SMDIC

SMDIC identifies immune cell populations driven by specific somatic mutations by integrating genomic and transcriptomic data to detect mutation-associated changes in tumor-infiltrating immune cell abundance.

Key Features:

• Integration of Genomic and Transcriptome Data: Integrates genomic data with transcriptome profiles to discover mutation-specific immune cell populations within tumors.
• Inference of Immune Cell Abundance: Infers a relative abundance matrix of tumor-infiltrating immune cells for each sample.
• Detection of Differential Abundance: Detects differential abundance of immune cells based on gene mutation status.
• Binary Matrix Conversion: Converts the abundance matrix of significantly dysregulated immune cells into two binary matrices representing upregulated and downregulated cells.
• Identification of Mutation-Driven Immune Cells: Compares gene mutation status with each immune cell in the binary matrices to identify somatic mutation-driven immune cells across samples.
• Visualization of Mutation–Immune Relationships: Visualizes immune cell abundance in relation to different mutation statuses for each gene.

Scientific Applications:

• Characterizing mutation-associated immune composition: Identifies tumor-infiltrating immune cell populations influenced by specific somatic mutations.
• Investigating neoantigen-driven responses: Links somatic mutations that can generate neoantigens to associated antitumor immune responses.
• Studying tumor progression and immune evasion: Assesses how specific genetic alterations influence tumor progression and immune evasion via changes in immune cell populations.
• Prioritizing immunotherapy targets: Supports identification of mutation-associated immune cell populations and mutations as potential targets for cancer immunotherapy.

Methodology:

Implemented in R; integrates genomic and transcriptomic datasets; infers a relative abundance matrix of tumor-infiltrating immune cells; detects differential abundance based on gene mutation status; converts significantly dysregulated cell abundances into upregulated and downregulated binary matrices; compares gene mutation status with each immune cell in the binary matrices to identify mutation-driven immune cells; provides visualization of immune cell abundance versus mutation status.