Prostate cancer (PCa) is the most commonly diagnosed cancer as well as the greatest source of cancer-related mortality in males of African ancestry (MoAA). Interestingly, this has been shown to be associated with single nucleotide polymorphisms around regions 2 and 3 of the 8q24 human chromosomal region. The non-protein coding gene locus Plasmacytoma Variant Translocation 1 (PVT1) is located at 8q24 and is overexpressed in PCa and, therefore, is also a candidate biomarker to explain the well-known disparity in this group. PVT1 has at least 12 exons that make separate transcripts which may have different functions, all of which are at present unknown in PCa. Our aim was to determine if any PVT1 transcripts play a role in aggressiveness and racial disparity in PCa. We used a panel of seven PCa cell lines including three derived from MoAA. Ribonucleic acid extraction, complementary deoxyribonucleic acid synthesis, and quantitative polymerase chain reaction (qPCR) were performed to evaluate expression of all 12 PVT1 exons. Each qPCR was performed in quadruplicates. At least four separate qPCR experiments were performed. Expression of PVT1 exons was inconsistent except for exon 9. There was no significant difference in exon 9 expression between cell lines derived from Caucasian males (CM), and an indolent cell line derived from MoAA. However, exon 9 expression in the aggressive MDA PCa 2b and E006AA-hT cell lines derived from MoAA was significantly higher than in other cell lines. Consequently, we observed differential expression of exon 9 of PVT1 in a manner that suggests that PVT1 exon 9 may be associated with aggressive PCa in MoAA. Prostate cancer (PCa) is the most common non-cutaneous cancer and the second leading cause of cancer-related death for men in the U.S. It is estimated that, in 2015, approximately 220,800 new cases of PCa will be diagnosed and 27,540 deaths will result from PCa . ConsequPCa is a heterogeneous disease, with multiple risks factors. The specific reasons for poor outcomes from PCa in males of African ancestry (MoAA) when compared to Caucasian males (CM) are not understood. However, it is widely believed that the causes of PCa disparities are complex and multifaceted. Two potential reasons are frequently proposed to explain this profound disparity in PCa: (1) MoAA present more often than CM with advanced incurable PCa due to more limited access to health care ,5,6; (2)The 8q24 human chromosomal region is one of the most important susceptibility genetic loci for PCa. Several studies have identified single nucleotide polymorphisms (SNPs) located in chromosome 8q24 as susceptibility markers for PCa ,10,11,12In recent years, non-protein coding RNAs (ncRNAs) have received special attention because they have been identified in many studies as being important in cancer biology. Substantial progress has been made in understanding the role of small non-coding RNAs such as microRNAs (miRNAs) in the development and progression of cancers ,15. HowePlasmacytoma variant translocation 1 (PVT1) is one of the transcribed lncRNAs located at the 8q24 PCa susceptibility locus . The PVTN-methyl-N-nitrosourea. WPE1-NA22 cells are mildly tumorigenic. MDA PCa 2b cells were derived from bone metastasis of prostate adenocarcinoma in a 64-year-old MoAA. PC-3 was derived from bone metastasis of a grade IV prostatic adenocarcinoma from a 62-year-old CM. The VCaP cell line was established from a vertebral bone metastasis from a 59-year-old CM with hormone refractory PCa. The E006AA cell line was established in 2004 by Koochekpour et al. [For this study, seven prostate epithelial cell lines were used, in representation of the heterogeneity notable in clinical PCa. The RWPE1 cells are epithelial cells derived from the peripheral zone of a histologically normal prostate from a 54-year-old CM. The cells were later transfected with a single copy of the human papilloma virus 18 (HPV-18) to finally establish the cell line. RWPE1 is non-tumorigenic. WPE1-NA22 cells were obtained by exposing RWPE1 to r et al. , from a All the cell lines were purchased from the American Type Culture Collection  except for the E006AA and E006AA-hT cell lines that were a kind gift from Dr. Shariar Koochekpour of the Roswell Park Cancer Institute .RWPE1 and WPE1-NA22 cells were maintained in Keratinocyte-Serum Free Medium  supplemented with 0.05 mg/mL bovine pituitary extract and 5 ng/mL human Epidermal Growth Factor. MDA PCa 2b cells were maintained in F-12K medium supplemented with 20% fetal bovine serum (FBS), 25 ng/mL cholera toxin, 10 ng/mL mouse epidermal growth factor, 0.005 mM phosphoethanolamine, 100 pg/mL hydrocortisone, 45 nM selenous acid and 0.005 mg/mL bovine insulin. PC-3 cells were maintained in F-12K medium supplemented with 10% FBS. VCaP cells were maintained in DMEM medium supplemented with 10% FBS. E006AA and E006AA-hT cells were maintained in DMEM medium supplemented with 10% FBS. All cell lines were also cultured with the presence of 1% Penicillin/Streptomycin.Prior to our study, the following PVT1 exons were described: 1a, 2, 3b, 4b, 7, 8 and 9 . HoweverAt 75% confluency, total RNA was extracted from cells in a 60 × 15 mm tissue culture dish, using RNeasy Mini Kit . After quantification with Nanodrop1000 spectrophotometer , 1 μg of RNA was reverse-transcribed into cDNA using QuantiTect Reverse Transcription kit . Amplification reactions were performed in 25 μL reaction volume using SYBR Green PCR master Mix , cDNA template and 0.4 μM final concentration for primers. The thermal cycle profile employed was as follows: 50 °C for 2 min, 10 min initial denaturation at 95 °C, and 40 cycles of 15 s denaturation at 94 °C, 1 min annealing at 65 °C. A dissociation curve was also added at the end of the cycle. The amplifications were carried out on the 7500 Real Time PCR machine . Messenger RNA (mRNA) expression was assessed in quadruplicates in at least 3 independent experiments and normalized to RPL32 mRNA expression. Relative expression levels were calculated by the Ct method (∆∆ Ct). Previously published RPL32 primer sequences were used [t test. p values less than 0.05 were deemed significant. A summary of all p values resulting from comparing each prostate cell line to the normal prostate cell line RWPE1 for all PVT1 exons are summarized in Data are presented as mean ± standard error of the mean (S.E.M) of at least three independent experiments. Statistical significance of differences was assessed using two-tailed Student’s We sought to assess the expression of 12 annotated PVT1 exons in various prostate cancer cell lines. Initial experiments focused on identifying exons that may be significantly overexpressed or underexpressed in PCa cell lines derived from MoAA.For PVT1 exon 1a and 1c, no significant differential expression was observed in the MDA PCa 2b PCA cell line derived from a male of African ancestry, when compared to the RWPE1 normal prostate epithelial cell line . For PVTp value = 0.00161) in relative expression by MDA PCa 2b in comparison to RWPE1 . However, PVT1 Exon 4a was overexpressed in the highly tumorigenic E006AA-hT also derived from a MoAA . Given that both MDA PCa 2b and E006AA-hT are highly tumorigenic and derived from MoAA, the dissimilar expression of PVT1 exon 4a in them suggests inconsistency and possible lack of importance.For PVT1 exon 2, we observed a small but significant decrease  higher . InteresThe long non-protein coding RNA (lncRNA) PVT1 has been shown to be important in cancer. PVT1 overexpression has been demonstrated in pancreatic cancer and colon cancer, and is related to poor prognosis in most of these cases ,28,29. HTo determine if a lncRNA is important, its cellular functions need to be elucidated. It will be important to determine if the lncRNA regulates important cellular functions or if it just represents “transcriptional noise” or background transcription. Although lncRNAs are sometimes aberrantly expressed in diseased tissues, suggesting specific functions in diseases, our knowledge of how these lncRNA act in the cell and which roles they might play in diseases in still very limited ,33. Theret al. reported that the amplification of PVT1 was involved in poor prognosis via the inhibition of apoptosis in colon cancers [Our results show that PVT1 exon 9 is consistently overexpressed in aggressively tumorigenic PCa cell lines derived from MoAA. This observed overexpression of PVT1 exon 9 in the aggressive PCa cell lines derived from MoAA suggests that PVT1 exon 9 may contribute to the high proportion of PCa aggressiveness in MoAA. To better understand the cellular and molecular mechanisms of this contribution, we need to understand the functional role of PVT1 exon 9 in PCa development and progression. To this end, its expression pattern in human PCa with varied clinico-pathological characteristics, its cellular functions, its molecular mechanisms of action, and its molecular targets will need to be determined. Some of this work has started to be done in other cancers. For example, in 2014, Takahashi  cancers .Assessing for a functional role for PVT1 exon 9 could be done by studying the effects of silencing of PVT1 transcripts using the RNAi approach. In this regard, Gray and colleagues were able to induce apoptosis in ovarian or breast cancer cell lines by silencing of PVT1 in cell lines with amplified chr.8q24 but they found no apoptosis in non-amplified cell lines . SimilarThe present study reports the overexpression of PVT1 exon 9 by aggressively tumorigenic PCa cell lines derived from MoAA. Based on this consistent overexpression of PVT1 exon 9 by aggressive PCa cell lines derived from MoAA, it is possible that PVT1 exon 9 is important and may contribute to the disproportionately increased aggressiveness of PCa in MoAA.


