Several years ago, the SUM panel of human breast cancer cell lines was developed, and these cell lines have been distributed to hundreds of labs worldwide. Our lab and others have developed extensive omics data sets from these cells. More recently, we performed genome-scale shRNA essentiality screens on the entire SUM line panel, as well as on MCF10A cells, MCF-7 cells, and MCF-7LTED cells. These gene essentiality data sets allowed us to perform orthogonal analyses that functionalize the otherwise descriptive genomic data obtained from traditional genomics platforms. To make these omics data sets available to users of the SUM lines, and to allow users to mine these data sets, we developed the SUM Breast Cancer Cell Line Knowledge Base. This knowledge base provides information on the derivation of each cell line, provides protocols for the proper maintenance of the cells, and provides a series of data mining tools that allow rapid identification of the oncogene signatures for each line, the enrichment of KEGG pathways with screen hit and gene expression data, an analysis of protein and phospho-protein expression for the cell lines, as well as a gene search tool and a functional-druggable signature tool. Recently, we expanded our database to include genomic data for an additional 27 commonly used breast cancer cell lines. Thus, the SLKBase provides users with deep insights into the biology of human breast cancer cell lines that can be used to develop strategies for the reverse engineering of individual breast cancer cell lines. The types of genomic data that are now available to clinicians and laboratory scientists include sequencing-based analyses that show the point mutations that occur in breast cancer, copy number data that provide information on the oncogenes activated by gene amplification as well as the tumor suppressor genes inactivated by point mutations or homozygous deletion, gene expression data from RNA-sequencing and gene expression arrays, and most recently, proteomics and phospho-proteomics data. In addition, a number of tools and databases have been developed, such as the CaBio Portal6, whereas others have been genome-scale and made use of libraries of varying complexities9. We have performed genome-scale shRNA screens for the SUM breast cancer cell line panel and some of the results from those experiments have been reported12. These functional screens have resulted in the generation of long lists of genes that have been found to play a direct role in either the proliferation or survival of specific human breast cancer cell lines. Moreover, when these sets of essential genes are analyzed with respect to the descriptive genomic data sets described above, the result is a functionalization of the genomic data that sheds light on the biology of individual breast cancer cell lines. The functionalization of genomic data is particularly important with respect to predicting the sensitivity or resistance of breast cancer cells to targeted drugs. Targeted drugs are effective when they inactivate a functional driver gene, but they are ineffective when they target a passenger gene, and this notion is supported by results of numerous clinical trials, both positive and negative. Because gene essentiality screens effectively distinguish driver from passenger genes, regardless of expression level or genomic status, these analyses are powerful ways to identify druggable targets in breast cancer cells. Thus, functional genomics has the potential to make accurate predictions about targeted drugs that are likely to have the largest impact on cancer cells with the highest therapeutic index.As powerful as these tools and data sets are, they are descriptive in nature and the inferences and conclusions that can be drawn from them are, as a result, correlative. Recently, a number of laboratories, including our own, have performed shRNA or CRISPR-Cas9-based gene essentiality screens on a large number of human breast cancer cell lines. Some of these screens have been focused on specific gene sets, such as the kinome12. More recently, we developed a series of web-based tools that allow breast cancer researchers who work with these cell lines to quickly and easily mine the data to identify the functional oncogenes, the most essential biological pathways, and the genes within those pathways that mediate growth and survival of the cells, as well as the best druggable targets in each cell line based on essentiality and druggability of each gene. Other data mining tools allow researchers to determine the status of any annotated gene in the genome in any cell line, along with its expression status at the mRNA and protein level, and genomic alterations that may be present in the gene. Finally, all of these data and data mining tools are presented in the context of a Knowledge Base that provides critical information on the derivation of each cell line, the patient from which the cells were derived, and the proper conditions for maintaining each cell line. Recently, we have expanded this Knowledge Base to include 27 additional breast cancer cell lines for which reliable genomics and functional screening data are available, and the data mining tools originally developed for the SUM lines can be used for a total of 40 breast cancer cell lines.To take full advantage of these functional genomics data sets, new tools need to be developed that allow breast cancer researchers to quickly identify the most essential genes, oncogenes, pathways, and druggable targets for any cell line, and then ultimately translate that knowledge to make predictions about breast cancer specimens from primary sites or metastatic lesions. Over the past 5 years, our laboratory has performed genome-scale shRNA screens on the SUM breast cancer cell lines developed in our laboratory, as well as on MCF10A cells, MCF-7 cells, and MCF-7LTED cellsThis Breast Cancer Cell Line Knowledge Base and the data mining tools contained within allow for rapid functional genomic analysis not only for the SUM breast cancer cell lines but for all breast cancer cell lines for which validated functional screens have been performed.16, as were breast cancer cell lines with HER2 amplifications for the development of HER2-targeted drugs24. More recently, palbociclib was identified in a drug screen using a large panel of breast cancer cell lines25. Despite the importance of breast cancer cell lines in the development of modern therapeutic modalities for breast cancer, most researchers know relatively little about the cell lines they work with, and thus, the full potential of the large panel of breast cancer cell lines that currently exists has not been fully realized. In attempt to address this gap in our understanding, and to increase the power and importance of breast cancer cell lines in research, we set out to develop a knowledge base that allows researchers using the SUM breast cancer cell lines, as well as other commonly used breast cancer cell lines, to have ready access to the genomic and functional genomic data that have been generated from these cells, and to be able to quickly and easily mine these complex data sets. The SUM Breast Cancer Cell Line Knowledge base is the result of these efforts and provides a gateway for the functional genomic analysis of breast cancer cell lines.Human breast cancer cell lines have been and continue to be a mainstay of breast cancer research worldwide. Indeed, breast cancer cell lines have played a key role not only in helping to elucidate the fundamental biology of breast cancer but also for the development of virtually every drug that is used to treat breast cancer patients. The MCF-7 cell line was critically important to the development of the hormonal therapies used to treat patients with estrogen-receptor positive breast cancer26. In addition, we incorporated into the database the list of targeted drugs that are linked to specific genes from the Genomics of Drug Sensitivity in Cancer database. We then designed and launched a series of web-based tools that allow these data sets to be mined in ways that shed light on the deep biology of each cell line and suggest targeted drug strategies that are likely to be effective in each of the lines.There were three overarching goals in the original development of the SLKBase: (1) to provide a rich source of information for anyone working with any of the SUM breast cancer cell lines, (2) to give researchers ready access to the large genomic data sets that have been developed with these cells, and (3) to allow researchers to perform orthogonal analyses of the various genomics data sets that we and others have obtained from the SUM lines. To build a platform for analysis of genomic data sets from the SUM lines, we first built a MySQL database that contains copy number data derived from array comparative genomic hybridization, gene expression data derived from Illumina bead arrays and more recently from RNA sequencing, point mutation data derived from whole-exome sequencing, and finally, data from the genome-scale shRNA screens for each of the SUM lines and for MCF10A, MCF-7, and MCF-7LTED cellsoncogene signatures for a given cell line, and we have reported on these for some of the SUM lines in previous publications12. We thus wanted to develop a tool that would allow anyone to ascertain the several types of oncogene signatures for any breast cancer cell line for which these genomic data sets are available, and this is now available on the SLKBase. By using the Oncogene Signature Tool, one can choose a breast cancer cell line and immediately identify three types of oncogene signatures. The first is the Candidate Oncogene Signature, which comprises genes that are genomically altered in the cell line, and are considered to be bona fide human oncogenes as indicated in the OncoKB database27. Thus, this list shows all candidate oncogenes that are genomically altered in the cell line, and their score in the shRNA screen provides information on the essentiality of each altered gene in the cell line. The second gene set is the Overall Oncogene Signature, which comprises any gene that is genomically altered in the cell line of interest that was also a hit in the functional screen, regardless of whether they are considered bone fide oncogenes. Thus, any gene that is genomically altered, by either gene amplification or point mutation, and was a hit in the functional screen is reported along with the expression level of the gene and its potential druggability. Finally, the Functional Oncogene Signature is the synthesis of the first two gene sets and shows the genes that are genomically altered, considered to be bona fide oncogenes, and were hits in the functional screen, along with their expression levels and druggability. The Candidate Oncogene Signature for the SUM-185 breast cancer cell line is shown in Table FGFR3, PIK3CA, and BCL2L1), and we have shown previously that targeting these oncogenes using low doses of appropriate targeted drugs yields dramatic synergistic and cell line-specific lethality12. The Candidate Oncogene Signature for the SUM-190 cell line is shown in Table 29 and others have published35, the SUM-190 cell line, derived from a patient with inflammatory breast cancer, has a HER2 gene amplification, and HER2 is overexpressed at the mRNA and protein level. And yet, HER2 was not a hit in the functional screen for these cells. This is contrast to the SUM-225 cells, which also has HER2 amplification29 and for which HER2 was a hit in the functional screen  are highly sensitive to this drug. Thus, in SUM-190 cells, for which HER2 lies upstream of PI3′Kinase signaling, PIK3CA is a better druggable target than HER2, a result predicted by the shRNA screen data. Interestingly, the HER2-amplified SUM-225 cell line is also highly sensitive to Alpelisib, an observation made with other HER2-amplified breast cancer cell lines, as can be observed using the Functional-Druggable Target tool on the SLKBase. The concentration–response curves for Alpelisib across the SUM cell line panel are shown in Supplementary Fig. One of the most powerful applications of genome-scale shRNA screens is the functionalization of genomic alteration data that are derived from sequencing or array-based applications. It is well-known