Prediction of carcinogenic potential by a toxicogenomic approach using rat hepatoma cells

Application of transfer learning to predict drug-induced human in vivo gene expression changes using rat in vitro and in vivo data

The liver is the primary site for the metabolism and detoxification of many compounds, including pharmaceuticals. Consequently, it is also the primary location for many adverse reactions. As the liver is not readily accessible for sampling in humans; rodent or cell line models are often used to evaluate potential toxic effects of a novel compound or candidate drug. However, relating the results of animal and in vitro studies to relevant clinical outcomes for the human in vivo situation still proves challenging. In this study, we incorporate principles of transfer learning within a deep artificial neural network allowing us to leverage the relative abundance of rat in vitro and in vivo exposure data from the Open TG-GATEs data set to train a model to predict the expected pattern of human in vivo gene expression following an exposure given measured human in vitro gene expression. We show that domain adaptation has been successfully achieved, with the rat and human in vitro data no longer being separable in the common latent space generated by the network. The network produces physiologically plausible predictions of human in vivo gene expression pattern following an exposure to a previously unseen compound. Moreover, we show the integration of the human in vitro data in the training of the domain adaptation network significantly improves the temporal accuracy of the predicted rat in vivo gene expression pattern following an exposure to a previously unseen compound. In this way, we demonstrate the improvements in prediction accuracy that can be achieved by combining data from distinct domains.

Quantitative analysis of artificial intelligence on liver cancer: A bibliometric analysis

Objective

To provide the current research progress, hotspots, and emerging trends for AI in liver cancer, we have compiled a relative comprehensive and quantitative report on the research of liver disease using artificial intelligence by employing bibliometrics in this study.

Methods

In this study, the Web of Science Core Collection (WoSCC) database was used to perform systematic searches using keywords and a manual screening strategy, VOSviewer was used to analyze the degree of cooperation between countries/regions and institutions, as well as the co-occurrence of cooperation between authors and cited authors. Citespace was applied to generate a dual map to analyze the relationship of citing journals and citied journals and conduct a strong citation bursts ranking analysis of references. Online SRplot was used for in-depth keyword analysis and Microsoft Excel 2019 was used to collect the targeted variables from retrieved articles.

Results

1724 papers were collected in this study, including 1547 original articles and 177 reviews. The study of AI in liver cancer mostly began from 2003 and has developed rapidly from 2017. China has the largest number of publications, and the United States has the highest H-index and total citation counts. The top three most productive institutions are the League of European Research Universities, Sun Yat Sen University, and Zhejiang University. Jasjit S. Suri and Frontiers in Oncology are the most published author and journal, respectively. Keyword analysis showed that in addition to the research on liver cancer, research on liver cirrhosis, fatty liver disease, and liver fibrosis were also common. Computed tomography was the most used diagnostic tool, followed by ultrasound and magnetic resonance imaging. The diagnosis and differential diagnosis of liver cancer are currently the most widely adopted research goals, and comprehensive analyses of multi-type data and postoperative analysis of patients with advanced liver cancer are rare. The use of convolutional neural networks is the main technical method used in studies of AI on liver cancer.

Conclusion

AI has undergone rapid development and has a wide application in the diagnosis and treatment of liver diseases, especially in China. Imaging is an indispensable tool in this filed. Mmulti-type data fusion analysis and development of multimodal treatment plans for liver cancer could become the major trend of future research in AI in liver cancer.

Use of deep learning methods to translate drug-induced gene expression changes from rat to human primary hepatocytes

In clinical trials, animal and cell line models are often used to evaluate the potential toxic effects of a novel compound or candidate drug before progressing to human trials. However, relating the results of animal and in vitro model exposures to relevant clinical outcomes in the human in vivo system still proves challenging, relying on often putative orthologs. In recent years, multiple studies have demonstrated that the repeated dose rodent bioassay, the current gold standard in the field, lacks sufficient sensitivity and specificity in predicting toxic effects of pharmaceuticals in humans. In this study, we evaluate the potential of deep learning techniques to translate the pattern of gene expression measured following an exposure in rodents to humans, circumventing the current reliance on orthologs, and also from in vitro to in vivo experimental designs. Of the applied deep learning architectures applied in this study the convolutional neural network (CNN) and a deep artificial neural network with bottleneck architecture significantly outperform classical machine learning techniques in predicting the time series of gene expression in primary human hepatocytes given a measured time series of gene expression from primary rat hepatocytes following exposure in vitro to a previously unseen compound across multiple toxicologically relevant gene sets. With a reduction in average mean absolute error across 76 genes that have been shown to be predictive for identifying carcinogenicity from 0.0172 for a random regression forest to 0.0166 for the CNN model (p < 0.05). These deep learning architecture also perform well when applied to predict time series of in vivo gene expression given measured time series of in vitro gene expression for rats.

