dataesr/scientific-paragraphs-categorization huggingface dataset:
Pathway enrichment analysis was performed using the STRING database (http://string-db. org). b Steady-state levels of significantly regulated metabolites in ARI-resistant cells when compared to WT LNCaP (FC > 1. 5, p < 0. 05 using a 1-way ANOVA with a Dunnett's multiple comparisons test). Selected metabolites were significantly modulated in at least 2 out of 3 conditions. c Isotopologue distribution of selected metabolites in ARI-resistant and WT LNCaP cells following  13  C-glucose incorporation for 1 h. d Isotopologue distribution of pyruvate and phosphoenolpyruvate in ARI-resistant and WT LNCaP cells following  13  C-glucose incorporation for 24 h. e Isotopologue distribution of reduced glutathione (GSH) in ARI-resistant and WT LNCaP cells following  13  C-glucose incorporation for 24 h.
b-e Data are presented as mean values +/-SD. c-e *p-value of labelled fraction < 0. 05 using a 1-way ANOVA with a Dunnett's multiple comparisons test. DHAP dihydroxyacetone-phosphate, PEP phosphoenolpyruvate, GSH reduced glutathione, GSSG oxidised glutathione. Source data are provided as a Source data File. significantly enriched in comparison to negative control H19 (Supplementary Fig. 5c ), suggesting that DECR1 might not be a direct AR-target. Altogether, these results suggested that DECR1 expression might be a part of an adaptive response to AR inhibition and prompted us to further explore the role of this protein in the context of CRPC. DECR1 is associated with poor prognosis in CRPC. In vivo, DECR1 expression was strongly upregulated in AR-proficient CRPC orthografts derived from LNCaP AI and VCaP CR cells grown under castrated condition (ADT) (Fig.
5f ). Using immunohistochemistry, we further examined DECR1 protein expression on a tissue microarray (TMA) of patients that had been biopsied before and after they developed resistance to ADT. In the majority of the patients (9 out of 14), DECR1 staining was increased after recurrence (positive Δ histoscore) (Fig. 5g ). Similarly, DECR1 expression was also upregulated in tumours persisting after castration therapy in the Nkx3. 1 Pten fl+/- Spry2 fl+/-genetically engineered mouse model, an AR-driven model of CRPC  26  (Supplementary Fig. 5d ). We next assessed the clinical relevance of DECR1 in prostate cancer. In silico analysis of the cancer genome atlas (TCGA) dataset revealed that DECR1 expression was frequently dysregulated in prostate cancer, at both gene and transcript levels (Fig. 5h ).
DECR1 mRNA expression level was significantly higher in prostate tumour samples than in normal adjacent tissues, and high DECR1 expression was associated with significantly reduced patient disease-free survival (Fig. 5i, j ). In another dataset  27  , high DECR1 expression was observed in more than 70% of metastases collected from CRPC patients, independent of the site of metastasis (Supplementary Fig. 5e ). Analysis of a third independent cohort  28  further confirmed that DECR1 expression was highest in metastatic tumours (Supplementary Fig. 5f ) and strongly correlated with worse outcome in metastatic patients (Supplementary Fig. 5g ). DECR1 maintains lipid homeostasis in CRPC cells. To evaluate the functional impact of DECR1 in CRPC, we used the isogenic pair of LNCaP (androgen-responsive) and LNCaP-AI (androgenindependent) cell lines.
5) for DECR1 using the TCGA dataset. i Gene expression analysis of DECR1 in normal and tumoural prostate tissues according to the TCGA dataset (n = 498). Centre line corresponds to median of data, top and bottom of box correspond to 75th and 25th percentile, respectively. Whiskers extend to adjacent values (minimum and maximum data points not considered outliers). j Kaplan-Meier survival analysis of prostate cancer patients stratified according to DECR1 expression using the TCGA dataset. b, c, f HSC70 is used as a sample loading control. d, e Data are presented as mean values +/-SD. d, e *p-value < 0. 05 using a 1-way ANOVA with a Dunnett's multiple comparisons test. g statistical analysis was performed using a Wilcoxon matched-pair signed-rank test (n = 14). i statistical analysis was performed using a pairwise ANOVA.
j statistical analysis was performed using a logrank test. Source data are provided as a Source data File. lipidomics performed on frozen tumours confirmed data from the Raman spectroscopy. DECR1 KO tumours presented a pronounced decrease in several highly abundant triglycerides species, which accounted for a large proportion of total lipid content (Fig. 7d ). Moreover, similar to what is observed in vitro, DECR1 KO tumours showed an increase in several ceramides and multiple polyunsaturated phospholipids (Fig. 7d ).
