News Article | October 26, 2016
Lapatinib, PLX-4032, trametenib, tarceva, ABT-199 and ABT-263 were purchased from Selleck-chem; QVD-OPh from Sigma; MG132 from Calbiochem; idarubicin and araC from Pharmacia and Upjohn. A-1210477 was made according to published methods26. Synthesis and characterization of S63845 is provided in the Supplementary Methods. Owing to light sensitivity, S63845 was stored in the dark. Following the previously published structure of MCL1 (PDB ID4WGI)43, a construct was designed with residues 173–321 of human MCL1 as a C-terminal fusion with maltose binding protein (MBP). In addition to the surface entropy-reducing (SER) mutations in MCL1 (K194A, K197A and R201A (ref. 43)), we also introduced E198A, E199A and K265A mutations into MBP (ref. 44). The plasmid encoding the MBP–MCL1 fusion protein was transformed into BL21(DE3)pLysS bacteria. A single colony was used to inoculate 5 ml terrific broth (Fisher BioReagents, (BP2468-2)) containing kanamycin and chloramphenicol at 100 μg ml−1 and 34 μg ml−1, respectively. After 3 h growth at 37 °C, the 5 ml culture was used to inoculate 2 l terrific broth containing the same antibiotics. At an OD of 0.7, the temperature was reduced to 18 °C before induction of MBP–MCL1 protein expression by addition of IPTG to a final concentration of 1 mM. Cells were harvested by centrifugation. Harvested cells were resuspended in 3 volumes of 20 mM Tris–HCl pH 7.4, 200 mM NaCl, 2 mM EDTA, 1 mM DTT and lysed by passing three times through an emulsiflex-C5 (Avestin). The lysate was clarified by centrifugation at 40,000 g, at 4 °C, for 60 min and applied to a 5-ml MBPTrap column (GE Healthcare). The MBP–MCL1 fusion protein was eluted in 20 mM Tris-HCl pH 7.4, 200 mM NaCl, 2 mM EDTA, 1 mM DTT, 10 mM maltose and further purified by size exclusion chromatography in 20 mM HEPES, 100 mM NaCl and 1 mM DTT. Protein eluted as a monomer was concentrated and used in crystallization studies. Apo crystals were grown at a concentration of 34 mg ml−1 (20 mM HEPES pH 7.5, 150 mM NaCl and 2 mM DTT) by the sitting drop vapour diffusion. 2 μl of the protein solution was mixed with 2 μl of the crystallization reservoir (25% PEG 3350, 0.2 M magnesium formate, 1 mM maltose) in a sitting drop plate. The plate was incubated at 284 K and suitable rod-like crystals appeared overnight. Individual crystals were harvested from the crystallization drops and transferred to a drop containing 4.5 μl of the crystallization reservoir solution plus 0.5 μl of S63845 (20 mM in DMSO). The mixture was incubated for 72 h at 284 K. Crystals were flash frozen in liquid nitrogen after cryoprotection using crystallization reservoir plus 20% ethylene glycol. Diffraction data were collected at the Soleil Synchrotron (France) on a beamline Proxima1 and were processed and scaled using XDS (ref. 45). The structure was solved by molecular replacement using MOLREP (ref. 46), using another crystal structure of an MBP–MCL1 fusion protein43. The data were subsequently refined using REFMAC5 (ref. 47). Interactive graphical model building was carried out with COOT. The ligand was clearly defined by the initial electron density maps. The progress of the refinement was assessed using R and the conventional R factor. Once refinement was completed, the structures were validated using various programs from the CCP4i package, CCP4. Statistic parameters are detailed in Extended Table 1. Fluorescence polarization assays were carried out in black-walled, flat-bottomed, low-binding, 384-well plates (Corning) in buffer A (10 mM HEPES, 150 mM NaCl, 0.05% Tween 20 pH 7.4 and 5% DMSO) in the presence of 10 nM fluorescein-PUMA (3-(((3′,6′-dihydroxy-3-oxo-3H-spiro(2-benzofuran-1,9′-xanthene)-5-yl)carbamothioyl)amino)-N-(6-oxohexyl)propanamide-AREIGAQLRRMADDLNAQY, from the polypeptide group, France). Final concentrations of MCL1, BCL-2 and BCL-X proteins were 10, 10 and 20 nM, respectively. The assay plates were incubated for 2 h at room temperature and the fluorescence polarization was measured on a Synergy 2 reader (exitation, 528 nm; emission, 640 nm; cut-off, 510 nm). The binding of increasing doses of the compound was expressed as a percentage reduction in mP compared to the window established between the ‘DMSO only’ and ‘total inhibition’ control (30 μM PUMA). The inhibitory concentrations of the drugs that gave a 50% reduction in mP (IC ) were determined, from 11-point dose response curves, in XL-Fit using a 4-parameter logistic model (Sigmoidal dose–response model). The K was subsequently calculated as described in ref. 48. All SPR measurements were performed on a BIAcore T200 instrument (BIAcore GE Healthcare). Direct binding experiments were performed at 20 °C on Series S NTA chips. 10 mM HEPES pH 7.4, 175 mM NaCl, 25 μM EDTA, 1 mM TCEP, 0.01% P20 and 1% DMSO was used as a running buffer (buffer B). The ligand surface was generated using double His-tagged proteins essentially as described in refs 49, 50. Serial dilutions of the compound in buffer B were injected over the protein surface. All sample measurements were performed at a flow rate of 30 μlper min (injection time 120 s, dissociation time 360 s). The sensor surface was regenerated by consecutive injections of 0.35 M EDTA pH 8.0 with 0.1 mg/ml−1 trypsin, 0.5 M imidazole and 45% DMSO (60 s, 15 μl per min). Data processing was performed using BIAevaluation 2.1 (BIAcore GE Healthcare Bio-Sciences Corp) software. Sensorgrams were double referenced before global fitting of the concentration series to a 1:1 binding model. Affinity determination by competition in solution experiments were performed at 30 °C in 10 mM HEPES pH 7.4, 150 mM NaCl, 3 mM EDTA, 1 mM TCEP, 2% glycerol, 0.05% P20 and 1% DMSO (buffer C). An MCL1-specific compound was immobilized on Series S CM5 chips by amine coupling as advised in the BIAcore GE Healthcare protocol. Serial dilutions of compounds in buffer B supplemented with fixed concentrations of protein were injected over the generated surface at a flow rate of 15 μl per min for 90 s. Calibration curves were generated using the same procedure by injecting different concentrations of protein over the same sensor chip. Affinity evaluations were performed using the affinity in solution model of BIA evaluation 2.1 (BIAcore GE Healthcare Bio-Sciences Corp) software. Mice were kept in either the Servier Research Institute or the Walter and Eliza Hall Institute (WEHI) specified pathogen-free animal areas for mouse experimental purpose (for Servier, facility license number B78-100-2). The care and use of animals was in accordance with European and national regulations for the protection of vertebrate animals used for experimental and other scientific purposes (directives 86/609 and 2003/65) and the requirements of the Servier Research Institute and WEHI Animal Ethics Committees. Sample sizes were chosen to reach statistical significance, and tumour measurements and all data analysis were performed in a blinded fashion. The Eμ-Myc transgenic mice are kept on a C57BL/6–Ly5.2+ background and have been described previously51. 