DNA Methylation Kit for Accurate Tumor Tissue-of-Origin Detection
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Solution Overview
Problem
Current methods for identifying the primary tumor site in cancer patients, particularly in cases of cancer of unknown primary (CUP), suffer from low accuracy and effectiveness, with immunohistochemistry (IHC) identifying primary sites in only 50-65% of patients and PET/CT diagnosing about 30% of CUP primary locations, and machine learning algorithms facing challenges in classifying tumor tissue origin accurately.
Innovation Solution
A DNA methylation-based technology using methylated adapters with inline barcodes and PCR amplification primers, combined with a computer-implemented data analysis method for methylation index prediction, to enhance the accuracy of tumor tissue-of-origin identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If immunohistochemistry (IHC) with hand-picked antibody panels is used to identify primary tumor sites, then the method is widely applicable, but the identification accuracy is limited to 50-65% of patients
Solution Approach 1:
The patent replaces the manual, subjective IHC antibody selection process with an automated machine learning system that analyzes methylation data. The ML algorithm objectively identifies tumor tissue origin based on methylation patterns, eliminating the limitations of hand-picked antibody panels and achieving superior accuracy while maintaining broad applicability across different cancer types.
Solution Approach 2:
The patent shifts from analyzing protein expression levels via IHC to analyzing DNA methylation patterns. This parameter change from post-translational protein detection to epigenetic modification analysis enables more accurate tumor tissue-of-origin identification, as methylation patterns are more stable and tissue-specific than protein expression profiles.
2Ease of operation
If PET/CT imaging is used to diagnose CUP primary locations, then non-invasive detection is achieved, but the diagnosis rate is limited to about 30% of CUP cases
Solution Approach 1:
The patent replaces imaging-based detection with molecular analysis of DNA methylation patterns in tumor cells. This substitution enables identification of primary tumor origin through epigenetic signatures that are more specific and sensitive than imaging capabilities, achieving higher diagnosis rates while maintaining a non-invasive workflow through liquid biopsy or tissue sample analysis.
Solution Approach 2:
The patent uses DNA methylation patterns as an intermediary biomarker to bridge the gap between non-invasive sampling and accurate primary site identification. Methylation signatures serve as molecular fingerprints that reveal tumor origin information without requiring direct visualization or intervention at the primary tumor site.
3Measurement precision
If machine learning algorithms are used for methylation-based tumor tracing, then classification accuracy improves, but the complexity of data analysis increases
Solution Approach 1:
The patent performs preliminary processing of methylation data by normalizing values, filtering informative CpG sites, and preparing features before applying machine learning classification. This preprocessing pipeline simplifies the subsequent analysis by reducing data dimensionality and noise, making the ML system more manageable while maintaining high classification accuracy.
Solution Approach 2:
The patent divides the complex analysis process into distinct segments: data preprocessing, feature selection, model training, and classification. This segmentation of the analytical workflow into modular steps reduces overall system complexity by allowing each component to be optimized and validated independently, while collectively achieving high tumor tissue origin identification accuracy.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The method achieves higher accuracy in determining the primary tumor site, improving prediction rates to 78.75% and 88.33% when considering the top-ranked and top-two predicted probabilities, outperforming current clinical methods.
Implementation Method 1
DNA methylation, the addition of a methyl group to the cytosine almost exclusively in the context of CpG dinucleotides, shows both cell- and tissue-specific patterns in the human genome
Implementation Method 2
PCR amplification primers and a PCR amplification reagent
Data Source
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AI summary
The present disclosure provides a kit for identifying tumor tissue-of-origin, including an adapter and PCR amplification primers, where the adapter includes nucleotide sequences of A01-T, A01-B, A02-T, A02-B, A03-T, A03-B, A04-T, A04-B, A05-T, A05-B, A06-T, and A06-B; and the PCR amplification primers include nucleotide sequences of RO1-F, R01-R, R02-F, and R02-R. In the present disclosure, new adapter nucleotide sequences and PCR amplification primers are designed, with higher accuracy and effectiveness. The present disclosure further provides a data analysis method for a kit for identifying tumor tissue-of-origin, including data preprocessing, alignment, methylation information statistics, quality control, and analysis. In the present disclosure, the analysis of a Beta value-based methylation index further improves a recognition ratio.