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Evolutionary unpredictability in cancer model systems

Chattopadhyay, Subhayan LU orcid ; Karlsson, Jenny LU ; Ferro, Michele LU ; Mañas, Adriana LU ; Kanzaki, Ryu LU orcid ; Fredlund, Elina LU ; Murphy, Andrew J. ; L. Morton, Christopher ; Andersson, Natalie LU orcid and A. Woolard, Mary , et al. (2025) In Scientific Reports 15(20334).
Abstract
Despite the advent of advanced molecular prognostic tools, it is still difficult to predict the course of disease for cancer patients at the individual level. This lack of predictability is also reflected in many experimental cancer model systems, begging the question of whether certain biological aspects of cancer (eg. growth, evolution etc.) can ever be anticipated or if there remains an inherent unpredictability to cancer, similar to other complex biological systems. We demonstrate by a combination of agent-based mathematical modelling, analysis of patient-derived xenograft model systems from multiple cancer types, and in-vitro culture that certain conditions increase stochasticity of the clonal landscape of cancer growth. Our findings... (More)
Despite the advent of advanced molecular prognostic tools, it is still difficult to predict the course of disease for cancer patients at the individual level. This lack of predictability is also reflected in many experimental cancer model systems, begging the question of whether certain biological aspects of cancer (eg. growth, evolution etc.) can ever be anticipated or if there remains an inherent unpredictability to cancer, similar to other complex biological systems. We demonstrate by a combination of agent-based mathematical modelling, analysis of patient-derived xenograft model systems from multiple cancer types, and in-vitro culture that certain conditions increase stochasticity of the clonal landscape of cancer growth. Our findings indicate that under those conditions, the cancer genome may behave as a complex dynamic system, making its long-term evolution inherently unpredictable. (Less)
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organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Scientific Reports
volume
15
issue
20334
article number
20334
publisher
Nature Publishing Group
external identifiers
  • pmid:40579452
  • scopus:105009351202
ISSN
2045-2322
DOI
10.1038/s41598-025-07407-6
language
English
LU publication?
yes
id
cf4e51ef-85a8-4acc-8e53-a28a11c57272
date added to LUP
2025-07-07 08:50:57
date last changed
2025-07-08 04:02:49
@article{cf4e51ef-85a8-4acc-8e53-a28a11c57272,
  abstract     = {{Despite the advent of advanced molecular prognostic tools, it is still difficult to predict the course of disease for cancer patients at the individual level. This lack of predictability is also reflected in many experimental cancer model systems, begging the question of whether certain biological aspects of cancer (eg. growth, evolution etc.) can ever be anticipated or if there remains an inherent unpredictability to cancer, similar to other complex biological systems. We demonstrate by a combination of agent-based mathematical modelling, analysis of patient-derived xenograft model systems from multiple cancer types, and in-vitro culture that certain conditions increase stochasticity of the clonal landscape of cancer growth. Our findings indicate that under those conditions, the cancer genome may behave as a complex dynamic system, making its long-term evolution inherently unpredictable.}},
  author       = {{Chattopadhyay, Subhayan and Karlsson, Jenny and Ferro, Michele and Mañas, Adriana and Kanzaki, Ryu and Fredlund, Elina and Murphy, Andrew J. and L. Morton, Christopher and Andersson, Natalie and A. Woolard, Mary and Hansson, Karin and Radke, Katarzyna and M. Davidoff, Andrew and Mohlin, Sofie and Pietras, Kristian and Bexell, Daniel and Gisselsson Nord, David}},
  issn         = {{2045-2322}},
  language     = {{eng}},
  month        = {{06}},
  number       = {{20334}},
  publisher    = {{Nature Publishing Group}},
  series       = {{Scientific Reports}},
  title        = {{Evolutionary unpredictability in cancer model systems}},
  url          = {{http://dx.doi.org/10.1038/s41598-025-07407-6}},
  doi          = {{10.1038/s41598-025-07407-6}},
  volume       = {{15}},
  year         = {{2025}},
}