1. Single-cell chromatin accessibility reveals malignant regulatory programs in primary human cancers.
- Author
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Sundaram L, Kumar A, Zatzman M, Salcedo A, Ravindra N, Shams S, Louie BH, Bagdatli ST, Myers MA, Sarmashghi S, Choi HY, Choi WY, Yost KE, Zhao Y, Granja JM, Hinoue T, Hayes DN, Cherniack A, Felau I, Choudhry H, Zenklusen JC, Farh KK, McPherson A, Curtis C, Laird PW, Demchok JA, Yang L, Tarnuzzer R, Caesar-Johnson SJ, Wang Z, Doane AS, Khurana E, Castro MAA, Lazar AJ, Broom BM, Weinstein JN, Akbani R, Kumar SV, Raphael BJ, Wong CK, Stuart JM, Safavi R, Benz CC, Johnson BK, Kyi C, Shen H, Corces MR, Chang HY, and Greenleaf WJ
- Subjects
- Humans, Neural Networks, Computer, Mutation, DNA Copy Number Variations, Breast Neoplasms genetics, Breast Neoplasms pathology, Chromatin metabolism, Chromatin genetics, Single-Cell Analysis, Neoplasms genetics, Gene Expression Regulation, Neoplastic
- 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.
- Published
- 2024
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