dataesr/scientific-paragraphs-categorization huggingface dataset:
However, a recent single cell transcriptome analysis of sequential CLL-RS samples revealed that, as compared to the CLL cells, RS cells downregulate genes related to BCR signaling and upregulate those involved in oxidative phosphorylation  21  , and therefore RS may be less sensitive to Ibrutinib. By applying a stringent cut-off to our transcriptomic score, generalized to all studied DLBCL datasets  [8] [9] [10] 42  , we identified a separate de novo DLBCL subset associated with a median PFS comparable to that of clonally-related RS. Ba ed on our observations, 4-8% of DLBCL diagnosed as de novo DLBCL, nonotherwise specified, may in fact be a subgroup of DLBCLs sharing common epigenetic and transcriptional features with clonally related RS, and with a similar unfavorable outcome. We propose a stable and reproducible expression-based classifier widely applicable to transcriptomic data, enabling the identification of this specific entity within supposedly de novo DLBCL, termed "RS-type DLBCL.
" Limitations of the transcriptome scoring method are dataset size and composition (DLBCL features associated with outcome), which by design prevent the exploration of single samples independently and may exert biases. However, the method also demonstrated the linear association of DLBCL scores with poor outcome and clinical variables of cancer aggressiveness, and so constitutes a means for improving the current DLBCL classification system. In conclusion, our study has revealed several relevant aspects of RS biology, including the complete RS hypomethylation profile and differentiation of clonal versus non-clonal RS according to DNAm patterns and gene expression profiles. The discovery of a CLL imprint allows clonal relationship assessment without the need for tumor DNA at the CLL stage. Subgrouping of primary RS samples according to extensive characterization of the epigenetic architecture has provided information underlying oncogenic processes, with clear clinical implications.
Ethics statement This study complies with all relevant ethical regulations and we have obtained written informed consent for all participants. No Fig. 6  | The gene expression-based LCS linearly classifies de novo DLBCL samples, with high scores enriched in N1, unclassified genomic profiles  10  , and shorter progression-free survival. Dataset from Wright et al. (n = 562; RNA-Seq; PMID: 32289277). Two-sided t tests were used to assess statistical significance. Top 25 LCS scores: enrichment in "other" subtype (e = 1. 51; p = 4. 6e-2); depletion in EZB subtype (e = 0; p = 3. 0e-2); enrichment in N1 subtype (e = 5. 99; p = 6. 4e-3). Top 141 (25%) LCS scores: enrichment in "other" subtype (e = 1. 28; p = 1. 4e-2); depletion in BN2 subtype (e = 0. 56; p = 1. 9e-2); enrichment in MCD subtype (e = 1.
57; p = 1. 7e-2); depletion in EZB subtype (e = 0; p = 1. 7e-8); enrichment in A53 subtype (e = 1. 62; p = 7. 5e-2); depletion in ST2 subtype (e = 0; p = 2. 6e-3); enrichment in N1 subtype (e = 2. 92; p = 7. 0e-3). Survival curves: Kaplan-Meier estimates of progression-free survival for n = 233 patients from a clinically and genomically annotated dataset from Wright and colleagues. Comparative PFS between patients with top 25% LCS and the rest of the cohort. Statistical comparisons were performed with the logrank test (p = 1e-4). Source data are provided as a Source Data file. ABC activated B cell like, A53 TP53 mutations/deletions-associated DLBCL subgroup, BN2 DLBCL subgroup associated with lesions of BCL6 and/or NOTCH2, COO cell of origin, DLBCL de novo diffuse large B cell lymphoma, EZB DLBCL subgroup associated with abnormalities of epigenetic regulators KMT2D, CREBBP, EP300, and/or EBF1, GCB germinal center B cell, LCS linear classifier score, MCD DLBCL subgroup associated with lesions of MYD88 and/or CD79B, N1 DLBCL subgroup associated with NOTCH1 gain of function, PFS progression-free survival, RS Richter syndrome, ST2 DLBCL subgroup associated with lesions of SGK1 and/or TET2.
