dataesr/scientific-paragraphs-categorization huggingface dataset:
As expected, Nutlin-3 or AT7519 led to the accumulation of the hypophosphorylated form of RB (Fig. 5a ) and to the induction of premature senescence in normal human cells, either MRC5 (Fig. 5b-d  and Supplementary Figure  S7a ) or IMR90 (Supplementary Figures  S8a-d ). Furthermore, Nutlin-3 or AT7519 notably repressed DNA repair gene expression through a P53/ P21/RB pathway (Fig. 5e  and Supplementary Figures  S7bc  and S8e )). This repression led to accumulation of DNA damage (Fig. 5f ,g and Supplementary Figures  S8f-g ) and induced DNA damage signaling as measured by (see figure on previous page) Fig. 2  RB pathway mediates repression of DNA repair genes in senescent cells. a The transcription factor ChIP-seq uniform peaks database from the Encode consortium was explored for 7 ChIP-seq experiments performed on E2F1 or E2F4 transcription factors.
Cells were treated with 4-OHT for 48 h or with H 2 O 2 for 24 h. RT-qPCR for the indicated DNA repair genes were then performed. Results were normalized against GAPDH levels. Means ± SD are presented in the graph. A statistically significant down-regulation and reversion were observed for all the genes described (t-test P value <0. 01). g MRC5 cells were transfected with a control siRNA (siCtrl) or with a siRNA directed against E2F1 (siE2F1). Three days after, RNA were prepared and RT-qPCR for the indicated genes were performed. Results were normalized against GAPDH levels. The different experiments shown are representative of at least two repeats. Statistically significant variations were observed for all the genes indicates (P < 0. 01) phosphorylation of ATM (Fig. 5h ). Importantly, these data reveal that DNA damage and DNA damage response processes can be initiated by RB activation and without direct lesions to DNA.
1. 1 (Agilent Technologies), then transferred to the Genespring GX 12. 6 software (Agilent Technologies) for data processing and data mining. All of the conditions were tested in three independent biological replicates for statistical analyses. Microarray probes were filtered using the Agilent flag filter to remove probes with a raw signal below 30 in at least one of the conditions tested. Genes differentially expressed between 4-OHT treated and untreated HMEC-MEK cells were defined using an unpaired t-test P-value <0. 01 with a Benjamani Hochberg correction and fold change cutoffs > or <2, for upregulation and downregulation, respectively. The GO tool from GeneSpring enabled us to determine statistically significant enrichments in biological processes, based on computation Pvalues described by standard hypergeometric distribution.
Immunofluorescence MRC5 cells were grown in 8 chamber tissue culture glass slides (Falcon, Corning). After the indicated treatments, cells were fixed in ice-cold methanol for 10 min at -20 °C, and blocked in PBS-Tween 0. 01% containing 1% Bovine Serum Albumin (PBST-BSA) for 2 × 15 min. Incubation with the 53BP1 antibody (dilution 1:300, Cell Signaling Technology, #4937) in PBST-BSA was performed overnight at 4 °C. After three washes with PBS, the slides were incubated with Alexa Fluor 488 dyeconjugated goat anti-rabbit antibody diluted in PBST-BSA (dilution 1:500) for 1 h at room temperature. After three washes in PBS, the slides were then mounted with DAPI Fluoromount G (SouthernBiotech). Images were acquired with a Nikon fluorescence microscope, and data were collected and analyzed with NIS software (Nikon).
Acknowledgements We thank laboratory members and  Agnès Tissier  for helpful suggestions and discussions. This work was supported by grants from the  Fondation ARC, la Ligue contre le Cancer , the  ANR French National Research Agency  ( ANR-14-CE12-0003 ), and  French National Cancer Institute (INCa   PLBIO-15-013 ,  INCa_11487 ). Marine Warnier  is supported by the  Fondation de France  and  Anda Huna  is supported by the  Fondation pour la Recherche Médicale.
