RanBP17 RNA expression levels in head and neck squamous cell carcinoma (HNSCC) cell lines with disruptive TP53 mutations.More than twenty years after its discovery, the role of the importin beta superfamily member Ran GTP-binding protein (RanBP) 17 is still ill defined. Previously, we observed notable RanBP17 on cell proliferation. FACS analysis was used for cell sorting according to their respective cell cycle phase and for BrdU assays. Immunocytochemistry was deployed for colocalization studies of RanBP17 with Nucleolin and SC35 (nuclear speckles) domains. TCGA analysis was performed for prognostic assessment and correlation analysis of RanBP17 in HNSCC patients.We deployed HNSCC cell lines as well as cell lines from other tumor entities such as HCT116, MDA-MB-231 and H460, which were derived from colon, breast and lung cancers respectively. RNAi was used to evaluate the effect of RanBP17, significantly reduced cell proliferation in HNSCC cell lines. This effect was also seen in the HNSCC unrelated cell lines HCT116 and MDA-MB-231. Similarly, inhibiting cell proliferation with cisplatin reduced RanBP17 in keratinocytes but lead to induction in tumor cell lines. A similar observation was made in tumor cell lines after treatment with the EGFR kinase inhibitor AG1478. In addition to previous reports, showing colocalization of RanBP17 with SC35 domains, we observed colocalization of RanBP17 to nuclear bodies that are distinct from nucleoli and SC35 domains. Interestingly, for HPV positive but not HPV negative HNSCC, TCGA data base analysis revealed a strong positive correlation of RanBP17 RNA with patient survival and CDKN2A.RNAi knockdown of RanBP17 splice isoforms and more so of non-coding circular RanBP17 RNA species. These aspects need to be addressed in more detail by future studies.Our data point to a role of RanBP17 in proliferation of HNSCC and other epithelial cells. Furthermore, RanBP17 could potentially serve as a novel prognostic marker for HNSCC patients. However, we noted a major discrepancy between RanBP17 RNA and protein expression levels with the used antibodies. These observations could be explained by the presence of additional The online version contains supplementary material available at 10.1186/s12885-022-09854-0. TP53 gene or due to inactivation of the p53 protein as a consequence of high-risk human papillomavirus (HPV) infection. Reduction or loss of p53 function is a major driving force for tumor development, progression and therapy resistance in HNSCC and other cancers. Early it was recognized that nuclear localization of p53 is required for its proper function emphasizing its role as a transcription factor [TP53 mutations such as those leading to a premature stop codon that result in truncated and cytoplasmically sequestered p53 proteins are distinct from non-disrupting TP53 mutations, like the typical hot spot mutations found in the DNA binding domain of the protein [TP53 mutation exhibit a significantly worse prognosis [RanBP (Ran GTP-binding protein) 17 transcript levels in cell lines with cytoplasmic mutant p53. RanBP17 is an ill-defined member of the importin beta superfamily (karyopherin). The gene encoding RanBP17 initially was identified due to sequence homology to its homologue RanBP16 that was isolated by affinity chromatography after binding to immobilized RanGTP. Both homologues, RanBP16 and RanBP17, exhibited the highest homology to exportins such as exportin 4 and exportin 1  [RanBP17 was independently discovered while investigating genes at the breakpoint region t  that is found in a significant number of acute lymphoblastic leukemias [RanBP17 presumably appeared late in evolution and likely is restricted to vertebrates, whereas its homologue RanBP16 is found widely distributed in many higher eukaryotes [Head and neck squamous cell carcinomas (HNSCC) are the most frequent cancers of the upper aerodigestive tract , 2. Virt protein , 5. Specrognosis . Analyzirognosis , we obseeukemias . Furtherkaryotes , the exakaryotes  have bee14) q34;q that is karyotes  and are karyotes –15. The TP53 mutations as reported for these cell lines [TP53 knock out HCT116 and H460 cell lines were generated and validated as previously described by Wanzel et al. [TP53 knockout clones were generated in the same manner as described for the HCT116 and H460 cells and successful TP53 knockout was validated by sequence and Western blot analysis. TP53 knockout cell lines were included since RanBP17 was initially found differentially regulated in cancer cells with disruptive TP53 mutations. All cells, except NHEK and Human Umbilical Vein Endothelial Cells (HUVEC), were cultured under standard conditions  in Dulbecco’s Modified Eagle Medium (DMEM) containing 10% fetal bovine serum (FBS), 100 U/ml penicillin, 100 µg/ml streptomycin, 50 µg/ml gentamicin, 2 mmol/L L-glutamine. NHEK  were grown in Keratinocyte Growth Medium 2 . HUVEC cells were cultured in EGMTM-2 Endothelial Cell Growth Medium-2 BulletKit™ (Lonza).Tumor tissues  was added to a final concentration of 10 µg/ml followed by incubation in a water bath for 90 min at 37 °C. Cells were pelleted and 90% of the medium supernatant was removed followed by resuspension of the cells in the remaining volume resulting in a cell concentration of 10 × 106 /ml. After incubation at 37 °C, cells were kept at 4 °C during all following steps. FACS tubes used for collection of sorted cells were previously filled with FCS and incubated for 1 h at 37 °C to coat the inner side of the tube aiming to prevent sticking of cells to the tube surface during cell sorting. After incubation, the FCS was removed leaving 500 µl FCS in the tube. Sorting of cells was performed with the MoFlow Astrios System  at the Flow Cytometry Core Facility . Cells were gated according to the G1/G0, S and G2/M phases of the cell cycle. In addition, cells from all 3 gates  were sorted into a single tube for reference, representing the whole population. Approximately 1 × 106 cells or more were collected for each cell cycle phase and exact cell numbers were documented. After sorting, cells were pelleted for 10 min at 300 x g (4 °C) and subsequently used for RNA and protein extraction. Same absolute amounts of “protein or RNA” or “protein or RNA levels adjusted to the respective cell number” were evaluated in the Western blot and RT-qPCR analyses. For the bromodeoxyuridine (BrdU) assay, the HNSCC cell lines UM-SCC-3 and UT-SCC-26A were treated with 20 µg/ml of the EGFR kinase inhibitor AG1478 . The BrdU assay was performed according to a modified protocol from BioLegend®  BrdU  was added to a final concentration of 10 µmol/L to the culture medium and incubation of cells  was continued for 2 more hours. Cells were harvested for FACS (70% ice cold ethanol) and Western blot analyses. Western blot analysis was performed as described below. Cells to be used for FACS were centrifuged for 10 min at 300 x g and washed in 0.5% BSA / PBS. The resulting pellet was incubated for 20 min at room temperature in 1 ml of 2 mol/L HCl, washed again and incubated for 2 min in 0.1 mol/L sodium tetraborate . After repeating the washing step, the pellet was resuspended in 50 µl dilution solution (0.5% BSA and 0.5% Tween-20 in PBS). Fluorescently labeled mouse anti BrdU antibody , 5 µl, Cat#: B35130, ThermoFisher Scientific/Invitrogen; BD Horizon™ V450 mouse anti-BrdU, 2.5 µl, Cat#: 560810) was added and cells were incubated for 20 min. Finally, cells were washed and resuspended in 500 µl propidium iodide solution (10 µg/ml in PBS) followed by flow cytometry. FACS data was analyzed with the FlowJo™ software .The two HNSCC cell lines, UM-SCC-3 and UT-SCC-26A, were sorted according to their cell cycle phase using a modified protocol from Arndt-Jovin and Jovin . Cells wSDS PAGE and Western blot analyses were performed under standard conditions . Rabbit RanBP17  were obtained from Dharmacon . Non-targeting small RNAs  were used as a negative control as described previously [Small interfering RNAs specific for p53 wt/wt, HCT116p53 −/−, H460p53 wt/wt, H460p53 −/−, MDA-MB-231p53 mut/mut and MDA-MB-231p53 −/− cells were grown until reaching 80% confluence. After washing in PBS (w/o Ca++ & Mg++), cells were detached by trypsin and counted. The cell number was adjusted to 100 cells/µl. Fifty µl of the cell suspension (5 × 103 cells) was added per well into a 96 well cell culture plate followed by incubation for 24 h. Transfection of cells with RanBP17 siRNA or non-targeting RNA was performed as described above and incubation was continued for 72 h. Experiments were performed at least in triplicate. In a preliminary experiment using UM-SCC-3 cells treated with RanBP17 siRNA or non-targeting RNA, CDDP  was added to a final concentration of 6.25, 12.5, 25, 50 or 100 µmol/L and incubation was continued for 24 more hours. The XTT viability assay (Cell Proliferation Kit II (XTT), Cat. No. 11 465 015 001, Roche Diagnostics GmbH, Mannheim, Germany) was performed according to the manufacturer’s instructions. Absorbance was measured with a DTX880 microplate reader  at 450 and 620 (reference wavelength) nm.UM-SCC-3, -4, -27, UT-SCC-26A, HCT116RanBP17 gene expression levels was performed by RT-qPCR. Total RNA was reversely transcribed into cDNA using the Transcriptor First Strand cDNA Synthesis Kit (Roche). Absolute quantitative RT-PCR was performed with the RanBP17  specific primers 5’-CCCAAGCAGGAGGTC-3’  and 5’-ATGGTCAGAAAAGTCGG-3’  to determine the copy numbers of RanBP17 RNA. For this, a standard curve of RanBP17 templates with known copy numbers were generated . The amount of amplified RNA in each probe was normalized against the ribosomal protein S18 . Quantitative RT-qPCR was performed with the same primers as used for absolute RT-qPCR. Samples were amplified with the Power SYBR Green PCR Master Mix (Applied Biosystems) and run in triplicate .Whole cellular RNA was isolated with the Trizol method  and the RNeasy Mini kit (Qiagen), according to the manufacturer’s protocol. RNA quantification and quality control was done with the Nanodrop and Experion systems. Gene expression patterns of HNSCC cell lines were determined by micro array analysis  as previously reported . Validat2 in the presence of 0, 5 and 50 µmol/L CDDP. Cells were subsequently trypsinized and collected by centrifugation at 300 x g for 10 min followed by protein extraction and Western blot analysis as described above. The experiment was repeated four times for the purpose of RNA extraction and RT-qPCR analysis. Similarly, two tumor cell lines, HCT116p53 wt/wt and UM-SCC-3, were exposed for 24 h to different CDDP levels  with subsequent RNA extraction and RT-qPCR using 11 different RanBP17 specific primer pairs (see Results below).In one preliminary experiment, keratinocytes were incubated for 24 h at 37 °C, 5% COCells were grown on coverslips in six well tissue culture dishes and cultured as described above. After reaching 50% confluence, cells were rinsed with PBS and fixed in cold (-20 °C) methanol for 5 min. Immunocytochemistry was performed as previously described . PrimaryRanBP17 RNAi treatment compared to the respective control  and (ii) between the AG1478 and DMSO groups at different cell cycle phases were calculated by an unpaired two-tailed t test. Thereby, the F-test did not calculate any differences between the variances of the individual groups in relation to each other. The one-way ANOVA with Tukey post hoc test was used to calculate: (i) Effect of cisplatin on RanBP17 gene expression, (ii) differences in the relative content of total RNA or protein per cell and, (iii) relative RanBP17 gene expression and percentage of cells during different cell cycle phases. For calculating differences of AG1478 treatment on RanBP17 or PCNA protein expression at different stages of the cell cycle, a two-tailed, one-sample t-test was used as the levels of AG1478 treated cells (UM-SSC-3 only) were compared with the expression levels in the corresponding DMSO controls . The GraphPad Prism 4.00 software  was used for statistical analysis. In all analyses a p value < 0.05 was considered as a significant difference between two groups.Statistic differences in: (i) the level of cell viability after RanBP17 gene exhibits a large number of validated and predicted splice variants, with the two