# 2.2.11 研究 |2021-单细胞转录组揭示肺腺癌特有的肿瘤微环境 ## 前言 题目：Single-cell RNA sequencing reveals distinct tumor microenvironmental patterns in lung adenocarcinoma 日期：2021-10-18 期刊：Oncogene 链接： 代码： ## 一句话概括 揭示两种不同的肺腺癌微环境模式，基于微环境提供额外的预后信息，预测潜在的目标细胞群用于治疗 ## 肺腺癌的细胞组成 10个正常+10个肺腺癌样本，得到114489个高质量的单细胞转录组数据 ![image-20211021154512130](https://jieandze1314-1255603621.cos.ap-guangzhou.myqcloud.com/blog/2021-10-21-074512.png) UMAP结果表明不管是组织类型还是不同病人，细胞都”混在一起“，说明不存在批次效应 ![image-20211021154652842](https://jieandze1314-1255603621.cos.ap-guangzhou.myqcloud.com/blog/2021-10-21-074653.png) 作者拿了epithelial, immune, stromal 三种类型细胞的 marker genes，分出了20,450 epithelial, 89,766 immune, and 4273 stromal，发现和其他文章一样，免疫细胞占据主体（D图中黄色部分占绝对优势）。不同病人之间的上皮细胞占比差异明显，而且solid/sarcomatoid（实体瘤/肉瘤样型）这种tumor的上皮细胞占比不到10%，而**lepidic/acinar carcinomas（鳞屑状/腺泡型）这种占比大于40%** ![image-20211021154941680](https://jieandze1314-1255603621.cos.ap-guangzhou.myqcloud.com/blog/2021-10-21-074941.png) ## 肿瘤上皮细胞的异质性 > 重点发现：不同病人的上皮细胞存在异质性，但组织类型变化的过程中，又存在一些共性的变化 ### **分群差异** 既然不同肿瘤样本展示了不同的上皮细胞占比差异，接下来作者将epi单独拿出来，重新分群，并且根据组织类型（normal和tumor）进行拆分（即：一整个epi的UMAP按照组织类型拆成了2张图） ![image-20211021155936251](https://jieandze1314-1255603621.cos.ap-guangzhou.myqcloud.com/blog/2021-10-21-075936.png) 对正常组织的上皮细胞再进行细胞注释（按照图B的marker基因），发现了：alveolar type 1 and 2, club, ciliated, and even a small cluster of neuroendocrine cells。肿瘤样本的epi体现出了明显的病人特异性（每个病人是单独的一团细胞） ![image-20211022094850060](https://jieandze1314-1255603621.cos.ap-guangzhou.myqcloud.com/blog/2021-10-22-014850.png) ### **拷贝数变异** inferCNV，发现重新分群结果和组织类型以及拷贝数变异结果都能对应上，并且肿瘤纯度高于90%，侧面反映了分群的准确性 ![image-20211021160209530](https://jieandze1314-1255603621.cos.ap-guangzhou.myqcloud.com/blog/2021-10-21-080210.png) ### **差异基因** 然后看了肿瘤上皮细胞的几个明显的差异基因表达量（EGFR, TFF3, CDKN2A, and SFTPA2），和免疫染色结果正相关 ![image-20211021161221429](https://jieandze1314-1255603621.cos.ap-guangzhou.myqcloud.com/blog/2021-10-21-081221.png) ### **信号通路** 根据一些致癌信号通路基因，看了这些通路在不同病人之间的确存在差异，尤其是EGFR, TGFβ, JAK/STAT, Hypoxia, and PI3K信号通路 ![image-20211021164721087](https://jieandze1314-1255603621.cos.ap-guangzhou.myqcloud.com/blog/2021-10-21-084721.png) 不过同时也发现，像是P034、033、030这几个病人，虽然他们的信号通路活性普遍比较高，但是他们的有丝分裂活性并不高 ![image-20211022094346190](https://jieandze1314-1255603621.cos.ap-guangzhou.myqcloud.com/blog/2021-10-22-014521.png) ### **从病人视角切换到组织视角，发现一些共性** 这次不按病人，而是按组织类型进行cluster的划分，并且看到第一个主成分（认为代表了最主要的生物差异因素）随着组织类型的变化，呈现梯度式的变化 ![image-20211022101334064](https://jieandze1314-1255603621.cos.ap-guangzhou.myqcloud.com/blog/2021-10-22-021334.png) 根据PC1 的PCA score，又找到了：**top 30 genes** positively and negatively correlated with PC1 were defined as an “alveolar/club-like” and “undifferentiated” tumor cell signature，其中*SCGB3A1* and *SCGB3A2* 又和肺的发育相关 ![image-20211022101447716](https://jieandze1314-1255603621.cos.ap-guangzhou.myqcloud.com/blog/2021-10-22-021447.png) 从undifferentiated到alveolar/club-like表型，肿瘤上皮细胞呈现出类似的组织类型和通路活性的变化，比如JAK/STAT, Hypoxia, EGFR and TGFβ信号在undifferentiated处于高位，而PI3K后来在alveolar/club-like表型中升高 ![image-20211022101602380](https://jieandze1314-1255603621.cos.ap-guangzhou.myqcloud.com/blog/2021-10-22-021602.png) ## 肿瘤基质细胞微环境 > 重点发现：存在2个肌成纤维细胞群，分别表现出“normal-like” and “cancer-associated” 的特性，并且各自都能主导基质的微环境 在A图中的第三个fibroblastic/muscle cell