A review of computational modeling, machine learning and image analysis in cancer metastasis dynamics

Abstract

Abstract Cancer is a life‐threatening process that stems from genetic mutations in cells, which leads to the formation of tumors, and is a major cause of deaths in the United States, with secondary metastasis being a major driver of fatality. The development of an optimal metastatic environment is an essential process prior to tumor metastasis. This process, called pre‐metastatic niche formation, involves the activation of resident fibroblast‐like cells and macrophages. Tumor‐mediated factors introduced to this environment transform resident cells that secrete additional growth factors and remodel the extracellular matrix, which is thought to promote tumor colonization and metastasis in the secondary environment. Furthermore, an important component of metastasis is the biological process of epithelial–mesenchymal transition, which can be exploited by cancer cells to change their phenotype, to migrate and proliferate as necessary. In this review, we discuss recent advances in the investigation of cancer growth and migration. Computational models that focus on biochemical signaling and multicellular dynamics are examined. Machine learning models and image analysis that classify cancer‐related data are also explored. Through this review, we highlight advances in the study of important aspects of cancer and metastasis signaling and computational tools to study these dynamics.

Keywords

• biochemical signaling
• cancer
• computational modeling
• epithelial–mesenchymal transition
• image analysis
• machine learning