Systems modeling of anti-apoptotic pathways in prostate cancer: psychological stress triggers a synergism pattern switch in drug combination therapy

A comprehensive review of computational cell cycle models in guiding cancer treatment strategies

This article reviews the current knowledge and recent advancements in computational modeling of the cell cycle. It offers a comparative analysis of various modeling paradigms, highlighting their unique strengths, limitations, and applications. Specifically, the article compares deterministic and stochastic models, single-cell versus population models, and mechanistic versus abstract models. This detailed analysis helps determine the most suitable modeling framework for various research needs. Additionally, the discussion extends to the utilization of these computational models to illuminate cell cycle dynamics, with a particular focus on cell cycle viability, crosstalk with signaling pathways, tumor microenvironment, DNA replication, and repair mechanisms, underscoring their critical roles in tumor progression and the optimization of cancer therapies. By applying these models to crucial aspects of cancer therapy planning for better outcomes, including drug efficacy quantification, drug discovery, drug resistance analysis, and dose optimization, the review highlights the significant potential of computational insights in enhancing the precision and effectiveness of cancer treatments. This emphasis on the intricate relationship between computational modeling and therapeutic strategy development underscores the pivotal role of advanced modeling techniques in navigating the complexities of cell cycle dynamics and their implications for cancer therapy.

HormoNet: a deep learning approach for hormone-drug interaction prediction

Several experimental evidences have shown that the human endogenous hormones can interact with drugs in many ways and affect drug efficacy. The hormone drug interactions (HDI) are essential for drug treatment and precision medicine; therefore, it is essential to understand the hormone-drug associations. Here, we present HormoNet to predict the HDI pairs and their risk level by integrating features derived from hormone and drug target proteins. To the best of our knowledge, this is one of the first attempts to employ deep learning approach for prediction of HDI prediction. Amino acid composition and pseudo amino acid composition were applied to represent target information using 30 physicochemical and conformational properties of the proteins. To handle the imbalance problem in the data, we applied synthetic minority over-sampling technique technique. Additionally, we constructed novel datasets for HDI prediction and the risk level of their interaction. HormoNet achieved high performance on our constructed hormone-drug benchmark datasets. The results provide insights into the understanding of the relationship between hormone and a drug, and indicate the potential benefit of reducing risk levels of interactions in designing more effective therapies for patients in drug treatments. Our benchmark datasets and the source codes for HormoNet are available in: https://github.com/EmamiNeda/HormoNet .

Systems Pharmacodynamic Model of Combined Gemcitabine and Trabectedin in Pancreatic Cancer Cells. Part I: Effects on Signal Transduction Pathways Related to Tumor Growth

Pancreatic ductal adenocarcinoma (PDAC) is often chemotherapy-resistant, and novel drug combinations would fill an unmet clinical need. Previously we reported synergistic cytotoxic effects of gemcitabine and trabectedin on pancreatic cancer cells, but underlying protein-level interaction mechanisms remained unclear. We employed a reliable, sensitive, comprehensive, quantitative, high-throughput IonStar proteomic workflow to investigate the time course of gemcitabine and trabectedin effects, alone and combined, upon pancreatic cancer cells. MiaPaCa-2 cells were incubated with vehicle (controls), gemcitabine, trabectedin, and their combinations over 72 hours. Samples were collected at intervals and analyzed using the label-free IonStar liquid chromatography-mass spectrometry (LC-MS/MS) workflow to provide temporal quantification of protein expression for 4,829 proteins in four experimental groups. To characterize diverse signal transduction pathways, a comprehensive systems pharmacodynamic (SPD) model was developed. The analysis is presented in two parts. Here, Part I describes drug responses in cancer cell growth and migration pathways included in the full model: receptor tyrosine kinase- (RTK), integrin-, G-protein coupled receptor- (GPCR), and calcium-signaling pathways. The developed model revealed multiple underlying mechanisms of drug actions, provides insight into the basis of drug interaction synergism, and offers a scientific rationale for potential drug combination strategies.

A novel computational predictive biological approach distinguishes Integrin β1 as a salient biomarker for breast cancer chemoresistance

Chemoresistance is a primary cause of breast cancer treatment failure, and protein-protein interactions significantly contribute to chemoresistance during different stages of breast cancer progression. In pursuit of novel biomarkers and relevant protein-protein interactions occurring during the emergence of breast cancer chemoresistance, we used a computational predictive biological (CPB) approach. CPB identified associations of adhesion molecules with proteins connected with different breast cancer proteins associated with chemoresistance. This approach identified an association of Integrin β1 (ITGB1) with chemoresistance and breast cancer stem cell markers. ITGB1 activated the Focal Adhesion Kinase (FAK) pathway promoting invasion, migration, and chemoresistance in breast cancer by upregulating Erk phosphorylation. FAK also activated Wnt/Sox2 signaling, which enhanced self-renewal in breast cancer. Activation of the FAK pathway by ITGB1 represents a novel mechanism linked to breast cancer chemoresistance, which may lead to novel therapies capable of blocking breast cancer progression by intervening in ITGB1-regulated signaling pathways.

