dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2015). Yet, regulators continue to take a cautious stand on this matter. Currently, 'omics data are mostly seen as supplementary information, for example for MoA analysis or to substantiate the application of read-across techniques. Main obstacles to the implementation of 'omics-based methods are uncertainties in how to use and validate such data, as well as their predictivity and performance for hazard and risk assessment when compared to established test methods. Moreover, in absence of clear causal relationships, predictability can be an issue. Without changes in long-established practices, it will be difficult to readily adapt 'omics data for the purpose of quantifiable risk assessment or to transpose test results to the classification and labelling (C&L) of substances, as implemented in the United Nations Globally Harmonised System of Classification and Labelling of Chemicals (GHS; United Nations, 2015) and Regulation (EC) No 1272/2008 on the classification, labelling and packaging of substances and mixtures (CLP; EP and Council of the EU, 2008).
From a current regulatory perspective, the biggest promise of 'omics data is use in ITSs, to identify biomarkers of adverse effects and to prioritise testing. Key scientific, legal and regulatory issues thus have to address the following questions: Is it the aim to provide supplementary information, to replace traditional toxicological endpoints or to address unresolved toxicological issues? How can molecular measures be converted into quantitative (or probabilistic) measures of adversity that are suitable for risk assessment? Can relevant biomarkers be identified, i. e. specific genes that define the pathological point-ofno-return that will ultimately lead to an adverse effect? How shall 'omics data be analysed and validated? Do validation studies include the right standards? Are the currently used test systems sufficiently complex? Also discussed was the need for blinded pilot studies (in which information on the test substances was not disclosed to the experimenter until after the completion of the study) in order to obtain a better understanding of the performance of 'omics-based methodologies in routine practice.
Andrew Worth (European Commission, Joint Research Centre, European Reference Laboratory for Alternatives to Animal Testing (EURL ECVAM), Italy) added further perspectives on the regulatory use of 'omics technologies. The primary mission of the EURL ECVAM is to make available validated test methods that support the 3Rs principle to replace, reduce and refine animal testing that was originally introduced by  Russell and Burch (1959). Possible applications of 'omics studies include the development of AOPs; the quality assurance of cell lines with respect to their identity, genetic stability, and contamination; the provision of supplementary data in WoE approaches; the support of grouping of substances and read-across technologies; and, finally, to set PoDs for risk assessment and to provide information relevant for C&L.
Generally, 'omics-based test methods need to be validated to demonstrate their reliability and reproducibility, their mechanistic relevance and utility, prior to routine use in these applications. On behalf of Leming Shi (Fudan University, Shanghai, China), Weida Tong (National Center for Toxicological Research (NCTR), U. S. Food and Drug Administration (FDA), USA) provided an overview on lessons learned from the first parts of the Microarray Quality Control (MAQC) and Sequencing Quality Control (SEQC) projects in striving for reproducible 'omics data for risk assessment. The goal of the FDA-led community-wide MAQC consortium was to assess the technical performance and application of 'omics technologies in clinical application, safety assessment and precision medicine. The MAQC consortium was established in 2005 and, by end of 2014, had completed three projects evaluating the performance of microarrays, genome-wide association studies and RNAsequencing (RNA-Seq), with particular reference to: 1.
Presentations work stream 1 Hans-Martin Kauffmann (BASF SE, Germany) presented the ECETOC concept for quality assurance of new technology data considering GLP requirements. The details on this framework are provided in  Kauffmann et al. (2017)  in this journal Supplement. Briefly, a GLP environment comprises a standard operating procedure (SOP) system, proper pre-planning and conduct documentation as well as independent quality assurance inspections  (OECD, 1998). Particular challenges to fulfil GLP requirements for 'omicsbased studies are likely to be associated with (1) the definition of raw data; (2) the reproducibility of final results based on stored raw data; (3) the transparent description of all data processing steps; and (4) the validation of procedures and software (that could be conducted as 'black box' validation).