Triple-Negative Breast Cancer (TNBC) is an aggressive disease with a poor prognosis. Clinically, TNBC patients have limited treatment options besides chemotherapy. The goal of this study was to determine the kinase dependency in TNBC cell lines and to predict compounds that could inhibit these kinases using integrative bioinformatics analysis.in vitro kinase binding data to determine the kinase dependency in 12 TNBC cell lines. We employed Kinase Addiction Ranker (KAR), a novel bioinformatics approach, which integrated these data sources to dissect kinase dependency in TNBC cell lines. We then used the kinase dependency predicted by KAR for each TNBC cell line to query K-Map for compounds targeting these kinases. Wevalidated our predictions using published and new experimental data.We integrated publicly available gene expression data, high-throughput pharmacological profiling data, and quantitative In summary, we implemented an integrative bioinformatics analysis that determines kinase dependency in TNBC. Our analysis revealed candidate kinases as potential targets in TNBC for further pharmacological and biological studies. TP53, BRCA1/2, PIK3CA, and PTEN mutations and activation of PI3K/AKT and RAS/RAF/MEK signaling pathways. Many of these genes and pathways are regulated by kinases ; therefore providing an opportunity to identify potential druggable targets by small moleculesfor TNBC therapy.Triple-negative breast cancer (TNBC) is a subtype of breast cancer that is lacking the expression ofestrogen receptor (ER), progesterone receptor (PR) and HER2 (ERBB2). TNBC, aEGFR in EGFR-mutated non-small cell lung cancers (NSCLC) by erlotinib or gefitinib[BRAF in BRAF-mutated melanoma by vemurafenib[ALK in EML4-ALK NSCLC by crizotinib[KIT and PDGFRA in gastrointestinal stromal tumors[ROS1 in ROS1-fusion NSCLC patients[in vitro kinase binding assays have been developed to capture the complex interactions between inhibitors and kinases[Protein kinases represent one of the largest “druggable” and well-studied protein families in the human genome. This clgefitinib-10, inhimurafeniband inhibrizotinib. Furtheral tumors and criz patients. Large-sd kinases-17.in vitro kinase binding assays to deconvolute kinase dependency in biological systems[in vitro kinase binding data, and genomics data for dissecting kinase dependency in cancer cells.High-throughput screening (HTS) provides a different perspective to interrogate biological systems using chemical biology. Large-scale HTS studies such as Cancer Cell Line Encyclopedia (CCLE), Genomicl systems. Howeverin vitro kinase binding data. We employed our recently developed Kinase Addiction Ranker (KAR) to integrate these data sourcesto dissect kinase dependency in TNBC cell lines[The goal of this study was to determine the kinase dependency in TNBC cell lines and to predict compounds that could inhibit these kinases using integrative bioinformatics analysis. In this study, we used publicly available gene expression data, HTS pharmacological profiling data, and quantitative ell lines. We thenell lines,27for co50).We obtained the HTS pharmacological profiling data of 12 TNBC cell lines from a recently published paper . The 12 We obtained comprehensive quantitative kinase inhibition data for 72 of the 180 screened kinase inhibitors from literature. Drug sensitivity data from these 72 drugs were used in our algorithm. This comprehensive inhibition data allows for better interpretation of high-throughput screening results as most kinase inhibitors interact with far more kinases than the ones that are most commonly reported.We obtained the TNBC microarray gene expression data from the Cancer Cell Line Encyclopedia (GSE36133). These cell lines were profiled using Affymetrix HG-U133 Plus 2.0 microarrays. Raw CEL files for these cell lines were normalized using Robust Multiarray Average (RMA) approach in Affymetrix Power Tools (APT).We have recently developed KAR (Kinase Addiction Ranker), a novel computational method that integrates high-throughput drug screening data, quantitative kinase binding data, and transcriptomics data to define kinase dependency for individual cancer cell lines . For eac50/Kd< 1 μM or percent of inhibition > 85%). Transcriptomics data is used to filter out low expressed kinases. Kinases are scored by adding or subtracting points based on the sensitivity bin of each drug that inhibits the kinase. Finally, p-values are computed using chi-square and Fisher's exact tests to determine if there is a significant association between a kinase being inhibited and the drug being sensitive (Sensitivity Bins 1-3) in the cell line. KAR returns the ranked list of kinases based on p-values and scaled scores. Kinases with p < 0.05 will be deemed as significant and dependent by the cancer cell line. We have implemented KAR in two programming languages: python and Matlab. KAR is freely available at: http://tanlab.ucdenver.edu/KAR.Next, quantitative kinase binding data is dichotomized as inhibited or not inhibited for each compound based on user-defined threshold [d)[The current K-Map reference database was builtbased on two recently published comprehensive analyses of kinase inhibitor selectivity ,16. The 50). The secFor each TNBC line, the top five kinases ranked by KAR were used as the query kinase profile and connected through the K-Map in this study.http://tanlab.ucdenver.edu/kMap.K-Map implementsthe pattern matching strategy based on the Kolmogorov-Smirnov (KS) statistics. The KS-test is a non-parametric, rank-based pattern-matching approach implemented in the connectivity map. The algHCC1806 was obtained from the American Type Culture Collection (ATCC). The cell line has been authenticated as previously described,30. CellErlotinib and bosutinib were obtained commercially  and prepared according to the manufacture's guidelines.50 values calculated from a minimum of three experiments.To evaluate the drug effects in TNBC cells, we used the CellTiter-Glo assay. In brief, cell viability assayswere performed using CellTiter-Glo (Promega) according to manufacturer's instructions. TNBC cells were seeded at 4000 cells/well in a 96-well plate, and exposed to increasing concentrations of erlotinib or bosutinib from 0 - 10 μmol/L for 96 hours. CellTiter-Glo measurements were obtained for these different concentrations to determine cell viability. Cell viability curves were derived from the data and ICin vitrokinase inhibition profile and inhibition of at least one kinase above threshold (Kd/EC50<1 μM or >85% inhibition). We used KAR (Kinase Addiction Ranker), a novel bioinformatics approach, which integrates gene expression, drug sensitivity, and kinase inhibition data to generate a ranked list of kinase dependency in these TNBC cell lines. As described in the methods section, KAR integrates three data sources  to delineate kinase dependency in individual cell lines. On average, KAR identified 24 kinases with a significant association with drug sensitivity in each cell line (range: 9 - 46)  activates MAPK8/JNK signaling. MAP4K4 is involved in cell migration and invasion in melanoma, ovarian, breast and prostate cancers[MAP4K4 correlated with larger tumor size, increased lymph node involvement, and recurrence in pancreatic ductal carcinoma[PRKD3 has been shown to promote proliferation and chemoresistance in TNBC [MAP4K4 and PRKD3 are so frequently associated with drug sensitivity in this dataset, they may represent targets that could benefit larger populations of TNBC patients. MAP4K4 and PRKD3 are simultaneously inhibited by CDK1/2 inhibitor III and PKR inhibitor, which were sensitive in nearly all of the 12 cell lines.The kinases most commonly associated with drug sensitivity in the TNBC cell lines were e cancers. Moreovecarcinoma. PRKD3 h in TNBC ,34. SincNext, we performed hierarchical clustering on the scaled KAR scores to reveal relationships between the 12 TNBC cell lines and kinases  from all 180 drugs[EGFR-dependent BT20 and HCC1806 grouped together. The other cell lines show less distinct groupings with many cell lines being paired with different cell lines than when clustered based on kinase score. This suggests that the kinase dependency relationships derived from KAR are different from the relationships derived from clustering pharmacological profiling data.We also clustered the pharmacological profiling data , as the top kinase for this cell line. Indeed, previously published papers have verified that this cell line expressed high levels of EGFR[KAR ranks Epidermal Growth Factor Receptor [EGFR as one of the kinase dependent in this cell line. From the clustering of kinase dependency score . Interese Figure , BT20 anAURKA) as one of the top five kinases in this cell line. Previously, we have tested two different Aurora Kinase inhibitors across a large panel of TNBC cell lines, and found that HCC70 is very sensitive to MLN8237 (IC50 = 0.1 μM)[50 = 0.549 μM)[AURKA dependence in this cell line. In fact, there is an ongoing Phase II clinical trial of treating TNBC patients with ENMD2076 .KAR ranks Aurora Kinase A (= 0.1 μM) and ENMD0.549 μM). This suMAP3K7, which is commonly known as the Transforming growth factor beta-activated kinase 1 (TAK1), as the top kinase for this cell line. Interestingly, this cell line is the only TNBC cell line analyzed that is a KRAS mutant (p.G13D) and is highly dependent on KRAS ("KRAS-dependent" cell line)[MAP3K7 expression, and is sensitive to the TAK1 kinase inhibitor 5Z-7-oxozeaenol [TAK1 could be a therapeutic target in KRAS-dependent lines [KAR ranks ell line). Indeed,ozeaenol . Previount lines . This coMAP4K2, MAP4K3) and CDK kinases . A previous study evaluating kinase expression in Estrogen Receptor (ER) positive vs. negative breast cancer samples identified a subgroup in the ER-negative samples also enriched with MAPKs[KAR results indicate that the other TNBC cell lines seem highly dependent on MAPKs  as the query to K-Map for predicting effective compounds. Compounds with p < 0.05 are selected and sorted by connectivity scores. Table Staurosporine, a multi-kinase inhibitor used as the positive control in the K-Map, was predicted as an effective compound for ten TNBC cell lines Table . This isEGFR dependency as determined by KAR, and one of the targets of bosutinib is EGFR. In addition to EGFR, bosutinib also inhibits top ranking kinases YES1, TNK2, MAP4K4, and LYN (Table EGFR). We would therefore predict that HCC1806 would be more sensitive to bosutinib than erlotinib. To validate this prediction, we tested HCC1806 in vitro with bosutinib and erlotinib using a CellTiter-Glo assay. As depicted in Figure 50 of bosutinib(3 μM) is lower than erlotinib (>10 μM) in HCC1806. This validates the K-Map prediction that this cell line is more sensitive to bosutinibthan erlotinib. We also validated BT20 with bosutinib and erlotinib, and found that bosutinib also exhibited lower IC50 than erlotinib. (data not shown).From Table PDGFR inhibitors, where PDGFRB is the top dependent kinase predicted by KAR for this cell line. Indeed, previous studies have demonstrated that Hs578T has high expression of PDGFRB (both at mRNA and protein levels), and this cell line is more sensitive to sunitinib[K-Map predicts SU11652 and sunitinib as potential compounds to be effective against Hs578T. Both compounds are sunitinib.MYC-dependence is synthetic lethal with CDK inhibitor in TNBC cell lines[MYC-dependent [CDK 1/2 inhibitor was predicted by K-Map as one of the compounds that inhibits the top five kinases in nine TNBC cell lines . Recent studies have suggested that ell lines. Indeed,ependent . This suEGFR dependence for BT-20 and HCC1806, but our experiments showed much lower sensitivity to Erlotinibthan Fink et al. . Fink et al.'s clustering of the drug sensitivity datarevealed co-clustering of HCC70, BT549, and MDA-MD468, and reported increased sensitivity of this group to PI3K pathway inhibitors[PIK3CB inhibition and drug sensitivity in HCC70 and BT549, but much higher correlations with drug sensitivity for other kinases , and does not require preliminary optimization of the drug screening list. K-Map allows for quick connection of essential kinases revealed by KAR to compounds for experimental testing. K-Map can help reveal drugs that may not have been part of the original screening set. While we used this approach with TNBC cell lines, a similar strategy can be used with patient samples to predict effective kinase inhibitor therapies and drug combinations.Here, we presented examples of how the KAR algorithm and K-Map research tool can be integrated to determine kinase dependency and predict effective cancer drugs for TNBC. KAR aids greatly in preventing misinterpretation of HTS data, as kinase inhibitors typically inhibit many more targets than are commonly reported. KAR therefore helps uncover kinase dependency that may be overlooked if only focusing on the commonly reported drug targets. Moreover, incorporation of gene expression data can help ensure that high-ranking kinases will be translationally applicable. Compared to previous approaches,46-48, Kin vitro kinase binding data, and gene expression data. We then queried the top five kinases from each TNBC cell lines to K-Map to predict compounds that could inhibit these sets of kinases. We validated the KAR and K-Map predictions using experiments and published studies. Using the integrative bioinformatics analysis, we have discovered kinase dependency in these TNBC cell lines. The data provide candidate kinases and drugs for further pharmacological and biological studies.We presented an integrative bioinformatics analysis to determine kinase dependency in TNBC. We integrated three different high-throughput data sources with the KAR algorithm: HTS pharmacological profiling data, quantitative AURKA: Aurora Kinase A; EGFR: Epidermal Growth Factor Receptor; HTS: High-throughput Screening; KAR: Kinase Addiction Ranker; NSCLC: Non-small cell lung cancer; TNBC: Triple Negative Breast Cancer.The authors declare that they have no competing interests.KAR and ACT conceived the study, coordinated the experiments, performed acquisition of data, participated in data analysis, and drafted the manuscript. LEH and JK contributed to the study design. KAR, JK, JS, MY and ACT conducted bioinformatics analysis. PK, AI, TMP, JTT, JRD and SGE conducted experimental validation. All authors read and approved the final manuscript.


