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Glioblastoma multiforme (GBM) is amongst the most lethal of all cancers. GBM consist of a heterogeneous population of tumor cells amongst which a tumor initiating and treatment-resistant subpopulation, here termed GBM stem cells (GSC), have been identified as primary therapeutic targets. Here, we describe a high-throughput small molecule screening approach that enables the identification and characterization of chemical compounds that are effective against GSC. The paradigm uses a tissue culture model to enrich for GSC derived from human GBM resections and combines a phenotype-based screen with gene target-specific screens for compound identification. We used 31,624 small molecules from seven chemical libraries that we characterized and ranked based on their effect on a panel of GSC-enriched cultures as well as their effect on the expression of a module of genes whose expression negatively correlates with clinical outcome: MELK, ASPM, TOP2A and FOXM1b. Of the 11 compounds meeting criteria for exerting differential effects across cell types used, 4 compounds demonstrated selectivity by inhibiting multiple GSC-enriched cultures compared to non-enriched cultures: Emetine, N-Arachidonoyldopamine (NADA), N-Oleoyldopamine (OLDA), and N-Palmitoyldopamine (PALDA). ChemBridge compounds #5560509 and #5256360 inhibited the expression of the 4 mitotic module genes. OLDA, Emetine, and compounds #5560509 and #5256360 were chosen for more detailed study and inhibited GSC in self-renewal assays in vitro and in a xenograft model in vivo. These studies demonstrate that our screening strategy provides potential candidates as well as a blueprint for lead compound identification in larger scale screens or screens involving other cancer types.
Glioblastoma multiforme (GBM) is almost universally fatal and new avenues of treatment are desperately needed. Several different lines of evidence exist to suggest that there are subpopulations of cells within GBM that have different capacities to initiate tumors in xenograft models. These tumor-initiating cells have at least some of the characteristics of stem cells in that they are capable of self-renewal and can produce the multiple cellular phenotypes that are found within the original tumor (1–3). Here, we use the term GBM stem cell (GSC) to denote these similarities, but not necessarily to imply cell of origin. These GSC have been shown in a number of different ways to be relatively resistant to radiation and chemotherapy (4, 5).
Multiple efforts have been undertaken to isolate GSC in order to study and better understand their biology, as well as to develop therapies that target them. However, success has been limited so far. In fact, there is an increasing body of evidence that several such subpopulations may exist within one tumor and that their isolation might require the use of multiple marker systems (6, 7). Hence, in an effort to derive large numbers of GSC that can be used for high throughput drug screening, we focused on more simple and reliable methods that have been shown to enrich for GSC subpopulations across virtually all subtypes of GBM, by culturing them in growth factor containing, serum-free neurosphere media (4). We took advantage of this culture model to develop multiple screening strategies that can be used to identify, characterize and categorize small molecules that specifically affect GSC. Our strategy was to first identify compounds that inhibited proliferation or killed GSC derived from one tumor. We then further analyzed and prioritized candidate compounds that had selective effects on GSC from some tumors compared to those derived from others, or had selective effects on cultures enriched for GSC compared to those depleted of GSC. In a parallel approach, we also analyzed candidate compounds from the primary screen for their ability to inhibit the expression of genes that are associated with patient outcome (8). Through the use of this strategy, we have identified novel classes of compounds for the study of therapeutic approaches for attacking GSC. This study not only provides interesting candidates for further investigation, but also represents a proof-of-principle for a screening paradigm that can be potentially utilized in a much larger scale for lead compound identification to develop new GSC-specific therapies.
