Modelling the order of driver mutations and metabolic mutations as structures in cancer dynamics

Recent works have stressed the important role random mutations have in the development of cancer phenotype. We challenge this current view by means of bioinformatic data analysis and computational modeling approaches. Not all the mutations are equally important for the development of metastasis. The survival of cancer cells from the primary tumour site to the secondary seeding sites depends on the occurence of very few driver mutations promoting oncogenic cell behaviours and on the order with which these mutations occur. We introduce a model in the framework of Cellular Automaton to investigate the effects of generic mutations and mutation order on cancer stemness and tumour cell migration. The metabolism of the cancer cell is a key factor in its proliferation rate. Bioinformatics analysis on a cancer mutation database shows that metabolism-modifying alterations constitute an important class of key cancer mutations. Our approach models three types of mutations: drivers, for which order is important, metabolic which supports cancer growth and are estimated from existing databases, and non driver mutations. Our results provide a quantitative basis of how the order of driver mutations and the metabolic mutations in different cancer clones could impact proliferation of therapy-resistant clonal populations and patient survival. Consequently, survival curves can be thought as the end point of the trajectories of the order-dependent and metabolic mutations. We believe that our work is novel because it quantifies two important factors in cancer spreading models: the order of driver mutations and the effects of metabolic mutations.