This project is set out to: i) Establish a collection of astrocytoma cell lines generated from Saudi patients, ii) Select drug resistant brain tumour CSCs, iii) Characterize drug resistant brain tumour CSCs individually, and iv) Deduce common features for drug resistant brain tumour CSCs.The Cancer Stem Cells (CSCs) hypothesis proposes that malignant brain tumours are organized into aberrant cell hierarchies where a subset of parent CSCs replicate asymmetrically and unlimitedly to produce differentiated daughter cell ,2. Thesein vivo tumourgenic assays.A range of methodologies will be applied including, tumour cell line derivation methods, the clonogenic selection assay, cytogenetics profiles, neuronal cancer stem cells characterisation assays and Optimisation of methods used to establish astrocytoma cell lines generated from Saudi patients was completed. To date two novel primary astrocytoma cell lines have been derived. Full clinical data related to the cell lines was retrieved. Currently, optimisation of the clonogenic selection assay and immunostaining for cancer stem cells markers are under progress.Successful retrieval of primary astrocytoma cell lines was possible to accomplish. Important clinical information data relevant to the cell lines were obtained providing clear identification reference. Further work is required for the characterization of the drug resistant cancer stem cell component within these cell lines. Once completed, this research will assist in understanding some of the molecular mechanisms relevant to drug resistance, especially for patients in the Kingdom of Saudi Arabia.


The authors have provided the following notice of correction regarding the contamination of two cell lines used in their study. Corrected versions of all figures and supporting information files can be found below or attached to this notice.HeLa cell populations. Experiments putatively using HCC-M and HCC-T cells were described in the main body and the figures of the manuscript several times.Cell line authentication  identified cell lines named HCC-M and HCC-T in the publication ’Comparative analysis of TGF-β/Smad signaling dependent cytostasis in human hepatocellular carcinoma cell lineś , e72252) as no outlying cell line in our study combining characteristics of both groups. In fact our results rather suggest that in HCC cell lines, TGF-β signaling can be classified exactly using the criteria applied in the present paper. In contrast, HeLa cells which originate from a different tumor entity  display a completely different TGF-β signaling pattern than HCC cell lines.Generally spoken, these results cannot be assigned to HCC-M or HCC-T specific characteristics, but need to be considered to serve as experimental controls. However, and most importantly, our finding directly strengthens the conclusion of the publication that HCC cell lines can be assorted into two clusters according to their sensitivity for TGF-β dependent cytostatic effects. Additionally, we can now state, that there is To further validate robustness of our findings with the other cell lines used in the study, we did confirm identity of those. Huh7, HLE, HLF, HepG2 and PLC/PRF/5 were purchased from major cell banks by SFB/TRR77 and provided to us right before the study was performed. STR analysis of Hep3B, HepG2, HLE, Huh6 and FLC-4 confirmed the correct identity. Yet, we need to note that FLC-4 was identified as JHH-4, as a reference STR profile for FLC-4 is not available since the cell line is not deposited at one of the major cell banks. However, JHH-4 is the parental cell line of FLC-4, which was obtained by starvation mutation from JHH-4 , and thus explains the analogous STR analysis results. Finally, we could exclude the possibility that the HeLa cross contamination of HCC-M and HCC-T occurred in our laboratory. Assessment of a new source of cells directly obtained from the originally providing laboratory  by STR analysis, showed the same HeLa cross contamination. Of course, we informed the laboratory of origin about this finding.Table S1Primary and secondary antibodies used for immunoblot analysis. Antibodies were obtained from Cell Signaling , Santa Cruz Biotechnology , Sigma Aldrich , BD Bioscience  or Epitomics . After blocking the membrane with 5 % non fat milk powder in TBST, the membrane was incubated with the first antibody over night at 4 °C applying careful agitation. After removal of excessive antibodies, the membrane was incubated with the second antibody in TBST for 3-4 h at room temperature.(TIF)Click here for additional data file.Table S2Origin of the cell line, the original patient characteristics , cell lines passages.(DOCX)Click here for additional data file.Figure S118S rRNA is a suitable reference gene for real time PCR analysis of the used liver cancer cell lines. (A) 18S rRNA is equally expressed between the different liver cancer cell lines. Real Time PCR experiments were performed using 18S rRNA as reference gene. An analysis of the Ct values of 18S rRNA revealed minor fluctuations confirming the suitability of 18S rRNA as reference gene. Results are shown as mean +/- SE for 3 (HCC-M and HuH6) to 4 independent experiments. (B) TGF-beta treatment of HCC cell lines resulted in no or negligible changes of expression of 18S rRNA in liver cancer cell lines. The diagram shows the mean deviation (ΔCt) of 18S rRNA Ct values from control and TGF-beta treated samples (24 h) of the same cell line. Results are presented as the mean +/- SE of 2-3 independent experiments.(TIF)Click here for additional data file.Figure S2Densitometric analysis (ImageJ software) of Western Blots in  Results are shown as mean +/- SE of the indicated numbers of independent experiments.(TIF)Click here for additional data file.Figure S3Densitometric analysis (ImageJ software) of Western Blots presented in  Results are shown as mean +/- SE of the indicated numbers of independent experiments.(TIF)Click here for additional data file.Figure S4Densitometric analysis (ImageJ software) of Western Blots presented in  Results are shown as mean +/- SE of the indicated numbers of independent experiments.(TIF)Click here for additional data file.Figure S5Densitometric Analysis (ImageJ software) of Western Blots in  Results are shown as mean +/- SE of the indicated numbers of independent experiments.(TIF)Click here for additional data file.Figure S6Densitometric analysis ((ImageJ software) of Western Blots in  Results are shown as mean +/- SE of the indicated numbers of experiments.(TIF)Click here for additional data file.


KRAS-mutant and both our analysis and a previous publication [KRAS-wild type. However, given the controversy we recommend that these cells are not be used as bladder cancer models.SW-850, included in our paper as a bladder cancer cell line, has been reported by several authors to be a pancreatic cancer cell line –5. This The Materials and Methods section of our paper indicated that the following cell lines were obtained from ATCC: 253 J, 575A, 639 V, JON, MGH-U4, SW-800, SW-1710, VM-CUB-2. However, these cultures have never been distributed by the ATCC. Therefore, they are available from us if other investigators are interested in using them.http://www.ncbi.nlm.nih.gov/biosample/SAMN03151953 ,http://web.expasy.org/cellosaurus/CVCL_8155, http://iclac.org/wp-content/uploads/Cross-Contaminations-v7_2.pdf). We have used fingerprinting analysis to confirm this fact and the genetic identity of the cells/DNAs used in our experiments . Nevertheless, our data indicate that the two cultures we used as VM-CUB-1 and VM-CUB-3 are distinct at the genomic level. Furthermore, as shown in Table It has been reported that VM-CUB-3 is a VM-CUB-1 contaminant (Following the publication of our recent article in BMC Genomics  a numberIn the last few years there has been much emphasis on the need to accurately designate, identify, and characterize cancer cell lines as they are precious tools for cell biology studies , 7. It i


Although natural products have been used by man since ancient times, it was only in the past century that active components of medicinal plants became available in chemically pure form. An impressive number of natural products have been introduced into medical practice and also used as lead or model molecules for structure optimization and for the development of more potent or better-tolerated drugs. A recent comprehensive review found that only 20.2% of the anticancer agents approved in the period 1981–2010 were purely synthetic, whereas the remaining 79.8% were natural products or inspired by natural products , indicating that structures from nature are indispensable in anticancer lead-finding research. Sclerocarya birrea gave rise to oxidative stress and apoptosis in HepG2 cells . Fractions of the methanolic extract of Croton sphaerogynus exhibited antiproliferative activity against adherent cancer cell lines . The mechanism of the anticancer action of the ethyl acetate extract of the traditional Chinese medicinal plant Euphorbia helioscopia was explored through the use of a hepatocellular carcinoma xenograft model in nude mice. The treatment with the extract resulted in apoptosis induction, decreased tumor growth, and invasion . Two papers in this issue concentrate on the isolation and subsequent investigation of natural products. Various antiproliferative xanthones were isolated from Garcinia species and two of them directly inhibited JAK kinase . The natural products hamigerone and radicinol, isolated from fungi by means of a bioassay-guided procedure, exhibited antiproliferative and proapoptotic properties in adherent cancer cell lines without affecting the viability of normal cells . Two further publications deal with previously identified compounds that were earlier not investigated in detail oncopharmacologically. Vitamin K1 displays antiproliferative activity, causes cell cycle arrest, and induces apoptosis in human colon cancer cell lines . The sesterterpene heteronemin exerted considerable growth-inhibiting activity against human cancer cell lines, including A498 renal carcinoma cells, resulting in apoptosis through the mitochondrial pathway and also autophagy in A498 cells . Two papers describe potentially relevant interactions between natural products and therapeutically utilized anticancer agents. Curcumin and one of its analogues exhibited additive properties with the proteasome inhibitor bortezomib against HL60 cells . Three ecdysteroids sensitized both multidrug-resistant and nonresistant adherent cell lines towards traditional anticancer agents, including doxorubicin . One of the presented papers reports on the validation of an in vitro method for the screening of potential anticancer extracts or compounds. Instead of the use of cultured cancer cell lines, slices of breast cancer tissues obtained from surgery were incubated with antiproliferative agents and the viabilities of the slices were determined .In this special issue, we present a set of original research papers concerning various aspects of the antiproliferative properties of plant extracts or isolated natural products. Three of them describe investigations with promising extracts. The methanolic root extract ofWe strongly trust that these publications in this special issue will contribute to the development of natural product-based and innovative drug candidates.


