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

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.

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Publication Details

Journal
Science
Published
2024-09-05
DOI
https://doi.org/10.1126/science.adk9217
Citations
36
Primary Topic
Cancer Genomics and Diagnostics
Type
article
Field-Weighted Citation Impact
10.27
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Single-cell chromatin accessibility reveals malignant regulatory programs in primary human cancers

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

Hyo Young Choi, Jean C. Zenklusen, Matthew Zatzman, M. Ryan Corces, Stephen C. Benz, Neal G. Ravindra, Matthew A. Myers, Shahab Sarmashghi, Bradley M. Broom, Ina Felau, Kyle Kai‐How Farh, Peter W. Laird, Benjamin J. Raphael, John N. Weinstein, Toshinori Hinoue, Cindy W. Kyi, Joshua M. Stuart, Roy Tarnuzzer, Andrew D. Cherniack, Laksshman Sundaram, Shadi Shams, Christina N. Curtis, Won-Young Choi, Jeffrey M. Granja, John A. Demchok, Howard Y. Chang, Alexander J. Lazar, Andrew McPherson, Ashley S. Doane, Hani Choudhry, Brian H. Louie, Yanding Zhao, Rory Johnson, Rehan Akbani, Christopher K. Wong, Mauro A. A. Castro, William J. Greenleaf, D. Neil Hayes, Kathryn E. Yost, Rojin Safavi, S. Tansu Bagdatli, Liming Yang, Hui Shen, Benjamin K. Johnson, Zhining Wang, Shwetha V. Kumar, Arvind Kumar, Adriana Salcedo, Ekta Khurana
article en
36 citations

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.

ScienceVol. 385(6713)
Broad Institute (US), National Institutes of Health (US), Van Andel Institute (US), Gladstone Institutes (US), Memorial Sloan Kettering Cancer Center (US), Buck Institute for Research on Aging (US), The University of Texas MD Anderson Cancer Center (US), Howard Hughes Medical Institute (US), University of Tennessee Health Science Center (US), Illumina (United States) (US), University of California, San Francisco (US), University of California, Santa Cruz (US), King Abdulaziz University (SA), Princeton University (US), Cornell University (US), Chan Zuckerberg Initiative (United States) (US), Nvidia (United States) (US), National Cancer Institute (US), Center for Cancer Research (US), Weill Cornell Medicine (US), Universidade Federal do Paraná (BR), Stanford University (US)
Good health and well-being
Openalex Percentile: Top 1%
Cancer Genomics and Diagnostics
10.27
DOIOpen Access PDFOpenAlex
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