Brain tumor specimens were collected following surgical resection at UCLA, with approval of institutional review boards. Tumors were graded using World Health Organization guidelines. Samples were dissociated as previously described (1) in either neurosphere media or serum containing media. Neurosphere media contained DMEM/F12 supplemented with B27 (GIBCO), bFGF (20 ng/ml, R&D Systems inc.), EGF (50 ng/mL, Peprotech), Penicillin/Streptomycin (1%, Invitrogen), L-Glutamine (Invitrogen) and heparin (5ug/ml, Sigma-Aldrich). Heparin, bFGF and EGF were added to the media every three days. Spheres were passaged every 7–14 days following either dissociation with TrypLE Express (Invitrogen) or chopping using a tissue chopper (Geneq Inc.). Serum media contained DMEMF12, 10%FBS and 1% Penicillin/Streptomycin. Under this condition, cells grew as attached monolayer cultures and were passaged when subconfluent. 293T, NHA and Human fetal astrocytes (gestational week 19) were cultured and expanded in serum-based media. Laminin-based adherent culture techniques were performed following protocols described by Pollard et al. (9) (See Suppl. Methods).
The high throughput screen was performed in a 384-well plate format using the ChemBridge DiverSet library (30,000 molecules; www.chembridge.com), and other collections of known bioactive compounds: Bioactive Lipids, Endocannabinoids, Ion Channel Ligands, Kinase and Phosphatase Inhibitors, Orphan receptor Ligands (204, 60, 72, 84 and 84 compounds, respectively; from www.enzolifesciences.com) and the Prestwick library (1120 FDA approved compounds; www.prestwickchemical.com), at concentrations recommended by the manufacturers. Cell number was estimated using ATPLite (Perkin Elmer), and values were analyzed and corrected for systematic effects using a parametric model developed specifically for this screen (10). The Z′ factors in the three assays were 0.479, 0.53 and 0.51 respectively, indicating an assay system of good quality. All screen data were stored and managed online, on the Collaborative Drug Discovery platform (www.collaborativedrug.com). Chemical classifications and predicted biological functions of hit candidates were determined using online databases (Pubchem, Pubmed, Lasso) and ADME software. Specific protocol details and hit candidate selection criteria are described in Supplemental Data.
For the RT-qPCR screen we used GBM146 that was cultured in sphere media. Spheres were dissociated and seeded into 96-well plates at 5×104 cells per well in sphere media. All experiments were done in duplicate. After 24h, compounds were added at 10μM and plates incubated for 6h at 37°C. Plates were then centrifuged, media removed and RNA extracted using the TRIzol method. RNA was transcribed into cDNA and gene expression levels quantified by RT-PCR using the PlusOnePlus™ system with SYBRR® Green method (Applied Biosystems). Expression levels of the target genes were normalized to GAPDH, plotted on a Log2 scale and Z-scores were calculated. Hit criteria were set at Z-score≤1.50. Expression levels of hit candidates were verified with RT-qPCR using larger cell numbers of different samples.
To assess self-renewal capacity, cells were treated with compound or DMSO and dissociated into a single cell suspension. Equal numbers of live cells were seeded into 96 well plates, in fresh media, at clonal density, which was pre-determined for each individual tumor culture by performing mixing experiments using fluorescently-labeled cells (11). These densities ranged between 5 and 10 cells per 96-well plate, depending on the GBM sample used. Three hours after plating, cell number was confirmed and plates were incubated until formation of spheres was observed. Spheres were fixed and incubated with Syto-9 dye (Molecular Probes) if not already expressing eGFP. Sphere number, size were assessed using an Acumen eX3 plate reader and. For the low-passage sphere formation studies, GBM312 was used at passage 6. Proliferation studies were performed using CFSE (carboxyfluorescein diacetate, succinimidyl ester) wash out and are described in Suppl. Methods.
Animal experimentation was done with institutional approval following NIH guidelines. To assess in vivo tumor formation and growth, a dissociated cell suspension was stereotactically injected into the neostriatum of NOD-Scid gamma(null) mice. Animals were sacrificed when symptomatic or after 8 months if no symptoms developed, perfusion-fixed and the brain tissue sectioned on a cryostat. Tumor formation was determined based on immunohistochemical studies (See Suppl. Methods and Suppl. Figure 1). For the drug treatment studies, cells stably expressing eGFP were used (12). Cells were exposed to either experimental drugs or DMSO ex vivo. After the incubation period, the drug was washed out and 50,000 live cells were transplanted in 2 μl of DMEM/F12. For the limiting dilution experiments, 500, 5,000 or 50,000 cells per animal were injected and mice were sacrificed 16, 12 or 10 weeks after transplantation, respectively. Tumor volumes were determined using fluorescence based imaging and data analysis. For further details, see Supplemental Data.
