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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
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
- 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
- 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
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Collected scRNA-seq datasets from 13 different cancer types, 217 patients and 526,261 cells
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Immune cells clustered by cell identity rather than patient origin: integrated 317,111 immune cells using canonical correlation analysis => 25 clusters
- For Current: to establish a pan-cancer immune classification system
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used immune cell type and state frequencies of the reference atlas as input for similarity assessment across the 13 cancer types
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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)
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- 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
To demonstrate the potential value of the atlas
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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)
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The applicability of the atlas as reference across species
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two liver metastases derived from mouse CRC organoids
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main subtypes and specific subpopulations could also be assigned using the human reference
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Spatial distribution of immune cells is important for ICI (immune checkpoint inhibitors) response
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Single-cell reference atlas immune profiles + Spatial transcriptome data
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SPOTlight : non-negative matrix factorization (NMF) based spatial deconvolution framework
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Analysis of oropharyngeal squamous cell carcinoma (SCC)
- cluster 1/2 (cancer cells) is surrounded by cluster 0 (stroma) and cluster 3 (immune cells)
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cluster1/2 presented a similar immune infiltration pattern, with an enrichment of proliferative T-cells and SPP1 macrophages
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cluster 3 presented a distinct immune infiltration pattern characterized by an enriched presence of (proliferative) B-cells
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cluster 0 harbored regulatory T-cells and terminally exhausted CD8 T-cells and was specifically enriched in M2 macrophages and naive T-cells.
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Analysis of ductal breast carcinoma (BC)
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also get a cancer-specific regional distribution:
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subclonal was directly associated with local enrichment of distinct immune cell states
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Foresee the regional distribution of immune cell types to become an important feature for the prediction of immuno-therapy outcome.
