DOCSLIB.ORG

Association of Mutations with All Drugs

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Association of Mutations with All Drugs Association of mutations with all drugs Sorafenib+NPM1:FLT3_ITD KW−2449+FLT3_ITD 12 Sunitinib+NPM1:FLT3_ITD Crenolanib+FLT3_ITD Sorafenib+FLT3_ITD 8 Patients 20 40 Pazopanib (GW786034)+NRAS 60 80 log10 overall FDR log10 overall − Vemurafenib (PLX−4032)+NRAS 4 Tivozanib (AV−951)+NRAS Crizotinib (PF−2341066)+KRAS NVP−TAE684+KRAS 0 −100 −50 0 50 100 Average Difference Association of mutations with 17−AAG (Tanespimycin) FLT3_ITD 6 NPM1:FLT3_ITD 4 Patients 20 40 TP53 log10 FDR 60 − FLT3_ITD:DNMT3A NPM1:FLT3_ITD:DNMT3A NPM1 2 FLT3_ITD:TET2 FLT3_ITD:ASXL1 NPM1:FLT3_ITD:TET2 FLT3_ITD:WT1 TP53:NRAS FLT3_ITD:IDH2 NPM1:DNMT3A FLT3_ITD:RUNX1 0 −50 −25 0 25 50 Average Difference Association of mutations with A−674563 NPM1:FLT3_ITD FLT3_ITD 4 3 Patients 20 40 2 FLT3_ITD:TET2 60 log10 FDR − 80 NPM1:FLT3_ITD:DNMT3A NPM1 FLT3_ITD:DNMT3A FLT3_ITD:IDH2 NPM1:IDH2 CREBBP 1 NPM1:FLT3_ITD:TET2 FLT3_ITD:WT1 0 −60 −30 0 30 60 Average Difference Association of mutations with ABT−737 1.00 0.75 Patients 10 20 0.50 30 log10 FDR 40 − 50 0.25 0.00 −50 −25 0 25 Average Difference Association of mutations with AT7519 1.00 0.75 Patients 20 0.50 40 log10 FDR 60 − 0.25 0.00 −25 0 25 Average Difference Association of mutations with AZD1480 FLT3_ITD 3 NPM1:FLT3_ITD NPM1:SRSF2:IDH2 2 Patients 20 40 60 log10 FDR − NPM1:SRSF2 FLT3_ITD:TET2 80 1 FLT3_ITD:IDH2 0 −60 −40 −20 0 20 Average Difference Association of mutations with Afatinib (BIBW−2992) 1.00 0.75 Patients 20 0.50 40 60 log10 FDR − 80 0.25 0.00 −20 0 20 40 Average Difference Association of mutations with Alisertib (MLN8237) NPM1:FLT3_ITD 1.2 NPM1:FLT3_ITD:TET2 0.8 Patients 20 40 60 log10 FDR − 80 0.4 0.0 −40 −20 0 20 Average Difference Association of mutations with Axitinib (AG−013736) 2.5 FLT3_ITD NPM1:FLT3_ITD 2.0 NPM1:FLT3_ITD:DNMT3A FLT3_ITD:DNMT3A NPM1 NPM1:DNMT3A 1.5 TP53:NRAS Patients 20 FLT3_ITD:IDH2 40 60 log10 FDR − 80 1.0 0.5 0.0 −30 0 30 60 Average Difference Association of mutations with BEZ235 RUNX1 1.00 0.75 Patients 20 0.50 40 log10 FDR 60 − 0.25 0.00 −75 −50 −25 0 25 Average Difference Association of mutations with BI−2536 1.00 0.75 Patients 5 10 0.50 15 log10 FDR − 20 0.25 0.00 −30 −20 −10 0 10 20 Average Difference Association of mutations with BMS−345541 2.0 PDS5B 1.5 Patients 20 1.0 40 60 log10 FDR − 80 0.5 0.0 −40 −20 0 20 Average Difference Association of mutations with Barasertib (AZD1152−HQPA) FLT3_ITD:IDH2 NPM1:FLT3_ITD FLT3_ITD 7.5 Patients 5.0 20 40 log10 FDR 60 − NPM1 NPM1:FLT3_ITD:TET2 FLT3_ITD:WT1 FLT3_ITD:TET2 2.5 FLT3_ITD:ASXL1 NPM1:IDH1 FLT3_ITD:DNMT3A PTPN11 NPM1:IDH2 NPM1:FLT3_ITD:DNMT3A ASXL1:SRSF2 NPM1:SRSF2 KRAS STAG2:SRSF2 0.0 −100 −50 0 50 Average Difference Association of mutations with Bay 11−7085 FLT3_ITD FLT3_ITD:IDH2 NPM1:FLT3_ITD 1.5 