introduce a mixed integer linear programming formulation to solve it exactly. On simulated data, we show that tumor clones can be identified reliably, especially when further taking into account the ancestral relationships that can be inferred from the input SNVs and CNAs. On 49 tumor samples from 10 prostate cancer patients, our reconciliation approach provides a higher resolution view of tumor evolution than previous studies.

BibTeX - Entry

@InProceedings{sashittal_et_al:LIPIcs.WABI.2021.9,
  author =	{Sashittal, Palash and Zaccaria, Simone and El-Kebir, Mohammed},
  title =	{{Parsimonious Clone Tree Reconciliation in Cancer}},
  booktitle =	{21st International Workshop on Algorithms in Bioinformatics (WABI 2021)},
  pages =	{9:1--9:21},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-200-6},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{201},
  editor =	{Carbone, Alessandra and El-Kebir, Mohammed},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2021/14362},
  URN =		{urn:nbn:de:0030-drops-143624},
  doi =		{10.4230/LIPIcs.WABI.2021.9},
  annote =	{Keywords: Intra-tumor heterogeneity, phylogenetics, mixed integer linear programming}
}

Keywords: Intra-tumor heterogeneity, phylogenetics, mixed integer linear programming
Collection: 21st International Workshop on Algorithms in Bioinformatics (WABI 2021)
Issue Date: 2021
Date of publication: 22.07.2021

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