Tuning of essential oil properties by enzymatic treatment: towards sustainable processes for the generation of new fragrance ingredients

In this review, several strategies of modification of essential oils by enzymatic treatment are presented. Being either applied before or after the production of the essential oil, enzymatic methods are shown to be particularly adapted to attain the required selectivity, specificity and efficiency in sustainable processes delivering products eligible for the natural grade. Examples dealing with the optimization of the properties of essential oils in terms of biological activity, odor and safety are provided, and it is likely that these strategies will address other type of properties in the future, such as the physico-chemical properties, for example.

Benzo[a]pyrene-induced transcriptomic responses in primary hepatocytes and in vivo liver: toxicokinetics is essential for in vivo-in vitro comparisons

The traditional 2-year cancer bioassay needs replacement by more cost-effective and predictive tests. The use of toxicogenomics in an in vitro system may provide a more high-throughput method to investigate early alterations induced by carcinogens. Recently, the differential gene expression response in wild-type and cancer-prone Xpa (-/-) p53 (+/-) primary mouse hepatocytes after exposure to benzo[a]pyrene (B[a]P) revealed downregulation of cancer-related pathways in Xpa (-/-) p53 (+/-) hepatocytes only. Here, we investigated pathway regulation upon in vivo B[a]P exposure of wild-type and Xpa (-/-) p53 (+/-) mice. In vivo transcriptomics analysis revealed a limited gene expression response in mouse livers, but with a significant induction of DNA replication and apoptotic/anti-apoptotic cellular responses in Xpa (-/-) p53 (+/-) livers only. In order to be able to make a meaningful in vivo-in vitro comparison we estimated internal in vivo B[a]P concentrations using DNA adduct levels and physiologically based kinetic modeling. Based on these results, the in vitro concentration that corresponded best with the internal in vivo dose was chosen. Comparison of in vivo and in vitro data demonstrated similarities in transcriptomics response: xenobiotic metabolism, lipid metabolism and oxidative stress. However, we were unable to detect cancer-related pathways in either wild-type or Xpa (-/-) p53 (+/-) exposed livers, which were previously found to be induced by B[a]P in Xpa (-/-) p53 (+/-) primary hepatocytes. In conclusion, we showed parallels in gene expression responses between livers and primary hepatocytes upon exposure to equivalent concentrations of B[a]P. Furthermore, we recommend considering toxicokinetics when modeling a complex in vivo endpoint with in vitro models.

Comparison of genotoxicant-modified transcriptomic responses in conventional and epigenetically stabilized primary rat hepatocytes with in vivo rat liver data

The concept of mechanistic toxicogenomics implies that compound-induced changes in gene expression profiles provide valuable information about their mode of action. A growing number of research groups have presented evidence that whole-genome gene expression profiling techniques might be used as tools for in vivo and in vitro generation of gene signatures and elucidation of molecular mechanisms after exposure to toxic compounds. An important issue to be investigated is the in vivo relevance of in vitro-obtained data. In the current study, we compare the gene expression profiles generated in vitro, after exposing conventional and epigenetically stabilized primary rat hepatocytes to well-known genotoxic hepatocarcinogens (aflatoxin B1, 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone and 2-nitrofluorene) with those derived in vivo after oral exposure of rats to these compounds. Similar statistical tools were applied on both sets of data. The major molecular pathways affected in the in vivo setting were DNA damage, detoxification and cell survival response, as previously described. In the conventional hepatocyte cultures, two of the three genotoxicants showed quite similar responses as in vivo with respect to these pathways. The third compound (2-nitrofluorene) revealed in vitro response which was not observed in vivo. In the epigenetically stabilized hepatocytes, in contrast to what was expected, the responses were less relevant for the in vivo situation. This study highlights the importance of in vitro/in vivo comparison of data that are generated using in vitro models and shows that conventional primary rat hepatocyte cultures represent an appropriate in vitro model to retrieve mechanistic information on the exposure to genotoxicants.

