Abstract 6626: Comprehensive, high sensitivity, high resolution, somatic variant detection in solid tumors with LinkPrep - an NGS assay

Abstract The characterization of somatic variants, including structural variants (SVs), copy number variations (CNVs), and single-nucleotide variants (SNVs), is essential for understanding tumor biology. Current methodologies for somatic variant profiling are fragmented across several assays and requires ample sample material. We present LinkPrep™ assay, a novel linked-read NGS assay utilizing DNA proximity ligation, optimized for high sensitivity and high-resolution whole genome somatic variant detection. LinkPrep data enables unified profiling of SVs, CNVs, and SNVs/InDels from the same dataset, providing a streamlined approach to comprehensive genomic analysis. We applied the LinkPrep assay to a benchmark cohort consisting of cell lines (ductal carcinoma, HCC1187; myelogenous leukemia, K562) and clinically relevant tumor samples (ovarian serous adenocarcinoma, metastatic ovarian carcinoma, and nasal osteosarcoma). LinkPrep achieved high concordance with whole-genome sequencing (WGS) for CNVs (correlations >89% across all samples) while also detecting SVs with superior mean read support and robust F1 scores (cell lines). Moreover, LinkPrep demonstrated excellent overlap with WGS for SNVs and InDels, exceeding 92% across all samples, and identified critical oncogenic candidates. Notably, LinkPrep uncovered additional high-confidence SVs (including low SV VAF down to 1%) in clinical samples that were missed by WGS, highlighting its enhanced sensitivity for somatic variant detection in low tumor fraction samples. Furthermore, we demonstrate the ability to use LinkPrep data to refine SV breakpoints down to base pair resolution. The data underscore the utility of the LinkPrep assay in translational research, where precise and comprehensive variant detection is imperative. This study establishes the LinkPrep assay as a next-generation tool for the detection of somatic variants, offering a robust and scalable solution for cancer genomics. Citation Format: Cory C. Padilla, Ericca Stamper, Jonathan Torchia, Daniel Hwang, Mark Wadolkowski, Ek Danwanichakul, J. Zachary Sanborn, Lisa Munding. Comprehensive, high sensitivity, high resolution, somatic variant detection in solid tumors with LinkPrep - an NGS assay [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 6626.

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Journal
Cancer Research
Published
2025-04-21
DOI
https://doi.org/10.1158/1538-7445.am2025-6626
Primary Topic
Radiomics and Machine Learning in Medical Imaging
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article
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article

Abstract 6626: Comprehensive, high sensitivity, high resolution, somatic variant detection in solid tumors with LinkPrep - an NGS assay

J. Zachary Sanborn, Cory C. Padilla, Ericca Stamper, Daniel Hwang et al.
1 links
Cancer Research
Radiomics and Machine Learning in Medical Imaging
article

Abstract 6626: Comprehensive, high sensitivity, high resolution, somatic variant detection in solid tumors with LinkPrep - an NGS assay

J. Zachary Sanborn, Cory C. Padilla, Ericca Stamper, Daniel Hwang, Jonathan Torchia, Mark Wadolkowski, Lisa Munding, Ek Danwanichakul
article en

Abstract

Abstract The characterization of somatic variants, including structural variants (SVs), copy number variations (CNVs), and single-nucleotide variants (SNVs), is essential for understanding tumor biology. Current methodologies for somatic variant profiling are fragmented across several assays and requires ample sample material. We present LinkPrep™ assay, a novel linked-read NGS assay utilizing DNA proximity ligation, optimized for high sensitivity and high-resolution whole genome somatic variant detection. LinkPrep data enables unified profiling of SVs, CNVs, and SNVs/InDels from the same dataset, providing a streamlined approach to comprehensive genomic analysis. We applied the LinkPrep assay to a benchmark cohort consisting of cell lines (ductal carcinoma, HCC1187; myelogenous leukemia, K562) and clinically relevant tumor samples (ovarian serous adenocarcinoma, metastatic ovarian carcinoma, and nasal osteosarcoma). LinkPrep achieved high concordance with whole-genome sequencing (WGS) for CNVs (correlations >89% across all samples) while also detecting SVs with superior mean read support and robust F1 scores (cell lines). Moreover, LinkPrep demonstrated excellent overlap with WGS for SNVs and InDels, exceeding 92% across all samples, and identified critical oncogenic candidates. Notably, LinkPrep uncovered additional high-confidence SVs (including low SV VAF down to 1%) in clinical samples that were missed by WGS, highlighting its enhanced sensitivity for somatic variant detection in low tumor fraction samples. Furthermore, we demonstrate the ability to use LinkPrep data to refine SV breakpoints down to base pair resolution. The data underscore the utility of the LinkPrep assay in translational research, where precise and comprehensive variant detection is imperative. This study establishes the LinkPrep assay as a next-generation tool for the detection of somatic variants, offering a robust and scalable solution for cancer genomics. Citation Format: Cory C. Padilla, Ericca Stamper, Jonathan Torchia, Daniel Hwang, Mark Wadolkowski, Ek Danwanichakul, J. Zachary Sanborn, Lisa Munding. Comprehensive, high sensitivity, high resolution, somatic variant detection in solid tumors with LinkPrep - an NGS assay [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 6626.

Cancer ResearchVol. 85(8_Supplement_1)
Dovetail Genomics (United States) (US)
Openalex Percentile: Top 87%
Radiomics and Machine Learning in Medical Imaging
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