Combined Toxic Exposures and Human Health: Biomarkers of Exposure and Effect

Procedures for risk assessment of chemical mixtures, combined and cumulative exposures are under development, but the scientific database needs considerable expansion. In particular, there is a lack of knowledge on how to monitor effects of complex exposures, and there are few reviews on biomonitoring complex exposures. In this review we summarize articles in which biomonitoring techniques have been developed and used. Most examples describe techniques for biomonitoring effects which may detect early changes induced by many chemical stressors and which have the potential to accelerate data gathering. Some emphasis is put on endocrine disrupters acting via epigenetic mechanisms and on carcinogens. Solid evidence shows that these groups of chemicals can interact and even produce synergistic effects. They may act during sensitive time windows and biomonitoring their effects in epidemiological studies is a challenging task

The first step in the development of Text Mining technology for Cancer Risk Assessment: identifying and organizing scientific evidence in risk assessment literature.

BACKGROUND: One of the most neglected areas of biomedical Text Mining (TM) is the development of systems based on carefully assessed user needs. We have recently investigated the user needs of an important task yet to be tackled by TM -- Cancer Risk Assessment (CRA). Here we take the first step towards the development of TM technology for the task: identifying and organizing the scientific evidence required for CRA in a taxonomy which is capable of supporting extensive data gathering from biomedical literature. RESULTS: The taxonomy is based on expert annotation of 1297 abstracts downloaded from relevant PubMed journals. It classifies 1742 unique keywords found in the corpus to 48 classes which specify core evidence required for CRA. We report promising results with inter-annotator agreement tests and automatic classification of PubMed abstracts to taxonomy classes. A simple user test is also reported in a near real-world CRA scenario which demonstrates along with other evaluation that the resources we have built are well-defined, accurate, and applicable in practice. CONCLUSION: We present our annotation guidelines and a tool which we have designed for expert annotation of PubMed abstracts. A corpus annotated for keywords and document relevance is also presented, along with the taxonomy which organizes the keywords into classes defining core evidence for CRA. As demonstrated by the evaluation, the materials we have constructed provide a good basis for classification of CRA literature along multiple dimensions. They can support current manual CRA as well as facilitate the development of an approach based on TM. We discuss extending the taxonomy further via manual and machine learning approaches and the subsequent steps required to develop TM technology for the needs of CRA.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

A Comparison and User-based Evaluation of Models of Textual Information Structure in the Context of Cancer Risk Assessment

BACKGROUND: Many practical tasks in biomedicine require accessing specific types of information in scientific literature; e.g. information about the results or conclusions of the study in question. Several schemes have been developed to characterize such information in scientific journal articles. For example, a simple section-based scheme assigns individual sentences in abstracts under sections such as Objective, Methods, Results and Conclusions. Some schemes of textual information structure have proved useful for biomedical text mining (BIO-TM) tasks (e.g. automatic summarization). However, user-centered evaluation in the context of real-life tasks has been lacking. METHODS: We take three schemes of different type and granularity - those based on section names, Argumentative Zones (AZ) and Core Scientific Concepts (CoreSC) - and evaluate their usefulness for a real-life task which focuses on biomedical abstracts: Cancer Risk Assessment (CRA). We annotate a corpus of CRA abstracts according to each scheme, develop classifiers for automatic identification of the schemes in abstracts, and evaluate both the manual and automatic classifications directly as well as in the context of CRA. RESULTS: Our results show that for each scheme, the majority of categories appear in abstracts, although two of the schemes (AZ and CoreSC) were developed originally for full journal articles. All the schemes can be identified in abstracts relatively reliably using machine learning. Moreover, when cancer risk assessors are presented with scheme annotated abstracts, they find relevant information significantly faster than when presented with unannotated abstracts, even when the annotations are produced using an automatic classifier. Interestingly, in this user-based evaluation the coarse-grained scheme based on section names proved nearly as useful for CRA as the finest-grained CoreSC scheme. CONCLUSIONS: We have shown that existing schemes aimed at capturing information structure of scientific documents can be applied to biomedical abstracts and can be identified in them automatically with an accuracy which is high enough to benefit a real-life task in biomedicine

