Abstract

Simple SummaryPhenotypic plasticity is emerging as a crucial feature across multiple axes of cancer progression. Cells undergoing the epithelial–mesenchymal transition (EMT) can fall into intermediate or hybrid epithelial/mesenchymal (E/M) cell states. Moreover, cancer cells across the EMT spectrum can exhibit the traits of cancer stem cells (CSCs) and communicate through several cell-cell signaling pathways. By integrating multiple analytical tools into a single computational framework, we investigated several single-cell RNA-sequencing (scRNA-seq) datasets and identified the emerging relationship between EMT, acquisition of CSC traits, and cell–cell communication. Our integrated analysis shows that the increase of EM plasticity correlates with high expression of CSC markers as well as intensification of cell–cell signaling between cancer cells. Furthermore, these observations are consistent across different cancer types and anatomical locations. Overall, our results shine light onto the interconnected and multi-dimensional landscape of cancer progression.Intermediate cell states (ICSs) during the epithelial–mesenchymal transition (EMT) are emerging as a driving force of cancer invasion and metastasis. ICSs typically exhibit hybrid epithelial/mesenchymal characteristics as well as cancer stem cell (CSC) traits including proliferation and drug resistance. Here, we analyze several single-cell RNA-seq (scRNA-seq) datasets to investigate the relation between several axes of cancer progression including EMT, CSC traits, and cell–cell signaling. To accomplish this task, we integrate computational methods for clustering and trajectory inference with analysis of EMT gene signatures, CSC markers, and cell–cell signaling pathways, and highlight conserved and specific processes across the datasets. Our analysis reveals that “standard” measures of pluripotency often used in developmental contexts do not necessarily correlate with EMT progression and expression of CSC-related markers. Conversely, an EMT circuit energy that quantifies the co-expression of epithelial and mesenchymal genes consistently increases along EMT trajectories across different cancer types and anatomical locations. Moreover, despite the high context specificity of signal transduction across different cell types, cells undergoing EMT always increased their potential to send and receive signals from other cells.

Highlights

  • The epithelial–mesenchymal transition (EMT) stands out as one of the main molecular processes that drive cancer progression by facilitating cell migration, metabolic reprogramming, and interactions between the tumor and immune system [1,2]

  • We have previously shown that unsupervised clustering with QuanTC leads to the identification of four cell types in the squamous cell carcinoma (SCC) dataset [10], which can be visualized in a low-dimensional projection space (Figures 1A and S1A)

  • To gain more insight into the biological role of these three states, we reconstructed the pseudotime ordering and EMT trajectories starting from the epithelial state with QuanTC

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Summary

Introduction

The epithelial–mesenchymal transition (EMT) stands out as one of the main molecular processes that drive cancer progression by facilitating cell migration, metabolic reprogramming, and interactions between the tumor and immune system [1,2]. On how to characterize these intermediate states and their connection with other axes of cancer progression, such as tumor-initiating ability and cell–cell signaling To tackle these questions, here we integrate existing tools for clustering and trajectory inference from scRNA-seq data with analysis of epithelial and mesenchymal gene signatures [11]. We evaluate cell plasticity along the epithelial–mesenchymal spectrum (or E-M plasticity) by computing an EMT circuit energy that is maximized when both epithelial and mesenchymal genes are highly expressed. Together, these methodologies provide unprecedented information on EMT progression and its intermediate states

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