Cell-Free DNA Variant Origin Classification With Allele Fraction Thresholds
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Solution Overview
Problem
Conventional systems struggle to automatically differentiate between germline and somatic mutations in cell-free DNA due to noise and biases, leading to ambiguous allele fraction measurements, especially in the presence of high germline background DNA.
Innovation Solution
A method using quantitative allele fraction (AF) measures, standard deviation (STDEV) thresholds, and AF thresholds to classify genomic loci as either somatic or germline by analyzing cell-free DNA sequencing data, employing binning techniques and comparing AF measures before and after cancer treatment to confirm origin.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional systems are used to detect genetic variants in cell-free DNA, then detection capability is provided, but automatic differentiation between germline and somatic mutations cannot be achieved due to noise and biases
Solution Approach 1:
The patent segments variants into different categories (germline, somatic, uncertain) based on allele fraction thresholds and confidence intervals. This segmentation allows automated classification by dividing the continuous spectrum of allele fractions into discrete categories with clear decision rules, resolving the contradiction between automation and precision.
Solution Approach 2:
The patent introduces multiple parameters beyond simple allele fraction, including confidence intervals, depth of coverage, and variant frequency distributions. By changing from a single-parameter to multi-parameter analysis, the system achieves both automated differentiation and maintained measurement precision through more robust statistical evaluation.
2Extent of automation
If quantitative allele fraction measures are used with threshold-based classification, then automatic origin identification is achieved, but measurement uncertainty due to noise and biases remains
Solution Approach 1:
The patent performs preliminary calculations of confidence intervals and statistical parameters before making classification decisions. By pre-computing these reliability metrics and incorporating them into the classification algorithm, the system maintains reliable automated identification despite measurement uncertainties from noise and biases.
Solution Approach 2:
The system uses confidence intervals and statistical feedback to adjust classification decisions. When measurements fall near threshold boundaries or show high variability, the feedback mechanism identifies these as uncertain cases requiring additional analysis or validation, thereby maintaining overall classification reliability while enabling automation.
3Measurement precision
If human experts or Tumor Boards are used to distinguish somatic mutations from germline variants, then differentiation accuracy can be maintained, but time consumption and operational complexity increase significantly
Solution Approach 1:
The patent implements self-service through automated algorithms that perform the differentiation task independently using statistical models and threshold-based classification. The system serves itself by incorporating expert-derived classification rules into automated software, eliminating the need for actual human expert involvement while maintaining differentiation accuracy and dramatically reducing time consumption.
Solution Approach 2:
The patent copies the decision-making logic of human experts into computational algorithms. By encoding expert knowledge into software rules and statistical models, the system replicates expert-level differentiation accuracy in an automated format that operates instantly without human time investment.
4Measurement precision
If allele fraction thresholds are set to be strict to reduce misclassification, then classification precision improves, but the number of uncertain or indeterminate cases increases
Solution Approach 1:
The patent segments variants into three distinct categories (germline, somatic, uncertain) rather than forcing binary classification. This segmentation allows strict thresholds to be applied to confident cases while gracefully handling ambiguous cases in a separate uncertain category, maintaining precision without oversimplifying the system complexity.
Solution Approach 2:
The classification system is dynamic and adaptive, adjusting confidence thresholds and classification decisions based on input data quality, depth of coverage, and observed variant frequency distributions. This dynamic behavior allows the system to maintain precision across varying conditions while managing complexity through context-dependent decision rules.
Data Source
AI summary
The present disclosure provides systems and methods to detect somatic or germline variants from cell-free DNA (cfDNA). Generally, the systems and methods comprise receiving sequencing information from cfDNA from said subject, determining whether measures are above or below a threshold; and classifying each as being of somatic origin or classifying each locus as being of germline origin.