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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: Jun 24, 2025

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Published on: August 16, 2020

Dissecting tumor microenvironment from spatially resolved transcriptomics data by heterogeneous graph learning.

Chunman Zuo^1,2, Junjie Xia^3,4, Luonan Chen^5,6,7

¹Institute of Artificial Intelligence, Shanghai Engineering Research Center of Industrial Big Data and Intelligent System, Donghua University, Shanghai, 201620, China. cmzuo@dhu.edu.cn.

Nature Communications

|June 13, 2024

Summary

This summary is machine-generated.

stKeep, a new computational method, uses heterogeneous graphs to analyze spatial transcriptomics data. It reveals complex tumor microenvironments, identifies cell states, and predicts cancer progression for improved diagnostics and immunotherapy.

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Published on: September 13, 2022

Area of Science:

Computational biology
Cancer research
Genomics

Background:

Spatially resolved transcriptomics (SRT) offers insights into tumor microenvironments (TME) by analyzing molecular networks and cell-cell communication (CCC).
Interpreting the complex TME structure is challenging due to limitations in computationally exploring intricate relationships between cells, genes, and histological regions.

Purpose of the Study:

To introduce stKeep, a novel heterogeneous graph (HG) learning method for unraveling TME complexity from SRT data.
To integrate multimodality and gene-gene interactions for a comprehensive analysis of TME.

Main Methods:

stKeep utilizes HG to learn cell-modules and gene-modules by incorporating diverse nodes (genes, cells, histological regions).
It identifies finer cell-states within TME and cell-state-specific gene-gene relations.
Contrastive learning is employed to infer CCC for each cell, ensuring comparability across cell-states.

Main Results:

stKeep outperforms existing tools in dissecting TME across various cancer samples.
It accurately detects specific cell populations like bi-potent basal cells, neoplastic myoepithelial cells, and metastatic cells.
Key transcription factors, ligands, and receptors relevant to disease progression are identified.

Conclusions:

stKeep enhances the interpretation of TME structure and cell-cell communication from SRT data.
Identified biomarkers are validated through clinical data analysis, demonstrating prognostic and immunotherapy potential.
The method offers significant clinical applications in cancer diagnostics and treatment strategies.