dataesr/scientific-paragraphs-categorization huggingface dataset:
eQTL analyses The influence of germline genetic variations on gene expression was assessed using a linear regression model, as implemented in the R library eMAP (http:// www. bios. unc. edu/~weisun/software. htm). An additive effect was assumed by modeling subjects' copy number of the rare allele, i. e. 0, 1 or 2 for a given genotype. Only relationships in cis (defined as those in which the SNP resided less than 1 MB up or down from the center of the transcript) were investigated. eQTL analyses were performed on both normal breast and tumor tissues, and included the following materials: Normal Breast: NB116 (n = 116) consists of samples from women of Caucasian ancestry recruited in Oslo, comprising expression data from normal breast biopsies (n = 73), reduction plastic surgery (n = 34) and adjacent normal (n = 9) (adjacent to tumour).
Genotyping was performed using the iCOGS SNP array, and gene expression levels were measured with Agilent 44K  [85, 86]. NB93 is the Caucasian fraction of the TCGA dataset for which adjacent normal breast expression data were available, n = 93 for the data normalized per gene, and n = 94 for the data normalized per isoform. Birdseed processed germline genotype data from the Affy6 SNP array were obtained from the TCGA dbGAP data portal  [87]. Gene expression levels were assayed by RNA sequencing, RSEM (RNAseq by Expectation-Maximization,  [88]  normalized both per gene and per isoform, as obtained from the TCGA consortium  [87]. The data was log2 transformed, and unexpressed genes were excluded prior to eQTL analysis. Breast carcinomas: BC241, is a Caucasian sample set recruited from Oslo, n = 241.
The sample set includes all stages of breast cancer, and genotypes were obtained with the iCOGS SNP array, and mRNA expression data was from the Agilent 44K array  [86, 89]. BC765 comprises samples from the TCGA breast cancer sample set of Caucasian origin  [87] , n = 765 for the data normalized per gene, and n = 766 for the data normalized per isoform. Genotyping platform was Affy6, and gene expression was measured using RNA sequencing. See NB93 for a more detailed description. For all sample sets, the genotyping data was processed as follows: SNPs with call rates < 0. 95 or minor allele frequencies < 0. 05 were excluded, as were SNPs out of Hardy Weinberg equilibrium with P < 10 -13. All samples with a call rate below 80% were excluded. Identity by state was computed using the R GenABEL package  [90] , and closely related samples with IBS > 0.
From top to bottom, ChIA-Pet interactions for PolII and CTCF in MCF7 cells and Hi-C interactions in HMEC cells. The ChIA-PET raw data available on GEO under the following accession (GSE63525. K56, GSE33664, GSE39495) were processed with the GenomicRanges package. C. Maps of mammary cell super-enhancer locations as defined in Hnisz et al. are shown in HMEC cells. Predicted enhancer-promoter determined interactions in MCF7 and HMEC cells, as defined by the integrated method for predicting enhancer targets (IM-PET) are shown. D. RNA-Seq data from MCF7 and HMEC cell lines. The value of the RNA-Seq analysis corresponds to the mean RPM value for FAM175A, MRPS18C, HELQ, AGPAT9, HSPE and COQ2 from four HMEC and 19 MCF7 datasets, respectively. The annotation was obtained through the Bioconductor annotation package TxDb.
and The Quebec Breast Cancer Foundation for the PERSPECTIVE project. Jacques Simard is Chairholder of the Canada Research Chair in Oncogenetics. Silje Nord is financed by a carrier grant from the Norwegian Regional Health authorities (Grant number 2014061). F2-2009-223175) (COGS). Funding for the iCOGS infrastructure came from: the European Community's Seventh Framework Programme under grant agreement n° 223175 (HEALTH-F2-2009-223175) (COGS), Cancer Research UK (C1287/A10118, C1287/A 10710, C12292/A11174, C1281/A12014, C5047/A8384, C5047/A15007, C5047/A10692, C8197/A16565), the National Institutes of Health (CA128978) and Post-Cancer GWAS initiative (1U19 CA148537, 1U19 CA148065 and 1U19 CA148112 -the GAME-ON initiative), the Department of Defence (W81XWH-10-1-0341), the Canadian Institutes of Health Research (CIHR) for the CIHR Team in Familial Risks of Breast Cancer, Komen Foundation for the Cure, the Breast Cancer Research Foundation, and the Ovarian Cancer Research Fund.
