Disease monitoring is important to detect cancer relapse, often as a result of a treatment-surviving cancer cell population. Detection of relapse is critical to determine the intensity and length of treatments and to predict the outcome of those treatments. Currently, karyotyping, fluorescent in situ hybridization (FISH), flow cytometry, PCR, and next-generation sequencing (NGS) are often used for this application. PCR is sensitive and highly effective, but as a targeted approach, it requires the identification of an appropriate biomarker. Karyotyping and FISH are used to follow structural variants, especially those detected at diagnosis and known to be drivers of carcinogenesis; however, karyotype has low sensitivity (~5-10% variant allele frequency (VAF)) and FISH is only targeted.We describe a novel workflow to find structural variants (SVs) using optical genome mapping (OGM) and to use those SVs as biomarkers for disease monitoring assessment. The initial SV profile from a cancer sample can be obtained by running the standard somatic workflow for OGM: 1.5 Tbp of high molecular genomic DNA is collected (without any amplification/selection), and molecules that can capture SV breakpoints are locally assembled into genome maps. These locally assembled genome maps are used to make high-confidence SV calls at least 5 kbp in size. Then, presumptive somatic variants are revealed by further comparison against an OGM control sample SV database (included in Bionano Access/Solve software). Subsequently, to detect residual cancer cells post-treatment, an ultra-deep coverage of approximately 5 Tbp of DNA can be collected, and these molecules are directly aligned to the genome maps in the initial cancer sample. This direct alignment approach is fast and sensitive in identifying remnants of pretreatment originator cells. We are currently applying this workflow to recover known leukemia-associated inter-chromosomal translocations, and our preliminary results indicate a sensitivity of detecting calls at a minimum of 0.5% VAF. Continual investigation on limit of detection is ongoing, but with the improvement on throughput and algorithms, we envision that this workflow to be essential in discovering low level clonal cancerous DNA.

Citation Format: Alex Chitsazan, Andy Pang, Alex Hastie. Applying optical genome mapping to detect genomic biomarkers and use for residual disease monitoring in hematologic malignancies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2224.