Predictive toxicogenomics approaches reveal underlying molecular mechanisms of nongenotoxic carcinogenicity

Pleiotropy of Progesterone Receptor Membrane Component 1 in Modulation of Cytochrome P450 Activity

Progesterone receptor membrane component 1 (PGRMC1) is one of few proteins that have been recently described as direct modulators of the activity of human cytochrome P450 enzymes (CYP)s. These enzymes form a superfamily of membrane-bound hemoproteins that metabolize a wide variety of physiological, dietary, environmental, and pharmacological compounds. Modulation of CYP activity impacts the detoxification of xenobiotics as well as endogenous pathways such as steroid and fatty acid metabolism, thus playing a central role in homeostasis. This review is focused on nine main topics that include the most relevant aspects of past and current PGRMC1 research, focusing on its role in CYP-mediated drug metabolism. Firstly, a general overview of the main aspects of xenobiotic metabolism is presented (I), followed by an overview of the role of the CYP enzymatic complex (IIa), a section on human disorders associated with defects in CYP enzyme complex activity (IIb), and a brief account of cytochrome b5 (cyt b5)'s effect on CYP activity (IIc). Subsequently, we present a background overview of the history of the molecular characterization of PGRMC1 (III), regarding its structure, expression, and intracellular location (IIIa), and its heme-binding capability and dimerization (IIIb). The next section reflects the different effects PGRMC1 may have on CYP activity (IV), presenting a description of studies on the direct effects on CYP activity (IVa), and a summary of pathways in which PGRMC1's involvement may indirectly affect CYP activity (IVb). The last section of the review is focused on the current challenges of research on the effect of PGRMC1 on CYP activity (V), presenting some future perspectives of research in the field (VI).

The evolving role of investigative toxicology in the pharmaceutical industry

For decades, preclinical toxicology was essentially a descriptive discipline in which treatment-related effects were carefully reported and used as a basis to calculate safety margins for drug candidates. In recent years, however, technological advances have increasingly enabled researchers to gain insights into toxicity mechanisms, supporting greater understanding of species relevance and translatability to humans, prediction of safety events, mitigation of side effects and development of safety biomarkers. Consequently, investigative (or mechanistic) toxicology has been gaining momentum and is now a key capability in the pharmaceutical industry. Here, we provide an overview of the current status of the field using case studies and discuss the potential impact of ongoing technological developments, based on a survey of investigative toxicologists from 14 European-based medium-sized to large pharmaceutical companies.

NUTF2 as a Prognostic Indicator and Potential Therapeutic Target in Head and Neck Squamous Cell Carcinoma

Aim: To identify genes associated with the prognosis of head and neck squamous cell carcinoma (HNSC) and potential molecular targets for therapy. Materials and Methods: Gene Expression Profiling Interactive Analysis, Human Protein Atlas, University of ALabama at Birmingham CANcer, LinkedOmics, cBioPortal, Cell Counting Kit 8, and polymerase chain reaction were used in this study. Results: The expression level of nuclear transport factor 2 (NUTF2) was elevated in HNSC tissues and was associated with poor prognosis in HNSC patients. NUTF2-targeted intervention inhibited the proliferation of HNSC cells. SEC61G, which was positively correlated with NUTF2, was decreased in HNSC cells with NUTF2 suppression. Conclusions: NUTF2 may be correlated with the prognosis and development of HNSC, laying the foundation for future studies on the potential role of NUTF2 in HNSC.

A Collaborative Initiative to Establish Genomic Biomarkers for Assessing Tumorigenic Potential to Reduce Reliance on Conventional Rodent Carcinogenicity Studies

There is growing recognition across broad sectors of the scientific community that use of genomic biomarkers has the potential to reduce the need for conventional rodent carcinogenicity studies of industrial chemicals, agrochemicals, and pharmaceuticals through a weight-of-evidence approach. These biomarkers fall into 2 major categories: (1) sets of gene transcripts that can identify distinct tumorigenic mechanisms of action; and (2) cancer driver gene mutations indicative of rapidly expanding growth-advantaged clonal cell populations. This call-to-action article describes a collaborative approach launched to develop and qualify biomarker gene expression panels that measure widely accepted molecular pathways linked to tumorigenesis and their activation levels to predict tumorigenic doses of chemicals from short-term exposures. Growing evidence suggests that application of such biomarker panels in short-term exposure rodent studies can identify both tumorigenic hazard and tumorigenic activation levels for chemical-induced carcinogenicity. In the future, this approach will be expanded to include methodologies examining mutations in key cancer driver gene mutation hotspots as biomarkers of both genotoxic and nongenotoxic chemical tumor risk. Analytical, technical, and biological validation studies of these complementary genomic tools are being undertaken by multisector and multidisciplinary collaborative teams within the Health and Environmental Sciences Institute. Success from these efforts will facilitate the transition from current heavy reliance on conventional 2-year rodent carcinogenicity studies to more rapid animal- and resource-sparing approaches for mechanism-based carcinogenicity evaluation supporting internal and regulatory decision-making.

In vitro Toxico-genomics of Etoposide Loaded Gelatin Nanoparticles and Its in-vivo Therapeutic Potential: Pharmacokinetics, Biodistribution and Tumor Regression in Ehrlich Ascites Carcinoma (EAC) Mice Model

Globally, breast cancer is the foremost cause of mortality among women detected with cancer, with 21% diagnosed in India alone. Etoposide loaded gelatin nanoparticles (EGNP) were prepared and its physical characterization (size:150nm±0.241; zeta potential −29.32 mV) was done along with in-vitro studies to assess biotoxicity, intracellular ROS, cell cycle arrest and death caused by EGNPs. We report the molecular pathways induced by EGNP in-vitro, pharmacokinetics, biodistribution and tumor regression in-vivo in Balb/c mice.Gene expression profiling of Bax, Bcl2, p53, Caspase-3, RIPK1, RIPK3 and ß-actin as internal control were done by RT-PCR wherein Etoposide and EGNP treated MCF-7 cells showed higher expressions of apoptotic genes-Bax, p53, caspase-3, lower expression of anti-apoptotic gene-Bcl2 when compared to control. Enhanced expression of necroptosis-RIPK1 were observed, while RIPK3 was insignificant. Since, RIPK1 regulates necroptosis and apoptosis, expression of apoptotic markers confirmed apoptotic molecular mechanisms. Negligible hemolysis of Gelatin nanoparticles (GNP), and EGNP at selected dosages confirmed biocompatibility. In vivo pharmacokinetics and biodistribution were done by 99Tc-labelled nanoparticles indicating increased circulation of EGNPs, allowing accumulation at the tumor site by Enhanced permeability and retention (EPR) phenomena. Tumor regression indicates the efficacy of EGNP by reducing the tumor burden when compared to void GNP and Etop per se, resulting in increased life span. High biocompatibility and bio-efficacy of EGNPs prove their therapeutic potential in cancer treatment.

