Computational Model for Cell-Free DNA Cellular Origin
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Solution Overview
Problem
Current liquid biopsy tests for cancer detection face challenges in identifying tumor-specific DNA markers due to their low abundance in plasma samples, making early-stage cancer detection difficult.
Innovation Solution
A method that determines the cellular origin of DNA molecules in cfDNA samples by analyzing genomic and epigenetic properties, such as fragment length, midpoint offset, and epigenetic status, using computational models to classify the origin of DNA molecules and diagnose diseases like cancer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current liquid biopsy tests rely on tumor originating somatic mutations in patient plasma cfDNA, then cancer diagnosis can be performed, but the detection sensitivity is insufficient when tumor originating cfDNA is present in very small quantities
Solution Approach 1:
The patent changes multiple parameters of cfDNA analysis simultaneously: fragment length distribution, nucleotide composition ratios (e.g., TTAG motif frequency), and epigenetic modification patterns. By analyzing these parameters in combination rather than relying solely on mutation detection, the method achieves high sensitivity even when tumor cfDNA constitutes a very small fraction of total plasma cfDNA
Solution Approach 2:
The patent employs a composite analytical approach that combines multiple types of cfDNA characteristics (fragmentomics, sequence composition, epigenetic markers) to create a comprehensive diagnostic signal. This composite analysis enables detection of tumor origin cfDNA at concentrations that would be undetectable by any single method alone
2Reliability
If liquid biopsy assays analyze cfDNA from early stage cancer patients, then early detection is achieved, but the amount of tumor originating cfDNA is extremely small making detection challenging
Solution Approach 1:
The patent monitors multiple cfDNA parameters including fragment length distributions, sequence motif frequencies (such as TTAG), and epigenetic modification patterns. This multi-parameter approach creates a composite signal that enhances detection reliability for early-stage cancers where tumor cfDNA is present at extremely low levels
Solution Approach 2:
The patent uses computational algorithms as intermediaries to process and integrate multiple cfDNA characteristics. These algorithms analyze fragmentomics data, sequence composition, and epigenetic patterns to generate a unified diagnostic interpretation, making the detection process manageable despite the complexity of analyzing multiple parameters simultaneously
Data Source
AI summary
Provided herein are methods for determining the cellular origin of cell-free DNA. In one aspect, the methods include constructing a distribution of sequence and/or epigenetic information from DNA molecules obtained from a cfDNA sample over a plurality of base positions of a set of differential genomic sections or loci that comprise genomic regions and/or epigenetic loci. The differential genomic loci exhibit one or more properties that differ between at least two cell types. The methods also include processing the distribution of the sequence and/or epigenetic information from the DNA molecules over the set of the differential genomic loci to determine the cellular origin of at least a subset of DNA molecules from the cfDNA sample. Other aspects are directed to methods of treating disease in subjects. Yet other aspects include related systems and computer readable media used to determine the cellular origin of cfDNA.