A single-cell tumor immune atlas for precision oncology

Abstract

The tumor immune microenvironment is a main contributor to cancer progression and a promising therapeutic target for oncology. However, immune microenvironments vary profoundly between patients, and biomarkers for prognosis and treatment response lack precision. A comprehensive compendium of tumor immune cells is required to pinpoint predictive cellular states and their spatial localization. We generated a single-cell tumor immune atlas, jointly analyzing published data sets of >500,000 cells from 217 patients and 13 cancer types, providing the basis for a patient stratification based on immune cell compositions. Projecting immune cells from external tumors onto the atlas facilitated an automated cell annotation system. To enable in situ mapping of immune populations for digital pathology, we applied SPOTlight, combining single-cell and spatial transcriptomics data and identifying colocalization patterns of immune, stromal, and cancer cells in tumor sections. We expect the tumor immune cell atlas, together with our versatile toolbox for precision oncology, to advance currently applied stratification approaches for prognosis and immunotherapy.

Differential analysis of binarized single-cell RNA sequencing data captures biological variation

“…The three datasets with the lowest correlation coefficient between logOR and logFC (ρ ≤ 0.62) were datasets generated using the Smart-seq protocol (Table 1 ). Across the datasets, we observed an average increase of 1.80 in logOR (median = 1.70, Q 1 = 1.59, Q 3 = 2.10), for every increase in logFC (see Figure 1B for the cancer atlas (2) dataset ( 8 )). The high degree of agreement of detected genes shows that BDA-LR performs on par with the Wilcoxon Rank Sum test, and the strong correlation of the logFC and logOR across all datasets shows that the results can be interpreted in a similar way.…”

“…For the aging mouse atlases ( 7 ), instead of annotated cell types we retrieved the tissue names. For the cancer atlas ( 8 ), the contrasting cell populations were defined by cell type, so we retrieved the tissue and the cancer-type for each cell. Each dataset was separately pre-processed.…”

The Novel Immune Checkpoint GPR56 Is Expressed on Tumor-Infiltrating Lymphocytes and Selectively Upregulated upon TCR Signaling

“…The dataset from the tumor Immune Cell Atlas study [36] was downloaded in the form of an RDS file containing the Seurat object. The cell types in the study were already defined.…”

The use of single-cell multi-omics in immuno-oncology

“…For example, using one Smart-Seq2 (deep sequencing depth and high sensitivity) scRNA-seq and 10X Genomics (suitable for detecting large cell populations due to its massive throughputs) scRNA-seq data from CD45 + immune cells, Zhang et al identified LAMP3 + dendritic cells as an important cell type originating from tumors, migrating to hepatic lymph nodes, and shaping the lymphocyte function through antigen-specific priming 8 . The fourth advantage is the ability to build single-cell atlas, such as the tumor immune atlas 9 , in order to provide a comprehensive compendium of immune cells and an inspection of gene expression patterns in different immune cell types.…”