Classifier System for Predicting ICI Response via Biological Process Segmentation
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Solution Overview
Problem
Current methods for predicting responses to immune checkpoint inhibitor (ICI) therapy in melanoma lack reliable biomarkers, with existing genomic markers showing limited reproducibility and clinical utility, necessitating the development of improved systems and methods for predicting medical treatment responses in subjects.
Innovation Solution
A method and system involving the selection of biological processes, generation of classifiers using algorithms such as Greedy forward feature selection, randomized forward feature selection, genetic algorithms, random forest, and neural networks to predict clinical outcomes from genome data, allowing for the treatment of subjects based on predicted responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing genomic biomarkers are used for predicting ICI response, then prediction can be performed, but the predictive performance and reproducibility are limited
Solution Approach 1:
The patent segments the genome into specific biological processes (e.g., DNA repair, chromatin modification, transcription) and evaluates mutations within each process separately. This segmentation allows identification of process-specific mutation patterns that are more reproducible and predictive than genome-wide approaches, directly resolving the contradiction between predictive performance and reproducibility.
Solution Approach 2:
The patent changes the parameter of analysis from genome-wide mutation burden to mutation patterns within specific biological processes. By transforming the approach from evaluating all mutations to evaluating mutations in context of their biological function, the patent achieves both improved predictive performance and reproducibility across different datasets.
2Loss of information
If comprehensive genomic analysis is performed, then more information is obtained, but the complexity of the system increases
Solution Approach 1:
The patent extracts and focuses on specific biological processes from the comprehensive genomic data, rather than analyzing all genomic information. This extraction of process-specific mutation patterns reduces system complexity while maintaining the essential predictive information, resolving the contradiction between information completeness and system complexity.
Solution Approach 2:
By segmenting the genome into functional biological processes, the patent makes the complex genomic data more manageable and interpretable. Each process can be analyzed independently, reducing the overall system complexity while preserving the predictive information contained in the comprehensive genomic analysis.
3Reliability
If multiple classification algorithms are evaluated, then the best predictor can be identified, but the computational resources and time required increase
Solution Approach 1:
The patent performs preliminary evaluation of multiple classification algorithms (e.g., Random Forest, Gradient Boosting, Neural Networks) during the development phase to identify the most effective algorithm for process-specific mutation analysis. This preliminary action establishes a robust predictive framework that can be efficiently applied to new patients without requiring repeated extensive algorithm testing, thus resolving the contradiction between predictor accuracy and computational time.
Data Source
AI summary
Described are systems and methods of predicting a response to a medical treatment in a subject. The systems and methods include the steps of selecting a set of mutations within at least one biological process, training a set of classifiers from the set of selected mutations via a training dataset, determining the performance level of each classifier via a validation dataset, applying a subset of high-performance level classifiers from the validation dataset via a test dataset, and predicting the response to the medical treatment based on the test dataset.