dataesr/scientific-paragraphs-categorization huggingface dataset:
Of note, in addition to modulating the enzymatic activity of CD38, these mAbs also have the capacity to induce cytotoxicity through diverse mechanisms, such as induction of complement activation, Ab-dependent cellular cytotoxicity (ADCC) or phagocytosis, and programmed cell death  (420). Albeit extensive clinical experience of utilizing the aforementioned mAbs against CD38overexpressing hematologic malignancies, the recently launched trial NCT03473730 constitutes the first application of a CD38specific mAb in patients with solid tumor malignancies. Another approach for limiting the intratumoral interstitial adenosine is the oxygenation of the TME  (293). As mentioned, hypoxia promotes build-up of extracellular adenosine at least by inducing upregulation of CD39 and CD73 as well as downregulation of adenosine transporters.
Three A2AR antagonists are currently being evaluated as single agents in Phase I/II trials to treat cancer patients bearing solid tumors. In particular, Corvus Pharmaceuticals, AstraZeneca, and Novartis have undertaken the clinical development of CPI-444 (NCT02655822), AZD4635 (NCT02740985), and NIR178 (NCT02403193, NCT03207867), respectively. As for A2AR, genetic deletion of A2BR reduces tumor growth rate  (399, 423)  while A2BR -/-tumor cells display reduced metastatic potential  (359, 367). Notably, administration of A2BR antagonists in tumor-bearing mice reduces tumor growth  (315, 398, 399)  and metastasis  (292, 359, 368)  eventually prolonging their survival  (359). Mechanistically, antagonism of A2BR in vivo augments the intra-tumoral presence of CD8 + T cells  (315, 398) , NKT  (315, 398)  as well as the mRNA levels of IFNγ and CXCL10  (399)  and the concentration of TNFα, IFNγ, and GzB  (398)  in the TME.
This intervention further results in decreased accumulation of MDSCc  (315, 398)  and IL-10 (398), as well as reduced levels of VEGF and angiogenesis  (315). Based on encouraging preclinical results, Palobiofarma recently launched a dose escalation Phase I study (NCT03274479) administering ¢ anti-PD-L1: • 9G2 (mAb) (96) • B7-DC/Fc (400) ¢ anti-PD-1mAb: • RMP1-14  (396, 401)  ¢ A2AR antagonists: • SCH58261 (96, 394, 401) • ZM241385 (400) • SYN115 (401) • CPI-444  (396)  ¢ Breast: • AT3 (401) • 4T1. 2 (394, 401) ¢ Melanoma: B16-F10 (394) ¢ Colon: • MC38 (396, 401) • CT26  (396)  ¢ Lung: • 344SQ (96) • LLC-JSP (96) ¢ Lymphoma: EL4 (400) ¢ Primary tumor expansion rate ↓ (96, 396, 400, 401) • IFN-γ (401) • Retained in perforin KO mice  (401)  ¢ Metastasis formation ↓ (394, 401) • Tumor CD73 (394) • NK cells > CD8 + T cells  (394)  ¢ Survival ↑ (394, 396, 401) • CD8 + T cells > NK cells (394) ↑ IFN-γ + CD8 + or tumor-specific T cells (401) ↑ GzB + CD8 + T cells (401) ↑ NK cells (394) PD-1 ICB & A2BR antagonism ¢ anti-PD-1mAb: • RMP1-14 (359) ¢ A2BR antagonist: • PSB1115 (359) ¢ Melanoma: B16-F10 (359) ¢ Breast: 4T1.
Indeed, dual therapy with an anti-CD73 mAb and an A2AR agonist confers superior tumor protection as compared to either one as a monotherapy  (384). However, this additive effect is lost when CD73 is targeted with a pharmacologic inhibitor, thus underscoring the capacity of CD73 to promote tumor progression in a catalytic activity-independent manner  (384). In light of these studies, Evotec and Exscientia have partnered to develop A2AR/CD73 bi-specific inhibitory molecules  (425) , whereas NCT03454451, NCT03549000 as well as the Phase Ib/II clinical trial NCT03381274 sponsored by MedImmune all include solid tumor-bearing patient cohorts scheduled to be treated with combinations of an anti-CD73 mAb along with a pharmacologic A2AR antagonist.
