Does chemically induced hepatocyte proliferation predict liver carcinogenesis?

Cell proliferation has long been recognized as having an important role in chemically induced carcinogenesis. Based on findings that certain nongenotoxic chemical carcinogens induced cell proliferation in the same organ that had an increased incidence of tumors, it has been hypothesized that a chemically induced response of enhanced DNA synthesis and cellular division causes cancer by increasing the rate of spontaneous mutations. It was further suggested that there would be no increased human risk of cancer by non-DNA-reactive compounds at doses that do not cause a proliferative response. An evaluation of the literature on the relationship between chemically induced cell proliferation and liver carcinogenesis reveals that very few systematic cell proliferation studies have been conducted over periods of extended exposure, and in many cases the exposure concentrations were not similar to those used in the cancer studies. The proliferative response resulting from exposure to many nongenotoxic carcinogens is not well sustained, whereas the carcinogenic response by these chemicals often requires prolonged exposure. The available literature leads to the conclusion that quantitative correspondences between cellular proliferation and carcinogenic responses have not been demonstrated and do not support the hypothesis that chemically induced cell proliferation is the primary mechanism by which nongenotoxic chemicals cause liver cancer. Studies of liver carcinogenesis in two-stage models point out the need to better understand chemical effects on cell loss as well as on cell replication, and demonstrate that measurements of cell proliferation alone are not sufficient to elucidate mechanisms of tumor development.     

Incorporation of inter-individual heterogeneity into the multistage carcinogenesis model: approach to the analysis of cancer incidence data

We investigate a multistage carcinogenesis frailty model to incorporate inter-individual heterogeneity into carcinogenic response. Attention is focused on inference concerning the effects of different sources of population heterogeneity on cancer rates. The authors consider unobserved variability arising from either carcinogen exposure or background characteristics. Gamma and Inverse-Gaussian distributions are selected for frailty models, and the baseline hazard function is the generalized Armitage-Doll model (i.e. non-frailty model) in which exposure effects shift the age scale instead of acting multiplicatively on cancer rates. For illustration, we apply the method to solid cancer data from a cohort of atomic bomb survivors to examine some features of proposed models. The results show that the Gamma frailty model for the heterogeneity of baseline rates provides the best goodness-of-fit of the model and a non-zero frailty variance. Parameter estimates are, for the most part, comparable between the Gamma and Inverse-Gaussian frailty models. In a heterogeneous population the exposure effects on young adulthood cancer rates might be underestimated for the non-frailty model. Meaningful information regarding each source of heterogeneity has been provided by the proposed method. Therefore, the multistage carcinogenesis frailty model approach is useful for analyses of epidemiological cancer data to assess population heterogeneity and heterogeneity-influenced exposure effects.     

Synthesis of evidence from epidemiological studies with interval-censored exposure due to grouping

We describe a method for assessing dose-response effects from a series of case-control and cohort studies in which the exposure information is interval censored. The interval censoring of the exposure variable is dealt with through the use of retrospective models in which the exposure is treated as a multinomial response and disease status as a binary covariate. Polychotomous logistic regression models are adopted in which the dose-response relationship between exposure and disease may be modeled in a discrete or continuous fashion. Partial conditioning is possible to eliminate some of the nuisance parameters. The methods are applied to the motivating study of the relationship between chorionic villus sampling and the occurrence of terminal transverse limb reduction.     

A latent variable approach to study gene-environment interactions in the presence of multiple correlated exposures

