Protein Network Ranking of Metastasis Drivers After Drug Intervention
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Solution Overview
Problem
Current methods fail to accurately identify heterogeneous molecules driving tumor metastasis and drug resistance in individual patients, leading to ineffective tumor treatment due to tumor heterogeneity.
Innovation Solution
A prediction system using high-throughput protein sequencing, bioinformatics, and computational biology to screen and rank key heterogeneous molecules by constructing protein interaction networks and calculating hazard ratios, followed by a sequential intervention system to target these molecules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional single-target treatment approaches are used, then treatment simplicity is maintained, but tumor heterogeneity leads to drug resistance and metastasis
Solution Approach 1:
The patent segments the tumor treatment approach by identifying and targeting multiple independent heterogeneous molecules (e.g., JWA, MDM2, FASN, USP9X, PFN1) separately. Each molecule represents a distinct therapeutic target that can be addressed individually through sequential interventions, allowing the treatment to adapt to the heterogeneous nature of tumor cells while maintaining manageable complexity through systematic analysis.
Solution Approach 2:
The patent changes the parameter of molecular targeting from single-target to multi-target by quantifying protein expression levels and identifying key heterogeneous molecules with significant expression changes. This parameter change enables the treatment to address tumor heterogeneity by targeting multiple molecules with different expression patterns across cell subpopulations.
2Measurement precision
If comprehensive protein analysis is performed to identify all heterogeneous molecules, then identification accuracy is improved, but analysis time and computational resources increase
Solution Approach 1:
The patent performs preliminary quantitative analysis of protein expression levels in tumor metastases before and after drug intervention to identify candidate heterogeneous molecules. This preliminary action filters the large set of proteins to a smaller subset of key molecules with significant expression changes, improving identification accuracy while reducing subsequent analysis time and computational resources.
Solution Approach 2:
The patent extracts key heterogeneous molecules from the comprehensive protein analysis results by selecting molecules with expression changes within specific ranges and those that are most significantly affected by treatment. This extraction process isolates the most relevant molecules from the complex proteomic data, maintaining high identification accuracy while enabling focused subsequent analysis.
3Reliability
If sequential intervention on multiple heterogeneous molecules is implemented, then tumor metastasis inhibition is improved, but treatment complexity and cost increase
Solution Approach 1:
The patent implements sequential intervention on heterogeneous molecules in a periodic manner, addressing one molecule at a time in a defined sequence (e.g., JWA first, then MDM2, FASN, USP9X, PFN1). This periodic approach improves tumor metastasis inhibition by systematically targeting each heterogeneous molecule while maintaining operational simplicity through a structured, step-by-step treatment protocol that can be implemented and monitored sequentially.
Data Source
AI summary
A prediction system for identifying key heterogeneous molecules that drive tumor metastasis including an input module, an analysis module, and an output module. The input module is configured to input a first quantitative analysis result of proteins and a second quantitative analysis result of proteins; the first quantitative analysis result of proteins is a collection of quantitative analysis results of various protein expression levels in tumor metastases of a target patient before drug intervention, and the second quantitative analysis result of proteins is a collection of quantitative analysis results of various protein expression levels in residual tumor metastases of the target patient after drug intervention. The analysis module includes a primary analysis submodule, a secondary analysis submodule, and a calculation and sorting submodule. The primary analysis submodule is used for preliminary screening analysis. The output module is used to output a sorted list of heterogeneous molecules.