Several clinical trials assessing anti-CD73 mAb treatment along with anti-PD-1 mAb (NCT03454451, NCT03549000) or anti-PDL-1 mAb (NCT02503774, NCT03773666, NCT03267589, NCT03334617) of advanced solid tumors are recruiting or underway. Intratumoral upregulation of CD38 and subsequent adenosine production was recently identified as a mechanism of acquired resistance to PD-1/PD-L1 blockade and mAb-mediated or pharmacologic inhibition of CD38 was shown to significantly improve the anti-tumor efficacy of an anti-PDL-1 mAb  (96). In terms of mechanisms, tumors from mice receiving the combinatorial therapy displayed higher accumulation of CD8 + T cells, effector memory CD8 + T cells, ICOS + CD4 + T cells and lower levels of MDSCs and Tregs as compared to tumors from single-agent treated mice  (96).
Moreover, the combination enables increased production of IFNγ and GzB by CD8 + tumor infiltrating T cells  (401)  while augmenting the intra-tumoral presence of NK cells  (394). Five clinical trials for the treatment of solid-tumor patient cohorts with A2AR antagonists along with anti-PD-1 Ab (NCT02403193, NCT03207867) or anti-PD-L1 Ab (NCT02655822, NCT03337698, NCT02740985) are ongoing. Finally, dual therapy comprising A2BR antagonism and PD-1 blockade is superior to either monotherapy at decreasing metastasis and improving survival of tumor-bearing mice  (359). However, no clinical trials have been launched to date to explore this combination in human cancer patients.
In light of such observations, the clinical trials NCT03611556 and NCT03742102 are set to decipher the potency of CT regimens when provided in combination with the CD73blocking Ab Oleclumab, supplemented or not by PD-1 blockade. Along the same lines, others explored if direct antagonism of A2AR and A2BR would augment the antitumor effects of CT agents. Indeed, tumor-bearing mice treated with Doxorubicin  (359, 405, 407) , Dacarbazine  (398) , or Oxaliplatin  (398, 407)  in combination with A2AR (405), A2BR  (359, 398) , or dual A2AR/A2BR antagonists (407) displayed superior tumor control  (398, 405, 407)  or survived longer  (359). Of note, tumors derived from mice treated with the combination of Dacarbazine and PSB1115, an A2BR antagonist, were more heavily infiltrated by CD8 + T cells as well as NKT cells and contained higher levels of GzB than tumors derived from counterpart mice subjected to Dacarbazine monotherapy  (398).
In terms of mechanisms, the combinatorial therapy significantly increases the intra-tumoral presence of CD8 + and CD4 + FoxP3 neg T cells while decreasing MDSCs  (408). In addition, melanoma patients harboring BRAF-mutant tumors exhibit a trend for elevated expression of CD73 whereas co-administration of an A2AR antagonist in mice bearing BRAF-mutant tumors increased the therapeutic benefit achieved either by BRAF inhibition or by the combination of BRAF and MEK inhibitors  (393). Finally, CD73 and A2AR are overexpressed in NSCLCs harboring EGFR mutations  (438)  and even though preclinical studies demonstrating increased efficacy of concomitant inhibition of EGFR and A2AR are not currently publicly available, the clinical trial NCT03381274 includes a cohort of patients with advanced NSCLC that will receive both an EGFR inhibitor and an A2AR antagonist.
D. Anderson 2017 Cell Carcinoma or Cancer Center Muscle Invasive Bladder Cancer A2A NCT02403193 NIR178 I/Ib ¢ Single agent Advanced NSCLC Palobiofarma 2015 ¢ In combination with spartalizumab (anti-PD-1) [-1pt] NCT03207867 NIR178 II ¢ Single agent Advanced solid Novartis 2017 ¢ In combination with malignancies spartalizumab (anti-PD-1) (Continued).
