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

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 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.

Document Details

Document Type
Pub Defense Publication
Publication Date
Jul 29, 2022
Source ID
10.1038/s41598-022-17456-w

Entities

People

  • Anuraag Bukkuri
  • Emma U. Hammarlund
  • Joel S. Brown
  • Kenneth J. Pienta
  • Patrick C. Walsh
  • Robert H. Austin
  • Sarah R Amend

Organizations

  • Crafoord Foundation
  • National Cancer Institute
  • National Cancer Research Institute
  • National Science Foundation
  • Prostate Cancer Foundation
  • Royal Swedish Academy of Sciences
  • Stiftelsen Längmanska Kulturfonden
  • Swedish Research Council
  • United States Department of Defense

Tags

Fields of Study

  • Biology

Readers

  • Molecular and genetic basis of cancer.
  • Neural Network Machine Learning.
  • Software Engineering.

Technology Areas

  • Biotechnology