Four-Gene Signature for Metastasis Risk and Chemotherapy Response
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Solution Overview
Problem
Current methods are inadequate for reliably identifying patients at risk of developing metastasis and predicting chemotherapy response in abdominal cancers such as pancreatic and ovarian cancers, with conflicting results on gene influences and lack of effective therapeutic options for peritoneal carcinomatosis.
Innovation Solution
A gene signature based on the combined expression levels of TNFRSF1B, TNFAIP6, HAPLN1, and HAS2 is used to predict metastasis risk and chemotherapy response, providing a reliable method for identifying patients at high risk of metastasis and poor chemotherapy responders.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current gene-based methods are used to identify patients at risk of metastasis, then some prediction capability is achieved, but the results are unreliable and conflicting due to individual gene analysis
Solution Approach 1:
The patent combines multiple gene expression markers (TNFRSF1B, TNFAIP6, HAPLN1, HAS2) into a single integrated gene signature. This merging of individual gene analyses into a composite signature resolves the conflicting results by capturing the complex interplay between these genes in the tumor microenvironment, thereby improving both reliability and measurement precision of metastasis risk prediction.
2Measurement precision
If individual gene markers are analyzed separately, then specific gene functions can be understood, but the overall predictive power for metastasis risk remains insufficient
Solution Approach 1:
The patent merges individual gene marker analyses into a unified gene signature that evaluates the combined expression patterns of TNFRSF1B, TNFAIP6, HAPLN1, and HAS2. This approach maintains the ability to understand specific gene functions while significantly improving overall predictive power for metastasis risk by capturing their synergistic interactions in the tumor microenvironment.
3Reliability
If comprehensive gene expression analysis is performed to improve prediction accuracy, then metastasis risk can be better identified, but the complexity of the diagnostic method increases
Solution Approach 1:
The patent combines multiple gene expression measurements into a single integrated gene signature score, which simplifies the diagnostic output while maintaining comprehensive analysis. This approach improves prediction reliability by considering multiple genes simultaneously but avoids excessive complexity by presenting the results as a unified metric rather than requiring interpretation of numerous individual gene expressions.
Data Source
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AI summary
The present invention relates to method for the identification of subjects with increased risk of suffering of metastasis and/or being bad responder to chemotherapy, by the determination of the expression level of a combination of genes: TNFRSF1B, TNFAIP6, HAPLN1 and HAS2, all of them connected to plasticity. When a surrogate value representing the combined expression of said four genes is higher than a reference value, the subject is at risk of suffering metastasis or is not responding adequately to the administered chemotherapy. The method allows the identification of such subjects very accurately, making possible changing the chemotherapy or taking other measures to control cancer progression and/or metastasis generation, thus improving the survival probability of the subject.