dataesr/scientific-paragraphs-categorization huggingface dataset:
Immunohistochemical stainings For the validation cohort, sections underwent immunohistochemical staining with an antibody against CD8, using steam heat-induced epitope retrieval, the Ventana Benchmark XT platform (Ventana-Roche Tissue Diagnostics, Meylan France) and DAB detection. The number of CD8 + cells was quantified using the HALO Image Analysis Software (Indica Labs). Cell culture and reagents Br16M3, Br25F4, and Br42M6 mouse melanoma cell lines were established from tumors arising from the LSL-BrafV600E;Tyr::CreERT2+/ o mouse model. 27 NR6. 1 mouse melanoma cell line was established from melanoma bearing Tyr:NRAS Q61K mice that were backcrossed on C57BL/6 J 28. BrafV600E mutation was induced in melanocytes by administrating Tamoxifen (Sigma) on the back skin. Established Br16M3, Br25F4, Br42M6, and NR6.
IHC staining analyses of mouse tumor samples Tumors were embedded in paraffin, and ZEB1 staining was performed using the anti-ZEB1 antibody (IHC-00419, 1/500, Bethyl) as well as the anti-CD8 antibody (clone 4SM15, 14-0808-82, 1/500, Invitrogen), the anti-CD4 antibody (clone 4SM95, 14-9766-82, 1/1000, Invitrogen), DAB (brown) or purple chromogen (for heavily pigmented tumors) detection and counterstaining with hematoxylin. Images were digitalized with a 3DHistech Pannoramic SCAN2 scanner on the Research Pathology Platform (CRCL). Quantification was done with HALO Image Analysis Software (Indica Labs).
Reverse transcription quantitative-PCR (RT-Q-PCR) Total RNA was isolated using RNeasy Kit (QIAGEN) and reverse transcribed using a high cDNA capacity reverse transcription kit following the manufacturer's instructions (Fisher Scientific). Real-time PCR intron-spanning assays were designed using the ProbeFinder software (Roche). All reactions, including no-template controls and RT controls were performed in triplicate on a CFX96 (Bio-Rad) and were analyzed with the Bio-Rad CFX manager software. Mouse HPRT1 was used for normalization. The primers are described in online supplemental table 5.
Statistical analyses Statistical analyses were performed using GraphPad Prism V. 8 software (GraphPad Software, Inc, San Diego, California, USA) or R software (V. 3. 6. 1). All experiments were performed at least in triplicate. Data are presented as mean±SD or ±SEM as specified in the figure legends. To assess significant correlation between signatures, a Pearson's correlation coefficient was performed. To determine significant differences between two groups, parametric data were analyzed using a t-test or Mann-Whitney test. All statistical tests were two tailed. The p values obtained were considered significant <0. 05. Heatmaps were generated with Excel.
ZEB1 expression was associated with an increase in overall immune infiltration (p=0. 0225, online supplemental figure  1A ) and was strongly correlated with cancer-associated fibroblast (CAF), endothelial cell, neutrophil, myeloid dendritic cell (R>0. 5, p<0. 0005), and NK cell scores (R=0. 3, p<0. 05) (online supplemental figure  1B ). This is consistent with the observation that ZEB1 is expressed by tumor cells and by stromal cells, endothelial cells, and some immune cell subsets. 29 Reanalysis of the published scRNA-Seq dataset from Jerby-Arnon et al  26  consistently revealed that ZEB1 is expressed by almost 50% of CAFs and endothelial cells, about 20% of T cells, 15% of B cells, 10% of macrophages, and 8% of NK cells (online supplemental figure  1C ). Hence, any conclusions drawn regarding a putative correlation between ZEB1 expression and immune subsets (online supplemental figure 1B) may have been severely biased in these conditions, thus pointing to the requirement of spatial analyses of tumors.
