Variable Allele Frequency Thresholds for Cancer Therapy Monitoring
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for monitoring a patient's response to cancer therapy using allele frequency thresholds do not account for differences between genes, single nucleotide polymorphisms (SNPs), types of mutations, and individual patient characteristics, leading to inadequate personalized therapy and monitoring.
Innovation Solution
The use of multiple allele frequency thresholds, set differently for various genes, SNPs, and types of mutations, and tailored to individual patients based on clinical and genetic data, with machine learning models to determine optimal thresholds for effective treatment response monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single generic allele frequency threshold is used for all variants, then the method is simple and easy to implement, but it does not account for differences between genes and SNPs leading to reduced measurement precision
Solution Approach 1:
The patent divides the single generic threshold into multiple variant-specific thresholds. Each observed variant (gene, SNP, mutation type) receives its own customized threshold value based on its characteristics, thereby improving measurement precision without requiring manual adjustment of each threshold individually through automated machine learning models.
Solution Approach 2:
The patent applies different threshold values to different variants based on their specific characteristics. Instead of using a uniform threshold across all variants, each variant receives a locally optimized threshold that accounts for its unique biological properties, improving the accuracy of treatment response monitoring for each specific variant.
2Adaptability or versatility
If multiple allele frequency thresholds are set for different variants, then the measurement precision and personalization are improved, but the device complexity and computational requirements increase
Solution Approach 1:
The patent performs preliminary actions by pre-calculating and storing optimal threshold values for multiple variants using machine learning models during a training phase. These pre-computed thresholds are then directly applied during clinical monitoring without requiring complex real-time calculations, thereby reducing processing complexity while maintaining high adaptability.
Solution Approach 2:
The patent uses machine learning models to learn optimal threshold values from training data and creates copies of these learned thresholds for application to patient samples. This allows the complex learning process to be performed once during model training, with simple threshold application during subsequent patient monitoring, reducing ongoing computational requirements.
3Reliability
If variant-specific allele frequency thresholds are used, then the reliability of treatment response determination is improved, but the ease of operation decreases due to more complex threshold management
Solution Approach 1:
The patent implements self-service by enabling the system to automatically determine and apply appropriate thresholds for each variant without requiring manual intervention. The machine learning model autonomously selects and applies the correct threshold values based on the observed variants in each patient sample, improving reliability while maintaining ease of operation.
Solution Approach 2:
The patent incorporates feedback mechanisms where the system continuously monitors treatment response using variant-specific thresholds and adjusts threshold values based on observed patterns and outcomes. This feedback loop improves the reliability of treatment response determination over time while the automated nature of the adjustments maintains ease of operation.
Data Source
AI summary
The present invention relates to monitoring a patient's response to therapy. In order to improve the monitoring of a patient's response to therapy, a method is provided to set a plurality of allele frequency thresholds to accounting for variations among tumours and patients. As the multiple allele frequency thresholds take into account differences between genes, single-nucleotide polymorphisms (SNPs), and/or patients, the multiple allele frequency thresholds may provide significant value to improve personalized therapy selection, disease surveillance, and monitoring to improve patient outcomes.