Enzymatic based methylation profiling of tumor tissue and circulating tumor DNA from pediatric patients with rhabdoid tumors

Rhabdoid tumors (RT) are highly aggressive malignancies that typically present in very young children. The most common anatomic sites are the central nervous system, (atypical teratoid/rhabdoid tumor (AT/RT)), kidney (malignant rhabdoid tumor or rhabdoid tumor of the kidney (RTK)) and extra-renal sites. The tumors are characterized predominantly by homozygous inactivation of the SMARCB1 tumor suppressor gene. Comprehensive epigenomic profiling using bisulfite conversion and array hybridization previously demonstrated distinct methylation subgroups, especially within AT/RTs, which have helped to define the origin and transcriptional programs in rhabdoid tumors from different sites. Integrating genome-wide methylation and high-resolution copy number analysis from samples with low DNA input remains challenging, which will ultimately be required in prospective studies with targeted treatment protocols that increasingly utilize cell-free DNA (cfDNA) from cerebrospinal fluid liquid biopsies to diagnose patients and monitor response. In this study, we applied enzymatic conversion followed by whole genome sequencing (EM-Seq) to a cohort of 24 central nervous system (CNS), 14 renal and 13 extrarenal RTs with and without germline SMARCB1 predisposing mutations or copy number alterations, as well as five cerebrospinal fluid (CSF) cfDNA samples from patients with AT/RT. Global methylation patterns confirmed the presence of five previously described subgroups (ATRT-TYR, ATRT-SHH, ATRT-MYC, RTK-like, and extra-renal-like). We leveraged EM-Seq to characterize genome-wide copy number alterations and copy-neutral loss of heterozygosity as well as exon-level copy number gains and losses in SMARCB1 . We further demonstrate the feasibility of extending this approach to CSF cfDNA, highlighting the clinical potential for minimally invasive tumor classification and molecularly informed surveillance that could inform precision medicine for this aggressive entity. These findings position EM-Seq as a unified platform for integrated methylation-based classification and copy number profiling of RT, with applicability to both tissue and liquid biopsy samples.