Evolution of cell motility in an individual-based model of tumour growth.
Tumour invasion is driven by proliferation and importantly migration into the surrounding tissue. Cancer cell motility is also critical in the formation of metastases and is therefore a fundamental issue in cancer research. In this paper we investigate the emergence of cancer cell motility in an evolving tumour population using an individual-based modelling approach. In this model of tumour growth each cell is equipped with a micro-environment response network that determines the behaviour or phenotype of the cell based on the local environment. The response network is modelled using a feed-forward neural network, which is subject to mutations when the cells divide. With this model we have investigated the impact of the micro-environment on the emergence of a motile invasive phenotype. The results show that when a motile phenotype emerges the dynamics of the model are radically changed and we observe faster growing tumours exhibiting diffuse morphologies. Further we observe that the emergence of a motile subclone can occur in a wide range of micro-environmental growth conditions. Iterated simulations showed that in identical growth conditions the evolutionary dynamics either converge to a proliferating or migratory phenotype, which suggests that the introduction of cell motility into the model changes the shape of fitness landscape on which the cancer cell population evolves and that it now contains several local maxima. This could have important implications for cancer treatments which focus on the gene level, as our results show that several distinct genotypes and critically distinct phenotypes can emerge and become dominant in the same micro-environment.
Gerlee
P
P
Niels Bohr Institute, Center for Models of Life, Copenhagen Ø, Denmark. gerlee@nbi.uk
Anderson
A R A
AR
D002465:Cell Movement; D005109:Extracellular Matrix / Q000502:physiology; D006801:Humans; D009361:Neoplasm Invasiveness; D009366:Neoplasm Seeding; D009369:Neoplasms / Q000473:pathology*; D014411:Neoplastic Stem Cells / Q000473:pathology; D009389:Neovascularization, Pathologic; D016571:Neural Networks, Computer; D010641:Phenotype