ACKNOWLEDGMENTS BCAC thanks all the individuals who took part in these studies and all the researchers, clinicians, technicians and administrative staff who have enabled this work to be carried out. This study would not have been possible without the contributions of the following:  Per Hall  (COGS);  Douglas F. Easton ,  Paul Pharoah ,  Kyriaki Michailidou ,  Manjeet K. Bolla ,  Qin Wang  (BCAC),  Andrew Berchuck  (OCAC),  Rosalind A. Eeles ,  Douglas F. Easton ,  Ali Amin Al Olama ,  Zsofia Kote-Jarai ,  Sara Benlloch  (PRACTICAL),  Georgia Chenevix-Trench ,  Antonis Antoniou ,  Lesley McGuffog ,  Fergus Couch  and  Ken Offit  (CIMBA),  Joe Dennis ,  Alison M. Dunning ,  Andrew Lee , and  Ed Dicks ,  Craig Luccarini  and the staff of the  Centre for Genetic Epidemiology Laboratory ,  Javier Benitez ,  Anna Gonzalez-Neira  and the staff of the CNIO genotyping unit,  Jacques Simard  and  Daniel C.
The work of the BBCC was partly funded by  ELAN-Fond of the University Hospital of Erlangen. The BBCS is funded by  Cancer Research UK  and  Breast Cancer Now and acknowledges NHS  funding to the  NIHR Biomedical Research Centre , and the  National Cancer Research Network (NCRN). ES is supported by  NIHR Comprehensive Biomedical Research Centre, Guy's & St. Thomas' NHS Foundation Trust  in partnership with King's  College London, United Kingdom. IT is supported by the  Oxford Biomedical Research Center. The BSUCH study was supported by the  Dietmar-Hopp Foundation , the  Helmholtz Society  and the  German Cancer Research Center (DKFZ). The CECILE study was funded by  Fondation de France ,  Institut National du Cancer (INCa) ,  Ligue Nationale contre le Cancer, Ligue contre le Cancer Grand Ouest, Agence Nationale de Sécurité Sanitaire (ANSES) ,  Agence Nationale de la Recherche (ANR).
HAC receives support from the  Lon V Smith Foundation  ( LVS39420 ). The ESTHER study was supportd by a grant from the  Baden Württemberg Ministry of Science, Research and Arts. Additional cases were recruited in the context of the VERDI study, which was supported by a grant from the  German Cancer Aid (Deutsche Krebshilfe). The GENICA was funded by the  Federal Ministry of Education and Research (BMBF)   Germany grants   01KW9975/5 ,  01KW9976/8 ,  01KW9977/0  and  01KW0114 , the  Robert Bosch Foundation, Stuttgart, Deutsches Krebsforschungszentrum (DKFZ) ,  Heidelberg , the  Institute for Prevention and Occupational Medicine of the German Social Accident Insurance, Institute of the Ruhr University Bochum (IPA) ,  Bochum , as well as the  Department of Internal Medicine, Evangelische Kliniken Bonn gGmbH, Johanniter Krankenhaus, Bonn, Germany.
