Abstract 583: Analysis of solid pediatric tumor methylomes reveals a shared cancer recurrence signature in cell-free DNA

Abstract

Abstract Cancer is the second most common cause of death in children aged 1-14 years in the United States, with approximately 11,000 new cases and 1200 deaths annually. Due in part to the rarity and diversity of childhood tumors, a non-invasive diagnostic to identify patients at high risk of recurrence does not exist for patients with pediatric solid cancer. Cell-free (cf)DNA is rapidly becoming the standard for non-invasive screening, diagnosis, treatment and monitoring of human tumors. In particular, cfDNA methylation detection has proven to be a robust platform for “liquid biopsies,” with several important advantages over simple somatic variant detection. Here we present a cfDNA methylation signature derived from a pan-methylome analysis of recurrent pediatric tumors. The methylomes spanned 34 tumor tissue samples, 13 patient-matched adjacent normal, and 16 patient-matched plasma samples from 27 individuals, representing 11 different pediatric tumor types, including 10 neuroblastomas, 4 osteosarcomas and 2 hepatoblastomas. In addition, 13 adjacent normal samples and 15 normal plasma samples were used as DNA methylation controls. These samples were obtained from the Pediatric Oncology Experimental Investigators' Consortium (POETIC) under informed consent. DNA methylation was analyzed for all samples by whole genome bisulfite sequencing (WGBS). Using consensus DNA methylation calling, we identified a set of 402 differentially methylated regions (DMR) in genomic DNA from tissue, with substantial enrichment of hypomethylated DMRs across a majority of samples. CpGs that were commonly hypomethylated were combined into consensus ‘hotspots.' In order to discriminate between tumor/normal samples in tissue, we applied supervised machine learning to train a classifier on these 402 features. Mean methylation across these 402 CpG hotspots was significantly (p < 0.01) lower in disease plasma compared to normal control plasma samples. We then tested the classifier on the 16 patient-matched cfDNA samples and achieved a classification accuracy of 95% and an area under the curve (AUC) of 0.935. In summary, we have identified a promising set of DNA methylation features also detected in cfDNA that can potentially be used for recurrence risk stratification and measurable minimal residual disease monitoring in solid pediatric tumors. Citation Format: David N. Buckley, Ben Tew, Gerald Gooden, Timothy Triche, Bodour Salhia. Analysis of solid pediatric tumor methylomes reveals a shared cancer recurrence signature in cell-free DNA [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 583.