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Cotargeting of MEK and PDGFR/STAT3 Pathways to Treat

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Cotargeting of MEK and PDGFR/STAT3 Pathways to Treat Published OnlineFirst June 15, 2017; DOI: 10.1158/1535-7163.MCT-17-0009 Small Molecule Therapeutics Molecular Cancer Therapeutics Cotargeting of MEK and PDGFR/STAT3 Pathways to Treat Pancreatic Ductal Adenocarcinoma Nisebita Sahu1, Emily Chan2, Felix Chu3, Thinh Pham3, Hartmut Koeppen3, William Forrest4, Mark Merchant2, and Jeff Settleman1 Abstract Pancreatic ductal adenocarcinoma (PDAC) is among the most ponatinib was effective in targeting pancreatic cancer cells both lethal human diseases and remains largely refractory to available in monolayer and spheroids by effectively blocking signaling drug treatments. Insufficient targeting of the known oncogenic via the PDGFRa and MEK kinases, while also preventing the drivers and activation of compensatory feedback loops and activation of STAT3- and S6-mediated compensatory feedback inability to prevent metastatic spread contribute to poor progno- loops in cancer cells. Furthermore, using xenograft models, sis for this disease. The KRAS-driven MEK pathway is mutationally we demonstrate that cotreatment with a MEK inhibitor and activated in most pancreatic cancers and is an important target for ponatinib causes significant tumor regression. PDAC patient therapeutics. Using a two-dimensional monolayer culture system samples also provided evidence of increased STAT3 activation as well as three-dimensional spheroid culture system, we con- in PDAC tumors and MAPK1 (ERK) activation in liver metas- ducted a screen of a large panel of anticancer agents and found that tases, implicating STAT3 and ERK as key drivers in primary MAP2K (MEK) inhibitors were most effective in targeting PDAC tumors and metastases, respectively. These results reveal a com- spheroids in comparison with monolayer cultures. Combination bination drug treatment strategy that may be effective in pancre- treatment with an MEK inhibitor and the multikinase inhibitor atic cancer. Mol Cancer Ther; 16(9); 1–10. Ó2017 AACR. Introduction sensitivity and resistance that could inform novel treatment strategies in pancreatic cancer. Pancreatic ductal adenocarcinoma (PDAC) is one of the most The majority of PDAC tumors are driven by KRAS mutation– malignant cancers, and numerous therapeutic approaches have driven activation of MAPK signaling (6–8), thereby highlighting been explored on the basis of drug response findings from MEK as an important candidate target for therapeutic intervention pancreatic cancer cells tested in two-dimensional culture models in PDAC patients. However, preclinical and clinical studies have (1, 2). However, thus far, these have largely failed to translate largely revealed a lack of efficacy upon MAPK pathway inhibition successfully to the clinic, potentially due to the insufficient reca- alone, potentially due to the rapid development of resistance to pitulation of the tumor context in vivo in this simple culture MAPK inhibitors through various compensatory mechanisms, condition. A variety of published studies have pointed to signif- thereby limiting the efficacy of the inhibitors and leading to icant differences in drug response data when testing cancer cells in emergence of drug-resistant tumors (9–11). Hence, an enhanced two-dimensional (2D) versus three-dimensional (3D) cultures understanding of the underlying mechanisms of sensitivity and (3–5). As 3D culture models have been shown to more closely resistance to MEK inhibitors may be critical for the identification reflect micrometastases that arise in vivo and can potentially better of effective therapeutic strategies for pancreatic cancer. In this recapitulate the mechanisms of tumor initiation, metastasis, and study, we compared the sensitivity of PDAC cancer cells in 2D and drug sensitivity in vivo, we explored mechanisms of drug response 3D cultures with small-molecule inhibitors in a high-throughput and resistance in 2D versus 3D culture conditions to enable the screening (HTS) assay to identify inhibitors that are most effective identification of physiologically relevant mechanisms of drug for targeting pancreatic cancer cells. 1Department of Discovery Oncology, Genentech, South San Francisco, Califor- Materials and Methods 2 nia. Department of Translational Oncology, Genentech, South San Francisco, Cell lines and reagents California. 3Department of Pathology, Genentech, South San Francisco, Cali- fornia. 