Objective:

To examine existing in silico tools for evaluating immune cell migration, communication, and function in the tumor microenvironment (TME).

Approach:

• Review of Computational Models: The review discusses various computational modelling techniques such as agent-based models, differential equation models, and machine learning models that simulate immune dynamics in the TME.
• Assessment of Bioinformatics Resources: The article evaluates bioinformatics databases like TCGA and TIMER for understanding the immune system composition and immunogenomics of tumors.
• Analysis of Immune Cell Populations: Specific in silico analyses of immune cell populations, including Tumour-associated macrophages (TAMs) and myeloid-derived suppressor cells (MDSCs), are examined to demonstrate predictive capabilities.

Key Findings:

• Immune cell trafficking in TMEs is crucial for determining tumor destruction or immune evasion.
• Computational models enhance understanding of immune-tumor interactions and the development of immune escape.
• Integration of multi-omics and spatial transcriptomic datasets aids in personalized modeling for immunotherapy responses.

Interpretation:

The findings support the increasing relevance of computational science in understanding immune-TME interactions and developing cancer immunotherapies.

Limitations:

• Data heterogeneity poses challenges for model validation.
• Bulk transcriptomic methods struggle with accurately resolving rare immune cell subpopulations.
• Validation of simulated immune interactions remains a significant barrier.

Conclusion:

The review emphasizes the importance of bioinformatics and computational tools in cancer research, particularly in the context of precision immunotherapy.