dataesr/scientific-paragraphs-categorization huggingface dataset:
25. The GSEA was performed using previously published gene signatures defining blood cDC1, cDC2, DC3, pDC, and AS-DC  20  , as well as newly generated signatures using the GeneSign module of BubbleMap  59  (Supplementary Table  6 ). Briefly, the transciptome of blood cDC1s and blood CD1c + cells was taken from previously published datasets  60. cDC1>CD1c + and CD1c + >cDC1 signatures were defined using the "Min(test) vs. Max(ref)" statistical method with a minimal linear fold change = 2 and a maximal FDR = 0. 01. Heatmaps displaying the expression pattern of gene signatures for cDC1, CD1c + cells, pDC, AS-DC, and cDC2 were generated using Morpheus (Broad Institute, https://software. broadinstitute. org/morpheus/). The 2000 genes defining the "in vitro culture imprinting" were identified using Morpheus as the mean difference of expression values between two groups: the in vitro generated cells (including cDC1, cDC2 CD206 -and cDC2 CD206 + ) versus ex vivo-isolated subsets (blood cDC1 and blood cDC2).
The 2872 genes defining the "in vivo signature" were identified by DEG analysis using the R package DESeq2 (version 1. 24. 0)  74 with a Benjamin-Hochberg p-value correction  75  (Log2FC > 1. 5, adjusted p-value < 0. 01). The volcano plot displaying the differentially expressed genes between in vivo and in vitro differentiated cDC2 was generated using R package ggplot2 (version 3. 2. 1)  76 (Log2FC > 1. 5, adjusted p-value < 0. 05). All the analysis from the raw counts matrix were performed in Rstudio (1. 2. 5001) using the version 3. 6. 1 of R. Pathway analysis was performed using ConsensusPathDB (cpdb. molgen. mpg. de)  77 and WikiPathways database. Mass cytometry acquisition and data analysis. For mass cytometry, preconjugated or purified antibodies were obtained from Invitrogen, Fluidigm (preconjugated antibodies), Biolegend, eBioscience, Becton Dickinson, or R&D Systems as listed in Supplementary Table  5.
Cells were incubated with 100 µL barcode in PBS for 30 min on ice, washed in permeabilization buffer and then incubated in FACS buffer for 10 min on ice. Cells were then pelleted and resuspended in 100 µL nucleic acid Ir-Intercalator (MAXPAR) in 2% PFA/PBS (1:2000), at room temperature. After 20 min, cells were washed twice with FACS buffer and twice with water before a final resuspension in water. In each set, the cells were pooled from all samples, counted, and diluted to 0. 5×10 6 cells/mL. EQ Four Element Calibration Beads (DVS Science, Fluidigm) were added at a 1% concentration prior to acquisition. Cell data were acquired and analyzed using a CyTOF Mass cytometer (Fluidigm). The CyTOF data were exported in a conventional flow cytometry file (. fcs) format and normalized using previously described software  79.
Events with zero values were randomly assigned a value between 0 and -1 using a custom R script employed in a previous version of mass cytometry software  80. Cells for each barcode were deconvolved using the Boolean gating algorithm within FlowJo. The CD45 + Lin (CD7/CD14/CD15/CD16/CD19/CD34) -HLA-DR + population of PBMC were gated using FlowJo and exported as an. fcs file. Marker expression values were transformed using the logicle transformation function  81. Random subsampling without replacement was performed to select 20,000 cell events. The transformed values of sub-sampled cell events were then subjected to UMAP dimension reduction 61,82 using all markers. We used the 2. 4. 0 release of UMAP, implemented in Python, but executed through the reticulate R package to interface R objects with Python.