Boswellia serrata, B. carterii) is used as traditional remedy to treat inflammatory diseases. The molecular effects of the active ingredients, the boswellic acids, on the immune system have previously been studied and verified in several clinical studies. Boswellic acids also inhibit cancer cell growth in vitro and in vivo. The molecular basis of the cytotoxicity of boswellic acids is, however, not fully understood as yet. By mRNA-based microarray, COMPARE, and hierarchical cluster analyses, we identified a panel of genes from diverse functional groups, which were significantly associated with sensitivity or resistance of α- or β-boswellic acids, such as transcription factors, signal transducers, growth regulating genes, genes involved in RNA and protein metabolism and others. This indicates that boswellic acids exert profound cytotoxicity on cancer cells by a multiplicity of molecular mechanisms.Frankincense ( B. sacra) are deciduous trees growing in China, India, the Arab peninsula, and some African countries . The resin gum of these trees is called frankincense or olibanum and is used not only for religious rituals, but also for medicinal purposes in different civilizations , the percentage growth was calculated as:The cytotoxicity of phytochemical compounds towards the NCI cell line panel was evaluated by determining the IC B assay  (http://Growth inhibition of 50% (GI50) was calculated from [(Ti-Tz)/(C-Tz)] × 100 = 50, which was the drug concentration resulting in a 50% reduction in the net protein increase (as measured by SRB staining) in control cells during the drug incubation. The drug concentration resulting in total growth inhibition (TGI) was calculated from Ti = Tz.2.4.http://dtp.nci.nih.gov). For hierarchical cluster analysis, objects were classified by calculation of distances according to the closeness of between-individual distances by means of. All objects were assembled into cluster trees (dendrograms). Previously, cluster models have been validated for gene expression profiling and for approaching molecular pharmacology of cancer [The mRNA microarray hybridization of the NCI cell line panel has been described ,14 and tf cancer ,15. Hier50 values for α- and β-boswellic acids were selected from the NCI database. The mRNA expression has been determined by microarray analyses as reported [50 values for the corresponding compound. To derive COMPARE rankings, a scale index of correlations coefficients  was created. In the standard COMPARE approach, greater mRNA expression in cell lines correlate with enhanced drug resistance, whereas in reverse COMPARE analyses greater mRNA expression in cell lines indicated drug sensitivity. Pearson's correlation test was used to calculate significance values and rank correlation coefficients as relative measure for the linear dependency of two variables. This test was implemented into the WinSTAT Program .For COMPARE analysis, the mRNA expression values of genes of interest and ICreported . COMPAREreported . Briefly3.3.1.50 values for both compounds have been determined over a dose range of 10−8 to 10−4 M in the cell line panel and deposited at the database of the NCI's Developmental Therapeutics Program. The log10 IC50 mean values for these cell lines grouped according to their tumor type are shown in 10 IC50 values of −8 to −5 M) than boswellic acids . Second, leukemia cell lines were most sensitive to standard anticancer agents, but were most resistant towards boswellic acids. On the other hand, prostate cancer which were most sensitive to boswellic acids, were only intermediate responsive towards standard agents  Boswellia carterii (syn. B. sacra) contains a number of other phytochemicals in addition to boswellic acids. Among them dipentene, farnesol, and borneol are also deposited in the NCI database. This allowed us to subject the IC50 values of α- and β-boswellic acid and those of dipentene, farnesol, and borneol to Pearson's correlation test and to investigate the cross-resistance of cell lines towards these five phytochemicals. Although the correlation of IC50 values for α-boswellic acid and those for β-boswellic acid reached a significance level of P < 0.001, the correlation coefficient was rather weak  and R < −0.55 (reverse COMPARE) are listed in We further investigated the microarray-based transcriptomic mRNA expression by COMPARE analyses to test whether sensitivity and resistance to the boswellic acids were correlated with expression of similar or different sets of genes. We mined the genome-wide mRNA expression database of the NCI and correlated the expression data with the ICPML, GLI1, PHKA1), RNA and protein metabolism  and others . Genes associated with sensitivity or resistance towards β-boswellic acid were also transcriptional factors and signal transducers , growth regulators  or others   .50 data for these compounds that had not been included before the cluster analysis. Indeed, the distribution of cell lines being sensitive or resistant to the compounds was significantly different between the branches of the dendrograms. The distribution of cell lines among the dendrogram in −7; χ2-test), but not towards β-boswellic acid or other phytochemicals of Boswellia carterii   . While stigation .50 values in 60 tumor cell lines showed that boswellic acids inhibited cell lines at higher concentrations than established anticancer drugs. This may indicate that boswellic acids are less effective than classical cytostatic drugs. However, the final efficacy is not only determined by the cytotoxicity of a compound, but also by the concentration window affecting tumor cells in comparison to normal cells. A major disadvantage of most established anticancer drugs is their severe toxicity on normal organs. In contrast, boswellic acids are well tolerated and severe side effects are rare events as pointed out in a recent meta-analysis of clinical trials on Boswellia preparations [In the present investigation, we analyzed the response of cancer cells towards boswellic acids. A comparison of the ICarations .in vitro results, it could be speculated that boswellic acids are favorable for the treatment of prostate cancer, albeit clinical experiences are still missing supporting this point of view.Another interesting feature of boswellic acids was that prostate cancer cell lines were more sensitive towards these compounds than other cell lines of other tumor types. In many cases, leukemia cells lines are more sensitive to cytotoxic compounds than cell lines from solid cancer types. From these 50 values for dipentene borneol, farnesol were available. Therefore, we have chosen these five phytochemicals to exemplarily analyze cross-resistance among these compounds. Interestingly, we observed that the 60 tumor cell lines did not exert cross-resistance between α- and β-boswellic acid on the one hand and dipentene, borneol, farnesol on the other hand. The development of drug resistance is a tremendous problem in clinical oncology. A general concept of drug resistance has been described by Goldie and Coldman [B. carterii provides a similar scenario: small subpopulations resistant to boswellic acids do not survive when they are treated with dipentene, borneol, or farnesol. Hence, boswellic acid resistance of the entire tumor cell population may be avoided. The point of view that phytotherapeutical preparations represent combination therapies, because they contain a multitude of different bioactive phytochemicals, has not been extensively discussed in the literature. Recently, we made comparable observations for different compounds of another medicinal herb, Artemisia annua L. [Frankincense gum resin preparations are commercially available rather than isolated boswellic acids. Therefore, the question arises, whether other compounds in addition to α- and β-boswellic acids may also exert cytotoxicity towards cancer cells. While the boswellic derivatives, acetyl-ß-boswellic acid, acetyl-boswellic acid, 11-keto-ß-boswellic acid, and acetyl-11-ß-boswellic acid, are not deposited in the NCI database, the IC Coldman . Startin Coldman . Goldie  Coldman . Sublethannua L. . It can 50 values for both compounds of 60 tumor cell lines with transcriptomic mRNA expression levels of this cell line panel [50 values for investigational drugs to identify candidate genes for drug resistance [Furthermore, we analyzed molecular determinants of sensitivity and resistance of cancer tumor cell lines towards α- and β-boswellic acids. By microarray-based gene expression and COMPARE analyses, we correlated the ICne panel . This apne panel . Clustersistance  and to isistance .We identified genes from diverse functional groups, which were tightly associated with the response of tumor cells to boswellic acids such as transcription factors and signal transducers, growth regulating genes, genes involved in RNA and protein metabolism and others. Although these genes have not yet been associated with cellular response to boswellic acids, the results can be reconciled with a proposed role of boswellic acid in a growth inhibitory activity towards cancer cells. The gene-hunting approach applied by us delivered several novel candidate genes that may regulate the response of cancer cells to boswellic acids. These results merit further investigation to prove the contribution of these genes to boswellic acid resistance. Remarkably, inflammation-related genes did not appear in our analysis. Cancer development is frequently preceded by inflammatory processes -10, and 4.The fact that genes associated with sensitivity or resistance against α- and β-boswellic acid were from diverse functional groups speaks for the multiplicity of mechanisms whereby boswellic acids exert their inhibitory effects towards cancer cells. Multiplicity of mechanisms can mean that boswellic acids either have multiple targets leading to multiple effects, or one target leading to activation or inactivation of multiple mechanisms downstream of this target. A general feature of natural products is their multi-specificity. Rather than acting on one single target, multiple targets and pathways are affected . Multi-s