Many existing cohort studies initially designed to investigate disease risk as a function of environmental exposures have collected genomic data in recent years with the objective of testing for gene-environment interaction (G × E) effects. In environmental epidemiology, interest in G × E arises primarily after a significant effect of the environmental exposure has been documented. Cohort studies often collect rich exposure data; as a result, assessing G × E effects in the presence of multiple exposure markers further increases the burden of multiple testing, an issue already present in both genetic and environment health studies. Latent variable (LV) models have been used in environmental epidemiology to reduce dimensionality of the exposure data, gain power by reducing multiplicity issues via condensing exposure data, and avoid collinearity problems due to presence of multiple correlated exposures. We extend the LV framework to characterize gene-environment interaction in presence of multiple correlated exposures and genotype categories. Further, similar to what has been done in case-control G × E studies, we use the assumption of gene-environment (G-E) independence to boost the power of tests for interaction. The consequences of making this assumption, or the issue of how to explicitly model G-E association has not been previously investigated in LV models. We postulate a hierarchy of assumptions about the LV model regarding the different forms of G-E dependence and show that making such assumptions may influence inferential results on the G, E, and G × E parameters. We implement a class of shrinkage estimators to data adaptively trade-off between the most restrictive to most flexible form of G-E dependence assumption and note that such class of compromise estimators can serve as a benchmark of model adequacy in LV models. We demonstrate the methods with an example from the Early Life Exposures in Mexico City to Neuro-Toxicants Study of lead exposure, iron metabolism genes, and birth weight.     

In vitro Cell Culture Model for Toxic Inhaled Chemical Testing

Cell cultures are indispensable to develop and study efficacy of therapeutic agents, prior to their use in animal models. We have the unique ability to model well differentiated human airway epithelium and heart muscle cells. This could be an invaluable tool to study the deleterious effects of toxic inhaled chemicals, such as chlorine, that can normally interact with the cell surfaces, and form various byproducts upon reacting with water, and limiting their effects in submerged cultures. Our model using well differentiated human airway epithelial cell cultures at air-liqiuid interface circumvents this limitation as well as provides an opportunity to evaluate critical mechanisms of toxicity of potential poisonous inhaled chemicals. We describe enhanced loss of membrane integrity, caspase release and death upon toxic inhaled chemical such as chlorine exposure. In this article, we propose methods to model chlorine exposure in mammalian heart and airway epithelial cells in culture and simple tests to evaluate its effect on these cell types.     

Data-Driven Method to Estimate Nonlinear Chemical Equivalence

There is great need to express the impacts of chemicals found in the environment in terms of effects from alternative chemicals of interest. Methods currently employed in fields such as life-cycle assessment, risk assessment, mixtures toxicology, and pharmacology rely mostly on heuristic arguments to justify the use of linear relationships in the construction of “equivalency factors,” which aim to model these concentration-concentration correlations. However, the use of linear models, even at low concentrations, oversimplifies the nonlinear nature of the concentration-response curve, therefore introducing error into calculations involving these factors. We address this problem by reporting a method to determine a concentration-concentration relationship between two chemicals based on the full extent of experimentally derived concentration-response curves. Although this method can be easily generalized, we develop and illustrate it from the perspective of toxicology, in which we provide equations relating the sigmoid and non-monotone, or “biphasic,” responses typical of the field. The resulting concentration-concentration relationships are manifestly nonlinear for nearly any chemical level, even at the very low concentrations common to environmental measurements. We demonstrate the method using real-world examples of toxicological data which may exhibit sigmoid and biphasic mortality curves. Finally, we use our models to calculate equivalency factors, and show that traditional results are recovered only when the concentration-response curves are “parallel,” which has been noted before, but we make formal here by providing mathematical conditions on the validity of this approach.     

Adding sequence context to a Markov background model improves the identification of regulatory elements

MOTIVATION: Many computational methods for identifying regulatory elements use a likelihood ratio between motif and background models. Often, the methods use a background model of independent bases. At least two different Markov background models have been proposed with the aim of increasing the accuracy of predicting regulatory elements. Both Markov background models suffer theoretical drawbacks, so this article develops a third, context-dependent Markov background model from fundamental statistical principles. RESULTS: Datasets containing known regulatory elements in eukaryotes provided a basis for comparing the predictive accuracies of the different background models. Non-parametric statistical tests indicated that Markov models of order 3 constituted a statistically significant improvement over the background model of independent bases. Our model performed slightly better than the previous Markov background models. We also found that for discriminating between the predictive accuracies of competing background models, the correlation coefficient is a more sensitive measure than the performance coefficient. AVAILABILITY: Our C++ program is available at ftp://ftp.ncbi.nih.gov/pub/spouge/papers/archive/AGLAM/2006-07-19     

Dose response problems in carcinogenesis.

