dataesr/scientific-paragraphs-categorization huggingface dataset:
For studies based on the effect of neo-adjuvant chemotherapy, data were obtained using total sections from 46 TN patients. For staining quantification, at least five distinct areas of each tumour were evaluated by two different and independent investigators. A score, named Hscore for "Histological score", was given as a function of the percentage of positive cells and the staining intensity from 0 to 4. For H2AX and c-H2AX staining, Hscores were established in both stromal fibroblasts and tumour epithelial cells. For all the other markers, Hscores were established in tumour epithelial cells. H2AX and c-H2AX have been performed independently on two different cohorts of TNBC when compared to other BC subtypes (LumA, HER2, TNBC) or analysed regarding the impact of chemotherapy (TNBC patient samples pre-and post-chemotherapy).
Gene expression profiling in human BC Only human tumours with a high content in epithelial tissue (at least 65%) have been used. Total RNA from LumA, HER2 and TN patients were extracted from frozen tumours with Trizol reagent (Life Technology, Inc. ) and purified using the RNeasy MinElute Cleanup kit (Qiagen #74204). RNA microarrays were conducted as described in  (Marty et al, 2008). For studies based on TN patients before and after chemotherapy, tumours with an epithelial content of 50% in average were selected. TN subtypes in BC tissues before and after chemotherapy were purified using miRNeasy kit (Qiagen #217004). RNA quality was checked on an Agilent 2100 bioanalyser for both cohorts. High-versus low-H2AX BC Accession number: The BC microarray data set is freely accessible in the Gene Expression Omnibus under the accession number: GSE45827.
Samples were hybridized on Affymetrix U133 plus 2. 0 arrays. Data were first normalized using GC-RMA algorithm (R version 2. 14. 1; http://cran. r-project. org). After normalization, only genes with a log2 intensity > 4 in at least 5% of all samples were considered for the analysis. Samples were classified into "High" or "Low" subgroup according to the median value of the H2AX epithelial histological score (Hscore) within each tumour subtype (TN or HER2). Genes differentially expressed according to this classification were then identified using a Welch's t-test (P-value < 0. 05 and fold change > 1. 5). Functional analysis using DAVID software was finally performed in order to define the Gene Ontology pathways significantly enriched in "Low"-versus "High"-H2AX tumour samples. Fold enrichment is defined as the ratio between input genes and background information.
Differentially expressed genes upon chemotherapy in TN patients Accession number: The microarray data set from TN patients, before and after chemotherapy (excluding patients with complete response to treatment), is freely accessible in the Gene Expression Omnibus under the accession number: GSE43816. TN ductal carcinomas were analysed on Human Gene 1. 1ST arrays (Affymetrix), before and after chemotherapy. The data were analysed using Partek Genomic Suite version 6. 6. Normalization was performed using RMA background correction with pre-background adjustment for GC content. Exons were summarized to genes using average method. Only probes with a log2 intensity > 5. 2 in at least 5% of all samples were kept for further analysis. A differential analysis using a paired-samples t-test was applied in order to detect genes differentially expressed before and after neoadjuvant chemotherapy within the same patient.
Good-versus poor-therapeutic response in TNBC patients Accession number: The microarray data set from TN patients before chemotherapy (including patients with complete response) is freely accessible in the Gene Expression Omnibus under the accession number: GSE45898. The biopsies (before treatment) were classified according to the clinical response of the patients: they have been classified into "Good" (major or complete) or "Poor" (absent or minor) therapeutic response subgroup. Genes differentially expressed between the two subgroups of patients were then identified using a Welch's t-test (P-value < 0. 05 and fold change > 1. 5). Functional analysis using DAVID software was finally performed in order to detect the Gene Ontology pathways significantly enriched in patients with "Good" therapeutic response.
Accession numbers GSE accession number for the gene expression profiling in Luminal-A, HER2 and Triple-Negative BC patient cohort is GSE45827. GSE accession number for the gene expression profiling in TNBC before and after chemotherapy is GSE43816, and for biopsies GSE45898. The microarray data sets will be freely available, using the accession numbers for above mentioned, at the publication time of the paper.
Statistical analysis All experiments were performed at least in triplicate. Data shown are means AE SEM (unless otherwise specified) from at least three independent experiments using adapted statistical test, as indicated in figure legends. Differences were considered to be statistically significant at values of P ≤ 0. 05. Edges of box-plot represent the 25 th and the 75 th percentiles; the solid line in the box presents the median value and the error bars indicate 90 th and 10 th percentiles from at least three independent experiments. The horizontal dark line on the scatter plots represents the mean and the error bars the SEM. Univariate Cox proportional hazards regression was conducted with SPSS 19. 0 software using the enter method. Survival analyses were carried out using Kaplan-Meier method and log-Rank test in R.
