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Identification of Somatic Structural Variants in Solid Tumors by Optical Genome Mapping
David Y Goldrich1, Brandon LaBarge1, Scott Chartrand2
1Department of Otolaryngology-Head and Neck Surgery, Pennsylvania State University College of Medicine, Hershey, PA 17033, USA.
Journal of Personalized Medicine
|March 6, 2021
Summary
Optical genome mapping (OGM) effectively identifies cancer-driving structural variants (SVs) in solid tumors. A novel protocol isolates high-molecular-weight DNA from small tissue samples, enabling comprehensive SV detection for cancer research.
Area of Science:
- Genomics
- Cancer Biology
- Molecular Diagnostics
Background:
- Somatic structural variants (SVs) are key drivers of cancer, but standard sequencing methods struggle to detect them.
- Optical genome mapping (OGM) excels at identifying large and complex SVs but requires ultra-high-molecular-weight DNA.
Purpose of the Study:
- To establish and validate a robust protocol for extracting high-molecular-weight DNA from solid tumors for OGM.
- To assess the utility of OGM in detecting clinically relevant somatic SVs across diverse solid tumor types.
Main Methods:
- Development and application of a paramagnetic nanobind disc-based protocol for DNA extraction from solid tumor tissues (as little as 6.5 mg).
- Performance of Optical Genome Mapping (OGM) on extracted DNA to generate genome maps.
- Identification and analysis of somatic SVs, with filtering against the GRCh38 human reference genome.
Main Results:
- Successful extraction of high-molecular-weight DNA from various solid tumors, yielding high map rates and effective OGM coverage.
- Identification of somatic SVs impacting cancer-related genes in all tested samples.
- Demonstrated high positive and negative predictive values for somatic SV detection using GRCh38 filtering, alongside evidence of intra-sample heterogeneity.
Conclusions:
- The developed DNA extraction protocol is effective for OGM across diverse solid tumors.
- OGM combined with SV analysis provides comprehensive genome-wide detection of functionally important SVs in cancer.
- This approach holds significant potential for cancer prognosis and treatment strategy development.

