dataesr/scientific-paragraphs-categorization huggingface dataset:
Downstream analysis of mRNA expression data using the gene set variation analysis (GSVA) approach  (Ha ¨nzelmann et al. , 2013) , a method to estimate pathway activity, also showed that the diversity of activity scores in cancer-related pathways (Molecular Signatures Database; http://software. broadinstitute. org/gsea/msigdb;  Liberzon et al. , 2011)  in PDTXs was similar to that observed in the breast cancer clinical population (Figure  S1C ). Furthermore, in matched pairs, the activity of breastcancer-related pathways (e. g. , PTEN, Tp53, BRCA1, Her2, and Cyclin D1) in the PDTXs was correlated with and predicted the activity scores in the originating breast cancer samples (Figure  S1D ). The subtype distribution of engrafted PDTXs was also reflected by the mutation frequencies identified using wholeexome sequencing (''WES'').
DISCUSSION The use of PDTXs in pre-clinical cancer drug development has become widespread  (Crystal et al. , 2014; Marangoni and Po-upon, 2014; Messersmith et al. , 2009). The data available  (DeRose et al. , 2011; Eirew et al. , 2015; Messersmith et al. , 2009) , to which we add extensively here, show PDTXs share most molecular and architectural features with their originating patient tumor sample. A recently published large study of 1,000 PDTX models adds a further crucial piece of evidence supporting their potential utility by showing the use of xenograft models to predict human clinical trial drug responses  (Gao et al. , 2015). The dataset presented here shows the unique value of a living biobank of breast cancer explants that preserve intratumor heterogeneity as a platform for drug screening, including the demonstration of reproducible drug responses across different xenograft passages.
The framework we developed of ex vivo PDTC drug screening followed by in vivo PDTX response validation is a cost-effective pipeline for pre-clinical drug development. The extensive detailed STAR Methods accompanying this report, including both processed and raw molecular profiling and drug sensitivity information, constitutes a publicly available dataset that we will continue to expand with more models and further drug testing. We will provide viable xenograft fragments to academic collaborators and will also make models available to the wider community through licensing. The extensive data generated already represent a valuable resource, which can be easily browsed in a purpose-built public web portal (http://caldaslab. cruk. cam. ac. uk/bcape). We are using the PDTX/PDTC platform to study mechanisms of drug resistance, to unravel clonal dynamics in response to therapeutic perturbation, and to perform genomewide perturbations with small hairpin RNA (shRNA) and CRISPR-CAS libraries  (Marcotte et al.
Cell Viability Assays Single cell suspensions generated from a 1. 5 cm 3 PDTX tumor were plated in triplicates at 40. 000 cells/well into 384-well plates. Drug was added to wells after 24h. To quantify drug responses in PDTCs, cell viability reading intensities were obtained 6 days post-treatment and normalized against positive and negative values. To independently validate data from our drug screening approach using CellTiter-Glo (CTG), a selection of 8 models were screened with 18 drugs and cell viability determined in parallel by CTG and two further tests (CyQUANT and SYTOX). 1) CellTiter-Glo (CTG): The methodology was adapted from the protocol previously described for cell lines  (Garnett et al. , 2012). 2) CyQUANT Direct Cell Proliferation Assay (C35011). The manufacturer's instructions were adapted to suit PDTC culture.
The sequencing was performed using 125bp paired-end reads. Short reads were aligned using novoalign (Novocraft) with our custom pipeline to remove mouse contamination. Bam files were merged, sorted and indexed using samtools. Duplicates were marked using Picard tools and insertions and deletions (indels) were realigned using GATK. For quality control purposes and to check for sample labeling mistakes, all samples were genotyped using GATK HaplotypeCaller and a few errors were identified and corrected. HaplotypeCaller was also employed for variant calling, and after that several filters were applied using the Bioconductor package VariantAnnotation: for single nucleotide variants (SNVs), a minimum genotyping quality of 20, at least 5 reads at the variant position, a strand bias Phred-scale p value smaller than 40 and no presence of homopolymers in the surrounding region.
