Computational Variant Classification Using cfDNA Fragment Length Metrics
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Solution Overview
Problem
Current methods for detecting clonal hematopoiesis variants in liquid biopsy assays are limited, often requiring additional sequencing reactions and being prone to false positives and inaccurate treatment responses.
Innovation Solution
The development of models that use feature sets derived from nucleic acid sequencing reactions of cell-free DNA fragments to distinguish between hematopoietic and solid tumor variants, eliminating the need for additional sequencing reactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If additional sequencing reactions are performed to distinguish clonal hematopoiesis variants from solid tumor variants, then measurement precision is improved, but device complexity and loss of time increase
Solution Approach 1:
The patent extracts and utilizes specific characteristics inherent to clonal hematopoiesis variants (such as presence in white blood cell-derived cell-free DNA, specific fragment length distributions, and characteristic variant allele fractions) to distinguish them from solid tumor variants. By focusing on these extracted features from the existing sequencing data, the method achieves accurate classification without requiring additional sequencing reactions, thereby resolving the contradiction between measurement precision and device complexity
Solution Approach 2:
The patent creates a computational model that replicates the diagnostic capability of additional sequencing reactions by analyzing patterns in the existing single sequencing reaction data. The model copies the information-gathering function of multiple reactions through sophisticated bioinformatics analysis of fragment lengths, variant allele fractions, and sequence coverage patterns, eliminating the need for physical additional sequencing while maintaining classification accuracy
2Reliability
If additional sequencing reactions are performed to reduce false positives, then reliability is improved, but loss of time increases
Solution Approach 1:
The patent performs preliminary computational analysis on the existing sequencing data by evaluating multiple characteristics (fragment length distributions, variant allele fractions, sequence coverage patterns, and presence in different DNA fractions) before making a classification decision. This preliminary action within the single sequencing reaction framework reduces false positives by thoroughly assessing multiple indicators simultaneously, eliminating the need for time-consuming additional sequencing reactions while maintaining high reliability
Solution Approach 2:
The patent introduces computational models and bioinformatics algorithms as intermediaries that process and interpret the sequencing data to distinguish clonal hematopoiesis variants from solid tumor variants. These computational intermediaries analyze patterns in fragment lengths, variant allele fractions, and sequence coverage to reduce false positives without requiring additional wet-lab sequencing reactions, thereby reducing diagnostic time while maintaining reliability
3Manufacturing precision
If comprehensive evaluation of cancer genome is performed using large NGS panels, then manufacturing precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent extracts and focuses analysis on specific informative characteristics from the NGS sequencing data, such as fragment length distributions, variant allele fractions, and sequence coverage patterns. By concentrating on these extracted features rather than requiring comprehensive analysis of all genomic data, the method achieves accurate distinction between clonal hematopoiesis and solid tumor variants while reducing the effective complexity of the evaluation process
Solution Approach 2:
The patent applies different analytical approaches to different aspects of the sequencing data: fragment length analysis for distinguishing cellular origins, variant allele fraction evaluation for determining variant characteristics, and sequence coverage pattern analysis for validation. This localized application of different quality assessment methods to specific data features enables comprehensive genomic evaluation with optimized complexity by matching analytical depth to the specific information needs of each genomic feature
Data Source
AI summary
A method of identifying a variant as a somatic variant derived from cell free DNA (cfDNA) identifies the variant at a locus based on differences between the nucleic acid sequence for a cfDNA fragment in a plurality of cfDNA fragments and a nucleic acid sequence for the locus in a reference sequence, where the cfDNA fragments are from a liquid biopsy sample from a subject. A set of cfDNA fragments comprising the variant, in the plurality of cfDNA fragments, determines fragment length metrics. A variant allele fraction (VAF) is determined based on comparison of the number of cfDNA fragments having the variant and the total number of cfDNA fragments mapping to the locus. Clonal hematopoiesis prevalence metrics for the variant are obtained. The fragment length metrics, VAF, and hematopoiesis metrics are inputted into a model thereby obtaining, as model output, whether the variant is a somatic variant derived from cfDNA.