Perivascular Three-Cell Structure Detection for Immunotherapy Prediction
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Solution Overview
Problem
Current methods fail to effectively investigate the association of perivascular tumor-associated macrophages with other immune cells in human tumors, limiting the prediction of responses to anti-tumor therapies and prognosis.
Innovation Solution
The use of immunofluorescence staining and quantitative image analysis to detect and quantify three-cell structures comprising T cells, immunosuppressive tumor-associated macrophages, and regulatory T cells in the tumor stroma, particularly around blood vessels, to predict the efficacy of immunotherapies and determine tumor prognosis and invasiveness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods are used to study immune cells in tumors, then the investigation is simpler and faster, but the ability to detect associations among perivascular immune cells is insufficient
Solution Approach 1:
The patent segments the complex task of immune cell analysis into distinct components: (1) immunofluorescence staining with multiple antibodies targeting specific cell markers, (2) image acquisition through microscopy, (3) image processing and analysis to identify cell types and their spatial relationships, and (4) quantification of perivascular cell associations. This segmentation enables precise detection of immune cell structures while managing complexity through systematic breakdown of the analytical process
Solution Approach 2:
The patent introduces quantitative image analysis as an intermediary tool that bridges the gap between raw immunofluorescence images and meaningful biological insights. This intermediary process automatically identifies cell types based on staining patterns, calculates distances between cells and blood vessels, and quantifies perivascular immune cell associations, thereby enhancing measurement precision without requiring direct manual observation of complex cellular interactions
2Reliability
If perivascular immune cell structures are not investigated, then the diagnostic process is simpler and faster, but the prediction of therapy response and prognosis is less accurate
Solution Approach 1:
The patent performs preliminary action by automatically identifying and characterizing perivascular immune cell structures during the image analysis phase, before clinical interpretation. The system pre-calculates key parameters such as the density of perivascular macrophages, T cells, and their spatial associations, storing these results for rapid clinical decision-making. This preliminary processing ensures reliable therapy response prediction without requiring extensive time during the actual diagnostic consultation
Solution Approach 2:
The patent replaces manual pathological examination with automated image analysis algorithms that objectively quantify perivascular immune cell structures. This substitution eliminates variability in human assessment and provides consistent, reproducible measurements of cell densities and spatial relationships, thereby improving the reliability of therapy response predictions while reducing the time required for accurate assessment
3Loss of information
If detailed analysis of three-cell structures is performed, then the understanding of immune contexture is deeper, but the complexity of the diagnostic method increases
Solution Approach 1:
The patent applies local quality by focusing the detailed analysis specifically on perivascular regions where immune cells interact with blood vessels. Instead of uniformly analyzing the entire tumor section, the system concentrates computational resources on identifying and characterizing immune cell structures within defined perivascular zones, thereby capturing critical local information about immune-tumor interactions while managing overall analytical complexity
Solution Approach 2:
The patent adds a spatial dimension to the analysis by measuring distances between immune cells and blood vessels, as well as the spatial relationships among different cell types within perivascular structures. This dimensional approach transforms simple cell counting into a comprehensive assessment of immune cell architecture and organization, preserving detailed information about cellular associations while using computational methods to manage the increased complexity
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach allows for the prediction of tumor responsiveness to immunotherapies and determination of tumor prognosis and invasiveness by correlating the density of these three-cell structures with treatment outcomes, enabling personalized therapy strategies.
Implementation Method 1
Using immunofluorescence staining techniques and quantitative image analysis, the present disclosure describes the discovery of a three-cell structure
Data Source
AI summary
Provided herein are methods for determining the chance of a tumor not responding to an anti-cancer therapy (e.g., a T cell-based immunotherapy) based on the presence, density, number, and/or location of certain three-cell structures as described herein. The three-cell structures may comprise a T cell, an immunosuppressive tumor-associated macrophage, and an immunosuppressive regulatory T cell. Such methods may be useful for identifying patients not likely to respond to T cell-based immunotherapy. Also provided herein are methods for determining the prognosis and/or invasiveness of a tumor. The present disclosure also encompasses methods for treating a tumor, as well as kits for performing the methods disclosed herein.