dataesr/scientific-paragraphs-categorization huggingface dataset:
Phosphorylated (p)-STAT5, a canonical target of GHR signalling, was detected in the nucleus of many cells within positive GHR staining regions (Figure  1E ). Importantly, the analysis of Ivy Glioblastoma Atlas project dataset showed that GHR mRNA expression was significantly enriched in the infiltrating tumour (IT) anatomical structure compared to the core of the tumour, termed here cellular tumour (CT) (Figure  1F ). Comparison between patients with GBM GHR high or GBM GHR low from ONT series revealed no difference in terms of prognosis (14. 47 months vs. 14. 00 months, p =. 87), age at diagnosis and sex ratio (Figure  S1B-D ). GBM GHR low samples being characterized by high EGFR expression (Figure  1C ), this overall suggests that the clinical impact of GHR overexpression was comparable to that of the oncogenic driver EGFR.
The clinical and molecular significance of GHR was also assessed in TCGA dataset. 13, 29 In this series, patient prognosis was not impacted either by GHR expression status. Of note, no amplification, deletion or somatic mutation of GHR were reported in this GBM series. Expectedly, EGFR amplified or mutant cases were underrepresented in GBM GHR high (Figure  1G ). Distribution analysis with previously described molecular and epigenetic subtypes  12, 30  revealed that the molecular subtypes were distributed differently between GBM GHR high and GBM GHR low (Figure  1G ). Indeed, an enrichment of glioma CpG island methylator phenotype (G-CIMP) and mesenchymal subtypes was observed in TCGA GBM GHR high samples, whereas the classical and proneural subtypes were underrepresented (Figure  1G ). Isocitrate dehydrogenase 1 (IDH1) somatic mutant R132H cases, which are associated with G-CIMP subtype,  30  were found to be enriched in GBM GHR high from ONT (18% in GBM GHR high vs.
Validation of these transformed PDCLs is shown in Figure  S4A-D. As expected,  33, 34  mild constitutive STAT5 signalling was observed only in 4339 CA-GHR or N13-1520 CA-GHR. GH stimulation markedly upregulated STAT5 signalling GH in 4339 WT-GHR and N13-1520 WT-GHR , but not in 4339 CA-GHR or N13-1520 CA-GHR , in agreement with the insensitivity of rabbit CA-GHR construct to human GH. The analysis of the global proteome of each cell population was then undertaken. Raw data have been deposited to the ProteomeXchange Consortium via the PRIDE  35  partner repository (identifier PXD004969). Proteomic expression data from 4339 WT-GHR (in the absence of hGH) or 4339 CA-GHR were compared to the data from 4339 GFP (Curie proteomic series, n = 3). Compared to 4339 GFP , we found 929 and 951 proteins differentially expressed (p <.
DISCUSSION We here identified GHR overexpression and active GHR downstream signalling as a novel oncogenic pathway in a subset of GBM (GBM GHR high ) falling out of the well-characterized subgroup of EGFR-overexpressing GBMs. SOCS2, the major negative regulator of GHR pathway,  [25] [26] [27]  is downregulated in GBM GHR high , presumably contributing to the maintenance of high-GHR expression and signalling. The fact that two genes acting in the same intracellular signalling pathway are modulated in a consistent direction (Figure  1 ) strongly supports the importance of the GHR pathway in this GBM subgroup, representing up to 30% of cases. The mutually exclusive GHR high /SOCS2 low /EGFR low and GHR low /SOCS2 high /EGFR high expression patterns iden-tified in the present series were also observed in five additional independent datasets.
Selection of patients Human GBM tissue samples were selected from OncoNeu-roTek (ONT; Paris Brain Institute) tumour tissue bank. Brain tumours fulfilling the following inclusion criteria were selected from our brain tumour database: (1) histologically proven newly diagnosed supratentorial GBM according to the World Health Organization classification system 2016, concordantly reviewed by two neuropathologists,  2  (2) a clinical history compatible with a newly diagnosed de novo GBM (no prior history of lower grade tumour), (3) available high-quality tumour RNA and/or frozen tissue allowing further molecular analysis and (4) written consent obtained from the patient for molecular analysis.