The estimation of risks from exposure to carcinogens is an important problem from the viewpoint of protection of human health. It also poses some very difficult dose-response problems. Two dose-response models may fit experimental data about equally well and yet predict responses that differ by many orders of magnitude at low doses. Mechanisms of carcinogenesis are not sufficiently understood so that the shape of the dose-response curve at low doses can be satisfactorily predicted. Mathematical theories of carcinogenesis and statistical procedures can be of use with dose-reponse problems such as this and, in addition, can lead to a better understanding of the mechanisms of carcinogenesis. In this paper, mathematical dose-response models of carcinogenesis are considered as well as various proposed dose-response procedures for estimating carcinogenic risks at low doses. Areas are suggested in which further work may be useful. These areas include experimental design problems, statistical procedures for use with time-to-occurrence data, and mathematical models that incorporate such biological features as pharmacokinetics of carcinogens, synergistic effects, DNA repair, susceptible subpopulations, and immune reactions.     

Using simulations to evaluate Mantel‐based methods for assessing landscape resistance to gene flow

Mantel‐based tests have been the primary analytical methods for understanding how landscape features influence observed spatial genetic structure. Simulation studies examining Mantel‐based approaches have highlighted major challenges associated with the use of such tests and fueled debate on when the Mantel test is appropriate for landscape genetics studies. We aim to provide some clarity in this debate using spatially explicit, individual‐based, genetic simulations to examine the effects of the following on the performance of Mantel‐based methods: (1) landscape configuration, (2) spatial genetic nonequilibrium, (3) nonlinear relationships between genetic and cost distances, and (4) correlation among cost distances derived from competing resistance models. Under most conditions, Mantel‐based methods performed poorly. Causal modeling identified the true model only 22% of the time. Using relative support and simple Mantel r values boosted performance to approximately 50%. Across all methods, performance increased when landscapes were more fragmented, spatial genetic equilibrium was reached, and the relationship between cost distance and genetic distance was linearized. Performance depended on cost distance correlations among resistance models rather than cell‐wise resistance correlations. Given these results, we suggest that the use of Mantel tests with linearized relationships is appropriate for discriminating among resistance models that have cost distance correlations <0.85 with each other for causal modeling, or <0.95 for relative support or simple Mantel r. Because most alternative parameterizations of resistance for the same landscape variable will result in highly correlated cost distances, the use of Mantel test‐based methods to fine‐tune resistance values will often not be effective.     

Acquisition of mitochondrial dysregulation and resistance to mitochondrial-mediated apoptosis after genotoxic insult in normal human fibroblasts: a possible model for early stage carcinogenesis

Acquisition of death-resistance is critical in the evolution of neoplasia. Our aim was to model the early stages of carcinogenesis by examining intracellular alterations in cells that have acquired apoptosis-resistance after exposure to a complex genotoxin. We previously generated sub-populations of BJ-hTERT human diploid fibroblasts, which have acquired death-resistance following exposure to hexavalent chromium [Cr(VI)], a broad-spectrum genotoxicant. Long-term exposure to certain forms of Cr(VI) is associated with respiratory carcinogenesis. Here, we report on the death-sensitivity of subclonal populations derived from clonogenic survivors of BJ-hTERT cells treated with 5 μM Cr(VI) (DR1, DR2), or selected by dilution-based cloning without treatment (CC1). Following Cr(VI) treatment, CC1 cells downregulated expression of the anti-apoptotic protein Bcl-2 and exhibited extensive expression of cleaved caspase 3. In contrast, the DR cells exhibited no cleaved caspase 3 expression and maintained expression of Bcl-2 following recovery from 24 h Cr(VI) exposure. The DR cells also exhibited attenuated mitochondrial-membrane depolarization and mitochondrial retention of cytochrome c and SMAC/DIABLO following Cr(VI) exposure. The DR cells exhibited less basal mtDNA damage, as compared to CC1 cells, which correlates with intrinsic (non-induced) death-resistance. Notably, there was no difference in p53 protein expression before or after treatment among all cell lines. Taken together, our data suggest the presence of more resilient mitochondria in death-resistant cells, and that death-resistance can be acquired in normal human cells early after genotoxin exposure. We postulate that resistance to mitochondrial-mediated cell death and mitochondrial dysregulation may be an initial phenotypic alteration observed in early stage carcinogenesis.     