T-PLL cells have shown a notable in vitro sensitivity towards JAK inhibition, which was not directly linked to the JAK/STAT mutation status  [15, 16]. First reports present individual clinical activity of tofacitinib (pan JAK inhibitor) and ruxolitinib (JAK 1/2 inhibitor) in relapsed T-PLL  [27, 28]. Although many studies identified JAK and STAT genes to be commonly mutated in T-PLL, these analyses have been performed in rather small cohorts not providing a sufficient dataset to determine reliable mutation and variant allele frequencies (VAFs). In addition, the publication overlap of these studies was unresolved and a systematic assessment for other potential genomic causes (e. g. , copy number alterations (CNAs)) has not been performed. Here, we conducted a meta-analysis that was supplemented by new primary data, hence providing the largest cohort to date that evaluated the genomic aberrations affecting JAK/STAT signaling in T-PLL.
After subtracting all cases reported in more than one study, we identified 275 unique T-PLL cases as the core cohort. Notably, data that were acquired by WGS, WES, and SNP array analysis were re-analyzed as primary data in cases, in which raw data were available (WES: 62. 2%, n = 46/74 T-PLL patients; WGS: 50. 0%, n = 4/8; SNP arrays: 100. 0%, n = 71/71), while data gained through Sanger seq. or TAS were not re-evaluated. By re-analyzing the WES and SNP array data through a uniform pipeline, homogeneity of the resulting dataset was obtained. JAK1, JAK2, JAK3, TYK2, STAT1, STAT2, STAT3, STAT4, STAT5A, STAT5B, and STAT6 are further referred to as 'any JAK or STAT gene'.
Mutations in JAK and STAT Genes are Predominantly Found at Subclonal Levels We included T-PLL cases with available data from WGS or WES in order to calculate the frequency of genomic variants in JAK and STAT genes. We identified a cumulative rate of 62. 1% (n = 54/87, Figure  1B ) cases with variants in any JAK or STAT gene. The vast majority of them were somatic single-nucleotide variants (SNVs) of missense type (96. 0%, n = 72/75 of variants detected by WGS/WES). The remaining three lesions (4. 0%, n = 3/75) were small in-frame deletions in JAK1 or JAK3. In the following these SNVs and small in-frame deletions are referred to as 'mutations'. Somatic variants were called and known single nucleotide polymorphisms were filtered out in those tumor samples for which a matched germline sample was available.
Data Acquisition Available literature was systematically screened for all genomic profiling studies that have information on the mutational status of any JAK or STAT gene in T-PLL, regardless of the sequencing technique. We identified a total of 10 publications meeting these criteria (Table  S6 ). Raw data of these publications was obtained from different sources, either publicly available or kindly provided by the corresponding authors. Full descriptions of the composed data set and detailed information on the sources of data are provided in Table  S6. Considered T-PLL patients had provided written informed consent and all the studies were originally approved by their institutional review boards.
Data Merging Considering the rarity of T-PLL and the strong collaboration network in T-PLL research, we assumed a pronounced exchange of patient samples between different centers and studies. To determine all redundantly analyzed patients in our cohort, we requested basic patient information to allow us to precisely identify potential overlaps while still ensuring anonymity of all the patients (Figure  1A ). In cases where genomic regions were sequenced by different methods, we included the information provided by the widest type of analysis, ranking WGS > WES > TAS > Sanger sequencing. In total, 275 distinct T-PLL patients were included in our analyses.
Raw Data Analysis Data acquired through whole-exome sequencing and SNP-array analysis was re-analyzed for those cases in which raw data was available, while data obtained through Sanger sequencing or targeted amplicon sequencing was not re-analyzed. Studies where raw data was not available were included by applying the already analyzed data. Detailed information on the data sources are listed in Table  S6  and on the applied tools with references in Table  S7. The below subsections describe the various genomic analyses performed for the meta-analysis.
