Pilocytic astrocytomas as well as other pediatric low-grade gliomas (pLGG) exhibit genetic events leading to aberrant activation of the MAPK pathway. The most common alterations are KIAA1549:BRAF fusions and BRAFV600E and NF1 mutations. Novel drugs targeting the MAPK pathway (MAPKi) are prime candidates for the treatment of these single-pathway diseases. We aimed to develop an assay suitable for preclinical testing of MAPKi in pLGGs with the goal to identify novel MAPK pathway–suppressing synergistic drug combinations. A reporter plasmid (pDIPZ) with a MAPK-responsive ELK-1–binding element driving the expression of destabilized firefly luciferase was generated and packaged using a lentiviral vector system. Pediatric glioma cell lines with a BRAF fusion (DKFZ-BT66) and a BRAFV600E mutation (BT-40) background, respectively, were stably transfected. Modulation of the MAPK pathway activity by MAPKi was measured using the luciferase reporter and validated by detection of phosphorylated protein levels. A screening of a MAPKi library was performed, and synergy of selected combinations was calculated. Screening of a MAPKi library revealed MEK inhibitors as the class inhibiting the pathway with the lowest IC50s, followed by ERK and next-generation RAF inhibitors. Combination treatments with different MAPKi classes showed synergistic effects in BRAF fusion as well as BRAFV600E mutation backgrounds. Here, we report a novel reporter assay for medium- to high-throughput preclinical drug testing in pLGG cell lines. The assay confirmed MEK, ERK, and next-generation RAF inhibitors as potential treatment approaches for KIAA1549:BRAF and BRAFV600E-mutated pLGGs. In addition, the assay revealed that combination treatments synergistically suppressed MAPK pathway activity.
This article is featured in Highlights of This Issue, p. 1573
Pediatric low-grade gliomas (pLGG) are the most common brain tumors in children (1) and comprise various World Health Organization grade I–II entities, including pilocytic astrocytomas (2). Complete surgical resection is the therapy of choice, but in case of unresectable tumors, chemo- or radiotherapy is applied (e.g., SIOP LGG 2004 trial, NCT00276640). The overall survival is good, with a 10-year survival rate of more than 90% (3, 4). However, recurrences occur frequently, leading to a poor 10-year event-free survival rate of only around 45% in this population (5). The clinical course can be variable, requiring repeated periods of treatment. This often leads to chronic morbidity of the affected patients with significant neurologic sequelae (6, 7). Therefore, in spite of a good overall survival, the management of pLGGs requires novel therapeutic approaches to tackle disease- and therapy-related morbidity.
Pilocytic astrocytoma is a single-pathway disease with virtually all driving aberrations occurring in the RAS/ERK MAPK pathway. Recent studies in pilocytic astrocytomas have shown that around 70% of the underlying MAPK alterations are KIAA1549:BRAF fusions, followed by NF1 (7%), BRAFV600E (5%), and FGFR1 (5%) mutations as the most frequent alterations (8). Other mutations affecting MAPK pathway members such as NTRK2, RAS, and RAF1 are usually rare (8–10). The majority (>80%) of pLGGs other than pilocytic astrocytoma also exhibit MAPK pathway activation (9). While the BRAF fusion is typical for pilocytic astrocytomas, BRAFV600E mutations are frequently observed in pleomorphic xanthoastrocytomas (66%) and gangliogliomas (18%; ref. 11). Because most pLGGs and all pilocytic astrocytomas are driven by activation of a single pathway, targeting this axis is a promising treatment approach. Indeed, several small-molecule MAPK inhibitors are currently under evaluation in clinical trials for pLGGs. The MEK inhibitor (MEKi), selumetinib, has shown promising efficacy in pediatric patients with recurrent or refractory pLGG (12). The MEKi, trametinib is studied in patients with NF1 and recurrent or refractory pLGG (and/or plexiform neurofibroma; refs. 13–15) or sporadic BRAF fusion–positive pLGG in a phase I/II trial (NCT03363217). Treatment of pediatric patients with a BRAFV600E-mutated pLGG with the combination of dabrafenib (V600E-specific BRAFi) and trametinib (MEKi) is currently being investigated in a phase I/II trial (NCT02684058). The novel RAF inhibitor, TAK-580, is in phase I clinical development in children with LGG and other MAPK-driven tumors (NCT03429803). The upcoming LOGGIC Europe trial (EudraCT No. 2018-000636-10) will randomize patients with pLGG in an MEKi (trametinib) treatment arm and compare with standard-of-care (SOC) carboplatin/vincristine and with vinblastine monotherapy, respectively. Similarly, the upcoming COG trial ACNS1831 (NCT03871257) will randomize NF1 patients with pLGG to receive selumetinib or SOC carboplatin/vincristine. Finally, new pan-RAF and ERK inhibitors in (pre-)clinical development are potential candidates for treatment of BRAF fusion–positive pLGGs (16–18).
Results of early clinical trials, however, emphasize the importance of fully understanding the underlying biology of MAPK signaling in pLGGs. Eighty-two percent (9/11) of patients with recurrent or progressive pLGG treated with sorafenib, a multikinase inhibitor including BRAF in its inhibitory spectrum, showed progressive disease under treatment in a phase I/II study leading to early termination of the study (19). Retrospectively, it was shown that sorafenib indeed induced paradoxical activation of the MAPK pathway (20, 21). These studies highlight the need for profound preclinical testing in suitable pLGG models and characterization of the mechanism of action of novel inhibitors before entering to clinical trials.
