Inference on spatial heterogeneity in tumor microenvironment using spatial transcriptomics data

Area of Science:

• Oncology
• Computational Biology
• Spatial Statistics

Background:

• The tumor microenvironment (TME) is crucial for cancer progression, immune response, and treatment efficacy.
• Intratumor heterogeneity exists at genetic, transcriptomic, and cellular composition levels.
• Quantitative assessment of spatial heterogeneity within the TME remains challenging.

Purpose of the Study:

• To develop a framework for analyzing spatial heterogeneity in the TME using network graph-based spatial statistical models.
• To apply this framework to spatial transcriptomics data to understand TME organization.
• To investigate the impact of spatial patterns on tumor characteristics and clinical implications.

Main Methods:

• Utilized network graph-based spatial statistical models on spatially annotated molecular data.
• Applied the framework to spatial transcriptomics data from pancreatic ductal adenocarcinoma (PDAC) samples.
• Analyzed spatial correlation patterns of tumor cells, immune cells, and pathway signatures.

Main Results:

• Observed significant global and local spatial correlation in tumor cell abundance scores.
• Found dispersed spatial patterns for immune cell types within the TME.
• Identified hypoxia, EMT, and inflammation signatures contributing to intra-tumor spatial variations.

Conclusions:

• Spatial patterns in cell type abundance and pathway signatures within the TME influence tumor growth and cancer hallmarks.
• Intra-tumor non-genetic spatial heterogeneity means single biopsies may not capture the full clinical picture.
• This framework provides insights into TME modularity and spatial heterogeneity for improved understanding of cancer.