Text Mining for Literature Review and Knowledge Discovery in Cancer Risk Assessment and Research

Research in biomedical text mining is starting to produce technology which can make information in biomedical literature more accessible for bio-scientists. One of the current challenges is to integrate and refine this technology to support real-life scientific tasks in biomedicine, and to evaluate its usefulness in the context of such tasks. We describe CRAB – a fully integrated text mining tool designed to support chemical health risk assessment. This task is complex and time-consuming, requiring a thorough review of existing scientific data on a particular chemical. Covering human, animal, cellular and other mechanistic data from various fields of biomedicine, this is highly varied and therefore difficult to harvest from literature databases via manual means. Our tool automates the process by extracting relevant scientific data in published literature and classifying it according to multiple qualitative dimensions. Developed in close collaboration with risk assessors, the tool allows navigating the classified dataset in various ways and sharing the data with other users. We present a direct and user-based evaluation which shows that the technology integrated in the tool is highly accurate, and report a number of case studies which demonstrate how the tool can be used to support scientific discovery in cancer risk assessment and research. Our work demonstrates the usefulness of a text mining pipeline in facilitating complex research tasks in biomedicine. We discuss further development and application of our technology to other types of chemical risk assessment in the future

Regulation of p53 and susceptibility to cell death in chemically-induced preneoplastic hepatocytes

Induction of preneoplastic lesions, termed enzyme-altered foci (EAF), is considered to be an early step in the development of liver cancer. In view of the relatively homogenous properties of the majority of EAF, these lesions are suggested to arise as an adaptive response to toxic stress. An overall goal of this thesis was to study the rote of adaptations to genotoxic stress in EAF development, and how resistance to toxicity in EAF hepatocytes was affected. In association with carcinogenesis much focus has been on the tumor suppressor gene p53. This gene is commonly mutated in cancer cells, thus indicating a critical rote in preventing cancer development. Previously, it has been found that EAF lesions from rats treated with the genotoxic agent diethylnitrosamine (DEN) display an attenuated p53 response to genotoxic stress, compared to normal liver tissue. In the first study the underlying mechanisms of the attenuated p53 response was examined in EAF hepatocytes. We found that in contrast to genotoxic agents, treatment of primary hepatocytes with the hypoxia-amimicking agent CoCl2 induced a p53 response in both EAF and normal cells. In addition, we further found decreased Levels of serine-15 phosphorylated p53 in EAF tissue upon toxic stress. The kinase ATM is involved in the phosphorylation of p53 after DNA damage, specifically at serine-15, and analysis of EAF tissue, employing immunohistochemistry and western blot analysis, revealed reduced levels of ATM compared to normal tissue. We hypothesized that an attenuated p53 response in EAF hepatocytes reflects an adaptation to acute cytotoxic and genotoxic stress seen at high doses. Rats received relatively Low doses of DEN for 10 or 20 weeks, and their livers were subsequently analyzed. All doses induced EAF Lesions and a majority of these demonstrated a relatively low p53 response after DEN treatment, as compared to surrounding tissue. Compared to rats receiving DEN at high dose rates, rats receiving the same cumulative dose at a low dose rate generally possessed larger preneoplastic lesions that were more "p53 -negative". These data argue against our hypothesis and suggest subtler genotoxic effects behind the alteration of the p53 response. EAF hepatocytes have been found to be relatively resistant to toxicological stress and apoptosis induced by xenobiotics. However, in vitro treatment with different sphingolipids, such as ceramide and sphingosine, was found to induce cell death, predominantly in EAF hepatocytes. TLC analysis and immunohistochemistry demonstrated altered levels of sphingosine, sphingosine-1 -phosphate and glucosylated ceramide in EAF tissue. In in vivo experiments, administration of sphingomyelin-supplemented diet to DEN-treated rats was found to reduce the number of preneoplastic lesions. Sphingolipids have been suggested to exert their apoptotic effects by decreasing Levels of phosphorylated Akt (pAkt). We found that a fraction of EAF displayed higher immunohistochemical staining of ceramide compared to the surrounding tissue, which correlated with tower staining of pAkt. Western blot analysis revealed that sphingosine was able to inhibit insulin-induced Akt phosphorylation in vitro. Furthermore, we found that the levels of p27 were increased in EAF and that these cells also were more sensitive to treatment with rapamycin, an inhibitor of mTOR. In conclusion, our results suggest that the DNA damage signaling pathway, including ATM and p53, is downregulated in EAF hepatocytes. This may result in a resistance to cell death in EAF hepatocytes and confer a growth advantage to EAF in a toxic environment. Furthermore, the sphingolipid levels and the pAkt-mTOR signaling pathway seem to be altered in many EAF cells. This may render these cells more susceptible to cell death induced by sphingolipids

