Minimal Residual Disease Detection via Multi-Sample Error Filtering
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Solution Overview
Problem
Current Next-Generation Sequencing (NGS) technologies face challenges in detecting DNA mutations present at very low rates due to high background error rates, making it difficult to distinguish between cancer-derived DNA and sequencing errors in blood samples of cancer patients.
Innovation Solution
A method involving the acquisition of sequencing data from tumor tissue, normal blood, and plasma samples, followed by background error filtering using genetic data from sample patients, to accurately detect minimal residual disease by reducing the background error rate and lowering the limit of detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If NGS technology is used to detect DNA mutations in blood samples, then the ability to identify cancer-derived DNA is improved, but the background error rate increases making it difficult to distinguish mutations from sequencing errors
Solution Approach 1:
The patent segments the detection process into multiple independent steps: (1) identifying candidate mutations from tumor tissue sequencing, (2) filtering candidates using normal blood sequencing to remove germline variants, (3) validating remaining candidates through plasma sequencing, and (4) confirming true positives by checking presence in multiple plasma samples. This multi-stage segmentation allows each step to focus on specific error types, collectively reducing the background error rate while maintaining detection sensitivity.
Solution Approach 2:
The patent introduces tumor tissue sequencing data as an intermediary to bridge the gap between normal blood sequencing and plasma sequencing. By first identifying mutations in tumor tissue (which contains high concentrations of cancer DNA), then using these as targets for detection in plasma (where cancer DNA is scarce), the system creates a reliable reference framework that enables accurate detection despite the low background error rate requirements.
2Measurement precision
If extensive sequencing data from multiple samples is analyzed to reduce background error rate, then the accuracy of minimal residual disease detection is improved, but the time and computing resources required increase
Solution Approach 1:
The patent performs preliminary actions by first sequencing tumor tissue samples to identify a comprehensive set of candidate mutations before analyzing plasma samples. This preliminary identification creates a focused list of target mutations that need to be detected in plasma, rather than attempting to detect all possible mutations simultaneously. This preliminary action significantly reduces the computational burden and time required for subsequent plasma sequencing analysis, as the system only needs to look for pre-identified candidate mutations rather than performing de novo mutation discovery.
Solution Approach 2:
The patent extracts and removes false positive mutations from the candidate list by comparing tumor tissue sequencing results with normal blood sequencing results. By taking out germline variants and technical artifacts that appear in both tumor and normal samples, the system creates a refined list of true cancer-specific mutations. This extraction process reduces the number of candidates requiring expensive and time-consuming validation sequencing, thereby reducing overall analysis time and resource requirements.
3Measurement precision
If the limit of detection is lowered to detect very low concentration tumor DNA, then the early detection capability is improved, but the ability to distinguish from background errors deteriorates
Solution Approach 1:
The patent merges information from multiple independent sources: tumor tissue sequencing data, normal blood sequencing data, and multiple plasma sequencing samples. By combining these independent data sources and requiring consistent detection across multiple samples, the system achieves a limit of detection of 0.1% tumor cell fraction while maintaining reliability. The convergence of evidence from multiple independent measurements distinguishes true low-concentration mutations from background errors, as random errors are unlikely to consistently appear across multiple independent sequencing experiments.
Solution Approach 2:
The patent implements feedback by using the results from plasma sequencing to validate and refine the candidate mutation list. Mutations detected in plasma samples provide feedback that confirms or refutes the presence of minimal residual disease. This feedback mechanism allows the system to adjust its detection threshold and filtering criteria based on actual observed data, thereby maintaining both low limit of detection and high reliability by dynamically optimizing the balance between sensitivity and false positive rate.
Data Source
AI summary
A method for detecting minimal residual disease using tumor information is provided, including acquiring first sequencing data associated with a first sample from a patient, acquiring second sequencing data associated with a second sample from the patient, acquiring third sequencing data associated with a third sample from the patient, and performing detection of minimal residual disease for the patient based on the first sequencing data, the second sequencing data, and the third sequencing data.