Pan-Cancer Interrogation of MUTYH Variants Reveals Biallelic Inactivation and Defective Base Excision Repair Across a Spectrum of Solid Tumors.

PURPOSE: Biallelic germline pathogenic variants of the base excision repair (BER) pathway gene MUTYH predispose to colorectal cancer (CRC) and other cancers. The possible association of heterozygous variants with broader cancer susceptibility remains uncertain. This study investigated the prevalence and consequences of pathogenic MUTYH variants and MUTYH loss of heterozygosity (LOH) in a large pan-cancer analysis. MATERIALS AND METHODS: Data from 354,366 solid tumor biopsies that were sequenced as part of routine clinical care were analyzed using a validated algorithm to distinguish germline from somatic MUTYH variants. RESULTS: Biallelic germline pathogenic MUTYH variants were identified in 119 tissue biopsies. Most were CRCs and showed increased tumor mutational burden (TMB) and a mutational signature consistent with defective BER (COSMIC Signature SBS18). Germline heterozygous pathogenic variants were identified in 5,991 biopsies and their prevalence was modestly elevated in some cancer types. About 12% of these cancers (738 samples: including adrenal gland cancers, pancreatic islet cell tumors, nonglioma CNS tumors, GI stromal tumors, and thyroid cancers) showed somatic LOH for MUTYH, higher rates of chromosome 1p loss (where MUTYH is located), elevated genomic LOH, and higher COSMIC SBS18 signature scores, consistent with BER deficiency. CONCLUSION: This analysis of MUTYH alterations in a large set of solid cancers suggests that in addition to the established role of biallelic pathogenic MUTYH variants in cancer predisposition, a broader range of cancers may possibly arise in MUTYH heterozygotes via a mechanism involving somatic LOH at the MUTYH locus and defective BER. However, the effect is modest and requires confirmation in additional studies before being clinically actionable.

The probable future of toxicology - probabilistic risk assessment.

Both because of the shortcomings of existing risk assessment methodologies, as well as newly available tools to predict hazard and risk with machine learning approaches, there has been an emerging emphasis on probabilistic risk assessment. Increasingly sophisticated AI models can be applied to a plethora of exposure and hazard data to obtain not only predictions for particular endpoints but also to estimate the uncertainty of the risk assessment outcome. This provides the basis for a shift from deterministic to more probabilistic approaches but comes at the cost of an increased complexity of the process as it requires more resources and human expertise. There are still challenges to overcome before a probabilistic paradigm is fully embraced by regulators. Based on an earlier white paper (Maertens et al., 2022), a workshop discussed the prospects, challenges and path forward for implementing such AI-based probabilistic hazard assessment. Moving forward, we will see the transition from categorized into probabilistic and dose-dependent hazard outcomes, the application of internal thresholds of toxicological concern for data-poor substances, the acknowledgement of user-friendly open-source software, a rise in the expertise of toxicologists required to understand and interpret artificial intelligence models, and the honest communication of uncertainty in risk assessment to the public.

Probabilistic risk assessment, initially from engineering, is applied in toxicology to understand chemical-related hazards and their consequences. In toxicology, uncertainties abound ­ unclear molecular events, varied proposed outcomes, and population-level assessments for issues like neurodevelopmental disorders. Establishing links between chemical exposures and diseases, especially rare events like birth defects, often demands extensive studies. Existing methods struggle with subtle effects or those affecting specific groups. Future risk assessments must address developmental disease origins, presenting challenges beyond current capabilities. The intricate nature of many toxicological processes, lack of consensus on mechanisms and outcomes, and the need for nuanced population-level assessments highlight the complexities in understanding and quantifying risks associated with chemical exposures in the field of toxicology.