that breast cancers, like most cancers, are genomically complex and that most of the genomic alterations that occur do not contribute directly to the malignant potential of the cells and are therefore poor drug targets. Thus, by combining data derived from essentiality screens with data derived from copy number analysis, gene expression analysis, and exome sequencing, one can quickly reduce the complexity of these data sets and identify the driving oncogenes in each cell line. We refer to the gene sets that are derived from such an analysis as the 12). Thus, elucidating the oncogene signatures of breast cancer cell lines is the starting point for developing targeted drug strategies that will be highly effective when used at low concentrations, and thus yield a high therapeutic index.The concept of oncogene addiction predicts that targeting functional-druggable oncogenes has a profound and specific effect on proliferation and/or survival in cells with those driving oncogenes. The oncogene signatures for the SUM lines make predictions about sensitivity to oncogene-targeted drugs, and these predictions are borne out by drug sensitivity data, as shown in Fig. https://www.cancerrxgene.org)36. This database lists all of the targeted drugs that have been tested experimentally against a panel of nearly 1000 cancer cell lines. The functional-druggable signature tool returns a list for each breast cancer cell line of genes that are both essential as determined by their hit status in the screen and druggable using a targeted agent. The functional-druggable signatures for three of the SUM lines are shown in Table Z-scores for this drug in each of the SUM lines are shown in Fig. 2a26 and the red bars indicate the cell lines for which either BCL2L2 or BCL2 (SUM-44) was a hit in the screen, and shows a good association between drug sensitivity and essentialness as determined in the screen. The concentration–response curves used to calculate the IC50 concentrations for Navitoclax for each of the SUM breast cancer cell lines are shown in Supplementary Fig. One of the advantages of genome-scale shRNA screens is that they identify a large number of essential genes, most of which are not genomically altered, and, therefore, not bona fide oncogenes. And yet, because these genes are essential to the growth and/or survival of the cells, they can be good drug targets as well. In order to fully leverage the data derived from our genome-scale functional screen, we created the Functional-Druggable Signature tool, which merges the shRNA or CRISPR screen hit data with data derived from the Genomics of Drug Sensitivity in Cancer database . Work currently in progress is aimed at performing a rigorous statistical analysis of the association between essentiality and drug sensitivity for all targeted drugs across the entire cell line panel.To begin to test this hypothesis more rigorously, we developed a version of the functional-druggable signature tool for the 27 additional breast cancer cell lines for which there are both functional genomic data from Project Achilles, and drug sensitivity data from the Genomics of Drug Sensitivity in Cancer database. Using this tool, one can directly examine the relationship between essentiality as determined by the screen hit score (CERES score) and the FGFR3 is a functional-driving oncogene activated by gene amplification, is highly dependent on this pathway for proliferation and survival. By contrast, other cell lines are less dependent on this pathway, and indeed, the SUM-229 cell line (lower left panel) has little or no reliance on this pathway for proliferation or survival as indicated by the small number of screen hit genes that map to the pathway.Another way to gain insight into the biology of human breast cancer cell lines is to use the screen hit data to identify the most important biological pathways for each cell line. To accomplish this goal, we developed the KEGG Pathway Engine, which maps the genes that were hits in the functional screen onto KEGG pathways. The KEGG Pathway Engine allows users to pick any KEGG pathway and determine its importance in any cell line in the database, obtain a visual picture of the pathway of interest with screen hits in the pathway displayed, and view the data associated with each hit gene in the pathway. A separate feature of this tool is the ability to view gene expression data for the specific cell line and pathway and compare that to the screen hit data. Figure KRAS-transformed cell line that falls into the basal/claudin-low subset of triple-negative breast cancers and has been predicted by others to be enriched for expression of genes in the TGF-beta pathway40. Thus, the Pathway Engine result is not only consistent with this prediction but also identifies the specific genes within the pathway that are most essential for these cells. The WNT signaling and TGF-beta signaling pathways were also found to be important in SUM-1315 and SUM-159 cells, the latter of which is also a MYC/HRAS transformed cell line. Thus, the KEGG pathway engine and Pathway Analysis tools, coupled with the functional-druggable signature tool, help to identify strategies and drug targets even in breast cancer cells that do not express functional-druggable oncogenes.To increase to power of the KEGG pathway engine, we developed a Pathway Analysis tool that calculates the relative essentialness of all KEGG pathways and rank-orders them based on the level of enrichment of the screen hit data to the pathway. The algorithm also takes into account the proportion of the genes in the pathway that were screen hits, as well as the relationship of hit genes in the pathway to each other, such that screen-hit genes that are directly linked to other screen hits in the pathway receive more weight. Table BCL2L1 gene, which is of developing interest as a therapeutic target in breast cancer, are shown in Table BCL2L1 is amplified, overexpressed, and a hit in the screen in SUM-185 cells. This gene, which is expressed at relatively normal levels in SUM-52, SUM-190, and SUM-149 cells, was a hit in the screen in these cell lines as well, and they are, indeed, sensitive to the targeted drug Navitoclax  and EIF4EBP1 as important driver genes from this amplicon in these cells48. In addition, SUM-44 cells overexpress BCL2, which was a strong hit in the screen, and the cells are exquisitely sensitive to Navitoclax and Venetoclax. CDK6 was also a strong hit in the shRNA screen in SUM-44 cells, and these cells are sensitive to palbociclib, as predicted from the functional druggable signature for these cells. By exploring important KEGG pathways, one can see that SUM-44 cells express a number of cytokines and chemokines, many of which, such as CCL1, LIFR, CCL25, and others, were strong hits in the functional screen, indicating their importance in the proliferation and survival of these breast cancer cells. Finally, these cells, having been derived from a patient with lobular breast cancer, have a point mutation in the CDH1 gene rendering them E-cadherin null, and thus, a good model of lobular breast cancer47. These findings demonstrate that overexpression and activation of the ER is just one feature of the SUM-44 cell line, and this phenotype occurs in the context of other molecular and cellular features important to the biology of the cells and the patient from whom they came. Thus, the SLKBase can be used to explore the biology of individual breast cancer cell lines, which yields novel and important observations on breast cancer cell lines that can be used to elucidate new strategies for reverse engineering of human breast cancer cells.Rather than considering the use of breast cancer cell lines because of one or two specific characteristics, it is now possible to consider cell lines as individual breast cancer patients, and use functional genomics to identify, in an unbiased manner, the most important genes, pathways, and druggable targets for each line. Such a systems-level approach can shed new light on many breast cancer cell lines. For example, the first line that we developed in our laboratory was SUM-44PE51 is the primary driving principle behind the orthogonal omics strategy that is key to the SLKBase. The principle of oncogene addiction proposes that cancer cells have one or more driving oncogenes to which the cells are addicted for their proliferation and survival, and the correctness of this principle has been shown in many laboratory and clinical studies. Based on this principle, we predicted that we could identify the functional-driving oncogenes in any cell line by an orthogonal analysis of gene amplification, gene expression and point mutation data, with shRNA screen data, which shows all of the genes in a given cell line that are essential for their growth and survival. Furthermore, the principle of oncogene addiction predicts that, for those functional-driving oncogenes that are druggable, cells should be exquisitely sensitive to drugs that target the products of those driving oncogenes. The results of our studies with the SUM lines support the predictions made by the principle of oncogene addiction.Oncogene addiction52 highlights the importance of linking gene essentiality data with drug sensitivity for the proper clinical development of targeted drugs.A second driving principle that underlies our approach to functional genomics is that since screen hits identify genes essential for growth and/or survival of the cancer cells, cells that express essential genes that are druggable should be sensitive to drugs that target those genes. For this reason, the SLKBase not only provides data on functional oncogenes in each breast cancer cell line, it also provides functional-druggable signatures for each cell line. Indeed, results of our experiments support the connection between essentialness as determined in the shRNA screens and drug sensitivity for a number of targeted drugs such as EGFR inhibitors, BCL2/BCLXL inhibitors, p38 MAP kinase inhibitors, PLK1 inhibitors, palbociclib, and others. In addition, our KEGG pathway analysis tools help to solidify the connection between functional druggable targets and essential pathways in individual cancer cell lines. The recent paper by Lin et al.HRAS point mutation, and in the SUM-159 screen, this gene was ranked 2378 out of approximately 15,000 genes queried in the screen. Had we chosen a more liberal cut-off point; this gene could have been considered a hit. For this reason, in all of the data that are returned on specific genes using the data mining tools in the SLKBase, the actual screen hit ranking is provided along with the hit status, so investigators can see for themselves how “essential” any gene is in any given cell line. We have identified other examples of false negatives for the SUM lines that are important to discuss. For example, SUM-44 cells express very high levels of ESR1 mRNA and ER protein, both of which can be seen on the SLKBase. Furthermore, we have previously shown that while SUM-44 cells are relatively estrogen-independent and Tamoxifen-resistant, these cells are sensitive to ER degraders such as fulvestrant, and knock-down of ESR1 using shRNAs resulted in profound loss of cell viability. Thus, the screen hit ranking of ESR1 of 4857 in SUM-44 cells appears to be a false-negative result and suggests that the shRNAs that target ESR1 in the Cellecta library did not have a strong enough effect on knock-down of the very high mRNA levels to result in a sufficiently high drop-out rate to score as a hit in the screen.One concern that investigators have with genome-wide screens is the possibility of false-positive results that point to genes that are incorrectly identified as essential. In our hands, using the Cellecta library of shRNAs and the statistical method we developed for analyzing the screen data, false positives have not been a significant issue. So far, we have never failed to confirm a screen hit using individual shRNA constructs that target putative essential genes. However, we do consider false negatives to be an issue with our screen data. The cut points that we chose to determine the genes considered to be hits in the screen typically yield approximately 1000 hits per cell line. We intentionally chose a conservative cut point so as to