De Novo Cell-Free DNA Fragmentation Mapping for Early Cancer Detection
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Solution Overview
Problem
Existing methods for analyzing cell-free DNA fragmentation patterns in cancer diagnostics are limited in characterizing genome-wide fragmentation aberrations, particularly at fine-scale gene-regulatory elements, and fail to identify unbiased regions of interest, which hinders the detection of early-stage cancers.
Innovation Solution
A computational approach, CRAG, is used to de novo characterize cell-free DNA fragmentation hotspots by integrating fragment size and coverage into a score, identifying regions with lower fragment coverage and smaller fragment size, and utilizing these hotspots for early-stage cancer detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing methods analyze cfDNA fragmentation patterns at selected known regulatory elements (TSS, TFBS, OCF), then the analysis is focused on specific regions, but it limits the opportunity to unbiasedly characterize genome-wide fragmentation aberrations at other regulatory regions
Solution Approach 1:
The genome is segmented into multiple regulatory regions including promoters, enhancers, insulators, and eukaryotic start sites. The method analyzes fragmentation patterns at each segment independently using de novo clustering, allowing comprehensive genome-wide characterization while maintaining focus on specific functional elements. This segmentation enables unbiased exploration of fragmentation aberrations across all regulatory regions without being limited to pre-selected regions.
2Ease of operation
If existing methods use single summary statistic scores (MDS) for each patient, then the analysis is simplified, but it does not allow further explorations of association with specific gene-regulatory elements
Solution Approach 1:
Instead of using a single summary statistic, the method segments the analysis into region-specific fragmentation patterns at promoters, enhancers, insulators, and eukaryotic start sites. Each region is analyzed independently through de novo clustering, preserving detailed information about fragmentation aberrations at specific gene-regulatory elements while maintaining the overall simplified workflow of processing cfDNA sequencing data.
3Area of stationary object
If existing methods use large-scale fragmentation patterns at mega-base level (DELFI), then the analysis covers broad regions, but it is challenging to associate with fine-scale gene-regulatory elements and druggable targets
Solution Approach 1:
The method segments the genome into functional regulatory regions at the fine-scale level, including promoters, enhancers, insulators, and eukaryotic start sites. This segmentation enables precise association of fragmentation patterns with specific gene-regulatory elements and potential druggable targets, while still maintaining comprehensive coverage across the entire genome. The de novo clustering approach processes data at this fine resolution level, resolving the contradiction between broad coverage and fine-scale precision.
Data Source
AI summary
A system and method for identifying genomic regions with higher fragmentation rates than the local and global backgrounds as part of diagnosing early stage cancer is provided. The method includes steps of: de-novo characterizing genome-wide cell-free DNA fragmentation regions with higher fragmentation rates than the local and global backgrounds from whole-genome sequencing by weighing the fragment coverages in each region by a ratio of average fragment sizes in the region versus that in the whole chromosome to generate a score; and identifying DNA fragmentation regions of interest based upon comparing the score with a threshold. The system and method can utilize identified DNA fragmentation hotspots for the detection and localization of multiple early-stage cancers (or certain other non-malignant disease).