cfDNA Transcription Start Site Coverage for Tumor Detection
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Solution Overview
Problem
Current methods for tumor prediction face challenges such as low precision and specificity, high costs, and limited scalability due to the use of serological markers, imaging techniques, genomic variations, and nucleosome-associated blotting, which hinder effective early tumor detection.
Innovation Solution
A cell-free DNA-based disease prediction model is constructed by obtaining sequencing data from diseased and control individuals, selecting genes with differential coverage at transcription start site regions, and training a prediction model using logistic regression or random forest algorithms to predict diseases like lung, liver, or colorectal cancer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If serological tumor markers are used for detection, then detection can be performed, but precision and specificity are lower
Solution Approach 1:
The patent changes the detection parameter from serological markers to cfDNA sequencing depth distribution at transcription start sites. This parameter change enables detection of tumor-specific genetic material in circulation, achieving both high precision in identifying tumor presence and high specificity in distinguishing tumor DNA from normal DNA through analysis of coverage patterns at gene TSS regions.
2Measurement precision
If CT or nuclear magnetic resonance imaging is used for detection, then tumor prediction can be performed, but false positive and false negative rates are higher and early screening is difficult
Solution Approach 1:
The patent performs preliminary detection of tumor DNA in circulation before tumors become detectable by imaging methods. By analyzing cfDNA sequencing depth distribution at transcription start sites, the method can identify tumor presence at very early stages, enabling early screening and intervention before anatomical changes are visible on CT or MRI scans.
3Measurement precision
If genomic variation detection at SNV level is used, then tumor prediction can be performed, but specific variation cannot be detected in all patients and cost is high
Solution Approach 1:
The patent creates a universal detection method that works for all patients regardless of specific tumor mutations. Instead of targeting specific SNV variants that may not be present in all tumors, the method analyzes the universal pattern of cfDNA sequencing depth distribution at transcription start sites across the genome, making it applicable to all tumor types and all patients.
4Measurement precision
If CNV-based detection is used, then tumor prediction can be performed, but only a small number of individuals have this type of variation
Solution Approach 1:
The patent changes from detecting CNV (copy number variation) to detecting sequencing depth distribution patterns at transcription start sites. This parameter change enables detection of tumor presence through epigenetic and genomic alterations in cfDNA that are present across all tumor types and all patients, significantly expanding population coverage while maintaining detection precision.
5Measurement precision
If genomic methylation detection is used, then tumor prediction can be performed, but cost is high and large-scale application is difficult
Solution Approach 1:
The patent uses a cost-effective approach by analyzing sequencing depth distribution patterns from standard cfDNA sequencing data rather than requiring expensive specialized methylation detection assays. The method extracts tumor detection information from routine sequencing data through bioinformatic analysis of coverage patterns at transcription start sites, making large-scale application and population screening economically feasible.
6Measurement precision
If nucleosome-associated blotting of cfDNA fragments is used, then tumor prediction can be performed, but higher sequencing depth is required and it is difficult to apply in clinical routine detection
Solution Approach 1:
The patent extracts the essential diagnostic information from cfDNA by analyzing sequencing depth distribution at transcription start sites, separating this key signal from the complexity of full nucleosome analysis. This extraction approach identifies tumor-specific patterns in cfDNA coverage without requiring complete nucleosome reconstitution or complex blotting procedures, simplifying the method for clinical application while maintaining high detection sensitivity.
Data Source
AI summary
Described is a free DNA-based disease prediction model and a construction method therefor and an application thereof. The construction method includes the steps of: 1) obtaining sequencing data of free DNA samples of diseased individuals and control individuals, the number of the diseased individuals and the number of the control individuals being both multiple; 2) selecting, according to the coverage of the sequencing data of the free DNA samples of the diseased individuals and the control individuals on a genome, a gene set having a difference in the coverage of a transcription initiation site region between the diseased individuals and the control individuals; and 3) for genes in the gene set, using the coverage of the sequencing data on the gene transcription initiation site region as an input prediction model for training so as to establishing a disease prediction model.