Integrative multi-omics characterization of 12 syngeneic mouse models

Area of Science:

• Cancer Research
• Immunology
• Proteomics

Background:

• Mouse syngeneic models are crucial for studying tumor-immune interactions and immunotherapy.
• Evaluating protein quantification methods is essential for robust preclinical research.

Purpose of the Study:

• To compare label-free protein quantification pipelines in mouse syngeneic models.
• To identify molecular mechanisms underlying response to immune checkpoint inhibitors (ICIs).
• To develop a web resource for sharing multi-omics data and analysis tools.

Main Methods:

• Comprehensive evaluation of six label-free protein quantification pipelines.
• Comparative analysis of data-independent acquisition (DIA) and data-dependent acquisition (DDA) methods.
• Integrative multi-omics analysis to identify biomarkers and pathways associated with treatment response.

Main Results:

• DIA significantly outperformed DDA in data coverage, reproducibility, and inter-model discrimination across 12 mouse syngeneic models.
• Identified Dnmt3a and Igf2r as potential markers associated with resistance to immune checkpoint inhibitors (ICIs).
• Highlighted interferon signaling and oxidative phosphorylation pathways that differentiate responders from non-responders.

Conclusions:

• DIA is a superior method for proteomic analysis in mouse syngeneic models for cancer immunotherapy research.
• Multi-omics analysis reveals key molecular players and pathways influencing ICI treatment outcomes.
• An interactive web resource is provided to facilitate data exploration and accelerate preclinical research for personalized cancer therapies.