Multi-protein classifier for ovarian cancer detection
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Solution Overview
Problem
Current methods for detecting ovarian cancer, particularly in its early stages, are inadequate due to the lack of sensitive screening tests and the fact that CA125, a well-known biomarker, is not expressed in 20% of ovarian cancers, leading to late diagnoses and low survival rates.
Innovation Solution
A method involving the measurement of mucin 16 (CA125), human epididymis protein 4 (HE4), integrin alpha-V (ITGAV), and seizure 6-like protein (SEZ6L) levels in biological samples, with a multi-protein classifier using these biomarkers to calculate an ovarian cancer risk score, improving sensitivity over CA125 alone.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If only CA125 is analyzed for ovarian cancer detection, then the method is simple and cost-effective, but the sensitivity is insufficient leading to late diagnoses
Solution Approach 1:
The patent combines multiple protein biomarkers (CA125, HE4, ITGAV, SEZ6L) into a single multi-protein classifier system. This merging of multiple detection targets into one integrated assay resolves the contradiction by achieving high detection sensitivity through protein level combinations while maintaining a unified testing platform that manages complexity.
Solution Approach 2:
The patent creates a composite biomarker profile by analyzing multiple proteins simultaneously rather than relying on a single biomarker. This composite approach resolves the sensitivity limitation of CA125 alone by integrating information from multiple protein sources, achieving superior detection capability while the composite nature handles the complexity through standardized measurement protocols.
2Reliability
If CA125 level is elevated, then ovarian cancer risk increases, but CA125 is not expressed in 20% of ovarian cancers leading to false negatives
Solution Approach 1:
The patent develops a multi-protein classifier that serves multiple detection functions simultaneously. By measuring CA125, HE4, ITGAV, and SEZ6L together, the system achieves universal detection coverage across different ovarian cancer types and stages, resolving the limitation of CA125 alone which misses 20% of cases. Each protein contributes different detection capabilities that collectively cover all cancer subtypes.
Solution Approach 2:
The patent changes the detection parameters from a single biomarker threshold to a multi-parameter protein profile analysis. By evaluating multiple protein levels simultaneously and using their combined pattern rather than individual thresholds, the system overcomes the parameter limitation where CA125 alone fails to detect cancers with normal or low CA125 expression.
3Measurement precision
If multi-protein classifier is used, then sensitivity increases for early-stage detection, but the measurement and analysis process becomes more complex
Solution Approach 1:
The patent segments the detection process into distinct measurement components for each protein (CA125, HE4, ITGAV, SEZ6L) that can be measured independently using standardized assays. This segmentation resolves the measurement complexity by breaking down the multi-protein analysis into manageable, individually-validated steps while achieving high early-stage detection precision through the combined results.
Data Source
AI summary
In one aspect, the present disclosure relates to a method for determining risk of ovarian cancer in a patient, the method including: providing a biological sample from the patient; measuring a level of CA1225 in the biological sample; measuring a level of SEZ6L in the biological sample; and identifying that the patient is at risk of ovarian cancer based on the level of CA125 and the level of SEZ6L. In one or more embodiments, the method further includes measuring a level of HE4 and ITGAV. In another aspect, the present disclosure relates to a kit, system, or computer program for performing a method described herein.