dataesr/scientific-paragraphs-categorization huggingface dataset:
We subsequently examined the transcriptomes of TCGA MPMs (available at https://xenabrowser. net/datapages/?dataset=TCGA-MESO. htseq_fpkm-uq. tsv&host=https%3A%2F%2Fgdc. xenahubs. net&removeHub=https%3A%2F%2Fxena. treehouse. gi. ucsc. edu%3 A443) stratified by the presence of a KRAS alteration alone (n = 10), a combined KRAS/TP53 alteration (n = 7), or none of the above (n = 69). Forty genes were biologically and statistically significantly overrepresented in KRAS/TP53-altered over KRASaltered over normal patients, which were able to cluster patients by genetic alteration in an unsupervised hierarchical fashion  (Fig 2A). KRAS/TP53-altered patients showed loss of a C>T mononucleotide signature that preponderated in KRAS/TP53-normal patients and displayed higher aneuploidy and genome alteration indices (Figs  2B  and C ).
Hence, we describe a molecular subclass of MPM patients in the TCGA dataset that involves ∼ 20% of patients, which harbor KRAS gain-of-function with or without TP53 loss-of-function. This molecular MPM subset features KRAS pathway activation, different mutation spectra, gene expression profiles, histology, and survival compared to other MPMs. To further test this, we interrogated KRAS and TP53 in our MPM patients, whose clinical characteristics are given in Appendix Table  S1. We employed digital droplet polymerase chain reaction (ddPCR) in order to detect KRAS codon 12/13 and 61 mutations, as well as TP53 CNA in pleural fluid and cell pellets of 45 patients with pleural effusions from our cohorts in Munich, Germany  (Klotz et al, 2019a (Klotz et al, , 2019b)). The effusions were caused A-D Pleural fluid cell pellets and supernatants from 45 patients (called ASK #) with pleural effusion from Munich, Germany  (Klotz et al, 2019a (Klotz et al, , 2019b)) , were subjected to digital droplet polymerase chain reaction (ddPCR) for the detection of mutant ( MUT ) copies of KRAS codon 12/13 (KRAS G12/13 ) and KRAS codon 61 (KRAS Q61 ), as well as copies of TP53 and TERT.
E-G Results summary (E), representative KRAS CNA segments (F), and data summary of individual cell line CNA z-score (G) from Affymetrix CytoScanHD Arrays of 33 primary MPM cell lines (called MESO #) from Nantes, France (GEO dataset GSE134349). Red lines denote the KRAS locus on chromosome 12p12. 1. H Data summary of mutant allelic frequency of KRAS compared with NF2 and BAP1 in all mutated samples from (A-G). Data information: In (A), data are presented as data summary of the highest mutant copy percentage detected per individual sample (KRAS plot) or of all individual samples assessed (TP53 plot). In (D), data are presented as number of patients (n). P, probability, hypergeometric test for enrichment of KRAS mutations in MPM versus other tumors. In (E), data are presented as individual cell lines (columns), genes (rows), legend, and number of patients (n in table).
Source data are available online for this figure. Wild-type (Wt), KRAS G12D , and Trp53f/f mice were intercrossed, and all possible offspring genotypes received 5 × 10 8 PFU intrapleural or intratracheal Ad-Cre and were sacrificed when moribund. In parallel, C57BL/6 mice received 10 consecutive weekly intraperitoneal injections of 1 g/kg urethane and were sacrificed after 6 months. Data summary (heatmap) and representative images of immunoreactivity of tissue sections of pleural and pulmonary tissues and tumors from these mice for different markers of human malignant pleural mesothelioma (MPM) and lung adenocarcinoma (LUAD). n = 10 mice/group were analyzed for each marker. Brown color indicates immunoreactivity and blue color nuclear hematoxylin counterstaining. Note the ubiquitous strong expression of Wilms' tumor 1 (WT1), patchy moderate expression of vimentin (VIM), ubiquitous moderate expression of mesothelin (MSLN), ubiquitous strong expression of calretinin (CALB2), podoplanin (PDPN), and osteopontin (SPP1), patchy moderate expression of cytokeratin 5/6 (CK5/6), and the absence of expression of surfactant protein C (SFTPC) in murine KRAS-driven mesotheliomas.