The HEBCS was financilly supported by the  Helsinki University Central Hospital Research Fund ,  Academy of Finland  ( 266528 ), the  Finnish Cancer Society, The Nordic Cancer Union  and the  Sigrid Juselius Foundation. The HMBCS was supported by a grant from the  Friends of Hannover Medical School  and www. impactjournals. com/oncotargetby the  Rudolf Bartling Foundation. Financial support for KARBAC was provided through the regional agreement on medical training and clinical research (ALF) between  Stockholm County Council and Karolinska Institutet , the  Swedish Cancer Society , The Gustav V Jubilee foundation and and Bert von Kantzows foundation. The KBCP was financially supported by the special Government Funding (EVO) of  Kuopio University Hospital  grants,  Cancer Fund of North Savo , the  Finnish Cancer Organizations , and by the strategic funding of the  University of Eastern Finland.
kConFab is supported by a grant from the  National Breast Cancer Foundation , and previously by the  National Health and Medical Research Council (NHMRC) , the  Queensland Cancer Fund , the  Cancer Councils of New South Wales, Victoria , Tasmania and  South Australia , and the  Cancer Foundation of Western Australia. Financial support for the AOCS was provided by the  United States Army Medical Research and Materiel Command  [ DAMD17-01-1-0729 ],  Cancer Council Victoria, Queensland Cancer Fund, Cancer Council New South Wales, Cancer Council South Australia , The  Cancer Foundation of Western Australia, Cancer Council Tasmania  and the  National Health and Medical Research Council of Australia  (NHMRC; 400413, 400281, 199600). G. C. T. and P. W. are supported by the  NHMRC. RB was a  Cancer Institute NSW Clinical Research Fellow.
and Margaret T. Grohne Family Foundation. MCCS cohort recruitment was funded by  VicHealth  and  Cancer Council Victoria. The MCCS was further supported by Australian  NHMRC  grants  209057 ,  251553  and  504711  and by infrastructure provided by  Cancer Council Victoria. Cases and their vital status were ascertained through the  Victorian Cancer Registry (VCR)  and the  Australian Institute of Health and Welfare (AIHW) , including the  National Death Index  and the Australian Cancer Database. The MEC was support by  NIH  grants  CA63464 ,  CA54281 ,  CA098758  and  CA132839. The work of MTLGEBCS was supported by the  Quebec Breast Cancer Foundation , the  Canadian Institutes of Health Research for the "CIHR Team in Familial Risks of Breast Cancer" program  -grant #  CRN-87521  and the  Ministry of Economic Development, Innovation and Export Trade  -grant #  PSR-SIIRI-701.
The NBCS has been supported by the  Research Council of Norway  grant  193387/V50  (to  A-L Børresen-Dale  and  V. N. Kristensen ) and grant  193387/H10  (to  A-L Børresen-Dale  and  V. N. Kristensen ),  South Eastern Norway Health Authority  (grant  39346  to  A-L Børresen-Dale  and  27208  to  V. N. Kristensen ) and the  Norwegian Cancer Society  (to  A-L Børresen-Dale  and  419616 -71248 -PR-2006-0282  to  V. N. Kristensen ). It has received funding from the  K. G. Jebsen Centre for Breast Cancer Research  ( 2012-2015 ). The OBCS was supported by research grants from the  Finnish Cancer Foundation , the  Academy of Finland  (grant number  250083 ,  122715  and  Center of Excellence  grant number  251314 ), the  Finnish Cancer Foundation , the  Sigrid Juselius Foundation , the  University of Oulu , the  University of Oulu Support Foundation  and the special Governmental EVO funds for Oulu University Hospitalbased research activities.
The  Ontario Familial Breast Cancer Registry (OFBCR)  was supported by grant  UM1 CA164920  from the  National Cancer Institute (USA). The content of this manuscript does not necessarily reflect the views or policies of the  National Cancer Institute  or any of the collaborating centers in the  Breast Cancer Family Registry (BCFR) , nor does mention of trade names, commercial products, or organizations imply endorsement by the  USA Government  or the BCFR. The ORIGO study was supported by the  Dutch Cancer Society  ( RUL 1997-1505 ) and the  Biobanking and Biomolecular Resources Research Infrastructure  ( BBMRI-NL CP16 ). The PBCS was funded by  Intramural Research Funds of the National Cancer Institute, Department of Health and Human Services, USA. The pKARMA study was supported by  Märit and Hans Rausings Initiative against Breast Cancer.
