A quantitative understanding of the advantages of nanoparticle-based drug delivery vis-à-vis conventional free drug chemotherapy has yet to be established for cancer or other disease despite numerous investigations. Here, we employ first-principles cell biophysics, drug pharmaco-kinetics and drug pharmaco-dynamics to model the delivery of doxorubicin (DOX) to hepatocellular carcinoma (HCC) tumor cells and predict the resultant experimental cytotoxicity data. The fundamental, mechanistic hypothesis of our mathematical model is that the integrated history of drug uptake by the cells over time of exposure, which sets the cell death rate parameter, and the uptake rate are the sole determinants of dose response relationship. A universal solution of the model equations is capable of predicting the entire, nonlinear dose response of the cells to any drug concentration based on just two separate measurements of these cellular parameters. This analysis reveals that nanocarrier-mediated delivery overcomes resistance to free drug because of improved cellular uptake rates, and that dose response curves to nanocarrier mediated drug delivery are equivalent to those for free-drug, but “shifted to the left,” i.e., lower amounts of drug achieve the same cell kill. We then demonstrate the model’s general applicability to different tumor and drug types, and cell-exposure time courses by investigating HCC cells exposed to cisplatin and 5-fluorouracil, breast cancer MCF-7 cells exposed to DOX, and pancreatic adenocarcinoma PANC-1 cells exposed to gemcitabine. The model will help in the optimal design of nanocarriers for clinical applications and improve the current, largely empirical understanding of in vivo drug transport and tumor response.
Drug delivery; mathematical modeling; mesoporous silica nanoparticle; pharmacokinetics-pharmacodynamics model; protocells
Multiscale models are commonplace in cancer modeling, where individual models acting on different biological scales are combined within a single, cohesive modeling framework. However, model composition gives rise to challenges in understanding interfaces and interactions between them. Based on specific domain expertise, typically these computational models are developed by separate research groups using different methodologies, programming languages, and parameters. This paper introduces a graph-based model for semantically linking computational cancer models via domain graphs that can help us better understand and explore combinations of models spanning multiple biological scales. We take the data model encoded by TumorML, an XML-based markup language for storing cancer models in online repositories, and transpose its model description elements into a graph-based representation. By taking such an approach, we can link domain models, such as controlled vocabularies, taxonomic schemes, and ontologies, with cancer model descriptions to better understand and explore relationships between models. The union of these graphs creates a connected property graph that links cancer models by categorizations, by computational compatibility, and by semantic interoperability, yielding a framework in which opportunities for exploration and discovery of combinations of models become possible.
tumor modeling; in silico oncology; model exploration; property graphs; neo4j
Long-living Ames dwarf (df/df) mice are homozygous for a mutation of the Prop1df gene. As a result, mice are deficient in growth hormone (GH), prolactin (PRL) and thyrotropin (TSH). In spite of the hormonal deficiencies, df/df mice live significantly longer and healthier lives compared to their wild type siblings. We studied the effects of calorie restriction (CR) on the expression of insulin signaling genes in skeletal muscle and adipose tissue of normal and df/df mice. The analysis of genes expression showed that CR differentially affects the insulin signaling pathway in these insulin target organs. Moreover, results obtained in both normal and Ames dwarf mice indicate more direct effects of CR on insulin signaling genes in adipose tissue than in skeletal muscle. Interestingly, CR reduced the protein levels of adiponectin in the epididymal adipose tissue of normal and Ames dwarf mice, while elevating adiponectin levels in skeletal muscle and plasma of normal mice only.
In conclusion, our findings suggest that both skeletal muscle and adipose tissue are important mediators of insulin effects on longevity. Additionally, the results revealed divergent effects of CR on expression of genes in the insulin signaling pathway of normal and Ames dwarf mice.
