12/16/2023 0 Comments Free download scite text editor![]() PhylEx leverages information about the single-nucleotide variants (SNVs) observed within a single cell to identify clones, improve clonal tree reconstruction, and facilitate highly accurate mapping of RNA expression profiles to clones. In this work, we introduce a Bayesian probabilistic method called PhylEx ( Phylo Expression) that integrates bulk DNA- and scRNA-seq data to meet this need. Hence, there is an unmet need for an integrative approach to simultaneously identify clonal population structure and the associated clonal genotypes from bulk DNA- and scRNA-seq data towards identifying intra-tumor heterogeneity in clonal gene expression profiles. However, the two-step approach does not fully utilize the available data as information in the scRNA data cannot be used to improve clonal population structure. Recent methods that seek to assign gene expression profiles to clones have treated the problem as a two-step procedure whereby the clonal population structure is identified and then scRNA data is aligned to clonal genotypes 17, 18. The increasing availability of single-cell RNA sequencing (scRNA-seq) data provides an approach to partially address this problem. Though the aforementioned methods can resolve clonal population structure, they cannot identify functional differences that result from the genomic heterogeneity. Recent advances in single-cell DNA sequencing (scDNA-seq) technologies have prompted the development of approaches better tailored to these data types 12, 13, 14 as well as methods that integrate scDNA-seq data with the bulk data for joint analysis for improved accuracy 15, 16. ![]() The limitations of clonal analysis using only the bulk method is well documented in the literature (e.g., refs. Early approaches used bulk sequence data coupled with computational deconvolution to address the admixed nature of bulk data 5, 6, 7, 8, 9. Inferring clonal population structure, genotypes, and trees from sequence data has been an active area of research in the past decade with implications for cancer treatment 2, 3, 4. The evolutionary relationship between clones can be represented by a phylogenetic tree or clonal tree. Though each cell is fundamentally distinct in cancer, there typically exist groups of cells that are genomically nearly identical, so-called clonal populations 1. 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.Ĭancer is an evolutionary process with ongoing mutational processes coupled with selection and drift leading to genetic diversity within the tumor cell populations. PhylEx outperforms the state-of-the-art methods both when comparing capacity for clonal tree reconstruction and for identifying clones. We evaluate PhylEx on synthetic and well-characterized high-grade serous ovarian cancer cell line datasets. ![]() 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. 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. Functional characterization of the cancer clones can shed light on the evolutionary mechanisms driving cancer’s proliferation and relapse mechanisms.
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