Whole-Exome Sequencing (WES) The sequenced raw exome reads were trimmed of B blocks from the end using Trimmomatic (RWTH Aachen, Aachen, Germany). The trimmed data were aligned to the human reference genome (GRCh build 37) using Burrows Wheeler Aligner (bwa-0. 7. 12, Wellcome Trust Sanger Institute, Cambridge, UK). After alignment the potential PCR duplicates were removed using Picard (Broad Institute of Harvard and MIT, Cambridge, MA, USA), and BAM files were generated, sorted, and indexed using SAMtools (Wellcome Trust Sanger Institute, Cambridge, UK). Exomic regions were re-aligned and the base quality scores were re-calibrated according to the Genome Analysis Toolkit Best Practices recommendations (GATK 4. 1. 3. 0). VarScan2. 2. 3 (The Genome Institute, St. Louis, MO, USA) was used to call somatic mutations with the following parameters: strand-filter 1, min-coverage-normal 8, min-coverage-tumor 6, somatic-p-value 0.
Somatic Copy-Number Alterations (sCNAs) Genotyping of Affymetrix GenomeWide SNP6 array (Thermo Fisher Scientific, Waltham, MA, USA) was performed using Genotype Console Software 4 (GTC4, Thermo Fisher Scientific) with the default parameters using Birdseed v2 algorithms (Broad Institute of Harvard and MIT). In order to infer copy number variations in the T-PLL genome, we used the pooled controls as a reference (non-tumor hematopoietic cell DNA as 'germline' from T-PLL patients, n = 13) created using the GTC pipeline. The Canary algorithm was used to call CN state using the default settings in GTC. We performed segmentation analysis on the basis of identified SNPs/CNVs using inbuilt algorithm. Copy number values and segmentation were visually assessed in the Integrative Genomics Viewer (Broad Institute, Cambridge, MA, USA).
Gene Expression Profiling (GEP) The processed normalized expression profiles generated with GenomeStudio were downloaded from GEO. Data were merged and adjusted for batch effects using linear regression. A differential expression analysis was performed using Student's t-test on expression values of patients and healthy controls (CD3+ pan T-cells from 10 healthy donors). The differentially expressed probes were mapped to the respective genes by Illumina provided annotation files. Principal component analysis of the whole gene expression set was performed with R.
Clinical Data and Statistical Analyses All publications were screened for accompanying clinical data and authors were contacted for further information. In total, we obtained and merged clinical data from nine publications  [9] [10] [11] [12] [13] [14] [15] [16] 18]. For patients who were reported in multiple studies, we included the most detailed clinical data set. To assess possible correlations between mutational status and clinical phenotypes, we performed a screening approach testing for a broad range of parameters (Table  S3 ), where the patients were stratified by overall mutational status, mutational status of specific genes, and by specific mutations. As a control, we used clinical data from T-PLL patients, who were unmutated in all JAK/STAT genes analyzed with WES/WGS. All statistical analyses were performed using R software and R packages (Table  S7 ).
Analysis of overall survival, as measured from day of diagnosis until day of event or censoring, was performed and graphed with R survival and R survminer packages. Log rank statistics were calculated to test for differences in survival distributions. Continuous clinical parameters were tested for normal distribution with Shapiro-Wilk test and further assessed for their associations to JAK/STAT mutational status with either Student's t-test or Wilcoxon rank sum test, depending on the normality test. Fisher's exact test was used for testing differences in categorical data in patients with JAK/STAT mutations compared to unmutated patients. A p < 0. 05 was considered to be statistically significant.
Immunoblots We performed Western blots of whole-cell lysates of primary T-PLL cells and CD3+ pan T-cell isolated from age matched healthy controls by magnetic-bead based cell enrichment. Positive selection of CD3+ pan T-cells of healthy controls was performed according to the manufactures instruction (Biolegend). Following antibodies from Cell Signaling Technology were used in a 1:1000 dilution according to the manufacturer's instruction: phospho-STAT5TYR694 (clone C11C5, catalogue #9359), STAT5 (polyclonal, #9363), and GAPDH (clone 14C10, #2118). As a secondary antibody we made use of HRP-coupled anti-rabbit (Dianova) in a 1:5000 dilution. Immunoblots were done using Western Bright ECL (Advansta, Menlo Park, CA, USA) and luminescence intensities were evaluated by densitometry (ImageJ software, National Institute of Health, Bethesda, MD, USA).