Most accounts of human life history propose that women have short reproductive spans relative to their adult lifespans, while men not only remain fertile but carry on reproducing until late life. Here we argue that studies have overlooked evidence for variation in male reproductive ageing across human populations. We apply a Bayesian approach to census data from Agta hunter-gatherers and Gambian farmers to show that long post-reproductive lifespans characterise not only women but also males in some traditional human populations. We calculate three indices of reproductive ageing in men  and identify a continuum of male reproductive longevity across eight traditional societies ranging from !Kung, Hadza and Agta hunter-gatherers exhibiting low levels of polygyny, early age at last reproduction and long post-reproductive lifespans, to male Gambian agriculturalists and Turkana pastoralists showing higher levels of polygyny, late-life reproduction and shorter post-reproductive lifespans. We conclude that the uniquely human detachment between rates of somatic senescence and reproductive decline, and the existence of post-reproductive lifespans, are features of both male and female life histories, and therefore not exclusive consequences of menopause. Human adult life is characterised by slow senescence, mid-life fertility decline and a long post-reproductive lifespan In contrast, most models postulate that as a rule men carry on reproducing into late life The models above have in common the fact that they do not investigate the possibility that male reproductive lifespans may vary among human populations. Current evidence strongly suggests that, as a rule, men remain fertile in late life, with a few reports of age-related declines in spermatogenesis, but no evidence of widespread male infertility in mid- or late life Here we analyse evidence for differences in the duration of male post-reproductive lifespans in eight traditional populations, the largest sample assembled in a comparative study of reproductive senescence in men, and the first to include hunter-gatherers , forager-horticulturalists , farmers  and pastoralists (Turkana). We apply a new Bayesian methodology to analyse census data for two populations  and show that a detachment between somatic and reproductive senescence and long post-reproductive lifespans may characterise not only menopausal women b represents the ‘rate of ageing’ or age-dependent increase in the probability of death Estimating survival trajectories for traditional populations is often problematic due to small samples and large number of right-censored cases  and hence indirect methods such as model life tables are frequently applied b represents the ‘rate of reproductive senescence’, or age-dependent increase in the probability of last reproduction (‘reproductive death’). Our definition of reproductive senescence therefore relates exclusively to the event of reproductive cessation in men and women, and not to any underlying physiological process (such as menopause in women). Based on the two coefficients somb (rate of ageing or somatic senescence) and repb (rate of reproductive senescence), we extend the comparison between somatic vs. reproductive senescence in !Kung women proposed by Alberts et al. R package BaSTAWe adapted the Bayesian procedure to calculate probabilities of last reproduction by age. In this case, we first built a matrix  for each population and both sexes, with data on each row describing an individual reproductive history, and each column representing a year in which reproduction has occurred (denoted by 1) or not occurred (denoted by 0). We used this matrix to estimate Gompertzian curves of age-dependent probability of last reproduction; the curves are therefore formally equivalent to mortality curves representing age-dependent probability of death. Since models are now based on reproduction data, the estimated Gompertz coefficient Data on oldest recorded age at last reproduction (OLR), age-dependent survival and fertility, and polygyny (meaning the fraction of married men having more than one wife) in the !Kung and Ache are respectively from Howell xm) by age-specific mortality (xl). Sums or integrals of age-dependent xmxl products are used in measures of fitness such as the Euler-Lotka equation or the net reproductive rate R after the age at last female reproduction) divided by total net reproductive rate . We also estimated post-reproductive representation (PrR), a measure of the post-reproductive fraction of adult life, based on survival and fertility curves from the eight populations and both sexes. For more details on definition and calculation of PrR see ref BaSTA and the Bayesian procedures described above and in Tuljapurkar et al. repb = 0.155, 95% credible interval = 0.135–0.178, DIC = 13573.6) is significantly higher than the Gompertz rate of ageing . The same is true for Gambian women . This is expected in women due to menopause, however differences between rates of somatic and reproductive senescence were also observed in men, both in the Agta  and rural Gambians . Therefore the detachment between rates of ageing and reproductive senescence, which differentiates humans from other primates, is not an exclusive consequence of menopause and is observed in men from two populations markedly differing in adult survival We compared rates of somatic and reproductive senescence in two populations using Gompertz models of age-dependent survival and probability of last reproduction . In AgtaWe also found significant evidence for variation in ages of reproductive termination in men across traditional populations. Male OLR reported in the Dobe !Kung was 54 years old xmxl products, or age-dependent fertility xm weighed by age-dependent survival xl) are significantly smaller than the fraction of male late-life realised fertility (based only on xm) calculated by Tuljapurkar et al. Like maximum observed longevity, OLR is sensitive to factors such as small sample size or a single exceptional individual The detachment between somatic and reproductive senescence in both sexes and variation in contribution of late-life reproduction suggests differences in the duration of male post-reproductive lifespans across populations. Levitis et al. Alberts et al. We also compiled data on polygyny levels in the eight populations . The ratThe Dobe !Kung, Hadza, Agta, Ache and Tsimane also show the lowest levels of MLR (13.8% or less), and polygyny levels below 10%. Yanomamo horticulturalists show high levels of polygyny (29.8%) and low levels of MLR (10.2%). High levels of MLR are observed in rural Gambians (29.9%) and Turkana pastoralists (51%), where polygyny is widespread at 40.2% and 79.8% respectively.We have shown that late-life reproduction and duration of post-reproductive lifespans in men are highly variable across traditional populations. Previous claims that men either reproduce until much later than women Between-population variation in the timing of male reproductive cessation contrasts with the more rigid pattern of reproductive senescence in women. While menopause occurs universally and prevents reproduction in late life, studies of male reproductive physiology have reported only ‘modest’ changes in spermatogenesis at old age, with possible effects on fertility remaining questionable Finally, our results are relevant for evolutionary accounts of human life history. Alberts et al. In summary, our study draws attention to the generally neglected topic of post-reproductive lifespans in men, which are significantly long in some traditional societies even in the absence of a post-fertile stage similar to observed in menopausal women. It also highlights a spectrum of variation in the duration of male reproductive spans across societies differing in polygyny levels and subsistence mode. Our sample of eight traditional populations is the largest so far assembled in a study of male reproductive senescence, and we hope that our study inspires collection and publication of data on male age-dependent reproduction in other traditional populations. More information, especially on pastoralist and farming societies, is needed to allow a proper statistical assessment of relationships between subsistence mode, polygyny and measures of reproductive senescence in men. In particular, data on Australian hunter-gatherers, ‘complex’ hunter-gatherers, and other pastoralist societies, presently unavailable or non-existent, could significantly extend our knowledge of variability and evolution of late-life reproduction, reproductive senescence and post-reproductive lifespans in men.File S1Additional information on Bayesian estimation of survival and probability of last reproduction curves.(DOCX)Click here for additional data file.


In this work, an efficient strategy for the synthesis of novel homoisoflavonoids, dioxolodioxol-5-ol which was reacted with 3-bromopropanoic acid followed by the reaction of oxalyl chloride to afford 6,7-dihydro-8H-dioxolochromen-8-one. The aldol condensation of the later compound with aromatic aldehydes led to the formation of the title compounds. Five novel derivatives 4a-e were tested for their cytotoxic activity against three human breast cancer cell lines including MCF-7, T47D, and MDA-MB-231 using the MTT assay.Our synthetic route started from benzodioxolochromen-8-one (4a) exhibited the highest activity against three cell lines. Also the analysis of acridine orange/ethidium bromide staining results revealed that 7-benzylidene-6,7-dihydro-8H-dioxolochromen-8-one (4a) and 7-(2-methoxybenzylidene)-6,7-dihydro-8H-dioxolochromen-8-one (4b) induced apoptosis in T47D cell line.Among the synthesized compounds, 7-benzylidene-6,7-dihydro-84b-4d was investigated in and it was revealed that it did not improve the activity of dioxolochromen-8-ones against MCF-7, T47D, and MDA-MB-231.Finally, the effect of methoxy group on the cytotoxicity of compounds  Ophiopogondioxol-5-yloxy)propanoic acid 2 and 6,7-dihydro-8H-dioxolochromen-8-one 3 prepared according to chromen-8-ones 4a-e was achieved using MTT colorimetric assay. The in-vitro cytotoxic activity of all synthesized compounds were evaluated against three human breast cancer cell lines including MCF-7, T47D and MDA-MB-231 using MTT colorimetric assay according to the method of Mosman dioxolodioxol-5-ol 1 -6,7-dihydro-8H-dioxolochromen-8-one (4d) .H-dioxolochromen-8-one (4b) and 7-(3-methoxybenzylidene)-6,7-dihydro-8H-dioxolochromen-8-one (4c) possessing methoxy (OMe) group at ortho and meta positions were prepared to compare their bioactivities against the studied cell lines with that of the control. Also other two derivatives, 7-benzylidene-6,7-dihydro-8H-dioxolochromen-8-one (4a) and 7--6,7-dihydro-8H-dioxolochromen-8-one (4e) were prepared to investigate the effect of methxoy substituent on the cytotoxicity -6,7-dihydro-8H-,3dioxoloin vitro cytotoxic activity of dioxolochromen-8-one derivatives 4, were tested against three human breast cancer cell lines including MCF-7, T47D, and MDA-MB-231. The 50% growth inhibitory concentration (IC50) for all derivatives were calculated and depicted in Table The ivity of ,3dioxoloH-dioxolochromen-8-one (4a) showed the highest activity against MCF-7, T47D, and MDA-MB-231 cell lines with IC50 values of 6.2 ± 0.1, 4.6 ± 0.1, and 9.3 ± 2.1 μg/ml, respectively. In contrast, 7--6,7-dihydro-8H-dioxolochromen-8-one (4e) did not show any cytotoxicity at the concentrations used. It seems that the presence of benzo[d]dioxole in benzylidene moiety decreases the cytotoxic activity of the corresponding compound. As can be seen in Table 4b, 4c, and 4d), different results were observed. All of them were inactive against MCF-7 cell line (IC50 > 100 μg/ml), whereas they exhibited good activity against T47D cell line with IC50 values of 5.7 ± 0.07, 18.8 ± 2.3, and 9.2 ± 2.9 μg/ml, respectively. It should be noted that compounds 4b and 4c were active against MDA-MB-231 cell line and 4d did not show any activity in this cell line. Presence of OMe in benzylidene moiety did not play crucial role on the improvement of cytotoxicity effects.According to MTT assay results in Table H-,3dioxoloH-dioxolochromen-8-one (4a) and 7-(2-methoxybenzylidene)-6,7-dihydro-8H-dioxolochromen-8-one (4b) induced apoptosis in T47D cell line  showed the highest activity against the three studied cell lines. Also the analysis of acridine orange/ethidium bromide staining results revealed that the cytotoxic effect of 7-benzylidene-6,7-dihydro-8H-dioxolochromen-8-one (4a) and 7-(2-methoxybenzylidene)-6,7-dihydro-8H-dioxolochromen-8-one (4b) may be due to inducing apoptosis in cancer cell lines.In conclusion, novel dioxolo[4,5-The authors declare that they have no competing interests.EA: Supervision of the synthetic part. ZM: Synthesis of the title compounds. ZS: Synthesis of the title compounds. MP: Performed the cytotoxic tests. MS: Performed the cytotoxic tests and collaborated in manuscript preparation. LF: Design of target compounds. NM: Synthesis of the title compounds. MS: collaborated in manuscript preparation. SKA: Supervision of the cytotoxic tests. AS: Collaboration in identifying the structures of target compounds. AF: Design of target compounds and supervision of the synthetic and pharmacological parts. All authors read and approved the final manuscript.


