dataesr/scientific-paragraphs-categorization huggingface dataset:
The estimation of the SARS-CoV-2 co-infection prevalence had been performed in 2020 by Liu and colleagues, who reported about 5% of co-infections in the United Arab Emirates. The low genetic diversity of SARS-CoV-2 observed during this period strongly limits the interpretation of this result  17. More recently, a study conducted on out-patients in the USA during the same period (November 2021 to February 2022) identified 20 Delta/Omicron coinfections out of 16,386 samples sequenced. The resulting prevalence of about 0. 1% for Delta/Omicron is highly consistent with the prevalence found herein. However, no BA. 1/BA. 2 co-infection was reported by the authors as they specifically looked for Delta/Omicron co-infections using a curated list of mutations  7. In contrast, the unbiased coinfection detection script presented herein and validated on mixed Delta:Omicron isolates was defined to detect likely co-infections whatever lineages are present in the sample and can be applied for all SARS-CoV-2 co-infections.
Samples used in this study were collected as part of an approved ongoing surveillance conducted by the NRC of HCL. The investigations were carried out in accordance with the General Data Protection Regulation (Regulation (EU) 2016/679 and Directive 95/46/EC) and the French data protection law (Law 78-17 on 06 January 1978 and Décret 2019-536 on 29 May 2019). Samples were collected for regular clinical management, with no additional samples for the purpose of this study. Patients were informed of the research and their nonobjection approval was confirmed. This study was approved by the ethics committee of the Hospices Civils de Lyon (HCL), Lyon, France and registered on the HCL database of RIPHN studies (AGORA N°41).
All co-infections were confirmed by visual examination of vcf plots using the lists of specific mutations from our lineage variant database (Fig S2, Fig S3 and Fig S4). Uniform frequencies of lineage-specific mutations along the genome were observed in each sample, except for two samples ("021228537801" among the Delta/BA. 1 co-infections and "722000801801" among the BA. 1/BA. 2 co-infections). In sample 021228537801, frequencies of Delta-and BA. 1-specific mutations were reversed between the first and second half of the genome (Fig S2). In sample 722000801801, frequencies of BA. 1-and BA. 2-specific mutations were reversed between the first fourth and rest of the genome (Fig S4). Delta/Omicron (BA. 1) co-infections were detected between weeks 50-2021 and 04-2022 (Fig 4). Considering only the period of Delta and Omicron (BA.
Acknowledgments We would like to thank all the members of the GenEPII sequencing platform who contributed to this investigation. We also thank all the laboratories, clinicians and patients involved in this work. This work was carried out within the framework of the French consortium on surveillance and research on infections with emerging pathogens via microbial genomics (consortium relatif à la surveillance et à la recherche sur les infections à pathogènes EMERgents via la GENomique microbienne EMERGEN; https://www. santepubliquefrance. fr/dossiers/coronavirus-covid-19/consortium-emergen).
High-purity NEN PDTOs mirror the gene expression of their parental tumors We next assessed whether NEN PDTOs maintained the gene expression profiles of their corresponding parental tumors using bulk RNA sequencing (RNA-seq) (Figures  3A  and S3A , Table  S2 ). To determine whether NEN PDTO samples and parental tumors show features of neuroendocrine identity, we examined the levels of expression for 3 canonical markers of neuroendocrine differentiation (CHGA, NCAM1, and SYP), and 3 neuroendocrine lineage transcription factors (ASCL1, INSM1, and NEUROD1)  (Andersson-Rolf et al. , 2021)   (Figure 3B and S3B). For 15 out of the 21 parental tumor-PDTO families in our dataset, expression levels for these markers were similar in PDTOs and their matched parental tumors and were consistent with the range of expression observed in reference samples (Figure  3B ).
Transcriptomic analysis of these high-purity NEN PDTO lines and their matched parental tumors, and a comparison with transcriptomes of NEN reference samples from previously published datasets, showed that they captured the molecular group of their parental tumor  (Alcala et al. , 2019; Alvarez et al. , 2018; Gabriel et al. , 2020)  (Figure  3C-D ). Embedding and clustering by UMAP using genes representative of known molecular groups of pulmonary NENs and small intestinal NETs largely showed that PDTOs and their parental tumors clustered together with the reference NEN tissue samples of the expected molecular group. Although LCNEC1 and mLCNEC23 were from extrapulmonary LCNEC tumors, their matched parental tumors and PDTOs clustered with the reference pulmonary LCNECs, suggesting that LCNECs arising in different tissue sites share similar expression profiles.