protein isoforms Q9H2T7-1 (= RanBP17 reference protein) and Q9H2T7-2 currently being the best characterized ones. The additional lower molecular weight bands as seen during Western blot analysis therefore could represent other isoforms or degradation products of RanBP17. Analysis of cDNA micro array data derived from a previous study [RanBP17 exon expression levels between the tested HNSCC cell lines, which further supports the notion of different HNSCC cell lines expressing different sets of RanBP17 splice variants ; Fig. Since RanBP17 RNAi knockdown was performed on a panel of HNSCC cell lines  including a colon (HCT116), a lung (H460) and a breast (MDA-MB-231) cancer cell line, the latter three together with their respective CRISPR/Cas9 TP53 knockout counterparts  was seen in most of the tested cell lines  RanBP17 specific primer pairs  worked for HCT116p53 wt/wt cells  and RanBP17 RNA and protein expression levels were evaluated for cells from each cell cycle phase Fig. . Cell cyses Fig. C. IntereProliferation and progression of HNSCC tumors is highly dependent on the activity of the receptor tyrosine kinase EGFR  . We deplhttps://www.proteinatlas.org/ENSG00000204764-RANBP17/cell). Although not colocalizing with nucleoli, the RanBP17 positive bodies or clusters on occasion were found adjacent to nucleoli  expressing HNSCC, a significantly higher cumulative survival is seen for patients with RanBP17 high HPV+ HNSCC. However, no such difference was observed in patients with HPV- HNSCC  [RanBP17 overexpression could also be derived from TILs present in the tumor. To further evaluate this aspect, we first looked into T-cell and B-cell marker expression levels and, consistent with previous reports, could observe significantly higher levels of these markers in HPV+ HNSCC compared with HPV- HNSCC  data sets [RanBP17 expression levels and therefore do not appear to account for the elevated RanBP17 levels in tumor tissues with high TIL levels as found in HPV+ HNSCC. However, this dataset [RanBP17 expression in TILs, we looked into another dataset, which included scRNAseq data from HPV- and HPV+ HNSCC derived immune cells [RanBP17 levels could be detected between immune cells derived from HPV- and HPV+ HNSCC  , the obsata sets , 26. As  dataset  only conRanBP17 derived circular RNA (circRanBP17) that is implicated in the pathogenesis of nasopharyngeal cancer [circRanBP17 to promote nasopharyngeal cancer cell proliferation and invasion by acting via mir-635 and RUNX2 and pointed out circRanBP17 as a potential target for nasopharyngeal cancer treatment. This report further highlights a role of RanBP17 and its derivatives in tumor progression. To address the miR-635/RUNX2 axis, we additionally compared the expression of RUNX2, in the presence of wild-type or mutant TP53, in the HPV-  and iPSC-derived neurons did not show age-related features and RanBP17 reduction. The authors concluded that aging of cells is related to a disturbance of the nucleocytoplasmic transport associated with RanBP17 [Interestingly, in a study by Mertens et al. the authors compared transcriptomes of differently aged human fibroblasts and observed an age dependent decrease of  RanBP17 . The obs RanBP17 . Since pRanBP17, which was the only of the five genes that exhibited a significantly reduced hazard ratio for GBM patients [RanBP17 levels correlated with an improved outcome. It will be interesting to clear up how RanBP17 is related to a favorable outcome of GBM and HNSCC patients.Wang and colleagues compared gene expression patterns between glioblastoma multiforme (GBM) and low grade glioblastoma (LGG) using the TCGA (The Cancer Genome Atlas) database. In their study they could identify a five gene system which allows to predict survival of GBM patients. Among these five genes they also included patients . This fiRanBP17 is a heavily spliced gene, which is giving rise to at least 22 splice variants (Ensembl-Gene: RANBP17 ENSG00000204764). Each of these variants could have a more or less distinct biological function that needs to be further characterized. The existence of different RanBP17 splice variants was further supported by micro array data of this study showing highly variable hybridization (expression) levels of RanBP17 exon-specific probes between the tested HNSCC cell lines . RNAi knockdown of RanBP17 resulted in endodermal differentiation. The authors suggested that RanBP17, among other karyopherins, could be involved in the maintenance of mESC pluripotency [RanBP17 in embryonic stem cells further emphasizes its possible role in cell renewal and proliferation.In a study by Sangel and coworkers, the authors investigated the expression of karyopherins in mESc. Here they observed high expression of ipotency . Howeveripotency . InvolvecircRanBP17 RNA species as products of the RanBP17 gene, which are implicated in tumor growth [circRanBP17 RNA species in the tested cells and cell lines could also explain, why in the micro array analysis  RNA [RanBP17 highly correlates with CDKN2A, the gene encoding the HPV surrogate marker p16INK4A. The recent discovery of circRanBP17 RNA and its implication in nasopharyngeal cancer prompted us to look at a possible involvement of circRanBP17 in the RanBP17 effects observed in our study. Since circRanBP17 reportedly was controlled via the “miR-635/RUNX2 axis”, we evaluated a possible correlation of RUNX2 and RanBP17 by TCGA analysis. Since no correlation of RUNX2 and RanBP17 gene expression was seen in the HPV+ HNSCC group, the observed positive prognostic effect in the RanBP17high patient population of the HPV+ group does not seem to be dependent on the miR-635/RUNX2 axis. However, in this context, it is interesting to note that in the initial micro array analysis, RanBP17 was found upregulated in CDDP resistant HNSCC cell lines carrying disruptive TP53 mutations but at the protein level, no major difference in expression was seen between CDDP resistant and sensitive HNSCC cell lines as would be expected from the micro array data. Therefore, at this point, we cannot exclude that circRanBP17 could explain this discrepancy. More detailed studies are required to better understand the possible role of circRanBP17 in HNSCC disease.In our study, we observed a major role of RanBP17 in HNSCC cell proliferation, which was also noted in cell lines derived from other tumor types. Surprisingly, TCGA analysis revealed higher V-1) RNA . A similRanBP17 is involved in the prognosis of HPV+ HNSCC will be an interesting task for future studies.Taken together, RanBP17 appears implicated in cell proliferation. As a member of the importin beta superfamily, RanBP17 could act as an importin or exportin, transporting cargoes such as transcription factors between the cytoplasm and nucleus that are implicated in cell cycle control. To investigate, how RanBP17 splice variants exist, which could be involved in different even opposite biological functions. Therefore, for future functional studies of RanBP17, it is necessary to take into consideration the presence of different RanBP17 splice isoforms at the RNA and protein level and in particular the possible involvement of circRanBP17 RNA.However, investigations into RanBP17 are highly complex due to the fact that numerous Additional file 1: Supplementary Table S1. Shown is the type and origin of the tissues used for Western blot analysis of RanBP17. Supplementary Table S2. Background information of cells and cell lines. Supplementary Figure S1. Uncropped Western blot images are shown for Supplementary Figure S2A (A), Supplementary Figure S2B (B), Figure 2A (C), Figure 4A (D), Figure 5A (E) and Figure 5B (F). Arrows point to the predicted size of the respective protein. Dashed rectangles refer to the region of the blot used in the final figure. Supplementary Figure S2. Western blot analysis, demonstrating protein expression of RanBP17 in tissues, cells and cell lines. A, A band at ~124 kDa (arrows) corresponding to the major RanBP17 isoform (UniProtKB/Swiss-Prot: Q9H2T7) was detected in all tested head and neck tumor tissues  including tonsil (N1) but not muscle (N2) tissues (Supplementary Table S1). B, Similarly, a ~124 kDa band was also observed in all head and neck tumor derived cell lines and other cell lines derived from different tumors (Supplementary Table S2). Smaller sized immunoreactive bands are seen in most of the tested samples, especially cell lines. GAPDH was used as an input control. Short and long exposures are shown to visualize strong and weak bands. Indicated at the bottom in B is the histologic tumor/tissue type from which cells and cell lines were derived from. epi=epithelial, end=endothelial, mes=mesenchymal, neu=neural, mel=melanocytic, lym=lymphocytic. Supplementary Figure S3. Expression of RanBP17 exon-specific micro array probes in different HNSCC cell lines. To allow for a more precise statement about the differing RanBP17 expression levels between the tested HNSCC cell lines during micro array analysis, we also were looking at expression of the individual exons. In order to obtain this information from the microarray files, we first searched for the sample IDs given for RANBP17 on the Human Gene 1.0 ST Affymetrix chip used. Then, the .CEL files obtained from the microarray sequencing were imported in the program R (v. 4.0.4) using the tool oligo (v 1.54.1) and the function "read.celfiles". Afterwards, a background correction was performed using "backgroundCorrect" followed by normalization using "normalize". Finally, the obtained expression data for the previously searched RANBP17 Probe IDs were written into a table using write.csv2 and the results were analyzed afterwards. A, Background corrected normalized intensity of the respective Probe signal for each tested cell line. B, Graphical depiction of the exon position for the single RanBP17 specific probes according to the RanBP17  reference sequence NM_022897.4. Supplementary Figure S4. Shown on the left are the primer pairs used for the quantitative PCR analysis in Fig. 3 with their corresponding sequence information. A and B show comparable efficiencies of all primer pairs in A) untreated HCT116p53wt/wt cells and B) untreated UM-SCC-3 cells. In UM-SCC-3 cells, only primer pairs P3, P5, and P11 yielded amplicons. Supplementary Figure S5. Specificity of the RanBP17 antibody. The RanBP17 signal  disappears  after preincubating the RanBP17 antibody (HPA029568) with its corresponding antigen peptide sequence (APrEST73986) thereby confirming specificity of the antibody. TP53 mutation in UM-SCC-3: p.R248Q (nuclear p53); TP53 mutation in UT-SCC-26A: p.Y236* (cytoplasmic p53). Supplementary Figure S6. Timer2.0 (http://timer.cistrome.org/) was used for the analysis of A, the immune association for RANBP17 and CD8+ T-cells (left side) and B-cells (right side) showing the Spearman correlation coefficients. Color filled boxes indicate significantly higher values (p<0.05) and B, for the correlation between the RANBP17 expression to CD8A/CD4/CD274 for HPV+ (upper part) and HPV- (lower part) samples of the TCGA-HNSC cohort (accessible at Gene_Corr of the Exploration tab), with the blue depicting the linear regression and the grey shaded area depicting the confidence interval. TPM: transcripts per kilobase million. Supplementary Figure S7. A, Boxplots for a scRNA-seq (single cell RNA-sequencing) HNSCC dataset (GEO103322) showing the expression of RANBP17, CD8a, CD4, and CD274 for the different cell types. Each dot represents the expression of one sequenced cell. B, Violin plots for the scRNA-seq immune cell HNSCC dataset (GEO139324), for which the expression details for each cell were extracted from the provided matrix files using Seurat for R. Plots were generated using the modules matplotlib and seaborn for python. Supplementary Figure S8. Boxplot analysis of the TP53 mutational status with regard to RUNX2 expression as well as correlation analysis of RUNX2 with RanBP17 for the A, HPV- and B, HPV+ TCGA HNSCC cohorts, showing no significant expression differences with regard to TP53 mutational status but a significant correlation in the HPV- cohort only. Analyses were performed using the TIMER2.0 Gene_Mutation module (http://timer.cistrome.org/). Left graphs: Wilcoxon test. Right graphs: linear regression with Spearman’s rho.


