Deciphering functional tumor states at single-cell resolution

Abstract

Within a tumor, cancer cells exist in different states that are associated with distinct tumor functions, including proliferation, differentiation, invasion, metastasis, and resistance to anti-cancer therapy. The identification of the gene regulatory networks underpinning each state is essential for better understanding functional tumor heterogeneity and revealing tumor vulnerabilities. Here, we review the different studies identifying tumor states by single-cell sequencing approaches and the mechanisms that promote and sustain these functional states and regulate their transitions. We also describe how different tumor states are spatially distributed and interact with the specific stromal cells that compose the tumor microenvironment. Finally, we discuss how the understanding of tumor plasticity and transition states can be used to develop new strategies to improve cancer therapy.

Keywords: EMT; cancer therapy; metastasis; single-cell; tumor heterogeneity.

Figure 1. Single‐cell RNA sequencing to unravel tumor heterogeneity

Schematic showing how tumors derived from different models can be used for transcriptional analysis at the single‐cell level of both malignant and stromal cells. CAF: cancer‐associated fibroblasts; EMT: epithelial‐to‐mesenchymal transition; GEMM: genetically engineered mouse model; PDX: patient‐derived xenograft.

Figure 2. Dynamic differentiation trajectories and states during EMT

(A) Schematic representation of EMT trajectories described in skin squamous cell carcinoma (SCC) undergoing spontaneous EMT. Epithelial cells give rise to two different EMT trajectories. It is still an open question whether these two EMT trajectories are divergent or whether hybrid EMT trajectory (EMT1) can give rise to late EMT (EMT2). EMT: epithelial‐to‐mesenchymal transition. (B) Tumor progression occurs through different tumor transition states. These tumor states are located in different niches, characterized by increasing number of immune cells and vascular density. Cells in the hybrid EMT state have the highest metastatic capacity. Circulating TCs can circulate individually or in clusters. CTCs: circulating tumor cells.

Figure 3. Single‐cell approaches enable the identification and targeting of tumor cell states responsible for therapy resistance

Anti‐cancer therapy induces epigenetic and/or transcriptional transition states that lead to the emergence of drug‐tolerant cells (DTCs), which upon prolonged drug exposure eventually develop mutations conferring resistance. Single‐cell RNA sequencing allows to identify and target these drug‐tolerant states, thus delaying/hampering tumor relapse. Here we show the mechanisms that have been described in melanoma. GRN: gene regulatory network; RXRγ: Retinoid X Receptor gamma; TC: tumor cell.