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

Seong-Hwan Jun, Hosein Toosi, Jeff Mold, Camilla Engblom, Xinsong Chen, Ciara O'Flanagan, Michael Hagemann-Jensen, Rickard Sandberg, Samuel Aparicio, Johan Hartman, Andrew Roth, Jens Lagergren, Nature communications 14, 982 (2023)


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.