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Tumor Progression

Tumor Progression

Tumor progression is a phenomenon where the pre-formed tumor acquires successive mutations to become clinically more aggressive and malignant. In the 1950s, Foulds first described the stepwise progression of cancer cells through successive stages.
Colon cancer is one of the best-documented examples of tumor progression. Early mutation in the APC gene in colon cells causes a small growth on the colon wall called a polyp. With time, this polyp grows into a benign, pre-cancerous tumor. Further...

Tumor Progression

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Tumor progression is a phenomenon where the pre-formed tumor acquires successive mutations to become clinically more aggressive and malignant. In the 1950s, Foulds first described the stepwise progression of cancer cells through successive stages.
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Related Experiment Video

Updated: Jul 14, 2026

Laser Capture Microdissection of Glioma Subregions for Spatial and Molecular Characterization of Intratumoral Heterogeneity, Oncostreams, and Invasion

Laser Capture Microdissection of Glioma Subregions for Spatial and Molecular Characterization of Intratumoral Heterogeneity, Oncostreams, and Invasion

Published on: April 12, 2020

Dissection of tumoral niches using spatial transcriptomics and deep learning.

Karla Paniagua¹, Yu-Fang Jin¹, Yidong Chen^2,3

¹Department of Electrical and Computer Engineering, KLESSE School of Engineering and Integrated Design, University of Texas at San Antonio, San Antonio, TX 78249, USA.

|April 15, 2025

Summary

This study presents TG-ME, a novel computational framework for analyzing tumor microenvironments (TME). TG-ME uses spatial transcriptomics and imaging to identify and characterize distinct tumoral niches, aiding in understanding cancer progression and treatment.

Keywords:

Artificial intelligence Cancer Microenvironment Transcriptomics

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Last Updated: Jul 14, 2026

Laser Capture Microdissection of Glioma Subregions for Spatial and Molecular Characterization of Intratumoral Heterogeneity, Oncostreams, and Invasion

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Digital Spatial Profiling for Characterization of the Microenvironment in Adult-Type Diffusely Infiltrating Glioma

Digital Spatial Profiling for Characterization of the Microenvironment in Adult-Type Diffusely Infiltrating Glioma

Published on: September 13, 2022

Area of Science:

Computational biology
Bioinformatics
Cancer research

Background:

Tumor microenvironments (TME) are complex and dynamic.
Understanding spatial organization of tumoral niches is crucial for cancer progression insights.
Current methods may lack the integrative power to dissect niche heterogeneity.

Purpose of the Study:

To introduce TG-ME, an innovative computational framework for dissecting tumoral niches.
To leverage spatial transcriptomics and morphological imaging for comprehensive niche characterization.
To provide a tool for uncovering microenvironmental signatures related to cancer prognosis and therapeutic outcomes.

Main Methods:

Integration of transformer and graph variational autoencoder (GraphVAE) models.
Multi-stage pipeline including data normalization, spatial information integration, and feature extraction.
Application of deep learning for robust clustering and profiling of tumor niches.

Main Results:

TG-ME effectively identifies and characterizes tumoral niches in benchmark and NSCLC datasets.
The framework provides insights into spatial organization and molecular changes within TME.
Demonstrated robust clustering and profiling of niches across different cancer stages.

Conclusions:

TG-ME is a powerful tool for analyzing spatial transcriptomics and morphological data.
The framework enhances understanding of tumor microenvironment heterogeneity and cancer progression.
TG-ME holds promise for guiding personalized treatment strategies through microenvironmental signature discovery.