Tumor Classification via Cell Surface Receptor Clustering Analysis
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Solution Overview
Problem
Current methods for classifying tumors based on responsiveness to therapeutic agents, such as anti-HER antibodies, are inadequate as they do not effectively distinguish between receptor antagonist responders and non-responders, leading to limited treatment efficacy in cancers like HER2-positive breast cancer due to therapeutic resistance.
Innovation Solution
The method involves determining the clustering status of cell surface receptor elements on tumor cells, specifically assessing the presence and distribution of receptor clusters, to classify tumors as responsive or non-responsive to therapeutic agents, allowing for improved patient stratification and treatment selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional tumor classification methods are used, then treatment selection is simplified, but treatment response prediction accuracy is insufficient
Solution Approach 1:
The invention segments the receptor expression analysis into two distinct dimensions: traditional receptor quantification (number of receptors per cell) and novel receptor clustering analysis (spatial distribution patterns). This segmentation allows independent optimization of each parameter and enables comprehensive tumor classification that captures both receptor abundance and spatial organization, thereby improving prediction accuracy without creating an unmanageable single complex assay.
Solution Approach 2:
The invention adds a new spatial dimension to traditional receptor analysis by examining the clustering status of receptors on cell surfaces. Instead of merely counting receptor numbers, the method analyzes the spatial arrangement and clustering patterns of receptors, transforming the analysis from a one-dimensional quantity measurement to a two-dimensional quantity-plus-distribution measurement, which significantly improves treatment response prediction.
2Reliability
If receptor expression levels are measured alone, then analysis is simple, but therapeutic resistance is not detected
Solution Approach 1:
The invention uses fluorescently labeled antibodies as intermediaries to visualize and detect receptor clustering patterns on cell surfaces. These labeled antibodies bind to the receptors and their fluorescent signals allow indirect observation of receptor spatial distribution and clustering status, making the detection of therapeutic resistance mechanisms accessible and reliable without requiring direct manipulation of the receptors themselves.
Solution Approach 2:
The invention employs fluorescent labeling that produces detectable color/fluorescence changes to visualize receptor clustering patterns. By using fluorescently labeled antibodies or ligands that emit light signals when bound to clustered receptors, the method transforms invisible molecular clustering events into visible optical signals, enabling reliable detection and classification of tumor cells based on their receptor spatial organization.
3Adaptability or versatility
If comprehensive receptor analysis is performed, then treatment selection accuracy is improved, but patient stratification becomes more complex
Solution Approach 1:
The invention segments the comprehensive receptor analysis into distinct, manageable classification categories: non-responsive tumors (impaired clustering), partially responsive tumors (intermediate clustering), and fully responsive tumors (unimpaired clustering). This segmentation transforms the complex continuous spectrum of receptor behavior into discrete, clinically actionable subgroups, enabling systematic patient stratification that is both comprehensive and practically manageable.
Solution Approach 2:
The invention changes the key parameter from traditional single-value receptor expression levels to a two-parameter system comprising both receptor quantity and clustering status. This parameter transformation enables tumors with similar receptor counts but different clustering patterns to be differentiated, creating more precise patient subgroups that respond differently to therapy, thereby enhancing stratification capability without overwhelming complexity.
Data Source
AI summary
Disclosed are methods for classifying tumors according to their responsiveness to a therapeutic agent based on the clustering status of a cell surface receptor element to which the therapeutic agent is capable of binding. Also disclosed are 5 methods for stratifying subjects with cancer into treatment subgroups based on this classification as well as methods for treating subjects so stratified.