Merkel cell carcinoma (MCC) is a highly lethal skin cancer. MCC tumors rapidly develop resistance to the chemotherapies tested to date. While PD-1/PD-L1 immune checkpoint blockade has demonstrated success in MCC treatment, a significant portion of MCC patients are nonresponsive. Therefore, the pressing need for effective MCC chemotherapies remains. We screened a library of natural products and discovered that one compound, glaucarubin, potently reduced the viability of Merkel cell polyomavirus (MCPyV)-positive MCCs, while remaining nontoxic to primary human fibroblasts and MCPyV-negative MCC cell lines tested. Protein array and Western blot analyses revealed that glaucarubin induces DNA damage and PARP-1 cleavage that correlates with the loss of viability in MCC cells. However, high basal expression of the antiapoptotic factor BCL-2 allowed a subpopulation of cells to survive glaucarubin treatment. Previous studies have shown that, while targeting BCL-2 family proteins significantly decreases MCC cell viability, BCL-2 antisense therapy alone was insufficient to inhibit tumor growth in patients with advanced MCC. We discovered that treatment with an FDA-approved BCL-2 inhibitor in the context of glaucarubin-induced DNA damage led to near complete killing in multiple MCPyV-positive MCC cell lines that express high levels of BCL-2. The combination of DNA damage-induced apoptosis and BCL-2 inhibition thus represents a novel therapeutic strategy for MCPyV-positive MCCs. Merkel cell carcinoma (MCC) is a neuroendocrine carcinoma associated with Merkel cell polyomavirus (MCPyV) ,2. MCC mDespite increasing prevalence and poor patient outcomes, there are no effective chemotherapeutic treatments available for metastatic MCC ,5. SeverMCPyV has a circular, double-stranded DNA genome of ~5 kb ,2. The vIn more than 80% of MCCs, MCPyV genomic DNA is clonally integrated into the tumor cell genome ,21. MCPyCompared to MCPyV-negative tumors, MCPyV-positive MCCs harbor very few somatic mutations ,25, suggIn this study, we screened the compounds in the National Cancer Institute (NCI) Nature Products Set IV library and discovered that a natural product, glaucarubin, could specifically reduce viability of the MCPyV-positive MCC cell line MKL-1, but spare most of the MCPyV-negative MCC cells and healthy human skin cells. We found that glaucarubin treatment kills MKL-1 cells via accumulation of dsDNA breaks, but that highly expressed antiapoptotic BCL-2 allows a small population of cells to survive. The combination of glaucarubin-induced DNA damage and treatment with the FDA-approved BCL-2 inhibitor ABT-199 completely inhibits the proliferation of this subpopulation of cancer cells. In several other MCPyV-positive cell lines tested with high BCL-2 expression, including ABT-199, treatment greatly enhanced the cytotoxicity of glaucarubin alone. The synergy between DNA damage and BCL-2 inhibition in MCPyV-positive cell lines provides an explanation for why downregulation of BCL-2 mRNA alone was insufficient to impede tumor growth in patients with metastatic or regionally recurrent MCC . The resTo identify candidate molecules with the potential to kill MCC cells, we performed cell cytotoxicity screening of the chemical compounds in the Natural Product Set IV provided by the NCI Developmental Therapeutics Program. This library contains 419 compounds isolated from natural products frequently consumed by humans. A subset of candidate compounds was identified in a luciferase reporter assay-based screen designed to isolate small molecules that can downregulate MCPyV oncogene transcription (data not shown). We then tested the cytotoxicity of those candidates in MKL-1  cells anFrom this study, we discovered that one of the natural product-derived compounds, glaucarubin, could effectively kill MKL-1 with an IC50 of approximately 149 nM . GlaucarTo explore the mechanism of action for glaucarubin cytotoxicity on MKL-1 cells, we sought to determine whether glaucarubin-related compounds were also toxic . We compTo explore possible explanations for the discrepancy between MKL-1 and HDF sensitivity to glaucarubin, and to identify targetable cellular factors underlying its mechanism of action, we applied reverse-phase protein microarray (RPPA) to probe glaucarubin-induced changes in the abundance of antigens with documented functions in cancer development and progression . We treaPrevious studies have shown that prosurvival BCL-2 and MCL-1 are highly expressed in up to 85% and 88% of MCCs, respectively ,39,40. BTo test the hypothesis that glaucarubin kills MCPyV-positive MCC cells by stimulating DNA damage-induced cell death response, we first validated our RPPA results in MKL-1 cells treated with glaucarubin via Western blot A. IndeedNext, we determined whether glaucarubin treatment induces DNA damage in other MCPyV-positive cell types . GlaucarTo test our hypothesis that canonical events downstream of DNA damage and γH2A.X accumulation incurred by glaucarubin were at the root of its cytotoxicity, we analyzed the p53 activation status and poly (ADP-ribose) polymerase (PARP-1) cleavage in each cell type in the presence of glaucarubin. p53 serine 15 phosphorylation, a marker for activated p53, is dramatically induced in glaucarubin-treated MKL-1, PeTa, and BroLi, where robust γH2A.X accumulation is observed . SignifiIn MKL-1 treated with glaucarubin, we consistently observed that nearly 20% of the cells remained alive, even after treatment with very high concentration of glaucarubin B. From tGiven that glaucarubin readily induces DNA damage in MCPyV-positive MCCs such as MKL-1, MKL-2, PeTa, and BroLi, but failed to inhibit BCL-2 in any of the cell types tested, we reasoned that BCL-2 activity in these MCPyV-positive MCC cells could confer resistance to complete loss of viability after glaucarubin treatment. Thus, we tested whether a combination of glaucarubin and the FDA-approved BCL-2 inhibitor, ABT-199, could circumvent resistance in those MCC cells A. As expCurrently, there are no effective chemotherapeutic strategies for combating metastatic MCCs, and those that have been attempted have failed to produce durable responses. The recently developed PD-1/PD-L1 immune checkpoint inhibitors have demonstrated promising results but, in many cases, the responses are temporary ,11,21,47Simarouba glauca [In this study, we performed a compound screening and identified the natural product glaucarubin as a potent inhibitor that can specifically repress the growth of MCPyV-positive MCC cells. Glaucarubin is a crystalline glycoside extracted from the tropical plant a glauca . We discTo search for possible molecular mechanisms underlying glaucarubin cytotoxicity observed in MCPyV-positive MCC cells, we performed a protein array analysis of putative oncogenes, tumor suppressors, and metastatic factors in normal healthy HDFs and MKL-1 cells after glaucarubin treatment. We found that γH2A.X is one of the most significantly increased antigens in MKL-1 cells after glaucarubin treatment, but it remained unchanged in HDFs under the same conditions . We alsoAn analysis of the MCPyV-positive and -negative MCC cell lines demonstrated that the antiproliferative activity of glaucarubin largely hinges on its ability to induce DNA-damage-associated cell death, though other pathways may be involved . For exaAlthough glaucarubin is effective at killing MCPyV-positive cells, nearly 20% of the MKL-1 cells remain viable even after treatment with as much as 0.1 mM of glaucarubin. RPPA analysis showed that the antiapoptotic regulator, BCL-2, is highly expressed in MKL-1 cells . BCL-2 iOur results suggest that p53 may contribute to the cell death induced by glaucarubin in some MCPyV-positive MCC cell lines such as MKL-1, PeTa, and BroLi . HoweverGlaucarubin has been used as an herbal medicine for treating intestinal amoebiasis for more than six decades ,60,61. IThe current standard of care for initial presentation of MCC is resection of tissue at the primary site, and irradiation of the resected site as well as draining lymph nodes. MCCs are considered highly radiation-sensitive tumors, as evidenced by the observation that disease is eliminated in many patients after radiation treatment alone. Recurrent and especially metastatic MCC, however, have much worse prognoses and are more difficult to treat. Given evidence of high expression of BCL-2 in many MCCs, and its potential as an escape mechanism from DNA-damage-mediated cell death, it would seem that BCL-2 inhibition may be best applied in combination with existing radiation treatments at first diagnosis. MCPyV-positivity and basal BCL-2 expression can be determined from primary tumor biopsies with relative ease and are likely predictors of the success of this combination. The great challenge of treating late-stage cancers with diverse mutational backgrounds lends credence to an aggressive treatment strategy for the primary tumor. ABT-199 treatment combined with radiation of the primary tumor and lymph nodes could prove effective at eliminating MCPyV-positive MCCs that would otherwise produce chemotherapeutically resistant metastases. Chemotherapeutically sensitizing tumors to DNA-damage-induced death may also make it possible for radiation specialists to reduce the dosages and concomitant toxicity needed for effective treatment.2. The remaining cell lines were grown in Dulbecco’s modified Eagle’s medium supplemented with 10% fetal calf serum. HDFs and human primary keratinocytes were isolated and cultured as described [MCPyV-positive MCC cell lines MKL-1, MKL-2 , MS-1 3, PeTa 4, WaGa 3, and Broescribed .http://dtp.cancer.gov). All glaucarubin-derived compounds were obtained from NCI/DTP Open Chemical Repository. BCL2 inhibitor ABT-199 (Cat. NO. 16233) was purchased from Cayman Chemical .All compounds were obtained from NCI Developmental Therapeutics Program Open Chemical Repository  following the manufacturer’s instructions. The validity of CellTiter-Glo results in measuring cell viability was confirmed by WST-1 assays  in pilot studies.Cells were seeded at a density of 5000 cells in 100 µL of medium per well. Cells were incubated at 37 °C in humidified air containing 5% COWestern blotting was performed as described previously . The priHDFs and MKL-1 treated in duplicate with DMSO or glaucarubin dissolved in DMSO for final concentrations equaling 1 µM or 10 µM, respectively. After 24 h, cell lysates were harvested for each condition. A total of 50 µg protein per sample was subjected to RPPA analysis using a set of validated antibodies targeting 304 antigens with documented roles in cancer. The RPPA assay was performed by the staff members of the MD Anderson Cancer Center core facility. All antibodies used in the RPPA assay were previously validated by Western blotting .t-test of GraphPad Prism software (Version 5.0)  to compare the data from the control and experimental groups. A two-tailed p value of <0.01 was considered statistically significant.Statistical analysis was performed using the unpaired A natural product, glaucarubin, induces dsDNA breaks, specifically in p53 wild-type MCPyV-positive MCCs. It is capable of significantly reducing the viability of those cells at the nanomolar range in vitro. High BCL-2 expression, however, enables a portion of p53 wild-type/virus-positive MCC cells to escape apoptotic cell death. Killing across multiple cell lines with these attributes is nearly total when glaucarubin-induced DNA damage is combined with treatment with an FDA-approved BCL-2 inhibitor, ABT-199.


