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

TL;DR

This paper introduces a cellular automata model to study how the order of driver mutations and metabolic mutations influence cancer progression, metastasis, and therapy resistance, supported by bioinformatics data analysis.

Contribution

It uniquely quantifies the impact of mutation order and metabolic alterations on cancer dynamics, integrating bioinformatics with computational modeling.

Findings

Mutation order significantly affects tumor growth and metastasis.

Metabolic mutations are crucial in cancer proliferation.

Model predicts mutation sequences linked to therapy resistance.

Abstract

Recent works have stressed the important role that random mutations have in the development of cancer phenotype. We challenge this current view by means of bioinformatic data analysis and computational modelling 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 occurrence 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 Automata to investigate the effects of metabolic mutations and mutation order on cancer stemness and tumour cell migration in bone metastasised breast cancers. 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, the order of which is relevant for the dynamics, metabolic which support 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. Further mathematical modelling of the order of mutations is presented in terms of operators. We believe 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.