Single-cell chromatin accessibility reveals malignant regulatory programs in primary human cancers

• DOI: 10.1126/science.adk9217
• Publication Date: 2024-09-05T18:00:11Z
• Authors: Laksshman Sundaram; Arvind Kumar; Matthew Zatzman; Adriana Salcedo; Neal Ravindra; Shadi Shams; Brian H. Louie; S. Tansu Bagdatli; Matthew A. Myers; Shahab Sarmashghi; Hyo Young Choi; Won-Young Choi; Kathryn E. Yost; Yanding Zhao; Jeffrey M. Granja; Toshinori Hinoue; D. Neil Hayes; Andrew Cherniack; Ina Felau; Hani Choudhry; Jean C. Zenklusen; Kyle Kai-How Farh; Andrew McPherson; Christina Curtis; Peter W. Laird; ; M. Ryan Corces; Howard Y. Chang; William J. Greenleaf; John A. Demchok; Liming Yang; Roy Tarnuzzer; Samantha J. Caesa...; Zhining Wang; Ashley S. Doane; Ekta Khurana; Mauro A. A. Castro; Alexander J. Lazar; Bradley M. Broom; John N. Weinstein; Rehan Akbani; Shwetha V. Kumar; Benjamin J. Raphael; Christopher K. Wong; Joshua M. Stuart; Rojin Safavi; Christopher C. Benz; Benjamin K. Johnson; Cindy Kyi; Hui Shen
• Keywords: Gene Expression Regulation, Neoplastic; DNA Copy Number Variations; Neoplasms; Mutation; Humans; Breast Neoplasms; Neural Networks, Computer; Single-Cell Analysis; Chromatin

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.