Reconstructing clonal tree for phylo-phenotypic characterization of cancer using single-cell transcriptomics.


Journal

Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555

Informations de publication

Date de publication:
22 02 2023
Historique:
received: 17 06 2021
accepted: 20 01 2023
entrez: 22 2 2023
pubmed: 23 2 2023
medline: 25 2 2023
Statut: epublish

Résumé

Functional characterization of the cancer clones can shed light on the evolutionary mechanisms driving cancer's proliferation and relapse mechanisms. Single-cell RNA sequencing data provide grounds for understanding the functional state of cancer as a whole; however, much research remains to identify and reconstruct clonal relationships toward characterizing the changes in functions of individual clones. We present PhylEx that integrates bulk genomics data with co-occurrences of mutations from single-cell RNA sequencing data to reconstruct high-fidelity clonal trees. We evaluate PhylEx on synthetic and well-characterized high-grade serous ovarian cancer cell line datasets. PhylEx outperforms the state-of-the-art methods both when comparing capacity for clonal tree reconstruction and for identifying clones. We analyze high-grade serous ovarian cancer and breast cancer data to show that PhylEx exploits clonal expression profiles beyond what is possible with expression-based clustering methods and clear the way for accurate inference of clonal trees and robust phylo-phenotypic analysis of cancer.

Identifiants

pubmed: 36813776
doi: 10.1038/s41467-023-36202-y
pii: 10.1038/s41467-023-36202-y
pmc: PMC9946941
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

982

Informations de copyright

© 2023. The Author(s).

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Auteurs

Seong-Hwan Jun (SH)

SciLifeLab, School of EECS, KTH Royal Institute of Technology, Stockholm, Sweden.
Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, USA.

Hosein Toosi (H)

SciLifeLab, School of EECS, KTH Royal Institute of Technology, Stockholm, Sweden.

Jeff Mold (J)

Department of Cell and Molecular Biology, Karolinska Institutet, Solna, Sweden.

Camilla Engblom (C)

Department of Cell and Molecular Biology, Karolinska Institutet, Solna, Sweden.

Xinsong Chen (X)

Department of Oncology and Pathology, Karolinska Institutet, Solna, Sweden.

Ciara O'Flanagan (C)

Department of Molecular Oncology, BC Cancer, Vancouver, BC, Canada.

Michael Hagemann-Jensen (M)

Department of Cell and Molecular Biology, Karolinska Institutet, Solna, Sweden.

Rickard Sandberg (R)

Department of Cell and Molecular Biology, Karolinska Institutet, Solna, Sweden.

Samuel Aparicio (S)

Department of Molecular Oncology, BC Cancer, Vancouver, BC, Canada.
Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, Canada.

Johan Hartman (J)

Department of Oncology and Pathology, Karolinska Institutet, Solna, Sweden.
Department of Clinical Pathology and Cytology, Karolinska University Laboratory, Stockholm, Sweden.

Andrew Roth (A)

Department of Molecular Oncology, BC Cancer, Vancouver, BC, Canada. aroth@bccrc.ca.
Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, Canada. aroth@bccrc.ca.
Department of Computer Science, University of British Columbia, Vancouver, Canada. aroth@bccrc.ca.

Jens Lagergren (J)

SciLifeLab, School of EECS, KTH Royal Institute of Technology, Stockholm, Sweden. jens.lagergren@scilifelab.se.

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Classifications MeSH