Abstract 7138: Whole genome sequencing-based detection of minimal residual disease using individual-specific features

Abstract The detection of minimal residual disease (MRD) following cancer treatment is critical for preventing recurrence and improving patient prognosis. However, existing MRD detection methods often lack the sensitivity required for precise monitoring. We have developed a highly sensitive method for MRD detection using whole-genome sequencing (WGS) analysis of cell-free DNA (cfDNA) with individual-specific features. The approach begins by identifying consensus single nucleotide variants (SNVs) and fusions that are consistently detected by multiple variant callers in the patient’s tumor WGS. Using a novel algorithm, we quantify supporting reads at the consensus SNV positions and assess their statistical significance based on the error distribution in the patient’s plasma WGS. Additionally, unique sequences corresponding to the consensus fusion positions in the plasma WGS are detected to diagnose MRD. Validation through cancer cell-line mix experiments demonstrated a sensitivity of less than 1:12, 800. Furthermore, the method effectively detected MRD in clinical samples from various cancer types, including lung, melanoma, breast, and liver. The limit of detection for the SNV quantification method was determined by the error rate in the individual BAM file, while the fusion sequence detection method exhibited no detection limit due to a limit of blank of zero. This algorithm has been implemented on a cloud platform, allowing individual users to analyze their data by uploading it. This highly sensitive approach for detecting MRD offers the potential to improve cancer patient prognosis significantly. Moreover, this technology holds great promise for broad applicability across various cancer types where cfDNA-based monitoring can provide clinical benefits. Citation Format: Jun Hyeok Lim, Jeong-Seon Ryu, Ji Won Byun, Jongsuk Chung, Hyun-Tae Shin. Whole genome sequencing-based detection of minimal residual disease using individual-specific features [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 7138.