The parental tumor and PDTOs from the pulmonary NET labeled LNET10 clustered with LCNEC samples. An additional three pulmonary NET samples, all from the reference datasets and previously reported as supra-carcinoids, also fell into the LCNEC cluster  (Alcala et al. , 2019)  (Figure  3C ). Consistent with the hypothesis that LNET10 is a supra-carcinoid, the parental tumor and PDTOs displayed features previously observed in supra-carcinoids: high expression levels of immune checkpoint genes and low expression of the putative prognostic marker for pulmonary NETs, OTP  (Alcala et al. , 2019; Moonen et al. , 2019 Moonen et al. , , 2022) )  (Figure  S3E ). The clinical data of the patient with LNET10 was also consistent with the diagnosis of supra-carcinoid: multiple metastases and recurrence following targeted therapy.
In the case of the 2 "mixed" PDTO-parental tumor WGS profiles of lung NETs in our dataset, LNET2 and LNET5, we identified somatic driver mutations that confirm that they contain tumoral tissue (Figure  S4B ). To identify somatic variants in samples for which WGS had not been performed, we called small variants in genes previously found to be altered in NENs identified in the RNA-seq reads for those genes  (Figure 4C and S4C). We identified 38 unique, putatively oncogenic mutations (see methods) in either the PDTOs, parental tumors, or both (Figure  S4C ). The mutated genes we identified involved common NEN targets, such as MEN1, ATRX, SMARCA4, ARID1B, TSC2, PIK3CA, and STK11  (Alcala et al. , 2019; Alvarez et al. , 2018; Fernandez-Cuesta et al. , 2014; George et al. , 2018). In all cases, when coverage of the region of interest for the relevant mutations and samples was sufficient, the mutations identified in the tumor were also found in the corresponding PDTO (true for 9/38 mutations and for 8/11 PDTO-parental tumor pairs) (Figure  4C  and S4C ).
LCNEC3 PDTOs were not dependent on EGF and we noted the best expansion in the absence of EGF. Given that LNET10 tumor tissue and PDTOs harbor a BRAF V600E mutation, which leads to EGF-independent activation of the MAPK pathway, we were surprised to observe that LNET10 PDTOs expanded more in all of the tested EGF concentrations compared to in media where EGF was omitted. Adaptive feedback activation of MAPK signaling has been observed in BRAF-mutant colon cancers treated with BRAF inhibitors, suggesting that EGF-mediated MAPK activation can play a functional role in promoting the growth of EGFR-expressing BRAF-mutant tumors  (Capdevila et al. , 2020; Prahallad et al. , 2012). We next asked whether LNET primary tumor tissue and matched PDTOs expressed the EGF receptor, EGFR. For this purpose, we analyzed RNAseq data from PDTOs and parental tumors and from previously published datasets of pulmonary NET tissue  (Alcala et al.
, 2019)  (Figure  S7B ). EGFR expression was present in most tumors and PDTOs in these datasets. IHC staining for EGFR confirmed membrane EGFR protein expression in tumor cells of 11 out of 13 LNET parental tumors (Figure  7E  and S7C, Table  S5 ). In line with the observation that LNET2 PDTOs were not dependent on EGF and that LNET19 PDTOs could not be propagated in EGF-containing media after passage 3, the staining for EGFR on LNET2 and LNET19 parental tumor tissue was entirely negative. LNET15 and LNET20 parental tumor tissue showed some staining for EGFR but their PDTOs stopped expanding at passage 3, suggesting that other factors beyond EGF might contribute to in vitro growth of some EGFR-expressing tumors. To ask whether EGFR expression is a common feature of pulmonary NETs, we performed IHC staining for EGFR on two sets of pulmonary NET tumor tissue microarrays containing a total of 216 cores from 73 pulmonary NETs (Figure  7F ).
DECLARATION OF INTERESTS Where authors are identified as personnel of the International Agency for Research on Cancer/World Health Organization, the authors alone are responsible for the views expressed in this article and they do not necessarily represent the decisions, policy or views of the International Agency for Research on Cancer/World Health Organization. H. C. 's full disclosure is given at https://www. uu. nl/staff/JCClevers/. H. C. is inventor of several patents related to organoid technology, cofounder of Xilis Inc. and currently an employee of Roche, Basel. Organoid lines reported in this manuscript can be requested from the lead author, h. clevers@hubrecht. eu and/or from talya. dayton@embl. es. Distribution of organoids to third parties will have to be authorized by the relevant ethical committee and a complete MTA will be required to ensure compliance with the Dutch 'medical research involving human subjects' act.