ZEB1-mediated impairment of T cell recruitment is independent of β-catenin pathway activation Since β-catenin pathway activation has been described as a major T cell exclusion mechanism in approximately 20%-30% of melanomas,  30  we wondered whether the effect of ZEB1 was independent of this pathway. We first determined a β-catenin score for each melanoma tumor based on RNA-Seq data, by using the previously described score from Spranger et al. 8  We showed that high ZEB1 expression was not correlated with activation of the β-catenin pathway, thus emphasizing that these two pathways may be uncoupled (figure  2A ). To further address this hypothesis by taking into account intratumor heterogeneity, we also reanalyzed the single cell RNA-seq dataset from Jerby-Arnon et al  26  and confirmed, at the single cell level, that the β-catenin score remained unaltered in ZEB1 high cells (figure  2B ).
Importantly, our study emphasizes the requirement for spatial analyses of tumors, allowing to tackle the issue of ZEB1 intratumoral heterogeneity. Indeed, we developed innovative multi-immunofluorescence stainings to specifically quantify ZEB1-expressing melanoma cells. We were thus able to exclude other ZEB1-expressing cells from the microenvironment, including CAFs, endothelial cells, and immune cells (T cells, macrophages and NK cells),  29  which significantly biased bulk RNA-seq analyses. Our strategy combining transcriptome and spatial analyses allowed to reliably address the crosstalk between melanoma cells and immune cells, more precisely to decipher the correlation between high ZEB1 expression in melanoma cells and decreased immune infiltration. Our findings highlight that studies on bulk tumors from public datasets, including the TCGA, may not be adequate to draw robust conclusions regarding markers shared by tumor cells and stroma, unless coupled with complementary spatial analyses.
Acknowledgements The authors would like to thank  Brigitte Manship  for critical reading,  Amélien Sanlaville  for his help with FACS analyses, and  Julien Marie  for RAG2 KO mice. Contributors MP performed and analyzed all experiments with the help of VB and MG. JB contributed to immunofluorescence stainings and analyses. LT performed RNAsequencing (RNA-seq) bioinformatics analyses. SD, JL, ML-B, and GT provided human samples and clinical data from the human sample experiments. BB and AdlF performed the pathological examination of the stainings. LL provided mouse models. YG-B, NB-V, BD, and CC helped to design and interpret data. AP provided grant support and advices. MP, JC, and SD conceived the project, designed experiments, and interpreted data. JC supervised the whole project, wrote the manuscript and acts as guarantor.
Funding This work was supported by the  Ligue Nationale contre le Cancer , the  LABEX DEVweCAN of the University of Lyon  ( ANR-10-LABX-0061 ), the  Lyon Integrated Research Institute in Cancer  ( SIRIC LYriCAN INCa-DGOS-Inserm_12563 ), the  Institut Convergence PLAsCAN  ( ANR-17-CONV-0002 ),the  Institut National contre le Cancer (INCa PAIR  melanoma  D22044 ,  INCA-DGOS PRTK Melpredict) , the  Fondation de France  (project  67134 ),  ARC (Sign'it grant Birdman) , the  Société Française de Dermatologie , the  ERiCAN program of Fondation MSD-Avenir  (Reference DS- 2018-0015 ), the  Fondation BMS , the association  Melarnaud and Vaincre le Mélanome. MelBase is sponsored by the  French National Cancer Institute (INCa). MP was supported by a fellowship from  'Région Rhônes Alpes ' and the  Association pour la Recherche contre le Cancer.
Most LRFR-induced genes in hypocotyls require both PIFs and YUCs In accordance with the established role of PIFs and YUCs for LRFR responses  [13] [14] [15] [16] [17] [18] 44  , gene expression in pif457 and yuc2589 was largely unresponsive to LRFR (Supplementary Fig. 1a ). PC  showed that hypocotyls of LRFR-treated pif457 and yuc2589 grouped with WL samples but yuc2589 cotyledons grouped closer to WT LRFR samples (Supplementary Fig. 1a ). We also evaluated the individual roles of PIFs and YUCs by analyzing the interactions between genotypes and light treatments. These comparisons revealed four groups among LRFR-upregulated genes: PIF and YUC dependent, only PIF dependent, only YUC dependent, and dependent on neither (Fig. 2a , Supplementary Fig. 2a , and Supplementary Data 3). In hypocotyls, most LRFR-induced genes required PIFs and YUCs, whereas the largest fraction depended on only PIFs in cotyledons (Fig.