The RBCS was funded by the  Dutch Cancer Society  ( DDHK 2004-3124 ,  DDHK 2009-4318 ). The SASBAC study was supported by funding from the  Agency for Science, Technology and Research of Singapore (A*STAR) , the  US National Institute of Health (NIH)  and the  Susan G. Komen Breast Cancer Foundation. The SBCS was supported by  Yorkshire Cancer Research   S295 ,  S299 ,  S305PA  and  Sheffield Experimental Cancer Medicine Centre. SEARCH is funded by a programme grant from  Cancer Research UK  [ C490/A10124 ] and supported by the  UK National Institute for Health Research Biomedical Research Centre  at the  University of Cambridge. SEBCS was supported by the  BRL (Basic Research Laboratory ) program through the  National Research Foundation of Korea  funded by the  Ministry of Education, Science and Technology  ( 2012-0000347 ).
SGBCC is funded by the  NUS start-up Grant, National University Cancer Institute Singapore (NCIS)   Centre Grant  and the  NMRC Clinician Scientist Award. www. impactjournals. com/oncotargetAdditional controls were recruited by the  Singapore Consortium of Cohort Studies-Multi-ethnic cohort (SCCS-MEC ), which was funded by the  Biomedical Research Council , grant number:  05/1/21/19/425. SKKDKFZS is supported by the  DKFZ. The SZBCS was supported by Grant  PBZ_KBN_122/P05/2004. The TNBCC was supported by: a  Specialized Program of Research Excellence (SPORE) in Breast Cancer  ( CA116201 ), a grant from the  Breast Cancer Research Foundation , a generous gift from the  David F. and Margaret T. Grohne Family Foundation. The UKBGS is funded by  Breast Cancer Now  and the  Institute of Cancer Research (ICR), London.
Long-range chromatin interactions. From top to bottom, ChIA-Pet interactions for PolII and CTCF in MCF7 cells and Hi-C interactions in HMEC cells. The ChIA-PET raw data available on GEO under the following accession (GSE63525. K56, GSE33664, GSE39495) were processed with the GenomicRanges package. C. Maps of mammary cell super-enhancer locations as defined in Hnisz et al. are shown in HMEC cells. Predicted enhancer-promoter determined interactions in MCF7 and HMEC cells, as defined by the integrated method for predicting enhancer targets (IM-PET) are shown. D. RNA-Seq data from MCF7 and HMEC cell lines. The value of the RNA-Seq analysis corresponds to the mean RPM value for FAM175A, MRPS18C, HELQ, AGPAT9, HSPE and COQ2 from four HMEC and 19 MCF7 datasets, respectively. The annotation was obtained through the Bioconductor annotation package TxDb.
The ABCS study was supported by the Dutch Cancer Society [grants NKI 2007-3839; 2009 4363]; BBMRI-NL, which is a Research Infrastructure financed by the Dutch government (NWO BCAC is funded by Cancer Research UK [C1287/ A10118, C1287/A12014] and by the European Community´s Seventh Framework Programme under grant agreement number 223175 (grant number HEALTH-.
1% for 20 min at 4 °C followed by a washing step in PBS FBS2% EDTA 2 mM azide 0. 1% and a washing step in PBS. Following the extracellular labelling, the cell viability was monitored using the efluor450 or efluor780 (ebioscience) in PBS for 20 min 4 °C, followed by a washing step with PBS FBS2% EDTA 2 mM azide 0. 1% and fixed in PFA 2%. Flow cytometry was performed using the MACSQUANT (Miltenyi Biotec) and data analysis was performed with the FlowJo software. Cytotoxicity assay. The cytolytic activity of engineered T-cells endowed with the different CARs (transfected with dose D 1/4 ), was assessed using a flow cytometry-based cytotoxicity assay. In this assay target cells presenting the CAR target antigen (Daudi, CD19 pos ) are labelled with CellTraceTM CFSE (Life Technologies) and control cells not presenting the CAR target antigen (K562, CD19 neg ) with CellTraceTM violet (Life Technologies).
