Target DNA Fragment Size Analysis for Low-Signal ctDNA Detection
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Solution Overview
Problem
Current methods for detecting circulating tumor DNA (ctDNA) in blood plasma face challenges due to low signal-to-noise ratio, difficulty in distinguishing somatic cancer mutations from non-cancerous cell mutations, and inefficiencies in fragment size analysis, particularly in patients with low tumor burden or specific cancer types.
Innovation Solution
A computer-implemented method utilizing fragment size analysis and copy number deviation features, employing classification algorithms trained on nucleic acid fragment sizes and copy number neutrality, to enhance the detection of ctDNA by classifying samples into cancerous or non-cancerous classes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-depth targeted sequencing is used to detect low levels of ctDNA, then detection sensitivity is improved, but false positive results from non-cancerous cells or clonal expansions increase
Solution Approach 1:
The invention segments the ctDNA detection process into two independent analysis streams: mutation analysis and fragment size analysis. By dividing the detection approach, the method can identify ctDNA through multiple pathways, reducing reliance on mutation analysis alone and thereby decreasing false positives from non-cancerous cells while maintaining high sensitivity for true ctDNA detection.
Solution Approach 2:
Fragment size analysis serves as an intermediary marker that bridges the gap between direct mutation detection and indirect ctDNA presence indication. By using fragment size characteristics (150-250 bp range) as an intermediate indicator of ctDNA, the method reduces false positives from somatic mutations in non-cancerous cells while maintaining ability to detect low-level ctDNA.
2Measurement precision
If mutation analysis methods are used to detect ctDNA, then ctDNA detection is achieved, but differences in chromatin organization or fragment size in ctDNA are not utilized
Solution Approach 1:
The invention makes the ctDNA detection system multi-functional by incorporating both mutation analysis and fragment size analysis. This universal approach allows the same sequencing data to be used for multiple purposes: identifying mutations and simultaneously analyzing fragment size characteristics, thereby preventing loss of chromatin organization information while maintaining detection accuracy.
Solution Approach 2:
The method performs preliminary fragment size analysis on sequencing data before final ctDNA detection conclusions are drawn. By examining fragment size characteristics in advance and combining this information with mutation analysis, the system preserves and utilizes chromatin organization information that would otherwise be lost, improving overall detection accuracy.
3Measurement precision
If ever-deeper sequencing is performed to improve ctDNA detection, then detection sensitivity increases, but false positive results from non-cancerous cells increase
Solution Approach 1:
The detection system is segmented into independent validation pathways: mutation analysis and fragment size analysis. By dividing the detection process, the method can cross-validate results between the two pathways, reducing false positives from non-cancerous cells even when using deep sequencing, while maintaining high sensitivity for true ctDNA detection.
Solution Approach 2:
The system implements feedback between mutation analysis and fragment size analysis results. When mutation analysis suggests ctDNA presence, fragment size analysis provides feedback validation, and vice versa. This feedback mechanism reduces false positives from deep sequencing by requiring concordance between multiple independent analytical pathways.
Data Source
AI summary
The present invention provides a computer-implemented method for detecting variant nucleic acid from a cell-free nucleic acid-containing sample. The method comprises (a) providing data representing fragment sizes of nucleic acid fragments obtained from said sample and/or representing a measure of deviation from copy number neutrality of the nucleic acid fragments obtained from said sample; b) processing the data from step a) according to a classification algorithm, wherein said classification algorithm operates to classify sample data into one of at least a first class containing the variant nucleic acid and a second class not containing the variant nucleic acid, based on a plurality of cell-free nucleic acid fragment size features and/or a deviation from copy number neutrality feature; and c) outputting the classification of the sample from step b, thereby determining whether the sample contains the variant nucleic acid or not, or a probability that the sample contains the variant nucleic acid. Related methods are also provided.