Cell-Free DNA End-Motif Analysis for Cancer Type Prediction
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Solution Overview
Problem
Current cancer diagnosis methods, particularly those involving tissue biopsy and tumor markers, are invasive, have limited accuracy, and are often ineffective at early detection, while existing liquid biopsies using cell-free DNA lack effective methods for predicting cancer types with high sensitivity and accuracy.
Innovation Solution
A method involving the extraction of nucleic acids from a biological sample, alignment with a reference genome, acquisition of end motif frequencies and sizes of nucleic acid fragments, generation of vectorized data, and analysis using a trained artificial intelligence model to diagnose and predict cancer types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If tissue biopsy is used for cancer diagnosis, then diagnostic accuracy is improved, but patient discomfort increases and invasiveness worsens
Solution Approach 1:
The patent uses cell-free nucleic acid fragments as an intermediary substance to indirectly detect cancer. Instead of directly sampling tumor tissue through biopsy, the method analyzes cfDNA fragments circulating in body fluids, which serve as a mediator that carries cancer diagnostic information without requiring invasive tissue sampling.
Solution Approach 2:
The patent replaces the mechanical tissue biopsy procedure with a biochemical analysis method. Instead of physically removing and examining tissue samples, the system uses next-generation sequencing to analyze the chemical composition and end motifs of cell-free nucleic acid fragments, substituting mechanical sampling with molecular detection.
2Ease of operation
If conventional tumor markers are used for cancer screening, then ease of operation is improved, but measurement precision deteriorates
Solution Approach 1:
The patent changes the detection parameters from conventional tumor marker concentrations to nucleic acid fragment end motif frequencies and sizes. By analyzing the sequence composition and length distribution of cfDNA fragments rather than traditional protein markers, the system achieves higher diagnostic precision while maintaining the ease of non-invasive sampling.
3Ease of operation
If liquid biopsy using cell-free DNA is used, then ease of operation and non-invasiveness are improved, but cancer type prediction capability deteriorates
Solution Approach 1:
The patent segments the cell-free DNA into individual fragments and analyzes their specific characteristics (end motifs and sizes) rather than treating cfDNA as a homogeneous mixture. This segmentation allows the system to extract cancer-type-specific information from individual fragment properties, enabling accurate cancer type prediction while maintaining non-invasive sampling.
Solution Approach 2:
The patent adds new analytical dimensions to liquid biopsy by introducing end motif frequency analysis and fragment size measurement. Instead of only analyzing bulk cfDNA composition, the system examines the sequence ends and length distribution of individual fragments, creating new dimensional data that enables cancer type differentiation.
4Reliability
If early cancer detection is pursued, then patient prognosis is improved, but detection capability deteriorates due to limited sensitivity of existing methods
Solution Approach 1:
The patent creates a molecular copy of cancer DNA in the form of cell-free nucleic acid fragments that circulate in body fluids. These fragments serve as replicable copies of tumor genetic material that can be detected in liquid samples, enabling early cancer detection before tumors become large enough for conventional biopsy methods.
Data Source
AI summary
Disclosed is a method for diagnosing cancer and predicting a cancer type using fragment end motif frequencies and sizes of cell-free nucleic acid, and more preferably, to a method for diagnosing cancer and predicting a cancer type by extracting nucleic acids from a biological sample to obtain sequence information, acquiring fragment end motif frequencies and sizes of nucleic acids based on the aligned reads, converting the fragment end motif frequencies and sizes of nucleic acids into vectorized data, inputting the vectorized data to a trained artificial intelligence model and analyzing a resulting calculated value. The method includes generating vectorized data and analyzing the same using an AI algorithm and thus is useful due to high sensitivity and accuracy thereof even in the case of low read coverage.