Effects of mutations and immunogenicity on outcomes of anti-cancer therapies for secondary lesions

Abstract

Cancer development is driven by mutations and selective forces, including the action of the immune system and interspecific competition. When administered to patients, anti-cancer therapies affect the development and dynamics of tumours, possibly with various degrees of resistance due to immunoediting and microenvironment. Tumours are able to express a variety of competing phenotypes with different attributes and thus respond differently to various anti-cancer therapies.In this paper, a mathematical framework incorporating a system of delay differential equations for the immune system activation cycle and an agent-based approach for tumour-immune interaction is presented. The focus is on those metastatic, secondary solid lesions that are still undetected and non-vascularised.By using available experimental data, we analyse the effects of combination therapies on these lesions and investigate the role of mutations on the rates of success of common treatments. Findings show that mutations, growth properties and immunoediting influence therapies' outcomes in nonlinear and complex ways, affecting cancer lesion morphologies, phenotypical compositions and overall proliferation patterns. Cascade effects

Agent-based computational modeling of glioblastoma predicts that stromal density is central to oncolytic virus efficacy

“…Computational agent-based models are particularly attractive modeling frameworks that support investigations into both temporal and spatial aspects of the many different cell types within tumors that may impact therapy ( Cess and Finley, 2020 ; Fadai et al., 2019 ; Gallaher et al., 2020 ; Ghaffarizadeh et al., 2018 ; Jenner et al., 2020b ; Metzcar et al., 2019 ; Norton et al., 2019 ), 2017 ; Ozik et al. (2018) ; Piretto et al. (2019) ; Rocha et al.…”

Optimising Hydrogel Release Profiles for Viro-Immunotherapy Using Oncolytic Adenovirus Expressing IL-12 and GM-CSF with Immature Dendritic Cells

“…Deterministic systems of ordinary differential equations (ODEs) are used to describe the key biophysical interactions and are then analysed to determine optimal therapeutic protocols [23]. In oncology, these techniques have been used to successfully optimise chemotherapy and immunotherapy treatment [25][26][27][28][29][30]. Zhu et al [31] optimised the scheduling of one cycle of chemotherapy drug VP-16 and found a new optimal drug regime which improved the clinical efficacy.…”