Alternative methods go green! Green toxicology as a sustainable approach for assessing chemical safety and designing safer chemicals

Green toxicology is marching chemistry into the 21st century. This emerging framework will transform how chemical safety is evaluated by incorporating evaluation of the hazards, exposures, and risks associated with chemicals into early product development in a way that minimizes adverse impacts on human and environmental health. The goal is to minimize toxic threats across entire supply chains through smarter designs and policies. Traditional animal testing methods are replaced by faster, cutting-edge innovations like organs-on-chips and artificial intelligence predictive models that are also more cost-effective. Core principles of green toxicology include utilizing alternative test methods, applying the precautionary principle, considering lifetime impacts, and emphasizing risk prevention over reaction. This paper provides an overview of these foundational concepts and describes current initiatives and future opportunities to advance the adoption of green toxicology approaches. Chal-lenges and limitations are also discussed. Green shoots are emerging with governments offering carrots like the European Green Deal to nudge industry. Noteworthy, animal rights and environ-mental groups have different ideas about the needs for testing and their consequences for animal use. Green toxicology represents the way forward to support both these societal needs with sufficient throughput and human relevance for hazard information and minimal animal suffering. Green toxi-cology thus sets the stage to synergize human health and ecological values. Overall, the integration of green chemistry and toxicology has potential to profoundly shift how chemical risks are evaluated and managed to achieve safety goals in a more ethical, ecologically-conscious manner.

Green toxicology aims to make chemicals safer by design. It focuses on preventing toxicity issues early during development instead of testing after products are developed. Green toxicology uses modern non-animal methods like computer models and lab tests with human cells to predict if chem­icals could be hazardous. Benefits are faster results, lower costs, and less animal testing. The principles of green toxicology include using alternative tests, applying caution even with uncertain data, con­sidering lifetime impacts across global supply chains, and emphasizing prevention over reaction. The article highlights European and US policy efforts to spur sustainable chemistry innovation which will necessitate greener approaches to assess new materials and drive adoption. Overall, green toxi­cology seeks to integrate safer design concepts so that human and environmental health are valued equally with functionality and profit. This alignment promises safer, ethical products but faces chal­lenges around validating new methods and overcoming institutional resistance to change.

Using in silico tools to predict flame retardant metabolites for more informative exposomics-based approaches.

Introduction: The positive identification of xenobiotics and their metabolites in human biosamples is an integral aspect of exposomics research, yet challenges in compound annotation and identification continue to limit the feasibility of comprehensive identification of total chemical exposure. Nonetheless, the adoption of in silico tools such as metabolite prediction software, QSAR-ready structural conversion workflows, and molecular standards databases can aid in identifying novel compounds in untargeted mass spectral investigations, permitting the assessment of a more expansive pool of compounds for human health hazard. This strategy is particularly applicable when it comes to flame retardant chemicals. The population is ubiquitously exposed to flame retardants, and evidence implicates some of these compounds as developmental neurotoxicants, endocrine disruptors, reproductive toxicants, immunotoxicants, and carcinogens. However, many flame retardants are poorly characterized, have not been linked to a definitive mode of toxic action, and are known to share metabolic breakdown products which may themselves harbor toxicity. As U.S. regulatory bodies begin to pursue a subclass- based risk assessment of organohalogen flame retardants, little consideration has been paid to the role of potentially toxic metabolites, or to expanding the identification of parent flame retardants and their metabolic breakdown products in human biosamples to better inform the human health hazards imposed by these compounds. Methods: The purpose of this study is to utilize publicly available in silico tools to 1) characterize the structural and metabolic fates of proposed flame retardant classes, 2) predict first pass metabolites, 3) ascertain whether metabolic products segregate among parent flame retardant classification patterns, and 4) assess the existing coverage in of these compounds in mass spectral database. Results: We found that flame retardant classes as currently defined by the National Academies of Science, Engineering and Medicine (NASEM) are structurally diverse, with highly variable predicted pharmacokinetic properties and metabolic fates among member compounds. The vast majority of flame retardants (96%) and their predicted metabolites (99%) are not present in spectral databases, posing a challenge for identifying these compounds in human biosamples. However, we also demonstrate the utility of publicly available in silico methods in generating a fit for purpose synthetic spectral library for flame retardants and their metabolites that have yet to be identified in human biosamples. Discussion: In conclusion, exposomics studies making use of fit-for-purpose synthetic spectral databases will better resolve internal exposure and windows of vulnerability associated with complex exposures to flame retardant chemicals and perturbed neurodevelopmental, reproductive, and other associated apical human health impacts.

