Anzeige
Erschienen in:
2023 | OriginalPaper | Buchkapitel
Simulating Tumor Evolution from scDNA-Seq as an Accumulation of both SNVs and CNAs
verfasst von : Zahra Tayebi, Akshay Juyal, Alexander Zelikovsky, Murray Patterson
Erschienen in: Bioinformatics Research and Applications
Verlag: Springer Nature Singapore
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Abstract
Ever since single-cell sequencing (scDNA-seq) was coined ‘method of the year’ in 2013, it has provided many insights into the evolution of tumors, viewed as a branching process of accumulating cancerous mutations that initiated with a single driver mutation — a model of clonal evolution which has been theorized almost half a century ago (Nowell, 1976). With this, is seen an explosion of methods for inferring the histories of such evolution, often in the form of a phylogenetic tree, from single-cell sequencing data. While the first methods modeled such evolution as an accumulation of point mutations (SNVs), copy number aberrations (CNAs, i.e., duplications or deletions of large genomic regions) are an important factor to consider. As a result, later methods began to bolster cancer phylogeny inference with bulk sequencing data, to account for CNAs. Despite the dozens of such inference methods available, there still does not exist much in the form of a unified benchmark for all such methods.
This paper moves to initiate such a benchmark, which can be built upon, by proposing a simulator which models both SNVs and CNAs jointly in generating an evolutionary scenario which can be interpreted as a scDNA-seq/matched bulk sample pair. The simulator models the accumulations of SNVs, and the duplication or deletion of chromosomal segments. We test this simulation on three methods: (a) a method which accounts for SNVs only, and under the infinite sites assumption (ISA), (b) a second more general method which models only SNVs, but allows for relaxations to the ISA, and (c) a third most general method which accounts for both SNVs and CNAs (and violations to the ISA). Results are consistent with the generality of these methods. This work is a step in the direction of developing a de-facto benchmark for cancer phylogeny inference methods.