For indels, we increased the width of the region to detect nearby homopolymers. Genotypes and variant allele frequencies (VAFs) were computed from these calls. All variants were annotated using annovar version March 2015 for gene/exon annotation, 1000 genomes version Oct 2014, dbSNP version138, repetitive regions genomicSuperDups database, SIFT, Polyphen 2, MutationTaster and MetaLR, all versions ljb26. Variants in intergenic, intronic or ncRNA intronic positions were discarded. In order to quantify variability in matched tumor/PDTX, different passages of PDTXs or matched PDTX/PDTC, all variants detected in at least one sample of each model were obtained. For those samples where those variants had not been detected GATK HaplotypeCaller was run again on those positions to see if this was a consequence of no reads in that region for that sample or a real absence of the variant.
These estimates were used to correct tumor VAFs or copy numbers where needed in the rest of the analyses. All variants that were present in the 1000 Genomes database or in any of our normal samples were labeled as germline. Regions marked as repetitive were also filtered, and insertions that represented a segmental duplication were removed if they were not present in at least three-fourths of all the samples for a given model or in 3 of them. Somatic variants that were not filtered were compiled for each model. Some manual curation was needed for genes like PI3KCA, where variants from a region of segmental duplication were included after manual inspection. Frequencies of mutations were compared to TCGA. Pearson correlations between VAFs in tumor and PDTX, different passages of the same model, PDTX and PDTCs of the same passage, mice replicates, technical replicates of the same sample and different tumors were computed.
For those samples with no shallow sequencing, CopywriteR was run on the whole exome sample. When possible, tumor/normal paired analysis was run on ASCAT. Filtering and quality control was carried out prior to running ASCAT; samples were removed if the mean coverage across all the mutations was less than 20. Mutations that were not sequenced to a depth of at least 10 reads in either the cancer or normal sample were also excluded. We then applied Pyclone to a total of 22 PDTX models, ranging between 2 and 15 samples for each model. Only mutations with a median coverage of at least 50 across all model samples were used. Additionally, for each model, mutations with a coverage smaller than 15 in any of the samples were removed from the analysis. Germline mutations (SNPs, duplications, and repeats present in the normal sample) were not utilized (but note that germline filtering was less stringent for PyClone analysis; in particular, variants that were present in the 1000 Genomes database were not removed, as we observed some VAF changes between samples that would be useful to distinguish variability from real changes).
sWGS The sWGS workflow uses low-coverage sequencing of the pre-capture exome-sequencing libraries to generate high-quality CNA calls. This is efficient in PDTX DNA usage, low-cost (around £30 per sample), and generates better quality copy-number data than what is achievable from WES. As described above in exome sequencing analysis, libraries were prepared using Nextera Rapid Capture Exome (Illumina Inc. , USA). Pre-capture libraries were normalized to 10uM and pooled for shallow whole genome sequencing (sWGS). 50 bp single-read whole-genome shallow sequencing was performed in parallel with the exome sequencing to provide a clean and accurate estimate of copy number. Alignment was performed using bwa with our custom pipeline to remove mouse contamination. Bam files were merged, sorted and indexed using samtools.
Duplicates were marked using Picard tools. The data were analyzed using the Bioconductor package QDNaseq. This method divides the genome in regions of 100Kb and counts all the reads within those bins. Those reads are then corrected for mappability and GC content and segmented using DNAcopy. Some additional filtering was applied to account for regions not properly mapped. We also observed that in a few occasions, the method shifted the log2 ratios because of the median normalization. Those cases were inspected perusing the VAF plots obtained with ASCAT and fixed. The segmented means of the tumors were corrected for normal contamination (as described in the exome pipeline) and copy numbers (HOMD, Homozygous deletions, HETD, Heterozygous deletions, NEUT, neutral copy number, GAIN, single copy gains and AMP, high-level amplifications), were called based on thresholds on the segmented mean log2-ratio (À1, À0.
