Abstract B053: Comprehensive, high-sensitivity, high-resolution somatic variant detection in clinical samples with linked-reads

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, Jon Torchia, Daniel Hwang, J. Zachary Sanborn, Lisa Munding. Comprehensive, high-sensitivity, high-resolution somatic variant detection in clinical samples with linked-reads [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Discovery and Innovation in Pediatric Cancer— From Biology to Breakthrough Therapies; 2025 Sep 25-28; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2025;85(18_Suppl_2):Abstract nr B053.