RNA extraction and processing of expression array data Approximately 50 mg of tissues from the initial 54 GBM samples were used to extract total RNA using the RNeasy Lipid Tissue mini kit (Quiagen, CA), following the instructions of the manufacturer. The quality of RNA obtained was checked using a Bioanalyser System (Agilent Technologies, Paolo Alto, CA) using the RNA Nano Chips. Genome U133 Plus 2. 0 Expression arrays data processing was done according to the manufacturer recommendations. Normalization was performed using the RMA method. 71 Clustering analysis and class comparison using a univariate t-test were performed using dChip software (http://biosun1. harvard. edu/complab/dchip/). 72  A p-value <. 005 was used to define differentially expressed genes. In order to compare the gene expression profile of the gliomas with normal brain, we used the gene expression data of five samples of corpus callosum (GSM175855, GSM175856, GSM175857, GSM175858, GSM176050) and five samples of cortex (GSM176049, GSM176344, GSM176345, GSM176346, GSM176347), available in the Gene Expression Omnibus repository (GSE7307).
Molecular subtypes and genomic alterations from additional series Expression data were extracted from ONT dataset, as well as five additional publicly available datasets,  9, 13, [18] [19] [20]  and plotted using pheatmap R function. For the analysis of molecular subtypes and genomic alterations in relation with GHR expression, a series of 273 patients was selected from the TCGA dataset  13  and accessed at www. cbioportal. org. 29 GHR expression data (available for 160 out of 273 patients only), molecular subtype and gene mutational and copy number status for each sample were obtained from Ref. [ 13 ]. High-GHR expression status was determined using an mRNA threshold of z-score 1. 3.
GHR mRNA expression analysis in tumour anatomical structures GHR RNA sequencing data were extracted from the Ivy Glioblastoma Atlas project dataset,  73  which comprises a total of 122 RNA samples generated from 10 tumours and sorted in 5 structures (Leading Edge, Infiltrating Tumour, Cellular Tumour, Microvascular Proliferation, and Pseudopalisading Cells Around Necrosis) identified by H&E staining. The Microvascular Proliferation structure was removed from our analysis to focus on the expression of GHR in tumour cells only.
Sections were stained with Mayers's hematoxylin (#MHS32, Sigma) and eosin (mix of eosin Y #341972Q and Orange G #1. 15925. 0025, VWR, Fontenay-sous-Bois, France). Sections were stained with anti-NuMA primary antibody (Thermo Fisher Scientific Cat# PA5-22285, RRID:AB_11157241) with secondary antibody goat anti-rabbit (Thermo Fisher Scientific Cat# A-11034, RRID:AB_2576217). For the cell invasion analysis in mice brains, NuMA-positive cells were quantified on coronal sections throughout entire brains using the QuPath cell detection software. For human FFPE sections, slides were deparaffinized with xylene, ethanol 100%, ethanol 90%, ethanol 70%, ethanol 50% and water baths. Antigens were retrieved by microwave heating (power 400 W) and blocked with 5% BSA, 5% goat serum in phosphate saline buffer (PBS) in.
GHR, GH, luciferase and CRISPR/Cas9 expression vectors Expression vectors for human WT and rabbit CA-GHR were generous gifts from Mike Waters and Andrew J. Brooks (University of Queensland, Australia). 33, 34 The CA-GHR vector was generated by removing the extracellular domain of rabbit GHR (to ensure the absence of human GH stimulation) and fusing the transmembrane and cytoplasmic domains to Jun zippers to achieve GH-independent forced functional dimerization. WT-GH vector was built using GH1 variant 1 sequence accession number NM_000515. 5. The control GFP vector was obtained from Addgene. 75 Luciferase/mKate2 vector was previously described. 76 These vectors were then inserted into lentiviral vectors (see Lentiviral production). Stable transduced PDCLs were generated by transduction using a multiplicity of infection of 10 and selection with puromycin for 5 days for GHR and GH vectors, or by FACS sorting for luciferase/mKate2 vectors (BioRad S3e Cell Sorter).