MICROARRAY EXPRESSION ANALYSIS RNA expression was analyzed using the Illumina HT-12 v3 platform. Raw data were processed with the beadarray package. The BASH algorithm was employed to correct for spatial artifacts. Summarization and probe selection based on quality was performed on the bead-level data using the detection thresholds recommended in the package and the re-annotation of the Illumina HT-12v3 platform as described previously  (Curtis et al. , 2012). The samples were classified into the intrinsic subtypes using PAM50, and the Three Gene classifier. ER, Her2 and PR status was inferred fitting a mixture model with the package mclust. Classification into Integrative Clusters (IntClusts). The PDTX samples were classified into one of the 10 Integrative Clusters  (Curtis et al. , 2012)  using the scripts provided in  (Ali et al.
, 2014)  and the R package iC10  (Ali et al. , 2014). Each sample was classified into the 10 Integrative Clusters and the assignment to each model was done by consensus (that is, the majority group for all the samples from that model). Copy number and expression data were used when available (n = 21). If not, only expression data (n = 17) or only copy number data (n = 0, no model had all samples with only copy number data). Comparing the assignments done with copy number and expression to the ones using only one type of data, there were no models that changed when using only expression data versus using combined CNA-expression data and 12 that changed when using only copy number data versus using combined CNA-expression data (2 from IntClust 1 to IntClus 9, 1 from InClust 5 to IntClust 10, 1 from IntClust 7 to IntClust 8, 2 from IntClust 10 to IntClust 3, 1 from IntClust 10 to IntClust 4, 5 from IntClust 10 to IntClust 9).
As the PDTX cohort might not represent all types of breast cancers, we included the 1980 Metabric samples  (Curtis et al. , 2012 ) and quantile-normalized them together with the PDTX samples to obtain more accurate classifications on all methods. Pathway activation scores. We downloaded the c6 oncogenic signatures from the Molecular Signatures Database (and incorporated an in-house PI3K/AKT/mTOR and an in-house DNA Damage Response DDR signature) and applied the GSVA package to infer sample specific pathway activation. Similarly, as the PDTX cohort might not represent all types of breast cancers, we also included the 1980 Metabric samples and quantile-normalized them together before converting the expression values into z-scores. For pathways that had a subset of genes that should be upregulated (UP) and another subset that should be downregulated (DOWN), the final score was obtained as UP-DOWN.
Heat maps and cluster analysis were performed using Euclidian distance and the Ward method. Pearson correlations between log-intensity expression values or pathway activation scores in tumor and PDTX, different passages of the same model, PDTX and PDTCs of the same passage, mice replicates, technical replicates of the same sample and different tumors were computed. A detailed comparison of pathway activity scores in tumor and PDTX and PDTX passages was performed fitting, to each pathway, a generalized additive model using the R package mgcv with the score of the tumor as the dependent variable and a smooth function of the score of all the PDTX passages as the independent variable. The deviance and the Spearman Correlation were computed as measures of predictive ability and reproducibility.
ANALYSIS OF HIGH-THROUGHPUT DRUG SCREENING USING PDTCS The observed response was computed as: 100 -(100 * (intensity-negative control)/(positive control -negative control). Quality Control was performed comparing response values in plates and screenings done in similar dates. Non-parametric isotonic regression using the R function isoreg was fit to the set of technical replicates of a given drug response for a given sample. The area under the curve (AUC) was computed on the model fits using the package flux which uses the trapezoid rule. The half maximal inhibitory concentration (IC 50 ) was predicted fitting a smoothing spline to the isotonic regression line. As a measure of variability around the AUC and IC 50 estimates, isotonic regression curves were fit to each of the technical replicates individually and their AUC and IC 50 also computed, producing error bars for the overall estimates based on the standard deviation.
Software Custom scripts to run the analyses are available at figshare: https://figshare. com/s/4a3f6bc543e5ba85834c Data Resources Raw sequencing and microarray files are available at EGA: EGAS00001001913. Normalized/Summarized data files are available at figshare: https://figshare. com/s/4a3f6bc543e5ba85834c. This repository also contains files with a comprehensive collection of plots including Pearson and Spearman correlation of SNVs, copy number, gene expression, pathway activation and methylation, pairwise scatterplots showing variant allele frequencies in each model, mutational profiles in each sample, copy number plots for each of the samples, pairwise scatterplots showing pathway scores in each model, pairwise smoothed scatterplots showing CpG methylation scores in each model, AUC and iC50 scores for all drugs in each model tested, dose-response curves for each drug and model tested, pairwise scatterplots showing AUC scores for drugs targeting the same pathway, boxplots showing Spearman correlation between cancer pathway activity score and AUCs for compounds targeting the same pathway and results of the Bliss model for each drug combination applied to each model.