NPM1:IDH2 SMC1A NPM1 Patients 10 1.0 NPM1:FLT3_ITD:DNMT3A 20 30 log10 FDR 40 − 50 0.5 0.0 −60 −40 −20 0 20 40 Average Difference Association of mutations with Bortezomib (Velcade) 1.00 0.75 Patients 20 0.50 40 60 log10 FDR − 80 0.25 0.00 −25 0 25 50 Average Difference Association of mutations with Bosutinib (SKI−606) FLT3_ITD 4 Patients 20 40 60 log10 FDR − FLT3_ITD:RUNX1 KRAS 80 NPM1:FLT3_ITD 2 FLT3_ITD:IDH2 FLT3_ITD:DNMT3A FLT3_ITD:TET2 NPM1:FLT3_ITD:DNMT3A 0 −60 −30 0 30 Average Difference Association of mutations with CHIR−99021 1.00 0.75 Patients 20 0.50 40 60 log10 FDR − 80 0.25 0.00 −40 −20 0 20 Average Difference Association of mutations with CI−1040 (PD184352) NRAS 3 Patients 2 20 NPM1:NRAS 40 60 log10 FDR − 80 NPM1:DNMT3A:NRAS 1 DNMT3A:NRAS 0 −50 −25 0 25 Average Difference Association of mutations with CYT387 FLT3_ITD NRAS NPM1:FLT3_ITD 2 SRSF2:NRAS Patients FLT3_ITD:DNMT3A 20 40 60 log10 FDR IDH1 − 80 1 0 −30 0 30 60 Average Difference Association of mutations with Cabozantinib FLT3_ITD NPM1:FLT3_ITD 10 FLT3_ITD:DNMT3A Patients NPM1:FLT3_ITD:DNMT3A 20 40 log10 FDR NPM1 60 − FLT3_ITD:IDH2 5 NPM1:FLT3_ITD:TET2 NPM1:DNMT3A TP53 FLT3_ITD:TET2 NPM1:TET2 NRAS TP53:NRAS KRAS NPM1:SRSF2 NPM1:IDH2 FLT3_ITD:RUNX1 FLT3_ITD:WT1 ASXL1 NPM1:FLT3 ASXL1:KRAS FLT3_ITD:TET2:DNMT3A DNMT3A ASXL1:SRSF2 STAG2:ASXL1 0 NPM1:TET2:DNMT3A FLT3_ITD:FLT3 −100 −50 0 50 100 Average Difference Association of mutations with Canertinib (CI−1033) FLT3_ITD:IDH2 2.0 FLT3_ITD NPM1:FLT3_ITD 1.5 FLT3_ITD:TET2 Patients SMC1A 20 1.0 40 log10 FDR 60 − 0.5 0.0 −60 −40 −20 0 20 Average Difference Association of mutations with Cediranib (AZD2171) RUNX1 FLT3_ITD:RUNX1 U2AF1 1.0 Patients 20 40 log10 FDR 60 − 0.5 0.0 −50 −25 0 25 Average Difference Association of mutations with Crenolanib FLT3_ITD NPM1:FLT3_ITD 10 Patients FLT3_ITD:DNMT3A 20 FLT3_ITD:FLT3 40 log10 FDR NPM1:FLT3_ITD:DNMT3A 60 − NPM1:FLT3_ITD:FLT3 5 NPM1:FLT3_ITD:TET2 NPM1 FLT3_ITD:TET2 NPM1:DNMT3A FLT3_ITD:SF3B1 KRAS FLT3_ITD:RUNX1 TP53 FLT3_ITD:WT1 ASXL1:KRAS NPM1:FLT3 SRSF2 NPM1:DNMT3A:FLT3 FLT3 NRAS SRSF2:NRAS DNMT3A:FLT3 0 CBFB.MYH11 −100 −50 0 50 Average Difference Association of mutations with Crizotinib (PF−2341066) KRAS 3 TP53:NRAS NRAS FLT3_ITD 2 Patients NPM1:FLT3_ITD 20 FLT3_ITD:SRSF2 40 60 log10 FDR − BCOR IDH2 80 SMC1A 1 NPM1:CEBPA 0 −30 0 30 60 Average Difference Association of mutations with DBZ NPM1:IDH2 4 3 Patients 20 2 40 log10 FDR 60 − FLT3_ITD:IDH2 FLT3_ITD NPM1:SRSF2:IDH2 IDH2 1 FLT3_ITD:IDH1 NPM1 ASXL1 0 −60 −40 −20 0 20 40 Average Difference Association of mutations with Dasatinib 1.5 PML.RARA TP53:NRAS TP53 1.0 KRAS Patients 20 40 60 log10 FDR − 80 0.5 0.0 −50 0 50 Average Difference Association of mutations with Doramapimod (BIRB 796) 1.00 0.75 Patients 20 0.50 40 60 log10 FDR − 80 0.25 0.00 −30 0 30 60 Average Difference Association of mutations with Dovitinib (CHIR−258) FLT3_ITD 12 