For both mass-cytometry datasets we used UMAP using 15 nearest neighbors (nn), a min_dist of 0. 2 and euclidean distance. Heatmaps displaying mean intensity values of CyTOF data were generated using Morpheus (Broad Institute, https://software. broadinstitute. org/morpheus/). Human dendritic cell differentiation in vivo. Human cord blood-derived CD34 + hematopoietic cells (5-15 × 10 4 cells/plug) were injected subcutaneously along with engineered stromal cells (1 : 1 to 1 : 5 ratio HSPC/MS5) in 200 μl of ice-cold Matrigel® (BD Biosciences). Mice were killed at day 12 of differentiation by cervical dislocation and Matrigel® plugs were collected. Subcutaneous Matrigel® plugs were recovered, cut in pieces and incubated in HBSS (Life Technologies) 1% FBS, 0. 37 U/ml Collagenase D (Roche), 10 μg/ml DNaseI (Roche), and 1 mg/ml Dispase (Sigma-Aldrich) for 30 minutes at 37 °C.
Slides were imaged using a SP5 (Leica Application Suite) and analyzed with Fiji software. Mixed lymphocyte reaction. Cord blood-derived DC subsets differentiated in vitro and in vivo were FACS-sorted into a V bottom 96-well plate (Corning) (10 4 cells/well) and activated overnight (16 h) using a TLR agonists cocktail containing LPS 10 ng/ml, R848 1 μg/ml, and Poly(I:C) 25 μg/ml. PBMCs were isolated by gradient centrifugation using Ficoll-Paque (GE Healthcare) and labeled with Cell Trace Violet (Thermo Fisher) according to manufacturer's user guide. CTV-labeled T cells were then isolated using a Pan naive T-cell isolation kit (Miltenyi Biotec) according to manufacturer's instructions and isolation purity (≥95%) was assessed by flow cytomery. Isolated naive T cells (10 5 cells/well) were seeded together with FACS-sorted DC (1 : 10 ratio DC/T cells) and incubated at 37 °C 5% CO 2 for 7 days.
Acknowledgements P. G. is a CNRS investigator. The research was supported by the  NC3Rs ,  BBSRC ,  CRUK ,  Rosetree Trust , and  King's Health Partners. Authors are recipient of awards and grants  BBSRC-BB/M029735/1 ,  CRUK-CIPA C57672/A22369 ,  WWCR-18-0422 ,  ANR-18-IDEX-0001 , and  ANR-17-CE11-0001-01. This work was supported by the  Institut Curie ,  Institut National de la Santé et de la Recherche Médicale ,  Labex DCBIOL  ( ANR-10-IDEX-0001-02 PSL  and  ANR-11-LABX0043 ),  SIRIC INCa-DGOS-Inserm_12554 , and  ANR JCJC  ( ANR-17-CE15-0004 ). We thank  Michael Ridley ,  Emily Hanton , and  Beth Ormrod  for technical assistance and  Matthew Nicholls  for schematic design. We also want to thank  Anthony Nolan Cord Blood Bank and Cell Therapy Centre  for providing the cord blood units used in this study.
Data availability Data that support the findings of this study have been deposited in NCBI's Gene Expression Omnibus (GEO) and are accessible through GEO accession codes GSE144435 [https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE144435] and GSE145803 [https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE145803].
In Brief Tlemsani et al. provide a unique resource, SCLC-CellMiner, integrating drug sensitivity and multi-omics data from 118 small cell lung cancer (SCLC) cell lines. They demonstrate that SCLCs have differential transcriptional networks driven by lineage-specific transcription factors (NEUROD1, ASCL1, POU2F3, and YAP1). Furthermore, YAP1-driven SCLCs have distinct drug sensitivity profiles.
Nevertheless, SCLC research has benefited from the systematic collection of a large number of tumor cell lines, most of them developed at the NCI in the NCI-VA and NCI-Navy Medical Oncology Branches  (Mulshine et al. , 2019). This collection has been distributed widely and included in the cancer drug genomic databases of the NCI, Broad Institute/MIT, and Sanger/Massachusetts General Hospital (MGH)  (Barretina et al. , 2012; Garnett et al. , 2012; Iorio et al. , 2016; Polley et al. , 2016). However, the data were until now accessible only from individual platforms, making it challenging to translate genomic knowledge of SCLC tumor biology and therapeutic possibilities. Additionally, a number of SCLC cell lines generated by the Minna-Gazdar group at UT Southwestern (UTSW) Medical Center  (McMillan et al.