Cell count is normalised to initial number of cells. a, g, h HSC70 is used as a sample loading control. b, d, e, f, i, j Data are presented as mean values +/-SD. b, e, f, i, j *p-value < 0. 05 using a 1-way ANOVA with a Dunnett's multiple comparisons test. DG: diacylglycerol, PC phosphatidylcholine, PE phosphatidylethanolamine, PG phosphatidylglycerol, PI phosphatidylinositol, PS phosphatidylserine, SM sphingomyelin, TG triglyceride. Source data are provided as a Source data File. the role of DECR1 in cancer has not been well-studied. In contrast to our data, DECR1 was proposed to act as a tumour suppressor in HER2-positive breast cancer  54. However, in the same study, DECR1 overexpression was also shown to protect cancer cells from glucose withdrawal-induced apoptosis. Interestingly, Decr1 -/-mice develop hypoglycaemia under metabolic stress conditions  55  , suggesting a link between DECR1 and glucose metabolism.
Former target ions selected for MS/MS were dynamically excluded for 25 s. The MS Raw files were processed with MaxQuant software 62 version 1. 5. 5. 1 and searched with Andromeda search engine  63  , querying UniProt  64  Homo sapiens (09/ 07/2016; 92,939 entries). The database was searched requiring specificity for trypsin cleavage and allowing maximum two missed cleavages. Methionine oxidation and N-terminal acetylation were specified as variable modifications, and cysteine carbamidomethylation as fixed modification. The peptide, protein and site false discovery rate (FDR) was set to 1%. Proteins were quantified according to the labelfree quantification algorithm available in MaxQuant  65. MaxQuant output was further processed and analysed using Perseus software version 1. 5. 5. 3  66. The common reverse and contaminant hits (as defined in MaxQuant output) were Fig.
7  DECR1 KO affects lipid metabolism and decreases CRPC tumour growth. a Representative pictures of LNCaP AI CTL (top image) or DECR1 KO (bottom image) tumour orthografts monitored by ultrasound imaging (left). Quantification of tumour volume using ultrasonography (right). b Representative pictures of hematoxylin/eosin staining on orthografts from LNCaP AI CTL (top) or DECR1 KO (bottom) cells. C = cancer cells, N = necrotic region. Quantification of the proportion of cancer cells (right). Scale bar represents 1000 µm. c Representative heatmaps on data from Raman spectroscopy using 532 nm excitation (intensity at frequency 2845 cm -1 /intensity at frequency 2935 cm -1 ) on LNCaP AI CTL (top) and DECR1 KO (bottom) derived orthografts (left). Average Raman spectra of LNCaP AI CTL (grey) or DECR1 KO (magenta) orthografts (middle) and quantification of tumour lipid content using the 2845 cm -1 -peak (right panel).
AU = arbitrary unit. d Heatmap illustrating the steady-state levels of significantly regulated lipids in DECR1deficient tumours when compared to CTL tumours (FC > 1. 5, p < 0. 05 using a two-tailed Student's t-test). Values are expressed as z-score. a-c Data are presented as mean values +/-SD. a-c *p-value < 0. 05 using a two-tailed Mann-Whitney U-test. BMP Bis(monoacylglycero)phosphate, Cer ceramide, PC phosphatidylcholine, PE phosphatidylethanolamine, PG phosphatidylglycerol, PI phosphatidylinositol, PS phosphatidylserine, TG triglyceride. Source data are provided as a Source data File. removed. Only protein groups identified with at least one uniquely assigned peptide were used for the analysis. Significantly enriched proteins were selected using a two-sided t-test analysis with a 5% FDR.
Lock masses were used and the mass accuracy obtained for all metabolites was below 5 ppm. Data were acquired with Thermo Xcalibur software. The peak areas of different metabolites were determined using Thermo TraceFinder 4. 0 software, where metabolites were identified by the exact mass of the singly charged ion and by known retention time on the HPLC column. Commercial standards of all metabolites detected had been analysed previously on this LC-MS system with the pHILIC column. The 13 C labelling patterns were determined by measuring peak areas for the accurate mass of each isotopologue of many metabolites. Intracellular metabolites were normalised to protein content of the cells, measured at the end of the experiment by the BCA assay (Thermo Fisher Scientific, Waltham, MA, USA). Lipidomic analysis.
