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

Paper Link: https://www.biorxiv.org/content/10.1101/2020.10.26.354829v1

Data Link (Restricted Access): https://zenodo.org/record/4036020#.X5uoHlMzaHF

Code Link: https://github.com/Single-Cell-Genomics-Group-CNAG-CRG/Tumor-Immune-Cell-Atlas

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.