Acknowledgements We thank  G. Almouzni ,  J. Hall  and  P. Bertrand  for fruitful discussion about the work and critical reviewing of the manuscript. We are grateful to  B. Fourquet ,  C. Massabeau  and  B. Sigal-Zafrani  for discussions on cohorts of BC patients following radiochemotherapy. We also thank  B. Tesson  for the transcriptomic data of cohort I, to  E. Padoy ,  L. Fuhrmann ,  M. Nourieh  and  O. Chouchane-Mlik.
For more information Gene ontology database: http://www. geneontology. org/GO. downloads. ontology. shtml F. Mechta-Grigoriou's laboratory: http://u830. curie. fr/fr/genetique-et-biologie-des-cancers/equipes/ equipe-stress-et-cancer/equipe-stress-et-cancer-00101 Institut Curie Hospital: http://curie. fr/en/soins/care/our-healthcare-offering.
This precursor is subsequently transported to the cytoplasm where it is processed by DICER, to form a mature microRNA of approximately 22 nucleotides. Pre-miRNAs were detected using forward primers that specifically hybridized with the pre-miRNA (but not the mature miRNA). In both case an reverse universal primer was used fo qPCR amplification. B-C. The levels of Let-7 pre-miRNAs (B) and mature miRNAs (C) were measured using qRT-PCR and normalized to U6 in two controls T-cell lines (CEM and Jurkat) and three HTLV-1 infected T-cells lines (Hut-102, C81-66 and ATL-2). Data are the means ± S. D. from three independent experiments ( ** p<0. 01). HTLV-1 infected cells lines (Figure  9 A-C ) and in CD8+ depleted PBMCs from ACs and from acute ATL patients (Figure  9 D-F ). We found that VPA treatment resulted in the restoration of dicer concomitant with decreased levels of hbz.
Western blot analysis Whole-cell lysates were prepared using RIPA buffer [10 mM Tris-HCl (pH 7. 4), 150 mM NaCl, 1% NP-40, 1 mM EDTA, 0. 1% SDS and 1 mM DTT], separated by electrophoresis on SD-PAGE gels, and transferred onto PVDF membranes (Millipore). Incubations with primary antibodies to detect Tax (NIH reagent), HBZ (Eurogentec), Dicer, Drosha (Bethyl Laboratory), Nucleoline (Santa Cruz Biotech) or Myc (Sigma-Aldrich) were followed by incubations with the appropriate secondary antibodies conjugated with horseradish peroxidase (HRP) (GE Healthcare) and by detection using enhanced luminescence (Roche). Protein bands were quantified following scanning by ImageJ software (http://imagej. nih. gov/ij/) [51].
RNA analysis Total RNA was prepared from whole cells using Trizol (Invitrogen) as previously described  [28]. Briefly, after reverse transcription (RT) using oligo-dT 12-18 primer (Invitrogen), the abundance of transcripts was assessed by real-time, quantitative qPCR analysis using the SYBR green PCR master mix (Roche Diagnostics) and gene-specific primer sets. Standard curves were generated from each PCR plate for all primer pairs using a serial dilution of an appropriate experimental sample. Samples were amplified in triplicate on each plate. Data were analyzed using LightCycler®480 Software (Roche Diagnostics). To measure the abundance of mature and precursor miRNAs, we used the QuantiMir detection kit (System Biosciences) according to manufacturer's instructions. Briefly, all cellular RNA were polyadenylated using Poly(A) polymerase, whereupon the oligo-dT adaptor was added to the reaction and annealing was allowed to proceed.
ACKNOWLEDGMENTS We kindly thank  Pr. Gatignol  and for  Dr Fischer  the gift of pCMV-RL let7 and pGL3-DICER-Prom vectors. We would like to thank  Dr Briant ,  Pr O. J Semmes  and  Dr Polakowski , for critical readings of manuscript. This work was supported by the  CNRS. H. G. was supported by a grant from the  Conseil Régional de la Martinique.
