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OncoNEM: inferring tumor evolution from single-cell sequencing data.

Published version
Peer-reviewed

Repository DOI


Change log

Authors

Ross, Edith M 

Abstract

Single-cell sequencing promises a high-resolution view of genetic heterogeneity and clonal evolution in cancer. However, methods to infer tumor evolution from single-cell sequencing data lag behind methods developed for bulk-sequencing data. Here, we present OncoNEM, a probabilistic method for inferring intra-tumor evolutionary lineage trees from somatic single nucleotide variants of single cells. OncoNEM identifies homogeneous cellular subpopulations and infers their genotypes as well as a tree describing their evolutionary relationships. In simulation studies, we assess OncoNEM's robustness and benchmark its performance against competing methods. Finally, we show its applicability in case studies of muscle-invasive bladder cancer and essential thrombocythemia.

Description

Keywords

Cancer evolution, Phylogenetic tree, Single-cell sequencing, Tumor evolution, Tumor heterogeneity, Algorithms, Clonal Evolution, Computational Biology, Evolution, Molecular, Genotype, High-Throughput Nucleotide Sequencing, Humans, Models, Statistical, Phylogeny, Polymorphism, Single Nucleotide, Single-Cell Analysis, Thrombocytopenia, Urinary Bladder Neoplasms

Journal Title

Genome Biol

Conference Name

Journal ISSN

1474-7596
1474-760X

Volume Title

17

Publisher

Springer Science and Business Media LLC
Sponsorship
Cancer Research UK (C14303/A17197)
Cancer Research UK (CB4320)
The authors would like to acknowledge the support of the University of Cambridge, Cancer Research UK and Hutchison Whampoa Limited. This work was funded by CRUK core grant C14303/A17197.