Prioritizing cancer hazard assessments for IARC Monographs using an integrated approach of database fusion and text mining

IARC Workshop on the Key Characteristics of Carcinogens: Assessment of End Points for Evaluating Mechanistic Evidence of Carcinogenic Hazards

BACKGROUND

The 10 key characteristics (KCs) of carcinogens form the basis of a framework to identify, organize, and evaluate mechanistic evidence relevant to carcinogenic hazard identification. The 10 KCs are related to mechanisms by which carcinogens cause cancer. The International Agency for Research on Cancer (IARC) Monographs programme has successfully applied the KCs framework for the mechanistic evaluation of different types of exposures, including chemicals, metals, and complex exposures, such as environmental, occupational, or dietary exposures. The use of this framework has significantly enhanced the identification and organization of relevant mechanistic data, minimized bias in evaluations, and enriched the knowledge base regarding the mechanisms of known and suspected carcinogens.

OBJECTIVES

We sought to report the main outcomes of an IARC Scientific Workshop convened by the IARC to establish appropriate, transparent, and uniform application of the KCs in future IARC Monographs evaluations.

METHODS

A group of experts from different disciplines reviewed the IARC Monographs experience with the KCs of carcinogens, discussing three main themes: a) the interpretation of end points forming the evidence base for the KCs, b) the incorporation of data from novel assays on the KCs, and c) the integration of the mechanistic evidence as part of cancer hazard identification. The workshop participants assessed the relevance and the informativeness of multiple KCs-associated end points for the evaluation of mechanistic evidence in studies of exposed humans and experimental systems.

DISCUSSION

Consensus was reached on how to enhance the use of in silico, molecular, and cellular high-output and high-throughput data. In addition, approaches to integrate evidence across the KCs and opportunities to improve methodologies of mechanistic evaluation of cancer hazards were explored. The findings described herein and in a forthcoming IARC technical report will support future working groups of experts in reporting and interpreting results under the KCs framework within the IARC Monographs or in other contexts. https://doi.org/10.1289/EHP15389.

Molecular and cell phenotype programs in oral epithelial cells directed by co-exposure to arsenic and smokeless tobacco

Chronic arsenic exposure can lead to various health issues, including cancer. Concerns have been mounting about the enhancement of arsenic toxicity through co-exposure to various prevalent lifestyle habits. Smokeless tobacco products are commonly consumed in South Asian countries, where their use frequently co-occurs with exposure to arsenic from contaminated groundwater. To decipher the in vitro molecular and cellular responses to arsenic and/or smokeless tobacco, we performed temporal multi-omics analysis of the transcriptome and DNA methylome remodelling in exposed hTERT-immortalized human normal oral keratinocytes (NOK), as well as arsenic and/or smokeless tobacco genotoxicity and mutagenicity investigations in NOK cells and in human p53 knock-in murine embryonic fibroblasts (Hupki MEF). RNAseq results from acute exposures to arsenic alone and in combination with smokeless tobacco extract revealed upregulation of genes with roles in cell cycle changes, apoptosis and inflammation responses. This was in keeping with global DNA hypomethylation affecting genes involved in the same processes in response to chronic treatment in NOK cells. At the phenotypic level, we observed a dose-dependent decrease in NOK cell viability, induction of DNA damage, cell cycle changes and increased apoptosis, with the most pronounced effects observed under arsenic and SLT co-exposure conditions. Live-cell imaging experiments indicated that the DNA damage likely resulted from induction of apoptosis, an observation validated by a lack of exome-wide mutagenesis in response to chronic exposure to arsenic and/or smokeless tobacco. In sum, our integrative omics study provides novel insights into the acute and chronic responses to arsenic and smokeless tobacco (co-)exposure, with both types of responses converging on several key mechanisms associated with cancer hallmark processes. The generated rich catalogue of molecular programs in oral cells regulated by arsenic and smokeless tobacco (co-)exposure may provide bases for future development of biomarkers for use in molecular epidemiology studies of exposed populations at risk of developing oral cancer.

CarD-T: Interpreting Carcinomic Lexicon via Transformers

The identification and classification of carcinogens is critical in cancer epidemiology, necessitating updated methodologies to manage the burgeoning biomedical literature. Current systems, like those run by the International Agency for Research on Cancer (IARC) and the National Toxicology Program (NTP), face challenges due to manual vetting and disparities in carcinogen classification spurred by the volume of emerging data. To address these issues, we introduced the Carcinogen Detection via Transformers (CarD-T) framework, a text analytics approach that combines transformer-based machine learning with probabilistic statistical analysis to efficiently nominate carcinogens from scientific texts. CarD-T uses Named Entity Recognition (NER) trained on PubMed abstracts featuring known carcinogens from IARC groups and includes a context classifier to enhance accuracy and manage computational demands. Using this method, journal publication data indexed with carcinogenicity & carcinogenesis Medical Subject Headings (MeSH) terms from the last 25 years was analyzed, identifying potential carcinogens. Training CarD-T on 60% of established carcinogens (Group 1 and 2A carcinogens, IARC designation), CarD-T correctly to identifies all of the remaining Group 1 and 2A designated carcinogens from the analyzed text. In addition, CarD-T nominates roughly 1500 more entities as potential carcinogens that have at least two publications citing evidence of carcinogenicity. Comparative assessment of CarD-T against GPT-4 model reveals a high recall (0.857 vs 0.705) and F1 score (0.875 vs 0.792), and comparable precision (0.894 vs 0.903). Additionally, CarD-T highlights 554 entities that show disputing evidence for carcinogenicity. These are further analyzed using Bayesian temporal Probabilistic Carcinogenic Denomination (PCarD) to provide probabilistic evaluations of their carcinogenic status based on evolving evidence. Our findings underscore that the CarD-T framework is not only robust and effective in identifying and nominating potential carcinogens within vast biomedical literature but also efficient on consumer GPUs. This integration of advanced NLP capabilities with vital epidemiological analysis significantly enhances the agility of public health responses to carcinogen identification, thereby setting a new benchmark for automated, scalable toxicological investigations.