Patients and materials A multicenter registry of RS accrual was established across nine centers affiliated to the French Innovative Leukemia Organization (Clinical-Trials. gov Identifier: NCT03619512). Sixty-four patients diagnosed with DLBCL-subtype RS were enrolled. Fresh frozen biopsies were gathered at RS diagnosis and met the criteria for DLBCL, including diffuse patterns of large B cells with the same size as macrophages or twice the size of normal lymphocytes  3, 50. For all patients, diagnoses were reviewed and confirmed by two independent pathologists. Only RS samples with at least 50% (median 80%, range 50-95%) high-grade component assessed by pathology review were selected for analysis. The same process was applied for assembling a validation cohort of 58 samples, further reduced to 52 QC-passed samples, which we processed to an independent EPIC 850K experiment.
This 52-sample validation cohort included 44 new samples: 18 new RS samples, the CLL component of 14 of these, 6 new DLBCL samples, and 6 additional CLLs. In addition, 8 samples from the training series were used as controls: 4 RS samples (3 clonally related and 1 clonally unrelated), with the 4 respective CLL components (Supplementary Fig. 16 ). Thus, this EPIC 850K experiment investigated 22 RS, 6 new DLBCLs, and 24 CLLs, including 18 paired-CLLs. Fifty-eight of the 64 enrolled patients with RS were from a previously described cohort, and both targeted NGS sequencing and DNAm exploration were performed; 56 of these 58 patients with RS underwent 18F-fluorodeoxyglucose positron emission tomography/computed tomography for initial diagnosis  51. For the other six patients with RS, the fresh frozen biopsy was too small for extracting both DNA and RNA, and due to the large cellular component (>70%), we prioritized gene expression data and only RNA sequencing was performed.
The minimal tumor purity was raised to 70% for RNA analysis, as contamination by signal from residual normal cells strongly influences global gene expression, especially for a subset of transcripts with very low expression in tumor cells but high expression in residual normal cells. Additional data for CLL (n = 215), and 92 normal B cells spanning the entire B lineage development were obtained as part of previously published studies  22, 25, 26, 34, 35. DNA methylation from 68 de novo DLBCL cases were also used as a reference. These DLBCLs originate from a larger lymphoma cohort gathered by the ICGC MMML-seq consortium  52. Finally, 10 lymph nodes from healthy subjects were analyzed as a control group for transcriptome sequencing.
Methylome data analyses EPIC microarray. DNAm status of 866,562 CpG sites was interrogated on the Infinium Methylation EPIC array (Illumina, San Diego, CA, USA; see Supplementals), later referred to as the EPIC 850K platform. Dataset generation. Datasets were created using the minfi package  53. The EPIC set comprises 90 distinct samples (58 RS, 25 CLL, plus a subset of 7 DLBCL replicates also available on 450K), interrogated on EPIC 850K. DNA methylation data from the control groups (215 CLL, 68 DLBCL, 92 normal B cells spanning the entire B-lineage) were acquired with the Illumina Infinium® HumanMethylation450 BeadChip (later referred to as the 450K platform)  22, 25, 26, 34. These and the EPIC 850K data were processed from IDAT files. Analyzes were run under R 3. 6 with Bioconductor 3. 10 and later versions.
The FULL dataset comprises 433 distinct samples (92 benign B cells, 215 CLLs, 68 DLBCLs and 58 RS), combined into a single 450K object containing probes shared by 850K and 450K microarrays: (i) raw IDAT files corresponding to 96 and 377 samples for the 850K (866,091 CpGs) and 450K (485,512 CpGs) platforms, respectively, and included technical replicates; (ii) each subset was loaded independently, stored into a dedicated RGChan-nelSet minfi object, along with full sample annotations, then both were combined into a third subset containing 473 samples × 452,567 CpGs using the combineArrays function with output type as "IlluminaHu-manMethylation450k"; (iii) the EPIC dataset stems from the first (850K) subset alone, the FULL dataset is obtained from the combined subsets. To reduce technological issues and biases, the same preparation protocol was applied to both EPIC (850K) and FULL (combined) subsets.
The main stages of the filtering and quality control pipeline are as follows: (i) technical checks, filtering, and evaluations (ii) data normalization with SWAN 54 ; (iii) probes located on X and Y chromosomes, flagged as cross-hybridization probes, or located near known SNPs were further removed with the rmSNPandCH function (with parameters dist = 2 and mafcut = 0. 05) available from the DMRcate package 55 ; (iv) imputation of the remaining failed β-value positions with imputePCA of the R missMDA package  56, 57  , (v) 2 × 2 sample correlation checks (Supplementary Figs. 32  and 33 ). Correlation heat maps were rendered with the R corrplot package; (vi) extended quality control step to remove sample outliers and check for residual postnormalization batch effects (Supplementary Fig. 34 ); (vii) ultimately, technical replicates were averaged into unique samples as all replicates were found comparable (Supplementary Fig.
