dataesr/scientific-paragraphs-categorization huggingface dataset:
47, 48 The integrative analysis of different epigenetic parameters offers a more complete and dynamic view of the epigenome and facilitates the functional interpretation of epigenetic changes. 49 Single-cell epigenomics allows interrogating the transcriptome and epigenetic marks (5mC, histone modification, non-coding RNA) at single-cell resolution, and therefore provides tissue and cell-type specificity. 50 However, these approaches require new computational pipelines for data analysis, which again poses a difficulty in establishing standards and comparing datasets that have been generated across different platforms and methods. 51 ignificant investments have also been made to coordinate research activities in epigenetics. Institutions have shown interest in building tools to establish standards and systematize knowledge, in particular with the goal of producing reference epigenomes.
After the NIH Roadmap Epigenomics Project led in 2015 to the profiling of 111 reference epigenomes,  49  the International Human Epigenome Consortium (IHEC) aims to complete at least 1000 reference epigenomes and establish standards for assays and metadata. 52 The NIEHS-funded TaRGET Consortium program is focused on 6 selected substances and aims to standardize research protocols in terms of exposure conditions, target and surrogate tissues, cell types, and assays. 53 The TaRGET consortium aims to generate epigenetic signatures of exposure across tissues that offer a baseline for assessing epigenetic effects of exposure, that further can support the production of human epigenetic data. Such scientific and institutional strategies will undoubtedly contribute to an increased understanding of epigenetic variability in the face of exposures and improve the comparability and reproducibility of epigenetic data in research.
However, this effort to homogenize and standardize research protocols will not immediately nor directly bear on the usefulness of epigenetic data for risk assessment, as it does not cover all of the knowledge needs of regulatory toxicology. For instance, the challenges of using noncoding RNAs (ncRNAs) as biomarkers do not only stem from the absence of standardized procedures for data normalization and referencing, and the need to develop statistical estimators and historical control data to ensure comparability and facilitate assessment. 32 They also relate to the need to incorporate them into the standardized in vitro assays and in vivo repeated-dose studies used in risk assessment. It may require adapting the guidelines for those assays, for example, defining test group sizes that will provide sufficient statistical power to detect ncRNA effect sizes.
Toxicological relevance of epigenetic data The second major issue in the use of epigenetic datasets is their interpretation. There is widespread agreement in the literature and among interviewed stakeholders on the challenges of translating epigenetic molecular mechanisms into relevant physiological endpoints. Epigenetic changes do not necessarily alter gene function; rather, akin to mutations, they can be neutral, adaptive, adverse, or emergent, that is, resulting in an adverse effect at a later stage. 54 In addition, in contrast with genetic mutations, the persistence of epigenetic changes is variable. 6] [57]  The identification of persistent adverse effects is at the core the hazard identification component of risk assessment, while they need to be quantifiable in order to be incorporated into a dose-response assessment.
73 rototype assessments can be used in conjunction with qualitative models such as adverse outcome pathways (AOPs). AOPs appear to be a promising avenue for the integration of epigenetic data into risk assessment. 33, 54, 74 An AOP is a structured representation of biological evidence that connects a molecular initiating event to an adverse outcome through the identification of key biological events, drawing on multiple, heterogeneous datasets. 75, 76 AOP frameworks demonstrate biological plausibility by organizing the existing evidence into holistic, visually representable networks. As sociology of science has shown, visual displays-charts, figures, and tablesorganize data in a way that not only helps represent it but in itself produces analyses. 77 Angrish and coauthors propose for example that an AOP framework can help interpret the multifarious epigenetic data related to arsenic exposure.
Facilitate the normalization and exchange of data In the words of our interlocutors, the integration of toxicoepigenetics into risk assessment further depends on "making sure that we are speaking each other's language. " Social studies of science have shown the efficiency of building "boundary objects," that is, scientific objects that help groups of actors coming from different perspectives with different objectives (eg, basic, regulatory, and industry scientists) share a frame of reference and work toward common goals. 81 In particular, information infrastructures (such as databases, directories, reporting tools, standard procedures, guiding principles of data interpretation) operate as boundary objects in 2 ways: first, they facilitate the circulation of information between practitioners; second, they support data interpretation and allow for the development of shared meanings.