Valuable chemicals by the enzymatic modification of molecules of natural origin: terpenoids, steroids, phenolics and related compounds

A renewed interest for using natural organic molecules for the production of valuable chemicals is observed in current organic processes. Natural compounds provide the access to natural grade chemicals when submitted to physical treatments or biotechnological processes. Dealing with structurally complex molecules, they can provide complex core structures for hemisynthesis purposes, and in many instances they offer the advantage of providing sustainable processes when using renewable resources. These assets could be synergistic with the assets of biocatalytic processes, to end-up with efficient and sustainable processes in the organic synthesis of valuable products. In this review, we have gathered a selection of examples on the use of enzymes for the modification of molecules of natural origin being either purified compounds (terpenoids, steroids, phenolics) or mixtures (essential oils, natural extracts) to access fine chemicals or organic polymers.

Comparison of hepatocarcinogen-induced gene expression profiles in conventional primary rat hepatocytes with in vivo rat liver

At present, substantial efforts are focused on the development of in vitro assays coupled with "omics" technologies for the identification of carcinogenic substances as an alternative to the classical 2-year rodent carcinogenicity bioassay. A prerequisite for the eventual regulatory acceptance of such assays, however, is the in vivo relevance of the observed in vitro findings. In the current study, hepatocarcinogen-induced gene expression profiles generated after the exposure of conventional cultures of primary rat hepatocytes to three non-genotoxic carcinogens (methapyrilene hydrochloride, piperonyl butoxide, and Wy-14643), three genotoxic carcinogens (aflatoxin B1, 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone, and 2-nitrofluorene), and two non-carcinogens (nifedipine and clonidine) are compared with previously obtained in vivo data after oral administration for up to 14 days of the same hepatocarcinogens to rats. In addition to the comparison of deregulated genes and functions per compound between in vivo and in vitro models, the major discriminating cellular pathways found in vivo in livers of exposed rats were examined for deregulation in vitro. Further, in vivo-derived gene signatures for the identification of genotoxic versus non-genotoxic carcinogens are used to classify in vitro-tested hepatocarcinogens and non-carcinogens. In the primary hepatocyte cultures, two out of the three tested genotoxic carcinogens mimicked the in vivo-relevant DNA damage response and were correctly assessed. Exposure to the non-genotoxic hepatocarcinogens, however, triggered a relatively weak response in the in vitro system, with no clear similarities to in vivo. This study contributes to the further optimization of toxicogenomics predictive tools when applied in in vitro settings.

Model study of the enzymatic modification of natural extracts: peroxidase-based removal of eugenol from rose essential oil

A protocol based on the use of horseradish peroxidase (HRP) is proposed for the removal of allergenic eugenol from rose essential oil without loss of the organoleptic quality and with a good conservation of the chemical composition. For the first time, an enzyme-based strategy is proposed for essential oils treatment and opens new opportunities in the detoxification of natural extracts used in perfumery and cosmetics. Our results on eugenol in rose essential oil constitute a first step toward the development of efficient and mild processes for the removal of more toxic compounds of natural extracts.

Genetic toxicology in the 21st century: reflections and future directions

A symposium at the 40th anniversary of the Environmental Mutagen Society, held from October 24-28, 2009 in St. Louis, MO, surveyed the current status and future directions of genetic toxicology. This article summarizes the presentations and provides a perspective on the future. An abbreviated history is presented, highlighting the current standard battery of genotoxicity assays and persistent challenges. Application of computational toxicology to safety testing within a regulatory setting is discussed as a means for reducing the need for animal testing and human clinical trials, and current approaches and applications of in silico genotoxicity screening approaches across the pharmaceutical industry were surveyed and are reported here. The expanded use of toxicogenomics to illuminate mechanisms and bridge genotoxicity and carcinogenicity, and new public efforts to use high-throughput screening technologies to address lack of toxicity evaluation for the backlog of thousands of industrial chemicals in the environment are detailed. The Tox21 project involves coordinated efforts of four U.S. Government regulatory/research entities to use new and innovative assays to characterize key steps in toxicity pathways, including genotoxic and nongenotoxic mechanisms for carcinogenesis. Progress to date, highlighting preliminary test results from the National Toxicology Program is summarized. Finally, an overview is presented of ToxCast™, a related research program of the U.S. Environmental Protection Agency, using a broad array of high throughput and high content technologies for toxicity profiling of environmental chemicals, and computational toxicology modeling. Progress and challenges, including the pressing need to incorporate metabolic activation capability, are summarized.

