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Spatial Transcriptomics Analysis and Differential Pseudotime Analysis of High-Grade Astrocytoma with Piloid Features

Cancer Genetics and Therapeutics
  • Primary Categories:
    • Cancer
  • Secondary Categories:
    • Cancer
Introduction:


High-grade astrocytoma with piloid features (HGAP) is a rare, newly classified tumor with ambiguous biological characteristics that challenge conventional treatment strategies. HGAP shares histological traits with both aggressive astrocytomas and pilocytic astrocytomas, suggesting unique origins and behavior. The tumor’s rapid progression and limited survival outcomes make it imperative to explore its underlying molecular mechanisms to improve diagnosis and therapeutic targeting.



The primary goal of this study aims to identify the specific genes that exhibit expression changes that are significantly correlated with tumor grease progression in HGAP, as determined by the differential pseudotime analysis model. This project integrates GeoMx Digital Spatial Profiler (DSP) transcriptomics with differential pseudotime analysis to explore the spatial heterogeneity and sequential evolutionary dynamics of HGAP. 

Methods:


Brain tumor samples from 3 patients, with regions of histological higher-grade tumor, lower-grade tumor, and non-neoplastic tissues were analyzed through GeoMx DSP to capture spatially resolved gene expression. Histological markers such as Glial Fibrillary Acidic Protein (GFAP), Neuronal Nuclei (NeuN), Nuclei, and Ionized Calcium-Binding Adapter Molecule 1 (IBA1), were employed to distinguish between variably differentiated areas and non-neoplastic tissue.



We utilized the grading differences as a pseudo-temporal sequence to model tumor progression. A differential pseudotime analysis was conducted, fitting gene expression data to a model representing progression from non-neoplastic to higher-grade tissue.Sample identity was included as a covariate to control for inter-sample variability. Data analysis was performed in R 4.3.1 using the Dyplyer, DESeq2, GeoMx Tools, GeoMx workflows, SpatialDecon and GSVA packages, which respectively facilitates quality control, batch correction, differential gene expression, cell deconvolution and pathway enrichment analysis. 

Results:


GeoMx DSP technology, which enables high-throughput RNA profiling within specific regions of interest (ROIs), is applied to understand the tumor microenvironment. By preserving spatial context, this method reveals the cellular architecture and gene expression differences within different tumor zones. 



Preliminary data from spatial transcriptomics has identified differential gene expression between higher-grade and lower-grade tumor regions. Immune response-related genes, such as IGHG (Immunoglobulin G heavy chains), are significantly upregulated in higher-grade areas, suggesting important differences in the tumor immune microenvironment.  KEGG pathways revealed pathways such as ribosome, proteasome and Neutrophil extracellular trap formation are enriched in tumor compartments of higher-grade regions compared to lower-grade regions. Cell deconvolution analysis revealed the presence of immune cells in all 3 types of  regions, which include macrophages, mast cells and naive B cells. An increase in immune cell populations in higher-grade areas was also noted from the analysis. 

Conclusion:


This research bridges innovative spatial transcriptomics with mathematical modeling to improve our understanding of HGAP. The approach allows for the identification of genes whose expression changes are associated with potential tumor progression, providing insights into potential therapeutic targets. The combination of gene expression profiling and predictive modeling offers potential for broader applications in the management of other aggressive tumors.

Agenda

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