Computational modelling of lung cancer evolution

Cancer is a disease characterised by the gradual development of invasive malignancy by subpopulations of cells. This process is usually marked by the acquisition of distinct genetic mutations, but whilst some specific mutations have known effects on cell fitness, the general influence of genetic evolution on the trajectory of cancer is unclear. Untangling the relationship between a cell's genetic profile and its phenotype is of crucial
importance to understanding and treating cancer. As the development of cancer cannot yet be fully simulated in vitro, many theoretical and computational models have been built to investigate the mechanisms of tumour formation. The outputs of these models
are usually compared with experimental data to assess the validity of their underlying premises. However, the application of these models has so far been limited by the need to make restrictive assumptions about the influence of mutations on cell fitness.
Here, we design agent-based models to rigorously test specific hypotheses about the influence of mutations on lung tumour formation. We apply this approach to an organoid model of tumorigenesis in non-small cell lung cancer (NSCLC) to suggest that the EGFR-L858R mutation induces cells to signal their neighbours not to divide.
In further work, we will use larger and more complex models to simulate the development of lung tumours. This model will be fitted and tested by comparing its outputs to genetic and transcriptomic data from the TRACERx cohort (a large dataset of untreated lung tumours taken from human patients). We will use this model to infer possible modes of evolution governing lung cancer development. Specifically, we will ask whether the fitness of lung cancer cells is determined by the accumulation of genetic mutations, or
evolves independently of them; whether cell fitness should generally be considered static or time-varying; and whether the mode of evolution governing a patient's disease can be predicted by known biomarkers. This understanding of evolutionary dynamics is crucial to the development of new treatments, especially those which aim to control patterns of evolution and affect the trajectory of disease.

Research Area: Mathematical Biology