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一份高分辨率的人类乳腺癌综合转录组图谱。

A highly resolved integrated transcriptomic atlas of human breast cancers.

作者信息

Chen Andrew Deru, Kroehling Lina, Ennis Christina, Denis Gerald V, Monti Stefano

出版信息

bioRxiv. 2025 May 3:2025.03.13.643025. doi: 10.1101/2025.03.13.643025.

Abstract

In this study, we developed an integrated single cell transcriptomic (scRNAseq) atlas of human breast cancer (BC), the largest resource of its kind, totaling > 600,000 cells across 138 patients. Rigorous integration and annotation of publicly available scRNAseq data enabled a highly resolved characterization of epithelial, immune, and stromal heterogeneity within the tumor microenvironment (TME). Within the immune compartment we were able to characterize heterogeneity of CD4, CD8 T cells and macrophage subpopulations. Within the stromal compartment, subpopulations of endothelial cells (ECs) and cancer associated fibroblasts (CAFs) were resolved. Within the cancer epithelial compartment, we characterized the functional heterogeneity of cells across the axes of stemness, epithelial-mesenchymal plasticity, and canonical cancer pathways. Across all subpopulations observed in the TME, we performed a multi-resolution survival analysis to identify epithelial cell states and immune cell types which conferred a survival advantage in both The Cancer Genome Atlas (TCGA) and METABRIC. We also identified robust associations between TME composition and clinical phenotypes such as tumor subtype and grade that were not discernible when the analysis was limited to individual datasets, highlighting the need for atlas-based analyses. This atlas represents a valuable resource for further high-resolution analyses of TME heterogeneity within BC.

摘要

在本研究中,我们构建了一个人类乳腺癌(BC)的综合单细胞转录组图谱(scRNAseq),这是同类研究中最大的资源库,涵盖了138名患者的超过60万个细胞。对公开可用的scRNAseq数据进行严格整合和注释,能够高度解析肿瘤微环境(TME)内上皮、免疫和基质的异质性。在免疫区室中,我们能够表征CD4、CD8 T细胞和巨噬细胞亚群的异质性。在基质区室中,解析了内皮细胞(ECs)和癌症相关成纤维细胞(CAFs)的亚群。在癌症上皮区室中,我们表征了细胞在干性、上皮-间质可塑性和经典癌症通路轴上的功能异质性。在TME中观察到的所有亚群中,我们进行了多分辨率生存分析,以确定在癌症基因组图谱(TCGA)和METABRIC中具有生存优势的上皮细胞状态和免疫细胞类型。我们还确定了TME组成与肿瘤亚型和分级等临床表型之间的强关联,这些关联在仅限于单个数据集的分析中无法识别,突出了基于图谱分析的必要性。该图谱为进一步高分辨率分析BC内TME的异质性提供了宝贵资源。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d3af/12051680/d67706bc6348/nihpp-2025.03.13.643025v2-f0001.jpg

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