8–10 week old female SCID mice (for transplantation with human AMO1 and H929 tumour cells) or Swiss Nude mice (Crl:NU(Ico)-Foxn1nu) (for transplantation with human MV4-11 tumour cells) were inoculated with 0.1 ml containing 5 × 106 of the indicated tumour cells subcutaneously into the right flank. The H929 and MV4-11 cells were resuspended in 100% matrigel (BD Biosciences) and the AMO1 cells in a 50:50 mixture of growth medium and matrigel. The width and length of the tumours were measured 2–3 times a week using an electronic caliper. Tumour volume was calculated using the formula: (length × width2)/2. When the tumour volume reached approximately 200 mm3, mice were randomized into different groups; that is, treatment with drug (different concentrations) or vehicle (n = 8 for each group). S63845 was formulated extemporaneously in 25 mM HCl, 20% 2-hydroxy propyl β-cyclo dextrin 20% (Fisher Scientifics) and administrated at the doses and schedules described in the figure legends. Tumour growth inhibition (TGI ) was calculated at the greatest response using the following equation: where day x is the day maximum where the number of animals per group in the control group is sufficient to calculate the TGI (%). For the statistical analysis of differences in tumour volume between treatment groups, a two-way ANOVA with repeated measures on day factor was performed on log-transformed data followed by Dunnett adjustment in order to compare each dose of drug to the control group. A complete tumour regression response was considered for the population with tumours 25 mm3 for at least three consecutive measurements. For ethical reasons, mice carrying tumours exceeding 2,000 mm3 were euthanized. Data are represented as mean of tumour volume ± s.e.m. over time (days) until at least one mouse per cohort had to be killed. Single-cell suspensions of 106 Eμ-Myc lymphoma cell lines (AH15A, AF47A, BRE966, 2253, MRE 721, 560), resuspended in phosphate-buffered saline (PBS), were injected into the tail vein of 8–9 week old female C57BL/6–Ly5.1+ mice. Mice were treated with either vehicle (25 mM HCl, 20% 2-hydroxy propyl β-cyclo dextrin) or 25 mg kg−1 S63845 (reconstituted in vehicle) on days 5–9 after transplant, administered by tail vein injection or, in some incidences when the tails became damaged, by retro-orbital injection. To generate the survival curves of the mice bearing the Eμ-Myc lymphoma cells, mice were killed when deemed unwell by experienced animal technicians. For the toxicity experiments, female C57BL/6–Ly5.1+ mice bearing Eμ-Myc lymphomas or non-tumour bearing C57BL/6–Ly5.1+ mice were killed 4 days after the 5-day drug treatment regimen had been completed (this equated to 13 days after transplantation of the tumour cells in the mice bearing the Eμ-Myc lymphoma cells). For the three mice injected with the AH15A Eμ-Myc lymphoma cells, those treated with vehicle were analysed after only 4 days of treatment because they were deemed too unhealty from the lymphoma to complete their prescribed regimen. For the maximal tolerated dose experiments, 7–8 week old C57BL/6 mice (3 male and 3 female mice in each arm) were treated with a dose of vehicle or S63845 (25 mg per kg, 40 mg per kg, 50 mg per kg or 60 mg per kg body weight) for 5 consecutive days by i.v. tail vein injection or by retro-orbital injection if the tails became damaged. The mice were analysed as they were killed, or for the mice surviving the entire course of treatment, 3 days after the 5-day treatment had been completed. For the initial toxicity studies, sections of spleen, lymph nodes, thymus, ovaries, uterus, kidneys, liver, pancreas, intestines, colon, heart, lung, sternum, backbone and muscle were fixed in 10% formalin and stained with haematoxylin and eosin. The weights of the spleen, thymus, (axillary, brachial and inguinal) lymph nodes, liver and kidneys were recorded. Cells were flushed from the bone marrow (two femurs and one tibia) and single cell suspensions of the spleen, thymus and lymph nodes were generated. The red blood cells were lysed by treatment with 0.168 M ammonium chloride and the white blood cell count was determined using a CASY cell counter (Scharfe System GmbH). All bone marrow or peripheral blood samples from patients with AML were collected after informed consent in accordance with guidelines approved by the Alfred and Royal Melbourne Hospital human research ethics committees. Mononuclear cells were isolated by Ficoll-Paque (GE Healthcare) density-gradient centrifugation, followed by red cell depletion in ammonium chloride (NH Cl) lysis buffer at 37 °C for 10 min. Cells were then resuspended in PBS containing 2% fetal bovine serum (FBS, Sigma). Mononuclear cells were suspended in RPMI-1640 (Gibco) medium containing penicillin and streptomycin (Gibco) and 15% heat-inactivated FBS (Sigma). Normal CD34+ progenitor cells from healthy donors were collected from granulocyte colony stimulating factor (G-CSF)-mobilized blood harvests and purified after Ficoll separation by CD34 positive selection using Miltenyi Biotec micobeads (Miltenyi Biotec. Cat. No. 130-046-703). The research with primary human cells was approved by the Human Research Ethics Committee (HREC) of Alfred Health. AML cells from patients and cells from normal donors were obtained following informed consent processes approved by the HRECs of Alfred Health and Melbourne Health. Colony-forming assays were performed on freshly purified and frozen mononuclear fractions from AML patients or normal cells from G-CSF mobilized normal, healthy donors. Primary cells were cultured in duplicate in 35-mm dishes (Griener-bio) at 104 to 105 cells. Cells were plated in 0.6% agar (Difco): AIMDM 2× (IMDM powder, Invitrogen), supplemented with NaHCO , dextran, penicillin and streptomycin, β-mercaptoethanol and asparagine, FBS (Sigma) at a 2:1:1 ratio of agar:AIMDM:FBS. For optimal growth conditions, all plates contained granulocyte/macrophage colony stimulating factor (100 ng per plate, genzyme), IL-3 (100 ng per plate, R&D Systems), stem cell factor (100 ng per plate, R&D Systems) and erythropoietin (4U per plate, Roche). Cells were cultured for 2–3 weeks in the presence or absence of drugs at 37 °C at 5% CO in a high humidity incubator. After incubation, plates were fixed with 2.5% glutaraldehyde in saline and scored using GelCount (Oxford Optronix). NCI-H929, RS4;11, MV4-11, HCT-116, BT-474, SK-Mel-2, PC-9 and H146 cells were cultured in RPMI 1640 medium, A2058 cells were cultured in DMEM medium and SK-MEL-28 cells were cultured in EMEM medium. All media were supplemented with 10% heat-inactivated FBS, 2 mM l-glutamine, 100 U ml−1 penicillin, 100 μg ml−1 streptomycin, and 10 mM HEPES pH = 7.4, at 37 °C, in 5% CO . For RS4;11 cells the medium was additionally supplemented with 4.5 g l−1 glucose. AMO1 cells were cultured in RPMI 1640 medium supplemented with 20% heat-inactivated FBS, 2 mM l-glutamine, 100 U ml−1 penicillin, 100 μg ml−1 streptomycin, and 10mM HEPES pH = 7.4. HeLa cells were cultured in DMEM medium (containing 10% heat-inactivated FBS, 10 mM HEPES, 100 U ml−1 penicillin, 100 μg ml−1 streptomycin). Cells were grown at 37 °C in a humidified atmosphere with 5% CO . All of the cell lines were purchased from the ATCC or DSMZ. Human Burkitt lymphoma (BL)-derived cell lines (Rael-BL, Kem-BL, BL2, BL30, BL31, BL41, and Ramos-BL, a gift from A.B. Rickinson and M. Rowe, University of Birmingham, UK) were cultured in RPMI 1640 medium supplemented with 10% heat-inactivated FBS, 1 mM glutamine, 1 mM sodium pyruvate, 50 μM α-thiogycerol (Sigma), and 20 nM bathocuproine disulfonic acid (Sigma) in a humidified incubator at 37 °C, 5% CO . The mouse Eμ-Myc lymphoma cell lines (AH15A, AF47A, 2253, BRE966, MRE 721 and 560) were cultured in high-glucose DMEM supplemented with 10% heat-inactivated FBS, 50 μM β-mercaptoethanol (Sigma), 100 μM asparagine (Sigma), 100 U ml−1 penicillin and 100 mg ml−1 streptomycin in a humidified incubator at 37 °C, 10% CO . The myeloma-derived cell lines were purchased from the ATCC, DSMZ or JCRB or provided by the laboratory of A. Spencer (XG1, KMS-26, ANBL6, WL-2 and OCI-MY1) and cultured as recommended by the suppliers at 37 °C in the presence of 5% CO . Bax−/−,Bak−/− H929, KMS-12-PE and AMO1 cells were generated using CRISPR/Cas9 genome editing as described below. HEK293T cells were cultured in DMEM supplemented with 10% heat-inactivated FBS at 37 °C in the presence of 10% CO . Media and supplements were purchased from Life Technologies unless specified otherwise. To test the sensitivity of 152 cell lines derived from several types of solid