82 However, there has been a significant lag in the construction of such boundary objects in toxicoepigenetics. Epigenomic data is noticeably absent from existing largescale governmental toxicology databases such as EPA's Toxicity Forecaster (ToxCast)  83  and related inter-agency governmental efforts such as Tox21. This lag is made more salient by the wealth of databases and tools related to epigenomics that have been built over the years. In particular, the NHGRI-funded Encyclopedia of DNA Elements (ENCODE) project, launched in 2003, compiles a comprehensive collection of chromatin features of the human genome including its methylome, open chromatin regions, and histone marks distributions. 84 Its extension effort, ModEncode, complemented the human data by adding large epigenomic datasets for model organisms such as Drosophila and C.
elegans. 85 Together with the emergence of these epigenomics datasets, tools to access, manipulate, and add individual datasets also emerged such as the Deepblue, USCS, and WashU Epigenome browsers. 86 ne first step to fill in the gap of toxicoepigenomic data in large toxicology databases is to introduce reporting guidelines that would provide a clear framework and format to share data and facilitate its uptake for risk assessment purposes. Most scientific journals request the submission of raw data, which could seem to enable full data access to epigenomic data for risk assessment purposes. Of course, to entirely reanalyze this data for risk assessment purposes would represent an enormous task that does not fit within the scope of risk assessment. Reporting guidelines offer a manageable middle ground.
A reporting guideline is "a checklist, flow diagram, or structured text to guide authors in reporting a specific type of research, developed using explicit methodology"; it is a guidance for writing that "provides a minimum list of information needed" to ensure a manuscript can be understood, replicated, used or included in a systematic review. 87 There are currently few reporting guidelines governing data production and interpretation and preferred tools in toxicoepigenetics. 71 Initiatives in that direction have been observed for the 'omics sciences, in particular with the Transcriptomics Reporting Framework (TRF) under the auspices of the ECETOC and OECD. 88 While not prescriptive, the TRF defines parameters that should be reported and standards for bioinformatic processing and statistical analysis in 'omics studies to be used in a regulatory context.
pS37) Reporting guidelines have been shown to positively impact the quality of data reporting in preclinical animal studies and in particular reporting of randomization, blinding, and sample-size estimation. 89, 90 The application of such standards to epigenomics could help form a consistent and interpretable data stream for risk assessment. Thanks, in particular, to its structure as a consortium involving laboratories with a legacy of research in epigenetics and toxicoepigenetics, the NIEHS and NIDA-funded Toxicant Exposures and Responses by Genomic and Epigenomic Regulators of Transcription (TaRGET) Program could help catalyze the production of such standards for the field through the publication of policies, pipelines, and quality control parameters. Secondly, and importantly, scientists producing toxicoepigenetic data will increase the probability of its being used by risk assessors if they use experimental and analytic tools that are accepted and used by risk assessment.
Such shared tools are so far lacking in the field. The use of shared software has proven fruitful for the uptake of gene expression data by risk assessment, gene expression that, although imperfect, can serve as a proxy for epigenetic changes. In particular, the integration of transcriptomic datasets into a benchmark dose (BMD) modeling through the BMDexpress software allows for the determination of a point of departure using changes in gene expression as a molecular endpoint. 91 This funneling of a data type into standard values that are interpretable by regulators in the context of risk assessment is precisely what is currently lacking for toxicoepigenomic data. The program TaRGET takes one step in this direction by making available softwares to standardize various pipeline and establish correlations between chromatin states through ATAQ-seq and gene expression data via RNAseq pipelines, data that might later be integrated in a BMD framework.
following financial support for the research, authorship, and/or publication of this article: A. L. G. and P. A. are supported by the  John Templeton Foundation. P. A. is also supported by  NIEHS   R01 ES027487  and the  Burroughs Wellcome Fund. S. L. is supported by the  French National Research Agency program , " Investissements d'avenir"  ( ANR-15-IDEX-02 ). H. B. was supported by the  French Institute on Research, Innovation and Society (IFRIS, SITES LabEx)  and the  French National Research Agency  ( ANR-13-SOIN-0005 ).