INTRODUCTION Recombinant adeno-associated virus (rAAV) vectors are among the most widely used viral vectors for gene therapy. 2] [3]  Moreover, the European Commission's approval of an AAV vector-based gene therapy for the treatment of lipoprotein lipase deficiency  4  has fueled the interest of pharmaceutical companies in the field. Clinical dosing of rAAV therapeutics is usually based on vector genome (vg) titer (vector strength (vg/ml)), thus having accurate quality control methods that determine the amount of vector being administered is important. In order to calibrate therapeutic vector doses used by different investigators, the research community recognized the need for well-characterized rAAV reference standard materials (RSMs). 6] [7]  Volunteer laboratories have produced and characterized these RSMs; the data have been published and they are accessible to the community through the American Type Culture Collection (ATCC, Manassas, VA)-LGC Standards (catalog numbers, VR-1616 and VR-1816).
The free-ITR method, including well-characterized controls, will help to calibrate doses to compare preclinical and clinical data in the field. UK regulatory agency (NIBSC/Medicines and Healthcare Products Regulatory Agency) has published data showing the variability in vg titration and also recommended the use of RSMs. 10  In 2012, Aurnhammer and colleagues established a molecular biological method that allows quantification of AAV serotype 2 (AAV2) genomes on the basis of qPCR targeting the inverted terminal repeat (ITR-2) sequence  11  (from now on named "ITR2 qPCR"). Because this method can be used universally for all ITR2-based vectors (included in the vast majority of vectors used in the field), it could be of interest to use it as a standardized assay for vg titrations. However, we have compared vg titers obtained using the ITR2 qPCR with that obtained for qPCR assays utilizing primers and probes with binding sites located internally to the vector and we found systematically higher titers (up to eightfold) using the ITR2 qPCR protocol.
Sequencing of ITR regions Sequencing of ITR regions from rAAV2 RSM, rAAV8 RSM and psub201 was carried out by ACGT (Wheeling, IL) using multiplexed Illumina sequencing. Briefly, rAAV vector DNA (single stranded) was extracted from rAAV2  RSM and rAAV8 RSM (ATCC) and dsDNA was PCR amplified from these ssDNA templates and used to prepare a barcoded, sized, pooled library. To sequence the ITRs from psub201, plasmid DNA was fragmented to an average 350 bp by ultrasonication and used to construct the sized pooled library. Each library was characterized by multiplexed Illumina sequencing using NextSeq 500. Trimmed and filtered reads were aligned with reference sequence from psub201 or pTR-UF11 (for AAV2 RSM and AAV8 RSM). No variants were observed in the ITR sequences confirming the integrity of the target sequence (data not shown).
Explanatory variables Using an extended patient questionnaire, which included the EQ-5D, EQ-VAS, MORISKY, PACIC and EUROPEP questionnaires, we obtained data on the following explanatory variables: demographic variables (gender, age, employment, education, marital status), duration of practice involvement, family physician visit frequency per year as well as data on comorbid conditions, which were self-reported, chosen from the list by patients themselves and confirmed by their physicians through an audit of patients' records, namely angina pectoris, myocardial infarction, stent or coronary by-pass, stroke, and peripheral artery disease. Furthermore, we also obtained data from patients on medication adherence (MORISKY questionnaire)  [24] , their assessment of chronic illness care (PACIC questionnaire)  [25, 26]  and their self-rated satisfaction of family practice care and clinical behaviour of their family physician (EUROPEP questionnaire)  [27, 28].
Data analysis We calculated descriptive statistics for patient characteristics. Demographic data were reported as frequencies (%) or mean (SD) values. Only patients with complete data on all explanatory variables were considered in the final model and included in the analyses. The characteristics of these patients were compared with those of patients who were excluded from the analysis using the chi-square test (gender), the independent-samples t-test (age) and the EQ-5D score (Mann-Whitney U test). Normal distribution of numerical variables was confirmed by the Shapiro-Wilks test. The main study outcome was HRQoL as measured by two dependent variables of the EQ-VAS and EQ-5D index score, which were also reported as frequencies (%) or mean (SD) and median (interquartile range) values. EQ-5D index score was obtained by applying preference weights from the health value scale defined according to a 3-level EQ-5D scale for Slovenian population  [22].