Predicting Anticancer Drug Resistance Mediated by Mutations

Cancer drug resistance presents a challenge for precision medicine. Drug-resistant mutations are always emerging. In this study, we explored the relationship between drug-resistant mutations and drug resistance from the perspective of protein structure. By combining data from previously identified drug-resistant mutations and information of protein structure and function, we used machine learning-based methods to build models to predict cancer drug resistance mutations. The performance of our combined model achieved an accuracy of 86%, a Matthews correlation coefficient score of 0.57, and an F1 score of 0.66. We have constructed a fast, reliable method that predicts and investigates cancer drug resistance in a protein structure. Nonetheless, more information is needed concerning drug resistance and, in particular, clarification is needed about the relationships between the drug and the drug resistance mutations in proteins. Highly accurate predictions regarding drug resistance mutations can be helpful for developing new strategies with personalized cancer treatments. Our novel concept, which combines protein structure information, has the potential to elucidate physiological mechanisms of cancer drug resistance.

Inferring microenvironmental regulation of gene expression from single-cell RNA sequencing data using scMLnet with an application to COVID-19

Inferring how gene expression in a cell is influenced by cellular microenvironment is of great importance yet challenging. In this study, we present a single-cell RNA-sequencing data based multilayer network method (scMLnet) that models not only functional intercellular communications but also intracellular gene regulatory networks (https://github.com/SunXQlab/scMLnet). scMLnet was applied to a scRNA-seq dataset of COVID-19 patients to decipher the microenvironmental regulation of expression of SARS-CoV-2 receptor ACE2 that has been reported to be correlated with inflammatory cytokines and COVID-19 severity. The predicted elevation of ACE2 by extracellular cytokines EGF, IFN-γ or TNF-α were experimentally validated in human lung cells and the related signaling pathway were verified to be significantly activated during SARS-COV-2 infection. Our study provided a new approach to uncover inter-/intra-cellular signaling mechanisms of gene expression and revealed microenvironmental regulators of ACE2 expression, which may facilitate designing anti-cytokine therapies or targeted therapies for controlling COVID-19 infection. In addition, we summarized and compared different methods of scRNA-seq based inter-/intra-cellular signaling network inference for facilitating new methodology development and applications.

β2 -adrenergic receptor signaling drives prostate cancer progression by targeting the Sonic hedgehog-Gli1 signaling activation

BACKGROUND

Considerable evidence suggests that the sympathetic nervous system, mainly via adrenergic signaling, contributes to prostate cancer (PCa) progression. However, the underlying molecular mechanisms remain unknown.

METHODS

The expression level of β₂ -adrenergic receptor (β₂ -AR) in tissue microarray was evaluated by immunohistochemistry. The effects of isoproterenol (ISO) or Sonic Hedgehog (Shh) signaling inhibitor on tumor growth were analyzed in proliferation and colony formation assays. The apoptosis of cells was analyzed by flow cytometry. Small hairpin RNA-based knockdown of β₂ -AR or Gli1 was validated by Western blot analysis and real-time PCR. Effects of β₂ -AR on prostate carcinogenesis in vivo were observed in a mouse xenograft model. The expression levels of the indicated proteins in xenograft tissues were evaluated by immunohistochemistry. Expression levels of Shh signaling components and downstream proteins were assessed by immunoblotting.

RESULTS

We determined that β₂ -AR was expressed at significantly higher levels in carcinoma than in normal prostate tissues. β₂ -AR signaling also played an essential role in sustaining PCa cell proliferation in vivo and in vitro. We also found that inhibition of Shh signaling or knockdown of Gli1 expression significantly restrained ISO-induced cell proliferation in vitro. ISO alleviated the apoptosis induced by suppressing or knocking down of Gli1. The β₂ -AR agonist ISO upregulated the transcription and protein expression of target genes of Shh signaling, including c-Myc, Cyclin D1, and VEGFA. Conversely, knocking down β₂ -AR markedly suppressed the expression of Shh components in vivo and in vitro. In Gli1 knockdown cells, ISO failed to increase the expression of target genes of Shh signaling.

CONCLUSIONS

In this study, we uncovered an important role of β₂ -AR signaling in regulating the Shh pathway activity in PCa tumorigenesis and provide further insight into the mechanism of the involvement of the Hh signaling pathway. Furthermore, given the efficacy of β₂ -adrenergic modulation on PCa, our study might also add evidence for potential therapeutic options of β-blockers for PCa.

Differential regulatory network-based quantification and prioritization of key genes underlying cancer drug resistance based on time-course RNA-seq data

Drug resistance is a major cause for the failure of cancer chemotherapy or targeted therapy. However, the molecular regulatory mechanisms controlling the dynamic evolvement of drug resistance remain poorly understood. Thus, it is important to develop methods for identifying key gene regulatory mechanisms of the resistance to specific drugs. In this study, we developed a data-driven computational framework, DryNetMC, using a differential regulatory network-based modeling and characterization strategy to quantify and prioritize key genes underlying cancer drug resistance. The DryNetMC does not only infer gene regulatory networks (GRNs) via an integrated approach, but also characterizes and quantifies dynamical network properties for measuring node importance. We used time-course RNA-seq data from glioma cells treated with dbcAMP (a cAMP activator) as a realistic case to reconstruct the GRNs for sensitive and resistant cells. Based on a novel node importance index that comprehensively quantifies network topology, network entropy and expression dynamics, the top ranked genes were verified to be predictive of the drug sensitivities of different glioma cell lines, in comparison with other existing methods. The proposed method provides a quantitative approach to gain insights into the dynamic adaptation and regulatory mechanisms of cancer drug resistance and sheds light on the design of novel biomarkers or targets for predicting or overcoming drug resistance.

