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2.2.5 研究 | 2020-原汁原味读--单细胞肿瘤免疫图谱

刘小泽写于2020.10.30
探索一种新的文献阅读方法,可以更快去理解到作者的主体思路 下面我会从这几块介绍:SOURCE(文章来源)、WHY(作者为什么做这个项目)、HOW(作者怎么做的项目)、GET WHAT(作者得到了什么主要结论)
其中不会有特别复杂的词语和语法,而且我会尽可能把文章逻辑层级写清楚

SOURCE

Title: A Single-Cell Tumor Immune Atlas for Precision Oncology
Date: 2020-10-26
Team: Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
Data Link (Restricted Access): https://zenodo.org/record/4036020#.X5uoHlMzaHF

WHY?

  • Immune microenvironments vary profoundly between patients and biomarkers for prognosis and treatment response lack precision
  • To pinpoint predictive cellular states of tumor immune cells and their spatial localization

HOW?

  • Analyzing >500,000 cells from 217 patients and 13 cancer types
  • Data projection: Seurat's anchor-transferring method
  • Using SPOTlight to combine single-cell and spatial transcriptomics data and identifying striking spatial immune cell patterns in tumor sections
  • ShinyApp (in progress) to project external data and to apply the immune classifier

GET WHAT?

GET 1: Generating a tumor immune cell atlas

  • Collected scRNA-seq datasets from 13 different cancer types, 217 patients and 526,261 cells
  • Immune cells clustered by cell identity rather than patient origin: integrated 317,111 immune cells using canonical correlation analysis => 25 clusters

GET2: Tumor subtype classifier

  • For Current: to establish a pan-cancer immune classification system
    • used immune cell type and state frequencies of the reference atlas as input for similarity assessment across the 13 cancer types
    • A hierarchical k-means clustering using immune cell proportions as features defined six clusters with largely different compositions (almost all cancer types were presented in each cluster)
  • For future: to facilitate the classification of immune profiles
    • trained an RF(random forest) classifier with the 25 immune cell population achieving a highly accurate classification
    • using the classifier, the pan-cancer immune classification system could be extended to additional cancer types

GET 3: A resource for immune cell annotation

To demonstrate the potential value of the atlas
  • The applicability of the atlas as reference across different cancer types
    • First: Project cells onto atlas using a reference-based projection (Fig. A)
    • Next: Typical clustering matching (Fig. B)
    • Third: Check correlation (Fig. C)
  • The applicability of the atlas as reference across species
    • two liver metastases derived from mouse CRC organoids
    • main subtypes and specific subpopulations could also be assigned using the human reference

GET 4: Spatial localization of immune cells in tumor sections

Spatial distribution of immune cells is important for ICI (immune checkpoint inhibitors) response
  • Single-cell reference atlas immune profiles + Spatial transcriptome data
  • SPOTlight : non-negative matrix factorization (NMF) based spatial deconvolution framework
  • Analysis of oropharyngeal squamous cell carcinoma (SCC)
    • cluster 1/2 (cancer cells) is surrounded by cluster 0 (stroma) and cluster 3 (immune cells)
    • cluster1/2 presented a similar immune infiltration pattern, with an enrichment of proliferative T-cells and SPP1 macrophages
    • cluster 3 presented a distinct immune infiltration pattern characterized by an enriched presence of (proliferative) B-cells
    • cluster 0 harbored regulatory T-cells and terminally exhausted CD8 T-cells and was specifically enriched in M2 macrophages and naive T-cells.
  • Analysis of ductal breast carcinoma (BC)
    • also get a cancer-specific regional distribution:
    • subclonal was directly associated with local enrichment of distinct immune cell states
  • Foresee the regional distribution of immune cell types to become an important feature for the prediction of immuno-therapy outcome.