Integrative cfDNA profiling from low-pass whole-genome sequencing enables tissue-of-origin prediction in cancer

Abstract

Cancer type classification is challenging due to tumor heterogeneity and undefined tissue of origin (TOO), particularly in cancers of unknown primary (CUP) and multiple primary cancers (MPC). Accurate TOO identification is critical for guiding treatment and prognosis. We developed a stacked ensemble machine learning classifier that integrates 11 multidimensional cfDNA features spanning genomic, fragmentomic, methylation/repeat, and microbial signals. Base models were constructed using five algorithms, including Deep Learning, Distributed Random Forest, Gradient Boosting Machine, Generalized Linear Model, and XGBoost, within a five-fold cross-validation framework, and their predictions were aggregated into a final ensemble optimized for top-1 accuracy. The classifier achieved robust performance across 17 cancer types, with top-1 and top-2 accuracies of 78% and 89% in the training cohort (n = 1,814), and 80% and 90% in an independent validation cohort (n = 1,221). Notably, predictive performance was retained in samples with low tumor fraction (71% top-1, 85% top-2). Sensitivity varied across tumor types, with the highest performance observed in head and neck and colorectal cancers. Among CUP cases, 11 of 15 (73.3%) predictions matched clinically inferred primary sites based on multimodal diagnostics. Feature importance analysis identified nucleosome positioning, fragment size distribution, and repeat elements as key contributors to model performance. Collectively, this cfDNA-based classifier provides a robust and non-invasive approach for accurate cancer type identification and has the potential to support clinical decision-making.