To date, the availability of in vitro and in vivo models of pLGGs for preclinical drug testing is limited as pLGG cells typically undergo senescence in vitro and do not from tumors in vivo. We have established the first patient-derived KIAA1549:BRAF fusion–expressing pilocytic astrocytoma cell line, DKFZ-BT66 (22). The model was shown to reflect the true biology of a pilocytic astrocytoma including activation of the MAPK pathway, slow growth behavior resulting from oncogene-induced senescence (OIS), and positivity for the senescence-associated secretory phenotype, as well as responsiveness to MAPKi (22, 23). However, the SV40 large T antigen expressed in DKFZ-BT66 cells, necessary to overcome OIS, limits the use of this cell line, as essential proapoptotic pathways are blocked (22, 24). Direct measurement of MAPK pathway activity circumvents this problem when testing MAPKi in the DKFZ-BT66 model, in addition to providing biological information by direct measurement of actual pathway activity rather than a surrogate measure such as viability. In this study, we have generated a novel ELK-1–driven luciferase reporter construct (pDIPZ) and applied it using an MAPKi compound library in both a BRAF fusion and a BRAFV600E-mutated pLGG background in a medium- to high-throughput manner.
Materials and Methods
Cell culture and cell lines
The patient-derived KIAA1549:BRAF fusion–positive pilocytic astrocytoma cell line, DKFZ-BT66 is described in (22) and the patient-derived BRAFV600E mutation–positive pediatric glioma cell line BT-40 in (25). The identity of all cell lines used was confirmed by multiplex cell line authentication service and proven to be free of contamination by multiplex cell contamination test (http://www.multiplexion.de; refs. 26, 27). After testing for identity and contamination, cells were aliquoted and frozen in liquid nitrogen until further use. To establish the readout conditions of the assay, DKFZ-BT66 cells stably transduced with human telomere reverse transcriptase (hTERT) were used (described in ref. 22); however, for the drug screen and following combination treatments, DKFZ-BT66 cells without overexpression of TERT were used. DKFZ-BT66 (±hTERT) cells (passage 9–14 for the native cell line and passage 18–30 for the hTERT cell line) were cultured in the presence of doxycycline (1 μg/mL) to induce proliferation and BT-40 cells (passage 12–20) were cultured as described in (22). Cell lines were tested for Mycoplasma contamination with VenorGeM Classic (catalog no. 11-1250, Minerva Biolabs) every 4 weeks. HEK293T cells (T. Brummer laboratory stock) were cultivated in DMEM [4.5 g/L glucose, 10% FCS (heat inactivated), 2 mmol/L l-glutamine, 10 mmol/L HEPES, 200 U/mL penicillin, and 200 μg/mL streptomycin) and transiently transfected as described previously in (28).
The vectors pDIPZ-ELK-1 binding site (BS)-CMVmin-desGFP-desFLuc (pDIPZ-CMV), pDIPZ-CMVmin-desGFP-desFLuc (pDIPZ-CMV w/o BS), pDIPZ-ELK-1 binding site-pFOSmin -desGFP-desFLuc (pDIPZ-pFOS), and pDIPZ-pFOSmin-desGFP-desFLuc (pDIPZ-pFOS w/o BS) were generated by modifying the pTRIPZ vector (catalog no. RHS4697, Dharmacon). For enzymatic digestion and ligation, the Anza Restriction Enzyme Cloning System (catalog no. IVGN3006, Thermo Fisher Scientific) was used according to the manufacturer's instructions. All primers were customized and purchased from Sigma-Aldrich. Primer sequences are listed in Supplementary Table S1. In summary, the gene cassette of the pTRIPZ vector, consisting of a doxycycline inducible promoter, a turboRFP cDNA, and the shRNAmir cassette, was eliminated by digestion with Anza NotI (catalog no. IVGN001-4, Thermo Fisher Scientific) and Anza XbaI (catalog no. IVGN012-6, Thermo Fisher Scientific) and replaced by a reporter gene cassette. The reporter gene cassettes were kindly provided by Sebastian Herzog (BIOSS, Freiburg, Germany) used in two variations: one vector containing an ELK-1–binding site [serum response element (SRE); ref. 29) linked to a CMVmin promoter (used for pDIPZ-CMV), controlling the expression of destabilized GFP (desGFP) and destabilized firefly luciferase (desFLuc), and the other one harboring a pFOSmin promoter instead of CMVmin (used for pDIPZ-pFOS). These sequences were extracted by PCR (Q5 High-Fidelity DNA Polymerase, catalog no. M0491S, NEB) using primers with NotI and XbaI overhangs (Supplementary Table S1). After digestion with the respective enzymes, the sequence of interest was inserted into the pTRIPZ backbone. To allow selection in the puromycin-resistant DKFZ-BT66 cells (22), the puromycin resistance gene was replaced by a blasticidin resistance gene. The blasticidin resistance gene was extracted from the pDEST vector by PCR and then ligated into the altered pTRIPZ vector using the NEBuilder HiFi DNA Assembly Cloning Kit (catalog no. E5520S, NEB) following the manufacturer's instructions resulting in the generation of pDIPZ-CMV and pDIPZ-pFOS, respectively. Finally, to generate pDIPZ-CMV w/o BS and pDIPZ-pFOS w/o BS, the ELK-1–binding site was eliminated by PCR amplification of the whole plasmid while excluding the binding site.
The cDNA for HA-tagged BRAFWT was amplified from pBabe-puro/BRAFWT-HA (10) using the oligonucleotides NotIBRAFfwd and BRAF C-term-HA (Supplementary Table S1). Both primers introduce flanking NotI sites into the amplicon, which was subcloned into pSC-A (Stratagene) for further propagation. The cDNA was then recovered by NotI digestion and subcloned into NotI linearized pMIBerry-NotI unique (30) to yield pMIBerry NotI unique/BRAFWT-HA. This retroviral vector allows for the expression of a bicistronic transcript encoding the protein of interest and dsRed2. The V600E mutation was introduced into this plasmid using site-directed mutagenesis standard procedures and the primers hBRAFV600Efwd and hBRAFV600Erev (Supplementary Table S1). For the generation of pMIBerry NotI unique/KIAA1549:BRAF-HA, the cDNA for long-form KIAA1549:BRAF (KEx16BEx9) fusion was amplified from pBABE-puro/KIAA1549-BRAF (10) using the primers MfeIKIAA1549fwdI and MfeIKIAA1549rev (Supplementary Table S1). The amplicon was subcloned into pSC-A for propagation, recovered by MfeI digestion, and subcloned into pMIBerry-NotI unique. The BRAF cDNAs of all pMIBerry-NotI unique constructs were confirmed by DNA sequencing.
