Comprehensive Bioinformatic Investigation of TP53 Dysregulation in Diverse Cancer Landscapes.

P53 overexpression plays a critical role in cancer pathogenesis by disrupting the intricate regulation of cellular proliferation. Despite its firmly established function as a tumor suppressor, elevated p53 levels can paradoxically contribute to tumorigenesis, influenced by factors such as exposure to carcinogens, genetic mutations, and viral infections. This phenomenon is observed across a spectrum of cancer types, including bladder (BLCA), ovarian (OV), cervical (CESC), cholangiocarcinoma (CHOL), colon adenocarcinoma (COAD), diffuse large B-cell lymphoma (DLBC), esophageal carcinoma (ESCA), head and neck squamous cell carcinoma (HNSC), kidney chromophobe (KICH), kidney renal clear cell carcinoma (KIRC), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), and uterine corpus endometrial carcinoma (UCEC). This broad spectrum of cancers is often associated with increased aggressiveness and recurrence risk. Effective therapeutic strategies targeting tumors with p53 overexpression require a comprehensive approach, integrating targeted interventions aimed at the p53 gene with conventional modalities such as chemotherapy, radiation therapy, and targeted drugs. In this extensive study, we present a detailed analysis shedding light on the multifaceted role of TP53 across various cancers, with a specific emphasis on its impact on disease-free survival (DFS). Leveraging data from the TCGA database and the GTEx dataset, along with GEPIA, UALCAN, and STRING, we identify TP53 overexpression as a significant prognostic indicator, notably pronounced in prostate adenocarcinoma (PRAD). Supported by compelling statistical significance (p < 0.05), our analysis reveals the distinct influence of TP53 overexpression on DFS outcomes in PRAD. Additionally, graphical representations of overall survival (OS) underscore the notable disparity in OS duration between tumors exhibiting elevated TP53 expression (depicted by the red line) and those with lower TP53 levels (indicated by the blue line). The hazard ratio (HR) further emphasizes the profound impact of TP53 on overall survival. Moreover, our investigation delves into the intricate TP53 protein network, unveiling genes exhibiting robust positive correlations with TP53 expression across 13 out of 27 cancers. Remarkably, negative correlations emerge with pivotal tumor suppressor genes. This network analysis elucidates critical proteins, including SIRT1, CBP, p300, ATM, DAXX, HSP 90-alpha, Mdm2, RPA70, 14-3-3 protein sigma, p53, and ASPP2, pivotal in regulating cell cycle dynamics, DNA damage response, and transcriptional regulation. Our study underscores the paramount importance of deciphering TP53 dynamics in cancer, providing invaluable insights into tumor behavior, disease-free survival, and potential therapeutic avenues.

Multiple epidemic waves as the outcome of stochastic SIR epidemics with behavioral responses: a hybrid modeling approach.

In the behavioral epidemiology (BE) of infectious diseases, little theoretical effort seems to have been devoted to understand the possible effects of individuals' behavioral responses during an epidemic outbreak in small populations. To fill this gap, here we first build general, behavior implicit, SIR epidemic models including behavioral responses and set them within the framework of nonlinear feedback control theory. Second, we provide a thorough investigation of the effects of different types of agents' behavioral responses for the dynamics of hybrid stochastic SIR outbreak models. In the proposed model, the stochastic discrete dynamics of infection spread is combined with a continuous model describing the agents' delayed behavioral response. The delay reflects the memory mechanisms with which individuals enact protective behavior based on past data on the epidemic course. This results in a stochastic hybrid system with time-varying transition probabilities. To simulate such system, we extend Gillespie's classic stochastic simulation algorithm by developing analytical formulas valid for our classes of models. The algorithm is used to simulate a number of stochastic behavioral models and to classify the effects of different types of agents' behavioral responses. In particular this work focuses on the effects of the structure of the response function and of the form of the temporal distribution of such response. Among the various results, we stress the appearance of multiple, stochastic epidemic waves triggered by the delayed behavioral response of individuals.

Application of Sensitivity Analysis to Discover Potential Molecular Drug Targets.

Mathematical modeling of signaling pathways and regulatory networks has been supporting experimental research for some time now. Sensitivity analysis, aimed at finding model parameters whose changes yield significantly altered cellular responses, is an important part of modeling work. However, sensitivity methods are often directly transplanted from analysis of technical systems, and thus, they may not serve the purposes of analysis of biological systems. This paper presents a novel sensitivity analysis method that is particularly suited to the task of searching for potential molecular drug targets in signaling pathways. Using two sample models of pathways, p53/Mdm2 regulatory module and IFN-ß-induced JAK/STAT signaling pathway, we show that the method leads to biologically relevant conclusions, identifying processes suitable for targeted pharmacological inhibition, represented by the reduction of kinetic parameter values. That, in turn, facilitates subsequent search for active drug components.

