dataesr/scientific-paragraphs-categorization huggingface dataset:
DISCUSSION We investigated the prevalence of NTRK fusion-positive cancers and their relation to other biomarkers in a large population of >295,000 cancer cases from the FoundationCORE database; the Fig. 3  Co-occurrence of genes among NTRK fusion-positive cancers. Co-occurrence of genes in all NTRK fusion-positive cancers (a). The prevalence of gene mutations was compared for NTRK fusion-positive and fusion-negative disease. Co-occurrence refers to genes that occurred in NTRK fusion-positive disease with an odds ratio greater than 1 compared with NTRK fusion-negative disease and the false discovery rate (FDR)-adjusted P-value was <0. 05. Lack of co-occurrence refers to genes that did not occur in NTRK fusion-positive disease with an odds ratio less than 1 compared with NTRK fusion-negative disease and the FDR-adjusted P-value was <0.
A fusion prevalence around 0. 30% with low frequency among common cancers and higher frequency within certain rare cancers is in line with previous estimations from smaller datasets  [4] [5] [6]. In the FoundationCORE database, Asian ancestry was associated with slightly increased NTRK gene fusions prevalence, possibly because of the higher proportion of NSCLC found in this cohort. Generally, NTRK gene fusions did not co-occur with other oncogenic drivers, supporting findings from smaller datasets  6. Due to the size of the FoundationCORE database and the large cohort of NTRK fusion-positive cases analysed, we were able to identify 88 different NTRK fusion partner pairs, of which 65. 9% had not previously been reported in other large public databases  4- 6,8,9,12,13. Importantly, although the predicted specificity of DNAbased assays is lower than that of RNA-based assays, and thus has greater potential for false-positive results, all of these fusions were predicted to be pathogenic based on conservative definitions for functional rearrangements and mutually exclusive with other known oncogenic drivers, further arguing for their pathogenicity.
Moreover, clinical bridging analyses in a selected clinical trial population  15  , estimated a response rate of 72. 2% in patients with NTRK fusion-positive tumours identified by DNA-based assays, providing more evidence that these assays can detect pathogenic fusions. The significant number of rearrangements identified in our study that were not previously reported highlights the value of analysing large datasets and underscores the need for highquality diagnostic methods ensuring identification of novel fusion partners. With the rarity of NTRK fusions, it seems important to cover known and unknown fusion events to identify patients qualifying for TRK inhibitor treatment. Due to their capacity to detect unknown fusions and to yield lower rates of false-negative and false-positive results than immunohistochemistry  16  , NGS assays are now integral to the process of identifying patients with tumours harbouring an NTRK fusion: when testing an unselected population, the ESMO guidelines for NTRK testing recommend front-line sequencing or screening by immunohistochemistry followed by sequencing of positive cases  8.
MSI status MSI ambiguous MSI-H MSS The large dataset analysed here allowed us to further describe the genomic landscape of NTRK fusion-positive cancers. In line with the assumption that NTRK-fusion-driven cancers are largely devoid of other oncogenic drivers, NTRK gene fusions were less likely to co-occur with common drivers, such as those involved in MAPK and PI3K signalling pathways (KRAS, PIK3CA) and with known oncogenic driver genes in breast cancer, CRC and NSCLC. Consequently, co-occurrence was seen with only 14 genes, including the most common fusion partner. The recent Rosen et al. study in NTRK fusion-positive cancers reported no cooccurrence with KRAS, NRAS, BRAF, EGFR, ALK, MET or ROS1 6 , and we did not observe co-occurrence with these genes either. Apart from CRC (owing to the over-representation of MSI-H CRC), median TMB was not different between NTRK fusion-positive and -negative cases.
This mutual exclusivity with BRAF V600E mutations suggests a yet unappreciated very rare subtype of spontaneous MSI-H CRC defined by the presence of NTRK gene fusions. Future studies will need to investigate the underlying biology of this observation. Importantly, these findings have immediate clinical implications, as testing for NTRK gene fusions in spontaneous MSI-H and BRAF wild-type CRC cases could identify patients who may benefit from NTRK-directed therapies. Our study has some limitations. NGS testing with Foundation Medicine Inc assays does not cover the whole exome/genome, so while NTRK1/2/3 are interrogated, including all exons and specific introns, the description of the genomic characteristics of NTRK fusion-positive cancers cannot be considered exhaustive. Furthermore, comparisons to clinical trial cohorts were limited by a lack of clinical and demographic information available in the Founda-tionCORE database  20.
