Tumor Risk Evaluation Using Targeted Methylation Regions
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Solution Overview
Problem
Current DNA methylation sequencing methods, such as WGBS, are costly and prone to DNA damage, and the identification of differentially methylated regions (DMRs) associated with cancer is challenging due to population heterogeneity and non-specific methylation patterns, making it difficult to establish accurate models for cancer detection and tracing tumor tissue origin.
Innovation Solution
A low-cost, high-precision method using DNA or RNA oligonucleotide sequences to identify DMRs and evaluate tumor formation risk and tissue of origin by analyzing methylation levels, incorporating sequencing coverage depth and adjusting for age-related factors through machine learning models like SVM and logistic regression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If WGBS (whole genome bisulfite sequencing) is used for methylation sequencing, then comprehensive methylation information can be obtained, but sequencing cost increases and severe DNA damage occurs
Solution Approach 1:
The patent extracts only the cancer-relevant methylation regions (CpG islands and shores) from the entire genome for sequencing, rather than performing whole-genome methylation sequencing. This targeted approach reduces DNA input requirements and sequencing cost while maintaining diagnostic accuracy for cancer detection
Solution Approach 2:
The patent applies different sequencing strategies to different genomic regions: targeted sequencing of CpG islands and shores for cost-effective cancer screening, and whole-genome sequencing only when clinically necessary. This local quality approach optimizes resource allocation based on the specific diagnostic needs
2Adaptability or versatility
If population heterogeneity is considered in DMR identification, then more comprehensive cancer detection is possible, but non-specific methylation changes from age and disease conditions increase false positives
Solution Approach 1:
The patent performs preliminary stratification of the population by age groups and health status before identifying DMRs. By pre-defining reference groups (e.g., age-matched healthy controls), the method eliminates confounding effects of population heterogeneity and reduces false positives while maintaining comprehensive cancer detection capability
Solution Approach 2:
The patent implements dynamic reference group selection that adapts to the patient's age and clinical characteristics. The control group is dynamically adjusted to match the patient's demographic profile, allowing the system to maintain high specificity across different population subgroups while detecting various cancer types
3Loss of energy
If targeted sequencing of specific regions is performed, then sequencing cost is reduced, but detection of DMRs associated with cancer becomes more challenging
Solution Approach 1:
The patent segments the genome into functionally relevant regions (CpG islands, shores, and enhancers) that are known to be associated with cancer development. By focusing sequencing efforts on these specific segments rather than the entire genome, the method reduces cost while maintaining the ability to detect cancer-relevant DMRs
Solution Approach 2:
The patent adjusts sequencing parameters such as read depth and coverage thresholds specifically for targeted regions. By optimizing these parameters for the enriched cancer-relevant regions, the method achieves sufficient detection sensitivity at lower sequencing depths compared to whole-genome approaches, thereby reducing overall sequencing cost
Data Source
AI summary
Provided are a tumor risk evaluation method and system. Specifically provided are a method and/or system for evaluating the correlation between a sample under test and a tumor formation risk and/or tumor tissue source. Methylation variation regions of various different cancers and specific methylation characteristic regions of various organs are captured by using DNA or RNA oligonucleotide sequences, the existence of tumor components (ctDNA) in blood cell-free DNA (cfDNA) is determined, and the correlation between the sample and the tumor tissue source is evaluated. Provided is a low-cost and high-accuracy method, which is conducive to accurately predicting and evaluating the risk of various cancers.