Ames dwarf; insulin; adipose tissue; skeletal muscle; adiponectin; obesity
In pedestrian detection methods, their high accuracy detection rates are always obtained at the cost of a large amount of false pedestrians. In order to overcome this problem, the authors propose an accurate pedestrian detection system based on two machine learning methods: cascade AdaBoost detector and random vector functional-link net. During the offline training phase, the parameters of a cascade AdaBoost detector and random vector functional-link net are trained by standard dataset. These candidates, extracted by the strategy of a multiscale sliding window, are normalized to be standard scale and verified by the cascade AdaBoost detector and random vector functional-link net on the online phase. Only those candidates with high confidence can pass the validation. The proposed system is more accurate than other single machine learning algorithms with fewer false pedestrians, which has been confirmed in simulation experiment on four datasets.
Cancer systems biology is an interdisciplinary, rapidly expanding research field in which collaborations are a critical means to advance the field. Yet the prevalent database technologies often isolate data rather than making it easily accessible. The Semantic Web has the potential to help facilitate web-based collaborative cancer research by presenting data in a manner that is self-descriptive, human and machine readable, and easily sharable. We have created a semantically linked online Digital Model Repository (DMR) for storing, managing, executing, annotating, and sharing computational cancer models. Within the DMR, distributed, multidisciplinary, and inter-organizational teams can collaborate on projects, without forfeiting intellectual property. This is achieved by the introduction of a new stakeholder to the collaboration workflow, the institutional licensing officer, part of the Technology Transfer Office. Furthermore, the DMR has achieved silver level compatibility with the National Cancer Institute’s caBIG®, so users can not only interact with the DMR through a web browser but also through a semantically annotated and secure web service. We also discuss the technology behind the DMR leveraging the Semantic Web, ontologies, and grid computing to provide secure inter-institutional collaboration on cancer modeling projects, online grid-based execution of shared models, and the collaboration workflow protecting researchers’ intellectual property.
Cancer modeling; model repository; oncology; Semantic Web; systems biology
IL-35 is a member of the IL-12 family of cytokines consisting of IL-12 p35 subunit and IL-12 p40-related protein subunit, EBV-induced gene 3 (EBI3). IL-35 functions through IL-35R and has a potent immune suppressive activity. Although IL-35 has been demonstrated to be produced by regulatory T cells, gene expression analysis has revealed that IL-35 is likely to have wider distribution including expression in cancer cells. In this study we have demonstrated that IL-35 is produced in human cancer tissues such as large B cell lymphoma, nasopharyngeal carcinoma and melanoma. In order to determine the roles of tumor-derived IL-35 in tumorigenesis and tumor immunity, we generated IL-35 producing plasmacytoma J558 and B16 melanoma cells, and observed that the expression of IL-35 in cancer cells does not affect their growth and survival in vitro, but stimulates tumorigenesis in both immune competent and Rag1/2 deficient mice. Tumor-derived IL-35 increases CD11b+Gr1+ myeloid cell accumulation in tumor microenvironment, and thereby promotes tumor angiogenesis. In immune competent mice, spontaneous CTL responses to tumors are diminished. IL-35 does not directly inhibit tumor antigen specific CD8+ T cell activation, differentiation and effector functions. However, IL-35-treated cancer cells had increased expression of gp130 and reduced sensitivity to CTL destruction. Thus, our study indicates novel functions of IL-35 in promoting tumor growth via enhancing myeloid cell accumulation, tumor angiogenesis and suppression of tumor immunity.