[25] ) of the two proteins. Residues colored in red correspond to identical residues in the two sequences. Sequence identity (≈12%) is further highlighted by the red vertical lines between the two sequences. Residues colored in green corresponds to homologous residues between the two sequences (≈35% sequence homology); (B) Cartoon representation of AVR-Pia (PDB: 6Q76  [26] ) (left) and (B) AVR-Pib (PDB: 5Z1V  [27] ) (right) structures. The six strands are labeled ß1 to ß6, and the structures are colored from the N-ter in dark blue to the C-ter in red. The SS-bridge in AVR-Pia is shown by sticks.
High-Pressure Unfolding Monitored with NMR Spectroscopy Crystal structures are available from the Protein Data Bank for AVR-Pia (PDB: 6Q76  [26] ) and AVR-Pib (PDB: 5Z1V  [27] ) and were used as a template for displaying the results. In the case of AVR-Pia, two solution structures are also available (PDB: 2N37  [28] , and 2MYW  [24] ), and the resonance assignment was deposited at the BMRB (n° 25460 for 2MYW). Nevertheless, the physical and chemical conditions used in these previous studies were different than those used in the present one, justifying the re-assignment of the amide resonances through guanidine titration experiments. The assigned HSQC for both proteins is given in the Supplementary Materials (Figure  S1 ). A series of 2D [ 1 H, 15 N] HSQC spectra of 15 N uniformly labelled proteins were recorded as a function of pressure in the conditions described in the Materials and Methods.
High-Pressure Unfolding Monitored with NMR Spectroscopy Crystal structures are available from the Protein Data Bank for AVR-Pia (PDB: 6Q76  [26] ) and AVR-Pib (PDB: 5Z1V  [27] ) and were used as a template for displaying the results. In the case of AVR-Pia, two solution structures are also available (PDB: 2N37  [28] , and 2MYW  [24] ), and the resonance assignment was deposited at the BMRB (n • 25460 for 2MYW). Nevertheless, the physical and chemical conditions used in these previous studies were different than those used in the present one, justifying the re-assignment of the amide resonances through guanidine titration experiments. The assigned HSQC for both proteins is given in the Supplementary Materials (Figure  S1 ). A series of 2D [ 1 H, 15 N] HSQC spectra of 15 N uniformly labelled proteins were recorded as a function of pressure in the conditions described in the Materials and Methods.
Simulations of Pressure Unfolding We applied constrained Cyana3 calculations to characterize the pressure-dependent unfolding process during which a protein undergoes transitions from a native compact structure to extended configurations. An ensemble of constraints' lists, generated through filtering the native constraints by the probability p ij values obtained from the normalized denaturation curves at each pressure, was used to compute the conformer ensembles by the torsion angle dynamics software Cyana3 (see Supplementary Materials). The total conformer ensemble sizes were similar for the two MAX effectors, with 23,108 conformers and 25,376 conformers for AVR-Pia and AVR-Pib, respectively. For each protein, the total set of conformers obtained for all the pressure ranges was sorted according to the fraction of native constraints (Q).
Simulations of Pressure Unfolding We applied constrained Cyana3 calculations to characterize the pressure-dependent unfolding process during which a protein undergoes transitions from a native compact structure to extended configurations. An ensemble of constraints' lists, generated through filtering the native constraints by the probability pij values obtained from the normalized denaturation curves at each pressure, was used to compute the conformer ensembles by the torsion angle dynamics software Cyana3 (see Supplementary Materials). The total conformer ensemble sizes were similar for the two MAX effectors, with 23,108 conformers and 25,376 conformers for AVR-Pia and AVR-Pib, respectively. For each protein, the total set of conformers obtained for all the pressure ranges was sorted according to the fraction of native constraints (Q).
