cfDNA Fragment Endpoint Analysis for Cancer Diagnosis
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Solution Overview
Problem
Current cell-free DNA (cfDNA) diagnostic methods rely on genotypic differences, which are uninformative when discriminating between cell populations or subjects, limiting their discriminatory power, especially in conditions like cancer diagnosis.
Innovation Solution
Applying a hidden Markov model to the frequency distribution of cfDNA fragment endpoint coordinates to identify physiological conditions, such as cancer, by training the model with reference samples and comparing summary statistics to threshold values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If cfDNA diagnostic methods rely on genotypic differences, then they can detect genetic abnormalities, but their discriminatory power is limited when discriminating between cell populations or subjects
Solution Approach 1:
The patent changes the analytical parameter from genotypic differences (nucleotide sequences) to spatial distribution patterns (fragment endpoint coordinates along the genome). This parameter transformation enables the method to capture epigenetic and chromatin structure information that is not visible through traditional genotypic analysis, thereby improving discriminatory power while avoiding the information loss inherent in relying solely on genetic sequence variation
Solution Approach 2:
The patent introduces a new dimensional approach by analyzing the spatial distribution of cfDNA fragment endpoints across the genome rather than focusing only on sequence composition. This dimensional shift from one-dimensional sequence analysis to two-dimensional spatial mapping (genome position + fragment endpoint location) reveals additional biological information related to chromatin accessibility and nucleosome positioning, enhancing the ability to discriminate between different physiological states
Data Source
AI summary
Methods for diagnosis of one or more physiological conditions using cfDNAs are disclosed. One embodiment of the invention is the computer implemented analysis of mapped circulating cell-free DNA fragment endpoint locations using a hidden Markov model to detect the presence of absence of cancer in a test subject. Another embodiment is a system for implementing the analysis of circulating cell-free DNA to detect the presence of absence of cancer using a hidden Markov model.