Lentiviral packaging and transduction were performed as described in (22). For antibiotic selection of DKFZ-BT66 (hTERT) and BT-40 cells, blasticidin (catalog no. A1113903, Thermo Fisher Scientific) was used in a final concentration of 6 μg/mL (for the hTERT cell line, 10 μg/mL) and 2 μg/mL, respectively, for 10 days.
MAPK inhibitors and other drugs
An MAPK inhibitor library (catalog no. L3400) and chemotherapeutics (carboplatin: catalog no. S1215, vinblastine: catalog no. S1248, and vincristine: catalog no. S1241) were purchased from Selleckchem. This predissolved set of drugs was stored at −80°C until usage. In addition, the following investigational MAPK inhibitors were added to the library: LXH254 (structure available here; ref. 31) and LTT462 (both generously provided by Novartis), RAF709 (catalog no. 23820, Cayman Chemical), PLX7904 (catalog no. S7964, Selleckchem), PLX8394 (catalog no. HY-18972, MedChemExpress), LY3009120 (catalog no. S7842, Selleckchem), LY3214996 (structure available here; ref. 32; generously provided by Eli Lilly and Company), (5Z)-7-oxo zeaenol (catalog no. 17459, Cayman Chemical), SCH772984 (catalog no. 19166, Cayman Chemical), and BI-882370 (catalog no. 24273, Cayman Chemical). These drugs were dissolved in DMSO, aliquoted, and stored at −80°C until usage. Inhibitors were diluted in cell culture medium and added to the cell culture at the indicated concentrations for the indicated time.
Measurement of metabolic activity was conducted in white flat bottom 384-well plates (catalog no. 3570, Corning) 72 hours after drug treatment, using a CellTiter-Glo One Solution Assay (catalog no. G8461, Promega) following the manufacturer's instructions. Cells were seeded 1 day before treatment with n = 3 × 103 DKFZ-BT66 cells per well and n = 6 × 103 BT-40 cells per well in 384-well plates. After drug treatment for 72 hours with concentrations ranging from 0.0043 to 25,000 nmol/L, metabolic activity was measured by Multimode Microplate Reader (Tecan). The metabolic activity screen was done in a single run with single measurements of each drug concentration step.
Luciferase reporter assay
Measurement of luciferase activity was conducted in white flat bottom 96- or 384-well plates (catalog no. 781094, 781096, Greiner Bio-One) after 24 hours of treatment using a Luciferase Assay System (catalog no. E1500, Promega). For experiments conducted in 96-well plates, 25 μL of luciferase substrate was added to each well. Steady-Glo Luciferase Assay System (catalog no. E2510, Promega) was used for experiments conducted in 384-well plates following the manufacturer's instructions. DKFZ-BT66 pDIPZ cells were seeded 24 hours prior to treatment in the presence of 1 μg/mL doxycycline with n = 5 × 104 cells per well in 96-well plates and n = 1 × 104 cells per well in 384-well plates. BT-40 pDIPZ cells were seeded 24 hours before treatment with n = 1 × 105 cells per well in 96-well plates and n = 2 × 104 cells per well in 384-well plates. After drug treatment for 24 hours, luciferase activity was measured using the FLUOstar OPTIMA Automated Plate Reader (BMG Labtech). For the MAPKi screen, cells were treated in nine concentration steps ranging from 0.001 to 10,000 nmol/L. IC50 values obtained from the screen (IC50screen) were validated (IC50validated) in three independent replicates (Supplementary Table S5). In the combination experiments, cells were treated with nine concentration steps in a serial dilution with each individual IC50validated as the middle concentration. All experiments (except the MAPKi reporter screen) were conducted in three biological replicates. The reporter screen was done in a single run with three technical replicates for each drug concentration step.
Western blot analysis
Western blots were performed as described previously (33). The following antibodies were used: monoclonal rabbit pERK (1/2) (Thr202/Tyr204) (1:500, catalog no. 4377, Cell Signaling Technology), monoclonal rabbit total ERK (1/2) (1:1,000, catalog no. 4695, Cell Signaling Technology), monoclonal rabbit anti-RSK1 p90 phospho T359 and S363 (1:1,000, catalog no. ab32413, Abcam), monoclonal rabbit RSK1 p90 (1:500, catalog no. 9333S, Cell Signaling Technology), monoclonal rabbit phospho-MEK1/2 (1:1,000, catalog no. 9121S, Cell Signaling Technology), monoclonal rabbit MEK1/2 (1:1,000, catalog no. 9122S, Cell Signaling Technology), monoclonal mouse HA-Tag (1:2,000, catalog no. 9110, Abcam), monoclonal rabbit BRAF C-19 (1:750, catalog no. sc-166, Santa Cruz Biotechnology), and monoclonal mouse GAPDH (1:10,000, catalog no. MAB374, Merck). Cells were treated with the indicated drugs in the indicated concentrations for 24 hours. Depicted blots are representative of at least three biological replicates. Quantification of Western blot bands was conducted using ImageJ on Windows.
Measurement of GFP was conducted using a Merck Guava EasyCyte HT flow cytometer. GFP and red fluorescent protein were measured using a 488 nm laser (500 long pass filter, 512/18 band pass filter) and a 561 nm laser (593 long pass filter, 620/52 band pass filter), respectively. Data were analyzed using FlowJo-V10 software and GuavaSoft version 3.1.1 (Merck Millipore).
