Single-cell chromatin accessibility reveals malignant regulatory programs in primary human cancers Journal Article
| Authors: | Sundaram, L.; Kumar, A.; Zatzman, M.; Salcedo, A.; Ravindra, N.; Shams, S.; Louie, B. H.; Bagdatli, S. T.; Myers, M. A.; Sarmashghi, S.; Choi, H. Y.; Choi, W. Y.; Yost, K. E.; Zhao, Y.; Granja, J. M.; Hinoue, T.; Hayes, D. N.; Cherniack, A.; Felau, I.; Choudhry, H.; Zenklusen, J. C.; Farh, K. K. H.; McPherson, A.; Curtis, C.; Laird, P. W.; The Cancer Genome Atlas Analysis Network, M. Ryan Corces; Chang, H. Y.; Greenleaf, W. J. |
| Article Title: | Single-cell chromatin accessibility reveals malignant regulatory programs in primary human cancers |
| 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: | genetics; mutation; neoplasm; neoplasms; metabolism; pathology; breast neoplasms; gene expression regulation; gene expression regulation, neoplastic; chromatin; breast tumor; artificial neural network; dna copy number variations; copy number variation; single cell analysis; single-cell analysis; humans; human; neural networks, computer |
| Journal Title: | Science |
| Volume: | 385 |
| Issue: | 6713 |
| ISSN: | 0036-8075 |
| Publisher: | American Association for the Advancement of Science |
| Date Published: | 2024-09-06 |
| Start Page: | eadk9217 |
| Language: | English |
| DOI: | 10.1126/science.adk9217 |
| PUBMED: | 39236169 |
| PROVIDER: | scopus |
| DOI/URL: | |
| Notes: | Article -- Source: Scopus |
Altmetric
Citation Impact
BMJ Impact Analytics
Related MSK Work