tumours or haematological malignancies (AML, lymphoma, bladder, central nervous system, colorectal, gastric, head and neck, liver, lung, breast, melanoma, ovarian, pancreas, prostate, renal, sarcoma and uterine) to S63845, cells were grown at 37 °C in a humidified atmosphere with 5% CO in RPMI 1640 medium (25 mM HEPES, with l-glutamine, Biochrom) supplemented with 10% (v/v) FBS (Sigma) and 0.1 mg ml−1 gentamicin (Life Technologies). Different culture media were used for VCap (DMEM, 10% FCS, 1% gentamycin), CALU1 (McCoy, 10% FCS 1% gentamycin) and U87MG (EMEM, 10% FCS 1% gentamycin). These cell lines were provided by the Children’s Hospital Cologne, the University Hospital Freiburg or the NCI or were purchased from ATCC, DSMZ, JCRB, ECACC or KCBL. Two cell lines used in this study were present in the list of commonly misidentified cell lines maintained by the International Cell Line Authentication Committee (ICLAC): NCI-H929 and U-937. For our study, H929 cells were obtained from authentic stocks (ATCC CRL-9068 and DSMZ ACC-163) and U937 cells were authenticated by STR analysis. All cell lines used in this study were verified to be mycoplasma negative before undertaking any experiments with them. Cell viability was measured using MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) colourimetric assay. Cells were seeded in 96-well microplates at a density to maintain control (untreated) cells in an exponential phase of growth during the entire experiment. Cells were incubated with compounds for 48 h followed by incubation with 1 mg ml−1 MTT for 4 h at 37 °C. Lysis buffer (20% SDS) was added and absorbance was measured at 540 nm 18 h later. All experiments were repeated at least three times. The percentage of viable cells was calculated and averaged for each well: per cent growth = (OD treated cells/OD control cells) × 100, and the IC , the concentration where the optical density was reduced by 50%, was calculated by a linear regression performed on the linear zone of the dose–response curve. Cells were harvested from exponential phase cultures, counted and plated in 96-well flat-bottom microtitre plates at a cell density depending on the cell line’s growth rate (4,000 to 30,000 cells for solid-tumour-derived cell lines, 10,000 to 60,000 for haematological cancer-derived cell lines). After a 24-h recovery period to allow the cells to resume exponential growth, 10 μl of culture medium (four control wells per plate) or of culture medium with the test compound were added by a liquid-handling robotic system and treated or untreated cells were cultured for a further 3 days. Compounds were applied in half-log increments at 10 concentrations in duplicate. After treatment of cells, 20 μl per well CellTiter-Blue reagent (Promega) was added. After incubation for up to 4 h, fluorescence was measured by using the Enspire Multimode Reader (Perkin Elmer, excitation λ = 531 nm, emission λ = 615 nm). IC values were calculated by 4-parameter, nonlinear curve fit using Oncotest Warehouse Software. To test the activity of S63845 in combination with the kinase inhibitors trametenib, tarceva, PLX-4032 and lapatinib, SK-MEL-28, BT-474, A2058, SK-Mel-2 and PC-9 cells were seeded into 96-well plates. After 24 h, cells were treated with the indicated compounds for 72 h and assayed for viability using the CellTiter-Glo reagent (Promega). Luminescence was measured at 0, 24, 48 and 72 h on independent plates seeded and treated at the same time. Results were normalized to the samples without treatment at time 0 h. To test the sensitivity of the multiple myeloma cell lines to S63845 cells were seeded into 96-well plates at 5,000 cells per well and treated at 5-point 1:8 serial dilutions of compounds starting from 10 μM. Cell viability was assessed at 48 h using the CellTiter-Glo Assay (Promega) following the manufacturer’s instructions and the plates were read using an Envision luminescence plate reader (Perkin Elmer). Results were normalized to the viability of cells that had been treated with 0.1% DMSO (vehicle) in medium for 48 h. The IC values were calculated using nonlinear regression algorithms in Prism software (GraphPad). To test the dependence of H929 cells on BCL-2, BCL-X , BCL-W, MCL1 or A1/BFL1 for their sustained survival, pools of cells stably expressing Cas9 (mCherry+) and inducibly expressing the relevant single guide RNA (sgRNA) (GFP+) were purified by flow cytometry (BD Biosciences) and seeded into 96-well plates (5,000 cells per well) in triplicates and their viability was determined by using the CellTiter-Glo assay 72 h after the addition of doxocycline (1 μg ml−1) to induce expression of the sgRNA targeting the corresponding genes. The data were normalized to the viability of cells infected with the empty vector. In some experiments, the viability (determined by propidium iodide exclusion) of the cells with or without co-treatment with the pan-caspase inhibitor QVD-OPh (25 μM; MP Biomedicals) for 12 h was also determined. Freshly purified mononuclear cells from AML patient samples were adjusted to a concentration of 2.5 × 105 per ml1 and 100 μl of cell suspensions were aliquoted per well into 96-well plates (Sigma). Cells were then treated with S63845, cytarabine (Pfizer), ABT-199 (Active Biochem) or idarubicin (Sigma) over a 6 log concentration range from 1 nM to 10 μM for 48 h and incubated at 37 °C, 5% CO . Cells were then stained with the Sytox blue nucleic acid stain (Invitrogen) and fluorescence measured by flow cytometric analysis using a LSR-II Fortessa machine (Becton Dickinson). FACSDiva software was used for data collection, and FlowJo software for data analysis. Blast cells were gated using forward and side light scatter properties. Viable cells excluding Sytox blue were determined at six concentrations for each drug and the 50% lethal concentration (LC , in μM) was calculated using nonlinear regression algorithms in Prism software (GraphPad). NCI-H929 cells were treated with the indicated compounds for 4 h, centrifuged and washed with binding buffer (10 mM HEPES, 140 mM NaCl, 2.5 mM CaCl ). Cells were incubated with 200 μl of binding buffer containing Annexin V–Alexa fluor 488 (Invitrogen) and propidium iodide (Sigma) for 15 min at 20 °C in the dark. 400 μl of binding buffer was added and samples were kept at 4 °C before flow cytometry analysis. For each sample, 104 cells were analysed by flow cytometry in an Epics XL/MCL flow cytometer (Beckman Coulter). Fluorescence was collected at 520 nm (Alexa fluor 488) and 630 nm ( propidium iodide). Human Burkitt lymphoma-derived cell lines and mouse Eμ-Myc lymphoma cell lines were plated at a density of 4 × 104 cells per well in flat-bottomed 96-well plates. These cells were treated with increasing doses of S63845 (typically 0.008, 0.025, 0.04, 0.2, 1, 5 μM) for 24 h. Cells were stained with Annexin V-FITC and propidium iodide, analysed on a FACS Calibur and live cells (Annexin V negative/propidium iodide negative) were recorded. Data are presented as per cent cell death induction relative to cells cultured in medium alone. Twenty-four hours after seeding, cells were treated with the indicated compounds for 6 h and harvested in lysis buffer (10 mM HEPES pH 7.4, 142.5 mM KCl, 5 mM MgCl , 1 mM EDTA, 1% NP40, protease and phosphatase inhibitors cocktails (Calbiochem)). Cleared lysates (5 μg protein) were prepared for immunodetection of cleaved PARP (a marker of apoptosis) by using the MSD apoptosis panel whole cell lysate kit (MSD) in 96-well plates according to manufacturer’s instructions, and were analysed on the Sector Image 2400. NCI-H929 cells were treated with S63845 for 4 h, washed with PBS and harvested in lysis buffer delivered with the cytochrome c release apoptosis assay kit (Qiagen). Cells were then homogenized using an ice-cold tissue grinder (40 passes). Homogenates were