minimize false positive hits. The consequence of this decision is more false-negative results that one has to be cautions of. For example, SUM-159 cells have a classic n of 1 problem, and truly make targeted cancer therapy precise and effective.In summary, our in-depth analysis of the SUM breast cancer cell lines, and more recently, other breast cancer cell lines, reinforces the importance of taking a systems-level approach to understanding breast cancer cell lines, and not lose sight of the fact that each cell line was derived from an individual patient with a specific set of molecular characteristics. As part of this approach, it is critical that investigators obtain and work with cancer cell lines appropriately to ensure that they are working with the cells they think they are, and to ensure that phenotypic drift is minimized, as any alteration in culture conditions from those originally used to develop the cell line can result in selection of subpopulations present within the cell line. By taking such measures, cancer cell lines are stable models of the type of breast cancer that was experienced by the patients from which they came, making it possible to use these model systems to develop novel and innovative reverse-engineering strategies for each cell line (patient) and ultimately use those strategies to solve the https://sumlineknowledgebase.com/?page_id=350). Briefly, SUM-44 and SUM-52 are luminal B cells; SUM-102, SUM-149, SUM-159, SUM-229, and SUM-1315 are triple-negative breast cancer cells. More specifically, SUM-159 and SUM-1315 cells map to the claudin-low subtype. SUM-190 and SUM-225 cells are HER2-positive breast cancer cells, and SUM-185 maps to the androgen-receptor enriched sup-type of breast cancer cells.All inhibitors were purchased from Selleckchem. The SUM breast cancer cell lines were maintained as described previously. MCF10A cells were a gift from Dr. Herb Soule at the Michigan Cancer Foundation. The molecular subtypes of each of the SUM lines along with additional information regarding each line are presented in the SLKBase (2 buffer (0.01 mM HEPES and 0.015 mM MgCl2) for 5 min, and lysed for 10 min with ethyl hexadecyldimethylammonium solution. For most of the cell lines, IC50s and the standard deviations were determined using GraphPad. For the SUM-229 and SUM-44 cell lines that were resistant to Alpelisib and as a result did not yield a large enough change in cell growth to result in a sigmoid curve interpretable by GraphPad, a local polynomial regression was used (LOESS with a span = 1) to fit a local regression curve over the range of data and determine the IC50 value. The 95% prediction interval was calculated around the curve, and the interval at the point where cell concentration was predicted to be 50% of the starting value (coinciding with the IC50 concentration) was inverted to derive the error for the IC50 value.Cells were plated in 24-well plates at a density of 15–30,000 cells per well. Cells were allowed to recover for 4 days before being treated in triplicate with the indicated inhibitors or DMSO control every 24 h for 4 days. On the fifty day, cell number was determined by harvesting and counting nuclei on a Z1 Coulter Counter . To prepare nuclei for counting, cells were washed three times with PBS, incubated on a rocker table with 0.5 ml per well HEPES/MgClMicroarrays with an average resolution of 35 kb (Agilent Human Genome CGH Microarray 44k chip) were hybridized after direct labeling of DNA with fluorescent dyes. DNA extraction was performed using standard column purification (Qiagen) and normal human female DNA was used as the reference. Dye-reversed replicates were performed. Regions of chromosomal amplification and deletion were determined based on circular binary segmentation provided by the Bioconductor DNA copy library.Total RNA was prepared using standard methods. RNA integrity was verified on an Agilent 2200 TapeStation  utilizing samples with RINs ≥8. Total RNA (100–200 ng) was used to prepare RNA-Seq libraries using the TruSeq RNA Sample Prep Kit following the protocol as described by the manufacturer . Libraries were clustered at a concentration to ensure at least 100 million reads per sample on the cBot as described by the manufacturer . Clustered RNA-seq libraries were paired-end sequenced using version 4 chemistry with 2 × 125 cycles on an Illumina HiSeq2500. Demultiplexing was performed utilizing bcl2fastq v2.17.1.14 to generate Fastq files for downstream analysis.2, 1 mM EGTA, 100 mM sodium fluoride, 10 mM sodium pyrophosphate, 1 mM sodium orthovanadate, 10% glycerol and protease/phosphatase inhibitors (Roche #05056489001/04906837001). Protein concentrations were determined by Bradford assay (BioRad) and concentrations were adjusted to 1 mg/ml. The samples were then mixed with 4× SDS sample buffer containing 0.2 M Tris–HCl (pH 8.0), 40% glycerol, and 8% SDS, boiled for 5 min, and stored at −80 °C until shipment to the RPPA Core Facility at MD Anderson for analysis.For RPPA analysis, cells were lysed in 100 μl RPPA lysis buffer containing 1% Triton X‐100, 50 mM HEPES, 150 mM NaCl, 1.5 mM MgCl53. Briefly, Agilent Sure Select XT reagents were used to prepare sequencing libraries. Hybrid capture was performed using Agilent Sure SelectXT Human All Exon V4 + UTRs, and 100 bp paired-end sequencing was performed on a HighSeq2000 achieving a median coverage of greater than 50-fold. Reads were aligned to the human reference genome GRCh37 using the Burrow-Wheeler Aligner. The data were processed further using the Genome Analysis Toolkit (GATK). For inclusion in the SLKBase, we cross-referenced all called SNVs with data in COSMIC and only report mutations that have occurred in COSMIC > 5 times. For the data from DepMap portal, we only report on the SLKBase mutations considered to be hot-spot mutations in COSMIC.Exome sequencing of SUM cell line DNA was performed essentially as described previously12. Briefly, virus pools expressing shRNA constructs were prepared according to the Cellecta Pooled Lentiviral shRNA Libraries User Manual protocol (www.cellecta.com). HEK 293 T cells were transfected with each of the three Cellecta library plasmid DNA pools (Human Modules 1–3) and the Cellecta Ready-to-Use Packaging Mix (Cat #CPCP-K2A). For each module, virus was titered and used to transduce 5 × 107 target cells at a MOI of ~0.3 in the presence of 5 µg/ml polybrene. Following transduction, cells were cultured for 3 days to allow expression of the resistance marker and non-transduced cells were eliminated from the culture by addition of the selective agent puromycin to the growth media. Three days after the addition of puromycin, cells were trypsinized and one half of the total population was harvested for genomic DNA preparation. The remaining cells were plated and grown for ~5–7 population doublings before harvesting for genomic DNA preparation. Genomic DNA was prepared by phenol:chloroform extraction according to the Cellecta Pooled Lentiviral shRNA Libraries User Manual protocol.The detailed methods that we used for our shRNA screens of the SUM lines have been reported previouslywww.usadellab.org) to trim read lengths to 18 nucleotides. Trimmed reads were deconvoluted using a Cellecta Barcode Analyzer and Deconvoluter software. Fold-depletion scores for each shRNA were calculated as the ratio of the read count at the reference time point versus the final time point.Barcode sequences were amplified from genomic DNA by two rounds of PCR as described previously. Amplified barcode sequences were run on a 3.5% agarose gel and purified using a QIAquick Gel Extraction Kit (Qiagen) according to the manufacturer’s instructions. Isolated barcode sequences were further purified using the PureLink Quick PCR Purification Kit (Invitrogen) according to the manufacturer’s instructions. For sequencing, purified barcodes were diluted to 0.75 ng/µl using buffer EB (Qiagen). Amplicons were clustered at 17 pM including 30% (v/v) PhiX to add sequence diversity. Single end (SE) clustering was performed on a Cbot according to the manufacturer’s protocol . A total of 36 cycles of SE sequencing were performed on an Illumina HiScanSQ. Custom primer GexSeqS  was added to the Illumina sequencing primers at 0.5 µM. Fastq files were generated using CASAVA 1.8.2 and processed using Trimmomatic software  would not be depleted, and their log-quantile scores would have a normal distribution. Based on this assumption, the median of the empirical distribution was used as an estimate of the mean of the null distribution. The estimate of the standard deviation of the null distribution was defined as the 97.5th quantile minus the 2.5th quantile, divided by 4. This was based on the knowledge that 95% of the data in a normally distributed variable falls between ± two standard deviations from the mean. Using this null distribution, all genes having log fold-depletion scores that were larger than the 95th percentile of the null distribution were identified as “hits”. Using this method, all genes that were hits in the screen had at least two, and usually more, shRNAs with depletion scores above the cut point.8, available at https://depmap.org/portal/download. We obtained metadata for each breast cancer cell line, such as the cell line’s Achilles ID, and then processed the data to prepare it for analysis with the tools available on the SLKBase. We performed median-centering normalization of the gene expression data. Specifically, to normalize the expression value of a gene within a cell line, we subtracted the expression value by the median expression of the gene among all breast cancer cell lines. Along with expression data, we obtained copy number amplification, mutation data (COSMIC hit count), and CRISPR scores of gene dependency effects that were calculated using the CERES method54. We followed DepMap by considering hits in the CRISPR screens to be those having CERES scores of ≤−0.5.We acquired data derived from other breast cancer cell lines from the Broad Institute’s Cancer Dependency Map project (DepMap)We developed a MySQL database to store the functional genomics data for the breast cancer cell lines. This database is hosted on Google App Engine. The database contains tables for cell lines, genes, and proteins. Gene tables contain basic information from Entrez  as well as a Boolean indicator if the gene is annotated as an oncogene by OncoKB. This allows us to unify gene essentiality information with a manual oncogene annotation. The database also contains linking tables between cell lines and genes that store the functional genomics data of a gene within a cell line (and likewise for proteomics data). For example, BCL2L1 has a linking table with each of the cell line tables; this linking table contains values for fold change, CNA, number of COSMIC mutations, etc.https://shiny.rstudio.com) to develop the tools available on the SLKBase website and the tools are deployed on RStudio’s shinyapps.io (https://shinyapps.io). Each of the apps uses the R package “RMySQL” to query the MySQL database for functional genomics data. Furthermore, the Pathway Engine tools utilize the “Pathview” package to map the genomics data to genes within KEGG pathways.We used the Shiny R package  and convert them to the format of an adjacency matrix suitable for input to NetworkX.The SLKBase web site was developed using WordPress tools and the HitMag style.Further information on research design is available in the Supplementary MaterialReporting Summary Checklist


