dataesr/scientific-paragraphs-categorization huggingface dataset:
We note two features of the algorithm. First, it uses a tree-automaton based approach for efficiency and correctness, but underlying it is the region (rather than zone) construction. Second, it works only with closed guards, while our approach works with closed and open guards. We note the following important points regarding our implementation: 1. The used in our implementation will be LU  [7] , without extrapolation and with global clock bounds. 2. The ToDo list used currently uses LIFO (stack) ordering for popping of elements. This corresponds to a DFS exploration of the zone-graph. But we can use other data structures for this purpose as well, e. g. , changing it to FIFO would give us a BFS exploration etc. 3. Both the simulation based and equivalence based approach are tested on PDTA with empty and non-empty languages, but we have ensured that both of them return an answer only after the entire exploration has been completed.
Closer examination of the RBFOX1 rearrangements reveals that 14 out of 52 samples have two separate events with most of them deleting CDS number 6 (Figure  S4D ), which encodes part of the RNA binding protein domain. Many of these genomic rearrangements resulted in fusion transcripts detected at the transcriptomic level; tumor suppressor genes (MTAP, BAP1, and NF2) were the most frequently affected by fusion transcripts (Figure  S4E ). Combining the MESOMICS dataset with the additional two other large datasets from  Bueno et al. (2016)  and the TCGA  (Hmeljak et al. , 2018) , we reached the sample size (n≈300) needed to detect low frequency (1%) MPM driver genes in such low-mutated tumors. We used the well-established Integrative OncoGenomics (IntOGen) pipeline on point mutations and indels  (Martínez-Jiménez et al.
, 2019)  was only correlated with the Morphology factor as well, further confirming that this factor mainly captures the molecular variation related to the morphological and not the molecular features, contrary to what has been shown for other cancer types  (Fu et al. , 2020). Here, we have uncovered three additional independent sources of major molecular variation, all with prognostic value, within MPM samples from the French MESOBANK. These findings were replicated using the the TCGA and Bueno datasets comprising mostly American patients, as well as in available data for MPM cell lines  (Blum et al. , 2019; Iorio et al. , 2016; de Reyniès et al. , 2014) , showing that the factors represent overlooked but robust processes present in all omic cohorts to date. Regarding prognostication, we found that the four factors provided complementary information about survival, and taking them all into account led to the most accurate prediction of prognosis.
Our study has provided some interesting hints for new therapeutic opportunities and also valuable information on how to define target populations 14 that will benefit the most from specific therapies, whether in clinical trials or already approved for MPM. Several examples are given below. Tumors with BAP1 alterations are expected to carry high levels of EZH2  (LaFave et al. , 2015) , suggesting that EZH2i could be an effective therapy for patients carrying BAP1-mutated cancers. Based on this, EZH2i were tested in BAP1 inactivated MPMs and a clinical trial is currently ongoing (NCT02860286). The molecular criteria for inclusion is lack of nuclear BAP1 staining by IHC, or evidence of loss of function by gene sequencing. However, we did not see a negative correlation between the expression of BAP1 and EZH2.
Artificial Intelligence analysis Whole-slide image based Artificial Intelligence (AI) prognostic score was computed using the AI MesoNet model, based on morphological features, developed by OWKIN, AI for Medical Research company  (Courtiol et al. , 2019). The model has been trained on a randomly selected training dataset of 2903 slides from the MESOPATH-NETMESO INCa network/MESOBANK (excluding samples from our MESOMICS cohort) and applied to the slides of our MESOMICS cohort. This model is trained to predict overall survival using only one H&E stained whole slide image per patient as input. It is therefore completely agnostic of any genomic information.
Data processing WGS reads were mapped to the reference genome GRCh38 (with ALT and decoy contigs) using our in-house workflow (https://github. com/IARCbioinfo/alignment-nf, release v1. 0), as described in  (Alcala et al. , 2019b). In summary, this workflow relies on the Nextflow domain-specific language (Di Tommaso et al. , 2017) and consists in 4 steps: reads mapping (software bwa version 0. 7. 15; Li and Durbin, 2009) , duplicate marking (software samblaster, version 0. 1. 24), reads sorting (software sambamba, version 0. 6. 6; Tarasov et al. , 2015) , and base quality score recalibration using GATK v4. 0. 12.