NSC757963 showed high selectivity towards the leukemia subpanel. Activities of both compounds strongly correlated to expression of NFKB1 and CSNK2B genes, implying that they may inhibit the NF-κB pathway. Immunocytochemical microscopy of OVCAR-3 cells showed clear cytosolic accumulation of the NF-κB p65 subunit following treatment. Western blotting showed dose dependent inhibition of the nuclear expression of the NF-κB p65 subunit with subsequent accumulation in the cytosol following treatment. Docking experiments showed binding of both compounds to the NF-κB activator IKKβ subunit preventing its translocation to the nucleus. Collectively, these results confirm the ability of our compounds to inhibit the constitutively active NF-κB pathway of OVCAR-3 cells. Furthermore, COMPARE analysis indicated that the activity of NSC757963 is similar to the antituberculosis agent rifamycin SV, this was confirmed by testing the antimycobacterial activity of NSC757963 against Mycobacterium tuberculosis, results revealed potent activity suitable for use in clinical practice. Molecular properties and Lipinski’s parameters predicted acceptable bioavailability properties with no indication of mutagenicity, tumorigenicity, irritability and reproductive effects. Oral absorption experiments using the human Caco-2 model showed high intestinal absorption of NSC745885 by passive transport mechanism with no intestinal efflux or active transport mechanisms. The unique molecular characterization as well as the illustrated anticancer spectra of activity and bioavailability properties warrant further development of our compounds and present a foundation brick in the pre-clinical investigations to implement such compounds in clinical practice.The novel compounds NSC745885 and NSC757963 developed at our laboratory were tested against a panel of 60 cancer cell lines at the National Cancer Institute, USA, and a panel of 39 cancer cell lines at the Japanese Foundation of Cancer Research. Both compounds demonstrated selective unique multi-log differential patterns of activity, with GI The anthracycline antibiotics daunorubicin and doxorubicin, having the anthraquinone moiety, were introduced in clinical practice more than 30 years ago for treatment of a wide variety of cancers, but have limited use due to cardiotoxicity and bone marrow toxicity , 2. Simi1, these effects are different than what is observed with its parent drug doxorubicin , as well as the full panel of 39 human cancer cell lines of the Japanese Foundation for Cancer Research (JFCR-39) , the relevant COMPARE analysis, and the Results of the initial single dose (10 μM) testing of NSC745885 and NSC757963 against the 60 cell lines of NCI are presented in 50), the 50% lethal concentration (LC50) and the total growth inhibition (TGI), compared to the values of untreated control cells.Both NSC745885 and NSC757963 satisfied the pre-determined threshold inhibition criteria of the NCI-60 One-Dose Screening, so they were tested against the panels of 60 cancer cell lines of NCI (NCI-60) and 39 cancer cell lines of JFCR (JFCR-39), each cell line was tested at five small doses of our compounds. Results are expressed as the 50% growth inhibition concentration  cell line showing a small sub-micro molar GI50 value of 0.16 μM followed by the leukemia MOLT-4, ovarian cancer OVCAR-3 and OVCAR-4, and melanoma LOX IMVI cell lines showing sub-micro molar GI50 values of 0.49, 0.55, 0.65 and 0.82 μM, respectively. The least growth inhibitory activity was for the non-Small cell lung cancer NCI-H322M cell line (GI50 = 17.4 μM)  followed by the lung cancer DMS114, ovarian cancer OVCAR-3 and melanoma LOX-IMVI cell lines showing sub-micro molar GI50 values of 0.5, 0.67, and 0.86 μM, respectively. The least activity was for the colon cancer HT-29 cell line (GI50 = 19 μM)   & S1 Fig= 19 μM)  & S2 Fig50 values ranged from 0.33 μM to 72.8 μM with the exception of one non-small cell lung cancer cell line (A549/ATCC), which showed a GI50 value of more than 100 μM. The leukemia cell lines showed a significant sensitivity to the compound; three out of five of the tested cell lines namely CCRF-CEM, HL-60(TB) and MOLT-4 showed sub-micro molar GI50 values as small as 0.33, 0.42 and 0.46 μM, respectively . The least activity was for the colon cancer HT-29 cell line (GI50 = 21 μM) and the lung cancer A549 cell line (GI50 = 20 μM)   & S4 FigDespite the potent anticancer activity of doxorubicin, its cardiotoxicity limits its use in clinical practice , 2. We d50 values. The mean GI50 values of all the NCI 60 cell line subpanels ranged from 0.97 to 7.2 μM. The strongest activity was towards the leukemia subpanel and the least one was towards the colon cancer subpanel. Similarly, the mean GI50 values of all the JFCR cell line subpanels ranged from 0.86 to 5.36 μM. The strongest activity was towards the melanoma subpanel and the least one was towards the colon cancer subpanel.To characterize the selectivity of NSC745885 and NSC757963 further among the different types of cancer, we determined their selectivity ratios towards each cell line subpanel used in the cytotoxicity experiments . NSC745850 values of the NCI subpanels ranging from 1.4 to 20.7 μM and of the JFCR subpanels ranging from 1.72 to 5.9 μM . It showed also a broad-spectrum inhibitory activity against the other NCI subpanels; the selectivity ratios of other NCI subpanels were less than 3. Similar broad-spectrum activity was observed among the JFCR subpanels where all of the tested subpanels showed selectivity ratio values of less than 3. Furthermore, NSC757963 showed remarkable growth inhibitory activity, which is evident from the low mean GIo 5.9 μM  Tables.Compounds having similar activity profiles (fingerprints) usually have similar mechanisms of action and modes of resistance. A method named “COMPARE analysis” was developed to measure the degree of correlation between the compounds of interest and the known drugs as well as the known molecular targets in the NCI databases using the Pearson’s correlation coefficient (PCC) determination as its principle . The PCCIn order to identify the molecular targets that may account for the activity of our compounds, we mined the NCI “Molecular Targets” database  to correlate the expression of such targets to the activity of our compounds. We identified multiple molecular targets that correlated strongly with our compounds (positive and negative correlations were identified). Results showed that the activity of NSC745885 is positively correlated to the expression of the following genes: ZNF184, NFKB1, PDHB, ARMC10, ZXDC, SYNCRIP, NXF1, RING1, CSNK2B and CDC5L with PCC values ≤ 0.566, while its activity is correlated to the suppression of the following genes: S100A10, PPP2R2C, PCSK6, TXN, TRIM16, EIF4E, CIB1, NXN, GCLC and CDC14B with PCC values ≤ -0.672 . MGMT is a cellular DNA repair protein that restores the damaged DNA to its normal state  transported with respect to time according to the following equation +, 299.9760 (C14H5ClN2O2S2+); found, 299.9766.For NSC745885, 1,2-diaminoanthraquinone  dissolved in tetrahydrofuran (30 mL) was reacted with thionyl chloride (0.5 mL) and triethylamine (3 mL) for 1 h at room temperature. The reaction mixture was treated with crushed ice (100 mL) and the resulting precipitate was washed with diethyl ether and purified by crystallization from ethanol  Abcam # ab32536, anti-TATA binding protein (TBP) mouse antibody (1TBP18), or anti-beta Tubulin rabbit antibody [NB600-936]. After washing, membranes were incubated with either goat anti-rabbit IgG H&L (HRP) (ab6721), or rabbit anti-mouse IgG H&L (HRP) (ab6728) secondary antibodies. Membranes were developed using the Immobilon Western chemiluminescent HRP substrate kit. Protein bands were normalized to either the beta Tubulin or TBP bands and quantified using ImageJ software.Mycobacterium tuberculosis to NSC757963 was tested using the BACTEC ™ MGIT ™ 960 SIRE Kit [Mycobacterium tuberculosis H37Rv reference strain was cultured in 96 well plates containing Middlebrook 7H9 broth with or without the compounds to be tested at different concentrations . Cultures were incubated for 14 days after which growth in every well was observed. Susceptibility to the tested drugs was calculated as the minimum concentration of the drug that can completely inhibit the growth of M. tuberculosis. Four antimycobacterial drugs, which are Streptomycin (1 μg/mL), Isoniazid (0.1 μg/mL), Rifampin (1 μg/mL), and Ethambutol (5 μg/mL) were tested along with NSC757963 as control drugs to assure the susceptibility of the tested strain and to validate the experimental procedures.The antimycobacterial susceptibility of SIRE Kit . BrieflyChromatographic separations and analysis were performed using a Hitachi L-2000 series system with UV detector  equipped with a C18 reverse-phase column . Analyses for NSC745885 and NSC757963 were performed using their maximum absorbance wavelengths . HPLC grade methanol was used as the mobile phase and ran at a rate of 1.0 mL/min.The properties displayed in 5 cells/cm2 onto Corning Transwell® polyester membrane inserts  in 12-well tissue culture plates and cultured in Dulbecco’s modified Eagle’s medium with high glucose (4500 mg/liter glucose) and L-glutamine (without pyruvate) supplemented with 10% FBS, 1% non-essential amino acids and 1% penicillin/streptomycin. Cells were incubated for at least 21 days at the specified conditions with the change of medium every two days. The TEER was measured using Millicell® ERS device for each insert after every change of medium to monitor the development of the cells monolayer. Cell monolayers with TEER values less than 300 Ω.cm2 at the end of the maintenance period were not suitable for the