Dynamics inside the cancer cell attractor reveal cell heterogeneity, limits of stability, and escape

Abstract

The observed intercellular heterogeneity within a clonal cell population can be mapped as dynamical states clustered around an attractor point in gene expression space, owing to a balance between homeostatic forces and stochastic fluctuations. These dynamics have led to the cancer cell attractor conceptual model, with implications for both carcinogenesis and new therapeutic concepts. Immortalized and malignant EBV-carrying B-cell lines were used to explore this model and characterize the detailed structure of cell attractors. Any subpopulation selected from a population of cells repopulated the whole original basin of attraction within days to weeks. Cells at the basin edges were unstable and prone to apoptosis. Cells continuously changed states within their own attractor, thus driving the repopulation, as shown by fluorescent dye tracing. Perturbations of key regulatory genes induced a jump to a nearby attractor. Using the Fokker-Planck equation, this cell population behavior could be described as two virtual, opposing influences on the cells: one attracting toward the center and the other promoting diffusion in state space (noise). Transcriptome analysis suggests that these forces result from high-dimensional dynamics of the gene regulatory network. We propose that they can be generalized to all cancer cell populations and represent intrinsic behaviors of tumors, offering a previously unidentified characteristic for studying cancer.cancer cell attractor | cell heterogeneity | edge cells | gene regulatory network | cell population dynamics

Cell population structure prior to bifurcation predicts efficiency of directed differentiation in human induced pluripotent cells

“…Single-cell gene expression profiling of the extreme tails of the cKIT distribution (outliers), cKIT low and cKIT high cells, mapped them to cell states primed for the M and En lineages, respectively. Thus, information on prospective fate is hidden in the bulk population distributions and seems most pronounced in the outlier subpopulations (25) as evident at days 2 and 2.5. Although at this point, the population is still unimodal with respect to cKIT expression, the cKIT high cells expressed higher levels of SOX17, whereas the cKIT low cells expressed higher levels of HAND1, and both displayed decreased expression of NANOG (Fig.…”

Epigenetic plasticity potentiates a rapid cyclical shift to and from an aggressive cancer phenotype

“…This adaptive tumor evolution has been traditionally cast in the form of new DNA mutations that confer selective growth advantage and lead to clonal disease spread . However, it is now recognized that even within genetically homogeneous cell populations, significant phenotypic differences can exist or arise in response to environmental cues . In particular, many tumors contain subpopulations of aggressive, drug‐resistant cells, sometimes termed “tumor‐initiating cells” due to their high serial tumorigenicity in mice, or “cancer stem cells” due their self‐renewal capacity and active transcriptional networks of pluripotency genes …”

Intracellular and intercellular signaling networks in cancer initiation, development and precision anti-cancer therapy

“…The seminal work of Stuart Kauffman , Sui Huang and Ingemar Ernberg [19] convincingly argued that cancer cells are trapped in abnormal attractors named as cancer attractors. A recent study [20] showed that subpopulations of cancer cells may re-populate the attractor-basin. Importantly, ‘edge-cells’ (i.e.…”

“…cancer cells having signaling network activation pattern situating them at the edge of the cancer-attractor basin) may jump to an adjacent attractor, which may represent an even more de-differentiated, aggressive or metastatic state. Increased noise accelerates this process [20,21]. Recent work developed an efficient simulation tool of signaling network dynamics, Turbine [21,22, http://turbine.ai], which is able to find cancer attractors and to determine multitarget intervention point sets shifting cancer cells from their abnormal attractors to attractors characterizing healthy cells and/or driving cancer cells to apoptosis.…”

“…Combination therapies and multitarget drugs designed by the dynamic network analysis of cancer-related signaling networks - shifting cancer cells from cancer attractors [19,20] to apoptosis - may overcome some of the problems listed above [3,21, http://turbine.ai]. Importantly, this analysis may be extended to the intercellular signaling networks of the cancer/stromal cell ecosystem, which may form cancer tissue attractors [19,64,65].…”