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The Spatiotemporal Heterogeneity of Tumor-Associated Stromal Cells: Reprogramming Plasticity to Unlock Precision
Yingying Lv1,2,3, Tingfei Duan1, Jinling Song4
1Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, P. R. China.
Tumor-associated stromal cells (TASCs) are crucial in the tumor microenvironment, influencing cancer growth and treatment. Understanding their plasticity and interactions is key to developing new immunotherapies for better cancer eradication.
Area of Science:
- Oncology
- Immunology
- Cell Biology
Background:
- Tumor-associated stromal cells (TASCs) are integral components of the tumor microenvironment (TME).
- Their heterogeneity and plasticity significantly impact tumor progression, metastasis, and response to therapy.
- Stromal-immune interactions mediated by TASCs are critical for shaping the tumor immune landscape.
Purpose of the Study:
- To review the molecular signatures and functional states of TASCs.
- To emphasize the role of TASCs in tumor dynamics and therapeutic resistance.
- To discuss novel strategies targeting TASC plasticity for enhanced cancer immunotherapy.
Main Methods:
- Literature review focusing on TASC heterogeneity, plasticity, and stromal-immune interactions.
- Analysis of molecular mechanisms governing TASC function and evolution within the TME.
- Exploration of therapeutic strategies targeting TASC plasticity.
Main Results:
- TASCs exhibit significant spatiotemporal heterogeneity, influencing tumor behavior and therapeutic outcomes.
- Stromal-immune interactions involving TASCs are pivotal in immune evasion and resistance to cancer treatments.
- Targeting TASC plasticity offers a promising avenue to overcome immune evasion and potentiate anti-tumor immunity.
Conclusions:
- A deeper understanding of TASC temporal evolution and spatial organization is needed for clinical translation.
- Innovative strategies targeting TASC plasticity can reverse immune evasion and promote tumor eradication.
- Future research should focus on multi-omics and machine learning for identifying spatially resolved biomarkers.
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