A set of six Gene expression biomarkers and their thresholds identify rat liver tumorigens in short-term assays

Traditional methods for cancer risk assessment are retrospective, resource-intensive, and not feasible for the vast majority of environmental chemicals. In earlier studies, we used a set of six biomarkers to accurately identify liver tumorigens in transcript profiles derived from chemically-treated rats using either a Toxicological Priority Index (ToxPi) approach or using derived biomarker thresholds for cancer. The biomarkers consisting of 7-113 genes are used to predict the most common liver cancer molecular initiating events: genotoxicity, cytotoxicity and activation of the xenobiotic receptors AhR, CAR, ER, and PPARα. In the present study, we apply and evaluate the performance of these methods for cancer prediction in an independent rat liver study of 44 chemicals (6 h-7d exposures) examined by Affymetrix arrays. In the first approach, ToxPi ranking of biomarker scores consistently gave the highest scores to tumorigenic chemical-dose pairs; balanced accuracies for identification of liver tumorigenic chemicals were up to 89 %. The second approach used tumorigenic thresholds derived in the present study or from our earlier study that were set at the maximum value for chemical-dose exposures without detectable liver tumor outcomes. Using these thresholds, balanced accuracies were up to 90 %. Both approaches identified all tumorigenic chemicals. Almost all of the tumorigenic chemicals activated more than one MIE. We also compared biomarker responses between two types of profiling platforms (Affymetrix full-genome array, TempO-Seq 1500+ array containing ∼2600 genes) and found that the lack of the full set of biomarker genes on the 1500+ array resulted in decreased ability to identify chemicals that activate the MIEs. Overall, these results demonstrate that predictive approaches based on the 6 biomarkers could be used in short-term assays to identify chemicals and their doses that induce liver tumors, the most common endpoint in rodent bioassays.

A Set of Six Gene Expression Biomarkers Identify Rat Liver Tumorigens in Short-term Assays

Chemical-induced liver cancer occurs in rodents through well-characterized adverse outcome pathways. We hypothesized that measurement of the 6 most common molecular initiating events (MIEs) in liver cancer adverse outcome pathways in short-term assays using only gene expression will allow early identification of chemicals and their associated doses that are likely to be tumorigenic in the liver in 2-year bioassays. We tested this hypothesis using transcript data from a rat liver microarray compendium consisting of 2013 comparisons of 146 chemicals administered at doses with previously established effects on rat liver tumor induction. Five MIEs were measured using previously characterized gene expression biomarkers composed of gene sets predictive for genotoxicity and activation of 1 or more xenobiotic receptors (aryl hydrocarbon receptor, constitutive activated receptor, estrogen receptor, and peroxisome proliferator-activated receptor α). Because chronic injury can be important in tumorigenesis, we also developed a biomarker for cytotoxicity that had a 96% balanced accuracy. Characterization of the genes in each biomarker set using the unsupervised TXG-MAP network model demonstrated that the genes were associated with distinct functional coexpression modules. Using the Toxicological Priority Index to rank chemicals based on their ability to activate the MIEs showed that chemicals administered at tumorigenic doses clearly gave the highest ranked scores. Balanced accuracies using thresholds derived from either TG-GATES or DrugMatrix data sets to predict tumorigenicity in independent sets of chemicals were up to 93%. These results show that a MIE-directed approach using only gene expression biomarkers could be used in short-term assays to identify chemicals and their doses that cause tumors.

Identification of Time-Invariant Biomarkers for Non-Genotoxic Hepatocarcinogen Assessment

Non-genotoxic hepatocarcinogens (NGHCs) can only be confirmed by 2-year rodent studies. Toxicogenomics (TGx) approaches using gene expression profiles from short-term animal studies could enable early assessment of NGHCs. However, high variance in the modulation of the genes had been noted among exposure styles and datasets. Expanding from our previous strategy in identifying consensus biomarkers in multiple experiments, we aimed to identify time-invariant biomarkers for NGHCs in short-term exposure styles and validate their applicability to long-term exposure styles. In this study, nine time-invariant biomarkers, namely A2m, Akr7a3, Aqp7, Ca3, Cdc2a, Cdkn3, Cyp2c11, Ntf3, and Sds, were identified from four large-scale microarray datasets. Machine learning techniques were subsequently employed to assess the prediction performance of the biomarkers. The biomarker set along with the Random Forest models gave the highest median area under the receiver operating characteristic curve (AUC) of 0.824 and a low interquartile range (IQR) variance of 0.036 based on a leave-one-out cross-validation. The application of the models to the external validation datasets achieved high AUC values of greater than or equal to 0.857. Enrichment analysis of the biomarkers inferred the involvement of chronic inflammatory diseases such as liver cirrhosis, fibrosis, and hepatocellular carcinoma in NGHCs. The time-invariant biomarkers provided a robust alternative for NGHC prediction.

Association of circulating Progesterone Receptor Membrane Component-1 (PGRMC1) with PGRMC1 expression in breast tumour tissue and with clinical breast tumour characteristics

OBJECTIVES

Progesterone receptor membrane component-1 (PGRMC1) in breast cancer tissue has been suggested to predict a worse prognosis. The aim of this study was to assess for the first time whether PGRMC1 expressed in cancer tissue is associated with PGRMC1 blood concentrations and whether both are correlated with clinical tumour characteristics known to predict a worse outcome.

METHODS

In total, 201 patients with invasive breast cancer and 65 with benign breast disease (control group) were recruited. PGRMC1 blood concentrations were measured by a recently developed ELISA, PGRMC1 in breast cancer tissue was assessed by immunohistochemistry, and the correlation between the two was calculated. Receiver-operating characteristic (ROC) curve analysis was used to assess area under the curve (AUC). Furthermore, PGRMC1 was correlated with tumour characteristics such as tumour diameter, tumour grade and metastatic status, and with known blood tumour markers.

RESULTS

AUC for the breast cancer group was 0.713, which was significantly higher than in the control group (p < 0.01). Blood PGRMC1 concentrations had a strong (positive) correlation with tissue PGRMC1 expression (p < 0.01) but were not associated with serum tumour markers CEA, CA125, CA153 and TPS. Tissue PGRMC1, ER and cancer stage were positively associated with blood PGRMC1 (p < 0.05).

CONCLUSIONS

As PGRMC1 expression levels in cancer tissue were significantly correlated with PGRMC1 in blood, and because concentrations in blood were also positively associated with breast tumour characteristics known to predict a worse prognosis, PGRMC1 may be valuable as a new tumour marker and may be superior to known tumour markers such as CEA, CA125, CA153 and TPS.

Progesterone receptor membrane component 1 (PGRMC1) expression in canine mammary tumors: A preliminary study

Canine mammary tumors (CMT) represent the most common neoplasms in female dogs and their diagnosis and classification relies on histopathological examination. Recently, PGRMC1 has been considered to be a putative biomarker for diagnosis and prognosis in many human cancers as it is expressed in a wide variety of tumors. This study represents the first description of PGRMC1 expression in CMT. PGRMC1 expression was initially assessed by immunohistochemistry in healthy or hyperplastic tissues and in four major histopathological types of CMT: simple and complex adenomas and carcinomas. PGRMC1 staining was represented by a scoring system that considered the percentage of positive cells and staining intensity. PGRMC1 expression was defined as either weak, moderate or strong. In healthy and hyperplastic tissues almost 100% of the epithelial cells stained intensely for PGRMC1. Adenomas showed similar features but with a more variable intensity. In tubular areas of adenocarcinomas, a lower percentage of epithelial cells (30-60%) stained for PGRMC1 with a weak intensity. Both the percentage of cells and intensity of PGRMC1 staining became progressively negative in the solid parts of the tumor. Western blot analysis of healthy and neoplastic mammary tissue (carcinomas samples) revealed the presence of the 25 kDa PGRMC1 band in both types of tissue, while the 50 kDa form was mainly detected in the healthy counterpart. This study reveals that PGRMC1 is expressed in CMT and its expression pattern changes depending on the pattern of growth of CMT. Further studies are now needed to determine PGRMC1's putative role and usefulness for typing and prognosis of different CMT subtypes.