experiment and discarded [3 (pH = 7.4) as the transport buffer in the basolateral compartment. NSC745885 was dissolved in the transport buffers with the aid of 1% DMSO to get final concentrations between 4–5 μM. Samples from the receiver compartments were withdrawn at different time intervals  and the amounts of transported drug were determined using the HPLC. After the transport experiments, the TEER was checked for every cell monolayer to assure that the monolayers were not compromised from the effect of the tested drug. The apparent permeabilities were calculated as mentioned in the previous section.The transport of our compounds through the Caco-2 cells was tested according to Hubatsch et al., 2007 . Brieflyiscarded . The intiscarded . The traMycobacterium tuberculosis H37Rv strain. Finally, results showed that NSC745885 will be completely absorbed from the human intestine if administered orally. In short, the findings of this study provide an integral part of the pre-clinical investigation of our lead compounds that warrant their further development as anticancer agents against the multi-drug resistant tumors with excellent oral absorption.Based on the cytotoxic spectra of activity of NSC745885 and NSC757963, their correlations to compounds of the NCI and the JFCR databases, and the identified molecular targets, we present two promising selective lead compounds with wide spectrum of cytotoxic activities against different cancer types rather than normal cells, and acceptable molecular, bioavailability and ADME properties. Both compounds exhibit activity spectra comparable to those of the standard drugs of the NCI database that are currently used in the clinical practice or in the clinical trials, with NSC757963 displaying high selectivity towards the leukemia subpanel. Both compounds have strong correlations with many effective molecular targets that are involved in the anticancer treatment strategies such as inhibiting the NF-κB which was confirmed by the immunocytochemical imaging, Western blotting and molecular docking. Moreover, activities of both compounds were not correlated to the expression of MGMT chemoresistance gene. Furthermore, NSC757963 may present a novel anti-tuberculosis agent, this was confirmed by determining the MIC against the S1 Fig10 scale, the zero represents log10 of the mean values (MID or MG-MID) of each of the GI50, TGI and LC50. Values to the right side of zero indicate more sensitivity of the cell lines to the tested compound than the mean value and those to the left side indicate more resistance to the tested compound than the mean value. Delta values are the difference between the mean values (MID or MG-MID) and the log10 of each corresponding values of the GI50, TGI and LC50 for the most sensitive cell line. Range values are the difference between log10 each of the GI50, TGI and LC50 values for the most resistant cell line and log10 each of the corresponding values of GI50, TGI and LC50 for the most sensitive cell line.X-axis is constructed based on the log(TIF)Click here for additional data file.S2 Fig10 scale; log10 of the mean values (MG-MID) of each of GI50, TGI and LC50 are represented by the zero on the X-axis. Delta values are the difference between the MG-MID and the log10 of each corresponding values of the GI50, TGI and LC50 for the most sensitive cell line. Range values are the difference between log10 each of the GI50, TGI and LC50 values for the most resistant cell line and log10 each of the corresponding values of GI50, TGI and LC50 for the most sensitive cell line. Values to the right side of zero indicate more sensitivity of the cell lines to the tested compound than the mean and those to the left side indicate more resistance to the tested compound than the mean. Br: breast, CNS: central nervous system, Co: colon, Lu: lung, Me: melanoma, Ov: ovary, Re: renal, St: stomach, xPg: prostate.X-axis is constructed based on the log(TIF)Click here for additional data file.S3 Fig10 scale, the zero represents log10 of the mean values (MID or MG-MID) of each of the GI50, TGI and LC50. Values to the right side of zero indicate more sensitivity of the cell lines to the tested compound than the mean value and those to the left side indicate more resistance to the tested compound than the mean value. Delta values are the difference between the mean values (MID or MG-MID) and the log10 of each corresponding values of the GI50, TGI and LC50 for the most sensitive cell line. Range values are the difference between log10 each of the GI50, TGI and LC50 values for the most resistant cell line and log10 each of the corresponding values of GI50, TGI and LC50 for the most sensitive cell line.X-axis is constructed based on the log(TIF)Click here for additional data file.S4 Fig10 scale; log10 of the mean values (MG-MID) of each of GI50, TGI and LC50 are represented by the zero on the X-axis. Delta values are the difference between the MG-MID and the log10 of each corresponding values of the GI50, TGI and LC50 for the most sensitive cell line. Range values are the difference between log10 each of the GI50, TGI and LC50 values for the most resistant cell line and log10 each of the corresponding values of GI50, TGI and LC50 for the most sensitive cell line. Values to the right side of zero indicate more sensitivity of the cell lines to the tested compound than the mean and those to the left side indicate more resistance to the tested compound than the mean. Br: breast, CNS: central nervous system, Co: colon, Lu: lung, Me: melanoma, Ov: ovary, Re: renal, St: stomach, xPg: prostate.X-axis is constructed based on the log(TIF)Click here for additional data file.S5 Fig(TIF)Click here for additional data file.S6 Fig(TIF)Click here for additional data file.S7 Fig(TIF)Click here for additional data file.S8 Fig(TIF)Click here for additional data file.S9 Fig(TIF)Click here for additional data file.S10 Fig(TIF)Click here for additional data file.S1 Tablea The average value of GI50 of every cell line panel tested in the five-dose NCI 60 cell line screen experiments. b The average value of GI50 of all of the tested cell lines in the five-dose NCI 60 cell line screen experiments and is equivalent to the mean graph midpoint (MID). c Compounds are rated as “selective” to the cell line panel if the ratio is more than 6, rated as “moderately selective” if the ratio is between 3 and 6, and rated as “non selective” if the ratio is less than 3.(DOCX)Click here for additional data file.S2 Tablea The average value of GI50 of every cell line panel tested in the five-dose JFCR 39 cell line screen experiments. b The average value of GI50 of all of the tested cell lines in the five-dose JFCR 39 cell line screen experiments and is equivalent to the mean graph midpoint (MG-MID). c Compounds are rated as “selective” to the cell line panel if the ratio is more than 6, rated as “moderately selective” if the ratio is between 3 and 6, and rated as “non selective” if the ratio is less than 3.(DOCX)Click here for additional data file.S3 Tablea Only compounds showing salient correlations with NSC745885 were selected and displayed in this table. Compounds appearing more than once in the COMPARE analysis results (due to difference in number of tested cell lines or in the hiConc of the compared experiments) were not included in this table. However the order of ranking of all of the compared compounds is retained. b This coefficient ranges from -1 to +1. Compounds with positive coefficient values approaching 1 have high similarities with the test compound, while those with negative coefficient values approaching -1 have high differences with the test compound.(DOCX)Click here for additional data file.S4 Tablea Only compounds showing salient correlations with NSC745885 were selected and displayed in this table. Compounds appearing more than once in the COMPARE analysis results (due to difference in number of tested cell lines or in the hiConc of the compared experiments) were not included in this table. However the order of ranking of all of the compared compounds is retained. b This coefficient ranges from -1 to +1. Compounds with positive coefficient values approaching 1 have high similarities with the test compound, while those with negative coefficient values approaching -1 have high differences with the test compound.(DOCX)Click here for additional data file.S5 Tablea This coefficient ranges from -1 to +1. Compounds with positive coefficient values approaching 1 have high similarities with the test compound, while those with negative coefficient values approaching -1 have high differences with the test compound.(DOCX)Click here for additional data file.S6 Tablea Only compounds showing salient correlations with NSC757963 were selected and displayed in this table. Compounds appearing more than once in the COMPARE analysis results (due to difference in number of tested cell lines or in the hiConc of the compared experiments) were not included in this table. However the order of ranking of all of the compared compounds is retained. b This coefficient ranges from -1 to +1. Compounds with positive coefficient values approaching 1 have high similarities with the test compound, while those with negative coefficient values approaching -1 have high differences with the test compound, and a value of zero indicates no correlation at all.(DOCX)Click here for additional data file.S7 Tablea This coefficient ranges from -1 to +1. Compounds with positive coefficient values approaching 1 have high similarities with the test compound, while those with negative coefficient values approaching -1 have high differences with the test compound, and a value of zero indicates no correlation at all.(DOCX)Click here for additional data file.


