Early-Stage Cancer MRD Detection from Urine Using MBD-Partitioned DNA
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Solution Overview
Problem
Current cancer detection methods often overlook genomic and epigenomic attributes of patient samples, leading to ineffective or suboptimal cancer therapies due to the omission of critical epigenetic variations such as methylation patterns, which are indicative of cancer.
Innovation Solution
A method involving the use of methyl binding domain (MBD) proteins to partition nucleic acid molecules based on methylation levels, followed by sequencing and analysis to detect minimal residual disease (MRD) in urine samples, utilizing machine learning techniques to identify cancer status with high sensitivity and specificity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional cancer screening tests are used, then general signs of health can be detected, but genomic and epigenomic attributes such as methylation patterns are overlooked
Solution Approach 1:
The detection method is segmented into multiple specialized components: MBD proteins for methylation-specific binding, molecular barcodes for epigenomic tagging, and sequencing analysis for genomic detection. Each component handles a specific aspect of cancer detection, allowing comprehensive information capture without requiring a single complex system to do everything.
Solution Approach 2:
Methyl binding domain (MBD) proteins serve as intermediaries that specifically bind to methylated DNA sequences, enabling the detection system to capture and analyze epigenetic information. These proteins act as mediators between the sample and the detection apparatus, translating biological signals into detectable patterns.
2Measurement precision
If MRD detection with high sensitivity is achieved, then early stage cancer can be detected, but the detection method becomes more complex
Solution Approach 1:
Molecular barcodes are attached to nucleic acid molecules before sequencing, during the library preparation phase. This preliminary tagging of epigenomic information allows the sequencing process to efficiently sort and analyze molecules based on their methylation status, achieving high sensitivity without requiring complex real-time analysis during sequencing.
Solution Approach 2:
The method detects cancer by measuring changes in methylation parameters across different genomic regions. By monitoring variations in methylation patterns rather than relying on single-marker detection, the system achieves high sensitivity for early-stage cancer while using standard sequencing technology rather than requiring novel complex instrumentation.
3Reliability
If methylation patterns are analyzed for cancer detection, then diagnostic accuracy improves, but the time required for analysis increases
Solution Approach 1:
The method merges genomic sequencing with epigenomic methylation analysis into a single unified workflow. By combining these analyses that would traditionally require separate experiments, the system achieves high diagnostic accuracy without proportionally increasing analysis time, as both types of information are obtained from the same sample preparation and sequencing run.
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 a sensitivity of at least 85% and specificity of at least 99% in detecting early-stage cancer, with a diagnostic accuracy of at least 99%, and integrates with health insurance claims data for personalized treatment recommendations.
Implementation Method 1
partitioning the plurality of nucleic acid molecules into a number of fractions based on a methylation level of the nucleic acid molecules
Implementation Method 2
combining a plurality of nucleic acid molecules derived from a subject with a solution including an amount of methyl binding domain (MBD) proteins to produce a nucleic acid-MBD protein solution
Implementation Method 3
combining at least a portion of the number of nucleic acid fractions with an amount of restriction enzyme that cleaves molecules with one or more unmethylated cytosines
Implementation Method 4
sequencing nucleic acid molecules derived from a urine sample obtained from a subject, analyzing sequence reads derived from the sequencing to identify one or more driver mutations
Data Source
AI summary
Disclosed herein are methods, compositions, and devices for use in early detection of cancer. The methods include sequencing a panel of regions in cell-free nucleic acid molecules and detecting one or more biomarkers that are indicative of a cancer, including from urine samples.