The efflux pump P-glycoprotein (P-gp) affects drug distribution after absorption in humans and animals. P-gp is encoded by the multidrug resistance gene (MDR1) gene in humans, while rodents  express the two isoforms Mdr1a and Mdr1b. Differences in substrate selectivity has also been reported. Our aim was to generate an in vitro cell model with tight barrier properties, expressing functional rat Mdr1a P-gp, as an in vitro tool for investigating species differences. The IPEC-J2 cell line forms extremely tight monolayers and was transfected with a plasmid carrying the rat Mdr1a gene sequence. Expression and P-gp localization at the apical membrane was demonstrated with Western blots and immunocytochemistry. Function of P-gp was shown through digoxin transport experiments in the presence and absence of the P-gp inhibitor zosuquidar. Bidirectional transport experiments across monolayers of the IPEC-J2 rMDR1a cell line and the IPEC-J2 MDR1 cell line, expressing human P-gp, showed comparable magnitude of transport in both the absorptive and efflux direction. We conclude that the newly established IPEC-J2 rMdr1a cell line, in combination with our previously established cell line IPEC-J2 MDR1, has the potential to be a strong in vitro tool to compare P-gp substrate profiles of rat and human P-gp. Rat Mdr1a has a nucleotide sequence homology of 85% with human MDR1, while the nucleotide sequence homology between rat Mdr1b and human Mdr1 is 83%. At the amino acid sequence level, mouse Mdr1a P-gp shares 87% and Mdr1b shares 80% homology to human P-gp, while amino acid sequences for rat Mdr1a and Mdr1b are 80% homologous to human P-gp  ethanesulfonic acid (HEPES) was from AppliChem GmbH . The radio-labelled isotopes, [3H]-Digoxin (26.3 Ci/mmol) and [14C]-Mannitol (0.057 Ci/mmol), together with Ultima Gold scintillation fluid, were purchased from PerkinElmer . [3H]-Olanzapine (80 Ci/mmol) and [3H]-Vinblastine (20 Ci/mmol) were from American Radio-Labelled Chemicals . All other compounds and reagents were purchased from Sigma-Aldrich , unless otherwise stated.Plasticware such as T-75 culture flasks and Transwell Permeable supports , and 100 U·mL−1 to 100 μg·mL−1 penicillin-streptomycin solution). The rat Mdr1a cDNA, codon-optimized for expression in Sus scrofa, was synthesized by Geneart  and inserted in the pcDNA3.1 (+) cloning vector under control of a cytomegalovirus (CMV) promoter (see 6 IPEC-J2 cells were stably transfected with either 5 µg purified rat MDR1-plasmid or 5 µg empty pcDNA 3.1 (+) plasmid (mock control) using a P3 Primary Cell Nucleofector X Kit L in a Amaxa 4D Nucleofector with Program CA167, according to the manufacturer’s protocol . As an additional control of transfection efficiency, cells were nucleofected with 1 µg green fluorescent protein (GFP) plasmid. After transfections, the cells were cultured to confluence and subsequently selected for 10 days by addition of 1 mg/mL Geneticin to the culture medium (now defined as passage 1) to kill cells that did not contain the pcDNA 3.1 (+) plasmid, including the GFP control. rMdr1a-transfected cells were further selected for 4 days in the presence of 2 μg/mL puromycin in the culture medium to kill cells that did not express functional P-gp, including the mock control. The rMdr1a-transfected cells were continuously cultured with 2 μg/mL puromycin in culture flasks to maintain the selection pressure. Cell lines were maintained in polycarbonate culture flasks and split once per week. Cells were seeded on permeable Transwell® inserts  at a density of 4 × 104 cells/insert and cultured for 15–18 days (with culture medium change three times per week) prior to experimentation. Stability of the transfection was estimated through bi-directional transport experiments . The IPEC-J2 cells were cultured in supplemented Dulbecco’s modified eagle’s medium-AQ . Primer sequences are shown in RNA was isolated from IPEC-J2 rMdr1a and IPEC-J2 Mock cells after 15–18 days of culture on permeable inserts. Cells from six permeable inserts obtained from the same passage were lysed and the RNA was isolated using a GeneElute Total RNA Purification kit and an On-Column DNase I Digestion Set according to the manufacturer’s protocol , permeabilized in 0.1% Triton-x100 in PBS and blocked with 2% bovine serum albumin (BSA) in PBS. The filters were cut out of the inserts into smaller pieces, which were incubated overnight at 4 °C in one of the following primary antibodies: rabbit anti-P-gp Ab129450, rabbit anti-ZO1 Ab59720 or rabbit anti-claudin-5 Ab15106 , diluted 1:100 in 2% BSA in PBS. The filters were then washed in 2% BSA in PBS and incubated in the fluorescent secondary antibody, goat anti-rabbit Alexa 488 AV1008 , diluted 1:200 in 2% BSA in PBS. The secondary antibody solution was added to 1.5 g) for 10 min at 4 °C. The protein concentration of the supernatant was quantified with the Bicinchoninic Acid Protein assay kit according to the manufacturer’s protocol. Protein samples containing 7.8 The protein, from IPEC-J2 rMdr1a and IPEC-J2 Mock-transfected cells grown on permeable inserts for 15–18 days, was isolated and analyzed in a Western blot. For both cell lines, six permeable inserts from three passages were pooled and frozen until the day of the experiment. The cells were lysed, and the protein was extracted using a NP-40 buffer containing 8.6% sucrose, 0.03% (ethylenediaminetetraacetic acid) EDTA, 0.04% egtazic acid (EGTA) and 0.24% Tris-HCl in 2% NP-40. The cells were homogenized, incubated on ice and centrifuged . TEER across empty permeable inserts was 8–25 Ω·cm2 using the chopstick electrode and 1–68 Ω·cm2 using the cup electrode.The cell monolayers on permeable inserts were allowed to equilibrate to room temperature for 20 min before the transepithelial electrical resistance (TEER) was measured prior to all experiments. The TEER values across the cell monolayers were measured using a chopstick electrode  or an Endohm-12 cup electrode  connected to a voltmeter  or the apical chamber (50 µL), respectively. The volumes of the collected samples were replaced with blank transport buffer (37 °C). All samples were mixed with 2 mL scintillation fluid and their radioactivity was measured in a liquid scintillation counter .The cell monolayers were washed and pre-incubated in transport buffer consisting of 10 mM HEPES, 0.05% BSA and 0.038% sodium bicarbonate in HBSS pH 7.4 for 15 min at 37 All transport experiments were conducted across cell monolayers from three independent passages, and for each passage, treatments were made in triplicates (three permeable inserts with cells). Values are presented as means ± standard error of the mean (SEM).2.The transepithelial resistance (TEER) values measured across cell monolayers were standardized by multiplication by the cross-sectional area of the permeable inserts to obtain a unit of Ω·cmapp) were calculated using Equation (1):−2·min−1), C0 is the initial concentration in the donor compartment, A is the cross-sectional area of the permeable inserts (1.13 cm2) and Qt represents the accumulated amount of drug in the receiver compartment at time t in minutes (corrected for the dilution arising from replacement of the sampled volume with blank transport buffer). Steady-state flux was calculated as the slope of the straight part of a plot of Qt versus time, to correct for lag-time effects. Papp values were calculated using six sampling time points . Efflux ratios were calculated as the ratio between the permeability coefficient in the basolateral to apical direction (PB-A) and the permeability coefficient in the apical to basolateral direction (PA-B), i.e., efflux ratio = PB-A/PA-B.Based on data from transport experiments, the apparent permeability coefficients  followed by a Dunnett’s multiple comparisons test. p-values less than 0.05 were considered significant.Statistical calculations were performed by comparing group means with either a two-tailed Student’s E. coli. IPEC-J2 wild-type (WT) cells were transfected, as described in the Methods Section. The transfected cells were treated with geneticin for 10 days to select for cells which had taken up the rMdr1a plasmid or the empty vector (mock cells). At this point, the IPEC-J2 mock cells were expanded and cryostored (passage 2). The transfected cells were further treated with the cytotoxic aminonucleoside and P-gp substrate, puromycin, in order to select for cells expressing functional P-glycoprotein. Puromycin treatment has previously been shown to be effective for selecting P-gp-expressing cells [2) were significantly lower than those of IPEC-J2 wild-type cells, 14,472 ± 635 Ω·cm2 (p = 0.0002). We have previously observed a similar drop in TEER across cell monolayers of IPEC-J2 cells transfected with the empty pcDNA 3.1(+) plasmid [2, the electrical resistance across IPEC-J2 rMdr1a cell monolayers was not different from those measured across IPEC-J2 WT cells (p = 0.9454). The bidirectional transport experiments with digoxin showed marked differences in P-gp function between the different cell monolayers (p = 0.0026), which indicates a marked efflux transport of digoxin across IPEC-J2 rMdr1a cells. The A–B and B–A permeabilities of digoxin were more comparable across monolayers of IPEC-J2 WT and IPEC-J2 mock cells with apparent efflux ratios of 2.1 and 1.8, respectively. The efflux ratio for IPEC-J2 mock cells was close to, but less than 2, which has been suggested as a cut-off value for active efflux [B-A value of (1.3 ± 1.7) × 10−7 cm s−1 for digoxin in IPEC-J2 WT cell monolayers was not significantly different from the corresponding PA-B value of (0.5 ± 0.5) × 10−7 cm s−1 (p = 0.4675). It is therefore unlikely that the observed efflux ratio of 2.1 for digoxin across IPEC-J2 WT cells reflects actual efflux transport.The plasmid encoding the rat Mdr1a gene (rMdr1a-pcDNA 3.1(+), see ng cells ,15. IPEC plasmid . With menolayers b. The tre efflux . The effp = 0.0019). This finding confirms that the polarized transport of digoxin observed across IPEC-J2 rMdr1a cells was mediated by P-glycoprotein. In similar transport experiments, the presence of 0.4 µM zosuquidar did not affect digoxin transport across monolayers of IPEC-J2 mock cells  × 10−5 cm s−1 and (1.6 ± 0.1) × 10−5 cm s−1 for the early and late group of passages, respectively. The permeability values in the A–B direction seemed to increase from (3.7 ± 1.0) × 10−7 cm s−1 in the early passages to (6.8 ± 6.2) × 10−7 cm s−1. However, the increase was not significant (p = 0.0548) in a Student’s t-test. The apparent increase in A–B permeability caused the efflux ratio to decrease from 47 in the early passages to 38 in the late passages. However, both efflux ratios are within the range of efflux ratios obtained in previous bidirectional transport experiments with digoxin (see above) and we therefore conclude that the expression of functional P-gp in monolayers of IPEC-J2 Mdr1 cells was stable up to passage 16.To test the stability of the functional expression of P-gp, IPEC-J2 rMdr1a cells from the same cryopreserved batch were cultured until passage 16; subsequently, bidirectional transport experiments with digoxin as substrate were completed for passages 5–7 and passages 14–16, and the obtained digoxin permeability values and efflux ratios were compared b. The app = 0.2461). The apparent expression level of rat Mdr1a in the IPEC-J2 rMdr1a cell monolayers, as indicated by the real-time PCR data, was more than 650-fold higher than the expression of porcine MDR1. The apparent expression level of porcine BCRP was more than 25-fold lower