NPM1:FLT3_ITD 8 Patients 20 FLT3_ITD:DNMT3A 40 log10 FDR 60 − NPM1:FLT3_ITD:DNMT3A FLT3_ITD:IDH2 4 NPM1 TP53 NPM1:FLT3_ITD:TET2 FLT3_ITD:TET2 NRAS TP53:NRAS FLT3_ITD:FLT3 NPM1:DNMT3A KRAS NPM1:IDH1 FLT3_ITD:RUNX1 FLT3_ITD:WT1 0 −50 0 50 Average Difference Association of mutations with Elesclomol PML.RARA 2.0 1.5 Patients 20 40 log10 FDR 1.0 60 − 0.5 0.0 0 50 100 Average Difference Association of mutations with Entospletinib (GS−9973) NPM1:FLT3_ITD 4 FLT3_ITD NPM1 3 NPM1:FLT3_ITD:DNMT3A Patients 10 NPM1:DNMT3A 20 2 FLT3_ITD:DNMT3A 30 log10 FDR FLT3_ITD:IDH2 40 − NPM1:IDH2 50 NPM1:TET2:DNMT3A FLT3_ITD:IDH1 STAG2 IDH2 1 NPM1:IDH1 NPM1:FLT3 DNMT3A NRAS DNMT3A:IDH2 0 −40 −20 0 20 40 Average Difference Association of mutations with Entrectinib 1.0 0.8 Patients 5 0.6 10 15 log10 FDR − 20 0.4 0.2 −40 −20 0 20 Average Difference Association of mutations with Erlotinib 8 FLT3_ITD 6 NPM1:FLT3_ITD Patients 20 4 40 60 log10 FDR − 80 NPM1:FLT3_ITD:DNMT3A NPM1 NPM1:FLT3_ITD:TET2 NRAS 2 FLT3_ITD:DNMT3A FLT3_ITD:TET2 KRAS NPM1:IDH2 NPM1:IDH1 STAG2:ASXL1 FLT3_ITD:IDH2 0 −40 −20 0 20 Average Difference Association of mutations with Flavopiridol 1.2 NRAS 0.9 Patients 20 0.6 40 log10 FDR 60 − 0.3 0.0 −50 −25 0 25 50 Average Difference Association of mutations with Foretinib (XL880) FLT3_ITD NPM1:FLT3_ITD 9 NPM1:FLT3_ITD:DNMT3A Patients 6 FLT3_ITD:DNMT3A 20 40 log10 FDR FLT3_ITD:IDH2 NPM1 60 − KRAS NPM1:FLT3_ITD:TET2 NPM1:FLT3 TP53 FLT3_ITD:FLT3 FLT3_ITD:TET2 NRAS 3 FLT3_ITD:WT1 TP53:NRAS NPM1:IDH2 NPM1:DNMT3A ASXL1:KRAS FLT3_ITD:RUNX1 ASXL1 FLT3 NPM1:SRSF2 IDH1:DNMT3A STAG2:ASXL1 FLT3_ITD:TET2:DNMT3A NPM1:TET2 IDH2 ASXL1:SRSF2 0 SMC1A −100 −50 0 50 Average Difference Association of mutations with GDC−0879 SMC1A 3 2 Patients 20 40 log10 FDR 60 − FLT3_ITD:IDH2 NPM1:IDH2 1 0 −40 −20 0 Average Difference Association of mutations with GDC−0941 FLT3_ITD:RUNX1 1.00 0.75 Patients 20 0.50 40 log10 FDR 60 − 0.25 0.00 −40 −20 0 20 40 Average Difference Association of mutations with GSK−1838705A NPM1:IDH2 3 FLT3_ITD:IDH2 2 Patients 20 40 log10 FDR IDH2 60 − SMC1A KRAS 1 0 −75 −50 −25 0 25 Average Difference Association of mutations with GSK−1904529A 1.00 0.75 Patients 20 0.50 40 log10 FDR 60 − 0.25 0.00 −40 −20 0 20 40 Average Difference Association of mutations with GSK690693 1.00 0.75 Patients 20 0.50 40 log10 FDR 60 − 0.25 0.00 −40 −20 0 20 Average Difference Association of mutations with GW−2580 IDH2 TP53:NRAS NRAS 2.0 TP53 KRAS 1.5 Patients NPM1:IDH2 20 40 log10 FDR 1.0 60 − 0.5 0.0 −25 0 25 50 Average Difference Association of mutations with Gefitinib FLT3_ITD 4 NPM1:FLT3_ITD Patients 20 NPM1:FLT3_ITD:DNMT3A 40 60 log10 FDR − NPM1 80 FLT3_ITD:IDH2 2 NPM1:IDH2 FLT3_ITD:DNMT3A NRAS FLT3_ITD:SRSF2 NPM1:DNMT3A TP53:NRAS 0 −25 0 25 Average Difference Association of mutations with Gilteritinib (ASP−2215) 6 NPM1:FLT3_ITD FLT3_ITD 4 Patients NPM1:FLT3_ITD:DNMT3A 10 20 FLT3_ITD:DNMT3A 