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

• Published in: Science (American Association for the Advancement of Science) 2024-09, Vol.385 (6713), p.eadk9217
• Main Authors: Sundaram, Laksshman, Kumar, Arvind, Zatzman, Matthew, Salcedo, Adriana, Ravindra, Neal, Shams, Shadi, Louie, Brian H, Bagdatli, S Tansu, Myers, Matthew A, Sarmashghi, Shahab, Choi, Hyo Young, Choi, Won-Young, Yost, Kathryn E, Zhao, Yanding, Granja, Jeffrey M, Hinoue, Toshinori, Hayes, D Neil, Cherniack, Andrew, Felau, Ina, Choudhry, Hani, Zenklusen, Jean C, Farh, Kyle Kai-How, McPherson, Andrew, Curtis, Christina, Laird, Peter W, Corces, M Ryan, Chang, Howard Y, Greenleaf, William J, Demchok, John A, Yang, Liming, Tarnuzzer, Roy, Caesar-Johnson, Samantha J, Wang, Zhining, Doane, Ashley S, Khurana, Ekta, Castro, Mauro A A, Lazar, Alexander J, Broom, Bradley M, Weinstein, John N, Akbani, Rehan, Kumar, Shwetha V, Raphael, Benjamin J, Wong, Christopher K, Stuart, Joshua M, Safavi, Rojin, Benz, Christopher C, Johnson, Benjamin K, Kyi, Cindy, Shen, Hui
• Format: Article
• Language: English
• Subjects: Accessibility; Breast cancer; Breast Neoplasms - genetics; Breast Neoplasms - pathology; Cancer; Cell culture; Chromatin; Chromatin - genetics; Chromatin - metabolism; Copy number; Data analysis; Deep learning; Deoxyribonucleic acid; DNA; DNA Copy Number Variations; DNA structure; Epithelial cells; Epithelium; Gene expression; Gene Expression Regulation, Neoplastic; Gene mapping; Gene regulation; Gene sequencing; Genes; Genetic analysis; Genetic diversity; Genetics; Genomes; Grammars; Humans; Immune system; Impact analysis; Impact prediction; Molecular modelling; Mutation; Neoplasms - genetics; Networks; Neural networks; Neural Networks, Computer; Nucleotides; Peptide mapping; Phenotypes; Population genetics; Regulatory proteins; Regulatory sequences; Single-Cell Analysis; Transcription factors; Transposase; Tumors
• ISSN: 0036-8075; 1095-9203; 1095-9203
• DOI: 10.1126/science.adk9217

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.