Previous research has shown that patient-derived GBM samples propagated in bFGF and EGF supplemented serum-free media maintain their tumorigenic potential, while the same GBM samples lose their tumorigenicity if propagated in traditional serum-supplemented media (4). We cultured and orthotopically transplanted nine GBM samples derived from different patients that we cultured under these two conditions (“sphere media” and “serum media”). Two to three months later, we observed tumor formation in all (41/41) animals that were injected with cells propagated in sphere media, whereas none of the 25 mice injected with serum-derived cells developed tumors (Suppl. Table 1). Hence, this culture model provides a suitable platform to investigate GSC and their non-tumorigenic counterparts in the frame of experiments that require large numbers of cells, such as high-throughput drug screening (HTS).
We tested the effect of 30,000 small molecules derived from the ChemBridge library on one of our human GBM samples (GBM#107) that we cultured in sphere media to enrich for GSC. This sample was especially suitable for high throughput screening because of its high proliferative nature as well as for its preference to grow as an adherent monolayer even in sphere media. The screen identified 694 compounds that significantly reduced cellular ATP concentration, as compared to DMSO treated control wells, suggesting a negative effect on GSC proliferation, metabolism and/or survival. These 694 compounds were further evaluated in subsequent screens.
For the primary screen we did not use a control cell line due to the large scale of the experiment. Therefore, we re-screened the 694 active compounds from the primary screen in a secondary screen on two GBM cell types (GBM107 and GBM1600) as well as on 293T human fibroblasts as a non-tumor control line. We then excluded all compounds that had an inhibitory effect on 293T cells only, as well as compounds inhibiting all three cell types, as nonspecific cytotoxins.
In addition to the 694 compounds identified in the primary screen, in this secondary round we also included multiple smaller compound collections, totaling 1624 known bioactive compounds. By excluding nonspecific killers from this pool of 2318 compounds, we identified 168 GSC-effective candidates (Suppl. Table 2). This compound collection was small enough to be further characterized using low-throughput strategies. For this, we pursued 2 different screening approaches as illustrated by the work flow chart in Figure 1.
In order to identify more selective compounds amongst our candidates, we screened them on an extended panel of different cell types and looked specifically for compounds that exhibited a differential effect profile. For this, we used several GBM samples (# 107, 146, 157, 167, 217 and 1600) that were cultured either in sphere or serum media and the non-cancer control cell types NHA (immortalized human astrocytes) and HFA (primary human fetal astrocytes). To control for effects of the different media, compound exposure all took place in sphere media, regardless of the media that cells were originally grown in. Although growth kinetics studies revealed only minor differences in proliferation rates between serum and sphere derived cells of the same cell type if cultured in screen (sphere) media, leaving little room for possible potentiating effects of the different media types, each screen condition was normalized to non-drug exposed controls of the same cell type (Suppl. Figure 2). Compounds were ranked based on their differential effects among cell types. The exclusion/inclusion criteria were calculated as described in Supplemental Methods and included differential effects between: 1. tumor and non-tumor control cells, 2. between distinct GBM samples and 3. between GSC-enriched and non-GSC enriched cultures of the same tumor sample. Since the high throughput screen was performed using a single drug concentration, we generated concentration-effect curves and calculated IC50 values for the top 30 compounds.
We identified 11 compounds (Emetine, N-Oleoyldopamine (OLDA), N-Palmitoyldopamine (PALDA), N-Arachidonoyldopamine (NADA), Anisomycin, Camptothecin, Chrysenequinone and the ChemBridge compounds # 5485415, 5181524, 5211950 and 5560509) that exhibited greater than one log(10) IC50 difference between different cell groups described in criteria 1–3 above. Eight of these compounds exhibited a more then 10-fold lower IC50 concentration in sphere cultures compared to serum cultures of at least one GBM sample, suggesting some selectivity in action against GSC (Table 1). In a separate experimental series using GBM cells that were cultured in serum and sphere media for different amount of times, we found that the differential compound effects between GSC -enriched and GSC -depleted cells were not due to a general protective effect of the serum-based media itself (Supplemental Data and Supplemental Figure 3).