Colorectal and glioblastoma cancer stem‐like cells (CSCs) are essential for translational research. Cell line authentication by short tandem repeat (STR) profiling ensures reproducibility of results in oncology research. This technique enables to identify mislabeling or cross‐contamination of cell lines. In our study, we provide a reference dataset for a panel of colorectal and glioblastoma CSCs that allows authentication. Each cell line was entered into the cell Line Integrated Molecular Authentication database 2.1 to be compared to the STR profiles of 4485 tumor cell lines. This article also provides clinical data of patients from whom CSCs arose and data on the parent tumor stage and mutations. STR profiles and information of our CSCs are also available in the Cellosaurus database (ExPASy) as identified by unique research resource identifier codes. Human cell lines obtained from cancer stem‐like cells represent an invaluable model for studying tumor properties. Cell line authentication by short tandem repeat (STR) profiling is an important tool to identify the potential mislabeling or cross‐contamination of cell lines. Here, the authors characterized 18 colorectal cancer stem‐like cell lines from 17 patients and 103 glioblastoma cancer stem‐like cell lines from 95 patients by STR profiling to create a reference dataset that allows the authentication of these cell lines and their identification through a unique research resource identifier. The results will help further ensure the reliability and reproducibility of research experiments. AMELXamelogenin in Xp22.1‐22.3AMELYamelogenin in Yp11.2CLASTRcellosaurus STR databaseCLIMACell Line Integrated Molecular Authentication databaseCSCcancer stem‐like cellCTSCcolorectal tumor stem‐like cellGSCglioblastoma stem‐like cellICLACInternational Cell Line Authentication CommitteeICLCCell Bank Interlab Cell Line CollectionMGMTO6‐methylguanine DNA methyltransferaseMSImicrosatellite instabilityPBMCperipheral blood mononuclear cellPCRpolymerase chain reactionSTRshort tandem repeat1Cancer research requires models that use patient‐derived cultured cells. These models allow to study tumor heterogeneity, in particular in the early stages of tumorigenesis. Isolation and in vitro cultivation of cancer stem‐like cells (CSCs), a small fraction of self‐renewing cells with stem‐like properties, provide a model to study the properties of these tumor initiating cells.https://web.expasy.org/cellosaurus/).STR profiling was carried out using standardized procedures for unambiguous authentication and identification of human cell lines according to the American National Standards Institute/American Type Culture Collection Standard ASN‐0002‐2011.The cell line profiles were challenged against public databases to exclude cross‐contamination with commercially available cell lines. Comparison of the cell line profiles against each other ruled out duplicates due to cross‐contamination. Duplicates were found only in those cell lines that derived from different regions of the same tumor.2Further method descriptions are included in 2.1CTSCs and GSCs were isolated from tumor samples through mechanical dissociation and cultured in a serum‐free medium supplemented with growth factors, as previously described.2.2All experiments were performed with mycoplasma‐free cells. Mycoplasma contamination in cell cultures was evaluated using MycoAlert Mycoplasma Detection Kit .2.3Single‐point mutations and small insertions/deletions in CTSCs were assessed by targeted DNA resequencing, focusing on 17 genes known to be frequently mutated in colon cancer, as previously described.Microsatellite instability (MSI) detection was performed using the Promega panel of mononucleotide MSI markers . The expression of four mismatch repair proteins, MLH1, MSH2, MSH6 and PMS2, was investigated in tumor tissues corresponding to CTSCs that showed MSI‐High.The expression of the stem cell marker CD133 and the epithelial antigen Ber‐EP4 in CTSCs was evaluated by flow cytometry, as previously described.2.4Tumor proliferation index was analyzed by immunohistochemistry on paraffin sections of glioblastoma samples using the avidin‐biotin‐peroxidase complex methods .2.5All human cell lines described have been authenticated by STR profiling within the last 3 years. Genomic DNA was isolated from cell pellets using the DNeasy Blood & Tissue Kit  and treated with RNase, according to the manufacturer's instructions. Yield was measured with NanoDrop 1000 spectrophotometer . One nanogram of DNA of each sample was used for the STR analysis. Samples were amplified and electrophoretically separated on an ABI Prism 3100 Genetic Analyzer (Applied Biosystems).STR profiles were analyzed by the GeneMapper ID software (Applied Biosystems), Version 3.2. The results showed highly reproducible cell line‐specific numeric patterns. The assay was performed by the Cell Bank Interlab Cell Line Collection (ICLC) of the Biological Resource Centre, IRCCS Ospedale Policlinico San Martino of Genoa, in collaboration with the Department of Legal and Forensic Medicine of Genoa University. Comparison of STR profiles using the CLIMA database was performed using the identification feature in CLIMA 2.1 version of the database, as previously described.33.1ACVR1B, AMER1, APC, BRAF, CTNNB1, FBXW7, KIAA1804, KRAS, MAP2K4, NRAS, PIK3CA, PTEN, SMAD2, SMAD4, SOX9, TCF7L2 and TP53 . Tumor site and disease stage  of CTSCs are shown in Table 53 Table .Analysis of MSI status using mononucleotide MSI markers showed that 6 CTSC lines had MSI‐High , one line had MSI‐Low (CTSC#417) and the other lines were microsatellite stable . In accoCTSCs were analyzed for the expression of CD133 and epithelial antigen Ber‐EP4 . CD133 iMGMT gene methylation status and proliferation index (Ki67) of parent tumors are shown in Table MGMT gene promoter status was methylated in 46 tumors (49%), unmethylated in 44 tumors (46%) and not available in 5 tumors (5%).We characterized 103 GSCs from 95 glioblastoma patients . Tumor location, disease stage (primary/recurrent tumor), For GSCs, the MSI status was not analyzed since its frequency, as determined through the amplification of the monucleotide loci, is rare in glioblastoma tumors. Single loci MSI are observed in a low percentage of glioblastoma samples and the presence of high MSI is not a typical feature of this tumor.GSCs were also analyzed for the expression of the markers CD133 and Sox2 . Express3.2Eighteen CTSCs and 103 GSCs were genetically identified using STR profiling with two different kits using 10 or 16 different loci . To protAmelogenin marker was used for gender determination.Failure in amelogenin sex gene detection is rare in healthy individualsAmong the GSCs that arose from 66 male individuals, 16 cell lines showed only AMELX . Therefore, about 24% of the male‐derived cell line profiles seem to have lost AMELY. It has been reported that about 40% to 45% of cell lines purportedly derived from males lacked the AMELY allele.3.3The analysis of STR profiles of CTSCs and GSCs by CLIMAIn some cell lines, PBMCs and the primary tumor tissues (T) were also analyzed for STR profiles. Results were as follows, GSC#298 and #298 PBMCs (100% of similarity); GSC#309s, #309T and #309 PBMCs (100% of similarity); 314T and 314 PBMCs (100%); GSC#318 and #318T (100%). All the other cell lines have unique profiles, that do not match with other profiles for a percentage higher than 90%. All unique profiles have been confirmed in CLASTR.4Human cell lines obtained from CSCs represent an invaluable model for studying the properties of tumors.In the present study, we detected MSI‐H in 7 out 18 CTSC cell lines (38%) that is higher than expected in CRC. There are two possible explanations for this result. The first is that 13 72%) tumor samples, which the CTSCs were isolated from, came from right‐sided colon cancers that harbor the MSI‐H phenotype most often.% tumor swww.ICLAC.org), a voluntary, independent scientific committee, established in 2012. ICLAC produces important guidelines, such as “Guide to Human Cell Line Authentication” and “Obtaining Cell Lines from Reliable Sources.” ICLAC was established after the publication of a consensus Standard for human cell line authentication by STR profiling.https://f1000research.com/articles/4-134/v2) determined the use of an unique research resource identifier to flag each established cell line for searches and data analysis.Despite the success of using cell lines as models to advance cancer research, misidentification of cell lines is a widespread problem.Using STR profiling, we generated for each CSC line a unique molecular identity pattern. STR profiles from CTSCs and GSCs were compared both with cell line profiles included in CLIMA 2.1 databaseThe cell lines used in our study will be available to researchers through Material Transfer Agreement (MTA).The authors declared no potential conflicts of interest.Glioblastoma tissue samples were harvested from patients undergoing craniotomy at the Institute of Neurosurgery, Università Cattolica del Sacro Cuore (UCSC), Rome, Italy. All the patients provided written informed consent according to the research proposals approved by the Ethical Committee of UCSC. Fresh human colorectal cancer tissues were obtained in accordance with the standards of the ethics committee on human experimentation of the Istituto Superiore di Sanità (authorization no. CE5ISS 09/282). All the patients provided written informed consent. Cell lines obtained from tumor stem‐like cells were de‐identified to protect patient health information.Appendix S1: Supporting InformationClick here for additional data file.


Neocryptodiscus papillaris based on moderate cytotoxic activity of the extract in COLO205, KM12 and MCF7 cancer cells. While the major prenylated furanocoumarin derivatives and osthol isolated from the dichloromethane extract were responsible for the activity in the colon and breast cancer cell lines, the 4′-acylated osthol derivatives including a novel coumarino-alkaloid; neopapillarine) demonstrated selective cytotoxic activity in A498 and UO31 renal cancer cell lines.Several simple and prenylated coumarin derivatives were isolated from the dichloromethane extract of the root of  Neocryptodiscus ; thrine (4) .Aspergillus species, also contain a coumarin nucleus as part of their polycylic heterocyclic structure +  indicating a molecular formula of C15H16O4 for 2 which that is in agreement with the hydoxylated isoprenyl side chain added to the osthol structure. Although this compound has previously been reported as a rat metabolite of osthol  strongly suggested that the C-4′ hydroxymethylene group was esterified with an acetyl group in 3. In addition, the position of the acetoxy group was corroborated by the 2D NOESY spectrum as C-4′ and furthermore, a sodium adduct molecular peak observed at m/z 325.1039 [M + Na]+  in the HRESIMS spectrum of 3 confirmed the structure of 3 as 4′-acetoxyosthol.The thol (3)  clearly 1H-NMR spectrum of neopapillarine (4)  confirming a molecular formula of C21H19NO5 for 4 which that is in agreement with a 4′-nicotinoyloxyosthol structure for compound 4.The rine (4)  showed sd esters , thus, tyl group  as with N. papillaris was tested in A498 and UO31 renal cancer cell lines  or nicotinoyloxy  group displayed a moderate inhibitory activity against the UO31 cell line. Furthermore neopapillarine (4), the 4′-nicotinoyloxy derivative of osthol, exhibited better inhibitory activity in the A498 renal cancer cell line in comparison with the 4′-senecioyloxy derivative of osthol (5).The cytotoxic activity of the coumarins isolated from ll lines . While mN. papillaris, a rare and endemic Apiaceae plant, yielded several coumarin derivatives with cytotoxic activity including a novel coumarino-alkaloid compound; named neopapillarine (4). The prenylated major coumarins of the root extract were osthol (1), isoimperatorin (8) and oxypeucedanin (9). Osthol (1) showed 20 and 24 μM IC50 values in COLO205 colon cancer and MCF7 breast cancer cell lines, respectively. Oxypeucedanin (9) displayed 30 and 31 μM IC50 values in the COLO205 and KM12 colon cancer cell lines, but much weaker IC50 values  against the MCF7 mammalian and A498 and UO31 renal cancer cell lines, respectively. Isoimperatorin (8) showed an IC50 value of 14 μM in the COLO205 cancer cell line but a weaker inhibitory activity  against the KM12 and MCF7 cancer cell lines, respectively. In contrast, only neopapillarine (4) and 4′-senecioyloxyosthol (5) showed inhibitory activity with an IC50 of 20 μM in the UO31 renal cancer cell line. While neopapillarine (4) displayed a moderate inhibitory activity with an IC50 of 67 μM in the A498 cell line, 4′-senecioyloxyosthol (5) showed relatively weak activity in the A498 cell line, this difference may be due to the nitrogen bearing ester group of neopapillarine (4).The Investigation of the dichloromethane extract of the root of 14) was the only other known nicotinic acid ester of a prenylated coumarin isolated from a plant species of the Rutaceae