cfDNA Microsatellite Instability Scoring from Repeat Length Profiles
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Solution Overview
Problem
Current methods for detecting microsatellite instability (MSI) in cell-free DNA (cfDNA) samples are underdeveloped, and the impact of variable tumor shedding on MSI detection has not been adequately evaluated, limiting the ability to assess MSI status and guide disease prognosis and treatment choices.
Innovation Solution
A method involving quantifying repeat lengths at microsatellite loci, generating site scores, and classifying MSI status based on trained thresholds, using probabilistic log likelihood-based scores to discriminate biological signals from noise, and identifying customized therapies based on MSI status.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If plasma-based next generation DNA sequencing (NGS) tests are used for comprehensive genomic profiling of cancer, then the ability to assess multiple genomic markers is improved, but the methods to detect MSI status from cell-free DNA (cfDNA) data remain underdeveloped
Solution Approach 1:
The patent segments the MSI detection process into distinct computational steps: (1) identifying microsatellite loci in cfDNA sequences, (2) quantifying repeat lengths at each locus, (3) calculating instability scores based on observed versus expected repeat patterns, and (4) classifying MSI status based on threshold criteria. This segmentation allows each step to be optimized independently, improving overall detection reliability while maintaining versatility across different cancer types
Solution Approach 2:
The patent performs preliminary computational actions on cfDNA data before final MSI classification, including: (1) pre-processing raw sequencing reads to identify microsatellite-containing fragments, (2) establishing expected repeat length distributions from reference genomes, and (3) setting locus-specific instability thresholds based on training data. These preliminary actions prepare the data structure needed for accurate MSI detection without requiring additional wet-lab procedures
2Measurement precision
If conventional polymerase chain reaction (PCR)-based MSI assessment approaches are used, then the gold standard for MSI detection is maintained, but the ability to analyze cell-free DNA samples is limited
Solution Approach 1:
The patent replaces the mechanical PCR amplification system with a computational analysis system that processes raw cfDNA sequencing data directly. Instead of using polymerase enzymes to amplify microsatellite regions, the invention uses bioinformatics algorithms to: (1) map sequencing reads to microsatellite loci, (2) measure repeat lengths from read lengths and alignment positions, and (3) calculate instability metrics. This substitution enables MSI analysis from cfDNA without requiring sufficient DNA for PCR amplification
Solution Approach 2:
The patent introduces computational intermediaries between the raw cfDNA sequencing data and the final MSI classification. These intermediaries include: (1) repeat length quantification algorithms that translate sequencing read data into microsatellite expansion measurements, (2) instability scoring systems that compare observed repeat patterns against expected distributions, and (3) classification thresholds that convert continuous instability scores into discrete MSI-High/MSI-Low categories. These computational intermediaries adapt the PCR-based MSI assessment methodology to work with cfDNA sequencing data
3Ease of operation
If methods are developed to detect MSI status from cell-free DNA data, then the ability to guide treatment decisions is improved, but the impact of variable tumor shedding on detection accuracy has not been evaluated
Solution Approach 1:
The patent implements feedback mechanisms to account for variable tumor shedding effects: (1) The instability scoring system incorporates feedback from the overall distribution of repeat length measurements across multiple microsatellite loci, allowing the system to distinguish between true MSI signals and artifacts from low tumor fraction or shedding variability. (2) The classification thresholds are determined through training on datasets with known MSI status, creating a feedback loop where historical data improves future detection accuracy. (3) The system provides feedback on the quality and quantity of cfDNA input required for reliable MSI detection, guiding sample collection and processing protocols
Data Source
AI summary
Provided herein are methods for determining the microsatellite instability status of samples. In one aspect, the methods include quantifying a number of different repeat lengths present at each of a plurality of microsatellite loci from sequence information to generate a site score for each of the plurality of the microsatellite loci. The methods also include comparing the site score of a given microsatellite locus to a site specific trained threshold for the given microsatellite locus for each of the plurality of the microsatellite loci and calling the given microsatellite locus as being unstable when the site score of the given microsatellite locus exceeds the site specific trained threshold for the given microsatellite locus to generate a microsatellite instability score, which includes a number of unstable microsatellite loci from the plurality of the microsatellite loci.