Tumor Immunophenotyping Using Tile-Based Immune Density Mapping
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for evaluating tumor-infiltrating lymphocytes (TILs) in cancer immunotherapy are labor-intensive, subjective, and lack predictive power due to ignoring local heterogeneity and dynamics of immune cell distributions, particularly CD8+ T cells, leading to imperfect biomarkers for patient response prediction.
Innovation Solution
A computational approach that divides tumor images into tiles to calculate epithelium and stroma immune cell densities and determines inflammation types, using machine learning to identify the epithelium-stroma interface and immune cell infiltration probabilities for accurate immunophenotyping.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual visual inspection of stained tissue sections is used to evaluate immune infiltrates, then pathologist expertise can be applied, but the process becomes labor-intensive, subjective, and error-prone with poor inter- and intra-observer concordance
Solution Approach 1:
The patent replaces manual visual inspection by pathologists with an automated computational image analysis system using machine learning algorithms. This substitution eliminates human subjectivity and variability while maintaining high measurement precision through consistent algorithmic evaluation of immune cell density and spatial distribution patterns across tissue sections.
Solution Approach 2:
The system enables self-service evaluation where the computational algorithm automatically processes and evaluates immune infiltrate patterns without requiring continuous human intervention. The automated pipeline performs tile generation, immune cell detection, density calculation, and spatial pattern recognition independently, freeing pathologists from labor-intensive manual analysis while preserving expert-level evaluation accuracy.
2Device complexity
If traditional TIL evaluation methods focusing on stromal compartment are used, then assessment can be simplified, but local heterogeneity and dynamics of immune cell distributions, particularly CD8+ T cells, are ignored reducing predictive power
Solution Approach 1:
The patent segments the tissue section into multiple tiles and further divides each tile into epithelium and stroma regions. This segmentation enables separate evaluation of immune cell density in both compartments, capturing local heterogeneity that traditional unified assessment misses. The tile-based approach allows detection of spatial patterns and dynamics while maintaining manageable computational complexity through systematic region-by-region analysis.
Solution Approach 2:
The system applies local quality assessment by evaluating immune cell distribution characteristics specifically in the epithelium versus stroma compartments separately. This localized evaluation captures the distinct biological roles and patterns in each compartment, particularly for CD8+ T cells in the epithelium, thereby improving predictive power without excessive complexity through targeted regional analysis.
3Ease of manufacture
If a single parameter is used to represent TME complexity, then the biomarker can be simple and reproducible, but it lacks sufficient predictive power for immunotherapy response
Solution Approach 1:
The patent creates a composite biomarker profile by integrating multiple parameters including immune cell density in epithelium and stroma, spatial distribution patterns, and architectural context from tile-based analysis. This composite approach captures the complexity of the tumor microenvironment while maintaining reproducibility through systematic computational evaluation, achieving both multi-parameter precision and practical applicability.
Solution Approach 2:
The computational framework provides multi-functional evaluation by simultaneously assessing multiple immune cell types, spatial patterns, and tissue architecture features within a unified analysis pipeline. This universal approach generates a comprehensive biomarker profile that predicts immunotherapy response across different tumor types and contexts, balancing complexity with broad applicability and reproducibility.
Data Source
AI summary
Described herein are methods, systems, and programming for determining a tumor immunophenotype of an image of a tumor. Some embodiments include dividing an image into tiles depicting tumor epithelium and/or tumor stroma. For each tile, an epithelium-immune cell density and a stroma-immune cell density may be calculated based on a number of immune cells identified in the tumor epithelium and the tumor stroma, respectively. Based on the epithelium-immune cell density and the stroma-immune cell density, an inflammation type of the type may be determined, and a tumor immunophenotype may be determined based on each tile's inflammation type.