Analysis of polar and non-polar lipids were conducted using an LC-MS system including an Ultimate 3000 HPLC (Thermo Fisher Scientific, Waltham, MA, USA) coupled to a Q-Exactive Orbitrap mass spectrometer (Thermo Fisher Scientific, Waltham, MA, USA). Q-Exactive Orbitrap MS instrument was operated in both positive and negative polarities, using the following parameters: mass range 240-1200 m/z (positive) and 240-1600 (negative), spray voltage 3. 8 kV (ESI+) and 3 kV (ESI-), sheath gas (nitrogen) flow rate 60 units, auxiliary gas (nitrogen) flow rate 25 units, capillary temperature (320 °C), full scan MS1 mass resolving power 70,000. Data dependent fragmentation (dd-MS/MS) parameters for each polarity as follows: TopN: 10, resolution 17,500 units, maximum injection time: 25 ms, automatic gain control target: 5e  5  and normalised collision energy of 20 and 25 (arbitrary units) in positive polarity.
TopN: 5, resolution 17,500 units, maximum injection time: 80 ms automatic gain control target: 5e  5  and normalised collision energy of 20 and 30 (arbitrary units) in negative polarity. The instrument was externally calibrated to <1 ppm using ESI positive and negative calibration solutions (Thermo Fisher Scientific, Waltham, MA, USA). Peak detection and integration from Raw data were processed using Compound Discoverer 3. 0 (Thermo Fisher Scientific, Waltham, MA, USA). Files were also converted to mgf format using MSConvert software and MS2 files were searched against LipidBlast database using LipiDex software  [69] [70] [71]. Peak intensities were normalised to cell number. Heatmaps were generated in R (v. 3. 6. 1, https://www. R-project. org) using pHeatmap package (v. 1. 0. 12, https://CRAN.
250 mm). Helium was used as a carrier gas with a flow rate of 1 ml min -1. For the separation of fatty acids, the initial gradient temperature was set at 50 °C for 1 min and increased at the ramping rate of 12 °C min -1 to 180 °C, following by a ramping rate of 1 °C min -1 to reach 200 °C. Finally, the final gradient temperature was set at 230 °C with a ramping rate of 5 °C min -1 for 2 min. The temperatures of the quadrupole and the source were set at 150 °C and 230 °C, respectively. The MS system was operated under electron impact ionisation at 70 eV and a mass range of 100-600 amu was scanned. After the acquisition by GC-MS, an inhouse Matlab M-file was used to extract mass distribution vectors, to integrate raw ion chromatograms and to correct by isotopologue distributions by the natural abundance  75.
Palmitic, stearic and oleic acid peak areas were extracted using mass-to-charge ratios (m/z) 270, 298 and 296, respectively. Mass Hunter B. 06. 00 software (Agilent) was used to quantify isotopomer peak areas before natural abundance isotope correction was performed using an in-house algorithm. Bioinformatics analysis. Gene expression data were downloaded from TCGA and the GEO website. TCGA RNASeqV2 data were shift log transformed and GSE21034 data were log transformed, using mean of probes per gene. Expression values were grouped according to sample type and group distributions were plotted using the matlab routine boxplot with the bar indicating the median, the box spanning from the 25th to the 75th percentiles, and whiskers spanning 2. 7\sigma. Outliers beyond that span are indicated in red.
Significant difference between the groups was measured using overall ANOVA. For survival analysis, gene expression data from human samples (excluding cell lines) was normalised as before, mean-centred, and clustered into three groups using k-means. Kaplan-Meier survival curves for the groups were plotted using the matlab routine kmplot. Human prostate cancer orthografts. In vivo orthograft experiments were performed in accordance with the ARRIVE guidelines  76  , and by a local ethics committee under the Project Licence P5EE22AEE in full compliance with the UK Home Office regulations (UK Animals (Scientific Procedures) Act 1986). Briefly, 20 × 10 6 cells/mouse were suspended in serum-free medium and mixed with Matrigel (Corning, NY, USA) in a 1:1 ratio. 50 µl of cell suspension were injected orthotopically into the anterior prostate lobe of CD1-nude mice (Charles River Laboratories, Wilmington, MA, USA).
The tissue sections were mapped using a water dipping objective, using a step size of 100 µm in x and y, with 1 s acquisition time, 100% laser power and a spectral center of 3000 cm -1. Renishaw Wire 4. 1 was used to perform basic pre-processing steps using the inbuilt functions in the software. Cosmic rays were removed followed by baseline subtraction. Baseline was subtracted using the in-built baseline subtraction intelligent fitting function (with an 11th order polynomial fitting and noise tolerance set to 1. 50 which was applied to the whole spectral dataset). Custom MATLAB® scripts were then used to perform further analysis. Outlier spectra (high intensity due to saturation or fluorescence) were removed using a threshold function. Spectra were cut to between 2800 and 3020 cm -1. Tissue regions were then selected based on the total spectral intensity for the map and all associated spectra were extracted and min-max scaled for comparison between conditions.
