dataesr/scientific-paragraphs-categorization huggingface dataset:
However, in terms of accuracy, BPRNACan with EpiDISH emerged as the best performing method for this dataset (Fig. 6b  and Additional file2: Figure  S6b ). These analyses demonstrated that since the CCLE_TIL10 signature matrix was generated based on in-silico samples, it is accurate for in-vitro and in-silico mixtures, whereas BPRNACan performs better on biological samples, in which cells are in a biologically relevant context, including the exchange of signals between them. Thus we next tested our two signature matrices on additional in-vivo cancer samples. To further benchmark the methods, we used a series of 59 multiple myeloma patient TME samples, generated by removal of cancer cells from bone marrow samples  [39]. For this dataset FACS analysis was performed to quantify NK, CD4 and CD8 T cells so we could compare our estimations for these cell types from the different methods to FACS results.
Although only 3 immune cell types had been assayed in this study, BPRNACan produced higher correlations with FACS for CD4 T and NK cells, and lower correlations for CD8 T cells compared to quanTIseq (Fig. 6c ), while for all three cell types it gives higher correlations than MCP-counter (Fig. 6c ). Finally, we examined a single-cell RNAseq dataset including 19 reconstructed primary melanoma non metastatic samples  [37]  and aggregated reads to produce an in-silico bulk sample for each patient to compare deconvolved proportions to the corresponding cell fractions estimated by counting single cells of each type from Racle et al. [36]  (CD8 T cells, macrophages, B cells and NK cells). We ran EpiDISH deconvolution using the BPRNACan signature matrix and measured high correlations with the cell-type proportions estimated in the publication (Pearson's R > 0.
The total PCHi-C network for haematopoietic cells consists of 249,511 chromatin fragments, of which 20,582 are promoter fragments (including 1127 fragments of sig gene promoters) and 228,929 other ends, (including 1131 fragments containing sig CpGs) (Additional file 2: Table  S3 ). We first mapped sig CpGs to their corresponding gene promoters, according to the definition of promoters used in the PCHi-C datasets. We found 645 genes that have sig CpGs in their promoter, of which 52 were already identified in sig genes and 314 were not in sig genes and had expression profiles in our reference datasets. Moreover, the comparison of the mapping between sig CpGs and genes harboring sig CpGs in their promoter shows that the PCHiC based annotation of CpGs to promoters reveals a higher overlap than Illumina annotation, Fisher's exact test p-value = 7.
8a ). To this end, we made predictors of response to anti-PD1 antibodies using different deconvolution results as features and evaluated their performance on 3 public melanoma datasets and one bladder cancer dataset  [40] [41] [42] [43]  with response to anti-PD1 through ElasticNet penalized logistic regression 62 (see Methods and Fig. 8b ). Our signature matrices in combination with Epidish or DeconRNA allowed us to train models with a performance that was better than random in a classification task (Receiving Operator Characteristic (ROC) Area Under the Curve (AUC) score above 0. 5 on the standard Cross Validation (CV) training for 10 models among 12) and better than using quanTIseq or MCPCounter. On specific datasets unseen during training (see Methods) our signatures reached AUC=0. 77, while training on all samples (standard CV), the BPRNACanProMet signature used by Epidish was the top performing model, with a ROC AUC score of 0.
703 (Fig. 8c ). The BPRNACan signature in combination with deconRNAseq is the only one that could produce models with a ROC AUC score always above 0. 5 (Fig. 8c ), even when predicting on datasets that had not been used for training. When excluding it from training, the Snyder bladder cancer dataset is the one with the overall worst performing models, which is probably because it is the only dataset with bladder cancer samples, all the other ones corresponding to melanoma  [40] [41] [42]. Looking at the coefficients in our regression models we can estimate which variables are associated with either progressive diseases or response to therapy. The coefficients of the model trained with the combination of the BPRNACanProMet signature and the Epidish method show that B cells  [57] [58] [59]  (presumably indicative of the presence of tertiary lymphoid structures) and M0 macrophages proportions are associated with patient response, whereas M2 macrophages  [60] , CD4, CD8 and NK cells associated with progressive disease in this model (Fig.
