dataesr/scientific-paragraphs-categorization huggingface dataset:
Interestingly, a not previously detected copy number gain was found for the KIT gene in the tumor tissue of patient T1. The gained region is involved in tumor cell proliferation, angiogenesis, and metastatic disease. Only 1 actionable pathway was found in common for both CAS patients. The variants found in the TP53 gene were highlighted as actionable alterations, as previously found in the WES study (TP53 p. Arg175His and TP53 p. Val143Met for patients T1 and T4, respectively). No Food and Drug Administrationapproved therapies for angiosarcoma were recommended for these 2 patients, but 3 therapies with clinical trials were observed in common for both patients: AZD1775 (NCT numbers NCT02576444 and NCT018227384), Transferrin Receptor-Targeted Liposomal p53 (NCT02354547), and Modified Vaccinia Virus Ankara Vaccine (NCT02432963).
Acknowledgments We are grateful to  Dr Manuel Serrano  from the  Tumour Suppression Group (CNIO)  for critical revision of the manuscript. We want to acknowledge the patients and the  BioBank of the Complejo Hospitalario Universitario de Santiago (CHUS)  ( PT17/0015/0002 ), integrated into the  Spanish National Biobanks Network , for their collaboration and  Centro Nacional de An alisis Gen omico (CNAG)  for the technical support with WES data.
Sources of Funding Benitez's laboratory is partially funded by  Centro de Investigaci on (CIBERER) ,  Horizon2020   BRIDGES  project, and by the  Spanish Ministry of Health  supported by  Federaci on Española de Enfermedades Raras (FEDER)  funds ( PI16/00440 ). Garcia-Pavia's group is partially supported by the  Instituto de Salud Carlos III (ISCIII)  (grants  CB16/11/00432  and  PI14/0967 ) and by the  Spanish Ministry of Economy and Competitiveness  (grant  SAF2015-71863-REDT ). Garcia-Pavia's and  Crespo-Leiro 's groups are supported by  FEDER  funds. Urioste's laboratory is funded by  Spanish Ministry of Health  supported by  FEDER  fund ( PI14/00459 ).
04 species km -1 and an average species range of 25 km. The polychaete assemblage in APEI#3 showed the lowest densities, lowest diversity as well as very low, distant-independent similarity with the other four study areas. Given that APEI#3 is located in an oligotrophic province and separated from the CCFZ by the Clarion Fracture Zone, our results call into question the representativeness and the appropriateness of APEI#3 to meet its purpose of preserving the biodiversity of the CCFZ fauna. Two methods for estimating the total number of polychaete species gave estimates that ranged from 498 to 240 000 species. Both methods are biased by the high frequency of singletons in the dataset, which likely result from under-sampling; our estimates thereby.
5 mM), 0. 5 µL of PCR nucleotide mix (final concentration of 0. 2 mM each dNTP), 9. 875 µL of nuclease-free water, 2. 5 μL of each primer (final concentration of 1 µM), 2 µL template DNA and 0. 125 U of GoTaq® G2 visualized after electrophoresis on 1 % agarose gel, were sent to the MacroGen Europe Laboratory in Amsterdam (the Netherlands) to obtain sequences, using the same set of primers as used for the PCR. Overlapping sequence (forward and reverse) fragments were aligned into consensus sequences using Geneious  Pro 8. 1. 7 2005 -2015 (Biomatters Ltd). For COI, the sequences were translated into amino-acid alignments and checked for stop codons to avoid pseudogenes. The minimum length coverage was 207 bp for 16S, 327 bp for COI and 1615 bp for 18S. The sequences were blasted in GenBank to check for the presence of contamination.
Each set of genes was aligned separately using the following plugins: MAAFT  (Katoh et al. , 2002)  for 16S and 18S; and MUSCLE  (Edgar, 2004)  for COI. All sequences obtained in this study have been deposited in BOLD (http://www. boldsystems. org; (Ratnasingham and Hebert, 2007) ) or GenBank (http://www. ncbi. nlm. nih. gov/genbank/).