The resultant spectral data set for each tissue map went through further quality control steps; firstly, excluding spectra with a total spectral intensity less than 60,000, followed by removing spectra out with one standard deviation of the mean. Spectra were scaled to the peak at 2933 cm -1 , and spectral data sets for all CTL and DECR1 KO samples were combined, with average for each condition plotted for comparison. The ratio of the intensities of the peaks at 2845 and 2935 cm -1 was determined for each spectral data point, as was the ratio of the intensities of the peaks at 2880 and 2935 cm -1. These values were used to create bar charts depicting the mean and standard deviation values in GraphPad Prism 7. GraphPad Prism 7 was used to perform a Mann-Whitney test to compare CTL to DECR1 KO for each ratio.
Panels b, i, j: n = 6 (three independent biological experiments performed in duplicates). Panels c, d: n = 3 independent biological experiments. Panels e, f: n = 3 independent wells from the same cell culture. Figure 7: Panel a: n = 6 mice per group. Panel b: n = 5 mice per group. Panel c: n = 7203 (CTL), 5425 (KO) peak intensities that were extracted from four mice per group. Panel d: n = 3 mice per group. Reporting summary. Further information on research design is available in the Nature Research Reporting Summary linked to this article. Data availability The raw files and the MaxQuant search results files have been deposited as partial submission to the ProteomeXchange Consortium via the PRIDE partner repository [Perez-Riverol Y, Csordas A, Bai J, Bernal-Llinares M, Hewapathirana S, Kundu DJ, Inuganti A, Griss J, Mayer G, Eisenacher M, Pérez E, Uszkoreit J, Pfeuffer J, Sachsenberg T, Yilmaz S, Tiwary S, Cox J, Audain E, Walzer M, Jarnuczak AF, Ternent T, Brazma A, Vizcaíno JA (2019) The PRIDE database and related tools and resources in 2019: improving support for quantification data.
Nucleic Acids Res 47(D1):D442-D450 (PubMed ID: 30395289)] with the dataset identifier PXD016836. The following databases were used in this study: The Cancer Genome Atlas (TCGA-https://tcga-data. nci. nih. gov/tcga/); GSE21034; STRING v11. 0 (https://string-db. org/cgi/input. pl). All the data supporting the findings of this study are available within the article and its supplementary information files and from the corresponding author upon reasonable request. A reporting summary for this article is available as Supplementary Information file. The source data underlying Figs. 1-7 and Supplementary Figs. 1-6 are provided as a Source data file. Raw data for lipidomics experiment are provided as Supplementary Data 2.
Acknowledgements This work was supported by  Cancer Research UK Beatson Institute core funding  ( C596/ A17196 ) and  CRUK core group  awarded to HYL ( A15151 ) and to SZ ( A12935 ). P. P. and E. H. were funded by grants from  "La ligue Contre le Cancer", "la région Bourgogne Franche-Comté " and " Canceropole Grand Est ". M. S. is a  Medical Research Council Clinical Research Fellow  ( MR/L017997/1 ). C. N. is the recipient of  CRUK Clinical Research Fellowship  (grant  300444-01 ). D. G. and K. F. acknowledge support from the  EPSRC  grant  EP/L014165/1  that supported L. J. S. -M. F. acknowledges FWO funding and  KU Leuven Methusalem co-funding.
Study design A prospective clinical study was conducted from October 2018 to April 2019 at the Psychiatric Hospital of the Cross (HPC, Jal El Dib, Metn, Lebanon). The database of in-patients with schizophrenia identified 306 eligible patients, of whom 206 were randomly selected using an online software (www. randomizer. org). Inclusion criteria were: age more than 18 years, a primary diagnosis of schizophrenia by a physician according to the Diagnostic and Statistical Manual 5th Edition (DSM-5) criteria, duration of hospitalization of more than a year (to be classified as chronic patients), clinical stability on current treatment without significant change in psychotropic drugs/doses for three months or more. Patients not included were those hospitalized for less than a year, with variable medication doses, who had autism or bipolar disorder or personality disorder or a severe organic disorder, including central nervous system disorders that may affect the cognitive function (dementia, multiple sclerosis, epilepsy, Parkinson's disease, and mental retardation), and those who refused to participate in the study.