8d ). Proportions of cancer cells, M1 macrophages, monocytes and neutrophils were not significantly associated with either of the two outcomes. We can also consider the combination of the BPRNACan signature with deconRNAseq that always produced models performing better than a random predictor, and that is the second best when the Snyder dataset was used as a test set. The coefficients of this model indicate that B cells, M0 and M1 macrophages  [61]  proportions are associated with response of patients, whereas CD4 and CD8 cells, Nk cells, monocytes and M2 macrophages are associated with progressive disease in this model (Fig. 8d ). Like the previous model, proportions of cancer cells and neutrophils were not strongly associated with any of the two outcomes. Thus, the two models differ in the importance of M1 macrophages and monocytes for predicting response or progressive disease.
Discussion Numerous reference-based deconvolution methods using DNA methylation or gene expression can be used to estimate the proportion of cell types in bulk datasets from cell mixtures, such as EpiDISH, MethylCibersort (for DNAm) and CIBERSORT, MCP-counter, quanTIseq, DeconRNASeq (for GE). Moreover, there are methods that can accurately predict the proportion of cancer cells in tumor samples (purity). However, for application in immuno-oncology it can be important to estimate the proportions of cancer and specific immune cells at the same time. Recently, Chakravarthy et al. [48]  have reported that deconvolution methods based on gene expression or DNA methylation could be complementary to each other in cases where both data types are available. Despite this, they did not further explore if the reference signature matrices for each type of data can be related.
Often in the clinic only gene expression data is available, so the integration of the two approaches is not common. Here we present five novel signature matrices for reference-based deconvolution: BPmetCan: which is able to deconvolve the proportion of cancer and immune cell types from DNA methylation data (Illumina Arrays or WGBS), based on WGBS signature matrices, which we validate against MethylCIBERSORT, EpiDISH and various methods to estimate tumor purity in blood and especially in tumor samples. CCLE_TIL10: which combines the TIL10 signature matrix  [29]  for immune cell types with a new list of genes identified to be cancer-cell specific using data from GTEx and CCLE, which displays excellent performance on in-vitro and in-silico mixtures of cancer cell lines and immune cells. BPRNACan: which combines our Blueprint derived immune cell signature matrix (BPRNA) with genes that are discriminant of cancer tissues compared to normal and outperforms or equals quanTIseq and MCP-Counter on cancer samples for many of the cell types.
BPRNACanProMet: which is an enhancement of the BPRNACan signature matrix by adding genes that have a sig CpG contained in their promoter, with demonstrated improvement in performance on many datasets BPRNACan3DProMet: which is an enhancement of the BPRNACan signature matrix by adding genes that have a sig CpG in their promoter and whose promoters also have a 3D contact with a fragment containing a sig CpG. We performed extensive validation using previous studies, such as whole blood mixtures, solid tumor-TCGA, PBMC, multiple myeloma patient bone marrow samples and melanoma non metastatic and metastatic samples. We compared the available DNAm or GE signature matrices to demonstrate that our novel signature matrices could faithfully estimate the fraction of cancer and specific immune cell compositions from DNA methylation and bulk gene expression data.
We are limited by the number of samples available for specific cell types (such as NK cells) and by the fact that these profiles are generated from purified cells that are isolated from their natural environment. This is especially true for cells that acquire specific phenotypes in a TME context, such as TAMs and TANs. The availability of single-cell RNAseq datasets from cancer samples, especially from technologies that also provide protein marker quantification such as CITEseq, will greatly improve our chances of generating relevant signature matrices for any cell type of interest. For this reason we provide code for the generation of new reference signature matrices in our openly available repository. Importantly, one of the main applications of deconvolution in immuno-oncology will be the prediction of response to immunotherapy, which can be made based on the inferred cell type proportions.
We therefore benchmarked our new signature matrices and methods by evaluating their accuracy in predicting response to anti-PD1 agents in 4 public datasets. This type of exercise is aimed at identifying which signature matrices and methods uncover the presence of specific cell subtypes that can impact immune checkpoint blocker response. These important subtypes might not correspond easily to literature definitions or FACS derived populations. M2 macrophages are known to impair response to immune checkpoint blockade  [60]  and we confirm their association with negative prognosis despite the discrepancy between our quantification of M2s and FACS estimates. This could be due to our signature capturing specific phenotypes that are not entirely determined by traditional cell surface markers used in FACS.
Our signature matrices are particularly suitable for the analysis of tumor samples from an immuno-oncology perspective, as they provide accurate estimates of immune cells as well as cancer cell proportions and have predictive power for estimating response to anti-PD1 agents. We make the signature matrices and all the code available to the community through a user-friendly snakemake pipeline that can use any given reference signature matrix to apply a variety of reference-based deconvolution methods. Additionally, the code to generate a signature matrix following the method used in this paper is also available, as well as a script to generate all the figures. Finally, we provide a user-friendly pipeline to apply our approach and make the code available to the research community, to promote the development of new more specific deconvolution signatures (https://github.
