A mechanism-based computational model to capture the interconnections among epithelial-mesenchymal transition, cancer stem cells and Notch-Jagged signaling

Abstract

Epithelial-mesenchymal transition (EMT) and cancer stem cell formation (CSCs) are two fundamental and well-studied processes contributing to cancer metastasis and tumor relapse. Cells can undergo a partial EMT to attain a hybrid epithelial/mesenchymal (E/M) phenotype or a complete EMT to attain a mesenchymal one. Similarly, cells can reversibly gain or lose 'stemness'. This plasticity in cell states is modulated by signaling pathways such as Notch. However, the interconnections among the cell states enabled by EMT, CSCs and Notch signaling remain elusive. Here, we devise a computational model to investigate the coupling among the core decisionmaking circuits for EMT, CSCs and the Notch pathway. Our model predicts that hybrid E/M cells are most likely to associate with stemness traits and exhibit enhanced Notch-Jagged signaling -a pathway that is implicated in therapeutic resistance. Further, we show that the position of the 'stemness window' on the 'EMT axis' is varied by altering the coupling strength between EMT and CSC circuits, and/or modulating Notch signaling. Finally, we analyze the gene expression profile of CSCs from several cancer types and observe a heterogeneous distribution along the 'EMT axis', suggesting that different subsets of CSCs may exist with varying phenotypes along the epithelialmesenchymal plasticity axis. Our computational model offers insights into the complex EMTstemness interplay and provides a platform to investigate the effects of therapeutic perturbations such as treatment with metformin, a common anti-diabetic drug that has been associated with decreased cancer incidence and increased lifespan of patients. Our mechanism-based model helps explain how metformin can both inhibit EMT and blunt the aggressive potential of CSCs simultaneously, by driving the cells out of a hybrid E/M stem-like state with enhanced Notch-Jagged signaling.

Toward understanding cancer stem cell heterogeneity in the tumor microenvironment

“…3G). This prediction is in good agreement with our previous observation that an E/M-CSC phenotype can switch to an M-CSC phenotype upon activation of TGF-β downstream targets (14). These results offer a unifying framework for the concepts of intratumoral heterogeneity, EMT, and CSCs and indicate the crucial role of JAG1 in simultaneously promoting a hybrid E/M phenotype and the acquisition of stem properties.…”

“…Notch-Jagged Signaling. Previous observations have suggested a key role of the Notch-Jagged signaling axis in mediating CSC properties (14,(42)(43)(44). Therefore, we next focused on what signals in the TME can promote Notch-Jagged signaling.…”

“…This classification is based on observations in multiple cancer types such as glioblastoma, pancreatic cancer, colon cancer, and basal-like breast cancer (BLBC) that Notch and Jagged are overexpressed in CSCs compared with non-CSCs (51-54). Further, our previous in silico results indicated that enhanced Notch-Jagged signaling is likely to correlate with the acquisition of stem-like traits (14). Thus, to elucidate the connection between inflammation, EMT, and CSCs, we determined the frequency of CSCs in a population of a (100 × 100) multicell lattice.…”

“…Notch-Jagged signaling has been studied in multiple developmental contexts for its role in cellular decision making and tissue patterning (11)(12)(13). Recent in silico, in vitro, and in vivo observations have suggested that Notch-Jagged signaling can promote a hybrid epithelial/mesenchymal (E/M) phenotype and traits similar to CSCs (5,(14)(15)(16)(17). Moreover, Notch-Jagged signaling among cancer cells can facilitate the formation of clusters of circulating tumor cells (CTCs)-the "bad actors" of cancer metastasis (15,16,18).…”

Toward Modeling Context-Specific EMT Regulatory Networks Using Temporal Single Cell RNA-Seq Data

“…Here, we will adopt a combined bioinformatics and systemsbiology modeling approach to construct context-specific core GRCs using the above-mentioned time-series scRNA-seq data from multiple cell lines and signaling treatment conditions (Figure 1). Many previous computational studies have been conducted to build EMT GRCs from literature support (Steinway et al, 2014;Hong et al, 2015;Burger et al, 2017;Huang et al, 2017;Bocci et al, 2018;Kohar and Lu, 2018;Ramirez et al, 2019). However, this approach is not optimized for the goal of this project, as there might not be sufficient literature data for a specific experimental condition.…”