Environmental mixtures and breast cancer: identifying co-exposure patterns between understudied vs breast cancer-associated chemicals using chemical inventory informatics

The Chemical and Products Database v4.0, an updated resource supporting chemical exposure evaluations

Since the initial release of the Chemical and Products Database (CPDat) in 2018, the United States Environmental Protection Agency has added a considerable amount of chemical exposure-related information to the database and has expanded its schema to accommodate new types of data. This data descriptor provides information regarding the structure and types of data contained within CPDat (both existing and new), new controlled vocabularies implemented to harmonize terminology across the different data types, application of a rigorous data curation and quality assurance tracking system, and various methods of accessing CPDat.

Prompting large language models to extract chemical‒disease relation precisely and comprehensively at the document level: an evaluation study

Given the scarcity of annotated data, current deep learning methods face challenges in the field of document-level chemical-disease relation extraction, making it difficult to achieve precise relation extraction capable of identifying relation types and comprehensive extraction tasks that identify relation-related factors. This study tests the abilities of three large language models (LLMs), GPT3.5, GPT4.0, and Claude-opus, to perform precise and comprehensive extraction in document-level chemical-disease relation extraction on a self-constructed dataset. Firstly, based on the task characteristics, this study designs six workflows for precise extraction and five workflows for comprehensive extraction using prompting engineering strategies. The characteristics of the extraction process are analyzed through the performance differences under different workflows. Secondly, this study analyzes the content bias in LLMs extraction by examining the extraction effectiveness of different workflows on different types of content. Finally, this study analyzes the error characteristics of extracting incorrect examples by the LLMs. The experimental results show that: (1) The LLMs demonstrate good extraction capabilities, achieving the highest F1 scores of 87% and 73% respectively in the tasks of precise extraction and comprehensive extraction; (2) In the extraction process, the LLMs exhibit a certain degree of stubbornness, with limited effectiveness of prompting engineering strategies; (3) In terms of extraction content, the LLMs show a content bias, with stronger abilities to identify positive relations such as induction and acceleration; (4) The essence of extraction errors lies in the LLMs' misunderstanding of the implicit meanings in biomedical texts. This study provides practical workflows for precise and comprehensive extraction of document-level chemical-disease relations and also indicates that optimizing training data is the key to building more efficient and accurate extraction methods in the future.

Applying cell painting in non-tumorigenic breast cells to understand impacts of common chemical exposures

The general population is exposed to many chemicals which have putative, but incompletely understood, links to breast cancer. Cell Painting is a high-content imaging-based in vitro assay that allows for unbiased measurements of concentration-dependent effects of chemical exposures on cellular morphology. We used Cell Painting to measure effects of 16 human exposure relevant chemicals, along with 21 small molecules with known mechanisms of action, in non-tumorigenic mammary epithelial cells, the MCF10A cell line. Using CellProfiler image analysis software, we quantified 3042 morphological features across approximately 1.2 million cells. We used benchmark concentration modeling to identify features both conserved and different across chemicals. Benchmark concentrations were compared to exposure biomarker concentration measurements from the National Health and Nutrition Examination Survey to assess which chemicals induce morphological alterations at human-relevant concentrations. We found significant feature overlaps between chemicals, including similarities between the organochlorine pesticide DDT metabolite p,p'-DDE and an activator of Wnt signaling CHIR99201. We validated these findings by assaying the activation of Wnt, as reflected by translocation of ꞵ-catenin, following p'-p' DDE exposure. Consistent with Wnt signaling activation, low concentration p',p'-DDE (25 nM) significantly enhanced the nuclear translocation of ꞵ-catenin. Overall, these findings highlight the ability of Cell Painting to enhance mode-of-action studies for toxicants which are common in our environment but incompletely characterized with respect to breast cancer risk.

