Personalized ctDNA Probe Assays for Low-Concentration Mutation Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods struggle to accurately detect disease-associated mutations, particularly in low concentrations of cell-free tumor DNA and fetal DNA, hindering early diagnosis of cancer and fetal genetic disorders.
Innovation Solution
A personalized assay using a unique combination of probes to detect segregating markers or mutations, combined with targeted sequencing of genomic DNA, allows for the identification of specific somatic mutations and determination of tumor fraction in patients, and the detection of fetal genetic markers in maternal blood.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional detection methods are used, then the detection process is simple, but the detection precision is insufficient for low-concentration tumor DNA and fetal DNA
Solution Approach 1:
The assay is divided into multiple sequential steps: (1) identifying segregating markers from tumor tissue or parental genomes, (2) designing patient-specific probes targeting these markers, (3) contacting probes with cell-free DNA, and (4) detecting probe-target hybridization signals. This segmentation allows each step to be optimized independently, achieving high detection precision for low-concentration tumor and fetal DNA while maintaining a manageable workflow
Solution Approach 2:
The method performs preliminary identification of patient-specific segregating markers from tumor tissue or parental genomes before the actual detection of cell-free DNA. This preliminary action creates a customized probe set tailored to each patient's unique genetic profile, enabling highly specific detection of their tumor or fetal DNA in the complex cell-free DNA mixture, thereby achieving high measurement precision
2Measurement precision
If targeted sequencing is performed to identify somatic mutations, then the detection accuracy improves, but the time and resource consumption increases
Solution Approach 1:
The method extracts and focuses only on the specific segregating markers relevant to each patient's tumor or fetal DNA from the entire genome. By taking out only these critical markers for probe targeting, the assay achieves high mutation detection accuracy while avoiding the time and resource expenditure of analyzing the complete genome, thus reducing detection time compared to comprehensive targeted sequencing
3Reliability
If personalized probe combinations are used, then the detection specificity increases, but the assay complexity and cost increase
Solution Approach 1:
The assay employs probes with locally optimized properties tailored to each patient's specific genetic profile. Each probe is designed with sequence specificity targeting patient-specific segregating markers, ensuring high detection reliability for their unique tumor or fetal DNA. This local quality approach concentrates resources on critical detection points rather than uniformly complexifying the entire assay
Solution Approach 2:
The method uses probe sequences that are complementary copies of the patient-specific segregating markers. These probe copies enable highly specific hybridization detection of the corresponding tumor or fetal DNA sequences in cell-free DNA, achieving high detection reliability through sequence matching without requiring complex detection instrumentation or procedures
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
Enhances the detection of disease-associated mutations and tumor fraction, providing accurate and non-invasive diagnosis of cancer and fetal genetic disorders, enabling early intervention and risk assessment.
Implementation Method 1
contacting said unique combination of probes to a nucleic acid sample
Implementation Method 2
probes are designed to detect either (i) a marker of interest or (ii) a segregating sequence at a marker of interest
Data Source
AI summary
The present disclosure relates to a laboratory execution system that provides for automation of laboratory processes. A centralized data management system may be dynamically updated and used to facilitate management of components of the laboratory execution system, such as an automation system and an analytics results management system that may facilitate complex analytical functions, such as synthesizing raw test data. Potential workflows include the detection of specific molecules of interest.