Cell-Free DNA End Distribution Model for Pan-Cancer Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing cancer diagnosis technologies based on cell-free DNA (cfDNA) suffer from limited universality and sensitivity, particularly in early-stage cancer detection, due to the high heterogeneity of tumor fragmentation patterns and the need for costly, specific statistical methods.
Innovation Solution
A cancer diagnosis model is constructed by analyzing the end distribution patterns of cfDNA in a non-cancer population, using whole genome sequencing to identify common fragmentation patterns that distinguish cancer patients from controls, without requiring tumor-specific markers or complex statistical analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If tumor-specific markers are used for cancer diagnosis, then diagnostic accuracy for specific cancer types is improved, but universality across different cancer types deteriorates
Solution Approach 1:
The patent applies universality by developing a diagnostic model based on global end distribution patterns of cfDNA that can detect multiple cancer types simultaneously. Instead of creating separate markers for each cancer type, the model identifies a universal signature characterized by deviations from normal end distribution patterns, enabling single-test detection across diverse cancers including lung, liver, stomach, and other malignancies
Solution Approach 2:
The patent merges the diagnostic approach by combining end distribution pattern analysis with fragmentation pattern characteristics into a unified diagnostic model. This integration allows the model to capture comprehensive cfDNA characteristics that reflect tumor presence across different cancer types, achieving both high accuracy and broad universality through combined feature analysis
2Measurement precision
If complex statistical methods are used to identify tumor markers, then diagnostic precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent extracts the essential diagnostic information by focusing solely on end distribution patterns and fragmentation characteristics of cfDNA, eliminating the need for complex statistical methods. The model directly analyzes these physical characteristics to identify cancer signatures, simplifying the diagnostic process while maintaining high precision through direct pattern recognition rather than complex statistical computation
3Reliability
If cfDNA fragmentation pattern analysis is used for cancer diagnosis, then diagnostic capability is improved, but sensitivity in early-stage cancer patients deteriorates
Solution Approach 1:
The patent applies dimensionality change by shifting from analyzing only fragmentation patterns to analyzing end distribution patterns across the entire genome. This dimensional expansion allows detection of subtle global deviations in end positioning that are characteristic of cancer even at early stages, significantly improving sensitivity while maintaining diagnostic capability through multi-dimensional pattern analysis
Data Source
AI summary
The present disclosure relates to a cell-free DNA-based cancer diagnosis model and use. A first aspect of the present disclosure provides a construction method for a cancer diagnosis model, including the steps of obtaining sequencing data of cell-free DNA of a non-cancer population; comparing the sequencing data of the cell-free DNA of the non-cancer population with reference genomes to obtain corresponding sites of the ends of the cell-free DNA on the reference genomes; and constructing the cancer diagnosis model based on the corresponding sites of the ends of the cell-free DNA on target genome intervals. The present disclosure adopts an idea that is opposite to the prevailing method. Instead of trying to identify diagnostic markers based on the specific fragmentation patterns of tumor cfDNA, the applicant constructs the cancer diagnosis model by looking for the end distribution patterns of cfDNA in non-cancer population.