Combined Exposures and Mixtures Research: An Enduring NIEHS Priority

BACKGROUND

The National Institute of Environmental Health Sciences (NIEHS) continues to prioritize research to better understand the health effects resulting from exposure to mixtures of chemical and nonchemical stressors. Mixtures research activities over the last decade were informed by expert input during the development and deliberations of the 2011 NIEHS Workshop "Advancing Research on Mixtures: New Perspectives and Approaches for Predicting Adverse Human Health Effects." NIEHS mixtures research efforts since then have focused on key themes including a) prioritizing mixtures for study, b) translating mixtures data from in vitro and in vivo studies, c) developing cross-disciplinary collaborations, d) informing component-based and whole-mixture assessment approaches, e) developing sufficient similarity methods to compare across complex mixtures, f) using systems-based approaches to evaluate mixtures, and g) focusing on management and integration of mixtures-related data.

OBJECTIVES

We aimed to describe NIEHS driven research on mixtures and combined exposures over the last decade and present areas for future attention.

RESULTS

Intramural and extramural mixtures research projects have incorporated a diverse array of chemicals (e.g., polycyclic aromatic hydrocarbons, botanicals, personal care products, wildfire emissions) and nonchemical stressors (e.g., socioeconomic factors, social adversity) and have focused on many diseases (e.g., breast cancer, atherosclerosis, immune disruption). We have made significant progress in certain areas, such as developing statistical methods for evaluating multiple chemical associations in epidemiology and building translational mixtures projects that include both in vitro and in vivo models.

DISCUSSION

Moving forward, additional work is needed to improve mixtures data integration, elucidate interactions between chemical and nonchemical stressors, and resolve the geospatial and temporal nature of mixture exposures. Continued mixtures research will be critical to informing cumulative impact assessments and addressing complex challenges, such as environmental justice and climate change. https://doi.org/10.1289/EHP14340.

Applying Cell Painting in Non-Tumorigenic Breast Cells to Understand Impacts of Common Chemical Exposures

There are a substantial number of chemicals to which individuals in the general population are exposed which have putative, but still poorly understood, links to breast cancer. Cell Painting is a high-content imaging-based in vitro assay that allows for rapid and unbiased measurements of the concentration-dependent effects of chemical exposures on cellular morphology. We optimized the Cell Painting assay and measured the effect of exposure to 16 human exposure relevant chemicals, along with 21 small molecules with known mechanisms of action, for 48 hours in non-tumorigenic mammary epithelial cells, the MCF10A cell line. Through unbiased imaging analyses using CellProfiler, we quantified 3042 morphological features across approximately 1.2 million cells. We used benchmark concentration modeling to quantify significance and dose-dependent directionality to identify morphological features conserved across chemicals and find features that differentiate the effects of toxicants from one another. Benchmark concentrations were compared to chemical exposure biomarker concentration measurements from the National Health and Nutrition Examination Survey to assess which chemicals induce morphological alterations at human-relevant concentrations. Morphometric fingerprint analysis revealed similar phenotypes between small molecules and prioritized NHANES-toxicants guiding further investigation. A comparison of feature fingerprints via hypergeometric analysis revealed significant feature overlaps between chemicals when stratified by compartment and stain. One such example was the similarities between a metabolite of the organochlorine pesticide DDT (p,p'-DDE) and an activator of canonical Wnt signaling CHIR99201. As CHIR99201 is a known Wnt pathway activator and its role in β-catenin translocation is well studied, we studied the translocation of β-catenin following p'-p' DDE exposure in an orthogonal high-content imaging assay. Consistent with activation of Wnt signaling, low dose p',p'-DDE (25nM) significantly enhances the nuclear translocation of β-catenin. Overall, these findings highlight the ability of Cell Painting to enhance mode-of-action studies for toxicants which are common exposures in our environment but have previously been incompletely characterized with respect to breast cancer risk.