Activation of the atypical NF-κB pathway induced by ionizing radiation is not affected by the p53 status.

DNA double-strand breaks induced by ionizing radiation can activate the atypical NF-κB pathway via ATM-mediated phosphorylation of NEMO/IKKγ. We aimed to determine whether the status of p53 influenced the activation of this particular NF-κB pathway. The NF-κB signaling was activated either by irradiation with a single 8 Gy dose or by TNFα cytokine in p53-proficient and p53-deficient variants of HCT116, RKO, and U2-OS human cancer cell lines. To assess pathway activation the kinetics of phosphorylation (Ser32) and proteolytic degradation of IκBα inhibitor and phosphorylation (Ser536) of RelA(p65) NF-κB subunit were analyzed. Though activation of the radiation-induced atypical pathway was delayed and weakened when compared to the cytokine-induced canonical pathway, no significant differences were noted between p53-proficient and p53-deficient variants, which indicated that activation of both NF-κB pathways was not affected by the p53 status. In marked contrast, the presence of p53 significantly affected downstream effects of NF-κB activation, i.e. transcription of NF-κB-dependent genes. However, different patterns of such interference were observed, which indicated gene-specific and cell-specific mechanisms of interactions between NF-κB and p53 at the transcription regulation level.

Petri nets and ODEs as complementary methods for comprehensive analysis on an example of the ATM-p53-NF-[Formula: see text]B signaling pathways.

Intracellular processes are cascades of biochemical reactions, triggered in response to various types of stimuli. Mathematical models describing their dynamics have become increasingly popular in recent years, as tools supporting experimental work in analysis of pathways and regulatory networks. Not only do they provide insights into general properties of these systems, but also help in specific tasks, such as search for drug molecular targets or treatment protocols. Different tools and methods are used to model complex biological systems. In this work, we focus on ordinary differential equations (ODEs) and Petri nets. We consider specific methods of analysis of such models, i.e., sensitivity analysis (SA) and significance analysis. So far, they have been applied separately, with different goals. In this paper, we show that they can complement each other, combining the sensitivity of ODE models and the significance analysis of Petri nets. The former is used to find parameters, whose change results in the greatest quantitative and qualitative changes in the model response, while the latter is a structural analysis and allows indicating the most important subprocesses in terms of information flow in Petri net. Ultimately, both methods facilitate finding the essential processes in a given signaling pathway or regulatory network and may be used to support medical therapy development. In the paper, the use of dual modeling is illustrated with an example of ATM/p53/NF-[Formula: see text]B pathway. Each method was applied to analyze this system, resulting in finding different subsets of important processes that might be prospective targets for changing this system behavior. While some of the processes were indicated in each of the approaches, others were found by one method only and would be missed if only that method was applied. This leads to the conclusion about the complementarity of the methods under investigation. The dual modeling approach of comprehensive structural and parametric analysis yields results that would not be possible if these two modeling approaches were applied separately. The combined approach, proposed in this paper, facilitates finding not only key processes, with which significant parameters are associated, but also significant modules, corresponding to subsystems of regulatory networks. The results provide broader insight into therapy targets in diseases in which the natural control of intracellular processes is disturbed, leading to the development of more effective therapies in medicine.

Piece-wise linear models of biological systems-Application, analysis, and comparison with nonlinear models using the example of the p53 regulatory module.

In this paper, we propose to use a linear system with switching methodology for description and analysis of complex biological systems. We show advantages of the proposed approach over the one usually used, which is based on ODE. We propose the detailed methodology of a full analysis of developed models, including analytical determination of the location and type of equilibrium points, finding an analytical solution, stability and bifurcation analysis. We illustrate the above with the example of the well-known p53 signalling pathway comparing the results with the results of a nonlinear, ODE-based version of the proposed model. The complex methodology proposed by us, especially due to the definition of model structure, which is easy to understand for biologists and medics, may be a bridge for closer cooperation between them and engineers in the future.

Influence of parameter perturbations on the reachability of therapeutic target in systems with switchings.