A human 150-gene mesothelioma signature derived from a cohort of 113 patients via comparison of MPM against multiple other malignancies (GSE42977; De Rienzo et al, 2013; Data ref: De Rienzo et al, 2012) was highly enriched in our KPM cell line signature  (Fig 9D). These data indicate that murine KRAS/TP53-driven MPM present Bap1 mutations, a gene expression profile that is highly similar to human MPM, and can be used for and druggable MPM models in syngeneic mice. Collectively, the murine and human findings support the existence of a KRAS-driven subset of MPM patients or clones that are likely missed during sequencing and/or sampling  (Comertpay et al, 2014; Li et al, 2020).
Discussion Our results demonstrate that, alone or in combination with TP53, KRAS is perturbed in a proportion of human MPM and can potentially drive the murine mesothelium toward MPM development. KRAS mutations, amplifications, and overexpression, as well as chromosome 12p gains, are shown to exist in 20% of patients from the TCGA MPM dataset and low allelic frequency KRAS mutations are discovered in 50% of MPM samples from our own human cohorts using sensitive techniques. Furthermore, KRAS mutations are shown to occasionally co-exist with TP53 mutations, to repulse NF2 mutations, and to be associated with biphasic MPM histology. Targeting of oncogenic KRAS G12D alone to the pleural mesothelium caused epithelioid MPM in mice and together with Trp53 deletion resulted in biphasic MPM with MPE.
Computational biologic analyses The   et al, 2018). Raw data from 86 human TCGA MPM patients were retrieved from the cBioPortal for Cancer Genomics (www. cbioportal. org/) using inputs "mesothelioma", "Mesothelioma (TCGA, PanCancer Atlas)", "Query by Gene KRAS and TP53", "Mutations", "Putative copy-number alterations from GISTIC", "mRNA expression z-scores", and "Protein expression z-scores" were downloaded and analyzed. Gene expression data from these patients, normalized with the log 2 (fpkm-uq + 1) method, were downloaded (https://xenabrowser. net/datapages/?dataset=TCGA-MESO. htseq_fpkm-uq. tsv&host=https%3A%2F%2Fgdc. xenahubs. net&removeHub=https%3A%2F%2Fxena. treehouse. gi. ucsc. edu%3 A443), ENSEMBL gene IDs were converted to gene symbols using https://www. biotools. fr/mouse/ensembl_symbol_converter, the data were filtered, differential gene expression (ΔGE) was analyzed, and heatmap visualization was performed using R* and packages limma R version 3.
42. 2 (https://bioconductor. org/packages/ release/bioc/html/limma. html) and edgeR (https://bioconductor. org/packages/release/bioc/html/edgeR. html). Both rows and columns were clustered using Pearson correlation and complete linkage. All mutations (n = 2,150) of all patients (n = 86) with MPM from the TCGA pan-cancer dataset were retrieved from www. cbioportal. org/ and were fed into the protein analysis through evolutionary relationships (PANTHER) Classification System (www. pantherdb. org/) using parameters: organism, Homo Sapiens; analysis, statistical overrepresentation test > PANTHER pathways or reactome pathways (both analyses were done); whole-genome reference list: Homo Sapiens; test type: binomial; and correction: false discovery rate. All raw data from the two independent PANTHER and reactome pathway analyses were retrieved, merged, and analyzed.
Gene set enrichment analysis (GSEA) was performed with the Broad Institute pre-ranked GSEA module software (http://software. broadinstitute. org/gsea/index. jsp; Subramanian et al, 2005). All aforementioned raw data were downloaded from the sources referenced above in *. csv format, are provided as source data files with this publication, and were reanalyzed using R*, Prism v8. 0 (GraphPad, La Jolla, CA), and Excel (Microsoft, Redmont, WA).