Illumina sequencing and identification of genetic variations Whole genome sequencing was performed at IGA Technology Services S. r. l. (Udine, Italy) using Illumina 2 × 100 bp paired-end reads HiSeq 2000 system. Reads were trimmed, quality filtered, and mapped against H 37 RvSiena as a reference sequence (CP007027. 1) using the Mosaik Assembler software  [20]. Genome fraction coverage and read depth were analyzed by Qualimap software  [21]. Single nucleotide substitutions, insertions, and deletions were retrieved with the VarScan software  [22]  by setting a minimum read depth of fifteen and a minimum variant frequency of 50% required to call a variant. This cutoff was selected to exclude variants located in genomic regions containing prophages and repetitive sequences, where most sequencing errors occur.
The morphometric analysis of involved lung area and granuloma size was performed using Sigmascan Pro Software (Systat Software Inc, San Jose, CA), as described  [25]. Cloning, expression, and purification of the wild type and His39Arg mutant PlcA protein Purified genomic DNA of M. tuberculosis H 37 RvSiena wild type and plcA His39Arg mutant strains was used as template for PCR amplification of plcA gene by using the primers shown in S2 Table. The PCR products were digested with BsphI and HindIII restriction enzymes, purified, and cloned into the pMyC plasmid at the Nco1 and HindIII cloning sites. The gene was under the control of the acetamidase promoter, as previously described  [26]. Recombinant plasmid pMyC-plcA was analyzed by nucleotide sequencing (GATC biotech) and used to transform Mycobacterium smegmatis mc 2 155 GroEL1ΔC  [27].
The work was funded in part by  NIH  grants  HL106788  and  AI104615  to MLG and in part by  European Community 's  Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement  FP7-223681  ( TB PAN-NET ) to GP. PS's PhD fellowship was financed by the  Ministère Francais de l'Enseignement Superieur et de la Recherche. We are also grateful for the support from the  Centre National de la Recherche Scientifique (CNRS). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
the sum of all annual ACS changes. Survivors' (green) and recruits' (orange) contribution are positive for ACS gains (survivors' ACS growth, new recruits' ACS and recruits' ACS growth) and negative for survivors' and recruits' ACS loss. Areas with higher levels of transparency and dotted lines are out of the calibration period (0-30 year). In the Guiana Shield and in nothwestern Amazonia, high levels of net ACS recovery are explained by large ACS gain from survivors' growth. Extrapolation was based on global rasters: topsoil bulk density from the Harmonized global soil database  (Nachtergaele et al. , 2008) , precipitation data from Worldclim  (Hijmans et al. , 2005)   in sites with less than 10 years of measurements (e. g. Peteco, Ecosilva, Iracema, Cumaru) and should be studied, together with the natural regeneration, in the future.
Site description Our study includes data from thirteen long-term (8-30 year) experimental forest sites located in the Amazon Basin and the Guiana Shield (Figure  1 -figure supplement 1). Sites meet the following criteria: (i) located in tropical forests with mean annual precipitation above 1000 mm; (ii) a total censused area above 1 ha; (iii) at least one pre-logging census and (iv) at least two post-logging censuses. For each site, we extracted annual precipitation and seasonality of precipitation data from WorldClim (RRID:SCR_010244)  (Hijmans et al. , 2005) , topsoil bulk density data from the Harmonized World Soil database  (Nachtergaele et al. , 2008) , and the synthetic climatic index from Chave et al. (Chave et al. , 2014) , using in all cases the highest resolution data available (30 arc-seconds).
For one of our sites (La Chonta, see Figure  1 -figure supplement 1), field measurements of precipitation (mean = 1580 mm yr À1 ) differed substantially from WorldClim data (1032 mm yr À1 ): in this particular case we used the measured value and adjusted the synthetic climatic index (E) in the allometric equation  (Chave et al. , 2014)  accordingly. Sites' data is available at Dryad Digital Repository  (Piponiot et al. , 2016).
ACS computation In all plots, diameter at breast height (DBH) of trees >20 cm DBH were measured, and trees were identified to the lowest taxonomic level: to the species level (75%) when possible, or to the genus level (15%); 10% of trees were not identified. To get the wood density, we applied the following standardized protocol to all sites: (i) trees identified to the species level were assigned the corresponding wood specific gravity value from the Global Wood Density Database (GWDD, doi:10. 5061/dryad. 234/1) (Zanne et al. , 2009) ; (ii) trees identified to the genus level were assigned a genus-average wood density; (iii) trees with no botanical identification or that were not in the GWDD were assigned the site-average wood density. The aboveground biomass (AGB) was estimated with the allometric equations from Chave et al.
