Data-Driven Discovery of Immune Contexture Biomarkers

Area of Science:

• Oncology
• Immunology
• Computational Biology

Background:

• Immune contexture (IC) features within the tumor microenvironment serve as crucial prognostic and predictive biomarkers.
• Identifying novel IC biomarkers is complex due to intricate immune-tumor cell interactions and numerous potential features.
• The tumor microenvironment's immune cell composition and spatial organization are key to understanding cancer progression and treatment efficacy.

Purpose of the Study:

• To present a data-driven approach for identifying novel immune contexture (IC) biomarkers.
• To mathematically define feature classes for IC biomarker discovery, including cell densities, cell-to-cell distances, and spatial heterogeneity.
• To rank candidate biomarkers based on their potential for stratifying patients for predictive purposes.

Main Methods:

• Development of a data-driven methodology for identifying IC biomarkers.
• Mathematical definitions for feature classes: cell densities, cell-to-cell distances, and spatial heterogeneity.
• Ranking of candidate biomarkers by their predictive potential for patient stratification.

Main Results:

• Evaluation on colorectal cancer (CRC) patients with varying microsatellite instability (MSI).
• Cell-to-cell distances and spatial heterogeneity emerged as the most promising feature classes for biomarkers.
• Features from both tumor and non-tumor compartments showed potential predictive value for therapy response.

Conclusions:

• The proposed data-driven approach streamlines the identification of promising IC biomarker candidates.
• This methodology can guide researchers in accelerating their biomarker discovery efforts.
• Further exploration of distance and heterogeneity-based features is warranted for clinical application.