Abstract
The cancer cell fraction (CCF), or proportion of cancerous cells in a tumor containing a single-nucleotide variant (SNV), is a fundamental statistic used to quantify tumor heterogeneity and evolution. Existing CCF estimation methods from bulk DNA sequencing data assume that every cell with an SNV contains the same number of copies of the SNV. This assumption is unrealistic in tumors with copy-number aberrations that alter SNV multiplicities. Furthermore, the CCF does not account for SNV losses due to copy-number aberrations, confounding downstream phylogenetic analyses. We introduce DeCiFer, an algorithm that overcomes these limitations by clustering SNVs using a novel statistic, the descendant cell fraction (DCF). The DCF quantifies both the prevalence of an SNV at the present time and its past evolutionary history using an evolutionary model that allows mutation losses. We show that DeCiFer yields more parsimonious reconstructions of tumor evolution than previously reported for 49 prostate cancer samples.
Original language | English (US) |
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Pages (from-to) | 1004-1018 |
Number of pages | 15 |
Journal | Cell Systems |
Volume | 12 |
Issue number | 10 |
Early online date | Aug 17 2021 |
DOIs | |
State | Published - Oct 20 2021 |
Keywords
- algorithm
- cancer cell fraction
- cancer genomics
- clustering
- copy-number aberrations
- single-nucleotide variants
- tumor heterogeneity
ASJC Scopus subject areas
- Pathology and Forensic Medicine
- Cell Biology
- Histology