Transcriptomic Effects of Low-Dose Inorganic Arsenic Exposure on Murine Bone Marrow-Derived Macrophages.

Both tissue-resident macrophages and monocytes recruited from the bone marrow that transform into tissue-resident cells play critical roles in mediating homeostasis as well as in the pathology of inflammatory diseases. Inorganic arsenic (iAs) is the most common drinking water contaminant worldwide and represents a major public health concern. Several diseases that macrophages have implicated involvement in are caused by iAs exposure, including cardiovascular disease, cancer, and increased risk of infectious disease. Therefore, understanding the effects of iAs exposure on macrophages can help us better grasp the full range of arsenic immunotoxicity and better design therapeutic targets for iAs-induced diseases particularly in exposed populations. In this study, we analyzed the transcriptome of low dose iAs-exposed male and female murine bone marrow-derived macrophages (BMDMs) with either M0, M1, or M2 stimulation. We identified differentially expressed genes by iAs in a sex- and stimulation-dependent manner and used bioinformatics tools to predict protein-protein interactions, transcriptional regulatory networks, and associated biological processes. Overall, our data suggest that M1-stimulated, especially female-derived, BMDMs are most susceptible to iAs exposure. Most notably, we observed significant downregulation of major proinflammatory transcription factors, like IRF8, and its downstream targets, as well as genes encoding proteins involved in pattern recognition and antigen presentation, such as TLR7, TLR8, and H2-D1, potentially providing causal insight regarding arsenic's role in perturbing immune responses to infectious diseases. We also observed significant downregulation of genes involved in processes crucial to coordinating a proinflammatory response including leukocyte migration, differentiation, and cytokine and chemokine production and response. Finally, we discovered that 24 X-linked genes were dysregulated in iAs-exposed female stimulation groups compared to only 3 across the iAs-exposed male stimulation groups. These findings elucidate the potential mechanisms underlying the sex-differential iAs-associated immune-related disease risk.

The Good, The Bad, and The Perplexing: Structural Alerts and Read-Across for Predicting Skin Sensitization Using Human Data.

In our earlier work (Golden et al., 2021), we showed 70-80% accuracies for several skin sensitization computational tools using human data. Here, we expanded the data set using the NICEATM human skin sensitization database to create a final data set of 1355 discrete chemicals (largely negative, ∼70%). Using this expanded data set, we analyzed model performance and evaluated mispredictions using Toxtree (v 3.1.0), OECD QSAR Toolbox (v 4.5), VEGA's (1.2.0 BETA) CAESAR (v 2.1.7), and a k-nearest-neighbor (kNN) classification approach. We show that the accuracy on this data set was lower than previous estimates, with balanced accuracies being 63% and 65% for Toxtree and OECD QSAR Toolbox, respectively, 46% for VEGA, and 59% for a kNN approach, with the lower accuracy likely due to the higher percentage of nonsensitizing chemicals. Two hundred eighty seven chemicals were mispredicted by both Toxtree and OECD QSAR Toolbox, which was approximately 20% of the entire data set, and 84% of these were false positives. The absence or presence of metabolic simulation in OECD QSAR Toolbox made no overall difference. While Toxtree is known for overpredicting, 60% of the chemicals in the data set had no alert for skin sensitization, and a substantial number of these chemicals were in fact sensitizers, pointing to sensitization mechanisms not recognized by Toxtree. Interestingly, we observed that chemicals with more than one Toxtree alert were more likely to be nonsensitizers. Finally, a kNN approach tended to mispredict different chemicals than either OECD QSAR Toolbox or Toxtree, suggesting that there was additional information to be garnered from a kNN approach. Overall, the results demonstrate that while there is merit in structural alerts as well as QSAR or read-across approaches (perhaps even more so in their combination), additional improvement will require a more nuanced understanding of mechanisms of skin sensitization.