Variant calling and filtering on DNA We performed somatic variant calling using software MuTect2 from GATK v4. 1. 5. 0  (Benjamin et al. ; Van der Auwera and O'Connor, 2020)  as implemented in our Nextflow workflow based on the GATK best practices (https://github. com/IARCbioinfo/mutect-nf release v2. 2b), using a set of 79 blood samples (16 from the MESOMICS cohort and 73 from  Gabriel et al. , 2021)  coming from the same sequencing machine from CNRGH in Paris as panel of normal and using GATK4's filtering module (FilterMutectCalls) with the recommended known variants VCF (gnomAD variants from GATK's Mutect2 bundle) and per sample estimates of the contamination rate obtained with GATK4's CalculateContamination (using the small panel of EXAC SNPs from GATK4's Mutect2 bundle). Multi-region samples were processed jointly using the multi-sample calling mode of Mutect2.
We called germline variants using Strelka2 v2. 9. 10-0 ( Normalization of resulting variant calling format (VCF) files was performed with BCFtools v1. 10-2 as implemented in our workflow (https://github. com/IARCbioinfo/vcf_normalization-nf v1. 1), and annotation was then performed with ANNOVAR (2018Aprl16)  (Wang et al. , 2010)  using the GENCODE v33 annotation, COSMIC v90, REVEL databases. To call somatic variants on tumor-only samples (72/115) a similar procedure was performed (Mutect2 tumor-only mode) but including further germline filtering steps using a random forest (RF) classifier. A total of 20 features (gnomad, cosmic, genomic location/impact, and features obtained directly from Mutect2) were selected to build a RF model to classify single nucleotide variants and small Indels into somatic or germline.
For training the RF model a total of 46 tumors with matched normal mesothelioma whole-genome sequences were used. Variants on this subset were called using both the tumor-only and matched modes of Mutect2. The matched somatic calls (ground-truth) were used to split the variants of the tumor-only calls into germline and somatic classes and subsampled to mitigate bias arising from class imbalance during training (1:1 somatic:germline ratio, n=407,984). The dataset was divided into 75% for training (n=305,988) and 25% for testing (n=101,996), and the trained model reached an accuracy of 0. 93 in the test set. A random forest model for SNVs (rfvs01) was trained using a total of 326,388 (80%) variants (1:1 ratio). For indels, a random forest model (rfvi01) was built using a total of 337,442 variants (1:1 ratio, including 305,988 SVNs and 31,454 indels).
To control the false positives (RF Model FDR=6. 4%), given the highest expected proportion of germline variants in the prediction set, we set a cutoff (RF votes) of 0. 5 and 0. 75 for coding and non-coding variants, respectively. Finally, the RF models (rfvs01 and rfvi01) were used to classify a total of 1,454,942 variants (SNVs=1,317,200 and indels=137,742) of which 217,436 variants (including SNVs and indels) were classified as somatic. The point mutation calls for the MESOMIC cohort (n=448,434) include the matched calls (43 WGS) and the filtered tumor-only calls (72 WGS) (Table  S5 ). The source code and the random forest models are available in the Github repository at https://github. com/IARCbioinfo/RF-mut-nf.
Copy number variant calling Somatic Copy Number Variants (CNVs) were called using the PURPLE software  (Priestley et al. , 2019)  , as implemented in our Nextflow workflow (https://github. com/IARCbioinfo/purple-nf, version 1. 0). We used a total of 57 (including multi-region samples) matched whole-genome sequences (WGS) of MPM for benchmarking the tumor-only mode of PURPLE. We ran PURPLE twice for each matched sample: first using as input the matched WGS Normal/Tumor pair, and second, using as input only the tumor WGS. We benchmarked the PURPLE tumor-only mode by comparing the estimation of tumor purity, tumor ploidy, number of segments, percentage of genome changed (amplified, deleted), percentage of genome in neutral LOH (Loss Of Heterozygosity), and major/minor copy number alleles at gene level for matched and tumor-only PURPLE calls.
Whole genome doubling samples were called in genomes with more than 10 autosomes with major allele copy number > 1. 5. Near haploid samples were identified as those with LOH genome percentage larger than 80%. Finally, recurrent genomic regions of DNA copy-number alterations in the 115 WGS were identified with GISTIC2. 0  (Mermel et al. , 2011, version 2. 20. 23, -conf 99%) using as input the PURPLE CNV calls (log2(totalcopynumber)-1) (Table  S8 ). For replication of the analyses using the whole-exome sequencing data from the TCGA and Bueno cohorts (Figure  S3A ), because PURPLE is only suited for WGS data, we used software Facets  (Shen and  Seshan 2016) instead, as implemented in our pipeline (https://github. com/IARCbioinfo/facets-nf v. 2. 0).
