Resistance formation after initial therapy response (acquired resistance) is common in high-risk neuroblastoma patients. YM155 is a drug candidate that was introduced as a survivin suppressant. This mechanism was later challenged, and DNA damage induction and Mcl-1 depletion were suggested instead. Here we investigated the efficacy and mechanism of action of YM155 in neuroblastoma cells with acquired drug resistance. The efficacy of YM155 was determined in neuroblastoma cell lines and their sublines with acquired resistance to clinically relevant drugs. Survivin levels, Mcl-1 levels, and DNA damage formation were determined in response to YM155. RNAi-mediated depletion of survivin, Mcl-1, and p53 was performed to investigate their roles during YM155 treatment. Clinical YM155 concentrations affected the viability of drug-resistant neuroblastoma cells through survivin depletion and p53 activation. MDM2 inhibitor-induced p53 activation further enhanced YM155 activity. Loss of p53 function generally affected anti-neuroblastoma approaches targeting survivin. Upregulation of ABCB1 (causes YM155 efflux) and downregulation of SLC35F2 (causes YM155 uptake) mediated YM155-specific resistance. YM155-adapted cells displayed increased ABCB1 levels, decreased SLC35F2 levels, and a p53 mutation. YM155-adapted neuroblastoma cells were also characterized by decreased sensitivity to RNAi-mediated survivin depletion, further confirming survivin as a critical YM155 target in neuroblastoma. In conclusion, YM155 targets survivin in neuroblastoma. Furthermore, survivin is a promising therapeutic target for p53 wild-type neuroblastomas after resistance acquisition (neuroblastomas are rarely p53-mutated), potentially in combination with p53 activators. In addition, we show that the adaptation of cancer cells to molecular-targeted anticancer drugs is an effective strategy to elucidate a drug's mechanism of action. YM155 (sepantronium bromide) was introduced as a transcriptional suppressor of survivin expression that displayed activity against a broad range of cancer types in preclinical models.3 However, further studies suggested that the YM155-induced inhibition of survivin expression may be a secondary effect downstream of YM155-induced DNA damage5 or associated with Myeloid Cell Leukemia 1 (Mcl-1) depletion.6Survivin, a member of the inhibitor of apoptosis protein (IAP) family, comprises a nodal protein implicated in a multitude of cellular pathways, including apoptosis and mitosis regulation, and is frequently found highly expressed in cancer cells, making it a potential target for anticancer therapies.rCDDP1000), doxorubicin (UKF-NB-6rDOX20), or vincristine (UKF-NB-3rVCR10 and UKF-NB-6rVCR10). Neuroblastoma is the most frequent solid extracranial pediatric cancer entity. About half of the patients are diagnosed with high-risk disease associated with overall survival rates below 50%, despite myeloablative therapy and differentiation therapy using retinoids.8 Although many neuroblastomas respond initially well to therapy, acquired drug resistance represents a major obstacle in clinical practice.8 Survivin had been previously shown to be a potential drug target in neuroblastoma.13 However, survivin had not been investigated as a therapeutic target in the acquired resistance setting in neuroblastoma prior to this study. Our principal findings are that survivin is a promising drug target in p53 wild-type neuroblastoma cells with acquired drug resistance and that YM155 impairs neuroblastoma cell viability in clinically achievable concentrations via survivin depletion. The drug-resistant cell lines displayed decreased sensitivity to YM155, with upregulation of the ATP-binding cassette (ABC) transporter ATP Binding Cassette Subfamily B Member 1  and downregulation of Solute Carrier Family 35 Member F2  as the major drug-specific resistance mechanisms and loss of p53 function as resistance mechanism that affects all approaches targeting survivin in neuroblastoma. In accordance with these findings, neuroblastoma cells adapted to YM155 displayed decreased levels of SLC35F2, increased levels of ABCB1, a p53 mutation, decreased levels of survivin, and decreased sensitivity to RNAi-mediated survivin depletion.Here we investigated the mechanism of action of YM155 in a panel consisting of the neuroblastoma cell lines UKF-NB-3 and UKF-NB-6 and their sublines with acquired resistance to cisplatin (UKF-NB-3rCDDP1000) or vincristine (UKF-NB-3rVCR10), as well as the UKF-NB-6 sublines resistant to doxorubicin (UKF-NB-6rDOX20) or vincristine (UKF-NB-6rVCR10), displayed substantially reduced YM155 sensitivity compared to the parental cell lines, resulting in IC50 values ranging from 5.32 nM (UKF-NB-3rCDDP1000) to 49.3 nM (UKF-NB-6rVCR10)  . There w6rVCR10) .14 Among the investigated cell lines, UKF-NB-3rVCR10, UKF-NB-6rDOX20, and UKF-NB-6rVCR10 display detectable ABCB1 activity  polymerase (PARP) cleavage  inhibition.of YM155  and p53 of YM155 , suggest4The anticancer mechanism of action of YM155 is under dispute. The compound was introduced as a suppressor of survivin expression. However, this mechanism was challenged and some studies suggested that the YM155-induced inhibition of survivin expression may be a secondary effect downstream of YM155-induced DNA damage.γ-histone H2A (γH2AX) levels indicating DNA double-strand breaks became only detectable after 24 h at YM155 concentrations of 10 nM or higher (γH2AX levels and Checkpoint Kinase 2 (Chk2) phosphorylation in UKF-NB-3 and UKF-NB-6 cells  not detected after 24 h treatment of p53-depleted UKF-NB-3 cells with YM155 concentrations of up to 25 nM , and that p53 is involved in mediating anti-neuroblastoma effects as a consequence of RNAi- or YM155-induced reduction of cellular survivin levels.rYM15520nM). UKF-NB-3rYM15520nM cells displayed a 497-fold increased resistance to YM155 (IC50: 303±8 nM) compared to parental UKF-NB-3 cells (IC50: 0.61±0.08 nM)  sensitized UKF-NB-3rYM15520nM cells to YM155 (IC50: 90.5±3.1 nM) but not to the level of parental UKF-NB-3 cells  . Moreove SLC35F2 , and har-3 cells .rYM15520nM cells also displayed reduced survivin levels (UKF-NB-3n levels  and redun levels . This furCDDP1000), doxorubicin (UKF-NB-6rDOX20), and vincristine (UKF-NB-3rVCR10 and UKF-NB-6rVCR10). The YM155 IC50 values ranged from 0.49 nM (UKF-NB-3) to 49.3 nM (UKF-NB-6rVCR10). This is in the range of therapeutically achievable plasma levels that were documented to reach about 50 nM.22 Notably, animal models indicated that the YM155 concentrations in tumor tissues are likely to be higher than those found in the plasma.23 Previously, YM155 IC50 values were reported to range from 3.2 to 11 nM in melanoma cells24 and from 2 to 50 nM in multiple myeloma cells.25 In a screen of 124 cell lines from 17 cancer entities, the YM155 IC50s ranged from 0.35 to >1000 nM.26 In a panel of 23 neuroblastoma cell lines, the YM155 IC50s ranged from 0.5 to >10 000 nM.10 This shows that the sensitivity to YM155-mediated survivin depletion may substantially vary even in cells from the same cancer type. Hence, it will be critical to understand which cancer cells are particularly sensitive to YM155 and survivin depletion in order to design tailored therapies.Here we investigated the efficacy of the survivin suppressant YM155 in a panel of neuroblastoma cell lines consisting of the V-Myc Avian Myelocytomatosis Viral Oncogene Neuroblastoma Derived Homolog (MYCN)-amplified neuroblastoma cell lines UKF-NB-3 und UKF-NB-6 and their sublines with acquired resistance to cisplatin , UKF-NB-6rDOX20 (from 11.80 to 1.35 nM), and UKF-NB-6rVCR10 (from 49.30 to 3.60 nM), but not to the level of the parental cell lines that displayed IC50 values of 0.49 nM (UKF-NB-3) and 0.65 nM (UKF-NB-6). Hence, additional resistance mechanisms appear to contribute to the decreased YM155 sensitivity that was observed in the drug-resistant neuroblastoma sublines in comparison to the respective parental chemosensitive cells.The parental cell lines UKF-NB-3 and UKF-NB-6 displayed a similar response to YM155 treatment. The YM155 IC50s were comparable , and YM155 induced survivin depletion and p53 induction in both cell lines. All investigated drug-resistant sublines of UKF-NB-3 and UKF-NB-6 showed substantially reduced YM155 sensitivity relative to the parental cell lines. Three mechanisms may explain the decreased YM155 sensitivity in the drug-resistant cancer cell lines (at least to a large degree): upregulation of ABCB1, downregulation of SLC35F2, and loss of p53 function. Upregulation of the ABC transporter ABCB1 had already been shown to affect the efficacy of YM155.15 The drug-resistant neuroblastoma cell lines UKF-NB-3rCDDP1000, UKF-NB-6rDOX20, and UKF-NB-6rVCR20 were characterized by reduced SLC35F2 expression relative to the respective parental cells. Hence, these decreased SLC35F2 levels are likely to contribute to the increased YM155 resistance observed in these cell lines when compared to the respective parental cell lines. This notion is further supported by our finding that SLC35F2 depletion increases the resistance of ABCB1-expressing neuroblastoma cells. Since SLC35F2 and ABCB1 affect intracellular YM155 concentrations, these represent drug-specific resistance mechanisms that may not be shared by other therapeutic strategies that target survivin.YM155 is transported by SLC35F2 into cells and lack of SLC35F2 has been associated with decreased cellular YM155 sensitivity.rVCR10 was the only drug-resistant cell line that did not display decreased SLC35F2 levels. However, ABCB1 inhibition did not resensitize UKF-NB-3rVCR10 cells to YM155 to the level of the parental UKF-NB-3 cell line. In contrast to the other investigated cell lines, UKF-NB-3rVCR10 harbors a C135F loss-of-function p53 mutation.18 Hence, loss of p53 function may impair the YM155 sensitivity of neuroblastoma cells. Indeed, p53 depletion resulted in decreased YM155 sensitivity of UKF-NB-3 and UKF-NB-6 cells. Moreover, the nutlin-3-resistant UKF-NB-3 subline UKF-NB-3rNutlin10μM that harbors a G245C loss-of-function p53 mutation18 displayed reduced sensitivity to YM155 relative to UKF-NB-3. Other reports had suggested that YM155 exerts its anticancer effects independently of the cellular p53 status.28 However, the relevance of the p53 status for cancer cell sensitivity to YM155 and survivin depletion may depend on the cellular context.UKF-NB-326 However, other studies suggested that YM155 may primarily induce DNA damage and that the reduction of survivin levels may rather be a consequence of DNA damage.4 In our study, however, survivin downregulation preceded the occurrence of DNA damage in response to YM155 treatment, suggesting that survivin depletion happens prior to DNA damage formation. In accordance, RNAi-mediated survivin depletion also resulted in the induction of DNA damage, demonstrating that DNA damage formation can be a consequence of the reduction of cellular survivin levels. Notably, we and others had previously shown that survivin