MONTE: Methylation-based Observation Normalization and Tumor purity Estimation

ABSTRACT Bulk DNA methylation profiling is widely used to study cancer epigenomics in clinical settings. However, these measurements aggregate signals from malignant and non-malignant cells, introducing composition-dependent confounding that complicates tumor-intrinsic interpretation and cross-cohort analyses. While existing methods can estimate tumor purity and, in some cases, correct methylation measurements, they are often constrained by cancer-specific reference models, predefined probe sets, or limited adaptability to new datasets and purity definitions. We present MONTE (Methylation-based Observation Normalization and Tumor purity Estimation), a cancer label–free method for tumor purity inference and CpG-resolved methylation purification from bulk DNA methylation data. MONTE learns probe-wise relationships between observed methylation and tumor purity using a stabilized linear model with empirical Bayes variance moderation and infers purity in new samples via signal-to-noise weighted aggregation of probe-level effects, without requiring matched normal samples or predefined probe sets. MONTE achieves robust purity estimation across cancer types using a single pan-cancer model and can be efficiently recalibrated to specific contexts through Bayesian transfer learning. MONTE provides a flexible, scalable, and interpretable framework for both methylation-based tumor purity estimation and correction, yielding tumor-intrinsic profiles for downstream analysis. Competing Interest Statement The authors have declared no competing interest.