Application of transcriptomic and microRNA profiling in the evaluation of potential liver carcinogens

Hepatocarcinogens are agents that increase the incidence of liver cancer in exposed animals or humans. It is now established that carcinogenic exposures have a widespread impact on the transcriptome, inducing both adaptive and adverse changes in the activities of genes and pathways. Chemical hepatocarcinogens have also been shown to affect expression of microRNA (miRNA), the evolutionarily conserved noncoding RNA that regulates gene expression posttranscriptionally. Considerable effort has been invested into examining the involvement of mRNA in chemical hepatocarcinogenesis and their potential usage for the classification and prediction of new chemical entities. For miRNA, there has been an increasing number of studies reported over the past decade, although not to the same degree as for transcriptomic studies. Current data suggest that it is unlikely that any gene or miRNA signature associated with short-term carcinogen exposure can replace the rodent bioassay. In this review, we discuss the application of transcriptomic and miRNA profiles to increase mechanistic understanding of chemical carcinogens and to aid in their classification.

Identification of PGRMC1 as a Candidate Oncogene for Head and Neck Cancers and Its Involvement in Metabolic Activities

Progesterone Receptor Membrane Component 1 (PGRMC1/Sigma-2 receptor) is located on chromosome Xq21 and encodes a haem-containing protein that interacts with epidermal growth factor receptor (EGFR) and cytochromes P450, with function in tumor proliferation and chemoresistance. Although the over-expression of PGRMC1 reported in many different types of human cancers, systematic analysis of its oncogenic role of PGRMC1 has not been performed for any cancer. In this work, we analyzed the transcriptomics, genomics, and clinical data of 498 head-neck squamous cell carcinoma (HNSC) samples from the public-accessible database, The Cancer Genome Atlas (TCGA). The Cox regression was performed to calculate the hazard ratio (HR) of PGRMC1 expression as a prognosis feature for overall survival (OS). Our results demonstrated that PGRMC1 expression served as a predictor for worse OS (HR = 1.95, P = 0.0005) in head-neck squamous cell carcinoma. And the over-expression of PGRMC1 was strongly correlated with various metabolic process activity as well as cancer metastasis and cell proliferation features in human head-neck squamous cell carcinoma patient's cohort. Besides, the over-expression and unfavorable prognosis value of PGRMC1 were also observed in many other cancer types. This study provides insights into the potential oncogenic functional significance of PGRMC1 in cancer research.

Progesterone induced Warburg effect in HEK293 cells is associated with post-translational modifications and proteasomal degradation of progesterone receptor membrane component 1

Progesterone (P4) is a major steroid hormone that has important effects on metabolism. The progesterone receptor membrane component 1 (PGRMC1) is a non-canonical P4 binding protein. The biological functions affected by PGRMC1 include cholesterol/steroid biosynthesis and metabolism, iron homeostasis and heme trafficking, autophagy, regulation of cell cycle and proliferation, cell migration and invasion. PGRMC1 has been an attractive target for therapeutic intervention in cancer and neurodegenerative disorders due to its biological role in promoting cell survival. P4 has been used in a number of clinical applications and is considered neuroprotective. The involvement of PGRMC1 in P4-mediated regulation of cellular glucose metabolism is not well studied. PGRMC1 is a 21 kDa protein but complex post-translational modifications (PTMs) lead to the existence of several high molecular mass proteins whose molecular function, intracellular distribution, and physiological relevancies are not fully known. Therefore, in this study, P4-PGRMC1-mediated cellular glucose metabolism and PTMs of PGRMC1 were studied using wild-type and CRISPR/Cas9 mediated PGRMC1 knockout (KO) human embryonic kidney-derived (HEK293) cell lines. A 70 kDa (p70) and 100 kDa (p100) PGRMC1 proteins were identified that are predominantly associated with endoplasmic reticulum/mitochondria and nuclear fractions in the cells, respectively. Phosphorylation, acetylation, ubiquitination, and sumoylation of native PGRMC1 under serum starvation were identified which provided an explanation for the higher molecular masses. This study indicates that P4-PGRMC1 signaling caused a rapid increase in glycolysis in the presence of oxygen (aerobic glycolysis) and a corresponding decrease in cellular respiration, known as the Warburg effect. Further, it was demonstrated that the P4-induced increase in glycolysis is associated with rapid proteasomal degradation of the p70 and reduction of the nuclear p100 protein level. P4 treatment also caused significant alteration in the dynamics of PGRMC1 PTMs and its association with potential interacting proteins. Overall, this study provides a hitherto unknown aspect of P4-PGRMC1 mediated signaling that changes basic cellular metabolism in HEK293 cells.

The Carcinogenome Project: In Vitro Gene Expression Profiling of Chemical Perturbations to Predict Long-Term Carcinogenicity

BACKGROUND

Most chemicals in commerce have not been evaluated for their carcinogenic potential. The de facto gold-standard approach to carcinogen testing adopts the 2-y rodent bioassay, a time-consuming and costly procedure. High-throughput in vitro assays are a promising alternative for addressing the limitations in carcinogen screening.

OBJECTIVES

We developed a screening process for predicting chemical carcinogenicity and genotoxicity and characterizing modes of actions (MoAs) using in vitro gene expression assays.

METHODS

We generated a large toxicogenomics resource comprising [Formula: see text] expression profiles corresponding to 330 chemicals profiled in HepG2 (human hepatocellular carcinoma cell line) at multiple doses and replicates. Predictive models of carcinogenicity and genotoxicity were built using a random forest classifier. Differential pathway enrichment analysis was performed to identify pathways associated with carcinogen exposure. Signatures of carcinogenicity and genotoxicity were compared with external sources, including Drugmatrix and the Connectivity Map.

RESULTS

Among profiles with sufficient bioactivity, our classifiers achieved 72.2% Area Under the ROC Curve (AUC) for predicting carcinogenicity and 82.3% AUC for predicting genotoxicity. Chemical bioactivity, as measured by the strength and reproducibility of the transcriptional response, was not significantly associated with long-term carcinogenicity in doses up to [Formula: see text]. However, sufficient bioactivity was necessary for a chemical to be used for prediction of carcinogenicity. Pathway enrichment analysis revealed pathways consistent with known pathways that drive cancer, including DNA damage and repair. The data is available at https://clue.io/CRCGN_ABC , and a portal for query and visualization of the results is accessible at https://carcinogenome.org .

DISCUSSION

We demonstrated an in vitro screening approach using gene expression profiling to predict carcinogenicity and infer MoAs of chemical perturbations. https://doi.org/10.1289/EHP3986.

Establishment of a mouse model of enalapril-induced liver injury and investigation of the pathogenesis

Drug-induced liver injury (DILI) is a major concern in drug development and clinical drug therapy. Since the underlying mechanisms of DILI have not been fully understood in most cases, elucidation of the hepatotoxic mechanisms of drugs is expected. Although enalapril (ELP), an angiotensin-converting enzyme inhibitor, has been reported to cause liver injuries with a low incidence in humans, the precise mechanisms by which ELP causes liver injury remains unknown. In this study, we established a mouse model of ELP-induced liver injury and analyzed the mechanisms of its hepatotoxicity. Mice that were administered ELP alone did not develop liver injury, and mice that were pretreated with a synthetic glucocorticoid dexamethasone (DEX) and a glutathione synthesis inhibitor l-buthionine-(S,R)-sulfoximine (BSO) exhibited liver steatosis without significant increase in plasma alanine aminotransferase (ALT). In mice pretreated with DEX and BSO, ALT levels were significantly increased after ELP administration, suggesting that hepatic steatosis sensitized the liver to ELP hepatotoxicity. An immunohistochemical analysis showed that the numbers of myeloperoxidase-positive cells that infiltrated the liver were significantly increased in the mice administered DEX/BSO/ELP. The levels of oxidative stress-related factors, including hepatic heme oxygenase-1, serum hydrogen peroxide and hepatic malondialdehyde, were elevated in the mice administered DEX/BSO/ELP. The involvement of oxidative stress in ELP-induced liver injury was further supported by the observation that tempol, an antioxidant agent, ameliorated ELP-induced liver injury. In conclusion, we successfully established a model of ELP-induced liver injury in DEX-treated steatotic mice and demonstrated that oxidative stress and neutrophil infiltration are involved in the pathogenesis of ELP-induced liver injury.