depletion resulted in increased yH2AX foci detection following irradiation30 that is functionally related to a direct physical interrelationship of survivin with components of DNA damage repair pathways.32The anticancer mechanism of action of YM155 has been under dispute and may depend on the cellular background. YM155 was introduced as a compound that suppresses the expression of survivin.6 However, YM155 reduced cellular Mcl-1 levels at substantially higher concentrations than survivin, and Mcl-1 depletion did not affect neuroblastoma cell viability. The finding that Mcl-1 depletion does not impair neuroblastoma cell viability is in accordance with previous results demonstrating that Mcl-1 depletion sensitizes neuroblastoma cells to paclitaxel, but does not exert significant effects on neuroblastoma cell viability on its own.33 Hence, the anti-neuroblastoma effects of YM155 do not appear to be caused by Mcl-1 depletion. Finally, survivin depletion-mediated X-linked inhibitor of apoptosis (XIAP) degradation had been shown to contribute to the effects of YM155 against breast cancer cells.34 However, survivin depletion did not decrease XIAP levels in our models  and the resistance-associated changes were investigated. We detected a R248W p53 mutation as target-specific resistance mechanism, and decreased SLC35F2 and increased ABCB1 as drug-specific resistance mechanisms. UKF-NB-3rYM15520nM cells also displayed decreased survivin levels and sensitivity to RNAi-mediated survivin depletion relative to UKF-NB-3, further confirming that YM155 primarily exerts its anti-neuroblastoma effects through survivin depletion.Next, UKF-NB-3 cells were adapted to growth in the presence of YM155  was purchased from Selleck Chemicals via BIOZOL Diagnostica GmbH , vincristine and cisplatin from TEVA GmbH , doxorubicin from Cell Pharm , and verapamil from Sigma-Aldrich .39 UKF-NB-3 and UKF-NB-6 sublines with acquired resistance to cisplatin (UKF-NB-3rCDDP1000), doxorubicin (UKF-NB-6rDOX20), vincristine (UKF-NB-3rVCR10 and UKF-NB-6rVCR10), or nutlin-3 (UKF-NB-3rNutlin10μM) were established by continuous exposure to increasing drug concentrations as described previously39 and derived from the Resistant Cancer Cell Line collection (www.kent.ac.uk/stms/cmp/RCCL/RCCLabout.html).The MYCN-amplified neuroblastoma cell lines UKF-NB-3 and UKF-NB-6 were established from stage 4 neuroblastoma patients.All cells were propagated in Iscove's modified Dulbecco's medium supplemented with 10% fetal calf serum, 100 IU/ml penicillin, and 100 mg/ml streptomycin at 37°C. Cells were routinely tested for mycoplasma contamination and authenticated by short tandem repeat profiling.41, using the Lentiviral Gene Ontology (LeGO) vector technology (www.lentigo-vectors.de).42p53-depleted neuroblastoma cells or neuroblastoma cells showing high expression of ABCB1 were established as described previouslySLC35F2-depleted cells were established using the same technology. The shRNA sequences (5′ to 3′) were sh1 SLC35F2, CTCTTTCTGTTTGGCTATA; sh2 SLC35F2, GAGGAATACATCGTGAAGA; sh3 SLC35F2, CAGGGAGTGATGTATTGAT.41Cell viability was determined by the 3--2,5-diphenyltetrazolium bromide (MTT) dye reduction assay after 120 h incubation, modified as described previously.6 cells were resuspended in 200 μl resuspension buffer R including 2.5 μM siRNA. The electroporation was performed using two 20 ms pulses of 1400 V. Subsequently, the cells were transferred into cell culture plates or flasks, containing prewarmed cell culture medium.Transient depletion of SLC35F2 was achieved using synthetic siRNA oligonucleotides (ON-TARGETplus SMARTpool) from Dharmacon . Non-targeting siRNA (ON-TARGETplus SMARTpool) was used as negative control. Cells were transfected by electroporation using the NEON Transfection System  according to the manufacturer protocol. Cells were grown to 60–80% confluence, trypsinized, and 1.2 × 10β-actin , SLC35F2 , ABCB1, Chk2, phosphorylated Chk2 (Thr68), Baculoviral IAP repeat containing 3 (cIAP-2), histone H2AX, phosphorylated histone H2AX (Ser139) (γH2AX), myeloid cell leukemia sequence-1 (Mcl-1), p21, cleaved PARP, XIAP , p53 , survivin and Baculoviral IAP repeat containing 2 (cIAP-1) . Protein bands were visualized by laser-induced fluorescence using infrared scanner for protein quantification .Cells were lysed using Triton-X-100 sample buffer, and proteins were separated by SDS-PAGE. Detection was done by using specific antibodies against A DNA damage kit that determines apurinic/apyrimidinic sites as indicators of DNA lesions by using an Aldehyde Reactive Probe  was used following the manufacturer's instructions.All coding exonic and flanking intronic regions of the human TP53 gene were amplified from genomic DNA with Platinum Taq DNA polymerase (Life Technologies) by multiplex PCR, using two primer pools with 12 non-overlapping primer pairs each, yielding ~180 bp amplicons. Each sample was tagged with a unique 8-nucleotide barcode combination using twelve differently barcoded forward and eight differently barcoded reverse primer pools. Barcoded PCR products from up to 96 samples were pooled, purified, and an indexed sequencing library was prepared using the NEBNext ChIP-Seq Library Prep Master Mix Set for Illumina in combination with NEBNext Multiplex Oligos for Illumina (New England Biolabs). The quality of sequencing libraries was verified on a Bioanalyzer DNA High Sensitivity chip (Agilent) and quantified by digital PCR. 2 × 250 bp paired-end sequencing was carried out on an Illumina MiSeq (Illumina) according to the manufacturer's recommendations at a mean coverage of 300 ×.43 To identify putative mutations, variant calling was performed using SAMtools in combination with VarScan2 (v2.3.9).44 Initially, SAMtools was used to create pileups with a base quality filter of 15. Duplicates, orphan reads, unmapped, and secondary reads were excluded. Subsequently, Varscan2 was applied to screen for SNPs and InDels separately, using a low-stringency setting with minimal variant frequency of 0.1, a minimum coverage of 20, and a minimum of 10 supporting reads per variant to account for cellular and clonal heterogeneity. Minimum average quality was set to 20 and a strand filter was applied to minimize miscalls due to poor sequencing quality or amplification bias. The resulting list of putative variants was compared against the IARC TP53 (R17) database to check for known p53 cancer mutations.Read pairs were demultiplexed according to the forward and reverse primers and subsequently aligned using the Burrows-Wheeler Aligner against the Homo sapiens Ensembl reference (rev. 79). Overlapping mate pairs were combined and trimmed to the amplified region. Coverage for each amplicon was calculated via SAMtools (v1.1).t-test. Three and more groups were compared by ANOVA followed by the Student–Newman–Keuls test. P values lower than 0.05 were considered to be significant.Results are expressed as mean±S.D. of at least three experiments. Comparisons between two groups were performed using Student's


Elucidating the poorly defined mechanisms by which inflammatory lesions are spatially restricted in vivo is of critical importance in understanding skin disease. Chemokines are the principal regulators of leukocyte migration and are essential in the initiation and maintenance of inflammation. The membrane-bound psoriasis-associated atypical chemokine receptor 2 (ACKR2) binds, internalizes and degrades most proinflammatory CC-chemokines. Here we investigate the role of ACKR2 in limiting the spread of cutaneous psoriasiform inflammation to sites that are remote from the primary lesion. Circulating factors capable of regulating ACKR2 function at remote sites were identified and examined using a combination of clinical samples, relevant primary human cell cultures, in vitro migration assays, and the imiquimod-induced model of psoriasiform skin inflammation. Localized inflammation and IFN-γ together up-regulate ACKR2 in remote tissues, protecting them from the spread of inflammation. ACKR2 controls inflammatory T-cell chemotaxis and positioning within the skin, preventing an epidermal influx that is associated with lesion development. Our results have important implications for our understanding of how spatial restriction is imposed on the spread of inflammatory lesions and highlight systemic ACKR2 induction as a therapeutic strategy in the treatment and prevention of psoriasis and potentially a broad range of other immune-mediated diseases. Chemokines, the primary in vivo regulators of leukocyte migration, are central to inflammatory pathogenesis . They biRecently, we showed that ACKR2 expression is particularly elevated in uninvolved skin of psoriatic patients . We hypoTo establish a mouse model appropriate for assessing the mechanism of differential ACKR2 regulation and the role of ACKR2 in regulating localized inflammation, we determined the role of ACKR2 in the well-characterized imiquimod (IMQ) mouse model of psoriasis . At rest+ T-cell localization in the IMQ model. There are no basal differences in total cutaneous or epidermal T-cell numbers in ACKR2-deficient, compared with WT, mice  could regulate human T-cell migration. For this purpose, we established an in vitro three-dimensional cell migration assay. The T-cell chemoattractant chemokine CCL20 has been associated with psoriasis . However–/– mice treated with or without IMQ  for 24 hours at 4 °C. For long-term storage, RNAlater-treated tissues were stored at –80 °C. Tissues for histology and immunohistochemistry were fixed in 10% neutral buffered formaldehyde (Sigma-Aldrich).RNA was extracted and purified on RNeasy Micro columns with on-column DNase digestion as per manufacturer’s instructions . Quantitative PCR was performed as described in the + staining, paraffin-embedded fixed skin was cut to 2-μm thickness. Heat-induced antigen retrieval was performed in pH 6 sodium citrate buffer for 100 seconds at 125 °C under pressure. Polyclonal rabbit anti-human CD3 (1:100 dilution) and polyclonal rabbit anti-Ki67 (1:400) , or negative controls, were used and counterstained with hematoxylin.For hematoxylin and eosin, Ki67, and CD3Details are provided in the Details are provided in the Details are provided in the Full-thickness mouse skin was submerged in 0.025-mmol/L EDTA  at 37 °C, after which the epidermal layer was curetted off, leaving the dermis intact. Both skin components were immediately lysed in Qiazol, using a Qiagen TissueLyser LT .t test, Mann-Whitney U tests, one-way analysis of variance, two-way analysis of variance, and correlation tests were performed in Prism Version 7.0 , with multiple comparisons tests as appropriate. Vector data were assessed by the Rayleigh test for vector data in chemotaxis plugin in ImageJ (available at http://imagej.nih.gov/ij/). P < 0.05 was deemed significant. ∗P < 0.05, ∗∗P < 0.01, ∗∗∗P < 0.005, ∗∗∗∗P < 0.0001. All data are n ≥ 3 and given as mean ± standard error of the mean unless otherwise stated.Student The authors state no conflict of interest.