For the assessment of GFP-positive cells (Fig. 1), DKFZ-BT66 or BT-40 cells ± pDIPZ constructs were seeded in 6-well plates (n = 1.5 × 105/well) 24 hours prior to measurement. After 24 hours, cells were prepared for flow cytometry by short enzymatic digestion with 0.05% trypsin-EDTA (catalog no. 25300054, Thermo Fisher Scientific) and subsequent addition of cold PBS plus 2% FBS (catalog no. F7524, Sigma-Aldrich). Depicted blots are representative of three technical replicates (Fig. 1). For evaluation of fluorescence under MAPKi treatment (Fig. 2A), DKFZ-BT66 pDIPZ cells (n = 5 × 104/well) or BT-40 pDIPZ cells (n = 1 × 105/well) were seeded in clear flat bottom 96-well plates (catalog no. 3072, Corning) 24 hours prior to treatment. After 24 hours of treatment, cells were prepared for flow cytometry as stated above. Depicted blots are representative of three biological replicates (Fig. 2A).
Drug combination analysis
Determination of the combination index (CI) and generation of isobolograms were conducted using the Chou-Talalay method and CompuSyn software on Windows for experiments using concentration ranges (34).
Synergism was further validated for selected RAFi/MEKi and RAFi/ERKi combinations using pERK or pRSK detection by Western blot as a readout. Concentrations were chosen according to the corresponding isobologram generated for the 0.9 fraction affected (Fa), that is, Drug1IC90, Drug2IC90, Drug1Combi + Drug2Combi leading to 90% inhibition of the pathway, Drug1Combi, and Drug2Combi. Western blot signal was quantified and the effect of the combination of both drugs was compared with the effect of each individual components, allowing the calculation of a CI value using the Bliss independence model as described in (35).
All experiments were conducted in at least three biological triplicates, except the flow cytometry validation (Fig. 1) and the reporter screening of MAPKis (Fig. 3; Supplementary Table S3), which were conducted in a single run with three technical replicates and the metabolic activity screen, which was conducted in a single run without replicates. Significance was calculated using the Tukey “Honest Significant Difference” method in R on Windows (36, 37) and P < 0.05 was considered significant. IC50 values were calculated using GraphPad Prism version 5.01 (GraphPad Software) on Windows. Graphs and CI tables were generated using GraphPad Prism version 5.01, FlowJo-V10 software, Microsoft PowerPoint 2010, and Microsoft Excel 2010 on Windows.
Metabolic activity readout is unsuitable to assess MAPKi treatment in SV40 large T–expressing DKFZ-BT66 cells
To identify novel treatment options for pLGG, an initial screen with different classes of MAPKi was performed. Metabolic activity was measured using an ATP-based assay in the KIAA1549:BRAF fusion–positive pilocytic astrocytoma cell line DKFZ-BT66 and the BRAFV600E mutation–positive pediatric glioma cell line BT-40 after treatment with various MAPKis for 72 hours (Supplementary Fig. S1). However, most MAPKis and other drugs including chemotherapy failed to reduce metabolic activity at clinically relevant concentrations in DKFZ-BT66 cells. This is most likely due to the fact that proapoptotic pathways are blocked by the SV40 large T antigen (present in DKFZ-BT66 but not in BT-40), as described in (22). Only compounds not dependent on, for example, p53 for induction of cell growth arrest, such as vincristine and vinblastine, showed an effect at clinically relevant concentrations in DKFZ-BT66 (Supplementary Fig. S1). In contrast, BT-40, which does not express SV40 large T antigen, showed reduced metabolic activity after MAPKi treatment (Supplementary Fig. S1). We thus concluded that metabolic activity is not suitable as a readout for a drug screen in the KIAA1549:BRAF fusion–positive model, DKFZ-BT66.
Generation of the novel ELK-1 reporter construct pDIPZ and transduction into two patient-derived pediatric glioma models
To enable medium- to high-throughput screening of MAPKi in a KIAA1549:BRAF fusion (in addition to a BRAFV600E mutant) background, we aimed at direct assessment of MAPK pathway activity instead of metabolic activity. We generated an ELK-1–responsive lentiviral reporter plasmid to directly measure MAPK pathway activity (29) and introduced it into both cell models. desGFP and desFLuc, separated by a T2A site and controlled by either a CMVmin or a pFOSmin promoter region (pDIPZ-CMV or -pFOS), were used as reporter genes (Fig. 1A; I and III). The promoter region was linked to an ELK-1–binding element, modulating the expression of the reporter genes depending on MAPK pathway activity (29). In addition, we generated both plasmids without the ELK-1–responsive element (pDIPZ-CMV/pFOS w/o binding site) as controls (Fig. 1A; II and IV). Lentiviral transduction efficiency was assessed by flow cytometry: approximately 56% and approximately 49% of DKFZ-BT66 hTERT cells (Fig. 1B), and approximately 37% and approximately 40% of BT-40 cells (Fig. 1C) transduced with pDIPZ-CMV and pDIPZ-pFOS, respectively, were assessed as GFP positive.
Characterization of reporter signal and promoter response
To evaluate the signal range of the different reporter genes and promoters, the highly selective MEKi, trametinib, and the BRAFV600E inhibitor (BRAFV600Ei), vemurafenib, were tested in both genetic backgrounds, KIAA1549:BRAF fusion (DKFZ-BT66 hTERT pDIPZ) and BRAFV600E mutation (BT-40 pDIPZ). A strong and significant decrease of the luminescence signal under MEKi treatment was detected in both the KIAA1549:BRAF fusion as well as the BRAFV600E mutation background (Fig. 2A). The luminescence signal decreased after trametinib (MEKi) treatment by 65%–67% in the KIAA1549:BRAF fusion background (CMV: 67.1% ± 3.6%; pFOS: 64.6% ± 9.4%; % reduction in luminescence of untreated control) and by 72%–74% in the BRAFV600E mutation background (CMV: 71.7% ± 7.2%; pFOS: 74.4% ± 1.7%; % reduction in luminescence of untreated control; Fig. 2A). The luminescence signal after vemurafenib (BRAFV600Ei) treatment decreased in a differential manner, as expected. The luminescence signal decreased by 59%–63% in the BRAFV600E mutation background (CMV: 58.6% ± 5.9%; pFOS: 63.0% ± 7%; % reduction compared with untreated control), while no decrease in signal was observed in the KIAA1549:BRAF fusion background (Fig. 2A). In contrast, the decrease in fluorescence signal determined by flow cytometry was not significant in the KIAA1549:BRAF fusion background, and only limited in the BRAFV600E mutation background (Fig. 2A) with a reduction of only, for example, 16%–32% as determined by flow cytometry (after trametinib treatment: CMV: 32.1% ± 5.8%; pFOS: 22.6% ± 6.1% and after vemurafenib treatment: CMV: 27.2% ± 4.0%; pFOS: 16.3% ± 2.8%; % reduction compared with untreated control; Fig. 2A). In conclusion, a significant reduction in luminescence, but not in fluorescence, in a mutational background–specific manner, was detectable in both cell lines. This is possibly because of prolonged protein stability of desGFP leading to slow response dynamics. We therefore chose luminescence as the reporter signal in the following experiments.