Which factors affect phytoplankton biomass in shallow eutrophic lakes?

The restoration and management of shallow, pond-like systems are hindered by limitations in the applicability of the well-known models describing the relationship between nutrients and lake phytoplankton biomass in higher ranges of nutrient concentration. Trophic models for naturally eutrophic small, shallow, endorheic lakes have not yet been developed, even though these are the most frequent standing waters in continental lowlands. The aim of this study was to identify variables that can be considered as main drivers of phytoplankton biomass and to build a predictive model. The influence of potential drivers of phytoplankton biomass (nutrients, other chemical variables, land use, lake use and lake depth) from 24 shallow eutrophic lakes was tested using data in the Pannonian ecoregion (Hungary and Romania). By incorporating lake depth, TP, TN and lake use as independent and Chl-a as dependent variables into different models (multiple regression model, GLM and multilayer perception model) predictive models were built. These models explained >50% of the variance. Although phytoplankton biomass in small, shallow, enriched lakes is strongly influenced by stochastic effects, our results suggest that phytoplankton biomass can be predicted by applying a multiple stressor approach, and that the model results can be used for management purposes.     

The LEC rat: a useful model for studying liver carcinogenesis related to oxidative stress and inflammation

Growing evidence indicates oxidative stress as a mechanism of several diseases including cancer. Oxidative stress can be defined as the imbalance between cellular oxidant species production and antioxidant capability shifted towards the former. Lipid peroxidation is one of the processes that takes place during oxidative stress. Lipid peroxidation products, such as malondialdehyde (MDA) and 4-hydroxy-2-nonenal (HNE), are closely related to carcinogenesis as they are potent mutagens and they have been suggested as modulators of signal pathways related to proliferation and apoptosis, two processes implicated in cancer development. Mechanisms by which oxidative stress leads to tumor formation are still under investigation. The need of suitable in vivo models that could reflect that inflammation-related human carcinogenesis is evident. In this regard, the mutant strain Long Evans Cinnamon-like (LEC) rat provides a promising model for investigation of the relationship between hepatitis induced by oxidative stress and hepatocarcinogenesis because it has been demonstrated to develop spontaneous liver tumor formation related to copper accumulation and oxidative stress. In this review, the findings regarding oxidative stress and its relation with liver pathologies in LEC rats are discussed; we focus on the mechanisms proposed for HNE carcinogenesis.     

Organoids in recapitulating tumorigenesis driven by risk factors: Current trends and future perspectives

Environmental and exogenous/ endogenous factors, in a setting of individual genetic predisposition, contribute to the cancer development. Over the years, epidemical evidence increasingly highlights the correlations of multiple cancer incentives and genetic alterations with cancer incidence. Unraveling the pivotal carcinogenesis events prompted by particular risk factors remarkably advances early surveillance and oncogenesis intervening. Traditional cell-based models and animal-based models are unrealistic and unreliable for translational study, respectively ascribing to the limited tumor heterogeneity and species-related variation. Organoid emerged as a fidelity model that well preserves the properties of its origin. With inherent quality of holistic perspective, organoid is therefore ideally suited for delineating the carcinogenesis under risk exposure, in favor of understanding pathogen-host interactions and alleviating cancer initiation. In this review, we have summarized the organoid model-based evidence that identified or validated carcinogenic risks, mainly including diet, aging, microbial infection, and chemical exposure. In addition, we envisioned the exciting prospect of organoid model in screening promising treatment and/or prevention during tumorigenesis. As a robust 3D in vitro system, organoid has been widespread applied in basial and clinical cancer research, which may elucidate crucial mechanisms of oncogenesis and develop novel targeting strategies.     