Out of these eight compounds, Emetine, OLDA, PALDA and NADA showed selectivity of sphere vs. serum grown cells across 5 (Emetine) and 3 GBM samples (OLDA, PALDA and NADA). OLDA, PALDA and NADA share not only common structural, but also biological characteristics, including an affinity to cannabinoid (CB1 and CB2) as well as to vanilloid (TRPV) receptors. While activation of such receptors has been associated with decreased glioma growth (13–17), our studies using agonists and antagonists of these receptors suggest but do not absolutely prove that OLDA, PALDA and NADA mediate their anti-tumor effect via other mechanisms (Suppl. Figures 4A–D). From these compounds, we chose OLDA as well as Emetine for subsequent characterization.
Next, we wanted to find out whether we could identify compounds that had the ability to influence the expression of important GBM genes. GBMs express modules of genes whose expression varies with each other. Amongst these genes, some are called “hubs” in that their expressions are amongst the most highly correlated with those of others. In one module, identified previously as the “mitotic” module, the expression levels of these hub genes are inversely associated with patient outcome (8). We reasoned that regardless of mechanism, important regulators of GBM stem cell proliferation could ultimately lead to the downregulation of these key hub genes. Using an RT-qPCR approach, we quantified the effect of the 168 compounds derived from the secondary screen, on the expression of 4 hub genes, ASPM, MELK, FOXM1b and TOP2A. We found 6 compounds that decreased the expression of MELK, 6 compounds that decreased ASPM, 7 compounds that decreased TOP2A and 10 compounds that decreased FOXM1b. Two small molecule compounds from the ChemBridge library, #5560509 and #5256360 inhibited the expression of all four genes, while Camptothecin, a Topoisomerase-I inhibitor, and compounds #5402594, #5551547, #5349968 and #5256272 reduced the expression of two of the four genes simultaneously (Table 2).
In order to confirm the selective effect of the hit compounds on GSC, we performed clonal sphere formation assays (18). Compound exposure significantly reduced the number of spheres formed, as compared to control cells, in a compound concentration and exposure time dependent manner (Fig. 2A,B and C). These results suggest that these compounds preferentiallydepleted the self-renewing cell population while relatively sparing the non-sphere forming ones. This effect diminished, but could be still observed after serial passaging of the primary spheres, suggesting a partial recovery of the sphere forming population upon drug removal (data not shown). These compounds also reduced the size of the clonally formed shperes as well as the total cell mass, in a dose-dependent manner. Interestingly, OLDA seemed to reduce the sphere number more than the sphere size or total cell number, which suggests a more selective inhibition of the sphere forming cells (Suppl. Fig. 5).
In order to further characterize the inhibitory effect of these compounds on our glioma cultures, we performed cell proliferation experiments using CFSE washout. Results revealed a dose-dependent inhibition of cell proliferation (Suppl. Fig. 6).
Next we used an ex vivo treatment strategy (19) to determine whether some of the highest priority compounds also had an effect on the ability of the cells to form tumors and grow in vivo. For this, we implanted GSC-enriched tumor cells that were pre-treated with Emetine, #5560509, #5256360 or OLDA, into the brain of immunosuppressed mice. We found a significantly reduced tumor mass in the compound treated groups, compared to the vehicle-treated transplants, with almost no tumor mass present if the cells were exposed to #5560509 or #5256360 (Fig. 3 and Table 3). In addition, limiting dilution experiments using very small cell numbers in the same experimental setting suggest that the decreased incidence of tumor formation in the treated group is associated with the specific loss of tumor initiating cells in the GBM samples upon drug treatment (Suppl. Table 3).