1 3. 4 11. 2 7. 5 PTTG1 Securin 11. 1 4. 4 5. 5 4. 3 RAD51 DNA repair protein RAD51 homolog 1 6. 3 2. 7 10. 7 4. 7 RAD51AP1 RAD51-associated protein 1 6. 5 5. 9 9. 7 10. 0 RAD54B DNA repair and recombination protein RAD54B 3. 6 2. 1 2. 1 2. 7 RAD54L DNA repair and recombination protein RAD54-like 7. 2 2. 1 9. 3 3. 8 RFC3 Replication factor C subunit 3 4. 9 4. 8 6. 1 9. 0 RFC4 Replication factor C subunit 4 3. 7 3. 2 4. 1 4. 3 RFC5 Replication factor C subunit 5 5. 8 2. 5 4. 5 4. 1 RNASEH2A Ribonuclease H2 subunit A 9. 1 3. 4 2. 7 2. 7 TRIP13 Pachytene checkpoint protein 2 homolog 19. 3 2. 3 11. 3 6. 6 UBE2T Ubiquitin-conjugating enzyme E2 T 6. 4 2. 4 5. 7 4. 0 USP1 Ubiquitin carboxyl-terminal hydrolase 1 7. 3 5. 3 3. 7 2. 5 Numbers indicate the average fold repression between senescent cells and controls for 39 DNA repair genes observed in WI-38 cells exposed to etoposide (WI38-ETO, GSE62701), in IMR90 cells overexpressing RAS (IMR90-RAS, GSE60652), in HUVEC cells during replicative senescence (HUVEC-RS, GSE7091), and in HMEC cells overexpressing MEK (HMEC-MEK, this study).
Purpose: Breast cancer is among the leading causes of cancer death; discovery of novel prognostic markers is needed to improve outcomes. Combining systems biology and epidemiology, we investigated microRNA-associated genes and breast cancer survival in a wellcharacterized population-based study. Experimental Design: A recently developed algorithm, ActMiR, was used to identify key microRNAs "activities" corresponding to target gene degradation which were predictive of breast cancer mortality in published databases. We profiled microRNA-associated genes in tumors from our well-characterized population-based cohort of 606 women with first primary breast cancer. Cox proportional hazards models were used to estimate hazard ratios (HR) and 95% confidence intervals (CI), after 15+ years of follow-up with 119 breast cancer-specific deaths.
In order to obtain robust results, we filtered out miRNAs whose number of target genes was smaller than 10. High miRNA activity corresponds to the high degradation effect on their target genes. To identify microRNAs of which activity was associated with breast cancer survival, we used "The Cancer Genome Atlas" database (tcga-data. nci. nih. gov/tcga/tcgaHome2. jsp) and data described in Dvinge et al. (14).
Kaplan-Meier survival (23) plots were built for visualization of unadjusted associations between gene expression and breast cancer-specific mortality. To estimate receiving operator characteristics (ROC), we first used R package pROC (24). This package allows to perform a standard ROC analysis and calculates the Area Under the Curve (AUC) for binary outcome, i. e. censored (death by breast cancer) vs. uncensored (survived at the end of followup). However, this is not the optimal estimator for such timedependent variable as patients survival, therefore we further re-analyzed our data using the survivalROC package (25). This package takes into account the survival duration and is considered more appropriate for this type of data (25). We used Nearest Neighbor Estimation (NNE) variant of the survivalROC function, with time point of 365 days and span parameter of 0.
Activity of miR-500a and breast cancer mortality The large public cancer dataset, the Cancer Genome Atlas (TCGA), contains multiple types of data (e. g. DNA copy number arrays, DNA methylation, exome sequencing, messenger RNA array, microRNA sequencing and reverse-phase protein arrays) performed on the same set of samples and provides comprehensive molecular portraits of several subtypes of breast cancers  (26). We previously reported that high activities of let-7d and miR-18a were associated with better survival outcomes ER-/HER2-breast cancer  (10). However, the majority of breast tumors in the United States are hormone receptor positive. We previously showed that the miRNA-mRNA correlation structure for ER + and ER -/HER2 -is markedly different, suggesting miRNA regulatory mechanisms are cancer-subtype specific  (10).
We subsequently evaluated relationships between miRNA and mRNAs expression levels in ER+ breast cancer using public databases. For each miRNA-mRNA pair, we calculated Pearson correlations between miRNA and mRNA expression levels. We inferred miRNA activity based on ActMiR method that we developed  (10). miR-500a emerged as a key prognostic miRNAs for ER + breast cancer. In the TCGA dataset, high "activity" of miR-500a was strongly associated with increased breast cancer mortality (Fig. 1A ); a similar association was observed using data from Dvinge et al. (14). It is worth noting that in comparison with the "activity", the "expression" level of miR-500a showed no associations with breast cancer survival (Fig. 1B ).
5, 95% CI 0. 4-0. 8, p=5. 3e-4). We selected the genes to be profiled based on their association with miR-500a "activity", both positive as well as negative associations. While a negative association suggests "direct targets" which represents the conventional mechanism of miRNA function, a positive association, which was also frequently observed, implicates mechanisms of indirect target or other modes of action. Fig. 4  depicts associations between HRs of each tested gene and its correlation with miR-500a activity by ActMiR algorithm for TCGA dataset. Based on our a priori hypothesis that high miR-500a activity confers increased breast cancer mortality, miR-500 associated genes should reside in Quadrant II or III, which is largely consistent with our findings (Fig. 4 ). There are a small number of genes resided in Quadrant I and IV implicating unknown modes of action.
