B-Cell Lymphoma Responsiveness Prediction via Marker Gene Expression
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Solution Overview
Problem
There is a need to identify predictive markers for the responsiveness of B-cell lymphoma patients to anti-CD40 antibody therapy, as not all B lymphoma cells are sensitive to anti-CD40 antibody-mediated cell death.
Innovation Solution
The method involves comparing the measured expression levels of specific marker genes such as UAP1, BTG2, CD40, VNN2, RGS13, CD22, IFITM1, CTSC, CD44, PUS7, BCL6, EPDR1, IGF1R, and CD79B in B-cell lymphoma samples to a reference level to predict responsiveness to anti-CD40 antibody treatment, using techniques like real-time quantitative reverse transcription PCR (qRT-PCR) and immunohistochemistry.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If anti-CD40 antibody therapy is administered to all B-cell lymphoma patients, then treatment coverage is maximized, but treatment effectiveness is reduced due to variable responsiveness
Solution Approach 1:
The patent applies preliminary action by measuring marker gene expression levels (such as CD40, BCL6, IGF1R, and other biomarkers) before initiating anti-CD40 antibody therapy. This pre-treatment assessment allows clinicians to identify patients most likely to respond to the therapy, ensuring that treatment is administered to the right population before starting the therapeutic regimen, thereby maximizing treatment effectiveness while maintaining appropriate coverage.
2Measurement precision
If marker gene expression analysis is performed to predict treatment responsiveness, then treatment precision is improved, but diagnostic complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the diagnostic process into distinct, manageable components: (1) measuring expression levels of specific marker genes (CD40, BCL6, IGF1R, etc.), (2) comparing measured levels to established thresholds, and (3) generating a responsiveness prediction. This segmented approach allows for systematic implementation of precise diagnostics while maintaining procedural clarity and reducing overall diagnostic complexity through structured analysis.
3Measurement precision
If multiple marker genes are analyzed to improve prediction accuracy, then responsiveness assessment precision is improved, but measurement time and resource requirements increase
Solution Approach 1:
The patent applies the extraction principle by identifying and measuring only the most clinically relevant marker genes (such as CD40, BCL6, IGF1R, and select additional markers) rather than performing comprehensive genomic analysis. This selective extraction of key biomarkers maintains high prediction accuracy for treatment responsiveness while significantly reducing measurement time, computational resources, and diagnostic complexity compared to analyzing all possible genetic markers.
Data Source
AI summary
The invention provides methods and kits useful for predicting or assessing responsiveness of a patient having B-cell lymphoma to treatment with anti-CD40 antibodies.