family, i.e., Murraya paniculata (L.) Jack, [15) [16)) [17)) [18), conyhydrine) [19)) [20)  [21) [To date, isomurralonginol nicotinate (.) Jack, . Interesck, [15) , dimeric5) [16)) ,31 and n)) [17)) ,33, dimehydrine) . Neverthe) [19))  and two anilate)  and a fute) [21)  have bee1H and 150 MHz for 13C and equipped with a 3 mm cryogenically cooled probe. HRESIMS data were acquired on an Agilent 6530 Accurate Mass Q-TOF instrument . Initial purification of the dichloromethane extract was carried out on a Sephadex LH-20  column. Further purification of column fractions was performed using silica gel F254 PLC plates (1 mm thickness) .UV spectra were recorded on the Shimadzu UV-Vis Spectrophotometer, UV-1700 . IR spectra (neat) were recorded on the Perkin-Elmer FT-IR Spectrometer, SPECTRUM 2000 . NMR spectra were acquired on a Bruker Avance III spectrometer  operating at 600 MHz for N. papillaris were collected from Aşağı Dilimli village, near Viranşehir, Şanlıurfa in June 2013 and identified by Prof. A. Duran. A voucher specimen (A. Duran 7780) was deposited in the Herbarium of Selçuk University, Faculty of Sciences, Department of Biology. Due to the endangered rare plant species status of Neocryptodiscus papillaris, only a portion of the root was taken from the plant  of the plant material, coarsely powdered root (200 g) and fruits (100 g) of N. papillaris were extracted sequentially with dichloromethane (4 L for the root and 2 L for the fruits) and methanol (4 L for the root and 2 L for the fruits) at room temperature and concentrated, in vacuo, to dryness . The dichloromethane root extract (4 g) was separated using a Sephadex LH-20 column (4.5 × 100 cm) packed in a hexane/dichloromethane/methanol (14:9:1) mixture followed by prep. TLC  for final purification of compounds. The known compounds isolated from the dichloromethane extract were osthol , 4′-senecioyloxyosthol , psoralen , bergapten , isoimperatorin , oxypeucedanin , oxypeucedanin hydrate , pranferol , scopoletin  and scoparone .Upon complete drying . White amorphous powder; IR (NaCl) νmax: 3450, 2918, 2850, 1730, 1607, 1498, 1403, 1281, 1252, 1120, 1091, 832 cm−1; UV (MeOH) λmax (log ε): 321 (3.80), 257 (3.39), 248 (sh) (3.40), 214 (3.94) nm; 1H and 13C-NMR .4′-Hydroxyosthol . White amorphous powder; IR (NaCl) νmax: 2933, 2850, 1742, 1730, 1609, 1498, 1436, 1403, 1367, 1281, 1251, 1118, 1090, 1028, 833 cm−1; UV (MeOH) λmax (log ε): 322 (3.91), 257 (3.51), 248 (3.50), 210 (3.99) nm; 1h and 13C-NMR .4′-Acetoxyosthol . White amorphous powder; IR (NaCl) νmax: 2919, 2850, 1723, 1608, 1498, 1436, 1281, 1251, 1161, 1117, 1089, 1024, 833, 742, 703 cm−1; UV (MeOH) λmax (log ε): 321 (3.23), 271 (sh) (2.98), 258 (3.11), 190 (sh) (3.63) nm; 1h and 13C-NMR .Neopapillarine  were diluted 1:25 with medium (192 μL) and then subjected to five 2:1 serial dilutions (100 μL each) on a 96-well plate. Duplicate 40 μL aliquots of each sample concentration were then transferred to a 384-well “dilution plate”, which could accommodate the duplicate samples from two 96-well plates. A 5 μL portion of each solution in the dilution plate was transferred to the cell cultures in the 384-well assay plates to give a final volume of 50 μL and a DMSO concentration of 0.4%. Control wells included 8 wells with the positive control sanguinarine chloride at 20 µM, as well as DMSO only controls and no cell controls. The Z’ factors for the individual plates were calculated and were >0.5 in all cases. The cells were incubated for 48 h at 37 °C in the presence of the test samples and then treated with the tetrazolium salt XTT . Viable cells reduced XTT to a colored formazan product, and after an additional 4 h incubation period the amount of formazan produced was quantified by absorption at 450 nm, using a 650 nm reference. Plates were read on a PerkinElmer EnVision (model # 2104) reader.The assay used for this study was a two-day, two cell line XTT bioassay , an in v


Tumor dissemination and recurrence is attributed to highly resistant cancer stem cells (CSCs) which may constitute a fraction of circulating tumor cells (CTCs). Small cell lung cancer (SCLC) constitutes a suitable model to investigate the relation of CTCs and CSCs due to rapid tumor spread and a high number of CTCs. Expansion of five SCLC CTC lines  in vitro at our institution allowed for the analysis of CSC markers and cytotoxicity of the CSC-selective drugs salinomycin and niclosamide against CTC single cell suspensions or CTC spheroids/ tumorospheres (TOS). Salinomycin exerted dose-dependent cytotoxicity against the SCLC lines but, with exception of BHGc7 TOS, there was no markedly enhanced activity against TOS. Similarly, niclosamide exhibits high activity against BHGc7 TOS and UHGc5 TOS but not against the other CTC spheroids. High expression of the CSC marker CD133 was restricted to three SCLC tumor lines and the BHGc10 CTC line. All SCLC CTCs are CD24-positive but lack expression of CD44 and ABCG2 in contrast to the SCLC tumor lines which show a phenotype more similar to that of CSCs. The stem cell marker SOX2 was found in all CTC lines and SCLC GLC14/16, whereas elevated expression of Oct-3/4 and Nanog was restricted to BHGc26 and UHGc5. In conclusion, the SCLC CTCs established from patients with relapsed disease lack a typical CSC phenotype in respect to chemosensitivity to CSC-selective drugs, surface markers, expression of pluripotent stem cell and transcription factors. Tumors seem to contain a small subpopulation of cancer stem cells (CSCs) which sustain primary tumors and metastases –4. CSCs liplatin . A few cliplatin , 27. In -catenin , 29. Add-catenin . The ant-catenin .Metastasis-initiating capabilities of CTCs based on stemness properties are difficult to detect since CTCs show extensive heterogeneity and only an extremely small fraction of these cells is able to establish secondary lesions , 33. In SCLC26A was established in our lab from a pleural effusion of a SCLC patient before treatment. GLC14 and GLC16 have been derived from the same patient before and after first cycles of chemotherapy , 42, DMSPictures of clusters of SCLC CTCs were acquired from tissue cultures and processed using Photoshop . Preparation of a TOS histological section was done as described previously .4 cells in form of single cells or tumorospheres (TOS) in 100 μl medium were distributed to wells of 96-well microtiter plates  and ten 2-fold dilutions of the test compounds were added from stock dilutions as described [50 values were determined from dose-response curves using Origin 9.1 software .1 × 10escribed . Assays Antibodies were obtained from Biolegend . Expression of CD133 (Clone 315-2C11), CD24 (ML5), CD44 (IM7) and ABCG2 (5D3) was determined in flow cytometry using corresponding secondary reagents (Sigma-Aldrich) and a Cytomics FC500 flow cytometer  in indirect immunofluorescence. Data analysis and histogram overlays were done employing the Kaluza flow analysis software (Beckman Coulter).Pluripotent stem cell markers were analyzed using the ARY010 Proteome Profiler Array  according to manufacturer’s instructions. Experiments were done in duplicate and the different tests were calibrated using the six reference spots included for each individual membrane. Arrays were evaluated using ImageJ and Origin 9.1 software .p < 0.05 regarded as significant difference using Origin software .Statistical significance was tested by t-tests and The cytotoxic activity of salinomycin against SCLC CTC and tumor cell lines was tested in MTT proliferation assays. An example of a dose-response curve for BHGc10 single cells and TOS is shown in Fig. 50 values for all SCLC cell lines are shown in Fig. 50 values in the 1 μM range. All other SCLC lines yield IC50 values for salinomycin in the 2–4 μM range, including BHGc7 single cells, BHGc10 and BHGc26TOS.The ICThe cytotoxic activity of niclosamide against CTC and tumor SCLC cell lines was tested in MTT proliferation assays Fig. . The celAnalysis of CSC marker expression was done using indirect immunofluorescence and flow cytometry. High expression of the CSC marker CD133 was detected in the BHGc10 CTC and DMS53 tumor cell lines, whereas expression was absent in the BHGc16 CTC cells Fig. .Fig. 4FlThese three SCLC cell lines showed expression of CD24 to a variable degree In addition to CD133-positive DMS53 and BHGc10, significant expression of this antigen in GLC14 and GLC16 SCLC lines and peak labeling of BHGc16 CTC and NCI-H69 cells was found , as well as unique mutations of p53 , 43. All+/CD24− CSC phenotype [50 values of salinomycin vary between 0.3 and 10 μM, depending on the cell type used and treatment period [With the single exception of the SCLC NCI-H69 cell line, salinomycin has more pronounced cytotoxic effects against the SCLC CTC cell lines including BHGc16, BHGc16 TOS, BHGc26 and UHGc5 TOS. TOS show higher resistance to chemotherapeutics such as cisplatin, topotecan and epirubicin compared to the corresponding single cell suspensions . Howeverhenotype . Cell dehenotype , 48–50. t period , 52. Howt period .Niclosamide showed high cytotoxic activity against most SCLC lines with exception of GLC14 and GLC16. Among the SCLC CTC lines, BHGc7 TOS and UHGc5 TOS were more resistant to niclosamide than the corresponding single cell fractions in contrast to BHGc10 TOS, BHGc16 TOS and BHGc26 TOS which exhibited the reversed effect. Again, this CSC-selective drug exhibits heterogenous activity against the SCLC CTC spheroids. Niclosamide has antitumor activity via inhibition of NF-κB, an increase in ROS and blocking of the Wnt/β-catenin, STAT3 and NOTCH signaling pathways but also targets mitochondria in cancer cells , 55. How+ CD90+ subpopulation expressing increased levels of Nanog and Oct-4. CD133 (Prominin 1) has been used to identify CSCs in lung cancer and was linked to high levels of ABCG2 and chemoresistance [−/CD87− cells showed the highest tumorigenic potential [SCLC is sensitive to the first-line therapy in most patients but invariably recurs as chemo- and radioresistant tumor within approximately one year . This nasistance . Severalsistance , 63. Thesistance . CD133 psistance , 66. Howotential . Signifiotential . In conchigh/CD24low cells, which have also been found in pleural metastases [+ CSCs were reported to be less proliferative and more resistant to chemotherapy compared to CD24− cells [+ SCLC cells and, in particular, from all SCLC CTC lines. No expression of the chemoresistance mediator ABCG2 could be detected in the SCLC lines studied here (data not shown). In conclusion, CD133 is not expressed in response to prior chemotherapy and a CD24+/CD44−/ABCG2− phenotype does not fit to the panel of markers generally expressed by typical CSCs of solid tumors, with the possible exception of BHGc10. Furthermore, SOX2 seems to represent a general marker of the neuroendocrine SCLC tumors which is not restricted to CSCs. Expression of Oct-3/4 and Nanog is limited to BHGc16 und BHGc26 which lack significant expression of CD133 and GLC14/16.In respect to CTCs, CD44 has been identified as CSC marker of gastric cancer and most of the CTC-positive patients showed CD44-positive CTCs . In breatastases . Recent tastases . Early dtastases . Our restastases , 42. CD24− cells . With ex−/CD24+/ABCG2− salinomycin-sensitive phenotype which lack CD133 expression. With exception of SOX2, pluripotent stem cell transcription factors Oct-3/4 and Nanog are expressed to a minor extent in the CTC cell lines studied. The SCLC CTC cell lines are chemosensitive to topotecan and epirubicin as single suspensions in good agreement with the lack of ABCG2 expression [The numerous studies reporting CSC traits in SCLC tumor cells point to a possible role of this phenotype in resident SCLC primaries and secondaries but our results preclude a role of CSC characteristics among SCLC CTCs , 74. It pression . Howeverpression , 76. CSC