86 vs 0. 62, Fig. 5B ). Lastly, we validated our findings in two public datasets, i. e. METABRIC dataset  (30)  which includes 1980 breast cancer cases (77% ER+), and a combined dataset of KMPlot service kmplot. com (31)  with 2862 cases (72% ER+). Most significant survival-associated genes from our study displayed the same direction of survival association in those datasets. (Fig. S6 , Fig. S7 ). As an example, Fig. S8  shows data for our top gene SUSD3: it is consistent among all 3 datasets, and for both ER+ and ER-subsets.
More importantly, miRNA "expression" level is not equivalent to its regulatory "activity"  (8, 36) , because activities of miRNAs are modulated through the accessibility of proteins or RNAs that is essential for miRNA functions and the relative abundance of miRNA to its target genes  (37) (38) (39). Thus, miRNA expression levels might not be the best functional indicator. For our cases, miR-500a expression levels were measured by qPCR on a small subset of paired tumor/adjacent premalignant tissues and no significant difference was observed (Fig. S9 ); therefore we did not measure miR-500a expression in the entire cohort. In our study, we reported that miR-500a activity (but not expression) was associated with survival of ER-positive patients with early stage breast cancer in two public databases.
We profiled a set of miR-500a associated genes, including potential direct and indirect targets, and aimed to investigate their prognostic significance in the well-characterized populationbased cohort of breast cancer patients with long-term follow-up. Our study revealed a set of genes strongly associated with the survival of breast cancer patients, in agreement with what is predicted by ActMiR  (10). Moreover, most of these associations were validated in two independent populations, including a large dataset of METABRIC  (30)  and the combined dataset provided by KMplot. com portal  (31). At the same time, gene expression data from the set of clinical samples used in this study are not sufficient to claim that ActMiRcalculated `miR-500a activity` matches real biological activity: more specific in vitro experiments need to be performed in order to justify that type of equivalence.
Among the high-risk (ER-PR-or high ROR) or low-risk (ER+/PR+, or low/medium ROR) group, we were able to identify a set of genes that show significant associations with survival and differentiate the group with different survival profiles. This is an important finding because it might potentially help to better classify patients and improve precision of treatment or general care for these patients. Although miR-500a has been identified as one of key players in breast cancer mortality, its exact role remains unknown. TargetScan  (40)  predicts that as many as 3816 possible target transcripts for this microRNA but none of them are experimentally validated. There is only a handful of publications which suggest that on miR-500a is a potential circulating biomarker of such pathologies as autism  (41) , lung cancer  (42) , endometriosis  (43) , and hepatocellular carcinoma  (44).
Our study discovered a large number of miR-500a genes that demonstrated strong associations with breast cancer survival. Many of these associated with poor survival in our population-based cohort of women with breast cancer are already part of PAM50 panel (such as BIRC5, CEP55, CENPF -all known to be markers of poor survival). However, there were many others where we are the first to be report that they are predictive of breast cancer survival, even once we adjusted for the PAM50 ROR score. For example, high expression of SUSD3 (Sushi Domain-Containing Protein 3) displayed significantly reduced mortality for both HR-positive and HR-negative breast cancer cases in our population, and this finding was validated in both the METABRIC and combined KMplot datasets (Fig. S6 ). Its association remained significant after adjustment for ROR (HR 0.
SUSD3 product was shown to be localized at plasma membrane, and its knockdown lead to decreased cell motility and proliferation  (47). These results cannot easily fit with the inverse association with breast cancer mortality in our study. The molecular mechanism of SUSD3 deserves to be further investigated experimentally. Another gene highly correlated with breast cancer survival after adjustment for PAM50-derived ROR scores (HR 0. 4, 95% CI 0. 2 -0. 6) was CROT (Carnitine O-Octanoyltransferase). Again, its association was highly significant not only in our dataset but also by KMplot data (METABRIC data not available for CROT: gene symbol not found in the list). This gene is known to be involved in lipid acids metabolism, but was not previously linked to cancer. Similar inverse association was shown for ARMCX1 (armadillo repeat containing, X-linked 1) gene in patients from all three datasets, including ROR-adjusted LIBCSP patients (HR 0.