Mathematical modeling and computational prediction of cancer drug resistance

Diverse forms of resistance to anticancer drugs can lead to the failure of chemotherapy. Drug resistance is one of the most intractable issues for successfully treating cancer in current clinical practice. Effective clinical approaches that could counter drug resistance by restoring the sensitivity of tumors to the targeted agents are urgently needed. As numerous experimental results on resistance mechanisms have been obtained and a mass of high-throughput data has been accumulated, mathematical modeling and computational predictions using systematic and quantitative approaches have become increasingly important, as they can potentially provide deeper insights into resistance mechanisms, generate novel hypotheses or suggest promising treatment strategies for future testing. In this review, we first briefly summarize the current progress of experimentally revealed resistance mechanisms of targeted therapy, including genetic mechanisms, epigenetic mechanisms, posttranslational mechanisms, cellular mechanisms, microenvironmental mechanisms and pharmacokinetic mechanisms. Subsequently, we list several currently available databases and Web-based tools related to drug sensitivity and resistance. Then, we focus primarily on introducing some state-of-the-art computational methods used in drug resistance studies, including mechanism-based mathematical modeling approaches (e.g. molecular dynamics simulation, kinetic model of molecular networks, ordinary differential equation model of cellular dynamics, stochastic model, partial differential equation model, agent-based model, pharmacokinetic–pharmacodynamic model, etc.) and data-driven prediction methods (e.g. omics data-based conventional screening approach for node biomarkers, static network approach for edge biomarkers and module biomarkers, dynamic network approach for dynamic network biomarkers and dynamic module network biomarkers, etc.). Finally, we discuss several further questions and future directions for the use of computational methods for studying drug resistance, including inferring drug-induced signaling networks, multiscale modeling, drug combinations and precision medicine.

Advances in computational approaches in identifying synergistic drug combinations

Accumulated empirical clinical experience, supported by animal or cell line models, has initiated efforts of predicting synergistic combinatorial drugs with more-than-additive effect compared with the sum of the individual agents. Aiming to construct better computational models, this review started from the latest updated data resources of combinatorial drugs, then summarized the reported mechanism of the known synergistic combinations from aspects of drug molecular and pharmacological patterns, target network properties and compound functional annotation. Based on above, we focused on the main in silico strategies recently published, covering methods of molecular modeling, mathematical simulation, optimization of combinatorial targets and pattern-based statistical/learning model. Future thoughts are also discussed related to the role of natural compounds, drug combination with immunotherapy and management of adverse effects. Overall, with particular emphasis on mechanism of action of drug synergy, this review may serve as a rapid reference to design improved models for combinational drugs.

ADRB2-Targeting Therapies for Prostate Cancer

There is accumulating evidence that β-2 adrenergic receptor (ADRB2) signaling contributes to the progression and therapy resistance of prostate cancer, whereas availability of clinically tested β-blocker propranolol makes this pathway especially attractive as potential therapeutic target. Yet even in tumors with active ADRB2 signaling propranolol may be ineffective. Inhibition of apoptosis is one of the major mechanisms by which activation of ADRB2 contributes to prostate cancer pathophysiology. The signaling network that controls apoptosis in prostate tumors is highly redundant, with several signaling pathways targeting a few critical apoptosis regulatory molecules. Therefore, a comprehensive analysis of ADRB2 signaling in the context of other signaling mechanisms is necessary to identify patients who will benefit from propranolol therapy. This review discusses how information on the antiapoptotic mechanisms activated by ADRB2 can guide clinical trials of ADRB2 antagonist propranolol as potential life-extending therapy for prostate cancer. To select patients for clinical trials of propranolol three classes of biomarkers are proposed. First, biomarkers of ADRB2/cAMP-dependent protein kinase (PKA) pathway activation; second, biomarkers that inform about activation of other signaling pathways unrelated to ADRB2; third, apoptosis regulatory molecules controlled by ADRB2 signaling and other survival signaling pathways.

Mechanisms underlying the effects of stress on tumorigenesis and metastasis (Review)

Stress is one of the fundamental survival mechanisms in nature. Although chronic or long-lasting stress can be detrimental to health, acute or short-term stress can have health benefits. The aim of the present review was to address the complexity and significance of stress in tumorigenesis. The review covers an evaluation of previously used and reported experimental animal models of stress, as well as the effects of stress on the neuroendocrine system, immune function, gut microbiota, and inflammation and multidrug resistance, all of which are closely associated with cancer occurrence, progression and treatment. The review concludes that understanding the efficacy of stress management (prevention and rehabilitation) is crucial to the development of comprehensive and individualized strategies for cancer prevention and treatment.