Flow cytometry parameters. Microcapillary flow cytometry system Guava ® EasyCyte TM 12HT (Merck Millipore, Darmstadt, Germany) with blue laser (488 nm) was used for the acquisition of the in vitro inflammation assay. The detection antibody of the assay contains Rphycoerythrin, thus yellow fluorescence allows to tract the concentration of TNF-α secreted by the cells (F cell %, Fig. 2a ). Dead cells were detected by the red fluorescence of propidium iodide (F cell %, Fig. 2d ). The mean flow velocity used was 35 µL/min. Each well was agitated for 10 s before each analysis using a rotatory agitator. Between each sample, the I n r e v i e w microcapillary and the agitator were washed. Data were treated using GuavaSoft (InCyte 3. 1. 1. ) software. 4. 9 Animals. Male Wistar rats (300 g; Janvier Labs, Le Genest St.
0 no response, 1 a short response or fast movement of the hindpaw (< 2 s), 2 a longer response (> 2 s), 3 licking of the hindpaw. This manipulation was performed three times for each hindpaw and the values were summed. Rotarod test. We used the Rotarod (IITC Life Science, Woodland Hills, CA). The speed of the roll increased progressively from 5 rpm to 20 rpm in 4 min. The time spend by the animal on the rotarod before falling was measured. This test was reproduced two times for each animal and the values were averaged. Beam walk test. The time needed by the rats to cross the beam (PVC bar of length: 130 cm; width: 4 cm, placed at 80 cm from the floor) was recorded. This test was reproduced three times for each animal and the values were averaged. 4. 12 Statistical analysis. Data are expressed as mean ± standard error of the mean (SEM).
Statistical tests were performed with GraphPad Prism 7. 05 (GraphPas Software, San Diego, California, USA). Sigmoid dose-response curves and EC50 values were calculated with the following equation:  =  + ( -)/(1 + 10^(( 50 -) *  With Y the response, X the concentration, Top (resp. Bottom) the plateaus at the top (resp. bottom) of the sigmoid in the units of the y axis, Hillslope the steepness of the curve and EC50 the concentration that gives a concentration halfway between bottom and top. Statistical analysis was performed using Student's t-test with a significance level of α=0. 05 meaning that differences between the negative control and the sample were considered significant when p<0. 05. Results were considered to be statistically significant if p-values were below 0. 05 (*), 0. 01 (**), and 0.
Discussion and conclusion We herein report the discovery of the first inhibitors of the YAP/ TAZ-TEAD interactions targeting the interface 2. The structures of five complexes with a high resolution allowed us to discover and characterize for the first time a cryptic site on the common surface of the YAP/TAZ/VGLL-binding domain of TEAD  [40, 41]. The phenol moiety of Y382, which, generally points towards the interface 2 (in the direction of S349) groove in almost all previous crystal structures of TEAD2, moved away. Re-analysis of all the TEAD units found in published crystallographic structures showed that, in two monomers (A and C) of mTEAD4 of the crystal complex of TAZ-mTEAD4 (PDB code: 5 GNO), this tyrosine residue (Y362 (Y382 in hTEAD2)) pointed towards C360 making a hydrogen bond with the thiol hydrogen atom when C360 is not covalently bonded to palmitate and in the two other monomers (B and D) pointed towards S329 (S349 in hTEAD2) (Fig.
The u-aminoalkyl or u-carboxyalkyl arm is supposed to help N-2 to engage hydrogen bond with the hydroxyl group of S349 through its electro-donating effect and head away the interface 2. We previously demonstrated the importance of the 3,4-dichlorophenyl moiety  [34]  and we planned in the future to delineate the relative importance of each chlorine atom of this moiety. Kaan et al. [32]  have previously reported the discovery of a fragment that targets the mTEAD4 interface 2 (pdb code: 5XJD). This fragment symmetrically binds to two molecules of TEAD. It is located closely to K389 at the third loop of a3 helix while our compounds bind to a pocket created by the flipping of Y382 (first loop of a3 helix) (Supplement Figure  12 ). Computational ligandmapping study allowed Kaan et al. to identify putative cryptic binding sites in mTEAD4.