The two reporter plasmids with different promoters, pDIPZ-CMV and pDIPZ-pFOS, were compared by measurement of luminescence after treatment with trametinib (MEKi) for 24 hours in the KIAA1549:BRAF fusion and the BRAFV600E mutation background. No significant difference between the two promoters was observed (Fig. 2B). Calculated relative IC50 values, as well as dose–response curve shapes were similar, indicating that both promoters perform equally well in the setting of this assay. Because no difference in signal output was detectable between both promoters, we arbitrarily chose the pDIPZ-CMV reporter plasmid for all subsequent measurements.
To control for unspecific changes in desFLuc expression upon MAPKi treatment, we measured luminescence using a pDIPZ-CMV reporter plasmid without the ELK-1–binding site (pDIPZ-CMV w/o ELK-1–binding site) and compared it with the pDIPZ-CMV reporter plasmid with the ELK-1–binding site (pDIPZ-CMV w/ELK-1–binding site). Importantly, modulation of MAPK pathway activity by trametinib treatment in both KIAA1549:BRAF fusion and BRAFV600E mutation background did not result in signal suppression when using the pDIPZ-CMV w/o ELK-1–binding site (Fig. 2C). Therefore, we conclude that the ELK-1–binding site is specific for mediating MAPK pathway activity to the reporter plasmid.
Finally, the results of the reporter assay were validated by Western blot analysis. As expected, a concentration-dependent decrease in phosphorylation of ERK was seen in accordance with the loss of MAPK-dependent signal, measured by the luminescence reporter pDIPZ-CMV (Fig. 2D). Further measurements using the luminescence assay were normalized to treatment with trametinib (1 μmol/L), because the MAPK pathway was maximally suppressed under this condition (Fig. 2B and D). In summary, the changes in reporter signal upon MAPKi treatment are indeed reflective of changes in MAPK pathway activity, and therefore the pDIPZ-CMV reporter is suitable for an MAPKi drug screen.
Screening of a MAPKi drug library reveals ERK inhibitors as a novel potent class beyond MEK and RAF inhibitors inhibiting the MAPK pathway in low-grade gliomas
To evaluate the effects of different inhibitors on MAPK pathway activity, we used our reporter assay to screen a commercially available MAPKi library customized to contain additional RAF, MEK, and ERK inhibitors (see Supplementary Table S2). MEKi was the dominant drug class inhibiting the pathway at very low IC50 levels as determined in the screen (IC50screen) in both the KIAA1549:BRAF fusion, as well as the BRAFV600E mutation background. Trametinib, a dual mechanism MEKi (38), was the top hit in both backgrounds (Fig. 3A–C). Of note, MEKi, for example, trametinib, pimasertib, or selumetinib, also paradoxically activated the MAPK pathway at lower concentrations in the BRAFV600E mutation background (BT-40 cells; Fig. 3B). All ERKis included in the library (SCH772984, ulixertinib, GDC-0994, LY3214996, LTT462, and (5Z)-7-oxo zeaenol) also showed potent inhibition of the MAPK pathway in both cell lines, with IC50screen values below 130 nmol/L.
As expected, strong differences in pathway inhibition were observed for RAFi between the KIAA1549:BRAF fusion and the BRAFV600E mutation background (Fig. 3C). Most RAFis, especially first- and second-generation RAFi, such as vemurafenib (17) paradoxically activated the pathway in the KIAA1549:BRAF fusion background (DKFZ-BT66; Fig. 3A), as has been described previously (20, 21, 39). Of note, the so-called paradox breakers (40), PLX7904 (PLX PB-4; ref. 41) and its optimized analog PLX8394 (PLX PB-3; ref. 21), did not show reduction of pathway activity in the KIAA1549:BRAF fusion background (Fig. 3A). This is in contrast to reports on PLX7904 impairing ERK phosphorylation in NRAS-mutant vemurafenib‐resistant melanoma cells (41), and PLX8394, which was described to fully abrogate the MAPK pathway in KIAA1549:BRAF fusion–expressing cell lines (21). There were, however, some newly developed third-generation pan-RAFi, for example, LY3009120 or LXH254 (17, 42, 43), which successfully inhibited the pathway, with IC50screen values ranging from 270 to 830 nmol/L in the KIAA1549:BRAF fusion background (Fig. 3A). Furthermore, both AZ628, a pan-RAF inhibitor which has a high potency against CRAF (44), and RAF709, a selective inhibitor of dimeric RAF and monomeric-mutant BRAF (45), were able to inhibit the MAPK pathway in the KIAA1549:BRAF fusion background (DKFZ-BT66) at higher concentration ranges. In the BRAFV600E mutation background (BT-40), almost all RAFis were effective, with BRAFV600E-specific inhibitors like dabrafenib or encorafenib scoring as top hits (Fig. 3B).
