Dynamic Quality Metric for Liquid Biopsy Accuracy
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Solution Overview
Problem
Current biopsy techniques, particularly liquid biopsies, face challenges in accurately detecting cancer due to low nucleic acid concentrations in bodily fluids, leading to false positives and false negatives, which undermine confidence and result in unnecessary interventions and increased mortality.
Innovation Solution
A computer system that determines a sample-specific dynamic quality metric by analyzing the ratio of tumor genetic molecules to total genetic molecules, along with sequencing coverage of cancer-specific genomic targets, to provide an indication of cancer presence and facilitate treatment recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If liquid biopsy is used to detect cancer in bodily fluids, then non-invasive sampling and comprehensive cancer detection are achieved, but the small amount of nucleic acids and variable recovery lead to incorrect results (false positives and false negatives)
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting the quality threshold based on the measured nucleic acid quantity and recovery rate for each sample. Instead of using a fixed threshold, the system calculates a sample-specific threshold that adapts to the actual conditions of the liquid biopsy, thereby maintaining high detection accuracy even when nucleic acid amounts vary significantly.
Solution Approach 2:
The patent implements dynamics by introducing a dynamic quality metric that changes based on real-time sample characteristics. The quality threshold is not static but is dynamically determined based on the measured nucleic acid concentration and recovery rate, allowing the system to optimize detection accuracy for each individual sample condition.
2Productivity
If liquid biopsy detects very low frequency sequences (1 in 1000 molecules), then comprehensive cancer detection is achieved, but the operating window edge leads to increased false positives and false negatives
Solution Approach 1:
The patent applies parameter changes by adjusting the quality threshold parameter based on the measured nucleic acid recovery rate. When recovery rate is low, the threshold is adjusted to account for potential false positives, while maintaining the ability to detect low frequency mutations. This dynamic parameter adjustment allows the system to operate effectively at the edge of the detection window.
Solution Approach 2:
The patent implements feedback by using the measured nucleic acid recovery rate as input to determine the quality threshold. The system measures the actual recovery rate, feeds this information back into the threshold calculation, and then uses the adjusted threshold for mutation detection. This feedback loop ensures that detection accuracy is optimized based on actual sample conditions.
3Device complexity
If fixed quality thresholds are used in biopsy analysis, then simple analysis procedures are maintained, but sample-specific variations lead to incorrect results and reduced confidence
Solution Approach 1:
The patent applies parameter changes by transitioning from a fixed quality threshold to a dynamic, sample-specific threshold. The threshold parameter is adjusted based on measured nucleic acid quantity and recovery rate, allowing the analysis procedure to adapt to sample variations while maintaining automated execution that does not significantly increase operational complexity.
Data Source
AI summary
During operation, a computer system may receive information indicating: a number of genetic molecules associated with normal tissue in a sample, and a number of tumor genetic molecules associated with a tumor in the sample. Then, the computer system may determine a sample-specific dynamic quality metric based at least in part on: a type of cancer, sequencing coverage of one or more cancer-specific genomic targets, and a first ratio of the number of tumor genetic molecules to a sum of the number tumor genetic molecules and the number of genetic molecules or a second ratio of the number of tumor genetic molecules to the number of genetic molecules. Next, based at least in part on a comparison of the sample-specific dynamic quality metric and a threshold, the computer system may selectively provide an indication of whether a mutation or the type of cancer is present in the sample.