Simulating cancer behavior across multiple biological scales in space and time, i.e., multiscale cancer modeling, is increasingly being recognized as a powerful tool to refine hypotheses, focus experiments, and enable more accurate predictions. A growing number of examples illustrate the value of this approach in providing quantitative insight on the initiation, progression, and treatment of cancer. In this review, we introduce the most recent and important multiscale cancer modeling works that have successfully established a mechanistic link between different biological scales. Biophysical, biochemical, and biomechanical factors are considered in these models. We also discuss innovative, cutting-edge modeling methods that are moving predictive multiscale cancer modeling toward clinical application. Furthermore, because the development of multiscale cancer models requires a new level of collaboration among scientists from a variety of fields such as biology, medicine, physics, mathematics, engineering, and computer science, an innovative Web-based infrastructure is needed to support this growing community.
cancer systems biology; discrete; continuum; hybrid; clinical translation; personalized medicine
This paper discusses the need for interconnecting computational cancer models from different sources and scales within clinically relevant scenarios to increase the accuracy of the models and speed up their clinical adaptation, validation, and eventual translation. We briefly review current interoperability efforts drawing upon our experiences with the development of in silico models for predictive oncology within a number of European Commission Virtual Physiological Human initiative projects on cancer. A clinically relevant scenario, addressing brain tumor modeling that illustrates the need for coupling models from different sources and levels of complexity, is described. General approaches to enabling interoperability using XML-based markup languages for biological modeling are reviewed, concluding with a discussion on efforts towards developing cancer-specific XML markup to couple multiple component models for predictive in silico oncology.
multi-scale computational tumor modeling; in silico oncology; model interoperability; XML markup languages
Molecular-focused cancer therapies, e.g., molecularly targeted therapy and immunotherapy, so far demonstrate only limited efficacy in cancer patients. We hypothesize that underestimating the role of biophysical factors that impact the delivery of drugs or cytotoxic cells to the target sites (for associated preferential cytotoxicity or cell signaling modulation) may be responsible for the poor clinical outcome. Therefore, instead of focusing exclusively on the investigation of molecular mechanisms in cancer cells, convection-diffusion of cytotoxic molecules and migration of cancer-killing cells within tumor tissue should be taken into account to improve therapeutic effectiveness. To test this hypothesis, we have developed a mathematical model of the interstitial diffusion and uptake of small cytotoxic molecules secreted by T-cells, which is capable of predicting breast cancer growth inhibition as measured both in vitro and in vivo. Our analysis shows that diffusion barriers of cytotoxic molecules conspire with γδ T-cell scarcity in tissue to limit the inhibitory effects of γδ T-cells on cancer cells. This may increase the necessary ratios of γδ T-cells to cancer cells within tissue to unrealistic values for having an intended therapeutic effect, and decrease the effectiveness of the immunotherapeutic treatment.
Applying a previously developed non-small cell lung cancer model, we assess ‘cross-scale’ the therapeutic efficacy of targeting a variety of molecular components of the epidermal growth factor receptor (EGFR) signalling pathway. Simulation of therapeutic inhibition and amplification allows for the ranking of the implemented downstream EGFR signalling molecules according to their therapeutic values or indices. Analysis identifies mitogen-activated protein kinase and extracellular signal-regulated kinase as top therapeutic targets for both inhibition and amplification-based treatment regimen but indicates that combined parameter perturbations do not necessarily improve the therapeutic effect of the separate parameter treatments as much as might be expected. Potential future strategies using this in silico model to tailor molecular treatment regimen are discussed.
agent-based model; multiscale; non-small cell lung cancer; epidermal growth factor receptor; transforming growth factor β; signalling pathway
A facile electrochemical sensor for the determination of nonylphenol (NP) was fabricated in this work. Cetyltrimethylammonium bromide (CTAB), which formed a bilayer on the surface of the carbon paste (CP) electrode, displayed a remarkable enhancement effect for the electrochemical oxidation of NP. Moreover, the oxidation peak current of NP at the CTAB/CP electrode demonstrated a linear relationship with NP concentration, which could be applied in the direct determination of NP. Some experimental parameters were investigated, such as external solution pH, mode and time of accumulation, concentration and modification time of CTAB and so on. Under optimized conditions, a wide linear range from 1.0 × 10−7 mol·L−1 to 2.5 × 10−5 mol·L−1 was obtained for the sensor, with a low limit of detection at 1.0 × 10−8 mol·L−1. Several distinguishing advantages of the as-prepared sensor, including facile fabrication, easy operation, low cost and so on, suggest a great potential for its practical applications.
