dataesr/scientific-paragraphs-categorization huggingface dataset:
METHODS The clinicians who had reported the occurrence of DRESS syndrome in patients being treated with raltegravir were invited to collaborate in a study to determine the patients' HLA genotypes. Those who agreed to collaborate then explained the study to their patients, and with the patients' informed consent, requested HLA ABC, DRB and DQB typing, which was performed at the relevant local laboratory. We used the RegiSCAR scoring system to classify each case as a possible, probable, or definite case of DRESS syndrome  [11] , and we used the Naranjo adverse drug reaction probability scale to estimate for each case the probability that DRESS syndrome was secondary to treatment with raltegravir  [12]. The structures of the relevant HLA molecules were compared using the crystal structure of HLA-B*53:01, Protein Data Bank code 1A1M.
Next, DOCKER was used to align the sequence of HLA-B*53:01 with HLA-B*35:01 (PileUp software; GCG Wisconsin Package), calculate sequence similarity based on a BLOSUM62 matrix, and output values for each protein position to correspond to atomic coordinates, which were plotted in 3 dimensions using PyMOL software (PyMOL Molecular Graphics System, version 1. 8; Schrödinger).
Allogeneic hematopoietic stem cell transplant (HSCT) remains the only curative immunotherapy for patients with multiple myeloma (MM). However, high rates of transplantassociated mortality (up to 30%) limits the applicability of this approach. Thus, to separate the beneficial post-HSCT "graft versus myeloma" effect from the antecedent toxicities, we developed a strategy to enrich autologous MM-specific T cells ex vivoby stimulating patient PBMCs with overlapping peptide libraries spanning 5 MM expressed tumor associated antigens (TAAs), PRAME, SSX2, MAGEA4, NY-ESO1 and Survivin. To test the safety and efficacy of these cells we have initiated a phase I/II study (NCT02291848) with 2 arms: >90 days (Group A) or <90 days (Group B) post-autoHSCT. To date, we have enrolled 9 patients (pt's) and completed multiTAA T cell manufacture for 8.
In summary, we have demonstrated the safety of multiTAA T cells in patients with MM and early evidence of clinical benefit co-incident with the in vivo expansion of tumorspecific T cells. Furthermore, we highlight the importance of simultaneously targeting multiple tumor-expressed antigens for clinical benefit. T-cell engineering is increasingly used in cancer immunotherapy, with long-term safety being a major issue. Sin e August 2002,we used TK cells after TCD haplo-HSCT to hasten immune reconstitution (IR), while controlling GvHDby suicide gene induction with ganciclovir (GCV). Effects of TK cells on outcome were assessed in 45 pts receiving 1-4 monthly doses (. 1-1. 0 × 10 7 /Kg; 21-49 days after HSCT) in a ph 2 trial  (n = 30; Lancet Oncol 2009; 10: 489)  and in the experimental arm of an ongoing ph 3 trial (n = 15; NCT00914628).
Long-term safety was assessed yearly. Endpoints: 1-year NRM, OS, LFS, RI and acute (a) or chronic (c) GvHD. Median follow-up: 3. 7 years (IQR 1. 5-8. 5). A pairmatched analysis (PMA), supportive of conditional marketing authorization recently granted for TK cells (www. ema. europa. eu / EPAR / Zalmoxis), compared data from both TK trials with control data from contemporaneous  HSCT (2000 HSCT ( -2013)). 34 pts (76%) had IR after a median of 2 TK-cell infusions (IQR 1-2), a median cumulative dose of 1. 3 × 10 7 /kg (1. 0-2. 4) and a median time from HSCT of 83 days (65-108). IR was not influenced by baseline risk factors or dose of TK cells, but was associated with improved NRM (P <. 0001), LFS (P =. 005) and OS (P <. 0001). Grade 2-4 aGvHD (35%; grade 3-4: 7%) was unrelated to TK-cell dose.
Microarray experiments Transcriptional profiling was performed using the Agilent chicken Gene Expression 4X44 Microarrays (AMADID: 026441, Agilent Technologies, Santa Clara, CA, USA). This microarray design is referenced within the Gene Expression Omnibus (GEO) database under the platform ID GPL15357. A dual color design was used to provide direct comparisons between infected and mock-infected CLEC213 cells. To reduce potential experimental biases, RNA samples were collected from 4 independent experiments. Arrays were hybridized according to the manufacturer's instructions and as previously described  [16]. Functional analysis of the data was done as described in reference  [17]. Differentially expressed genes were identified using a moderated T-test. A Benjamini-Hochberg False Discovery Rate (FDR) was then used as multiple testing correction method and a corrected p-value cutoff of 1% was applied.