Finally, as we did not collect clinical outcomes data, we are unable to investigate the prognostic value of NTRK fusions in our cohort. Despite this, our study included the largest population used to profile the characteristics and genomic landscape of NTRK fusion-positive cancers in a tumour-agnostic setting. The FoundationCORE database of >295,000 patient records, with an overall prevalence of 0. 30% for NTRK fusion-positive cancers, allowed us to describe the largest cohort of NTRK fusionpositive cancers to date. From these 889 cases, we were able to identify 88 unique fusion partners of which two-thirds had not been reported before, underscoring the critical need for appropriate testing to identify this very small subgroup of cancers. Importantly, we were able to describe a subtype of spontaneous MSI-H CRC defined by the presence of NTRK fusions and the absence of otherwise pathogenic BRAF V600E mutations.
FoundationCORE database samples Comprehensive genomic profiling (CGP), including TMB  21  and genetic ancestry prediction  22  , were carried out in a Clinical Laboratory Improvement Amendments certified, College of American Pathologists accredited laboratory (Foundation Medicine Inc. , Cambridge, MA, USA). Data from 295,676 de-identified, consented-for-research cases between January 2013 and December 2019 from 75 different solid tumour types were profiled. Detailed methods for this assay were previously described by Chmielecki, et al. 23. Briefly, haematoxylin-and-eosin-stained slides or Wright-Giemsa stained blood/aspirate smears were used to confirm the pathologic diagnosis of each case. Samples containing a minimum of 20% tumour cells were selected for subsequent RNA and/or DNA extraction, from 10μm formalin-fixed paraffin-embedded (FFPE) sections or fresh blood/bone marrow aspirates, and genomic analysis.
The sequences of captured libraries (median exon coverage depth >600x using Illumina, San Diego, CA, USA) were analysed for select gene fusions, indels, base substitutions and copy number alterations, as previously described  25, 26. Variants removed from the dataset included germline variants (1000 Genomes Project [dbSNP142] or dbSNP database http://www. ncbi. nlm. nih. gov/SNP/), those with ≥2 counts in the ExAC database (http://exac. broadinstitute. org/) except for known cancer drivers (e. g. BRCA1/2 and TP53 mutations), and recurrent variants of unknown significance predicted to be germline by an internally developed algorithm  27. Known confirmed somatic alterations according to the Catalogue of Somatic Mutations in Cancer (COSMIC) were highlighted as biologically significant. Approval was obtained from the Western Institutional Review Board (Protocol No.
NGS from the FoundationCORE database Co-occurrence of NTRK1/2/3 gene fusions with known and likely somatic alterations in each of >300 cancer-related genes was assessed across all samples. Odds ratios for mutational co-occurrence were generated using two-sided Fisher's exact test. False discovery rate (FDR)-adjusted P-values calculated using the Benjamini-Hochberg correction were used to determine significance (P < 0. 05). Co-occurrence was also evaluated with alterations of disease-specific driver genesets in their respective indications. Throughout this analysis, NTRK fusion-positive cases were defined as those harbouring any NTRK1/2/3 rearrangement known or suspected to result in a fusion protein, consistent with definitions used in other pansolid tumour prevalence studies  [4] [5] [6].
An assessment of NTRK fusion detection rate was conducted for the FoundationOne CDx platform using COSMIC v92 (cancer. sanger. ac. uk) as the reference baseline. Using 10 internal process-matched normal control specimens (each an equal mixture of 10 diploid HapMap cell lines), the mean sequence coverage at all genomic loci was calculated. Conservatively, using 100x as the minimum for which custom FoundationOne CDx algorithms would detect breakpoints, all annotated fusion breakpoints in NTRK1/2/3 in the COSMIC v92 database (TPM3:NTRK1, TPR:NTRK1 variant I, TPR:NTRK1 variant II, TFG:NTRK1, LMNA:NTRK1, TP53:NTRK1, QKI:NTRK2, NACC2:NTRK2, ETV6:NTRK3 variant I, ETV6:NTRK3 variant II, ETV6:NTRK3 variant III, and ETV6:NTRK3 variant IV) were compared with the empirically measured coverage profile of FoundationOne CDx.
ACKNOWLEDGEMENTS We would like to thank  Dean Pavlick  for his assistance in evaluating expected NTRK fusion detection rates in silico. M. G. K. would like to acknowledge support by  NIHR Manchester Biomedical Research Centre. Third-party medical writing assistance, under the direction of the authors, was provided by  Laura Vergoz PhD , of  Ashfield MedComms , an  Ashfield Health company , and was funded by  F. Hoffmann-La Roche Ltd.
