Immuno-Oncology Score for Predicting Immunotherapy Responsiveness
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Solution Overview
Problem
Current technologies lack effective biomarkers for determining patient responsiveness to immunomodulation therapies, such as immune checkpoint inhibitors, which are crucial for stratifying patient populations and optimizing treatment strategies in cancer care.
Innovation Solution
The development of an immuno-oncology score (IO score) using a combination of mesenchymal (M), mesenchymal stem-like (MSL), and immunomodulatory (IM) gene expression signatures, assessed through gene expression analysis, to predict patient responsiveness to immunomodulation therapies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional single biomarker approaches are used for predicting immunotherapy responsiveness, then the simplicity of the test is maintained, but the accuracy and reliability of patient stratification is insufficient
Solution Approach 1:
The patent combines multiple gene expression signatures (mesenchymal, mesenchymal stem-like, and immunomodulatory) into a single integrated immuno-oncology score. This merging of multiple biomarkers into one composite metric achieves both improved prediction accuracy through multi-factor assessment and maintains relative simplicity by providing a unified scoring system rather than requiring separate evaluation of multiple independent biomarkers
Solution Approach 2:
The immuno-oncology score functions as a composite biomarker that integrates information from three distinct gene expression domains. This composite approach synthesizes data from mesenchymal (10 genes), mesenchymal stem-like (15 genes), and immunomodulatory (27 genes) signatures into a single predictive metric, enhancing measurement precision while managing complexity through systematic integration
2Reliability
If comprehensive gene expression analysis is performed to assess tumor immunology, then the accuracy of patient stratification is improved, but the time and resources required for testing increase
Solution Approach 1:
The patent performs preliminary action by pre-defining the three gene expression signatures and their associated gene sets before clinical application. The mesenchymal, mesenchymal stem-like, and immunomodulatory signatures are established with specific gene members identified in advance, allowing clinical laboratories to directly implement the scoring system without needing to perform de novo signature development, thereby reducing implementation time while maintaining reliability
Solution Approach 2:
The comprehensive gene expression analysis is segmented into three distinct, non-overlapping signature components that can be evaluated separately and then integrated. This segmentation of the 52 total genes into three functional categories (mesenchymal, mesenchymal stem-like, immunomodulatory) allows for systematic assessment that improves reliability through comprehensive coverage while managing testing complexity through structured organization
Data Source
AI summary
Cancer is the second leading cause of death in the United States. Increasingly, immune modulating therapies, such as therapy with immune checkpoint inhibitors (ICI) are being explored as promising potential therapies for many cancers. Presented herein are systems and methods for prediction, and especially automated prediction, of subject response to cancer therapies. Also presented herein are methods for selection of cancer therapies based upon predicted subject response and/or technologies for administering cancer therapies to appropriate subjects.