, 2018)  had not been integrated in the NCI (NCI-SCLC), Broad Institute (Cancer Cell Line Encyclopedia [CCLE]/Cancer Therapeutics Response Portal  [CTRP] ), and Sanger/MGH (Genomics of Drug Sensitivity in Cancer  [GDSC] ) databases. To extend our understanding of the genomics of SCLC, we performed genome-wide promoter methylation on the NCI set of 66 SCLC cell lines and whole-genome RNA sequencing (RNA-seq) for 72 cell lines of the UTSW set. We integrated those data in a global drug and genomic database (SCLC-Global) encompassing 118 SCLC lines from 115 individual patients. The integrated data, SCLC-CellMiner-CrossDataBase (SCLC-Cell-Miner), are available from a web-based tool (https://discover. nci. nih. gov/SclcCellMinerCDB/) derived from our CellMiner cross-database (CDB) web application  (Rajapakse et al.
SCLC-CellMiner also makes accessible whole-exome mutation data for 12,537 genes across 72 cell lines of the UTSW SCLC database in addition to the previously released exome sequencing data for 52 cell lines from CCLE and 62 cell lines from GSDC. Tested clinical drugs and investigational compounds in each dataset and across data sources are summarized in Figure  1D. The NCI dataset provides the largest number (n = 526), followed by the CTRP (n = 481), GDSC (n = 297), and CCLE (n = 224). SCLC-CellMiner allows multiple analyses (Table  1 ): confirming cell line reproducibility and identity across datasets, drug activity reproducibility, determinants of gene expression (on the basis of DNA copy number, promoter methylation, and microRNA expression), exploration and validation of genomic networks, classification of the cell lines on the basis of metadata such as the NAPY, epithelial-mesenchymal transition (EMT) and antigen-presenting machinery (APM) scores, and validation and discovery of drug response determinants.
Reproducibility across datasets can be tested with CellMi-nerCDB by plotting the same gene (expression, copy number, or promoter methylation), drug, or microRNA on the x and the y axes. For instance, Schlafen 11 (SLFN11), whose expression is highly predictive of response to a broad range of frontline treatments of SCLC (etoposide, topotecan, cis-and carboplatin) as well as drugs under investigation such as the poly(ADP-ribose polymerase) inhibitors  (Farago et al. , 2019; Gardner et al. , 2017; Murai et al. , 2019; Zoppoli et al. , 2012)  measured by RNA-seq in the UTSW database, shows a 0. 92 Pearson correlation with its measured values by Affymetrix microarray in the NCI database (Figure  2B ). SLFN11 promoter DNA methylation in the NCI database also shows a Pearson correlation of 0. 9 with its value in the GDSC (Figure  2C ).
CDB analyses are shown in Figure  2  for MYC, which is commonly amplified and drives proliferation of SCLC  (Ireland et al. , 2020) , for BCL2, which encodes a canonical antiapoptotic protein targeted by navitoclax (ABT-263)  (Rudin et al. , 2012) , and for two SCLC drugs, etoposide and topotecan. MYC amplification (in NCI) is correlated with its overexpression (by RNA-seq in CCLE) (Figure  2D ). Navitoclax activity is correlated with BCL2 expression (Figure  2E ). Response to etoposide is correlated in the NCI and CTRP despite different assays; cells responding to etoposide overlap for topotecan  (Figures 2F and 2G). Integrating the CellMinerCDB database of more than 1,000 cell lines of all lineages, which includes 74 and 53 SCLC cell lines in  GDSC and CCLE (Figures 1A and 1C)   (Rajapakse et al.