Human studies All human experiments conformed to the principles set out in the WMA Declaration of Helsinki and the Department of Health and Human Services Belmont Report. The Munich clinical study was prospectively approved by the Ludwig-Maximilians-University Munich Ethics Committee  (approvals #623-15 and #711-16). All patients gave written informed consent a priori. Diagnoses were made according to current standards by a board-certified pathologist at the Asklepios Fachkliniken Gauting, Munich, Germany. Pleural fluid was centrifuged at 300 g for 10 min at 4°C, genomic DNA was extracted from cell pellets, supernatants, and pleural tumor tissues using TRIzol (Thermo Fisher) and purified using GenElute Mammalian Genomic DNA Miniprep (Sigma Aldrich), and 200 ng DNA were used to analyze KRAS codons 12/13 and 61, and TP53 copies with ddPCR KRAS G12/G13, KRAS G61, TP53 CNV, and TERT CNV Kits and QuantaSoft Analysis Pro software (BioRad, Hercules, CA) as described elsewhere  (Poole et al, 2019).
1 mg/ml streptomycin, and 10% heat-inactivated fetal calf serum and cultured at 37°C in 5% CO 2 -95% air. Genomic DNA from 33 MPM cell lines was extracted with Nucleospin Blood kit (Macherey-Nagel, D€ uren, Germany) and 500 ng were hybridized to Affymetrix CytoScanHD Arrays (Thermo Fisher). Detection, quantification, and visualization of single nucleotide variations (SNV) and copy number alterations (CNA) were performed using Affymetrix Chromosome Analysis Suite v3. 1. 1. 27 (Thermo Fisher)  and data are available at GEO datasets (GSE134349; Data ref:  Blanquart et al, 2019). The cell lines were also sequenced in a targeted fashion focusing on 21 genes and the TERT promoter on a MiSeq system (Illumina, San Diego, CA)  (Quetel et al, 2020). The MAPED (Clinical identification of malignant pleural effusions in the emergency department) study entailed a few samples from patients enrolled in a prospective clinical trial (preprint:  Marazioti et al, 2021).
MAPED was registered with ClinicalTrials. gov (#NCT03319472), and written informed consent was obtained from all patients a priori. MAPED was approved by the University of Patras Ethics Committee  (approval #22699/21. 11. 2013). Pleural fluid was centrifuged at 300 g for 10 min at 4°C, RNA and DNA were extracted from cell pellets using TRIzol (Thermo Fisher) and purified using GenElute Mammalian Genomic DNA Miniprep (Sigma-Aldrich), and 200 ng RNA/ DNA were used for RT-PCR, qPCR, and Sanger sequencing. The Istanbul study was approved by the KocUniversity Ethics Committee on Human  Research (approval #2021. 223. IRB2. 042/06. 05. 2021). Both Nantes pleural fluid and Istanbul pleural tumor specimens were processed and analyzed identical to the Munich study.
For solid tumor formation, C57BL/6 mice received 10 6 subcutaneous PMC, KPM, or AE17 cells in the rear flank, three vertical tumor dimensions (δ 1 , δ 2 , δ 3 ) were monitored serially, and the formula πδ 1 δ 2 δ 3 /6 was used to calculate tumor volume. RNA sequencing was done on an IonTorrent sequencer (Thermo Fisher); data were deposited at GEO datasets (GSE94415) and were analyzed using Bioconductor (Data ref:  Stathopoulos et al, 2017). Gene set enrichment was done with the Broad Institute pre-ranked GSEA module  (Subramanian et al, 2005).
RNA sequencing RNA sequencing was done on an IonTorrent sequencer (Thermo Fisher), and data were analyzed using Bioconductor (https://www. bioconductor. org/). File alignments were performed with Τmap (https://github. com/iontorrent/TMAP). Coverage and alignments plot from sequencing were generated using Integrative genome viewer  (Robinson et al, 2011). Alignments are represented as gray polygons with reads mismatching the reference indicated by color. Loci with a large percentage of mismatches relative to the reference are flagged in the coverage plot as color-coded bars. Alignments with inferred small insertion or small deletion are represented with vertical or horizontal bars, respectively. Gene set enrichment analysis (GSEA) was performed with the Broad Institute pre-ranked GSEA module software (http://software.