Modeling of signaling crosstalk-mediated drug resistance and its implications on drug combination

The efficacy of pharmacological perturbation to the signaling transduction network depends on the network topology. However, whether and how signaling dynamics mediated by crosstalk contributes to the drug resistance are not fully understood and remain to be systematically explored. In this study, motivated by a realistic signaling network linked by crosstalk between EGF/EGFR/Ras/MEK/ERK pathway and HGF/HGFR/PI3K/AKT pathway, we develop kinetic models for several small networks with typical crosstalk modules to investigate the role of the architecture of crosstalk in inducing drug resistance. Our results demonstrate that crosstalk inhibition diminishes the response of signaling output to the external stimuli. Moreover, we show that signaling crosstalk affects the relative sensitivity of drugs, and some types of crosstalk modules that could yield resistance to the targeted drugs were identified. Furthermore, we quantitatively evaluate the relative efficacy and synergism of drug combinations. For the modules that are resistant to the targeted drug, we identify drug targets that can not only increase the relative drug efficacy but also act synergistically. In addition, we analyze the role of the strength of crosstalk in switching a module between drug-sensitive and drug-resistant. Our study provides mechanistic insights into the signaling crosstalk-mediated mechanisms of drug resistance and provides implications for the design of synergistic drug combinations to reduce drug resistance.

Predicting the impact of combined therapies on myeloma cell growth using a hybrid multi-scale agent-based model

Multiple myeloma is a malignant still incurable plasma cell disorder. This is due to refractory disease relapse, immune impairment, and development of multi-drug resistance. The growth of malignant plasma cells is dependent on the bone marrow (BM) microenvironment and evasion of the host's anti-tumor immune response. Hence, we hypothesized that targeting tumor-stromal cell interaction and endogenous immune system in BM will potentially improve the response of multiple myeloma (MM). Therefore, we proposed a computational simulation of the myeloma development in the complicated microenvironment which includes immune cell components and bone marrow stromal cells and predicted the effects of combined treatment with multi-drugs on myeloma cell growth. We constructed a hybrid multi-scale agent-based model (HABM) that combines an ODE system and Agent-based model (ABM). The ODEs was used for modeling the dynamic changes of intracellular signal transductions and ABM for modeling the cell-cell interactions between stromal cells, tumor, and immune components in the BM. This model simulated myeloma growth in the bone marrow microenvironment and revealed the important role of immune system in this process. The predicted outcomes were consistent with the experimental observations from previous studies. Moreover, we applied this model to predict the treatment effects of three key therapeutic drugs used for MM, and found that the combination of these three drugs potentially suppress the growth of myeloma cells and reactivate the immune response. In summary, the proposed model may serve as a novel computational platform for simulating the formation of MM and evaluating the treatment response of MM to multiple drugs.

Mathematical model of perineural tumor spread: a pilot study

BACKGROUND

Perineural spread (PNS) of pelvic cancer along the lumbosacral plexus is an emerging explanation for neoplastic lumbosacral plexopathy (nLSP) and an underestimated source of patient morbidity and mortality. Despite the increased incidence of PNS, these patients are often times a clinical conundrum-to diagnose and to treat. Building on previous results in modeling glioblastoma multiforme (GBM), we present a mathematical model for predicting the course and extent of the PNS of recurrent tumors.

METHODS

We created three-dimensional models of perineurally spreading tumor along the lumbosacral plexus from consecutive magnetic resonance imaging scans of two patients (one each with prostate cancer and cervical cancer). We adapted and applied a previously reported mathematical model of GBM to progression of tumor growth along the nerves on an anatomical model obtained from a healthy subject.

RESULTS

We were able to successfully model and visualize perineurally spreading pelvic cancer in two patients; average growth rates were 60.7 mm/year for subject 1 and 129 mm/year for subject 2. The model correlated well with extent of PNS on MRI scans at given time points.

CONCLUSIONS

This is the first attempt to model perineural tumor spread and we believe that it provides a glimpse into the future of disease progression monitoring. Every tumor and every patient are different, and the possibility to report treatment response using a unified scale-as "days gained"-will be a necessity in the era of individualized medicine. We hope our work will serve as a springboard for future connections between mathematics and medicine.

Chi3l3: a potential key orchestrator of eosinophil recruitment in meningitis induced by Angiostrongylus cantonensis

BACKGROUND

Angiostrongylus cantonensis, an important foodborne parasite, can induce serious eosinophilic meningitis in non-permissive hosts, such as mouse and human. However, the characteristics and mechanisms of the infection are still poorly understood. This study sought to determine the key molecules and its underlying mechanism in inducing brain eosinophilic infiltration caused by Angiostrongylus cantonensis.

METHODS

Mathematical models were established for prediction of significantly changing genes and the functional associated protein with RNA-seq data in Angiostrongylus cantonensis infection. The expression level of Chi3l3, the predicted key molecule, was verified using Western blotting and real-time quantitative PCR. Critical cell source of Chi3l3 and its relationship with eosinophils were identified with flow cytometry, immunohistochemistry, and further verified by macrophage depletion using liposomal clodronate. The role of soluble antigens of Angiostrongylus cantonensis in eosinophilic response was identified with mice airway allergy model by intranasal administration of Alternaria alternate. The relationship between Chi3l3 and IL-13 was identified with flow cytometry, Western blotting, and Seahorse Bioscience extracellular flux analyzer.

