cfDNA Endpoint Analysis for Early Cancer Detection
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Solution Overview
Problem
Current methods for analyzing cell-free DNA (cfDNA) fragment endpoints are inadequate for early and accurate detection, diagnosis, and monitoring of cancer, as they lack sensitivity and specificity in identifying predictive genomic locations.
Innovation Solution
An analytics system processes cfDNA fragments from cancer and non-cancer samples to create position vectors, identifying statistically significant endpoints, which are used to train a cancer classifier to predict cancer presence and type based on endpoint counts at specific genomic locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional cfDNA analysis methods are used, then the analysis process is simple, but the detection accuracy and ability to identify predictive genomic locations is insufficient
Solution Approach 1:
The patent segments the genomic landscape into specific regions of interest (ROIs) based on endpoint density patterns. Instead of analyzing the entire genome uniformly, the method divides it into manageable segments (ROIs) that are enriched for cancer-specific endpoint patterns, thereby improving detection accuracy while reducing the complexity of analysis to focused regions.
Solution Approach 2:
The patent introduces a new dimension of analysis by examining endpoint density patterns across multiple genomic regions simultaneously. It creates a multi-dimensional feature space characterized by endpoint counts, densities, and distributions across different ROIs, transforming the one-dimensional endpoint position data into a comprehensive multi-dimensional profile for improved cancer detection.
2Reliability
If comprehensive genomic analysis is performed to improve cancer detection accuracy, then the detection precision improves, but the computational resources and time required increase
Solution Approach 1:
The patent performs preliminary action by pre-identifying and storing Regions of Interest (ROIs) based on endpoint density patterns from training data before actual cancer detection. These ROIs are pre-characterized and stored as reference profiles, allowing the analysis system to quickly compare new samples against established patterns without performing comprehensive genome-wide analysis, thereby reducing analysis time while maintaining high reliability.
Solution Approach 2:
The patent applies local quality by focusing analysis resources on specific genomic regions (ROIs) that exhibit cancer-specific endpoint patterns rather than uniformly analyzing the entire genome. Each ROI is analyzed with appropriate depth and specificity, allocating computational resources efficiently to regions most likely to provide diagnostic information.
3Measurement precision
If more cfDNA fragments are analyzed to improve statistical significance, then the measurement precision improves, but the quantity of data processing increases
Solution Approach 1:
The patent extracts only the most relevant information from cfDNA fragment data by focusing on endpoint positions within pre-defined Regions of Interest. Instead of processing and storing all raw fragment data, it extracts and analyzes only the endpoint count information within ROIs, significantly reducing data volume while maintaining measurement precision through targeted analysis of biologically relevant regions.
Data Source
AI summary
A system and method for determining a presence of cancer in a test sample from a test subject comprising a set of fragments of deoxyribonucleic acid (DNA) is described. Locations along a genome of the test subject that are predictively significant in cancer detection may be identified through probabilistic analyses based on a comparison of the count of non-cancer fragments expected to terminate at a location and a count of fragments observed to terminate at the location. Based on the comparison, a p-value for each location is determined and is compared to a p-value threshold to determine predictively significant genomic locations, and a classifier is trained based on these locations. The system inputs a test feature vector containing counts of endpoint fragments from a test sample to the classifier, which generates a cancer prediction describing a likelihood the test sample has cancer and/or is of a particular cancer type.