文献解读,单细胞肿瘤免疫图谱 A Single-Cell Tumor Immune Atlas for Precision Oncology
SOURCE
WHY?
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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?
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Analyzing>500,000cells from217patients and13cancer types -
Data projection: Seurat's anchor-transferring method -
UsingSPOTlightto 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
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Collected scRNA-seq datasets from13 different cancer types, 217 patients and 526,261 cells

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Immune cells clustered by cell identity rather than patient origin: integrated317,111immune cells usingcanonical correlation analysis=> 25clusters
Get 2: Tumor subtype classifier
For Current:to establish a pan-cancer immune classification system
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usedimmune cell type and state frequenciesof the reference atlas as input for similarity assessment across the 13 cancer types -
Ahierarchical k-means clusteringusing 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
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trained aRF(random forest) classifierwith the 25 immune cell population achieving a highly accurate classification -
using the classifier, the pan-cancer immune classification system could be extended toadditional cancer types
GET 3: A resource for immune cell annotation
To demonstrate the potential value of the atlas
The applicability of the atlas as referenceacross different cancer types
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First:Project cells onto atlas using a reference-based projection (Fig. A)
Next: Typical clustering matching (Fig. B)

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Third: Check correlation (Fig. C)
The applicability of the atlas as referenceacross species
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two liver metastases derived frommouse CRC organoids -
main subtypes and specific subpopulations could also be assignedusing the human reference
GET 4: Spatial localization of immune cells in tumor sections
Spatialdistribution of immune cells is important forICI (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)
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cluster 1/2(cancer cells) is surrounded bycluster 0(stroma) andcluster 3(immune cells)
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cluster1/2presented a similar immune infiltration pattern, with an enrichment of proliferativeT-cellsandSPP1 macrophages
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cluster 3presented a distinct immune infiltration pattern characterized by an enriched presence of (proliferative)B-cells
cluster 0harbored regulatory T-cells and terminally exhausted CD8 T-cells and was specifically enriched inM2 macrophagesandnaive T-cells.
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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Foreseethe regional distribution of immune cell types to become an important feature for the prediction of immuno-therapy outcome.