Microsatellite Instability Detection in Cell-Free DNA
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Solution Overview
Problem
Current methods for detecting microsatellite instability (MSI) in circulating tumor DNA (ctDNA) are unreliable due to its low presence relative to other molecules in cell-free DNA (cfDNA), making it challenging to accurately diagnose cancer using cfDNA samples.
Innovation Solution
A processing system separates cfDNA and genomic DNA, generates sequence reads, and calculates viability, significance, entropy, and divergence scores for markers to determine MSI likelihood, filtering and analyzing these scores to assess the presence of MSI in cancer samples.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing MSI detection methods are applied to cfDNA samples, then cancer diagnosis can be performed, but the detection accuracy is poor due to low ctDNA presence
Solution Approach 1:
The method segments the cfDNA sample into multiple components: ctDNA (circulating tumor DNA), cfDNA (cell-free DNA), and gDNA (genomic DNA). By separating and analyzing these components independently, the method can specifically target ctDNA markers while filtering out background noise from other DNA sources, thereby improving MSI detection accuracy despite low ctDNA concentration
Solution Approach 2:
The method changes multiple parameters to optimize detection: it adjusts marker selection criteria (choosing specific microsatellite loci), modifies scoring parameters (viability, significance, entropy, and divergence scores), and transforms the detection approach from direct MSI detection to a multi-parameter statistical analysis. These parameter changes enable accurate MSI detection even when ctDNA concentration is very low
2Measurement precision
If multiple scoring parameters are calculated for each marker, then marker significance can be determined, but the computational complexity increases
Solution Approach 1:
The method performs preliminary actions by pre-calculating and storing reference data for control reads (from normal tissue or non-tumor cells) before analyzing the test sample. This preliminary characterization of normal microsatellite patterns establishes baseline expectations, allowing the subsequent test analysis to focus on detecting deviations from these pre-established norms, thereby reducing computational complexity during the actual diagnostic process
Solution Approach 2:
The method introduces intermediary scoring parameters (viability score, significance score, entropy score, divergence score) that act as mediators between the raw sequence data and the final MSI determination. These intermediary scores break down the complex analysis into manageable computational steps, each evaluating a specific aspect of marker behavior, making the overall process more tractable while maintaining precision
3Measurement precision
If cfDNA and gDNA are separated and analyzed independently, then MSI detection accuracy improves, but the processing time and complexity increase
Solution Approach 1:
The method merges the analysis of cfDNA and gDNA by using gDNA as a reference framework for interpreting cfDNA results. Instead of treating them as completely separate analyses, the gDNA data informs the expected microsatellite patterns in cfDNA, allowing parallel processing where both DNA types contribute to a unified MSI determination, thereby reducing total processing time while maintaining accuracy
Data Source
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AI summary
For some cancers, microsatellite instability (MSI) in cell-free DNA can indicate the presence of a cancer in a subject. Subjects can generate a DNA sample for analysis to determine a likelihood that MSI exists and, thereby, determine a likelihood that the sample includes cancer. A system determines a likelihood that the sample includes MSI by selecting a set of markers from the sample and determining if those markers include MSI associated with cancer. The system determines if a marker is significant in by calculating: a viability score, a significance score, an entropy score, and a divergence score. The processing system determines an instability score representing a likelihood that the sample includes MSI based on the determined marker significances. Based on the instability score, the processing system can determine that a sample includes MSI and inform a method of treatment for the subject.