SOURCE
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>500,000cells 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
Collected scRNA-seq datasets from13 different cancer types, 217 patients and 526,261 cells

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
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
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
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 referenceacross species
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)
cluster 1/2(cancer cells) is surrounded bycluster 0(stroma) andcluster 3(immune cells)
cluster1/2presented a similar immune infiltration pattern, with an enrichment of proliferativeT-cellsandSPP1 macrophages
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)
also get a cancer-specific regional distribution:
subclonal was directly associated with local enrichment of distinct immune cell states
Foreseethe regional distribution of immune cell types to become an important feature for the prediction of immuno-therapy outcome.
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