For RNA sequencing, RNA quality was assessed on a Fragment Analyzer (Agilent Technologies). From 40 ng total RNA, mRNA was isolated with the NEBNext Poly(A) mRNA Magnetic Isolation Module. RNA-seq libraries were then prepared from the mRNA using the NEB-Next Ultra II Directional RNA Library Prep Kit for Illumina. Libraries were quantified by a fluorimetric method, and their quality assessed on a Fragment Analyzer (Agilent Technologies). Cluster generation was performed with the resulting libraries using Illumina HiSeq 3000/ 4000 SR Cluster Kit reagents. Libraries were sequenced on the Illumina HiSeq 4000 with HiSeq 3000/4000 SBS Kit reagents for 150 cycles. Sequencing data were demultiplexed with the bcl2fastq Conversion Software (v. 2. 20, Illumina; San Diego, California, USA).
5-min hold) and a 15 °C/min ramp to 320 °C. Injection (1 µl) was done in splitless mode; injector and mass spectrometry detector temperatures were set to 250 °C. The ion source in a EI set at 70 eV, the mass range is 40-700 m/ z Raw data files were processed using the vendor-specific MassHunter (version B. 07. 00, Agilent). Quantification of sterols was based on peak areas, which were derived from total ion current and using cholestanol as the internal standard. Each sterol was normalized to the total amount of detected sterols and presented as a percentage of the total.
Acknowledgements We thank  Martina Legris ,  Laure Allenbach Petrolati ,  Olivier Michaud ,  Mieke de Wit ,  Anupama Goyal ,  Ana Lopez Vazquez ,  Ganesh Mahadeo Nawkar , and  Maud Lagier  for providing resources, technical support and/or comments on the manuscript;  René Dreos  for advice on statistical methods;  Niko Geldner ,  Ersin Gül  ( ETH Zurich ),  Sinem Celebioven  ( University Zurich ),  Soner Yildiz  ( Icahn School of Medicine ) and  Yasin Dagdas  ( GMI, Vienna ) for suggestions and comments on the manuscript;  Christian Hardtke ,  Teva Vernoux  ( ENS Lyon ),  Richard Vierstra  ( Washington University, St Louis ),  Dany Geelen  ( University Gent ) and  Yasin Dagdas  for providing resources. We are grateful to the  CIF  (https:// cif. unil. ch/)for help with microscopy, the  GTF  (https://wp.
unil. ch/gtf/)for RNA-seq. experiments and the  Metabolomics Unit  (https://www. unil. ch/metabolomics/en/home. html) for the lipidomic analysis and the  Bordeaux-Metabolome  platform for sterol analysis (https://www. biomemb. cnrs. fr/en/lipidomic-plateform/). Work in the Fankhauser lab is supported by the  University of Lausanne  and the  Swiss National Science Foundation  ( 310030B_179558 ) and the  Velux Foundation  (Project  1455 ). The  Bordeaux Metabolome-Lipidome Facility-MetaboHUB by a grant  from  ANR  (no. ANR-11-INBS-0010 ). Manon Genva  thanks the  National Fund for Scientific Research (FNRS, Belgium , through grant  2022/V 3/5/053-40010130-JG/JN-2724 ) and the  University of Liège  for mobility grants.