35 ). These filtering steps led to the final EPIC (90 samples × 794,927 CpGs) and FULL (433 samples × 397,769 CpGs) datasets. Technical checks, filtering, evaluations, and quality control. These steps included failed CpGs removal (>10% samples with a detection p value >0. 01), gender check between clinical data and gender returned by the getSex function, and genotype checks (Supplementary Data 10) between RNA-seq data (see Supplementals) and genotypes inferred with the beta2genotype function available from the R OmicsPrint package  58. Cell composition deconvolution. Cell type composition was estimated for each sample with the estimateCellCounts function against a library of 6 normal white blood cells (CD8 T cells, CD4 T cells, NK, B cells, monocytes, and granulocytes) (Supplementary Data 11).
Downstream bioinformatics Supervised analyses. As a rule, β-values were used for direct interpretation and graphical representation, while M-values were favored for statistics and computations. Linear modeling based on empirical Bayesian methods was used to assess for CpG differential methylation. When applicable, these models included cell deconvolution results as added covariates to correct for B cell content. Additionally, at this point, any unwanted methylation variation such as residual batch effects were removed by using the RUVm function from package missMethyl  59. The overall dispersion was calculated on the entire dataset, then p values for each comparison were obtained with a twoway moderated t test and adjusted for FDR following the Benjamini-Hochberg procedure. At probe level, an FDR < 0.
01 indicated statistical significance. Differentially methylated region (DMR) determination was performed on the same linear models with dmrcate (package DMRcate), with lambda = 1000 and C = 3. FDR cut-off for first allowing a CpG to initiate a DMR was set to FDR = 0. 01, and DMRs were considered statistically significant if both min_smoothed_fdr and HMFDR output probabilities were <0. 01. Unsupervised analyses. Explorations were conducted on β-values, and all methods used Euclidean distances as (dis)similarity metrics. PCAs were performed with R packages FactoMineR and factoextra, on the entire datasets or a subset of the top variant CpGs across all considered samples. Hierarchical clusterings included complete and average linkage criteria, and resulting heat maps and dendrograms were rendered with the R package ComplexHeatmap.
Non-Negative Matrix Factorizations were performed with the R package NMF, either on all CpGs or a subset of the most variant ones according to the context, with method lee, and parameters ranking = 3 and iterations = 50. Feature annotations. Methylome data were analyzed using the available Illumina 450K and EPIC platform annotations, which strongly rely upon the hg19 assembly. As several tools like minfi and DMRcate still use those by default, CpG and DMR locations/annotations were lifted to hg38 coordinates as an after-computation-process when required, especially when dealing with integrations with transcriptomic and epigenomic data. Additional CpG annotations included B cell development modules  34  , UCSC tracks for the EBV-transformed GM12878 cell line, such as DNaseI and chromatin marks from ENCODE and transcription factor ChIP-Seq peaks from ENCODE3, histone modifications, and chromHMM chromatin states for 7 reference CLL epigenomes (2 U-CLLs and 5 M-CLLs)  26.
Any liftOver of coordinates between hg19-and hg38-annotated data was achieved with the UCSC table browser or with R packages liftOver and XGR. Gene set and pathway analyses. Unbiased functional annotations on ontological terms (GO) and KEGG pathways were achieved at CpG and DMR levels with the R package missMethyl. Additional enrichment analyses were conducted on curated gene lists with Enrichr 60. Reactome pathway overrepresentations and enrichment analyses were performed with ReactomePA 61 on curated sets of unique genes associated with identified DMRs. Enrichments in sets of CpGs, DMRs, target genes, GO terms, or pathways were calculated as the occurrences of the selection against a background representing the entire dataset (enrichment = observed frequency/expected frequency). p values associated with enrichment analyses were obtained with (i) an overrepresentation test, (ii) Fisher's exact test, or (iii) a Chi-square test, depending on the context and group size.
Transcriptomics All samples were processed within the same batch. Demultiplexed single-end sequencing data corresponding to 50-nucleotide-long reads were available in FASTQ files, one for each of the 87 samples, and used in the next processing steps. The cohort was composed of 47 RS samples, 2 paired-CLLs, 28 DLBCLs, and 10 controls from normal lymph nodes. The 10 normal controls were added for methodologic purposes: (i) normalization; (ii) checking benign profiles against B cell malignancies; (iii) checking the feeble amplitude of the transcriptomic component separating inflammatory (n = 3) from non-inflammatory lymph nodes (n = 7); and (iv) validation of the efficiency of the developed scoring methods. Transcriptome reconstruction pipeline. First quality controls were conducted using FastQC v0.