The embryonic stem cell test combined with toxicogenomics as an alternative testing model for the assessment of developmental toxicity

One of the most studied in vitro alternative testing methods for identification of developmental toxicity is the embryonic stem cell test (EST). Although the EST has been formally validated, the applicability domain as well as the predictability of the model needs further study to allow successful implementation of the EST as an alternative testing method in regulatory toxicity testing. Genomics technologies have already provided a proof of principle of their value in identification of toxicants such as carcinogenic compounds. Also within the EST, gene expression profiling has shown its value in the identification of developmental toxicity and in the evaluation of factors critical for risk assessment, such as dose and time responses. It is expected that the implementation of genomics into the EST will provide a more detailed end point evaluation as compared to the classical morphological scoring of differentiation cultures. Therefore, genomics may contribute to improvement of the EST, both in terms of definition of its applicability domain as well as its predictive capacity. In the present review, we present the progress that has been made with regard to the prediction of developmental toxicity using the EST combined with transcriptomics. Furthermore, we discuss the developments of additional aspects required for further optimization of the EST, including kinetics, the use of human embryonic stem cells (ESC) and computational toxicology. Finally, the current and future use of the EST model for prediction of developmental toxicity in testing strategies and in regulatory toxicity evaluations is discussed.

Alternative (non-animal) methods for cosmetics testing: current status and future prospects-2010

The 7th amendment to the EU Cosmetics Directive prohibits to put animal-tested cosmetics on the market in Europe after 2013. In that context, the European Commission invited stakeholder bodies (industry, non-governmental organisations, EU Member States, and the Commission's Scientific Committee on Consumer Safety) to identify scientific experts in five toxicological areas, i.e. toxicokinetics, repeated dose toxicity, carcinogenicity, skin sensitisation, and reproductive toxicity for which the Directive foresees that the 2013 deadline could be further extended in case alternative and validated methods would not be available in time. The selected experts were asked to analyse the status and prospects of alternative methods and to provide a scientifically sound estimate of the time necessary to achieve full replacement of animal testing. In summary, the experts confirmed that it will take at least another 7-9 years for the replacement of the current in vivo animal tests used for the safety assessment of cosmetic ingredients for skin sensitisation. However, the experts were also of the opinion that alternative methods may be able to give hazard information, i.e. to differentiate between sensitisers and non-sensitisers, ahead of 2017. This would, however, not provide the complete picture of what is a safe exposure because the relative potency of a sensitiser would not be known. For toxicokinetics, the timeframe was 5-7 years to develop the models still lacking to predict lung absorption and renal/biliary excretion, and even longer to integrate the methods to fully replace the animal toxicokinetic models. For the systemic toxicological endpoints of repeated dose toxicity, carcinogenicity and reproductive toxicity, the time horizon for full replacement could not be estimated.

Toxicogenomic profiling of chemically exposed humans in risk assessment

Gene-environment interactions contribute to complex disease development. The environmental contribution, in particular low-level and prevalent environmental exposures, may constitute much of the risk and contribute substantially to disease. Systematic risk evaluation of the majority of human chemical exposures, has not been conducted and is a goal of regulatory agencies in the U.S. and worldwide. With the recent recognition that toxicological approaches more predictive of effects in humans are required for risk assessment, in vitro human cell line data as well as animal data are being used to identify toxicity mechanisms that can be translated into biomarkers relevant to human exposure studies. In this review, we discuss how data from toxicogenomic studies of exposed human populations can inform risk assessment, by generating biomarkers of exposure, early effect, and/or susceptibility, elucidating mechanisms of action underlying exposure-related disease, and detecting response at low doses. Good experimental design incorporating precise, individual exposure measurements, phenotypic anchors (pre-disease or traditional toxicological markers), and a range of relevant exposure levels, is necessary. Further, toxicogenomic studies need to be designed with sufficient power to detect true effects of the exposure. As more studies are performed and incorporated into databases such as the Comparative Toxicogenomics Database (CTD) and Chemical Effects in Biological Systems (CEBS), data can be mined for classification of newly tested chemicals (hazard identification), and, for investigating the dose-response, and inter-relationship among genes, environment and disease in a systems biology approach (risk characterization).