However, even if individual parts of 'omics studies cannot be addressed in full GLP-compliance, an 'as GLP-like as possible' procedure serves to promote the regulatory acceptance of 'omics data. This introductory lecture was followed by presentations exploring the feasibility of collecting specific types of 'omics data for regulatory purposes in a GLP-compliant manner. Ben van Ravenzwaay presented a case study to exemplify the state-of-the-art collection of metabolomics data under GLP and GLPlike conditions. In this case study, all steps of the animal studies were conducted in-house, under GLP conditions. The metabolome analysis was undertaken externally in a test facility with GLP status for mass spectrometry analytics. The different steps of bioanalytical measurements and archiving of the bioanalytical data were also conducted following GLP criteria.
Summary of breakout session work stream 1 The outcome of the subsequent breakout session Feedback on the GLP framework served to update the draft yielding the Framework for the quality assurance of 'omics technologies considering GLP requirements presented in  Kauffmann et al. (2017)  in this journal Supplement. The workshop participants agreed that the establishment of a GLP-like context for collecting, storing and curating 'omics data would be a valuable contribution to fostering the regulatory acceptance and use of 'omics data. Nevertheless, a key question raised in the discussions was whether GLP criteria would alone provide the guidance for conducting high-quality 'omics studies, or whether more detailed SOPs and analysis criteria, such as the ones provided by the MAQC consortium, would be required to capture the levels of detail and flexibility required for accommodating the use of 'omics data in regulatory toxicity studies.
Workshop participants defined raw data, in terms of 'omics technologies, as data produced by the 'omics instruments following the application of a data acquisition tool (e. g. the data produced from a microarray scan following the application of the image analysis software tools provided by the respective vendor). It was noted that the sheer magnitude of 'omics datasets, and the fact that such data might exhibit a high degree of between-sample heterogeneity and variation as a result of vendors' methods variations, may pose special challenges for quality assurance auditing. With respect to the software tools, the availability of audit trails to prevent uncontrolled manipulation was considered especially valuable. In concluding work stream 1, Ben van Ravenzwaay highlighted that advancing technology standardisation is essential to improve best practice for 'omics studies.
However, he cautioned that GLP guidelines serve to ensure the reproducibility of studies, based on existing raw data, but not necessarily their quality. GLP compliance ensures the traceability of study results, and it prevents data manipulation. While audit trails are important (particularly for the electronic storage of raw data), pending their inclusion in all relevant software, the need for audit trails can be circumvented by using paper records of the data with every manipulation. If this is not possible for technical reasons, such as for extremely large datasets (e. g. , RNA-Seq), checksums can be computed on each intermediate dataset, storing both the checksums and the datasets. Brazma et al. , 2001)  and the work of the MAQC consortium  (MAQC Consortium et al. , 2006 ; SEQC/MAQC-III Consortium, 2014) successfully addressed issues to consider in generating and storing 'omics data, the ECETOC framework focuses on the second step of 'omics studies, i.
e. data processing. Data processing encompasses (i) the selection of raw data; (ii) data normalisation; (iii) the 'filtering' of data to remove outliers; and (iv) statistical analysis. Weida Tong summarised lessons learned from the MAQC and SEQC projects and presented next steps towards reproducible 'omics for risk assessment. Generally, the reproducibility of DEG lists can be affected by (i) sample size; (ii) the endpoint under investigation (reproducibility increases with increasing treatmentrelated effect); and (iii) the complexity of the study design (taking into account the availability of technical and biological replicates). The MAQC criterion to include fold change and p-value thresholds to account for cross-laboratory and cross-platform variation as well as variance filters to account for signal/noise ratio has improved the reproducibility of transcriptomics data.