centrifuged at 700 g for 10 min at 4 °C. The supernatants were transferred into fresh tubes and centrifuged at 10 000 g for 30 min at 4 °C. The supernatants were collected as cytosolic fractions. Cytochrome c release was determined by western blotting using the cytochrome c antibody provided in the kit. Lysates were also analysed by immunoblotting using an anti-LDH antibody (Rockland 200-1173; used as protein loading control). Total protein extracts of myeloma cells were generated in lysis buffer (20 mM Tris-HCl pH 7.4, 135 mM NaCl, 1.5 mM MgCl , 1 mM EDTA, 10% glycerol) containing 1% Triton X-100 and complete protease inhibitors (Roche). Protein extracts of the other cell lines were generated in lysis buffer containing 10 mM HEPES pH 7.4, 142.5 mM KCl, 5 mM MgCl , 1 mM EDTA, 1% NP40, protease and phosphatase inhibitors cocktails (Calbiochem). Lysates were stored at −80 °C. Protein content was quantified using the Bradford assay (Bio-Rad). Lysates were diluted with LDS sample buffer (Invitrogen) at a 3:1 ratio and denatured at 95 °C for 7–10 min. 20–40 μg of protein extracts were separated by SDS–PAGE (NuPAGE 10% Bis Tris gels) and proteins transferred onto nitrocellulose membranes. The membranes were blocked in 5% skimmed milk in PBS and 0.1% Tween20 (blocking buffer) before incubation with antibodies. Rat monoclonal antibodies to BAX (21C10; WEHI) or BAK (7D10; WEHI) and mouse monoclonal antibody against HSP70 (N6; used as a loading control) were used. All antibodies were diluted in blocking buffer. Commercially available antibodies were also used: rabbit polyclonal antibodies against MCL1 (Santa Cruz, S-19, sc-819), PARP (Cell Signaling, 9542), BIM (Cell Signaling, C34C5 2933), Phospho-ERK (Cell Signaling, 9101), total ERK (Cell Signaling, 9102), BAK (BD 556396), BAX (Santa Cruz, sc-493) BCL-X (Transduction Laboratory, 610212) and mouse monoclonal antibodies against actin (Millipore, MAB1501R; used as a loading control), NOXA (Calbiochem, OP180), Flag-M2 (Sigma) and p53 (Santa Cruz, sc-126). HeLa cells were transiently transfected, using the Effecten reagent (Qiagen), with expression vectors encoding 3× Flag-tagged MCL1, BCL-X or BCL-2 (p3×Flag–CMV10, Sigma). After 24 h, cells were treated for 4 h with S63845 and then harvested in lysis buffer (10 mM HEPES pH 7.5, 150 mM KCl, 5 mM MgCl , 1 mM EDTA, 0.4% Triton X100, protease and phosphatase inhibitors cocktails (Calbiochem)). The cleared lysates were subjected to immunoprecipitation with anti-Flag M2 agarose beads (Sigma). The immunoprecipitates and inputs were analysed by immunoblotting using the antibodies listed above. Total RNA was extracted using RNeasy mini kit with DNase I treatment (Qiagen) and reverse transcripted using a high-capacity cDNA reverse transcription kit with RNAse inhibitor (Life Technologies). Conventional real-time PCR was performed on an ABI 7900HT system in 50 μl reaction volumes containing 2× TaqMan universal PCR master mix, 2.5 μl of 20× target/control assay mix and 5 μl of respective cDNA in an optical 96-well plate. NTCs (no template controls) using RNase-free water were included in the plate. Cycling conditions were 95 °C (10 min), followed by 40 PCR cycles at 95 °C (15 s) and 60 °C (1 min). TaqMan Gene Expression Assays (Life Technologies) for MCL1 evaluated the anti-apoptotic long (L) isoform NM_021960 (reference assay Hs01050896_m1). Two out of five reference genes including GAPDH, PPIA, 18S, UBC and SDHA, were selected on geNorm software as the optimal number of reference target genes (geNorm pairwise variation cut off V < 0.15). As such, the optimal normalization factor was calculated as the geometric mean (GM) of reference targets SDHA and PPIA (ref genes) and calculation of −ΔC was achieved as follows: Data are presented as fold change of relative quantification calculated as 2–ΔΔCt, with . Pair-wise comparisons were evaluated with a t-test. Aliquots of cells were stained in 24G2 (anti-FcγR, (Fcγ gamma receptor)) antibody containing hybridoma supernatant, containing fluorescently (FITC, R-PE or APC) labelled monoclonal antibodies against cell surface markers, and analysed on an LSR11C (Becton Dickinson) excluding propidium iodide + (dead) cells. The following antibodies were used: anti-CD25 (clone PC61), anti-CD4 (clones GK1.5 (Biolegend) and H129), anti-CD8 (clones 53-6.7 (Biolegend) and YTS 169), CD44 (clone IM7), anti-B220 (220 kDa form of CD45 expressed on B cells, clones RA3-6B2 (Biolegend)), anti-GR1 (granulocyte antigen 1, clone RB6-8C5), anti-MAC1 macrophage antigen 1, clone M1/70), anti-SCA1 (stem cell antigen 1, clone E13-161.7), anti-c-KIT (clone 2B8 (Biolegend)), anti-TCR (T cell receptor, Biolegend), anti-TER119 (clone TER-119), anti-IgM (clone 5.1), anti-IgD (clone 11-26C), anti-Ly5.1 (clone A20.1) and anti-Ly5.2 (clone S.450-15.2). Data were processed using FlowJo Version 9.9 (TreeStar). Blood cell counts and cell subset composition were determined using an ADVIA 2120 haematology analyser (Siemens). The vectors for the constitutive expression of Cas9 and the inducible expression of the sgRNAs have been previously described30. To target the BCL-2 family members, sgRNAs were designed ( http://crispr.mit.edu) and cloned into pFH1tUTG (ref. 30) with the exception of sgRNAs for MCL1, which have been previously described30. The sequences of the sgRNAs used in this study as well as the primers for amplifying the targeted regions for DNA sequence analysis are detailed in Supplementary Tables 1 and 2, respectively. The vectors to express the BIM variants have been previously described12, 13. To produce lentiviruses, the constructs of interest were co-transfected into HEK293T cells with the packaging viruses pMDLg/pRRE, pRSV RRE and pCMV VSV-G (all from Addgene) using the Effectene transfection reagent (Qiagen). The lentiviruses were harvested, filtered and used to infect target cells as previously described13, 30. Multiple-myeloma-derived cell lines were serially infected with lentiviruses that stably co-express Cas9 and the fluorescent marker, mCherry, and inducibly express the indicated sgRNAs plus stably express GFP. Double positive cells (mCherry+ GFP+) were purified using a BD FACSAria Fusion Sorter (BD Biosciences). Expression of the sgRNA was induced by the addition of doxycycline (1 μg ml−1; Sigma). The experiments targeting BCL-2, BCL-X , BCL-W, MCL1 and BFL1 were undertaken with pools of infected cells. To generate the BAX−/−,BAK−/− H929 clone, a BAX-deficient H929 clone was infected with a lentivirus expressing a sgRNA to target BAK, re-cloned and verified by DNA sequencing and western blotting (Fig. 2c). Sequences of sgRNAs and primers for targeted PCR used in this study are shown in Supplementary Tables 1 and 2. DNA sequence verification was carried out as previously described30. Briefly, genomic DNA was isolated using the DNeasy kit (Qiagen) and mutation of targeted DNA confirmed by the Illumina MiSeq30. The unique PCR primers with overhang sequences for each sgRNA are listed in Supplementary Table 2. Graphpad Prism software was used for generating Kaplan–Meier animal survival plots of vehicle and S63845 treated mice and performing statistical analysis (using a log-rank test (Mantel–Cox)). Graphpad Prism was also used to perform multiple unpaired two-tailed t-tests of vehicle-treated and S63845-treated mice to look for significant changes in the data generated from the ADVIA analysis of the blood and from the FACS analysis of the number of different cells present in the spleen, thymus, lymph nodes and bone marrow. Graphpad Prism was used to generate IC curves for cell lines treated with S63845 in vitro. GraphPad Software was used for statistical analysis. All data are expressed as mean ± s.d. P < 0.05 was considered to be significant. The PDB deposition code for the X-ray structure of the MBP-MCL1 complex with S63845 is 5LOF.
