近日，
为了识别与癌症相关的基因调控变化，研究人员在《癌症基因组图谱》项目中生成了涵盖八种肿瘤类型的单细胞染色质可及性图谱。肿瘤染色质的可及性受到拷贝数改变的强烈影响，这些改变可以用于识别亚克隆，然而潜在的顺式调控图谱仍保留了特定癌症类型的特征。
通过使用器官匹配的健康组织，研究人员确定了多种癌症中的“最近健康”细胞类型，显示出基底样亚型乳腺癌的染色质特征最类似于分泌型的腔型上皮细胞。
通过神经网络模型学习癌症中的调控程序，研究人员揭示了模型优先选择的癌症相关基因附近的体细胞非编码突变富集，表明分散的、非重复的非编码突变在癌症中具有功能性。
总体而言，这些数据和可解释的癌症与健康组织基因调控模型，为理解癌症特异性基因调控提供了框架。
附：英文原文
Title: Single-cell chromatin accessibility reveals malignant regulatory programs in primary human cancers
Author: 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, The Cancer Genome Atlas Analysis Network, M. Ryan Corces, Howard Y. Chang, William J. Greenleaf, John A. Demchok, Liming Yang, Roy Tarnuzzer, Samantha J. Caesar-Johnson, 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
Issue&Volume: 2024-09-06
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.
DOI: adk9217
Source: https://www.science.org/doi/10.1126/science.adk9217