30 log10 FDR − 40 NPM1 2 NPM1:IDH2 NPM1:DNMT3A NRAS FLT3_ITD:TET2 NPM1:FLT3_ITD:TET2 0 −50 −25 0 25 Average Difference Association of mutations with Go6976 1.00 0.75 Patients 5.0 7.5 0.50 10.0 log10 FDR 12.5 − 15.0 0.25 0.00 −25 0 25 Average Difference Association of mutations with INK−128 4 FLT3_ITD 3 Patients 20 NPM1:FLT3_ITD 40 2 60 log10 FDR − 80 1 CEBPA 0 −50 −25 0 25 50 Average Difference Association of mutations with Ibrutinib (PCI−32765) 5 NPM1 NPM1:FLT3_ITD FLT3_ITD NPM1:DNMT3A 4 NPM1:FLT3_ITD:DNMT3A 3 Patients 20 40 log10 FDR 60 − 2 FLT3_ITD:DNMT3A FLT3_ITD:IDH2 NPM1:IDH2 DNMT3A 1 NPM1:SRSF2:IDH2 0 −50 −25 0 25 50 Average Difference Association of mutations with Idelalisib 1.00 0.75 Patients 20 0.50 40 log10 FDR 60 − 0.25 0.00 −25 0 25 Average Difference Association of mutations with Imatinib NRAS 2.0 FLT3_ITD TP53:NRAS 1.5 Patients FLT3_ITD:RUNX1 20 40 1.0 KIT 60 log10 FDR − 80 0.5 0.0 −30 0 30 60 Average Difference Association of mutations with JAK Inhibitor I FLT3_ITD 2 NPM1:FLT3_ITD Patients 20 40 60 log10 FDR − 80 1 0 −50 −25 0 25
Load more

Recommended publications
  • A Human Multi-Lineage Hepatic Organoid Model for Liver Fibrosis
    bioRxiv preprint doi: https://doi.org/10.1101/2020.09.01.278473; this version posted September 2, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. A Human Multi-Lineage Hepatic Organoid Model for Liver Fibrosis Yuan Guan1, Annika Enejder2, Meiyue Wang1, Zhuoqing Fang1, Lu Cui3, Shih-Yu Chen4, Jingxiao Wang1, Yalun Tan1, Manhong Wu1, Xinyu Chen1, Patrik K. Johansson2, Issra Osman1, Koshi Kunimoto3, Pierre Russo5, Sarah C. Heilshorn2 and Gary Peltz1* 1Department of Anesthesia, Stanford University School of Medicine, Stanford CA, 94305; 2Department of Materials Science and Engineering, Stanford University, Stanford CA, 94305; 3Department of Pathology, Institute of Stem Cell Biology and Regenerative Medicine (ISCBRM), Stanford University School of Medicine, Stanford, 94305, CA, USA; 4Shih-Yu Chen, Institute of Biomedical Sciences, Academia Sinica, Taipei, 11529, Taiwan; 5Perelman School of Medicine at The University of Pennsylvania, Philadelphia, PA 19104 The authors have declared that no conflict of interest exists. *Address Correspondence to: [email protected] 300 Pasteur Dr. Room L232 Stanford, CA 94305. bioRxiv preprint doi: https://doi.org/10.1101/2020.09.01.278473; this version posted September 2, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Summary Despite its devastating consequences, liver fibrosis has no treatments. Genome engineering and a hepatic organoid system was used to produce the first in vitro model for human liver fibrosis. Hepatic organoids engineered to express the most common causative mutation for Autosomal Recessive Polycystic Kidney Disease (ARPKD) developed the key features of ARPKD liver pathology (abnormal bile ducts and hepatic fibrosis) in only 21 days.
