Objective:

To summarize the integration of multilayer regulatory information across various omics levels into computational models for virtual perturbation analysis in cancer immunology, highlighting its significance for precision oncology.

Key Findings:

• In silico knockout provides a framework for predicting system-wide responses to genetic perturbations, crucial for identifying therapeutic targets.
• Complex regulatory networks in the tumor microenvironment are influenced by genetic, epigenetic, and metabolic factors, impacting treatment responses.
• Traditional experimental approaches face challenges in deconstructing multidimensional networks due to resource intensity and dimensionality constraints, limiting their applicability in precision oncology.

Interpretation:

The integration of computational modeling with multi-omics data offers a promising avenue for advancing precision immunotherapy by identifying immune regulatory mechanisms and therapeutic targets, ultimately improving patient outcomes.

Limitations:

• Challenges in multi-omics integration and biological complexity, such as data heterogeneity and computational limitations.
• Traditional assays provide only static snapshots, failing to capture dynamic regulatory states and the temporal aspects of immune responses.

Conclusion:

The review highlights how digital experiments redefine the ability to decode and therapeutically target the regulatory essence of cancer immunity, paving the way for innovative immunotherapeutic strategies.