Here, we have developed a screening strategy that enables the identification and categorization of chemical compounds based on their effect on GSC. These cells are particularly highly resistant to radiation therapy (5). Although they do show some sensitivity to Temozolomide (20), resistance is clearly present or develops, as the vast majority of tumors recur, even with this treatment. One of our goals was to determine whether some compounds selectively act on GSC compared to less tumorigenic cells from the same tumor. This selectivity may allow for the delineation of pathways and processes that are highly important to these cells. Furthermore, by making sure that a drug candidate has the potential to attack GSC, one might ensure the highest chance of therapeutic success. However, it is also important to note that such selectivity is not a critical requirement for the development of therapies, and may not even be desirable. The GSC component represents only a portion of cells in the tumor that may be the most highly tumorigenic at the time of assay. It is possible that other cells in the tumor have the ability to take on a greater self-renewal and tumorigenic capacity over time, and drugs that attack both the GSC and the non-GSC component will be needed.
For our assays, we adopted a cell culture model that enriches GSC content of primary human GBM samples as described previously (4) and here. The major advantage in using this culture model to enrich for GSC is the ability to produce a large number of cells, which is a prerequisite for high throughput screens and which can be problematic using alternative enriching techniques like FACS. Several cell-sorting approaches have been described in order to enrich for the tumor initiating sub-population of GBM. These include the use of cell surface markers CD133, SSEA-1 (CD15), Hoechst dye exclusion, or cell auto-fluorescence (6, 21–24). However, it is unclear whether any of these approaches can be used reliably and routinely to enrich for GSC across all GBM subtypes. For example, both CD133 positive and negative cells possess self-renewal and tumor initiating potential (25–27). In fact, self-renewing tumor initiating cells do not necessarily consist of a single subpopulation of GBM cells that uniformly express a single cell surface marker. Rather, there may be multiple stem cell populations expressing different markers (6, 7, 28, 29). Another important factor to be considered is the inter-patient heterogeneity of GBM that is fueled by an extensive repertoire of mutation patterns in this patient population (30, 31) that could conceivably give rise to GSC possessing very different sets of markers. Such markers or marker systems, once established, will be of great value and necessity to further explore the effect of hit candidates on different glioma subpopulations. The cell culture system we are using here seems to enrich for GSC across most GBM samples, independently of their mutational status or molecular characteristics. Although the degree to which the neurosphere cultures are enriched for GSC is not exactly known, the simplicity and practicality of this method to quickly and reliably expand GSC populations makes it more useful than FACS sorting, at least for purposes where a 100% pure GSC population is not a requirement for a successful experiment.
While the large number of cells needed for high throughput screens and the above-described challenges of GSC enrichment make the use of freshly dissociated tumor tissue impossible, the use of an in vitro enrichment model raises potential concerns, as the artificial environment can change characteristics of the primary tumor cells. Furthermore, any in vitro study ignores the importance of the in vivo niche, an important component to understanding GSC biology (32). However, despite these drawbacks, there are important features of GSC that are preserved in the sphere culture model. Multiple studies have revealed that the sphere culture environment preserves many of the fundamental characteristics of the parent tumor, including cell heterogeneity (1) and the ability to form heterogeneous tumors in animal models, recapitulating the parent tumor’s cell composition and the invasiveness of GBM (2, 4). These cells and the tumors that they form maintain the genotypic and phenotypic signature of the original parent tumor that they were derived from (4). Additionally, the capacity of the original tumor cells to form neurospheres is by itself an indicator of the in vivo aggressiveness of the tumor (12, 33, 34). Taken together, these data support the notion that the neurosphere culture technique is a valid and useful model to investigate at least some characteristics of GSC.
As a potential alternative to sphere derived cultures, Pollard et al. has recently developed a laminin-based adherent culture technique that was shown to be suitable for high throughput screen purposes (9). In that study the authors examined the effects of a relatively small group of compounds with known mechanism of action. Although this selection had only marginal overlap with our database, some compounds (or sometimes compounds from the same chemical family) were found to be effective in both studies. These were Doxorubicin, Nifedipin, Fluphenazine/Perphenazine, Fluvastatin/Itavastatin, Chlorothiazid/Phenothiazid and Fluoxetine. While these compounds provide independent validation for both screening approaches, there were also compounds that seemed to be not effective in one study while effective in the other and vice versa. More study will be needed to determine whether these differences are due to the different GBM samples used, or due to the different culturing and screening approaches.
