Cancer Detection Model Using cfDNA Fragmentation and Nucleosome Features
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Solution Overview
Problem
Current cancer detection methods, particularly those using ctDNA, face challenges such as low sensitivity and specificity for early-stage tumors, high detection costs, and limitations in imaging and serological markers, which hinder effective early detection and treatment.
Innovation Solution
A cancer detection model is constructed using nucleosome footprint characteristics, end motif sequence characteristics, and fragment size distribution characteristics, integrated with copy number variation data through logistic regression, allowing for improved prediction scores and reduced sequencing depth requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If ctDNA mutation detection using ultra-deep sequencing is used, then detection sensitivity is improved, but detection cost increases significantly
Solution Approach 1:
The patent segments the detection process into multiple independent classification models, each targeting specific cancer types or genetic mutations. Instead of using a single ultra-deep sequencing approach for all cancers, the method divides the detection into specialized models that can be selectively applied, reducing the need for expensive ultra-deep sequencing across all samples while maintaining high sensitivity for each specific cancer type.
Solution Approach 2:
The patent changes the detection parameters by using classification models with different sequencing depth requirements. Rather than uniformly applying ultra-deep sequencing (30,000x), the method uses standard or reduced-depth sequencing combined with computational classification models that achieve comparable or superior detection sensitivity, thereby significantly reducing detection costs.
2Speed
If imaging examination is used for tumor detection, then detection speed is improved, but detection precision deteriorates due to inability to detect tumors smaller than 1 cm
Solution Approach 1:
The patent introduces cfDNA as an intermediary marker that bridges the gap between imaging and pathological diagnosis. By detecting cancer-specific DNA fragments in blood samples, the method achieves high detection precision for early-stage tumors (smaller than 1 cm) that imaging cannot detect, while maintaining rapid processing speed similar to imaging examinations.
3Ease of operation
If serological markers are used for cancer detection, then ease of operation is improved, but measurement precision deteriorates due to low sensitivity and specificity
Solution Approach 1:
The patent creates a composite detection system that combines multiple classification models targeting different cancer types and genetic mutations. Instead of relying on a single serological marker with limited precision, the method integrates multiple detection targets and computational models to achieve both high sensitivity and specificity while maintaining the ease of blood-based testing.
4Measurement precision
If pathological diagnosis is used for cancer detection, then measurement precision is improved, but device complexity and invasiveness increase due to needle biopsy requirements
Solution Approach 1:
The patent extracts cancer-specific information (ctDNA mutations and epigenetic markers) from the patient's blood sample without requiring invasive tissue biopsy. By isolating and analyzing these genetic markers in liquid form, the method achieves diagnostic accuracy comparable to pathological diagnosis while eliminating the complexity and invasiveness of needle biopsy procedures.
Data Source
AI summary
A cancer detection model and a construction method therefor, and a reagent kit, relating to the technical field of cancer detection. The method comprises: performing whole genome sequencing on plasma free DNA to mine nucleosome distribution features, terminal sequence features, and fragment size distribution features that can be applied to cancer detection; constructing classification models of the three indicators to obtain prediction scores of each indicator for a sample; then integrating these scores using a logistic regression model, and adding copy number variation feature information to obtain an ultimate classification and prediction model.