Particle automata model of renal cancer progression

Abstract

Even though the cancer mortality rate is slowly decreasing, it is still one of the leading causes of morbidity and mortality worldwide. One of the most common types of this disease is renal cancer, occurring in kidneys. A total of 63,340 new renal cancer cases (42,680 in men and 22,660 in women) and 14,970 deaths from renal cancer (10,010 men and 4,960 women) are projected to occur only in the US in 2018, with 1 in 48 lifetime risk for developing kidney cancer for men and 1 in 83 for women. Tumor growth is a complex, multiscale phenomena with many coupled microscopic and macroscopic factors that have to be accounted for while studying the disease. Despite a tremendous amount of work on understanding cancerogenesis and developing an effective anticancer therapies we still do not fully understand the mechanics of the malignant tissue development. Even though it is impossible to fully simulate and control cancer growth, numerical model allows for identification and investigation of the most crucial tumor growth factors and possible scenarios of its proliferation. The purpose of this article is to create model of renal tumor that uses the particle automata model[1,2]. We would also like to clarify if smooth particle hydrodynamics (SPH) method can be used to improve modelling of this particular biological process[3]. In the particle automata model both cancerous and healthy tissues are made of particles interacting with each other via spring harmonic forces and in SPH model we assume that biological tissues are represented as viscous fluids. In each model healthy tissue serves as an environment in which the renal tumor develops. Both healthy and cancerous cells have a life cycle in which they can be proliferating, dormant or necrotic. We use oxygen concentration, external pressure and time as restrictive factors for tissue growth. Herein we hope to reproduce in vivo tumor growth results inside in silico model and gain more insight into the rules governing the spread of the disease.