SOCS2 promoter methylation SOCS2 promoter methylation was assessed in the panel of PDCLs following a previously described method (Base-specific cleavage and Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry -MALDI-TOF MS -assay  32  ) and outsourced to Varionostic (Ulm Germany). In the GBM sample panel, promoter methylation was measured by pyrosequencing by the Epigénomique Fonctionnelle team from Université de Paris. Pyrosequencing primers were designed using the PyroMark Assay Design Software 2. 0 (Qiagen). An amount of 500-ng genomic DNA was subjected to bisulfite conversion using an EpiTect Bisulfite Kit (Qiagen, Cat# 59124). PCR reactions (list of primers in Table  2 ) were performed in a final volume of 25 μl, using a PyroMark PCR kit (Qiagen, Cat# 978703), with one of the primers biotinylated and containing 12.
SILAC labelling and proteomic analysis A SILAC-based proteomic analysis was carried out to accurately quantify the proteomes of three constructstransduced PDCLs (4339 GFP, 4339 WT-GHR, 4339 CA-GHR) derived from 4339 PDCL. All cell populations were metabolically encoded by a 3-week exposure to one of the following SILAC labelling included in the culture medium: (1) light labelling: light l-arginine and l-lysine; (2) medium labelling: l-arginine-HCl,  13  C 6 + l-lysine-2HCl, 4,4,5,5-d 4 ; (3) heavy labelling: l-Arginine-HCl, 13 C 6 , 15 N 4 + l-lysine-2HCl,  13  C 6 , 15 N 2 (Thermo Fisher Scientific). For each of the three replicates, labelling was inverted for each cell line variant. All three cell populations (heavy, medium and light) were mixed before proteomic analysis, lysed with RIPA buffer supplemented with phosphatase and protease inhibitor cocktail (Pierce, Brebieres, France), separated on SDS-PAGE gels and analysed by nano-LC-MS/MS (see Proteomic data acquisition [Institut Curie Series]).
Proteomic data acquisition (Institut Curie Series) Protein extracts separated on SDS-PAGE gels (10%, Invitrogen at 30 mA during 1 h 30 min) were stained with colloidal blue staining (LabSafe GEL Blue G Biosciences). Gel slices were then excised (12 bands), and proteins were reduced with 10-mM DTT prior to alkylation with 55-mM iodoacetamide. After washing and shrinking the gel pieces with 100% MeCN, in-gel digestion was performed using trypsin (Promega) overnight in 25-mM NH 4 HCO 3 at 30 • C. Peptides were extracted from gel slices and analysed by nano-LC-MS/MS using an RSLCnano system (Ultimate 3000, Thermo Scientific) coupled to an Orbitrap Fusion mass spectrometer (Q-OT-qIT, Thermo Fisher Scientific). Samples were loaded on a C18 precolumn (300-μm inner diameter × 5 mm; Thermo Scientific) at 20 μl/min in 2% MeCN,.
6 Th with the quadrupole, HCD fragmentation with normalized collision energy of 35 and rapid scan MS analysis in the ion trap. The MS2 ion count target was set to 104 and the max injection time was 100 ms and only those precursors with charge state 2-7 were sampled for MS2. The dynamic exclusion duration was set to 60 s with a 10-ppm tolerance around the selected precursor and its isotopes. The instrument was run in top speed mode with 3-s cycles. Data were acquired using the Xcalibur software (v 3. 0) and the resulting spectra were interrogated by Sequest HT through Proteome Discoverer (v 1. 4, Thermo Scientific) with the SwissProt Homo Sapiens database (032015). Carbamidomethyl cysteine, oxidation of methionine, Nterminal acetylation, heavy 13 C 6 15 N 2 -lysine (Lys8) and 13 C 6 15 N 4 -arginine (Arg10) and medium 2 H 4 -lysine (Lys4) and  13  C 6 -arginine (Arg6) were set as variable modifications.