Statistical analyses qRT-PCR measurements were expressed as the mean ± standard error of the mean (SEM) of at least 3 independent experiments, and statistical analyses were performed using the Student's t-test for pair wise comparisons and ANOVA for multiple comparisons. Ontological analysis was carried out with IPA (Qiagen) and used the right-tailed Fisher's exact test to calculate a pvalue determining the probability that each biological function and disease assigned to that data set is due to chance alone. The activation status of the pathways was predicted using IPA "Upstream Regulator Analysis" tool by calculating a regulation z-score. The z-score is used to infer likely activation states of upstream regulators based on comparison with a model that assigns random regulation directions.
Eventually, the resulting data were processed using the GeneSpring software (Agilent). A total of 2,723 differentially expressed genes (DEGs) were obtained and used for further ontological analyses. Among these genes, 2,151 were upregulated and 572 downregulated during the viral infection (Fig 1A  and 1B ). We used IPA software to identify the functions and pathways associated to this set of DEG. We focused on the 25 most significantly regulated canonical pathways (Fig  1C, p-value<1% ). As expected, several inflammatory pathways appeared to be induced during the AIV infection: Interleukin 1, 6, 8 (IL1, IL6, IL8) and acute phase response signaling. The cellular functions mediated through G-proteins were also predominant in the CLEC213 innate response: Gα12/13, RhoGDI, G-Protein Coupled Receptor, Gαi and Rho Family GTPases signaling.
In order to validate changes in gene expression and to confirm the microarray data, we performed qRT-PCR on independently-produced samples. The qRT-PCR assay confirmed the potent induction of inflammatory mediators with a 600 fold increase for IL6 (Fig  4A ). As displayed by microarray data, the components of the oxidative phosphorylation metabolic pathway were largely induced by the H6N2 infection (Fig 4B ), with slightly higher levels of expression in the qRT-PCR assay. For instance, Mitochondrially Encoded ATP Synthase 6 (MT-ATP6) was induced 8. 3 fold vs. mock in qRT-PCR data and 3. 9 fold in microarray data. The homogeneous regulation of the genes encoded by the mitochondria is likely due to the polycistronic organization of the mitochondrial genome  [19].
This work was partially funded by the  Conseil regional d'I ˆle-de-France  through  DIM Malinf ("Domaine d'Interêt Majeur Maladies Infectieuses, parasitaires et nosocomiales emergentes ", Grant#:  dim100157 ). LM was supported by a grant of the  Animal Health Division of INRA , OL was supported by  "DIM Malinf" doctoral fellowships  awarded by the " Conseil regional d'I ˆle-de-France ".
In the first step, candidate human templates for CDR grafting were identified by separately entering the sequences of the VL and VH domains in the IMGT/ DomainGapAlign search program  [31]  and by restricting the search to human sequences in IMGT/GENE-DB  [32]. The closest human VH gene, IGHV1-3*01, showed 67. 34% of identity with the murine counterpart. This identity rose up to 92. 85% after grafting the murine 12G4 CDR-IMGT into the human FR-IMGT. The closest human VL gene, IGK1-9*01, showed 62. 76% of identity with the murine counterpart. However, IGKV1-5*01 was preferred because the IMGT/GeneFrequency tool  [31]  indicated that IGK1-9*01 is not very frequently expressed. IGKV1-5*01 has an identity of 58. 51% with the VL of 12G4 that was increased to 88. 29% after grafting. After production in YB2/0 cells and purification, the binding capacity of humanized 12G4 (h12G4) to the MISRII-Fc fusion protein was tested and compared with that of mouse 12G4 by ELISA (Figure  1A ).
Templates selected to build the initial model were the PDB structures 2OSL (for the light and heavy chains of ch12G4) and 3EO9 and 2EH7 (for the light and heavy chain of h12G4, respectively). (D) CDR loops, shown as sticks and with a different color than the light and heavy chains, were specifically rebuilt and refined using the Discovery Studio software (Modeler and Looper algorithms). The 3C23K model was built from the h12G4 model by replacing the four residues mutated during the maturation affinity process (I47T, S49P, E54K, Q216R showed as sticks and balls). The conformation of the mutated residues and of the surrounding residues that lie within a 5Å cutoff radius was optimized using the MODELER algorithm. (E) The electrostatic potentials of the three MAbs, calculated with the Delphi program included in the Discovery studio software, are visualized using a surface graphic view.