News Article | February 15, 2017
WILMINGTON, Mass.--(BUSINESS WIRE)--Charles River Laboratories International, Inc. (NYSE: CRL) today reported its results for the fourth-quarter and full-year 2016 and provided guidance for 2017. For the quarter, revenue from continuing operations was $466.8 million, an increase of 31.9% from $353.9 million in the fourth quarter of 2015. Revenue growth was driven primarily by the Discovery and Safety Assessment and Manufacturing Support segments. Research Models and Services revenue also increased. The acquisitions of WIL Research, Agilux Laboratories, Blue Stream Laboratories, and Oncotest contributed 20.9% to consolidated fourth-quarter revenue growth, both on a reported basis and in constant currency. The addition of a 53rd week at the end of 2016, which is periodically required to align to a December 31st calendar year end, contributed approximately 5.1% to reported fourth-quarter revenue growth. The impact of foreign currency translation reduced reported revenue growth by 2.4%. Excluding the effect of these items, organic revenue growth was 8.3%. On a GAAP basis, net income from continuing operations attributable to common shareholders was $44.7 million for the fourth quarter of 2016, an increase of 36.4% from $32.8 million for the same period in 2015. Fourth-quarter diluted earnings per share on a GAAP basis were $0.93, an increase of 34.8% from $0.69 for the fourth quarter of 2015. On a non-GAAP basis, net income from continuing operations was $58.3 million for the fourth quarter of 2016, an increase of 23.3% from $47.3 million for the same period in 2015. Fourth-quarter diluted earnings per share on a non-GAAP basis were $1.21, an increase of 21.0% from $1.00 per share for the fourth quarter of 2015. Both the GAAP and non-GAAP earnings per share increases were driven primarily by the acquisition of new businesses, notably WIL Research, as well as higher revenue for legacy operations. A gain from the Company’s venture capital investments contributed $0.02 per share in the fourth quarter of 2016, compared to a negligible impact for the same period in 2015. James C. Foster, Chairman, President and Chief Executive Officer, said, “Our fourth-quarter results provided a strong finish to an exceptional year in which we met our long-term revenue goals for all of our businesses except Discovery, and our long-term operating margin targets for the three business segments. We were very pleased that three of our businesses, Safety Assessment, Microbial Solutions, and Biologics Testing Solutions, reported low-double-digit organic revenue growth for the full year. Client demand for our unique portfolio of essential products and services remained strong across each of our client segments, particularly for our biotechnology clients, who were the primary driver of our revenue growth in 2016.” “Our continued investments to broaden our early-stage portfolio, the scientific expertise of our staff, our focus on productivity and efficiency initiatives, and our ability to offer flexible partnership structures are the primary reasons that we are the partner of choice for many of our clients. Based on our view of the opportunities in 2017, we believe we will again deliver high single-digit organic revenue growth and earnings per share growth at a faster rate than revenue,” Mr. Foster concluded. Revenue for the RMS segment was $124.7 million in the fourth quarter of 2016, an increase of 9.5% from $113.8 million in the fourth quarter of 2015. Organic revenue growth was 5.7%. Revenue growth was driven primarily by higher sales of research model services, and sales of research models also increased. In the fourth quarter of 2016, the RMS segment’s GAAP operating margin increased to 26.7% from 24.1% in the fourth quarter of 2015. On a non-GAAP basis, the operating margin increased to 27.3% from 25.4% in the fourth quarter of 2015. Both the GAAP and non-GAAP operating margin increases were due primarily to higher sales volume and the benefit of efficiency initiatives. Revenue from continuing operations for the DSA segment was $241.7 million in the fourth quarter of 2016, an increase of 50.6% from $160.5 million in the fourth quarter of 2015. Growth was driven primarily by the acquisitions of WIL Research, Agilux Laboratories, and Oncotest, which contributed 41.6% to DSA revenue growth. Organic revenue growth was 7.9%. Low-double-digit growth in the legacy Safety Assessment business was partially offset by lower revenue for the legacy Discovery Services business, which declined due primarily to softer demand from global clients for Early Discovery services. Robust demand from biotechnology clients continued to drive revenue growth in the DSA segment. In the fourth quarter of 2016, the DSA segment’s GAAP operating margin declined to 18.1% from 23.1% in the fourth quarter of 2015. The margin decline was due to costs associated with the evaluation and integration of acquisitions, including amortization of intangible assets, as well as the benefit from a tax law change in Quebec in the fourth quarter of 2015. On a non-GAAP basis, the operating margin decreased to 23.8% from 27.1% in the fourth quarter of 2015, due primarily to the tax law change in Quebec, which benefited both the GAAP and non-GAAP DSA operating margin by approximately 230 basis points in the fourth quarter of 2015. The acquisition of WIL reduced the fourth-quarter operating margin by approximately 100 basis points, and foreign exchange benefited the DSA operating margin by approximately 80 basis points due primarily to a weaker British pound. Revenue for the Manufacturing segment was $100.3 million in the fourth quarter of 2016, an increase of 26.2% from $79.5 million in the fourth quarter of 2015. The acquisitions of Blue Stream Laboratories and WIL Research’s contract development and manufacturing (CDMO) services contributed 9.2% to Manufacturing revenue growth in the fourth quarter of 2016. Organic revenue growth was 12.9%, primarily driven by robust growth in the Microbial Solutions and Biologics Testing Solutions businesses. In the fourth quarter of 2016, the Manufacturing segment’s GAAP operating margin increased to 31.0% from 23.7% in the fourth quarter of 2015. The GAAP operating margin increase was primarily driven by lower acquisition costs related to Celsis, as well as leverage from higher revenue in the Microbial Solutions business. On a non-GAAP basis, the operating margin increased to 34.2% from 33.8% in the fourth quarter of 2015, driven by operating margin improvement in the Microbial Solutions business as a result of higher revenue and the benefit of efficiency initiatives. For 2016, revenue increased by 23.3% to $1.68 billion from $1.36 billion in 2015. Organic revenue growth was 7.7%. On a GAAP basis, net income from continuing operations attributable to common shareholders was $154.5 million in 2016, an increase of 2.8% from $150.3 million in 2015. Diluted earnings per share on a GAAP basis in 2016 were $3.22, an increase of 2.2% from $3.15 in 2015. On a non-GAAP basis, net income from continuing operations was $218.9 million in 2016, an increase of 22.1% from $179.3 million in 2015. Diluted earnings per share on a non-GAAP basis in 2016 were $4.56, an increase of 21.3% from $3.76 in 2015. For 2016, RMS revenue was $494.0 million, an increase of 5.0% from $470.4 million in 2015. Organic revenue growth was 4.1%. On a GAAP basis, the RMS segment operating margin increased to 27.6% in 2016 from 25.7% in 2015. On a non-GAAP basis, the operating margin increased to 28.4% in 2016 from 27.1% in 2015. For 2016, DSA revenue was $836.6 million, an increase of 36.7% from $612.2 million in 2015. Organic revenue growth was 8.9%. On a GAAP basis, the DSA segment operating margin decreased to 16.5% in 2016 from 19.9% in 2015. On a non-GAAP basis, the operating margin decreased to 22.7% in 2016 from 23.3% in 2015. For 2016, Manufacturing revenue was $350.8 million, an increase of 25.0% from $280.7 million in 2015. Organic revenue growth was 11.3%. On a GAAP basis, the Manufacturing segment operating margin increased to 29.8% in 2016 from 26.6% in 2015. On a non-GAAP basis, the operating margin increased to 33.8% in 2016 from 32.6% in 2015. Charles River completed the divestiture of its CDMO business on February 10, 2017, to Quotient Clinical, a portfolio company of specialist healthcare investment adviser GHO Capital Partners LLP, based in London, England, for $75.0 million in cash, subject to certain post-closing adjustments. The CDMO business, which represented approximately 1% of Charles