ML Classifier for Checkpoint Inhibition Prediction
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Solution Overview
Problem
Current methods for predicting patient responsiveness to checkpoint inhibition therapies in cancer treatment are limited, as they often rely on single features or limited combinations, failing to accurately account for the complex interplay of genomic and cellular features that determine treatment effectiveness.
Innovation Solution
A computer-implemented method using a trained machine learning classifier that inputs genomic information from a non-training subject, including features from a tumor profile, to predict responsiveness to checkpoint inhibition therapies. The classifier is trained on genomic information and responsiveness data from a plurality of training subjects, allowing it to generate a predictive classification of the non-training subject's responsiveness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple genomic and cellular features are considered to predict patient responsiveness, then prediction accuracy is improved, but system complexity increases
Solution Approach 1:
The patent segments the prediction system into distinct functional modules: feature extraction module (processing genomic and cellular data), machine learning classification module (performing prediction), and reporting module (presenting results with feature importance). This segmentation allows each module to handle specific aspects of complexity independently, improving overall system manageability while maintaining high prediction accuracy through comprehensive feature consideration.
Solution Approach 2:
The machine learning classifier acts as an intermediary between the raw genomic and cellular features and the final responsiveness prediction. It processes and integrates multiple features (TMB, leukocyte infiltration, checkpoint expression) to produce a simplified predictive output, mediating the complexity of multi-feature interaction while delivering accurate predictions.
2Reliability
If comprehensive genomic features are analyzed to determine treatment responsiveness, then prediction reliability is improved, but data processing complexity increases
Solution Approach 1:
The patent divides data processing into separate stages: (1) feature extraction from genomic and cellular data, (2) feature selection and transformation, and (3) classification prediction. This segmentation reduces the complexity of handling comprehensive genomic features by processing them in manageable steps through specialized modules, while maintaining high reliability through thorough feature analysis.
Solution Approach 2:
The system extracts and isolates specific predictive features from the comprehensive genomic data (such as TMB, specific leukocyte markers, checkpoint expressions) and processes only those relevant features through the machine learning model. This extraction approach reduces data processing complexity by focusing computational resources on the most predictive features rather than processing all genomic data uniformly.
3Loss of information
If feature importance and directional effects are reported for each feature, then contextual information is improved, but reporting space requirements increase
Solution Approach 1:
The patent transitions from traditional linear or tabular reporting to a radial visualization format where feature importance and directional effects are represented in angular and radial dimensions. This dimensional transformation allows comprehensive contextual information about multiple features to be displayed within a compact circular layout, reducing the overall reporting space required while preserving complete feature information.
Solution Approach 2:
The reporting system merges multiple information dimensions (feature name, importance level, directional effect, and visual representation) into a single integrated radial display. By combining these information types into one unified visualization approach, the system reduces the total space required compared to separate reporting sections for each feature attribute, while maintaining comprehensive contextual information.
Data Source
AI summary
Provided is a computer-implemented method, including inputting to a trained machine learning classifier genomic information of a non-training subject that includes features from a tumor sample, wherein the trained machine learning classifier trained on features of tumor samples obtained from training subjects and their a responsiveness to checkpoint inhibition treatment and the machine-learning classifier is trained to predict responsiveness to the treatment, and generating a checkpoint inhibition responsiveness classification predictive of the subject's responding to the checkpoint inhibition with the trained machine-learning classifier, and reporting the checkpoint inhibition responsiveness classification using a graphical user interface. Also provided are a computer system for performing the method and a machine learning classifier trained by the method.