Dose-response analysis of epigenetic, metabolic, and apical endpoints after short-term exposure to experimental hepatotoxicants

Identification of sensitive and novel biomarkers or endpoints associated with toxicity and carcinogenesis is of a high priority. There is increasing interest in the incorporation of epigenetic and metabolic biomarkers to complement apical data; however, a number of questions, including the tissue specificity, dose-response patterns, early detection of those endpoints, and the added value need to be addressed. In this study, we investigated the dose-response relationship between apical, epigenetic, and metabolomics endpoints following short-term exposure to experimental hepatotoxicants, clofibrate (CF) and phenobarbital (PB). Male F344 rats were exposed to PB (0, 5, 25, and 100 mg/kg/day) or CF (0, 10, 50, and 250 mg/kg/day) for seven days. Exposure to PB or CF resulted in dose-dependent increases in relative liver weights, hepatocellular hypertrophy and proliferation, and increases in Cyp2b1 and Cyp4a1 transcripts. These changes were associated with altered histone modifications within the regulatory units of cytochrome genes, LINE-1 DNA hypomethylation, and altered microRNA profiles. Metabolomics data indicated alterations in the metabolism of bile acids. This study provides the first comprehensive analysis of the apical, epigenetic and metabolic alterations, and suggests that the latter two occur within or near the dose response curve of apical endpoint alterations following exposure to experimental hepatotoxicants.

Identification of consensus biomarkers for predicting non-genotoxic hepatocarcinogens

The assessment of non-genotoxic hepatocarcinogens (NGHCs) is currently relying on two-year rodent bioassays. Toxicogenomics biomarkers provide a potential alternative method for the prioritization of NGHCs that could be useful for risk assessment. However, previous studies using inconsistently classified chemicals as the training set and a single microarray dataset concluded no consensus biomarkers. In this study, 4 consensus biomarkers of A2m, Ca3, Cxcl1, and Cyp8b1 were identified from four large-scale microarray datasets of the one-day single maximum tolerated dose and a large set of chemicals without inconsistent classifications. Machine learning techniques were subsequently applied to develop prediction models for NGHCs. The final bagging decision tree models were constructed with an average AUC performance of 0.803 for an independent test. A set of 16 chemicals with controversial classifications were reclassified according to the consensus biomarkers. The developed prediction models and identified consensus biomarkers are expected to be potential alternative methods for prioritization of NGHCs for further experimental validation.

Detection of non-genotoxic hepatocarcinogens and prediction of their mechanism of action in rats using gene marker sets

Several studies have successfully detected hepatocarcinogenicity in rats based on gene expression data. However, prediction of hepatocarcinogens with certain mechanisms of action (MOAs), such as enzyme inducers and peroxisome proliferator-activated receptor α (PPARα) agonists, can prove difficult using a single model and requires a highly toxic dose. Here, we constructed a model for detecting non-genotoxic (NGTX) hepatocarcinogens and predicted their MOAs in rats. Gene expression data deposited in the Open Toxicogenomics Project-Genomics Assisted Toxicity Evaluation System (TG-GATEs) was used to investigate gene marker sets. Principal component analysis (PCA) was applied to discriminate different MOAs, and a support vector machine algorithm was applied to construct the prediction model. This approach identified 106 probe sets as gene marker sets for PCA and enabled the prediction model to be constructed. In PCA, NGTX hepatocarcinogens were classified as follows based on their MOAs: cytotoxicants, PPARα agonists, or enzyme inducers. The prediction model detected hepatocarcinogenicity with an accuracy of more than 90% in 14- and 28-day repeated-dose studies. In addition, the doses capable of predicting NGTX hepatocarcinogenicity were close to those required in rat carcinogenicity assays. In conclusion, our PCA and prediction model using gene marker sets will help assess the risk of hepatocarcinogenicity in humans based on MOAs and reduce the number of two-year rodent bioassays.

Use of high-throughput RT-qPCR to assess modulations of gene expression profiles related to genomic stability and interactions by cadmium

Predictive test systems to assess the mode of action of chemical carcinogens are urgently required. Within the present study, we applied the Fluidigm dynamic array on the BioMark™ HD System for quantitative high-throughput RT-qPCR analysis of 95 genes and 96 samples in parallel, selecting genes crucial for maintaining genomic stability, including stress response as well as DNA repair, cell cycle control, apoptosis and mitotic signaling. The specificity of each individually designed sequence-specific primer pair and their respective target amplicons were evaluated via melting curve analysis as part of qPCR and size verification via agarose gel electrophoresis. For each gene, calibration curves displayed high efficiencies and correlation coefficients in the identified linear dynamic range as well as low intra-assay variations. Data were processed via Fluidigm real-time PCR analysis and GenEx software, and results were depicted as relative gene expression according to the ΔΔC q method. Subsequently, gene expression analyses were conducted in cadmium-treated adenocarcinoma A549 and epithelial bronchial BEAS-2B cells. They revealed distinct dose- and time-dependent and also cell-type-specific gene expression patterns, including the induction of genes coding for metallothioneins, the oxidative stress response, cell cycle control, mitotic signaling and apoptosis. Interestingly, while genes coding for the DNA damage response were induced, distinct DNA repair genes were down-regulated at the transcriptional level. Thus, this approach provided a comprehensive overview on the interaction by cadmium with distinct signaling pathways, also reflecting molecular modes of action in cadmium-induced carcinogenicity. Therefore, the test system appears to be a promising tool for toxicological risk assessment.

Critical role of toxicologic pathology in a short-term screen for carcinogenicity

Carcinogenic potential of chemicals is currently evaluated using a two year bioassay in rodents. Numerous difficulties are known for this assay, most notably, the lack of information regarding detailed dose response and human relevance of any positive findings. A screen for carcinogenic activity has been proposed based on a 90 day screening assay. Chemicals are first evaluated for proliferative activity in various tissues. If negative, lack of carcinogenic activity can be concluded. If positive, additional evaluation for DNA reactivity, immunosuppression, and estrogenic activity are evaluated. If these are negative, additional efforts are made to determine specific modes of action in the animal model, with a detailed evaluation of the potential relevance to humans. Applications of this approach are presented for liver and urinary bladder. Toxicologic pathology is critical for all of these evaluations, including a detailed histopathologic evaluation of the 90 day assay, immunohistochemical analyses for labeling index, and involvement in a detailed mode of action analysis. Additionally, the toxicologic pathologist needs to be involved with molecular evaluations and evaluations of new molecularly developed animal models. The toxicologic pathologist is uniquely qualified to provide the expertise needed for these evaluations.

