Immunotherapy Resistance Scoring With Plasma Proteomics
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current predictive biomarkers for immunotherapy response, such as tumor PD-L1 expression and tumor mutational burden, are not sufficiently accurate in predicting patient response to immune checkpoint inhibitor therapies, particularly in metastatic non-small cell lung cancer, due to their inability to capture the complexity of tumor-immune system interactions and heterogenous mechanisms of resistance.
Innovation Solution
A method using plasma proteomics and machine learning algorithms to integrate PD-L1 levels and plasma proteomic data, calculating a resistance score from factor expression levels in subjects, to predict response to monotherapy or combination therapy with anti-PD-1/PD-L1 immunotherapy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If tumor PD-L1 expression and tumor mutational burden are used as predictive biomarkers, then treatment decisions can be informed, but prediction accuracy is insufficient
Solution Approach 1:
The patent combines multiple biomarkers (PD-L1 expression, tumor mutational burden, plasma proteomic features) into an integrated predictive model. This merging of multiple measurement dimensions enables more accurate prediction of immunotherapy response while capturing the complexity of tumor-immune interactions that single biomarkers cannot detect
2Measurement precision
If comprehensive characterization of tumor microenvironment and immune cells is performed, then predictive performance improves, but multiple assays and tissue specimens are required
Solution Approach 1:
The patent uses plasma proteomic features as an intermediary that reflects tumor microenvironment and immune cell dynamics without requiring direct analysis of tissue specimens. This plasma-based mediator captures systemic immune responses and tumor-derived signals, enabling comprehensive characterization through a single minimally invasive blood draw rather than multiple tissue assays
3Ease of operation
If plasma proteomics is used to predict immunotherapy response, then a single minimally invasive assay is achieved, but integration with PD-L1 levels is needed
Solution Approach 1:
The patent merges plasma proteomic data with PD-L1 expression levels into a unified predictive model. This combination integrates local tumor surface marker information (PD-L1) with systemic immune and tumor microenvironment signals (plasma proteomics), achieving both ease of sampling through blood draw and high prediction accuracy by capturing multiple biological dimensions
Data Source
AI summary
Methods of predicting response of a subject suffering from cancer to an anti-PD-1/L1 immunotherapy, as a monotherapy or combination therapy, comprising calculating a resistance score for factors expressed by the subject, summing the resistance score to produce a total resistance score, wherein a total resistance score beyond a predetermined threshold indicates a subject is predicted to be resistant to the anti-PD-1/L1 immunotherapy as a monotherapy or combination therapy, are provided.