Detection and classification of lymphoma from cell-free methylome data

Abstract Diagnosing lymphoma traditionally relies on invasive tissue biopsies, which can yield insufficient material for histopathological evaluation and carry a risk of complications. Minimally invasive assessment of cell-free DNA (cfDNA) in plasma offers a promising alternative for lymphoma detection that could aid the rapid evaluation of malignant vs. benign lymphadenopathy. Here, we examine the methylome of plasma samples from 165 lymphoma patients and 47 controls using cell-free methylated DNA immunoprecipitation and high-throughput sequencing (cfMeDIP-seq). Differential methylation analysis of a discovery cohort (142 out of 212 samples) revealed 13,897 hypermethylated genomic regions in lymphoma cases, which were subsequently used for classification using regularized binomial generalized linear models. In a validation cohort (70 samples), we identified lymphomas with an accuracy of 0.89, positive predictive value (PPV) of 0.90 and negative predictive value (NPV) of 0.87. cfDNA methylation scores were significantly associated with orthogonal measures of cfDNA tumor burden, stage, and clinical outcomes. Our results highlight the feasibility of cfDNA methylation profiling as a sensitive and minimally invasive method for detecting lymphoma.