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Plasticity

Plasticity

Plasticity is the property where an object loses its elasticity and undergoes irreversible deformation, even after the deformation forces are eliminated. If a material deforms irreversibly without increasing stress or load, then this is called ideal plasticity. For example, when a force is applied to an aluminum rod, it changes its shape, but it does not return to its original shape once the force is removed. Plastic deformation or ductility is thus a permanent deformation or change in the...

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Deep learning-based feature discovery for decoding phenotypic plasticity in pediatric high-grade gliomas single-cell

Abicumaran Uthamacumaran¹

¹Department of Surgical and Interventional Sciences, McGill University, Montreal, Canada; Department of Physics (Alumni), Concordia University, Montreal, Canada; Department of Psychology (Alumni), Concordia University, Montreal, Canada; Oxford Immune Algorithmics, Reading, UK.

Computers in Biology and Medicine

|August 23, 2025

Summary

This summary is machine-generated.

AI systems medicine reveals key plasticity drivers in pediatric high-grade gliomas (pHGGs). Understanding these networks offers new precision therapy targets for stabilizing aggressive brain tumors.

Keywords:

Artificial intelligence Deep learning Features Pediatric high-grade gliomas Precision oncology Predictive biomarkers Systems medicine

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Area of Science:

Systems medicine and artificial intelligence (AI)
Computational biology and bioinformatics
Neuro-oncology and developmental neurobiology

Background:

Pediatric high-grade gliomas (pHGGs) exhibit significant heterogeneity and plasticity.
Understanding the molecular determinants of lineage plasticity is crucial for developing effective therapies.
Current therapeutic strategies face challenges due to tumor adaptability and resistance.

Purpose of the Study:

To identify critical determinants of lineage-specific plasticity in pediatric high-grade glioma (pHGG) subtypes using AI-powered systems medicine.
To elucidate network interactions regulating glioma morphogenesis and cell fate decision-making.
To uncover potential therapeutic vulnerabilities and precision medicine strategies for pHGGs.

Main Methods:

Application of complex network dynamics and graph-based machine learning to single-cell transcriptomics data.
Analysis of pediatric high-grade glioma (pHGG) subtypes: IDHWT glioblastoma and K27M-altered diffuse midline glioma.
Identification of transition genes, hub genes, and regulatory network interactions.

Main Results:

Identified critical network interactions involving the tumor-immune microenvironment, neurodevelopmental programs, and signaling pathways (MAPK/ERK, WNT).
Discovered specific transition genes (e.g., DKK3, NOTCH2, H3F3A) and hub genes (e.g., ITM2C, H3F3A) regulating glioma plasticity.
Revealed that pHGGs are developmentally trapped, exhibiting hybrid cellular identities and plasticity as stress responses.

Conclusions:

pHGGs display maladaptive behaviors and hybrid cellular identities driven by disrupted neuro-differentiation hierarchies.
Tumor heterogeneity and plasticity are stress-response patterns influenced by the immune-inflammatory microenvironment and oxidative stress.
Targeting developmental trajectories and plasticity networks offers promising precision medicine strategies, including transition therapy towards neuronal differentiation.