A multiscale cell-based model of tumor growth for chemotherapy assessment and tumor-targeted therapy through a 3D computational approach

• Autor(a) principal: Jafari Nivlouei, Sahar
• Data de Publicação: 2022
• Outros Autores: Soltani, Madjid, Shirani, Ebrahim, Salimpour, Mohammad Reza, Travasso, Rui D., Carvalho, João
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• DOI: 10.1111/cpr.13187
• Texto Completo: http://hdl.handle.net/10316/103214; https://doi.org/10.1111/cpr.13187
• topic: cellular Potts model; chemotherapy; computational biology; signaling transduction; targeted therapy; tumor-induced angiogenesis; Cell Proliferation; Humans; Models, Biological; Neoplasms; Neovascularization, Pathologic; Signal Transduction; Computer Simulation

Objectives: Computational modeling of biological systems is a powerful tool to clarify diverse processes contributing to cancer. The aim is to clarify the complex biochemical and mechanical interactions between cells, the relevance of intracellular signaling pathways in tumor progression and related events to the cancer treatments, which are largely ignored in previous studies. Materials and Methods: A three-dimensional multiscale cell-based model is developed, covering multiple time and spatial scales, including intracellular, cellular, and extracellular processes. The model generates a realistic representation of the processes involved from an implementation of the signaling transduction network. Results: Considering a benign tumor development, results are in good agreement with the experimental ones, which identify three different phases in tumor growth. Simulating tumor vascular growth, results predict a highly vascularized tumor morphology in a lobulated form, a consequence of cells' motile behavior. A novel systematic study of chemotherapy intervention, in combination with targeted therapy, is presented to address the capability of the model to evaluate typical clinical protocols. The model also performs a dose comparison study in order to optimize treatment efficacy and surveys the effect of chemotherapy initiation delays and different regimens. Conclusions: Results not only provide detailed insights into tumor progression, but also support suggestions for clinical implementation. This is a major step toward the goal of predicting the effects of not only traditional chemotherapy but also tumor-targeted therapies.