Correcting for measurement error in fractional polynomial models using Bayesian modelling and regression calibration,with an application to alcohol and mortality

Exposure measurement error can result in a biased estimate of the association between an exposure and outcome. When the exposure–outcome relationship is linear on the appropriate scale (e.g. linear, logistic) and the measurement error is classical, that is the result of random noise, the result is attenuation of the effect. When the relationship is non‐linear, measurement error distorts the true shape of the association. Regression calibration is a commonly used method for correcting for measurement error, in which each individual's unknown true exposure in the outcome regression model is replaced by its expectation conditional on the error‐prone measure and any fully measured covariates. Regression calibration is simple to execute when the exposure is untransformed in the linear predictor of the outcome regression model, but less straightforward when non‐linear transformations of the exposure are used. We describe a method for applying regression calibration in models in which a non‐linear association is modelled by transforming the exposure using a fractional polynomial model. It is shown that taking a Bayesian estimation approach is advantageous. By use of Markov chain Monte Carlo algorithms, one can sample from the distribution of the true exposure for each individual. Transformations of the sampled values can then be performed directly and used to find the expectation of the transformed exposure required for regression calibration. A simulation study shows that the proposed approach performs well. We apply the method to investigate the relationship between usual alcohol intake and subsequent all‐cause mortality using an error model that adjusts for the episodic nature of alcohol consumption.     

Oxidative stress and apoptosis in metal ion-induced carcinogenesis

Epidemiological evidence suggests that exposure to certain metals causes carcinogenesis. The mechanisms of metal-induced carcinogenesis have been pursued in chemical, biochemical, cellular, and animal models. Significant evidence has accumulated that oxidative stress may be a common pathway in cellular responses to exposure to different metals. For example, in the last few years evidence in support of a correlation between the generation of reactive oxygen species, DNA damage, tumor promotion, and arsenic exposure has strengthened. This article summarizes the current literature on metal-mediated oxidative stress, apoptosis, and their relation to metal-mediated carcinogenesis, concentrating on arsenic and chromium.     

IKKbeta couples hepatocyte death to cytokine-driven compensatory proliferation that promotes chemical hepatocarcinogenesis

IkappaB kinase beta (IKKbeta), required for NF-kappaB activation, links chronic inflammation with carcinogenesis. We investigated whether IKKbeta is involved in chemically induced liver cancer, a model not involving overt inflammation. Surprisingly, mice lacking IKKbeta only in hepatocytes (Ikkbeta(Deltahep) mice) exhibited a marked increase in hepatocarcinogenesis caused by diethylnitrosamine (DEN). This correlated with enhanced reactive oxygen species (ROS) production, increased JNK activation, and hepatocyte death, giving rise to augmented compensatory proliferation of surviving hepatocytes. Brief oral administration of an antioxidant around the time of DEN exposure blocked prolonged JNK activation and compensatory proliferation and prevented excessive DEN-induced carcinogenesis in Ikkbeta(Deltahep) mice. Decreased hepatocarcinogenesis was also found in mice lacking IKKbeta in both hepatocytes and hematopoietic-derived Kupffer cells. These mice exhibited reduced hepatocyte regeneration and diminished induction of hepatomitogens, which were unaltered in Ikkbeta(Deltahep) mice. IKKbeta, therefore, orchestrates inflammatory crosstalk between hepatocytes and hematopoietic-derived cells that promotes chemical hepatocarcinogenesis.     

