TY - JOUR T1 - Cancer phylogenetics using single-cell RNA-seq data JF - bioRxiv DO - 10.1101/2021.01.07.425804 SP - 2021.01.07.425804 AU - Jiří C. Moravec AU - Rob Lanfear AU - David L. Spector AU - Sarah D. Diermeier AU - Alex Gavryushkin Y1 - 2021/01/01 UR - http://biorxiv.org/content/early/2021/07/27/2021.01.07.425804.abstract N2 - Phylogenetic methods are emerging as a useful tool to understand cancer evolutionary dynamics, including tumor structure, heterogeneity, and progression. Most currently used approaches utilize either bulk whole genome sequencing (WGS) or single-cell DNA sequencing (scDNA-seq) and are based on calling copy number alterations and single nucleotide variants (SNVs). Here we explore the potential of single-cell RNA sequencing (scRNA-seq) to reconstruct cancer evolutionary dynamics. scRNA-seq is commonly applied to explore differential gene expression of cancer cells throughout tumor progression. The method exacerbates the single-cell sequencing problem of low yield per cell with uneven expression levels. This accounts for low and uneven sequencing coverage and makes SNV detection and phylogenetic analysis challenging. In this paper, we demonstrate for the first time that scRNA-seq data contains sufficient evolutionary signal and can be utilized in phylogenetic analyses. We explore and compare results of such analyses based on both expression levels and SNVs called from our scRNA-seq data. Both techniques are shown to be useful for reconstructing phylogenetic relationships between cells, reflecting the clonal composition of a tumor. Without an explicit error model, standardized expression values appear to be more powerful and informative than the SNV values at a lower computational cost, due to being a by-product of standard expression analysis. Our results suggest that scRNA-seq can be a competitive alternative or useful addition to conventional scDNA-seq phylogenetic reconstruction. Our results open up a new direction of somatic phylogenetics based on scRNA-seq data. Further research is required to refine and improve these approaches to capture the full picture of somatic evolutionary dynamics in cancer.Competing Interest StatementThe authors have declared no competing interest. ER -