BACKGROUND: Examination of physiological processes and the influences of the drugs on them can be efficiently supported by mathematical modeling. One of the biggest problems is related to the exact fitting of the parameters of a model. Conditions inside the organism change dynamically, so the rates of processes are very difficult to estimate. Perturbations in the model parameters influence the steady state so a desired therapeutic goal may not be reached. Here we investigate the effect of parameter deviation on the steady state in three simple models of the influence of a therapeutic drug on its target protein. Two types of changes in the model parameters are taken into account: small perturbations in the system parameter values, and changes in the switching time of a specific parameter. Additionally, we examine the systems response in case of a drug concentration decreasing with time. RESULTS: The models which we analyze are simplified, because we want to avoid influences of complex dynamics on the results. A system with a negative feedback loop is the most robust and the most rapid, so it requires the largest drug dose but the effects are observed very quickly. On the other hand a system with positive feedback is very sensitive to changes, so small drug doses are sufficient to reach a therapeutic target. In systems without feedback or with positive feedback, perturbations in the model parameters have a bigger influence on the reachability of the therapeutic target than in systems with negative feedback. Drug degradation or inactivation in biological systems enforces multiple drug applications to maintain the level of a drug's target under the desired threshold. The frequency of drug application should be fitted to the system dynamics, because the response velocity is tightly related to the therapeutic effectiveness and the time for achieving the goal. CONCLUSIONS: Systems with different types of regulation vary in their dynamics and characteristic features. Depending on the feedback loop, different types of therapy may be the most appropriate, and deviations in the model parameters have different influences on the reachability of the therapeutic target.

Application of bifurcation theory and siRNA-based control signal to restore the proper response of cancer cells to DNA damage.

Many diseases with a genetic background such as some types of cancer are caused by damage in the p53 signaling pathway. The damage changes the system dynamics providing cancer cells with resistance to therapy such as radiation therapy. The change can be observed as the difference in bifurcation diagrams and equilibria type and location between normal and damaged cells, and summarized as the changes of the mathematical model parameters and following changes of the eigenvalues of Jacobian matrix. Therefore a change in other model parameters, such as mRNA degradation rates, may restore the proper eigenvalues and by that proper system dynamics. From the biological point of view, the change of mRNA degradation rate can be achieved by application of the small interfering RNA (siRNA). Here, we propose a general mathematical framework based on the bifurcation theory and siRNA-based control signal in order to study how to restore the proper response of cells with damaged p53 signaling pathway to therapy by using ionizing radiation (IR) therapy as an example. We show the difference between the cells with normal p53 signaling pathway and cells with abnormalities in the negative (as observed in SJSA-1 cell line) or positive (as observed in MCF-7 or PNT1a cell lines) feedback loop. Then we show how the dynamics of these cells can be restored to normal cell dynamics by using selected siRNA.

A novel mathematical model of ATM/p53/NF- κB pathways points to the importance of the DDR switch-off mechanisms.

BACKGROUND: Ataxia telangiectasia mutated (ATM) is a detector of double-strand breaks (DSBs) and a crucial component of the DNA damage response (DDR) along with p53 and NF- κB transcription factors and Wip1 phosphatase. Despite the recent advances in studying the DDR, the mechanisms of cell fate determination after DNA damage induction is still poorly understood. RESULTS: To investigate the importance of various DDR elements with particular emphasis on Wip1, we developed a novel mathematical model of ATM/p53/NF- κB pathways. Our results from in silico and in vitro experiments performed on U2-OS cells with Wip1 silenced to 25 % (Wip1-RNAi) revealed a strong dependence of cellular response to DNA damages on this phosphatase. Notably, Wip1-RNAi cells exhibited lower resistance to ionizing radiation (IR) resulting in smaller clonogenicity and higher apoptotic fraction. CONCLUSIONS: In this article, we demonstrated that Wip1 plays a role as a gatekeeper of apoptosis and influences the pro-survival behaviour of cells - the level of Wip1 increases to block the apoptotic decision when DNA repair is successful. Moreover, we were able to verify the dynamics of proteins and transcripts, apoptotic fractions and cells viability obtained from stochastic simulations using in vitro approaches. Taken together, we demonstrated that the model can be successfully used in prediction of cellular behaviour after exposure to IR. Thus, our studies may provide further insights into key elements involved in the underlying mechanisms of the DDR.

The role of stochastic gene switching in determining the pharmacodynamics of certain drugs: basic mechanisms.

