Patient-Specific Pathway Activity Inference via Probabilistic Modeling
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Solution Overview
Problem
Current methods fail to comprehensively integrate multiple data sources to identify reproducible and interpretable molecular signatures of tumorigenesis and progression in cancer, leading to incomplete understanding of genomic changes and therapeutic resistance mechanisms, particularly in cancers like ERBB2-positive breast cancers.
Innovation Solution
A method for generating dynamic pathway maps by cross-correlating and assigning influence levels to pathway elements using a probabilistic pathway model, incorporating various genomic alterations such as copy number, DNA methylation, somatic mutations, and microRNA expression to predict pathway activity in patient samples.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple data sources are integrated to identify molecular signatures, then the precision and sensitivity of causal interpretations improve, but the device complexity and data processing requirements increase
Solution Approach 1:
The patent segments the complex data integration task into distinct modules: a probabilistic pathway model that incorporates curated pathway interactions, an analysis engine that processes multiple data sources (genomic, transcriptomic, epigenomic), and a dynamic pathway map generation component. This segmentation allows each module to handle specific aspects of data integration independently, improving precision while managing complexity through modular architecture.
Solution Approach 2:
The patent introduces a probabilistic pathway model as an intermediary layer between raw multi-omic data and clinical interpretations. This intermediary integrates multiple data sources through a unified probabilistic framework that incorporates known pathway interactions, enabling precise molecular signature identification without directly managing the full complexity of raw data integration.
2Measurement precision
If multiple data sources are integrated to identify molecular signatures, then the sensitivity of causal interpretations improves, but the difficulty of detecting and measuring increases
Solution Approach 1:
The probabilistic pathway model serves as a universal framework that can process multiple types of data sources (genomic alterations, gene expression, epigenetic modifications) through a single integrated analysis engine. This multi-functional approach increases sensitivity of causal interpretations by simultaneously considering evidence from diverse data types without requiring separate analysis pipelines for each data source.
Solution Approach 2:
The patent transforms heterogeneous data from multiple sources into a unified parameter space using the probabilistic pathway model. By converting diverse molecular data (copy number variations, expression levels, methylation patterns) into standardized pathway activity parameters, the system increases sensitivity of detection while reducing the difficulty of integrating and measuring data from different sources.
3Reliability
If dynamic pathway maps are generated using probabilistic models, then the ability to stratify cancer patients and predict treatment responses improves, but the loss of time for computation and analysis increases
Solution Approach 1:
The patent pre-computes and stores curated pathway interactions and probabilistic models in a database before clinical analysis. This preliminary preparation of pathway knowledge and computational frameworks allows the analysis engine to quickly generate dynamic pathway maps and patient stratifications without performing full probabilistic computations from scratch during clinical decision-making, thereby improving reliability while reducing analysis time.
Solution Approach 2:
The system creates simplified copies or representations of complex probabilistic pathway models that can be rapidly evaluated. By pre-processing and storing key pathway relationships and interaction patterns, the analysis engine can efficiently generate patient-specific dynamic pathway maps without requiring extensive real-time computation, balancing reliability of predictions with acceptable analysis time.
Data Source
AI summary
The present invention relates to methods for evaluating the probability that a patient's diagnosis may be treated with a particular clinical regimen or therapy.