Recent reports have identified distinct genomic patterns in ovarian carcinoma, including proliferative and mesenchymal-like groups, with worse outcome. The exact mechanisms driving the onset and progression of these tumors are still poorly understood. Additionally, researchers are concerned about the correct subtype stratification of the available cell line models, and the exploration of alternatives to monolayer culture. Identification of biomarkers to stratify cell lines, characterization of important processes as epithelial-mesenchymal transition (EMT), and the use of three-dimensional (3D) cultures as alternative models could be useful for cell line classification.In this work, we present a descriptive analysis of 16 commonly used ovarian cancer cell lines. We have studied their morphology in 2- and 3D culture, and their response to cisplatin, observing in the majority of them an increased resistance in 3D. We have also performed an immunohistochemical analysis for proliferation marker Ki-67, and EMT related markers to establish phenotypes. Epithelial cells tend to show higher proliferative rates, and mesenchymal cells show an increase in EMT related markers, especially when cultured in 3D conditions.in vitro experiments must be carefully chosen. Our results also support that tridimensional culture could be a very helpful alternative in ovarian cancer research. Regarding EMT, a very important process for the development of this disease, some related biomarkers might be further characterized for their role in this disease development.We have stated the complex heterogeneity of ovarian cancer models, resembling primary tumors, agreeing with the argument that the cell line model for  The vast majority of malignant ovarian cancer cases are carcinomas, presumably originating from the Müllerian epithelium of the ovarian surface and the fallopian tube. The World Health Organization (WHO) classification distinguishes serous, mucinous, endometrioid and clear cell as the main histological subtypes. In the serous category, accounting for over 70% of all ovarian carcinomas (OC), the existence of two different diseases is now well established, low- and high-grade tumors, with distinct morphological and molecular characteristics . The firOne of these mechanisms, epithelial to mesenchymal transition (EMT), is a developmental program that transiently disrupts cell-cell adhesion and converts epithelial cells into more migratory and invasive mesenchymal cells . This prThe study of relevant pathways for cancer development has been facilitated by cell-based models. Immortalized OC cell lines have been used as an alternative to freshly isolated tumor cells since they have extended lifespan and their clonal nature reduces inter-experimental variability. However, when cultured as a two-dimensional (2D) monolayer, these cell lines demonstrate low functionality and an altered phenotype compared to primary cultures. There is an urgent need to characterize the existing models in order to use the most suitable ones for research. Additionally, alternatives to the classical monolayer culture such as three-dimensional (3D) cell culture that better represent tumor features, should be considered.The main objective of this study was to establish 3D OC models, comparing them with bidimensional culture, and to characterize them for their growth, response to treatment, and expression of EMT markers.Two-dimensional morphology was analyzed by phase contrast imaging. We found two different patterns: round or epithelial , and mesenchymal or fibroblast-like  Figure . The douSpheroids individually created per well on ultra low attachment (ULA) plates (Corning) were quite reproducible, as previously described . Using tWhen cultured in monolayer, OC cell lines do not display any morphological features to indicate histology. However, on 3D, H&E stain revealed some features, including the presence of papillae in some of them, and existence of acellular central regions due to apoptosis or necrosis, as in tumor tissue (data not shown). Changes in size, measured in bright field microscopy, could be a surrogate marker of cell growth, but only in compact spheroids, so we used Ki-67 staining for evaluation of proliferation, that diminishes with time in compact spheres such as SKOV3, and remains constant in aggregates, as represented by PEO1 .50 values calculated for CDDP in ovarian cancer cell lines spheroids and monolayer cultures. Four groups of cell lines were established according to their IC50 values, taking IOSE cells as a reference (IC50 = 2 μM): sensible cells (IC50 < 3 μM), partially sensitive cells (IC50 between 3 and 6 μM), resistant cells (IC50 between 6 and 12 μM), and very resistant cells (IC50 > 12 μM).Monolayer cultures and spheroids were treated with increasing concentrations of CDDP for 72 h. Dose-dependent reduction in cell viability was observed, but differences in drug response in 2D experiments are independent of histotype, phenotype or spheroid formation categories. Table 50 values  whilst the other two show an increase when cultured in 3D . The remaining group of aggregate forming cell lines, with the exception of two of them (A2780CIS and OVCAR3), did not reach the IC50 values when cultured on 3D conditions at the same concentration range set for 2D experiments (100 µM top concentration), and only one (A2780CIS), experimented a decrease in IC50  Table . Additiop < 0.05), and decreasing for spindle subtype .Ki-67 was employed to evaluate cell proliferation of cultures . The perTP53 mutation, a ubiquitous molecular sign in high-grade serous ovarian carcinoma (HGSOC) [TP53 mutated by different authors. PEO16, A2780 and 2780CIS showed an abnormal staining pattern; however, they are not reported as mutated on previous studies. (HGSOC) . Twelve p = 0.04); no significant differences were observed on Ki-67 expression between 2- or 3D culture (data not shown).Table We also assessed the expression of master regulators of the EMT process in 2- and 3D culture conditions by IHC , endometrioid, clear cell and mucinous. HGSOC accounts for more than 70% of cases, and is responsible for the majority of ovarian cancer related deaths . RecentlEstablished cell lines are the most common model in cancer research, and their use has contributed to the understanding of cancer biology in the last decades, but they also have limitations. It is now widely accepted that some of the most frequently used ovarian cancer cell lines, including A2780, SKOV3 and OVCAR3, do not seem to be the most suitable models for the study of HGSOC –16.in vitro studies must be carefully chosen, depending on numerous factors, including the endpoint of the study, growth characteristics, or histological or genomic background [We agree with other authors that the optimal cell line for ckground , 17.in vivo drug efficacy. More recently, 3D cell culture models, expected to bridge the gap between 2D and animal models, are becoming more widely used in scientific research, including drug screening and new compounds development fields. Ovarian cancer has common tumor growth characteristics, such as a hypoxic environment, absent in traditional culture models. However, it also has some unique features, such as peritoneal spread as individual cells or aggregates. For all these reasons, 3D unattached culture could be a more representative model for these tumors.2D culture is easy to handle and highly reproducible, but an objection to this model is that it does not totally mimic some properties of tumors, and sometimes fails to reproduce Previous work has been published on ovarian cancer spheroids culture using different methods –21. HoweIn this study, we characterized ovarian cancer spheroids from 16 commonly used cell lines, using ULA plates, in a highly uniform way, as previously described . This meRegarding compact spheroids experiments, assessment of cell number and days of incubation is crucial. An estimated diameter of 400 μm could be optimal to mimic the diffusion state in the tumor, which is about 100 μm in depth for nutrients and oxygen, and 14 days as final time point was also effective to avoid excessive necrotic areas –25. Prol50 values, and other two that experiment an increase. The same behavior was seen for the aggregate 3D group where, with the exception of one cell line, all had an increase in the IC50 values. Moreover, most of them do not even reach this value under the same CDDP concentration range tested on 2D. These results are in agreement with the reported by other authors, supporting that cells tend to be more resistant to treatment when growing on 3D [Differences in cellular response to platinum were determined to be cell line specific. This can be explained because every cell line was established at a different treatment time-points: before or after first line, on different relapse states, or even after alternative chemotherapy regimes. In this study, 2- and 3D comparison in the compact spheroids group, show two cell lines that retain similar ICng on 3D . In advang on 3D . The comng on 3D , 30–32.TP53 itself, and these situations have been associated with poor prognosis and chemoresistance in different types of tumors [TP53 mutation status [P53 pathway is disrupted in the majority of human cancers, mainly through mutation or deletion of f tumors , 34. In f tumors , and lowf tumors , 36. In n status . Howevern status , 17, 38.We have also evaluated Ki-67 proliferation activity, which revealed a similar proliferation rate in 2- and 3D culture, although it has been shown to decrease in other reports . InteresDuring ovarian cancer progression, both EMT and the reverse process, mesenchymal-epithelial transition (MET) occur in a dynamic way, and a related phenotype has been pointed to have a more aggressive behavior , 40. We In most cell lines, there is an increased expression of master EMT regulators in 3D models comparing with 2D. Other authors have described changes in EMT regulators between 2 and 3D ovarian cancer models and this could be in agreement with the role of the EMT process in disease dissemination, when the cells are growing unattached, like the ascitic cells in the abdominal cavity, before seeding the peritoneum . Therefo2 incubator. Spheroids were cultured using ULA plates  as previously described [Ovarian cancer cell lines were obtained from the European Collection of Authenticated Cell Cultures (ECACC), and cultured based on the guidelines of the repository. Cells were regularly tested for mycoplasma infection and discontinued after 15 consecutive passages. Some of these lines were established from the same patient during the course of disease, and had received different treatment schemes prior to their establishment: PEA1/PEA2, PEO1/PEO4/PEO6 and PEO14/PEO23/T014 . The celescribed , for up 3 cells per well in 96-well plates (MW96). Cellular confluence was measured at different time points . For 3D experiments, cells were seed at a variable density, set before for each cell line (from 500 to 10000 cells per well) in ULA plates. Growth was tracked measuring spheroid diameter at days 4, 7, 10 and 14. Image acquisition and analysis were performed with Celigo S plate cytometer .For monolayer experiments, cells were seeded at a density of 6 × 1050 values were calculated using linear regression with GraphPad Prism 7 software .Monolayer culture experiments were carried out in a similar way as proliferation assays and 24 hours after seeding, cells were exposed to different concentrations of (CDDP) for 72 h. After this time, cellular confluence was measured with Celigo S and IC50 by using linear regression.For 3D experiments, after 4 days of culture, spheroids were exposed to CDDP for 72 h. Cell viability was measured using CellTiter-Glo Luminescent assay . Additionally, in compact spheroids, cells were treated with a fluorescent staining solution containing calcein-AM (CAM) (BD Biosciences) to detect live cells, propidium iodide (PI) (Sigma-Aldrich) for dead cells and Hoechst 33342 (Sigma-Aldrich) to counterstain all nuclei. Mean CAM intensity values obtained by Celigo analysis were used to calculate ICHistological studies were performed both in cell pellets from monolayer cultures and aggregates or spheroids. Cell pellets were collected, fixed with 70% ethanol for 24 h, and paraffin embedded. Spheroids were fixed in 4% paraformaldehyde solution, embedded in 4% noble agar (Sigma-Aldrich) and then paraffin embedded. Sections were stained with haematoxylin and eosin (H&E).We performed IHC for the markers described below. Briefly, four-μm sections were cut with a semiautomatic microtome HM3508 (MICROM), deparaffinized and rehydrated in water. Antigen retrieval was performed in a DAKO PT Link . Peroxidase activity was blocked with Dako Protein block for 10 minutes (containing 0.25% casein in PBS), then incubated for 30 minutes with primary antibodies, detected with Dako Envision Plus kit, and counterstained with haematoxylin. Antibodies used include: Ki-67 (#IS626), P53 (#IS616), ECAD (#IR059), PANCK (#IR053), VIM (#IR630), all from Dako; NCAD (#ab1221), Snail (#ab135708), Slug (#ab38551), Zeb1 (#ab180905), Twist (#ab50581), and Twist2 (#ab57997), all from Abcam , and Zeb2 (#sc-48789) from Santa Cruz .Evaluation of immunohistochemical stains was performed as follows: ECAD, NCAD, PANCK, and VIM membrane staining was evaluated as conserved (C), reduced (R) or absent (A); cytoplasmic immunostaining of Snail, Slug, Twist1 and Twist2, and nuclear expression of Zeb1 and Zeb2, was evaluated as positive  or negative. P53 and Ki-67 were analyzed as a percentage, for ki-67 as continuous value of positive stained nuclei, and for P53 as two categories: aberrant expression (less than 10% or more than 60% positive nuclei), and normal expression (ranging from 11% to 59%), as previously described , 44, 45.Phenotypical classification of the cell lines was done according to the expression of epithelial (ECAD/PANCK) and mesenchymal (VIM/NCAD) markers. Cells were classified as purely epithelial or mesenchymal when they only expressed epithelial or mesenchymal markers; intermediate phenotypes were established when co-expression of both groups of markers was observed.p-values < 0.05. Graphs and statistical analysis were performed using Microsoft Office Excel , SPSS version 15.0 , and GraphPad Prism 7 (GraphPad Software).Results are representative of at least three independent experiments in triplicate to sextuplicate, and are represented as mean ± standard deviation (SD), except 3D experiments which were performed once with 6 replicates for each condition. Chi-Square test and Fischer’s exact test (2-sided) were performed for comparisons, and these were considered significant when Spheroids show different features compared with 2D culture. Particularly in OC, epithelial ovarian cancer cells growing as spheroids are frequently detected in the ascites of the patients. We have successfully used a reproducible technique to obtain uniform ovarian cancer spheroids over 16 established cell lines, and characterized their growth, proliferation, and drug response. Compelling evidence has suggested the critical role of EMT in cancer development and progression, but their regulation mechanisms need to be further characterized. We propose the use of 3D models for this purpose that, with optimal instrumentation and computational coupled resources, could represent a promising model for high throughput studies.


