【文献分享】A comprehensive single-cell breast提供了代码和数据

介绍

我们构建了一个包含 236,363 个细胞（来自 119 个活检样本，涵盖八个数据集）的原发性乳腺肿瘤微环境（TME）的综合单细胞 RNA 测序图谱。在本研究中，我们利用这一资源对免疫细胞和癌上皮细胞的异质性进行了多项分析。我们通过六个亚群定义了乳腺 TME 中的自然杀伤（NK）细胞异质性。由于 NK 细胞异质性与上皮细胞异质性相关，我们以单基因表达、分子亚型以及反映肿瘤内转录异质性的 10 个类别来表征上皮细胞。我们开发了 InteractPrint，它考虑了癌上皮细胞异质性对癌症-免疫相互作用的影响。我们使用 T 细胞 InteractPrint 来预测两项乳腺癌临床试验中对免疫检查点抑制（ICI）的反应（这些试验测试了新辅助抗 PD-1 治疗）。T 细胞 InteractPrint 在两项试验中均能预测反应情况（与 PD-L1 相比，AUC 分别为 0.82、0.83 与 0.50、0.72）。该资源使我们能够对乳腺 TME 进行更深入的高分辨率研究。

We present an integrated single-cell RNA sequencing atlas of the primary breast tumor microenvironment (TME) containing 236,363 cells from 119 biopsy samples across eight datasets. In this study, we leverage this resource for multiple analyses of immune and cancer epithelial cell heterogeneity. We define natural killer (NK) cell heterogeneity through six subsets in the breast TME. Because NK cell heterogeneity correlates with epithelial cell heterogeneity, we characterize epithelial cells at the level of single-gene expression, molecular subtype, and 10 categories reflecting intratumoral transcriptional heterogeneity. We develop InteractPrint, which considers how cancer epithelial cell heterogeneity influences cancer-immune interactions. We use T cell InteractPrint to predict response to immune checkpoint inhibition (ICI) in two breast cancer clinical trials testing neoadjuvant anti-PD-1 therapy. T cell InteractPrint was predictive of response in both trials versus PD-L1 (AUC = 0.82, 0.83 vs. 0.50, 0.72). This resource enables additional high-resolution investigations of the breast TME.

代码

https://github.com/ChanLab-UTSW/BreastCancer_Integrated/tree/main/Analysis

参考

• A comprehensive single-cell breast tumor atlas defines cancer epithelial and immune cell heterogeneity and interactions predicting anti-PD-1 therapy response
• https://github.com/ChanLab-UTSW/BreastCancer_Integrated/tree/main/Analysis