RESULTS

We analyzed the skewed cytokine pattern in brains of Angiostrongylus cantonensis-infected mice and found Chi3l3 to be an important molecule, which increased sharply during the infection. The percentage of inflammatory macrophages, the main source of Chi3l3, also increased, in line with eosinophils percentage in the brain. Network analysis and mathematical modeling predirect a functional association between Chi3l3 and IL-13. Further experiments verified that the soluble antigen of Angiostrongylus cantonensis induce brain eosinophilic meningitis via aggravating a positive feedback loop between IL-13 and Chi3l3.

CONCLUSIONS

We present evidences in favor of a key role for macrophave-derived Chi3l3 molecule in the infection of Angiostrongylus cantonensis, which aggravates eosinophilic meningitis induced by Angiostrongylus cantonensis via a IL-13-mediated positive feedback loop. These reported results constitute a starting point for future research of angiostrongyliasis pathogenesis and imply that targeting chitinases and chitinase-like-proteins may be clinically beneficial in Angiostrongylus cantonensis-induced eosinophilic meningitis.

HIDEEP: a systems approach to predict hormone impacts on drug efficacy based on effect paths

Experimental evidence has shown that some of the human endogenous hormones significantly affect drug efficacy. Since hormone status varies with individual physiological states, it is essential to understand the interplay of hormones and drugs for precision medicine. Here, we developed an in silico method to predict interactions between 283 human endogenous hormones and 590 drugs for 20 diseases including cancers and non-cancer diseases. We extracted hormone effect paths and drug effect paths from a large-scale molecular network that contains protein interactions, transcriptional regulations, and signaling interactions. If two kinds of effect paths for a hormone-drug pair intersect closely, we expect that the influence of the hormone on the drug efficacy is significant. It has been shown that the proposed method correctly distinguishes hormone-drug pairs with known interactions from random pairs in blind experiments. In addition, the method can suggest underlying interaction mechanisms at the molecular level so that it helps us to better understand the interplay of hormones and drugs.

Stress alters the expression of cancer-related genes in the prostate

Background

Prostate cancer is a major contributor to mortality worldwide, and significant efforts are being undertaken to decipher specific cellular and molecular pathways underlying the disease. Chronic stress is known to suppress reproductive function and promote tumor progression in several cancer models, but our understanding of the mechanisms through which stress contributes to cancer development and progression is incomplete. We therefore examined the relationship between stress, modulation of the gonadotropin-releasing hormone (GnRH) system, and changes in the expression of cancer-related genes in the rat prostate.

Methods

Adult male rats were acutely or repeatedly exposed to restraint stress, and compared to unstressed controls and groups that were allowed 14 days of recovery from the stress. Prostate tissue was collected and frozen for gene expression analyses by PCR array before the rats were transcardially perfused; and brain tissues harvested and immunohistochemically stained for Fos to determine neuronal activation.

Results

Acute stress elevated Fos expression in the paraventricular nucleus of the hypothalamus (PVH), an effect that habituated with repeated stress exposure. Data from the PCR arrays showed that repeated stress significantly increases the transcript levels of several genes associated with cellular proliferation, including proto-oncogenes. Data from another array platform showed that both acute and repeated stress can induce significant changes in metastatic gene expression. The functional diversity of genes with altered expression, which includes transcription factors, growth factor receptors, apoptotic genes, and extracellular matrix components, suggests that stress is able to induce aberrant changes in pathways that are deregulated in prostate cancer.

Conclusions

Our findings further support the notion that stress can affect cancer outcomes, perhaps by interfering with neuroendocrine mechanisms involved in the control of reproduction.

Electronic supplementary material

The online version of this article (10.1186/s12885-017-3635-4) contains supplementary material, which is available to authorized users.

The novel Indole-3-formaldehyde (2-AITFEI-3-F) is involved in processes of apoptosis induction?

AIM AND OBJECTIVES

Balancing between Bax and Bcl-2 plays critical roles in both proliferation and self-renewal activation of cancer cells. Indole-3-formaldehyde derivatives limit the growth and facilitate cell death in different cell systems. In this study, we introduced a novel indole derivative (2-AITFEI-3-F) with tendency to facilitate apoptosis in NB4 line in comparison to basal Indole-3-formaldehyde (I3F).

METHODS

The NB4 cells were cultured in RPMI1640 medium contained 2-AITFEI-3-F and I3F (15.12-1000μg/mL) for 24, 48 and 72h. Inhibition of cell proliferation was assessed by trypan blue staining technique and MTT assay. The fold changes of Bax/Bcl-2 expression against β-actin were determined by real-time-PCR technique. Western blotting analysis was also applied for evaluating the expression of Bax and Bcl2 at protein level. Data were analyzed by student t and repeated measure tests. Differences were considered significant if (P<0.01).