Data availability The RNA-seq data discussed in this publication have been deposited in NCBI's Gene Expression Omnibus  105  and are accessible through GEO Series accession number GSE174655. The MS data for lipid measurements were deposited in Metabolights (https://www. ebi. ac. uk/ metabolights/). Untargeted lipidomic analysis with the unique identifier MTBLS2796, MTBLS5753 for the sphingolipidomics, and MTBLS5766 for sterols. Source data are provided with this paper.
ACKNOWLEDGEMENTS Author contributions: All authors contributed to the conceptual setting and structure of the manuscript during a workshop of the  I-Link  project  PlasticsInBiofilms. All authors contributed writing and to research for supporting literature. SM contributed to provide graphical support. All authors provided comments on the final version of the manuscript as well as on facing reviewers' comments. This publication was funded by the  Consejo Superior de Investigaciones Científicas, Ministerio de Ciencia Innovación y Universidades of Spain  ( I-Link  project PlasticsInBiofilms ref:  LINKA20169 ). EM and HG gratefully acknowledge funding from the  Plan Nacional de I1D1I, Retos Investigación del Ministerio de Ciencia, Innovación y Universidades of Spain  (Fluvial-P-removal, ref:  PID2019-111803RB-I00/AEI/10.
In 2002, Van Glabbeke et al. [12]  published a pooled analysis with independent patient data calculating progression-free rates for first-line or pre-treated STS patients who had been included in phase II trials of the European Organisation for Research and Treatment of Cancer (EORTC) e Soft Tissue and Bone Sarcoma Group (STBSG) database. Efficacy thresholds were estimated in order to make a distinction between active and inactive antineoplastic agents. In first-line, a 6month rate of 30e56% was considered as a reference value for drug activity depending on histology. For the pre-treated population, a 3-month rate !40% suggested drug activity and 20% inactivity for any histologic subtype. These values have been applied extensively (>420 citations) to design new studies for all STS. In a previous study by our group  (Kantidakis et al.
Extracting information for the meta-analyses For this work, the focus was on the second and third most prevalent STS in the database; LPS and SS. Two meta-analysis databases were designed with a row per treatment arm and treatment line (first-line versus pretreated population). For each of them, G. K. extracted the number of evaluable LPS or SS patients for PFSR (those included in the efficacy dataset based on the statistical plan's criteria), the PFSR at 3 and 6 months together with the 95% confidence intervals (95%-CIs), and the year of study activation. Placebo/best supportive care arms, arms with <10 patients, mixed treatment lines, or studies activated before 2000 were removed from the database. When summary PFS estimates (at 3 and 6 months) could not be retrieved from a publication, they were requested from first authors and/or study sponsors.
Statistical methods In both databases (for LPS and SS), a random-effects model was employed to estimate the overall PFSR at 3 and 6 months per line of treatment (first-line versus pretreated). The DerSimonian and Laird method was used to estimate the between-study variance in clinical trials  [18, 19]. The inverse variance method was used to pool treatment-specific PFS estimates (more weight is given to larger studies). For each treatment arm, the number of cases (patients alive and progression-free) was approximated based on the total number of evaluable patients and the recorded PFS estimate; the equivalent PFS proportion is defined as cases/evaluable patients. The calculated number of cases was employed under a binomial distribution to estimate the variance (unknown quantity) for each drug or combination and the 95%-CIs  [20, 21].
Clinical trials included The study selection is provided in Fig. 1. In total, 38 studies were potentially relevant for the meta-analyses (35 for LPS, 16 for SS): 25 trials were included in the LPS meta-analyses [30e54] and 13 trials  [30,32,38,41,42,46e48,52,53,55e57]  in the SS metaanalyses. Table  1  Main characteristics of all studies included in the LPS or SS meta-analyses. Studies in the SS database are presented in shade. Treatments were classified as recommended (yes or no) according to ESMO 2021 guidelines  [23]. Study period Z period of first to last patient accrual. Evaluable patients were those who satisfied the study's statistical plan criteria for inclusion in efficacy datasets. Trabectedin 24h Z trabectedin 24-h infusion treatment schedule. The 3-h infusion treatment arm was excluded from the LPS meta-analysis due to limited number of patients (n Z 6 < 10).
