Hallmarks of transcriptional intratumour heterogeneity across a thousand tumours.

Each tumour contains diverse cellular states that underlie intratumour heterogeneity (ITH), a central challenge of cancer therapeutics1. Dozens of recent studies have begun to describe ITH by single-cell RNA sequencing, but each study typically profiled only a small number of tumours and provided a narrow view of transcriptional ITH2. Here we curate, annotate and integrate the data from 77 different studies to reveal the patterns of transcriptional ITH across 1,163 tumour samples covering 24 tumour types. Among the malignant cells, we identify 41 consensus meta-programs, each consisting of dozens of genes that are coordinately upregulated in subpopulations of cells within many tumours. The meta-programs cover diverse cellular processes including both generic (for example, cell cycle and stress) and lineage-specific patterns that we map into 11 hallmarks of transcriptional ITH. Most meta-programs of carcinoma cells are similar to those identified in non-malignant epithelial cells, suggesting that a large fraction of malignant ITH programs are variable even before oncogenesis, reflecting the biology of their cell of origin. We further extended the meta-program analysis to six common non-malignant cell types and utilize these to map cell-cell interactions within the tumour microenvironment. In summary, we have assembled a comprehensive pan-cancer single-cell RNA-sequencing dataset, which is available through the Curated Cancer Cell Atlas website, and leveraged this dataset to carry out a systematic characterization of transcriptional ITH.

Elucidating the diversity of malignant mesenchymal states in glioblastoma by integrative analysis.

BACKGROUND: Multiple glioblastoma studies have described a mesenchymal (MES) state, with each study defining the MES program by distinct sets of genes and highlighting distinct functional associations, including both immune activation and suppression. These variable descriptions complicate our understanding of the MES state and its implications. Here, we hypothesize that there is a range of glioma MES states, possibly reflecting distinct prior states in which a MES program can be induced, and/or distinct mechanisms that induce the MES states in those cells. METHODS: We integrated multiple published single-cell and bulk RNA sequencing datasets and MES signatures to define a core MES program that recurs across studies, as well as multiple function-specific MES signatures that vary across MES cells. We then examined the co-occurrence of these signatures and their associations with genetic and microenvironmental features. RESULTS: Based on co-occurrence of MES signatures, we found three main variants of MES states: hypoxia-related (MES-Hyp), astrocyte-related (MES-Ast), and an intermediate state. Notably, the MES states are differentially associated with genetic and microenvironmental features. MES-Hyp is preferentially associated with NF1 deletion, overall macrophage abundance, a high macrophage/microglia ratio, and M2-related macrophages, consistent with previous studies that associated MES with immune suppression. In contrast, MES-Ast is associated with T cell abundance and cytotoxicity, consistent with immune activation through expression of MHC-I/II. CONCLUSIONS: Diverse MES states occur in glioblastoma. These states share a subset of core genes but differ primarily in their association with hypoxia vs. astrocytic expression programs, and with immune suppression vs. activation, respectively.