Cancer Tissue Heterogeneity is an important consideration in cancer research as it can give insights into the causes and progression of cancer. It is known to play a significant role in cancer cell survival, growth and metastasis. Determining the compositional breakup of a heterogeneous cancer tissue can also help address the therapeutic challenges posed by heterogeneity. This necessitates a low cost, scalable algorithm to address the challenge of accurate estimation of the composition of a heterogeneous cancer tissue.In this paper, we propose an algorithm to tackle this problem by utilizing the data of accurate, but high cost, single cell line cell-by-cell observation methods in low cost aggregate observation method for heterogeneous cancer cell mixtures to obtain their composition in a Bayesian framework.The algorithm is analyzed and validated using synthetic data and experimental data. The experimental data is obtained from mixtures of three separate human cancer cell lines, HCT116 , A2058 (Melanoma) and SW480 .The algorithm provides a low cost framework to determine the composition of heterogeneous cancer tissue which is a crucial aspect in cancer research. Cancer tissue heterogeneity is a very important aspect in cancer research with widespread implications. It is a phenomenon observed in almost all cancers including breast cancer , colon cIn  an accurIn this paper we extend the gene expression based methods presented in , 10 so tThe proposed algorithm requires the expensive cell-by-cell observation of individual subpopulations only once and can then be used to determine the composition of any number of heterogeneous cancer tissues composed of those subpopulations.n different cell lines represented as C=. Let there be m different quantitatively measured attributes. These attributes are chosen such that they are independent and the different cell lines have dissimilar attribute profiles. The idea of the algorithm is to use the expensive cell-by-cell observation of attributes to create a database of the mean and standard deviation of the attributes for these n cell lines in isolation. This is only a one time process as the mean and the standard deviation of the attributes of the cell lines are assumed to remain consistent for different heterogeneous cancer tissues. This is under the assumption that the cells in a heterogeneous cancer tissue do not affect the attribute value of each other. Once this is done we can analyze any heterogeneous cancer tissue composed of any subset of these n cell lines by collecting only low cost aggregate attribute observations. The algorithm takes as an input the mean and standard deviation of the attributes for the cell lines from the database and the aggregate attribute observations of the heterogeneous cancer tissue and gives the compositional breakup of the heterogeneous tissue as the output.Let us assume that we need to study heterogeneous cancer tissues composed of a given set of n cell lines by making high cost cell-by-cell attribute observations for them. To do this, we measure the value of the m attributes for individual cells of a particular cell line. We do this separately for all the n different cell lines. For a particular cell line, say ith cell line, these individual cell observations are considered to be the samples of the random attribute vector Ei=. The algorithm uses the sample mean and sample standard deviation as the estimate of the mean and standard deviation of the attributes. The first step of the algorithm is to profile (estimate the mean and standard deviation of the attributes) each of these eijk are the samples of the random variable Eij. Let μ and σ be n x m matrices whose elements are μij and σij, the true mean and true standard deviation of the attributes for different cell lines. It is important to have sufficiently large number of samples to arrive at an accurate estimate of the mean and standard deviation.where N= where Ni represents the number of cells of cell line Ci in the tissue. Let the corresponding ratio be represented by π. Assume that the true composition of the heterogeneous tissue is given by Esum which is the sum of the attributes of all the cells in the mixture. The objective of the algorithm is to take Esum, i≤n, 1≤j≤m and generate an accurate estimate of N and π represented as n different random vectors with independent components and the mean and standard deviation of each component of those random vectors. From this sum, mean and standard deviations it estimates how many samples of different random vectors were added to get this sum.Assume that the aggregate attribute vector is represented by Esum which is an m-dimensional vector. Let the jth component of Esum be represented by Esumj for 1≤j≤m. As a hypothetical example, let us consider the case shown in Fig. Esumj can be written as: Now we focus on Eisumj is the contribution of ith cell line in the jth attribute of the aggregate attribute vector.where Ni cells of the ith cell line in the heterogeneous mixture and the summation of the jth attribute of each of these cells gives Eisumj. The jth components of the attribute vector of each of these cells are independent, as the attribute value of one cell does not affect the attribute value of another cell. They are also identically distributed with the same distribution as Eij. Hence, by Central Limit Theorem, for sufficiently large Ni, Eisumj can be approximated by a Gaussian Distribution with mean Niμij and variance N. For practical purposes, the cell lines which form a significant part of heterogeneous cancer tissue satisfy the condition of large Ni. Hence, Eisumj has a Gaussian Distribution irrespective of the distribution of Eij. This is a very important implication as it gives the independence of choosing any feature as a part of the attribute vector irrespective of the probability distribution of the same. The only condition is that the aggregate attribute value of the heterogeneous cancer tissue should be given by the summation of the attributes of individual cells in the tissue.There are Eisumj for different values of i in Eq. Esumj can be approximated as Gaussian with mean Esumj can be approximated by: Esum are independent, the probability of Esum is given by: As the components of N in order to obtain a maximum likelihood estimate of N. However the complex expression makes it difficult to solve this problem analytically. Another approach can be to evaluate the expression in Eq. N. However, the complexity of the algorithm will become exponential in that case and hence it will be infeasible when the number of different cell lines is large. Hence we use a Bayesian approach to estimate N.This needs to be maximized over N are assumed to have a uniform prior from 0 to an arbitrarily large number, say M. The posterior probability of Ni is given by: All the components of Ni− represents all the components of N excluding the ith component and where P can be calculated from Eq. Ni from Eq. Ni which is given by The Metropolis algorithm comes in handy when it is difficult to exactly evaluate the posterior probability. In such a scenario, if it is possible to sample directly from the posterior distribution, we can generate independent identically distributed samples and use them to approximate the posterior probability distribution. However, in our case, it is not possible to sample directly from Eq. s samples of Ni from the posterior distribution in the set . We then consider adding the proposal value Nis. We follow the following steps: uniform.1. 2. Compute the acceptance ratiomin or Nis otherwise.3. Assign Suppose we have P and P from Eqs. M cancels and hence the algorithm is independent of M. The Markov chain formed by following the aforementioned steps has the same stationary distribution as the posterior distribution of N. The Markov chain needs to run for a few initial iterations before it reaches stationarity and only after that the sampling has to be done. An important consideration is the length of the neighborhood for the proposal distribution. If the neighborhood is too small, the Markov chain will take too long to reach stationarity and the samples will be too close to each other. Too large a neighborhood would result in too many samples being rejected once the Markov chain has reached stationarity. Hence the value of neighborhood parameter needs to be tuned appropriately. We draw samples from this Markov Chain after running it till it reaches stationarity. These samples are used to estimate the posterior distribution of N. To do this, we use a non parametric probability density function estimation, Kernel Density Estimation.Substituting Ni1,Ni2,…,Nik) be the samples of the posterior distribution of Ni drawn from the Metropolis algorithm. The Kernel Density Estimate of the posterior distribution is given by: Let  estimate (the mode of the posterior distribution) of N, represented as Once the posterior density function estimation is done, we can evaluate the posterior mean, the posterior mode, the confidence interval, etc. Such properties of Ni for the corresponding cell line. There are a variety of methods available to study the cell lines present in a heterogeneous cancer tissue, some of which are experimental whereas others are algorithmic. Fluorescent in situ hybridization(FISH) or FISH coupled with immunofluorescence, are methods based on amplification of specific regions in the chromosome to detect heterogeneity. Another approach is to sequence genes known to be frequently mutated for the cancer under study. There have been other studies based on the study of whole genomes. A good summary of the experimental methods to detect the subpopulations of a heterogeneous tissue is provided in . This value of μ ensures that all the cell lines contribute in all the attributes, making the problem challenging, and there is a difference in the attribute mean for the different cell lines. We set the standard deviation assuming constant coefficient of variation of 1. For the cell-by-cell analysis we generate 2000 samples of each cell line from a Gaussian distribution with the corresponding mean and standard deviation. The algorithm estimates the mean, Esum by generating Ni samples for the ith cell line for all the cell lines. We add the attribute values of all the samples to obtain Esum. Table N. The first case is the one where all the cell lines are present in equal proportion. The second case is when the all the cell lines are present in unequal proportions. The third case is when only half of the cell lines are actually present in the mixture. The last case is when there is only one cell line in the mixture. The third and the last case demonstrate how the algorithm can be used without knowing exactly how many cell lines are present in the heterogeneous tissue.We first look at the performance of the algorithm for different cases of μ to be a cyclic matrix with the first element of first row being 1000k for 0≤k≤1, last element being 1000 and the rest of the elements being equally spaced between 1000k and 1000. For instance, for k=0.55, the first row is [550 600 650 700 750 800 850 900 950 1000] and the rest of the rows are obtained through cyclic permutation of the