nonylphenol (NP); cetyltrimethylammonium bromide (CTAB); determination; electrochemical sensor
Dopamine, its receptors and transporter are present in the brain beginning from early in the embryonic period. Dopamine receptor activation can influence developmental events including neurogenesis, neuronal migration and differentiation raising the possibility that dopamine imbalance in the fetal brain can alter development of the brain and behavior. We examined whether elevated dopamine levels during gestation can produce persisting changes in brain dopamine content and dopamine-mediated behaviors. We administered L-3,4-dihydroxyphenylalanine (L-DOPA) in drinking water to timed-pregnant CD1 mice from the 11th day of gestation until the day of parturition. The prenatal L-DOPA exposure led to significantly lower cocaine conditioned place preference, a behavioral test of reward, at postnatal day 60 (P60). However, in vivo microdialysis measurements showed significant increases in cocaine-induced dopamine release in the caudate putamen of P26 and P60 mice exposed to L-DOPA prenatally, ruling out attenuated dopamine release in the caudate putamen as a contributor to decreased conditioned place preference. Although dopamine release was induced in the nucleus accumbens of prenatally L-DOPA exposed mice at P60 by cocaine, the dopamine release in the nucleus accumbens was not significantly different between the L-DOPA and control groups. However, basal dopamine release was significantly higher in the prenatally L-DOPA exposed mice at P60 suggesting that the L-DOPA exposed mice may require a higher dose of cocaine for induction of cocaine place preference than the controls. The prenatal L-DOPA exposure did not alter cocaine-induced locomotor response, suggesting dissociation between the effects of prenatal L-DOPA exposure on conditioned place preference and locomotor activity. Tissue concentration of dopamine and its metabolites in the striatum and ventral midbrain were significantly affected by the L-DOPA exposure as well as by developmental changes over the P14 to P60 period. Thus, elevation of dopamine levels during gestation can produce persisting changes in brain dopamine content, cocaine-induced dopamine release and cocaine conditioned place preference.
Dopamine; L-DOPA; cocaine; conditioned place preference; locomotor activity
Multiscale modeling is increasingly being recognized as a promising research area in computational cancer systems biology. Here, exemplified by two pioneering studies, we attempt to explain why and how such a multiscale approach paired with an innovative cross-scale analytical technique can be useful in identifying high-value molecular therapeutic targets. This novel, integrated approach has the potential to offer a more effective in silico framework for target discovery and represents an important technical step towards systems medicine.
computational modeling; multiscale; epidermal growth factor receptor; non-small cell lung cancer; signaling pathway
The 14-3-3 family is comprised of highly conserved proteins that are functionally important in the maintenance of homeostasis. Their involvement with the cell cycle, their association with proto-oncogenes and oncogenes, and their abnormal expression in various tumors has linked this family of proteins to the etiology of human cancer. Mounting evidence now indicates that 14-3-3σ is a cancer suppressor gene but the roles of the other 14-3-3 isoforms and their interactions in tumorigenesis have not yet been elucidated. In our current study, we examined the expression of 14-3-3β, γ, ε, ζ, η and τ in a large series of vulvar squamous cell carcinomas to evaluate any clinical significance.
Tumor biopsies from 298 vulvar carcinomas were examined by immunohistochemistry for the expression of 14-3-3β, γ, ε, ζ, η and τ. Statistical analyses were employed to validate any associations between the expression of any 14-3-3 isoform and clinicopathologic variables for this disease.