Our results demonstrate that a short time interval between treatments caused more unrepaired DNA damages and more cell kill, especially at higher temperatures. Although hyperthermia before ionizing radiation may result in slightly more DNA damage, the sequence between hyperthermia and ionizing radiation yielded similar effects on cell survival. Cervical cancers are almost exclusively caused by an infection with the human papillomavirus (HPV). When patients suffering from cervical cancer have contraindications for chemoradiotherapy, radiotherapy combined with hyperthermia is a good treatment option. Radiation-induced DNA breaks can be repaired by nonhomologous end-joining (NHEJ) or homologous recombination (HR). Hyperthermia can temporarily inactivate homologous recombination. Therefore, combining radiotherapy with hyperthermia can result in the persistence of more fatal radiation-induced DNA breaks. However, there is no consensus on the optimal sequence of radiotherapy and hyperthermia and the optimal time interval between these modalities. Moreover, the temperature of hyperthermia and HPV-type may also be important in radiosensitization by hyperthermia. In this study we thoroughly investigated the impact of different temperatures (37–42 °C), and the sequence of and time interval (0 up to 4 h) between ionizing radiation and hyperthermia on HPV16 Most cases of cervical cancer are caused by infection with human papillomavirus (HPV). HPV types 16 and 18 are responsible for >70% of all cases of cervical cancer in the world ,3, incluHyperthermia has the ability to change the microenvironment and make cells more sensitive to radiotherapy. The precise mechanisms on how hyperthermia enhances tumor oxygenation are not fully understood. Hyperthermia can induce hypoxia-inducible factor-1 (HIF-1), a potent regulator of tumor vascularization and metabolism, which can push surviving cells toward glycolysis . HyperthMultiple mechanisms are responsible for the radiosensitization effect by hyperthermia, each operating at different temperatures and each with different optimal sequences and time intervals ,14. Hype+ (SiHa and Caski), HPV18+ (HeLa and C4I), and HPV negative (C33A and HT3) cell lines, which sequence and time interval (0 up to 4 h) between applying ionizing radiation and hyperthermia caused the most damage to the tumor cells at different temperatures (37 to 42 °C). Effects on cell cycle distribution, DNA damage level, and cell survival fraction were assessed. Finally, different mechanisms explaining these results will be discussed.In this study we investigated in in vitro models of HPV16+, HPV18+, and HPV-negative cervical carcinoma cell lines. A schematic overview of treatments is given in + SiHa (red) and Caski (orange) (+ HeLa (green) and C4I (yellow)  were tested and the effect of different temperatures was investigated for HPV16(orange) B; HPV18+(yellow) C; and HP(yellow) D. Per th+ SiHa (red) and Caski (orange) (+ HeLa (green) and C4I (yellow)  E; HPV18+(yellow) F; and HP(yellow) G cervica+ cell lines (+ cell lines HeLa (green), C4I (yellow), HPV-negative cell lines  between ionizing radiation and hyperthermia resulted in the lowest cell survival. Moreover, there is no significant difference between the different sequences in clonogenic capacity.Cell cycle distribution was studied by the incorporation of BrdU. A schematic overview of treatments is given in Apoptosis after different time intervals and for the different sequences of ionizing radiation (4 Gy) and hyperthermia (42 °C) was measured by the Nicoletti assay. A short time interval between ionizing radiation and hyperthermia resulted in the highest apoptotic levels measured by flow cytometry. All cell lines showed that approximately 30–35% of cells were apoptotic after a 0 h time interval between ionizing radiation and hyperthermia, compared to approximately 20–30% after a 4 h time interval between ionizing radiation and hyperthermia . All cel+ SiHa and Caski , was measured by nuclear γ-H2AX staining, which may be very important to understand the earlier described differences in cell survival. To prevent the counting of nonradiation induced DSBs, exclusion of S-phase cells was done using Edu-exclusion A. In Fignd Caski C, HPV18+ and C4I D, and HP and C4I E cell li+ cell lines  between the two treatment modalities, suggesting cells are halted and the damage is too severe to continue to the G2 phase. Since the number of cells in S phase was unchanged or larger after treatment, there is no indication that cells went into cell senescence. The outcome of ionizing radiation is affected by the cell cycle, mitotic cells are hypersensitive because they can inactivate DSB repair, so during interphase, cell survival is maximal when cells are irradiated during the early G1 and G2 phase of the cell cycle and is minimal during the S phase . PreviouHigher levels of DNA damage were observed after treatment with a shorter time interval between ionizing radiation and hyperthermia, indicating that there was more remaining DNA damage after a short time interval between the two therapies. Blocking DNA repair was clearly the dominant radiosensitizing effect of hyperthermia in our in vitro experiments, the question is: which pathways are involved? Currently, it is generally accepted that hyperthermia downregulates BRCA2 , a proteAll the in vitro data converge to the conclusion that a higher temperature 42 °C vs. 39 or 41 °C of HT and the shortest time interval (0 h) between ionizing radiation and hyperthermia results in the lowest cancer cell survival. In six different cervical cancer cell lines, all experiments were conducted independently and performed at least three times. Our results confirm the data reported in a study of Van Leeuwen et al. , in whicDecades ago, similar experiments were performed on mammalian cell lines, in which Chinese hamster ovary (HA-1) and mouse mammary sarcoma (EMT-6) cells were investigated regarding the interaction between hyperthermia and ionizing radiation. Li and Kal concluded that in HA-1 cells the radiosensitizing effect of hyperthermia was larger when applied before ionizing radiation , but in in vivo model, in which the tumor microenvironment is taken along as well as the relevant healthy tissues around the target region. In patients, a short time interval has been shown to result in lower in-field recurrence and a longer overall survival   data are expressed as the mean with standard deviation of at least three independent experiments. Statistical analyses for cell survival, γ-H2AX staining, and apoptosis levels were performed using two-way ANOVA analysis using GraphPad Prism version 8 .In summary, hyperthermia is an effective tumor sensitizer for ionizing radiation. However, optimizing therapies is important to find the best clinical outcome. Our in vitro results demonstrated that the time interval between hyperthermia and ionizing radiation is an important factor that determines the effectiveness of this combinational therapy. Moreover, our results may indicate that HPV-negative cell lines are already quite sensitive to ionizing radiation alone, while the HPV-positive cell lines require hyperthermia in order to reduce cell survival. The HPV16+ cell line seems to be more sensitive to the combination of hyperthermia and ionizing radiation, already at lower doses of ionizing radiation, compared to the HPV18+ cells. However, the survival fraction, cell cycle distribution, apoptotic levels, and DNA damage repair is more dependent on the time interval between the two treatments, rather than on the type of HPV. In conclusion: a shorter time interval between ionizing radiation and hyperthermia results in lower survival fractions than after a longer time interval, and the sequence between the two modalities did not demonstrate any significant differences on cervical cancer cell lines. However, studying the effects on normal tissue should be taken into account before the most optimal way of applying hyperthermia and radiotherapy can be determined.


The origin recognition complex (ORC) cooperates with CDC6, MCM2-7, and CDT1 to form pre-RC complexes at origins of DNA replication. Here, using tiling-sgRNA CRISPR screens, we report that each subunit of ORC and CDC6 is essential in human cells. Using an auxin-inducible degradation system, we created stable cell lines capable of ablating ORC2 rapidly, revealing multiple cell division cycle phenotypes. The primary defects in the absence of ORC2 were cells encountering difficulty in initiating DNA replication or progressing through the cell division cycle due to reduced MCM2-7 loading onto chromatin in G1 phase. The nuclei of ORC2-deficient cells were also large, with decompacted heterochromatin. Some ORC2-deficient cells that completed DNA replication entered into, but never exited mitosis. ORC1 knockout cells also demonstrated extremely slow cell proliferation and abnormal cell and nuclear morphology. Thus, ORC proteins and CDC6 are indispensable for normal cellular proliferation and contribute to nuclear organization. Saccharomyces cerevisiae is the best characterized system, from which individual proteins involved in DNA replication have been identified and studied extensively, including functional reconstitution of the entire pre-RC assembly and the regulated initiation of DNA replication from these pre-RCs with purified proteins , comprising Orc1-6 subunits, binding to each potential DNA replication origin  at origins of DNA replication throughout the genome . Among eproteins . In S. cn origin . Chromatn origin . The Mcmn origin . During n origin . The funn origin .S. cerevisiae and human cells, the ORC1-5 subunits contain a AAA+ or a AAA+-like domain and a winged-helix domain (WHD)  . In yeasin (WHD) . ORC binin (WHD) . On the in (WHD) . ORC subin (WHD) . One or  mitosis . In huma mitosis .S. cerevisiae, S. pombe, Xenopus, and Drosophila, since mutation or depletion of ORC prevents CDC6 binding and MCM loading onto DNA  cells in which expression of either ORC1 or ORC2 subunit was eliminated using CRISPR-Cas9-mediated gene ablation could still proliferate  strategies have shown that targeting regions within protein domains typically show significantly higher degree of negative selection phenotypes . This isll types . Other mproteins . Subsequferation .Streptococcus pyogenes Cas9) across each exon of ORC1-6 and CDC6 were designed and synthesized. Pooled CRISPR libraries also included control guide RNAs targeting either known core essential genes such as CDK1, PCNA etc. as positive controls, or those targeting non-essential gene loci or no loci at all as negative controls  since essential protein domains correlate with higher negative selection phenotype, we hypothesized that analyzing the effect of every possible guide RNA target site in the ORF might uncover new functional regions, and (b) a high-throughput screen of this nature would provide incontrovertible evidence about the essentiality of ORC and CDC6 proteins, at least in the cell lines tested. A recent study validated this approach by analyzing tiling-sgRNA data from Munoz et. al. and found that up to 17.7% of the regions that displayed a CRISPR knockout hyper-sensitive (CKHS) phenotype, did not overlap with previously annotated domain or known function . Thus, gcontrols . The totNeGeCKO’ , negativNeGeCKO’ , Rosa26,egatives  and 43 phttps://toolkit.tuebingen.mpg.de/frpred) server that calculates a conservation score based on amino acid variability as well as the probability of it being a functional ligand binding or catalytic site at each amino acid position of the input sequence (https://consurf.tau.ac.il/) server which analyses the probability of structural and functional conservation despite amino acid variability for any given position of input sequence (http://bioinf.cs.ucl.ac.uk/psipred/) that scores for intrinsically disordered regions (IDRs) that are usually not well conserved yet found to be functionally essential in many proteins  Consequence   dataset was compared to a combined RNAi dataset of cell lines, it indicated that using the CRISPR method, with a gene effect score of less than −0.5, ll lines . It is eed study . For ORChers not . Of note-1 cells . CompariORC4, which is entirely structured, and thus none of the sgRNAs target NAD regions. For all other genes, the fraction of NAD targeting sgRNAs that scored as essential, as well as their locations within these regions were comparable between cell lines rather than between proteins  – that is sgRNAs targeting AD regions showed significantly higher depletion compared to those targeting non-annotated domain (NAD) regions  and 7a. proteins . This suin vitro . In yeasin vitro . Anotherin vitro .p53 status of the cell, post-translational modifications, local structure of the gene locus can confound analyses from high-throughput tiling-sgRNA CRISPR screening  was constructed, and the ORC2 cDNA was edited to harbor multiple mismatches based on two of the four sgRNAs, ORC2-1 and ORC2-2  TIR1 (OsTIR1) gene that encodes a plant auxin‐binding receptor that interacts with the conserved E3 ubiquitin ligase SCF complex to degrade mAID-tagged proteins and the U2OS cell line. In the dropout CRISPR/Cas9 experiment, cells expressing a positive control RPA3 sgRNA and all four ORC2 sgRNAs, but not the negative control Neg15 sgRNA  or as rounded, mitotic-like cells (30%)  .gr proteins in ORC2_H-2, H-4, and H-5 cell lines were depleted following 4 hr of auxin treatment  and CHK1(S345) phosphorylation were detected in all three ORC2 mutant cell lines  (pH3S10) followed by flow cytometry analysis . During the second cell cycle, in the absence or presence of doxycycline and auxin, it took TO-HCT116 cells about 35–50 min to progress from early prophase to chromosome segregation , n = 10.ORC1 and ORC2 genes in p53-/- HCT116 cells (-/-ORC1 (B14 clone) and -/-ORC2 (P44 clone) cell lines described in this study as a gift from Dr. Anindya Dutta and performed several experiments on them. Using the ORC2 validated sgRNAs , we first tested if the -/-ORC2 cell line was sensitivity to the four ORC2 sgRNAs we used in our study  to determine the nature of the ORC2 transcripts in the -/-ORC2 cells . To determine the CNV status we performed SMASH . We also compared HCT116 -/-ORC1 (B14) and HCT116 -/-ORC2 (P44) cell lines with the parental lines, that is either +/+p53 and -/-p53 background by confocal microscopy .To further address this question of the role of ORC2, we performed a new G1 synchronization experiment with ORC2 depletion and the result is shown in new Figure 4—figure supplement 3. While arresting the cells in G1, we harvested two time points after auxin treatment. The nuclear volume remained unchanged. CENP-C staining was also unchanged (data not shown). We suggest that this abnormal heterochromatin phenotype is due to cells lacking ORC2 during S phase that affects sister chromatids cohesion loading , therefore, the centromeric region showed decompaction in late S/G2 phase. We have also provided new evidence of heterochromatin decondensation after 48 hr of ORC2 depletion in Figure 4—figure supplement 2.It is hard to tell whether aberrant mitosis is an indirect effect of incomplete DNA replication, checkpoint activation or due to a direct effect of ORC loss. Because most cells arrest at G1, late S, or G2 phase with checkpoint activation, only a very small percentage of cells enter mitosis to exhibit the abnormal phenotype. ORC2 localizes at the kinetochore during M phase, and we suggest that those mitotic cells lacking ORC2 cannot properly align and segregate sister chromatids during M phase. This could be due to direct outcome of ORC2 removal or that ORC2 is essential for recruiting other factors before M phase. In Figure 5 we’ve shown that cells can progress normally during first cell cycle upon release from double thymidine block, which indicates loss of ORC2 during mitosis doesn’t affect cell division. Nonetheless, ORC2 could still be part of an essential components for recruiting or loading important factors for chromosome congression, and this action can be traced back to G1 or S phase, which will then be hard to discriminate from its replication function.We have added new discussion about the loss of ORC1 in the cells and the possibilities of how they can have the limited proliferation. We have also added a discussion of a more recent paper by Shibata and Dutta, 2020, on the reported loss of ORC2 and ORC5 in human cell lines.


FGFR1 regulon to be involved in the characterization of the NB group, where neuroendocrine signature genes were significantly upregulated, and further β-catenin was shown to have significantly higher nuclear localization. LP groups were mainly driven by the regulons ERBB2, FOXA1, GATA3, and PPARG, and they showed upregulation of the genes involved in epithelial differentiation and urogenital development, while the activity of EGFR, FOXM1, STAT3, and HIF1A was implicated for the regulation of BS group. Collectively, our results and classifications may serve as an important guide for the selection and use of bladder cancer cell lines for experimental strategies, which aim to manipulate regulons critical for bladder cancer development.  Bladder cancer is one of the most frequent cancers and causes more than 150.000 deaths each year. During the last decade, several studies provided important aspects about genomic characterization, consensus subgroup definition, and transcriptional regulation of bladder cancer. Still, much more research needs to be done to characterize molecular signatures of this cancer in depth. At this point, the use of bladder cancer cell lines is quite useful for the identification and test of new signatures. In this study, we classified the bladder cancer cell lines according to the activities of regulons implicated in the regulation of primary bladder tumors. Our regulon gene expression-based classification revealed three groups, neuronal-basal (NB), luminal-papillary (LP), and basal-squamous (BS). These regulon gene expression-based classifications showed a quite good concordance with the consensus subgroups assigned by the primary bladder cancer classifier. Importantly, we identified  Bladder cancer is a heterogeneous group of tumors, where transitional cell carcinoma constitutes the great majority of the cases. Classically, bladder cancer is diagnosed in two histopathological classes as ‘muscle invasive bladder cancer (MIBC)’ and ‘non-muscle invasive bladder cancer (NMIBC)’ with different prognostic and molecular characteristics . In the last decade, there have been a number of studies characterizing the genomic landscape of both MIBC and NMIBC and defining the molecular subgroups . A more recent study aimed to define the consensus subgroups of MIBC using the gene expression data in combination with several studies , where the six consensus subgroups were referred to as ‘luminal papillary’, ‘luminal nonspecified’, ‘luminal unstable’, ‘stroma-rich’, ‘basal/squamous’, and ‘neuroendocrine-like’. In this study, the authors, in addition, associated these subgroups with distinct regulon activities, previously defined in . These regulons implicated in bladder carcinogenesis include transcription factors and growth factor receptors, determined according to their gene regulatory activity in bladder cancer . Bladder cancer cell lines have been extensively used for modeling the development, progression and molecular characteristics of bladder cancer. In addition to the focused characterization of cell lines, where only two/three of them are used , there are a few other studies, which provided details about the molecular and genomic characterization of bladder cancer cell lines collectively. In one study, a classification based on the subgroups defined by , ‘“Urobasal A”, “Urobasal B”, “Genomically Unstable”and “SCC-like” were established for 40 bladder cancer cell lines . Another study performed exome sequencing for 25 bladder cancer cell lines and identified the frequently mutated genes among analyzed cell lines . A more recent study provided a comprehensive review about molecular characteristics, origin, and tumorigenic properties of more than 150 murine and human bladder cancer cell lines . In addition, the Cancer Cell Line Encyclopedia of the Broad Institute (CCLE database) provides a unique source for the transcriptomic and genomic data produced in a variety of cancer cell lines including bladder cancer . Although regulon activities have been significantly associated with primary bladder cancer subgroups , there has not been yet a study, which characterized the bladder cancer cell lines according to regulon activities defined for the primary bladder cancers . In this study, we classified the bladder cancer cell lines into 3 groups according to their regulon activities and associated the upregulated genes in each cell line group with the targets of the regulons. Our results reveal previously unknown cooperative regulatory activities in bladder cancer cells and can serve as a guide for modeling bladder cancer according to different regulon activities. 2. The two bladder cancer cell lines 5637 and RT112 were obtained from DSMZ and J82 was kindly provided by Dr. S. Senturk . 5637 and RT112 were cultured in RPMI 1640 (Gibco BRL), J82 was cultured in DMEM (Dulbecco’s Modified Eagle Medium). All media were supplemented with %10 FBS and %1 Penicillin-Streptomycin. Cells were cultured at 37 °C and 5% COIn 24 well plates, J82 was plated 10000/well, RT112 was plated 20000/well, 5637 was plated 40000/well. Cells were incubated overnight on glass coverslips and rinsed with 1x PBS the following day. Cells were fixed with 4% formaldehyde for 15 min at RT, and 0.2% TritonX was used for permeabilization. Fixed cells were blocked with 2% Donkey serum for 45 min. Afterwards, cells were incubated with β-catenin antibody  diluted in 2% donkey serum overnight at 4°C. Next day, cells were rinsed 2 times with 1x PBS. Goat Anti-Rabbit Alexa Fluor 594 was used as a secondary antibody. DAPI was used for nucleus staining. Coverslips were mounted onto slides for imaging with Zeiss LSM880. Images were acquired as Z-stack using ZEN 2 software. Images with maximum intensity were used for further analysis. Quantification of the images were done with ImageJ program. Splitted DAPI channel images were used to determine region of interests for nuclear β-catenin signal intensities. A total of 17 cells per cell line were used for quantification. Integrated Density Values (IDV) were used for statistical analysis. CCLE RNAseq gene expression data for bladder cancer cell lines (RPKM) were downloaded from Cancer Cell Line Encyclopedia (CCLE) database  and were accessed at cbioportal . Regulon definitions were based on . Mutation data for bladder cancer cell lines were obtained using cbioportal . Neuroendocrine differentiation gene definitions are based on the information provided in Supplementary Table 3 from .Using the gene expression values for the regulon genes, we clustered 25 bladder cancer cell lines using kmeans option (k = 6), within pheatmap package (Kolde 2019). Only the regulons that have min 1 rpkm  expression value in at least one cell type analyzed were included in clustering. This resulted in 19 number of regulons which contributed to the clustering analysis. In order to determine the consensus classification of bladder cancer cell lines, we utilized the “Molecular Classification of Bladder Cancer” classifier developed by Kamoun et al.,  (134.157.229.105:3838/BLCAclassify). Gene expression matrix for the cell lines in rpkm ) was uploaded to the classifier and resulting consensus classifications are presented in Figure 1b and Supplementary Table S1.Differential gene expression analysis, where one cell line group was compared with the other groups, was performed using cbioportal . Basically, custom cell line groups were formed based on our classifications , and differentially expressed genes were identified using ‘Compare’ and ‘mRNA’ options. Upregulated genes were defined using q value threshold of 0.1 and log Ratio of 0.5. Gene ontology analysis for the upregulated gene sets was performed using the ConsensusPathDB (CPDB) database of Max Planck Institute . Overrepresentation function of the CPDB was used, and only Level 4 GO terms  were included for further analysis. “GOChord” function of “GOplot” R package was used for visualization . In chord graphs, maximum top 20 GO terms with adjusted p-value <0.05 were shown. For the limit parameter of the “GOChord” function, a minimum number of genes belonging to a specific GO term was determined as 5 if the number of the genes in upregulated gene set was >100, otherwise the number was set as 4 genes minimum. Genes, which are linked with at least 4 different GO terms, were displayed on the plots together with their logFC value representations. Regulon – target gene association table was downloaded from  (Table S2.25) . Genes, which are positively