Manta was run in somatic SV discovery mode using the tumor/normal WGS (--normalBAM and -tumorBAM options) and excluding the non-chromosome contig sequences (alt and decoy) of hg38 (--callRegions option). The somatic SVs passing all Manta filters (minPassSomaticScore >=30) were considered for the consensus step. SvABA was run using our in-house Nextflow workflow (https://github. com/IARCbioinfo/svaba-nf, revision number 1. 0) to identify somatic and germline SVs using the tumor/normal WGS. The somatic SVs passing all SvABA filters were considered for the consensus step. The overlap of filtered somatic SV calls was performed using SURVIVOR (merge subcommand) considering as matching SV breakpoints those at a maximum distance of 1kb (ignoring SV type and SV strand). Somatic SVs (minimum SV size 50bp) identified by at least two callers and single caller predictions with a minimum read support of 15 pairs (including pair-end and split-read evidence) were included in the consensus set of each matched sample.
To filter germline SVs in tumor-only samples we trained a random forest model for each SV caller. The SV random forest model includes a total of 19 features, which are associated with external SV 26 databases, custom panel-of-normal SVs, genomic regions, and SV features derived from SV callers. The training of the random forest model for each SV caller was performed using the matched WGS (57 including multi-region samples). First, the somatic calls from the matched WGS were used as the ground-truth during training and evaluation of each SV random forest model. Second, tumor-only calls were generated for the matched data using the tumor WGS for Manta and Delly. For SvABA, the somatic and germline SVs called by the somatic mode were merged to generate the tumor-only calls from the matched data.
SV call set for each tumor-only sample was created using the same steps performed for the matched WGS (merging Delly, SVaba and Manta calls with SURVIVOR and keeping single caller predictions with read support >= 15). Moreover, SVs found in more than 4 samples in the tumor-only series were also classified as potentially germline and removed from the final consensus set. The SVs calls for the MESOMIC cohort (Table  S6 , n=12,914) include the matched SV calls (43 WGS, n=4,685) and the filtered tumor-only SV calls  (72 WGS, n=8, 229). The source code and the SV random forest models are available in the Github repository at https://github. com/IARCbioinfo/ssvht.
Data processing The 126 raw reads files from the MESOMICS cohort and the 21 files from the  Iorio and colleagues (2016)  mesothelioma cohort (downloaded from the EGA and SRA websites, datasets EGAS00001000828 and PRJNA523380) were processed in three steps using the RNA-seq processing workflow based on the Nextflow language and accessible at https://github. com/IARCbioinfo/RNAseq-nf (release v2. 3), as described first in Alcala, Leblay,  Gabriel et al. (2019b). In summary, reads were trimmed for the adapter sequence using Trim Galore v0. 4. 2, then mapped to reference genome GRCh38 (using annotation gencode version 33) with STAR software (v2. 7. 3a). Then, reads were realigned locally using ABRA2  (Mose et al. , 2019 ; workflow https://github. com/IARCbioinfo/abra-nf release v3. 0), and base quality scores were recalibrated using GATK (workflow https://github.
com/IARCbioinfo/BQSR-nf release v1. 1). Once processed, expression was quantified for each sample, generating a raw read count table with gene-level quantification for each gene of the comprehensive gencode gene annotation file (release 33), as well as a table with Gene fragments per kilobase million (FPKM), using StringTie software (v2. 1. 2) (Nextflow 29 pipeline accessible at https://github. com/IARCbioinfo/RNAseq-transcript-nf release v2. 2). Quality control of the samples was performed at each step. FastQC software (v0. 11. 9; https://www. bioinformatics. babraham. ac. uk/projects/fastqc/) was used to check raw reads quality and RSeQC software (v3. 0. 1) was used to check alignment quality.
Normalisation and quality controls The raw read counts of the 59,607 genes in the expression data matrix, from MESOMICS, TCGA, and Bueno cohorts, from which we removed non-chimionaif samples, were normalised using the variance stabilisation transform (vst function from R package DESeq2 v1. 14. 1); this transformation enables comparisons between samples with different library sizes and different variances in expression across genes. We performed dimensional reduction on expression data as quality control, using Principal Component Analysis (PCA) (function dudi. pca from R package ade4 v1. 7-16). PCA was performed on the variance-stabilised read counts of the 5,000 most variable genes for (i) (MESOMICS) 109, (ii)  (Bueno)  180, (TCGA) 73, and (iii) (3-cohorts: MESOMICS, Bueno, and TCGA) 362 samples (Table  S3 ).