For statistical analysis, the t-test is recommended over the Analysis of Variance (ANOVA) model, since parameters affecting variance are not necessarily known and hence cannot be fully accounted for by ANOVA. Further investigations should aim at assessing how different levels of gene expression (large versus small modulations) affect reproducibility and how different approaches to process and analyse transcriptomic data affect the resulting lists of DEGs. Further, it is unknown how the interpretation of the biological relevance of the resulting lists of DEGs is affected by the specific pathway database used for data interpretation. Tim Ebbels (Imperial College London, UK) presented a metabolomics case study to explore aspects of data processing relevant for this type of 'omics study. Metabolomics can be applied either in targeted or untargeted mode, i.
Summary of breakout session work stream 2 The outcome of the breakout session Feedback on the framework of best practices for analysing 'omics data served to update the corresponding draft yielding a generic Transcriptomics Reporting Framework (TRF) for 'Omics Data Processing and Analysis  (Gant et al. , 2017)  presented in this journal Supplement. Generally, workshop participants found it premature, or even unfeasible, to provide specific guidance on how to conduct the different steps of data processing. Instead, the imminent value of presenting a framework for reporting the specific steps of 'omics data processing used in a particular study was highlighted and proposed as a generic TRF. Initially focusing on data processing, the TRF should eventually include parameters for experimental design, and, in the long term, it might be expanded to cover all types of 'omics (e.
g. , transcriptomics, proteomics, etc. ) and the related technologies (e. g. , microarrays, RNA-Seq, etc. ). The TRF should be organised flexibly to allow for the inclusion of new evidence, and new methods or new technologies, as they become available. Adherence to the TRF would provide transparency on the data processing and evidence that best practice had been followed, reflecting the current maturity of the technology and further taking into consideration the complexity and diversity of 'omics technologies. Compliance with the steps of the TRF would enable the generation of reproducible 'omics data, which would facilitate their regulatory applicability. Also, a Topical Scientific Workshop on New Approach Methodologies (that include 'omics-based studies) organised by the European Chemicals Agency (ECHA, 2016) came to the conclusion that reporting templates were required for such methodologies to encourage their use for regulatory purposes.
As such, issues on the reproducibility of 'omics data could relate to between-study reproducibility as well as to the reproducibility of the analysis of a given set of raw data. In the future, the TRF could also provide the basis for the development of technology-specific analysis frameworks, in which process the vendors of the technologies should be involved. Case studies, using reference samples, and possibly also validation studies, should substantiate the appropriateness of the steps of the TRF. In wrapping up work stream 2, Weida Tong and Tim Gant expressed the wish that the TRF would also be beneficial for the work of the OECD AOP programme to ensure that all newly developed AOPs utilising 'omics data are founded on transparently collected, reproducible data sets. The TRF would be flexible and sufficiently general to accommodate future advancements and new technologies, while also being sufficiently specific to be useful.
Kamin Johnson (Dow Chemical Company, USA) presented a preliminary proposal to pivot from employing 'omics data for hazard identification to using them to generate bioactivity-based PoDs for the risk assessment of substances. This tiered approach is intended to facilitate the application of 'omics data, not only to investigate MoAs, but also to derive PoDs for regulatory hazard and risk assessment. Since prevailing knowledge gaps generally make it difficult to link 'omics data to a specific apical effect, the goal to identify an apical effect might be replaced by the goal to determine bioactivity, i. e. the perturbation of a biological pathway at the molecular level. If no bioactivity is observed, there should be no hazard. In a future paradigm, in which bioactivity in combination with exposure constitutes risk, PoDs could be based upon the benchmark dose that affects the most sensitive biological pathway(s)  (Thomas et al.
This in vivo PoD is combined with predictions of human exposure and internal dosimetry to generate a MoE, and substances with a Tier 1 MoE below a specific threshold are submitted to Tier 2 Tier 2: Generation of bioactivity PoD, using 'omics data recorded for all relevant organs in an in vivo repeated-dose toxicity study encompassing all possible critical routes of exposure (e. g. an extended one generation-like study). When applying such a bioactivity-based risk assessment approach, it will have to be considered whether the recorded bioactivity could ultimately lead to an apical effect. However, the limited data generated to date comparing 'omics and apical PoDs for cancer endpoints suggest that these PoDs lie within an order of magnitude  (Bercu et al. , 2010; Thomas et al. , 2013a, b; Jackson et al.