For AVR-Pib, we observe also a secondary branch starting at Q = 0. 5 deviating from the major conformer profile that corresponds to inversions of the conformer topology relative to the native topology (Supplementary Materials, Figure  S7C ), where the ß3 and ß4 strands cross each other. By qualitatively comparing the spreading of the landscapes, we draw the conclusion that conformer diversity, seen by its larger distribution and heterogeneity, is greater in the case of AVR-Pib than for AVR-Pia. A clustering approach was used to pool similar conformers into families giving, in fine, a schematic view of the conformer populations. To avoid over-simplification, the clustering parameters of the MaxCluster software were fine-tuned (see Supplementary Materials). The Nearest Neighbor clustering  [31]  success over all conformers was 86% and 85% for AVR-Pia and AVR-Pib, respectively.
Energy Calculations The internal energy of each conformer was calculated by evoEF2 software  [32]. The conformer was first submitted with the RepairStructure option that optimizes the energy of the provided model by combinatorial exploration of the side-chain rotamers. Then, the resulting model was submitted to evoEF2 with the ComputeStability option to calculate the free energy (evoEF2 arbitrary units), and more specific contribution terms (Van der Waals and H-Bonds energies) of the model. The complete EvoEF2 energy function is written as: E EvoEF2 = E VDW + E ELEC + E HB + E DESOLV + E SS + E AAPP + E RAMA + E ROT -E REF  (5)  Here, E VDW , E ELEC , E HB , E DESOLV and E REF represent the total Van der Waals, electrostatic, hydrogen bonding, de-solvation and reference energy terms for a protein system, respectively.
Clustering We used the MaxCluster (http://www. sbg. bio. ic. ac. uk/~maxcluster/download. html, accessed on 15 May 2019) software that is fast enough for computing pairs of r. m. s. d. for a large set of structures (in the order of 104 structures). The Nearest Neighbor (NN) clustering algorithm in MaxCluster is based on the method of Shortle et al. [31]. The clustering must perform two goals: (i) global shape recognition and clustering at low Q values; and (ii) more local r. m. s. d. clustering at high Q values. Accordingly, we adapted MaxCluster parameters that perform well with these two tasks. Since we performed 4 Cyana3 calculations for each constraint list, yielding four conformers per list, we choose the minimum number of conformers to form a cluster to be five (default value). The ceiling threshold (T m ) was let at the default value (8 Å) in order to be able to cluster conformers, according to their global shapes.
Contact Maps Statistics Statistical tests assume a null hypothesis of no relationship or no difference between groups. Then, they determine whether the observed data fall outside of the range of values predicted by the null hypothesis. The computational flowchart for generating randomly scrambled conformer populations (null hypothesis) is essentially identical to Figure  10 , except that at each pressure and at each filter value (f ) the residue specific data (p i ) were scrambled before generating the constraint files. That has the effect of de-correlating the pressure to sequence specificity, if any. In other words, it disconnects the pressure and structure. To perform the statistical analysis, we pooled these randomly scrambled conformer populations to the original 'pressure' data, and the obtained merged data at each Q were re-clustered with the default minimum cluster size or with minimum size of 10.
Under such null hypothesis assumption, the statistical significance of any cluster was assessed by the alpha value threshold α < 0. 05, which means that in a given cluster less than 5% of the conformers could be generated by random-scrambling. Accordingly, any cluster having more than 95% of conformers generated from the original 'pressure' dataset was selected to be statistically relevant. Fractional contact maps were computed from the upper bound restraint files used to generate these conformers. In these maps, any factional contact corresponds to the normalized frequency (0 to 1) of observing that contact in the selected cluster(s) of the original 'pressure' dataset. Molecular structure Figures and Videos were executed with PyMOL v. 1. 6. [45]. The other Figures and Videos were executed with the MatLab 2019 package.
