A physical mechanism of cancer heterogeneity

Unveiling the Dynamics behind Glioblastoma Multiforme Single-Cell Data Heterogeneity

Glioblastoma Multiforme is a brain tumor distinguished by its aggressiveness. We suggested that this aggressiveness leads single-cell RNA-sequence data (scRNA-seq) to span a representative portion of the cancer attractors domain. This conjecture allowed us to interpret the scRNA-seq heterogeneity as reflecting a representative trajectory within the attractor's domain. We considered factors such as genomic instability to characterize the cancer dynamics through stochastic fixed points. The fixed points were derived from centroids obtained through various clustering methods to verify our method sensitivity. This methodological foundation is based upon sample and time average equivalence, assigning an interpretative value to the data cluster centroids and supporting parameters estimation. We used stochastic simulations to reproduce the dynamics, and our results showed an alignment between experimental and simulated dataset centroids. We also computed the Waddington landscape, which provided a visual framework for validating the centroids and standard deviations as characterizations of cancer attractors. Additionally, we examined the stability and transitions between attractors and revealed a potential interplay between subtypes. These transitions might be related to cancer recurrence and progression, connecting the molecular mechanisms of cancer heterogeneity with statistical properties of gene expression dynamics. Our work advances the modeling of gene expression dynamics and paves the way for personalized therapeutic interventions.

Perspectives on the landscape and flux theory for describing emergent behaviors of the biological systems

We give a review on the landscape theory of the equilibrium biological systems and landscape-flux theory of the nonequilibrium biological systems as the global driving force. The emergences of the behaviors, the associated thermodynamics in terms of the entropy and free energy and dynamics in terms of the rate and paths have been quantitatively demonstrated. The hierarchical organization structures have been discussed. The biological applications ranging from protein folding, biomolecular recognition, specificity, biomolecular evolution and design for equilibrium systems as well as cell cycle, differentiation and development, cancer, neural networks and brain function, and evolution for nonequilibrium systems, cross-scale studies of genome structural dynamics and experimental quantifications/verifications of the landscape and flux are illustrated. Together, this gives an overall global physical and quantitative picture in terms of the landscape and flux for the behaviors, dynamics and functions of biological systems.

Deciphering the molecular mechanism of the cancer formation by chromosome structural dynamics

Cancer reflects the dysregulation of the underlying gene network, which is strongly related to the 3D genome organization. Numerous efforts have been spent on experimental characterizations of the structural alterations in cancer genomes. However, there is still a lack of genomic structural-level understanding of the temporal dynamics for cancer initiation and progression. Here, we use a landscape-switching model to investigate the chromosome structural transition during the cancerization and reversion processes. We find that the chromosome undergoes a non-monotonic structural shape-changing pathway with initial expansion followed by compaction during both of these processes. Furthermore, our analysis reveals that the chromosome with a more expanding structure than those at both the normal and cancer cell during cancerization exhibits a sparse contact pattern, which shows significant structural similarity to the one at the embryonic stem cell in many aspects, including the trend of contact probability declining with the genomic distance, the global structural shape geometry and the spatial distribution of loci on the chromosome. In light of the intimate structure-function relationship at the chromosomal level, we further describe the cell state transition processes by the chromosome structural changes, suggesting an elevated cell stemness during the formation of the cancer cells. We show that cell cancerization and reversion are highly irreversible processes in terms of the chromosome structural transition pathways, spatial repositioning of chromosomal loci and hysteresis loop of contact evolution analysis. Our model draws a molecular-scale picture of cell cancerization from the chromosome structural perspective. The process contains initial reprogramming towards the stem cell followed by the differentiation towards the cancer cell, accompanied by an initial increase and subsequent decrease of the cell stemness.