Overall, IC50screen estimated for RAF and ERK inhibitors were significantly lower in the BRAFV600E-expressing cell line than the BRAF fusion model (Fig. 3C). Other drugs such as JNK and p38α inhibitors or chemotherapeutics showed no inhibitory effect on measured MAPK pathway activity in either the KIAA1549:BRAF fusion or the BRAFV600E mutation background, and thus IC50screen values could not be estimated.
To ensure that the decrease in luminesce signal under treatment resulted from MAPK pathway inhibition and is thus ELK-1 dependent (29), cells transduced with the control vector pDIPZ w/o ELK-1–binding site were treated with the IC50screen concentrations of each drug and luminescence was subsequently measured. Only TAK-632, carboplatin, sorafenib and sorafenib tosylate, or sorafenib tosylate and PLX-4720 showed a signal reduction below 80% in DKFZ-BT66 or BT-40, respectively, indicating that these could be false positive hits in the screen (Supplementary Table S3).
To validate key findings in the screen, pERK protein levels after treatment with selected inhibitors were determined by Western blot analysis. The difference in response to vemurafenib treatment is shown in Fig. 4A. Paradoxical activation was observed for DKFZ-BT66, whereas pERK signal was reduced in BT-40, similar to the results obtained from the screen (Fig. 3A and B). In contrast to vemurafenib, the second-generation RAFi AZ628 was able to reduce pERK levels in the KIAA1549:BRAF fusion background as seen in Fig. 4B, further validating the reporter assay being suitable to distinguish between positive and negative hits.
Finally, the differential response to the so-called paradox breakers in both backgrounds was validated by assessment of pERK levels (Fig. 4C). Treatment with the third-generation RAFi PLX8394, the optimized analog of PLX7904 (40), significantly reduced pERK levels in the BRAFV600E background in low concentrations (1 nmol/L; Fig. 4C), as expected from the reporter assay data. In contrast, pERK levels were significantly reduced only at very high concentrations in the BRAF fusion background, in accordance with the signal observed in the reporter assay (Fig. 3A). Importantly, paradoxical activation on pERK level was not observed in the BRAF fusion background.
To validate our findings in additional genetic models and to evaluate whether this observation is independent of the genetic backgrounds of the cells, HEK293T cells overexpressing different MAPK pathway alterations were treated with PLX8394 (Fig. 4D and E). The overexpression of BRAFV600E protein was validated by detection of the HA-tag and of the KIAA1549:BRAF protein by detection of the fusion-length BRAF protein (Supplementary Fig. S2). Reduction of pMEK (as direct readout of RAF inhibition) and pERK (as direct readout of MEK inhibition) levels were achieved under lower concentrations of PLX8394 for BRAFV600E mutation compared with KIAA1549:BRAF fusion–expressing HEK293T cells (Fig. 4D and E). In conclusion, the BRAFV600E-mutated background is more susceptible to the treatment with the paradox breaker PLX8394, as predicted by the reporter assay (Fig. 3A and B).
In summary, MEKi and ERKi effectively inhibited MAPK pathway activity in both the KIAA1549:BRAF fusion as well as the BRAFV600E mutation background, as measured by the reduction in luminescence signal. In case of RAFi, pathway inhibition depended on the type of MAPK aberration and RAFi class, as expected. Other drugs tested, apart from MEKi, ERKi, and RAFi, were not able to reduce the MAPK pathway signal output.
Combination of different classes of MAPKi show synergistic effects on pathway inhibition in the KIAA1549:BRAF fusion as well as the BRAFV600E mutation background
To further assess novel potential treatment regimens for pLGG, we tested combinations of different classes of MAPKi for synergistic inhibition of the MAPK pathway. Combinations of different MAPKi were chosen on the basis of lowest IC50screen values for each respective background, as well as matching compounds from a single pharmaceutical company in a pragmatic approach to model possible future clinical trials (Supplementary Table S4). IC50screen values generated in the screen of the drugs chosen for combination testing were validated (IC50validated; Supplementary Table S5). IC50validated were used for the combination experiments. Synergistic effects were observed in all tested RAFi and MEKi combinations in both KIAA1549:BRAF fusion (LXH254 plus trametinib and AZ628 plus selumetinib), as well as the BRAFV600E mutation background (AZ628 plus selumetinib, vemurafenib plus cobimetinib, and dabrafenib plus trametinib; Fig. 5A; Supplementary Figs. S3–S5). All synergies measured by CI plotting were corroborated by isobologram analysis (Supplementary Fig. S5).
Synergy of the combination of RAFi and ERKi was detected only for one of the ERKis tested. Only the ERKi, LTT462 showed synergy in combination with the RAFi, LXH254 in the KIAA1549:BRAF fusion and with dabrafenib in the BRAFV600E mutation background, respectively (Fig. 5A; Supplementary Figs. S3–S5). All other RAFi plus ERKi combinations (LY3009120 plus LY3214996, dabrafenib plus ulixertinib, encorafenib plus ulixertinib, and encorafenib plus GDC-0994), as well as MEKi plus ERKi combinations (trametinib plus LTT462 and pimasertib plus SCH772984) revealed only additive or even antagonistic effects, however, mostly in the form of buffering antagonism (ref. 46; Fig. 5A).
To validate the synergistic effects observed for the combination of RAFi and MEKi, and RAFi and ERKi, respectively, Western blots were conducted and synergistic effects were calculated using the Bliss independence model (ref. 35; Fig. 6). For the RAFi, AZ628 in combination with the MEKi, selumetinib synergistic effects were observed in both cell lines using pERK as readout (Fig. 6A and B) with CI values below 0.9. For the combination of the RAFis (LXH254 and dabrafenib) with the ERKi (LTT462), pRSK levels, as a downstream target of pERK, were determined to evaluate synergism, because pERK cannot be used as readout due to accumulation of pERK upon inhibition with the ERKi (as described previously; ref. 47), especially in the KIAA1549:BRAF fusion background (Fig. 6A). For both backgrounds, CI values around 1.0 were calculated from the protein quantification by Western blot analysis, indicating additive effects (Fig. 6A and B).