first row. We set the standard deviation assuming constant coefficient of variation of 1. Similarity of the attribute means is controlled by the value of k. Higher value of k would imply more similarity of the attribute means between cell lines. When k=1, there would be no difference in the attribute profiles of the cell lines and it would be impossible for the algorithm to differentiate between the different cell lines. We study the effect of similarity on the error performance of the algorithm and the confidence interval. To test the algorithm, we set N = [100 200 300 400 500 600 700 800 900 1000]. On expected lines, the error increases as shown in Fig. k.Next, we analyze the performance of the algorithm by varying the similarity in the attribute means of different cell lines. We set μ to be a cyclic matrix with the first row being [100 200 300 400 500 600 700 800 900 1000]. We also set N = [100 200 300 400 500 600 700 800 900 1000]. We vary the coefficient of variation to study its effect on the error performance of the algorithm and the confidence interval. As is expected, the error increases as shown in Fig. We next analyze the effect of varying standard deviation of the attributes on the performance of the algorithm. For this analysis we set The algorithm was validated using the heterogeneous mixtures of three separate human cancer cell lines, HCT116 , A2058 (Melanoma) and SW480 . There were two different mixtures. Mixture 1 was approximately mixed in the ratio [1/3 1/3 1/3] and Mixture 2 was approximately mixed in the ratio [7/20 3/20 1/2]. Each mixture was perturbed and imaged under three different conditions: untreated, treated with Lapatinib and treated with Temsirolimus. Hence, overall there were six test cases. The experiment involved imaging the single cell lines and the mixtures on a cell-by-cell level. The attribute vector was composed of red, green and blue fluorescence. Although we have the cell-by-cell data for the mixtures too, the algorithm only takes the summation of the attribute values as the aggregate input. The cells were marked with fluorophores such that red fluorescence was emitted only by HCT116 and green fluorescence was emitted only by A2058. The blue fluorescence was used for detection of a cell and was emitted by all the three cell lines.Note that the estimated ratio from the proposed algorithm can vary from the approximate ratio due to multiple reasons. Firstly, the estimation done by the instrument to populate the cell well is not accurate. Secondly, during the time between the cell lines being mixed and fluorescence being recorded, the cells may multiply at different rates leading to a change in the ratio. This effect might vary in the three groups due to the impact of the drugs on cell multiplication. Lastly, imaging only captures a portion of the well and it might not be a representation of the true ratio of cells in the mixture. Hence, instead of comparing the estimated ratio from the proposed aggregate observation based algorithm to the approximate ratio, we compare it to the result obtained using cell-by-cell mixture analysis algorithm proposed in . Let theThe proposed aggregate attribute value based algorithm was run for the six test cases. As an input, the algorithm took the summation of the mixture cell-by-cell attribute values as the aggregate input and the single cell line cell-by-cell data for the corresponding group to estimate the mean and the standard deviation of the attributes. The output of the algorithm was the posterior probability of the number of cells for each case as presented in Figs. πcbc, πagg and e. As it can be observed, the estimate of the number of cells obtained from the algorithm is close to the approximate number of cells obtained by cell-by-cell analysis of the mixture for HCT116 and A2058.Table However, the estimate of the number of cells of SW480 is very inaccurate. In particular, we see that the estimate of number of SW480 cells is low for mixture 1 and high for mixture 2. As per the experiment design, the blue fluorescence of all the three cell lines impacts the estimate of number of SW480 cells. Hence, we look at the mean blue attribute value in the single cell line cell-by-cell data and the mixture cell-by-cell data given in Table Hence, by this analysis, we observe that the algorithm performs well if the parameters of the attribute vector remain consistent in the single cell line cell-by-cell analysis and the heterogeneous mixture. Variation in these parameters leads to inaccurate results.The proposed algorithm enables low cost estimation of the composition of heterogeneous cancer tissue which is an important factor in cancer diagnosis and research. As demonstrated by the simulation results, the algorithm gives an accurate estimate of the different cell lines in the tissue. A crucial aspect of the method proposed is the accurate experiment design. An inconsistent experiment design in the parameter estimation phase and aggregate measurement phase may result in inaccurate estimates of the composition of cell lines as is evident in the experimental results for SW480 cell line. This calls for standardization of the experiment design to ensure the scalability of the algorithm.In this work we address the challenge of determining the composition of any heterogeneous cancer tissue. It uses the advantage offered by the expensive cell-by-cell analysis methods while actually utilizing the low cost aggregate attribute methods. The algorithm takes as inputs the characteristics of the attribute vector of the individual cell lines and the aggregate attribute values of the heterogeneous cancer tissue. Based on these inputs, the algorithm uses a Bayesian approach to estimate the number of cells of different cell lines that are present in the heterogeneous mixture. In order to estimate the posterior probability, the algorithm uses the Metropolis algorithm to gather samples from the posterior distribution and Kernel Density Estimation to estimate the distribution from these samples.


A biocompatible fluorescent hydroxyquinoline-affixed polyfluorene (PF-HQ) nanoparticle-based drug delivery system (DDS: PF-HQ–DOX) is utilized for cancer cell imaging, transport and delivery of the FDA-approved drug doxorubicin (DOX), and theranostics applications. via three-dimensional growth of a polymeric backbone in a higher water fraction (THF : H2O = 1 : 9) was observed. The nanoparticles showed incredible dual-state optical and fluorescence properties, which were further explored in multi-color cell imaging in both cancer and normal cells. The cell viability assay in various normal cells confirmed the biocompatible nature of PF-HQ, which was further supported by an ex vivo  model. This encouraged us to fabricate PF-HQ-based new drug delivery systems (DDS: PF-HQ–DOX) upon conjugation with the FDA-approved anti-cancer drug doxorubicin (DOX) by filling the hydrophobic pockets of the polymer nanoparticles. The enhanced anti-cancer activity of the DDS (PF-HQ–DOX) compared with that of free DOX was observed in mouse melanoma cancer cells (B16F10) and a subcutaneous mouse (C57BL6/J) melanoma tumor model upon administration of PF-HQ–DOX. Ex vivo biodistribution studies using a fluorescence quantification method demonstrated the enhanced accumulation of DOX in tumor tissues in the PF-HQ–DOX-treated group compared to that of the free drug, signifying the drug delivery efficacy of the delivery system by a passive targeting manner. Based on the above biological data , these robust and versatile fluorescent hydroxyquinoline-affixed polyfluorene (PF-HQ) nanoparticles could be effectively utilized for multifunctional biomedical applications (as they are biocompatible and can be used for bio-imaging and as a drug delivery vehicle).The accumulation of fluorescent hydroxyquinoline-affixed polyfluorene (PF-HQ) nanoparticles and their utility for multi-color bio-imaging and drug delivery for cancer treatment are reported. The formation of nanoparticles (PF-HQ) containing hydrophobic pockets  Hydroxyquinolines guided the main chain aggregation in water and are shown to form nanoparticles (PF-HQ) with ordered self-assemblies, thus they present a novel class of luminescent compound. Although known as a classical ACQ molecule, PF-HQ behaves uniquely with a marked increase in the fluorescence intensity with increasing concentration accompanied by a red shift in the spectrum. Surprisingly, it does not follow classical AIE rules either. Hence, we compromise with the poor quantum yield in an aqueous medium.in vitro and ex vivo) and excellent fluorescence property, PF-HQ is utilized for live cell imaging (in vitro) in various cell lines. PF-HQ also shows mild dose-dependent cytotoxicity toward cancer cells, observed by a cell viability assay. Furthermore, a drug delivery system (DDS: PF-HQ–DOX) using PF-HQ as a delivery vehicle and doxorubicin (DOX) as an anti-cancer drug was also demonstrated. Administration of the PF-HQ–DOX system showed remarkable anti-cancer efficacy compared to free DOX in B16F10 cancer cells and subcutaneous mouse melanoma tumor models (in vivo) in a passive targeting manner through the enhanced permeability and retention (EPR) effect. These robust fluorescent polymer nanoparticles (PF-HQ) could be beneficial for multifunctional biomedical, imaging, and cancer theranostics applications.Due to its biocompatibility (Doxorubicin (DOX), Dulbecco’s Modified Eagle’s medium (DMEM), phosphate-buffered saline (PBS), kanamycin, streptomycin, penicillin, ribonuclease (RNase), fetal bovine serum (FBS), HBSS buffer , MTT -2,5-diphenyltetrazolium bromide), and propidium iodide (PI) were procured from Sigma Aldrich Chemicals, USA and used without any purification. For the chick chorioallantoic membrane assay (CAM) assay, fertilized chicken eggs were brought from the Directorate of Poultry Research, Rajendra Nagar, Hyderabad.Cercopithecus aethiops monkey kidney fibroblast cell lines, NIH-3T3: mouse fibroblast cell lines, B16F10: mouse melanoma cell lines, and A549: human lung cancer cell lines were bought from ATCC, USA.COS-1: Female C57BL6/J mice (8–9 weeks old and each weighing ∼18–20 g) were purchased from the National Institute of Nutrition (NIN), Hyderabad. All mouse experiments were performed after approval by the animal ethical committee of CSIR IICT, Hyderabad .Ki-67 antibody  was purchased from Thermo Scientific, USA. Phycoerythrin tagged goat anti-rabbit IgG (cat. number # SC-3739) was purchased from Santa Cruz Biotechnology, Inc. USA.