DATA AVAILABILITY The data generated and analysed during this study are described in the following data record: https://doi. org/10. 6084/m9. figshare. 14604465  32. The data were generated and analysed under the auspices of Roche, which is a member of the Vivli Center for global clinical research data. Data access conditions are described at https://vivli. org/ourmember/roche/. To request access to individual patient-level data from the clinical trials, first locate the clinical trial in Vivli (https://search. vivli. org/ requires sign up and log in) using the trial registration number (given above), then click the 'Request Study' button and follow the instructions. In the event that you cannot see a specific study in the Roche list, an Enquiry Form can be submitted to confirm the availability of the specific study.
The Helsinki Study imputed genotypes using a Finnish population-specific reference panel and the BEAGLE software. All genotype data were based on genome build hg38. Genotyping platforms and cohort-specific quality control and analysis parameters are provided in eTable 3. GWAS of stroke cases and controls were conducted within sites or within groupings of sites, and then meta-analyzed. Prior to analysis, we removed stroke cases from analysis if there were fewer than 40 in the analysis strata, if they could not be assigned to a genetic ancestry group, or if there was not an adequate number of controls. Filtering on these criteria left 16,927 (of 17,077) early onset cases for analysis. The overall analytic design is depicted in Figure  1. Our primary analysis was a transethnic meta-analysis. In parallel, we performed a European only meta-analysis.
Using summary level association results (VTE results from the INVENT Consortium 21 ), we then estimated pairwise genetic correlations among early onset stroke, late onset stroke, and VTE using LD Score Regression analysis (LDSC)  22. We then tested if genetic predisposition to VTE, as measured by a polygenic risk score (PRS), was more prominently associated with early compared to later onset stroke. For this purpose, we generated a VTE PRS for individuals from the EOSC and late onset stroke subset from SiGN based on a large prior GWAS of VTE  23  using PRSice software  24. The VTE PRS included 255 SNPs using a GWAS p-value threshold of 1 × 10 -5 (see eMethods). We tested the association between the VTE PRS score with stroke in the European ancestry sample using logistic regression with 10 principal components for ancestry and sex included as covariates.
Effect sizes between early and late onset stroke were compared using a Wald test (See eMethods). We identified several disorders and plasma biomarkers (late onset stroke, VTE, and plasma levels of von Willebrand factor (VWF) and Factor VIII) for which associations at the ABO locus have previously been reported and then used the coloc software  25  to assess evidence that the same causal SNPs associated with EOS also drove associations with the second trait. Briefly, coloc utilizes a Bayesian approach and summary level association level results for two traits to calculate the posterior probabilities of five competing hypotheses (H0-H4) that assess whether the associations are due to the same (corresponding to H4) or a different (corresponding to H3) causal variant (see eMethods). Standard Protocol Approvals, Registrations, and Patient Consents.
Associations of ABO blood group-defining SNPs with early and late onset stroke. We tested associations of these blood group-defining haplotypes with early and late onset stroke in the European ancestry subgroup. As indicated previously, the top ABO SNPs identified in our GWAS, rs8176685 and rs529565, are in high linkage disequilibrium with the tagging SNPs for haplotypes A1 and O1, which were not included in our dataset. We also used rs1137827 as a high LD tag for rs8176743 (blood group B) (Table  1 ). Consistent with the VTE findings from Goumidi et al, our analyses indicate haplotype A1 (rs8176685) to be strongly associated with early onset stroke (OR = 1. 18, 95% CI: 1. 11-1. 25; p = 1. 95 × 10 -8 and blood group O1 (rs529565) to be protective (OR = 0. 88, 95% CI: 0. 85-0. 91; p = 3. 17 × 10 -13 ) (Table  1 ).
Fixation was performed for 10 min in 1% formaldehyde (Thermo Scientific, 28908) at room temperature (RT). After washing the cells twice in PBS, they were permeabilised with 0. 1% v/v Triton X-100 in PBS (PBSTx) supplemented with 3% donkey serum (Sigma-Aldrich, D9663) for 30 min at RT. Primary antibodies (diluted in PBSTx with 3% donkey serum) were applied for 2 h at RT or overnight at 4°C in a volume of 1 ml per dish. After three washes in PBSTx, secondary antibodies (2 μg/ml in PBSTx with 3% donkey serum) were applied for 2h at RT. Cells were washed three times in PBSTx, nuclei counterstained with 4',6-diamidino-2-phenylindole (DAPI; Sigma-Aldrich, D9542), and imaged with an inverted Leica SP8 confocal microscope using a 40× oil immersion objective. Acquisition was performed using the LASX acquisition software suite.