Classification of NEN PDTOs as "high-purity" or "mixed" Established NEN PDTO lines were classified as either "high-purity" or "mixed" according to available data derived from: morphological/histological analyses and molecular analyses. The term "high-purity" was applied to PDTO lines for which there was no discernible contamination in the organoids from non-tumor cells as identified by either histology or molecular analyses. The term "mixed" was applied to PDTOs for which histology and/or molecular analyses provided evidence for the presence of both tumor cells and non-tumor cells. The following criteria were defined as evidence of tumor cells in the culture: 1) presence of neuroendocrine marker-expressing cells identified by immunohistochemical staining (CHGA, SYP, or CD56/NCAM); 2) levels of neuroendocrine marker expression within the range observed for corresponding subtype in reference dataset (Figures  3  and S3 ; Table  S2 ); 3) UMAP clustering of organoid sample together with corresponding parental tumor tissue (Figures  3  and S3 ); 4) identification of shared genetic driver or NEN-associated alterations in PDTO and parental tumors by WGS (when available); 5) identification of genetic driver or NEN-associated alterations in RNAseq reads (applied when WGS data was not available) (Figures  4  and S4 ; Table  S4 ).
In all cases, a sample was defined as "pure" by criteria 1 when 60% or more of cells were NE-marker positive, and "mixed" when less than 60% of cells were NE-marker positive. For lines that were defined as "mixed" by criteria 1, evidence of tumor cell content by criterias 2 -5 were used to determine whether the NE-marker+ cells were likely to be tumor cells (i. e. criteria 2 -5 were used to classify lines for which criteria 1 showed less than 60% of cells were NE-marker+). When WGS data was available, tumor purity was estimated jointly with the copy number alterations using software PURPLE (see "copy number variant calling" paragraph below). One sample, LNET2Np7, derived from tumor adjacent normal tissue of LNET2 patient was assessed using this criteria. However, WGS analyses were inconclusive with regards to tumor cell content.
Early passage was defined as passages 1 to 3, intermediate passage as 4 to 6, and late passage as passages 7 and beyond. Processing. Raw reads were processed using our in-house whole-genome sequencing processing workflow https://github. com/IARCbioinfo/alignment-nf v 1. 2, which uses software bwa to align reads to reference genome GRCh38 with decoy genome and alt contigs, and GATK to perform base quality score recalibration. We performed quality controls using fastQC for raw reads and qualimap for aligned reads (using workflow https://github. com/IARCbioinfo/qualimap-nf v. 1. 1), and confirmed that files of a same experiment came from the same individual using NGSCheckMate (using workflow https://github. com/IARCbioinfo/NGSCheckMate-nf v. 1. 1a).
RNA libraries were prepared using TruSeq Stranded mRNA polyA kit (Illumina) and sequenced on either an Illumina Nextseq 2000 (paired-end, 2 x 150 bp) or an Illumina Novaseq 6000 (paired-end, 2 x 150 bp). Library preparation and sequencing was performed by USEQ (Utrecht Sequencing Facility). Processing. Processing of 47 RNA-seq from the experiments and public data for 210 RNA-seq from LNEN  (Alcala et al. , 2019) , 30 LNETs  (Laddha et al. , 2019) , and 88 RNA-seq from SINET  (Alvarez et al. , 2018; Hofving et al. , 2021)  were all done using our RNA-seq pre-processing workflow (https://github. com/IARCbioinfo/RNAseq-nf v. 2. 4) to ensure mitigation of potential batch effects between cohorts due to differences in processing (software, version, or operating system); as described in  Gabriel et al.
(2020) , this workflow uses trimgalore to trim reads for adapter sequences and STAR to map reads to reference genome GRCh38. Base quality scores were then recalibrated to improve subsequent variant calling using GATK (https://github. com/IARCbioinfo/BQSR-nf v. 1. 1), and alignments were realigned locally using ABRA2 to improve their quality, in particular at splicing junctions (https://github. com/IARCbioinfo/abra-nf v. 3. 0). Expression Quantification. Gene expression quantification was performed with Stringtie  (Pertea et al. , 2015) , in one-pass mode, using our workflow https://github. com/IARCbioinfo/RNAseq-transcript-nf v 2. 2 with the gencode v33 comprehensive gene annotation as reference, providing expression in raw read count and TPM formats. We also processed the SINET RNA-seq datasets EGAS00001003358 and SRP107025  (Alvarez et al.