11. 5 [http://www. bioinformatics. babraham. ac. uk/projects/fastqc/] results as a guideline. No adapter content or known overrepresented sequence needed to be removed at this step. Read mapping and the main filtering were performed using HISAT2 v2. 0. 4  62  against a reference index built to account for human population SNPs as well as known transcripts (this index can be obtained from ftp://ftp. ccb. jhu. edu/pub/infphilo/hisat2/data/grch38_ snp_tran. tar. gz). The following scoring constraints were applied during alignment:-score-min L, 0,-0. 2-sp 10. 3-dta. Samtools v1. 3. 1 [http:// github. com/samtools/samtools] was used for manipulating the alignment files throughout the downstream analysis. PCR duplicates were flagged with Picard v1. 13 MarkDuplicates [https://broadinstitute. github.
io/picard/]. Differentially spliced transcripts were assembled from the obtained alignments with StringTie v2. 1. 0  63. The present protocol took advantage of the proposed workflow for identifying known as well as novel isoforms, using an annotation file for hg38 in gtf format as a guide [ftp://ftp. ensembl. org/pub/release88/gtf/homo_ sapiens/Homo_sapiens. GRCh38. 90. gtf. gz]. The following parameters were used: (i) first step is applied for each sample -f 0. 2 -j 3 -c 10 -M 0. 5, (ii) second step merges all transcripts of all samples-merge -m 200 and (iii) the last step estimates abundances and read coverage for all the merged transcripts, for each sample -A -C -f 0. 2 -j 3 -c 10 -M 0. 5. Two tables were generated from these results, one compiling raw read count at the gene level, and another at the transcript level.
Raw abundance filtering and normalization. Raw counts were filtered by applying a minimum expression threshold for a gene or transcript. Those had to be expressed (non-zero value) in at least two samples and present an average expression value across all samples higher than 1/5,000,000 of the average library size (64 ± 3 million reads per sample), that is, at least 20 reads per feature. Data was further adjusted with the TMM normalization method  64  , and finally was log2 and cpm (count per million) transformed  65. A total of 23,508 genes and 77,491 transcripts were identified and reported at the end of the process. Pearson's correlations for gene expression levels averaged at 0. 92 for genes and 0. 75 for transcripts and were very stable across samples (data not shown). Gene and transcript annotations.
All transcriptomic analyses were performed using the hg38 reference assembly of the human genome. Results were fully annotated with known symbols corresponding to gene and transcript genomic locations whenever possible. Upon completion of the transcript assembly, gene symbols were assigned Ensembl IDs based on overlapping positions with known transcripts (90% overlap minimum). In case of failed overlap, custom and unique IDs were used. Therefore, gene and transcript assignments were based on the Ensembl 66 GRCh38 annotations available in both core and funcgene databases, version 90. These were downloaded from ftp:// ftp. ensembl. org/pub/release-90/mysql/ for local installation and query with in-house custom tools). Transcriptome explorations. Unsupervised analyses were all carried out with hierarchical and K-means clustering techniques, as previously described  67.
Expression values were median-centered, and uncentered Pearson's correlation was used as distance metrics. Supervised analyses were performed through linear modeling (empirical Bayes), and differential expression p values were obtained using a two-way moderated t test then adjusted for FDR following the Benjamini-Hochberg procedure. An FDR < 0. 01 indicated statistical significance. Cluster dissection was achieved with functional annotation tools for target gene associations, such as the Open Targets platform  68  , and gene signature correlation with public datasets from multiple databases, such as GEO (Gene Expression Omnibus), with Enrichr [https:// maayanlab. cloud/Enrichr].
Methylome and transcriptome data integrations Here we focused on the RS cohort, for which 41 RS samples overlapped between methylome and transcriptome experiments. A subset of the methylome EPIC dataset (M-values, normalized and curated) and part of the transcriptome dataset (gene and transcript CPMsalso normalized and filtered) were integrated to eliminate unwanted signals and pinpoint the functional mechanisms linking DNA methylation of regulatory regions with gene expression in RS. Both datasets were re-annotated with biomaRt  69  and linked using two methods: (i) with shared Ensembl identifiers; and (ii) by genomic coordinates for refined feature overlap when the first method failed. We used "TSS200," "TSS1500," and "first exon" CpG information to define associations with promoter regions in the next analysis steps, and overlap was considered successful within 2 kb between CpG and gene transcription start sites (TSS).
