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Single-cell chromatin accessibility landscapes reveal nucleotide-resolved malignant regulatory programs in primary human cancers

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To identify gene regulatory changes associated with malignancy, we generated a single-cell atlas of chromatin accessibility landscapes of cancer from 74 samples comprising 227,063 nuclei across eight tumor types as part of The Cancer Genome Atlas (TCGA). Chromatin accessibility landscapes in cancer are strongly influenced by copy number alterations that can also be used to identify subclones, yet underlying cis-regulatory landscapes retain strong cancer type-specific features. Using data from organ-matched healthy tissues, we identify the nearest-healthy cell types in diverse cancers, demonstrating that the epigenetic signature of basal-like subtype breast cancer is most similar to secretory-type luminal epithelial cells. Neural network models trained to learn regulatory programs in cancer revealed enrichment of model-prioritized somatic non-coding mutations near cancer-associated genes, suggesting that dispersed, non-recurrent non-coding mutations in cancer are functional. Overall, these data and interpretable gene regulatory models for cancer and healthy tissue provide a framework for understanding cancer-specific gene regulation.

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