Selecting the optimal cancer cell line for an experiment can be challenging given the diversity of lines available. Here, we present CNpare, which identifies similar cell line models based on genome-wide DNA copy number.https://github.com/macintyrelab/CNpare. All analysis performed in the manuscript can be reproduced via the code found at https://github.com/macintyrelab/CNpare_analyses.CNpare is available as an R package at Bioinformatics online. Immortalized cancer cell lines are an integral part of cancer research and the development of new therapies .CNpare is designed to compare and contrast genome-wide cancer cell line copy number profiles. The user inputs one or more genome-wide absolute copy number profiles in the form of a segment table  and these are compared to a precomputed database of cancer cell line profiles. This includes profiles of 1170 human cancer cell lines from the Cancer Cell Line Encyclopaedia (CCLE) (segment tables are converted to bin tables where the copy number is reported for evenly sized bins across the genome (default 500 kb). To adjust for noise at copy number boundaries, a window-based smoothing procedure is used to align boundaries across samples , and in real-world scenarios (finding the nearest match for cell lines and assessing suitability).We used separate cultures of the same cell lines profiled as part of the CCLE and GDSC projects (304 pairs) and observed the ability of CNpare to identify, for each GDSC line, the correctly matched line in the CCLE database . In 100%To compare against alternative approaches, we computed gene-based copy number, chromosome arm-based copy number, ploidy and gene expression for all cell lines in the database. Using these data to compare the cell line culture pairs, we found that gene-based copy number matched 63% of the cell lines correctly, arm-based copy number 91%, ploidy 43% and gene expression 38% . This sur, 93.9% (567/604) of the matched cell lines were significant. Using Manhattan’s distance 41.6% (251/604) were significant. This suggests that a good, ploidy agnostic match can nearly always be found using CNpare; however, the current database only supports good, ploidy aware matches ∼40% of the time.For each cell line in the CCLE database, we sought its nearest match using CNpare and observed whether this match was significant (the observed correlation measure was greater than expected by chance) or non-significant (a random match). Using Pearson’s r= 0.42, p-value = 0.02, with 54.13% genome difference, r = 0.43, p-value = 0.02: r = 0.38, p-value = 0.03; and Pearson‘s r= 0.48, p-value = 0.01; We also explored a specific match scenario using the high-grade serous ovarian cancer cell line OVKATE. First, we directly compared the OVKATE copy number profile with copy number profiles in the database. The cell line which had the most similar profile was PANC0203  resolution—comparison is made at 500 kb resolution (default bin size). While it is possible to increase the resolution up to 30 kb, resolution beyond this is limited as the SNP6 technology underpinning the database does not facilitate higher resolution; (ii) total absolute copy number as input. Total absolute copy number is currently required input and performance is dependent on the accuracy of the method used to compute it. We recommend using ASCAT , our results demonstrate that CNpare can be used to select appropriately matched cancer cell line models, providing a valuable tool for improving cancer research in the context of studying CIN.btac371_Supplementary_DataClick here for additional data file.


However, the effect of allo-HCT is limited by the occurrence of acute graft-patients .versus-tumor (GVT) activity. The authors also discuss the biology of GVHD in mouse models as well as aspects of clinical translation.In this Research Topic, the authors cover recent advances of pathophysiology, uncommon manifestations, prevention and treatment strategies for GVHD, as well as approaches to enhance graft-Huang et al. describe how metabolic modulation of donor T cells could help to reduce GVHD without loss of GVT by targeting glycolysis. The beneficial effects of allo-HCT against malignant tumor cells is not restricted to hematological malignancies as Bates et al. for example explored allo-HCT as platform for the treatment of neuroblastoma. The authors assessed how combining immunocytokine treatment and ex vivo activated NK cell infusions could serve as intervention to provide GVT activity in a murine GD2+ neuroblastoma model. Ex vivo expanded allogeneic T cells may also have activity in anti-viral immunity. Kim et al. describe how antigen specific T cells from donors recovered from Covid-19 can be expanded and manufactured to treat severe disease in partial HLA-matched recipients.GVHD is mediated by alloreactive donor T cells, which recognize MHC molecules as foreign. While alloreactivity is causative for GVHD, it is also necessary to provide the beneficial GVT effect. Jiang et al. review current literature concerning the roles of different T cell subset and their respective cytokine signatures in the context of GVHD and GVT. They also outline preclinical data on the role of these subsets in both, GVHD and GVT effects, and subsequently address strategies to translate these findings to prevent GVHD in patients. Another factor that can be modified to reduce GVHD is the pre-transplant conditioning as it leads to release of danger signals -GVHD . Drugs used as conditioning regimen pre allo-HCT, but also immunosuppressive drugs used to prevent GVHD are known to cause renal damage. However, renal diagnostic criteria are yet to be defined, as AKI often is the result of multiple etiologies . GI manifestations of GVHD however mostly contribute to reduced quality of life and mortality  and are NS)-GVHD , 13. CliNS)-GVHD , 15. HowNS)-GVHD . Vinnakoiologies . TherefoKelly and Rasko discuss MSCs and GVHD in their contribution to this Research topic. The activity of MSCs is controversial, as different clinical studies showed responses to MSCs or failed to improve GVHD-related mortality  products are a promising treatment that is under intensive investigation for GVHD. ortality , 19 whicThis Research Topic presents recent advances in the field of translational research of GVHD and discuss how these advances are connected to increased mechanistic understanding of the underlying pathophysiology.MC, RZ and TT performed literature research, discussed the articles and wrote the manuscript. All authors contributed to the article and approved the submitted version.This study was supported by the Deutsche Forschungsgemeinschaft, Germany, SFB-1479 – Project ID: 441891347 (P01 to RZ), SFB1160 (RZ).RZ received honoraria from Novartis, MNK, Incyte and Sanofi.The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


HAP1, a near-haploid human leukemic cancer cell line is often used in combination with CRISPR-Cas9 gene editing technology for genetic screens. HAP1 carries the Philadelphia chromosome (Ph) and an additional ~ 30 Mb fragment of chromosome 15 inserted into chromosome 19. The potential use of an in vitro cell line as a model system in biomedical research studies depends on its ability to maintain genome stability. Being a cancer cell line with a near-haploid genome, HAP1 is prone to genetic instability, which is further compounded by its tendency to diploidise in culture spontaneously. Moreover, CRISPR-Cas9 gene editing coupled with prolonged in-vitro cell culturing has the potential to induce unintended ‘off-target’ cytogenetic mutations.To gain an insight into chromosomal instability (CIN) and karyotype heterogeneity, 19 HAP1 cell lines were cytogenetically characterised, 17 of which were near-haploids and two double-haploids, using multiplex fluorescence in situ hybridisation (M-FISH), at single cell resolution. We focused on novel numerical (N) and structural (S) CIN and discussed the potential causal factors for the observed instability. For each cell line we examined its ploidy, gene editing status and its length of in-vitro cell culturing.Sixteen of the 19 cell lines had been gene edited with passage numbers ranging from 10 to 35. Diploidisation in 17 near-haploid cell lines ranged from 4 to 35% and percentage of N- and S-CIN in [1n] and [2n] metaphases ranged from 7 to 50% with two cell lines showing no CIN. Percentage of cells with CIN in the two double-haploid cell lines were 96% and 100% respectively. The most common S-CIN observed was deletion followed by translocation of both types, non-reciprocal and Robertsonian. Interestingly, we observed a prevalence of S-CIN associated with chromosome 13 in both near-and double-haploid cell lines, with a high incidence of Robertsonian translocation involving chromosome 13. Furthermore, locus-specific BAC  FISH enabled us to show for the first time that the additional chromosome 15 fragment is inserted into the p-arm rather than the q-arm of chromosome 19 of the HAP1 genome.Our study revealed a high incidence of CIN leading to karyotype heterogeneity in majority of the HAP1 cell lines with the number of chromosomal aberrations varying between cell lines. A noteworthy observation was the high frequency of structural chromosomal aberrations associated with chromosome 13. We showed that CRISPR-Cas9 gene editing technology in combination with spontaneous diploidisation and prolonged in-vitro cell culturing is potentially instrumental in inducing further chromosomal rearrangements in the HAP1 cell lines with existing CIN. We highlight the importance of maintaining cell lines at low passage and the need for regular monitoring to prevent implications in downstream applications. Our study also established that the additional fragment of chromosome 15 in the HAP1 genome is inserted into chromosome 19p rather than 19q.The online version contains supplementary material available at 10.1186/s13039-022-00625-x. Chromosomal instability (CIN) is a form of genomic instability that encompasses ongoing changes in chromosome complements resulting in abnormal DNA content in cells. CIN involves changes in chromosome number (N-CIN), by gains or losses of whole chromosomes; or chromosome structure (S-CIN) by partial gains or losses of chromosomes such as deletions, translocations, amplifications, inversions and complex rearrangements. Both N- and S-CIN often co-exist within a given cell or tumour. Continuous acquisition of novel chromosomal aberrations generates cell to cell variations resulting in karyotype heterogeneity. , 2.in-vitro cell lines used in biomedical research. Rigorous quality control of karyotype integrity is required in cell lines used in studies of gene expression and function, response to drugs and pathogens, elucidation of cellular mechanisms as well as of drug discovery. Ideally, the rates of CIN in model in-vitro cell lines should be low enough to ensure that significantly different phenotypes observed between ‘treated’ and ‘control’ experiments are the ‘effect’ of target treatments and not the result of differential expression of cells with clonal chromosomal alterations. Despite the importance of