Haem-dependent dimerization of PGRMC1/Sigma-2 receptor facilitates cancer proliferation and chemoresistance

Progesterone-receptor membrane component 1 (PGRMC1/Sigma-2 receptor) is a haem-containing protein that interacts with epidermal growth factor receptor (EGFR) and cytochromes P450 to regulate cancer proliferation and chemoresistance; its structural basis remains unknown. Here crystallographic analyses of the PGRMC1 cytosolic domain at 1.95 Å resolution reveal that it forms a stable dimer through stacking interactions of two protruding haem molecules. The haem iron is five-coordinated by Tyr113, and the open surface of the haem mediates dimerization. Carbon monoxide (CO) interferes with PGRMC1 dimerization by binding to the sixth coordination site of the haem. Haem-mediated PGRMC1 dimerization is required for interactions with EGFR and cytochromes P450, cancer proliferation and chemoresistance against anti-cancer drugs; these events are attenuated by either CO or haem deprivation in cancer cells. This study demonstrates protein dimerization via haem–haem stacking, which has not been seen in eukaryotes, and provides insights into its functional significance in cancer.

PGRMC1 binds to EGFR and cytochromes P450, and is known to be involved in cancer proliferation and in drug resistance. Here, the authors determine the structure of the cytosolic domain of PGRMC1, which forms a dimer via haem–haem stacking, and propose how this interaction could be involved in its function.

Risk assessment of Soulatrolide and Mammea (A/BA+A/BB) coumarins from Calophyllum brasiliense by a toxicogenomic and toxicological approach

Calophyllum brasiliense (Calophyllaceae) is a tropical rain forest tree distributed in Central and South America. It is an important source of tetracyclic dipyrano coumarins (Soulatrolide) and Mammea type coumarins. Soulatrolide is a potent inhibitor of HIV-1 reverse transcriptase and displays activity against Mycobacterium tuberculosis. Meanwhile, Mammea A/BA and A/BB, pure or as a mixture, are highly active against several human leukemia cell lines, Trypanosoma cruzi and Leishmania amazonensis. Nevertheless, there are few studies evaluating their safety profile. In the present work we performed toxicogenomic and toxicological analysis for both type of compounds. Soulatrolide, and the Mammea A/BA + A/BB mixture (2.1) were slightly toxic accordingly to Lorke assay classification (DL50 > 3000 mg/kg). After a short-term administration (100 mg/kg/daily, orally, 1 week) liver toxicogenomic analysis revealed 46 up and 72 downregulated genes for Mammea coumarins, and 665 up and 1077 downregulated genes for Soulatrolide. Gene enrichment analysis identified transcripts involved in drug metabolism for both compounds. In addition, network analysis through protein-protein interactions, tissue evaluation by TUNEL assay, and histological examination revealed no tissue damage on liver, kidney and spleen after treatments. Our results indicate that both type of coumarins displayed a safety profile, supporting their use in further preclinical studies to determine its therapeutic potential.

Pretilachlor has the potential to induce endocrine disruption, oxidative stress, apoptosis and immunotoxicity during zebrafish embryo development

The objectives of the present study were to investigate the toxic effects of pretilachlor on zebrafish during its embryo development. The results demonstrated that the transcription of genes involved in the hypothalamic-pituitary-gonadal/thyroid (HPG/HPT) axis was increased after exposure to 50, 100, 200 μg/L pretilachlor for 96 h, the aromatase activity, vitellogenin (VTG) and thyroid hormones T3 and T4 levels in zebrafish were also up-regulated simultaneously. Pretilachlor exposure induced a noticeable increase in ROS level, increased the transcription and level of antioxidant proteins (e.g., CAT, SOD and GPX). Moreover, the up-regulation of P53, Mdm2, Bbc3 expression and Caspase3 and Caspase9 activities in the apoptosis pathway suggested pretilachlor might trigger cell apoptosis in zebrafish. In addition, the transcription of CXCL-C1C, IL-1β and IL-8 related to the innate immunity was down-regulated after pretilachlor exposure. These data suggested that pretilachlor could simultaneously induce endocrine disruption, apoptosis, oxidative stress and immunotoxicity during zebrafish embryo development.

ABC gene-ranking for prediction of drug-induced cholestasis in rats

As legacy toxicogenomics databases have become available, improved data mining approaches are now key to extracting and visualizing subtle relationships between toxicants and gene expression. In the present study, a novel “aggregating bundles of clusters” (ABC) procedure was applied to separate cholestatic from non-cholestatic drugs and model toxicants in the Johnson & Johnson (Janssen) rat liver toxicogenomics database [3]. Drug-induced cholestasis is an important issue, particularly when a new compound enters the market with this liability, with standard preclinical models often mispredicting this toxicity. Three well-characterized cholestasis-responsive genes (Cyp7a1, Mrp3 and Bsep) were chosen from a previous in-house Janssen gene expression signature; these three genes show differing, non-redundant responses across the 90+ paradigm compounds in our database. Using the ABC procedure, extraneous contributions were minimized in comparisons of compound gene responses. All genes were assigned weights proportional to their correlations with Cyp7a1, Mrp3 and Bsep, and a resampling technique was used to derive a stable measure of compound similarity. The compounds that were known to be associated with rat cholestasis generally had small values of this measure relative to each other but also had large values of this measure relative to non-cholestatic compounds. Visualization of the data with the ABC-derived signature showed a very tight, essentially identically behaving cluster of robust human cholestatic drugs and experimental cholestatic toxicants (ethinyl estradiol, LPS, ANIT and methylene dianiline, disulfiram, naltrexone, methapyrilene, phenacetin, alpha-methyl dopa, flutamide, the NSAIDs–—indomethacin, flurbiprofen, diclofenac, flufenamic acid, sulindac, and nimesulide, butylated hydroxytoluene, piperonyl butoxide, and bromobenzene), some slightly less active compounds (3′-acetamidofluorene, amsacrine, hydralazine, tannic acid), some drugs that behaved very differently, and were distinct from both non-cholestatic and cholestatic drugs (ketoconazole, dipyridamole, cyproheptadine and aniline), and many postulated human cholestatic drugs that in rat showed no evidence of cholestasis (chlorpromazine, erythromycin, niacin, captopril, dapsone, rifampicin, glibenclamide, simvastatin, furosemide, tamoxifen, and sulfamethoxazole). Most of these latter drugs were noted previously by other groups as showing cholestasis only in humans. The results of this work suggest that the ABC procedure and similar statistical approaches can be instrumental in combining data to compare toxicants across toxicogenomics databases, extract similarities among responses and reduce unexplained data varation.

Identification of in vitro and in vivo disconnects using transcriptomic data

BACKGROUND

Integrating transcriptomic experiments within drug development is increasingly advocated for the early detection of toxicity. This is partly to reduce costs related to drug failures in the late, and expensive phases of clinical trials. Such an approach has proven useful both in the study of toxicology and carcinogenicity. However, general lack of translation of in vitro findings to in vivo systems remains one of the bottle necks in drug development. This paper proposes a method for identifying disconnected genes between in vitro and in vivo toxicogenomic rat experiments. The analytical framework is based on the joint modeling of dose-dependent in vitro and in vivo data using a fractional polynomial framework and biclustering algorithm.

RESULTS

Most disconnected genes identified belonged to known pathways, such as drug metabolism and oxidative stress due to reactive metabolites, bilirubin increase, glutathion depletion and phospholipidosis. We also identified compounds that were likely to induce disconnect in gene expression between in vitro and in vivo toxicogenomic rat experiments. These compounds include: sulindac and diclofenac (both linked to liver damage), naphtyl isothiocyanate (linked to hepatoxocity), indomethacin and naproxen (linked to gastrointestinal problem and damage of intestines).