In summary (Fig. 5B), synergistic effects were observed for treatment with RAFi and MEKi, and some of the RAFi and ERKi combinations in both the KIAA1549:BRAF fusion as well as the BRAFV600E mutation background. The combination of MEKi and ERKi did not reveal unequivocal synergism, but rather additive and/or antagonistic (if buffered) effects.
pLGGs are chronic condition often associated with multiple recurrences and therapeutic interventions in the course of a patient's lifetime, and new effective drug treatments are urgently needed. Despite several ongoing early clinical trials testing MAPKi (alone and in combination) in pLGGs (e.g., NCT02285439, NCT01089101, NCT03363217, and NCT02684058), extensive preclinical studies analyzing the efficacy of MAPKi in pLGGs are still missing. The most important reason for this is the lack of suitable pLGG models that faithfully reflect the biological features of these tumors, including genetic background, slow growth, and induction of senescence. The strength of our study is the use of patient-derived pLGG models and a fast and cost-effective reporter system suitable for high-throughput analysis.
Previous studies have established several in vitro and in vivo pLGG models (e.g., refs. 10, 21, 48–50), most of them genetically engineered to overexpress the most common BRAF aberrations. However, the underlying MAPK driver mutation, specifically the KIAA1549:BRAF fusion, is not expressed endogenously in these models. The expression levels and relative stoichiometry of BRAF, CRAF, and the BRAF fusion are altered, and therefore interactions and feedback mechanisms within the MAPK pathway are likely to be artificially changed. In our study, we have used two well-characterized patient-derived pediatric glioma cell lines, endogenously expressing the KIAA1549:BRAF fusion or the BRAFV600E mutation without genetic overexpression (22, 25).
Widely used methods to determine MAPK pathway activity are, for example, Western blot analysis for pERK, qPCR for MAPK pathway genes, and SRE luciferase reporter assays based on transient transfection (51, 52). These methods are not well suited for high-throughput analysis of the MAPK pathway due to workload, time, and scalability reasons. Here, we used a reporter assay which comes with several advantages: stable lentiviral transduction, no individual sample processing after treatment, fast measurement, scalability, and automatability of the cost-effective readout. In addition, the ELK-1–responsive design of the reporter assay provides information on the transcriptional sum output of the MAPK pathway instead of measurement of phosphorylation status of single components of the pathway, such as pERK, alone. The importance of this information is emphasized by past studies, for example, showing that in tumors with mutations in BRAF or receptor tyrosine kinase, although having similar levels of pERK, elevated transcriptional output of the MAPK pathway was detected only in BRAF-mutated tumors (53). Consequently, only BRAF-mutated tumors were dependent on ERK signaling for proliferation and MEKi sensitivity (53). The reporter assay described here can not only measure the actual transcriptional output of the MAPK pathway, but also compare relative changes upon treatment with MAPKi. This allows for comparison of relative potencies of MAPKi in pLGG.
The pattern of effectiveness in MAPK inhibition both on the single compound, as well as the MAPKi class level warrants a closer look at the molecular effectors of MAPK inhibition. Most of the early-generation RAF inhibitors led to paradoxical activation, as expected, and not all pan-RAFis could inhibit the MAPK pathway. Conversely, novel third-generation pan-RAFi showed inhibitory activity with minimal paradoxical activation in the BRAFV600E background, as well as in the KIAA1549:BRAF fusion–positive cell line. Strikingly, all of the RAF inhibitors capable of inhibiting the pathway in the BRAF fusion–expressing cell line belong to type II inhibitors, which stabilize the αC-helix in the IN and the DFG motif in the OUT conformation (17, 54). This mechanism prevents negative allosteric movements of the second protomer of the RAF dimer, which keeps its αC-helix IN conformation. As a result, the inhibitor is able to bind to the second RAF protomer in similar concentrations, to completely abolish kinase activity. Interestingly, the paradox breakers, PLX7904 and PLX8394, were less effective in BRAF fusion–containing cells compared with their BRAFV600E-positive counterparts. Similarly, Weinberg and colleagues (30) observed that the paradox breakers, PLX7904 and PLX8394, were more effective in suppressing MEK/ERK phosphorylation triggered by BRAFV600E than by the TTYH3:BRAF fusion protein. This might be explained by the fact that PLX7904 and PLX8394 were developed with vemurafenib as starting point (40). Vemurafenib was optimized for the conformation of V600E that is stabilized by the mutation-specific salt-bridge created by E600 (55). The kinase domain of BRAF fusions, however, is not mutated and is therefore probably much more flexible, leading to less sensitivity to the paradox breakers. Alternatively, but not excluding this possibility, other mechanisms might contribute to the insensitivity of KIAA1549:BRAF to PLX8394. Recently, Botton and colleagues also reported the insensitivity of various BRAF fusion–driven melanoma lines toward PLX8394. They suggested that this paradox breaker, which was originally selected to impair the activity of RAS-induced BRAF/RAF1 heterodimers, fails to disrupt RAS-independent kinase homodimers of the BRAF kinase domains whose stability might be additionally influenced by their fusion partner (56). In that regard, it should be noted that, despite its frequency as BRAF fusion partner, very little is known about the tertiary and quaternary structures of KIAA1549.
MEKis were the most effective class of MAPKi in both genetic backgrounds based on IC50screen reporter values. Specifically, trametinib, a potent inhibitor of MEK1/2, which also reduces the activation of MEK by RAF by disrupting the conformation of the MEK1/2 activation loop sites (a so-called “feedback buster”; refs. 38, 57), showed the lowest IC50s in both backgrounds. Furthermore, our data indicate that MEKis are acting in clinically achievable concentrations (Supplementary Figs. S3 and S4), suggesting a high potential of sufficient MAPK pathway suppression also in patients. ERKis were also an effective class of MAPKi in both backgrounds. Current clinical phase I studies (e.g., NCT02857270, NCT02711345, and NCT01875705) will show whether these promising MAPKis will qualify as potential candidates for future pLGG trials (18).