karyotype assessment, few studies have focused on reporting levels of CIN in currently used in-vitro model cell lines, particularly those that are inherently unstable or cancer derived like HAP1, as well as those bought commercially and kept at high passage across laboratories and institutions.Assessment of CIN is important for model In this context, the near-haploid HAP1 cell line, is a powerful model, widely used in gene function studies because mutation of a single allele causes loss-of-function phenotypes in the cell line. Of a male chronic myeloid leukemic origin, HAP1 is a fibroblast – like derivation of the near-haploid KBM-7 cell line , 4. BetwDespite its popularity in biomedical research, CIN in HAP1 cell lines remains under-reported and it is unknown to which extent new unintended chromosome alterations arise, whether some rearrangements are prone to occur more than others and if there is a correlation between the gene editing method (CRISPR-Cas9) and the appearance of novel chromosome aberrations.HAP1 being a cancer cell line is inherently unstable; its near-haploid genome is prone to spontaneous ‘diploidisation’ due to a rapid enrichment of diploid cells in culture. To avoid this, such cell lines are frequently flow sorted to maintain a high percentage of haploid cells Accumulation of CIN is further enhanced by prolonged in-vitro cultures Gene editing technology, CRISPR-Cas9 can cause ‘off-target’ and sometimes, ‘at or near-target loci’ unintended mutations manifested as large-scale chromosomal rearrangements; thus, use of this technique comes with the increased need of monitoring cell line genome integrity , 9.The current information can be summarised as below:Therefore, to gain an insight into CIN we undertook a detailed analysis of characterisation of chromosomal aberrations in HAP1 cell lines with the help of M-FISH karyotyping, a single cell-based assay. M-FISH has an advantage over molecular based analyses which use pools of cells, it provides information about individual cells, identifying cryptic chromosomal rearrangements whilst reflecting inter- and intra-tumour genomic changes leading to karyotype heterogeneity in the cell lines.in-vitro cell culturing. We focussed on novel N- and S-CIN and explored the potential underlying causal factors that may have triggered CIN in the cell lines.In our study, we characterised CIN in 19 HAP1 cell lines majority of which had been CRISPR-Cas9 gene edited and underwent extended Further, FISH with locus-specific probes derived from bacterial artificial chromosome (BAC) clones provided information on the correct localisation and orientation of the additional ~ 30 Mb long chromosome 15 fragment into the p-arm of chromosome 19 of the HAP1 genome.Overall, our study revealed CIN and the extent of it in the HAP1 cell lines with variations in the rate of CIN between cell lines. We highlight the importance of regular monitoring of unstable cell lines for genetic instability due to novel, unintended and undetected genomic alterations to avoid further implications in downstream analyses leading to misinterpretation of data.Using metaphases from the reference cell line GM15510 and HAP1 cell line 4C1-R1 for locus-specific FISH we were able to validate the localisation and orientation of the probes selected to map to regions of chromosomes 15 and 19.FISH with BAC probes Fig.  as reporin-vitro cell culturing as outlined in Table M-FISH karyotyping and DAPI banding allowed us to investigate N-and S-CIN and the extent of karyotype heterogeneity in 19 HAP1 cell lines, of which 17 were near-haploids and two double-haploids. We focused on novel chromosomal rearrangements while examining the underlying causal factors that may have triggered CIN in the cell lines. For each cell line we examined ploidy, CRISPR-Cas9 gene editing status and the length of In-vitro cell culturing or ‘passage numbers’ of the cell lines ranged from a minimum of 10  to a maximum of 35 (1 N HAP1-P19) with unknown passage numbers in two cell lines namely HAP1 GR 5.10 and HAP1 GR S2. N- and S-CIN resulting in karyotype heterogeneity was observed in 15 near-haploid and two double-haploid cell lines. Number of novel chromosomal aberrations varied between cell lines as enlisted in Additional file Sixteen of the 19 HAP1 cell lines underwent gene editing by CRISPR-Cas9 technology. HAP1 cell lines showing ploidy status, gene editing status, passage numbers of cell culture and percentages of haploidy and diploidy. *HAP1 F3 carries 1 triploid metaphase [3n].Diploidisation, a spontaneous phenomenon of haploid metaphases in near-haploid [1n] cell lines becoming double-haploid 2n] metaphases over time, ranged from 4 (HAP1 A1 and HAP1 GR S2) to 35% (HAP1 Pool Cas9 + Blast)  to 50% (HAP1 Pool Cas9 + Blast). No CIN was observed in two cell lines viz. C6 HAP1 and HAP1 GR S2. In [1n] metaphases percentage of CIN ranged from 3 to 40% and prevalence of S-CIN was observed over N-CIN. Percentage of CIN in [2n] metaphases ranged from 3 to 30% and both N- and S-CIN were observed in the metaphases of the cell lines. It must be noted that often both N- and S-CIN co-existed in the same metaphase and that the majority of the cell lines with high passage numbers showed CIN with the exception of C6 HAP1  have also been observed in the cell lines. Incidentally, twelve cell lines carried deletions and the percentage ranged from 3 (HAP1 A2) to 23% (B3 HAP1 P17). Robertsonian translocation, yet another prevalent structural aberration was observed in 8 near-haploid cell lines (47%), majority of which were associated with chromosome 13. Figure Novel rearrangements identified may indicate potential clonality as observed in certain cell lines such as HAP1 GR 5.10 with three metaphases carrying a derivative involving chromosomes 9 and 13 [der(13)t],  was observed in six out of 30 metaphases in HAP1-P53-KO along with del(Xp) in five metaphases, as enlisted in S2. Similarly in HAP1-2n-C, a diploid cell line, with del(Xp) has been observed in five out of 25 metaphases.Identifying the precise location and orientation of a sequence within a genome is important to define the linkage background for genes on a chromosome and is relevant for downstream applications, especially to determine whether position and orientation changes impact gene expression. We have demonstrated for the first time that the ~ 30 Mb additional fragment of chromosome 15 is inserted in an inverted orientation onto the ‘p’ arm of chromosome 19 and not the ‘q’ arm as reported previously by Esseltzbichler et. al. .in-vitro model often used in gene editing [in-vitro models particularly in therapeutic applications is highly dependent on their genome integrity and reproducibility of data. This is true for haploid models, such as HAP-1, where maintenance of a haploid state throughout experiments is crucial to the testing of hypotheses. Hence, genome stability in this cell line is crucial for any downstream analyses and applications.Haploid genetics has always been important for understanding genome evolution and function. With natural haploidy absent in Vertebrates, scientists have often attempted to obtain haploid cells from fish, mice and particularly embryonic stem cells via culturing methods . However editing , 15. HowIn this study, we speculated that the underlying instability of the HAP1 cell line due to its inherent haploid status coupled with CRISPR-Cas9 machinery may have been potentially instrumental in inducing CIN in the form of ‘unintended off-target’ chromosomal aberrations , 8. AccuOne common feature of the cell lines analysed in the study was the presence of diploid cells in an otherwise haploid model. Ploidy instability, leading to diploidisation is a natural feature of haploid cultures in general. For instance, decrease in the percentage of haploid cells has been observed by several authors , 16, in in-vitro cell culturing and a growing number of scientific publications demonstrate that passage number affects a cell line’s characteristic over time. Cell lines at high passage numbers experience alterations often manifested as chromosomal aberrations resulting in cell to cell heterogeneity, eventually heterogeneous karyotypes, in comparison to lower passaged cell cultures. The evidence for passage number related effects on cell lines is compelling [During diploidisation, HAP1 cells in culture may potentially develop CIN generating intra-lineage diversity due to progressive accumulation of new chromosomal aberrations, thus forming heterogeneous karyotypes within a cell population. CIN increases further in such unstable cell lines by prolonged mpelling , 17. Thumpelling , 18.Seventeen out of 19 HAP1 cell lines, in our study, had been CRISPR-Cas9 gene edited. HAP1, an unstable cancer cell line when gene edited by CRISPR-Cas9 may have an increased level of genome instability in comparison to primary cell lines with stable karyotypes. Incidentally, primary cell lines had been used in the development of CRISPR methodology . As prevIn our study S-CIN was more prevalent than N-CIN. The three main structural aberrations observed were deletions, non-reciprocal translocations and Robertsonian translocations. But the predominant structural CIN observed was deletion. Chromosomal deletion has always been described as one of the hallmarks of cancer and deleted regions have been widely demonstrated to contain tumour suppressor genes. Such segmental losses from chromosomes provide the cells with selective growth advantage particularly in unstable cancer-derived cell lines like HAP1 , 20.Chromosomal translocation is yet another hallmark of cancer that drives genome instability , 22. NonBRCA2 gene, retinoblastoma gene and the alveolar rhabdomyosarcoma gene FOXO1A to mention a few, have been identified on chromosome 13. B cell chronic lymphocytic leukaemia (CLL) is one of the most common leukaemia in the western world and approximately 10% of CLL patients have a homozygous deletion in 13q14.3 [Our study has shown a high frequency of chromosomal aberrations associated with chromosome 13 Table . Eleven  13q14.3 . Our obsM-FISH the technique used to analyse the cell lines is a cost-effective method for visualising chromosomal aberrations. As in any technique M-FISH has its limitation, it fails to detect S-CIN below 3–5 Mb. It is also labour intensive, requires live cells, assesses small sample sizes and is highly specialized. A more detailed comprehensive analysis of CIN could perhaps be provided by single-cell genomics a technique that is continually evolving with increasing accuracy of DNA amplification alongside novel methods that do not require pre-amplification of DNA . HoweverIn this study we have performed an in-depth molecular cytogenetic characterisation of 19 HAP1 cell lines by M-FISH karyotyping. M-FISH provided estimates of incidence of CIN in HAP1 lineages along with insights into possible events responsible for it. We conjectured that standard CRISPR-Cas9 gene editing technology in combination with diploidisation in an unstable near-haploid cancer cell line like HAP1 with existing CIN and undergoing prolonged in vitro cell