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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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Related Experiment Video

Updated: Oct 16, 2025

Using Microarrays to Interrogate Microenvironmental Impact on Cellular Phenotypes in Cancer

Using Microarrays to Interrogate Microenvironmental Impact on Cellular Phenotypes in Cancer

Published on: May 21, 2019

Deciphering the cancer microenvironment from bulk data with EcoTyper.

Andrea Rolong¹, Bob Chen², Ken S Lau³

¹Epithelial Biology Center, Vanderbilt University Medical Center, Nashville TN, USA; Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville TN, USA.

|October 15, 2021

Summary

This summary is machine-generated.

Researchers developed EcoTyper software to analyze tumor-microenvironment interactions using bulk transcriptomics data. This tool enhances predictions for tumor progression and patient prognosis across diverse cancer types.

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Area of Science:

Oncology
Bioinformatics
Computational Biology

Background:

Understanding tumor-microenvironment (TME) interactions is crucial for cancer research.
High-volume bulk transcriptomics data presents challenges for deconvolving cellular composition.
Accurate prediction of tumor progression and patient prognosis remains a key clinical need.

Purpose of the Study:

To introduce EcoTyper, a novel software for deconvolving TME interactions.
To assess the effectiveness of EcoTyper in analyzing high-volume transcriptomics data.
To demonstrate improved prediction of tumor progression and patient prognosis using EcoTyper.

Main Methods:

Development of the EcoTyper computational tool.
Application of EcoTyper to analyze bulk transcriptomics data from various tumor types.
Validation of EcoTyper's performance using multiple independent data sources.

Main Results:

EcoTyper successfully deconvolves complex TME interactions from bulk transcriptomics data.
The software demonstrates significant improvements in predicting tumor progression.
Enhanced patient prognosis predictions were achieved across multiple cancer datasets.

Conclusions:

EcoTyper provides a powerful approach to dissecting TME composition and function.
This tool has the potential to advance personalized cancer medicine through improved prognostic capabilities.
EcoTyper facilitates the analysis of large-scale transcriptomic datasets for clinical applications.