River’s 2016 consolidated revenue, provides services to support the formulation design and manufacture of oral drug dosages for biopharmaceutical clients, specializing in high-potency compounds. Charles River acquired the CDMO business in April 2016 as part of the acquisition of WIL Research. Following a strategic review, Charles River determined that the CDMO business was not optimized within Charles River’s portfolio at its current scale, and that the capital could be better deployed in other long-term growth opportunities. The Company is providing the following revenue growth and earnings per share guidance for 2017. This guidance reflects the divestiture of the CDMO business. Earnings per share in 2017 are expected to benefit from both higher revenue and operating margin expansion. The benefit is expected to be partially offset by foreign exchange, which is expected to reduce 2017 earnings per share by approximately $0.10, and lower gains from the Company’s venture capital investments. The Company’s 2016 earnings per share included a $0.13 gain on venture capital investments, and 2017 guidance includes an estimated $0.04 gain on these investments, consistent with the Company’s expected return on invested capital. Footnotes to Guidance Table (1) The contribution from acquisitions reflects only those acquisitions which were completed in 2016. (2) Organic revenue growth is defined as reported revenue growth adjusted for acquisitions, the divestiture of the CDMO business, the 53rd week, and foreign currency translation. (3) GAAP earnings per share guidance does not include the expected net gain and tax impact related to the divestiture of the CDMO business because the disposition accounting has not yet been finalized. (4) These charges relate primarily to the Company’s planned efficiency initiatives in 2017, including site consolidation costs, asset impairments, and severance. Other projects in support of the global productivity and efficiency initiatives are expected, but these charges reflect only the decisions that have already been finalized. (5) These adjustments are related to the evaluation and integration of acquisitions and the divestiture of the CDMO business, and primarily include transaction, advisory, and certain third-party integration costs, as well as certain costs associated with acquisition-related efficiency initiatives. Charles River has scheduled a live webcast on Tuesday, February 14, at 8:00 a.m. ET to discuss matters relating to this press release. To participate, please go to ir.criver.com and select the webcast link. You can also find the associated slide presentation and reconciliations of GAAP financial measures to non-GAAP financial measures on the website. Charles River will present at the Leerink 6th Annual Global Healthcare Conference in New York on Thursday, February 16, at 9:30 a.m. ET. Management will provide an overview of Charles River’s strategic focus and business developments. A live webcast of the presentation will be available through a link that will be posted on the Investor Relations section of the Charles River website at ir.criver.com. A webcast replay will be accessible through the same website approximately three hours after the presentation and will remain available for approximately two weeks. The Company reports non-GAAP results in this press release, which exclude often one-time charges and other items that are outside of normal operations. A reconciliation of GAAP to non-GAAP results is provided in the schedules at the end of this press release. In addition, the Company reports results from continuing operations, which exclude results of the Phase I clinical business that was divested in 2011. The Phase I business is reported as a discontinued operation. Use of Non-GAAP Financial Measures This press release contains non-GAAP financial measures, such as non-GAAP earnings per diluted share, which exclude the amortization of intangible assets, inventory purchase accounting adjustments, and other charges related to our acquisitions; expenses associated with evaluating and integrating acquisitions and divestitures, as well as fair value adjustments associated with contingent consideration; charges related to modifications of purchase options on remaining non-controlled equity interests, and re-measurement of previously held equity interests; charges, gains and losses attributable to businesses or properties we plan to close, consolidate or divest; severance and other costs associated with our efficiency initiatives; executive transition costs; a reversal of indemnification assets associated with acquisitions and corresponding interest; write-off of and adjustments to deferred financing costs and fees related to debt financing; gain on bargain purchase; and costs related to a U.S. government billing adjustment and related expenses. This press release also refers to our revenue in both a GAAP and non-GAAP basis: “constant currency,” which we define as reported revenue growth adjusted for the impact of foreign currency translation, and “organic revenue growth,” which we define as reported revenue growth adjusted for foreign currency translation, acquisitions, the divestiture of the CDMO business, and the 53rd week. We exclude these items from the non-GAAP financial measures because they are outside our normal operations. There are limitations in using non-GAAP financial measures, as they are not prepared in accordance with generally accepted accounting principles, and may be different than non-GAAP financial measures used by other companies. In particular, we believe that the inclusion of supplementary non-GAAP financial measures in this press release helps investors to gain a meaningful understanding of our core operating results and future prospects without the effect of these often-one-time charges, and is consistent with how management measures and forecasts the Company's performance, especially when comparing such results to prior periods or forecasts. We believe that the financial impact of our acquisitions and divestitures (and in certain cases, the evaluation of such acquisitions and divestitures, whether or not ultimately consummated) is often large relative to our overall financial performance, which can adversely affect the comparability of our results on a period-to-period basis. In addition, certain activities and their underlying associated costs, such as business acquisitions, generally occur periodically but on an unpredictable basis. We calculate non-GAAP integration costs to include third-party integration costs incurred post-acquisition. Presenting revenue on a constant-currency basis allows investors to measure our revenue growth exclusive of foreign currency exchange fluctuations more clearly. Non-GAAP results also allow investors to compare the Company’s operations against the financial results of other companies in the industry who similarly provide non-GAAP results. The non-GAAP financial measures included in this press release are not meant to be considered superior to or a substitute for results of operations prepared in accordance with GAAP. The Company intends to continue to assess the potential value of reporting non-GAAP results consistent with applicable rules and regulations. Reconciliations of the non-GAAP financial measures used in this press release to the most directly comparable GAAP financial measures are set forth in this press release, and can also be found on the Company’s website at ir.criver.com. This press release includes forward-looking statements within the meaning of the Private Securities Litigation Reform Act of 1995. Forward-looking statements may be identified by the use of words such as “anticipate,” “believe,” “expect,” “intend,” “will,” “may,” “estimate,” “plan,” “outlook,” and “project,” and other similar expressions that predict or indicate future events or trends or that are not statements of historical matters. These statements also include statements regarding our projected future financial performance including revenue (on both a reported, constant-currency, and organic growth basis), operating margins, earnings per share, the expected impact of foreign exchange rates, and the expected benefit of our life science venture capital investments; the future demand for drug discovery and development products and services, including our expectations for future revenue trends; our expectations with respect to the impact of acquisitions on the Company, our service offerings, client perception, strategic relationships, revenue, revenue growth rates, and earnings; the