A small number of somatic mutations drive the development of cancer, but all somatic mutations are markers of the evolutionary history of a tumor. Prominent methods to construct phylogenies from single-cell-sequencing data use single-nucleotide variants (SNVs) as markers but fail to adequately account for copy-number aberrations (CNAs), which can overlap SNVs and result in SNV losses. Here, we introduce SCARLET, an algorithm that infers tumor phylogenies from single-cell DNA sequencing data while accounting for both CNA-driven loss of SNVs and sequencing errors. SCARLET outperforms existing methods on simulated data, with more accurate inference of the order in which mutations were acquired and the mutations present in individual cells. Using a single-cell dataset from a patient with colorectal cancer, SCARLET constructs a tumor phylogeny that is consistent with the observed CNAs and suggests an alternate origin for the patient's metastases. SCARLET is available at: github.com/raphael-group/scarlet. Both single-nucleotide variants (SNVs) and copy-number aberrations (CNAs) accumulate during cancer evolution, and these mutations may overlap on the genome. We introduce SCARLET (single-cell algorithm for reconstructing loss-supported evolution of tumors), an algorithm to construct phylogenies from single-cell DNA sequencing data using both SNVs and CNAs.
All Science Journal Classification (ASJC) codes
- Pathology and Forensic Medicine
- Cell Biology
- single-cell DNA sequencing
- tumor heterogeneity
- tumor phylogenetics