Environmental data Environmental data were compiled from  Hauquier et al. (2018)  and  Volz et al. (2018). Sediment samples were collected with a multi-corer or a gravity corer during the same cruise and in the same areas (see Martinez Arbizu and Haeckel, 2015 for details). The sediment characteristics studied by  Hauquier et al. (2018)  included a clay fraction (< 4 µm), a silt fraction (4-63 µm), total nitrogen (TN in weight%), total organic carbon (TOC in weight%) and chloroplastic pigment equivalents (CPE in µg ml -1 ). Nodules were weighed onboard for each box-core sample to calculate nodule density (kg m -2 ; Table  1 ). Particulate organic carbon flux (POC, mg C m -2 d -1 ) at the seafloor for our study areas were provided by  Volz et al. (2018). At the regional scale, POC flux (mg C m -2 d -1 ) at the seafloor was approximated using net surface primary production provided by the Ocean Productivity site  (Westberry et al.
All analyses were conducted using the R language (R Core Team, 2018) with RStudio (RStudio Team, 2015) and the following specific packages or functions: adespatial  (Dray et al. , 2019) , BiodiversityR  (Kindt and Coe, 2005) , fossil  (Vavrek, 2011) , vegan  (Oksanen et al. , 2015) , VennDiagram  (Chen, 2018) , beta. div. comp (Legendre, 2014) , ness  (Menot, 2019).
5-2 % of the organic carbon derived from sea-surface primary production  (Rowe et al. , 1991; Smith et al. , 1997; Smith et al. , 2008a). Variations in sea-surface primary productivity divide the NE Pacific abyss into three main areas  (Sokolova, 1997; Hannides and Smith, 2003; Smith and Demopoulos, 2003) : the eutrophic abyss in the equatorial upwelling zone (-5˚S-5˚N) with POC flux about 1-2 g C m -2 year -1 ; the mesotrophic abyss in the Equatorial North Pacific (5-15˚N) with a POC flux of about 0. 5-1. 5 g C m -2 year -1 and the oligotrophic abyss underlying the North Pacific Subtropical Gyre (15-35˚N) with a POC flux lower than 0. 5 g C m -2 year -1. Our metadata analysis confirmed that polychaete abundance was significantly and positively correlated with POC flux, distinguishing areas in the oligotrophic abyss (APEI#3, CLIMAX II, DOMES A, EqPac 9 and Kaplan West) with low abundance (4-21 ind.
Integrative taxonomy thus not only provides more accurate estimates of species diversity, but also facilitates comparisons across datasets. Over 90 % of the species in the abyssal Pacific are new to science  (Glover et al. , 2002)  and there are few attempts to try to name them  (Paterson et al. , 2016; Bonifácio and Menot, 2019) , although DNA sequences can easily be matched. Therefore, 26 MOTUs are shared between  Janssen et al. (2015)  and our study. The overlap is low but it should be noted that we had only 71 COI sequences belonging to 45 MOTUs to compare with the 556 COI sequences belonging to 233 MOTUs from  Janssen et al. (2015). This highlights a shortcoming of COI-based barcoding because success rates for COI sequencing are generally low and a combination of several genetic markers plus morphology is essential to accurately assess species diversity.
Acknowledgement The research leading to these results has received funding from the  Ifremer program  " Ressources Minérales Marines" (REMIMA) , the  JPI Oceans pilot action "Ecological Aspects of Deep-Sea Mining " and the  European Union   Seventh Framework Program  ( FP7/2007-2013 ) under the  MIDAS  project, grant agreement no. 603418. We are grateful to the crew of the RV Sonne, and all people involved in the field sampling and sample processing during the SO239 cruise. We would like to thank for their expertise in washing and sieving samples to  Dr. Stefanie Kaiser ,  Sarah Schnurr  and  Dr. Ana Hilário ; and for live-sorting the worms to Lenka Neal. Also, thanks to  Baptiste François  for sample sorting in the laboratory. We are grateful, as well, to all people involved in molecular analysis:  Dr.