(Chave et al. , 2014). Biomass was assumed to be 50% carbon  (Penman et al. , 2003). The ACS of every tree i was then computed as follows: ÂCS i ¼ exp À 1:803 À 0:976 Â E þ 0:976 Â lnðWD i Þþ þ2:673 Â lnðDBH i Þ À 0:0299 Â lnðDBH 2 i Þ Â 0:5 (1) where WD i and DBH i are the specific wood density and diameter at breast height of the tree i and E is the synthetic climatic index  (Chave et al. , 2014). The ACS changes data that was generated is available at Dryad Digital Repository  (Piponiot et al. , 2016).
Covariates To model ACS changes, we chose six covariates : (1) loss disturbance intensity, i. e. percentage of initial ACS loss; (2) acs0 mean ACS of the site; (3) dacs relative ACS of the plot, as a % of acs0; (4) prec annual precipitation; (5) seas precipitation seasonality; (6) bd topsoil bulk density. To give equivalent weight to all covariates, we centred and standardized them in order to have a mean of zero and a standard deviation of one over all observations. The uncertainty associated with ACS covariates (loss, acs0, dacs) is less than 10%  (Chave et al. , 2014). Climatic covariates (annual precipitation prec and precipitation seasonality seas) were extracted from Worldclim rasters (RRID:SCR_010244). Error in Worldclim precipitation data was estimated to be <10 mm in Amazonia  (Hijmans et al.
Inference Bayesian hierarchical models were inferred through MCMC methods using an adaptive form of the Hamiltonian Monte Carlo sampling  (Carpenter et al. , 2015). Each observation was given a weight proportional to the size of the plot. Codes were developed using the R language (RRID:SCR_ 001905) (R Developement Core Team, 2015) and the Rstan package  (Carpenter et al. , 2015). A detailed list of priors is provided in Table  1.
Prediction maps Maps were obtained with the following steps: (i) spatially-explicit covariates are extracted at the resolution of 30 arc-second from: the pan-tropical carbon map of Avitabile et al. for pre-disturbance aboveground carbon stocks  (Avitabile et al. , 2016) ; WorldClim (RRID:SCR_010244)  (Hijmans et al. , 2005)  for annual precipitation and seasonality of precipitation, and the Harmonized World Soil database  (Nachtergaele et al. , 2008)  for topsoil bulk density; (ii) disturbance intensity is set to 40% of pre-logging ACS loss, which is a common value for disturbance intensity after conventional logging in Amazonia  (West et al. , 2014; Blanc et al. , 2009; Martin et al. , 2015)  , and the relative forest maturity dacs is set to zero; (iii) parameters are drawn from their previously calibrated distribution; (iv) to simulate random effects, all five parameters (a) are taken from their distribution N ða 0 ; s 2 a Þ; (v) for every pixel, we estimate the five cumulative ACS changes (cSg, cSl, cRr,cRg,cRl) 10 years after the 40% ACS loss, given the parameters value and the pixel covariates values extracted from global rasters.
Steps (iii) to (v) are repeated 200 times and summary statistics are calculated for every pixel. Because a significant part of our sites have experiment duration lower than 10 years (Figure  1 -figure supplement 1), we are less confident in Amazonian-wide predictions after that 10 year period. Maps were elaborated under the R statistical software (RRID:SCR_001905) (R Developement Core  Team, 2015).
Acknowledgements We are in debt with all technicians and colleagues who helped setting up the plots and collecting data over years. Without their precious work, this study would have not been possible and they may be warmly thanked here. We are grateful to  CIRAD , the  GFclim  project (FEDER 2014-2020, Project  GY0006894 ) and the  Sao Paulo Research Foundation  ( FAPESP: 2013/16262-4  and  2013/50718-5 ) for financial support. This study was partially funded by an  Investissement d'Avenir grant  of the  ANR  ( CEBA :  ANR-10-LABEX-0025 ) and carried out in the framework of the Tropical managed  Forests Observatory (TmFO) , supported by the  Sentinel Landscape program  of  CGIAR (Consultative Group on International Agricultural Research ) - Forest Tree and Agroforestry Research Program.