We performed experiments comparing the laminin-based and the sphere culture methods side by side, using our top compound candidates. We found that our compounds can be efficacious in laminin-based cultures as well. However the data showed a slight right-shift in the concentration effect studies in laminin-based cultures. The reason for this mild difference is not entirely clear, but it might be due to the smaller cell surface area that is available for the compounds to act on, in the adherent compared to the floating cell cultures, or it might be due to a protecting effect of the laminin itself (Suppl. Figure 7).
We have shown that tumor cells that were exposed to experimental compounds ex vivo produced significantly reduced sized xenografts after transplantation into immunosuppressed animals, demonstrating that the compounds affected cells that contribute to tumor initiation and/or tumor growth, and do not simply inhibit factors that contribute to growth in vitro. In addition to these findings, limiting dilution experiments using very small cell numbers in the same experimental setting suggest a specific loss of tumor initiating cells caused by compound treatment. We do not yet know whether direct in vivo administration of these compounds will be effective. Further characterization and possibly chemical modification of the lead compounds will be needed prior to extensive studies in vivo using already established tumors in experimental animals.
Our study complements a prior study (35), that used a different approach by probing for inhibitors of normal murine neural stem cell proliferation with the hypothesis that, because of the close relationship between neural stem cells and GSC, such compounds would be potential therapeutic candidates. That study screened 1,267 compounds and identified small molecules that are known to affect neurotransmission. Our screen similarly identified some of these neural stem cell inhibiting compounds to have at least some effect on human GSC, including the dopamine antagonist Eticlopride hydrochloride, the serotonin antagonist Metergoline, and the glutamate receptor blocker Ifenprodil tartrate. However, our screen also identified GSC-inhibiting compounds with known neuromodulating effects, like OLDA, that had no effect on normal neural stem cells and hence were not identified as hits in the above mentioned study.
We chose to screen several compound libraries combining uncharacterized as well as characterized small molecules. Using uncharacterized compounds carries the possibility of identifying novel lead candidates, but the mechanisms of action of these compounds are unknown. In contrast, collections of already FDA-approved drugs, like the Prestwick library, enable a faster transition of potential hit candidates to clinical application (so called “drug repurposing”) and can also supply a particular screen with positive or negative controls. For example, our screen identified Camptothecin, a known topoisomerase inhibitor that had been previously tested as an anti cancer agent (36). Moreover, using a panel of substances with known mechanisms of action, one might gain additional information about the particular cell type used for the screen, or may assess the role of certain chemical classes for particular cell types (35). It is important to point out however, that even when one knows potential mechanisms of candidate compounds based on other studies, this does not necessarily mean that actions on GSC are mediated via these mechanisms. For example, OLDA, PALDA and NADA are agonists for cannabanoid and vanilloid receptors and cannabinoid receptor agonists have known inhibitory effects on GBM. However, we found no evidence that these mechanisms are responsible for the effects we see on GBMSCLC using these compounds. To understand and identify the bona fide molecular targets of these active compounds, additional studies will be necessary utilizing techniques, such as the recently developed DARTS (37).
In our multi-step HTS strategy, differential effect profile played a key role in the identification of specific and non-specific inhibitors. We were interested in compounds that have the capacity to selectively inhibit GSC compared to other cells in the tumor, but also in compounds that had differential effects on different GBM samples. These latter ones are in fact potentially interesting candidates, as such a differential effect profile indicates a specific mechanism involving pathways that are vital for one GBM sample but not for the other.
