cfDNA Detection via SNV Locus Panel Noise Comparison
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for accurately measuring the level of cancer-specific DNA in a sample are challenging due to the low fraction of DNA from diseased tissue, requiring high sensitivity approaches like custom qPCR or enrichment techniques.
Innovation Solution
The method involves comparing nucleic acid sequencing data to determine the rate of SNV reads from diseased tissue versus a background factor indicative of sequencing false positive errors, allowing for the measurement of disease level, recurrence, progression, or regression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high sensitivity schemes like custom qPCR or custom enrichment are used to detect cfDNA from diseased tissue, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent segments the genome into multiple loci regions, sequencing at least a portion of the genome at multiple loci rather than focusing on a single region. This segmentation allows the system to distribute the detection burden across multiple locations, improving overall detection accuracy without requiring hyper-complex enrichment techniques at any single locus.
Solution Approach 2:
The patent transitions from traditional single-locus or bulk DNA analysis to a multi-locus genomic sequencing approach. By adding the dimension of multiple genomic locations, the system achieves superior detection of cancer-specific nucleic acids without proportionally increasing device complexity, as standard sequencing platforms can handle multiple loci efficiently.
2Measurement precision
If deep sequencing methods are used to ensure accurate variant calls at specific targeted loci, then measurement precision is improved, but use of energy and time increase
Solution Approach 1:
The patent applies partial action by sequencing only the necessary portions of the genome at multiple loci rather than performing complete deep sequencing of the entire genome. This selective approach provides sufficient accuracy for variant calling while significantly reducing the time and computational resources required compared to whole-genome deep sequencing.
Solution Approach 2:
The patent changes the parameter of sequencing depth distribution - instead of applying uniform deep sequencing across the entire genome, it applies moderate to high sequencing depth specifically at multiple targeted loci. This parameter optimization maintains variant calling accuracy while reducing overall sequencing time and energy consumption.
3Measurement precision
If targeted nucleic acid sequencing methods are used to look for mutations in known driver genes, then measurement precision is improved, but adaptability to different disease states decreases
Solution Approach 1:
The patent creates a universal sequencing approach that can detect cancer-specific nucleic acids across multiple loci simultaneously. This multi-functional system can monitor various disease states, track treatment response, and detect recurrence using the same platform, eliminating the need for separate targeted assays for different cancer types or stages.
Solution Approach 2:
The patent implements a dynamic monitoring capability where the same multi-locus sequencing platform can adapt to different disease scenarios by analyzing variant frequencies and patterns across multiple genomic locations. This allows the system to dynamically adjust its analytical approach based on the specific disease state being monitored, enhancing versatility without sacrificing precision.
Data Source
AI summary
Described herein are methods, devices, and systems for measuring a level of a disease (such as cancer), for example a fraction of nucleic acid molecules (such as cell-free DNA) in a sample from an individual that relate to diseased tissue (such as cancer tissue). Also described are methods, devices, and systems for measuring a presence, recurrence, progression, or regression of the disease in the individual. Certain methods include comparing, using nucleic acid sequencing data associated with the individual, a signal indicative of a rate at which sequenced loci selected from a personalized disease-associated small nucleotide variant (SNV) locus panel are derived from a diseased tissue to a background factor indicative of a sequencing false positive error rate, or a noise factor indicative of a sampling variance, across the selected loci.