The March cut was excluded from the analyses as it represented biomass production from the previous autumn, before the onset of CO 2 treatment. For each species and each of the four treatments, resilience to ECE was estimated by the biomass resilience index (RESIL), calculated as the ratio of above-ground biomass harvested after the ECE (November cut) to above-ground biomass harvested before the ECE (mean of April and June cuts). A RESIL value of 1 corresponds to full recovery of species biomass. This index was chosen from among others  (Ingrisch and Bahn, 2018)  because the biomass data harvested in November integrate ECE resistance and recovery periods. This index allows comparison of the treatments because it is calculated for each replicate macrocosm. Fig. 1. Experimental design and time course of the experiment.
Statistical analysis Statistical analyses were carried out with R software  (RStudio Team, 2015). Data were transformed when necessary (log, inverse, square, power 4, 5, or 6, boxcox) before analysis of variance (ANOVA) with the 'lmtest' package to conform to the assumptions of normality (Shapiro-Wilk test), homogeneity of variances (Bartlett test), and independence (Durbin-Watson test) or normality (Shapiro-Wilk test) of residues. For data for which the assumptions of normality, homogeneity of variances, and/or normality of residues were not obtained, analyses were carried out with the non-parametric Kruskal-Wallis test. For each trait, the factors species (D. glomerata, H. lanatus), CO 2 concentration (390, 520 ppm), climatic condition (Con, ECE), and date (D1, D2), as well as all of their interactions, were explored.
Acknowledgements This study was supported by an  ANR   VALIDATE  project grant and by the  EC FP7 Animal Change  project financially supported by the  European Community 's  Seventh Framework Programme  ( FP7/2007-2013 ) under the grant agreement number  266018. This study benefited from the  CNRS  staff and technical resources allocated to the  ECOTRONS Research Infrastructure , as well as from the state allocation ' Investissement d'Avenir' AnaEE-France   ANR-11-INBS-0001. We thank the technical staff of  INRA UREP and Herbipole groups  for extracting the intact soil monoliths;  Pascal Chapon ,  Olivier Darsonville ,  Lionel Thiery , and the  Ecotron team  for their technical support during this experiment; and  Pierre-Jean Haupais  for his contribution to soluble carbohydrate analysis. AA and MLB received a post-doctorate position through an  INRA  scientific package (2010)(2011)(2012)(2013)(2014); AA was also supported by the European  FP7 ExpeER Transnational Access programme.
Taxonomic diversity analysis Data were used to calculate the Taxonomic Richness (TR, number of taxa per 0. 25 m²) and abundance (individuals per 1/32 m² or 0. 25 ind. m -2 ) and the most widely-used biodiversity indices for each station, i. e. the Shannon-Weaver diversity index (H') in log2 and Pielou's evenness (J) for the five stations and four dates. Data analysis was performed using the PRIMER® version 6 software package (Plymouth Routines in Multivariate Ecological Research)  (Clarke and Gorley, 2006). The Ecological Status indicating the quality of the station was estimated from H' values according to the thresholds defined previously by  Vincent et al. (2002) : 0-1, bad; 1-2: poor; 2-3: moderate; 3-4 good and > 4: high. Dauvin et al. (2017)  had proposed thresholds for J', respectively <0.
General characteristics of the fauna A total of 147 taxa and a total of 8,404 individuals were recorded during the one-year sampling effort of 5 m². Among these taxa, the macrofauna is dominated by Polychaetes (64 species and 46. 0 % of abundance), crustaceans (39 species and 32. 8 %), molluscs (32 species and 19. 3 %), sipunculids (6 species and 0. 9 %), echinoderms (3 species and 0. 8 %), Cnidaria (2 species and 0. 1 %) and Chordata (1 species and 0. 1 %). The five dominant species are the polychaetes    2 ). The mean abundance per core sample varies from a minimum of 5. 6 for the Zostera-Tractor station in spring to 108. 6 for the Oyster station in summer. Most of the values are comprised between 40 and 80 individuals per 1/32 m² (Table  3 ). A significant seasonal pattern is observed for three of the five stations: Oyster, Zostera-Oyster and Zostera-Tractor (Table  4 ).