3C23K induces ADCC and ADCP, but no CDC Based on the findings that 3C23K-FcKO did not have any in vivo effect (Figure  3B ) and that 3C23K did not induce apoptosis in vitro (Figure  4B ), the potential 3C23K Fc-related mechanisms of action were investigated. First, CDC was assessed using a cytotoxicity detection kit based on the measurement of lactate dehydrogenase (LDH) activity released by damaged cells. COV434-MISRII cells were incubated with increasing concentrations of 3C23K or 3C23K-CHO (0 to 5,000ng/ml) in the presence of baby rabbit serum, as a source of complement (1/10 dilution). After 1 hour of incubation at 37°C, no cell lysis was observed, whatever the treatment (data not shown). Lack of CDC activity in our experimental conditions could be attributed to the too low MISRII antigenic density (2x10 4 receptors/cell  [20] ) for CDC.
As 3C23K does not bind to mouse MISRII, our experiments in mice gave us no clue on its possible toxicity towards normal tissues. However, as a first step towards clinical trials, we performed regulatory toxicological studies in cynomolgus monkeys and 3C23K did not elicit any sign of toxicity with no observed adverse effect level (NOAEL) at doses ≥ 300 mg/kg  [53]. In conclusion, on the basis of the previous characterization of the mouse 12G4 MAb  [20, 25]  and the present data, the anti-MISRII MAb 3C23K appears to be a promising new EMABling ® therapeutic candidate for OC treatment. In 2016, a phase I clinical trial (NCT02978755) was initiated with 3C23K (now named GM-102) in gynecological cancers.
Humanization of the murine MAb 12G4 12G4 variable regions were amplified from total RNA by using the 5′ RACE kit (Invitrogen). The variable regions of both chains were cloned into the TOPO TA cloning vector (Invitrogen) and were confirmed by sequencing (MWG). The human acceptor FRs were identified according to Pelat  [54]. Briefly, the mouse sequences of the heavy and light chain Fv fragments were used for alignment to human germline sequences in the IMGT database (www. imgt. org). Based on these alignments, amino acid differences at specific positions between the human and murine FRs were detected. The properties of these residues (involvement in disulfide bridges or CDR flanking regions) were analyzed manually with the help of a "collier de perles" representation  [31]  and led to the construction of FRs for grafting the 12G4 CDRs of the heavy and light chains.
Bound antibodies were detected using a TECAN LS Reloaded laser scanner (Tecan). Data were extracted and analyzed using the Array-Pro ® Analyzer software. The antibody binding potencies to the different peptides were compared using the Student t-test and the Statgraphics Centurion XV V15. 2. 06 (StatPoint, Inc) software. Differences were considered as statistically significant when p < 0. 05.
Molecular modeling of anti-MISRII antibodies All algorithms used for antibody modeling were extracted from the Discovery studio V3. 1 software (Accelrys software Inc). The Fab structure of ch12G4 and h12G4 were modeled with a sequence homology approach using MODELER  [56]. The best homologous templates were identified by BLAST search against a sequence database of known antibody 3D structures (extracted from the PDB database). The following PDB structures were the templates selected to build the initial model: i) 2OSL for the light and heavy chains of ch12G4; ii) 3EO9 and 2EH7 for the light and heavy chains of h12G4; and iii) 3E09 for the interface. CDR loops were specifically rebuilt and refined using the LOOPER algorithm. 70. Starting from the h12G4 model, the 3C23K model was built by replacing the four mutated residues generated by the maturation affinity process: I47T, S49P, E54K in the VL and Q3R in the VH.
The conformation of the mutated residues and of the surrounding residues that lie within a 5Å cutoff radius was optimized using the MODELER algorithm. The three models were superimposed based on their sequence alignment, and the main chain atom rmsd calculated using the 3DMA program. The electrostatic potential of the three antibodies was calculated using Delphi, a program that solves the Poisson-Boltzmann equation on a cubical lattice using the finite-difference technique  [57].
Apoptosis assay Approximately 350,000 cells were seeded in 6-well/ plates and incubated or not with 50 µg/ml MAb, 20 nM MIS or 150 nM staurosporin, as positive control, for 24 h and then stained using the annexin V-FITC Apoptosis Detection www. impactjournals. com/oncotarget Kit (Beckman Coulter IM3614). Both adherent and detached cells were collected and centrifuged at 1,000 rpm for 5 minutes. After washing with PBS, cells were stained with 130 µl of a mix containing 10 µl FITC-labeled annexin V and 20 µl propidium iodide (PI) in 100 µl annexin buffer on ice in the dark for 15 minutes. After addition of 400 µl annexin buffer, cells were analyzed by flow cytometry within 30 minutes and data analyses performed using the Kaluza Flow Analysis software (Beckman Coulter).
