A review on in-silico analysis of immune cell trafficking and interactions with the tumour microenvironment

The tumour microenvironment (TME) contains a diverse mix of cells and components, including cancer cells, immune cells, connective tissue, and biochemical factors; all of these are constantly interacting as well as influencing both the progression and spread of cancer and the ability of the immune system to recognise and respond to it. Accumulating evidence indicates that immune cell trafficking in TMEs is a major factor in determining whether tumours are destroyed by immune defences or evade immune surveillance. However, advances in experimental techniques do not provide a complete picture of how immune-tumour cell interactions occur with respect to their spatial, temporal, and molecular characteristics. This review examines existing in silico tools for evaluating how immune cells migrate, communicate, and function in the TME. The components that affect how immune cells infiltrate tumours will be summarized (i.e., chemotactic gradients, adhesion molecules, extracellular matrix remodelling, hypoxia, and metabolic reprogramming), and their role in immune exclusion and the development of immune escape will be emphasized. Computational modelling techniques (e.g., agent-based models, ordinary and partial differential equation models, systems biology models, network biology models, and machine learning prediction models) enable multiscale simulation of immune dynamics. This capability helps further our understanding of how tumours escape immune surveillance and develop into malignancies. The use of bioinformatics databases and major bioinformatics resources such as TCGA, TIMER, TCIA, etc., that may assist in understanding the composition of the immune system and immunogenomics of tumours is assessed. To demonstrate the predictive ability of computational models to establish patterns of immune cell traffic and predict the efficacy of immunotherapy, we examine specific in silico analyses of distinct immune cell populations, such as Tumour-associated macrophages (TAMs) and myeloid-derived suppressor cells (MDSCs). Furthermore, integrating multi-omics and spatial transcriptomic datasets enables personalised modelling of potential responses to immune checkpoint therapy. Despite these advantages, precision immunotherapy faces many challenges, including data heterogeneity, model validation, and translation limitations, as well as future perspectives on precision immunotherapy using digital twin technology. Overall, the findings from this review support the increasing relevance of bioinformatics and computational science in understanding immune-TME interactions and developing novel cancer immunotherapies.