Methylation Pattern Analysis for Tissue Deconvolution in DNA Mixtures
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Solution Overview
Problem
Current methods for determining the contributions of different tissues to a biological sample containing a mixture of cell-free DNA molecules, such as in plasma, are limited in accuracy and specificity, particularly in identifying diseased states and sequence imbalances, due to inadequate testing and application of methylation pattern analysis.
Innovation Solution
The method involves analyzing methylation patterns at specific genomic sites to determine fractional contributions of various tissue types by solving a system of linear equations, using both type I and type II genomic sites that have high variability across tissues and individuals, allowing for accurate identification of diseased states and sequence imbalances through comparison with reference values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If only genomic sites specific to one tissue type are used, then tissue identification is simplified, but accuracy in determining contributions from multiple tissue types deteriorates
Solution Approach 1:
The patent segments the genomic sites into two distinct types: type I sites that are specific to particular tissue types and type II sites that have different methylation levels across multiple tissue types. This segmentation allows the method to simultaneously leverage the simplicity of tissue-specific markers and the discriminative power of variable markers, resolving the contradiction between operational simplicity and measurement precision.
Solution Approach 2:
The patent creates a universal methylation analysis approach that works across multiple tissue types by incorporating both type I and type II genomic sites. The type II sites serve as universal markers that can distinguish between different tissue types in a mixed DNA sample, enabling the method to accurately determine contributions from various tissue types while maintaining ease of operation through a unified analytical framework.
2Measurement precision
If genomic sites with high variability across tissues are used, then accuracy in determining tissue contributions is improved, but the complexity of analyzing methylation patterns increases
Solution Approach 1:
The patent segments the analysis into two distinct components: type I sites that provide tissue-specific information and type II sites that provide differential methylation information across tissues. This segmentation organizes the complexity by categorizing genomic sites according to their functional characteristics, making the overall analysis more manageable while maintaining high accuracy.
Solution Approach 2:
The patent changes the parameters used for analysis by focusing on methylation levels at specific genomic sites rather than requiring complex multi-parameter analysis. By selecting genomic sites with high variability in methylation levels across tissues, the method simplifies the analysis parameters while improving measurement precision in determining tissue contributions.
3Reliability
If methylation pattern analysis is applied to determine tissue contributions, then detection of diseased states is improved, but the difficulty of detecting and measuring sequence imbalances increases
Solution Approach 1:
The patent substitutes complex mechanical or computational methods for detecting sequence imbalances with a methylation-based approach. By analyzing methylation levels at genomic sites, the method replaces difficult sequence imbalance detection with a more straightforward epigenetic marker analysis, improving reliability in diseased state detection while reducing measurement difficulty.
Solution Approach 2:
The patent uses methylation levels as an intermediary marker to indirectly detect sequence imbalances and diseased states. Instead of directly measuring difficult-to-detect sequence variations, the method uses methylation patterns at genomic sites as a proxy that reflects underlying biological changes, thereby improving detection reliability while reducing the difficulty of measurement.
Data Source
AI summary
The contributions of different tissues to a DNA mixture are determined using methylation levels at particular genomic sites. Tissue-specific methylation levels of M tissue types can be used to deconvolve mixture methylation levels measured in the DNA mixture, to determine fraction contributions of each of the M tissue types. Various types of genomic sites can be chosen to have particular properties across tissue types and across individuals, so as to provide increased accuracy in determining contributions of the various tissue types. The fractional contributions can be used to detect abnormal contributions of a particular tissue, indicating a disease state for the tissue. A differential in fractional contributions for different sizes of DNA fragments can also be used to identify a diseased state of a particular tissue. A sequence imbalance for a particular chromosomal region can be detected in a particular tissue, e.g., identifying a location of a tumor.