Cell-Free DNA Methylation Profiling for Early Colorectal Cancer Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current cancer screening tools for colorectal cancer face challenges with high false positive and false negative rates, low sensitivity in detecting early-stage tumors, and insufficient prevalence of methylation biomarkers, necessitating more sensitive and specific blood-based screening methods.
Innovation Solution
A methylation signature panel comprising specific genomic regions, such as ITGA4, EMBP1, TMEM163, SFMBT2, and ZNF543, is used to analyze cell-free nucleic acids from blood samples, combined with machine learning classifiers to distinguish between healthy individuals and those with colorectal cancer, enabling early detection and monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If tumor size is increased to release detectable circulating tumor markers, then detection sensitivity improves, but early detection capability deteriorates
Solution Approach 1:
The patent changes the detection parameter from tumor size-based marker release to DNA methylation pattern detection. By detecting hypermethylated DNA sequences in cell-free DNA from even small tumors, the method achieves high detection sensitivity without requiring tumors to grow to detectable sizes, thus resolving the contradiction between detection sensitivity and early detection capability
Solution Approach 2:
The patent replaces the mechanical/biological process of waiting for tumors to grow large enough to release detectable markers with a molecular biology-based detection method. By using methylation-specific PCR and sequencing to detect epigenetic modifications in circulating tumor DNA, the system can detect cancer at much smaller sizes, eliminating the need to wait for tumor growth
2Ease of operation
If methylation biomarkers are used for cancer detection, then non-invasive screening is enabled, but sensitivity is reduced due to insufficient prevalence in tumors
Solution Approach 1:
The patent segments the detection approach by focusing on specific hypermethylated DNA sequences rather than relying on the presence of any methylation biomarker. By identifying and detecting multiple specific hypermethylated regions (such as in genes like ITGA4, EMBP1, TMEM163, SFMBT2, and ZNF543) that are highly prevalent in colorectal cancer, the method maintains non-invasive screening capability while significantly improving detection sensitivity
Solution Approach 2:
The patent uses a composite detection strategy combining multiple methylation-specific assays targeting different hypermethylated genomic regions. By analyzing a panel of multiple methylation biomarkers simultaneously through methods like methylation-specific PCR and next-generation sequencing, the system overcomes the limitation of individual biomarkers with low prevalence, achieving high sensitivity while maintaining non-invasive screening
3Device complexity
If conventional screening tools are used, then simplicity is maintained, but false positive and false negative rates increase
Solution Approach 1:
The patent applies partial action by implementing a tiered detection strategy. The initial screen uses targeted methylation detection of specific hypermethylated regions in cell-free DNA, which has high sensitivity and specificity. This partial approach focuses resources on the most informative markers rather than attempting to detect all possible cancer types or stages, thereby maintaining operational simplicity while reducing false positives and negatives
Solution Approach 2:
The patent introduces cell-free DNA methylation analysis as an intermediary between conventional simple screening tools and complex tissue biopsy. This intermediary approach uses non-invasive blood or stool samples to detect cancer-specific methylation patterns, providing higher reliability than conventional tools while avoiding the complexity and invasiveness of immediate biopsy, thus reducing false positives and negatives
Data Source
AI summary
The present disclosure provides methods and systems for screening or detecting a colorectal cancer or following colorectal disease progression that may be applied to cell-free nucleic acids such as cell-free DNA. The method may use detection of methylation signals within a single sequencing read in identified genomic regions as input features to train a machine learning model and generate a classifier useful for stratifying populations of individuals. The method may comprise extracting DNA from a cell-free sample obtained from a subject, converting the DNA for methylation sequencing, generating sequencing reads, and detecting colon proliferative cell disorder-associated signals in the sequencing information and training a machine learning model to provide a discriminator capable of distinguishing groups in a subject population such as healthy, cancer or distinguishing disease subtype or stage. The method may be used for, e.g., predicting, prognosticating, and/or monitoring response to treatment, tumor load, relapse, or colorectal cancer development.