One approach that we utilized to stratify compounds was to assay effects on the expression of downstream regulators of GBM proliferation. Several approaches have been undertaken to identify a subset of such genes, playing a key role in GBM initiation, proliferation, therapy resistance and recurrence (38, 39). Global gene expression analysis of clinical GBM samples identified a gene coexpression module, consisting of key mitosis hub genes (8). Hub genes are those whose expression are most correlated with other genes of the expression module. Among these genes, the expression level of ASPM, FOXM1, MELK, PRC1, PTTG1, and TOP2A negatively correlate with patient survival (www.probesetanalyzer.com ). While not all of these gene candidates have been characterized in great detail, knockdown of MELK has a direct inhibitory effect on GBM stem cell-like cell proliferation and self-renewal (40). We reasoned that compounds with the capacity to diminish expression of multiple (or all) hub genes would be potential regulators of highly critical processes in GBM. We identified two previously uncharacterized compounds #5560509 and #5256360 that inhibit the expression of at least 3 of the 4 genes that we investigated here.
Our screen also identified a number of compounds that inhibited one or a few of these genes (Table 2), some with previously identified biological activities. Camptothecin, a known Topoisomerase I inhibitor, whose analogues Topotecan and Irinotecan have been used extensively as cytotoxic agents in cancer therapy, downregulated the expression of both MELK and ASPM, uncovering new potential mechanisms through which this compound mediates its tumor growth inhibiting effect. Ro 31–8220, also known as Bisindolylmaleimide, down-regulated MELK expression. This compound has been described to inhibit Protein kinase C, MAPKAP-K1β and p70 S6 kinase and has been proposed as a therapeutic agent in glioma (41–43) (44). C8 Ceramine, a compound that has been shown to induce nitric oxide synthase and cell death in medulloblastoma cells (45, 46), has also down-regulated MELK expression in our screen. Chelidonine, that we identified as a ASPM down-regulating compound, has been shown to induce apoptosis (47) and senescence by decreasing hTERT expression in human hepatocellular carcinoma cells (48) while its derivative, Ukrain has been shown to induce apoptosis in Glioblastoma cells (49).
Our strategy utilized different types of tertiary screens where each approach delivered several hit compounds. No compound however seemed to meet all selection criteria. While such compounds might exist in libraries other than used here, the data imply that our different approaches are selecting for distinct compound qualities and therefore using parallel screening strategies in general might capture a broader range of compounds delivering more useful hits than sequential screening designs.
One important question is how our screening strategy differs, in terms of hit recognition, from more traditional screening techniques that use established, serum-derived cancer cell lines. Such cell lines have an advantage in that they are easily expandable and serum-based media are much cheaper than the growth-factor supplemented media used here. However, these lines are likely to be depleted of the very cells that we are interested in, GSC, and therefore screens using them could miss compounds that show selectivity against GSC compared to the general tumor population. Thus, compounds, such as OLDA, PALDA and NADA, that have IC50’s against serum-derived cultures of 10–15μM, would not be detected as hits in most screens using these cells.
In summary, we developed a screening strategy that marries phenotype-based high-throughput techniques with specific target based low-throughput approaches, in order to categorize and characterize large number of compounds based on their effect on GSC. Using this approach, we identified several known as well as previously uncharacterized small molecule compounds that were effective against GSC using in vitro and in vivo assays, making them to potential lead candidates for further drug development. Furthermore, our strategy can be adopted for large-scale screens involving more extensive and more diverse libraries to identify lead compounds for pharmacological therapy of glioblastoma and potentially, other cancer types.
We thank Jong Sang Lee at the UCLA Molecular Screening Shared Resource for his help with the screens, Dr. Michael Haykinson and Ric Grambo at the Biological Chemistry Imaging Facility at UCLA for their help with the Typhoon imaging, Dr. Michael E. Jung for his expert advise, Dr. Paul Mischel for the GBM1600 cell line and Dr. Eric Wechsler for the human astrocyte cultures. We thank Andre Gregorian and Jantzen Sperry for outstanding technical assistance. The NHA line was obtained from Dr. Russel Pieper.
Dr. Miriam & Sheldon G. Adelson Medical Research Foundation, NINDS grant NS052563 and NCI grant CA124974. RD and KAB were supported by NIH awards AI67769 and CA016042.
Conflicts of interest
The authors have no conflicts of interest to declare.