Acquisition times were defined to obtain 30,000 counts for each projection (generally 60-120 seconds were required) with 24 projections. The total scan time was generally around 30-60 minutes. Concurrent micro-CT whole-body images were acquired (55kV, 500 milliseconds, 240 projections) for anatomic co-registration with the SPECT data. Reconstructed data from SPECT and CT images were visualized and coregistered using Invivoscope ®. Images and maximum intensity projections (MIPs) were reconstructed using the dedicated Invivoscope ® (Bioscan, Inc. , Washington, USA) and Mediso InterViewXP ® software programs (Mediso, Budapest, Hungary). The InvivoQuant ® software was alternatively used for image reconstruction.
Black bars: murine 12G4; Grey bars: 3C23K. Panels(D, E) Modeled structure of chimeric 12G4 (ch12G4), humanized 12G4 (h12G4) and affinity matured 3C23K using a sequence homology approach. Templates selected to build the initial model were the PDB structures 2OSL (for the light and heavy chains of ch12G4) and 3EO9 and 2EH7 (for the light and heavy chain of h12G4, respectively). (D) CDR loops, shown as sticks and with a different color than the light and heavy chains, were specifically rebuilt and refined using the Discovery Studio software (Modeler and Looper algorithms). The 3C23K model was built from the h12G4 model by replacing the four residues mutated during the maturation affinity process (I47T, S49P, E54K, Q216R showed as sticks and balls). The conformation of the mutated residues and of the surrounding residues that lie within a 5Å cutoff radius was optimized using the MODELER algorithm.
ACKNOWLEDGMENTS AND FUNDING We thank  Véronique Garambois  and  Sabine Bousquié  for excellent technical assistance. This work was supported by the grant  Projet collaboratif de R&D FEDER/Oséo  "MISR-test" from  Région Languedoc-Roussillon/Occitanie. This work was supported by the www. impactjournals. com/oncotargetprogram " Investissement d'Avenir " (grant agreement:  Labex MabImprove ,  ANR-10-LABX-53-01 ).
The different cell groups were cultured for 4 days and myogenic differentiation was assessed by immuno-staining using an anti-myosin antibody (My32, red), counterstained with DAPI (blue). Bar scale corresponds to 100 μm. (b) Quantitative analyses of myogenic differentiation of competent cells. The percentage of myogenic differentiation was determined microscopically. Total CD34 + cell population was used as a reference and myogenic differentiation was arbitrarily estimated at 100%. Statistics: n = 3, t-test P < 0. 0001 (**). significantly reduced (data not shown). Unexpectedly, we reproducibly noticed that the passages of the CD34 + / Sca1 + cells improved the myogenic effects of the conditioned media. Supposedly, the passage of the CD34 + /Sca1 + cells induced selection. However, because the myogenic effect might be due to different proliferation rates of the myogenic lineage, we examined the kinetics of proliferation in presence of control media and conditioned media (Supplementary Fig.
The expression of the putative myogenic candidates identified from the omics analyses was analysed by q-RT-PCR. Three distinct cultures were examined, initial fraction cells (1), CD34 + /Sca1 + primary cells (used in omics analyses and efficient for myogenic differentiation) (2), and CD34 + /Sca1 + cells after three passages (highly efficient for myogenic differentiation, S3 in Fig. 2c ) (3). The data are expressed relative to Gapdh transcription (2 -ΔCt ). (b) Complement C3 is present in conditioned media. Purified human C3, control media (DMEM/decomplemented FCS) and conditioned media S3 (see Fig. 2c ) were resolved in SDS-PAGE and analyzed by western blotting with anti-C3 antibodies. (c) Growth media supplemented with serum (active complement) promoted myogenic differentiation. Initial fraction cells were cultured in DMEM/decomplemented FCS (Control), myogenic conditioned media S3 (Conditioned media S3), DMEM/decomplemented FCS supplemented with 1% mouse serum (Control + 1% mouse serum) and DMEM/decomplemented FCS supplemented with 1% mouse serum It is generally believed that cytokines induce cell differentiation through the activation of signalling pathways.
Nuclei were counterstained with bis-benzimide. Quantitative analysis of myogenic differentiation were carried out by quantifying myosin with MY32 antibody and nuclei with DAPI staining in 96-well plates cultures using a fluorometer (POLARstar Omega -BMG labtech). Myogenic differentiation was expressed by the ratio of the FITC myosin signal/Bis-Benzimide DNA signal. Quantification was based on two to four independent experiments with at least four to six wells each. The percentages of myogenic nuclei were quantifying directly from microscopic observations (Nikon Ni-E microscope) by calculating the ratio of the nuclei within myosin expressing cells/the total number of nuclei/cm 2 using ImageJ software (http://rsb. info. nih. gov/ij/). Two to four independent experiments with at least three to four wells were done and four microscopic fields per well were counted.