–1 of PF-HQ was prepared in H2O–THF at a volume ratio of 9 : 1 and used for all biological studies.A stock solution of 2.7 μg μLl-glutamine, at 37 °C in a humidified CO2 incubator. Samples were sterilized by UV irradiation for 10–15 minutes before any treatment.All cancer (A549 and B16F10) and normal (COS-1 and NIH-3T3) cell lines were cultured in DMEM complete media supplemented with FBS (10%), antibiotics (0.005% penicillin–streptomycin–kanamycin), and 5% 4 cells per well) and cultured for 24 h in a humidified cell incubator. All the cell lines were incubated with PF-HQ (50–100 μg mL–1) for 14 hours. All the treated cell lines were washed extensively with PBS and finally, the fluorescence images were captured using a fluorescence microscope (Nikon Eclipse TE2000-E). The fluorescence emission in the green field (λem = 525 nm) was collected after excitation at λex = 420–495 nm and red emission (λem = 605 nm) was collected after excitation at λex = 510–560 nm with a 20× microscope objective. Similarly, the blue fluorescence emission (λem = 485 nm) was collected after excitation at λex = 380 nm at 20× magnification.20All normal and cancer cell lines  were seeded in 24-well plates (2 × 104 cells per well) were seeded in 96-well plates and cultured for 24 h. A cell viability assay of all normal and cancer cell lines was carried out using MTT reagents after 24 h incubation with PF-HQ at different doses (27–540 μg mL–1) according to the published procedure.–1 in PBS) and incubated for 4 h under dark conditions. The MTT solution was replaced by freshly prepared DMSO : MeOH (1 : 1) to solubilize the formazan dye and the absorbance of each well was recorded at λ = 575 nm. The cell viability results were calculated as the percent cell viability using the following equation: % cell viability = {[A570 (treated cells) – background]/[A570 (untreated cells) – background]} × 100.21All normal and cancer cell lines   for 4 days before the experimental study. During the experiment, a small window was created very carefully in the top of the egg shell. Sterile filter paper discs were soaked in 100 μg of PF-HQ solutions and were placed on the egg yolks for about 4 h. Finally, bright field images of the untreated and PF-HQ-treated eggs were captured at different times (0 h and 4 h) of treatment using a stereomicroscope (Leica).22The CAM assay is a typical assay for the analysis of the –1 concentration) was added in 1 mL of PF-HQ (1 mg mL–1) in an Eppendorf under vortex conditions for 45–60 minutes. The orange-brownish intense PF-HQ–DOX nanoconjugate solution was ultra-centrifuged (14 000 rpm at 14 °C for 30 min) using centrifugation . The PF-HQ–DOX nanoconjugate pellet was collected (50 μL) and utilized for all the physicochemical characterizations and biological studies (in vitro and in vivo). The supernatant of the PF-HQ–DOX nanoconjugate was used to determine the % attachment of DOX in the PF-HQ–DOX pellets using the spectrofluorimetry technique after making a standard curve. Similarly, PF-HQ was also centrifuged (14 000 rpm at 14 °C for 30 minutes) and the pellet was used for all biological experiments for comparison with PF-HQ–DOX.In order to prepare the nanoconjugates (PF-HQ–DOX), DOX (100 μg of 1 mg mL–1), (ii) DOX (0.625–5 μM), and (iii) PF-HQ–DOX  for 24 hours. All the untreated and treated cell lines were washed extensively with PBS to remove surface-attached nanoparticles/nanoconjugates. Finally, the cell viability of the untreated and treated cell lines was determined using an MTT assay according to our previously published protocols.20To determine the cancer cell killing ability of PF-HQ–DOX, murine melanoma cancer cell lines (B16F10) were incubated with (i) PF-HQ (5.25–42 μg mL5 cells per well) were cultured in 6-well dishes in DMEM for 24 h. In order to check the cellular DNA content of the B16F10 cancer cells, B16F10 cancer cell lines were incubated with (i) PF-HQ (21 μg mL–1), (ii) DOX (2.5 μM), and (iii) PF-HQ–DOX (the concentration of DOX = 2.5 μM and the concentration of PF-HQ = 21 μg mL–1) for 24 h. Untreated B16F10 cells were kept as a control. After 24 h of treatment, all the treated or untreated cell lines were extensively washed with DPBS and trypsinized, and cell cycle analysis was carried out using PI-RNase staining by a flow cytometer  according to our already published protocols.23B16F10 cancer cell lines (4 × 105 cells per well) were cultured in a 6-well dish in DMEM complete media for 24 h. To check the extent of apoptosis, B16F10 cancer cell lines were incubated with (i) PF-HQ (21 μg mL–1), (ii) DOX (2.5 μM), and (iii) PF-HQ–DOX (the concentration of DOX = 2.5 μM and the concentration of PF-HQ = 21 μg mL–1) for 24 h. Untreated B16F10 cells were kept as a control. Finally, the treated and untreated cell lines were trypsinized and stained using the FITC Annexin V Apoptosis Detection Kit (BD Biosciences) using an FACScan flow cytometer, as per the manufacturer’s protocol.20B16F10 cancer cell lines  into the lower left abdomen of each mouse. When the tumor volume reached ∼50–75 mm3 (post 16–17 days of cancer cell implantation), the tumor-bearing mice were erratically categorized into four distinct groups (n = 3): Gr-I: the untreated control group; Gr-II: the mice treated with PF-HQ (34 mg per kg b.w.); Gr-III: mice treated with free DOX (2.5 mg per kg b.w.); and Gr-IV: mice treated with PF-HQ–DOX . All the treatments were injected intraperitoneally (IP) on alternate days over a period of 10 days . The tumor volume was measured using the formula 0.5 × ab2, where a symbolizes the greatest dimension and b corresponds to the shortest dimension of the tumors, measured by a digital vernier caliper. Also, the volume, weight, and optical images of the untreated and treated tumors were taken after the sacrifice of the respective mice by CO2 euthanasia upon completion of the animal experiment. All the mice were regularly observed for weight loss/gain, allergies, mortality, or morbidity during the tumor regression experiment.Initially, the 1H and 13C NMR spectroscopy  calculations (Table S12O (at 523 nm), the aggregated polymer quantum yield was calculated to be only 1.18%. The intensity of the emission peak at 412 nm decreases  and T2 = 1.568 ns (8.8%), whereas in water, tri-exponential decay was observed with the species having a longer lifetime (T1 = 0.265 ns (13.52%), T2 = 1.1 ns (37.27%), and T3 = 3.638 (49.20%))  Fig. S6. Further) Fig. S6. PF-HQ s) Fig. S6. This hi2O = 1 : 9) showed a bright red fluorescence property. However, the fluorescence emission peak was broad when PF-HQ was mixed with PBS or HBSS in a buffer (–1) for 12–14 h. The fluorescence images were recorded after extensive washing with PBS using fluorescence microscopy. PF-HQ exhibited intense green (λex = 420–495 nm and λem = 525 nm) and red (λex = 510–560 nm and λem = 605 nm) fluorescence inside the different normal and cancer cell lines (λem = 485 nm at λex = 380 nm) exclusively from B16F10 melanoma cancer cells, but not in normal cells (NIH-3T3 and COS-1)   and S9†.2O = 10 : 90 solvent system) in a dose-dependent manner (27–540 μg mL–1) for 24 h  in different cancer cell lines (A549 and B16F10), indicating the insignificant cancer cell killing ability of PF-HQ.A cell viability assay, a critical assay for the determination of cytotoxicity, was performed for various normal (NIH-3T3 and COS-1) and cancer (A549 and B16F10) cell lines after incubation of the cells with PF-HQ  for 4 h  assay or egg yolk assay is a standard test to evaluate the  for 4 h . Untreat for 4 h . The CAM for 4 h . Several for 4 h . This inξ) of PF-HQ–DOX (–25.1 ± 6.4 mV) decreased compared to the ξ of PF-HQ (–25.4 ± 5.6 mV) after DOX attachment. The decrease in the negative zeta potential further supported the possible attachment of positively charged DOX (+2.7 ± 0.8 mV) with negatively charged PF-HQ by weak electrostatic attraction.–1 (assigned as –NH stretching from a secondary amine of PF-HQ) was shifted to 1598 cm–1 upon conjugation with DOX  with the free lone pair of the secondary amine of PF-HQ  using PF-HQ as a delivery vehicle and DOX as an anti-cancer drug. Since PF-HQ shows anti-cancer properties, we hypothesized that PF-HQ–DOX could exhibit enhanced anti-cancer effects compared to pristine DOX when combined in synergy with each other. An elegant fabrication technique was developed to pack the hydrophobic pockets  of the P Fig. S12. This in Fig. S12.28 Hence Fig. S12.in vitro cytotoxicity nature of the nanoconjugates (PF-HQ–DOX), melanoma cancer (B16F10) cell lines were incubated with (i) PF-HQ, (ii) DOX, and (iii) PF-HQ–DOX in a dose-dependent manner  for 24 h (–1), (ii) DOX (2.5 μM), and (iii) PF-HQ–DOX  (in vivo) in the C57BL6/J mouse melanoma tumor model.In order to test the for 24 h . The celf PF-HQ) . The DDSAccording to published reports, the enhanced cytotoxicity of PF-HQ–DOX in cancer cell lines can cause arrest in any of the four cell cycle phases, namely sub-G1, G0/G1, S, and G2/M.in vitro cell viability data showed significant inhibition of melanoma cancer cell proliferation using PF-HQ–DOX which encouraged us to perform the tumor regression studies (in vivo) in the subcutaneous mouse murine melanoma model (C57BL6/J).Malignant melanoma is the sixth most recurrent cancer in the U.S., with estimated deaths of ∼48 000 worldwide every year.n = 3). The tumor regression results revealed that PF-HQ–DOX caused a significant reduction in the melanoma tumor compared to pristine DOX and PF-HQ, as confirmed by the tumor volume and tumor weight data (before and after the sacrifice of the mice) . Additionally, the biodistribution of DOX in the mice treated with DOX and PF-HQ–DOX was determined by spectrofluorimetry (in vitro), observed by fluorescence microscopy  on alternate days with (i) PF-HQ (34 mg per kg b.w.), (ii) DOX (2.5 mg per kg b.w.), and (iii) PF-HQ–DOX (DOX = 2.5 mg per kg b.w. and PF-HQ = 34 mg per kg b.w.). The untreated tumor-bearing mice were kept as a control group (he mice) . The opthe mice) . Interesorimetry . The biocroscopy . Hence, r organs . The quaNumerous studies have demonstrated the role of the cancer proliferation marker like Ki-67 in the impediment of tumor growth. Ki-67 is a nuclear protein which is subsequently linked with the ribosomal RNA transcription process.38392O solvent system (1 : 9) in cancer and normal cell lines (B16F10 and CHO) was reported previously.in vitro) and in mouse melanoma tumor models (in vivo) with high efficiency and improved anti-cancer effects. A smart fabrication technique was developed to fill the hydrophobic pockets with a high drug loading efficacy  is increasingly becoming a challenging task to overcome the complexity and high cost, and to develop a multifunctional approach. Metal nanoparticle-based nano-theranostics agents often involve serious problems regarding bio-degradability, toxicity, and clearance from the body.efficacy  of the c Fig. S15 thereby in vitro) and in CAM assay (ex vivo). PF-HQ was further utilized to design and fabricate a drug delivery system (PF-HQ–DOX) that successfully inhibited the cancer cell proliferation and tumor growth in a mice melanoma tumor model. Mechanistic studies demonstrated the inhibition of Ki-67 and subsequent apoptosis for the PF-HQ–DOX-treated tumor. Altogether, the in vitro and in vivo studies revealed that fluorescent hydroxyquinoline-appended polyfluorene (PF-HQ) nanoparticles are highly probable candidates for various bio-medical applications as demonstrated in this work.In conclusion, PF-HQ displayed dual-state emission properties with an unusually high red shift >150 nm in an aqueous medium which facilitated bio-imaging inside live cells to acquire bright multi-color green and red fluorescence. More importantly, PF-HQ showed high biocompatibility in normal cell lines (Authors have no conflicts of interest.Supplementary informationClick here for additional data file.