Acknowledgements This study was carried out within the framework of the  CIZO (Cartography and Interaction Eelgrass-Shellfish ) programme funded by the  Seine Normandy Water Agency  and the  Manche Departmental Council  with the support of  CRC   Manche/Mer-du-Nord. We thank  Louis Lefort ,  Magalie Legrain ,  Vincent Lefebvre ,  Suzy Moal ,  Naida Laisney , and  Morgane Ravache  for their participation in the sampling on the foreshore. We are grateful to  Michael Carpenter  for postediting the English style and grammar, and the two reviewers for their useful comments on the first version of this paper.
A Common Molecular Basal-Like Subgroup Can Be Distinguished in Different HNSCC Data Sets Over the last decade, several molecular classifications of HNSCC have described different head and neck tumor subgroups  [5] [6] [7] [8] [9]  (for a recent review, see  [17] ), that have been given different names. We showed that there are common subgroups with similar molecular identities, when considering four different datasets  [5] [6] [7] [8]  (Supplementary Materials Figure  S1A ). We focused on one subgroup, named "Basal" in  [6] [7] [8]  and "Group1" in  [5] , which comprises about 30% of HNSCC tumors. These tumors are mainly located in the oral cavity and to some extent in the oropharynx, HPV-negative, and composed of well-differentiated tumors (Figure  1A ). Signaling pathway analysis across the public datasets established that basal tumors display up-regulation of genes involved in the EGFR signaling pathway (amphiregulin (AREG), EREG), adaptation to hypoxia (Hypoxia Inducible Factor-1α (HIF1A), Carbonic Anhydrase 9 (CA9)), and differentiation/keratinization of basal epithelial cells (Cadherin 3 (CDH3), Cadherin 13 (CDH13)).
We investigated whether the same subgroups could be identified in our transcriptomic analysis of 98 HNSCC samples  [18]. We found four equivalent expression subgroups in our collection (i. e. , atypical (n = 28/98), basal (n = 40/98), classical (n = 17/98), and mesenchymal (n = 11/98) tumors), and confirmed the presence of the basal subgroup by analyzing characteristic overexpressed genes. We initially identified 18 genes that were up-regulated in the basal subgroups (Supplementary Materials Table  S1 ) of the three available datasets  [6] [7] [8]. They included EREG and AREG, which encode ligands of the EGF receptor (epiregulin and amphiregulin, respectively)  [14, 19]. Using RT-qPCR-based gene expression assays on RNA extracted from the 98 tumor samples, we found a statistically significant higher expression of both AREG and EREG in the basal tumors (ANOVA p < 0.
However, these studies are not directly comparable, since different drugs and samples (cell lines versus tumor specimens) were tested. We addressed this issue using a high-throughput comprehensive approach. We initially classified cell lines into basal and non-basal subtypes (Supplementary Materials Figure  S3 ) using a pattern of gene expression that categorized tumor samples into the corresponding subtypes and gene expression data from the Genomics Drug Sensitivity in Cancer project  [20]  and the CCLE  [21]  databases. We selected 10 basal-like and 15 nonbasal-like HNSCC cell lines (Supplementary Materials Table  S2 ). These cell lines were screened for   [5] [6] [7] [8]. (B) Boxplot of the gene expression of AREG and EREG genes measured by RT-qPCR in basal, mesenchymal, atypical, and classical HNSCC.
However, these studies are not directly comparable, since different drugs and samples (cell lines versus tumor specimens) were tested. We addressed this issue using a high-throughput comprehensive approach. We initially classified cell lines into basal and non-basal subtypes (Supplementary Materials Figure  S3 ) using a pattern of gene expression that categorized tumor samples into the corresponding subtypes and gene expression data from the Genomics Drug Sensitivity in Cancer project  [20]  and the CCLE  [21]  databases. We selected 10 basal-like and 15 non-basal-like HNSCC cell lines (Supplementary Materials Table  S2 ). These cell lines were screened for their in vitro sensitivity to three drugs that target EGFR (i. e. , Cetuximab, Erlotinib, Afatinib; see Material and Methods). Standard dose-response curves were established for all of the drugs and cell lines, and the areas under the curve (AUC), which inversely correlate with drug sensitivity, were used to evaluate drug response (Table  1 ).