High cytoplasmic levels of 14-3-3β, γ, ζ, ε and η were observed in 79%, 58%, 50%, 86% and 54% of the vulvar carcinomas analyzed, respectively, whereas a low nuclear expression of 14-3-3τ was present in 80% of these cases. The elevated cytoplasmic expression of 14-3-3β, γ, ε, ζ and η was further found to be associated with advanced disease and aggressive features of these cancers. The overexpression of cytoplasmic 14-3-3β and ε significantly correlated with a poor disease-specific survival by univariate analysis (P = 0.007 and P = 0.04, respectively). The independent prognostic significance of these factors was confirmed by multivariate analysis (P = 0.007 and P = 0.009, respectively).
We reveal for the first time that the 14-3-3β, γ, ε, ζ, η and τ isoforms may be involved in the progression of vulvar carcinomas. Furthermore, our analyses show that high cytoplasmic levels of 14-3-3β and ε independently correlate with poor disease-specific survival.
To date, parameters defining biological properties in multiscale disease models are commonly obtained from a variety of sources. It is thus important to examine the influence of parameter perturbations on system behavior, rather than to limit the model to a specific set of parameters. Such sensitivity analysis can be used to investigate how changes in input parameters affect model outputs. However, multiscale cancer models require special attention because they generally take longer to run than does a series of signaling pathway analysis tasks. In this article, we propose a global sensitivity analysis method based on the integration of Monte Carlo, resampling, and analysis of variance. This method provides solutions to (1) how to render the large number of parameter variation combinations computationally manageable, and (2) how to effectively quantify the sampling distribution of the sensitivity index to address the inherent computational intensity issue. We exemplify the feasibility of this method using a two-dimensional molecular-microscopic agent-based model previously developed for simulating non-small cell lung cancer; in this model, an epidermal growth factor (EGF)-induced, EGF receptor-mediated signaling pathway was implemented at the molecular level. Here, the cross-scale effects of molecular parameters on two tumor growth evaluation measures, i.e., tumor volume and expansion rate, at the microscopic level are assessed. Analysis finds that ERK, a downstream molecule of the EGF receptor signaling pathway, has the most important impact on regulating both measures. The potential to apply this method to therapeutic target discovery is discussed.
agent-based model; analysis of variance; multiscale; non-small cell lung cancer; sensitivity analysis
To determine the pharmacokinetic and pharmacodynamic dose-response effects of insulin glargine administered subcutaneously in individuals with type 2 diabetes.
RESEARCH DESIGN AND METHODS
Twenty obese type 2 diabetic individuals (10 male and 10 female, aged 50 ± 3 years, with BMI 36 ± 2 kg/m2 and A1C 8.3 ± 0.6%) were studied in this single-center, placebo-controlled, randomized, double-blind study. Five subcutaneous doses of insulin glargine (0, 0.5, 1.0, 1.5, and 2.0 units/kg) were investigated on separate occasions using the 24-h euglycemic clamp technique.
Glargine duration of action to reduce glucose, nonessential fatty acid (NEFA), and β-hydroxybutyrate levels was close to or >24 h for all four doses. Increases in glucose flux revealed no discernible peak and were modest with maximal glucose infusion rates of 9.4, 6.6, 5.5, and 2.8 μmol/kg/min for the 2.0, 1.5, 1.0, and 0.5 units/kg doses, respectively. Glargine exhibited a relatively hepatospecific action with greater suppression (P < 0.05) of endogenous glucose production (EGP) compared with little or no increases in glucose disposal.
A single subcutaneous injection of glargine at a dose of ≥0.5 units/kg can acutely reduce glucose, NEFA, and ketone body levels for 24 h in obese insulin-resistant type 2 diabetic individuals. Glargine lowers blood glucose by mainly inhibiting EGP with limited effects on stimulating glucose disposal. Large doses of glargine have minimal effects on glucose flux and retain a relatively hepatospecific action in type 2 diabetes.