RESULTS

There was a significant difference in cell viability, when various concentrations of 2-AITFEI-3-F (but similar to I3F) were used for 24, 48 and 72h in comparison to I3F regarding the cellular viability (P<0.05). Real time PCR and Western blotting analysis indicated that the gene and protein expression level of Bcl-2 down-regulated while Bax was up-regulated in compare to untreated control cells and cells treated with I3F (P<0.01).

CONCLUSION

According to these findings, the novel indole derivative 2-AITFEI-3-F probably triggered apoptosis of NB4 cells by modulating Bax/Bcl-2 ratio. Furthermore, the 2-AITFEI-3-F had markedly displayed anti-cancer activity than I3F.

Brefeldin A enhances docetaxel-induced growth inhibition and apoptosis in prostate cancer cells in monolayer and 3D cultures

Docetaxel is a commonly used chemotherapeutic drug for patients with late stage prostate cancer. However, serious side effect and drug resistance limit its clinical success. Brefeldin A is a 16-membered macrolide antibiotic from mangrove-derived Fungus Aspergillus sp. (9Hu), which exhibited potent cytotoxicity against human cancer cells. In the present study, we determined the effect of brefeldin A on docetaxel-induced growth inhibition and apoptosis in human prostate cancer PC-3 cells. Brefeldin A in combination with docetaxel inhibited the growth of PC-3 cells in monolayer and in three dimensional cultures. The combination also potently stimulated apoptosis in PC-3 cells as determined by propidium iodide staining and morphological assessment. Mechanistic studies showed that growth inhibition and apoptosis in PC-3 cells treated with brefeldin A and docetaxel were associated with decrease in the level of Bcl-2. The present study indicates that combined brefeldin A with docetaxel may represent a novel approach for improving the efficacy of docetaxel, and Bcl-2 may serve as a target for brefeldin A to enhance the effects of docetaxel chemotherapy.

Mathematical and Computational Modeling in Complex Biological Systems

The biological process and molecular functions involved in the cancer progression remain difficult to understand for biologists and clinical doctors. Recent developments in high-throughput technologies urge the systems biology to achieve more precise models for complex diseases. Computational and mathematical models are gradually being used to help us understand the omics data produced by high-throughput experimental techniques. The use of computational models in systems biology allows us to explore the pathogenesis of complex diseases, improve our understanding of the latent molecular mechanisms, and promote treatment strategy optimization and new drug discovery. Currently, it is urgent to bridge the gap between the developments of high-throughput technologies and systemic modeling of the biological process in cancer research. In this review, we firstly studied several typical mathematical modeling approaches of biological systems in different scales and deeply analyzed their characteristics, advantages, applications, and limitations. Next, three potential research directions in systems modeling were summarized. To conclude, this review provides an update of important solutions using computational modeling approaches in systems biology.

Effect of Dynamic Interaction between microRNA and Transcription Factor on Gene Expression

MicroRNAs (miRNAs) are endogenous noncoding RNAs which participate in diverse biological processes in animals and plants. They are known to join together with transcription factors and downstream gene, forming a complex and highly interconnected regulatory network. To recognize a few overrepresented motifs which are expected to perform important elementary regulatory functions, we constructed a computational model of miRNA-mediated feedforward loops (FFLs) in which a transcription factor (TF) regulates miRNA and targets gene. Based on the different dynamic interactions between miRNA and TF on gene expression, four possible structural topologies of FFLs with two gate functions (AND gate and OR gate) are introduced. We studied the dynamic behaviors of these different motifs. Furthermore, the relationship between the response time and maximal activation velocity of miRNA was investigated. We found that the curve of response time shows nonmonotonic behavior in Co1 loop with OR gate. This may help us to infer the mechanism of miRNA binding to the promoter region. At last we investigated the influence of important parameters on the dynamic response of system. We identified that the stationary levels of target gene in all loops were insensitive to the initial value of miRNA.

Combined Treatment with Stattic and Docetaxel Alters the Bax/Bcl-2 Gene Expression Ratio in Human Prostate Cancer Cells

Docetaxel, recognized as a stabilizing microtubule agent, is frequently administrated as a first line treatment for prostate cancers. Due to high side effects of monotherapy, however, combinations with novel adjuvants have emerged as an alternative strategy in cancer therapy protocols. Here, we investigated the combined effects of stattic and docetaxel on the DU145 prostate cancer cell line. Cytotoxicity was evaluated by MTT assay. To understand molecular mechanisms of stattic action, apoptotic related genes including Bcl-2, Mcl-1, Survivin and Bax were evaluated by real-time RT-PCR. Alteration in the expression of pro-apoptotic Bax and anti-apoptotic Bcl-2 genes and Bax/Bcl-2 ratio were investigated via the 2^ΔΔCT method. The IC₅₀ values for docetaxel and stattic were 3.7 ± 0.9 nM and 4.6±0.8 µM, respectively. Evaluation of key gene expression levels revealed a noticeable decrease in antiapoptotic Bcl-2 and Mcl-1 along with an increase in pro-apoptotic Bax mRNA levels (p<0.05). Our results suggest that combination of a STAT3 inhibitor with doctaxel can be considered as a potent strategy for induction of apoptosis via increasing Bax mRNA expression.

