Abstract
We present a Minimal Event Distance Aneuploidy Lineage Tree (MEDALT) algorithm that infers the evolution history of a cell population based on single-cell copy number (SCCN) profiles, and a statistical routine named lineage speciation analysis (LSA), whichty facilitates discovery of fitness-associated alterations and genes from SCCN lineage trees. MEDALT appears more accurate than phylogenetics approaches in reconstructing copy number lineage. From data from 20 triple-negative breast cancer patients, our approaches effectively prioritize genes that are essential for breast cancer cell fitness and predict patient survival, including those implicating convergent evolution.The source code of our study is available at https://github.com/KChen-lab/MEDALT.
Highlights
Aneuploidy, the phenomenon that genomes acquire or lose chromosomal fragments, has been causally implicated in a wide variety of human diseases such as neuropsychiatric disorders and cancer [1,2,3]
Overview of the methods To address these challenges, we propose a new computational framework that performs lineage tracing from single-cell copy number (SCCN) data and detects significant focal and broad copy number alterations (CNAs) associated with lineage expansion (Fig. 1)
When we performed gene set enrichment analysis on genes identified by lineage speciation analysis (LSA), we found that the lineages of higher CNA rates have more DNA damage repair (DDR) genes affected by the CNAs than the lineages of lower CNA rates (Additional file 1: Fig. S7)
Summary
Aneuploidy, the phenomenon that genomes acquire or lose chromosomal fragments, has been causally implicated in a wide variety of human diseases such as neuropsychiatric disorders and cancer [1,2,3]. Recent advances in single-cell DNA sequencing (e.g., tagmentation-based approach [15] and single-cell CNV solution by the 10x Genomics) have enabled large-scale acquisition of single-cell copy number (SCCN) profiles in tens of thousands of cells at around 100-kb resolution (~ 0.1X sequencing coverage per cell) [16,17,18,19]. Other platforms such as single-cell RNA-sequencing [20, 21] and singlecell ATAC-sequencing [22] have been utilized for SCCN profiling. Tree, taking into account sparsity in cell population sampling, multiplicity in subset partitioning, and propensity of the alteration at a particular genomic location, etc. [36]
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