An agent-based modelling framework to study growth mechanisms in EGFR-L858R mutant cell alveolar type II cells.
Helena CogganClare E WeedenPhilip PearceMohit P DalwadiAlastair MagnessCharles SwantonKaren M PagePublished in: Royal Society open science (2024)
Mutations in the epidermal growth factor receptor (EGFR) are common in non-small cell lung cancer (NSCLC), particularly in never-smoker patients. However, these mutations are not always carcinogenic, and have recently been reported in histologically normal lung tissue from patients with and without lung cancer. To investigate the outcome of EGFR mutation in healthy lung stem cells, we grow murine alveolar type II organoids monoclonally in a three-dimensional Matrigel. Our experiments show that the EGFR-L858R mutation induces a change in organoid structure: mutated organoids display more 'budding', in comparison with non-mutant controls, which are nearly spherical. We perform on-lattice computational simulations, which suggest that this can be explained by the concentration of division among a small number of cells on the surface of the mutated organoids. We are currently unable to distinguish the cell-based mechanisms that lead to this spatial heterogeneity in growth, but suggest a number of future experiments which could be used to do so. We suggest that the likelihood of L858R-fuelled tumorigenesis is affected by whether the mutation arises in a spatial environment that allows the development of these surface protrusions. These data may have implications for cancer prevention strategies and for understanding NSCLC progression.
Keyphrases
- epidermal growth factor receptor
- advanced non small cell lung cancer
- small cell lung cancer
- tyrosine kinase
- induced apoptosis
- stem cells
- single cell
- cell cycle arrest
- cell therapy
- wild type
- end stage renal disease
- newly diagnosed
- endoplasmic reticulum stress
- brain metastases
- oxidative stress
- peritoneal dialysis
- signaling pathway
- current status
- electronic health record
- prognostic factors
- squamous cell carcinoma
- big data
- molecular dynamics
- pi k akt