Identification of Somatic Mutation-Driven Immune Cells by Integrating Genomic and Transcriptome Data

Area of Science:

• Oncology
• Immunology
• Bioinformatics

Background:

• Somatic mutations in tumors can create neoantigens, potentially activating antitumor immune responses.
• Immune cell activity significantly impacts tumor progression, but tools for linking specific mutations to immune cells are lacking.

Purpose of the Study:

• To develop a computational method for systematically identifying tumor-infiltrating immune cells driven by specific somatic mutations in cancer.
• To provide a user-friendly tool for discovering mutation-specific immune cell responses.

Main Methods:

• Developed SMDIC, an R-based software package integrating genomic and transcriptome data.
• Implemented a pipeline for inferring immune cell abundance, detecting differential abundance based on mutation status, and identifying mutation-driven immune cells.
• Included visualization of immune cell abundance across different mutation statuses.

Main Results:

• SMDIC enables automated identification of somatic mutation-driven immune cells.
• The software facilitates the discovery of mutation-specific immune cell responses.
• Provides a means to visualize immune cell abundance relative to gene mutation status.

Conclusions:

• SMDIC is a novel, user-friendly tool for identifying somatic mutation-specific immune cell responses.
• This tool can advance the understanding of anticancer immunity mechanisms.
• SMDIC may help identify novel targets for cancer immunotherapy.