Immune Cell Phenotype Clustering for Immunotherapy Response Prediction
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Solution Overview
Problem
Current methods lack a reliable test or biomarker to predict which cancer patients will respond to immune-based therapies, such as immunotherapy, and there is a limited understanding of why some patients respond while others do not, leading to inefficiencies in treatment.
Innovation Solution
A method involving the clustering of immune cell phenotypes from patient data, generation of violin plots, and statistical analysis to predict a cancer patient's response to immune-based or targeted therapy, using algorithms like SPADE and t-SNE, and recommending appropriate therapies based on the analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple types of immune cell markers are used to characterize the immune system, then the comprehensiveness of immune system analysis is improved, but the complexity of data interpretation increases significantly
Solution Approach 1:
The patent segments the complex immune system data into distinct cell populations (T cells, B cells, NK cells, monocytes, etc.) and further segments them into functional subsets based on marker expression patterns. This segmentation transforms the overwhelming complexity of analyzing all immune cells simultaneously into manageable, interpretable groups that can be individually assessed for their relationship to treatment response.
Solution Approach 2:
The patent adds a new dimension of analysis by creating a hierarchical classification system that organizes immune cells across multiple levels: cell type (e.g., T cell), functional subset (e.g., cytotoxic, helper, regulatory), and activation state. This dimensional organization transforms complex multi-parameter flow cytometry data into a structured framework that reveals patterns predictive of immunotherapy response.
2Reliability
If comprehensive immune cell phenotyping is performed to understand treatment response, then the accuracy of response prediction is improved, but the time and resources required for analysis increase
Solution Approach 1:
The patent performs preliminary clustering and characterization of immune cell populations from baseline samples before treatment begins. By pre-identifying and quantifying relevant cell subsets (such as regulatory T cells, exhausted T cells, and myeloid-derived suppressor cells) at baseline, the system establishes predictive markers in advance, enabling rapid assessment of likely treatment response without requiring extensive post-treatment analysis.
Solution Approach 2:
The patent extracts and focuses on specific, clinically relevant immune cell subsets that are most predictive of immunotherapy response, rather than analyzing all immune cell populations equally. By identifying and isolating the key predictive markers (such as the ratio of effector to regulatory T cells, or specific activation states), the system reduces the analysis burden to only the most informative parameters.
3Loss of information
If immune cell phenotypes are clustered and analyzed in detail, then the understanding of treatment response mechanisms is improved, but the difficulty of detecting and measuring relevant patterns increases
Solution Approach 1:
The patent employs computational algorithms that iteratively refine the clustering of immune cell populations based on marker expression patterns, using feedback from the data itself to identify natural groupings. This unsupervised learning approach allows the system to automatically detect meaningful patterns without requiring pre-defined hypotheses, and the results can be validated against clinical outcomes to ensure they capture true biological variation related to treatment response.
Solution Approach 2:
The patent introduces computational biology tools and algorithms as intermediaries between the raw flow cytometry data and clinical interpretation. These computational mediaries perform dimensionality reduction, clustering, and pattern recognition that transform complex multi-parameter data into simplified visualizations and quantitative metrics that are easier for clinicians to interpret and use for treatment decision-making.
Data Source
AI summary
An example method for quantitatively predicting a cancer patient's response to immune-based or targeted therapy is described herein. The method can include receiving patient data for the cancer patient. The patient data is derived from a blood or tissue sample. The method can also include clustering a plurality of immune cell phenotypes present in the patient data, and generating a plurality of violin plots of signal intensity for at least one of the immune cell phenotypes. The clustered patient data can include a plurality of nodes, and each of the violin plots can capture a number of events. The method can further include statistically analyzing the violin plots to predict the cancer patient's response to immune-based or targeted therapy.