Coggan, Helena;
(2025)
Agent-based modelling of early lung cancer evolution.
Doctoral thesis (Ph.D), UCL (University College London).
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Abstract
Lung cancer is the world’s leading cause of cancer-related death. Understanding the early stages of cancer development is key to developing effective treatment, but these are difficult to study directly, as a tumour is usually highly-evolved by the time a patient arrives in the clinic. To better understand the mechanisms driving the evolution of lung cancer, we can build simulations in which cells are represented as interacting ‘agents’. These computational models can be used to test whether particular hypotheses about cancer evolution give rise to realistic tumours. We can also combine these models with machine learning frameworks to infer the rules of tumour evolution from real cancer data. In Chapter 2 of this thesis, we discuss how agent-based models can be used to study the interactions between cells in the very first stages of lung cancer growth. We build a three-dimensional model to replicate the spatial structure of organoids (cell clusters) grown from mouse cells. We find that a particular cancer-associated mutation (EGFRL858R) changes the rules of cell division, to limit proliferation to small protrusions on the organoid’s surface. In Chapter 3, we construct a larger-scale model of tumour evolution, in which cells compete for survival within tissue regions. We find that models which assume that cells compete directly for existing space in the tumour are better able to replicate the properties of growing lung cancers. In Chapter 4, we use this model to study how the presence of heritable and semi-heritable selection can be distinguished using multi-region sequencing data. The results of this thesis highlight how different modes of evolution can shape our interpretation of tumour development. A deeper understanding of the forces driving cancer evolution should influence the selection of future drug targets, as well as personalised cancer prevention and treatment strategies.
| Type: | Thesis (Doctoral) |
|---|---|
| Qualification: | Ph.D |
| Title: | Agent-based modelling of early lung cancer evolution |
| Open access status: | An open access version is available from UCL Discovery |
| Language: | English |
| Additional information: | Copyright © The Author 2025. Original content in this thesis is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) Licence (https://creativecommons.org/licenses/by-nc/4.0/). Any third-party copyright material present remains the property of its respective owner(s) and is licensed under its existing terms. Access may initially be restricted at the author’s request. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Mathematics |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10206853 |
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