Stochastic modeling suggests that noise reduces differentiation efficiency by inducing a heterogeneous drug response in glioma differentiation therapy

Background

Glioma differentiation therapy is a novel strategy that has been used to induce glioma cells to differentiate into glia-like cells. Although some advances in experimental methods for exploring the molecular mechanisms involved in differentiation therapy have been made, a model-based comprehensive analysis is still needed to understand these differentiation mechanisms and improve the effects of anti-cancer therapeutics. This type of analysis becomes necessary in stochastic cases for two main reasons: stochastic noise inherently exists in signal transduction and phenotypic regulation during targeted therapy and chemotherapy, and the relationship between this noise and drug efficacy in differentiation therapy is largely unknown.

Results

In this study, we developed both an additive noise model and a Chemical-Langenvin-Equation model for the signaling pathways involved in glioma differentiation therapy to investigate the functional role of noise in the drug response. Our model analysis revealed an ultrasensitive mechanism of cyclin D1 degradation that controls the glioma differentiation induced by the cAMP inducer cholera toxin (CT). The role of cyclin D1 degradation in human glioblastoma cell differentiation was then experimentally verified. Our stochastic simulation demonstrated that noise not only renders some glioma cells insensitive to cyclin D1 degradation during drug treatment but also induce heterogeneous differentiation responses among individual glioma cells by modulating the ultrasensitive response of cyclin D1. As such, the noise can reduce the differentiation efficiency in drug-treated glioma cells, which was verified by the decreased evolution of differentiation potential, which quantified the impact of noise on the dynamics of the drug-treated glioma cell population.

Conclusion

Our results demonstrated that targeting the noise-induced dynamics of cyclin D1 during glioma differentiation therapy can increase anti-glioma effects, implying that noise is a considerable factor in assessing and optimizing anti-cancer drug interventions.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-016-0316-x) contains supplementary material, which is available to authorized users.

A Hierarchical Mechanism of RIG-I Ubiquitination Provides Sensitivity, Robustness and Synergy in Antiviral Immune Responses

RIG-I is an essential receptor in the initiation of the type I interferon (IFN) signaling pathway upon viral infection. Although K63-linked ubiquitination plays an important role in RIG-I activation, the optimal modulation of conjugated and unanchored ubiquitination of RIG-I as well as its functional implications remains unclear. In this study, we determined that, in contrast to the RIG-I CARD domain, full-length RIG-I must undergo K63-linked ubiquitination at multiple sites to reach full activity. A systems biology approach was designed based on experiments using full-length RIG-I. Model selection for 7 candidate mechanisms of RIG-I ubiquitination inferred a hierarchical architecture of the RIG-I ubiquitination mode, which was then experimentally validated. Compared with other mechanisms, the selected hierarchical mechanism exhibited superior sensitivity and robustness in RIG-I-induced type I IFN activation. Furthermore, our model analysis and experimental data revealed that TRIM4 and TRIM25 exhibited dose-dependent synergism. These results demonstrated that the hierarchical mechanism of multi-site/type ubiquitination of RIG-I provides an efficient, robust and optimal synergistic regulatory module in antiviral immune responses.

Multi-scale Modeling in Clinical Oncology: Opportunities and Barriers to Success

Hierarchical processes spanning several orders of magnitude of both space and time underlie nearly all cancers. Multi-scale statistical, mathematical, and computational modeling methods are central to designing, implementing and assessing treatment strategies that account for these hierarchies. The basic science underlying these modeling efforts is maturing into a new discipline that is close to influencing and facilitating clinical successes. The purpose of this review is to capture the state-of-the-art as well as the key barriers to success for multi-scale modeling in clinical oncology. We begin with a summary of the long-envisioned promise of multi-scale modeling in clinical oncology, including the synthesis of disparate data types into models that reveal underlying mechanisms and allow for experimental testing of hypotheses. We then evaluate the mathematical techniques employed most widely and present several examples illustrating their application as well as the current gap between pre-clinical and clinical applications. We conclude with a discussion of what we view to be the key challenges and opportunities for multi-scale modeling in clinical oncology.

Bifurcation-based approach reveals synergism and optimal combinatorial perturbation

Cells accomplish the process of fate decisions and form terminal lineages through a series of binary choices in which cells switch stable states from one branch to another as the interacting strengths of regulatory factors continuously vary. Various combinatorial effects may occur because almost all regulatory processes are managed in a combinatorial fashion. Combinatorial regulation is crucial for cell fate decisions because it may effectively integrate many different signaling pathways to meet the higher regulation demand during cell development. However, whether the contribution of combinatorial regulation to the state transition is better than that of a single one and if so, what the optimal combination strategy is, seem to be significant issue from the point of view of both biology and mathematics. Using the approaches of combinatorial perturbations and bifurcation analysis, we provide a general framework for the quantitative analysis of synergism in molecular networks. Different from the known methods, the bifurcation-based approach depends only on stable state responses to stimuli because the state transition induced by combinatorial perturbations occurs between stable states. More importantly, an optimal combinatorial perturbation strategy can be determined by investigating the relationship between the bifurcation curve of a synergistic perturbation pair and the level set of a specific objective function. The approach is applied to two models, i.e., a theoretical multistable decision model and a biologically realistic CREB model, to show its validity, although the approach holds for a general class of biological systems.