The Abstract section is missing. The publisher apologizes for the error. The Abstract can be viewed here:N1 type constitute a widely used class of anticancer drugs, the effect of which on human non small-cell lung cancer (NSCLC) has not been adequately studied although frequently combined with platinum-based chemotherapy. We thus studied the effect of N-methyl-N-nitrosourea (MNU), a model SN1 methylating agent, on two human NSCLC cell lines; A549 (p53wt) and H157 (p53null). The mechanism of MNU-induced cell death was investigated through a time course gene expression profiling study, 24, 48 and 72h following treatment. Differentiated genes, biological processes and cellular pathways were identified using appropriate bioinformatics tools. The results were validated through RT-PCR of selected differentiated genes. The number of statistically significant differentiated genes, presenting a minimum of 2 fold alteration in their expression, was 920 for A549 and 541 for H157 cells. In both cell lines, the MNU-induced alterations in gene expression became prominent 48h after treatment. Between the two cell lines, only 86 genes were found in common. Gene Ontology-based analysis revealed that the most significantly altered processes, regarding A549 cells, were DNA damage response and repair, mitotic relative functions, cell cycle and proliferation while in H157 cells, they were mainly related to transcription regulation, cholesterol and fatty acid biosynthesis, apoptosis, inflammatory response, cell cycle and adhesion. MNU induced distinct responses at the gene expression level in the above cell lines; in A549 cells, the contribution of pyroptosis in the MNU-induced cell death was further validated by caspase-1 western blotting and inhibition, DNA repair processes were down-regulated and a significant number of p53 downstream genes were up-regulated indicating the induction of a p53-, caspase-1-dependent cell death mechanism, while in H157 cells genes related to apoptosis were induced accompanied by down-regulation of pro-inflammatory genes. Our results support the use of SN1 methylating agents in platinum-based combination regimen against advanced NSCLC.Despite concerted research efforts, lung cancer remains the leading cause of cancer death worldwide, therefore, new therapeutic approaches are eagerly needed. Methylating agents of S


Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive and lethal malignancies. Long non-coding microRNAs (lncRNAs) are a newly discovered type of regulatory molecule with both diagnostic and prognostic value, but the role of lncRNA in PDAC has not been well investigated until now. Here, we present evidence that shows that the lncRNA DGCR5 is significantly reduced in PDAC tissues as well as in PDAC cell lines and that the downregulation of DGCR5 predicts poor prognosis. Ectopic expression of DGCR5 inhibits the proliferation and migration, and promotes 5-FU resistances of PDAC cells. Further experiments demonstrated that DGCR5 and miR-320a regulate each other in a reciprocal manner and that DGCR5 reverses the inhibition of PDCD4 by miR-320a, which is involved in the regulation of the PDAC cell phenotype and response to 5-FU. Our findings provide novel information about the functions of lncRNAs in PDAC, some of which might be beneficial to the precise diagnosis, prognosis and individualized therapy of patients with PDAC in the future. Pancreatic cancer (PC) is one of the most common lethal malignant diseases worldwide, as PC has almost equal mortality and incidence rates  and is rIn recent decades, the role of non-coding RNA in the development of human diseases, including cancers, has been widely studied. Although microRNA has been well studied, the investigation into long non-coding RNA has just begun. LncRNAs, which belong to a class of ncRNA >200 nucleotides in length, have been implicated in various biological processes such as chromatin reprogramming, cis- or trans- regulation of neighboring genes and post-transcriptional regulation of mRNA processing –8. LncRNIn this study, we investigated the possible role of DGCR5 in PDAC cells and further explored its possible mechanism. We found that DGCR5 is significantly reduced in clinical PDAC samples and PDAC cell lines and that a downregulated DGCR5 level is associated with tumor-free survival and the malignant phenotype of PDAC cells. In addition, we found that DGCR5 and miR-320a mutually regulate each other and that DGCR5 reverses the inhibition of PDCD4 by miR-320. Our study elucidates how DGCR5 affects the proliferation, migration and the resistance of PDAC cells to 5-FU, and provides a theoretical basis for the diagnosis and treatment of PDAC.In our preliminary study, we found that several lncRNAs were differentially expressed in clinical PDAC samples compared with normal pancreatic tissues according to microarray experiments (data not shown). We also found that DGCR5 was significantly downregulated in PDAC. In addition, by deep sequencing, DGCR5 was demonstrated to be under-expressed . To furtTo study the role of DGCR5 in the regulation of the phenotype of PDAC cells, we first synthesized siRNA for DGCR5 and constructed a DGCR5 overexpression plasmid. We validated the efficiency of si-DGCR5 in HAPC cells, which have a relatively high expression level of DGCR5. The efficiency of the DGCR5 overexpression plasmid was validated in PANC-1 cells, which have a relatively low expression level of DGCR5 Figure . Then, aTo determine the possible role of DGCR5 in the response of pancreatic cancer cells to 5-FU, we generated 5-FU-resistant HPAC and PANC-1 cells. As shown in Figure LncRNAs exert various bio-functions. Some function as a scaffold, some are involved in the epigenetic modification of chromatin, and others regulate the function of microRNA. To determine whether DGCR5 functions as a regulator of microRNA, we conducted a microRNA array experiment to compare the discrepant expression of microRNA between PANC-1 cells transfected with the DGCR5 expression plasmid or the control plasmid. Several microRNAs were significantly differentially expressed between DGCR5-overexpressing cells and control cells Figure ; miR-320To further validate the mutual regulation of DGCR5 and miR-320a in PDAC cells, we performed a rescue assay. In both HPAC and PANC-1 cells, the DGCR5 expression plasmid significantly increased the level of DGCR5; the simultaneous transfection of these cells with miR-320a mimics reversed the efficiency of the DGCR5 expression plasmid Figure  and 5B. To determine whether the impacts of DGCR5 on PDAC cell phenotype could be reversed by miR-320a mimics, we first transfected HPAC or PANC-1 cells with the DGCR5 expression plasmid alone or simultaneously with miR-320a; the qRT-PCR results demonstrated that the transfection was efficient Figure  and 6B. We attempted to determine whether miR-320 is involved in the resistance of PDAC cells to 5-FU, which is regulated by DGCR5. We transfected 5-FU-resistant HPAC or PANC-1 cells with DGCR5 alone or simultaneously with miR-320a mimics, and the qRT-PCR results confirmed the transfection efficiency Figure  and 7B. In this study, we demonstrated that the lncRNA DGCR5 was significantly reduced in both clinical PDAC samples and PDAC cell lines and that a lower DGCR5 level might imply a poor survival. In addition, it was found that miR-320a and DGCR5 mutually regulated each other and that DGCR5 reversed the inhibition of the miR-320a target gene PDCD4, which in turn inhibited the proliferation, migration and 5-FU resistance of PDAC cells Figure .An increasing amount of evidence has demonstrated that ncRNAs participate in the progression of many cancers including pancreatic cancer. The role of microRNAs in PDAC has been systematically studied, but as novel regulatory molecules, the role of lncRNAs in PDAC has not been well studied. Only a few studies have investigated the role of lncRNAs in PDAC, and considering the potential clinical and prognostic significance of lncRNAs in PDAC, more extensive studies in this field are required. In this study, we found that the lncRNA DGCR5 was significantly downregulated in PDAC tissues compared with non-PDAC pancreatic tissues. It was also revealed that DGCR5 is involved in the regulation of proliferation, migration and 5-FU resistance of PDAC cells. Further experiments suggested that DGCR5 and miR-320a mutually regulate each other and that DGCR5 reverses the inhibition of PDCD4 by miR-320a.DGCR5 was first reported to be downregulated in Huntington’s disease , and othUnlike protein coding genes, lncRNAs elicit their functions via various pathways. Some lncRNAs function as a scaffold on which many protein factors bind together to form a large functional unit –20, whilIn conclusion, here we showed the way in which a novel lncRNA (DGCR5) regulates the progression and chemotherapeutic response of PDAC. DGCR5 and miR-320a reciprocally regulate each other, and DGCR5 reverses the inhibition of PDCD4 by miR-320a, which in turn affects the phenotype and chemotherapeutic response of PDAC cells. Downregulated DGCR5 in PDAC tissues indicates a poor prognosis for patients, and the area under the ROC curve suggested that DGCR5 might have potential diagnostic value for PDAC. Our study sheds new light on the molecular mechanism of PDAC progression and response to 5-FU with respect to lncRNA.Thirty matched primary pancreatic cancer specimens and matched adjacent non-tumorous pancreatic tissues were collected from Huai’an First People’s Hospital of Nanjing Medical University with informed consent of the patients. The samples were placed into liquid nitrogen and then transferred to a freezer for storage at -80°C. All the procedures were in accordance with the guidelines of the ethics committee of the university. All cell lines were purchased from ATCC and were maintained according to the guidelines of ATCC.–ΔΔCt method.Total RNA of the clinical samples and the cell lines was extracted with TRIzol reagent  per the manufacturer’s instructions, and cDNAs were synthesized with a PrimeScript RT Reagent Kit  according to the standard protocol provided by the manufacturer. Real-Time PCR and SYBR Premix Ex Taq II  was conducted to detect the expression level of target genes. The level of DGCR5 was normalized to that of GAPDH, and the level of miR-320a was normalized to that of U6 snRNA; the fold change in the expression of the target genes was calculated using the 2 The cells transfected with the DGCR5 expression plasmid, the knockdown plasmid or the respective control were seeded into a 96-well plate at an initial density of 4,000 cells per well. CCK8 was added to the culture medium at 24 h, 48 h, 72 h or 96 h after the cells were seeded. The absorbance at 450 nm was then detected using a microplate reader. For the colony formation assay, approximately 400 cells per well were seeded in a 24-well plate, and the medium was replaced every two days. Sixteen days after seeding, the cells were fixed and the number of colonies was counted.5 cells were seeded. The medium in the chamber did not contain serum, while the medium in the 24-well plates contained 20% FBS. Then, 36 hours after seeding, the cells that did not pass through the filter were removed by a cotton swab, whereas cells on the lower surface were fixed and stained with formaldehyde and crystal violet, respectively. The cells on the lower side of the filters were then counted.The cells transfected with the DGCR5 expression plasmid, the knockdown plasmid or the respective control were seeded into a chamber coated with or without fresh Matrigel (diluted 1:6 in serum-free medium)  in a 24-well plate; approximately 2 × 10 The cells transfected with the DGCR5 expression plasmid, the knockdown plasmid or the respective control were seeded into a 96-well plate at an initial density of 4,000 cells per well. A different concentration of 5-FU was added to the medium, and each concentration was tested in triplicate. Twenty-four hours after 5-FU treatment, an MTT assay was conducted, and the well with 50% of 570 nm absorbance of the well without 5-FU treatment was the IC50 of 5-FU.Data are presented as the means ± SD. SPSS 22.0 software  was used for the statistical analysis. The survival calculations were illustrated with Kaplan-Meier curves, and differences between the survival curves were tested by the log-rank test.


