Cancer Hallmarks Analytics Tool (CHAT): a text mining approach to organize and evaluate scientific literature on cancer.

Motivation: To understand the molecular mechanisms involved in cancer development, significant efforts are being invested in cancer research. This has resulted in millions of scientific articles. An efficient and thorough review of the existing literature is crucially important to drive new research.

Text mining for improved exposure assessment.

Chemical exposure assessments are based on information collected via different methods, such as biomonitoring, personal monitoring, environmental monitoring and questionnaires. The vast amount of chemical-specific exposure information available from web-based databases, such as PubMed, is undoubtedly a great asset to the scientific community. However, manual retrieval of relevant published information is an extremely time consuming task and overviewing the data is nearly impossible.

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.

Gender differences in cancer susceptibility: role of oxidative stress.

Cancer is a leading cause of death worldwide and environmental factors, including chemicals, have been suggested as major etiological incitements. Cancer statistics indicates that men get more cancer than women. However, differences in the known risk factors including life style or occupational exposure only offer partial explanation.

Grouping chemicals for health risk assessment: A text mining-based case study of polychlorinated biphenyls (PCBs).

As many chemicals act as carcinogens, chemical health risk assessment is critically important. A notoriously time consuming process, risk assessment could be greatly supported by classifying chemicals with similar toxicological profiles so that they can be assessed in groups rather than individually. We have previously developed a text mining (TM)-based tool that can automatically identify the mode of action (MOA) of a carcinogen based on the scientific evidence in literature, and it can measure the MOA similarity between chemicals on the basis of their literature profiles (Korhonen et al.

Automatic semantic classification of scientific literature according to the hallmarks of cancer.

Motivation: The hallmarks of cancer have become highly influential in cancer research. They reduce the complexity of cancer into 10 principles (e.g.

Evaluation of carcinogenic modes of action for pesticides in fruit on the Swedish market using a text-mining tool.

Toxicity caused by chemical mixtures has emerged as a significant challenge for toxicologists and risk assessors. Information on individual chemicals' modes of action is an important part of the hazard identification step. In this study, an automatic text mining-based tool was employed as a method to identify the carcinogenic modes of action of pesticides frequently found in fruit on the Swedish market.

Active learning-based information structure analysis of full scientific articles and two applications for biomedical literature review.

Motivation: Techniques that are capable of automatically analyzing the information structure of scientific articles could be highly useful for improving information access to biomedical literature. However, most existing approaches rely on supervised machine learning (ML) and substantial labeled data that are expensive to develop and apply to different sub-fields of biomedicine. Recent research shows that minimal supervision is sufficient for fairly accurate information structure analysis of biomedical abstracts.

Exocrine pancreatic carcinogenesis and autotaxin expression.

Exocrine pancreatic cancer is an aggressive disease with an exceptionally high mortality rate. Genetic analysis suggests a causative role for environmental factors, but consistent epidemiological support is scarce and no biomarkers for monitoring the effects of chemical pancreatic carcinogens are available. With the objective to identify common traits for chemicals inducing pancreatic tumors we studied the National Toxicology Program (NTP) bioassay database.

Gender differences in chemical carcinogenesis in National Toxicology Program 2-year bioassays.

Differences in cancer incidences between men and women are often explained by either differences in environmental exposures or by influences of sex hormones. However, there are few studies on intrinsic gender differences in susceptibility to chemical carcinogens. We have analyzed the National Toxicology Program (NTP) database for sex differences in rat responses to chemical carcinogens.

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.

Data and literature gathering in chemical cancer risk assessment.

In recent years, chemical cancer risk assessment has faced major challenges: the demand for cancer risk assessment has grown considerably with strict legislation regarding chemical safety, whereas cancer hazard identification has turned increasingly complex due to the rapid development and high publication rate in biomedical sciences. Thus, much of the scientific evidence required for hazard identification is hidden in large collections of biomedical literature. Extensive guidelines have been produced to support cancer risk assessment under these circumstances.

Weakly supervised learning of information structure of scientific abstracts--is it accurate enough to benefit real-world tasks in biomedicine?

Motivation: Many practical tasks in biomedicine require accessing specific types of information in scientific literature; e.g. information about the methods, results or conclusions of the study in question.

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.

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.

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.

Chemical induced alterations in p53 signaling.

The p53 protein is one of the most important tumor suppressors. The present review summarizes aspects of p53 function and its role in cancer development. Some of the most well-characterized molecular mechanisms affecting p53 regulation, stabilization, inactivation and downstream events are described.

Dietary sphingolipids suppress a subset of preneoplastic rat liver lesions exhibiting high PTEN, low phospho-Akt and high levels of ceramide species.

Rat liver glutathione-S-transferase Pi-(GST-P)-positive enzyme-altered foci (EAF) are preneoplastic lesions that develop in response to carcinogenic stress. They are often used as endpoints in e.g.

Induction of preneoplastic rat liver lesions with an attenuated p53 response by low doses of diethylnitrosamine.

Previous reports have documented an attenuated p53 response to DNA-damage in preneoplastic enzyme-altered foci (EAF). Data suggest that this alteration is an adaptation to genotoxic stress induced by carcinogens. Here, we have studied whether the altered p53 response in EAF can be related to acutely apoptotic or cytotoxic doses of the carcinogen diethylnitrosamine (DEN).

Characterizing the role of MDM2 in diethylnitrosamine induced acute liver damage and development of pre-neoplastic lesions.

Pre-neoplastic lesions in rodent liver often express high levels of MDM2 and lack a p53 response to DNA damage. The question we posed was whether there is a liver-specific regulation of the p53/MDM2 feedback loop and if it can be related to the development of pre-neoplastic lesions, referred to as enzyme altered foci (EAF) in rats. Acute responses of p53 and MDM2 to diethylnitrosamine (DEN) were characterized by employing immunohistochemistry, western blotting, RT-PCR and in situ hybridization.

Sphingolipids suppress preneoplastic rat hepatocytes in vitro and in vivo.

Sphingolipids can modulate cell growth, differentiation and apoptosis. In the present investigation, selective death of hepatocytes localized in enzyme-altered foci (EAF hepatocytes) was shown to be induced by sphingolipids. Sphingosine (20 micro M) caused rapid cell death predominantly of EAF hepatocytes in vitro.