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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: Jan 6, 2026

Enrichment and Characterization of the Tumor Immune and Non-immune Microenvironments in Established Subcutaneous Murine Tumors

Enrichment and Characterization of the Tumor Immune and Non-immune Microenvironments in Established Subcutaneous Murine Tumors

Published on: June 7, 2018

NITUMID: Nonnegative matrix factorization-based Immune-TUmor MIcroenvironment Deconvolution.

Daiwei Tang¹, Seyoung Park², Hongyu Zhao¹

¹Department of Biostatistics, Yale School of Public Health, New Haven, CT 06511, USA.

Bioinformatics (Oxford, England)

|October 9, 2019

Summary

This summary is machine-generated.

We developed NITUMID, a new computational framework to profile tumor microenvironments from RNA data. It accurately estimates tumor and immune cell proportions and outperforms existing methods in detecting clinical signals.

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Last Updated: Jan 6, 2026

Enrichment and Characterization of the Tumor Immune and Non-immune Microenvironments in Established Subcutaneous Murine Tumors

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Area of Science:

Computational biology
Genomics
Immunology

Background:

Computational methods profile tumor microenvironments (TME) from bulk RNA data, aiding understanding of therapeutic responses.
Existing methods struggle with variable mRNA levels and tumor cell proportions.

Purpose of the Study:

To introduce the Nonnegative Matrix Factorization-based Immune-TUmor MIcroenvironment Deconvolution (NITUMID) framework.
To address limitations in current TME profiling methods regarding tumor proportion and cell-specific mRNA levels.

Main Methods:

Developed a Nonnegative Matrix Factorization-based framework (NITUMID).
Designed to simultaneously estimate tumor and immune cell proportions.
Accommodates variable mRNA levels across different cell types.

Main Results:

NITUMID accurately estimates tumor fractions and cell-specific mRNA levels, features lacking in other methods.
Demonstrated superior cell type profiling accuracy compared to existing deconvolution methods.
Showed enhanced ability to detect clinical and prognostic signals from tumor gene expression data.

Conclusions:

NITUMID offers a robust solution for TME deconvolution from bulk RNA data.
The framework improves upon existing methods by accounting for tumor purity and cell-specific expression.
NITUMID enhances the detection of clinically relevant signals for improved patient stratification and treatment strategies.