Risk of publication bias The contour enhanced funnel plots did not indicate systematic asymmetry between the studies included for LPS or SS meta-analyses with the exception of pretreated LPS population at 6 months. Tests for funnel plot asymmetry indicated low risk of publication bias in the databases for SS and first-line LPS, as well as high risk of bias for pre-treated LPS at 6 months (see Appendix sections 2. 3 and 2. 4). However, publication bias cannot be excluded for first-line SS patients because of the very limited number of studies (three trials, five treatment regimens).
Sensitivity meta-analyses Regarding LPS (see Appendix section 2. 3), Baujat plots detected 'Blay 2014: Trabectedin'  [53]  as a potentially influential treatment regimen for first-line at 3 and 6 months (overall pooled PFSR decreased 2% and 3% after the exclusion of this treatment regimen). Overall heterogeneity slightly decreased. For patients previously treated with systemic therapy, 'Samuels 2017: pazopanib'  [44]  was identified by Baujat plots and diagnostics (overall PFSR decreased 2% and 1% at 3 and 6 months but heterogeneity did not go down). Results were robust to the candidate outlier in the pre-treated setting and less robust in the first-line setting. Secondly for SS (see Appendix section 2. 4), the plots and diagnostics for first-line agents pointed out 'Judson 2014: doxorubicin þ ifosfamide'  [32]  as the most influential study (overall pooled PFSR decreased 6% at 3 months and 10% at 6 months after removing it from the database).
Discussion This research project yielded efficacy thresholds to design new phase II clinical trials for advanced/metastatic LPS or SS patients with PFSR at 3 or 6 months as the primary endpoint, based on meta-analyses of summary data collected from sponsors and published papers (2003e2018). Reference values were estimated for the parameter of null hypothesis (P 0 ) as the overall pooled PFSR per treatment line, and new values were calculated for the parameter of alternative hypothesis (P 1 ) using the recommended treatment effects to target by the ESMO-MCBS recommendations  [15]. Two decades ago, the Van Glabbeke study  [12]  suggested benchmarks for various STS patients who participated in phase II clinical trials of the EORTC-STBSG database for treatment with inactive (used for P 0 ) or active agents (used for P 1 ).
Acknowledgements Georgios Kantidakis's work as a Fellow at EORTC Headquarters was supported by a grant from the  EORTC Soft Tissue and Bone Sarcoma Group and Leiden University Medical Center department of Medical Oncology  as well as from the  EORTC Cancer Research Fund (ECRF). Marie Vinches 's work as a Fellow at EORTC Headquarters was supported by a grant from  Fonds Cancer (FOCA)  from  Belgium. This publication was supported by a donation from  Kom Op Tegen Kanker (Stand up to Cancer) , the  Flemish Cancer Society from Belgium. The authors would like to express their appreciation to all the primary investigators (PI) who shared valuable PFS estimates 3 and 6 months for liposarcoma (LPS) and/or synovial sarcoma (SS) patients, namely,  Dr Akira Kawai  (1 study; LPS),  Dr PA Cassier  (1 study; LPS),  Dr Mark A.
Proteomics analysis of the cellular adaptation to ECM stiffness and CNF1 toxin activity To understand how host cells adapt their proteome to ECM stiffness to eventually control bacterial invasion, we performed label-free, quantitative proteomics analysis of HUVECs cultured on fibronectin ECM of different stiffnesses (low 1 kPa, 4 kPa, high 50 kPa). To study the impact of CNF1 toxin on this cellular response, we cultured cells overnight and then assigned them to either the untreated (NT) or CNF1-treated group (CNF1) upon treatment for an additional 2 h with the toxin (Supplementary Fig. 2A ). Protein lysates from four replicates per condition were processed for liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis. A total of 3515 protein groups were identified, and 1804 were reliably quantified (protein groups with at least three valid LFQ intensity values in one of the experimental conditions) (listed in Supplementary Data 1 and 2).
