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MUC1-C 可将 IFN-γ 通路的激活与三阴性乳腺癌肿瘤免疫微环境的抑制相结合。

MUC1-C integrates activation of the IFN-γ pathway with suppression of the tumor immune microenvironment in triple-negative breast cancer.

机构信息

Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA.

Department of Biostatistics and Bioinformatics, Roswell Park Cancer Institute, Buffalo, New York, USA.

出版信息

J Immunother Cancer. 2021 Jan;9(1). doi: 10.1136/jitc-2020-002115.

DOI:10.1136/jitc-2020-002115
PMID:33495298
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7839859/
Abstract

BACKGROUND

Immune checkpoint inhibitors (ICIs) have had a profound impact on the treatment of many tumors; however, their effectiveness against triple-negative breast cancers (TNBCs) has been limited. One factor limiting responsiveness of TNBCs to ICIs is a lack of functional tumor-infiltrating lymphocytes (TILs) in 'non-inflamed' or 'cold' tumor immune microenvironments (TIMEs), although by unknown mechanisms. Targeting MUC1-C in a mouse transgenic TNBC tumor model increases cytotoxic tumor-infiltrating CD8+ T cells (CTLs), supporting a role for MUC1-C in immune evasion. The basis for these findings and whether they extend to human TNBCs are not known.

METHODS

Human TNBC cells silenced for MUC1-C using short hairpin RNAs (shRNAs) were analyzed for the effects of MUC1-C on global transcriptional profiles. Differential expression and rank order analysis was used for gene set enrichment analysis (GSEA). Gene expression was confirmed by quantitative reverse-transcription PCR and immunoblotting. The The Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) datasets were analyzed for effects of MUC1 on GSEA, cell-type enrichment, and tumor immune dysfunction and exclusion. Single-cell scRNA-seq datasets of TNBC samples were analyzed for normalized expression associations between MUC1 and selected genes within tumor cells.

RESULTS

Our results demonstrate that MUC1-C is a master regulator of the TNBC transcriptome and that MUC1-C-induced gene expression is driven by STAT1 and IRF1. We found that MUC1-C activates the inflammatory interferon (IFN)-γ-driven JAK1→STAT1→IRF1 pathway and induces the IDO1 and COX2/PTGS2 effectors, which play key roles in immunosuppression. Involvement of MUC1-C in activating the immunosuppressive IFN-γ pathway was extended by analysis of human bulk and scRNA-seq datasets. We further demonstrate that MUC1 associates with the depletion and dysfunction of CD8+ T cells in the TNBC TIME.

CONCLUSIONS

These findings demonstrate that MUC1-C integrates activation of the immunosuppressive IFN-γ pathway with depletion of TILs in the TNBC TIME and provide support for MUC1-C as a potential target for improving TNBC treatment alone and in combination with ICIs. Of translational significance, MUC1-C is a druggable target with chimeric antigen receptor (CAR) T cells, antibody-drug conjugates (ADCs) and a functional inhibitor that are under clinical development.

摘要

背景

免疫检查点抑制剂(ICIs)对许多肿瘤的治疗产生了深远影响;然而,它们对三阴性乳腺癌(TNBC)的疗效有限。限制 TNBC 对 ICI 反应的一个因素是缺乏功能肿瘤浸润淋巴细胞(TILs)在“非炎症”或“冷”肿瘤免疫微环境(TIME)中,尽管其机制尚不清楚。在小鼠转基因 TNBC 肿瘤模型中靶向 MUC1-C 会增加细胞毒性肿瘤浸润 CD8+T 细胞(CTL),支持 MUC1-C 在免疫逃逸中的作用。这些发现的基础以及它们是否扩展到人类 TNBC 尚不清楚。

方法

使用短发夹 RNA(shRNA)沉默 MUC1-C 的人类 TNBC 细胞被分析 MUC1-C 对全局转录谱的影响。差异表达和等级排序分析用于基因集富集分析(GSEA)。通过定量逆转录 PCR 和免疫印迹法确认基因表达。分析了癌症基因组图谱乳腺浸润性癌(TCGA-BRCA)和乳腺癌国际联合会(METABRIC)数据集,以研究 MUC1 对 GSEA、细胞类型富集以及肿瘤免疫功能障碍和排除的影响。分析了 TNBC 样本的单细胞 scRNA-seq 数据集,以确定肿瘤细胞内 MUC1 与选定基因之间的归一化表达关联。

结果

我们的结果表明,MUC1-C 是 TNBC 转录组的主要调节因子,MUC1-C 诱导的基因表达受 STAT1 和 IRF1 驱动。我们发现 MUC1-C 激活炎症性干扰素(IFN)-γ驱动的 JAK1→STAT1→IRF1 途径,并诱导 IDO1 和 COX2/PTGS2 效应物,它们在免疫抑制中发挥关键作用。通过分析人类批量和 scRNA-seq 数据集,扩展了 MUC1-C 参与激活免疫抑制 IFN-γ途径的作用。我们进一步证明 MUC1 与 TNBC TIME 中 CD8+T 细胞的耗竭和功能障碍有关。

结论

这些发现表明,MUC1-C 将免疫抑制 IFN-γ途径的激活与 TNBC TIME 中 TIL 的耗竭整合在一起,并为 MUC1-C 作为一种潜在的治疗靶点提供了支持,可单独或与 ICI 联合用于治疗 TNBC。具有重要转化意义的是,MUC1-C 是一种可靶向药物靶点,具有嵌合抗原受体(CAR)T 细胞、抗体药物偶联物(ADC)和一种正在临床开发中的功能性抑制剂。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ebc2/7839859/7af9a0a70698/jitc-2020-002115f06.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ebc2/7839859/27242e0e083b/jitc-2020-002115f01.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ebc2/7839859/4bedab8d50de/jitc-2020-002115f02.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ebc2/7839859/b8600ed4b55b/jitc-2020-002115f03.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ebc2/7839859/6ca317f2a1c1/jitc-2020-002115f04.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ebc2/7839859/4cc0a63f55e0/jitc-2020-002115f05.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ebc2/7839859/7af9a0a70698/jitc-2020-002115f06.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ebc2/7839859/27242e0e083b/jitc-2020-002115f01.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ebc2/7839859/4bedab8d50de/jitc-2020-002115f02.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ebc2/7839859/b8600ed4b55b/jitc-2020-002115f03.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ebc2/7839859/6ca317f2a1c1/jitc-2020-002115f04.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ebc2/7839859/4cc0a63f55e0/jitc-2020-002115f05.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ebc2/7839859/7af9a0a70698/jitc-2020-002115f06.jpg

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