Statistical inference of the rates of cell proliferation and phenotypic switching in cancer

Einar Bjarki Gunnarsson, Jasmine Foo, Kevin Leder

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Recent evidence suggests that nongenetic (epigenetic) mechanisms play an important role at all stages of cancer evolution. In many cancers, these mechanisms have been observed to induce dynamic switching between two or more cell states, which commonly show differential responses to drug treatments. To understand how these cancers evolve over time, and how they respond to treatment, we need to understand the state-dependent rates of cell proliferation and phenotypic switching. In this work, we propose a rigorous statistical framework for estimating these parameters, using data from commonly performed cell line experiments, where phenotypes are sorted and expanded in culture. The framework explicitly models the stochastic dynamics of cell division, cell death and phenotypic switching, and it provides likelihood-based confidence intervals for the model parameters. The input data can be either the fraction of cells or the number of cells in each state at one or more time points. Through a combination of theoretical analysis and numerical simulations, we show that when cell fraction data is used, the rates of switching may be the only parameters that can be estimated accurately. On the other hand, using cell number data enables accurate estimation of the net division rate for each phenotype, and it can even enable estimation of the state-dependent rates of cell division and cell death. We conclude by applying our framework to a publicly available dataset.

Original languageEnglish (US)
Article number111497
JournalJournal of Theoretical Biology
Volume568
DOIs
StatePublished - Jul 7 2023

Bibliographical note

Publisher Copyright:
© 2023 Elsevier Ltd

Keywords

  • Cancer evolution
  • Epigenetics
  • Mathematical modeling
  • Maximum likelihood estimation
  • Parameter identifiability
  • Phenotypic switching

PubMed: MeSH publication types

  • Journal Article
  • Research Support, U.S. Gov't, Non-P.H.S.
  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

Fingerprint

Dive into the research topics of 'Statistical inference of the rates of cell proliferation and phenotypic switching in cancer'. Together they form a unique fingerprint.

Cite this