Toxicogenomic biomarkers for liver toxicity

Toxicogenomics (TGx) is a widely used technique in the preclinical stage of drug development to investigate the molecular mechanisms of toxicity. A number of candidate TGx biomarkers have now been identified and are utilized for both assessing and predicting toxicities. Further accumulation of novel TGx biomarkers will lead to more efficient, appropriate and cost effective drug risk assessment, reinforcing the paradigm of the conventional toxicology system with a more profound understanding of the molecular mechanisms of drug-induced toxicity. In this paper, we overview some practical strategies as well as obstacles for identifying and utilizing TGx biomarkers based on microarray analysis. Since clinical hepatotoxicity is one of the major causes of drug development attrition, the liver has been the best documented target organ for TGx studies to date, and we therefore focused on information from liver TGx studies. In this review, we summarize the current resources in the literature in regard to TGx studies of the liver, from which toxicologists could extract potential TGx biomarker gene sets for better hepatotoxicity risk assessment.

Toxicogenomics: transcription profiling for toxicology assessment

Toxicogenomics, the application of transcription profiling to toxicology, has been widely used for elucidating the molecular and cellular actions of chemicals and other environmental stressors on biological systems, predicting toxicity before any functional damages, and classification of known or new toxicants based on signatures of gene expression. The success of a toxicogenomics study depends upon close collaboration among experts in different fields, including a toxicologist or biologist, a bioinformatician, statistician, physician and, sometimes, mathematician. This review is focused on toxicogenomics studies, including transcription profiling technology, experimental design, significant gene extraction, toxicological results interpretation, potential pathway identification, database input and the applications of toxicogenomics in various fields of toxicological study.

Improving prediction of chemical carcinogenicity by considering multiple mechanisms and applying toxicogenomic approaches

While scientific knowledge of the potential health significance of chemical exposures has grown, experimental methods for predicting the carcinogenicity of environmental agents have not been substantially updated in the last two decades. Current methodologies focus first on identifying genotoxicants under the premise that agents capable of directly damaging DNA are most likely to be carcinogenic to humans. Emphasis on the distinction between genotoxic and non-genotoxic carcinogens is also motivated by assumed implications for the dose-response curve; it is purported that genotoxicants would lack a threshold in the low dose region, in contrast to non-genotoxic agents. However, for the vast majority of carcinogens, little if any empirical data exist to clarify the nature of the cancer dose-response relationship at low doses in the exposed human population. Recent advances in scientific understanding of cancer biology-and increased appreciation of the multiple impacts of carcinogens on this disease process-support the view that environmental chemicals can act through multiple toxicity pathways, modes and/or mechanisms of action to induce cancer and other adverse health outcomes. Moreover, the relationship between dose and a particular outcome in an individual could take multiple forms depending on genetic background, target tissue, internal dose and other factors besides mechanisms or modes of action; inter-individual variability and susceptibility in response are, in turn, key determinants of the population dose-response curve. New bioanalytical approaches (e.g., transcriptomics, proteomics, and metabolomics) applied in human, animal and in vitro studies could better characterize a wider array of hazard traits and improve the ability to predict the potential carcinogenicity of chemicals.

Databases applicable to quantitative hazard/risk assessment--towards a predictive systems toxicology

The Workshop on The Power of Aggregated Toxicity Data addressed the requirement for distributed databases to support quantitative hazard and risk assessment. The authors have conceived and constructed with federal support several databases that have been used in hazard identification and risk assessment. The first of these databases, the EPA Gene-Tox Database was developed for the EPA Office of Toxic Substances by the Oak Ridge National Laboratory, and is currently hosted by the National Library of Medicine. This public resource is based on the collaborative evaluation, by government, academia, and industry, of short-term tests for the detection of mutagens and presumptive carcinogens. The two-phased evaluation process resulted in more than 50 peer-reviewed publications on test system performance and a qualitative database on thousands of chemicals. Subsequently, the graphic and quantitative EPA/IARC Genetic Activity Profile (GAP) Database was developed in collaboration with the International Agency for Research on Cancer (IARC). A chemical database driven by consideration of the lowest effective dose, GAP has served IARC for many years in support of hazard classification of potential human carcinogens. The Toxicological Activity Profile (TAP) prototype database was patterned after GAP and utilized acute, subchronic, and chronic data from the Office of Air Quality Planning and Standards. TAP demonstrated the flexibility of the GAP format for air toxics, water pollutants and other environmental agents. The GAP format was also applied to developmental toxicants and was modified to represent quantitative results from the rodent carcinogen bioassay. More recently, the authors have constructed: 1) the NIEHS Genetic Alterations in Cancer (GAC) Database which quantifies specific mutations found in cancers induced by environmental agents, and 2) the NIEHS Chemical Effects in Biological Systems (CEBS) Knowledgebase that integrates genomic and other biological data including dose-response studies in toxicology and pathology. Each of the public databases has been discussed in prior publications. They will be briefly described in the present report from the perspective of aggregating datasets to augment the data and information contained within them.