Environmental and dietary exposure to 24 polycyclic aromatic hydrocarbons in a typical Chinese coking plant

Polycyclic aromatic hydrocarbons (PAHs), known for their health risks, are prevalent in the environment, with the coking industry being a major source of their emissions. To bridge the knowledge gap concerning the relationship between environmental and dietary PAH exposure, we explore this complex interplay by investigating the dietary exposure characteristics of 24 PAHs within a typical Chinese coking plant and their association with environmental pollution. Our research revealed Nap and Fle as primary dietary contaminants, emphasizing the significant influence of soil and atmospheric pollution on PAH exposure. We subjected our data to non-metric multidimensional scaling (NMDS), Spearman correlation analysis, Lasso regression, and Weighted Quantile Sum (WQS) regression to delve into this multifaceted phenomenon. NMDS reveals that dietary PAH exposure, especially within the high molecular weight (HMW) group, is common both within and around the coking plant. This suggests that meals prepared within the plant may be contaminated, posing health risks to coking plant workers. Furthermore, our assessment of dietary exposure risk highlights Nap and Fle as the primary dietary contaminants, with BaP and DahA raising concerns due to their higher carcinogenic potential. Our findings indicate that dietary exposure often exceeds acceptable limits, particularly for coking plant workers. Correlation analyses uncover the dominant roles of soil and atmospheric pollution in shaping dietary PAH exposure. Soil contamination significantly impacts specific PAHs, while atmospheric pollution contributes to others. Additionally, WQS regression emphasizes the substantial influence of soil and drinking water on dietary PAHs. In summary, our study sheds light on the dietary exposure characteristics of PAHs in a typical Chinese coking plant and their intricate interplay with environmental factors. These findings underscore the need for comprehensive strategies to mitigate PAH exposure so as to safeguard both human health and the environment in affected regions.

Association of hormone receptors and human epidermal growth factor receptor-2/neu expressions with clinicopathologic factors of breast carcinoma: a cross-sectional study in a tertiary care hospital, Kabul, Afghanistan

BACKGROUND

Breast cancer (BC) is one of the major causes of death worldwide. It is the most common cause of death before the age of 70 years. The incidence and mortality of BC are rapidly increasing, posing great challenges to the health system and economy of every nation.

METHODOLOGY

A cross-sectional analytical study was conducted at the Department of Pathology and Clinical Laboratory of the French Medical Institute for Mothers and Children (FMIC) to demonstrate the association of human epidermal growth factor receptor 2 (Her2/Neu) and estrogen receptor (ER)/ progesterone receptor (PR) with clinical as well as pathological parameters among women with BC. A consecutive nonprobability sampling method was used for this study over a span of one and a half years.

RESULTS

One hundred twenty participants diagnosed with breast cancer were included in the study. The mean age at diagnosis was 44.58 ± 11.16 years. Out of the total patients, 68 (56.7%) were above 40 years old, 108 (90%) were married, 94 (78.3%) were multiparous, and 88 (73.3%) had a history of breastfeeding. 33.3% of cases were within the age range of menopause (40-50 years). The positive expression rates of ER, PR, and Her2/neu were found to be 48.8%, 44.6%, and 44.6%, respectively, and Her2/neu overexpression was found to be higher among ER/PR-negative cases.

CONCLUSION

In our study, we demonstrated that among Afghan women, grade II invasive ductal carcinoma, not otherwise specified, was the most common type of BC and frequently affected women above the age of 40. We also revealed that the percentage of negative ER (50.4%), negative PR (54.4%), and concordant ER/PR-negative cases were high compared to other possibilities. Additionally, the study revealed that expression of Her2/neu was in contrast with the expression of ER and PR receptors. The findings of our study still support the importance of performing immunohistochemical stains for hormonal receptor classification in terms of better clinical outcomes and prognosis.

Suspect Screening Analysis of Pooled Human Serum Samples Using GC × GC/TOF-MS

Humans interact with thousands of chemicals. This study aims to identify substances of emerging concern and in need of human health risk evaluations. Sixteen pooled human serum samples were constructed from 25 individual samples each from the National Institute of Environmental Health Sciences' Clinical Research Unit. Samples were analyzed using gas chromatography (GC) × GC/time-of-flight (TOF)-mass spectrometry (MS) in a suspect screening analysis, with follow-up confirmation analysis of 19 substances. A standard reference material blood sample was also analyzed through the confirmation process for comparison. The pools were stratified by sex (female and male) and by age (≤45 and >45). Publicly available information on potential exposure sources was aggregated to annotate presence in serum as either endogenous, food/nutrient, drug, commerce, or contaminant. Of the 544 unique substances tentatively identified by spectral matching, 472 were identified in females, while only 271 were identified in males. Surprisingly, 273 of the identified substances were found only in females. It is known that behavior and near-field environments can drive exposures, and this work demonstrates the existence of exposure sources uniquely relevant to females.