than the expression of rat Mdr1a  in IPEC-J2 rMdr1a and IPEC-J2 mock cells was investigated by real-time PCR . Real-tiat Mdr1a b. Appareat Mdr1a b.The high expression of rat Mdr1a mRNA found in the PCR analysis of IPEC-J2 rMdr1a cells was confirmed by Western blotting c. A dist2 and a cell height of 10–18 µm. The tight junctional proteins ZO-1 and claudin-5 were expressed and both proteins were mainly localized to the cell junctional zones. The localization of ZO-1 appeared to be less defined compared to claudin-5. Staining for P-gp showed expression of P-gp and the xz-stack indicated that P-gp was predominantly expressed in the apical membrane  × 10−5 cm s−1 in the A–B direction. This corresponded to an efflux ratio of 1.9, indicating that olanzapine was not actively effluxed across monolayers of IPEC-J2 rMdr1a cells. Similar observations were done across IPEC-J2 MDR1 cells, where the apparent permeability of olanzapine was (4.0 ± 0.3) × 10−5 cm s−1 in the B–A direction and (2.3 ± 0.4) × 10−5 cm s−1 in the A–B direction, which resulted in an efflux ratio of 1.8. Directly compared, the B–A transport of olanzapine across IPEC-J2 rMdr1a cells was not significantly different from the B–A transport across IPEC-J2 MDR1 cells (p = 0.9302), and in a similar manner, the A–B transport across IPEC-J2 rMdr1a cells was not significantly different from the A–B transport across IPEC-J2 MDR1 cells (p = 0.7111). Thus, the bidirectional transport experiments with olanzapine across the IPEC-J2 rMdr1a cell monolayers overexpressing rat P-gp and across the IPEC-J2 MDR1 cells overexpressing human P-gp did not indicate species differences.Across IPEC-J2 rMdr1a cell monolayers, the apparent permeability of olanzapine was (4.4 ± 0.3) × 10−7 cm s−1 and (2.1 ± 0.2) × 10−5 cm s−1 respectively, while the A–B and B–A transport of digoxin across IPEC-J2 MDR1 cells was (2.8 ± 0.3) × 10−7 cm s−1 and (2.1 ± 0.2) × 10−5 cm s−1, respectively. No significant difference in efflux transport of digoxin was observed between the two cell lines (p = 0.9614). The bidirectional transport experiments with digoxin across IPEC-J2 rMdr1a and IPEC-J2 MDR1 demonstrated that the observed in vitro efflux transport of a P-gp substrate in cell monolayers overexpressing rat P-gp was not different from efflux transport of the same substrate across monolayers of a similar cell line overexpressing human P-gp.Digoxin, on the other hand, exhibited clear efflux transport across both cell lines with efflux ratios of 61.4 across IPEC-J2 rMdr1a cell monolayers and 75.7 across IPEC-J2 MDR1 cells b. The A–vinca alkaloid chemotherapeutic drug compound and a well-known P-gp substrate [−5 cm s−1 and not significantly different from the B–A apparent permeability of (1.9 ± 0.6) × 10−5 cm s−1 across IPEC-J2 MDR1 cells (p = 0.8142). Similarly, the A–B apparent permeability of (1.6 ± 0.2) × 10−6 cm s−1 across monolayers of IPEC-J2 rMdr1a cells was not significantly different from the corresponding apparent permeability of (1.5 ± 0.2) × 10−6 cm s−1 across IPEC-J2 MDR1 cells (p = 0.8475).Vinblastine, a ubstrate ,18,19,20−7 cm s−1 across IPEC-J2 rMdr1a cell monolayers and not significantly different from the A–B apparent permeability of (2.5 ± 0.2) × 10−7 cm s−1 across cell monolayers of IPEC-J2 MDR1 cells (p = 0.2786). The B–A apparent permeability was (4.4 ± 1.1) × 10−7 cm s−1 across IPEC-J2 rMdr1a cells and (3.7 ± 0.4) × 10−7 cm s−1 across IPEC-J2 MDR1 cells, with an insignificant difference between the two (p = 0.3845). Overall, the bidirectional data obtained for mannitol across IPEC-J2 rMdr1a and IPEC-J2 MDR1 cells show that mannitol permeated the cell monolayers of both cell lines passively with a low permeability. This observation corroborates the high TEER values measured across the IPEC-J2 rMdr1a cell monolayers in the present study and those reported for the IPEC-J2 MDR1 cells by Saaby et al. [Finally, we tested the bidirectional transport of mannitol, a small hydrophilic compound, which can be used to probe for barrier properties and as a marker for paracellular diffusion d. Mannity et al. .In summary, despite the previous reports of efflux transport across MDCK II cells overexpressing human P-gp, olanzapine was neither a substrate for P-gp across IPEC-J2 rMdr1a cell monolayers nor across IPEC-J2 MDR1 cells overexpressing rat P-gp and human P-gp, respectively. Moreover, the bidirectional transport data observed for olanzapine was not different between the two models, which indicates that the interaction with rat Mdr1a P-gp is not different from human P-gp. This was also observed for the known P-gp substrates digoxin and vinblastine. Both compounds exhibited marked and quantitatively similar efflux transport across monolayers of the two cell lines.2 and <100 to 10,000 Ω·cm2 [2 of IPEC-J2 rMdr1a cell monolayers suggest that this cell line forms a much tighter barrier than the Caco-2 and MDCK MDR1 cell lines. In summary, the bidirectional transport data with digoxin, the expression studies and the measured electrical resistance values demonstrate that the IPEC-J2 rMdr1a cells form tight monolayers with high levels of functional rat P-gp. Overall, the performance of the IPEC-J2 rMdr1a cells, in terms of P-gp functionality and barrier properties, is consistent with the similar cell line IPEC-J2 MDR1, previously established by the group [There is a risk that P-gp substrate profiling data produced in humanized in vitro models and clinical trials with human volunteers may not correlate with data from animal studies using rodents such as mice and rats. This can in part be due to differences in P-gp expression levels between humans and experimental animal models and between in vitro and in vivo models but may in part also be due to differences in amino acid sequences between P-glycoproteins expressed in different animal species. As a consequence, there is an interest in assessing the impact of species differences in P-gp function. As an example, cell lines overexpressing the human P-gp and cell lines overexpressing the murine P-gp (encoded by the Mdr1a gene) have been employed to assess species differences in efflux transport profiles for different substrates . Along t00 Ω·cm2 ,27,28,29he group .The IPEC-J2 rMdr1a cells offer the combination of exceptionally tight monolayers and at the same time, a high expression of functional rat P-gp. Real-time PCR showed that the expression of mRNA for rMdr1a was markedly higher than the expression of endogenous porcine MDR1. This is comparable to our previously established cell line IPEC-J2 MDR1 overexpressing human P-gp, where the expression of hMDR1 mRNA was more than a ~1000-fold higher than the expression of porcine MDR1 mRNA . ExpressThe IPEC-J2 rMdr1a cell monolayers offer a combination of an expression level of Mdr1a, which was markedly higher than that of endogenous porcine MDR1, while at the same time forming extremely tight barriers. The tight monolayers minimize the paracellular leak of compounds through the cell barrier, which in turn increases the sensitivity for detecting weak substrates. Using the IPEC-J2 MDR1 cells overexpressing human P-gp, we have previously reported the identification of citalopram and atenolol as P-gp substrates, even though these compounds were identified as non-substrates in other in vitro models . Considep,uu). Using an MDCK II MDR1 cell line overexpressing human P-gp, Summerfield et al. observed significant efflux transport of olanzapine with a reported efflux ratio of 4.9. Based on this finding, a Kp,uu value less than 1 would be expected. However, from steady-state intravenous brain distribution experiments in rats, Summerfield and co-workers could calculate a Kp,uu value of 2.13, which indicates that the concentration of free unbound olanzapine in the brain is approximately 2-fold larger than the free unbound plasma concentration [p = 0.9302), and the same was true for the A–B transport (p = 0.7111). Based on these findings, we can conclude that the bidirectional transport of olanzapine across IPEC-J2 rMdr1a cells, which overexpressed rat P-gp, was not discernably different from bidirectional transport observed across IPEC-J2 MDR1 cells overexpressing human P-gp. In addition to the rat Kp,uu value larger than unity, Summerfield et al. also reported a Kp,uu value of 6.9 in mice [p,uu values larger than unity could indicate active uptake transport of olanzapine across the BBB in the rodent animal models. This is corroborated by a report by Dickens et al., who found that clozapine, a second-generation antipsychotic structurally similar to olanzapine, was actively taken up by the human brain endothelial cell line hCMEC/D3 [p,uu values larger than unity in animal models can be explained by active uptake processes outcompeting potential weak efflux transport, rather than species differences. We also conducted parallel bidirectional transport with two known P-gp substrates, digoxin and vinblastine [p = 0.8142). This is in concordance with findings of Takeuchi and co-workers, who found that the efflux ratio for vinblastine was of similar magnitude across LLC-PK1 cells transfected with rat Mdr1a (18.4) and LLC-PK1 transfected with human MDR1 (12.5) [p = 0.9614). However, due to slight differences in the A–B transport between the two cell lines, an efflux ratio of 61.4 was obtained across monolayers of Mdr1a cells, while the calculated efflux ratio was 75.7 across MDR1 cell monolayers. In comparison, Takeuchi et al. reported a digoxin efflux ratio of 20.9 across LLC-PK1 cells transfected with rat Mdr1a and 15.3 across LLC-PK1 cells transfected with human MDR1 [−7 cm s−1) was compared with the B-A transport across cell monolayers of IPEC-J2 MDR1 ((3.7 ± 0.4) × 10−7 cm s−1), demonstrating no significant difference (p = 0.3845) between monolayers of the two cell lines. The low apparent permeability values observed for mannitol (2.5 × 10−7 to 4.4 × 10−7 cm s−1) were consistent with the high TEER-values measured for the two cell lines.To investigate whether known P-gp substrates interact differently with rat and human P-gp, we conducted parallel bidirectional transport experiments with olanzapine, vinblastine, digoxin and mannitol across the IPEC-J2 rMdr1a cell monolayers overexpressing rat P-gp and IPEC-J2 MDR1 cell monolayers, which overexpress human P-gp . OlanzaphCMEC/D3 . It is tblastine ,18,19,201 (12.5) . In the man MDR1 , indicat−1 mg protein−1 in a study by Takeuchi et al. [−1 mg protein−1. The consistent and similar transport data between the IPEC-J2 rMdr1a and IPEC-J2 MDR1 cell lines observed in the present study is a noteworthy finding. Further studies with the IPEC-J2 rMDR1a cell line will address the contribution from endogenous efflux transporters through a series of bidirectional transport experiments with relevant substrates and inhibitors. However, based on the results from the present study, transport experiments using IPEC-J2 rMdr1a and IPEC-J2 MDR1 cell lines appear to represent a unique screening platform, which can be used to compare substrate profiles of rat Mdr1a and human P-gp in order to identify species differences in P-gp-mediated efflux transport.In the present study, we have generated a cell line, IPEC-J2 rMdr1a, which formed very tight monolayers and had a high expression of functional rat P-gp. In addition, we demonstrated consistent bidirectional transport data for olanzapine, digoxin, vinblastine and mannitol across monolayers of the IPEC-J2 rMdr1a cell line and the similar IPEC-J2 MDR1 cell line overexpressing human P-gp. Remarkably, there were no significant differences between A–B and B–A transport of the respective compounds between the two cell lines. Differences in absolute values for, e.g., clearance, permeability coefficients and efflux ratios can normally vary between different cell-based barrier models due to differences in paracellular tightness and expression levels of transport proteins. As an example, the efflux transport of digoxin across LLC-PK1 cells transfected to express P-glycoproteins of different species  ranged between 9.2 and 15.6 µL mini et al. . Similar2) and low paracellular permeability of the low-molecular weight marker mannitol (~3 × 10−7 cm s−1). Through real-time PCR, Western blotting and immunocytochemistry, we have demonstrated that IPEC-J2 rMdr1a cells express high levels of rat P-gp localized to the apical membrane and that the expression of endogenous porcine ABC-type efflux transporters was low in comparison. In addition, we found that the high functional expression of rat P-gp was stable for at least 14 passages. In bidirectional transport experiments, the IPEC-J2 rMdr1a cells exhibited marked efflux transport of the prototypical P-gp substrate digoxin (efflux ratio of 76) and the known P-gp substrate vinblastine (efflux ratio of 13). Thus, we conclude that the newly established IPEC-J2 rMdr1a cell line represents a promising tool to screen drug compounds for interactions with rat P-gp. In combination with similar cell barrier models overexpressing human P-gp  with rat Mdr1a, we have generated a new cell line—the IPEC-J2 rMdr1a. IPEC-J2 rMdr1a cells formed extremely tight monolayers, as judged from the very high transepithelial electrical resistance (~14–15,000 Ω∙cm


