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The Tumor Microenvironment

The Tumor Microenvironment

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: Oct 1, 2025

Visualization, Quantification, and Mapping of Immune Cell Populations in the Tumor Microenvironment

Visualization, Quantification, and Mapping of Immune Cell Populations in the Tumor Microenvironment

Published on: March 25, 2020

spatialGE: quantification and visualization of the tumor microenvironment heterogeneity using spatial

Oscar E Ospina¹, Christopher M Wilson¹, Alex C Soupir¹

¹Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL 33612, USA.

Bioinformatics (Oxford, England)

|March 8, 2022

Summary

This summary is machine-generated.

New software, spatialGE, analyzes spatial transcriptomics data to reveal tumor microenvironment heterogeneity and its link to clinical data, improving cancer insights.

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Visualization, Quantification, and Mapping of Immune Cell Populations in the Tumor Microenvironment

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Area of Science:

Oncology
Bioinformatics
Computational Biology

Background:

Spatially resolved transcriptomics offers insights into the tumor microenvironment.
Existing analytical methods lack the ability to correlate spatial tumor heterogeneity with clinical data.

Purpose of the Study:

To develop a novel software tool, spatialGE, for analyzing spatial transcriptomics data.
To enable the exploration of associations between tumor microenvironment heterogeneity and clinical information.

Main Methods:

Development of spatialGE, an R package for spatial transcriptomics analysis.
Implementation of gene expression surface visualization and heterogeneity statistics.
Inclusion of spot-level cell deconvolution and spatially informed clustering.

Main Results:

spatialGE provides quantitative measures of tumor microenvironment heterogeneity.
The software facilitates comparisons between spatial data and clinical information.
New data object enables simultaneous storage of data and analysis results.

Conclusions:

spatialGE addresses the need for analytical methods linking spatial tumor heterogeneity to clinical data.
The software enhances understanding of the tumor microenvironment for improved cancer prognosis and therapies.