Intercellular signaling reinforces single-cell level phenotypic transitions and facilitates robust re-equilibrium of heterogeneous cancer cell populations

Cancer cells within tumors exhibit a wide range of phenotypic states driven by non-genetic mechanisms, such as epithelial-to-mesenchymal transition (EMT), in addition to extensively studied genetic alterations. Conversions among cancer cell states can result in intratumoral heterogeneity which contributes to metastasis and development of drug resistance. However, mechanisms underlying the initiation and/or maintenance of such phenotypic plasticity are poorly understood. In particular, the role of intercellular communications in phenotypic plasticity remains elusive. In this study, we employ a multiscale inference-based approach that integrates single-cell transcriptomic data to predict phenotypic changes and tumor population dynamics. Our computational framework combines ligand-receptor interaction inference (CellChat), transcription factor activity estimation (decoupleR), and causal signaling network reconstruction to analyze single-cell RNA sequencing (scRNA-seq) data and investigate how intercellular interactions influence cancer cell phenotypes, with a particular focus on EMT-related gene programs. We further use mathematical models based on ordinary differential equations, informed by network inferences, to examine how intercellular communication shapes phenotypic dynamics at the population level from a dynamical systems perspective. Our work highlights the critical role of intercellular signaling in sustaining intratumoral heterogeneity, and our approach of computational analysis of scRNA-seq data can infer inter- and intra-cellular signaling networks in a holistic manner.