Processing of publicly available expression array data Raw expression array CEL files from Iorio and colleagues  (Iorio et al. , 2016)  and de Reynies and colleagues  (de Reyniès et al. , 2014)  were downloaded from public repositories (GEO: GSE29354 and ArrayExpress: E-MTAB-1719, respectively) and processed using the RMA algorithm (justRMA function from the affy R package v1. 68. 0). Annotations were downloaded from the hgu219. db and hgu133plus2. db packages (v3. 2. 3).
Immune contexture deconvolution from expression data The proportion of cells that belong to each of ten immune cell types (B cells, macrophages M1, macrophages M2, monocytes, neutrophils, NK cells, CD4+ T cells, CD8+ T cells, CD4+ regulatory T cells, and dendritic cells) were estimated from the RNA-seq data using software quanTIseq (downloaded 14 September 2020) using our workflow for parallel processing of samples (https://github. com/IARCbioinfo/quantiseq-nf release v1. 1). Additionally, as technical validation, we used EpiDISH R package (v2. 6. 0) to estimate seven immune cell types (B cells, monocytes, neutrophils, NK cells, CD4+ T cells, CD8+ T cells, and eosinophils) as well as epithelial cells and fibroblasts from the DNA methylation data. The immune cell types for which the association with archetypes were the strongest (absolute Pearson's correlation coefficient r > 0.
31. CC-BY-NC-ND 4. 0 International license perpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in For each sample, 600 ng of purified DNA were bisulfite converted using the EZ DNA Methylation kit (Zymo Research Corp. , CA, USA) following the manufacturer's recommendations for Infinium assays. Then, 250 ng of bisulfite-converted DNA was used for amplification, fragmentation and finally hybridisation on Infinium Methylation EPIC beadchip, following the manufacturer's protocol (Illumina Inc. ). Chips were scanned using Illumina iScan to produce two-colour raw data files (IDAT format).
Data processing The resulting IDAT raw data files were pre-processed using R packages minfi  (v. 1. 34. 0) and ENmix (v. 1. 25. 1). We first performed quality control checks on the raw data. Two-colour intensity data of internal control probes were manually inspected to check the quality of successive sample preparation steps (bisulfite conversion, hybridisation, extension, and staining; function plotQC, ENmix). There was one outlier, the technical replicate MESO_056_T1, when comparing per sample log2 methylated and unmethylated chip-wise median signal intensity (function getQC, minfi), and no samples displayed an overall p-detection value > 0. 01 (function detectionP, minfi). The poor quality sample, MESO_056_T1, was excluded from subsequent processing. Sex was assigned using a predictor based on the median total signal intensity of sex chromosomes, with a cutoff of -2 log2 estimated copy number difference between males and females (function getSex, minfi).
One sample was identified to be discordant between predicted (female) and clinically reported (male) sex, MESO_071_T. Whole genome sequencing results from matched blood confirmed that the participant was male, whilst the tumour displayed losses on chrY and gains on chrX. Raw data were then normalised using functional normalisation (function preprocessFunnorm, minfi) to reduce technical variation within the data, and probe removal steps were performed to ensure reliability and accuracy of the final dataset. Four groups of probes were removed: (i) poor performing probes with a p-detection value > 0. 01 in at least one sample (16,497 probes discarded), p-detection value was computed by comparing the total signal (methylated and unmethylated) of each probe with the background signal level from non-negative control probes (function detectionP, minfi) (ii) cross-reactive probes (42,552 probes discarded), cross-reactive probes co-hybridise to multiple locations within the genome and therefore cannot be reliably investigated (Pidsley et al.
Genome Biology 2016 PMID: 27717381) (iii) probes on the sex chromosomes (17,144 probes discarded), and (iv) probes with SNPs within the single base extension site, or target CpG site, at a minor allele frequency of > 5% (database dbSNP build 137), (8,411 probes discarded, function dropLociWithSnps, minfi). This resulted in a normalised, filtered dataset of 781,245 probes for 139 samples. Finally, beta and M-values were extracted (functions getBeta and getM, minfi). Nine probes recorded m-values of -∞ for at least one sample, and these values were replaced with the next lowest m-value in the dataset. The three normal tissues and one remaining technical replicate were then removed from the beta and m matrices for the subsequent analyses. This resulted in 135 samples, 122 for discovery and an additional 13 for ITH analyses.