Combining QSAR Modeling and Text-Mining Techniques to Link Chemical Structures and Carcinogenic Modes of Action

There is an increasing need for new reliable non-animal based methods to predict and test toxicity of chemicals. Quantitative structure-activity relationship (QSAR), a computer-based method linking chemical structures with biological activities, is used in predictive toxicology. In this study, we tested the approach to combine QSAR data with literature profiles of carcinogenic modes of action automatically generated by a text-mining tool. The aim was to generate data patterns to identify associations between chemical structures and biological mechanisms related to carcinogenesis. Using these two methods, individually and combined, we evaluated 96 rat carcinogens of the hematopoietic system, liver, lung, and skin. We found that skin and lung rat carcinogens were mainly mutagenic, while the group of carcinogens affecting the hematopoietic system and the liver also included a large proportion of non-mutagens. The automatic literature analysis showed that mutagenicity was a frequently reported endpoint in the literature of these carcinogens, however, less common endpoints such as immunosuppression and hormonal receptor-mediated effects were also found in connection with some of the carcinogens, results of potential importance for certain target organs. The combined approach, using QSAR and text-mining techniques, could be useful for identifying more detailed information on biological mechanisms and the relation with chemical structures. The method can be particularly useful in increasing the understanding of structure and activity relationships for non-mutagens

The first step in the development of Text Mining technology for Cancer Risk Assessment: identifying and organizing scientific evidence in risk assessment literature.

BACKGROUND: One of the most neglected areas of biomedical Text Mining (TM) is the development of systems based on carefully assessed user needs. We have recently investigated the user needs of an important task yet to be tackled by TM -- Cancer Risk Assessment (CRA). Here we take the first step towards the development of TM technology for the task: identifying and organizing the scientific evidence required for CRA in a taxonomy which is capable of supporting extensive data gathering from biomedical literature. RESULTS: The taxonomy is based on expert annotation of 1297 abstracts downloaded from relevant PubMed journals. It classifies 1742 unique keywords found in the corpus to 48 classes which specify core evidence required for CRA. We report promising results with inter-annotator agreement tests and automatic classification of PubMed abstracts to taxonomy classes. A simple user test is also reported in a near real-world CRA scenario which demonstrates along with other evaluation that the resources we have built are well-defined, accurate, and applicable in practice. CONCLUSION: We present our annotation guidelines and a tool which we have designed for expert annotation of PubMed abstracts. A corpus annotated for keywords and document relevance is also presented, along with the taxonomy which organizes the keywords into classes defining core evidence for CRA. As demonstrated by the evaluation, the materials we have constructed provide a good basis for classification of CRA literature along multiple dimensions. They can support current manual CRA as well as facilitate the development of an approach based on TM. We discuss extending the taxonomy further via manual and machine learning approaches and the subsequent steps required to develop TM technology for the needs of CRA.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are