Genome Biology (Nov 2023)

ConDoR: tumor phylogeny inference with a copy-number constrained mutation loss model

  • Palash Sashittal,
  • Haochen Zhang,
  • Christine A. Iacobuzio-Donahue,
  • Benjamin J. Raphael

DOI
https://doi.org/10.1186/s13059-023-03106-5
Journal volume & issue
Vol. 24, no. 1
pp. 1 – 23

Abstract

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Abstract A tumor contains a diverse collection of somatic mutations that reflect its past evolutionary history and that range in scale from single nucleotide variants (SNVs) to large-scale copy-number aberrations (CNAs). However, no current single-cell DNA sequencing (scDNA-seq) technology produces accurate measurements of both SNVs and CNAs, complicating the inference of tumor phylogenies. We introduce a new evolutionary model, the constrained k -Dollo model, that uses SNVs as phylogenetic markers but constrains losses of SNVs according to clusters of cells. We derive an algorithm, ConDoR, that infers phylogenies from targeted scDNA-seq data using this model. We demonstrate the advantages of ConDoR on simulated and real scDNA-seq data.

Keywords