The integromes generated at this step represented 475,148 and 674,567 associations at the gene and transcript levels, respectively. As described in a similar setup  70  , Spearman's correlations were calculated for each association. Correlations at the gene level were used for generating density plots and presenting a general view, whereas transcripts were used for precise analyses and final results. These were filtered into candidate transcriptional effector locations, by selecting "promoter regions" containing at least three negativelycorrelated CpGs (rho < -1/3; p value <0. 001) or three positively correlated CpGs (rho > 1/3; p value <0. 001) with features corresponding to "TSS200," "TSS1500," "first exon," or "TSSoverlap2kb" (each linked to the same transcript identifier). Manhattan representations were plotted against the background with the R CMplot package.
Gene set enrichment and pathway analyses of selected candidate lists were carried out as described in Methylome data analyses. Interaction networks of putative TFs encoded by candidate genes, protein domain enrichments, and effector functions were performed with STRING tools [https://string-db. org/]  71. A curated database of 1639 human TFs with DNA-binding domain information was obtained from http://humantfs. ccbr. utoronto. ca/. Regulatory networks were built with NetworkAnalyst [www. networkanalyst. ca]  72. Methodology for building the gene expression-based scoring system CLL-derived RS signature. A 215-gene set was obtained by extracting two clusters of strongly correlated up-and down-regulated profiles from the transcriptome hierarchical clustering tree (Fig. 3a , Supplementary Fig.
36 , and Supplementary Data 9). The two initial clusters displayed a very high enrichment in CLL genes and mainly drove the whole sample aggregation process. These were further reduced to protein-coding genes, to avoid biases when applying the signature to transcriptomes of different origins, which may not contain ncRNAs or genes of undefined biotype. The reduced set was then overlapped with genes integrating significantly between transcriptome and methylome. The resulting 215-gene signature contained 93 protein-coding genes underexpressed in CLL-derived RS and 122 protein-coding genes overexpressed in CLL-derived RS. Linear classifier score (LCS). For each analyzed dataset, scores were obtained according to the following procedure, to render the process as reproducible as possible. (i) When applicable, raw expression data with relevant sample annotations were retrieved from the Gene Expression Omnibus curated database (https://www.
After a distribution check, no negative outliers were found in any dataset. (vi) For each of the 215 genes, weights were assigned: those originating from the upregulated cluster were weighted +1 and those originating from the downregulated cluster were weighted -1. (vii) Finally, LCS scores were computed as the mean of weighted gene expressions for each sample S of the dataset: LCS S ð Þ = 1 n X n i = 1 G i :W i ð3Þ with n the number of genes in the signature, and G i representing the gene i weighted by W i. LCS scores were then standardized (meancentering to 0 and standard-deviation-reducing to obtain scores fully comparable between datasets). The obtained Z-scores were compared to a normal distribution in a one-way test to calculate a p value, used to define the initial LCS cutoff (p < 0.
Data availability Raw DNA methylation, gene expression and targeted NGS data generated in this study from RS samples have been deposited in the European Genome-Phenome Archive (study EGAS00001005495) under accession number EGAD00010002194 for DNA methylation data; accession number EGAD00001007922 for transcriptomic data, and accession number EGAD00001009509 for targeted NGS data. The raw data are protected and available under restricted access. Clinical and genomic data can be obtained by contacting the data access committee, according to the European Genome-Phenome Archive's procedure. Data access will be granted if their use complies with the data use conditions, including a commitment to strictly use these data for a clearly identified academic research programs and according to good practice recommendations.
The Data Access Committee will respond to requests within 2 weeks. Once access to the data is granted, these are available until the end of the research program they support. Previously published DNA methylation datasets from the ICGC MMMLseq consortium that were used in this study are available upon request from the data access committee at the ICGC consortium data portal [https://dcc. icgc. org/]. Published datasets can be found under the following accession codes: GSE103265; GSE66770; GSE10846; GSE98588; GSE87371. All other data supporting the findings of this study are available from the corresponding authors upon request. Source data are provided with this paper.