The primers for AXL were designed by using Oligo 7 (version 7. 60) and sequence specificity checked using-BLAST software, the primers for CTGF were taken from Nagaraja et al. [54]  and Cyr61 from Chen et al. [55]. They were manufactured by Eurogentec. Primer sequences are listed below. The relative expression value of each target gene (AXL, CTGF and Cyr61) was utilizing the 2 ÀDDCT method using MAN2B1 for normalization. All experiments were performed at least in triplicates.
Acknowledgments This work was financially supported by grants from  le Minist ere de l'Education et de la Recherche  and the  University of Lille  (M. S. and P. S. ) and fundings from le  Canc eropôle Nord-Ouest  ( Mobility Support Program for Young Researchers , M. S. ). We express our thanks to the  NMR facility (Lille University) , the  Region Hauts-de-France (France) , the  Minist ere de la Jeunesse, de l'Education Nationale et de la Recherche (MJENR)  and the  Fonds Europ eens de D eveloppement R egional (FEDER)  and the  Lille University Molecular Interactions facility. This work was also supported by the  French Infrastructure for Integrated Structural Biology (FRISBI)   ANR-10-INSB-05-01. These experiments were performed at BL13-.
Amino acid residues highlighted are those establishing Van der Waals interactions with compound C58. B. 2D structure of hit fragment C58. C. 2D structures of putatively interesting explored modifications around fragment C58. X-ray crystal structure of C58 bound to syntenin was resolved (PDB code 6R9H) and revealed that C58 is located within the syntenin-PDZ2 domain on the same binding pocket as syndecan-2 peptide as shown in Figure  1A  (a superimposition view of binding mode of compounds C58 and C-terminal end peptide of Syndecan is given Figure  S1A  in Supp. Data). The phenyl ring of fragment C58 exhibits a strong π-stacking with Phe213, its nitrogen atom interacts with the backbone carbonyl of Phe211 through hydrogen bonds and its carboxylic acid forms the expected canonical hydrogen-bonding interactions with backbone amide group of Val209 and Gly210  [19].
Results were analyzed with a two-wavelength signal ratio: [intensity (665 nm)/intensity (620 nm)]*10 4. Percentage of inhibition was calculated using the following equation: % inhibition = [(compound signal)-(min signal)]/[(max signal)-(min signal)]*100, where 'max signal' is the signal ratio with the compound vehicle alone (DMSO) and 'min signal' the signal ratio without protein B. For IC50 measurements, values were normalized and fitted with Prism (GraphPad software) using the following equation: Y=100/(1+((X/IC50)^Hill slope)).
4-3-2. Design of virtual library The creation of the virtual focused library relied on the procedure described in the DOTS methodology  [30]. Briefly, a focused library was generated starting from an activated substructure (here, fragment 45 that contains an aryl halide reactive function), a collection of commercially available BBs and a set of in silico encoded medicinal chemistry relevant chemical reactions. This chemistry-driven strategy is expected to design accessible compounds with high reaction yields. The single Suzuki rule (#31 according to the reference study  [36]  was enabled for the design stage. A raw focused library of 1865 compounds was automatically designed. Then, several fully automated post-processing stages were applied to this raw focused library to extract a subset of duplicate-free compounds with a focus on structures with Then, yuck filters, which were used to design the clean subset of the ZINC database (http://zinc.
4-4-2. Cancer cell viability The effects of the compounds C58 and SyntOFF were tested both on MCF-7 and MCF-7 SyntKO cells. Briefly, the cells were treated with increasing concentrations of the compounds for 48hours and early/late apoptosis was measured by using the FITC Annexin V apoptosis detection kit with 7-AAD (Biolegend, San Diego, CA) according to the manufacturer. Annexin V positive but 7-AAD negative (early apoptotic cells) and Annexin V positive and 7-AAD positive (late stage apoptosis) was determined by using FACS LSRII flow cytometer (BD Biosciences) and data were analyzed with Flowjo software (Tree Star).
Acknowledgements This work was supported by grants from the  National Research Agency (ANR, Investissements d'Avenir, A *   MIDEX  project  ANR-11-IDEX-0001-02 ), the  Institut National du Cancer (INCa , subvention  2013-105 ), the  Belgian Foundation  against cancer (STK,  FA/2014/294 ), the  French National Research Agency  ( ANR-18-CE13-0017 , Project SynTEV), the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Sklodowska-Curie  grant agreement No  747025 , the  French Infrastructure for Integrated Structural Biology (FRISBI)   ANR-10-INSB-05-01  with the use of the  AFMB laboratory Xray facility. The authors acknowledge the  European Synchrotron Radiation Facility  ( ESRF ) as well as the SOLEIL synchrotron (Proxima 1 and Proxima 2 beamlines) for provision of synchrotron radiation facilities.
