Multi-Pathway Score Model for Cancer Risk Assessment
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Solution Overview
Problem
Current approaches to treating tumors and cancers are often empirical and fail to account for the individual heterogeneity of tumors, leading to ineffective therapies and wasted time, as they do not accurately assess the functional state of cellular signaling pathways over time.
Innovation Solution
A method that determines the activity levels of the TGF-β, PI3K, Wnt, ER, and HH cellular signaling pathways using a Multi-Pathway Score (MPS) model, allowing for accurate prediction of clinical events such as cancer development and progression, enabling tailored therapeutic strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional empirical treatment approaches are used for tumors, then treatment simplicity is maintained, but treatment efficacy deteriorates due to failure to account for individual tumor heterogeneity
Solution Approach 1:
The patent segments the assessment process into distinct modules: genomic data acquisition, proteomic data acquisition, pathway activity calculation, and risk score generation. Each module handles a specific aspect of the complex assessment, making the overall system more manageable and implementable while capturing tumor heterogeneity through multiple data dimensions
Solution Approach 2:
The patent transforms raw genomic and proteomic data into meaningful pathway activity parameters through mathematical modeling. By converting high-dimensional data into pathway-specific activity scores (e.g., Wnt pathway activity, TGF-beta pathway activity), the system makes complex biological information interpretable and actionable for clinical decision-making
2Measurement precision
If traditional class-based treatment is applied without considering pathway activity, then treatment simplicity is maintained, but measurement precision of tumor characteristics deteriorates
Solution Approach 1:
The patent introduces pathway activity models as intermediary layers between raw genomic/proteomic data and clinical treatment decisions. These models act as mediators that translate complex molecular data into interpretable pathway activity scores, enabling precise tumor characterization without requiring clinicians to directly analyze high-dimensional data
Solution Approach 2:
The patent replaces traditional mechanical classification methods (visual histology, simple biomarker testing) with computational modeling approaches. By using in silico pathway simulations and mathematical models to assess pathway activity, the system achieves higher measurement precision while automating the complex assessment process
3Measurement precision
If comprehensive pathway analysis is performed to improve risk assessment accuracy, then prediction accuracy improves, but loss of time in treatment decision-making increases
Solution Approach 1:
The patent performs preliminary pathway model calibration and validation using reference datasets before clinical application. By pre-establishing pathway activity thresholds and risk score cutoffs based on training data, the system eliminates the need for complex real-time calculations during patient assessment, enabling rapid clinical decision-making with comprehensive analysis
Solution Approach 2:
The patent uses computational models that replicate in vivo pathway behavior in silico, allowing comprehensive pathway analysis to be performed on digital copies of patient data rather than requiring lengthy wet-lab experiments. This virtual modeling approach maintains high prediction accuracy while dramatically reducing assessment time
4Reliability
If multiple signaling pathways are analyzed simultaneously, then reliability of risk assessment improves, but device complexity increases
Solution Approach 1:
The patent merges multiple pathway analysis results into a unified risk assessment framework. By integrating activities from Wnt, TGF-beta, PI3K, and other pathways into a coordinated evaluation model, the system achieves reliable multi-pathway assessment while presenting a unified, simplified output to clinicians
Data Source
AI summary
A bioinformatics method for determining a risk score that indicates a risk that a subject, in particular a human, will experience a negative clinical event within a certain period of time. The risk score is based on a unique combination of activities of two or more cellular signaling pathways in a subject, wherein the selected cellular signaling pathways are the TGF-β pathway and one or more of a PI3K pathway, a Wnt pathway, an ER pathway, and an HH pathway. The invention includes an apparatus with a digital processor configured to perform such a method, a non-transitory storage medium storing instructions that are executable by a digital processing device to perform such a method, and a computer program comprising program code means for causing a digital processing device to perform such a method. The bioinformatics invention allows for more accurate prognosis of specific negative clinical events in a patient with, for example, a tumor or cancer, such as disease progression, recurrence, development of metastasis, or even death.