Gene Expression Profiling for Anti-CD20 Therapy Response Prediction
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Solution Overview
Problem
Current treatments for diffuse large B-cell lymphoma (DLBCL) using anti-CD20 therapy, such as rituximab, exhibit varying response rates among patients, necessitating a method to predict which patients will benefit from this therapy and which will not, to tailor treatment effectively.
Innovation Solution
A method involving the determination of gene expression levels in patient samples using diagnostic probes or antibodies for genes listed in Tables 1 and 2, compared to control samples, to predict response to anti-CD20 therapy, employing statistical methods like Support Vector Machine or k-nearest-neighbor algorithms, and the use of kits containing these probes for in vitro prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If anti-CD20 therapy is administered to all DLBCL patients, then some patients will achieve response and survival benefit, but many patients will not respond and undergo unnecessary treatment with associated side effects and costs
Solution Approach 1:
The patent performs gene expression profiling and computational analysis before administering anti-CD20 therapy to predict patient response. This preliminary action identifies likely responders and non-responders, allowing clinicians to tailor treatment decisions and avoid unnecessary therapy in patients predicted to have poor response.
Solution Approach 2:
The patent introduces gene expression profiles and computational algorithms as intermediaries between the patient's tumor characteristics and the treatment decision. These intermediaries translate complex molecular data into actionable predictions about therapy response, enabling personalized treatment selection.
2Adaptability or versatility
If gene expression profiling is performed to predict therapy response, then personalized treatment decisions can be made, but the complexity and cost of the diagnostic process increases
Solution Approach 1:
The patent develops computational algorithms that can process multiple gene expression profiles simultaneously and adapt to different patient samples. The system is designed to handle various input formats and provide standardized output predictions, making the complex analysis process universally applicable across different clinical settings.
Solution Approach 2:
The patent creates computational models that replicate the complex biological relationships between gene expression patterns and therapy response. Instead of requiring direct complex experimental analysis for each patient, the system uses trained computational models that copy and apply learned patterns from training data to new patient samples, simplifying the diagnostic process.
Data Source
AI summary
This invention provides methods, compositions, and kits relating to biomarkers whose expression levels are correlated with diffuse large B-cell lymphoma (DLCBL) patients' response to treatment with a CD20 antagonist, such as a CD20 antibody, exemplified by rituximab. The methods, compositions, and kits of the invention can be used to identify DLBCL patients who are likely or not likely, to respond to anti-CD20 treatments.