Further alternative approaches to avoid using arbitrary fold change thresholds may be pathway-or patternmatching approaches, applying read-across from common databases to match the generated data. Such approaches consider the rank orders and the direction of gene expression changes  (ECETOC, 2008) , but not their amplitude or magnitude. Pathway-or patternmatching approaches include a probability analysis of the similarity of the gene expression profiles, which can either be based on comparative techniques (using, e. g. , a pre-defined predictive gene set or a rank-based correlation analysis) or on multivariate analyses (e. g. , principal component analyses or clustering techniques)  (ECETOC, 2008). Further approaches to avoid fold change thresholds involve the use of generalised linear models, e.
Using pathway databases, recurrent changes that are not of toxicological interest can be identified and sorted out  (ECETOC, 2013). As it is unlikely that measurements from a single 'omics technology will identify all biologically relevant changes, it might be necessary to have a "fusion" of 'omics derived data to truly distinguish between meaningful and irrelevant changes linked to a toxicological outcome. An understanding of the inter-relationship of these 'omics measurements linked to apical pathology will help identify those measurements reflective of a toxicological effect. Such an approach will require pathway experts collaborating in multidisciplinary teams to interpret and agree the relationships between 'omics measurements and their relationship with toxicologically relevant 'omics changes.
, 2012). Again, expert knowledge is required to establish such links, in addition to distinguishing between adaptive and adverse effects. At best, such considerations should address by how many orders of magnitude adaptive and adverse effects differ, and they should take into account the dose-and time-dependence of the transition from normal variability, through adaptive response, to adverse effect  (ECETOC, 2010). Aggregate measures may be required for biological pathways to be able to assess the orders of magnitude difference between adaptive and adverse effects. However, it is not always possible to establish quantitative relationships between gene expression changes and phenotypic changes. Generally, it was considered difficult to determine the human health relevance of 'omics data (just as other toxicological data) obtained under very controlled study conditions at present.
To use 'omics to derive a PoD, it should be evident that the identified biological pathway is causally related to an adverse effect  (ECETOC, 2008 (ECETOC, , 2010)). Importantly, the applicability of 'omics-based PoDs depends on the intended use within regulatory toxicology (e. g. to prioritise substances for hazard assessment or to exclude the need for further testing). In most cases, 'omics data are not being used on their own, but embedded in a tiered process. Thus, frameworks are needed to enable the integration of 'omics data into lines of evidence, e. g. by applying the quantitative WoE approach presented in work stream 4. In wrapping up work stream 3, Alan Poole emphasised the need to discriminate whether 'omics-based studies were intended to be used to supplement toxicity studies or to supplant toxicity studies.
Presentations work stream 4 Mark Pemberton (Systox Ltd. , UK; ECETOC Scientific Committee) and Jim Bridges (Surrey University, UK), in two video presentations, provided further details on the utilisation and integration of 'omics data within QWoE approaches. A QWoE approach is defined as the identification, objective analysis and scoring (using predefined scientifically justified criteria) of all potentially relevant studies, for both their reliability and relevance in testing a hypothesis. Accordingly, the steps of a QWoE approach comprise the formulation of the hypothesis and data collection; the assessment of the reliability and relevance of the studies; the identification of critical endpoints; the evaluation of the concordance of findings both within and between lines of evidence; comparisons with findings for related substances; and, finally, the conclusion and the characterisation of uncertainties.
The draft QWoE framework is applicable to any hypothesis, e. g. both to identify MoAs and to determine PoDs for subsequent hazard assessment. By providing transparency in the way that data are evaluated and integrated, it minimises the opportunity for bias and provides a basis for dialogue between stakeholders. When applying the QWoE framework to 'omics data, their relative weighting as compared to traditional toxicological data or within a set of 'omics data has to be taken into consideration. Amber Goetz (Syngenta Crop Protection LLC, USA) presented a case study to demonstrate how 'omics data could support WoE approaches when assessing the human health relevance of experimental findings. The hypothesis tested in this study was that the hepatocarcinogenic MoA of the fungicide Sedaxane is initiated by activation of the constitutive activated receptor (CAR;  Oshida et al.