development and performance of our services and products; market and industry conditions including the outsourcing of services and spending trends by our clients; the potential outcome of and impact to our business and financial operations due to litigation and legal proceedings, including with respect to our ongoing investigation of inaccurate billing with respect to certain government contracts; and Charles River’s future performance as delineated in our forward-looking guidance, and particularly our expectations with respect to revenue, the impact of foreign exchange, and enhanced efficiency initiatives. Forward-looking statements are based on Charles River’s current expectations and beliefs, and involve a number of risks and uncertainties that are difficult to predict and that could cause actual results to differ materially from those stated or implied by the forward-looking statements. Those risks and uncertainties include, but are not limited to: the ability to successfully integrate businesses we acquire; the ability to execute our efficiency initiatives on an effective and timely basis (including divestitures and site closures); the timing and magnitude of our share repurchases; negative trends in research and development spending, negative trends in the level of outsourced services, or other cost reduction actions by our clients; the ability to convert backlog to revenue; special interest groups; contaminations; industry trends; new displacement technologies; USDA and FDA regulations; changes in law; continued availability of products and supplies; loss of key personnel; interest rate and foreign currency exchange rate fluctuations (including the impact of Brexit); changes in tax regulation and laws; changes in generally accepted accounting principles; and any changes in business, political, or economic conditions due to the threat of future terrorist activity in the U.S. and other parts of the world, and related U.S. military action overseas. A further description of these risks, uncertainties, and other matters can be found in the Risk Factors detailed in Charles River's Annual Report on Form 10-K as filed on February 12, 2016, as well as other filings we make with the Securities and Exchange Commission. Because forward-looking statements involve risks and uncertainties, actual results and events may differ materially from results and events currently expected by Charles River, and Charles River assumes no obligation and expressly disclaims any duty to update information contained in this news release except as required by law. Charles River provides essential products and services to help pharmaceutical and biotechnology companies, government agencies and leading academic institutions around the globe accelerate their research and drug development efforts. Our dedicated employees are focused on providing clients with exactly what they need to improve and expedite the discovery, early-stage development and safe manufacture of new therapies for the patients who need them. To learn more about our unique portfolio and breadth of services, visit www.criver.com. (1) Charles River management believes that supplementary non-GAAP financial measures provide useful information to allow investors to gain a meaningful understanding of our core operating results and future prospects, without the effect of often-one-time charges and other items which are outside our normal operations, consistent with the manner in which management measures and forecasts the Company’s performance. The supplementary non-GAAP financial measures included are not meant to be considered superior to, or a substitute for results of operations prepared in accordance with U.S. GAAP. The Company intends to continue to assess the potential value of reporting non-GAAP results consistent with applicable rules, regulations and guidance. (2) This item includes operating losses related primarily to the Company's Shrewsbury, Massachusetts facility. (3) These adjustments are related to the evaluation and integration of acquisitions, which primarily include transaction, third-party integration, and certain compensation costs, and fair value adjustments associated with contingent consideration. (1) Charles River management believes that supplementary non-GAAP financial measures provide useful information to allow investors to gain a meaningful understanding of our core operating results and future prospects, without the effect of often-one-time charges and other items which are outside our normal operations, consistent with the manner in which management measures and forecasts the Company’s performance. The supplementary non-GAAP financial measures included are not meant to be considered superior to, or a substitute for results of operations prepared in accordance with U.S. GAAP. The Company intends to continue to assess the potential value of reporting non-GAAP results consistent with applicable rules, regulations and guidance. (2) These amounts represent the reversal of an uncertain tax position and an offsetting indemnification asset primarily related to the acquisition of BioFocus. (3) The amounts relate to the acquisition of Sunrise Farms, Inc. and represents the excess of the estimated fair value of the net assets acquired over the purchase price. (4) The amount represents a $1.5 million charge recorded in connection with the modification of the option to purchase the remaining 13% equity interest in Vital River, partially offset by a $0.7 million gain on remeasurement of previously held equity interest in an entity acquired in a step acquisition. (1) Charles River management believes that supplementary non-GAAP financial measures provide useful information to allow investors to gain a meaningful understanding of our core operating results and future prospects, without the effect of often-one-time charges and other items which are outside our normal operations, consistent with the manner in which management measures and forecasts the Company’s performance. The supplementary non-GAAP financial measures included are not meant to be considered superior to, or a substitute for results of operations prepared in accordance with U.S. GAAP. The Company intends to continue to assess the potential value of reporting non-GAAP results consistent with applicable rules, regulations and guidance. (2) The contribution from acquisitions reflects only those acquisitions which were completed during fiscal year 2016 and 2015. (3) Organic revenue growth is defined as reported revenue growth adjusted for acquisitions, the 53rd week, and foreign exchange.
Schueler J.,Albert Ludwigs University of Freiburg |
Wider D.,Albert Ludwigs University of Freiburg |
Klingner K.,Oncotest |
Siegers G.M.,University of Western Ontario |
And 4 more authors.
PLoS ONE | Year: 2013
Background: We systematically analyzed multiple myeloma (MM) cell lines and patient bone marrow cells for their engraftment capacity in immunodeficient mice and validated the response of the resulting xenografts to antimyeloma agents. Design and Methods: Using flow cytometry and near infrared fluorescence in-vivo-imaging, growth kinetics of MM cell lines L363 and RPMI8226 and patient bone marrow cells were investigated with use of a murine subcutaneous bone implant, intratibial and intravenous approach in NOD/SCID, NOD/SCID treated with CD122 antibody and NOD/ SCID IL-2Rγ(null) mice (NSG). Results: Myeloma growth was significantly increased in the absence of natural killer cell activity (NSG or αCD122-treated NOD/SCID). Comparison of NSG and áCD122-treated NOD/SCID revealed enhanced growth kinetics in the former, especially with respect to metastatic tumor sites which were exclusively observed therein. In NSG, MM cells were more tumorigenic when injected intratibially than intravenously. In NOD/SCID in contrast, the use of juvenile long bone implants was superior to intratibial or intravenous cancer cell injection. Using the intratibial NSG model, mice developed typical disease symptoms exclusively when implanted with human MM cell lines or patient-derived bone marrow cells, but not with healthy bone marrow cells nor in mock-injected animals. Bortezomib and dexamethasone delayed myeloma progression in L363- as well as patient-derived MM cell bearing NSG. Antitumor activity could be quantified via flow cytometry and in vivo imaging analyses. Conclusions: Our results suggest that the intratibial NSG MM model mimics the clinical situation of the disseminated disease and serves as a valuable tool in the development of novel anticancer strategies. © 2013 Schueler et al.
