Outcome-Driven Persona-Typing for Precision Oncology
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Solution Overview
Problem
Current precision oncology approaches rely heavily on genetic information, which may not be sufficient due to the complexity of crosstalk within and between dysregulated pathways, leading to low success rates of targeted inhibitors, and oncologists struggle to keep up with the vast number of new drugs and clinical trial data.
Innovation Solution
A method and system using a variable weighting-based scoring algorithm that analyzes molecular data, biomolecular interaction networks, and patient history to generate outcome-driven personas for personalized treatment recommendations, incorporating multi-omic profiling and machine learning to match therapy options with patient-specific characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If single-gene biomarkers are used for treatment recommendations, then the treatment approach is simple and easy to implement, but the success rate is low due to failure to account for pathway crosstalk
Solution Approach 1:
The patent combines multiple biomarkers across different pathways into an integrated multi-omic profile. Instead of relying on single-gene biomarkers, the system merges genomic, transcriptomic, proteomic, and metabolomic data to create a comprehensive molecular signature that captures pathway crosstalk and biological context, thereby improving treatment prediction accuracy.
Solution Approach 2:
The patent uses composite multi-omic profiles that integrate multiple layers of molecular information (DNA, RNA, proteins, metabolites) similar to how composite materials combine different substances to achieve superior properties. This composite approach allows the system to capture complex biological interactions that single biomarkers cannot detect.
2Measurement precision
If comprehensive multi-omic profiling is performed to account for pathway crosstalk, then treatment accuracy improves, but the complexity of analysis and data processing increases significantly
Solution Approach 1:
The patent introduces computational algorithms and bioinformatics tools as intermediaries that process and integrate multi-omic data. These computational intermediaries translate complex multi-layered molecular data into actionable treatment recommendations, bridging the gap between comprehensive profiling and clinical decision-making without overwhelming oncologists with raw data complexity.
Solution Approach 2:
The patent segments the complex multi-omic data analysis into manageable components, processing genomic, transcriptomic, proteomic, and metabolomic data through separate analytical pipelines before integrating results. This segmentation allows each data type to be analyzed with appropriate methods while reducing overall computational complexity.
3Loss of information
If oncologists review all new drugs and clinical trial data manually, then comprehensive knowledge is achieved, but the time and effort required is unsustainable given the explosion of new treatments
Solution Approach 1:
The patent implements a self-updating knowledge base that automatically incorporates new drug approvals, clinical trial results, and published literature. The system performs self-service by continuously scanning and integrating new information without requiring manual review by oncologists, ensuring knowledge completeness while eliminating the time burden of staying current with rapidly evolving treatment options.
Solution Approach 2:
The patent incorporates feedback loops where treatment outcomes from the patient population are continuously analyzed and used to refine treatment recommendations. This feedback mechanism allows the system to learn from real-world results and automatically update its knowledge base, reducing the need for manual information gathering while maintaining comprehensive and current treatment knowledge.
Data Source
AI summary
A method and system are disclosed for performing supervised outcome-driven persona-typing, including receiving a first data set having patient specific data, treatment data, and outcome data for each of a plurality of first patients. Once received, the plurality of first patients are grouped based on the first data set, and a persona for each of patient groups is generated. A second data set may then be received for a second patient including second patient specific data. A comparison is then carried out on the second data set to identify an existing persona, and then associate the second patient with the identified persona. Based on each of the above steps, a patient care plan is then created.