First Organoid Intelligence (OI) workshop to form an OI community.

The brain is arguably the most powerful computation system known. It is extremely efficient in processing large amounts of information and can discern signals from noise, adapt, and filter faulty information all while running on only 20 watts of power. The human brain's processing efficiency, progressive learning, and plasticity are unmatched by any computer system. Recent advances in stem cell technology have elevated the field of cell culture to higher levels of complexity, such as the development of three-dimensional (3D) brain organoids that recapitulate human brain functionality better than traditional monolayer cell systems. Organoid Intelligence (OI) aims to harness the innate biological capabilities of brain organoids for biocomputing and synthetic intelligence by interfacing them with computer technology. With the latest strides in stem cell technology, bioengineering, and machine learning, we can explore the ability of brain organoids to compute, and store given information (input), execute a task (output), and study how this affects the structural and functional connections in the organoids themselves. Furthermore, understanding how learning generates and changes patterns of connectivity in organoids can shed light on the early stages of cognition in the human brain. Investigating and understanding these concepts is an enormous, multidisciplinary endeavor that necessitates the engagement of both the scientific community and the public. Thus, on Feb 22-24 of 2022, the Johns Hopkins University held the first Organoid Intelligence Workshop to form an OI Community and to lay out the groundwork for the establishment of OI as a new scientific discipline. The potential of OI to revolutionize computing, neurological research, and drug development was discussed, along with a vision and roadmap for its development over the coming decade.

Probabilistic risk assessment - the keystone for the future of toxicology.

Safety sciences must cope with uncertainty of models and results as well as information gaps. Acknowledging this uncer-tainty necessitates embracing probabilities and accepting the remaining risk. Every toxicological tool delivers only probable results. Traditionally, this is taken into account by using uncertainty / assessment factors and worst-case / precautionary approaches and thresholds. Probabilistic methods and Bayesian approaches seek to characterize these uncertainties and promise to support better risk assessment and, thereby, improve risk management decisions. Actual assessments of uncertainty can be more realistic than worst-case scenarios and may allow less conservative safety margins. Most importantly, as soon as we agree on uncertainty, this defines room for improvement and allows a transition from traditional to new approach methods as an engineering exercise. The objective nature of these mathematical tools allows to assign each methodology its fair place in evidence integration, whether in the context of risk assessment, sys-tematic reviews, or in the definition of an integrated testing strategy (ITS) / defined approach (DA) / integrated approach to testing and assessment (IATA). This article gives an overview of methods for probabilistic risk assessment and their application for exposure assessment, physiologically-based kinetic modelling, probability of hazard assessment (based on quantitative and read-across based structure-activity relationships, and mechanistic alerts from in vitro studies), indi-vidual susceptibility assessment, and evidence integration. Additional aspects are opportunities for uncertainty analysis of adverse outcome pathways and their relation to thresholds of toxicological concern. In conclusion, probabilistic risk assessment will be key for constructing a new toxicology paradigm - probably!

Similarities and Differences in Gene Expression Networks Between the Breast Cancer Cell Line Michigan Cancer Foundation-7 and Invasive Human Breast Cancer Tissues.