culturing may have been instrumental in inducing further genome instability. Such chromosomal instability caused cell to cell variation resulting in karyotype heterogeneity, the rate of which varied between cell lines.Locus-specific FISH refined the location and orientation of the additional ~ 30 Mb fragment of chromosome 15 showing its integration, in an inverted pattern, into the ‘p’ arm of chromosome 19 and not the ‘q’ arm of chromosome 19, as previously thought.Our data brings further evidence to highlight the importance of maintaining cell lines used in biomedical research laboratories at low passage and the need for regular monitoring to avoid significant accumulation of mutations which may otherwise have important implications in basic research and clinical applications. We addressed the concern of genome instability in the HAP1 cell lines whilst exploring and conjecturing the potential underlying causal factors that may have induced instability in the cell lines.Metaphase suspensions of HAP1 cell line, 4C1 R1 and control cell line GM15510, a human female transformed cell line were used in the locus-specific FISH experiment. Metaphase chromosomes were harvested following a standard protocol . The adhHuman bacterial artificial chromosome (BAC) clones Table  mapping Probes were generated from purified BAC DNA by whole genome amplification using GenomePlex® Whole Genome Amplification kit (Sigma-Aldrich), as described previously by Gribble et. al . Probes Metaphase slides made from the single cell suspensions were pre-treated for 10 min. in Acetone (Sigma-Aldrich) at room temperature followed by baking in a 62 °C oven for an hour. Slides were then denatured in an alkaline denaturation solution  for 7 to 8 min. followed by rinses in 1 M Tris–HCl (pH 7.4) solution and 1 The remaining FISH procedures largely followed Gribble et. al . The proPost-hybridisation washes involved a 30 min wash in 2 Slides were mounted in Vectashield® Vibrance™ Antifade mounting medium containing DAPI , Vector laboratories.Imaging was carried out using 63 nalysed. .Metaphase chromosomes were harvested from the HAP1 cell lines Table  followinThe probe mix of 24 colour human M-FISH paint was denatured at 65 °C for 10 min. before applying onto the denatured slides. Hybridisation was carried out over two nights at 37 °C. Post-hybridisation washes involved a 30 min wash in 2 Imaging was carried out using 63 Additional file 1: Table S1. Novel N- and S-CIN in near-haploid cell lines.Additional file 2: Table S2. Novel N- and S-CIN in double-haploid cell lines.


We recently established clinical-grade human embryonic stem cell (hESC) line KthES11 in accordance with current good manufacturing practice standards in Japan. Despite this success, the establishment efficiency was very low at 7.1% in the first period.To establish clinical-grade hESC lines, we used xeno-free chemically defined medium StemFit AK03N with the LM-E8 fragments instead of feeder cells. The protocol was then optimized, especially in the early culture phase.We established five hESC lines  with 45.5% efficiency. All five hESC lines showed typical hESC-like morphology, a normal karyotype, pluripotent state, and differentiation potential for all three germ layers. Furthermore, we developed efficient procedures to prepare master cell stocks for clinical-grade hESC lines and an efficient strategy for quality control testing.Our master cell stocks of hESC lines may contribute to therapeutic applications using human pluripotent stem cells in Japan and other countries. •We established five clinical-grade hESC lines without feeders at high efficiency.•The hESC lines had typical hESC-like characteristics with a normal karyotype.•We prepared master cell stocks of clinical-grade hESCs for therapeutic application.•These clinical-grade hESC lines are ready to be distributed. HoweverClinical studies using human pluripotent stem cells (hPSCs) have been conducted in at least 10 countries, and significantly in the United States and China. By the end of 2019, there were at least 54 clinical studies of using hPSCs for the treatment of 22 diseases. Of these studies, 32 clinical studies are performed with hESC-derived cell products .In Japan, “The Act on the Safety of Regenerative Medicine” and “Pharmaceuticals, Medical Devices, and Other Therapeutic Products Act” were enacted in November 2014 to realize and commercialize regenerative medicine [To establish hESC lines for clinical use, we chose E8 fragments of laminin (LM-E8s) instead of feeder cells. Rodin et al. and we showed that laminin-511/521 are a suitable alternative culture substrate to support hPSC culture compared with other extracellular matrix proteins and Matrigel ,16. FurtWe started to establish clinical-grade hESCs in 2017 and reported the first clinical-grade hESC line, KthES11, in 2018 [In this study, we developed a system to establish clinical-grade hESC lines with high efficiency. Furthermore, we performed QC tests separately during the manufacturing process for master cell stocks.22.1The study was approved by the ethics committee of Life and Medical Sciences, Kyoto University , for derivation and characterization, including early differentiation of hESC lines from donated blastocysts, where informed consent was obtained from donors’ parents. Additionally, the study was approved by the Ministry of Education, Culture, Sports, Science, and Technology (license number: 0630) and the Ministry of Health, Labour, and Welfare (MHLW) (license number: 0630) in Japan.2.2Derivation of clinical-grade hESC lines and their banking were performed in our CPF operated with GMP-level management. In line with “The Act on the Safety of Regenerative Medicine,” the facility obtained a manufacturing license (#FA5160004) from MLHW after passing a site visit by the Pharmaceuticals Medical Devices Agency in Japan. Long-term culture and QC tests of our master cell stocks were performed outside the CPF.2.32, iMatrix-511MG, Matrixome, Osaka, Japan) in StemFit AK03N  with Y-27632 . Although ICMs had fully attached by the next day, we used Y-27632 during ICM culture for the initial several passages. At approximately 7 days, ICMs with outgrowths were mechanically dissected into small pieces and transferred into a new culture well precoated with iMatrix-511MG. After 14–16 days of culture in an incubator at 37 °C with 5% CO2 and 5% O2, morphologically hESC-like cells had appeared. The cells were subsequently cultured for three to four passages by mechanical dissection using 31 G needles, and the resulting cells appeared to be stabilized as hESCs. At this stage, the hESCs were capable of passaging as small clumps using a non-enzymatic solution (5 mM EDTA in PBS) and cryopreservation for early stocks. When seeded at 1–2 × 105 cells/well (6-well plate), the cells became confluent every 3–4 days. During culture, cell viability was examined at each passage by a Via1-Cassette™ with NucleoCounter NC-200 . Cell viability was generally higher than 75%.We gently isolated the inner cell mass (ICM) from day 5–6 blastocysts using a micromanipulator . The ICMs were seeded on a Falcon Center Well Organ Culture Dish  precoated with LM-E8s  and Y-27632 . The number of viable collected cells was determined by double staining with acridine orange and DAPI (ChemoMetec). The vials were labeled for traceability and placed in a CoolCell freezing container  in a −80 °C mechanical freezer . The next day, the vials were transferred to the vapor phase of a liquid nitrogen tank .hESC lines at early passages were harvested in a batch of five to 10 vials as seed stocks with 5 × 102.5Current drafts for QC testing are described in For biological safety tests, sterility tests were performed during both seed stock and master cell stock preparations. Other biological safety tests were performed during master cell stock preparation.Conversely, characterization and potency were examined as long-term culture tests from more than three independent trials using our master cell stocks. Karyotype analysis was performed using seed and master cell stocks, and after every five passages during long-term culture tests. Detailed QC testing protocols have been described previously .For ectodermal differentiation, we used STEMdiff™ Trilineage Ectoderm Medium  in accordance with the manufacturer's instructions except that the number of induction days was extended from 7 to 11 days.For neuronal cell differentiation, we used a Quick-Neuron™ Dopaminergic - mRNA Kit  in accordance with the manufacturer's instructions. Then, the cells were fixed in 4% paraformaldehyde, permeabilized with 0.2% Triton X-100 in PBS, and stained with the primary and secondary antibodies described in ABO genotyping was performed by allele-specific PCR as described previously  with the33.11.Six to 10 weeks were required to produce master cell stocks from 5–6-day blastocysts. Therefore, we prepared seed stocks before the master stock and divided the manufacturing process into two parts to allow for flexibility in the manufacturing schedule. We generally prepared five to 10 frozen vials as seed stocks.2.We performed QC tests separately in stages as shown in 3.Long-term culture tests were performed in a specially designated facility with an air conditioning system with a HEPA filter. All materials and solutions were the same grades as those in the CPF.4.Before master cell stock preparation, we continuously cultured some hESCs from seed stock preparation outside the CPF and developed optimal culture protocols for each cell line. Furthermore, we performed some QC tests, such as gene expression analysis and flow cytometry, to confirm that seed stocks had some hESC characteristics in addition to their morphology.5.Karyotype analyses were carried out during master cell stock preparation and every five passages during long-term culture.To prepare stocks efficiently in a compact space with a small number of people, we developed a system for clinical-grade hESC banking as shown in 3.2We established only one clinical-grade hESC line, KthES11, from 14 blastocysts in period 1. One major problem was poor adhesion of the ICM to LM-E8s. We previously showed that LM-E8s promote strong adhesion of hPSCs compared with other culture substrates, such as Matrigel and intact laminin , suggestAnother major issue was the sensitivity against subculturing hESC-like cells during initial passages, even when we used non-enzymatic 5 mM EDTA/PBS and added Y-27632 to the culture medium. Therefore, we applied mechanical dissociation using 31 G needles for the stages. After hESC-like cells had appeared to stabilize with a sufficient number (passage 3 to 4), the cells were passaged as small colonies using 5 mM EDTA in PBS as described in the Materials and Methods.With these improvements, our establishment efficiency was increased to 45.5% (five out of 11) in Period 2 from 7.1% (one out of 14) in Period 1 .Table 2L2, but the cells could be generally maintained using iMatrix-511MG at a lower concentration . At this concentration, cell viability during subculture was approximately 90%.Furthermore, we established clinical-grade hESCs