Stochastic epigenetic dynamics of gene switching

Epigenetic modifications of histones crucially affect eukaryotic gene activity, while the epigenetic histone state is largely determined by the binding of specific factors such as the transcription factors (TFs) to DNA. Here, the way in which the TFs and the histone state are dynamically correlated is not obvious when the TF synthesis is regulated by the histone state. This type of feedback regulatory relation is ubiquitous in gene networks to determine cell fate in differentiation and other cell transformations. To gain insights into such dynamical feedback regulations, we theoretically analyze a model of epigenetic gene switching by extending the Doi-Peliti operator formalism of reaction kinetics to the problem of coupled molecular processes. Spin-1 and spin-1/2 coherent-state representations are introduced to describe stochastic reactions of histones and binding or unbinding of TFs in a unified way, which provides a concise view of the effects of timescale difference among these molecular processes; even in the case that binding or unbinding of TFs to or from DNA is adiabatically fast, the slow nonadiabatic histone dynamics gives rise to a distinct circular flow of the probability flux around basins in the landscape of the gene state distribution, which leads to hysteresis in gene switching. In contrast to the general belief that the change in the amount of TF precedes the histone state change, flux drives histones to be modified prior to the change in the amount of TF in self-regulating circuits. Flux-landscape analyses shed light on the nonlinear nonadiabatic mechanism of epigenetic cell fate decision making.

Breast cancer: Muscarinic receptors as new targets for tumor therapy

The development of breast cancer is a complex process that involves the participation of different factors. Several authors have demonstrated the overexpression of muscarinic acetylcholine receptors (mAChRs) in different tumor tissues and their role in the modulation of tumor biology, positioning them as therapeutic targets in cancer. The conventional treatment for breast cancer involves surgery, radiotherapy, and/or chemotherapy. The latter presents disadvantages such as limited specificity, the appearance of resistance to treatment and other side effects. To prevent these side effects, several schedules of drug administration, like metronomic therapy, have been developed. Metronomic therapy is a type of chemotherapy in which one or more drugs are administered at low concentrations repetitively. Recently, two chemotherapeutic agents usually used to treat breast cancer have been considered able to activate mAChRs. The combination of low concentrations of these chemotherapeutic agents with muscarinic agonists could be a useful option to be applied in breast cancer treatment, since this combination not only reduces tumor cell survival without affecting normal cells, but also decreases pathological neo-angiogenesis, the expression of drug extrusion proteins and the cancer stem cell fraction. In this review, we focus on the previous evidences that have positioned mAChRs as relevant therapeutic targets in breast cancer and analyze the effects of administering muscarinic agonists in combination with conventional chemotherapeutic agents in a metronomic schedule.

A global and physical mechanism of gastric cancer formation and progression

Gastric cancer is regarded as a major health issue for human being nowadays. The Helicobacter pylori (H. pylori) infection has been found to accelerate the development of gastritis and gastric cancer. Significant efforts have been made towards the understanding of the biology of gastric cancer on both genetic and epigenetic levels. However the physical mechanism behind the gastric cancer formation is still elusive. In this study, we constructed a model for investigating gastric cancer formation by explored the gastric cancer landscape and the flow flux. We uncovered three stable state attractors on the landscape: normal, gastritis and gastric cancer. The definition of each attractor is based on the biological function and gene expression levels. The global stabilities and the switching processes were quantified through the barrier heights and dominant kinetic paths. To investigate the underlying mechanism of the process from normal through the gastritis to the gastric cancer caused by genetic or epigenetic factors, we simulate the oncogenesis of gastric cancer through changes of several gene regulation strengths and H. pylori infection. The simulated results can illustrate the developmental and metastasis process of gastric cancer. Different H. pylori infection degrees accelerating the process from gastritis to gastric cancer can be quantified. Then we applied global sensitivity analysis, one key gene and four key regulations were found. These results are consist with the experimental results and can be used to design the polygenic anti-cancer agents through multiple key genes or regulations. The landscape approach provides a physical and simple strategy for analyzing gastric cancer in a systematic and quantitative way. It also offers new insight into treatment strategy for gastric cancer by adjusting relevant polygenic genes and regulations.