News Article | December 6, 2016
CULVER CITY, Calif. & SHOHAM, Israel--(BUSINESS WIRE)--NantHealth, Inc., (Nasdaq: NH), a leading next-generation, evidence-based, personalized healthcare company, today announced that it has entered into an exclusive reseller agreement for GPS Cancer, the leading molecular test that helps guide treatment strategies including choice of standard chemotherapy for oncologists, with Oncotest-Teva, a pioneer in the field of personalized medicine and a subsidiary of Teva Pharmaceutical Industries Ltd. in Israel. This landmark agreement—which expands the GPS Cancer footprint globally—adds a key international distributor to the growing set of health plans, health systems, and Fortune 50 companies that have committed to covering or using GPS Cancer since its commercial availability in June 2016. "This is a ground-breaking partnership with Oncotest-Teva, and it builds on the growing momentum we’ve had with payers, providers, and self-insured employers in the United States by bringing GPS Cancer globally,” said Patrick Soon-Shiong, MD, CEO of NantHealth. “Cancer is a devastating disease, not only in the United States, but across the globe. This opportunity to partner with Oncotest-Teva for GPS Cancer truly puts into perspective the depth of our mission—to fight cancer worldwide and to evolve from the era of genomics into the 21st century of quantitative proteomics. With GPS Cancer tests now available in Israel, our goal of expanding treatment options for patients beyond the U.S. is becoming a reality. We’re looking forward to bringing patients—no matter where they are—the opportunity to receive personalized care and better informed decision making even when deciding on standard chemotherapy.” Under the terms of the agreement, Oncotest-Teva will have exclusive rights to distribute GPS Cancer to physicians in Israel. GPS Cancer, which integrates quantitative proteomics with whole genome (DNA) and transcriptome (RNA) sequencing, is the only integrated comprehensive molecular test of its type conducted in CLIA-certified and CAP-accredited laboratories. It provides oncologists with a comprehensive molecular profile of a patient’s cancer to inform personalized treatment strategies. As a cornerstone of the Cancer MoonShot 2020 program, GPS Cancer provides key insights based on the unique biology of a patient’s tumor—from the DNA to the RNA to the protein. This rich information helps doctors build more effective treatment plans based on FDA-approved drugs and active clinical trials, while enabling cancer researchers to design new clinical trials that harness the potential of the immune system. "Oncotest-Teva is continuously striving to bring oncologists in Israel the highest quality solutions to understand diseases that will help them provide patients with better treatments and outcomes," said Dr. Lior Soussan-Gutman, managing director at Oncotest-Teva. "GPS Cancer raises the bar when it comes to advanced care options. This partnership is not only fantastic for our team to be able to bring GPS Cancer tests to local oncologists, but it’s truly an opportunity to offer patients a new standard of care—one that is personalized and has been favorable amongst other cancer care providers as seen with the Cancer MoonShot 2020 program.” Unlike other tests on the market, GPS Cancer sequences the whole genome of 20,000+ genes and 3 billion base pairs and matches against the patient’s normal DNA, providing oncologists with an expansive view of alterations to inform personalized treatment strategies. GPS Cancer extends from genomics to proteomics not only through analysis of RNA, but also utilizes quantitative proteomics through mass spectrometry to measure the amounts of clinically relevant proteins that are the targets of or essential for various therapeutics. This clinically relevant information helps oncologists to better understand how patients may potentially respond to chemotherapies, targeted therapies and immunotherapies. Cautionary Note Concerning Forward-Looking Statements This press release contains forward-looking statements within the meaning of the Private Securities Litigation Reform Act of 1995, including, among others, statements regarding the capabilities and anticipated utility of our GPS Cancer, including predicting patient response and resistance to therapeutics, enabling diagnoses by physicians and accelerating efforts to bring novel combinations of therapeutic agents to cancer patients, as well as our contribution to the Cancer 2020 initiative. Forward-looking statements are subject to numerous risks and uncertainties that could cause actual results to differ materially from currently anticipated results. Factors that may cause future results to differ materially from management’s current expectations include, among other things, that GPS Cancer may not perform as anticipated, that sufficient physicians may not adopt GPS Cancer to assist their diagnoses or that healthcare payers may not provide reimbursement for GPS Cancer as expected. Our business is subject to numerous additional risks and uncertainties, including, among others, risks relating to market acceptance of our products; our ability to successfully launch new products and applications; competition; our sales, marketing and distribution capabilities; our planned sales, marketing, and research and development activities; unanticipated increases in costs or expenses; and risks associated with international operations. Information on these and additional risks, uncertainties, and other information affecting our business and operating results can be found in our existing and future filings with the Securities and Exchange Commission. These forward-looking statements speak only as of the date hereof. We disclaim any obligation to update these forward-looking statements except as may be required by law. About NantHealth, Inc. NantHealth, Inc. a member of the NantWorks ecosystem of companies, is a next-generation, evidence-based, personalized healthcare company enabling improved patient outcomes and more effective treatment decisions for critical illnesses. NantHealth‘s unique systems-based approach to personalized healthcare applies novel diagnostics tailored to the specific molecular profiles of patient tissues and integrates this molecular data in a clinical setting with large-scale, real-time biometric signal and phenotypic data to track patient outcomes and deliver precision medicine. For nearly a decade, NantHealth has developed an adaptive learning system, CLINICS, which includes its unique software, middleware and hardware systems infrastructure that collects, indexes, analyzes and interprets billions of molecular, clinical, operational and financial data points derived from novel and traditional sources, continuously improves decision-making and further optimizes our clinical pathways and decision algorithms over time. For more information please visit www.nanthealth.com and follow Dr. Soon-Shiong on Twitter @DrPatSoonShiong. About GPS Cancer™ GPS Cancer™ is a comprehensive molecular profile available through NantHealth. GPS Cancer integrates whole genome (DNA) sequencing, whole transcriptome (RNA) sequencing, and quantitative proteomics through mass spectrometry, providing oncologists with unprecedented insight into the molecular signature of each patient’s cancer to inform personalized treatment strategies. GPS Cancer profiling is conducted in CLIA-certified and CAP-accredited laboratories, and is a key enabler for Cancer MoonShot 2020, the world’s most comprehensive cancer collaborative initiative seeking to accelerate the potential of combination immunotherapy as the next generation standard of care in cancer patients. For more information, visit www.gpscancer.com and www.cancermoonshot2020.org. About Oncotest-Teva Oncotest-Teva is a pioneer in the field of personalized medicine in Israel. For almost two decades, Oncotest-Teva has been focusing on identification of novel approaches to diagnosing malignant diseases and predicting the course of disease, according to the unique genetic profile of the patient and tumor cells, and more precise and more effective matching of therapeutic directions.
Lee J.Y.,Sungkyunkwan University |
Kim S.Y.,Sungkyunkwan University |
Park C.,Sungkyunkwan University |
Kim N.K.D.,Samsung |
And 25 more authors.
Oncotarget | Year: 2015
Background: In this study, we established patient-derived tumor cell (PDC) models using tissues collected from patients with metastatic cancer and assessed whether these models could be used as a tool for genome-based cancer treatment. Methods: PDCs were isolated and cultured from malignant effusions including ascites and pleural fluid. Pathological examination, immunohistochemical analysis, and genomic profiling were performed to compare the histological and genomic features of primary tumors, PDCs. An exploratory gene expression profiling assay was performed to further characterize PDCs. Results: From January 2012 to May 2013, 176 samples from patients with metastatic cancer were collected. PDC models were successfully established in 130 (73.6%) samples. The median time from specimen collection to passage 1 (P1) was 3 weeks (range, 0.5-4 weeks), while that from P1 to P2 was 2.5 weeks (range, 0.5-5 weeks). Sixteen paired samples of genomic alterations were highly concordant between each primary tumor and progeny PDCs, with an average variant allele frequency (VAF) correlation of 0.878. We compared genomic profiles of the primary tumor (P0), P1 cells, P2 cells, and patient-derived xenografts (PDXs) derived from P2 cells and found that three samples (P0, P1, and P2 cells) were highly correlated (0.99-1.00). Moreover, PDXs showed more than 100 variants, with correlations of only 0.6-0.8 for the other samples. Drug responses of PDCs were reflective of the clinical response to targeted agents in selected patient PDC lines. Conclusion(s): Our results provided evidence that our PDC model was a promising model for preclinical experiments and closely resembled the patient tumor genome and clinical response.
PubMed | Sungkyunkwan University, Hanyang University, Oncotest and Samsung
Type: Journal Article | Journal: Oncotarget | Year: 2015
In this study, we established patient-derived tumor cell (PDC) models using tissues collected from patients with metastatic cancer and assessed whether these models could be used as a tool for genome-based cancer treatment.PDCs were isolated and cultured from malignant effusions including ascites and pleural fluid. Pathological examination, immunohistochemical analysis, and genomic profiling were performed to compare the histological and genomic features of primary tumors, PDCs. An exploratory gene expression profiling assay was performed to further characterize PDCs.From January 2012 to May 2013, 176 samples from patients with metastatic cancer were collected. PDC models were successfully established in 130 (73.6%) samples. The median time from specimen collection to passage 1 (P1) was 3 weeks (range, 0.5-4 weeks), while that from P1 to P2 was 2.5 weeks (range, 0.5-5 weeks). Sixteen paired samples of genomic alterations were highly concordant between each primary tumor and progeny PDCs, with an average variant allele frequency (VAF) correlation of 0.878. We compared genomic profiles of the primary tumor (P0), P1 cells, P2 cells, and patient-derived xenografts (PDXs) derived from P2 cells and found that three samples (P0, P1, and P2 cells) were highly correlated (0.99-1.00). Moreover, PDXs showed more than 100 variants, with correlations of only 0.6-0.8 for the other samples. Drug responses of PDCs were reflective of the clinical response to targeted agents in selected patient PDC lines.Our results provided evidence that our PDC model was a promising model for preclinical experiments and closely resembled the patient tumor genome and clinical response.