This research was supported with funding from  Cancer Research UK  and from the  European Union  to the  EUROCAN Network of Excellence  ( FP7 ; grant num-ber 260791). M. C. has received funding from the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Sk1odowska-Curie  grant agreement no. 660060 and was supported by the  Department of Experimental Oncology and Molecular Medicine, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy. R. N. B. is supported by the  Wellcome Trust  PhD Programme in Mathematical Genomics and Medicine. S-J. S. is supported by the  Wellcome Trust PhD Programme for Clinicians in Cambridge. A. Bruna , O. M. R. , E. M. , V. S. , and C. C. are members of the  EurOPDX Consortium. We are very grateful for the generosity of all the patients that donated samples for implantation.
We present LOFAR High-Band Array (HBA) observations of the Herschel-ATLAS North Galactic Pole survey area. The survey we have carried out, consisting of four pointings covering around 142 square degrees of sky in the frequency range 126-173 MHz, does not provide uniform noise coverage but otherwise is representative of the quality of data to be expected in the planned LOFAR wide-area surveys, and has been reduced using recently developed 'facet calibration' methods at a resolution approaching the full resolution of the datasets (∼ 10 × 6 arcsec) and an rms off-source noise that ranges from 100 µJy beam -1 in the centre of the best fields to around 2 mJy beam -1 at the furthest extent of our imaging. We describe the imaging, cataloguing and source identification processes, and present some initial science results based on a 5-σ source catalogue.
This concatenation gives sufficient signal-to-noise on the target field for per-band phase calibration. 3 The facet calibration scripts may be found at https://github. com/tammojan/facet-calibration; the code for the implementation described in this paper is available at https://github. com/ mhardcastle/surveys-pipeline. 4 https://github. com/revoltek/losoto The next stage was to generate a model to allow us to calibrate the fields. To do this we phase calibrated band 20 (data at frequencies of 150-152 MHz, around the frequency range where the HBA is most sensitive) using the observatory-provided global sky model, which is based on low-resolution observations from other telescopes. We then imaged this dataset with AWIMAGER  (Tasse et al. 2013 ) at ∼ 10-arcsec resolution, derived a list of Gaussians from the image with PYBDSM 5   (Mohan & Rafferty 2015)  and cross-matched them with the FIRST catalogue to obtain a list of sources in the field detected by both LOFAR and FIRST, with their 151-MHz and 1.
In practice, we always started the run for the data at 150-158 MHz before the other five, so that problems with, for example, the definition of the facets could be ironed out using a single dataset; such problems generally showed up as a failure of phase or amplitude self-calibration, resulting in poor solutions and/or increased residuals after subtraction, and were dealt with by increasing the size of the calibration region to include more sources or changing the phase/amplitude solution intervals. The whole facet calibration process, for the six spectral windows, took between one and two weeks per field, depending on the number of facets and the number of problems encountered; typically the selfcalibration, imaging and subtraction step for one facet in one spectral window took 4-5 hours on a 16-core node with 64 GB RAM and 2.
Of these, we elected not to use the 150-MHz data, although catalogues were available, in order to be able to use them as comparison datasets later (see below). The VLSS covers the whole survey area and should be properly calibrated on the scale of  Scaife & Heald (2012) , hereafter SH (we use the VLSSr catalogue of  Lane et al. 2014). The B2 survey also covers the whole survey area, and SH report that it needs no systematic correction to their flux scale; we assign errors to the flux densities based on the recipes given by  Colla et al. (1973). WENSS is more problematic. Firstly, the flux density of 3C 286 in WENSS is a factor 1. 23 above the SH value. SH report an overall scaling factor for WENSS of 0. 90, but in order to ensure that 3C 286's flux density is correct we scale all WENSS flux densities for NVSS objects down by a factor 0.