Uncovering the Underlying Mechanisms of Cancer Metabolism through the Landscapes and Probability Flux Quantifications

Cancer metabolism is critical for understanding the mechanism of tumorigenesis, yet the understanding is still challenging. We studied gene-metabolism regulatory interactions and quantified the global driving forces for cancer-metabolism dynamics as the underlying landscape and probability flux. We uncovered four steady-state attractors: a normal state attractor, a cancer OXPHOS state attractor, a cancer glycolysis state attractor, and an intermediate cancer state attractor. We identified the key regulatory interactions through global sensitivity analysis based on the landscape topography. Different landscape topographies of glycolysis switch between normal cells and cancer cells were identified. We uncovered that the normal state to cancer state transformation is associated with the peaks of the probability flux and the thermodynamic dissipation, giving dynamical and thermodynamic origin of cancer formation. We found that cancer metabolism oscillations consume more energy to support cancer malignancy. This study provides a quantitative understanding of cancer metabolism and suggests a metabolic therapeutic strategy.

Landscape perspectives of tumor, EMT, and development

A tumor is rarely fatal until becoming metastatic. Recent discoveries suggest that epithelial mesenchymal transition(EMT) is an important process which contributes to not only cancer metastasis but also increased stemness. Cancer cells with stem cell characteristics are called cancer stem cells (CSCs). We review recent efforts to quantify and delineate the relationship among EMT, CSC and tumor development. When the gene regulatory network is tightly regulated through the effectively fast regulatory binding, Cancer, Premalignant, Normal, CSC, stem cell (SC), Lesion and Hyperplasia states emerged. The corresponding landscape topography for all of these states can be quantified to a global way for uncovering the relationship among the tumor, metastasis, and development. On the other hand, phenotypic and functional heterogeneity is regarded as one of the greatest challenge in cancer treatment. Cancer and CSCs are heterogeneous and give rise to tumorigenic and non-tumorigenic cells during self-renewal, differentiation and epigenetic diversification. Further, if the gene regulatory network is weakly regulated through the effective slow regulatory binding (by DNA methylation or histone modification etc), multiple meta-stable states can emerge. This model can provide an epigenetic and physical rather than genetic and fixed origin of heterogeneity. Elucidating the origin of and dynamic nature of tumor cells will likely help better understand the cellular basis of therapeutic response, resistance, and relapse.

Assessment of the toxicogenic effects and cell death potential of the ester (Z)-methyl 4-((1,5-dimethyl-3-oxo-2-phenyl-2,3-dihydro-1H-pyrazol-4-yl)amino)-4-oxobut-2-anoate in combination with cisplatin, cyclophosphamide and doxorubicin

Despite rapid advances in both the early detection and treatment of cancer, the mortality from this disease remains high, which justifies the development of new products that are more selective and effective and have fewer side effects. Accordingly, a novel ester was synthesized that contains two pharmacophores with important biological activities: (I) 4-aminoantipyrine, which has anti-inflammatory and antioxidant effects, and (II) the pharmacophore 1,4-dioxo-butenyl, which has cytotoxic activity. When administered alone, this compound is non-genotoxic, and it does not cause an increasing in splenic phagocytosis. Nevertheless, it can induce cell death. When administered in combination with commercial chemotherapeutic agents, such as doxorubicin, cisplatin, and cyclophosphamide, the ester shows antigenotoxic activity and decreases phagocytosis and reduces the potential to cause cell death. These results indicate that the compound should not be used in combination with chemotherapeutic agents that exert their effect through DNA damage, an important feature of antitumor drugs.