The SCLC Methylome Two prior studies described the promoter methylation profiles of SCLC with limited data for cell lines; 18 were examined by  Kalari et al. (2013)  and 7 by  Poirier et al. (2015)  together with primary tumors and patient-derived xenograft (PDX) samples. Here we analyzed the methylome of the 66 cell lines of the NCI and processed the methylome of the whole 985 GDSC cancer cell line dataset, including its 61 SCLC cell lines. Individual probe analysis for the Illumina 850K platform in the NCI SCLC cell lines is reported in a parallel publication  (Krushkal et al. , 2020) , while SCLC-CellMiner provides promoter methylation score  (Reinhold et al. , 2017). The promoter methylation data are highly reproducible between the NCI and GDSC datasets for the 43 common cell lines despite the different Illumina platforms (850K versus 450K)   Resource (Figures  2A  and 2C ).
SCLC DNA Copy Number versus Methylome as Drivers of Gene Expression To evaluate the relative importance of promoter methylation and gene copy number, we derived copy number data from the Illumina 850K methylome array and correlated the expression of each gene with DNA copy number and methylation in the NCI-SCLC dataset (Figure  3E )  (Reinhold et al. , 2017). Correlations for individual genes can be readily displayed with SCLC-CellMiner (https://discover. nci. nih. gov/SclcCellMinerCDB/), and snapshots of genes involved in SCLC carcinogenesis and driven by methylation (NEUROD1, ASCL1, POU2F3, YAP1, and SLFN11) are presented in Figure  S1. Unlike the histone and epithelial genes, the expression of SCLC growth-driving genes, such as the oncogenes (MYC,   S5 ). CellMinerCDB snapshots showing increased (MYC, MYCL, and MYCN) or decreased (BAP1 and VHL) copy number variation are provided in Figure  S1.
SCLC-Global Integrates Transcriptomes and Molecular and Phenotypic Data for 116 Cell Lines To integrate expression data from microarray and/or RNA-seq across the five data sources (Figure  1 ), we created the ''SCLC-Global'' expression set by regrouping all datasets by Z score normalization, which enables CDB analyses of gene expression (and also other genomic, epigenomic, and phenotypic drug response information). Principal-component and correlation analyses (Circos-and CAT-plots) validated the approach (Figures S2A-S2D and S2F). The ''SCLC-Global'' data are available in the pull-down tabs ''x-Axis Cell Line Set'' and ''y-Axis Cell Line Set'' (https://discover. nci. nih. gov/SclcCellMinerCDB/). The ''SCLC-Global'' mRNA dataset shows very high correlation with each dataset (NCI-SCLC, GDSC, CCLE, and UTSW) (Figure  S2E ).
For example, ASCL1 expression in SCLC-Global versus SCLC NCI/DTP is highly correlated (r = 0. 99, p = 1. 9e-55). SCLC-Global offers many other features, including cross-correlation with other databases for DNA methylation, DNA copy number, DNA mutation, microRNA expression, and drug activity. SCLC-Global can also be used to retrieve all the genes correlated with the expression of any given gene. For instance, for MYCN, the top correlate (p = 0. 967) is MYCNOS (Figures  S2G-S2I ), the MYCN Opposite Strand antisense RNA. The data for individual cell lines can also be visualized by plotting MYCNOS against MYCN in the SCLC-Global database (Figure  S2H ). Plotting MYCN versus MYCNOS in the CCLE database using CellMi-nerCDB extends the finding that MYCN is co-expressed with its antisense RNA in both SCLC and brain tumor cell lines (Figure  S2I ).