Failure to adequately characterize cell lines, and understand the differences between in vitro and in vivo biology, can have serious consequences on the translatability of in vitro scientific studies to human clinical trials. This project focuses on the Michigan Cancer Foundation-7 (MCF-7) cells, a human breast adenocarcinoma cell line that is commonly used for in vitro cancer research, with over 42,000 publications in PubMed. In this study, we explore the key similarities and differences in gene expression networks of MCF-7 cell lines compared to human breast cancer tissues. We used two MCF-7 data sets, one data set collected by ARCHS4 including 1032 samples and one data set from Gene Expression Omnibus GSE50705 with 88 estradiol-treated MCF-7 samples. The human breast invasive ductal carcinoma (BRCA) data set came from The Cancer Genome Atlas, including 1212 breast tissue samples. Weighted Gene Correlation Network Analysis (WGCNA) and functional annotations of the data showed that MCF-7 cells and human breast tissues have only minimal similarity in biological processes, although some fundamental functions, such as cell cycle, are conserved. Scaled connectivity-a network topology metric-also showed drastic differences in the behavior of genes between MCF-7 and BRCA data sets. Finally, we used canSAR to compute ligand-based druggability scores of genes in the data sets, and our results suggested that using MCF-7 to study breast cancer may lead to missing important gene targets. Our comparison of the networks of MCF-7 and human breast cancer highlights the nuances of using MCF-7 to study human breast cancer and can contribute to better experimental design and result interpretation of study involving this cell line.

Complex abdominal wall reconstruction after oncologic resection in a sequalae of giant omphalocele: A case report.

INTRODUCTION: Trauma injuries and oncologic resection are common aetiologies of complex abdominal wall defect. Reconstruction of abdominal wall is an everlasting question for general, paediatric and reconstructive surgeons. The plethora of techniques, bioprosthetic and engineered tissues offer countless possibilities. PRESENTATION OF CASE: The patient was a 28 years old woman, with past history of untreated giant liver omphalocele, admitted for a suspicious hepatic tumefaction without specific clinical signs. The thoraco abdominopelvic CT scan revealed lung metastasis and a bilobed left hepatic tumour. Pre-operative cytologic findings of mild differentiated hepatocellular carcinoma differed from the post-operative findings of hepatoblastoma. The full-thickness abdominal wall defect after a radical resection was reconstructed with a combined acellular dermal matrix, NPWT and skin graft solution. A total epithelization was obtained after 8 weeks follow-up. DISCUSSION: Hepatoblastoma in adult is rare, with no consensus. A radical resection in context of giant untreated omphalocele is an unusual challenge for the surgical team. The pre-operative evaluation, the defect classification and the general conditions of the patient are paramount steps for an appropriate reconstruction. Primary or delayed reconstruction with myocutaneous flap as gold standard, depends on the oncologic management and anticipated post-operative complications. Acellular dermal matrix used for a bridged fascial repair directly on viscera and covered by NPWT, favourited a healthy granulation tissue. The full-thickness defect was then reconstructed with an ADM, NPWT and skin graft instead of an association with the myocutaneous flap. The patient follow-up was emphasized in the hepatoblastoma, but the complications of this reconstruction strategy are unknown. A total epithelization was obtained, the abdominal bulge or hernia is the first complication under surveillance. CONCLUSION: Delayed reconstruction after an oncologic large abdominal wall resection has the advantage to manage post-operative complications and prepare alternative solutions. Acellular dermal matrix was not first designed for skin tissue regeneration, some authors as us experimented the conclusion that this matrix could be used for permanent abdominal wall reconstruction.

Avoiding Regrettable Substitutions: Green Toxicology for Sustainable Chemistry.

Green chemistry seeks to design less hazardous chemicals, but many of the efforts to replace chemicals have resulted in so-called "Regrettable Substitutions", when a chemical with an unknown or unforeseen hazard is used to replace a chemical identified as problematic. Here, we discuss the literature on regrettable substitution and focus on an oft-mentioned case, Bisphenol A, which was replaced with Bisphenol S-and the lessons that can be learned from this history. In particular, we focus on how Green Toxicology can offer a way to make better substitutions.

Organophosphorus flame retardants are developmental neurotoxicants in a rat primary brainsphere in vitro model.

