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)

• 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.