545, p = 2. 45e-10) between ASCL1 and REST, the transcriptional target of NOTCH (Figure  5A ). The NEUROD1 subset of NE-SCLC (SCLC-N) did not show significant correlation between NEU-ROD1 and DLL3 expression (r = À0. 18, p = NS) (Figures  S3J  and S3K ), questioning whether DLL3 downregulates the NOTCH pathway in SCLC-N cell lines. Of the 116 SCLC cell lines in SCLC-CellMiner, 9 belong to the YAP subset (Figure  4 ). Because expression of YAP1 is a feature   S3 ). Cell Reports 33, 108296, October 20, 2020 9 Resource in a wide variety of solid tumors (Figure  4E ), we explored the YAP transcriptional network (Figure  5C ). The first notable finding is that YAP1 expression is highly correlated with the expression of its heterodimeric partner TAZ (encoded by the WWTR1/TAZ gene) both in the SCLC-Global dataset (Figures  5C  and 5D ) and across the 986 cell lines of the GDSC (Figure  S4 ), suggesting a master transcriptional regulator upstream of both genes, or YAP1 acting as super-enhancer (Figure  5C ).
Global analyses of the NOTCH pathway across 1,036 cell lines from the 22 different tissue types of CCLE (Figures  5G, S4D,  and S4E ) show that NOTCH2 and NOTCH3 are co-expressed in many tumor types and that the NE-SCLC cell lines are characterized by low NOTCH expression (Figures  5G  and S4D ). In contrast, the SCLC-Y-and -P cells are among the highest NOTCH-expressing cells. The SCLC-Y Transcriptome Clusters with NSCLC Cell Lines Next, we examined the relationship between the SCLC-Y and the NSCLC cell lines (Figure  5I ). tSNE (t-distributed stochastic neighbor embedding) is a method to highlight strong patterns by reducing the dimensionality of a dataset while preserving as much ''variability'' as possible. tSNE analysis using gene expression data between NSCLC (n = 100) and SCLC (n = 60) cell lines from the GDSC grouped the SCLC-Y with the NSCLC cell lines.
Cancer cells (typically glioblastomas) with MGMT inactivation are selectively sensitive to TMZ. Analyses of SCLC-Global reveals lack of Resource MGMT expression in 33% (N = 38) of the cell lines (Figure  S6D ). Notably, the non-NE SCLC cell lines all express MGMT, indicating that the SCLC-P-and -Y cancer cells are predicted to be poor candidates for TMZ-based therapies  (Farago et al. , 2019). The SCLC-Y cell lines show the greatest resistance to the standard-of-care drugs (etoposide, cisplatin, and topotecan) (Figure  6B ). This result is not limited to SCLC, as a highly significant drug resistance phenotype is observed between YAP1 expression and response to etoposide and camptothecin across the whole database of the CCLE-CTRP (Figure  S6E ). To determine whether the NAPY classification predicts sensitivity to drugs not commonly used as standard of care for SCLC, we analyzed 526 compounds of the NCI database  (Polley et al.
DISCUSSION SCLC-CellMiner (https://discover. nci. nih. gov/SclcCellMinerCDB/) provides a unique and first-of-its-kind resource of patientderived SCLC cell lines characterized comprehensively using multi-omics and drug sensitivity. It also includes new high-resolution methylome, detailed in a complementary publication  (Krushkal et al. , 2020). SCLC-CellMiner enables interrogation of different databases. The data are highly reproducible across databases, which allowed us to build an integrated platform (''SCLC-Global'') to examine genomic characteristics and drug sensitivities across 116 SCLC cell lines. Patient-derived cancer cell lines remain the most widely used models and the primary basis to study the biology of cancers. They enable high-throughput testing of new drugs and determinant-of-response hypotheses.
The database of 116 SCLC cell lines reported here models the genetic and molecular diversity of SCLC, as exemplified by their stratification across the four recently proposed subgroups (NAPY classification)  (Rudin et al. , 2019). Several studies of human cancer cell lines have revealed a drift at the transcriptomic level for individual cell lines over multiple passages, or passages in different laboratories. This raised the concern that cancer cell lines bear more resemblance to each other, regardless of the tissue of origin, than to the clinical samples that they model. However, several other studies have come to the opposite conclusion, demonstrating the need for human cancer cell line panels  (Barretina et al. , 2012; Neve et al. , 2006; Reinhold et al. , 2019; Wang et al. , 2006; Weinstein, 2012; Zoppoli et al.