clusters中，发现从normal到tumor，原来fibroblast占主导（两个蓝色），变成了myofibroblast占主导（两个绿色） B图中展示的是myofibroblast和fibroblast的maker gene：Myofibroblast clusters were characterized by expression of **both fibroblastic marker genes**, such as PDGFRA and LUM, **and smooth muscle marker genes**, such as MYLK and ACTA2 ![image-20211022111551049](https://jieandze1314-1255603621.cos.ap-guangzhou.myqcloud.com/blog/2021-10-22-031551.png) 然后看到myofibroblast的cluster2，都存在于tumor样本；而cluster1则是在tumor、normal均有，其中几个基因（COL5A2、COL6A3、SULF1、MMP11）与extracellular matrix remodeling有关 ![image-20211022111922027](https://jieandze1314-1255603621.cos.ap-guangzhou.myqcloud.com/blog/2021-10-22-031922.png) 对比myofibroblast的2个cluster通路活性，发现cluster2在TGFβ 、JAK/STAT和hypoxia-induced pathways 均高于cluster1 ![image-20211022112255732](https://jieandze1314-1255603621.cos.ap-guangzhou.myqcloud.com/blog/2021-10-22-032256.png) 另外，这两个cluster在fibroblastic/muscle cell的占比，在不同病人之间也是负相关的 ![image-20211022112506029](https://jieandze1314-1255603621.cos.ap-guangzhou.myqcloud.com/blog/2021-10-22-032506.png) ## 肿瘤免疫微环境 > 重点发现：正常肺组织到腺癌的过程中，免疫细胞组成发生了什么变化；导致不同患者之间异质性的肿瘤免疫微环境是什么样的 拿到的细胞类型包括：tissue-resident and monocyte-derived macrophages, monocytes, myeloid and plasmacytoid dendritic cells, mast cells, and T, NK, B, and plasma cells 比较明显的是不同细胞类型的数量变化： * tumor myeloid（骨髓） cell中，monocyte-derived macrophages、dendritic cells数量上升，tissue-resident macrophages、monocytes数量下降 * tumor lymphoid（淋巴） cell中，CD8+ T, B, and plasma cells数量上升，NK and conventional T cells下降 ![image-20211022113222401](https://jieandze1314-1255603621.cos.ap-guangzhou.myqcloud.com/blog/2021-10-22-033222.png) ## 研究肿瘤微环境的构成模式 PCA将不同的病人样本进行划分： * P018, P019, P024, P031, P032, and P033： **N³MC pattern**【**normal-like myofibroblasts**，non-inflammatory monocyte-derived macro- phages, NK cells, myeloid dendritic cells and conventional T cells】 * P023, P027, P030 and P034：**CP²E pattern**【**cancer-associated myofibroblasts**, proin- flammatory monocyte-derived macrophages, plasmacytoid den- dritic cells and exhausted CD8+ T cells】 ![image-20211022115457976](https://jieandze1314-1255603621.cos.ap-guangzhou.myqcloud.com/blog/2021-10-22-035458.png) ![image-20211022132938932](https://jieandze1314-1255603621.cos.ap-guangzhou.myqcloud.com/blog/2021-10-22-052939.png) ![image-20211022133819567](https://jieandze1314-1255603621.cos.ap-guangzhou.myqcloud.com/blog/2021-10-22-053820.png) 做了细胞通讯分析，发现： **tumor cells in the CP2E** environment receive potential paracrine signals from cancer-associated **myofi- broblast cluster 2** activating **Ephrin, FGF, WNT, TGFβ, and BMP signaling**, and from proinflammatory monocyte-derived **macrophages cluster 2** potentially activating **JAK/STAT** signaling ![image-20211022132819928](https://jieandze1314-1255603621.cos.ap-guangzhou.myqcloud.com/blog/2021-10-22-052820.png) 分析了TCGA lung adenocarcinoma cohort，发现 N³MC 相关的gene signature预后更好 ![image-20211022133923384](https://jieandze1314-1255603621.cos.ap-guangzhou.myqcloud.com/blog/2021-10-22-053924.png) 最后得出结论： * 免疫活化的CP²E微环境由癌症相关的肌纤维细胞，促炎单核细胞衍生的巨噬细胞，血浆骨质树突树突细胞和排出的CD8 + T细胞组成，并且预后不利 * 惰性N³MC 微环境主要包括正常的肌纤维细胞，非炎症单核细胞衍生的巨噬细胞，NK细胞，骨髓树突细胞和常规T细胞，并与良好的预后有关 --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. 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