A statistical test on single-cell data reveals widespread recurrent mutations in tumor evolution
Abstract
The infinite sites assumption, which states that every genomic position mutates at most once over the lifetime of a tumor, is central to current approaches for reconstructing mutation histories of tumors, but has never been tested explicitly. We developed a rigorous statistical framework to test the assumption with single-cell sequencing data. The framework accounts for the high noise and contamination present in such data. We found strong evidence for recurrent mutations at the same site in 8 out of 9 single-cell sequencing datasets from human tumors. Six cases involved the loss of earlier mutations, five of which occurred at sites unaffected by large scale genomic deletions. Two cases exhibited parallel mutation, including the dataset with the strongest evidence of recurrence. Our results refute the general validity of the infinite sites assumption and indicate that more complex models are needed to adequately quantify intra-tumor heterogeneity.
02 /Specification & Classification
Click any tag to browse matching papers- Organism
- Assay
- Research Focus
- Approach
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Single-cell mutation calling and phylogenetic tree reconstruction with loss and recurrence
Jack Kuipers, Jochen Singer, Niko Beerenwinkel·BioinformaticsPeer-reviewed·Aug 24, 2022
- Tree inference for single-cell data
Katharina Jahn, Jack Kuipers, Niko Beerenwinkel·bioRxiv·Apr 9, 2016
- Inferring Cancer Progression from Single-cell Sequencing while Allowing Mutation Losses
Simone Ciccolella, Camir Ricketts, … Gianluca Della Vedova·BioinformaticsPeer-reviewed·Aug 11, 2020
- Integrative inference of subclonal tumour evolution from single-cell and bulk sequencing data
Salem Malikić, Katharina Jahn, … Niko Beerenwinkel·Nature CommunicationsPeer-reviewed·Jun 21, 2019
- FiMO: Inferring the Temporal Order of Mutations on Clonal Phylogeny under Finite-sites Models
Avesh Kumar Agrawal, Hamim Zafar·bioRxiv·Jan 23, 2022
- SiFit: A Method for Inferring Tumor Trees from Single-Cell Sequencing Data under Finite-site Models
Hamim Zafar, Anthony Tzen, … Luay K. Nakhleh·Genome biologyPeer-reviewed·Sep 19, 2017