Predicting cancer rates in astronauts from animal carcinogenesis studies and cellular markers

The radiation space environment includes particles such as protons and multiple species of heavy ions, with much of the exposure to these radiations occurring at extremely low average dose-rates. Limitations in databases needed to predict cancer hazards in human beings from such radiations are significant and currently do not provide confidence that such predictions are acceptably precise or accurate. In this article, we outline the need for animal carcinogenesis data based on a more sophisticated understanding of the dose-response relationship for induction of cancer and correlative cellular endpoints by representative space radiations. We stress the need for a model that can interrelate human and animal carcinogenesis data with cellular mechanisms. Using a broad model for dose-response patterns which we term the "subalpha-alpha-omega (SAO) model", we explore examples in the literature for radiation-induced cancer and for radiation-induced cellular events to illustrate the need for data that define the dose-response patterns more precisely over specific dose ranges, with special attention to low dose, low dose-rate exposure. We present data for multiple endpoints in cells, which vary in their radiosensitivity, that also support the proposed model. We have measured induction of complex chromosome aberrations in multiple cell types by two space radiations, Fe-ions and protons, and compared these to photons delivered at high dose-rate or low dose-rate. Our data demonstrate that at least three factors modulate the relative efficacy of Fe-ions compared to photons: (i) intrinsic radiosensitivity of irradiated cells; (ii) dose-rate; and (iii) another unspecified effect perhaps related to reparability of DNA lesions. These factors can produce respectively up to at least 7-, 6- and 3-fold variability. These data demonstrate the need to understand better the role of intrinsic radiosensitivity and dose-rate effects in mammalian cell response to ionizing radiation. Such understanding is critical in extrapolating databases between cellular response, animal carcinogenesis and human carcinogenesis, and we suggest that the SAO model is a useful tool for such extrapolation.     

Influence of blood flow and millimeter wave exposure on skin temperature in different thermal models

Recently we showed that the Pennes bioheat transfer equation was not adequate to quantify mm wave heating of the skin at high blood flow rates. To do so, it is necessary to incorporate an "effective" thermal conductivity to obtain a hybrid bioheat equation (HBHE). The main aim of this study was to determine the relationship between non-specific tissue blood flow in a homogeneous unilayer model and dermal blood flow in multilayer models providing that the skin surface temperatures before and following mm wave exposure were the same. This knowledge could be used to develop multilayer models based on the fitting parameters obtained with the homogeneous tissue models. We tested four tissue models consisting of 1-4 layers and applied the one-dimensional steady-state HBHE. To understand the role of the epidermis in skin models we added to the one- and three-layer models an external thin epidermal layer with no blood flow. Only the combination of models containing the epidermal layer was appropriate for determination of the relationship between non-specific tissue and dermal blood flows giving the same skin surface temperatures. In this case we obtained a linear relationship between non-specific tissue and dermal blood flows. The presence of the fat layer resulted in the appearance of a significant temperature gradient between the dermis and muscle layer which increased with the fat layer thickness.     

Levelling-off of the risk of lung and bladder cancer in heavy smokers: an analysis based on multicentric case-control studies and a metabolic interpretation

The shape of the dose-response relationship between carcinogenic exposure and cancer risk is a key issue, both from a theoretical (models of carcinogenesis) and practical (risk assessment) point of view. Human populations exposed to Polycyclic Aromatic Hydrocarbons (PAH) via air pollution showed a non-linear relationship between levels of exposure and WBC-DNA adducts. Among highly exposed subjects, the DNA adduct level per unit of exposure was significantly lower than measured at environmental exposures. The same exposure-dose non-linearity was observed in lung DNA from rats exposed to PAH. We have analyzed 11 case-control studies on bladder cancer (4584 incident cases and 9360 hospital controls) and eight case-control studies on lung cancer (5092 incident cases and 6083 population controls), conducted in Europe in recent years. All the studies collected detailed information on smoking histories with a similar methodology. We have estimated the relationship between the number of cigarettes smoked and the risk of cancer, with and without adjustment by duration of smoking. We have observed a levelling-off of the relationship between the number of cigarettes smoked and the relative risks for lung and bladder cancer, both in men and women. The levelling-off occurred at an odds ratio of about 5 for bladder cancer, while it occurs at about 20 for lung cancer (in men). A potential explanation for such levelling-off involves metabolic pathways and individual susceptibility. It has been suggested that some metabolic polymorphisms exert an effect that is more important at low levels of exposure.