cfDNA Microsatellite Instability Detection Using Site-Specific Thresholds
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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 effectiveness of using MSI as a biomarker for cancer prognosis and treatment.
Innovation Solution
A method involving quantifying repeat lengths at microsatellite loci, generating site scores, and classifying MSI status using trained thresholds, with probabilistic log likelihood-based scores to discriminate biological signals from noise, enabling accurate MSI detection and guiding treatment decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If PCR-based MSI assessment methods are used, then measurement precision is improved, but device complexity and ease of operation are worsened due to requiring complex laboratory procedures
Solution Approach 1:
The patent replaces complex mechanical laboratory procedures (PCR-based assessment requiring specialized equipment and facilities) with a computational approach using probablistic log likelihood-based scores that can be performed through bioinformatics analysis of sequencing data, thereby maintaining measurement precision while reducing device and procedural complexity
Solution Approach 2:
The patent introduces probabilistic log likelihood-based scores as an intermediary computational tool that bridges the gap between raw sequencing data and MSI status determination, enabling accurate MSI detection through statistical modeling without requiring direct application of complex PCR-based methods
2Measurement precision
If probabilistic log likelihood-based scores are used, then measurement precision is improved, but loss of information is reduced by discriminating biological signals from noise
Solution Approach 1:
The patent employs probabilistic log likelihood-based scores that incorporate feedback mechanisms through statistical modeling, where the scoring system continuously evaluates and adjusts the discrimination between biological signals and noise based on observed data patterns, thereby improving measurement precision while preserving information integrity
Solution Approach 2:
The patent changes the parameter of information representation by transforming raw sequencing data into probabilistic log likelihood-based scores, which optimally encode the signal-to-noise relationship and enable precise MSI detection by capturing the statistical properties of the data without information loss
3Ease of operation
If cfDNA-based MSI detection is developed, then ease of operation is improved, but measurement precision is worsened due to variable tumor shedding
Solution Approach 1:
The patent applies preliminary action by using probabilistic log likelihood-based scores that are pre-calibrated and trained to account for variable tumor shedding effects, allowing the method to compensate for these variations before they impact measurement precision, thus maintaining both ease of operation and accuracy
Solution Approach 2:
The patent addresses the precision issue by changing the analytical parameter from direct MSI status determination to probabilistic log likelihood-based scoring, which transforms the data in a way that makes the results robust to variations in tumor shedding, thereby preserving measurement precision while maintaining the ease of cfDNA-based operation
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.