Abstract

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.

The functional changes of individual clones in single cell RNA sequencing (scRNA-seq) data remain elusive. Here, the authors develop PhylEx that integrates bulk genomics data with co-occurrences of mutations revealed by scRNA-seq data and apply it to high-grade serous ovarian cancer cell line and breast cancer datasets.

Details

Title
Reconstructing clonal tree for phylo-phenotypic characterization of cancer using single-cell transcriptomics
Author
Jun, Seong-Hwan 1   VIAFID ORCID Logo  ; Toosi, Hosein 2 ; Mold, Jeff 3 ; Engblom, Camilla 3   VIAFID ORCID Logo  ; Chen, Xinsong 4   VIAFID ORCID Logo  ; O’Flanagan, Ciara 5 ; Hagemann-Jensen, Michael 3   VIAFID ORCID Logo  ; Sandberg, Rickard 3   VIAFID ORCID Logo  ; Aparicio, Samuel 6   VIAFID ORCID Logo  ; Hartman, Johan 7   VIAFID ORCID Logo  ; Roth, Andrew 8   VIAFID ORCID Logo  ; Lagergren, Jens 2   VIAFID ORCID Logo 

 SciLifeLab, School of EECS, KTH Royal Institute of Technology, Stockholm, Sweden (GRID:grid.5037.1) (ISNI:0000000121581746); Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, USA (GRID:grid.412750.5) (ISNI:0000 0004 1936 9166) 
 SciLifeLab, School of EECS, KTH Royal Institute of Technology, Stockholm, Sweden (GRID:grid.5037.1) (ISNI:0000000121581746) 
 Karolinska Institutet, Department of Cell and Molecular Biology, Solna, Sweden (GRID:grid.465198.7) 
 Karolinska Institutet, Department of Oncology and Pathology, Solna, Sweden (GRID:grid.465198.7) 
 BC Cancer, Department of Molecular Oncology, Vancouver, Canada (GRID:grid.465198.7) 
 BC Cancer, Department of Molecular Oncology, Vancouver, Canada (GRID:grid.465198.7); University of British Columbia, Department of Pathology and Laboratory Medicine, Vancouver, Canada (GRID:grid.17091.3e) (ISNI:0000 0001 2288 9830) 
 Karolinska Institutet, Department of Oncology and Pathology, Solna, Sweden (GRID:grid.465198.7); Karolinska University Laboratory, Department of Clinical Pathology and Cytology, Stockholm, Sweden (GRID:grid.24381.3c) (ISNI:0000 0000 9241 5705) 
 BC Cancer, Department of Molecular Oncology, Vancouver, Canada (GRID:grid.24381.3c); University of British Columbia, Department of Pathology and Laboratory Medicine, Vancouver, Canada (GRID:grid.17091.3e) (ISNI:0000 0001 2288 9830); University of British Columbia, Department of Computer Science, Vancouver, Canada (GRID:grid.17091.3e) (ISNI:0000 0001 2288 9830) 
Pages
982
Publication year
2023
Publication date
2023
Publisher
Nature Publishing Group
e-ISSN
20411723
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2778775502
Copyright
© The Author(s) 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.