1, 4 vs. 1 or 50 vs. 4) in either the non-treated or CNF1-treated group (Fig. 2b  and Supplementary Data 4). We found common terms shared between the NT-and CNF1-treated conditions (keywords: SH3 domain, cell adhesion, metal-binding, LIM domain, zinc), while the CNF1 analysis also identified terms associated with metabolism (aminoacyl-tRNA biosynthesis, fatty-acid elongation) and innate immune responses (natural killer cell-mediated cytotoxicity). The CNF1 toxin induces Rac1 permanent activation, an event sensed by the HACE1 E3 ligase that polyubiquitylates the active form of Rac1 as a function of its strength of activation for subsequent proteasomal degradation  16, 22. In the search for potential partners of the Rac1-HACE1 system regulated by ECM stiffness, we used Cytoscape to build the interaction network corresponding to the common enhanced keywords of NT (yellow nodes) and CNF1 (yellow nodes with orange edges) conditions in our enrichment analysis, which we further merged with the HACE1 network (light-blue nodes) retrieved from the IntAct database (Fig.
0001. involved in muscular dystrophies  60. Moreover, OPTN localization to FAs was described in podocytes  61. OPTN belongs to the group of proteins with differentially upregulated expression between 1 and 50 kPa in our dataset (Supplementary Data 3, cluster2). Therefore, OPTN represents a good candidate. We verified the expression of OPTN as a function of ECM compliance by western blots. For this, fibronectin was coated on hydrogels of increasing stiffness, spanning elastic modulus values from 1 to 50 kPa, and OPTN levels were monitored by western blots following 15 h of adhesion (Fig. 2d, e ). The OPTN levels increased gradually with the ECM stiffness. In contrast, the expression of HACE1 was constant at the different ECM compliances (Fig. 2d  and Supplementary Fig. 2D ). Given that OPTN expression can be transcriptionally induced by TNF-α and IFN-γ  62  , we assessed whether stiffness-mediated induction of  OPTN is through a transcriptional mechanism.
We then challenged our hypothesis by overexpressing OPTN in cells plated on low stiffness ECM (1 kPa), where endogenous OPTN expression is low (Fig. 2d ), and detected an OPTN-induced increase in Rac1 polyubiquitylation (Fig. 3d ). Altogether, these data showed that Rac1 ubiquitylation by HACE1 is tuned by ECM stiffness via modulation of OPTN levels. We next assessed the importance of OPTN in the ability of HACE1 to interact with its target Rac1. As shown in Fig. 3e , when OPTN was knocked down, HACE1 was impaired in its ability to bind to active Rac1Q61L. Together, these data showed that OPTN is required for the correct binding of HACE1 to its target Rac1 for ubiquitylation. We then analyzed the impact of OPTN on Rac1 activity in cells. We used a GST-PAK-RBD affinity column with lysates from the control or OPTN knockdown cells cultured on fibronectin-coated surfaces (Fig.
Results from non-CNF1-treated cells (NT) and CNF1 toxin-treated cells (CNF1) are analyzed separately. c Network of association between the HACE1 protein interactome and the five sets of proteins belonging to common enriched UP_Keywords retrieved from the non-CNF1-treated cells (NT) and CNF1 toxintreated cells (CNF1) analysis. Rhomboid boxes represent common UP_Keywords. White nodes in the network represent proteins detected in the dataset that are not significantly upregulated. Yellow nodes correspond to upregulated proteins in any of the pairwise NT comparisons (50 vs. 1, 4 vs. 1 or 50 vs. 4), a dark yellow edge of the node represents upregulation also for the CNF1 samples in any of the pairwise comparisons. Blue nodes correspond to the Hace1 interactome. Larger protein nodes are those that belong both to the protein-protein interaction network and to the UP_Keywords network.