UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Parsimonious clone tree reconciliation in cancer

Sashittal, P; Zaccaria, S; El-Kebir, M; (2021) Parsimonious clone tree reconciliation in cancer. In: 21st International Workshop on Algorithms in Bioinformatics (WABI 2021). (pp. 9:1-9:21). Schloss Dagstuhl -- Leibniz-Zentrum für Informatik: Dagstuhl, Germany. Green open access

[thumbnail of LIPIcs-WABI-2021-9.pdf]
Preview
Text
LIPIcs-WABI-2021-9.pdf - Accepted Version

Download (1MB) | Preview

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.

Type: Proceedings paper
Title: Parsimonious clone tree reconciliation in cancer
Event: 21st International Workshop on Algorithms in Bioinformatics (WABI 2021)
ISBN-13: 978-3-95977-200-6
Open access status: An open access version is available from UCL Discovery
DOI: 10.4230/LIPIcs.WABI.2021.9
Publisher version: https://doi.org/10.4230/LIPIcs.WABI.2021.9
Language: English
Additional information: © Palash Sashittal, Simone Zaccaria, and Mohammed El-Kebir; licensed under Creative Commons License CC-BY 4.0
Keywords: Intra-tumor heterogeneity, phylogenetics, mixed integer linear programming
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Cancer Institute
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Cancer Institute > Research Department of Oncology
URI: https://discovery.ucl.ac.uk/id/eprint/10135269
Downloads since deposit
61Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item