Due to regulatory bans and voluntary substitutions, halogenated polybrominated diphenyl ether (PBDE) flame retardants (FR) are increasingly substituted by mainly organophosphorus FR (OPFR). Leveraging a 3D rat primary neural organotypic in vitro model (rat brainsphere), we compare developmental neurotoxic effects of BDE-47-the most abundant PBDE congener-with four OPFR (isopropylated phenyl phosphate-IPP, triphenyl phosphate-TPHP, isodecyl diphenyl phosphate-IDDP, and tricresyl phosphate (also known as trimethyl phenyl phosphate)-TMPP). Employing mass spectroscopy-based metabolomics and transcriptomics, we observe at similar human-relevant non-cytotoxic concentrations (0.1-5 µM) stronger developmental neurotoxic effects by OPFR. This includes toxicity to neurons in the low µM range; all FR decrease the neurotransmitters glutamate and GABA (except BDE-47 and TPHP). Furthermore, n-acetyl aspartate (NAA), considered a neurologic diagnostic molecule, was decreased by all OPFR. At similar concentrations, the FR currently in use decreased plasma membrane dopamine active transporter expression, while BDE-47 did not. Several findings suggest astrogliosis induced by the OPFR, but not BDE-47. At the 5 µM concentrations, the OPFR more than BDE-47 interfered with myelination. An increase of cytokine gene and receptor expressions suggests that exposure to OPFR may induce an inflammatory response. Pathway/category overrepresentation shows disruption in 1) transmission of action potentials, cell-cell signaling, synaptic transmission, receptor signaling, (2) immune response, inflammation, defense response, (3) cell cycle and (4) lipids metabolism and transportation. Taken together, this appears to be a case of regretful substitution with substances not less developmentally neurotoxic in a primary rat 3D model.

Evaluation of the global performance of eight in silico skin sensitization models using human data.

Allergic contact dermatitis, or the clinical manifestation of skin sensitization, is a leading occupational hazard. Several testing approaches exist to assess skin sensitization, but in silico models are perhaps the most advantageous due to their high speed and low-cost results. Many in silico skin sensitization models exist, though many have only been tested against results from animal studies (e.g., LLNA); this creates uncertainty in human skin sensitization assessments in both a screening and regulatory context. This project's aim was to evaluate the accuracy of eight in silico skin sensitization models against two human data sets: one highly curated (Basketter et al., 2014) and one screening level (HSDB). The binary skin sen­sitization status of each chemical in each of the two data sets was compared to the prediction from eight in silico skin sensitization tools (Toxtree, PredSkin, OECD's QSAR Toolbox, UL's REACHAcross™, Danish QSAR Database, TIMES-SS, and Lhasa Limited's Derek Nexus). Models were assessed for coverage, accuracy, sensitivity, and specificity, as well as optimization features (e.g., probability of accuracy, applicability domain, etc.), if available. While there was a wide range of sensitivity and specificity, the models generally performed comparably to the LLNA in predicting human skin sensitization status (i.e., approximately 70-80% accuracy). Additionally, the models did not mispredict the same com­pounds, suggesting there might be an advantage in combining models. In silico skin sensitization models offer accurate and useful insights in a screening context; however, further improvements are necessary so these models may be con­sidered fully reliable for regulatory applications.

Mapping Chemical Respiratory Sensitization: How Useful Are Our Current Computational Tools?