In this paper we analyze the impact of the stochastic fluctuation of genes between their ON and OFF states on the pharmacodynamics of a potentially large class of drugs. We focus on basic mechanisms underlying the onset of in vitro experimental dose-response curves, by investigating two elementary molecular circuits. Both circuits consist in the transcription of a gene and in the successive translation into the corresponding protein. Whereas in the first the activation/deactivation rates of the single gene copy are constant, in the second the protein, now a transcription factor, amplifies the deactivation rate, so introducing a negative feedback. The drug is assumed to enhance the elimination of the protein, and in both cases the success of therapy is assured by keeping the level of the given protein under a threshold for a fixed time. Our numerical simulations suggests that the gene switching plays a primary role in determining the sigmoidal shape of dose-response curves. Moreover, the simulations show interesting phenomena related to the magnitude of the average gene switching time and to the drug concentration. In particular, for slow gene switching a significant fraction of cells can respond also in the absence of drug or with drug concentrations insufficient for the response in a deterministic setting. For higher drug concentrations, the non-responding fraction exhibits a maximum at intermediate values of the gene switching rates. For fast gene switching, instead, the stochastic prediction follows the prediction of the deterministic approximation, with all the cells responding or non-responding according to the drug dose.

The pharmacodynamics of the p53-Mdm2 targeting drug Nutlin: the role of gene-switching noise.

In this work we investigate, by means of a computational stochastic model, how tumor cells with wild-type p53 gene respond to the drug Nutlin, an agent that interferes with the Mdm2-mediated p53 regulation. In particular, we show how the stochastic gene-switching controlled by p53 can explain experimental dose-response curves, i.e., the observed inter-cell variability of the cell viability under Nutlin action. The proposed model describes in some detail the regulation network of p53, including the negative feedback loop mediated by Mdm2 and the positive loop mediated by PTEN, as well as the reversible inhibition of Mdm2 caused by Nutlin binding. The fate of the individual cell is assumed to be decided by the rising of nuclear-phosphorylated p53 over a certain threshold. We also performed in silico experiments to evaluate the dose-response curve after a single drug dose delivered in mice, or after its fractionated administration. Our results suggest that dose-splitting may be ineffective at low doses and effective at high doses. This complex behavior can be due to the interplay among the existence of a threshold on the p53 level for its cell activity, the nonlinearity of the relationship between the bolus dose and the peak of active p53, and the relatively fast elimination of the drug.

Oscillations and bistability in the stochastic model of p53 regulation.

The p53 regulatory pathway controls cell responses, which include cell cycle arrest, DNA repair, apoptosis and cellular senescence. We propose a stochastic model of p53 regulation, which is based on two feedback loops: the negative, coupling p53 with its immediate downregulator Mdm2, and the positive, which involves PTEN, PIP3 and Akt. Existence of the negative feedback assures homeostasis of healthy cells and oscillatory responses of DNA-damaged cells, which are persistent when DNA repair is inefficient and the positive feedback loop is broken. The positive feedback destroys the negative coupling between Mdm2 and p53 by sequestering most of Mdm2 in cytoplasm, so it may no longer prime the nuclear p53 for degradation. It works as a clock, giving the cell some time for DNA repair. However, when DNA repair is inefficient, the active p53 rises to a high level and triggers transcription of proapoptotic genes. As a result, small DNA damage may be repaired and the cell may return to its initial "healthy" state, while the extended damage results in apoptosis. The stochasticity of p53 regulation, introduced at the levels of gene expression, DNA damage and repair, leads to high heterogeneity of cell responses and causes cell population split after irradiation into subpopulations of apoptotic and surviving cells, with fraction of apoptotic cells growing with the irradiation dose.

Single TNFalpha trimers mediating NF-kappaB activation: stochastic robustness of NF-kappaB signaling.

BACKGROUND: The NF-kappaB regulatory network controls innate immune response by transducing variety of pathogen-derived and cytokine stimuli into well defined single-cell gene regulatory events. RESULTS: We analyze the network by means of the model combining a deterministic description for molecular species with large cellular concentrations with two classes of stochastic switches: cell-surface receptor activation by TNFalpha ligand, and IkappaBalpha and A20 genes activation by NF-kappaB molecules. Both stochastic switches are associated with amplification pathways capable of translating single molecular events into tens of thousands of synthesized or degraded proteins. Here, we show that at a low TNFalpha dose only a fraction of cells are activated, but in these activated cells the amplification mechanisms assure that the amplitude of NF-kappaB nuclear translocation remains above a threshold. Similarly, the lower nuclear NF-kappaB concentration only reduces the probability of gene activation, but does not reduce gene expression of those responding. CONCLUSION: These two effects provide a particular stochastic robustness in cell regulation, allowing cells to respond differently to the same stimuli, but causing their individual responses to be unequivocal. Both effects are likely to be crucial in the early immune response: Diversity in cell responses causes that the tissue defense is harder to overcome by relatively simple programs coded in viruses and other pathogens. The more focused single-cell responses help cells to choose their individual fates such as apoptosis or proliferation. The model supports the hypothesis that binding of single TNFalpha ligands is sufficient to induce massive NF-kappaB translocation and activation of NF-kappaB dependent genes.