Cell-Free Nucleic Acid Fragmentation Analysis for Tumor Detection
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Solution Overview
Problem
Current methods for detecting tumors using cell-free nucleic acid analysis lack sensitivity and specificity, particularly in early-stage tumor detection, leading to high false positive and false negative results due to low tumor-derived DNA amounts and low prevalence of the condition.
Innovation Solution
A method involving the analysis of cell-free nucleic acid molecules to determine cancer levels by determining genomic positions of nucleic acid ends, computing relative abundances, and processing against cutoff values, combined with techniques like count-based and size-based assays, and using majority voting or cutoff values to enhance detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current methods are used for tumor detection using cell-free nucleic acid analysis, then the detection process is simple, but the sensitivity and specificity are low leading to high false positive and false negative results
Solution Approach 1:
The patent segments the detection process into multiple independent assays including size-based analysis, count-based analysis, and fragmentation pattern analysis. Each assay targets specific characteristics of tumor-derived nucleic acids, allowing comprehensive detection while maintaining modularity. This segmentation enables the system to achieve high sensitivity and specificity by combining results from multiple specialized assays rather than relying on a single complex method.
Solution Approach 2:
The patent introduces multiple dimensions of analysis beyond simple presence/absence detection. These dimensions include nucleic acid fragment size distribution, fragmentation patterns at specific genomic locations, and relative abundance ratios. By analyzing nucleic acids across multiple dimensional characteristics simultaneously, the system achieves superior detection accuracy while using established laboratory techniques for each dimension.
2Reliability
If tumor-derived DNA amount is low in early stage tumors, then early detection is possible, but the sensitivity of the test decreases
Solution Approach 1:
The patent changes the parameters being measured from simple DNA quantity to multiple characteristic parameters including fragment size distribution, fragmentation patterns at specific genomic breakpoints, and relative abundance ratios. By monitoring these parameter changes rather than just total DNA amount, the assay can detect early-stage tumors with low DNA quantities. The method identifies specific fragmentation signatures that remain detectable even when overall tumor-derived DNA is minimal.
Solution Approach 2:
The patent uses specific genomic fragmentation patterns as intermediary markers that amplify the detectability of tumor-derived DNA. Instead of directly measuring low quantities of tumor DNA, the method identifies characteristic fragmentation intermediaries at specific genomic locations that serve as reliable proxies for tumor presence. These intermediary markers enhance sensitivity by providing detectable signals even when the original tumor-derived DNA quantity is very low.
3Measurement precision
If the prevalence of early stage tumor is low, then early detection is valuable, but the specificity of the test decreases leading to more false positives
Solution Approach 1:
The patent segments the detection criteria into multiple independent characteristics that must all be satisfied for a positive diagnosis. These segments include specific fragment size ranges, particular fragmentation patterns at defined genomic locations, and threshold relative abundance ratios. By requiring concordance across multiple segmented criteria rather than relying on a single marker, the assay achieves high specificity and minimizes false positives even in low-prevalence screening populations.
Solution Approach 2:
The patent implements a feedback mechanism where results from multiple assays (size-based, count-based, fragmentation pattern) are integrated to adjust the interpretation of individual markers. The system uses feedback from concordant results across different assay types to confirm positive findings and reduce false positives. This multi-layered feedback approach ensures that only samples showing consistent abnormal patterns across multiple independent measurements are classified as positive, thereby maintaining high specificity.
Data Source
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AI summary
Various embodiments are directed to applications (e.g., classification of biological samples) of the analysis of the count, the fragmentation patterns, and size of cell-free nucleic acids, e.g., plasma DNA and serum DNA, including nucleic acids from pathogens, such as viruses. Embodiments of one application can determine if a subject has a particular condition. For example, a method of present disclosure can determine if a subject has cancer or a tumor, or other pathology. Embodiments of another application can be used to assess the stage of a condition, or the progression of a condition over time. For example, a method of the present disclosure may be used to determine a stage of cancer in a subject, or the progression of cancer in a subject over time (e.g., using samples obtained from a subject at different times).