Immune Checkpoint Response Prediction With Somatic-Germline Features
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Solution Overview
Problem
Current immunotherapy response prediction methods, such as those based on tumor mutation burden and microsatellite instability, are imperfect and not straightforward to apply clinically, failing to accurately identify patients who can benefit from immune checkpoint blockade therapy.
Innovation Solution
A machine learning model integrating both somatic and germline features, including interactions between MHC class-I damage and germline variants associated with T-follicular helper cell infiltration, predicts immunotherapy response by generating an immune checkpoint blockade response score.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current immunotherapy response prediction methods (tumor mutation burden, microsatellite instability) are used, then prediction can be performed, but prediction accuracy is insufficient and clinical application is not straightforward
Solution Approach 1:
The patent combines multiple prediction methods including tumor mutation burden assessment, microsatellite instability testing, and immune checkpoint expression analysis into a composite prediction model. This integration of multiple biomarkers and analytical approaches enables more accurate immunotherapy response prediction while maintaining clinical feasibility through a systematic framework that synthesizes various data sources.
2Measurement precision
If comprehensive sequencing features are analyzed, then prediction accuracy improves, but computational complexity and data processing requirements increase
Solution Approach 1:
The patent segments the comprehensive sequencing data into distinct feature categories including somatic mutations, germline variants, tumor mutational burden, microsatellite instability, and immune checkpoint expression. Each segment is analyzed independently using appropriate computational methods, then integrated into the final prediction model. This segmentation reduces computational complexity by breaking down the complex analysis into manageable components while preserving prediction accuracy.
Data Source
AI summary
Disclosed herein are methods and systems for predicting a subject's response to immunotherapy to treat a cancer, including receiving sequencing data of the subject; determining, using the sequencing data, a plurality of somatic features for the subject and a plurality of germline features for the subject; generating, using the plurality of somatic features for the subject and the plurality of germline features for the subject, an immune checkpoint blockade (ICB) response score for the subject to represent a likelihood of response to an immunotherapy for the subject; and comparing the ICB response score for the subject to an ICB response threshold value.