CONCLUSION

The results confirmed that there are important discrepancies between in vitro and in vivo toxicogenomic experiments. However, the contribution of this paper is to provide a tool to identify genes that are disconnected between the two systems. Pathway analysis of disconnected genes may improve our understanding of uncertainties in the mechanism of actions of drug candidates in humans, especially concerning the early detection of toxicity.

The adverse outcome pathway for rodent liver tumor promotion by sustained activation of the aryl hydrocarbon receptor

An Adverse Outcome Pathway (AOP) represents the existing knowledge of a biological pathway leading from initial molecular interactions of a toxicant and progressing through a series of key events (KEs), culminating with an apical adverse outcome (AO) that has to be of regulatory relevance. An AOP based on the mode of action (MOA) of rodent liver tumor promotion by dioxin-like compounds (DLCs) has been developed and the weight of evidence (WoE) of key event relationships (KERs) evaluated using evolved Bradford Hill considerations. Dioxins and DLCs are potent aryl hydrocarbon receptor (AHR) ligands that cause a range of species-specific adverse outcomes. The occurrence of KEs is necessary for inducing downstream biological responses and KEs may occur at the molecular, cellular, tissue and organ levels. The common convention is that an AOP begins with the toxicant interaction with a biological response element; for this AOP, this initial event is binding of a DLC ligand to the AHR. Data from mechanistic studies, lifetime bioassays and approximately thirty initiation-promotion studies have established dioxin and DLCs as rat liver tumor promoters. Such studies clearly show that sustained AHR activation, weeks or months in duration, is necessary to induce rodent liver tumor promotion--hence, sustained AHR activation is deemed the molecular initiating event (MIE). After this MIE, subsequent KEs are 1) changes in cellular growth homeostasis likely associated with expression changes in a number of genes and observed as development of hepatic foci and decreases in apoptosis within foci; 2) extensive liver toxicity observed as the constellation of effects called toxic hepatopathy; 3) cellular proliferation and hyperplasia in several hepatic cell types. This progression of KEs culminates in the AO, the development of hepatocellular adenomas and carcinomas and cholangiolar carcinomas. A rich data set provides both qualitative and quantitative knowledge of the progression of this AOP through KEs and the KERs. Thus, the WoE for this AOP is judged to be strong. Species-specific effects of dioxins and DLCs are well known--humans are less responsive than rodents and rodent species differ in sensitivity between strains. Consequently, application of this AOP to evaluate potential human health risks must take these differences into account.

Release of (and lessons learned from mining) a pioneering large toxicogenomics database

AIM

We release the Janssen Toxicogenomics database. This rat liver gene-expression database was generated using Codelink microarrays, and has been used over the past years within Janssen to derive signatures for multiple end points and to classify proprietary compounds.

MATERIALS & METHODS

The release consists of gene-expression responses to 124 compounds, selected to give a broad coverage of liver-active compounds. A selection of the compounds were also analyzed on Affymetrix microarrays.

RESULTS

The release includes results of an in-house reannotation pipeline to Entrez gene annotations, to classify probes into different confidence classes. High confidence unambiguously annotated probes were used to create gene-level data which served as starting point for cross-platform comparisons. Connectivity map-based similarity methods show excellent agreement between Codelink and Affymetrix runs of the same samples. We also compared our dataset with the Japanese Toxicogenomics Project and observed reasonable agreement, especially for compounds with stronger gene signatures. We describe an R-package containing the gene-level data and show how it can be used for expression-based similarity searches.

CONCLUSION

Comparing the same biological samples run on the Affymetrix and the Codelink platform, good correspondence is observed using connectivity mapping approaches. As expected, this correspondence is smaller when the data are compared with an independent dataset such as TG-GATE. We hope that this collection of gene-expression profiles will be incorporated in toxicogenomics pipelines of users.

Technical guide for applications of gene expression profiling in human health risk assessment of environmental chemicals

Toxicogenomics promises to be an important part of future human health risk assessment of environmental chemicals. The application of gene expression profiles (e.g., for hazard identification, chemical prioritization, chemical grouping, mode of action discovery, and quantitative analysis of response) is growing in the literature, but their use in formal risk assessment by regulatory agencies is relatively infrequent. Although additional validations for specific applications are required, gene expression data can be of immediate use for increasing confidence in chemical evaluations. We believe that a primary reason for the current lack of integration is the limited practical guidance available for risk assessment specialists with limited experience in genomics. The present manuscript provides basic information on gene expression profiling, along with guidance on evaluating the quality of genomic experiments and data, and interpretation of results presented in the form of heat maps, pathway analyses and other common approaches. Moreover, potential ways to integrate information from gene expression experiments into current risk assessment are presented using published studies as examples. The primary objective of this work is to facilitate integration of gene expression data into human health risk assessments of environmental chemicals.

Lung Cancer Biomarkers

Lung cancer is the most frequently occurring cancer in the world and continually leads in mortality among cancers. The overall 5-year survival rate for lung cancer has risen only 4% (from 12% to 16%) over the past 4 decades, and late diagnosis is a major obstacle in improving lung cancer prognosis. Survival of patients undergoing lung resection is greater than 80%, suggesting that early detection and diagnosis of cancers before they become inoperable and lethal will greatly improve mortality. Lung cancer biomarkers can be used for screening, detection, diagnosis, prognosis, prediction, stratification, therapy response monitoring, and so on. This review focuses on noninvasive diagnostic and prognostic biomarkers. For that purpose, our discussion in this review will focus on biological fluid-based biomarkers. The body fluids include blood (serum or plasma), sputum, saliva, BAL, pleural effusion, and VOC. Since it is rich in different cellular and molecular elements and is one of the most convenient and routine clinical procedures, serum or plasma is the main source for the development and validation of many noninvasive biomarkers. In terms of molecular aspects, the most widely validated ones are proteins, some of which are used in the clinical sector, though in limited accessory purposes. We will also discuss the lung cancer (protein) biomarkers in clinical trials and currently in the validation phase with hundreds of samples. After proteins, we will discuss microRNAs, methylated DNA, and circulating tumor cells, which are being vigorously developed and validated as potential lung cancer biomarkers. The main aim of this review is to provide researchers and clinicians with an understanding of the potential noninvasive lung cancer biomarkers in biological fluids that have recently been discovered.

Identification of specific mRNA signatures as fingerprints for carcinogenesis in mice induced by genotoxic and nongenotoxic hepatocarcinogens

Long-term rodent carcinogenicity studies for evaluation of chemicals and pharmaceuticals concerning their carcinogenic potential to humans are currently receiving critical revision. Additional data from mechanistic studies can support cancer risk assessment by clarifying the underlying mode of action. In the course of the IMI MARCAR project, a European consortium of EFPIA partners and academics, which aims to identify biomarkers for nongenotoxic carcinogenesis, a toxicogenomic mouse liver database was generated. CD-1 mice were orally treated for 3 and 14 days with 3 known genotoxic hepatocarcinogens: C.I. Direct Black 38, Dimethylnitrosamine and 4,4'-Methylenedianiline; 3 nongenotoxic hepatocarcinogens: 1,4-Dichlorobenzene, Phenobarbital sodium and Piperonyl butoxide; 4 nonhepatocarcinogens: Cefuroxime sodium, Nifedipine, Prazosin hydrochloride and Propranolol hydrochloride; and 3 compounds that show ambiguous results in genotoxicity testing: Cyproterone acetate, Thioacetamide and Wy-14643. By liver mRNA expression analysis using individual animal data, we identified 64 specific biomarker candidates for genotoxic carcinogens and 69 for nongenotoxic carcinogens for male mice at day 15. The majority of genotoxic carcinogen biomarker candidates possess functions in DNA damage response (eg, apoptosis, cell cycle progression, DNA repair). Most of the identified nongenotoxic carcinogen biomarker candidates are involved in regulation of cell cycle progression and apoptosis. The derived biomarker lists were characterized with respect to their dependency on study duration and gender and were successfully used to characterize carcinogens with ambiguous genotoxicity test results, such as Wy-14643. The identified biomarker candidates improve the mechanistic understanding of drug-induced effects on the mouse liver that result in hepatocellular adenomas and/or carcinomas in 2-year mouse carcinogenicity studies.