Mathematical Modeling of Therapy-induced Cancer Drug Resistance: Connecting Cancer Mechanisms to Population Survival Rates

Drug resistance significantly limits the long-term effectiveness of targeted therapeutics for cancer patients. Recent experimental studies have demonstrated that cancer cell heterogeneity and microenvironment adaptations to targeted therapy play important roles in promoting the rapid acquisition of drug resistance and in increasing cancer metastasis. The systematic development of effective therapeutics to overcome drug resistance mechanisms poses a major challenge. In this study, we used a modeling approach to connect cellular mechanisms underlying cancer drug resistance to population-level patient survival. To predict progression-free survival in cancer patients with metastatic melanoma, we developed a set of stochastic differential equations to describe the dynamics of heterogeneous cell populations while taking into account micro-environment adaptations. Clinical data on survival and circulating tumor cell DNA (ctDNA) concentrations were used to confirm the effectiveness of our model. Moreover, our model predicted distinct patterns of dose-dependent synergy when evaluating a combination of BRAF and MEK inhibitors versus a combination of BRAF and PI3K inhibitors. These predictions were consistent with the findings in previously reported studies. The impact of the drug metabolism rate on patient survival was also discussed. The proposed model might facilitate the quantitative evaluation and optimization of combination therapeutics and cancer clinical trial design.

Adrenergic Stimulation of DUSP1 Impairs Chemotherapy Response in Ovarian Cancer

PURPOSE

Chronic adrenergic activation has been shown to associate with adverse clinical outcomes in cancer patients, but the underlying mechanisms are not well understood. The focus of the current study was to determine the functional and biologic effects of adrenergic pathways on response to chemotherapy in the context of ovarian cancer.

EXPERIMENTAL DESIGN

Increased DUSP1 production by sympathetic nervous system mediators (e.g., norepinephrine) was analyzed by real-time quantitative RT-PCR and by Western blotting. In vitro chemotherapy-induced cell apoptosis was examined by flow cytometry. For in vivo therapy, a well-characterized model of chronic stress was used.

RESULTS

Catecholamines significantly inhibited paclitaxel- and cisplatin-induced apoptosis in ovarian cancer cells. Genomic analyses of cells treated with norepinephrine identified DUSP1 as a potential mediator. DUSP1 overexpression resulted in reduced paclitaxel-induced apoptosis in ovarian cancer cells compared with control; conversely, DUSP1 gene silencing resulted in increased apoptosis compared with control cells. DUSP1 gene silencing in vivo significantly enhanced response to paclitaxel and increased apoptosis. In vitro analyses indicated that norepinephrine-induced DUSP1 gene expression was mediated through ADRB2 activation of cAMP-PLC-PKC-CREB signaling, which inhibits JNK-mediated phosphorylation of c-Jun and protects ovarian cancer cells from apoptosis. Moreover, analysis of The Cancer Genome Atlas data showed that increased DUSP1 expression was associated with decreased overall (P= 0.049) and progression-free (P= 0.0005) survival.

CONCLUSIONS

These findings provide a new understanding of the mechanisms by which adrenergic pathways can impair response to chemotherapy and have implications for cancer management.

Systematic synergy modeling: understanding drug synergy from a systems biology perspective

Owing to drug synergy effects, drug combinations have become a new trend in combating complex diseases like cancer, HIV and cardiovascular diseases. However, conventional synergy quantification methods often depend on experimental dose–response data which are quite resource-demanding. In addition, these methods are unable to interpret the explicit synergy mechanism. In this review, we give representative examples of how systems biology modeling offers strategies toward better understanding of drug synergy, including the protein-protein interaction (PPI) network-based methods, pathway dynamic simulations, synergy network motif recognitions, integrative drug feature calculations, and “omic”-supported analyses. Although partially successful in drug synergy exploration and interpretation, more efforts should be put on a holistic understanding of drug-disease interactions, considering integrative pharmacology and toxicology factors. With a comprehensive and deep insight into the mechanism of drug synergy, systems biology opens a novel avenue for rational design of effective drug combinations.

β-Adrenergic Receptor Signaling in Prostate Cancer

Enhanced sympathetic signaling, often associated with obesity and chronic stress, is increasingly acknowledged as a contributor to cancer aggressiveness. In prostate cancer, intact sympathetic nerves are critical for tumor formation, and sympathectomy induces apoptosis and blocks tumor growth. Perineural invasion, involving enrichment of intra-prostatic nerves, is frequently observed in prostate cancer and is associated with poor prognosis. β₂-adrenergic receptor (ADRB2), the most abundant receptor for sympathetic signals in prostate luminal cells, has been shown to regulate trans-differentiation of cancer cells to neuroendocrine-like cells and to affect apoptosis, angiogenesis, epithelial–mesenchymal transition, migration, and metastasis. Epidemiologic studies have shown that use of β-blockers, inhibiting β-adrenergic receptor activity, is associated with reduced prostate cancer-specific mortality. In this review, we aim to present an overview on how β-adrenergic receptor and its downstream signaling cascade influence the development of aggressive prostate cancer, primarily through regulating neuroendocrine differentiation.