Funding: This research was funded by the  DFG Research Unit   FOR1961  (Control-T;  HE3553/4-2 ) and the  DFG CRC SFB 1076  (Project  B9 ), by the  Köln Fortune program , by the  Fritz Thyssen Foundation  ( 10. 15. 2. 034MN ), by the  Academy of Finland  (grants  292611 , 295504 ,  310507 ,  326238 ), and by the  Cancer Society of Finland. This work was also supported by the  EU   Transcan-2 consortium 'ERANET-PLL ' and by the  ERAPerMed consortium 'JAKSTAT-TARGET'. A. S. is funded by a scholarship through the  José Carreras Leukemia Foundation  ( DJCLS 03 F/2016 ).
Acknowledgments We thank the patients,  Bounthaphany Bounxouei  (Associate Professor and Director), and staff of  Mahosot Hospital, especially the microbiology laboratory  staff and the ward staff for their technical help and support. We also thank  Bounnack Saysanasongkham  (Associate Professor and Director of  Department of Health Care, Ministry of Health ) and  Bounkong Syhavong  ( Associate Professor and Minister of Health), Laos , for their kind help and support. We thank the staff of the Meningococcal Reference Unit of the  Health Protection Agency (Manchester, UK)  for N. meningitidis typing and the staff of the Respiratory and Vaccine Preventable Bacteria Reference Unit of  Public Health England (Colindale, UK ) for Haemophilus influenzae typing. We also thank  Mavuto Mukaka  for his help with statistical analysis and  Nicholas Day  for comments on the paper.
Literature review: rapid evidence assessment Literature search Three databases were searched for articles published between January 2005 and November 2016 containing keywords related to meningococcal vaccines and economic evaluation: the National Health Service Economic Evaluation Database (UK-based; includes > 16,000 economic evaluations of healthcare interventions), MED-LINE (searched via PubMed), and Google Scholar. Of the 31 articles included in the analysis, most analyzed serogroup B (MenB) vaccines (12/31) followed by quadrivalent conjugate vaccines against serogroups A, C, W, and Y (MenACWY; 9/31). High-income countries such as the United Kingdom (5/31), the United States (5/31), and Canada (3/31) produced the most published evaluations; of the 26 studies that provided full economic evaluations, 7 studies deemed vaccination against MenB, serogroup C (MenC), or MenACWY to be cost-effective; 5 concluded that the evaluated vaccine was not cost-effective; 11 provided mixed results (cost-effectiveness varied based on cost per dose); and 3 were considered unclear (experts were uncertain about criteria for being deemed cost-effective).
Acknowledgements The authors would like to acknowledge  Dr Maria Espinoza  of the  University of Toronto (Toronto, ON, Canada ) and  Dr Ivar Kristiansen  of the  University of Oslo (Oslo, Norway)  for helpful discussions and critical reading of the manuscript. Editorial/medical writing support was provided by  Jill E. Kolesar, PhD , at  Complete Healthcare Communications, LLC (North Wales, PA, USA ), a  CHC Group Company , and was funded by  Pfizer Inc. Sanofi Pasteur  and consultancy fees from  IMS Health  and  AstraZeneca , paid to her employer. She is supported by the  NIHR Health Pro-tection Research Unit in Evaluation of Interventions at the University of Bristol. The views expressed in this article are those of the authors and not necessarily those of the  NHS , the  NIHR , or the  Department of Health and Social Care.
Cytokine/ Chemokine profile in aqueous humor The Bio-Plex Mouse 23-Plex Panel assay (Bio-Rad, Marne-la-Coquette, France) was used according to the manufacturer's recommendations to measure cytokine and chemokine levels in aqueous humor. As each pool of aqueous humor from eight eyes consisted of �40μl, duplicates of 20μl samples were analyzed. The assay plate layout consisted in a standard series, one blank well and duplicates of 20 μL samples, diluted to 50 μL with BioPlex mouse serum diluent. Data were analyzed with Bio-Plex Manager TM software V1. 1 (BioRad). The fold induction index was calculated as the ratio of cytokine concentration in infected mice and the mean concentration of PBS injected mice.
