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

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

  • By

  • Kharan P.

  • Amy S. Mathew

  • Payel Ghosh

  • Syama H. P.

  • July 3, 2026

  • 0 min

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Clinical Report: Overview of Computational Studies on Immune Cell Movement

Overview

This review discusses the role of computational models in understanding immune cell dynamics within the tumor microenvironment (TME).

Background

The tumor microenvironment is crucial in cancer progression and treatment outcomes, consisting of various cell types that interact dynamically.

Data Highlights

No numerical data or trial data provided in the article.

Key Findings

  • Immune cell trafficking in TMEs significantly influences tumor destruction or immune evasion.
  • Computational models can simulate immune dynamics and help understand tumor escape mechanisms.
  • Factors affecting immune cell infiltration include chemotactic gradients, adhesion molecules, and metabolic reprogramming.
  • Bioinformatics resources like TCGA and TIMER assist in analyzing immune system composition and immunogenomics.
  • Challenges in precision immunotherapy include data heterogeneity and model validation.

Clinical Implications

The integration of computational models in cancer research can enhance the understanding of immune responses in the TME.

Conclusion

Computational science is important for elucidating immune-tumor interactions.

Related Resources & Content

  1. Frontiers in Immunology, 2026 -- Microfluidic chips for decoding cancer-immune crosstalk in immunotherapy
  2. Frontiers in Oncology, 2026 -- Immune-excluded and immune-suppressive tumor microenvironments: mechanisms, spatial biomarkers, and therapeutic rewiring
  3. Frontiers in Immunology, 2026 -- The theory of tumor immuno-ecodynamics
  4. Frontiers in Immunology, 2026 -- The Immune Landscape of Melanoma Microenvironmental Crosstalk
  5. ESMO basic requirements for AI-based biomarkers in oncology (EBAI) - PubMed
  6. The predictive value of intratumoral tertiary lymphoid structures on the response to immunotherapy in cancer patients: a systematic review and meta-analysis - PMC
  7. ESMO basic requirements for AI-based biomarkers in oncology (EBAI) - PubMed
  8. The predictive value of intratumoral tertiary lymphoid structures on the response to immunotherapy in cancer patients: a systematic review and meta-analysis - PMC

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