Blockade of growth hormone (GH), decreased insulin-like growth factor-1 (IGF1) action and increased insulin sensitivity are associated with life extension and an apparent slowing of the aging process. We examined expression of genes involved in insulin action, IR, IRS1, IRS2, IGF1, IGF1R, GLUT4, PPARs and RXRs in the hearts of normal and GHR−/− (KO) mice fed ad libitum or subjected to 30% caloric restriction (CR). CR increased the cardiac expression of IR, IRS1, IGF1, IGF1R and GLUT4 in normal mice and IRS1, GLUT4, PPARα and PPARβ/δ in GHR-KO animals. Expression of IR, IRS1, IRS2, IGF1, GLUT4, PPARγ and PPARα did not differ between GHR-KO and normal mice. These unexpected results suggest that CR may lead to major modifications of insulin action in the heart, but high insulin sensitivity of GHR-KO mice is not associated with alterations in the levels of most of the examined molecules related to intracellular insulin signaling.
Caloric restriction; aging; GHR-KO; insulin; fatty acid
To date, there are no data investigating the effects of GABAA activation on counterregulatory responses during repeated hypoglycemia in humans. The aim of this study was to determine the effects of prior GABAA activation using the benzodiazepine alprazolam on the neuroendocrine and autonomic nervous system (ANS) and metabolic counterregulatory responses during next-day hypoglycemia in healthy humans.
RESEARCH DESIGN AND METHODS
Twenty-eight healthy individuals (14 male and 14 female, age 27 ± 6 years, BMI 24 ± 3 kg/m2, and A1C 5.2 ± 0.1%) participated in four randomized, double-blind, 2-day studies. Day 1 consisted of either morning and afternoon 2-h hyperinsulinemic euglycemia or 2-h hyperinsulinemic hypoglycemia (2.9 mmol/l) with either 1 mg alprazolam or placebo administered 30 min before the start of each clamp. Day 2 consisted of a single-step hyperinsulinemic-hypoglycemic clamp of 2.9 mmol/l.
Despite similar hypoglycemia (2.9 ± 1 mmol/l) and insulinemia (672 ± 108 pmol/l) during day 2 studies, GABAA activation with alprazolam during day 1 euglycemia resulted in significant blunting (P < 0.05) of ANS (epinephrine, norepinephrine, muscle sympathetic nerve activity, and pancreatic polypeptide), neuroendocrine (glucagon and growth hormone), and metabolic (glucose kinetics, lipolysis, and glycogenolysis) counterregulatory responses. GABAA activation with alprazolam during prior hypoglycemia caused further significant (P < 0.05) decrements in subsequent glucagon, growth hormone, pancreatic polypeptide, and muscle sympathetic nerve activity counterregulatory responses.
Alprazolam activation of GABAA pathways during day 1 hypoglycemia can play an important role in regulating a spectrum of key physiologic responses during subsequent (day 2) hypoglycemia in healthy man.
The disruption of the growth hormone (GH) axis in mice promotes insulin sensitivity and is strongly correlated with extended longevity. Ames dwarf (Prop1df, df/df) mice are GH, prolactin (PRL), and thyrotropin (TSH) deficient and live approximately 50% longer than their normal siblings. To investigate the effects of GH on insulin and GH signaling pathways, we subjected these dwarf mice to twice-daily GH injections (6 μg/g/d) starting at the age of 2 weeks and continuing for 6 weeks. This produced the expected activation of the GH signaling pathway and stimulated somatic growth of the Ames dwarf mice. However, concomitantly with increased growth and increased production of insulinlike growth factor-1, the GH treatment strongly inhibited the insulin signaling pathway by decreasing insulin sensitivity of the dwarf mice. This suggests that improving growth of these animals may negatively affect both their healthspan and longevity by causing insulin resistance.