An integrated and renewable interface for capture, release and analysis of circulating tumor cells

Circulating tumor cells (CTCs) have now emerged as a type of promising circulating biomarkers in liquid biopsy and can predict the occurrence and development of cancers. In this work, an integrated and renewable interface is fabricated for the capture, release and quantitative analysis of CTCs. As designed, folate receptor-positive CTCs are captured by folic acid-modified DNA probes at the interface through the receptor-ligand interaction, and are efficiently released from the interface with the aid of bleomycin-ferrous complex-regulated cleavage. Taking MCF-7 cells as the model, the functional interface demonstrates high efficiency to selectively capture the folate receptor-positive tumor cells, and the bleomycin-ferrous complex-regulated cleavage not only easily releases the captured cells with well-maintained viability and proliferation ability, but also releases silver nanoparticles that are labeled at the cell surface for highly sensitive quantification by adopting electrochemical techniques with a detection limit of 6 cells/mL. At the meanwhile, the interface is proved to be regenerated through a simple cleavage-hybridization event and reused with high stability. Therefore, our work may provide a new idea for the collection and downstream researches of circulating tumor cells in the future.

Wrangling Whole Mixtures Risk Assessment: Recent Advances in Determining Sufficient Similarity

Human health risk assessments for complex mixtures can address real-world exposures and protect public health. While risk assessors typically prefer whole mixture approaches over component-based approaches, data from the precise exposure of interest are often unavailable and surrogate data from a sufficiently similar mixture(s) are required. This review describes recent advances in determining sufficient similarity of whole, complex mixtures spanning the comparison of chemical features, bioactivity profiles, and statistical evaluation to determine "thresholds of similarity". Case studies, including water disinfection byproducts, botanical ingredients, and wildfire emissions, are used to highlight tools and methods. Limitations to application of sufficient similarity in risk-based decision making are reviewed and recommendations presented for developing best practice guidelines.

Cutting-edge computational chemical exposure research at the U.S. Environmental Protection Agency

Exposure science is evolving from its traditional "after the fact" and "one chemical at a time" approach to forecasting chemical exposures rapidly enough to keep pace with the constantly expanding landscape of chemicals and exposures. In this article, we provide an overview of the approaches, accomplishments, and plans for advancing computational exposure science within the U.S. Environmental Protection Agency's Office of Research and Development (EPA/ORD). First, to characterize the universe of chemicals in commerce and the environment, a carefully curated, web-accessible chemical resource has been created. This DSSTox database unambiguously identifies >1.2 million unique substances reflecting potential environmental and human exposures and includes computationally accessible links to each compound's corresponding data resources. Next, EPA is developing, applying, and evaluating predictive exposure models. These models increasingly rely on data, computational tools like quantitative structure activity relationship (QSAR) models, and machine learning/artificial intelligence to provide timely and efficient prediction of chemical exposure (and associated uncertainty) for thousands of chemicals at a time. Integral to this modeling effort, EPA is developing data resources across the exposure continuum that includes application of high-resolution mass spectrometry (HRMS) non-targeted analysis (NTA) methods providing measurement capability at scale with the number of chemicals in commerce. These research efforts are integrated and well-tailored to support population exposure assessment to prioritize chemicals for exposure as a critical input to risk management. In addition, the exposure forecasts will allow a wide variety of stakeholders to explore sustainable initiatives like green chemistry to achieve economic, social, and environmental prosperity and protection of future generations.

