DeCiFering the elusive cancer cell fraction in tumor heterogeneity and evolution

Gryte Satas, Simone Zaccaria, Mohammed El-Kebir, Benjamin J. Raphael

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

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 languageEnglish (US)
Pages (from-to)1004-1018.e10
JournalCell Systems
Volume12
Issue number10
DOIs
StatePublished - Oct 20 2021

All Science Journal Classification (ASJC) codes

  • Pathology and Forensic Medicine
  • Histology
  • Cell Biology

Keywords

  • algorithm
  • cancer cell fraction
  • cancer genomics
  • clustering
  • copy-number aberrations
  • single-nucleotide variants
  • tumor heterogeneity

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