If verified in clinical cohorts of immunotherapy-treated patients, this finding might enable patient selection. Second, we highlight potential surface markers that could be targeted on the basis of the NAPY subgroups. For example, SCLC-Y cells express neither the therapeutically relevant surface epitopes DLL3 or CEACAM5  (Das, 2017; Morgensztern et al. , 2019; Rudin et al. , 2017) , which tend to be specific for the SCLC-A (and N). However, SCLC-Y express CD151 and EPHA2 (Figure  6K ) and might respond to the YAP1 and NOTCH inhibitors in clinical development  (Crawford et al. , 2018; Leonetti et al. , 2019). Our analyses demonstrate the value of cancer cell line databases and imply that updating drug testing with new clinical drug candidates shall provide valuable information to guide clinical trials.
METHOD DETAILS DNA methylation data Gene-level methylation using the 850K Illumina Infinium MethylationEPIC BeadChip array was summarized based on  (Reinhold et al. , 2017). In short, methylation data were normalized using the minfi package using default parameters, where probe-level beta-values and detection p values were calculated for each probe. This provided 866,091 methylation probe measurements. Methylation probe beta-values for individual cell lines with detection p values > = 10-3 were set to missing. Also probes with median p value > = 10-6 were set to missing for all cells and removed from the analysis. Probe locations on the human genome (hg19 version) defined by Illumina was used for the analysis, annotating proximal gene transcripts and CpG islands. Probes were designated as category ''1'' or ''2,'' with category ''1'' considered to be most informative.
Global expression data We generate a new Global SCLC dataset (SCLC-Global) using all combined cell line resources: NCI SCLC, CCLE, CTRP, GDSC and UTSW. The data sources have a mixture of microarray and RNA-seq gene expression. For each experiment, genes were scaled across all cell lines to create a z-score normalized dataset. The SCLC-Global expression was calculated by averaging the z-scored gene expressions from all sources. To test for removal of batch effects by gene scaling (z-score normalization), we clustered the cell lines based on gene expression using the raw data (Figure  S2A ) and the normalized data (Figure  S2B ) in R using the hclust() for clustering, and the ape package (version 5. 3) to create the clustering dendrograms.
Predictive power of DNA copy number and methylation on transcript expression Testing the predictive power of DNA copy number and methylation on transcript expression was performed with linear regression analysis (as seen in Figure  3E. For each of the 15,798 genes with all three forms of data available (transcript, methylation, and copy number levels) a linear regression model was fit, with both copy number and methylation as independent variables and transcript expression as the dependent variable. The model provided coefficients for the copy number and methylation that gave the lowest squared error between fitted values and true expression. We separated individual contributions of these two factors for gene expression prediction using the method of relative importance  (Gromping, 2006) , using the lmg method  (Bacher, 1980)  from the R package relaimpo to compute individual R 2 values.
SCLC subtypes and heatmaps The SCLC cell lines were classified into the NAPY subtypes using the expression of NEUROD1, ASCL1, POU2F3 and YAP1 with the SCLC-Global expression dataset. Clustering was performed using distance matrix based on Euclidean distance and ''ward. D'' clustering using the hclust() function in R programming language. SCLC neuroendocrine score Cell line neuroendocrine score was calculated based the method reported in  Zhang et al. (2018)  that uses a gene set of 25 neuroendocrine and 25 non-neuroendocrine genes for classification. For each cell line, the expression values of genes were correlated with the expression averages of neuroendocrine [NE] cells and non-neuroendocrine [non-NE] cells from  Zhang et al. (2018). The NE score was calculated with the following formula: NEscore = correlð½X i ; ½NEÞ À correlð½X i ; ½nonNEÞ 2 where X i denotes the gene expression values of cell line i, [NE] is the mean expression of genes in neuroendocrine cells from  Zhang et al.