Statistical analysis Statistical analysis was performed using SPSS software version 25. 0. Descriptive statistics were calculated for all variables in the study. The normality of the variables within each group was verified by the Kolmogorov-Smirnov test. The Pearson's correlation test was used to check for association between continuous variables. A p ≤ 0. 05 was considered statistically significant. The validity of the construction of the SNS scale in this sample was confirmed by launching a factor analysis for the 20 items of the questionnaire, using the principal component analysis technique, with a varimax rotation since the extracted factors were not found to be significantly correlated. The Kaiser-Meyer-Olkin (KMO) measurement and the Bartlett sphericity test were performed to ensure the adequacy of the sampling.
Therefore, our results may not be generalized to the whole population of Lebanese schizophrenic patients. Moreover, we only included chronic patients with a hospitalization of more than a year so that we could guarantee that they would still be at the hospital after 6 weeks for the retest. Finally, the level of insight was not assessed in the present study, but previous data showed no correlation between the level of insight and the SNS scores  [10]. Nevertheless, despite all these limitations, and to the best of our knowledge, this is first test-retest study for the validation of the SNS that enrolls a large number of randomly selected patients.
MATERIALS AND METHODS The KATHERINE study was a randomized, multicenter, open-label, phase 3 trial (NCT01772472/ BO27938/NSABP B-50-I/GBG 77), the methods for which have been reported previously. 6 Briefly, eligible patients had histologically confirmed, HER2-positive, nonmetastatic, invasive primary breast cancer; had received taxane-and trastuzumab-containing neoadjuvant therapy (with or without anthracyclines) followed by surgery; and had residual invasive disease in the breast and/or axillary nodes at surgery. Patients were randomly assigned to receive up to 14 cycles of adjuvant treatment with trastuzumab (6 mg/kg intravenously every 3 weeks) or T-DM1 (3. 6 mg/kg intravenously every 3 weeks). Both groups received adjuvant endocrine and radiation therapy per local standards of care. Patients who discontinued T-DM1 because of adverse events were permitted to switch to trastuzumab.
Statistical Analysis Summaries of the compliance rates by treatment arm were obtained at each assessment time point. The compliance rate was defined as the number of patients who completed each assessment divided by the total number of eligible patients at that time point. When scoring the QLQ-C30 and QLQ-BR23 questionnaires, if more than 50% of the constituent items were completed, a prorated score was computed, consistent with the scoring manuals and validation studies. 10 For subscales with fewer than 50% of the items completed, the subscale was considered to be missing. There was no data imputation. Summary statistics (mean, standard deviation, median, 25th and 75th percentiles, and range) of absolute scores and change from baseline scores of the QLQ-C30 and QLQ-BR23 subscales were summarized at each assessment time point for the 2 treatment arms.
Counterstaining was performed with Mayer hematoxylin freshly prepared (Dako, #S3309). Tissue sections were then submitted to serial gradients of xylen and mounted with coverslip in an automatic device (Sakura, Tissue-Tek DRS). In each sample, CAF marker staining was evaluated as a histological score (Hscore), defined by staining intensity (ranging from 0 to 4) multiplied by the percentage of stained fibroblasts. For LNs, only fibroblasts in metastatic zonesassessed by EPCAM staining-were evaluated. For each PT and LN sample, CAF enrichment was established applying an algorithm developed by the team, which takes as input CAF markers H-scores, as described in refs. 32, 33. In brief, the thresholds were first defined in a learning dataset on the distribution (1st quartile, median and 3rd quartile) of each marker using FACS data.
Design of a decision tree for CAF subset prediction. CAF identity was determined by using an algorithm developed by the team (shown in Fig. 2d ), which takes as input histological scores of CAF markers  32, 33. The thresholds were first defined, in a learning dataset, on the distribution (1st quartile, median and 3rd quartile) of each marker using FACS data. Thresholds were then transposed to IHC data. CAF subsets and epithelial cells at cellular level in situ in invaded LNs. IHC staining from consecutive sections were scanned on Philips Ultra Fast Scanner; 5× images of CAF markers including FAP, CD29, FSP1 and SMA (markers used in the decision tree algorithm, see also Fig. 2d ) as well as EPCAM staining from the same Fig. 8  CAF subset content in LNs is a prognostic marker. a Kaplan-Meier curves showing patient disease-free survival according to percentage (%) of stroma relative to epithelium in LN sections (n = 119).