Ames dwarf; Aging; Insulin; Growth hormone
We present a multiscale agent-based non-small cell lung cancer model that consists of a 3D environment with which cancer cells interact while processing phenotypic changes. At the molecular level, transforming growth factor β (TGFβ) has been integrated into our previously developed in silico model as a second extrinsic input in addition to epidermal growth factor (EGF). The main aim of this study is to investigate how the effects of individual and combinatorial change in EGF and TGFβ concentrations at the molecular level alter tumor growth dynamics on the multi-cellular level, specifically tumor volume and expansion rate. Our simulation results show that separate EGF and TGFβ fluctuations trigger competing multi-cellular phenotypes, yet synchronous EGF and TGFβ signaling yields a spatially more aggressive tumor that overall exhibits an EGF-driven phenotype. By altering EGF and TGFβ concentration levels simultaneously and asynchronously, we discovered a particular region of EGF-TGFβ profiles that ensures phenotypic stability of the tumor system. Within this region, concentration changes in EGF and TGFβ do not impact the resulting multi-cellular response substantially, while outside these concentration ranges, a change at the molecular level will substantially alter either tumor volume or tumor expansion rate, or both. By evaluating tumor growth dynamics across different scales, we show that, under certain conditions, therapeutic targeting of only one signaling pathway may be insufficient. Potential implications of these in silico results for future clinico-pharmacological applications are discussed.
Supplementary information: Supplementary data are available at Bioinformatics online.
Glutathione S-transferase pi (GST pi) is a subgroup of GST family, which provides cellular protection against free radical and carcinogenic compounds due to its detoxifying function. Expression patterns of GST pi have been studied in several carcinomas and its down-regulation was implicated to be involved in malignant transformation in patients with Barrett's esophagus. However, neither the exact role of GST pi in the pathogenesis nor its prognostic impact in squamous esophageal carcinoma is fully characterized.
Immunohistochemistry was used to investigate GST pi expression on 153 archival squamous esophageal carcinoma specimens with a GST pi monoclonal antibody. Statistic analyses were performed to explore its association with clinicopathological factors and clinical outcome.
The GST pi expression was greatly reduced in tissues of esophageal carcinomas compared to adjacent normal tissues and residual benign tissues. Absent of GST pi protein expression in cytoplasm, nuclear and cytoplasm/nucleus was found in 51%, 64.7% and 48% of all the carcinoma cases, respectively. GST pi deficiency in cytoplasm, nucleus and cytoplasm/nucleus was significantly correlated to poor differentiation (p < 0.001, p < 0.001 and p < 0.001, respectively). UICC stage and T stage were found significantly correlated to negative expression of GST pi in cytoplasm (p < 0.001 and p = 0.004, respectively) and cytoplasm/nucleus (p = 0.017 and p = 0.031, respectively). In univariate analysis, absent of GST pi protein expression in cytoplasm, nucleus and cytoplasm/nucleus was significantly associated with a shorter overall survival (p < 0.001, p < 0.001 and p < 0.001, respectively), whereas only GST pi cytoplasmic staining retained an independent prognostic significance (p < 0.001) in multivariate analysis.
Our results show that GST pi expression is down regulated in the squamous esophageal carcinoma, and that the lack of GST pi expression is associated with poor prognosis. Therefore, deficiency of GST pi protein expression may be an important mechanism involved in the carcinogenesis and progression of the squamous esophageal carcinoma, and the underlying mechanisms leading to decreased GST pi expression deserve further investigation.
CDC25 phosphatases are important regulators of the cell cycle. Their abnormal expression detected in a number of tumors implies that their dysregulation is involved in malignant transformation. However, the role of CDC25s in vulvar cancer is still unknown. To shed light on their roles in the pathogenesis and to clarify their prognostic values, expression of CDC25A, CDC25B and CDC25C in a large series of vulvar squamous cell carcinomas were examined.
Expression of CDC25A, CDC25B, CDC25C and phosphorylated (phospho)-CDC25C (Ser216) were examined in 300 vulvar carcinomas using immunohistochemistry. Western blot analysis was utilized to demonstrate CDC25s expression in vulvar cancer cell lines. Kinase and phosphatase assays were performed to exclude cross reactivity among CDC25s isoform antibodies.