Third, in the absence of on-label treatment options, patients must be matched with the specific portfolio of investigational therapies and clinical trials available in each hospital (or hospital network), which are subject to continuous changes. Failure to address these issues or the inability to perform them in a clinically acceptable time frame can impair the outcome of individual patients and precision cancer medicine initiatives. Clinical decision support systems (CDSS) can tackle these challenges by implementing efficient data analysis and reporting processes. Several commercial CDSS software are currently available, but in-house solutions are often used to better accommodate the specific needs of each center. In fact, we believe that the capacity of academic institutions to develop custom CDSS accelerates the use of emerging biomarkers and promotes precision medicine across.
Technical RepoRT NATurE CANCEr healthcare professionals. We have therefore developed the MTBP, a CDSS that creates a unified framework to interpret sequencing results across the seven comprehensive cancer centers that form Cancer Core Europe (CCE)  1  at present. Importantly, the portal is integrated in the CCE clinical workflows and provides a single platform to distribute the results and support shared discussions at scale  2. Seamless communication among clinical investigators is essential to leverage the collective expertise of the community in this era of rapidly changing precision-oncology landscape. To our knowledge, this is an unprecedented effort for codeveloping new anticancer therapies and biomarkers under a harmonized infrastructure in Europe. Here, we describe our approach and discuss the results of using the MTBP in a consecutive cohort of 500 advanced solid tumors evaluated from January 2019 to January 2021 in the context of the Basket of Baskets (NCT03767075) study, an ongoing CCE multibasket phase 2 clinical trial matching molecular biomarkers with immunotherapies and targeted drugs.
The functional relevance evaluates the allele-centric effect of the observed variant, whereas context-dependent interpretation factors in additional considerations (such as whether the variant is germline or somatic, co-occurring alterations in the same or other genes and/or the cancer type of the patient's tumor). Questions that are addressed in each step are exemplified here for a given BRCA1 mutation (HRR, homologous recombination repair; PARPi, poly-ADP ribose polymerase inhibitors). basket trial for advanced tumors selected according to predefined molecular profiles (NCT03767075). Overall, 36% of the patients of the CCE cohort presented here were recommended for one of the Basket of Baskets treatment arms available at the moment of the molecular tumor board discussion (Fig. 5a ). These were mostly associated with the use of immune checkpoint inhibitors in the presence of loss-of-function events in DNA damage repair genes, as estimated by the MTBP variant interpretation (Methods).
For variants that are not located in mutation hotspots, we decided to use the combined annotation dependent depletion (CADD) score  16  to estimate the functional relevance of missense mutations in tumor suppressor genes (TSGs), as functional impact predictions perform worse in other scenarios (data not shown). The method and associated thresholds were selected according to our own benchmarking, based on the performance observed for mutations with curated effects (upper violin plot) and in silico simulations (lower violin plot) (Methods). FN, false negative (given these thresholds); FP, false positive; TN, true negative; TP, true positive. the majority (60%) of these patients were not finally enrolled in the Basket of Baskets trial, mostly because of the deterioration of their clinical condition or subsequent screening failures.
This further emphasizes the importance of deploying systems that can support an efficient and rapid trial recruitment at the point of clinical decision-making. The MTBP retrieves the trials' eligibility criteria from in-house databases gathering clinical, pathological and molecular requisites. This information is curated following an ad-hoc syntax adapted to the growing complexity of cancer biomarkers, which are defined by the presence (or absence) of a given combination of genomic alterations and/or genomic signatures. This syntax also defines prioritization rules in case that the allocation to multiple trials is possible, as well as variant interpretation nuances to be used by the MTBP interpretation framework. One example of the latter is the evidence required for considering variants in a given actionable gene as functionally relevant; in general, we only match clinical trials with tumor variants whose effect is based on well-curated studies or bona fide biological assumptions, but lower evidence, such as bioinformatic predictions, is also considered for emerging biomarkers associated with less characterized genes.