PLK1) inhibition in CREBBP-mutant SCLC cells. We validated this association in vitro using four mutated and four wild-type SCLC cell lines and two PLK1 inhibitors (Volasertib and BI2536), confirming that the effect of PLK1 inhibitors depended on the mutational status of CREBBP. Besides, DrugSniper was validated in-silico with several known clinically-used treatments, including the sensitivity of Tyrosine Kinase Inhibitors (TKIs) and Vemurafenib to FLT3 and BRAF mutant cells, respectively. These findings show the potential of genome-wide loss-of-function screens to identify new personalized therapeutic hypotheses in SCLC and potentially in other tumors, which is a valuable starting point for further drug development and drug repositioning projects.The development of predictive biomarkers of response to targeted therapies is an unmet clinical need for many antitumoral agents. Recent genome-wide loss-of-function screens, such as RNA interference (RNAi) and CRISPR-Cas9 libraries, are an unprecedented resource to identify novel drug targets, reposition drugs and associate predictive biomarkers in the context of precision oncology. In this work, we have developed and validated a large-scale bioinformatics tool named DrugSniper, which exploits loss-of-function experiments to model the sensitivity of 6237 inhibitors and predict their corresponding biomarkers of sensitivity in 30 tumor types. Applying DrugSniper to small cell lung cancer (SCLC), we identified genes extensively explored in SCLC, such as Aurora kinases or epigenetic agents. Interestingly, the analysis suggested a remarkable vulnerability to polo-like kinase 1 ( In recent years, genetic alterations, such as DNA mutations, translocations, or copy number variations, have been used as a source of therapeutic targets and therapy response biomarkers in cancer . HoweverHER2-inhibitors for tumors with ERBB2 amplification in breast cancer, the vemurafenib sensitivity of BRAF-mutant cells in melanoma, or the relevance of EGFR mutation for EGFR-inhibitor treatments in non-small cell lung cancer (NSCLC). This approach is especially relevant in tumors that still lack a clear treatment guideline, such as small cell lung carcinoma (SCLC).Analyzing genetic essentiality in specific aberrant phenotypes has been demonstrated as an unprecedented approach to identify drug targets and develop personalized therapies in cancer. This is the case for the sensitivity to PLK1) [CDKs) [SCLC, accounting for around 15% of all lung cancers, is an aggressive neuroendocrine malignancy characterized by a high proliferation rate and early development of widespread metastases. These features contribute to the extremely poor prognosis with a median survival of 7 months after patients are diagnosed , revealiPLK1) ,4 or cyc) [CDKs) . In thisLarge-scale loss-of-function screens, such as RNA interference (RNAi) and CRISPR-based libraries, have been used to delineate novel potential drug targets and predictive biomarkers in human cancer cells. In this context, the Project Achilles ,9,10, thIn this research, we present the DrugSniper tool, a bioinformatics software to identify response biomarkers for targeted therapies in 30 tumor types for which cell lines have been derived and included in the Cancer Cell Line Encyclopedia (CCLE). To this aim, DrugSniper modeled the sensitivity of 6237 protein-targeting drugs with the essentiality scores of their corresponding targets for 412 cancer cell lines. In the present work, we will refer to a specific study carried out on SCLC cell lines using DrugSniper.PLK1) inhibition in CREBBP-mutant SCLC cells. We validated this association in vitro using four CREBBP mutated and four wild-type SCLC cell lines and two PLK1 inhibitors (Volasertib and BI2536), confirming that the effect of PLK1 inhibitors depended on the mutational status of CREBBP. Besides, we further validated DrugSniper in-silico in tumor types different from SCLC, focusing on several known personalized treatments, including the sensitivity of Olaparib in BRCA mutant patients and to Vemurafenib in BRAF mutant cells.By integrating protein-targeted drug information with the mutational landscape of SCLC cells, we identified genes extensively explored in SCLC as potential targets, such as Aurora kinases or epigenetic agents. Interestingly, the analysis suggested a remarkable vulnerability to polo-like kinase 1  and a predictive biomarker, which indicates whether the treatment will be effective in a patient or not. In this study, we developed an application (DrugSniper) that models drug sensitivity by the essentiality scores of genes associated with protein targets (therapy) and predicts mutations (predictive biomarkers) that discriminate resistant and sensitive cells. In total, the current version of DrugSniper includes 6237 compounds and 17,098 targets in 412 cancer cell lines.DrugSniper’s functionality facilitates the identification and selection of precision medicine therapies in four real-life scenarios A. In the50 or Ki values were below 1 µM. Integrating the three lists of drugs and targets, we were left with 6237 compounds that constituted DTIs with 2284 protein targets.DrugSniper integrates three sources of large-scale datasets: targeted drug information, genome-wide RNAi libraries and mutational profiles A. We incProject Achilles measures how individual genes affect cell survival through RNAi experiments. The DEMETER score ,18 is a We integrated mutational profiles with DEMETER scores for 412 cell lines and developed a statistical pipeline to identify gene mutations as predictive biomarkers for targeted therapies B,C. The As a proof of concept validation of DrugSniper, we focused on essential gene-predictive mutation pairs that were already in clinical use and checked their statistical significance in DrugSniper. Interestingly, DrugSniper identified several of these known drugs and biomarkers that define current clinical guidelines.FLT3-mutated AML [FLT3-mutant AML cell lines  = 0.18). Another example is Vemurafenib, a drug widely used to treat tumors driven by pathogenic variants in the BRAF gene, e.g., melanoma or skin cancers. This drug inhibits BRAF activity when BRAF is altered [BRAF vulnerability when BRAF was mutated in skin cancer . Likewise, Olaparib is a drug that exploits synthetic lethality with successfully proven efficacy [PARP polymerases that are essential for DNA repair in the presence of mutated BRCA genes [PARP in tumors with BRCA2 mutant variants . Similarly, recent publications showed the efficacy of using Trametinib (MEK inhibitor) in lung cancer cells with ATM mutations [MEK is a gene family that contains several mitogen-activated protein kinases (MAPK). DrugSniper found MAPK7—a MEK family gene—to be essential in lung cancer when ATM was altered  is characterized by the absence of associated treatments. However, Tyrosine Kinase Inhibitors (TKIs) were recently prescribed as a possible therapy for ated AML . DrugSni altered . DrugSniefficacy . This drCA genes . DrugSniutations . MEK is EGFR-mutant tumors is Erlotinib. This drug is frequently used to treat non-small cell lung cancer but could be used to treat different EGFR-mutant cancers [EGFR becomes essential when EGFR is mutated in cancer cells because of oncogene addiction [p-value = 2.74 × 10−2 and local FDR = 0.03).An essential therapy for  cancers . EGFR beddiction . DrugSniKRAS–KRASmut pair (top 1 pair when filtering by String), which is a well-known oncogene addiction [KRAS due to its activating mutation. More examples of associations between clinically used drugs and biomarkers found by DrugSniper are collected in Remarkably, Drugsniper is capable of identifying not only loss-of-function but also gain-of-function mutations as predictive biomarkers of genetic essentiality, as shown in a supplementary case study using lung adenocarcinoma . Specifiddiction , triggerPARP1–BRCA2, MAP3K7–ATM and EGFR–EGFR were validated in the tab “Visualize case-by-case” focusing only on the statistical significance between such pairs. This means that they do not appear in the current pipeline “Predict Biomarkers for a Target Gene” due to the stringent filters applied . We set these filters to minimize the false positives when screening, which is crucial for wet lab hypothesis validation. Therefore, these filters eliminated some of the well-known and clinically used pairs mentioned above, although they were statistically significant when assessed as pairs (It is important to note that the pairs: n = 22) to evaluate the potential application of this approach in a blinded situation. We considered the first of the scenarios mentioned above, i.e., we did not have a priori selected drugs or targets to test in SCLC. The goal was to obtain a list of putative drugs/targets with their corresponding predictive mutation biomarkers for this elusive and aggressive cancer type. In this case, the tool was first used to explore SCLC cell viability for all available gene silencing experiments. The filters used to select the first list of essential genes were:To be essential in >20% of SCLC cell lines, with a threshold of −2 for the DEMETER score for essentiality. Using this filter, we require that at least 20% of SCLC samples die when genes considered “essential” are knocked down.To be specific for SCLC cell lines with an odds ratio >1 . This specificity parameter states that at most for 20% of the other cell-lines, the selected gene is essential. Henceforth, the gene is hypothesized to be a good drug target specifically for SCLC.To have a minimal expression score of 1 TPM (transcripts per million) in more than 75% of the SCLC cell lines.We applied DrugSniper to SCLC cell lines , is involved in the development and tissue-specific differentiation [CASP8AP2/FLASH, which supports cancer cells’ epithelial-to-mesenchymal (EMT) transition and inactivates Notch signaling [SMARCA4/BRG1, a component of the SWI/SNF-B (PBAF) chromatin remodeling complex, which activates neuroendocrine transcription and has been associated with relevant regulating roles in SCLC [PLK1, a serine/threonine-protein kinase that is a key regulator of mitotic progression and is also required for the spindle assembly checkpoint [. PLK1 has already been proposed as a potential target in SCLC and other tumors. A Phase II trial with BI2536, a PLK1 inhibitor, has been carried out, although it did not show efficacy in the treatment of relapsed SCLC [Using the above-mentioned filters, DrugSniper predicted 28 putative protein targets with their corresponding predictive mutation biomarkers . Interesntiation  and is antiation ; CASP8APignaling ,32; SMAR in SCLC ; or PLK1eckpoint . PLK1 haPLK1 inhibition with the mutation of CREBBP as a response biomarker , and also the KEGG pathways spliceosome  = 1.62 × 10−5), ribosome (FDR = 0.0049) or cell cycle (FDR = 0.0049).To validate the accuracy of these results, we selected the top hypothesis: iomarker . The prePLK1 has several experimental inhibitory drugs, so we were able to validate the prediction derived from DrugSniper by exploring the efficacy of two commercially available PLK1 inhibitors, Volasertib and BI2536, over a panel of SCLC cells in a cell viability (MTS) assay. We compared the relative cell growth after 72 h of treatment with serial dilutions of PLK1 inhibitors and analyzed the statistical results using the R library drc [rary drc .50 values significantly higher for the non-mutated SCLC cell lines NCI-H841, NCI-H889, NCI-H2171 and NCI-H146 than for the CREBBP mutated SCLC cell lines NCI-H1048, NCI-H1963, NCI-H211 and HCC33  . IC50 values for each cell line can be found in the The dose–response curves for Volasertib revealed IC× 10−12) A. Very sPLK1 inhibitors was also measured using a colony formation assay. The CREBBP mutated (CREBBP-MUT) cell line NCI-H1048 reduced its ability to form colonies significantly after exposure to 2.5 nM of Volasertib (p = 7.24 × 10−12) and 5 nM of BI2536 (p = 9.37 × 10−7), showing a significant global effect of both inhibitors (p = 4.84 × 10−14). In contrast, the CREBBP wild-type (CREBBP-WT) cell line NCI-H841 was more resistant to Volasertib or BI2536 treatment , confirming that cells were more sensitive or resistant to both treatments according to our computational predictions. These results and the recall of multiple clinically-used treatments, such as Olaparib or Vemurafenib, suggest the validity of the methodology.We applied this tool to SCLC, as it is a tumor with an urgent unmet medical need. DrugSniper predicted a range of drug targets and their corresponding companion biomarkers of sensitivity. Within the top-ranked discoveries, DrugSniper suggested a remarkable vulnerability to PLK1 plays an essential role in several oncogenic functions including regulation of mitosis and cytokinesis, modulation of genomic stability, induction of cell survival and regulation of cell division [PLK1 has long been considered an effective target for anti-mitotic agents and has been the subject of an extensive effort for anti-cancer drug discovery [CREBBP, which is mutated in 10% of patients, is an acetyltransferase that plays a central role in histone acetylation, chromatin stability and transcription [CREBBP are frequently found in SCLC tissues [CREBBP and PLK1 are involved in regulating the activity of FOXM1, a transcription factor that orchestrates the transcription of essential genes for cell cycle progression [PLK1 interacts with FOXM1 by mediating its phosphorylation on Ser-724 and stimulates its transcriptional activity [These findings are especially relevant in SCLC for several reasons. On the one hand, division . Interesdivision . PLK1 haiscovery . On the cription . Inactiv tissues , where i tissues . Remarkagression . Additioactivity .PLK1 inhibitors involved in clinical trials have shown no response in different cohorts of lung cancer (both SCLC and NSCLC), so far. Volasertib, which is currently in phase II [CREBBP mutation could be more sensitive to PLK1 inhibitors. Indeed, the PLK1 stratification based on the companion biomarker-mutated CREBBP that we report herein constitutes a promising approach to improve SCLC therapies, as shown by the in vitro validation. Likewise, this strategy could be extensible to NSCLC (8% of patients have a CREBBP mutated background) and other cancer types.Most phase II , showed phase II . AlthougDrugSniper’s approach allows the identification of drugs and biomarkers in a tumor histological subtype or a specific cohort of cell lines. However, the activity and safety of a drug depend on many other factors , which are not modeled by this pipeline. The output of DrugSniper should be considered as a hypothesis that helps a posterior drug development project, which should be validated and tested.The applicability of this computational pipeline will increase as more loss-of-function experiments are carried out in a wider range of tumors. So far, around 30 cancer types have genome-wide RNAi experiments available, some of which have only a few screened samples. In this context, DrugSniper opens another encouraging alternative: analyzing not only cells by primary site, but also by other tumor characteristics such as tumor histology. Another interesting future line is including genome-wide inhibition screens of normal samples. These experiments would complement DrugSniper’s approach as they would allow the consideration of target inhibition toxicity in normal tissue. DrugSniper methodology can also be extended to other types of biomarkers (not only mutations), such as copy number variations, expression, splicing or, even, epigenetic footprints. Nevertheless, mutations, or, in general, DNA alterations, can be more easily measured than epigenetics or RNA expression and are, therefore, more useful biomarkers.Large-scale loss-of-function screens are opening a wide range of opportunities to identify new personalized therapeutic strategies in cancer, which is an interesting starting point for further drug development projects. This work proposes a methodology to analyze these valuable resources and discover new applications that use mutations to help define more efficient clinical decisions in cancer.CCLE  providesWe integrated genome-wide RNAi libraries  of 412 cancer cell lines of the Project Achilles  with theProject Achilles interrogated these 412 cell lines for gene essentiality using RNAi screens. From that basis, the DEMETER score was created to quantify the competitive proliferation of the cell lines and minimize the effect of off-target hybridizations by using a statistical model. We used the DEMETER score as a measure of essentiality. The more negative the DEMETER score is, the more essential a gene is for a given cell line. Authors of DEMETER established a cut-off of −2 in this score as a threshold of essentiality. Genes with a DEMETER score lower than this threshold can be considered essential for a cell line. Missing scores of DEMETER were imputed using the nearest neighbor averaging algorithm . Combinit denote the number of RNAi target genes and n denote the number of screened samples. Let E be a i in sample j.Let p denote the number of mutated genes in the same n screened samples. Let M be a j is mutant in gene i. The resulting Let n’ be a subset of screened samples (columns of E and M matrices) that yields an essentiality matrix E’ and a mutation matrix M’. Let t for the n’ cell lines cohort. Let p for the n’ cell lines. For each pair of RNAi targets (t) and mutations (p), a null hypothesis is defined asLet p-values. Dealing with these p-values implies the correction of a huge number of multiple hypotheses (more than 20 million hypotheses).This null hypothesis is, therefore “the essentiality score of a gene is identical in mutant and wild-type cell lines”. To test this hypothesis, we used a moderated t-test implemented in limma . We applp-values corresponding to all tests according to the gene that is mutated in each case. For each group, we computed the local false discovery rate , which estimates the probability of the null hypothesis to be true, conditioned by the observed p-values [z is the observed p-values, π0 is the proportion of true null hypotheses estimated from the data, p-value, not on its inclusion in a larger set of possible values” as it occurs with previous multiple hypothesis correction methods such as q-values or the standard False Discovery Rate. The local FDR and π0 were estimated using the Bioconductor’s R Package q-value [To correct for multiple hypotheses, we followed a methodology similar to the IHW (Independent Hypothesis Weighting) procedure , which ip-values . The fortated in , “the ad q-value .2 in a humidified incubator, except for NCI-H2171 and NCI-H889 cells, which were grown in a HITES medium , 5 ng/mL murine EGF and 1% of an insulin–transferrin–selenium mix  with 5% FetalClone. Cell lines were authenticated and routinely tested for mycoplasma.The human SCLC cell lines NCI-H841, NCI-H1048, NCI-889, NCI-2171, NCI-146, NCI-H1963, HCC33 and NCI-H211 were obtained from the American Type Culture Collection . Cells were grown in RPMI 1640 with 10% FetalClone  at 37 °C with 5% COCell viability was determined using the 3--5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium (MTS) reduction assay  according to manufacturer specifications. Absorbance was measured at 540 and 690 nm in a SpectroStar nano reader . The experiments were performed in sextuplicate and repeated at least three times.PLK1 inhibitors Volasertib and BI2536 were obtained from SelleckChem. SCLC cells were seeded in 96-well plates at a density of 2.5 × 103 cells/well and incubated in a medium with threefold serial dilutions of PLK1 inhibitors, starting from 10 µM, for 72 h. After incubation with MTS reagent for 3 h, the absorbance was measured. Absorbance reading from the cells incubated without the inhibitor was used for 100% survival. Data were collected from six technical and three biological replicates for each cell line.PLK1 inhibitors (Volasertib and BI2536) for 72 h. The medium was replaced with a drug-free medium every 72 h for 14 days. Clones were fixed in 4% formaldehyde for 30 min, stained with crystal violet for 5 min, scanned and counted. The clonogenic assays were carried out in triplicate and repeated at least three times.The human adherent SCLC cells NCI-H841 and NCI-H1048 were used in colony formation assays. Cells were seeded at a density of 500 cells/well in a 6-well plate, treated with 0.1% DMSO or increasing doses (2.5–20 nM) of PLK1 inhibitors Volasertib or BI2536. After 24 h of treatment, cells were collected by centrifugation, washed with PBS, fixed with 70% ethanol, incubated with 0.5 mg/mL RNase  at 37 °C for 30 min and stained with propidium iodide . Cell fluorescence was analyzed by flow cytometry on a FACSCalibur platform fitted with a Cell Quest Pro software package . Cell cycle results were analyzed with FlowJo v9 software .SCLC cell lines were synchronized in a starving medium with 0.5% FetalClone for 12 h. Then, the medium was replaced with a fresh medium with or without 10 nM of the 50) of each inhibitor was determined by nonlinear regression using the R package drc. The statistical significance was also determined using this package.Data are presented as mean ± SD from 3 or more independent repetitions. Analyses of MTS and clonogenic assays were performed using a quasi-Poisson statistical model. Cell proliferation data were fitted and compared (WT vs. MUT). The half-maximal inhibitory concentration .