Discussion HNSCC tumors can be divided into four groups based on their molecular characteristics: classical, basal, mesenchymal, and atypical  [5] [6] [7] [8] [9] 18]. Interestingly, basal tumors overexpress genes linked to activation of the EGFR pathway, indicating sensitivity to inhibition of EGFR  [9, 10]. We confirmed this hypothesis with a collection of HNSCC cell lines that resemble basal or non-basal tumors, based on their molecular features reported in several databases (the Genomics of Drug Sensitivity in Cancer (GDSC) project  [11]  and/or the Cancer Cell Line Encyclopedia  [21] ). Cell lines were treated with the small TKIs Erlotinib and Afatinib, as well as the anti-EGFR monoclonal antibody Cetuximab. Erlotinib and Afatinib have lower AUCs and more closely related efficacies compared to Cetuximab.
The PI3K/mTor inhibitor Gedatolisib was more efficient than any of the other treatments. Interestingly, Gedatolisib, in combination with the CDK4/6 Inhibitor Palbociclib, is being evaluated for patients with various solid tumors (including head and neck cancer), in the NCT03065062 phase I trial (ClinicalTrial. gov reference) that is currently recruiting participants. We tested a combination with a HER-2 inhibitor (Trastuzumab) because there is some evidence that HER-2 overexpression is associated with poor outcomes in HNSCCC  [30, 31]  and that HER-2 can affect the response to the EGFR blockade. HER-2 can affect signaling by forming heterodimers with other members of the ErbB family  [32]. EGFR/HER2 heterodimers decrease the efficacy of EGFR blockade in HNSCC cell lines  [33, 34]. In addition, acquired Cetuximab resistance in HNSCC xenografts can be overcome with a dual EGFR-HER2 kinase inhibitor  [35].
Curve fitting of the dose-response data was performed to determine several descriptive parameters, including IC50, area under the curve (AUC), and the fitting correlation parameter of the dose response curve. The AUC was used to analyze the results. For analysis of drug combinations, dose-response matrix data (single replicate) were obtained using six concentrations of each compound (1. 3 nM-10 µM, 6-fold dilutions for the drugs; 0. 37 nM-2. 85 µM, 12-fold dilutions for Cetuximab; 1. 54 nM-12 µM; 12-fold dilutions for Trastuzumab). As described above, cells were incubated at day four with the cell viability reagent and fluorescent signal intensities acquired. Curve fitting and synergy scoring (Bliss reference model) were done with SynergyFinder  [48].
Next Generation Sequencing DNA was extracted from cultured cells (5 million cells harvested in 200 µL PBS) using the QIAamp DNA Mini Kit (Qiagen, Courtaboeuf, France). Double strand DNA was quantified using a fluorometric method (Qubit dsDNA BR Assay, Thermo Fisher Scientific) and qualified by real-time PCR (FFPE QC Kit, Illumina, Paris, France). Mutation screening was performed by next generation sequencing (NGS) on a MiSeq Illumina platform using the Tumor Hotspot MASTR Plus assay (Multiplicom-Agilent, Les Ulis, France), which targets frequently occurring mutations (hotspots) in a panel of 26 genes (Supplementary Materials Table  S1 ). Sequencing data were aligned to human genome hg19 using the BWA-MEM algorithm (Burrows-Wheeler Aligner-maximal exact matches). Variants were called using three different variant callers: VarScan, GATK HaplotypeCaller (GATK-HC), and GATK Unified Genotyper (GATK-UG).