Specificity of digestion was set for trypsin and allowed two missed cleavage sites. Mass tolerances in MS and MS/MS were set to 10 ppm and. 5 Da, respectively. The resulting files were further processed using myProMS. 82 he Sequest HT target and decoy search result were val-idated at 1% false discovery rate (FDR) with Percolator. For SILAC-based protein quantification, peptides XICs (Extracted Ion Chromatograms) were retrieved from Proteome Discoverer. Scale normalization was applied to compensate for mixing errors of the different SILAC cultures. Protein ratios were computed as the geometrical mean of related peptides ions ratios using the R package limma  83  and p-values were adjusted using Benjamini-Hochberg FDR control threshold set to. 05. Analysis of biological replicates of all the samples identified a total of 9535 proteins at an FDR of 1% (5639 common proteins).
Proteomic data acquisition (Yale University Serie) Proteins were precipitated by Chloroform:MeOH:water (1:4:4) for shipping. Protein pellet was washed twice with methanol then vacuum dried briefly and reconstituted in a urea:ammonium buffer (8 M:. 4 M), digested with Lys-C (overnight) followed by trypsin (Promega, Inc. ; for 5 h) and desalted using an RP macro-spin (The Nest Group) desalting cartridge. The eluted peptides were reconstituted in. 1% formic acid prior to injection onto an LC-MS/MS mass spectrometer. LC-MS/MS was performed on an LTQ Orbitrap Elite (Thermo Fisher Scientific) equipped with a Waters Symmetry C18 (180 μm × 20 mm) trap column and a 1. 7 μm, 75 μm × 250 mm nanoAcquity UPLC column (35 • C). Trapping was done using 99% Buffer A (100% water,. 1% formic acid) and peptide separation was accomplished using a linear gradient of solvents A (.
1% Formic Acid in Water) and B (. 075% formic acid in acetonitrile) over 210 min, at a flow rate of 300 nl/min. MS spectra were acquired in the Orbitrap using 1 microscan and a maximum injection time of 900 ms followed by three data dependent MS/MS acquisitions in the ion trap (with precursor ions threshold of >3000). The total cycle time for both MS and MS/MS acquisitions was 2. 4 s. Peaks targeted for MS/MS fragmentation by collision induced dissociation were first isolated with a 2-Da window followed by normalized collision energy of 35%. Dynamic exclusion was activated where former target ions were excluded for 30 s. Data were processed and analysed as previously described in myProMS. 82.
Statistics 9] [20]  Pearson's chi-square tests were performed to compare molecular subgroups and gene alterations distribution between GHR high and GHR low GBMs in the TCGA dataset. Both analyses were performed on R. All other statistical tests were performed using GraphPad Prism 6. Fisher's exact test was performed to compare age at diagnostic and sex ratio in the ONT series. OS curves were generated using the Kaplan-Meier method and compared with a log-rank or Gehan-Breslow-Wilcoxon tests. Analysis for identification of signalling pathways and biological modules (p-values and activation z-score) from expression arrays and proteome data was performed using Ingenuity Pathway Analysis software (Qiagen). 84 For all other analysis, one-way ANOVA with Tukey's test for one parameter multiple comparisons, two-way ANOVA with Sidak's test for two parameters multiple comparisons, or two-tailed t-test for single comparisons were performed on the mean ± s.
such as infiltrative tumour (IT), leading edge (LE), cellular tumour (CT) and pseudopalisading cells around necrosis (PAN). ***p ≤. 001; *p ≤. 05. (G) Proportional distribution of EGFR genomic alterations (mutations or amplification) and molecular subtypes in GBM GHR high (n = 60) versus GBM GHR low (n = 456) (TCGA dataset, z-score of 1. 3). Results were analysed by Pearson's chi-square test.