Both irradiation modalities induced early cytotoxic effects in line #1 with small effects on cell cycle, whereas a proliferative G2/M cytostatic block was observed in line #83. MR spectroscopy signals from mobile lipids (ML) increased in spectra of line #1 after photon and C-ion irradiation with effects on lipid unsaturation level, whereas no effects were detected in line #83 spectra. Gamma-Aminobutyric Acid (GABA), glutamic acid (glu) and Phosphocreatine (pCr) signals showed a significant variation only for line #1 after carbon ion irradiation. Glucose (glc) level and lactate (Lac) extrusion behaved differently in the two lines. Our findings suggest that the differences in irradiation response of GSCs #1 and #83 lines are likely attributable to their different metabolic fingerprint rather than to the different radiation types.Glioblastoma multiforme (GBM) is a malignant primary brain tumor with very poor prognosis, high recurrence rate, and failure of chemo-radiotherapy, mainly due to a small fraction of cells with stem-like properties (GSCs). To study the mechanisms of GSCs resistance to radiation, two GSC lines, named line #1 and line #83, with different metabolic patterns and clinical outcome, were irradiated with photon beams and carbon ions and assessed by  Glioblastoma multiforme (GBM) is the most common malignant glioma in adults. Despite innovative research efforts for targeted therapies, the outcome remains poor . To enhaThe cancer stem cell (CSC) hypothesis attributes treatment failure to a small fraction of self-renewing cells with stem-like properties and high resistance to radiation ,5 that sMetabolomics and metabolic profiling, as biochemical picture of a particular phenotype, can be explored by MRS as a tool to further understand metabolic changes occurring during tumor initiation, progression, and radiotherapy resistance . Improve137Cs photons and C-ions  at different Linear Energy Transfer (LET) value at a dose of 20 Gy. Effects on cell survival and cell cycle were also evaluated.To study the mechanisms involved in GSCs radiation resistance and evaluate whether their response to irradiation is due to different radiation type or to intrinsic GSCs characteristics, two GSC lines, named #1 and #83, were selected on the basis of their metabolic profiles and genetic signatures, deeply analyzed in previous studies ,21. PartGSC lines #1 and #83 were selected from a panel of GSCs on the basis of their biological and metabolic characteristics ,21. AlthApoptosis for both lines is shown in Metabolomics and metabolic profiling of the two cell lines have been explored by MRS. As already reported, the analysis of metabolic profiles for lines #1 and #83 indicated that these two lines strongly differ in several metabolites as shown in box plots and Fold Change evaluations shown in The whole MR spectra of the two lines, the analysed metabolic signals , selected region of 2D COSY spectra and the corresponding intensity values are shown in In particular, 1D and 2D spectra of photon beam irradiated samples (green trace) and their controls (blue trace) of lines #1 and #83 together with the spectral regions of interest are shown in A and B panels of p values, calculated on at least 5 samples, are shown in Mean values of lipid, glutathione and energy metabolites signal intensities, standard deviations and The intensities of signals from lipids (ML) increased in spectra of line #1 after both photon and C ion irradiation A,C,E,F, It is interesting to note that the level of lipid unsaturation, measured as the B/A ratio of cross peak intensities B, was stThe signals of GSH, gln, glu and GABA were influenced in line #1 and line #83 after irradiation with photons and carbon ions, though to a different extent .Unsupervised Cluster analysis of GSH, gln, glu signal intensities from spectra was also performed. The resulting dendrogram is shown in In addition, the signals of phosphocreatine (pCr) and total creatine (tCr) in line #1 spectra were strongly influenced by irradiation with carbon ions C,F, whilcon/Lacirr ratio for both types of irradiation in both lines increased as a function of time,  clearly decreased in the spectra of line #1 after irradiation with photon and carbon ions A,C,E,F, of time, B, being Effects on the selected metabolites after irradiation with both photon beams and carbon ions as a function of time are reported in Finally, a synthesis of the metabolic response of the two GSC cell lines to photon and C-ion irradiation is reported in In GBM the self-renewing, tumorigenic GSC population likely contributes to tumor initiation and therapeutic resistance. In addition, as normal stem and progenitor cells participate in tissue development and repair, these developmental strategies re-emerge in GSCs to support the progressive growth of tumors ,31,32,33High GBM heterogeneity seems to play a relevant role in GSC ability to escape therapies. A recent study that used single-cell RNA sequencing of primary GBMs revealed extraordinary diversity within each tumor . The higMetabolomics may help in clarifying stem cell fate and reprogramming function ,42. In tA previous study on 52 GSC lines analyzed in terms of LD50 after photon irradiation reported a similar response to a 20 Gy irradiation for both lines . HoweverCell cycle checkpoints and mechanisms involved in DNA repair are clearly interdependent, with the choice of repair mechanism consistently adjusted throughout the cell cycle ,46. ExisLipids detected as Mobile Lipids play a relevant role in GSC escaping mechanisms to radiotherapy. The lipid signal increased in line #1 after irradiation, while only small fluctuations could be detected in line #83. This increase in MLs in line #1 can be attributed to the accumulation of lipids in the cytoplasm as a consequence of metabolism impairment ,52. In cThe two lines belong to distinct metabolic clusters, where lipid content is one of the important metabolic parameters differentiating mesenchymal from neuronal fingerprint ,21. The l-glutamate involving glutamic acid decarboxylase (GAD) and pyridoxal-l-phosphate (PLP) as cofactor [As GSH is concerned it is well known the role of this metabolite as the most important active oligopeptide in the maintenance of redox homeostasis, with a radiation protection role against oxidative stress. The absence, after irradiation, of relevant changes of this metabolite in both cell lines shows that GSH is not a key metabolite in protecting GSCs from irradiation. On the contrary, the GABA increase, concomitantly with a glu decrease in irradiated cells from line #1, is particularly relevant after carbon ion irradiation and suggests a GABA synthesis increase. Indeed, GABA is synthesized via enzymatic decarboxylation of cofactor . Unfortucofactor . Noteworcofactor . The GABcon/Lacirr.Cancer cells have significant heterogeneity in glucose metabolism. Dysfunction of mitochondria, loss of tumor suppressors, the hypoxic microenvironment, and oncogene-driven metabolic reprogramming are initiating events of abnormal energy metabolism in cancer cells . At presIn tumor cells, glycolysis is the main supplier of intracellular ATP and cytosolic phosphocreatine-creatine kinase (CK) was found associated with enzymes of glycolysis ,62,63 thIn conclusion metabolism of line #83, analyzed by MRS, is unresponsive to irradiation, while in line #1 cells alterations of metabolic patterns of FA, GABA, pCr, and glc confirm the involvement of cell metabolism, including energy pathways, in cell radiosensitivity. Differences in radiation response may, therefore, be likely attributed to genetic and metabolic differences between the two lines, representative of mesenchymal and proneural profiles, regardless of radiation type. Further experiments are foreseen to have more insight in the observed effects. In this respect, proton MRS may contribute to study cell metabolism in GSCs with different metabolic and genetic signatures after different radiotherapeutic approaches.Tumor tissue samples were collected from adult patients with GBM tumors (WHO grade IV) undergoing complete or partial surgical resection at the Institute of Neurosurgery, Catholic University School of Medicine in Rome. Informed consent was obtained from the patients before surgery. All patients provided written informed consent according to the research proposals approved by the Ethical Committee of the Catholic University School of Medicine, UCSC (Prot. 4720/17). Patients were eligible for the study if a diagnosis of glioblastoma multiforme was established histologically according to the WHO classification . DetailsThe expression of the proliferation marker Ki-67 and of Phosphatase and Tensin Homolog were characterized on tumor specimen by immunohistochemistry on deparaffinized sections using the avidin-biotin-peroxidase complex methods , anti-Ki67 monoclonal antibody  and anti-Phosphatase and Tensin Homolog mouse monoclonal antibody . O6-methylguanine-DNA methyltransferase promoter methylation patterns were assessed on genomic DNA extracted from paraffin-embedded tissue by methylation-specific PCR as previously described ,67. LeveGSCs were isolated through mechanical dissociation of the tumor tissue and cultured in a serum-free medium supplemented with epidermal growth factor and basic fibroblast growth factor as previously described . The in Cell number in biological experiments has been evaluated by a Scepter 2.0 Coulter Counter .Clones from GSCs were obtained by plating single cells into 96 wells plate. After 4 weeks, single clones were mechanically dissociated and replated to expand the culture.6 cells/vial in nitrogen and 50 vials were prepared to be used for all the experiments at identical starting conditions.For irradiation experiments GSCs were seeded to be expanded. At 3th passage cells were pooled and stored as 1 × 102 flasks at a density of about 4 × 104/mL and 2 × 104/mL in 50 mL total volume, respectively. Just before irradiation, cells were collected and placed in different sterilized sample holders, depending on the irradiation facility used: commercial 1 mL polystyrene cuvettes (INFN-LNS and ISS) and commercial UVette (CNAO). These sample holders were then centrifuged at low speed to obtain cell pellets 4 mm and 2 mm thick, respectively.Seven days before the experiments, the GSC #1 and GSC #83 cell lines were seeded in T175 cmIrradiation with C-ions was carried out at the multidisciplinary irradiation beamline facility of the INFN-LNS in Catania and at the CNAO hadrontherapy facility in Pavia. At the INFN-LNS, 62 AMeV in energy carbon ions, exited in air and reached the entrance of the cell suspensions at 52 MeV/u, corresponding to a Linear Energy Transfer (LET) value of 42 keV/µm. However, the thickness of the 4-mm cell pellet does affect the LET of C-ions, with a significant variation in the average LET from 42 to 56 keV/µm. Absolute dosimetry was performed following the IAEA TRS 398 Code of Practice using a plane parallel PTW Markus ionisation chamber . At the 137Cs γ-rays were obtained from a Gammacell 40 Exactor (Nordion) at the ISS, Rome, with a dose rate of about 0.8 Gy min−1.All irradiations were performed at room temperature, with a single high radiation dose of 20 Gy comparable with: total doses delivered during radiation therapy in a fractionated regimen, single high doses used in other therapeutic modalities , and the boost before the chemo-radiotherapic treatments used for the treatment of patient with Carbon ions . The sam2O and 2 mM Sodium 3-(trimethylsilyl) propionate-2,2,3,3-d4 (TMSP) used as a frequency standard. A 15 μL aliquot of the suspension was inserted into a 1 mm MRS microtube and centrifuged to obtain a packed cell volume. Cells were removed, washed in Phosphate-buffered saline (PBS) and centrifuged at 162 rcf for 3 min. The pellet was suspended in PBS with 20% D2O and 2 mM TMSP and transferred into a 1 mm MRS microtube. All MRS reagents were purchased from Cambridge Isotope Laboratories, Inc., Tewksbury, MA, USA.Conditioned growth media, collected from cell cultures, were added with 20% D1H MRS experiments were run on a digital Bruker Avance spectrometer  at 400.14 MHz, equipped with a 1mm microprobe. Both one dimensional (1D) and two-dimensional Correlation SpectroscopY (2D COSY) experiments were performed, at T = 298 K.1H MRS spectra of GSCs and culture media were acquired with a 90° RF pulse, the number of scans (ns) was equal to 1000  for cell spectra while ns = 4000 was used for culture media spectra. When indicated, a Lorentzian-Gaussian function was applied in the time domain, before Fourier transformation.1D 2D COSY spectra were acquired with a 90°-t1–90°-t2 pulse sequence and ns = 32 for cell or ns = 128 for culture media samples. Spectra were acquired as a matrix of 512 × 128 data points in time domain.NMR parameters were obtained in at least three independent experiments and data are expressed as mean ± standard deviation (SD) values. WINNMR software  was used to perform 1D signal deconvolution and 2D cross peak integration as reported in . MacromoTM Paris, France) version 2012.2.02.Unsupervised agglomerative hierarchical Clustering, Principal Component Analysis and Student’s t test were performed utilizing XLSTAT software (Addinsoft