broadinstitute. org/gsea/index. jsp; Subramanian et al, 2005). The raw *. bam files, one for each RNA-Seq sample, were summarized to a gene read counts table, using the Bioconductor package GenomicRanges. In the final read counts table, each row represented one gene, each column one RNAseq sample, and each cell the corresponding read counts associated with each row and column. The gene counts table was normalized for inherent systematic or experimental biases (e. g. , sequencing depth, gene length, and GC content bias) using the Bioconductor package DESeq after removing genes that had zero counts over all RNASeq samples (20,007 genes). The output of the normalization algorithm was a table with normalized counts, which can be used for differential expression analysis with statistical algorithms developed specifically for count data.
(Bott et al, 2011; Enomoto et al, 2012; Mezzapelle et al, 2013; Shukuya et al; /cance Guo et al, 2015; Lo Iacono et al, 2015; Bueno et al, 2016; De Rienzo et al, 2016; Kato et al, 2016; Hmeljak _data=n). The dataset for Fig 1B was generated by manual curation of the main text and supplementary data of publications(Bott et al, 2011;Enomoto et al, 2012;Mezzapelle et al, 2013;Shukuya et al; 2014;Guo et al, 2015;Lo Iacono et al, 2015;Bueno et al, 2016;De Rienzo et al, 2016;Kato et al, 2016;Hmeljak.
Acknowledgements The authors thank  Dr. YC Gary Lee  ( University of Western Australia, Perth, Australia ) for donating AE17 cells and the cluster LUNG innovation ( LUNG O2 ) for logistic support. This work was supported by  European Research Council 2010 Starting Independent Investigator  (# 260524 ) and 2015 Proof of Concept (# 679345 ) grants, the  Graduate College (Graduiertenkolleg, GRK)  # 2338  of the  German Research Society (Deutsche Forschungsgemeinschaft, DFG) , the target validation project for pharmaceutical development ALTERNATIVE of the  German Ministry for Education and Research (Bundesministerium f€ ur Bildung und Forschung, BMBF) , and a  Translational Research Grant  by the  German Center for Lung Research (Deutsches Zentrum f€ ur Lungenforschung, DZL)  (all to GTS); the  Greek State Scholarship Foundation Program  "Reinforcement of Postdoctoral Researchers-1 st and 2 nd cycles" co-financed by the  European Union Social Fund and Greek national funds  ( NSRF 2014-2020  and  MIS-5033021 ) (to IG, MS, and IL);  General Secretariat for Research and Innovation and Hellenic Foundation for Research and Innovation  grant # 1853a  (to MS); REPSIRE  European Respiratory Society Fellowship  ( LTRF 2015-1824 ) (to IP);  INSERM, CNRS , the  "Institut de Recherche en Sant e Respiratoire des Pays de la Loire" ,  ARSMESO44 , the  National Research Agency  under the  Programme d' Investissements d' Avenir  ( ANR-16-IDEX-0007 ), and the  Pays de la Loire Region research program  (all to CB, MG, and SD); as well as INSERM, the  Ligue Contre le Cancer (Ile de France committee) , and the  Chancellerie des Universit es de Paris (Legs POIX)  (all to DJ).
Data availability Affymetrix CytoScanHD Microarray data: GEO dataset GSE134349 (https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE1343 49). IonTorrent RNA sequencing data: GEO dataset GSE94415 (https:// www. ncbi. nlm. nih. gov/geo/query/acc. cgi?&acc=GSE94415). Expanded View for this article is available online. Angers: https://www. crcina. org/?lang=en The Koc University School of Medicine: https://medicine. ku. edu. tr/en/ The cancer genome atlas (TCGA) pan-cancer human malignant pleural mesothelioma (MPM) dataset available at cBioportal: https://www. cbioportal. org/study/summary?id=meso_tcga_pan_can_atlas_2018 The cancer genome atlas (TCGA) pan-cancer human malignant pleural mesothelioma (MPM) gene expression dataset available at: https:// xenabrowser. net/datapages/?dataset=TCGA-MESO.