A deep-learning approach for identifying prospective chemical hazards

With the aim of helping to set safe exposure limits for the general population, various techniques have been implemented to conduct risk assessments for chemicals and other environmental stressors; however, none of these tools facilitate the identification of completely new chemicals that are likely hazardous and elicit an adverse biological effect. Here, we detail a novel in silico, deep-learning framework that is designed to systematically generate structures for new chemical compounds that are predicted to be chemical hazards. To assess the utility of the framework, we applied the tool to four endpoints related to environmental toxicants and their impacts on human and animal health: (i) toxicity to honeybees, (ii) immunotoxicity, (iii) endocrine disruption via ER-α antagonism, and (iv) mutagenicity. In addition, we characterized the predicted potency of these compounds and examined their structural relationship to existing chemicals of concern. As part of the array of emerging new approach methodologies (NAMs), we anticipate that such a framework will be a significant asset to risk assessors and other environmental scientists when planning and forecasting. Though not in the scope of the present study, we expect that the methodology detailed here could also be useful in the de novo design of more environmentally-friendly industrial chemicals.

Research Recommendations for Selected IARC-Classified Agents: Impact and Lessons Learned

BACKGROUND

The International Agency for Research on Cancer (IARC) Monographs program assembles expert working groups who publish a critical review and evaluation of data on agents of interest. These comprehensive reviews provide a unique opportunity to identify research needs to address classification uncertainties. A multidisciplinary expert review and workshop held in 2009 identified research gaps and needs for 20 priority occupational chemicals, metals, dusts, and physical agents, with the goal of stimulating advances in epidemiological studies of cancer and carcinogen mechanisms. Overarching issues were also described.

OBJECTIVES

In this commentary we review the current status of the evidence for the 20 priority agents identified in 2009. We examine whether identified Research Recommendations for each agent were addressed and their potential impact on resolving classification uncertainties.

METHODS

We reviewed the IARC classifications of each of the 20 priority agents and identified major new epidemiological and human mechanistic studies published since the last evaluation. Information sources were either the published Monograph for agents that have been reevaluated or, for agents not yet reevaluated, Advisory Group reports and literature searches. Findings are described in view of recent methodological developments in Monographs evidence evaluation processes.

DISCUSSION

The majority of the 20 priority agents were reevaluated by IARC since 2009. The overall carcinogen classifications of 9 agents advanced, and new cancer sites with either "sufficient" or "limited" evidence of carcinogenicity were also identified for 9 agents. Examination of published findings revealed whether evidence gaps and Research Recommendations have been addressed and highlighted remaining uncertainties. During the past decade, new research addressed a range of the 2009 recommendations and supported updated classifications for priority agents. This supports future efforts to systematically apply findings of Monograph reviews to identify research gaps and priorities relevant to evaluation criteria established in the updated IARC Monograph Preamble. https://doi.org/10.1289/EHP12547.

Updated systematic assessment of human, animal and mechanistic evidence demonstrates lack of human carcinogenicity with consumption of aspartame

Aspartame has been studied extensively and evaluated for its safety in foods and beverages yet concerns for its potential carcinogenicity have persisted, driven primarily by animal studies conducted at the Ramazzini Institute (RI). To address this controversy, an updated systematic review of available human, animal, and mechanistic data was conducted leveraging critical assessment tools to consider the quality and reliability of data. The evidence base includes 12 animal studies and >40 epidemiological studies reviewed by the World Health Organization which collectively demonstrate a lack of carcinogenic effect. Assessment of >1360 mechanistic endpoints, including many guideline-based genotoxicity studies, demonstrate a lack of activity associated with endpoints grouped to key characteristics of carcinogens. Other non-specific mechanistic data (e.g., mixed findings of oxidative stress across study models, tissues, and species) do not provide evidence of a biologically plausible carcinogenic pathway associated with aspartame. Taken together, available evidence supports that aspartame consumption is not carcinogenic in humans and that the inconsistent findings of the RI studies may be explained by flaws in study design and conduct (despite additional analyses to address study limitations), as acknowledged by authoritative bodies.