4Department of Bioinformatics and Computational Biology, Genentech, All cell lines were from ATCC except for PA-TU-8988T cells, South San Francisco, California. which were from DSMZ, and SUIT-2 cells, which were from JCRB. All cell lines were purchased between 2009 and 2010 and were Note: Supplementary data for this article are available at Molecular Cancer Therapeutics Online (http://mct.aacrjournals.org/). banked at the Genentech cell line core facility that routinely performs SNP and STR analysis to confirm cell line identity. All Current address for J. Settleman: Calico Life Sciences, South San Francisco, CA. cell lines were routinely cultured in RPMI medium (Gibco) Corresponding Author: Jeff Settleman, Calico Life Sciences, 1170 Veterans supplemented with 10% FBS, 2 mmol/L L-glutamine, 100 U/mL Boulevard, San Francisco, CA 94080. Phone: 650-769-5558, Fax: 650-225- of penicillin and streptomycin. Rapamycin, ponatinib, dasatinib, 3485; E-mail: [email protected] nilotinib, and imatinib were from Selleckchem, crenolanib doi: 10.1158/1535-7163.MCT-17-0009 was from Arog Pharmaceuticals, and cobimetinib, GDC-0941, Ó2017 American Association for Cancer Research. and GDC-0980 were synthesized at Genentech. The Human www.aacrjournals.org OF1 Downloaded from mct.aacrjournals.org on September 29, 2021. © 2017 American Association for Cancer Research. Published OnlineFirst June 15, 2017; DOI: 10.1158/1535-7163.MCT-17-0009 Sahu et al. Phospho-Kinase Array Kit was from R&D Systems (ARY003B), the compliance with National Institutes of Health Guide for the Care Cignal-45-Pathway reporter array was from Qiagen (CCA-901L), and Use of Laboratory Animals. All dosing regimens were well and the human tyrosine kinases RT2 Profiler PCR array was from tolerated in xenografts. Two xenograft models were used: the Qiagen (PAHS-161Z). All assays with these kits were performed human PDAC-derived cell line KP4 and cells derived from tumors according to the manufacturer's instructions. isolated from KPP genetically engineered mouse model (GEMM) mice. Cells (5 Â 106) were subcutaneously implanted into the Western blotting and cell viability assays right flanks of 6–8 weeks old immunodeficient athymic female Cells were seeded in 10-cm dishes and treated with 1 mmol/L nude mice (Charles River Laboratories) without using any Matri- of small-molecule inhibitors for 24 hours. Cell lysates were gel. Mice were randomized after tumors reached approximately 3 prepared in RIPA lysis buffer (Thermo Fisher Scientific) con- 200 mm and then treated with vehicle (MCT þ 25 mmol/L citrate taining a protease inhibitor cocktail (Thermo Fisher Scientific); buffer, pH 2.75), cobimetinib (5 mg/kg) Æ ponatinib (30 mg/kg), SDS-PAGE was performed and proteins were transferred to orally once daily. Tumor volumes were measured using Ultra-Cal nitrocellulose membranes. Immunoblotting was performed IV calipers at the indicated intervals. To appropriately analyze the using standard methods. Protein bands were quantified using repeated measurement of tumor volumes from the same animals ImageJ software. Primary antibodies used were p-MAPK1 over time, a mixed modeling approach was used (14). This (ERK), p-STAT3, p-RPS6 (S6), total-S6, p-PDGFRa,p-PDGFRb, approach addresses both repeated measurements and modest p-RPS6KA2 (RSK3), cleaved PARP, BCL2L1 (Bcl-xL), MCL1, dropouts due to any non–treatment-related deaths of animals BIRC5 (survivin), RAB11, p-AKT1, ACTB1 (b-Actin), GAPDH before the study end. Cubic regression splines were used to fita fi (Cell Signaling Technology), total-STAT3, total-PDGFRa (Santa nonlinear pro le to time courses of log2 tumor volume at each Cruz Biotechnology), p-EPHA7 (Gene Tex), and p-EPHA2/3, dose level. These nonlinear profiles were then related to the dose EPHA2/5 (MyBioscource). within the mixed model. Tumor growth inhibition as a percentage Cell viability assays were performed by treating cells with a dose of vehicle (%TGI) was calculated as the percentage of the area titration (0.001–10 mmol/L) or fixed dose (1 mmol/L) of the under the fitted curve (AUC) for the respective dose group per day various pharmacologic inhibitors for 72 hours and measuring in relation to the vehicle, using the following formula: %TGI ¼ Â À viability using CellTiter Glo (Promega). Bliss scores were calcu- 100 (1 AUCdose/AUCveh). To determine the uncertainty inter- lated as described previously (12). vals (UI) for %TGI, the fitted curve and the fitted covariance matrix were used to generate a random sample as an approximation to RNAi knockdown the distribution of %TGI. The random sample was composed of fi Lentiviral particles for shSTAT3 were generated as described 1,000 simulated realizations of the tted-mixed model, where the previously (13). shSTAT3 in a doxycycline-inducible system was %TGI has been recalculated for each realization. The reported UIs obtained from LakePharma, Inc. The target sequences used for were the values for which 95% of the time, the recalculated values fi shSTAT3 were as follows: of %TGI would fall in this region given the tted model. The 2.5 shSTAT3 #1: AATCTTAGCAGGAAGGTGCCT and 97.5 percentiles of the simulated distribution were used as the > shSTAT3 #2: AATGAATCTAAAGTGCGGGGG
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