MONTE: Methylation-based Observation Normalization and Tumorpurity Estimation

Abstract

Bulk DNA methylation profiling is widely used to study cancer epigenomics in clinical settings, but 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 typically require cancer-specific reference models, matched normal samples, or predefined probe sets, limiting their applicability to rare cancers, different clinical cohorts, and cross-dataset comparisons. We present MONTE (Methylation-based Observation Normalization and Tumor purity Estimation), a unified, cancer label-free framework for tumor purity inference and CpG-resolved methylation correction from bulk DNA methylation data. MONTE learns probe-wise relationships between methylation and tumor purity using an empirical Bayes-moderated linear model and infers purity in new samples via signal-to-noise weighted aggregation, without requiring matched normals, cancer labels, or predefined probe sets. A single pan-cancer MONTE model outperforms existing cancer-specific methods for purity estimation across 21 cancer types, generalizes across purity references, and runs orders of magnitude faster on full-dataset analyses. MONTE also introduces Bayesian transfer learning, which enables efficient recalibration to alternative purity definitions, validated on three independent external cohorts. Methylation correction with MONTE further amplifies tumor-relevant regulatory signal and improves the reproducibility of differential methylation analyses. By unifying purity estimation and correction in a single flexible, scalable, and interpretable framework, MONTE broadens the accessibility of tumor-intrinsic methylation analysis across cancer types and datasets.