A Narrative Literature Review of Natural Language Processing Applied to the Occupational Exposome

The evolution of the Exposome concept revolutionised the research in exposure assessment and epidemiology by introducing the need for a more holistic approach on the exploration of the relationship between the environment and disease. At the same time, further and more dramatic changes have also occurred on the working environment, adding to the already existing dynamic nature of it. Natural Language Processing (NLP) refers to a collection of methods for identifying, reading, extracting and untimely transforming large collections of language. In this work, we aim to give an overview of how NLP has successfully been applied thus far in Exposome research. Methods: We conduct a literature search on PubMed, Scopus and Web of Science for scientific articles published between 2011 and 2021. We use both quantitative and qualitative methods to screen papers and provide insights into the inclusion and exclusion criteria. We outline our approach for article selection and provide an overview of our findings. This is followed by a more detailed insight into selected articles. Results: Overall, 6420 articles were screened for the suitability of this review, where we review 37 articles in depth. Finally, we discuss future avenues of research and outline challenges in existing work. Conclusions: Our results show that (i) there has been an increase in articles published that focus on applying NLP to exposure and epidemiology research, (ii) most work uses existing NLP tools and (iii) traditional machine learning is the most popular approach.

CCDB: A database for exploring inter-chemical correlations in metabolomics and exposomics datasets

Inter-chemical correlations in metabolomics and exposomics datasets provide valuable information for studying relationships among chemicals reported for human specimens. With an increase in the number of compounds for these datasets, a network graph analysis and visualization of the correlation structure is difficult to interpret. We have developed the Chemical Correlation Database (CCDB), as a systematic catalogue of inter-chemical correlation in publicly available metabolomics and exposomics studies. The database has been provided via an online interface to create single compound-centric views. We have demonstrated various applications of the database to explore: 1) the chemicals from a chemical class such as Per- and Polyfluoroalkyl Substances (PFAS), polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), phthalates and tobacco smoke related metabolites; 2) xenobiotic metabolites such as caffeine and acetaminophen; 3) endogenous metabolites (acyl-carnitines); and 4) unannotated peaks for PFAS. The database has a rich collection of 35 human studies, including the National Health and Nutrition Examination Survey (NHANES) and high-quality untargeted metabolomics datasets. CCDB is supported by a simple, interactive and user-friendly web-interface to retrieve and visualize the inter-chemical correlation data. The CCDB has the potential to be a key computational resource in metabolomics and exposomics facilitating the expansion of our understanding about biological and chemical relationships among metabolites and chemical exposures in the human body. The database is available at www.ccdb.idsl.me site.

Automated Network Assembly of Mechanistic Literature for Informed Evidence Identification to Support Cancer Risk Assessment

BACKGROUND

Mechanistic data is increasingly used in hazard identification of chemicals. However, the volume of data is large, challenging the efficient identification and clustering of relevant data.

OBJECTIVES

We investigated whether evidence identification for hazard assessment can become more efficient and informed through an automated approach that combines machine reading of publications with network visualization tools.

METHODS

We chose 13 chemicals that were evaluated by the International Agency for Research on Cancer (IARC) Monographs program incorporating the key characteristics of carcinogens (KCCs) approach. Using established literature search terms for KCCs, we retrieved and analyzed literature using Integrated Network and Dynamical Reasoning Assembler (INDRA). INDRA combines large-scale literature processing with pathway databases and extracts relationships between biomolecules, bioprocesses, and chemicals into statements (e.g., "benzene activates DNA damage"). These statements were subsequently assembled into networks and compared with the KCC evaluation by the IARC, to evaluate the informativeness of our approach.

RESULTS

We found, in general, larger networks for those chemicals which the IARC has evaluated the evidence to be strong for KCC induction. Larger networks were not directly linked to publication count, given that we retrieved small networks for several chemicals with little support for KCC activation according to the IARC, despite the significant volume of literature for these specific chemicals. In addition, interpreting networks for genotoxicity and DNA repair showed concordance with the IARC KCC evaluation.

DISCUSSION

Our method is an automated approach to condense mechanistic literature into searchable and interpretable networks based on an a priori ontology. The approach is no replacement of expert evaluation but, instead, provides an informed structure for experts to quickly identify which statements are made in which papers and how these could connect. We focused on the KCCs because these are supported by well-described search terms. The method needs to be tested in other frameworks as well to demonstrate its generalizability. https://doi.org/10.1289/EHP9112.

An integrative model of cancer cell differentiation with immunotherapy. Phys Biol 2021;18. [PMID: 34633296 DOI: 10.1088/1478-3975/ac2e72] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

In order to improve cancer treatments, cancer cell differentiation and immunotherapy are the subjects of several studies in different branches of interdisciplinary sciences. In this work, we develop a new population model that integrates other complementary ones, thus emphasizing the relationship between cancer cells at different differentiation stages and the main immune system cells. For this new system, specific ranges were found where transdifferentiation of differentiated cancer cells can occur. In addition, a specific therapy against cancer stem cells was analysed by simulating cytotoxic cell vaccines. In reference to the latter, the different combinations of parameters that optimize it were studied.