Processing of publicly available DNA methylation data DNA methylation array data (IlluminaHumanMethylation450k BeadChip array IDAT files) from the TCGA mesothelioma cohort  (Hmeljak et al. , 2018)  were downloaded from the GDC legacy archive 32. CC-BY-NC-ND 4. 0 International license perpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for this this version posted September 29, 2021. ; https://doi. org/10. 1101/2021. 09. 27. 461908 doi: bioRxiv preprint (https://portal. gdc. cancer. gov/legacy-archive/search/), and from the  Iorio et al. (2016)  cell line cohort from GEO repository (dataset GSE68379), respectively. Datasets were then imported into R and pre-processed using R packages minfi (v.
1. 34. 0) and ENmix (v. 1. 25. 1) individually. Data processing was performed as per the MESOMICS cohort, no samples failed QC steps or were discordant for sex. Probes with p-detection value > 0. 01, cross-reactive probes  (Chen et al. , 2013) , probes on sex chromosomes, and those associated with SNPs were discarded. This resulted in normalised, filtered datasets of 439,417 probes for 74 samples for the TCGA cohort, and 436,125 probes for 21 samples for the Iorio cell line cohorts. Beta and M-values were extracted (functions getBeta and getM, minfi), and probes recording M-values of -∞ for at least one sample were replaced with the next lowest m-value in the dataset. Where DNA methylation array data was required for the MESOMICS and TCGA cohorts together (see Integrative unsupervised analyses), data were combined and processed as follows.
IDAT files for 126 MESOMICS samples (excluding ITH samples), and 74 TCGA samples were imported into R as separate RGSets. The TCGA RGSet was converted to array type IlluminaHumanMethylationEPIC and combined with the MESOMICS RGSet (function convertArray and combineArrays from R package minfi). All samples passed QC. One sample was identified to be discordant between predicted and clinically reported sex, MESO_071_T, as previously described. Subsequent processing was as per the MESOMICS cohort, and 56,308 probes were discarded (16,588 with p-detection value > 0. 01, 26,254 cross-reactive, 8,838 sex chromosome, and 4,628 SNP-associated). This resulted in a normalised, filtered dataset of 396,145 probes for 200 samples. Beta and M-values were extracted (functions getBeta and getM, minfi), one hundred and twenty eight probes recorded m-values of -∞ for at least one sample and were replaced with the next lowest M-value in the dataset.
The dataset of 781,245 probes contained 102,341 promoter-assigned probes, 23,858 enhancer-assigned probes, and 317,281 gene body assigned probes. Average M and beta values were calculated for each individual sample across all promoter, enhancer and gene body probes to obtain mean promoter, enhancer and gene body methylation levels per sample respectively. The mean M-values were used for statistical analysis of associations between regional methylation levels and features of interest, while beta values were used for plotting significant findings.
Pre-processing of expression data We used normalised read counts matrices (see RNA Sequencing) for (i), (ii),(iii) and (iv) encompassing 59,607 genes. Among these genes, those having less than one FPKM difference across the samples have been excluded from the unsupervised analyses. Also, in order to mitigate sex influence on the expression profiles, we removed genes from the sex-chromosomes. For each analysis, the top 5,000 most variable genes were selected. Similarly, the 5,000 most variable genes from the normalized array expression of cell lines (see expression array processing section) were selected. Whenever several probes were available for a same gene, the one with the highest intensity was selected. Pre-processing of methylation data DNA methylation was available for both MESOMICS and TCGA cohorts.
Pre-processing of copy number changes Copy number changes data was available for both MESOMICS and TCGA cohorts. We assessed the global (Total) and the minor (Minor) allele copy number states at the gene level using respectively the total (total) and the minor (minor) copy number estimate given by PURPLE (see Copy number variant calling section) on hg38 genome for the MESOMICS cohort, and SNP array estimates downloaded from the GDC portal for the TCGA MESO cohort. Of note, for the TCGA samples, the copy number state has been aligned on the hg19 genome. Hence, specifically for analysis (iv), the transformation of hg19 coordinates into hg38 has been required to integrate copy number data from both MESOMICS and TCGA samples within the same data set. To do so, we used liftOver R package (v. 1.