Sigma (σ) receptors have attracted great interest since they are implicated in various cellular functions and biological processes and diseases, including various types of cancer. The receptor family consists of two subtypes: sigma-1 (σ1) and sigma-2 (σ2). Both σ receptor subtypes have been proposed as therapeutic targets for various types of cancers, and many studies have provided evidence that their selective ligands (agonists and antagonists) exhibit antiproliferative and cytotoxic activity. Still, the precise mechanism of action of both σ receptors and their ligands remains unclear and needs to be elucidated. In this study, we aimed to simultaneously determine the expression levels of both σ receptor subtypes in several human cancer cell lines. Additionally, we investigated the in vitro antiproliferative activity of some widely used σ1 and σ2 ligands against those cell lines to study the relationship between σ receptor expression levels and σ ligand activity. Finally, we ran the NCI60 COMPARE algorithm to further elucidate the cytotoxic mechanism of action of the selected σ ligands studied herein. Sigma receptor is a class of receptors that have been the subject of intensive pharmacological research since their discovery in 1976 by Martin et al. ,3 and isInterestingly, both sigma subtypes have been proposed as therapeutic targets for several diseases and pathological conditions, with various types of cancer being amongst them . HoweverIn this study, we try to shed some light on the relationship of the σ1 and σ2 receptors and the anticancer activity of sigma ligands. To this aim, we used Western blot (WB) to study the expression levels of σ1, PGRMC1, and σ2/TMEM97 receptors in several human cancer cell lines, representing nine different cancer types. Most of the tested cell lines included in this study are also included in the NCI60 cell line panel list that is widely used for the in vitro testing of potential anticancer drugs . In addi2 air atmosphere.All cell lines used were purchased from the National Cancer Institute , and were cultured in RPMI 1640  medium  supplemented with 5% fetal bovine serum , 2 mM L-glutamine , and 100 U/mL penicillin and 100 μg/mL streptomycin . Cell cultures were maintained at 37 °C in a humidified incubator with a 5% COSQ Transfer Membrane . Membranes were blocked for 1 h at room temperature with the appropriate blocking buffer, according to each antibody’s specific protocol, and then incubated overnight at 4 °C with rabbit anti-SIGMA1 antibody , rabbit anti-PGRMC1 antibody , or rabbit anti-TMEM97 antibody . All antibodies were used at a 1:1000 dilution. Finally, membranes were incubated with the secondary horseradish peroxidase-conjugated anti-rabbit antibody  at 1/6000 dilution, developed with Clarity Western enhanced chemiluminescence (ECL) Substrate  and detected with Uvitec Cambridge chemiluminescence imaging system with the help of Alliance Software (ver. 16.06) .Cell lysis was performed in T-PER tissue protein extraction reagent  supplemented with 1% protease and phosphatase inhibitor cocktail . Cell lysates were then sonicated  and centrifuged at 13,000 rpm for 30 min at 4 °C, and supernatants were then collected. Protein concentration was determined using a Pierce BCA Protein Assay Kit . Lysates containing equal amount of protein (20 μg) were run on a 10% acrylamide gel and transferred to a 0.22 μm pore size polyvinylidene difluoride (PVDF) membrane, Immobilon-Pv/v) trichloroacetic acid (TCA)  to each well and stained for 1 h at 4 °C. Cells were washed (three times) with slow running tap water; excess water was removed by gentle tapping onto a paper towel and the plates were allowed to air-dry at room temperature (RT). A solution of 0.04% (w/v) SRB  in 1% (v/v) acetic acid  was subsequently used to dye cells for 10 min at RT. After incubation, the excess dye was removed by repeated rinses with 1% (v/v) acetic acid  and cell monolayers were allowed to dry at RT. The protein-bound dye was dissolved in 10 mM Tris base solution by incubation for 10 min at 37 °C followed by solubilization in an orbital shaker for 10 min. Absorbance was measured at 540 nm in a BioTek EL311 microplate reader . From the generated dose-response curves, we determined the GI50 (50% cell growth inhibition), TGI , and LC50  parameters. Growth inhibition of 50% (GI50) is calculated from ((Ti-Tz)/(C-Tz)) x 100 = 50. The drug concentration resulting in total growth inhibition (TGI) is calculated from Ti = Tz. The LC50 indicating a net loss of cells following treatment is calculated from ((Ti-Tz)/Tz) x 100 = − 50. In these formulas Tz represents a measurement of the cell population for each cell line at the time of drug addition, Ti is a measurement representing the growth of cells in the presence of a given concentration of the drug and C the measurement of the control cells at the end of the 48 h incubation period.The cytotoxic effect of the sigma ligands Siramesine (kindly offered by H. Lundbeck A/S), PB28 dihydrochloride , rimcazole dihydrochloride , BD1047 dihydrobromide , and SM-21 maleate  was determined using sulforhodamine B (SRB) colorimetric assay. Cell viability was determined at the beginning of each experiment by trypan blue dye exclusion method and was always greater than 97%. Cells were seeded into a flat bottom 96-well plate at a density of 5000 cells per well, 24 h before treatment. Sigma ligands were dissolved in dimethyl sulfoxide (DMSO) and were serially diluted in culture medium to acquire the desired concentrations. The final concentration of DMSO in each cell culture was no higher than 0.1%. After 48 h treatment, cells were fixed by gentle addition of 50%  was calculated to measure the strength of the linear relationship between the antiproliferative efficacy of the selected sigma ligands to the expression levels of sigma receptors. The NCI60 COMPARE algorithm was used to calculate the Pearson correlation coefficient (PCC) of the sigma ligands against compounds with known mechanism of action (MoA) in the National Cancer Institute (NCI) database. Differences were considered significant (rejection of the null hypothesis) when p ≤ 0.05.All data are presented as the mean ± SD of at least 3 independent experiments. A two-tailed Student’s For the first time in the literature, the expression levels of σ1 receptor, PGRMC1, and σ2/TMEM97 were studied simultaneously by Western blot analysis in 23 human cancer cell lines. The cell lines used represent nine different types of cancer: lung, colon, CNS, melanoma, ovarian, breast, renal, prostate, and pancreatic ductal adenocarcinoma (PDAC). The in vitro antiproliferative efficacy of five common sigma ligands was investigated in the same panel of the 23 human established cancer cell lines in which we earlier investigated the expression levels of σ1, PGRMC1, and σ2/TMEM97 receptors. The selected sigma ligands were siramesine and PB28 dihydrochloride, referred to in the literature as σ2 agonists with high σ2 receptor affinities ,13, and Twenty-three established human cancer cell lines were treated with the sigma ligands for 48 h at various concentrations , and a cell viability assay (SRB) was performed. Dose–response curves were generated after incubation with different concentrations of the various sigma ligands .50, TGI, and LC50 parameters were calculated for each cell line, representing the antiproliferative, cytostatic, and cytotoxic effects, respectively. Therapeutic index (TI) was also calculated as LC50 to GI50 ratio, determining the relative safety of the compounds . Rimcazole (mean GI50 = 22.3 μΜ) and PB28 (mean GI50 = 24.6 μΜ) demonstrated moderate antiproliferative efficacy, while SM21 (mean GI50 = 73.6 μΜ) and BD1047 (mean GI50 = 92.1 μΜ) showed no significant cell growth inhibition efficacy. Siramesine exhibited the best cytostatic and cytotoxic efficacy, as indicated by the low TGI mean parameter of 15.3 μΜ and low LC50 mean parameter of 45 μΜ.Siramesine was found to exhibit the best in vitro antiproliferative activity among the sigma ligands tested, as indicated by the low GI50, TGI, and LC50 parameters of the selected sigma ligands against the 23 established human cancer cell lines were further visualized by heat correlation maps  in all the tested cancer cell lines, which contrasted with GI50 parameter values for the other tested sigma ligands, which were in the cold color region . The same pattern was observed for TGI and LC50 parameters, as siramesine was in the warm region of the color scale in several cell lines, in contrast to the rest of the sigma ligands tested. This indicates that among the sigma ligands studied, the σ2 agonist siramesine exhibited the best antiproliferative, cytostatic, and cytotoxic activity. Unlike siramesine, PB28 was found to exhibit significant antiproliferative activity against only four cell lines . Interestingly, rimcazole’s highest activity was observed against the two melanoma cell lines tested and two out of the four pancreatic cancer cell lines (AsPC1 and MiaPaCa2) tested.GIion maps . The par50 to GI50 ratio as an indicator of the relative safety of a substance (The therapeutic index (TI) of the selected ligands was also calculated as an LCubstance . High TI50 values were normalized using the z-score statistical parameter and grouped by cancer type , while positive values, projected to the left, represent cell lines less sensitive to the mean . We next sought to identify possible patterns in the sigma ligands’ sensitivity profiles that could be related to the sigma ligands activity as either agonists or antagonists. GIcer type . The z-sr) was calculated to investigate the relationship between the antiproliferative efficacy of the selected sigma ligands to the expression levels of sigma receptors. The Pearson’s r is a statistical measure of the strength of the linear relationship between two variables and is constrained between values −1 ≤ r ≤ 1. Positive r values denote positive linear correlation while negative values denote negative linear correlation. An r of −1 indicates a perfect negative linear relationship, an r of 0 indicates no linear relationship, and an r of 1 indicates a perfect positive linear relationship. Scatter plots of the tested sigma ligands’ antiproliferative effect (GI50) versus σ1 expression levels  between siramesine antiproliferative effect and σ1 expression levels, while no other significant correlation was reported between the other tested sigma ligand efficacies and the σ1 expression levels. r = −0.2 and r = −0.3, respectively), while no other significant correlation was reported between the other tested sigma ligand efficacies and the PGRMC1 expression levels. r = −0.22), while no other significant correlation was reported between the other tested sigma ligand efficacies and the σ2/TMEM97 expression levels.Pearson’s linear correlation coefficient  was investigated using the COMPARE algorithm. The COMPARE algorithm from the Developmental Therapeutics Program, NCI, was used at the GIilar MoA ,18. The e curves  and conva ligand . Positivell line , the meaThe highest PCC 0.884) was reported for rimcazole to thalicarpine. Thalicarpine  is a natural vinca alkaloid with antineoplastic activity, which induces single strand breaks in DNA and arrests cancer cells at the G2/M and G1 phase of the cell cycle. It is also reported to exhibit RNA- and protein synthesis-inhibiting activities . Interes was repoSigma receptors have attracted increased research interest over the last few decades, as reflected in the growing number of extensive and complex literature published in this rapidly evolving field. It is now undisputed that σ1 receptor is an allosteric modulator of multiple cellular signaling systems that, in the context of cancer, act as a chaperone protein, which is a component of the cancer cell support machinery . Until rSeveral studies report overexpression of σ receptors in cancer; however, these studies mainly involved the indirect detection of σ receptors by radio-binding of their selectively labeled ligands ,29,30,31https://dtp.cancer.gov/), the most successful and comprehensive program worldwide established since 1955 and dedicated to the discovery and development of anticancer drugs. Through this program, many important anticancer drugs such as taxanes, vinca alkaloids, and even more advanced anticancer approaches such as sipeulucel-T have entered clinics. Our data suggest that under the experimental conditions tested in this study, there was no distinct sensitivity pattern between the sigma receptor agonists compared to the sigma receptor antagonists studied. Pearson’s linear correlation coefficient (r) was calculated to further investigate the relationship between the antiproliferative efficacy of the selected sigma ligands and the expression levels of sigma receptors. Our data confirmed that there was no significant relationship between sigma receptor expression levels and the sigma ligands’ antiproliferative effect (−0.3 < r < 0.22). These results indicate that the compounds tested in this study exert their effect mainly through mechanisms that do not involve the σ receptors, even though they show in vitro anticancer activity. A recent study by the group of Mach et al. reported that sigma-2 receptor/TMEM97 and PGRMC1 do not mediate sigma-2 ligand cytotoxicity [On the basis of our data showing that σ1, PGRMC1, and σ2/TMEM97 receptors are highly expressed in many of the cell lines tested, we further explored whether sigma receptors mediate the in vitro anticancer activities of five commonly used sigma ligands. In this context, we investigated the in vitro antiproliferative efficacy of siramesine, PB28 dihydrochloride, rimcazole dihydrochloride, BD1047 dihydrobromide, and SM-21 maleate under the same experimental conditions and against the same panel of the 23 human established cancer cell lines in which the expression levels of σ1, PGRMC1, and σ2/TMEM97 receptors were previously studied. The selected sigma ligands were representative sigma agonists and antagonists with high affinities for σ1 and/or σ2 receptor. It is worth noting that in the literature, the classification of sigma ligands as agonists or antagonists is based on the recapitulation of the σ1 gene overexpression or knockdown phenotypes . First, toxicity , supportAn essential missing element in sigma receptor biology in cancer is a clear definition of the underlying molecular MoA that translates into the cellular response of the sigma receptor ligand effect. In the literature, many potential mechanisms of action have been proposed to mediate sigma ligands’ anticancer efficacy, including apoptosis ,41, autoIn conclusion, we show that sigma receptors are indeed expressed in the majority of the 23 human cancer cell lines assessed in this study. We provided evidence that sigma receptor expression does not follow a cancer-type pattern or (with some exceptions) a pairwise expression pattern and that it may not mediate the antiproliferative efficacy of a panel of some very common σ ligands studied in this report. Among the ligands tested, the σ2 receptor ligand siramesine showed the most promising anticancer activity, especially against pancreatic cancer. These findings also provide a platform that rationally warrants the further evaluation of siramesine as a potential anticancer compound, especially against pancreatic cancer.


