Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
Department of Computer Science, Stanford University, Stanford, CA, USA.
Science. 2024 Sep 6;385(6713):eadk9217. 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.
为了识别与癌症相关的基因调控变化，我们作为癌症基因组图谱的一部分，生成了八个肿瘤类型的单细胞染色质可及性图谱。肿瘤染色质可及性受拷贝数改变的强烈影响，这些改变可用于识别亚克隆，但潜在的顺式调控景观保留了癌症类型特异性特征。使用器官匹配的健康组织，我们在不同的癌症中鉴定了“最近的健康”细胞类型，表明基底样亚型乳腺癌的染色质特征与分泌型腔上皮细胞最相似。经过训练以学习癌症中调控程序的神经网络模型揭示了优先考虑模型的体细胞非编码突变在癌症相关基因附近的富集，这表明癌症中分散的、非重复的、非编码突变是有功能的。总的来说，这些数据和用于癌症和健康组织的可解释基因调控模型为理解癌症特异性基因调控提供了一个框架。