associated with the regulons , were referred to as the target of the respective regulons. Afterwards, upregulated genes for each cell line group were intersected with the targets of the regulons and the results were presented as percent intersection rate . Statistical analyzes were performed utilizing the R/Bioconductor packages (www.bioconductor.org). ANOVA was used to check the statistical difference among the groups for Figures 3a, 4a, 5a, and Supplementary Figure S2. Subsequently, Bonferroni post-hoc test was applied to the results of ANOVA test. Spearman correlation test was applied for Figures 3c, 3d, 4c, 4d, and 5b. Dunnett’s multiple comparisons test was used for statistical analysis of the immunostaining images . We determined the expression of the regulon genes in 25 bladder cancer cell lines and classified these cell lines according to the expression profile of the regulon genes. Our unsupervised clustering analysis using kmeans (k = 6) clustered the bladder cell lines into 3 groups . In order to find out to what extent our regulon-based classifications are legitimate, we additionally classified the cell lines using the consensus classifier algorithm provided in . This analysis identified 5 out of 9 cell lines in group 1 to be assigned to neuroendocrine-like subgroup; 6 out of 6 cell lines in group 2 were identified to belong to luminal papillary and 10 out of 10 cell lines in group 3 as basal-squamous . Among the group 1 cell lines, one cell line (J82) had almost equal annotation scores (0.383 vs 0.385) for neuroendocrine-like and basal squamous classes, and, for two of the cell lines (SW1710 and TCCSUP), annotation scores were rather close as well (Supplementary Table S1). Therefore, we named the group 1-3 as ‘neuronal-basal (NB)’, ‘luminal papillary (LP)’ and ‘basal squamous (BS)’, respectively.FGFR1, which is highly enriched in ‘stromal-rich’ subgroup in consensus classification of bladder cancer , mainly separates the NB group from the two other groups. The regulon cluster 4 driven by the expression of FGFR3, ERBB3, TP63, and FOXA1 was mainly enriched for LP class; regulon cluster 6 constituted by PPARG and GATA3 expression was enriched in LP class and partially in BS class. Regulon cluster 5, driven by luminal-papillary markers RARG, RXRA  and basal marker KLF4  was relatively enriched in LP class, with partial enrichments in NB and BS classes. Regulon cluster 3, dominated by the basal markers, EGFR, FOXM1, STAT3 ,and HIF1A  were similarly enriched in all cell line groups. Although luminal and basal terms are classically used for bladder cancer cell lines , our regulon expression-based analysis here brought additional features, characteristics of each group. Our analysis revealed that the expression status of For each of the 3 groups, we determined with the clustering analysis , we performed differential gene expression analysis contrasting one group with all other groups and determined the upregulated genes for each group. This analysis identified 327 and 570 upregulated genes in NB and LP classes, respectively. However, within the significance thresholds we used, we failed to detect upregulated genes for the BS class. The reason behind this can be attributed to the heterogeneous structure of this group, as it can be seen in the heatmap  and in PCA analysis  as well.FGFR1 targets (18.96%), followed by GATA6 (4.89%) and FOXM1 (4.89%) . FGFR1 was also significantly upregulated in the NB group . FGFR1 targets, which are upregulated in the NB class were mainly involved in neurogenesis, neuron differentiation, nervous system development . Further, expression of the genes VIM and ZEB1 implicated in epithelial to mesenchymal transition , highly correlated with the expression of FGFR1, emphasizing the role of this regulon in the transcriptomic constitution of the NB group .Having determined the upregulated genes in different cell line groups we defined, next, we tempted to relate those genes with the regulon targets. We identified the genes positively associated with the regulons using the information provided in . This analysis showed that cell line groups constituted according to regulon expression profiles were in concordance with the regulon activity. For the NB group, upregulated genes had the highest intersection rate with ERBB2, FOXA1, PPARG, ERBB3, FGFR3, RARG, and GATA3 targets . We identified that almost all these regulons were significantly upregulated in the LP class . Target genes of the regulons upregulated in LP class were involved in epithelial cell differentiation, cell junction organization, and urogenital system development . Remarkably, expressions of FOXA1 (ρ = 0.71) and GRHL3 (ρ = 0.60) significantly correlated with the expression of ERBB2 , indicating the luminal characteristics of the LP group. Upregulated genes in the LP class mainly intersected with FGFR1 targets upregulated in the NB group, prompted us to decipher this connection in more detail. As FGFR1 is the main player characterizing this group, we checked the enrichment of FGFR1 regulon activity in each consensus subgroup of primary bladder cancer . We discovered that although FGFR1 has the highest enrichment score in stromal-rich consensus subgroup , it was also moderately enriched in neuroendocrine-like subgroup  ). To strengthen this association further, we checked the expression of genes marker of neuroendocrine differentiation  in the cell line groups we determined. This analysis also revealed that genes involved in neuroendocrine differentiation were significantly higher expressed in NB group  . Additionally, expression of FGFR1 highly correlated with the expression of neuroendocrine markers . Collectively, these results highly argue for the neuronal characteristics of the NE group and involvement of FGFR1 in this signature. Our finding, which shows the enrichment of neurogenesis-related genes in the APC mutation and one had CTNNB1 mutation. On the contrary 2 had APC or CTNNB1 mutation in the two other cell line groups . Based on this information, we checked the β-catenin localization in one of the NB group cell lines we had in lab J82 and the other two cell lines, 5637 (BS group) and RT112 (LP group) as controls (no mutation in CTNNB1 or APC). The staining of β-catenin in 5637 and RT112 was concentrated at the cytoplasm and the membrane while in J82 it was concentrated at the nucleus of the cells. Our data showed that β-catenin showed significantly higher nuclear localization in J82 compared to the other two cell lines . This finding strengthens our conclusions about the involvement of WNT/β-catenin pathway in neuronal differentiation of bladder cancer cells. The information we provide for the potential involvement of FGFR1 in neuroendocrine features of bladder cancer , identification of significantly increased nuclear localization of β-catenin in a cell line belonging to NB group  collectively strengthens the neuronal/neuroendocrine characteristics of the cell lines present in NB group according to our classifications. We recently showed that the WNT/β-catenin pathway is associated with the active regulatory elements characterizing neuronal bladder cancer . Within this frame, to check any connection of the NB group with WNT/β-catenin pathway deregulation, we scanned the cell lines we used in this study for the mutation status of β-catenin and β-catenin destruction complex components. Among the NB group cell lines, 3 of them had Bladder cancer cell lines serve as important models for modeling bladder tumorigenesis, invasive characteristics and treatment responses . So far, several studies characterized the genomic and transcriptomic properties of bladder cancer cell lines . In this study, we aimed to characterize the bladder cancer lines in terms of their regulon activity, defined for the primary bladder cancers in literature . Our results showed that bladder cancer cell lines have differential regulon activities, reflecting their transcriptomic signatures and their consensus classifications . FGFR1 and were involved in neuronal differentiation. Accordingly, the expression of the genes marker of neuroendocrine differentiation  was significantly higher in the NB group compared to the two other cell line groups. In literature, FGFR1 has been shown be expressed at higher levels in bladder cancers showing mesenchymal features . Knock-down of FGFR1 in JMSU1 and UMUC3 cell lines, belonging to NB group in our results, resulted in a significant reduction in the anchorage-independent ability of these cells . Further FGFR1 expression was high in most small cell carcinoma of the bladder , which is a rare type of bladder cancer with neuroendocrine differentiation . These existing literature and our findings highly support the association of FGFR1 with NB characteristics and neuronal differentiation of bladder cancer. Genes significantly upregulated in cell lines belonging to the NB group were mainly intersected the targets of APC, a component of β-catenin destruction complex . It is known that the immune gene expression signature is relatively depleted from small cell neuroendocrine carcinoma of the bladder , and neuroendocrine-like bladder cancer show decreased levels of immune infiltrate . It was also identified that Wnt/β-catenin signaling can decrease the T-cell infiltration in melanoma mouse models. Thus, inhibition of Wnt signaling has been suggested to prevent immunotherapy resistance . In addition, inhibition of FGFR1 has been shown to enhance the immune checkpoint inhibitor response in breast cancer . Based on all these information, we checked the expression of CXCL16, T cell chemoattractant  in bladder cancer cell lines and identified a significant negative correlation with FGFR1 expression . Our data and existing literature together suggest a regulatory axis involving FGFR1, WNT/ β-catenin signaling, and tumor immune microenvironment in regulation of NB cell lines. Therefore, we suggest that combinatorial treatment strategies disrupting this regulatory axis can be applied on NB cell lines. We previously showed that WNT/β-catenin pathway is deregulated in neuronal subtype of bladder cancer . In this study, we identified significantly higher accumulation β-catenin in nucleus in J82 cell line belonging to NB group, which has a mutation in ERBB2 has been identified to be overexpressed in high-risk non-muscle invasive bladder cancer  and as one of the major prognostic factors for survival status of the patients . FOXA1 expression was adequate for separating non-basal subtype of bladder cancer from the basal subtype . Furthermore, GATA3, FOXA1, and PPARG have been shown to drive the luminal fate in a collaborative manner . Thus, within this frame, our regulon-based classifications confirm the luminal character of the LP class we defined. Regulons implicated in LP group cell lines are mainly known for early bladder cancer, mostly non-muscle invasive and luminal associations. EGFR, FOXM1 and STAT3 as the main regulons, driving the basal characterization of this group . EGFR has been previously shown to be enriched in basal-like bladder cancer, and some groups of muscle invasive bladder cancers have been determined to respond to EGFR inhibitors . In addition, expression of FOXM1 as a prognostic factor in the survival of muscle invasive bladder cancer patients , STAT3 expression, and phosphorylation was identified to be substantially higher in basal-like bladder cancer . Further, STAT3 activated transgenic mice directly developed invasive bladder cancer without going through the intermediate noninvasive stages . Our results here collectively emphasize the role of EGFR, FOXM1, and STAT3 in basal characteristics of BS cell lines. Our differential gene expression analysis did not identify significantly upregulated genes in the BS class, largely because of the heterogeneity of this group . However, we determined To conclude, our regulon-based classification of bladder cancer cell lines may serve as an important guideline for studying the different regulons implicated in bladder cancer and trial of drug candidates relevant for targeting regulons. Aleyna Eray: Design of the study, computational and experimental analysis, writing of the manuscript. Serap Erkek-Ozhan: Design, supervision of the study, writing of the manuscript.