Faculty, Staff and Student Publications

Publication Date

9-6-2024

Journal

Science

DOI

10.1126/science.adk9217

PMID

39236169

PMCID

PMC12289346

PubMedCentral® Posted Date

7-24-2025

PubMedCentral® Full Text Version

Author MSS

Abstract

To identify cancer-associated gene regulatory changes, we generated single-cell chromatin accessibility landscapes across eight tumor types as part of The Cancer Genome Atlas. Tumor chromatin accessibility is strongly influenced by copy number alterations that can be used to identify subclones, yet underlying cis-regulatory landscapes retain cancer type-specific features. Using organ-matched healthy tissues, we identified the "nearest healthy" cell types in diverse cancers, demonstrating that the chromatin 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 noncoding mutations near cancer-associated genes, suggesting that dispersed, nonrecurrent, noncoding 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.

Keywords

Humans, Chromatin, Single-Cell Analysis, Neoplasms, Gene Expression Regulation, Neoplastic, Neural Networks, Computer, Mutation, DNA Copy Number Variations, Breast Neoplasms

Published Open-Access

yes

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