Estimating tumour immune infiltration: methodological convergence across histology and spatial technologies

Area of Science:

• Oncology
• Immunology
• Computational Biology

Background:

• Tumor immune infiltration is crucial for cancer biology and predicting treatment response.
• Diverse experimental and computational platforms exist for quantifying immune infiltration, but optimal choices remain unclear.

Purpose of the Study:

• To provide a comprehensive overview of platforms and computational methods for estimating tumor immune profiles.
• To compare technologies based on resolution, specificity, spatial context, and clinical interpretability.
• To discuss emerging trends in multimodal data integration for immune profiling.

Main Methods:

• Review of traditional histopathology, immunohistochemistry, AI-based digital pathology, bulk RNA sequencing, and spatial omics.
• Comparative analysis of technological capabilities and computational approaches.
• Discussion of multimodal data integration strategies (mapping-based and fusion-based).

Main Results:

• Each platform offers unique insights into tumor immune profiling.
• Key comparative factors include technological resolution, cell-type specificity, spatial context, and clinical interpretability.
• Multimodal data integration presents emerging opportunities for enhanced immune profiling.

Conclusions:

• The review highlights methodological opportunities and translational potential in immune infiltration estimation.
• Guidance is provided for designing more actionable immune profiling strategies.
• Informed platform and methodological choices are essential for advancing cancer research and treatment.