Anomalous Fragment Detection in cfDNA Methylation Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for analyzing methylation sequencing data from cell-free DNA lack sensitivity and specificity for early cancer detection, as fragments with anomalous methylation patterns are often masked by those from healthy individuals, making it difficult to discriminate between cancerous and non-cancerous samples.
Innovation Solution
A method involving the conversion of unmethylated cytosines to uracils in cfDNA fragments, followed by sequencing and comparison to a reference genome to identify methylation states, using probabilistic analysis and a classifier trained on methylation sequencing data to identify anomalously methylated fragments and determine cancer presence with high sensitivity and specificity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If methylation sequencing data from all cfDNA fragments is analyzed uniformly, then comprehensive coverage is achieved, but sensitivity and specificity for cancer detection deteriorate due to noise from healthy fragments
Solution Approach 1:
The patent segments cfDNA fragments into two distinct groups: anomalously methylated fragments (potential cancer markers) and normally methylated fragments (healthy controls). This segmentation is achieved through probabilistic analysis that compares observed methylation patterns against expected patterns from healthy individuals, thereby isolating the cancer-specific signal from the background noise of healthy DNA fragments.
Solution Approach 2:
The patent extracts and identifies anomalously methylated fragments by comparing test sample methylation patterns against a reference genome and healthy control data. The probabilistic analysis extracts fragments with statistically significant deviations from normal methylation patterns, separating these informative fragments from the majority of normal fragments that would otherwise obscure the cancer signal.
2Measurement precision
If probabilistic analysis is applied to all fragments, then accurate identification of anomalous fragments is improved, but computational complexity increases
Solution Approach 1:
The patent performs preliminary actions by first establishing a reference framework of normal methylation patterns from healthy control samples before analyzing test samples. This pre-computed reference data structure enables subsequent probabilistic analyses to proceed more efficiently by comparing against pre-established baselines rather than performing complete analyses from scratch, thereby reducing computational complexity while maintaining accuracy.
3Measurement precision
If all CpG sites are analyzed in detail, then comprehensive methylation profiling is achieved, but analysis time and computational resources increase
Solution Approach 1:
The patent applies local quality by focusing detailed analysis primarily on CpG sites and fragments that exhibit anomalous methylation patterns, rather than uniformly analyzing all CpG sites with equal depth. The probabilistic analysis identifies regions of interest where methylation patterns deviate from normal, concentrating computational resources on these locally significant regions while reducing analysis of normal regions.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach allows for the accurate identification of anomalously methylated cfDNA fragments, enhancing the sensitivity and specificity of cancer detection by filtering out noise and focusing on regions most informative for distinguishing cancer patients from healthy subjects.
Implementation Method 1
The plurality of cfDNA fragments in the test sample are treated to convert unmethylated cytosines to uracils
Data Source
AI summary
An analytics system creates a data structure counting strings of methylation vectors from a healthy control group. The analytics system enumerates possibilities of methylation state vectors given a sample fragment from a subject, and calculates probabilities for all possibilities with a Markov chain probability. The analytics system generates a p-value score for the subject's test methylation state vector by summing the calculated probabilities that are less than or equal to the calculated probability of the possibility matching the test methylation state vector. The analytics system determines the test methylation state vector to be anomalously methylated compared to the healthy control group if the p-value score is below a threshold score. With a number of such sample fragments, the analytics system can filter the sample fragments based on each p-value score. The analytics system can run a classification model on the filtered set to predict whether the subject has cancer.