Funding Funder Grant reference number  Author Agence Nationale de la Recherche   ANR-10-LABEX-0025   Camille Piponiot Bruno Herault Fundaca ˜o de Amparo aPesquisa do Estado de Sa ˜o Paulo FAPESP :  2013/16262-4  and  2013/50718-5   Edson Vidal European Regional Development Fund FEDER   2014-2020 ,  GY0006894 Camille Piponiot Bruno He  ´rault The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
)Figure 4. 4 Figure 4. Predicted cumulative ACS changes (Mg C ha À1 ) over the first 10 year after losing 40% of ACS. Extrapolation was based on global rasters: topsoil bulk density from the Harmonized global soil database (Nachtergaele et al. , 2008), Worldclim precipitation data (Hijmans et al. , 2005) and biomass stocks from Avitabile et al. map (Avitabile et al. , 2016). Cumulative ACS changes are obtained by integrating annual ACS changes through time. We here show the median of each pixel. Top graphs are ACS gain and bottom graphs are ACS loss. (a) ACS gain from survivors' growth. (b) ACS gain from new recruits. (c) ACS gain from recruits' growth. (d) ACS loss from survivors' mortality. (e) ACS loss from recruits' mortality. Black dots are the location of our experimental sites. Survivors' ACS changes (a and d) show strong regional variations unlike to recruits' ACS changes (b,c and e).
Further investigation of pleiotropic effects We further investigated pleiotropy between cancer sites using conditional Q-Q plots to examine enrichment of association signals in one cancer when conditioning on significance of P values of a second cancer. Enrichment is reflected in a leftward deflection of the Q-Q plot with decreasing P value categories of the conditioning cancer  (24) , indicating higher pleiotropy between cancer sites than expected by chance. Functional Significance eQTL analysis-We obtained non-tumor lung eQTL data of 1,111 patients from three studies: Laval University (n = 409, GSE23352), The University of British Columbia (n = 339) and the University of Groningen (n = 363). Gene expression profiles were obtained using an Affymetrix array (GEO platform GPL10379). Genotyping was performed using the Illumina Human1M-Duo genotyping BeadChip.
Analyses were adjusted for age, sex and smoking status. eQTL data are deposited in the Gene Expression Omnibus (GEO) database with accession numbers GSE23352, GSE23529 and GSE23545 for the three studies respectively. Further details of this study are published elsewhere  (25). We investigated validated lung cancer associated variants from our study. A statistically significant eQTL for a variant was declared if 2 of 3 studies showed a significant P value after Bonferroni correction for multiple comparisons. We also evaluated eQTL data for variants in LD (R 2 > 0. 7) with our validated variants. We also obtained TCGA eQTL data for 402 high-grade serous ovarian cases and 145 prostate tumor samples again investigating variants with validated associations and those in high LD (R 2 > 0. 7) with them.
Gene expression values for high-grade serous ovarian cases were assessed by P value. Gene expression values for prostate cancer were adjusted for somatic copy number and CpG methylation as previously described  (26). Significant associations were defined as those having both P value and false discovery rate (Benjamini-Hochberg method) of less than 0. 05. Function/prediction-We used FuncPred from SNPinfo (http://snpinfo. niehs. nih. gov/) to assist with variant function prediction. The software evaluates a variant's potential function in splicing regulation, TFBS prediction, miRNA binding site prediction and regulatory potential using in-house algorithms and tools developed elsewhere. In addition, we used RegulomeDB to further assess regulatory potential for variants of interest (http:// www.
13q13. 1 (known breast and lung locus) confirmed for serous ovarian cancer- We observed a significant pleiotropic associations for rs11571833 (ASSET two-sided P value: = 6. 14 × 10 -10 ). This variant (Fig. S3 ) is potentially functional and genome-wide significant associations between this variant and breast and lung cancer (driven by SqCC) were previously reported, with the latter study using a subset of the lung cancer data use here  (7, 10). More recently, a study focusing specifically on rs11571833 reported an association with serous ovarian cancer using the iCOGS data which formed our replication data set (P = 3 × 10 -5 ). Our results from the GAME-ON data set confirm the association with serous ovarian cancer (GAME-ON OR = 1. 76, 95% CI 1. 30-2. 39; P = 2. 49 × 10 -4 ; with the rare allele associated with increased risk)) and reached genome-wide significance when we combined the two data sets (P = 3.
Discussion Using data from the GAME-ON Network and GECCO we conducted a cross-cancer GWAS analysis investigating pleiotropic associations for five cancer sites (lung, breast, colorectal, ovary and prostate) including histology and subtypes. We identified three new pleiotropic associations that were supported by results in GAME-ON/GECCO data and our independent replication data sets. We identified a novel pleiotropic association at the 1q22 region involving breast cancer and lung SqCC, neither of which was previously known to be associated with genetic variation in this region. The association with lung SqCC was further supported by its functional significance as demonstrated by eQTL analysis. Further novel results include a locus at CASP8/ALS2CR12, known to be associated with breast cancer and melanoma, that was found to be associated with prostate cancer; while genetic variation at the 9p21.