Third, multi-center nature of this study (more than 30 participating hospitals) assumes there could be certain heterogeneity in choice of the treatment strategies and the way it was reported. Finally and importantly, by definition, a confounder (i. e. treatment) has to be related to both the independent variable (i. e. gene expression) and outcome (i. e. survival). In our case, all tumor samples were collected prior to treatment other than surgery, the treatment and molecular profile of the tumor should be considered independent. Also, treatment is part of the causal pathway (being impacted by tumor stage or hormone receptor status at diagnosis) and including a causal intermediate in the model can bias the effect estimates  (49). In summary, by leveraging public databases and our own epidemiologic studies and cooperating systems biology approach, we identified a key microRNA, miR-500a, that appeared to be strongly linked with breast cancer mortality across platforms.
Results were validated in independent datasets. These results suggest miR-500a associated gene panel may improve prognostic efficiency of current molecular phenotyping and ultimately improve breast cancer survival. Survival curves associated with inferred activity (A) and expression levels (B) of miR-500a-5p, based on published datasets of TCGA. Age-and stage-adjusted correlation of individual miR500a-related genes expression levels with long-term breast cancer-specific survival of LIBCSP patients. Each dot corresponds to a gene, with horizontal axis displaying hazard ratio of high expression group comparing to low expression group (i. e. genes on the left are associated with better survival, genes on the right are associated with worse survival) and vertical axis displaying logarithm of p-value of the same Cox model.
Genes included in the PAM50 panel are marked with pink color. Co-expression of miR-500a-related genes with strongest association with survival, according to LIBCSP results. Blue color in gene labels and horizontal bar indicates genes associated with worse survival, red color associated with better survival. Color of the plot cells shows the correlation in respective pair of genes (red color indicating positive correlation and blue color -negative correlation). Concordance between two characteristics: association of gene expression with miR-500a activity (vertical axis: correlation coefficient) calculated by ActMiR algorithm for TCGA dataset, and association of gene expression with survival (horizontal axis: hazards ratio) calculated for Cox model for LIBCSP dataset.
Background: Data on Severe cutaneous drug reactions (CADRs) are not common among in sub-Saharan Africa children. The purpose of this study was to document the clinical, etiological and evolutionary aspects of Severe CDRs in children hospitalized at the dermatology department of university hospitals of Conakry. Material and Methods: Retrospective study, conducted from 1 January 2000 to 31 December 2014. Were included all children aged 0-17 years hospitalized for severe CARDs. The data collected were Socio-demographic, clinical, para-clinical and evolution variables. The data was entered and analyzed using the Excel 8. 0 software. Results: During a study period, 4437 patients of all ages was hospitalized in dermatology department. 35 patients were included with an average age of 11. 3 years and a sex ratio of 1.
Cells were then washed and incubated on-ice for 30 min with mouse anti-mouse CD45. 2-FITC (BD PharMingen, #553772), rat anti-mouse CD4-APC (eBioscieince, #17-0041-83), rat anti-mouse CD45R/B220-PE (BD PharMingen, #553090), rat anti-mouse GL7-BV421 (BD Horizon, #562967), hamster anti-mouse FAS-BV510 (BD Horizon, #563646), hamster anti-mouse PD1-PerCP-eFluor710 (eBioscience, #46-9985-82, all 1/200), biotinylated rabbit anti-HEL (Abcam, #Ab34620, 1/500) and Streptavidin-APC-eFluor780 (eBioscience, #47-4317-82, 1/100) diluted in PBS/BSA/ EDTA (1x/2%/2mM). Acquisitions were performed on a FACS Verse (BD Biosciences) and data analysis was carried out with FlowJo v10. Antigen extraction and B cell polarization 0. 25x10 6 B lymphoma or 0. 4x10 6 spleen B cells were plated on poly-L-lysine-coated slides and stimulated with BCR-ligand + beads (cell:beads ratio: 1:2) containing OVA ± Gal8 for indicated time at 37 C, fixed in 4% PFA for 12 min at room temperature, quenched and blocked with PBS/BSA/Glycine (1x/2%/100 mM) and stained for OVA, the centrosome (g-Tubulin) and the lysosomes (Lamp-1).
The orbitrap cell analyzed the peptides in full ion scan mode, with the resolution set at 120 000 with a m/z range of 350 -1550. Higher-energy collisional dissociation (HCD) activation with a collisional energy of 28% was used for peptide fragmentation with a quadruple isolation width of 1. 6 Da. The linear ion trap was employed in top-speed mode in order to acquire the MS/MS data. Maximum ion accumulation times were set to 250 ms for MS acquisition and 60 ms for MS/ MS acquisition in parallelization mode. For the identification step, all MS and MS/MS data were processed with the Proteome Discoverer software (Thermo Scientific, version 1. 4) and with an in-house Mascot search engine (Matrix Science, version 2. 5. 1). The mass tolerance was set to 7 ppm for precursor ions and 0. 5 Da for fragments.
