驱动癌细胞转录失调的顺式调节元件的注释对于提高我们对肿瘤生物学的理解至关重要。在此，我们以单细胞分辨率展示了人类乳腺肿瘤和手术切除后立即处理的健康乳腺组织的匹配染色质可及性 (scATAC-seq) 和转录组 (scRNA-seq) 概况的概要。我们确定了管腔乳腺肿瘤和基底乳腺肿瘤最有可能的起源细胞，然后引入了一种新的方法，该方法实施线性混合效应模型，以系统地量化恶性肿瘤中染色质可及性区域（即调控元件）与基因表达之间的关联。细胞与正常乳腺上皮细胞。这些数据揭示了从正常细胞基因表达沉默子到癌细胞基因表达增强子的调节元件，导致临床相关癌基因的上调。为了将该数据集的效用转化为易于处理的模型，我们为乳腺癌细胞系生成了匹配的 scATAC-seq 和 scRNA-seq 图谱，揭示了每种亚型的体外和体内细胞之间保守的致癌基因表达程序。总之，这项工作强调了致癌过程中非编码调控机制的重要性，以及单细胞多组学在单细胞分辨率下定义 BC 细胞调控逻辑的能力。

Annotation of the cis -regulatory elements that drive transcriptional dysregulation in cancer cells is critical to improving our understanding of tumor biology. Herein, we present a compendium of matched chromatin accessibility (scATAC-seq) and transcriptome (scRNA-seq) profiles at single-cell resolution from human breast tumors and healthy mammary tissues processed immediately following surgical resection. We identify the most likely cell-of-origin for luminal breast tumors and basal breast tumors and then introduce a novel methodology that implements linear mixed-effects models to systematically quantify associations between regions of chromatin accessibility (i.e. regulatory elements) and gene expression in malignant cells versus normal mammary epithelial cells. These data unveil regulatory elements with that switch from silencers of gene expression in normal cells to enhancers of gene expression in cancer cells, leading to the upregulation of clinically relevant oncogenes. To translate the utility of this dataset into tractable models, we generated matched scATAC-seq and scRNA-seq profiles for breast cancer cell lines, revealing, for each subtype, a conserved oncogenic gene expression program between in vitro and in vivo cells. Together, this work highlights the importance of non-coding regulatory mechanisms that underlie oncogenic processes and the ability of single-cell multi-omics to define the regulatory logic of BC cells at single-cell resolution.