The rationale behind combination treatments is based on the inhibition of potential escape mechanisms from therapy via feedback activation, as well as the possibility to reduce individual drug concentrations, and thus drug toxicities, in combination settings. Reconstitution of ERK signaling as a resistance mechanism, for example, via RAF dimer formation, has been observed in malignant transformation of pLGGs (although this is a rare event; ref. 58). Other resistance mechanisms described in, for example, melanoma (59) are BRAF amplification or MEK mutation, leading to resistance to MAPKi single treatment by reconstitution of MAPK pathway signaling. Combination treatments targeting several components of the pathway could effectively prevent tumor progression under such circumstances (60). Synergistic effects of a pan-RAF/MEKi combination were confirmed for BRAFV600Ei-resistant melanoma and colorectal carcinoma cell lines (61). In addition, the phase I clinical trial of vemurafenib in patients with melanoma revealed that a complete shutdown of the MAPK pathway is necessary for significant tumor response (55), which could be more easily achieved using synergistic combination treatments. Our results suggest that strong synergy depends on the combination of certain classes of MAPKi. Synergistic effects were observed when RAFi were combined with either MEKi or ERKi, possibly due to directly targeting the BRAF alteration in both mutational backgrounds. In case of the combination of MEKi with ERKi, synergistic effects were virtually absent. This is consistent with a recently published study describing that MEKi and ERKi combinations act synergistically only in RAS-mutant models but not in BRAF-mutant models as a consequence of distinct feedback productivity (62).
Finally, the results obtained from the reporter assay could be validated by Western blot analysis: synergistic effects were confirmed for RAFi combined with a MEKi. The combinations of RAFis and ERKi revealed rather additive effects instead of the synergism indicated by the reporter assay. The downstream target, pRSK, was chosen as a suitable readout for ERK inhibition because pERK accumulates upon ERKi treatment. Indeed, a reduction of pRSK, indicative of ERK inhibition, was readily detectable, considering the measurement of synergism by Western blot analysis; however, detection of rather small effects (such as phospho-protein changes) by Western blot can be challenging. Measuring the phosphorylation of a single protein such as RSK, downstream of pERK, as a readout could disregard its own feedback mechanisms interfering with a strong dynamic reaction. Our assay, using a sensitive luminescence signal as a surrogate marker of transcriptional activity at the downstream end of the MAPK pathway might be more suitable to evaluate synergistic effects on the global signaling output.
In summary we have generated a novel MAPK-specific reporter assay in a pLGG-specific background. This reporter assay enables direct assessment of transcriptional activation status of the MAPK pathway and response to MAPKi treatment. Our results indicate that, in addition to MEKis, ERKis and next-generation pan-RAFi are novel potential candidates for the treatment of pLGGs. The synergy of the combination of RAFi with either MEKi or ERKi detected in both genetic backgrounds (KIAA1549:BRAF fusion and BRAFV600E mutation) indicates strong clinical potential of those MAPKi combinations. Clinical trials are urgently needed to test the efficacy of MAPKi combination therapies, especially RAFi and MEKi, in pLGGs.
Disclosure of Potential Conflicts of Interest
D. Hargrave has acted as a paid/unpaid consultant and advisor for AstraZeneca, Roche, and Novartis and received research funding from AstraZeneca. C.M. van Tilburg participated at advisory boards of Novartis and Bayer. O. Witt is an advisory board member of Novartis, AstraZeneca, Janssen, and Roche. No potential conflicts of interest were disclosed by the other authors.
Conception and design: D. Usta, R. Sigaud, F. Selt, O. Witt, T. Milde
Development of methodology: D. Usta, R. Sigaud, S. Pusch, T. Brummer, T. Milde
Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): D. Usta, R. Sigaud, J.L. Buhl, V. Marquardt, J. Jansen, A.C. Sommerkamp, T. Rubner, D.T.W. Jones, M. Remke, T. Brummer
Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): D. Usta, R. Sigaud, J.L. Buhl, F. Selt, D. Pauck, J. Ecker, T. Hielscher, J. Vollmer, T. Rubner, S.M. Pfister, T. Brummer, T. Milde
Writing, review, and/or revision of the manuscript: D. Usta, R. Sigaud, J.L. Buhl, F. Selt, S. Pusch, J. Ecker, T. Rubner, D. Hargrave, C.M. van Tilburg, S.M. Pfister, D.T.W. Jones, M. Remke, T. Brummer, O. Witt, T. Milde
Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): D. Usta
Study supervision: D. Usta, O. Witt, T. Milde
We thank Daniela Kuhn, Isabel Büdenbender, and Sandra Braun for excellent technical assistance. This work was financially supported by The Brain Tumour Charity (The Everest Centre for Low-Grade Paediatric Brain Tumours; GN-000382 to S.M. Pfister, D.T.W. Jones, and O. Witt). D. Usta received a scholarship from the Friedrich-Ebert-Foundation (“Friedrich-Ebert-Stiftung”). J. Vollmer received a scholarship from The German National Academic Foundation (“Studienstiftung des deutschen Volkes”). J. Ecker received a postdoc stipend from the Medical Faculty, Heidelberg University, through the Physician Scientist Program. T. Brummer acknowledges support by the German Research Foundation by a Heisenberg-Professorship and BR3662/4–1. O. Witt is grateful to Christian Scheu and his family for their financial support of this study. S.M. Pfister, D.T.W. Jones, and O. Witt received financial support from A Kids' Brain Tumor Cure (PLGA Foundation). O. Witt, D.T.W. Jones, and S.M. Pfister received financial support from Children's Tumor Foundation Synodos low-grade glioma initiative. O. Witt and T. Milde received donations from private charities (anonymous, “Verein für krebskranke Kinder Odenwald e.V.” and the “DLFH Verband Pfalz e.V.”). D. Hargrave was supported by funding from the NIHR Great Ormond Street Hospital Biomedical Research Centre. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR, or the Department of Health.
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