High nuclear CDC25A and CDC25B expression were observed in 51% and 16% of the vulvar carcinomas, respectively, whereas high cytoplasmic CDC25C expression was seen in 63% of the cases. In cytoplasm, nucleus and cytoplasm/nucleus high phospho-CDC25C (Ser216) expression was identified in 50%, 70% and 77% of the carcinomas, respectively. High expression of CDC25s correlated significantly with malignant features, including poor differentiation and infiltration of vessel for CDC25B, high FIGO stage, presence of lymph node metastases, large tumor diameter, poor differentiation for CDC25C and high FIGO stage, large tumor diameter, deep invasion and poor differentiation for phospho-CDC25C (Ser216). In univariate analysis, high expression of phospho-CDC25C (Ser216) was correlated with poor disease-specific survival (p = 0.04). However, such an association was annulled in multivariate analysis.
Our results suggest that CDC25C and phospho-CDC25C (Ser216) play a crucial role and CDC25B a minor role in the pathogenesis and/or progression of vulvar carcinomas. CDC25B, CDC25C and phospho-CDC25C (Ser216) were associated with malignant features and aggressive cancer phenotypes. However, the CDC25s isoforms were not independently correlated to prognosis.
We have extended our previously developed 3D multi-scale agent-based brain tumor model to simulate cancer heterogeneity and to analyze its impact across the scales of interest. While our algorithm continues to employ an epidermal growth factor receptor (EGFR) gene-protein interaction network to determine the cells’ phenotype, it now adds an implicit treatment of tumor cell adhesion related to the model’s biochemical microenvironment. We simulate a simplified tumor progression pathway that leads to the emergence of five distinct glioma cell clones with different EGFR density and cell ‘search precisions’. The in silico results show that microscopic tumor heterogeneity can impact the tumor system’s multicellular growth patterns. Our findings further confirm that EGFR density results in the more aggressive clonal populations switching earlier from proliferation-dominated to a more migratory phenotype. Moreover, analyzing the dynamic molecular profile that triggers the phenotypic switch between proliferation and migration, our in silico oncogenomics data display spatial and temporal diversity in documenting the regional impact of tumorigenesis, and thus support the added value of multi-site and repeated assessments in vitro and in vivo. Potential implications from this in silico work for experimental and computational studies are discussed.
glioma; epidermal growth factor receptor; cancer heterogeneity; agent-based model
HIWI, the human homologue of Piwi family, is present in CD34+ hematopoietic stem cells and germ cells, but not in well-differentiated cell populations, indicating that HIWI may play an impotent role in determining or maintaining stemness of these cells. That HIWI expression has been detected in several type tumours may suggest its association with clinical outcome in cancer patients.
With the methods of real-time PCR, western blot, immunocytochemistry and immunohistochemistry, the expression of HIWI in three esophageal squamous cancer cell lines KYSE70, KYSE140 and KYSE450 has been characterized. Then, we investigated HIWI expression in a series of 153 esophageal squamous cell carcinomas using immunohistochemistry and explored its association with clinicopathological features.
The expression of HIWI was observed in tumour cell nuclei or/and cytoplasm in 137 (89.5%) cases, 16 (10.5%) cases were negative in both nuclei and cytoplasm. 86 (56.2%) were strongly positive in cytoplasm, while 49 (32.0%) were strongly positive in nuclei. The expression level of HIWI in cytoplasm of esophageal cancer cells was significantly associated with histological grade (P = 0.011), T stage (P = 0.035), and clinic outcome (P < 0.001), while there was no correlation between the nuclear HIWI expression and clinicopathological features.
The expression of HIWI in the cytoplasm of esophageal cancer cells is significantly associated with higher histological grade, clinical stage and poorer clinical outcome, indicating its possible involvement in cancer development.