Integrated DNA Sequencing Workflow for Somatic and Methylation Signals
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Solution Overview
Problem
Current methods for analyzing cell-free DNA in liquid biopsies are inadequate for accurately detecting cancer due to low concentration and heterogeneity, focusing primarily on sequence modifications rather than non-sequence modifications like methylation, which can provide crucial disease information.
Innovation Solution
An integrated library preparation method that involves dividing DNA into subsamples, enriching specific target regions, and combining them for sequencing, allowing for detailed analysis of both sequence-variable and epigenetic signatures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current methods focus on sequence modifications in cell-free DNA, then sequencing can be performed, but sensitivity and accuracy for cancer detection remain inadequate due to low concentration and heterogeneity
Solution Approach 1:
The DNA sample is divided into multiple subsamples, with at least one subsample enriched for specific target regions (e.g., CpG islands, promoter regions) and at least one subsample representing the remaining DNA. This segmentation allows targeted analysis of cancer-relevant regions while maintaining representation of the overall DNA landscape, thereby improving detection accuracy despite low total DNA concentration.
Solution Approach 2:
Different subsamples are prepared with different enrichment characteristics - some subsamples are enriched for specific target regions while others are not. This local quality approach allows the analysis to focus on cancer-relevant regions (improving sensitivity) while also considering the broader genomic context, resolving the contradiction between detecting rare cancer signals and accounting for tumor heterogeneity.
2Loss of information
If DNA from cells in or around cancer is analyzed, then epigenetic information can be obtained, but the contribution of cancer-related DNA to the sample is relatively small compared to other cells
Solution Approach 1:
The method extracts and enriches specific epigenetically relevant target regions (such as CpG islands and promoter regions) from the cell-free DNA subsamples. By taking out these specific regions that are known to contain cancer-related epigenetic signatures, the analysis can detect cancer information even when the overall contribution of cancer cell DNA to the sample is small.
Solution Approach 2:
The DNA is divided into subsamples with enrichment for target regions before sequencing. This preliminary action of enriching cancer-relevant regions beforehand ensures that when sequencing is performed, the epigenetic information from cancer cells is amplified and made detectable, compensating for their small overall contribution to the liquid biopsy sample.
3Adaptability or versatility
If the DNA sample is divided into subsamples with different enrichment levels, then targeted and whole genome analysis can be performed, but the workflow complexity increases
Solution Approach 1:
The library preparation method is designed to be universally applicable to different analysis goals. The same workflow can generate subsamples suitable for targeted sequencing, whole genome sequencing, or a combination thereof. This multi-functionality allows a single method to provide both targeted and whole genome analysis flexibility without requiring completely separate workflows, thereby managing complexity while maintaining adaptability.
Data Source
AI summary
Provided herein are methods of analyzing DNA comprising dividing a DNA sample into a plurality of subsamples, treating at least one of the plurality of subsamples, combining at least a portion of the DNA of at least two of the subsamples, and sequencing the combined subsample. Some such methods facilitate detection of both epigenetic and genetic characteristics of the DNA within a combined workflow.