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Related Concept Videos

The Tumor Microenvironment02:17

The Tumor Microenvironment

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Every normal cell or tissue is embedded in a complex local environment called stroma, consisting of different cell types, a basal membrane, and blood vessels. As normal cells mutate and develop into cancer cells, their local environment also changes to allow cancer progression. The tumor microenvironment (TME) consists of a complex cellular matrix of stromal cells and the developing tumor. The cross-talk between cancer cells and surrounding stromal cells is critical to disrupt normal tissue...
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Updated: Aug 30, 2025

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
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Inference on spatial heterogeneity in tumor microenvironment using spatial transcriptomics data.

Antara Biswas1, Bassel Ghaddar1, Gregory Riedlinger1

  • 1Rutgers Cancer Institute, Rutgers the State University of New Jersey, New Brunswick, New Jersey, USA.

Computational and Systems Oncology
|August 29, 2022
PubMed
Summary
This summary is machine-generated.

Spatial analysis of the tumor microenvironment (TME) reveals significant heterogeneity. Network graph models show tumor cells are spatially correlated, while immune cells are dispersed, impacting cancer progression and treatment.

Keywords:
cancerheterogeneitymultivariate analysisspatial transcriptomicstissue microenvironment

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Area of Science:

  • Oncology
  • Computational Biology
  • Spatial Statistics

Background:

  • The tumor microenvironment (TME) is crucial for cancer progression, immune response, and treatment efficacy.
  • Intratumor heterogeneity exists at genetic, transcriptomic, and cellular composition levels.
  • Quantitative assessment of spatial heterogeneity within the TME remains challenging.

Purpose of the Study:

  • To develop a framework for analyzing spatial heterogeneity in the TME using network graph-based spatial statistical models.
  • To apply this framework to spatial transcriptomics data to understand TME organization.
  • To investigate the impact of spatial patterns on tumor characteristics and clinical implications.

Main Methods:

  • Utilized network graph-based spatial statistical models on spatially annotated molecular data.
  • Applied the framework to spatial transcriptomics data from pancreatic ductal adenocarcinoma (PDAC) samples.
  • Analyzed spatial correlation patterns of tumor cells, immune cells, and pathway signatures.

Main Results:

  • Observed significant global and local spatial correlation in tumor cell abundance scores.
  • Found dispersed spatial patterns for immune cell types within the TME.
  • Identified hypoxia, EMT, and inflammation signatures contributing to intra-tumor spatial variations.

Conclusions:

  • Spatial patterns in cell type abundance and pathway signatures within the TME influence tumor growth and cancer hallmarks.
  • Intra-tumor non-genetic spatial heterogeneity means single biopsies may not capture the full clinical picture.
  • This framework provides insights into TME modularity and spatial heterogeneity for improved understanding of cancer.