ACKNOWLEDGMENTS This work was supported by grants from  Gobierno Vasco-Departamento de Salud 2013111114  (to M. L. M. -C),  ELKARTEK 2016 ,  Departamento de Industria del Gobierno Vasco  (to M. L. MÀC),  Ministerio de Ciencia, Innovación y Universidades MICINN :  SAF2017-87301-R , and  RTI2018-096759-A-100  integrado en el  Plan Estatal de Investigación Científica y Técnica y Innovación, cofinanciado con Fondos FEDER  (to M. L. MÀ T. C. D respectively);  MCIU/AEI/FEDER ,  UE  ( RTI2018-095134-B-100 ) (to P. A. ),  AECC Bizkaia (M. S-M) ;  Asociación Española  contra el Cáncer (T. C. D),  Fundación Científica de la Asociación Española Contra el Cáncer (AECC Scientific Foundation) Rare Tumor Calls 2017  (to M. L. M, J. M. B. , M. A. A. , J. J. G. M. ),  La Caixa Foundation Program  (to M. L.
M and J. M. B. ), 2018  BBVA Foundation Grants for Scientific Research Teams  (to M. L. M. -C. ), Ayudas para apoyar grupos de investigación del sistema Universitario Vasco  IT971-16  (P. A. ). MyFirst Grant AIRC n. 16888,  Ricerca Finalizzata Ministero della Salute   RF-2016-02364358 ,  Ricerca corrente Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico  (to LV), the  European Union  ( EU) Programme Horizon 2020  (under grant agreement No. 777377 ) for the project  LITMUS-"Liver Investigation: Testing Marker Utility in Steatohepatitis " (to LV),  Fondazione IRCCS Ca' Granda "Liver BIBLE "  PR-0391 ,  Fondazione IRCCS Ca ' Granda core COVID-19  Biobank  ( RC100017A ) (to LV). This research was funded by the  CIBERehd  ( EHD15PI05/2016 ) and " Fondo de Investigaciones Sanitarias ,  Instituto de Salud Carlos III", Spain  ( PI16/00598  and  PI19/ 00819 , co-funded by  European Regional Development Fund/European Social Fund , " Investing in your future ");  Spanish Ministry of Economy, Industry and Competitiveness  ( SAF2016-75197-R) ;  "Junta de Castilla y Leon " (SA063P17);  AECC Scientific Foundation  ( 2017/2020 ), Spain; " Centro Internacional sobre el Envejecimiento " (OLD-HEPAMARKER,  0348_CIE_6_E ), Spain;  University of Salamanca Foundation, Spain  ( PC-TCUE18-20_051 ), and  Fundació Marato TV3  (Ref.
Pharmacokinetic (PK) Modeling for Eplerenone and KBP-5074 Plasma levels of KBP-5074 and eplerenone from Day 1 and Day 26 collected at 1, 2, 4, and 7 h post first dose were used to determine maximum plasma drug concentration (C max ) and area under the concentration time-curve from zero to last measurement (AUC 0-last ). PK modeling analysis for eplerenone and KBP-5074 was performed by FMD K&L (Fort Washington, PA, United States) and used data for groups 2-5 of studies A (eplerenone) and B (KBP-5074). For PK modeling for KBP-5074, pre-dose data for serum K + and UACR were not used. Day 7 data were only available for UACR and not K +. A turnover model was selected for analysis of delayed effects and fitted using post-hoc PK parameters. Stochastic approximation expectation maximization (SAEM) was used with the R package nlmixr 1.
TI EC 50 for serum K + EC 50 for UACR (1) PK/pharmacodynamic (PD) Modeling Analysis for UACR and Serum K + PK/PD modeling analysis for UACR and serum K + was performed by FMD K&L (Fort Washington, PA, United States) using data for groups 2-5 of studies A and B. Sequential fits of PK and data were performed. KBP-5074 and eplerenone plasma concentrations were fitted to a one-compartment, first order absorption PK model with a single transit compartment and first order elimination, shown below. For eplerenone, the definition of k tr k a was used, as k tr derived a large k a value (47. 8). The Akaike information criterion (AIC), calculated for models with k tr k a and k tr ≠k a, was 6,299 and 6,303, respectively. Hence, the k tr k a model was used for the PD analysis of eplerenone. I max was fixed to 1 to decrease the number of estimated parameters.