Multi-modality in gene regulatory networks with slow promoter kinetics

Phenotypical variability in the absence of genetic variation often reflects complex energetic landscapes associated with underlying gene regulatory networks (GRNs). In this view, different phenotypes are associated with alternative states of complex nonlinear systems: stable attractors in deterministic models or modes of stationary distributions in stochastic descriptions. We provide theoretical and practical characterizations of these landscapes, specifically focusing on stochastic Slow Promoter Kinetics (SPK), a time scale relevant when transcription factor binding and unbinding are affected by epigenetic processes like DNA methylation and chromatin remodeling. In this case, largely unexplored except for numerical simulations, adiabatic approximations of promoter kinetics are not appropriate. In contrast to the existing literature, we provide rigorous analytic characterizations of multiple modes. A general formal approach gives insight into the influence of parameters and the prediction of how changes in GRN wiring, for example through mutations or artificial interventions, impact the possible number, location, and likelihood of alternative states. We adapt tools from the mathematical field of singular perturbation theory to represent stationary distributions of Chemical Master Equations for GRNs as mixtures of Poisson distributions and obtain explicit formulas for the locations and probabilities of metastable states as a function of the parameters describing the system. As illustrations, the theory is used to tease out the role of cooperative binding in stochastic models in comparison to deterministic models, and applications are given to various model systems, such as toggle switches in isolation or in communicating populations, a synthetic oscillator, and a trans-differentiation network.

Identifying the optimal anticancer targets from the landscape of a cancer-immunity interaction network

Cancer immunotherapy, an approach of targeting immune cells to attack tumor cells, has been suggested to be a promising way for cancer treatment recently. However, the successful application of this approach warrants a deeper understanding of the intricate interplay between cancer cells and the immune system. Especially, the mechanisms of immunotherapy remain elusive. In this work, we constructed a cancer-immunity interplay network by incorporating interactions among cancer cells and some representative immune cells, and uncovered the potential landscape of the cancer-immunity network. Three attractors emerge on the landscape, representing the cancer state, the immune state, and the hybrid state, which can correspond to escape, elimination, and equilibrium phases in the immunoediting theory, respectively. We quantified the transition processes between the cancer state and the immune state by calculating transition actions and identifying the corresponding minimum action paths (MAPs) between these two attractors. The transition actions, directly calculated from the high dimensional system, are correlated with the barrier heights from the landscape, but provide a more precise description of the dynamics of a system. By optimizing the transition actions from the cancer state to the immune state, we identified some optimal combinations of anticancer targets. Our combined approach of the landscape and optimization of transition actions offers a framework to study the stochastic dynamics and identify the optimal combination of targets for the cancer-immunity interplay, and can be applied to other cell communication networks or gene regulatory networks.

Mesenchymal stem cells over-expressing cxcl12 enhance the radioresistance of the small intestine

The chemokine C-X-C motif chemokine 12 (CXCL12) greatly impacts various biological processes in mammals, including cell survival, growth and migration. Mesenchymal stem cells (MSCs) are promising tools for carrying foreign genes to treat radiation-induced injuries in the intestinal epithelium. In this study, human adipose-derived MSCs were constructed to over-express the mouse cxcl12 gene to treat such injuries. In vitro, because of the high levels of mouse CXCL12 in conditioned medium produced by mouse cxcl12 gene-modified cells, phosphorylation of Akt at Ser473 and Erk1/2 at Thr202/Thr204 was increased within crypt cells of irradiated organoids compared with unmodified controls. Moreover, intracellular stabilization of β-catenin was achieved after treatment of mouse cxcl12 gene-modified cells with conditioned medium. As a result, survival of crypt cells was maintained and their proliferation was promoted. When delivering mouse cxcl12 gene-modified cells into irradiated BALB/c nude mice, mice were rescued despite the clearance of cells from the host within 1 week. Irradiated mice that received mouse cxcl12 gene-modified MSCs exhibited reduced serum levels of interleukin-1α (IL-1α) and IL-6 as well as elevated levels of CXCL12. Additionally, epithelial recovery from radiation stress was accelerated compared with the irradiated-alone controls. Moreover, mouse cxcl12 gene-modified MSCs were superior to unmodified cells at strengthening host repair responses to radiation stress as well as presenting increased serum CXCL12 levels and decreased serum IL-1α levels. Furthermore, the number of crypt cells that were positive for phosphorylated Akt at Ser473 and phosphorylated Erk1/2 at Thr202/Thr204 increased following treatment with mouse cxcl12 gene-modified MSCs. Thus, cxcl12 gene-modified MSCs confer radioresistance to the intestinal epithelium.