Acknowledgments Our work has been supported by the  Fondation de Recherche Medicale  ( Retinal Physiopathology program ),  Universitede Strasbourg, and Hopitaux Universitaires de Strasbourg. E ´. R. received a Berthe Fouassier Ph. D. scholarship from the  Fondation de France  (grants no. 12165  and  2012-32622 ). We want to thank  Lydia Arnoux ,  Rachel Huber  and  Sylvie Matern  for their valuable technical help.
Data Processing and Analysis for CyTOF Data were normalized using Rachel Finck's MATLAB normalizer  [24]. Replicates were concatenated using the FCS file concatenation tool (Cytobank, Mountain View, CA, USA). Four-Element Calibration Beads were excluded by manual gating on the Ce 140 channel. Singlets were selected on an 191 Ir DNA intercalator/cell length bivariate plot (Fig. 1 ). We consistently observed a clear population of cells showing a strong background in all channels and in   Cell events were selected within singlets and normalization beads excluded using iridium-191 nucleus staining. Then, cell events were manually gated on the CD66abce/CD3 bivariate plot to exclude CD3/CD66abce double-positive cells, eosinophils that bind all metal complexes on the antibodies. Cell clustering was performed using the SPADE algorithm, grouping phenotypically related cells in a homogenous cluster.
We performed a deeper analysis of these nodes using our recently released SPADEVizR R-package  [27] , dedicated to cytometry data analysis and focusing primarily on SPADE-derived data. The SPADE nodes were categorized by calculating the 1st and 99th percentiles of the expression distribution of the total cells for each marker and dividing this range into five expression categories (online suppl. Fig. 1 ). The mean of the median marker expression of cells contained in each node was then used to assign the expression of each marker to one of the five categories. Data for individuals with less than 50 events in a given node were excluded from the calculation to avoid biasing the phenotypical characterization of these small cell nodes. Hierarchical clustering was performed using the Euclidean metric and complete linkage method we recently published  [27].
1, BD Bioscience), CD11b V450 (ICRF144, BD Bioscience), CD3 BV605 (SP34. 2, BD Bioscience), CD8a BV605 (RPAT8, BD Bioscience), CD20 BV605 (2H7, BD Bioscience), CD62L BV711 (SK11, BD Bioscience), CD32abc BV786 (FLI8. 26, BD Bioscience), CD14 FITC (M5E2, BD Bioscience), CD16 PerCP-Cy55 (3G8, BD Bioscience), CDw125 PE (A14, BD Bioscience), CXCR4 PE-Dazzle 594 (12G5, Biolegend), CD89 PE-Cy7 (A59, Biolegend), HLADR AF700 (L234, Biolegend), and CD66abce APC-Vio770 (TET2, Myltenyi Biotec). Samples were then lysed and fixed using BD fluorescence-activated cell sorting (FACS) Lysing (BD Bioscience) for 15 min. After an additional wash with PBS, acquisition was performed on a BD FORTESSA flow cytometer (BD Bioscience) and analyzed using FlowJo software. The fluorescence of the bioparticles increases with acidification of the phagolysosome.
Acknowledgments This work was supported by the " Programme Investissements d'Avenir" (PIA) , managed by the  ANR  under reference  ANR-11-INBS-0008 , funding the  Infectious Disease Models and Innovative Therapies  ( IDMIT, Fontenay-aux-Roses, France) infrastructure , and  ANR-10-EQPX-02-01 , funding the  FlowCyTech facility (IDMIT, Fontenay-aux-Roses, France). We thank  Gilead  and  ViiV  for providing the antiretroviral drugs. Animal studies were supported by the  ANRS  and  MSD Avenir , as part of the  pVIS-CONTI research program , and the  SIVART ANRS-IDMIT CO1 research program. We thank all members of the  FlowCyTech, Animal Science and Welfare, and Infectiology Immunology Laboratory core  facilities of the  IDMIT infrastructure  for their excellent expertise and outstanding contribution.