, 2018; Hofving et al. , 2021)  with the same worflows, which is also the version that was used to process the LNEN data by Gabriel and colleagues  (Gabriel et al. , 2020) , allowing integration of the different datasets with minimal batch effects. Unsupervised analyses. Analyses were performed separately on (i) lung and pancreatic NENs, and (ii) on small intestine NETs. Raw counts from all samples were normalized using the variance stabilization transform (R package DESeq2) to provide approximately normally distributed values, separately for (i) and (ii) and using the "blind" mode so each tumor type is processed in an independent and completely unsupervised manner (samples can have their own variance/mean relationship) but biologically meaningful differences between SINETs and other NENs are not removed.
UMAP was then performed separately on (i) and (ii), using features known to be informative about molecular groups, and setting the number of neighbors to the maximal value (number of samples) so that both short-and long-distance relationships between samples are preserved, as in our recent integrative study  (Gabriel et al. , 2020). The subset of genes for the lung and pancreatic NENs (i) were extracted from a published list of "core" genes that are differentially expressed between all pairs of lung NEN molecular groups  (Alcala et al. , 2019) , excluding genes that were discarded between gencode v19 and v33 or that changed ENSEMBL ID between the releases (54/1459 genes). Cluster annotations were extracted from  Gabriel et al. (2020)  (https://github. com/IARCbioinfo/DRMetrics/data/Attributes.
txt. zip), assuming as shown in the article that clusters A1, A2, and B from  Alcala et al. (2019)  respectively correspond to clusters LC1, LC3, and LC2 from  Simbolo et al. (2019) , and grouping together under the term LCNEC (resp. SCLC) LCNEC and LCNEC-like SCLC (resp. SCLC and SCLC-like LCNEC). The subset of genes for the SINET samples (ii) included all 520 genes from Alvarez et al. (  2018 ) except NME1-NME2, which is a readthrough transcription between neighboring genes NME1 and NME2 that was not quantified by StringTie. Supervised analyses. A Partial Least Squares (PLS) analysis was performed on matched parental tumors ("parent" group) and PDTOs ("organoid" group) in order to find genes whose expressions are altered by the PDTO formation process, using the pls function from R package mixOmics in "regression" mode and selecting the first 10 components of the expression matrix (named X in the PLS framework, while the group matrix is Y), after running the variance stabilization transform on the entire dataset simultaneously using a design comparing the "parent" and "PDTO" groups, so differences between the groups is not removed during the transformation.
PLS components separating parents and PDTOs were identified using ANOVA F-tests with each of the ten components as a function of the groups. We found only two components associated with the groups (F-tests, component 1, p<2. 2✕10 -16 , component 2, p=0. 01293, other components p>0. 35). To understand which genes were responsible for the separation between parents and PDTOs, we computed the Pearson correlation between the expression of each gene and each of the two PLS components associated with the groups and performed integrated gene set analysis on the correlation p-values using the ActivePathways R package with GO terms as gene sets. ActivePathways allows combining p-values from multiple sources (here components 1 and 2) to find which pathways are associated with each source separately and in combination.
We found 108 significantly enriched pathways (see results in Fig. S3G  and Table  S2 ). Pathways were then aggregated into super-pathways using the EnrichmentMap module of cytoscape, which forms a network of pathways based on their similarity in terms of shared genes, using the default cutoff of 0. 375 similarity to connect two pathways. Results are presented Fig. S3H , and show a vast majority (89/108) of pathways belonging to immune-related pathways and pathways sharing genes with these immune-related pathways. We also found two additional super pathways-one comprising four blood-vessel-related pathways and one comprising five synaptic-related pathways-a small group of 3 pathways and 6 additional isolated pathways.
Somatic alteration calling Small variants calling. Single nucleotide variants were called from WGS data using software Mutect2 from GATK4 (using our workflow https://github. com/IARCbioinfo/mutect-nf v. 2. 2b), and indels were called using the intersection of Mutect2 and strelka2 calls (using our workflow https://github. com/IARCbioinfo/strelka2-nf v. 1. 2a) in order to filter out false positive calls, more numerous in indels than in SNVs. Annotations were performed with ANNOVAR (using our workflow https://github. com/IARCbioinfo/table_annovar-nf v. 1. 1. 1). To ensure comparisons with previous studies on NETs  (Alcala et al. , 2019; Fernandez-Cuesta et al. , 2014)  and common cancers  (Hoadley et al. , 2018) , that mostly relied on whole-exome sequencing, tumor mutational burdens were computed using the exonic ranges from the SureSelect Human All Exon v7 panel from Agilent (bed file downloaded from the manufacturer's website, approximately covering 36Mb), and focusing on non silent mutations (nonsnononymous SNVs, nonsense mutations, nonstop mutations, and indels).