thank Edward Voss and Jean Kanyo from MS & Proteomics Resource for their help in sample preparation and MS data collection. Finally, we thank Vincent Beaudoin and Camille Levasseur for technical support. Part of this work was carried out in the CELIS, Histomics and ICONICS core facilities of the ICM. F I N A N C I A L S U P P O R T This work is part of the national program Cartes d'Identité des Tumeurs R (CIT) (http://cit. ligue-cancer. net/) funded and developed by La Ligue Contre le Cancer. This work was supported by grants from: -L'Association pour la Recherche sur les Tumeurs Cérébrales (ARTC) (AI/MV) -La Fondation ARC pour la Recherche sur le Cancer GlioTEx project (AI) -Cancéropôle Ile-de France Emergence program 2015-1-EMERG-17-INSERM 6-1 (AI/MV/VG) -US National Institutes of Health NIDDK R01-DK107441 (SJF) -US Veterans Administration Merit Review 1I01BX003718 (SJF) -FRM DGE 20121125630 (FDi, DL) -NIH A09562 MIMED-Neurovascular Renegeration (JLT) -SiRIC CURAMUS grant INCa-DGOS-Inserm_12560 (MS) -Program "investissements d'avenir" ANR-10-IAIHU-06 (MS) -Ligue Nationale contre le Cancer (LNCC; équipe labellisée) (MS) -Yale School of Medicine (TTL) -Office of The Director, National Institutes of Health (S10OD02365101A1, S10OD019967 and S10OD018034) (TTL).
Genotyping and imputation Each individual from the Milieu Intérieur cohort was genotyped by the HumanOmniExpress-24 BeadChip (Illumina), covering 719,665 SNPs. 245,766 rare functional variants were also genotyped on a HumanExome-12 BeadChip (Illumina). After quality control, both datasets were merged, for a total of 723,341 SNPs. Next, IMPUTE v. 2 was used to perform genotype imputation, on 1Mb windows buffered by an additional 1Mb. Before imputation, SNPs were phased using 500 conditioning haplotypes, 50 MCMC, 10 burn-in and 10 pruning iterations. SNPs and allelic states were aligned to the imputation reference panel from the 1000 Genome Project Phase 1 v3 (2010/11/23). SNPs with dissimilar alleles (even after flipping) or ambiguous C/G or A/T alleles were filtered out. Imputation yielded a total of set of 37,895,612 SNPs.
, 2017  22  and Deming et al. , 2016  23. For each dataset, the percentage of replication (y-axis) as a function of the significance threshold (x-axis) was computed as the number of cis-regulated proteins reported in this workthat were also reported in the corresponding dataset as cis-regulated (the "replicated" proteins) divided by the total number of proteins reported as cis-regulated in a previous study that were analyzed in our work (the "replicable" proteins). The dashed vertical lines represent the p-value significance threshold corresponding to the FDR of cis-pQTLs (red) and to the nominal replication threshold (orange).
recruited at BioTrial (Rennes, France). The Milieur Intérieur cohort was approved by the Comité de Protection des Personnes -Ouest 6 (Committee for the protection of persons) on June 13th, 2012 and by French Agence nationale de sécurité du médicament (ANSM) on June 22nd, 2012. The study is sponsored by Institut Pasteur (Pasteur ID-RCB Number: 2012-A00238-35), and was conducted as a single centre interventional study without an investigational product. The original protocol was registered under ClinicalTrials. gov (study# NCT01699893). The samples and data used in this study were formally established as the Milieu Interieur biocollection (NCT03905993), with approvals by the Comité de Protection des Personnes -Sud Méditerranée and the Commission nationale de l'informatique et des libertés (CNIL) on April 11, 2018.