Chemical respiratory sensitization is an immunological process that manifests clinically mostly as occupational asthma and is responsible for 1 in 6 cases of adult asthma, although this may be an underestimate of the prevalence, as it is under-diagnosed. Occupational asthma results in unemployment for roughly one-third of those affected due to severe health issues. Despite its high prevalence, chemical respiratory sensitization is difficult to predict, as there are currently no validated models and the mechanisms are not entirely understood, creating a significant challenge for regulatory bodies and industry alike. The Adverse Outcome Pathway (AOP) for respiratory sensitization is currently incomplete. However, some key events have been identified, and there is overlap with the comparatively well-characterized AOP for dermal sensitization. Because of this, and the fact that dermal sensitization is often assessed by in vivo, in chemico, or in silico methods, regulatory bodies are defaulting to the dermal sensitization status of chemicals as a proxy for respiratory sensitization status when evaluating chemical safety. We identified a data set of known human respiratory sensitizers, which we used to investigate the accuracy of a structural alert model, Toxtree, designed for skin sensitization and the Centre for Occupational and Environmental Health (COEH)'s model, a model developed specifically for occupational asthma. While both models had a reasonable level of accuracy, the COEH model achieved the highest balanced accuracy at 76%; when the models agreed, the overall accuracy was 87%. There were important differences between the models: Toxtree had superior performance for some structural alerts and some categories of well-characterized skin sensitizers, while the COEH model had high accuracy in identifying sensitizers that lacked identified skin sensitization reactivity domains. Overall, both models achieved respectable accuracy. However, neither model addresses potency, which, along with data quality, remains a hurdle, and the field must prioritize these issues to move forward.

Posttraumatic pseudoaneurysm of a superficial branch of the ulnar artery: A case report.

INTRODUCTION: Hand and Upper limb pseudoaneurysms are uncommon and misdiagnosed. The delayed diagnostic and treatment lead to severe vascular and nerve complications. Many mechanisms are involved like acute injury, chronic micro traumatism of the hand, with specific clinic and para clinic signs. PRESENTATION OF CASE: The patient was a 30-years old woman, right-handed, admitted at the emergencies for a penetrating hand injury at the junction of the middle third - distal third of the left forearm, palmar surface, with a good radial and ulnar pulse, without sensory or motor deficit. No vessels injuries observed per operatively. A post-operative worsened pain opposite to the scar with a purplish pulsatile swelling appeared after 20 days of complete wound healing. A needle puncture with a red blood contain, motivated an US Doppler revealing a pseudoaneurysm of a superficial artery of the ulnar artery, surgically resected, without complication. DISCUSSION: Two main mechanisms are involved in upper limb especially hand pseudoaneurysm: penetrating trauma and repeated micro traumatism. Superficial vessels are rarely damaged compare to deep subfascial vessels according to Laplace law. The diagnostic is clinical confirmed with the medical imaging. A pseudoaneurysm is suspected in front of a pulsatile painful tumefaction following a vessel path, with medical imaging in favor. A delayed misdiagnosis lead to a delayed care with severe complications as thrombosis, embolism and vessel. The therapeutic care is mainly surgical. CONCLUSION: This case reports a delayed diagnostic of posttraumatic pseudoaneurysm of a superficial branch of the ulnar artery, managed with a surgical resection.

Author Correction: Functionally Enigmatic Genes in Cancer: Using TCGA Data to Map the Limitations of Annotations.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Internationalization of read-across as a validated new approach method (NAM) for regulatory toxicology.

Read-across (RAx) translates available information from well-characterized chemicals to a substance for which there is a toxicological data gap. The OECD is working on case studies to probe general applicability of RAx, and several regulations (e.g., EU-REACH) already allow this procedure to be used to waive new in vivo tests. The decision to prepare a review on the state of the art of RAx as a tool for risk assessment for regulatory purposes was taken during a workshop with international experts in Ranco, Italy in July 2018. Three major issues were identified that need optimization to allow a higher regulatory acceptance rate of the RAx procedure: (i) the definition of similarity of source and target, (ii) the translation of biological/toxicological activity of source to target in the RAx procedure, and (iii) how to deal with issues of ADME that may differ between source and target. The use of new approach methodologies (NAM) was discussed as one of the most important innovations to improve the acceptability of RAx. At present, NAM data may be used to confirm chemical and toxicological similarity. In the future, the use of NAM may be broadened to fully characterize the hazard and toxicokinetic properties of RAx compounds. Concerning available guidance, documents on Good Read-Across Practice (GRAP) and on best practices to perform and evaluate the RAx process were identified. Here, in particular, the RAx guidance, being worked out by the European Commission's H2020 project EU-ToxRisk together with many external partners with regulatory experience, is given.