Datasets Four public datasets of transcriptomic profiles of HNSCC tumors were used: the 60 transcriptomic profiles of Chung et al. [5]  (GSE686), the 138 transcriptomic profiles of Walter et al. [6]  (GSE39368), the 303 transcriptomic profiles of the Cancer Genome Atlas  [7] , and the 134 transcriptomic profiles of Keck et al. [8]  (GSE40774). Two public datasets of transcriptomic profiles of HNSCC cell lines were studied: the 51 HNSCC cell lines of the Cancer Cell Line Encyclopedia (CCLE)  [21]  and the 47 HNSCC cell lines of the Genomics Drug Sensitivity in Cancer (GDSC) project  [20]. The transcriptomic profiles of our 98 HNSCC tumor samples  [18]  were used to predict the basal subtype in our tumor collection (E-MTAB-1328).
Data Analyses We used ANOVA models and the moderate t-test to identify differentially expressed genes. The association between the partitioning of the samples and the bio-clinical factors was evaluated in chi-squared tests or Fisher-exact tests when required. We used hypergeometric tests to measure the association between a gene list and the 17,306 biological pathways collected from Kyoto Encyclopedia of Genes and Genomes (KEGG), MSigDB, Gene Ontology (GO), or Biocarta. The four public datasets of HNSCC tumors originated four HNSCC molecular classifications  [5] [6] [7] [8]. Centroids were built for each of these subtypes with the common differentially expressed genes. Pearson correlations were used to compare the resulting centroids. The four transcriptomic datasets were scaled and aggregated.
A centroid-based predictor was then built on this aggregated dataset to predict the basal subtype in the two transcriptomic datasets of cell lines  [20, 21]. A cell line was defined as basal if the prediction was basal in the two datasets, and a cell line was defined as non-basal if the prediction was non-basal in the two datasets, or if the prediction was non-basal in one dataset and the cell line was not present in the second dataset. The criteria of fold change and area under the curve (AUC) were used to select the best markers of the basal subtype.
Gene Expression Assays Gene expression assays in tumor samples were performed by extracting the total RNA from 98 HNSCC frozen tissues  [18]  using the RNeasy kit (Qiagen, Courtaboeuf, France), according to the manufacturer's instructions. The integrity of extracted RNA was verified on an Agilent 2100 Bioanalyser (Agilent Technologies, Palo Alto, CA, USA). RNA was retro-transcribed using the Goscript reverse transcription system (Promega, Charbonnières-les-Bains, France), and real-time quantitative PCR was performed using the LightCycler ® 480 real-time PCR system (Roche, Mannheim, Germany). qRT-PCR data were analyzed using the LightCycler ® 480 software. The expression levels of each gene were normalized to the geometric mean Ct values of two internal controls, Ribosomal Protein Long P0 (RPL0) and Ubiquitin B (UBB).
The following primer pairs were used: AREG (5 -CCACAGTGCTGATGGATTTG-3 and 5 -GCCAGGTATTTGTGGTTCGT-3 ), EREG (5 -TCCCAGGAGAGTCCAGTGAT-3 and 5 -GTGTTCACATCGGACACCAG-3 ), RPLP0 (5 -GAAGGCTGTGGTGCTGATGG-3 and 5 -CCGGATATGAGGCAGCAGTT-3 ) and UBB (5 -GCTTTGTTGGGTGAGCTTGT-3 and 5 -CGAAGATCTGCATTTTGACCT-3 ). Gene expression assays on cultured cells were performed by extracting total RNA from pelleted cells using a standard TRIZol procedure (TRI Reagent ® : TR 118 Molecular Research Center), according to manufacturer's instructions. RNA was retro-transcribed using the Goscript reverse transcription system (Promega), and real-time quantitative PCR was performed using the LightCycler ® 480 real-time PCR system (Roche). AREG and EREG expression was measured using the pairs of primers shown above, and normalized to the expression of 18S RNA (ARN18S Forward: 5 -tgtggtgttgaggaaagcag-3 ; ARN18S Reverse: 5 -tccagaccattggctaggac-3 ) using the 2 -∆∆Ct method.