cfDNA Deviation Scoring for Tumor Progression Detection
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Solution Overview
Problem
Existing methods for assessing tumor progression from cell-free DNA (cfDNA) in bodily fluids are insensitive and noisy due to overwhelming signals from non-tumor DNA, making it challenging to detect and monitor tumor progression accurately.
Innovation Solution
A method involving measuring cfDNA counts at multiple genomic regions, normalizing these counts against reference values, calculating deviation scores, and applying a logarithmic transformation to generate a Change in Deviation (CID) score, which is used to detect tumor progression when it satisfies a predetermined criterion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If cell-free DNA analysis is performed using conventional methods, then the detection can be conducted with standard techniques, but the sensitivity and specificity are low due to overwhelming signals from non-tumor DNA
Solution Approach 1:
The patent extracts and isolates tumor-derived cfDNA fragments from the mixture of cfDNA in bodily fluids by analyzing fragmentation patterns. Tumor cfDNA has distinct fragmentation characteristics compared to non-tumor cfDNA, allowing selective identification and quantification of tumor signal while excluding the overwhelming non-tumor background noise.
Solution Approach 2:
The patent applies local quality analysis by examining specific fragmentation patterns and size distributions of cfDNA fragments at different genomic regions. By analyzing the local characteristics of DNA fragment lengths and breakpoints, the method identifies tumor-specific signatures that differ from the uniform pattern of non-tumor cfDNA, enabling precise tumor detection.
2Reliability
If cfDNA counts are measured at multiple genomic regions, then more comprehensive tumor coverage is achieved, but the complexity of data processing increases
Solution Approach 1:
The patent segments the genome into multiple predefined regions of interest and measures cfDNA fragment counts independently in each region. By dividing the complex genomic data into manageable segments and analyzing fragmentation patterns in each, the method achieves comprehensive tumor coverage while simplifying the overall data processing through structured regional analysis.
Solution Approach 2:
The patent transforms the raw cfDNA count data by applying mathematical transformations including logarithmic scaling and deviation calculations. These parameter changes convert the complex multi-regional data into a simplified CID score that reflects tumor progression status, reducing data complexity while preserving diagnostic reliability.
3Measurement precision
If deviation scores are calculated and logarithmic transformation is applied to generate CID scores, then tumor progression detection sensitivity is enhanced, but the computational requirements increase
Solution Approach 1:
The patent performs preliminary normalization of cfDNA counts against reference values and calculates deviation scores before the final CID score computation. These preliminary processing steps standardize the data and reduce variability, enabling more sensitive tumor detection while reducing the computational burden of the subsequent logarithmic transformation and aggregation steps.
Data Source
AI summary
The present disclosure provides methods of assessing tumor progression in a subject. In an aspect, a method for assessing tumor progression of a subject can comprise: measuring a count of a plurality of cell-free DNA (cfDNA) molecules at each of a plurality of genomic regions, wherein the plurality of cfDNA molecules is obtained or derived from a bodily fluid sample of the subject; processing the counts measured at each of the genomic regions to obtain quantitative measures of deviation of the counts relative to a plurality of reference values, to produce deviation scores; determining a difference between the deviation scores and a plurality of reference deviation scores to produce changes in deviation (CID) values, and calculating a CID score based on the CID values; and detecting a tumor progression of the subject when the CID score satisfies a pre-determined criterion.