Spatial and Compositional Biomarkers in Tumor Microenvironment Predicts Clinical Outcomes in Triple-Negative Breast...

Area of Science:

• Oncology
• Immunology
• Computational Biology

Background:

• Triple-negative breast cancer (TNBC) presents limited therapeutic options, necessitating new targets.
• Understanding the tumor microenvironment (TME) is crucial for linking anti-tumor immunity to clinical outcomes.
• Existing research on TME-immunity connections in TNBC remains limited.

Approach:

• Utilized imaging mass cytometry on 58 TNBC patient specimens for single-cell resolution analysis.
• Applied multi-scale computational algorithms to quantify cell distribution and spatial relationships.
• Developed a deep learning model to predict treatment response based on baseline TME features.

Key Points:

• Distinct cellular distribution patterns were observed, potentially related to tumor vasculature and fibroblast heterogeneity.
• Ten recurrent cellular neighborhoods (CNs) were identified, characterized by unique cell compositions.
• The prevalence of immune hotspot CNs and inter-CN interactions correlated with improved patient survival.

Conclusions:

• TNBC TME architecture is defined by cellular composition, spatial organization, vasculature, and molecular profiles.
• Spatial TME features can predict treatment response in TNBC patients, as demonstrated by a deep learning model.
• These findings suggest novel imaging-based biomarkers for TNBC treatment development.