Pan-cancer classification of single cells in the tumour microenvironment

Abstract

Single-cell RNA sequencing can reveal valuable insights into cellular heterogeneity within tumour microenvironments (TMEs), paving the way for a deep understanding of cellular mechanisms contributing to cancer. However, high heterogeneity among the same cancer types and low transcriptomic variation in immune cell subsets present challenges for accurate, high-resolution confirmation of cells’ identities. Here we present scATOMIC; a modular annotation tool for malignant and non-malignant cells. We trained scATOMIC on >300,000 cancer, immune, and stromal cells defining a pan-cancer reference across 19 common cancers and employ a hierarchical approach, outperforming current classification methods. We extensively confirm scATOMIC’s accuracy on 225 tumour biopsies encompassing >350,000 cancer and a variety of TME cells. Lastly, we demonstrate scATOMIC’s practical significance to accurately subset breast cancers into clinically relevant subtypes and predict tumours’ primary origin across metastatic cancers. Our approach represents a broadly applicable strategy to analyse multicellular cancer TMEs.

Multi-omics Identification and Route-Specific Characterization of Metastasis-specific EMT Genes and Their Microenvironmental Interactions

“…We extracted significantly differentially expressed genes in each of the original raw clusters from the analyzed samples. For elaborate mapping between clusters and cell type, we used two algorithms with Single R [33] for robust immune cell mapping and scATOMIC [34] for adjusting immune and cancer cell signals. Finally, we confirmed cell types using manual curation based on the consensus of three researchers.…”

Detecting phenotype-specific tumor microenvironment by merging bulk and single cell expression data to spatial transcriptomics

“…These methods focused on the association inference between single cells and bulk samples, rather than prediction of cellular phenotypes. Meanwhile, scATOMIC ( [22]) represents a comprehensive pancancer tool for classifying malignant and non-malignant cell employing random forest models. However, it is only based on single-cell data without phenotypic information from bulk samples.…”

Molecular, Metabolic, and Subcellular Mapping of the Tumor Immune Microenvironment via 3D Targeted and Non-Targeted Multiplex Multi-Omics Analyses

“…However, TCGA mainly uses bulk tissue samples, and therefore the data cannot be used to address key questions including how intertumoral and intratumoral heterogeneity develop, how tumor cells and various stromal cell types interact in the TIME, and how certain histological patterns within tumors such as the location of activate CD8+ T cells play an important role in modulating the malignant phenotypes of tumor cells [ 7 , 8 , 9 ]. Recent single-cell RNA sequencing (scRNAseq) projects and datasets from various tumor types allow the study of tumor heterogeneity [ 10 , 11 , 12 , 13 ]. However, the initial tissue dissociation step disrupts any three-dimensional spatial context, thus again lacking any information about cell–cell or cell-ECM interactions.…”