Different genomic technologies have been applied to cell line authentication, but only one method (short tandem repeat [STR] profiling) has been the subject of a comprehensive and definitive standard (ASN-0002). Here we discuss the power of this document and why standards such as this are so critical for establishing the consensus technical criteria and practices that can enable progress in the fields of research that use cell lines. We also examine other methods that could be used for authentication and discuss how a combination of methods could be used in a holistic fashion to assess various critical aspects of the quality of cell lines. While definitive methods for human cell line authentication have been compiled in a documentary standard, this Perspective maintains that more research is required to provide similar vetting of methods for non-human cell lines. Cell line authentication is still poorly reported, despite the prevalence of misidentification, the calls for community action, and the estimates of wasted research dollars –6. Studihttp://www.scoop.it/t/cell-line-contamination/p/4040895974/2015/04/08/which-journals-ask-for-cell-line-authentication) lists journals with authentication policies and links to those policies. The Prostate Cancer Foundation has had an initiative requiring cell line authentication and contamination testing for grantees since 2013, and since then a campaign to encourage cell line authentication has been initiated by the Global Biological Standards Institute [The critical nature of the issue of cell line authentication has led to requirements being put in place by funding agencies and publishers for authentication and purity of cell lines. The National Institutes of Health (NIH) has revised guidelines to applications for funding  and provnstitute .Authentication of Human Cell Lines: Standardization of STR Profiling. This is a very thorough and helpful document that explains why, and precisely how, to authenticate human cell lines [Why are more labs not authenticating their human cell lines? Possibly, many researchers are unaware of the existence of a documentary standard that codifies specific guidance and laboratory protocols and the availability of authentication services that can make the process relatively easy. The American National Standards Institute (ANSI) and the American Type Culture Collection (ATCC) provide a documentary standard (ASN-0002) for human cell lines based on the use of short tandem repeats (STR) entitled ll lines , and proUnlike the situation for human cell lines, authentication of nonhuman cell lines is less clear and is burdened by the immature state of development of kits, testing services, databases, and accepted standards for STR or other DNA markers.In addition to authentication, there are other issues that should be considered for assessing the quality of an experimental cell line. By quality, we mean those characteristics that allow assessment of the appropriateness of the cell line for its purpose. As we consider how to develop and encourage the use of methods and standards for qualifying cell lines, we should keep in mind that the use of cell lines in biomedical research is only one application that requires quality assessment. The need for quality assessment of cell lines that are used for clinical products in the form of cell and/or gene therapies and for production of pharmaceutical products will inform and be informed by the research community’s needs for rapid and reliable quality control data. These methods and standards may also find application to identification and quality assurance of biopsied tissues and food products. Similarly, cell lines modified in the context of synthetic biology also require technologies for qualification.Here we suggest a comprehensive view of cell line assessment in order to lay out a larger landscape that might be considered as the community makes decisions about technologies and standards in this arena.There are a number of methods available that provide relevant data on the quality of cell lines, and some of the more commonly used methods appear in Sequence-specific profiles of SNP and STR provide the power to discriminate between individuals of the same species. Interspecies contamination can also be detected with these methods if appropriate primers or probes are included in the test. Whole genome sequencing (WGS) is a powerful technique that, in principle, can provide information about species identity as well as the presence of mutations and contaminating species, including adventitious agents. At the current time, the technical and bioinformatic resources required make WGS expensive; however, as the cost continues to decrease for sequencing and bioinformatic approaches become more routine, this approach will become more practical for routine characterization of cell lines. Phenotypic characteristics such as morphology or gene expression profiles can also be indicative but can be strongly influenced by the culture environment, so strict experimental controls would have to be in place to conclude misidentification. But if one wants to reproduce an experiment, quantitative phenotypic and gene expression profiles can support a claim that cell culture environment is highly similar. Depending on circumstances, it is possible that all of the methods listed in The development of community-vetted documentary standards for these methods can play an important role in their acceptance and ease of use. The ANSI/ATCC document on STR profiling for cell line authentication that was developed by an international working group of experts led by ATCC and contributed to by NIST and others is an excellent example of how to develop comprehensive standards. While most if not all of the supporting information for the standard is provided in traditional peer-reviewed publications such as , officiaFor the development of ASN-0002, STR profiling was chosen as the preferred method for human cell line authentication because of the extensive work that has been done in human identification for forensics purposes. Forensic identification is almost exclusively based on STR analysis for the following reasons: (1) discriminatory power, (2) established testing infrastructure, (3) cost, (4) effectiveness for degraded DNA samples, (5) comparability of STR profiling data from various platforms, and (6) ability to detect human DNA mixtures . As a rehttp://www.ncbi.nlm.nih.gov/books/NBK144066/. STR profiling was selected as the basis of a standard for human cell line authentication because of its ability to discriminate human cell lines with a relatively small number of allelic markers; comparable data can be achieved across different laboratories, making it feasible to establish a database against which data can be compared; STR profiling kits are available so tests can be run in individual laboratories; it is a relatively rapid and low-cost test; and it doesn’t require as high a level of technical expertise as some other methods.The ASN-0002 documentary standard is summarized in an electronic book that is available online  at http:The standard consists of over 100 pages that include detailed protocols for DNA extraction, STR profiling, data analysis, quality control of the data, interpretation of results, and implementation of a public searchable database. Different methods are compared, all necessary algorithms are provided, and guidance on how to evaluate and interpret data and troubleshoot anomalies is described. How to assess the quality of the data and the interpretation are addressed rigorously, including the use of an allelic ladder , how to assess assay artifacts such as stutter and allelic dropout, appropriate controls, accurate quantification of DNA, etc. A carefully considered basis for assigning a “match” or “mismatch” is based on a matching metric of ≥80% . The carhttp://www.atcc.org/STR%20Database.aspx; http://www.jhsf.or.jp/bank/Category-Index.html; https://www.dsmz.de/services/services-human-and-animal-cell-lines/online-str-analysis.html). The DSMZ database allows profile searching against cell lines from all of these databases. The existence of STR profile databases is critical for providing a benchmark profile against which a user can compare their cell line by searching by name of the cell line or by STR profile. The National Center for Biotechnology Information (NCBI) also hosts the BioSample database (http://www.ncbi.nlm.nih.gov/biosample/), which contains the STR profiles of the most frequently used cell lines from the BRCs (listed above) as well as the International Cell Line Authentication Committee (ICLAC) database (http://iclac.org/databases/cross-contaminations/), which list human cell lines that are misidentified based on their STR profiles.As a result of this standard, the STR profiles of many human cell lines have been collected by the Biological Resource Centers (BRCs), namely the ATCC, the Deutsche Sammlung von Mikroorganismen und Zellkulturen (DSMZ), the Japanese Collection of Research Bioresources (JCRB), and RIKEN, and these data are available in public databases . Standard methods for CO1 DNA barcoding for distinguishing animal cell lines at the level of species have been developed by the Consortium for the Barcode of Life [Species-Level Identification of Animal Cells through Mitochondrial Cytochrome c Oxidase Subunit 1 (CO1) DNA Barcodes is now available [http://www.barcodinglife.org, and NCBI, http://www.ncbi.nlm.nih.gov/genbank/barcode) for matches. Species-specific primer assays [Standards for using SNP markers for authentication of human cell lines have been proposed ,21 and s of Life . The ATCvailable . The conr assays ,23 proviWhen considering authentication of nonhuman cell lines, the extensive body of technical knowledge on STR profiling, which is captured in the ANSI standard and by other organizations , and the proven record of practicality and fit-for-purpose of STR profiling, make it an obvious choice for consideration as a standard method for nonhuman cell lines.A number of issues that accompanied the development of the ANSI/ATCC standard for human cell lines will have to be considered for nonhuman cell lines. These include the following: identifying reliable polymorphic STR markers that provide intraspecies discrimination; having many laboratories collect STR data on the same cell lines, establishing and accumulating consensus data for informative STR markers that produce reliable results to consistently characterize a cell line; determining the metrics by which the raw data will be deemed to be of sufficient quality; and determining the best methods for assessing concordance in STR marker data and what level of concordance is sufficient to call a “match.” These are tasks that will need to be undertaken in a community effort. STR databases for nonhuman species need to be developed using this process.Having community-vetted documentary standards is a critical factor that is helpful for facilitating implementation of cell line authentication. Measurements can provide confidence in the use of cell lines. Community action around how to apply measurements reliably and the development of comparative data are key to achieving a comprehensive toolkit for assuring the quality of cell lines. The existence of the ANSI/ATCC standard (ASN-0002) for STR profiling of human cell lines and ASN-0003 for species barcoding are critical first steps and provide excellent examples that can be built on. Similar activities should be undertaken for the other methods discussed here.Despite the existence of the very thorough and highly vetted documentary standard, human cell line authentication is still not commonly performed. In part, this may be due to the lack of awareness of this document and the need for increased education and training programs on cell line authentication. The ASN-0002 document could provide an excellent basis for educational materials to be used for training in procedures for achieving robust qualifying data. It is also likely that greater availability of contract testing services and clearer funding and publishing priorities will be required to stimulate the application of the capabilities and standards that are already in place for human cell line identification and to spur the development of similar standards for the important nonhuman cell lines. Consensus standards that are produced in a careful, open, and official process are an integral part of the success of this endeavor. Standards help to assure that data are sharable and can be the basis of decision-making and compliance. The collection of high-quality data from a large range of cell lines and different labs is an initial necessary step. What will follow are consensus standards and access to the data, which will facilitate labs and companies to provide the testing services and encourage the widespread adoption of this essential part of quality assurance of any process that utilizes cell lines.S1 Text(DOCX)Click here for additional data file.S2 Text(DOCX)Click here for additional data file.


The benefits of single-use technologies in both upstream and downstream operations are now widely acknowledged by the biopharmaceutical industry, and have resulted in radical changes to the design and operation of many pharmaceutical processes. In mammalian cell culture, multi-use, cleanable glass or stainless steel stirred tank reactors (STRs) have been used successfully for growth of suspended cell lines for both small and large scale systems. However, to achieve the same or better performance from a single-use bioreactor presents a significant challenge to product designers and developers.The poster describes the studies aimed at characterizing the fluid dynamics of the Allegro STR 1000 bioreactor by measurement and modelling of specific physical parameters known to be critical for mammalian cell culture: more specifically, oxygen transfer rates, mixing times and carbon dioxide stripping rate.The study was conducted to determine the mixing efficiency, kLa O2 and CO2evacuation rate. For the mixing study, pH probes were located in eight different locations. For kLa O2 and CO2 evacuation rate determination two probes in the standard probe inserts were used (P1 & P2). A cell culture media simulant was used for all experiments: bicarbonate buffer, pluronic F68, antifoam A.Mixing studies were conducted by performing a single shot addition of 4M NaOH and then measuring the change in pH of 1000 L of cell culture media simulant at ambient temperature. No significant difference was seen in the mixing time measured between the eight probe positions. Mixing time was defined as the time required to reach 100 ± 5 % of the final pH value. 1000 L of media simulant was heated up to 37 ºC and brought down to pH 6.5 using CO2 gas and maintained stable for a duration of 2 minutes. A pCO2 probe recorded the initial pCO2. Air was then sparged through the ring sparger of the bioreactor at the desired flow rate and the change in pH measured.Our studies have shown that, the Allegro STR 1000 single-use bioreactor can be engineered suitably to provide a practical alternative to stainless steel bioreactors. By evaluating the hydrodynamics of the Allegro STR 1000bioreactor, we established that the direct drive large impeller could provide power input for fluid volumes up to 1000 L that can support the growth of mammalian cells. This power transfer in conjunction with a customized cubical biocontainer with integrated baffles produced homogeneous mixing. User experience is also enhanced by providing features that make the installation and use of the bioreactor intuitive and easy.