Adhesive structures between cells and with the surrounding matrix are essential for the development of multicellular organisms. In addition to providing mechanical integrity, they are key signalling centres providing feedback on the extracellular environment to the cell interior, and vice versa. During development, mitosis and repair, cell adhesions must undergo extensive remodelling. Post-translational modifications of proteins within these complexes serve as switches for activity. Tyrosine phosphorylation is an important modification in cell adhesion that is dynamically regulated by the protein tyrosine phosphatases (PTPs) and protein tyrosine kinases. Several PTPs are implicated in the assembly and maintenance of cell adhesions, however, their signalling functions remain poorly defined. The PTPs can act by directly dephosphorylating adhesive complex components or function as scaffolds. In this review, we will focus on human PTPs and discuss their individual roles in major adhesion complexes, as well as Hippo signalling. We have collated PTP interactome and cell adhesome datasets, which reveal extensive connections between PTPs and cell adhesions that are relatively unexplored. Finally, we reflect on the dysregulation of PTPs and cell adhesions in disease. Cell adhesions are an essential feature of multicellular life and function as both mechanical links between cells and their environment and as major signalling hubs. In addition to maintaining tissue integrity, adhesion complexes must rapidly remodel to accommodate growth, renewal and repair. Reversible protein tyrosine phosphorylation, which expands the static proteome, allows dynamic changes to protein function that can facilitate such remodelling. The coordinated actions of protein tyrosine kinases and phosphatases (PTPs) control phosphotyrosine levels with high spatiotemporal specificity and are both key players in the regulation of cell adhesions. The repertoire of kinases and PTPs has expanded in concert with increased organismal complexity, reflecting a need for greater regulation and specificity . In thisAt their core, adhesions consist of transmembrane receptors that couple the cell exterior to the intracellular cytoskeleton, through adaptor proteins. Focal adhesions and hemidesmosomes comprise integrins localised to the basal plasma membrane that bind to extracellular matrix (ECM) components and link to intracellular actin and intermediate filaments (e.g. keratins), respectively . Both coCat), regulate the surface levels of the cadherins . P. Pin vitactivity . Howeveractivity . This raRelatively few PTPs have been identified as direct regulators of tight junctions in comparison with adherens junctions and less is known about the role of tyrosine phosphorylation in coordinating tight junction dynamics. Our adhesome analysis suggests a much wider involvement of both receptor and non-receptor PTPs in tight junction regulation .Focal adhesions are mechanosensitive, integrin-based adhesions linking the actin cytoskeleton to the ECM allowing the cell to sense and adapt to changes in their immediate extracellular environment . TyrosinPtprf knockout mice have revealed this is due to premature involution of the mammary gland [The receptor PTPRF (LAR) reportedly localises to focal adhesions . It alsory gland . WhetherPtpra null mouse embryonic fibroblasts (MEFs) display impaired cell spreading and migration [PTPRA is also a key phosphatase needed to coordinate integrin signalling events during the formation of focal adhesions. PTPRA dephosphorylates the inhibitory Src Y527 phosphosite ,123, religration ,125. Intigration . Phosphoigration  in additigration ,129 and igration   is a focal adhesion regulator recruited to its substrate FAK by PIN1(protein interacting with NIMA 1). It directly dephosphorylates FAK Y397, an autophosphorylation site that facilitates binding to SFKs, phosphoinositide 3-kinase (PI3K) and phosphoinositide phospholipase C gamma (PLCγ). PTPN12 dephosphorylation of FAK reduces the number of focal contacts and consequently increases migration ,132. In blastoma .Ptpn1 null MEFs revealed hyperphosphorylated tyrosine residues on a number of adhesion-related proteins, including paxillin and caveolin-1 [PTP1B has been shown to both promote  and inhiveolin-1 . Followiveolin-1  and EphAveolin-1 . PTP1B lveolin-1 , suggestveolin-1 . Indeed,veolin-1 . In addiveolin-1 .In addition to the defined roles for PTPRA, PTPRF, PTPN12 and PTP1B in focal adhesion biology, other PTPs are associated with focal adhesions . In partDownstream of integrin engagement, the Rho GTPase network is vital for mediating the intracellular responses that regulate cell adhesion and spreading . GEFs prPTPN12 interacts with and modulates the phosphorylation of VAV2, a RhoA and Rac1 GEF as well as p190RhoGAP, a Rho GAP in cells. This is proposed to coordinate VAV2 and p190RhoGAP activities to couple protrusions at the leading edge with tail retraction at the rear, in order to coordinate cell migration ,151. p19Drosophila, the Hippo signalling has emerged as a key mechanotransduction pathway, linking cell adhesion to transcriptional responses [First described in esponses . Fundameesponses . The patesponses . Given tPTPN13 and PTPN14 are present in the Hippo pathway interactome  and PTPNSeveral RPTPs had enriched interactions with the Hippo pathway including PTPRK . PTPRK cWe have described how PTPs regulate cell adhesions, however, cell adhesions can also feedback to PTPs to control their activity and localisation. This is particularly true for cell surface RPTPs. For example, the R2B receptor family form extracellular homophilic interactions that stabilise the receptors at cell–cell contacts ,161–164.2O2), are important regulators of wound healing [5)R motif [2O2, which is thought to enter cells through aquaporins [2O2 production [2O2 may act to inhibit PTP catalytic activity thereby promoting the phosphorylation of downstream integrin mediators, such as FAK [phox was proposed to oxidise PTPN12 (PTP-PEST), downstream of Rac GTPase activation [phox was recruited to focal adhesions by scaffolding proteins Hic-5 and TRAF4. Hic-5 had previously been shown to recruit PTPN12 to focal adhesions [PTP oxidation is another regulatory mechanism that can occur at cell adhesions. Reactive oxygen species (ROS), such as hydrogen peroxide R motif , which i)R motif . PTPs ca)R motif ,175. In )R motif . Superoxuaporins . In addioduction . It has h as FAK ,179. ROStivation . p47phoxdhesions . PTPN12 Rac is a known NOX activator , promotiDuring the development of the nervous system, cell adhesion is critical for promoting neurite connections and several PTPs have been implicated in regulating this process. PTPRM has been shown to regulate N-cadherin-dependent neurite outgrowth  and thisDrosophila orthologue of the three mammalian R2A receptors and is highly expressed in developing neurons. DLar is critical for motor axon guidance decisions [Drosophila axon guidance, including Trio [Drosophila identified immunoglobulin superfamily cell adhesion molecule (CAM) Sticks and Stones (Sns) as a novel ligand for dLar [C. elegans and has two isoforms: PTP-3A and PTP-3B, which differ by length of the extracellular domain [The role of R2A RPTPs in axon guidance was first characterised in invertebrate systems by genetic studies. Leukocyte common antigen–related (dLar) is the ecisions  and for ecisions ,192. A ning Trio , Liprin-ing Trio . Recent for dLar . The autfor dLar . The intr domain . The twor domain .In vertebrates, the R2A family enact three general roles; mediating cell–cell adhesion at synapses, regulation of presynaptic differentiation and supporting postsynaptic differentiation . The R2APtprj knock out mouse retinas have misrouted axons. PTPRJ function in axon guidance is associated with the regulation of repulsive signals from Ephrin and c-Abl activities [Finally, there are emerging roles for the type 3 RPTPs (R3 family) in the field of axon guidance, in particular PTPRJ and PTPRO. Members of the R3 family are able to interact with and dephosphorylate Eph receptors, which coordinate axonal projections . A recentivities . Thus, sDysregulation of protein tyrosine phosphorylation is often a key driving event in oncogenesis leading to uncontrolled proliferation and cell survival. Despite functioning as negative regulators of tyrosine phosphorylation, PTPs can act as both tumour suppressors and oncogenes . The likThere are several examples where RPTP loss of function leads to increased cell motility and invasion including PTPRM and PTPRK in breast cancer ,212, PTPPTPs are implicated in cancer through their cross-talk with Hippo signalling, which has been linked to solid tumour initiation and growth . Indeed,PTPs are important therapeutic targets in a number of disease settings ,231. It The receptor PTPRS is a target in spinal cord injury. PTPRS binds to extracellular CSPGs, which inhibit sensory neuron outgrowth during regeneration . DeletinPatients with cardiovascular diseases can exhibit compromised VEGF/VEGFR2 signalling resulting in impaired angiogenesis following injury . A cell-PTPs remain challenging targets so it will be essential to understand specific regulatory mechanisms, signalling functions and protein–protein interactions to develop new approaches, such as allosteric modulators. Additionally, knowledge of substrates may reveal additional modalities to manipulate PTP activity, or further targets that mediate adhesive processes in disease. Finally, the key roles for RPTPs in cell adhesion control and EMT raises the possibility of targeting their extracellular domains, for example, with monoclonal antibodies .The recent explosion of proteomics datasets has enabled us to visualise the extensive connections between PTPs, core adhesive complexes and signalling pathways. Their involvement ranges from core scaffolding components within adhesive complexes to dynamic regulators of tyrosine phosphorylation. Although challenging to study, in part due to a lack of specific tools and a need to understand upstream phosphorylation, the PTPs are clearly central to the coordinated regulation of cell adhesion. Individual PTPs associate with multiple complexes, suggesting they could mediate cross-talk between adhesion types. Moreover, PTPs can confer precise, spatiotemporal control through distinct subcellular localisations and local control by upstream signals such as ROS. There are no general rules for how PTPs impact on adhesive processes. Just as PTPs can be both oncogenic and tumour suppressive, they can differentially regulate processes such as EMT. This highlights the need to understand the functions of each individual PTP in defined contexts. PTPs have historically been ‘undruggable' targets, however, the recent development of allosteric inhibitors for SHP2 highlights the importance of understanding regulatory mechanisms to inform drug development. Many open questions remain. Multiple PTPs are associated with the adherens junction cadherin–catenin module. Do PTPs carry out redundant functions or do they fine tune the phosphorylation code within specific complexes? Several PTPs interact with adhesive complex components, yet are likely to be excluded from the core complex, in particular the RPTPs. Additionally, they receive feedback from adhesions. Do PTPs play a role in delimiting adhesions or controlling their size? We anticipate that phosphoproteomics and interactome studies in 3D models such as organoids, as well as super-resolution microscopy, will uncover the interplay between PTPs and cell adhesions, as well as their roles in signalling to elicit transcriptional or metabolic changes.