Navigating the bridge between wet and dry lab toxicology research to address current challenges with high-dimensional data

Toxicology research has rapidly evolved, leveraging increasingly advanced technologies in high-throughput approaches to yield important information on toxicological mechanisms and health outcomes. Data produced through toxicology studies are consequently becoming larger, often producing high-dimensional data. These types of data hold promise for imparting new knowledge, yet inherently have complexities causing them to be a rate-limiting element for researchers, particularly those that are housed in "wet lab" settings (i.e., researchers that use liquids to analyze various chemicals and biomarkers as opposed to more computationally focused, "dry lab" researchers). These types of challenges represent topics of ongoing conversation amongst our team and researchers in the field. The aim of this perspective is to i) summarize hurdles in analyzing high-dimensional data in toxicology that require improved training and translation for wet lab researchers, ii) highlight example methods that have aided in translating data analysis techniques to wet lab researchers; and iii) describe challenges that remain to be effectively addressed, to date, in toxicology research. Specific aspects include methodologies that could be introduced to wet lab researchers, including data pre-processing, machine learning, and data reduction. Current challenges discussed include model interpretability, study biases, and data analysis training. Example efforts implemented to translate these data analysis techniques are also mentioned, including online data analysis resources and hands-on workshops. Questions are also posed to continue conversation in the toxicology community. Contents of this perspective represent timely issues broadly occurring in the fields of bioinformatics and toxicology that require ongoing dialogue between wet and dry lab researchers.

Wildfire Variable Toxicity: Identifying Biomass Smoke Exposure Groupings through Transcriptomic Similarity Scoring

The prevalence of wildfires continues to grow globally with exposures resulting in increased disease risk. Characterizing these health risks remains difficult due to the wide landscape of exposures that can result from different burn conditions and fuel types. This study tested the hypothesis that biomass smoke exposures from variable fuels and combustion conditions group together based on similar transcriptional response profiles, informing which wildfire-relevant exposures may be considered as a group for health risk evaluations. Mice (female CD-1) were exposed via oropharyngeal aspiration to equal mass biomass smoke condensates produced from flaming or smoldering burns of eucalyptus, peat, pine, pine needles, or red oak species. Lung transcriptomic signatures were used to calculate transcriptomic similarity scores across exposures, which informed exposure groupings. Exposures from flaming peat, flaming eucalyptus, and smoldering eucalyptus induced the greatest responses, with flaming peat grouping with the pro-inflammatory agent lipopolysaccharide. Smoldering red oak and smoldering peat induced the least transcriptomic response. Groupings paralleled pulmonary toxicity markers, though they were better substantiated by higher data dimensionality and resolution provided through -omic-based evaluation. Interestingly, groupings based on smoke chemistry signatures differed from transcriptomic/toxicity-based groupings. Wildfire-relevant exposure groupings yield insights into risk assessment strategies to ultimately protect public health.

Exposure forecasting - ExpoCast - for data-poor chemicals in commerce and the environment

Estimates of exposure are critical to prioritize and assess chemicals based on risk posed to public health and the environment. The U.S. Environmental Protection Agency (EPA) is responsible for regulating thousands of chemicals in commerce and the environment for which exposure data are limited. Since 2009 the EPA's ExpoCast ("Exposure Forecasting") project has sought to develop the data, tools, and evaluation approaches required to generate rapid and scientifically defensible exposure predictions for the full universe of existing and proposed commercial chemicals. This review article aims to summarize issues in exposure science that have been addressed through initiatives affiliated with ExpoCast. ExpoCast research has generally focused on chemical exposure as a statistical systems problem intended to inform thousands of chemicals. The project exists as a companion to EPA's ToxCast ("Toxicity Forecasting") project which has used in vitro high-throughput screening technologies to characterize potential hazard posed by thousands of chemicals for which there are limited toxicity data. Rapid prediction of chemical exposures and in vitro-in vivo extrapolation (IVIVE) of ToxCast data allow for prioritization based upon risk of adverse outcomes due to environmental chemical exposure. ExpoCast has developed (1) integrated modeling approaches to reliably predict exposure and IVIVE dose, (2) highly efficient screening tools for chemical prioritization, (3) efficient and affordable tools for generating new exposure and dose data, and (4) easily accessible exposure databases. The development of new exposure models and databases along with the application of technologies like non-targeted analysis and machine learning have transformed exposure science for data-poor chemicals. By developing high-throughput tools for chemical exposure analytics and translating those tools into public health decisions ExpoCast research has served as a crucible for identifying and addressing exposure science knowledge gaps.