using iMatrix-511MG at a concentration of 0.5 μg/cm3.3In the second period, we established another five clinical-grade hESC lines of both sexes with different blood types . We had For karyotype analysis, we performed G-banding every five passages from our seed stocks. All six cell lines showed normal karyotypes in long-term culture .To assess pluripotency, hESCs in long-term culture were grown in suspension in custom mTeSR™1 . The cells formed embryoid bodies (EBs) A, and thOur master cell stocks of all clinical-grade hESCs were sterile, free of mycoplasma, and negative for the viruses described in the Materials and Methods. Furthermore, the cell lines passed an endotoxin test. These data are available from the corresponding author.4We established five clinical-grade hESC lines without feeders at high efficiency. Furthermore, we developed an efficient system for QC testing. All five hESC lines were pluripotent with normal karyotypes. The hESC lines also showed a differentiation potential in an EB formation assay and other assessment methods.ICM is inside the blastocyst and covered with trophectoderm, which expresses integrin αVβ3 ,22. AccoWe used a non-enzymatic EDTA solution for subculture. This method does not dissociate cells into single cells and appears to be less damaging than dissociation by enzymatic solutions such as TrypLE Select (Thermo Fisher Scientific). Although there was good cell viability with normal passaging, cell viability was often low in the first phase of the establishment process. The preparation of master cell stock was often very slow. Therefore, we dissociated the cells mechanically when cultured in a center well organ culture dish, i.e., when the number of cells was small. These changes greatly improved the efficiency of establishment.2 to facilitate cell detachment and increase cell viability in passages. These pretests led to efficient preparation of the master cell stocks and QC testing.Our aims when establishing clinical-grade hESC lines consisted of three evaluation stages: preparation of seed stocks, preparation of master cell stocks, and evaluation by long-term culture using the master cell stocks. We did not perform all QC tests for every step, but they were assigned to each stage. When we prepared seed stocks, we performed sterility tests for biological safety. We also cultured the cells outside of the CPF using surplus cells from cryopreservation. We confirmed that the cell lines had a normal karyotype by G-binding. We examined whether the cell stocks had hESC characteristics by FACS analysis and immunocytochemistry. The reason why the established cells were hESCs was because of their morphology. Additionally, we optimized the culture method. For some cell lines, the concentration of iMatrix-511MG was lowered to 0.25 μg/cmNext, when preparing the master cell stocks, biological safety tests were the focus. In period 1, we used antibiotics in the medium during early culture. Therefore, we examined antibiotic residues using the highly sensitive LC/MS/MS method. Subsequently, we did not add antibiotics and did not need to conduct such a test. Evaluation tests for long-term culture were conducted outside of the CPF in a specially designated facility with an air conditioning system that used HEPA filters, while the reagents used for culture were the same as those used in the CPF. We have frozen and stored some of the master cell stocks and long-term cultured cells as evaluation samples for further development of QC testing.Currently, we are distributing these clinical-grade hESC lines upon request. We expect to reveal more characteristics of our hESC lines through collaboration with our distribution partners. For example, we may find unique characteristics of the cell lines that can easily differentiate into specific cell types, cell lines that maintain genome stability even after long-term culture, or cell lines suitable for genetic recombination.https://hpscreg.eu/search?cell-type=hesc), in which we reported our cell lines, including those in this study. However, in Japan, only two centers, our institute and the National Research Institute for Child Health and Development [Because of the great potential of hESCs, many hESC lines have been established worldwide and have been registered in hPSCreg (elopment , have su5We established hESCs for clinical use with high efficiency. Our master cell stocks of the hESC lines may contribute to therapeutic applications using hESCs in Japan and other countries.The authors declare no competing interests.


ASCL1 (SCLC‐A), NEUROD1 (SCLC‐N), POU2F3 (SCLC‐P) and YAP1 (SCLC‐Y). Here, we investigated the proteomic landscape of these molecular subsets with the aim to identify novel subtype‐specific proteins of diagnostic and therapeutic relevance.Small‐cell lung cancer (SCLC) molecular subtypes have been primarily characterized based on the expression pattern of the following key transcription regulators: Pellets and cell media of 26 human SCLC cell lines were subjected to label‐free shotgun proteomics for large‐scale protein identification and quantitation, followed by in‐depth bioinformatic analyses. Proteomic data were correlated with the cell lines’ phenotypic characteristics and with public transcriptomic data of SCLC cell lines and tissues.YAP1‐driven subtype as the most distinct SCLC subgroup. Using sparse partial least squares discriminant analysis, we identified proteins that clearly distinguish four SCLC subtypes based on their expression pattern, including potential diagnostic markers for SCLC‐Y .Our quantitative proteomic data highlighted that four molecular subtypes are clearly distinguishable at the protein level. The cell lines exhibited diverse neuroendocrine and epithelial–mesenchymal characteristics that varied by subtype. A total of 367 proteins were identified in the cell pellet and 34 in the culture media that showed significant up‐ or downregulation in one subtype, including known druggable proteins and potential blood‐based markers. Pathway enrichment analysis and parallel investigation of transcriptomics from SCLC cell lines outlined unique signatures for each subtype, such as upregulated oxidative phosphorylation in SCLC‐A, DNA replication in SCLC‐N, neurotrophin signalling in SCLC‐P and epithelial–mesenchymal transition in SCLC‐Y. Importantly, we identified the We report for the first time, the protein expression differences among SCLC subtypes. By shedding light on potential subtype‐specific therapeutic vulnerabilities and diagnostic biomarkers, our results may contribute to a better understanding of SCLC biology and the development of novel therapies. The first mass spectrometry–based proteomic analysis on small‐cell lung cancer (SCLC) cell lines, reporting unique proteomic profiles of its proposed molecular subtypes.SCLC subgroups detected by proteomics are consistent with mRNA‐based subtypes.Unique proteomic profiles of SCLC subtypes highlight potential subtype‐specific therapeutic vulnerabilities and diagnostic biomarkers. Parallel detection and quantitation of thousands of proteins, including those with lower abundance, is feasible with modern high‐resolution mass spectrometers and advanced sample preparation workflows, thus achieving a better understanding of molecular interactions and signalling pathways in cancer.In the current work, we conducted a comparative proteomic analysis of SCLC subtypes using cell lines categorized based on the relative expression of four key transcription factors. Our label‐free shotgun proteomic approach assessing both the cell pellet (CP) and culture media (CM) resulted in the confident quantification of nearly 9000 proteins. This comprehensive proteomic evaluation of SCLC cell lines integrated with knowledge from existing transcriptomic datasets can give a clearer definition of SCLC subtypes and might provide insights into their specific features that govern therapy response.22.12. SCLC cell lines were either purchased from ATCC or kindly provided by our collaborators. All cell lines were regularly checked for mycoplasma contamination using the luminescence‐based MycoAlert mycoplasma detection kit (Lonza) with supernatant from cells cultured for >3 days and used within 10 passages after authentication. The cell lines and their key characteristics are listed in Table Human SCLC cell lines were maintained in RPMI‐1640 with 10% fetal calf serum , 100 U/ml penicillin and 10 mg/ml streptomycin  at 37°C in a humidified incubator with 5% CO2.2ASCL1: Hs00269932_m1, NEUROD1: Hs00159598_m1, POU2F3: Hs00205009_m1, YAP1: Hs00371735_m1, GAPDH: Hs02786624_g1; Thermo Fisher Scientific). Gene expression was calculated as 2−ΔCt using glyceraldehyde 3‐phosphate dehydrogenase (GAPDH) as reference gene.Cells were incubated in T25 flasks until 70% confluency. Total RNA was isolated with TRIzol reagent and reverse transcribed with MMLV reverse transcriptase (Thermo Fisher Scientific) according to the manufacturer's protocol. Gene expression was analysed by quantitative polymerase chain reaction (qPCR) using TaqMan gene expression assays on the Applied Biosystems 7500 Fast Real‐Time PCR System , following the manufacturer's instructions.CPs and CM from 26 cell lines were processed and subjected to an MS‐based proteomic analysis. In brief, the CPs were solubilized with protein extraction buffer , 100 mM triethylammonium bicarbonate [TEAB], pH 8), incubating for 5 min at 95°C with 500 rpm shaking. The volume of the buffer was adjusted to the number of cells in each sample, that is 250 μl of protein extraction buffer was added to samples containing 5 M cells. Proteins were extracted via a 20 min sonication at 4°C  with 40 cycles (15 s on/15 s off), followed by a brief centrifugation at 20000 × Additionally, the filtered CM samples were concentrated using spin concentrators  to ∼100 μl. Protein determination was performed with NanoDrop (DeNovix DS‐11 FX +). Prior to digestion, SDS was added to a final concentration of 3%, and 100 mM TEAB was added to increase the pH. Reduction was performed with 10 mM DTT and a 1 h incubation at 37°C.g). Digestion buffer (50 mM TEAB) containing LysC at 1:50 enzyme‐to‐protein ratio was added on top of the filters, incubating for 2 h at 37°C. Next, digestion buffer containing trypsin at 1:50 enzyme‐to‐protein ratio was added to the samples, incubating overnight at 37°C. On the following day, the peptides were eluted in three steps, first with 80 μl of digestion buffer, then with 80 μl of 0.2% formic acid, and finally with 80 μl of 50% acetonitrile (ACN) containing 0.2% formic acid. All peptides were dried down in a vacuum concentrator. Peptide determination was performed for all samples using Pierce Quantitative Colorimetric Peptide Assay kit (Thermo Scientific), following the manufacturer's instructions.Protein digestion for both the CP and media (100 μg of proteins per sample) was accomplished using the S‐Trap technology (ProtiFi) with few modifications, as previously described by our group.2.4The nLC–MS/MS analysis was performed on a Q Exactive HF‐X mass spectrometer coupled to a Dionex UltiMate 3000 RSLCnano UPLC system (Thermo Scientific), with an EASY‐Spray ion source. Peptides from CPs and media were injected in triplicates , utilizing two MS methods detailed later. All samples were loaded onto an Acclaim PepMap 100 C18  trap column and separated on an Acclaim PepMap RSLC C18 column  (Thermo Scientific) using a flow rate of 300 nl/min, a column temperature of 60°C. A 145‐min gradient was applied for separation, using solvents A (0.1% formic acid) and B (0.1% formic acid in 80% ACN), increasing solvent B from 2% to 25% in 115 min, then to 32% in the next 10 min, and to 45% in 7 min. Finally, the gradient increased to 90% solvent B in 8 min, continuing for another 5 min.Regarding the MS approach, peptides from the CP were analysed with one data‐dependent acquisition (DDA) and two data‐independent acquisitions (DIA), whereas peptides from the CM were analysed with two DDA and one DIA runs.m/z 375–1500, resolution of 120000 (at 200 m/z), target AGC value of 3 × 106 and maximum injection time of 100 ms. Fragmentation was done with an NCE of 28, and the isolation window was set to 1.2 m/z. MS2 scans were acquired with a resolution of 15000 (at 200 m/z), target AGC value of 1 × 105, maximum injection time of 50 ms, ion selection threshold of 8 × 103 and dynamic exclusion of 40 s.The top 20 DDA method was applied with full MS1 scans at m/z 375–1455, with a resolution of 120000 (at 200 m/z), target AGC value of 3 × 106 and maximum injection time of 50 ms. The MS2 scans were acquired with a resolution of 30000 (at 200 m/z), fragmentation with NCE of 28, target AGC value of 1 × 106, automatic maximum injection time, fixed first mass of 200 m/z and the variable isolation windows were 13.0, 16.0, 26.0 and 61.0 m/z .For the DIA analysis, a complete acquisition cycle consisted of 3 MS1 full scans, each followed by 18 MS2 DIA scans with variable isolation windows. MS1 full scans were acquired at See Table 2.52.5.1Database search was done on Proteome Discoverer v2.4 using SEQUEST HT search engine combined with spectral library search, using the UniProtKB human database (accessed on 15 January 2019) and Proteome Tools spectral libraries. Dynamic modifications included oxidation of methionine and N‐terminal acetylation, whereas carbamidomethylation of cysteine was implemented as a static modification. Precursor tolerance was set to 10 ppm and fragment mass tolerance was set to 0.02 Da. Additionally, a maximum of two missed cleavages were allowed, and a false discovery rate (FDR) of 1% was set both on peptide and protein levels. For protein quantitation, the top three average methods were used .2.5.2https://github.com/bszeitz/SCLC_proteomics for details on all used packages.The data‐processing steps were performed separately for the CP and CM samples. Both the processing and the statistical tests were performed in R v.4.2.02‐transformed, and individual measurements were median normalized by centring their intensities around the global median. Measurements from the same batch were then checked for outliers. After removal of low‐quality measurements in the CM data ,https://www.proteinatlas.org/humanproteome/blood+protein, accessed on 23 July 2022) was used to annotate proteins detectable in human blood plasma by immunoassay, MS or proximity extension assays. This was supplemented with a list of proteins actively secreted into the blood (https://www.proteinatlas.org/search/sa_location%3ASecreted+to+blood). In total, 3076 and 3126 plasma proteins were identified in the CP and CM, respectively, of which 2763 and 2823 were quantified in at least 80% of the samples. Among the proteins actively secreted in the blood, 229 and 334 were found in the CP and CM, respectively, and 172 and 278 proteins were quantified in min. 80% of the samples.Additionally, information from the Human Protein Atlas  and the in silico human surfaceome by Bausch‐Fluck et al.http://wlab.ethz.ch/surfaceome/, accessed on 28 July 2022). In the CP and CM, we found a total of 682 and 549 cell‐surface proteins that are included in both databases, of which 477 and 416 were quantified in at least 80% of the samples.Two databases were downloaded to retrieve a list of cell‐surface proteins, namely The Cancer Surfaceome Atlashttps://www.proteinatlas.org/humanproteome/tissue/druggable, accessed on 22 October 2021), which contains the protein targets of the current Food and Drug Administration (FDA)‐approved drugs, including enzymes, transporters, ion channels, and receptors. Functional annotations were collected from the UniProthttps://www.uniprot.org/, Release 2022_03) for selected proteins, and the list of FDA‐approved drugs that directly interact with those proteins as part of their mechanism of action was gathered from the DrugBank database (www.drugbank.ca).To annotate ‘druggable’ proteins with subtype specific profiles, we used The druggable proteome database available at the Human Protein Atlas website , were used to assess the expression profile differences between selected transcripts across subtypes (SCLC‐A/N/P/Y).The mRNA data of SCLC tissue samples was accessed from cBioPortal2.6.2https://depmap.org/portal/.35 Compared to our set of cell lines, only five cell lines are not included in the mutation table , and two additional cell lines are not present in the RNA sequencing table (H378 and H1688). The RNA‐Seq data contains the measurements from 50 SCLC cell lines, which were also categorized into subtypes by Rudin et al.2 CPM counts were calculated using the ‘cpm’ function from edgeR v.3.32.12‐transformation). This expression matrix was used to display the differential expression of selected transcripts across subtypes.Mutation and RNA‐Seq data  of Cancer Cell Line Encyclopedia (CCLE) cancer cell lines were accessed on 28 June 2022 and 23 August 2021, respectively, from the Cancer Dependency Map website, 2.6.3ftp://ftp.sanger.ac.uk/pub/project/cancerrxgene/releases/current_release/) on 18 July 2022. Both the Genomics of Drug Sensitivity in Cancer 1 (GDSC1) and GDSC2 datasets were investigated. In total, the subtype classification of 38 SCLC cell lines is known,CancerRxGene drug sensitivity data2.72.7.1p‐values and Tukey's honestly significant difference (HSD) post hoc tests. Comparisons were done between culture types  and subtypes . A protein was considered significant for a comparison if both the ANOVA FDR and the corresponding pairwise Tukey's HSD test p‐value were less than 0.05. Subtype‐specific proteins were selected based on the following criteria: ANOVA FDR < 0.05, as well as Tukey's HSD test p < 0.05 for all comparisons of the subtype of interest against the other subtypes. Differential expression analysis for the CCLE RNA‐Seq data was performed via limma by fitting a linear model for the six subtype comparisons, followed by Empirical Bayes smoothing of standard errors. Multiple testing correction with the BH method was applied and FDR < 0.05 was considered significant. For the transcriptomic dataset by George et al., pairwise Wilcoxon tests were performed on the Z‐scored values to check gene expression differences between the subtypes. Significance level was set to nominal p < 0.05.Differential expression analyses for the proteomic data were performed via ANOVA, followed by multiple testing correction  of ANOVA Overrepresentation analyses (ORAs) were conducted using clusterProfiler v.3.18.12.7.2Two previously published gene sets were used to introduce a scoring system for the NE and epithelial–mesenchymal transition (EMT) characteristics of cell lines. For NE characteristics, a 50‐gene NE signature built for SCLC cell lines described by Zhang et al.Z‐score of NE markers) − (Mean Z‐score of non‐NE markers).NE score =  − .EMT score = , whereas NEUROD1 did not show sufficiently large variance (SD = 0.50), and ASCL1 was not quantified in enough samples to be included in the list. The resulting proteome data  were resampled 1000 times via the bootstrap method with a probability of 0.8 for selecting any item (i.e. sample) and any feature (i.e. protein). The bootstrap sample datasets were clustered using the partitioning around medoids method with the Pearson distance and complete linkage, exploring the range of two to seven clusters. The visual inspection of consensus matrices, as well as the silhouette plots for K = 4 and K = 5 revealed that a cleaner separation of the clusters can be achieved with K = 4; moreover, the average silhouette width was higher for K = 4. Based on these observations, and considering the relatively low number of samples, consensus clustering with K = 4 was used as sample grouping. Graphical outputs from the ConsensusClusterPlus R package and silhouette plots for K = 4 and K = 5 are shown in Figure Samples were grouped in an unsupervised manner using the consensus clustering algorithm, implemented in the ConsensusClusterPlus R package v.1.54.0.2.7.410 , calculated for all proteins/genes used in the statistical analyses). The pairwise test was the Tukey's HSD post hoc test for proteomics, whereas for CCLE RNA‐Seq, the p‐values were derived from the moderated t‐test statistic. The BH method was used for p‐value adjustment in pGSEA. To obtain the list of subtype‐characteristic gene sets, the following filter was applied on the pGSEA results: p < 0.01 in all comparisons of the subtype of interest against the other subtypes with an unequivocal sign (either positive or negative) normalized enrichment score (NES). Gene sets with a p < 0.01 for at least one comparison are listed in Table p‐value of the same gene set was less than 0.1 in relevant comparisons of the other dataset, as well as its NES values showed the same direction, the gene set was declared significant in both proteomics and transcriptomics. In contrast, if the gene set in the other dataset did not meet the previous criteria, the gene set was declared significant in only one dataset. A visual explanation for the filtering steps can be seen in Figure Pre‐ranked GSEA (pGSEA) was performed via the ‘GSEA’ function of the clusterProfiler R package for all six subtype comparisons. As input, the hallmark,https://github.com/broadinstitute/ssGSEA2.0 (accessed on 05 February 2022). The parameters were set as follows: sample.norm.type = ‘rank’, weight = 0.75, statistic = ‘area.under.RES’, output.score.type = ‘NES’, nperm = 1000, min.overlap = 5, and correl.type = ‘z.score’. The gene sets were then filtered for the most representative gene sets . The full CP and CM data (filtered and imputed) were analysed separately. First, a PLS‐DA model was fitted with 10 components to evaluate the performance and to select the optimal number of components for the final model. The performance plot showed that five and four components with centroid distance measure are sufficient for good performance  in the CP and CM data, respectively .The sparse partial least squares discriminant analysis (sPLS‐DA) was performed via the mixOmics R package v.6.14.0,y Figure . Then sPy Figure  showed ty Figure . Additio2.7.6Z‐scored protein expression tables. PCA biplots were drawn using the ggbiplot R package v.0.55 (https://github.com/vqv/ggbiplot). Further visualizations were done using