cfDNA Fragment Size Analysis for Tumor Mutation Classification
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Solution Overview
Problem
Current methods for detecting somatic mutations in cell-free DNA (cfDNA) face challenges in distinguishing between tumor-derived and clonal hematopoietic mutations, due to low mutation fractions and interference from germline alterations and sequencing artifacts, which complicates early-stage cancer detection and analysis.
Innovation Solution
A computer-implemented method and system that analyze size profiles of cfDNA fragments to classify mutations of clonal hematopoietic origin versus tumor origin using a predictive model, generating size profiles and defining regions of interest to differentiate between the two types based on fragment lengths, thereby improving the sensitivity and specificity of mutation detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If mutation detection sensitivity is increased to detect low-abundance tumor mutations in cfDNA, then early-stage cancer detection capability is improved, but false positives from clonal hematopoietic mutations and sequencing artifacts increase
Solution Approach 1:
The patent segments the cfDNA population into distinct subpopulations based on fragment length characteristics. By analyzing fragment length distributions and identifying specific length ranges associated with tumor-derived DNA versus clonal hematopoietic DNA, the method separates true tumor mutations from false positives, enabling sensitive detection while maintaining reliability
Solution Approach 2:
The patent changes the analytical parameter from simply detecting mutation presence to analyzing fragment length distributions. By measuring and comparing fragment lengths of mutant versus wild-type alleles, the method transforms the detection approach to distinguish tumor mutations from clonal hematopoietic mutations based on physical characteristics of the DNA fragments
2Object-affected harmful factors
If cfDNA analysis is performed to enable noninvasive cancer detection, then patient safety and convenience are improved, but analytical sensitivity is limited by low ctDNA blood levels
Solution Approach 1:
The patent changes the measurement parameter to fragment length, which provides additional discriminatory information beyond mutation presence. This allows the method to achieve high analytical sensitivity in liquid biopsy by exploiting the different fragment length profiles of tumor-derived versus hematopoietic-derived cfDNA, enabling detection even at low ctDNA concentrations
3Measurement precision
If comprehensive cfDNA sequencing is performed to identify all potential mutations, then mutation detection coverage is improved, but interference from germline alterations and sequencing artifacts increases
Solution Approach 1:
The patent segments mutant cfDNA fragments into distinct groups based on fragment length. By identifying length ranges that are enriched for tumor-derived DNA and depleted of clonal hematopoietic and germline mutations, the method isolates true tumor signals from background noise, improving the signal-to-noise ratio while maintaining comprehensive mutation detection coverage
Solution Approach 2:
The patent extracts and analyzes only the fragment length information from sequenced cfDNA molecules. By taking out and examining the length parameter specifically, the method separates tumor-derived mutations from germline alterations and sequencing artifacts without requiring additional sequencing depth, thus improving signal-to-noise ratio while maintaining detection coverage
Data Source
AI summary
The genomic data processing systems and methods described herein can accurately detect mutations in nucleic acid (e.g., cell free DNA (cfDNA) sequence reads associated with plasma nucleic acid samples. The genomic data processing system of the present disclosure distinguishes mutations derived from a tumor from mutations derived of clonal hematopoietic (CH) origin. The origin of mutated DNA fragments can be more accurately determined by analyzing fragment sizes in cfDNA to generate tumor and CH regions of interest (ROIs) in corresponding size profiles. A mutation can be more accurately classified using a metric based on proportions of fragments in the ROIs.