Abstract 5024: Analytical performance of a genome-wide methylome enrichment platform to detect minimal residual disease from plasma-derived cell-free DNA

Abstract Introduction: Plasma-derived cell-free DNA (cfDNA) can be used to identify cancer signals, including minimal residual disease (MRD), in patients who have undergone curative cancer treatments. The cell-free methylated DNA immunoprecipitation and high-throughput sequencing (cfMeDIP-seq) methodology is combined with custom algorithms that leverage differentially methylated regions (DMRs) found in cfDNA to distinguish between cancer and non-cancer signals. This novel non-degradative, tissue-agnostic approach was developed to bypass the limitations of bisulfite-sequencing and tissue-informed methods used in other liquid biopsy technologies. Here we present preliminary analytical performance metrics of an algorithm in development for detecting MRD. Methods: In the genome-wide methylome enrichment platform, plasma-derived cfDNA is subjected to standard library preparation, combined with DNA filler, denatured, and immunoprecipitated using an anti-5-mC antibody. Captured DNA is amplified and sequenced. For this study, a candidate algorithm comprised of differentially methylated regions (DMRs) and 12 cancer types was used to quantify cancer-specific methylation. Control samples from 12 non-cancer donors were used to establish a selected 95% true negative rate threshold. This threshold was used to determine assay sensitivity, which was evaluated using contrived cancer samples intended to mimic low-level circulating tumor DNA (ctDNA) representative of MRD. Samples were contrived by titrating fragmented DNA from three immortalized tumor-derived cell-lines into plasma-derived cfDNA in a titration series targeting 1% ctDNA levels. Cell-lines were of non-small cell lung cancer and head and neck squamous cell carcinoma origin; titration series were replicated for a total of 65 tests. Results: All non-cancer and contrived cancer samples met in-process quality control metrics. This included ≥98. 5% methylation specificity and ≥80 million unique molecules. Limit of detection calculations at 95% sensitivity (LoD95) were 0. 1%. Conclusions: Tissue-agnostic approaches for detecting cancer signals from plasma have significant benefit, especially in settings where tissue is not accessible for evaluation. However, these tests must have highly sensitive methods of cancer signal detection for clinical applications. Our preliminary analytical data demonstrates the use of a blood-based, tissue- agnostic genome-wide methylome enrichment platform utilizing non-degradative methodology combined with specific algorithms and DMRs for MRD quantification and prognostic prediction, using treatment-naïve plasma samples. Future studies in post-treatment and longitudinal samples are ongoing to evaluate the utility of this genome-wide methylome enrichment platform for cancer management. Citation Format: Shu Yi Shen, Iulia Cirlan, Felicia Vincelli, Ben Brown, Jun Min, Justin Burgener, Junjun Zhang, Yulia Newton, Margaret Gruca, Abel Licon, Jing Zhang, Anne-Renee Hartman, Alan Williams, Hestia Mellert, Daniel D. De Carvalho. Analytical performance of a genome-wide methylome enrichment platform to detect minimal residual disease from plasma-derived cell-free DNA abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts) ; 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84 (6Suppl): Abstract nr 5024.