During imaging, the cells were kept at 37°C in a humidified atmosphere (7% CO 2 ). Images on single focal planes were acquired with a 63× oil immersion objective on an inverted LSM800 confocal Zeiss microscope, using the ZEN Blue acquisition software suite. For alkaline phosphatase staining, 300 EKOiE, EKOiE Nr5a2 KO (c2 and c4), and EKOiE Nr5a2 KO Repair ESCs (c4. 2 and c16. 1) were plated in single wells of 6-well plates coated overnight with 0. 01% poly-L-ornithine (Sigma-Aldrich,P4957) at 4°C, washed and coated for 2 h with 10 μg/ml laminin (Millipore, CC095) in PBS. After culture in FCS/LIF or 2i/LIF media in the presence or absence of 1 μg/ml doxycycline (Sigma-Aldrich, I5148) for 7 days, cells were fixed and stained using an alkaline phosphatase staining kit (Sigma-Aldrich, 86R-1KT) according to the manufacturer's instructions.
10 Flow cytometry E14Tg2a, Esrrb-T2a-GFP or Nr5a2-T2a-GFP ESCs were plated at low density (2000 cells/cm 2 ) in FCS/LIF medium and cultured for 3 days before analysis. After trypsinisation, cells were resuspended in FCS/LIF without Phenol Red, and analysed using a LSR II flow cytometer system or a Luminex Image Stream MK2 instrument with a 60× magnification objective. Data were analysed using the FlowJo software suite.
To determine the number of clusters to use, we relied on the enrichment of differentially expressed genes in proximity of ChIP-seq peaks, and found that k=5 was the first k at which enrichment was robust. Differential binding analysis was performed using DESeq2  (Love et al. , 2014). Gene Ontology analyses were made with GREAT using standard parameters. De novo motif discovery on Esrrb/ Nr5a2-bound regions in FCS/L used the Regulatory Sequence Analysis Tools (RSAT) with standard parameters (rsat. sb-roscoff. fr) (Nguyen et al. , 2018). To locate motifs preferentially bound by Esrrb and Nr5a2, two PFM matrixes were created to reflect a perfect match to the consensus sequence TCAAGGTCA or TCAAGGCCA, and motifs including 0 or 1 mismatches identified with the TFBSTools R package  (Tan and Lenhard, 2016).
org) with standard parameters. To determine enrichments of each group of differentially expressed genes in proximity to the ChIP-seq clusters, we calculated hypergeometic right tail Pvalues for the association between differentially expressed genes within x bp of a ChIP-seq peak belonging to a cluster, compared with a background of all genes within x bp of a cluster peak, for x in [1, 1e+6] bp, using the Julia package ProximityEnrichment. jl (https://github. com/owensnick/ ProximityEnrichment. jl). Data visualisation was made in R using ggplot2  (Wickham, 2016)  and ComplexHeatmaps  (Gu et al. , 2016b)  packages.
Comparisons with published datasets Oct4 ChIP-seq datasets from  Buecker et al. (2014) and Festuccia et al. (2018a)  were obtained through the European Nucleotide Archive database and aligned with Bowtie2  (Langmead and Salzberg, 2012)  to the mm10 genome, with default options. Coverage in the classes of Esrrb/Nr5a2dependent or -independent regulatory regions identified in this study was quantified in each external dataset using the R packages bamsignals  (Mammana and Helmuth, 2021) , Rsamtools  (Morgan et al. , 2021)  and GenomicRanges  (Lawrence et al. , 2009). Processed data for RNA-seq correlations were obtained from supplementary tables available in the previously published studies  (Argelaguet et al. , 2019; Boroviak et al. , 2015; Buecker et al. , 2014; Dunn et al. , 2019; Festuccia et al.
, 2018a; Martello et al. , 2013; Ye et al. , 2013; Yi et al. , 2011). Focusing on differentially expressed genes, Pearson correlation coefficients were then calculated based on the fold changes in expression of each gene in the external datasets, compared with the fold change in expression observed after depletion of Esrrb, Nr5a2 or both Esrrb and Nr5a2 in FCS/LIF or 2i/LIF. Heatmaps displaying the correlation between datasets were generated using the ComplexHeatmap R package  (Gu et al. , 2016b). Processed data for RNAseq analysis of Esrrb and Nr5a2 relative levels of expression was obtained from supplementary tables available in the previously published studies  (Atlasi et al. , 2019; Buecker et al. , 2014; Cruz-Molina et al. , 2017; Dunn et al. , 2019; Ficz et al. , 2013; Gu et al. , 2016a; Hsu et al.
