Metastasis Risk Prediction via Gene Expression Profiling
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Solution Overview
Problem
Current methods fail to accurately predict the risk of metastasis in melanoma tumors, leading to delayed and ineffective treatment, as existing prognostic factors have limited predictive accuracy and are not suitable for early intervention.
Innovation Solution
A method involving the determination of the expression levels of specific nucleic acid sequences (CDH1, ECM1, EIF1B, FXR1, HTR2B, ID2, LTA4H, MTUS1, RAB31, ROBO1, and SATB1) in tumor samples, correlated with a risk of metastasis using a validated data set and nucleic acid array, allowing for early identification of high-risk tumors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional prognostic factors are used to predict metastasis risk, then treatment decisions can be made, but the predictive accuracy is limited and treatment may be delayed
Solution Approach 1:
The patent changes the parameters used for prediction from conventional clinical factors to gene expression levels of specific nucleic acid sequences. By measuring expression levels of genes such as CDH1, ECM1, EIF1B, FXR1, HTR2B, ID2, LTA4H, MTUS1, RAB31, ROBO1, and SATB1, the method achieves superior predictive accuracy for metastasis risk, directly resolving the contradiction between measurement precision and treatment timing.
2Loss of time
If early intervention is implemented, then treatment timing is improved, but accurate identification of high-risk tumors is required
Solution Approach 1:
The patent performs preliminary assessment of metastasis risk by measuring gene expression levels before treatment decisions are made. This preliminary action using molecular biomarkers enables early identification of high-risk tumors with high accuracy, allowing timely intervention while maintaining diagnostic precision.
3Measurement precision
If more comprehensive testing is performed, then predictive accuracy improves, but test complexity increases
Solution Approach 1:
The patent extracts and focuses on a specific panel of 11 key nucleic acid sequences that are most predictive of metastasis risk. By selecting and measuring only these critical genes rather than performing comprehensive genomic analysis, the method achieves high predictive accuracy while maintaining test simplicity and clinical feasibility.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables accurate prediction of metastatic risk, facilitating timely and informed treatment decisions by distinguishing between high-risk and low-risk tumors with high sensitivity and specificity.
Implementation Method 1
The array consists of nucleic acid sequences capable of hybridizing to at least three nucleic acid sequences selected from the group of nucleic acids consisting of CDH1, ECM1, EIF1B, FXR1, HTR2B, ID2, LMCD1, LTA4H, MTUS1, RAB31, ROBO1, and SATB1
Data Source
AI summary
The invention encompasses methods and compositions for predicting the risk of metastasis. In particular, the invention encompasses a method for correlating the level of expression of one or more nucleic acid sequences with a risk of metastasis.