A novel scoring method based on RNA-Seq immunograms describing individual cancer-immunity interactions

Area of Science:

• Immunology
• Bioinformatics
• Oncology

Background:

• Cancer-immune interactions are complex, necessitating multi-biomarker approaches for treatment prediction and resistance management.
• The "immunogram" framework integrates immunological variables to comprehensively assess anti-tumor immunity.
• Existing methods require robust tools to translate molecular data into actionable immunogram insights.

Purpose of the Study:

• To develop a computational method for converting transcriptomic data into quantitative immunogram scores (IGS).
• To establish a standardized framework for assessing 10 key immune profiles across diverse cancer types.
• To create an accessible web resource for exploring cancer immunograms.

Main Methods:

• Utilized single-sample gene set enrichment analysis (ssGSEA) on 9417 bulk RNA-Seq samples from The Cancer Genome Atlas (TCGA).
• Calculated immunogram scores (IGS) based on 10 molecular profiles including innate immunity, T cell response, and myeloid-derived suppressor cells.
• Normalized enrichment scores using z-scores and applied the formula IGS = 3 + 1.5 × Z for score distribution.

Main Results:

• Successfully generated IGS for 9362 cancer patients across 29 solid tumor types.
• Demonstrated a method to quantify complex immune profiles from transcriptomic data.
• Developed "The RNA-Seq based Cancer Immunogram Web" for public access to these immunograms.

Conclusions:

• The developed method provides a quantitative and comprehensive assessment of the tumor immune microenvironment.
• Immunogram scores derived from transcriptomic data can aid in predicting patient response to cancer immunotherapies.
• The accessible web tool facilitates further research into cancer immunology and personalized treatment strategies.