Functionally Enigmatic Genes in Cancer: Using TCGA Data to Map the Limitations of Annotations.

Cancer is a comparatively well-studied disease, yet despite decades of intense focus, we demonstrate here using data from The Cancer Genome Atlas that a substantial number of genes implicated in cancer are relatively poorly studied. Those genes will likely be missed by any data analysis pipeline, such as enrichment analysis, that depends exclusively on annotations for understanding biological function. There is no indication that the amount of research - indicated by number of publications - is correlated with any objective metric of gene significance. Moreover, these genes are not missing at random but reflect that our information about genes is gathered in a biased manner: poorly studied genes are more likely to be primate-specific and less likely to have a Mendelian inheritance pattern, and they tend to cluster in some biological processes and not others. While this likely reflects both technological limitations as well as the fact that well-known genes tend to gather more interest from the research community, in the absence of a concerted effort to study genes in an unbiased way, many genes (and biological processes) will remain opaque.

The exposome - a new approach for risk assessment.

Complementing the human genome with an exposome reflects the increasingly obvious impact of environmental exposure, which far exceeds the role of genetics, on human health. Considering the complexity of exposures and, in addition, the reactions of the body to exposures - i.e., the exposome - reverses classical exposure science where the precise measurement of single or few exposures is associated with specific health or environmental effects. The complete description of an individual's exposome is impossible; even less so is that of a population. We can, however, cast a wider net by foregoing some rigor in assessment and compensating with the statistical power of rich datasets. The advent of omics technologies enables a relatively cheap, high-content description of the biological effects of substances, especially in tissues and biofluids. They can be combined with many other rich data-streams, creating big data of exposure and effect. Computational methods increasingly allow data integration, discerning the signal from the noise and formulating hypotheses of exposure-effect relationships. These can be followed up in a targeted way. With a better exposure element in the risk equation, exposomics - new kid on the block of risk assessment - promises to identify novel exposure (interactions) and health/environment effect associations. This may also create opportunities to prioritize the more relevant chemicals for risk assessment, thereby lowering the burden on hazard assessment in an expo-sure-driven approach. Technological developments and synergies between approaches, quality assurance (ultimately as Good Exposome Practices), and the integration of mechanistic thinking will advance this approach.

Adaptation of the Systematic Review Framework to the Assessment of Toxicological Test Methods: Challenges and Lessons Learned With the Zebrafish Embryotoxicity Test.

Systematic review methodology is a means of addressing specific questions through structured, consistent, and transparent examinations of the relevant scientific evidence. This methodology has been used to advantage in clinical medicine, and is being adapted for use in other disciplines. Although some applications to toxicology have been explored, especially for hazard identification, the present preparatory study is, to our knowledge, the first attempt to adapt it to the assessment of toxicological test methods. As our test case, we chose the zebrafish embryotoxicity test (ZET) for developmental toxicity and its mammalian counterpart, the standard mammalian prenatal development toxicity study, focusing the review on how well the ZET predicts the presence or absence of chemical-induced prenatal developmental toxicity observed in mammalian studies. An interdisciplinary team prepared a systematic review protocol and adjusted it throughout this piloting phase, where needed. The final protocol was registered and will guide the main study (systematic review), which will execute the protocol to comprehensively answer the review question. The goal of this preparatory study was to translate systematic review methodology to the assessment of toxicological test method performance. Consequently, it focused on the methodological issues encountered, whereas the main study will report substantive findings. These relate to numerous systematic review steps, but primarily to searching and selecting the evidence. Applying the lessons learned to these challenges can improve not only our main study, but may also be helpful to others seeking to use systematic review methodology to compare toxicological test methods. We conclude with a series of recommendations that, if adopted, would help improve the quality of the published literature, and make conducting systematic reviews of toxicological studies faster and easier over time.