Methylation Pattern Mapping for Early Cancer Discrimination
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current cancer detection methods are unsatisfactory, particularly for early detection, and existing biomarker approaches fail to effectively leverage complex nucleic acid sequencing data to identify and differentiate cancer conditions.
Innovation Solution
A method involving methylation sequencing to generate interval maps with nodes representing genomic regions, scanning for qualifying methylation patterns (QMPs) that satisfy specific criteria, and using these patterns to train classifiers for cancer condition discrimination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current screening tests (mammography, colonoscopy, Pap smears, PSA testing) are used for cancer detection, then cancer screening can be performed, but early detection capability is insufficient and many cancers remain undetectable until too late
Solution Approach 1:
The patent changes the detection parameter from traditional structural imaging (mammography, colonoscopy) to molecular parameter detection (DNA methylation patterns). By detecting methylation status at specific CpG sites in circulating cell-free DNA, the system achieves earlier and more accurate cancer detection before structural changes occur.
Solution Approach 2:
The patent uses circulating cell-free DNA as an intermediary biomarker to detect cancer. Instead of directly imaging tumors or requiring tissue biopsies, the system detects methylation patterns in cell-free DNA circulating in blood, providing a non-invasive early detection method that bridges the gap between cancer occurrence and traditional detection methods.
2Measurement precision
If methylation sequencing is performed to identify biomarkers, then cancer detection sensitivity improves, but data complexity and analysis difficulty increase
Solution Approach 1:
The patent segments the complex methylation sequencing data into discrete, analyzable units (CpG sites and methylation patterns). By breaking down the genome into specific CpG regions and analyzing methylation status at individual sites, the system transforms complex sequencing data into manageable patterns that can be systematically evaluated for cancer indicators.
Solution Approach 2:
The patent performs preliminary processing of methylation sequencing data to identify and filter significant methylation patterns before cancer detection analysis. By pre-processing to remove technical artifacts and identify biologically relevant methylation changes, the system reduces downstream analysis complexity while maintaining high detection sensitivity.
3Measurement precision
If traditional reductionism approach (precision oncology focusing on single mutations) is used, then specific mutations can be identified, but cancer complexity is underappreciated and treatment effectiveness remains limited
Solution Approach 1:
The patent merges multiple types of molecular information (methylation patterns, gene expression, and genomic data) into an integrated cancer detection and classification system. By combining epigenetic methylation data with traditional genomic sequencing, the system achieves both mutation identification and comprehensive cancer condition differentiation, overcoming the limitations of reductionism.
Solution Approach 2:
The patent adds the epigenetic dimension (methylation patterns) to the traditional genomic dimension (DNA sequences). This dimensional expansion allows the system to detect cancer at multiple levels simultaneously—both the sequence mutations and the epigenetic regulation patterns—providing a more comprehensive view of cancer biology that improves both detection accuracy and treatment adaptability.
Data Source
AI summary
Systems and methods of identifying methylation patterns discriminating or indicating a cancer condition are provided. First and second datasets are obtained. Each dataset comprises a plurality of fragment methylation patterns determined by methylation sequencing of nucleic acids obtained from a first or second set of subjects and comprising a methylation state of each CpG site in a corresponding plurality of CpG sites. Each plurality of subjects has a respective first or second state of the cancer condition. First and second interval maps are generated for each respective dataset, each comprising a plurality of nodes characterized by a start methylation site, an end methylation site, a representation of each different fragment methylation pattern and a count of fragments. The first and second interval maps are scanned for qualifying methylation patterns within a predetermined range of CpG sites, satisfying one or more selection criteria, thereby identifying methylation patterns discriminating a cancer condition.