The following modifications were allowed: oxidation (M), carbamidomethylation (Cys), phosphorylation (Ser, Thr, Tyr), acetylation (N-term of protein), deamidation (Asn, Gln). The SwissProt database (10/2014) with the Mus musculus taxonomy was used to identify proteins. Peptide FDR (False Discovery Rate) were calculated with the Percolator algorithm. Protein were considered as identified if their mascot scores were above 50 and if more than 2 peptides per protein were identified.
ACKNOWLEDGMENTS We acknowledge the  Nikon Imaging Center at CNRS-Institut Curie and PICT-IBiSA, Institut Curie (Paris, France) , a member of the  France-BioImaging national research infrastructure , for support in image acquisition. We gratefully thank  Helene D. Moreau  for the design of the graphical abstract. D. O. was supported by fellowships from  Ecole Doctorale BioSPC, UniversiteParis Diderot and UniversiteParis Descartes , and  Fondation pour la Recherche Medicale  ( FDT20150532056 ). We thank  Region Ile-de-France  (SESAME),  Paris-Diderot University (ARS) , and  CNRS  for funding part of the LC-MS/MS equipment. M. E. was funded by a  Junior Team Leader starting grant  from the  Laboratory of Excellence in Research on Medication and Innovative Therapeutics (LabEx LERMIT)  supported by a grant from  ANR  ( ANR-10-LABX-33 ) under the  program ''Investissements d'Avenir''  ( ANR-11-IDEX-0003-01 ) and by an  ANR @RAction starting grant  ( ANR-14-ACHN-0008 ).
M. -I. Y. was supported by a research grant from  FONDECYT  ( 1141182 ). Funding was obtained from  Association Nationale pour la Recherche  ( ANR-PoLyBex-12-BSV3-0014-001  to A. -M. L. -D. ), the  European Research Council  ( ERC-Strapacemi-GA 243103  to A. -M. L. -D. ), ECOS ( C12S02 International Research grant  to M. -I. Y. and A. -M. L. -D. ), and  CONICYT   Basal Financial Program  ( AFB-170005  to A. G. ).
Statistics All graphs and statistical analysis were performed with GraphPad Prism 7. No statistical method was used to predetermine sample size. Kolmogorov-Smirnov test was used to assess normality of all datasets. Mann-Whitney test was used to determine statistical significance excepted when mentioned. Boxes in boxplots extend from the 25th to 75th percentile, with a line at the median and whiskers extend from the 10th to the 90th percentile. Bar graphs show the mean ± sem. Statistical details of experiments (sample size, replicate number, statistical significance) can be found in the figures and figure legends.
Acknowledgments The authors acknowledge  Joseph Butterfield  at  Mayo Clinic, Rochester, MN  for providing HMC-1. 1 and -1. 2 cell lines, and the  Flow Cytometry Core at the University of Minnesota  for their excellent technical support and services. This work was supported by  National Institutes of Health, National Heart, Lung , and Blood Institute T32 training grant  T32HL007062  (H. D. Y. ),  National Cancer Institute  grants  P01 CA111412  (M. F. and J. S. M. ),  P01 CA65493  (M. F. and J. S. M. ), and  US Department of Defense  grant  CA150085  (M. F. ).
Patients and tumor samples We studied 40 pediatric patients with tumors initially diagnosed as supratentorial ependymomas by histopathological assessment between 1993 and 2014, retrieved from the archives of Sainte-Anne and Necker-Enfants-Malades Hospitals in Paris. Patients were operated at Necker and treated at Gustave Roussy. The use of clinical data and biologic material was reviewed in conformity with the institutional internal review boards. All patients were treated by maximal safe surgical excision, followed by radiotherapy in patients older than three years and adjuvant chemotherapy in patients younger than three years and/or incomplete surgical excision. All cases included had the neuropathological diagnosis of supratentorial ependymoma in the initial pathological report. Cases diagnosed as subependymomas (WHO grade I) were not included.
Histopathological re-review by senior pathologists (PV and FA) according to the revised WHO 2016 classification was performed subsequently. The cases were also subjected to local radiological (NB) review (Magnetic resonance imaging exams and if available computed tomography preoperative scans). The Kaplan-Meier analyses were performed for survival data using the log-rank test. The level of significance was P < 0. 05. Analyses were performed using IBM SPPSS statistics software (v20).