Metaplasia as a transitional cell-of-origin state revealed by epigenomeinformed cancer prediction
Year: 2026
Type: conference-paper
Abstract: Understanding the cellular origin of cancer is essential for elucidating tumorigenesis and improving early detection and therapeutic strategies. Here, we leverage large-scale whole-genome sequencing and single-cell chromatin accessibility data to develop a machine learning framework for predicting the cell of origin (COO) across multiple cancer types. By integrating mutational landscapes with cell-type-specific epigenomic profiles, our approach accurately assigns tumors to their originating cellular populations at high resolution, demonstrating that somatic mutation patterns retain a record of the pre-malignant chromatin state. Our model reveals both expected and previously unrecognized cellular origins, including a predominant basal cell origin for small cell lung cancer, challenging traditional assumptions of a neuroendocrine origin. Importantly, beyond static COO prediction, we identify dynamic cellular trajectories during tumorigenesis, highlighting the presence of intermediate metaplastic states in several gastrointestinal cancers. These findings support a model in which metaplasia represents a transitional and potentially permissive state linking normal tissue and malignant transformation, rather than a terminal differentiation endpoint. This work demonstrates that integrative, epigenome-informed machine learning enables robust prediction of cancer cell of origin while uncovering metaplastic intermediates as critical components of tumor evolution. These insights have important implications for understanding cancer initiation, refining early detection strategies, and targeting pre-malignant cellular states.
Language: English
Subfield: Cancer Research
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7204064331
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