Artificial Neural Network Analysis of Flow Cytometry Data for Cancer Diagnosis
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Solution Overview
Problem
Current cancer diagnostic methods lack sensitivity and generate numerous false positives and negatives, are often invasive, and are costly, making them inefficient for early detection and recurrence testing.
Innovation Solution
The use of artificial neural networks to analyze flow cytometry data, eliminating the need for manual gating and providing a computationally efficient representation of cell populations, enabling the detection of cancer-specific cell populations like MDSCs with high accuracy and specificity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional flow cytometry gating methods are used for cancer detection, then the analysis process is simple and widely applicable, but the detection accuracy is insufficient and generates numerous false positives and negatives
Solution Approach 1:
The patent replaces the manual gating mechanism with an automated machine learning-based classification system. The machine learning model automatically processes high-dimensional flow cytometry data, eliminating the need for manual gate definition and providing consistent, accurate cancer detection without human intervention in the analysis process
Solution Approach 2:
The patent transforms the analysis approach by changing from traditional two-dimensional gating parameters to multi-dimensional feature space analysis. The system uses multiple markers (CD11b, CD15, CD66b, HLA-DR) measured across multiple flow cytometry channels to create a comprehensive cellular phenotype profile, enabling precise distinction between MDSCs and other cell types
2Productivity
If manual gating methods are used for flow cytometry data analysis, then the operation is straightforward and widely understood, but the process is labor intensive and subjective
Solution Approach 1:
The system performs self-service by automatically processing flow cytometry data through machine learning algorithms without requiring operator intervention. The machine learning model independently performs cell population identification, MDSC detection, and cancer diagnosis, eliminating the need for manual gate adjustment and subjective interpretation
Solution Approach 2:
The patent replaces manual gating operations with automated computational algorithms. The machine learning model processes high-dimensional data automatically, providing consistent results without human subjectivity and significantly improving analysis throughput and efficiency
3Loss of information
If conventional gating methods are used to isolate specific cell populations, then the method is easy to implement, but it results in a coarse representation that obscures important information
Solution Approach 1:
The patent applies dimensionality change by moving from traditional two-dimensional gating plots to high-dimensional feature space analysis. The system uses multiple flow cytometry channels measuring different markers (CD11b, CD15, CD66b, HLA-DR) simultaneously, creating a comprehensive multi-dimensional representation that preserves all relevant information about cell populations
Solution Approach 2:
The system segments the complex high-dimensional data into meaningful biological categories by using machine learning classification. The model divides the cellular population into distinct groups (MDSCs, lymphocytes, other myeloid cells) based on their marker profiles, providing both detailed information preservation and biologically meaningful organization
4Measurement precision
If liquid biopsy methodologies with DNA or RNA sequencing are used, then the cancer detection sensitivity is improved, but the test cost and operational complexity increase significantly
Solution Approach 1:
The patent uses inexpensive flow cytometry technology with disposable fluorescent antibodies instead of expensive DNA or RNA sequencing. The system employs standard flow cytometry instruments and commercially available fluorescently conjugated antibodies to achieve cancer detection, providing a cost-effective alternative to expensive sequencing platforms
Solution Approach 2:
The patent replaces complex molecular sequencing technology with simpler flow cytometry-based cellular analysis. Instead of sequencing DNA or RNA, the system directly analyzes cell surface and intracellular markers using fluorescent antibodies, providing a more operationally simple and cost-effective approach to cancer detection
5Ease of manufacture
If flow cytometry is used to detect MDSCs in peripheral circulation, then the method is inexpensive and non-invasive, but the detection accuracy is insufficient for screening tests
Solution Approach 1:
The patent changes the detection parameters by using multiple flow cytometry channels to measure multiple markers simultaneously (CD11b, CD15, CD66b, HLA-DR). This multi-parameter approach creates a comprehensive cellular phenotype profile that significantly improves MDSC detection accuracy while maintaining the inexpensive, non-invasive nature of flow cytometry
Solution Approach 2:
The system uses composite fluorescent staining approaches combining multiple fluorescently conjugated antibodies against different markers on MDSCs. This composite staining strategy enables simultaneous detection of multiple cell characteristics in a single flow cytometry run, improving detection accuracy without increasing test complexity or cost
Data Source
AI summary
The present disclosure provides methods for applying artificial neural networks to flow cytometry data generated from biological samples to diagnose and characterize cancer in a subject.