SpatialSNV: A novel method for identifying and analyzing spatially resolved SNVs in tumor microenvironments

Area of Science:

• Oncology
• Genomics
• Bioinformatics

Background:

• Single-nucleotide variants (SNVs) are crucial in tumor development, but their role in shaping tumor microenvironments is understudied.
• Spatial transcriptomics enables mapping SNVs within the tumor context, offering new insights into microenvironment dynamics.

Purpose of the Study:

• To develop a method for identifying effective SNVs across tumor sections using spatial transcriptomics.
• To explore the relationship between SNVs, tumor evolution, and the tumor microenvironment.

Main Methods:

• Developed SpatialSNV tool for SNV identification across spatial transcriptomics platforms.
• Analyzed SNV distribution, mutational profiles at tumor margins, and correlation with microenvironmental factors.
• Identified spatially correlated SNV groups and specific mutations like S100A11L40P.

Main Results:

• SNVs reflect regional tumor evolutionary traces and impact beyond RNA expression.
• Tumor margins show distinct mutational profiles, with SNVs decreasing with distance from the boundary.
• SNVs are linked to inflammatory and hypoxic microenvironments and tumor-immune crosstalk.
• Identified S100A11L40P as a tumor region-specific mutation potentially producing neoantigens.

Conclusions:

• SpatialSNV provides a framework for studying tumor-immune crosstalk using spatial transcriptomics and SNV data.
• The approach aids in exploring tumor heterogeneity and identifying therapeutic targets.