SNP-Locus Methylation Analysis for Low-Level Tumor Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Sequencing errors and statistical noise obscure cancer detection signals, leading to delayed diagnoses and ineffective treatments due to low sensitivity and specificity in disease detection.
Innovation Solution
A method involving the analysis of methylation percentages at single nucleotide polymorphism (SNP) loci using both population-level and subject-specific sequencing data to generate predictions about medical conditions, disease stages, or individual samples, with error accounting and enrichment using capture probes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional sequencing methods are used to detect cancer, then the detection process is simple, but sequencing errors and statistical noise obscure cancer signals, leading to low sensitivity and specificity
Solution Approach 1:
The method segments the detection process by first identifying SNPs to define specific loci, then analyzing methylation patterns at those loci separately. This segmentation allows focused analysis on cancer-relevant regions, improving signal detection while filtering out background noise from the rest of the genome.
Solution Approach 2:
The patent introduces methylation status as an intermediary biomarker that links SNP identification to cancer detection. By measuring methylation percentages at SNP-containing loci and comparing them to reference values, the method creates an intermediate measurement layer that enhances cancer signal detection beyond what direct sequencing alone can achieve.
2Reliability
If methylation analysis at SNP loci is performed to improve cancer detection, then sensitivity and specificity improve, but computational complexity and processing requirements increase
Solution Approach 1:
The method performs preliminary actions by first identifying SNPs and defining specific loci before conducting methylation analysis. Reference methylation values are pre-established for comparison. This preliminary structuring of the data and analysis framework reduces the computational complexity during the actual detection phase by focusing calculations only on relevant loci rather than the entire genome.
Solution Approach 2:
The patent applies local quality by analyzing methylation patterns specifically at loci containing SNPs, rather than uniformly across the entire genome. This localized approach concentrates computational resources on regions with highest cancer-relevance, improving detection reliability while reducing overall computational burden compared to whole-genome methylation analysis.
3Measurement precision
If tumor DNA is present at low levels in the sample, then the sample represents early-stage or minimal disease, but sequencing errors make it difficult to distinguish tumor signals from noise
Solution Approach 1:
The method changes the measurement parameter from direct sequence variant detection to methylation percentage measurement at specific loci. Methylation status provides an additional dimension of information that is less susceptible to sequencing errors. By measuring the proportion of methylated reads at each locus and comparing to reference values, the method can detect low-level tumor DNA signals that would be indistinguishable from sequencing noise using conventional approaches.
Data Source
AI summary
Provided herein are methods and system for using methylation data to improve disease detection.