Recurrence of cancer cell states across diverse tumors and their interactions with the microenvironment

Abstract

While genetic tumor heterogeneity has long been recognized, recent work has revealed significant variation among cancer cells at the epigenetic and transcriptional levels. Profiling tumors at the single-cell level in individual cancer types has shown that transcriptional heterogeneity is organized into cancer cell states, implying that diverse cell states may represent stable and functional units with complementary roles in tumor maintenance and progression. However, it remains unclear to what extent these states span tumor types, constituting general features of cancer. Furthermore, the role of cancer cell states in tumor progression and their specific interactions with cells of the tumor microenvironment remain to be elucidated. Here, we perform a pan-cancer single-cell RNA-Seq analysis across 15 cancer types and identify a catalog of 16 gene modules whose expression defines recurrent cancer cell states, including 'stress', 'interferon response', 'epithelial-mesenchymal transition', 'metal response', 'basal' and 'ciliated'. Using mouse models, we find that induction of the interferon response module varies by tumor location and is diminished upon elimination of lymphocytes. Moreover, spatial transcriptomic analysis further links the interferon response in cancer cells to T cells and macrophages in the tumor microenvironment. Our work provides a framework for studying how cancer cell states interact with the tumor microenvironment to form organized systems capable of immune evasion, drug resistance, and metastasis.

Intercellular extrachromosomal DNA copy number heterogeneity drives cancer cell state diversity

“…Thirty-one modules without significant correlation to at least 50% of other modules were considered uncommon and removed from downstream analyses. Average gene activity scores for each meta and submodule followed by gene set enrichment analysis (GSEA) revealed high activity of genes involved in cell cycle progression and cell division for Meta Module 1 (e.g., KIF18B, ASPM, KIF14), in line with recent findings in other cancer entities (26). In particular, submodules of this cell cycle meta module showed enrichment of replication (S1) and cell division (S8).…”

“…Recent large-scale efforts into transcriptional cell states pan-cancer have identified cell-cycle related modules, but not any associated with ribosome biogenesis or cell-cell interactions (26). We thus hypothesised that the latter modules might be specific to neuroblastoma biology and indicative of MYCN -mediated upregulation of ribosome biogenesis (27,28) and downregulation of neurogenesis (29).…”

“…The enriched pathways demonstrate biological functionality of the observed MYCN expression heterogeneity on the single-cell level and its consistency across a cohort of MYCN -amplified primary neuroblastomas. We describe its association with changes in ribosomal activity and cell-cell interaction and to a weaker degree with cell cycle transition, potentially indirectly through co-activation of other cancer pathways (26).…”

“…The cell-cell interaction meta module was divided into submodules associated with neuronal differentiation (S3), sensory perception (S4), regulation of cell size (S5), axonogenesis (S7) and synaptic signalling (S9) [Figure 1d; Table S3, Additional File 2]. Recent large-scale efforts into transcriptional cell states pan-cancer have identified cell-cycle related modules, but not any associated with ribosome biogenesis or cell-cell interactions (26). We thus hypothesised that the latter modules might be specific to neuroblastoma biology and indicative of MYCN-mediated upregulation of ribosome biogenesis (27,28) and downregulation of neurogenesis (29).…”

Disrupting cellular memory to overcome drug resistance

“…Extending scMemorySeq beyond state switching in melanoma, there are a number of biological contexts in which specific drugs or ligands could be shifting gene expression states and population dynamics simultaneously. This is particularly relevant in cancer where there is a growing body of literature describing considerable heterogeneity at the single-cell level that shows variable degrees of memory (Tirosh et al 2016; Barkley et al 2021; Neftel et al 2019). Here, we show that the scMemorySeq approach can provide a detailed systematic overview of these populations, and can also be used to test how closely related cells from the same lineage will react to different conditions.…”