Single nucleotide variants were called from RNA-seq data in the samples without WGS data using software Mutect2 from GATK4 as for WGS, but in tumor-only mode (also using our workflow https://github. com/IARCbioinfo/mutect-nf RNAseq branch) and annotated using ANNOVAR as described above. In addition, calls were further stringently filtered to reduce false somatic calls: we excluded variants reported with a population frequency >1% in any of the populations from database GNOMAD (ANNOVAR column "non_cancer_AF_popmax"), in ExAC excluding TCGA samples (ANNOVAR column ExAC_nontcga_ALL), or in the 1000 genomes project (ANNOVAR column 1000g2015aug_all), we excluded nonsynonymous variants with a REVEL pathogenicity score <0. 7, and we excluded variants never reported in the COSMIC v92 catalog of coding cancer variants.
Whenever a variant was found in either parental tumor or a PDTO, the corresponding position in the matched PDTO or parental tumor was classified as "no_coverage" whenever the read depth was 9 or less, below recommended depth for variant detection  (Koboldt, 2020). Structural Variant Calling. Structural variants (SVs) were called from WGS data using a two-step ensemble method combining results from 3 software: SVaba  (Wala et al. , 2018) , Delly  (Rausch et al. , 2012) , and Manta  (Chen et al. , 2016). In the discovery step, following  Mangiante et al. (2021) , we independently called SVs in each of the 23 tumor samples with WGS data using the SURVIVOR consensus calling  (Jeffares et al. ) , merging SVs within a distance of 1kb and requiring either agreement between 2 SV callers or a strong support by a single caller (at least 15 reads), as implemented in our workflow (https://github.
com/IARCbioinfo/sv_somatic_cns, v. 1. 0). This step led to a list of high-quality SVs. In the subsequent recovery step, for each experiment and each SV that was not detected in all samples from this experiment, we checked whether one of the SV callers found reads supporting this SV with breakpoints within 1kb of those detected (SVs marked as "recovered" in Table  S4 ), in unfiltered calls from SVaba and Manta, and initial calls from DELLY (no unfiltered calls are reported by the software). SVs were annotated based on their type (inversion, translocation, duplication, deletion), the position of their breakpoints (intergenic, within exons or introns) and their strands as described in  Mangiante et al. (2021) , using the gencode v33 annotation. Copy Number Variant calling. Copy number variants (CNVs) were called from WGS data using software PURPLE using our custom workflow https://github.
com/IARCbioinfo/purple-nf (branch dev_multi-sample). This workflow relies on the recommended PURPLE workflow using AMBER for B-allele frequency (BAF) estimation and COBALT for read depth ratio (RDR) estimation (https://github. com/hartwigmedical/hmftools/tree/master/purple ), but relying on multi-sample segmentation of using the multipcf function from R package copynumber  (Nilsen et al. , 2012)  instead of the simple pcf function used in the recommended workflow; this allows to infer more consistent breakpoints across samples from the same experiment. In addition, we used the option to include the list of SNVs called by Mutect2 (see above) to improve the CNV estimation by using the variant allelic fractions. Analyses. We identified as damaging mutations the nonsynonymous mutations predicted as damaging by REVEL (score greater than or equal to 0.
5) which combines multiple other damage predictions to maximize the evidence of pathogenicity  (Ioannidis et al. , 2016) , along with stop gains, stop losses, indels, and splicing mutations. We retrieved driver gene lists from a literature search of lung and SI neuroendocrine neoplasms  (Banck et al. , 2013; Derks et al. , 2018; Fernandez-Cuesta et al. , 2014; George et al. , 2018; Miyoshi et al. , 2017; Pelosi et al. , 2018; Rekhtman et al. , 2016; Sei et al. , 2015; Simbolo et al. , 2017 Simbolo et al. , , 2018;; Walter et al. , 2018)  (see Table  S4  for the complete list). The waterfall plots (Figure  4B-C ) were produced using R package maftools  (Mayakonda et al. , 2018). Circos plots were produced using R package circlize  (Gu et al. , 2014).