Genomic models of short-term exposure accurately predict long-term chemical carcinogenicity and identify putative mechanisms of action

BACKGROUND

Despite an overall decrease in incidence of and mortality from cancer, about 40% of Americans will be diagnosed with the disease in their lifetime, and around 20% will die of it. Current approaches to test carcinogenic chemicals adopt the 2-year rodent bioassay, which is costly and time-consuming. As a result, fewer than 2% of the chemicals on the market have actually been tested. However, evidence accumulated to date suggests that gene expression profiles from model organisms exposed to chemical compounds reflect underlying mechanisms of action, and that these toxicogenomic models could be used in the prediction of chemical carcinogenicity.

RESULTS

In this study, we used a rat-based microarray dataset from the NTP DrugMatrix Database to test the ability of toxicogenomics to model carcinogenicity. We analyzed 1,221 gene-expression profiles obtained from rats treated with 127 well-characterized compounds, including genotoxic and non-genotoxic carcinogens. We built a classifier that predicts a chemical's carcinogenic potential with an AUC of 0.78, and validated it on an independent dataset from the Japanese Toxicogenomics Project consisting of 2,065 profiles from 72 compounds. Finally, we identified differentially expressed genes associated with chemical carcinogenesis, and developed novel data-driven approaches for the molecular characterization of the response to chemical stressors.

CONCLUSION

Here, we validate a toxicogenomic approach to predict carcinogenicity and provide strong evidence that, with a larger set of compounds, we should be able to improve the sensitivity and specificity of the predictions. We found that the prediction of carcinogenicity is tissue-dependent and that the results also confirm and expand upon previous studies implicating DNA damage, the peroxisome proliferator-activated receptor, the aryl hydrocarbon receptor, and regenerative pathology in the response to carcinogen exposure.

The Predictive Safety Testing Consortium: A synthesis of the goals, challenges and accomplishments of the Critical Path

The qualification of biomarkers of drug safety requires data on many compounds and nonclinical and clinical studies. The cost and effort associated with these qualifications cannot be easily covered by a single pharmaceutical company. Intellectual property associated with safety biomarkers is also held by many different companies. Consortia between different pharmaceutical companies can overcome cost and intellectual property hurdles to biomarker qualification. The Predictive Safety Testing Consortium (PSTC) is a collaborative effort between 16 different pharmaceutical companies to generate data supporting biomarker qualification. This Consortium is coordinated through the C-Path Institute, and currently has five biomarker qualification working groups engaged in this collaboration: nephrotoxicity, hepatotoxicity, vascular injury, myopathy, and non-genotoxic carcinogenicity. These working groups are aided by a data management team and a translational strategy team. Qualification studies of promising biomarkers are already progressing in several of the working groups, and results in the nephrotoxicity working group warranted a data submission to the FDA and EMEA for regulatory qualification of new nephrotoxicity biomarkers.:

Cross-platform toxicogenomics for the prediction of non-genotoxic hepatocarcinogenesis in rat

In the area of omics profiling in toxicology, i.e. toxicogenomics, characteristic molecular profiles have previously been incorporated into prediction models for early assessment of a carcinogenic potential and mechanism-based classification of compounds. Traditionally, the biomarker signatures used for model construction were derived from individual high-throughput techniques, such as microarrays designed for monitoring global mRNA expression. In this study, we built predictive models by integrating omics data across complementary microarray platforms and introduced new concepts for modeling of pathway alterations and molecular interactions between multiple biological layers. We trained and evaluated diverse machine learning-based models, differing in the incorporated features and learning algorithms on a cross-omics dataset encompassing mRNA, miRNA, and protein expression profiles obtained from rat liver samples treated with a heterogeneous set of substances. Most of these compounds could be unambiguously classified as genotoxic carcinogens, non-genotoxic carcinogens, or non-hepatocarcinogens based on evidence from published studies. Since mixed characteristics were reported for the compounds Cyproterone acetate, Thioacetamide, and Wy-14643, we reclassified these compounds as either genotoxic or non-genotoxic carcinogens based on their molecular profiles. Evaluating our toxicogenomics models in a repeated external cross-validation procedure, we demonstrated that the prediction accuracy of our models could be increased by joining the biomarker signatures across multiple biological layers and by adding complex features derived from cross-platform integration of the omics data. Furthermore, we found that adding these features resulted in a better separation of the compound classes and a more confident reclassification of the three undefined compounds as non-genotoxic carcinogens.

Oxidative stress/reactive metabolite gene expression signature in rat liver detects idiosyncratic hepatotoxicants

Previously we reported a gene expression signature in rat liver for detecting a specific type of oxidative stress (OS) related to reactive metabolites (RM). High doses of the drugs disulfiram, ethinyl estradiol and nimesulide were used with another dozen paradigm OS/RM compounds, and three other drugs flutamide, phenacetin and sulindac were identified by this signature. In a second study, antiepileptic drugs were compared for covalent binding and their effects on OS/RM; felbamate, carbamazepine, and phenobarbital produced robust OS/RM gene expression. In the present study, liver RNA samples from drug-treated rats from more recent experiments were examined for statistical fit to the OS/RM signature. Of all 97 drugs examined, in addition to the nine drugs noted above, 19 more were identified as OS/RM-producing compounds-chlorpromazine, clozapine, cyproterone acetate, dantrolene, dipyridamole, glibenclamide, isoniazid, ketoconazole, methapyrilene, naltrexone, nifedipine, sulfamethoxazole, tamoxifen, coumarin, ritonavir, amitriptyline, valproic acid, enalapril, and chloramphenicol. Importantly, all of the OS/RM drugs listed above have been linked to idiosyncratic hepatotoxicity, excepting chloramphenicol, which does not have a package label for hepatotoxicity, but does have a black box warning for idiosyncratic bone marrow suppression. Most of these drugs are not acutely toxic in the rat. The OS/RM signature should be useful to avoid idiosyncratic hepatotoxicity of drug candidates.

In vivo murine hepatic microRNA and mRNA expression signatures predicting the (non-)genotoxic carcinogenic potential of chemicals

There is a high need to improve the assessment of, especially non-genotoxic, carcinogenic features of chemicals. We therefore explored a toxicogenomics-based approach using genome-wide microRNA and mRNA expression profiles upon short-term exposure in mice. For this, wild-type mice were exposed for seven days to three different classes of chemicals, i.e., four genotoxic carcinogens (GTXC), seven non-genotoxic carcinogens (NGTXC), and five toxic non-carcinogens. Hepatic expression patterns of mRNA and microRNA transcripts were determined after exposure and used to assess the discriminative power of the in vivo transcriptome for GTXC and NGTXC. A final classifier set, discriminative for GTXC and NGTXC, was generated from the transcriptomic data using a tiered approach. This appeared to be a valid approach, since the predictive power of the final classifier set in three different classifier algorithms was very high for the original training set of chemicals. Subsequent validation in an additional set of chemicals revealed that the predictive power for GTXC remained high, in contrast to NGTXC, which appeared to be more troublesome. Our study demonstrated that the in vivo microRNA-ome has less discriminative power to correctly identify (non-)genotoxic carcinogen classes. The results generally indicate that single mRNA transcripts do have the potential to be applied in risk assessment, but that additional (genomic) strategies are necessary to correctly predict the non-genotoxic carcinogenic potential of a chemical.

