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Stochastic models of Mendelian and reverse transcriptional inheritance in state-structured cancer populations

Bukkuri, Anuraag LU ; Pienta, Kenneth J. ; Austin, Robert H. ; Hammarlund, Emma U. LU ; Amend, Sarah R. and Brown, Joel S. (2022) In Scientific Reports 12(1).
Abstract

Recent evidence suggests that a polyaneuploid cancer cell (PACC) state may play a key role in the adaptation of cancer cells to stressful environments and in promoting therapeutic resistance. The PACC state allows cancer cells to pause cell division and to avoid DNA damage and programmed cell death. Transition to the PACC state may also lead to an increase in the cancer cell’s ability to generate heritable variation (evolvability). One way this can occur is through evolutionary triage. Under this framework, cells gradually gain resistance by scaling hills on a fitness landscape through a process of mutation and selection. Another way this can happen is through self-genetic modification whereby cells in the PACC state find a viable... (More)

Recent evidence suggests that a polyaneuploid cancer cell (PACC) state may play a key role in the adaptation of cancer cells to stressful environments and in promoting therapeutic resistance. The PACC state allows cancer cells to pause cell division and to avoid DNA damage and programmed cell death. Transition to the PACC state may also lead to an increase in the cancer cell’s ability to generate heritable variation (evolvability). One way this can occur is through evolutionary triage. Under this framework, cells gradually gain resistance by scaling hills on a fitness landscape through a process of mutation and selection. Another way this can happen is through self-genetic modification whereby cells in the PACC state find a viable solution to the stressor and then undergo depolyploidization, passing it on to their heritably resistant progeny. Here, we develop a stochastic model to simulate both of these evolutionary frameworks. We examine the impact of treatment dosage and extent of self-genetic modification on eco-evolutionary dynamics of cancer cells with aneuploid and PACC states. We find that under low doses of therapy, evolutionary triage performs better whereas under high doses of therapy, self-genetic modification is favored. This study generates predictions for teasing apart these biological hypotheses, examines the implications of each in the context of cancer, and provides a modeling framework to compare Mendelian and non-traditional forms of inheritance.

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author
; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Scientific Reports
volume
12
issue
1
article number
13079
publisher
Nature Publishing Group
external identifiers
  • pmid:35906318
  • scopus:85135180634
ISSN
2045-2322
DOI
10.1038/s41598-022-17456-w
language
English
LU publication?
yes
id
65ab1bd8-57eb-4cf6-8ee8-8f3f91d4bb2b
date added to LUP
2022-10-06 10:26:32
date last changed
2024-06-27 22:44:50
@article{65ab1bd8-57eb-4cf6-8ee8-8f3f91d4bb2b,
  abstract     = {{<p>Recent evidence suggests that a polyaneuploid cancer cell (PACC) state may play a key role in the adaptation of cancer cells to stressful environments and in promoting therapeutic resistance. The PACC state allows cancer cells to pause cell division and to avoid DNA damage and programmed cell death. Transition to the PACC state may also lead to an increase in the cancer cell’s ability to generate heritable variation (evolvability). One way this can occur is through evolutionary triage. Under this framework, cells gradually gain resistance by scaling hills on a fitness landscape through a process of mutation and selection. Another way this can happen is through self-genetic modification whereby cells in the PACC state find a viable solution to the stressor and then undergo depolyploidization, passing it on to their heritably resistant progeny. Here, we develop a stochastic model to simulate both of these evolutionary frameworks. We examine the impact of treatment dosage and extent of self-genetic modification on eco-evolutionary dynamics of cancer cells with aneuploid and PACC states. We find that under low doses of therapy, evolutionary triage performs better whereas under high doses of therapy, self-genetic modification is favored. This study generates predictions for teasing apart these biological hypotheses, examines the implications of each in the context of cancer, and provides a modeling framework to compare Mendelian and non-traditional forms of inheritance.</p>}},
  author       = {{Bukkuri, Anuraag and Pienta, Kenneth J. and Austin, Robert H. and Hammarlund, Emma U. and Amend, Sarah R. and Brown, Joel S.}},
  issn         = {{2045-2322}},
  language     = {{eng}},
  number       = {{1}},
  publisher    = {{Nature Publishing Group}},
  series       = {{Scientific Reports}},
  title        = {{Stochastic models of Mendelian and reverse transcriptional inheritance in state-structured cancer populations}},
  url          = {{http://dx.doi.org/10.1038/s41598-022-17456-w}},
  doi          = {{10.1038/s41598-022-17456-w}},
  volume       = {{12}},
  year         = {{2022}},
}