Translational microarray systems for outcome prediction of hepatocellular carcinoma

DNA microarray technology has revolutionized our understanding of the molecular basis of hepatocellular carcinoma (HCC), one of the most fatal human cancers with a high recurrence rate. Many researchers have used DNA microarray technology to reclassify HCC with respect to metastatic potential and to develop predictors for the outcome of HCC. However, developed predictors have reached the level only of small retrospective studies, and their current status is far from that required for clinical use. This is due to the lack of transparent data, the high cost and data instability associated with the high dimensionality of the technique, the infancy of bioinformatics, and the complicated nature of recurrent HCC. This comprehensive review summarizes: (i) class comparison studies to identify genes or pathways involved in HCC metastasis (ii) class discovery studies that have resulted in the identification of a new molecular subclass of HCC with respect to metastasis, and (iii) class prediction studies to develop multidimensional predictors for HCC outcome. We also discuss issues that need to be addressed so that the power of array-based predictors can be estimated prospectively in large independent cohorts of HCC patients.

Prediction of a carcinogenic potential of rat hepatocarcinogens using toxicogenomics analysis of short-term in vivo studies

The carcinogenic potential of chemicals is currently evaluated with rodent life-time bioassays, which are time consuming, and expensive with respect to cost, number of animals and amount of compound required. Since the results of these 2-year bioassays are not known until quite late during development of new chemical entities, and since the short-term test battery to test for genotoxicity, a characteristic of genotoxic carcinogens, is hampered by low specificity, the identification of early biomarkers for carcinogenicity would be a big step forward. Using gene expression profiles from the livers of rats treated up to 14 days with genotoxic and non-genotoxic carcinogens we previously identified characteristic gene expression profiles for these two groups of carcinogens. We have now added expression profiles from further hepatocarcinogens and from non-carcinogens the latter serving as control profiles. We used these profiles to extract biomarkers discriminating genotoxic from non-genotoxic carcinogens and to calculate classifiers based on the support vector machine (SVM) algorithm. These classifiers then predicted a set of independent validation compound profiles with up to 88% accuracy, depending on the marker gene set. We would like to present this study as proof of the concept that a classification of carcinogens based on short-term studies may be feasible.

A gene expression biomarker provides early prediction and mechanistic assessment of hepatic tumor induction by nongenotoxic chemicals

There are currently no accurate and well-validated short-term tests to identify nongenotoxic hepatic tumorigens, thus necessitating an expensive 2-year rodent bioassay before a risk assessment can begin. Using hepatic gene expression data from rats treated for 5 days with one of 100 structurally and mechanistically diverse nongenotoxic hepatocarcinogens and nonhepatocarcinogens, a novel multigenebiomarker (i.e., signature) was derived to predict the likelihood of nongenotoxic chemicals to induce liver tumors in longer term studies. Independent validation of the signature on 47 test chemicals indicates an assay sensitivity and specificity of 86% and 81%, respectively. Alternate short-term in vivo pathological and genomic biomarkers were evaluated in parallel for comparison, including liver weight, hepatocellular hypertrophy, hepatic necrosis, serum alanine aminotransferase activity, induction of cytochrome P450 genes, and repression of Tsc-22 or alpha2-macroglobulin messenger RNA. In contrast to these biomarkers, the gene expression-based signature was more accurate. Unlike existing tests, an understanding of potential modes of action for hepatic tumorigenicity can be derived by comparison of the signature profile of test chemicals to hepatic tumorigens of known mechanism, including regenerative proliferation, proliferation associated with xenobiotic receptor activation, peroxisome proliferation, and steroid hormone-mediated mechanisms. This signature is not only more accurate than current methods, but also facilitates the identification of mode of action to aid in the early assessment of human cancer risk.