ChIP-seq has been commonly applied to identify genomic occupation of transcription factors (TFs) in a context-specific manner. It is generally assumed that a TF should have similar binding patterns in cells from the same or closely related tissues. Surprisingly, this assumption has not been carefully examined. To this end, we systematically compared the genomic binding of the cell cycle regulator FOXM1 in eight cell lines from seven different human tissues at binding signal, peaks and target genes levels. We found that FOXM1 binding in ER-positive breast cancer cell line MCF-7 are distinct comparing to those in not only other non-breast cell lines, but also MDA-MB-231, ER-negative breast cancer cell line. However, binding sites in MDA-MB-231 and non-breast cell lines were highly consistent. The recruitment of estrogen receptor alpha (ERα) caused the unique FOXM1 binding patterns in MCF-7. Moreover, the activity of FOXM1 in MCF-7 reflects the regulatory functions of ERα, while in MDA-MB-231 and non-breast cell lines, FOXM1 activities regulate cell proliferation. Our results suggest that tissue similarity, in some specific contexts, does not hold precedence over TF-cofactors interactions in determining transcriptional states and that the genomic binding of a TF can be dramatically affected by a particular co-factor under certain conditions. Transcription factors (TFs) are crucial proteins that mediate gene transcriptional regulation by binding to specific DNA sequences in all living organisms. Several technologies have been developed to investigate the binding of TFs in a high-throughput manner . Among tet al. performed ChIP-chip analysis and found that the binding sites varied extensively between human and mouse even for TFs that are highly conserved during evolution [et al. compared the pseudohyphal regulators STE12 and TEC1 binding sites in three yeast species, S. cerevisiae, S. mikatae, and S. bayanus under pseudohyphal conditions and reached a similar conclusion [et al. showed that the target genes of MCM1 have diverged substantially in three related yeast species; however, in all species MCM1 is involved in regulating cell cycle and mating processes [Previous studies have reported a rapid turnover rate of binding sites of homologous TFs in different species –14, suggvolution . Bornemanclusion . Other tnclusion , 16. In rocesses . Moreoverocesses .et al. compared the REST genomic occupancy in 16 different human cell lines and found that only 7% of binding peaks are shared by all cell lines [i.e., the accessibility as measured by DNase I hypersensitivity analysis [On the other hand, the genomic occupancy of a TF in multiple cell types of the same organism shows different degrees of variation. For some TFs, a high degree of shared occupancy between cell types has been observed. Investigation of CTCF binding in 19 human cell lines, for instance, indicates that on average 72% of CTCF sites were shared between any two cell types . Additioll lines , 21 and ll lines , 23 leveanalysis , 25) thaanalysis , 27. In analysis –32. Furtanalysis  indicateanalysis , 35. GivIn this study, we investigate FOXM1 binding in several cell lines and show that overall genetic similarity of cell lines does not hold precedence over context-specific TF-co-factor interactions in determining TF binding profiles. FOXM1, forkhead box protein, is a crucial cell cycle regulator , 37, whiTo compare FOXM1 binding sites in different human cell lines, we searched for all available FOXM1 ChIP-seq datasets in the Gene Expression Omnibus (GEO) database  and obtaWe first sought to investigate the difference in FOXM1 binding events between different cell types. We divided the whole human genome into 100 bp bins, and for each bin we calculated the normalized binding signal (mean coverage per million reads) based on the continuous-valued TF binding signal provided in the bedGraph files. Bins with non-zero signal in at least one of the 23 ChIP-seq profiles were selected for PCA analysis. The results showed that all MCF-7 cell ChIP-seq profiles clustered together according to the first principle component, whereas MDA-MB-231 profiles were grouped with HEK293, HeLa and U2OS Figure . This isSecond, we called FOXM1 binding peaks for all ChIP-seq experiments using Model-based Analysis of ChIP-Seq (MACS2) . The numCDC25B, a well-known FOXM1 target gene [VMP1, a gene encoding a vacuole membrane protein [Figure get gene , which w protein , was onl protein , 60 and  protein .2-transformation was applied after performing the motif enrichment analysis so that a positive value indicates enrichment and a negative value indicates depletion. The results showed that experiments in MCF-7 cell lines (green bar) clustered together based on their enriched motifs compared with the other cell lines, whereas those from MDA-MB-231 (brown bar) cell lines clustered with other non-breast cells  and ESR1 (Mann Whitney Wilcoxon Test P = 3e-05) were significantly enriched in MCF-7 cell lines compared to others  but higher enrichment in an estrogen receptor related pathway, ER non-genomic pathway , when compared to other cell lines   C2 pathws Figure . These rMCF-7. Similarly, iRASMDA indicates FOXM1 activity inferred based on its target genes in MDA-MB-231 cell line.Several studies have shown that the transcriptional activity of FOXM1 is more predictive than its mRNA expression in cancers , 48–50. MCF-7 showed significant difference between ER+ and ER− samples . However, when we stratified patients into ER+ and ER- groups, iRASMCF-7 is not associated with prognosis any more (P > 0.1). This indicates that FOXM1 targets in MCF-7 cell lines reflect estrogen receptor activity in breast tumor samples, implying that ERα serves as the major factor that mediates FOXM1 genomic binding in ER+ breast cancer. Namely, in majority binding sites, ERα mediates FOXM1 through its DNA motifs, and thus regulating the transcription of ERα target genes. We further divided patients into two groups based on iRASMCF-7, patients with high iRASMCF-7 (iRASMCF-7 > 0) and those with low iRASMCF-7 (iRASMCF-7 < 0), and compared their prognosis. Patients with high iRASMCF-7 exhibited better prognosis compared to those with low iRASMCF-7 . These observations further implied that iRASMCF-7 primarily reflects ERα activity in a tumor sample where higher ERα activity indicates greater sensitivity to hormone treatment, even though the major function of FOXM1 is to regulate cell cycle division.We first compared the inferred regulatory activities of FOXM1 in ER-positive (ER+) versus ER-negative (ER−) breast tumor samples. When FOXM1 targets identified in MCF-7 were used for activity inference, the resulting iRASs Figure . As showMDA. We observed significantly lower iRASMDA in ER+ than in ER- tumor samples , which is opposite to what we observed for iRASMCF-7. Furthermore, patients with low iRASMDA had better prognosis compared to those with high iRASMDA . These results suggested that iRASMDA reflects the activity of FOXM1 in regulating cell proliferation. Lower iRASMDA indicates slower proliferation of tumor cells in a patient's sample, and therefore informs better prognosis. The relevance of iRASMDA with cell proliferation was further confirmed by its high positive correlation with the E2F4 score  in group 4 contained 23.1% of METABRIC patients with only 35.1% of these patients being ER+. This observation further suggests that the calculated iRASMCF-7 reflects the activity of estrogen receptor, which is consistent with Figure MCF-7 than ER- patients. More interestingly, when we compared group 1 with group 4, we found that ER+ patients are more likely to maintain lower cell proliferation. We further found that patients in group 1 have the best outcome compared to the other 3 groups , as these patients are associated with higher ERα activity and lower proliferation. Moreover, patients in group 1 and 2 had better prognosis compared to those in group 3 and 4 which implies that proliferation is more prognosticative than ER activity in breast cancer. These observations suggest that the combination of the inferred activities of both ERα and proliferation can provide more precise prognostic predictions in breast cancer.Our observations indicate that FOXM1 activity inferred based on its targets in MCF-7 cell line serves as a proxy for ERα activity, while iRASA Figure . For exaTFs play crucial roles in regulating gene expression by binding to many cis-regulatory elements that decide cell fate. ChIP-seq technology has been widely utilized to investigate the binding sites of several TFs in a panoply of biological contexts. Intuitively, we would expect to observe more similar genomic binding profiles of a TF in cell lines from the same or more closely related tissues. However, our analyses on FOXM1, a well-known cell cycle regulator , provideIn this study, we focus on the binding events of FOXM1 using 23 ChIP-seq experiments from 8 human cell lines that encompass 7 different tissues  FOXM1 binds with its motif without interacting with a co-factor. (3) FOXM1 interacts with another TF x, but DNA binding is mediated by FOXM1 motif. (4) FOXM1 interacts with another TF x that binds with its motif. These four binding patterns may co-exist in MCF-7 and non-breast cancer cell lines. When ChIP-seq data for FOXM1 are generated in more cell lines, we may expect to observe in some cell lines that FOXM1 binding events are mainly mediated by the interaction of another TF x with its DNA motif as observed in MCF-7 cells.In view of our analyses, we inferred the binding patterns of FOXM1 in different cell lines based on its physical interactions with different co-factors. In MCF-7 cell lines FOXM1 binding might be mediated by ERα, supporting the following two possible patterns: (1) FOXM1 interacts with ERα and both bind their own motifs, eliciting a cooperative binding pattern, and (2) FOXM1 interacts with ERα but only ERα binds its DNA motif Figure . Our motet al. . In contet al. [in vitro. In summary, our analysis robustly demonstrated altered FOXM1 binding as a result of ERα activity and provided new insights concerning the binding modes of FOXM1 in different cell types.Although the FOXM1 ChIP-seq data were collected from 6 different studies with different FOXM1 antibodies and sequence depths, we achieved consistent results according to the comparisons based on raw signals, binding peaks, motifs and target genes. Our systematic analyses revealed that binding of FOXM1 in MCF-7 cell line differs with MDA-MB-231 and other non-breast cell lines. Moreover, the binding of FOXM1 exhibited highly similarities in cell lines from MDA-MB-231 and six other different tissues. Our results provide new insights into TF binding and suggest that some TFs might not be as affected by tissue type as previously thought. However, there exists some limitations to our methods. First, noise caused by the hybridization process in ChIP-seq experiments[et al.  revealedWe retrieved 23 FOXM1 ChIP-seq experiments from the NCBI GEO database   and Non-T(i). The maximum deviation between these two functions infers the similarity score between the binding affinity data and sample gene expression. After 1000 times random permutation, we normalized those scores and got the iRAS which infers the activity of FOXM1 in a specific sample.According to the target gene sets identified using our previously TIP algorithm , we firsyed BASE , a rank-p-value. Multivariate cox regression model was used to examine the independent prognostic power of iRAS considering important clinical variables including age, tumor stage, tumor grade, ER status, PR status and HER2 status. The R package “survival” was used to implement survival analysis.The Kaplan-Meier method was applied to compare survival prognosis of patients in different groups and the log-rank test was used to calculate the


Aspergillus and Penicilium, and is responsible for mycotoxicosis in animals and humans.Patulin is a mycotoxin produced by some molds, especially There is still not very detailed data about the anti-cancer potency of patulin, but some reports demonstrated that it induces cellular apoptosis and toxicity.To determine the efficacy of patulin as a therapeutic strategy for cervical and colorectal cancers, we investigated its effects on HeLa,SW-48, and MRC-5 cell lines. Cell lines were exposed to various concentrations of patulin , then using methyl thiazolyl tetrazolium (MTT) and bromo-2′-deoxyuridine (BrdU) assays, the rates of apoptosis and cell viability were determined.The obtained results showed a significant reduction in cell viability and apoptosis induction in a dose-dependent manner. Among all the cell lines, the highest growth inhibition rate was obtained at the 4 μM concentration of patulin.Our results suggested that patulin could significantly decrease tumor growth in human cervical and colorectal cancer models. Penicillium and Aspergillus, and is usually found in apples and apple-derived products including juice and compote. Several reports have demonstrated that exposure to different levels of this polyketide metabolite is associated with immunotoxicity, genotoxicity, embryotoxicity, and teratogenicity in animals and some microorganisms  and human immortalized keratinocyte (HaCaT) , as wellIn the current study, MTT assay demonstrated that the inhibition rates of HeLa and SW-48 cell lines, which were incubated with 2 μM of patulin, were 40% and 30%, respectively . PatulinHowever, a study by Luft et al. , it was It is worth mentioning that MTT assay indirectly quantifies cell number and cannot obviously confirm the induction of apoptosis and reduced cell number; thus, the apoptotic effect of patulin on tumor cell growth was evaluated using an ELISA-based BrdU incorporation assay.Several studies have confirmed that patulin can induce apoptosis in various cell lines . Wu et aFusarium equiseti, exhibited IC50 values ranging from 10 nM to 2.5 μM against HaCat, P9-WT, MCF-7, MDA-231, SV-HUC, UM-UC14, and PC3 cell lines [Previously, BrdU has been used as a rapid test to determine DNA synthesis in proliferating cells exposed to different mycotoxins. In the study of Nagashima et al. using this assay, the IC50 value of nivalenol against the promyelocyte-derived cell line, HL60, was 0.51 µM . Also, fll lines . The current results regarding patulin are in line with those obtained by Wu and Liu, who concluded that patulin induced apoptosis in HL-60 and HEK293 cells as well as similar to other mycotoxins , 26.We believe that because of verified induction of toxicity and apoptosis in our report and other studies, patulin can be considered an extreme candidate for anti-cancer agents. Fungus usually produces many important metabolites that may have anti-cancer benefits. For the first time we have demonstrated that patulin was able to decrease tumor growth in human colorectal and cervical cancer models.