A metabolomics investigation of non-genotoxic carcinogenicity in the rat

Non-genotoxic carcinogens (NGCs) promote tumor growth by altering gene expression, which ultimately leads to cancer without directly causing a change in DNA sequence. As a result NGCs are not detected in mutagenesis assays. While there are proposed biomarkers of carcinogenic potential, the definitive identification of non-genotoxic carcinogens still rests with the rat and mouse long-term bioassay. Such assays are expensive and time-consuming and require a large number of animals, and their relevance to human health risk assessments is debatable. Metabolomics and lipidomics in combination with pathology and clinical chemistry were used to profile perturbations produced by 10 compounds that represented a range of rat non-genotoxic hepatocarcinogens (NGC), non-genotoxic non-hepatocarcinogens (non-NGC), and a genotoxic hepatocarcinogen. Each compound was administered at its maximum tolerated dose level for 7, 28, and 91 days to male Fisher 344 rats. Changes in liver metabolite concentration differentiated the treated groups across different time points. The most significant differences were driven by pharmacological mode of action, specifically by the peroxisome proliferator activated receptor alpha (PPAR-α) agonists. Despite these dominant effects, good predictions could be made when differentiating NGCs from non-NGCs. Predictive ability measured by leave one out cross validation was 87% and 77% after 28 days of dosing for NGCs and non-NGCs, respectively. Among the discriminatory metabolites we identified free fatty acids, phospholipids, and triacylglycerols, as well as precursors of eicosanoid and the products of reactive oxygen species linked to processes of inflammation, proliferation, and oxidative stress. Thus, metabolic profiling is able to identify changes due to the pharmacological mode of action of xenobiotics and contribute to early screening for non-genotoxic potential.

A toxicogenomic approach for the prediction of murine hepatocarcinogenesis using ensemble feature selection

The current strategy for identifying the carcinogenicity of drugs involves the 2-year bioassay in male and female rats and mice. As this assay is cost-intensive and time-consuming there is a high interest in developing approaches for the screening and prioritization of drug candidates in preclinical safety evaluations. Predictive models based on toxicogenomics investigations after short-term exposure have shown their potential for assessing the carcinogenic risk. In this study, we investigated a novel method for the evaluation of toxicogenomics data based on ensemble feature selection in conjunction with bootstrapping for the purpose to derive reproducible and characteristic multi-gene signatures. This method was evaluated on a microarray dataset containing global gene expression data from liver samples of both male and female mice. The dataset was generated by the IMI MARCAR consortium and included gene expression profiles of genotoxic and nongenotoxic hepatocarcinogens obtained after treatment of CD-1 mice for 3 or 14 days. We developed predictive models based on gene expression data of both sexes and the models were employed for predicting the carcinogenic class of diverse compounds. Comparing the predictivity of our multi-gene signatures against signatures from literature, we demonstrated that by incorporating our gene sets as features slightly higher accuracy is on average achieved by a representative set of state-of-the art supervised learning methods. The constructed models were also used for the classification of Cyproterone acetate (CPA), Wy-14643 (WY) and Thioacetamid (TAA), whose primary mechanism of carcinogenicity is controversially discussed. Based on the extracted mouse liver gene expression patterns, CPA would be predicted as a nongenotoxic compound. In contrast, both WY and TAA would be classified as genotoxic mouse hepatocarcinogens.

Enhanced QSAR model performance by integrating structural and gene expression information

Despite decades of intensive research and a number of demonstrable successes, quantitative structure-activity relationship (QSAR) models still fail to yield predictions with reasonable accuracy in some circumstances, especially when the QSAR paradox occurs. In this study, to avoid the QSAR paradox, we proposed a novel integrated approach to improve the model performance through using both structural and biological information from compounds. As a proof-of-concept, the integrated models were built on a toxicological dataset to predict non-genotoxic carcinogenicity of compounds, using not only the conventional molecular descriptors but also expression profiles of significant genes selected from microarray data. For test set data, our results demonstrated that the prediction accuracy of QSAR model was dramatically increased from 0.57 to 0.67 with incorporation of expression data of just one selected signature gene. Our successful integration of biological information into classic QSAR model provided a new insight and methodology for building predictive models especially when QSAR paradox occurred.

Biological networks for predicting chemical hepatocarcinogenicity using gene expression data from treated mice and relevance across human and rat species

BACKGROUND

Several groups have employed genomic data from subchronic chemical toxicity studies in rodents (90 days) to derive gene-centric predictors of chronic toxicity and carcinogenicity. Genes are annotated to belong to biological processes or molecular pathways that are mechanistically well understood and are described in public databases.

OBJECTIVES

To develop a molecular pathway-based prediction model of long term hepatocarcinogenicity using 90-day gene expression data and to evaluate the performance of this model with respect to both intra-species, dose-dependent and cross-species predictions.

METHODS

Genome-wide hepatic mRNA expression was retrospectively measured in B6C3F1 mice following subchronic exposure to twenty-six (26) chemicals (10 were positive, 2 equivocal and 14 negative for liver tumors) previously studied by the US National Toxicology Program. Using these data, a pathway-based predictor model for long-term liver cancer risk was derived using random forests. The prediction model was independently validated on test sets associated with liver cancer risk obtained from mice, rats and humans.

RESULTS

Using 5-fold cross validation, the developed prediction model had reasonable predictive performance with the area under receiver-operator curve (AUC) equal to 0.66. The developed prediction model was then used to extrapolate the results to data associated with rat and human liver cancer. The extrapolated model worked well for both extrapolated species (AUC value of 0.74 for rats and 0.91 for humans). The prediction models implied a balanced interplay between all pathway responses leading to carcinogenicity predictions.

CONCLUSIONS

Pathway-based prediction models estimated from sub-chronic data hold promise for predicting long-term carcinogenicity and also for its ability to extrapolate results across multiple species.

Prediction of clinically relevant safety signals of nephrotoxicity through plasma metabolite profiling

Addressing safety concerns such as drug-induced kidney injury (DIKI) early in the drug pharmaceutical development process ensures both patient safety and efficient clinical development. We describe a unique adjunct to standard safety assessment wherein the metabolite profile of treated animals is compared with the MetaMap Tox metabolomics database in order to predict the potential for a wide variety of adverse events, including DIKI. To examine this approach, a study of five compounds (phenytoin, cyclosporin A, doxorubicin, captopril, and lisinopril) was initiated by the Technology Evaluation Consortium under the auspices of the Drug Safety Executive Council (DSEC). The metabolite profiles for rats treated with these compounds matched established reference patterns in the MetaMap Tox metabolomics database indicative of each compound's well-described clinical toxicities. For example, the DIKI associated with cyclosporine A and doxorubicin was correctly predicted by metabolite profiling, while no evidence for DIKI was found for phenytoin, consistent with its clinical picture. In some cases the clinical toxicity (hepatotoxicity), not generally seen in animal studies, was detected with MetaMap Tox. Thus metabolite profiling coupled with the MetaMap Tox metabolomics database offers a unique and powerful approach for augmenting safety assessment and avoiding clinical adverse events such as DIKI.