htseq_fpkm-uq. tsv&host= https%3A%2F%2Fgdc. xenahubs. net&removeHub=https%3A%2F%2Fxena. treehouse. gi. ucsc. edu%3A443 The catalogue of somatic mutations in cancer (COSMIC) human MPM dataset: https://cancer. sanger. ac. uk/cosmic/browse/tissue?wgs=off&sn=pleura&ss= all&hn=mesothelioma&sh=&in=t&src=tissue&all_data=n Human MPM datasets at Gene Expression Omnibus: https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE51024, https://www. ncbi. nlm. nih. gov/geo/query/ acc. cgi?acc=GSE134349, https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc= GSE42977 Novel mouse MPM cell line and normal mesothelial cell RNA sequencing dataset at Gene Expression Omnibus: https://www. ncbi. nlm. nih. gov/geo/query/ acc. cgi?&acc=GSE94415 Using Pleural Effusions to Diagnose Cancer (MAPED) study page at ClinicalTrials.
gov: https://www. clinicaltrials. gov/ct2/show/NCT03319472?term= maped&draw=2&rank=1 Links to patient support, advocate, and charity organizations: https://www. mesotheliomagroup. com/, https://www. mesothelioma. com/, https://www. mesotheliomahelp. org/, https://www. asbestos. com/support/, https:// mesothelioma. net/mesothelioma-support/, https://www. curemeso. org/, https:// www. mesotheliomahope. com/resources/cancer-foundations/, https://www. mesothelioma. uk. com/.
Results We systematically interrogate KRAS alterations in the TCGA pancancer dataset of human MPM and in MPM patients from our centers employing sensitive techniques. 20% of TCGA and 50% of our patients show activating mutations or amplification of KRAS, in 30% of the cases accompanied by TP53 mutations or loss. These changes are associated with enhanced signaling downstream of KRAS. KRAS and TP53 are shown to cooperate for MPM development in conditional mouse models. Three new MPM cell lines are developed that are highly similar to the human disease, and these experimental MPM models are shown to be actionable by a novel KRAS inhibitor.
▸Figure 2. 2 Figure 2. KRAS pathway activation in MPM from the cancer genome atlas (TCGA) pan-cancer MPM dataset. A-F Molecular and clinical features of the cancer genome atlas (TCGA) pan-cancer MPM patients (n = 87) stratified by the presence of KRAS standalone (n = 10) and combined KRAS/TP53 (n = 7) alterations. Shown are unsupervised hierarchical clustering of n = 86 patients (gene expression data were not available for one patient) by 40 genes significantly overexpressed in KRAS/TP53-altered over KRAS-altered over KRAS/TP53-normal patients (A) and data summaries of mononucleotide change signatures (B), of indices of genomic instability and mutation burden (C), of clinical features and KRAS/TP53/NF2 co-mutation frequency (D, E), and of overall survival (F). G KRAS/TP53 pathway adapted from Matallanas et al (2011) and Tikoo et al (1994).
6, 7 The 3WINTERS-IPS (Type 3 Von Willebrand International Registries Inhibitor Prospective Study) is an investigator-initiated, multicenter, European/Iranian observational study that aims to comprehensively evaluate the clinical symptoms of VWD3 patients,  11  their phenotypic laboratory findings, and the VWF variants, as well as the safety and efficacy of replacement therapy for the treatment and prevention of bleeding manifestations (registered at www. clinicaltrials. gov as #NCT02460458). We report here the spectrum of VWF variants in 2 large groups of VWD3 patients from Europe and the Islamic Republic of Iran (Iran). in the largest cohort of VWD3 patients investigated thus far and to compare the VWF variants between the European VWD3 (EU-VWD3) and the Iranian VWD3 (IR-VWD3) patients. Our findings highlight the distinct features regarding the heterogeneity and the localization of the VWF variants identified in these 2 populations.