, 2015). The study design encompassed oral 22-day toxicity studies using male CD-1 mice and in vitro studies using primary hepatocytes from rats and humans. Organ weight changes, liver enzyme activation, CAR3 transactivation and pregnane X receptor (PXR) activation were assessed. Gene expression changes were evaluated using qRT-PCR and Agilent microarray technologies. The microarray data were analysed using the GeneSpring™ 13 software and applying >1. 5 fold-change and <0. 05 p-value thresholds. Bioinformatics analyses were conducted using Ingenuity Pathways Analysis (IPA™) applying a 2-way ANOVA. Multiple testing correction was performed using Benjamini-Hochberg false discovery rate. The analysis showed that the expression of xenobiotic metabolising enzyme genes and other genes associated with CAR and/or PXR activation increased in a dose-dependent manner.
, 2014; Becker et al. , 2015). Importantly, the QWoE framework should not become a 'new check box approach', just as the application of any scoring system should not override case-by-case expert judgment during hazard and risk assessment. For this reason, it was also suggested that it might be more appropriate to name the framework 'semi-quantitative' WoE approach. Case studies would serve to assess how consideration of traditional and 'omics-based data (either in combination or each on their own) within the QWoE affected the outcome of hazard and risk assessment. Such case studies would have to distinguish between the use of 'omics data to determine MoAs and to determine PoDs. In wrapping up work stream 4, Alan Poole emphasised that the QWoE framework was intended to ensure transparency of the process, but not to place undue weight on any given numerical score.
Close of the workshop Closing the workshop, Alan Poole thanked all participants for their valuable contributions to achieve best practice for the acquisition, storage, processing, and interpretation of 'omics data. After the workshop, the draft frameworks were amended, taking into account the recommendations from the breakout sessions and plenary discussions. The frameworks for a GLP-like context for collecting, storing and curating data; for the processing of 'omics data; and for QWoE for integrating 'omics data were conceived as reporting templates to enhance the standardisation, transparency and reproducibility of 'omics studies. Overall, this work is closely linked to the activities of the OECD EAGMST to achieve the ultimate goal of regulatory acceptance and use of 'omics studies.
Methods: In the present study, PAR-1 gene expression was determined in lung tissue from patients with non-smallcell lung cancer (NSCLC) using a combination of publicly available RNA microarray datasets and in house-made tissue microarrays including tumor biopsies of 94 patients with NSCLC (40 cases of adenocarcinoma, 42 cases of squamous cell carcinoma and 12 cases of other type of NSCLC at different stages). Results: PAR-1 gene expression strongly correlated with tumor stromal markers (i. e. macrophage, endothelial cells and (myo) fibroblast markers) but not with epithelial cell markers. Immunohistochemical analysis confirmed the presence of PAR-1 in the tumor stroma and showed that PAR-1 expression was significantly upregulated in malignant tissue compared with normal lung tissue. The overexpression of PAR-1 in tumor stroma of NSCLC appeared to be independent from tumor type, tumor stage, histopathological differentiation status, disease progression and patient survival.
Mining of publically available RNA microarray dataset The datasets were derived from Gene Expression Omnibus (http://www. ncbi. nlm. nih. gov/gds) using the R2 microarray analysis and visualization platform (http://r2. amc. nl). Correlation of gene expression between PAR-1 and markers of different stromal cell types in NSCLC cancer patients were derived by the R2 program from five different datasets, including Bild (n = 114, GSE3141), Peitsch (n = 150, GSE43580), EXPO (n = 121, GSE2109), Mao (n = 124, GSE 31852) and Hou (n = 156, GSE 19188).