ecDNA Signature Detection via Copy Number and Fragment Length Analysis
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Solution Overview
Problem
Current methods in clinical oncology are insufficient for identifying extrachromosomal DNA (ecDNA) due to limitations in whole exome sequencing and targeted panels, which focus on gene mutations rather than the surrounding genomic context, hindering accurate detection and classification of ecDNA-positive cancers.
Innovation Solution
Development of ecDNA signatures that combine gene copy number, gene fusions, fragment length, allele frequency, and genomic location with other biomarkers to improve identification in non-WGS panels, allowing for classification of ecDNA status and guiding treatment decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If whole exome sequencing and targeted panels are used to focus on gene mutations, then the depth of mutation analysis is improved, but the ability to detect ecDNA and surrounding genomic context deteriorates
Solution Approach 1:
The patent segments the detection approach into two complementary parts: (1) targeted sequencing for deep mutation analysis, and (2) ecDNA-specific signature detection using computational algorithms that analyze copy number variations, fragment lengths, and allele frequencies. This segmentation allows each method to excel at its specialized function while the integration recovers the lost genomic context information.
Solution Approach 2:
The patent changes the analytical parameters from standard mutation-focused metrics to ecDNA-specific parameters including copy number variation patterns, fragment length distributions, and allele frequency deviations. These parameter changes enable the detection of ecDNA structures that would be invisible to conventional mutation-only approaches.
2Device complexity
If conventional sequencing methods are used, then the cost and complexity are reduced, but the accuracy of ecDNA detection deteriorates
Solution Approach 1:
The patent introduces computational algorithms as an intermediary layer between conventional sequencing data and ecDNA detection. These algorithms process standard sequencing outputs and extract ecDNA signatures through pattern recognition, serving as a bridge that enables ecDNA detection without requiring specialized sequencing hardware or methods.
Solution Approach 2:
The patent makes conventional sequencing platforms multi-functional by enabling them to perform both their traditional mutation detection function and the additional ecDNA detection function through software-based signature analysis. This universality allows a single sequencing run to generate data for both purposes without requiring separate specialized equipment.
3Measurement precision
If comprehensive genomic analysis is performed to detect ecDNA, then the detection accuracy is improved, but the computational resources and time required increase
Solution Approach 1:
The patent performs preliminary action by pre-defining ecDNA signatures and establishing threshold criteria before actual sample analysis. The computational algorithms are pre-trained on reference datasets to recognize ecDNA patterns, so that during clinical analysis, the system can quickly compare patient data against established signatures rather than performing de novo analysis, significantly reducing computation time.
Solution Approach 2:
The patent applies partial action by focusing computational resources on specific genomic regions and features most likely to contain ecDNA signatures, rather than analyzing the entire genome uniformly. The system prioritizes analysis of regions with copy number variations and other hallmarks of ecDNA, performing excessive analysis only where needed to confirm presence.
Data Source
AI summary
Provided herein are methods and systems for detecting ecDNA positive cancers and methods and systems for developing signatures of ecDNA positive cancers.