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全面的单细胞乳腺癌肿瘤图谱定义了上皮和免疫异质性以及预测抗 PD-1 治疗反应的相互作用。

A comprehensive single-cell breast tumor atlas defines epithelial and immune heterogeneity and interactions predicting anti-PD-1 therapy response.

机构信息

Department of Internal Medicine, Division of Hematology and Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA; Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA.

Department of Biomedical Engineering, Johns Hopkins University Whiting School of Engineering, Baltimore, MD, USA.

出版信息

Cell Rep Med. 2024 May 21;5(5):101511. doi: 10.1016/j.xcrm.2024.101511. Epub 2024 Apr 12.

Abstract

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.

摘要

我们展示了一个整合的原发性乳腺肿瘤微环境(TME)单细胞 RNA 测序图谱,其中包含来自 8 个数据集的 119 个活检样本中的 236363 个细胞。在这项研究中,我们利用这一资源对免疫和癌症上皮细胞异质性进行了多种分析。我们通过乳腺 TME 中的六个亚群来定义自然杀伤(NK)细胞的异质性。由于 NK 细胞异质性与上皮细胞异质性相关,我们在单细胞基因表达、分子亚型和 10 个反映肿瘤内转录异质性的类别水平上对上皮细胞进行了特征描述。我们开发了 InteractPrint,它考虑了癌症上皮细胞异质性如何影响癌症免疫相互作用。我们使用 T 细胞 InteractPrint 来预测两项乳腺癌临床试验中接受新辅助抗 PD-1 治疗的免疫检查点抑制(ICI)的反应,这两项临床试验都在测试抗 PD-1 治疗。T 细胞 InteractPrint 在两项试验中均优于 PD-L1(AUC=0.82,0.83 与 0.50,0.72)。这一资源为乳腺 TME 的进一步高分辨率研究提供了可能。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/daa9/11148512/1e8103a2b01b/fx1.jpg

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