-650 and 3'-UTR c. -150). In Sheffield and Milan, genetic evaluation was initially performed by using NGS analysis. At Sheffield, the custom oligonucleotide baits were designed by using Agilent Technologies SureDesign (https:// earray. chem. agilent. com/suredesign/index. htm). The enriched libraries were prepared by using a SureSelect XT reagent kit (Agilent Technologies Inc. , Palo Alto, CA), and the pooled library underwent paired-end sequencing on the HiSeq 2500 system (Illumina, Inc. , San Diego, CA). In Milan, the custom panel was designed by using Illumina Design Studio on Human (hg19) reference genome (https:// emea. support. illumina. com/sequencing/sequencing_software/ designstudio. html). The VWF was amplified by using TruSeq Custom Amplicon strategy (Illumina, Inc. ), and the paired-end sequencing was performed on a MiSeq sequencing platform (Illumina, Inc.
). NGS data were analyzed according to the guidelines reported by the Broad Institute (https://software. broadinstitute. org/ gatk/best-practices/), and the variant call format files were annotated by ANNOVAR. 15 Further details of the NGS analysis are reported in the supplemental Materials. All variants identified in Sheffield and Milan were confirmed by Sanger sequencing using the BigDye Terminator v1. 1 Cycle Sequencing Kit (Applied Biosystems, Warrington, UK) on a 3730 and 3130 DNA Analyzer (Applied Biosystems), respectively. In Hamburg, genetic analysis was performed by using direct Sanger sequencing. Exons 2 to 52, including intron-exon boundaries, were amplified by polymerase chain reaction (PCR), and PCR products were analyzed on an ABI Prism 3130 Genetic Analyzers (Applied Biosystems) using the genomic kit ABI Prism BigDye Terminator Cycle Sequencing Ready Reaction (Applied Biosystems).
16 Reactions were set up as described by MRC Holland (MLPA General Protocol), and fragment size analysis was performed by using either the 3730 DNA Analyzer (Sheffield) or the 3130 Genetic Analyzer (Hamburg and Milan) (Applied Biosystems). Screening of VWF variants. The variants identified were prioritized on the basis of allelic frequency and type of mutation. Missense, splicing, nonsense, insertion, duplication, and deletion variants with minor allele frequency ,0. 01 were considered as candidate pathogenic variants. Any detected VWF sequence variation was checked to determine whether they had been earlier recorded in online databases. They were sought in the Coagulation Factor Variant Databases portal supported by the European Association for Haemophilia and Allied Disorders (EAHAD)  17  (https://grenada.
lumc. nl/LOVD2/VWF/home. php?select_db=VWF), the Human Gene Mutation Database (HGMD)  18  (http://www. hgmd. cf. ac. uk/ac/ gene. php?gene=VWF), and the Ensembl database  19  (www. ensembl. org), all accessed January 2021. In silico analysis. The impact of the missense changes on VWF protein structure and function was evaluated by using the following tools: Combined Annotation-Dependent Depletion PHRED score  20  (applying a threshold of 20),  21  Sorting Intolerant From Tolerant  22  (https://sift. bii. a-star. edu. sg/), Polymorphism Phenotyping v2,  23  Poly-Phen-2 (http://genetics. bwh. harvard. edu/pph2/), and Mutation-Taster  24  (http://www. mutationtaster. org/StartQueryEngine. html), all accessed November 2020. Pathogenicity of potential splice site variants was evaluated by using splice site prediction tools such as var-SEAK (https://varseak.
Number of VWF variants in the 3WINTERS-IPS cohort A total of 231 patients (Europe/Iran [EU/IR] 5 121/110), previously diagnosed with VWD3, were investigated at the molecular level. The variants identified in these 231 patients are reported in supplemental Tables  1  and 2 ; the data reported hereafter represent only the unrelated patients from 206 families (EU/IR 5 115/91). The majority (134 [EU/IR 5 57/77]) were homozygous for the variants identified, but no variant was identified in five EU-VWD3 patients and in six IR-VWD3 patients (Figure  1 ). A total of 154 different unique variants were identified in the 2 populations, 101 in European patients and 58 in Iranian patients (Figure  2 ). Only 5 variants were found in both populations. Three of these are among the 11 "hotspot" arginine codons present in the VWF, 30 p.