Albert (Insitro) §, Darragh Duffy §, Lluis Quintana-Murci §, ¶ unless otherwise indicated, partners are located at Institut Pasteur, Paris § co-coordinators of the Milieu Intérieur Consortium Additional information can be found at: www. milieuinterieur. fr Sample Information Samples came from the Milieur Intérieur Cohort, which was approved by the Comité de Protection des Personnes -Ouest 6 (Committee for the protection of persons) on June 13th, 2012 and by French Agence nationale de sécurité du médicament (ANSM) on June 22nd, 2012. The study is sponsored by Institut Pasteur (Pasteur ID-RCB Number: 2012-A00238-35), and was conducted as a single centre interventional study without an investigational product. The original protocol was registered under ClinicalTrials. gov (study NCT01699893).
Acknowledgments This work benefited from support of the  French government's Invest  in the Future programme. This programme is managed by the  Agence Nationale de la Recherche , reference  ANR-10-LABX-69-01. The  Clinical Bioinformatics Laboratory  of the  Imagine Institute  was partly supported by the  French National Research Agency (ANR)  " Investissements d'Avenir" Program  (Grant  ANR-10-IAHU-01 ).
Results: After 12 weeks, improvement was reported in 78. 6% of patients (PGI-C). Physician assessment (CGI-I) indicated clinical improvement in 82. 8% of patients. Patient assessment of lymphedema symptoms showed a signi cant decrease in discomfort (p = 0. 02) and swelling (p = 0. 01). Signi cant declines in mean global GLS scores (p < 0. 0001), and in the proportion of patients reporting heaviness, tightness, swelling or urinary dysfunction (p < 0. 05 for all) were also observed. LyQLI scores decreased (indicating improved QoL) in each of the physical, psychosocial (p = 0. 05) and practical domains. The compressive garment was well tolerated with high compliance and adverse events (due to swelling or discomfort) led to permanent discontinuation in only 3 patients. Conclusions: Use of a new genital compression garment over 12 weeks improves QoL and clinical measures in patients with genital lymphedema (NCT04602559; Registration: 10/20/2020).
Study design and patient population This was a multi-center prospective open-label single-armed study (NCT04602559) conducted within three specialist centers for vascular medicine (CHU Montpellier, CHU Toulouse, and Cognacq-Jay Hospital, Paris, France) between December 2020 and July 2021. Eligible patients were individuals aged ≥15 years with secondary genital lymphedema or primary (stage II or III as de ned by the International Society of Lymphology criteria  [21] ) presenting discomfort related to lymphedema, able to provide informed consent and participate over the 12 week period of the study follow up. Exclusion criteria included those patients with recent genital surgical interventions in the last three months or intensive lymphedema reduction therapy within four weeks, pregnancy or planned pregnancy, or recognized contraindications for compression therapy e.
Outcome measures Baseline characteristics were assessed at inclusion. The patient global impression of change (PGI-C) questionnaire was used to evaluate the changes observed in terms of limitation of activities, symptoms, emotions and overall QoL after wearing the garment for 12 weeks. Patients maintained a daily diary to record compliance with shorts and pad wear instructions (in terms of days and hours per day). There is no precise de nition of compliance for pressure garment wear in the literature. We de ned compliance as good when use was ≥5 days each week with a minimum of 6 hours during daytime and 6 hours overnight. Safety and tolerance data were collected via the patient's diary and recorded by the investigating physicians during follow-up visits. At these visits, all data were recorded by the physicians in electronic case report forms (eCRF).
Data analysis Descriptive analyses were performed for all study variables. Continuous variables are presented as mean values ± standard deviation [SD] or median and interquartile range (IQR) i. e. , rst quartile to third quartile (Q1-Q3). Categorical variables are presented as frequency counts and percentages. Analyses were performed for intention-to-treat (ITT) and the per-protocol populations (PP). The ITT population included all patients who wore the investigational garment at least once and who attended for at least one postbaseline visit. The PP population included all patients of the safety population without major deviations. Safety analyses were conducted in the entire population. Poor compliance i. e. , wearing the garment for less than 4 days a week (or for less than 50% of the day) was considered a major protocol deviation.