, 2019; von Meyenn et al. , 2016). Processed single cell RNA-seq  (Kolodziejczyk et al. , 2015)  was obtained from the ESpresso database (https://espresso. teichlab. sanger. ac. uk/), and data plotted with the FlowJo software suite. Data from  Buecker et al. (2014 ), Festuccia et al. (2018a)  and  Heurtier et al. (2019)  are available in Gene Expression Omnibus (GEO) under accession numbers GSE118898, GSE118907 and GSE56138, respectively.
Funding N. F. , during his stay at the LMS, was funded by an  Imperial College London Research Fellowship  and a  Medical Research Council Career Development Award. P. N. acknowledges the  European Research Council  (Erc-cog-2017, BIND), the  Labex Revive  ( Investissement d'Avenir ;  ANR-10-LABX-73 ), the  Institut Pasteur  and the  Centre National de la Recherche Scientifique  for funding. Deposited in PMC for immediate release.
We therefore cultured the bacteria in a specific medium, "simulating genital tract secretion" (SGTS) medium  42  (Table  1 ). In these conditions, 17β-estradiol had no visible effect on biofilm structure (Fig. 6B ). Biofilm images analysis with COMSTAT2 software revealed that the mean thickness (μm), mean biomass volume (μm 3 /μm 2 ) and roughness coefficients of control and 17β-estradiol-treated bacteria were unaffected by treatment (Fig. 6C ).
Methods Bacterial strain and culture conditions. Lactobacillus crispatus CIP104459 was obtained from the Institut Pasteur Collection (CRBIP-Microorganism biobank catalogue, Paris, France). It was isolated in 1955 from a vaginal swab from a non-menopausal woman at the "La Croix-Rousse" maternity center (Lyon, France). In our laboratory, this strain was stored at -140 °C in a cryofreezer (Thermo Fisher Scientific, Waltham, Massachusetts, USA). The draft genome sequence of L. crispatus CIP 104459 has recently been determined and deposited in the DDBJ/ENA/GenBank under accession number VOMA00000000  34. This bacterium was grown in anoxic conditions in conical 15 mL tubes (Corning, Thermo Fisher Scientific, Waltham, Massachusetts, USA) containing (filled to maximal capacity) De Man, Rogosa and Sharpe (MRS) medium (VWR, Fontenay-sous-Bois, France), at 37 °C, without shaking.
, Männedorf, Switzerland). The excitation and emission wavelengths were set at 365 and 425 nm, respectively. Each measurement was performed in triplicate. Membrane anisotropy (r value) was calculated as described by Lakowicz  63. Data were analyzed with SparkControl software 2. 1 (Tecan Group Ltd. , Männedorf, Switzerland). Fluorescence polarization is inversely related to membrane fluidity. Increases in anisotropy indicate a decrease of membrane fluidity, and vice versa. In a second series of experiments, the bacteria were cultured for 6, 12 or 24 h in normal MRS medium, collected and exposed immediately to 17β-estradiol and to the fluorescent probe. Changes in fluorescence polarization were measured over a period of 3 h, with the same equipment.
The potential auto-aggregation and structure of L. crispatus CIP104459 was also studied by flow cytometry with a CytoFlex S flow cytometer (Beckman coulter Life science, Indianapolis, USA) and the CytExpert software. After 18 h of culture in the presence or absence of 17β-estradiol, the bacterial suspension was harvested by centrifugation as previously described and resuspended in PBS. Bacteria were not stained and aliquots (200 µL) were distributed into 96-well microplates (Thermo Fisher Scientific, Waltham, Massachusetts, USA). After incubation in static condition for 30 min, the samples were subjected to flow cytometry. At least 10,000 events at OD 488 ± 4 nm (SSC channel) and OD 525 ± 20 nm (FSC channel) were recorded at a flow rate of 10 µL. min -1 for each treatment. Data were analyzed with Cytexpert software.
1. Aliquots (1 mL) of suspension, with or without 17β-estradiol, were added to 24-well microplates with flat glass bottoms (Sensoplate, Greiner Bio-One, Germany), which were incubated for 48 h in anoxic conditions, without shaking, for biofilm formation. Wells were washed twice with PW to remove the remaining planktonic bacteria, and biofilms were stained with SYTO 9 Green Fluorescent Nucleic Acid Stain (Thermo Fisher Scientific, Waltham, Massachusetts, USA). Stained samples were examined under an LSM 710 inverted confocal laser scanning microscope (Zeiss, Marly-le-Roi, France) with the Zen 2009 software package (version 12. 0. 1. 362). Images were reconstructed and analyzed with COMSTAT2 software. The mean thickness (μm), biomass volume (μm 3 /μm 2 ) and roughness coefficient of the biofilm were calculated for a least 30 observations per treatment.