0, 0. 7, 0. 6, 0. 5, 0. 4, 0. 3, 0. 2 or lower). Multiple regression modelling included characteristics that were found to be significant by univariate regression only. Statistical analysis was performed using IBM SPSS software, version 21. 0 (IBM Corp. , Armonk, NY). Statistical significance was set at p < 0. 05.
Results In total, 36 family medicine practices took part in this study, with 768 CHD patients, representing 71. 1% of the target sample of 1080 patients. A total of 312 patients refused to participate or did not return questionnaires. To continue, 345 patients were excluded due to unclear coding (n = 15), missing data (n = 178) or non-fulfilment of inclusion criteria (n = 152). As a result, the analysis included 423 patients, which is 55. 1% of the sample of eligible patients. Despite the high volume of excluded data, there were no statistically significant differences between the excluded (n = 345) and analysed (n = 423) samples by mean age (68. 7 ± SD 10. 5 and 68. 0 ± SD 10. 8 respectively, p = 0. 362), gender (64. 3% of male patients excluded from the analysis and 64. 8% of all male patients participating in the study, respectively, p = 0.
Comparison of the models led to contradictory dilemmas. On this basis we decided to analyse the data using ordinal logistic regression model due to non-linearity of the outcome variable  [37]. There are several reasons as to why the sample might not be representative of the Slovenian CHD population: the low response rate, the exclusion of patients who did not understand Slovenian and those with diabetes, family physicians may have failed to include random sample of participants. The final number of patients in the sample was much smaller due to several missing values in the patient questionnaire, which was very long and cognitively demanding in some parts (e. g. , PACIC). For the analysis, we used only a part of the instrument. Only patients who completed the questionnaire in full were included in the analysis.
Purpose: Studies examining high-dose chemotherapy with autologous hematopoietic stem cell transplantation (HDC-AHSCT) strategies in inflammatory breast cancer (IBC), showed encouraging results in terms of disease-free survival (DFS), and overall survival (OS). The lack of data regarding HER2 status in all of these studies prevented any prognostic analysis involving breast cancer subtypes. Methods: All consecutive female patients treated for IBC with HDC and AHSCT at Institut Paoli-Calmettes between 2003 and 2012 were included. Since 2005, trastuzumab was included in initial treatment. Patient, tumor and treatment characteristics were collected. Patients were categorized in three subtypes based on hormonal receptor (HR) and HER2 status of the primary tumor: Luminal, (HR+/HER2-), HER2 (HER2+, any HR), and triple negative (TN) (HER2-and HR-).
Patient population The patient population was identified from our prospectively maintained institutional cell therapy database. Inclusion criteria were as follows: all consecutive female patients treated for IBC with HDC and AHSCT at Institut Paoli-Calmettes between 2003 (the year from which testing for HER2 overexpression was carried out systematically) and 2012. Patient, tumor and treatment characteristics were collected. Minimum criteria required for the diagnosis of IBC included the following: T4d according to American Joint Committee on Cancer (7 th edition) breast cancer staging, characterised by diffuse erythema and oedema, PEV2 (inflammation, erythema, and/or edema localized to <50% of the breast surface), or PEV3 (generalized inflammation and edema occupying >50% of the breast surface) according to the Institut Gustave-Roussy classification.
Metastatic patients were excluded. A flowchart illustrating consecutive steps in the selection process is provided in figure  1. The study was approved by the Institut Paoli-Calmettes (IPC) Institutional Review Board (IRB, Comité d'Orientation Stratégique, COS). All patients undergoing HDC-AHSCT are required to provide signed informed consent, which covers the use and transfer of anonymized data for further clinical research. No further consent was requested by the IRB for the conduct of the present study.