R v.4.2.0 and GraphPad Prism v.8 for Windows, GraphPad Software, San Diego, California USA, www.graphpad.com.Individual clustering settings for heat map visualizations were hierarchical clustering, Euclidean distance and complete linkage, unless specified otherwise in the figure legends. Heat maps were plotted using ComplexHeatmap R package v.2.6.2.33.1We characterized 26 cell lines derived from primary or metastatic human SCLC lesions Table  using laASCL1, NEUROD1, POU2F3 and YAP1 mRNA expression patterns – we grouped the cell lines into one of the four respective subgroups: SCLC‐A, SCLC‐N, SCLC‐P and SCLC‐Y   family universally expressed across our samples, such as MYC and protein L‐Myc (MYCL),MNE score = 0.71, MEMT score = −0.70). SCLC‐N was found to be a rather NE subtype with mixed epithelial–mesenchymal characteristics (MNE score = 0.56 and MEMT score = 0.39). SCLC‐P exhibited moderate non‐NE characteristics in our dataset ; however, a high expression of epithelial markers was detected (MNE score = −0.05 and MEMT score = −0.61) in this subtype. In contrast, SCLC‐Y cell lines exhibited prominent non‐NE and mesenchymal traits (MNE score = −1.35 and MEMT score = 0.75). In line with these findings, delta‐like protein 3 (DLL3) protein, an inhibitory Notch pathway ligand,The cell lines were further characterized by their NE and EMT features. NE scores built from 19 NE and 17 non‐NE markers,Comparing NE and EMT scores between cell lines with different properties, such as culture type, cell line origin and treatment with chemotherapy, we found that adherent cell lines harbour significantly lower NE scores than the non‐adherent ones Figure . In addi3.2p < 0.05) downregulated in suspension cell lines compared to adherent cell lines in the CP and CM, respectively, whereas 244 and 244 proteins were significantly upregulated in suspension cell lines in the CP and CM, respectively  enriched both in CP and CM, as well as other pathways such as endocytosis in CP and gap junction in CM were overrepresented, thereby supporting the phenotypic cell line differences on protein level  grew in suspension, 3 (11.5%) in a semi‐adherent form and the other 13 (50.0%) grew adherent on the plastic Figure . Adhereny Figure , left. Al Figure .3.3POU2F3 mRNA expression than other SCLC‐A cell lines , the levels of which differ in a given subtype compared to all the three other subtypes in the CP and CM data, respectively  from the CP data. Furthermore, we performed the same analysis on the RNA‐Seq data of 50 SCLC cell lines from CCLEn = 9237 genes) and finally assessed the relationship between proteomic and transcriptomic results  KEGG processes in SCLC‐A according to subtype‐specific proteins (n = 33) include oxidative phosphorylation (OXPHOS), as well as phenylalanine metabolism and leukocyte transendothelial migration  contributed to the significant overrepresentation of KEGG pathways, such as cell cycle, phagosome, riboflavin metabolism and peroxisome proliferator‐activated receptor signalling pathway , three significantly enriched KEGG pathways were detected, namely phospholipase D signalling, lysosome as well as other glycan degradation , such as extracellular matrix (ECM)‐receptor interaction, focal adhesion, spliceosome, peroxisome or O‐glycan biosynthesis  of subtype‐specific processes outlined earlier in the SCLC tissue transcriptomic dataset published by George et al.In order to verify the subtype‐specificity observed in our cell lines in SCLC tissues as well, we investigated the behaviour of representative gene sets (3.5GPX8), pyruvate dehydrogenase kinase isoform 2 (PDK2) and tyrosine–protein kinase receptor UFO (AXL) , basal cell adhesion molecule , GTP‐binding protein 1 , keratin‐18 , noelin  and phospholipase C‐gamma‐2 . The most promising blood‐based biomarker from this list is the protein UFO, which has been previously detected via immunoassay as well.Among the proteins confirmed on tissue transcriptome level, six are also detectable in human blood plasma by MS, namely tyrosine–protein kinase receptor UFO , ephrin type‐A receptor 2 (EPHA2), integrin alpha‐V and beta‐1 , histone deacetylase 1 (overexpressed in SCLC‐A/N/P vs. SCLC‐Y), and mast/stem cell growth factor receptor Kit . Multiple drugs that directly interact with the aforementioned proteins as part of their mechanism of action could be delineated , we could find ‘druggable’ proteinsed Table 40, nameed Table . Generaled Table  accordined Table .4Although clinically SCLC is still regarded as a homogeneous tumour type with NE characteristics, it was described decades ago that besides the ‘classical’ form of SCLC with suspension growth type and NE phenotype, a ‘variant’ form (NE‐low type) of SCLC also exists, mainly forming adherent cell cultures.ASCL1+, NEUROD1+, POU2F3+ and YAP1+ cell lines, respectively. In the global proteomic data, these transcription factors showed higher abundances in their respective subtype, with only the elevated level of neurogenic differentiation factor 1 (NEUROD1) in SCLC‐N cell lines being not significantly different in all three comparisons.Recent profiling studies highlighted that the NE and non‐NE classes of SCLC can be further divided into additional subtypes, namely SCLC‐A and ‐N  and SCLC‐P and ‐Y (non‐NE types).According to the protein expression of NE and non‐NE markers described by Zhang et al.,SD in CP = 0.85, MSD in CM = 1.20). Another driver of the proteomic differences was the culture type, affecting both the CP and CM.Importantly, we could categorize our SCLC cell lines into four subgroups based on their comprehensive cellular proteomic profiles, which agreed with the pre‐defined mRNA‐based subtypes (only one cell line was misclassified in proteomics). Of note, the overall high variability of cell media proteins compared to cellular proteins probably interfered with the detection of subtype‐specific signatures in this dataset  and transcripts (from CCLE transcriptomic data of SCLC cell lines) across the subtypes. Accordingly, we uncovered a list of potential subtype‐specific therapeutic vulnerabilities.ASCL1 in neural differentiation,Due to the well‐known regulatory role of SCLC‐N cell lines, which predominantly formed suspension cultures in our study, showed a concordant downregulation of cell adhesion pathways. Harvesting the protein‐level data also highlighted the increased activity of DNA replication and transcription and the depletion of cytokine‐mediated signalling in this subtype. Interestingly, a similar trend was described in a study where gene expression differences between SCLC and normal tissue were addressed.POU2F3‐driven SCLC.In the epithelial‐like SCLC‐P cell lines, upregulation of the lamellipodium organization pathway was observed, which constitutes a crucial step in EMT leading to increased cell motility and invasive capacities.YAP1+ cell lines. Recently, Caeser et al. reported that in SCLC‐A, the activation of the MAPK pathway rather has inhibitory functions, compared to SCLC‐N/P cell lines. However, no cell lines from the SCLC‐Y subtype were tested by these authors.YAP1‐driven cell lines, we also observed that focal adhesion, ECM organization and cell–substrate protein pathways were uniformly upregulated in the SCLC‐Y subgroup, just as peroxisome and endocytosis‐related proteins. Tlemsani et al. described that SCLC‐Y cell lines demonstrate high presenting and native immune predisposition.SCLC‐Y cell lines formed a clearly defined subgroup in our SCLC samples. A unique finding in the proteomic data was the downregulation of protein acetylation, chromatin modification  and DNA double‐strand break repair pathways in this subtype. Moreover, we also detected overexpression of the MAPK‐cascade and Rho‐GTPase signalling members in this subtype. Rho is crucial for YAP/TAZ activityYAP1‐driven subtype.GPX8 was previously correlated with YAP1 expression in SCLC cell lines.AXL gene (encoding the UFO protein) is associated with the mesenchymal phenotype and a potential target for overcoming resistance to epidermal growth factor (EGF) receptor inhibitors.Of note, controversies around SCLC‐Y still exists, because comprehensive immunohistochemical and histopathologic analyses of SCLC subtypes in patient samples failed to identify a distinct EPHA2, ITGAV and ITGB1, overexpressed in SCLC‐Y), HDAC1 (downregulated in SCLC‐Y) and KIT (upregulated in SCLC‐P). Cells with high levels of ASCL1 also showed stronger expression of DDC,POU2F3‐driven SCLCs, whereas YAP1‐driven tumours are more resistant to vorinostat (targeting HDAC1).Our data outlined six proteins that show remarkable differences in abundance across the subtypes and are also FDA‐approved drug targets, namely DDC (overexpressed in SCLC‐A), EPHA2, ITAV and ITB1 .Cell lines, however undoubtedly not ideal models for profiling studies, offer many advantages that complement the use of tumour tissues and animal models for the examination of SCLC.Taken together, our results should primary be considered hypothesis‐generating for future studies, and all finding should be examined in light of the abovementioned limitations. Accordingly, proteomic analyses of larger SCLC cohorts, preferably including fresh tissue samples of patients with homogeneous treatment histories, are needed to validate our findings.5YAP1‐driven subtype with specific proteomic features could be also distinguished. Comprehensive proteomic profiling of these subtypes has uncovered a list of candidate subtype‐specific therapeutic vulnerabilities for this once enigmatic cancer. Additionally, we also identified several potential IHC‐ and blood‐based biomarkers that might facilitate subtype diagnosis in the future. Altogether, our results might pave the way for future subtype‐specific personalized therapeutic approaches in SCLC.To conclude, this is the first large‐scale proteomic study in human SCLC cell lines concerning the newly defined molecular subtypes. We report that SCLC cell lines can be divided into four distinct subtypes by MS‐based proteomics, which accords well with the qPCR‐based classification. Importantly, a distinct The authors have no conflicts of interest to declare.BS is a recipient of the Semmelweis 250+ Excellence PhD Scholarship (EFOP‐3.6.3‐VEKOP‐16‐2017‐00009). BD, AMS and ZM acknowledge funding from the Hungarian National Research, Development and Innovation Office . BD was also supported by the Austrian Science Fund . ZM was supported by the UNKP‐20‐3 and UNKP‐21‐3 New National Excellence Program of the Ministry for Innovation and Technology of Hungary, and by the Hungarian Respiratory Society (MPA #2020), PH was supported by the Netherlands X‐omics Initiative . VL is a recipient of the Bolyai Research Scholarship of the Hungarian Academy of Sciences and the UNKP‐19‐4 New National Excellence Program of the Ministry for Innovation and Technology. KS was supported by the Austrian Science Fund (FWF No. T 1062‐B33) and the City of Vienna Fund for Innovative Interdisciplinary Cancer Research. MR acknowledges funding from the Mrs. Berta Kamprad´s Cancer Foundation (FBKS‐2020‐22‐(291)).Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.