Statistical analyses. For cell culture studies, at least three independent experiments were carried out. Microscopic analyses were performed at least in duplicate and at least ten random fields were imaged per sample. Fluorimetry analyses were performed at least on six culture wells. Data are presented as mean ± SEM. Differences between groups were tested for statistical significance an unpaired two-tailed Student's t test. P < 0. 0001 was considered statistically highly significant and P < 0. 005 was considered statistically significant.
Acknowledgements We gratefully acknowledge  Ross Tomaino  of the  Taplin Mass Spectrometry Facility at Havard Medical School  for expert technical assistance and the genomics core facility of the  University of Nantes. We thank  Drs Angélique Galvani ,  Jeffrey J. Hayes  and  Yves-Henri Sanejouand  for critical comments. This work was financially supported in part by grants from the  AFM  ( AFM 16774 ) to G. A. -B. and C. T. and  CNRS  to C. T.
Provisional identification of Transcription factors of the breast cancer stem cell phenotype and Inference of its modules. In order to identify TFs that may be master regulators (MRs) of breast cancer stem cells, we assessed the expression of TFs in bCSC-enriched samples sorted by FACS based on ALDH1 activity. We carried out a consensus analysis of our three paired samples of bCSC and total tissue, bCSC/Bulk dataset, and 8 paired samples of bCSC and cancer cells, bCSC/CC dataset. We found 17 TFs differentially expressed in the bCSCs in both comparisons. Most of them (13) were up-regulated (PRRX1, SNAI2, TWIST1, ID4, BNC2, GATA6, ZNF503, FOXF2, HOXA5, HOXB3, TSC22D1, TBX5 and CREB3L1) and four down-regulated (SCML4, ZNF831, IKZF3 and SP140). These TFs present a consistent pattern of expression in the two datasets (Pearson correlation coefficient, r = 0.
84; p < 0. 001; Supplementary Figure  S1 ). We wondered what could be the transcriptional programs controlled by these TFs. Consequently, to infer the regulons of each of these TFs, in the context of breast cancer, we applied a reverse engineering strategy on a larger dataset as required by the ARACNe algorithm (breast cancer NACT dataset, Supplementary Table  S1 )  19, 20. Regulons of the 17 TFs resulting from the bCSCs consensus analysis (Table  1  and Supplementary Table  S1 ) were used to build a network visualised in Cytoscape (Fig. 1a ). In this independent dataset, the four regulons controlled by the TFs downregulated in bCSC (SCML4, ZNF831, IKZF3, and SP140) are largely overlapping and set apart from the regulons of TFs up-regulated in bCSC. The Master Regulator Analysis -Fisher's Exact Test (MRA-FET) algorithm computes the statistical significance of the overlap between the regulon of individual TFs (i.
e. , its ARACNE-inferred targets) and the signature of interest, giving as output the p-values computed by Fisher's Exact Test. We used the list of differentially expressed genes from the "bCSC/CC" dataset as a signature for the breast cancer stem cell phenotype. MRA-FET indicates that regulons of 11 TFs contribute significantly to the bCSC signature: eight are positively correlated with the bCSC phenotype (BNC2, PRRX1, TBX5, SNAI2, TWIST1, ID4, HOXA5, and four are negatively correlated (IKZF3, SCML4, SP140, ZNF831) (Table  1 ). Of these 11 TFs, 9 are organised in two well-connected modules with the topology of logical circuits  16, 17  , Figure  1b  and c display the regulatory circuits. One formed by BNC2, PRRX1, TBX5, SNAI2, TWIST1 (Fig. 1b ), and the other by IKZF3, SCML4, SP140, ZNF831 (Fig.
Inference of the biological processes associated with the bCSC Transcription Factor Modules expression. We constructed a bCSC metagene using the level of expression of these nine TFs (coefficient of 1 for bCSC TFs and -1 for immune module TFs) and ranked the samples in the NACT dataset and in TCGA-BRCA (Fig. 2c  and Supplementary Figure  S2 ). In these two gene expression datasets generated from distinct platforms (DNA-array and RNAseq), we again confirmed that the TFs IKZF3, SCML4, SP140, ZNF831 were inversely (and co-ordinately) expressed compared to BNC2, PRRX1, TBX5, SNAI2 and TWIST1. This was in accordance with the predicted structure of the regulons from the bCSCs database (Supplementary Figure  S1  and Supplementary Table  S1 ). It was of note that in the neoadjuvant dataset (NACT), low expression of the bCSC metagene correlated with pathological complete response to chemotherapy (3.