Association, artefact rejection and optical identification The source catalogue was the starting point for our source association and optical identification processes, which were carried out in parallel. Optical identification was carried out using images and catalogues from SDSS Data Release 12  (Alam et al. 2015) , hereafter DR12. Initially, we carried out a simple positional crossmatch for low-z galaxies, selecting compact (deconvolved size < 10 arcsec) LOFAR sources whose position matched that of an optical source from the MPA-JHU 10  catalogue within 8 arcsec (chosen based on the distribution of offsets). This identified 1,048 LOFAR sources, of which we would expect around 30 to be chance coincidences given the number of MPA-JHU sources in the survey area. We then visually inspected the LOFAR, SDSS, FIRST and NVSS images for all the 16,084 remaining sources, initially with a single author (one of GG, MJH or SCR) inspecting each source.
Flux scale tests: TGSS crossmatch For comparison with a deeper survey than 7C at 151 MHz we make use of the data from the TGSS survey made with the GMRT with a resolution of 25 × 25 arcsec  (Intema et al. 2016). As with the NVSS data, we made a single large mosaic of the images, extracted   2449 ) can be cross-matched with LOFAR data. Surprisingly, given the good agreement between LOFAR and NVSS flux scales (Section 4. 2), we see non-negligible per-field differences in the mean LOFAR and TGSS flux densities. There is no overall flux scale offset (as measured from median ratios of all matched sources), but the median TGSS/LOFAR ratios for the individual fields vary between 0. 86 and 1. 10. GMRT flux calibration is itself not reliable to better than about 10 per cent, and the overall medians will be dominated by the sources close to the centre of each field, so it is perfectly possible that much of this scatter comes from GMRT calibration uncertainties.
In addition, the GMRT's flux scale can be adversely affected by bright sources in the field, and this is apparent, for example, in the flux for the calibrator source 3C 286, which is significantly offset in the GMRT catalogue from the reference 150-MHz value of SH. We therefore do not attempt to use the TGSS images to derive further corrections to the perfield flux scale, but simply report the TGSS comparison here for the benefit of future workers. Plotting the LOFAR (corrected) total flux densities against TGSS flux densities (we restrict the comparison to sources that should be unresolved to TGSS) shows a good correlation, but, as with 7C, the scatter is larger than would be expected from the nominal errors (Fig. 5 ), indicating some residual calibration errors in either or both of the TGSS and LOFAR datasets.
Positional accuracy tests: FIRST crossmatch The 7C crossmatch shows that there are no gross astrometric errors in the catalogue, but to investigate positional accuracy in more detail we need a larger sample with higher resolution. For this purpose we cross-matched the source catalogue with the FIRST survey data in the field. There are 9,856 FIRST sources in the survey area, after filtering out sources with FIRST sidelobe probability (i. e. probabil- ity of being an artefact) > 0. 05. We restricted the crossmatching to compact LOFAR sources (fitted size less than 15 arcsec) with welldetermined positions (nominal positional error less than 5 arcsec). 3,319 LOFAR/FIRST matches were obtained by this method, with a mean offset over the whole field of δ RA = 0. 16 ± 0. 01 arcsec and δ DEC = 0. 01 ± 0.
It can be seen that, although the overall in-band spectral index distribu- tion is reasonable and peaked around the expected value (0. 6-0. 7), the catalogues for the four fields have rather different distributions. The central field, in particular, shows a peak at flat spectral index values which must be the result of the generally poorer quality of the data in this field, while the NE field has an excess of steepspectrum sources. By contrast, the normalization of the power-law fits at 150 MHz is generally in good agreement with the broad-band total flux density we measure. We conclude that in-band spectral indices cannot be reliably compared between fields in this dataset, though sources with extreme apparent in-band spectral index remain interesting topics for further investigation. Reliable absolute in-band spectral index measurements will require the LOFAR gain transfer problems to be solved by the use of a correctly normalized beam model.