    [Show full text]
  • Targeting Fibrosis in the Duchenne Muscular Dystrophy Mice Model: an Uphill Battle
    bioRxiv preprint doi: https://doi.org/10.1101/2021.01.20.427485; this version posted January 21, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. 1 Title: Targeting fibrosis in the Duchenne Muscular Dystrophy mice model: an uphill battle 2 Marine Theret1#, Marcela Low1#, Lucas Rempel1, Fang Fang Li1, Lin Wei Tung1, Osvaldo 3 Contreras3,4, Chih-Kai Chang1, Andrew Wu1, Hesham Soliman1,2, Fabio M.V. Rossi1 4 1School of Biomedical Engineering and the Biomedical Research Centre, Department of Medical 5 Genetics, 2222 Health Sciences Mall, Vancouver, BC, V6T 1Z3, Canada 6 2Department of Pharmacology and Toxicology, Faculty of Pharmaceutical Sciences, Minia 7 University, Minia, Egypt 8 3Developmental and Stem Cell Biology Division, Victor Chang Cardiac Research Institute, 9 Darlinghurst, NSW, 2010, Australia 10 4Departamento de Biología Celular y Molecular and Center for Aging and Regeneration (CARE- 11 ChileUC), Facultad de Ciencias Biológicas, Pontificia Universidad Católica de Chile, 8331150 12 Santiago, Chile 13 # Denotes Co-first authorship 14 15 Keywords: drug screening, fibro/adipogenic progenitors, fibrosis, repair, skeletal muscle. 16 Correspondence to: 17 Marine Theret 18 School of Biomedical Engineering and the Biomedical Research Centre 19 University of British Columbia 20 2222 Health Sciences Mall, Vancouver, British Columbia 21 Tel: +1(604) 822 0441 fax: +1(604) 822 7815 22 Email: [email protected] 1 bioRxiv preprint doi: https://doi.org/10.1101/2021.01.20.427485; this version posted January 21, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder.
    [Show full text]
  • FLT3 Inhibitors in Acute Myeloid Leukemia Mei Wu1, Chuntuan Li2 and Xiongpeng Zhu2*
    Wu et al. Journal of Hematology & Oncology (2018) 11:133 https://doi.org/10.1186/s13045-018-0675-4 REVIEW Open Access FLT3 inhibitors in acute myeloid leukemia Mei Wu1, Chuntuan Li2 and Xiongpeng Zhu2* Abstract FLT3 mutations are one of the most common findings in acute myeloid leukemia (AML). FLT3 inhibitors have been in active clinical development. Midostaurin as the first-in-class FLT3 inhibitor has been approved for treatment of patients with FLT3-mutated AML. In this review, we summarized the preclinical and clinical studies on new FLT3 inhibitors, including sorafenib, lestaurtinib, sunitinib, tandutinib, quizartinib, midostaurin, gilteritinib, crenolanib, cabozantinib, Sel24-B489, G-749, AMG 925, TTT-3002, and FF-10101. New generation FLT3 inhibitors and combination therapies may overcome resistance to first-generation agents. Keywords: FMS-like tyrosine kinase 3 inhibitors, Acute myeloid leukemia, Midostaurin, FLT3 Introduction RAS, MEK, and PI3K/AKT pathways [10], and ultim- Acute myeloid leukemia (AML) remains a highly resist- ately causes suppression of apoptosis and differentiation ant disease to conventional chemotherapy, with a me- of leukemic cells, including dysregulation of leukemic dian survival of only 4 months for relapsed and/or cell proliferation [11]. refractory disease [1]. Molecular profiling by PCR and Multiple FLT3 inhibitors are in clinical trials for treat- next-generation sequencing has revealed a variety of re- ing patients with FLT3/ITD-mutated AML. In this re- current gene mutations [2–4]. New agents are rapidly view, we summarized the preclinical and clinical studies emerging as targeted therapy for high-risk AML [5, 6]. on new FLT3 inhibitors, including sorafenib, lestaurtinib, In 1996, FMS-like tyrosine kinase 3/internal tandem du- sunitinib, tandutinib, quizartinib, midostaurin, gilteriti- plication (FLT3/ITD) was first recognized as a frequently nib, crenolanib, cabozantinib, Sel24-B489, G-749, AMG mutated gene in AML [7].
    [Show full text]
  • From Celebrex to a Novel Class of Phosphoinositide-Dependent Kinase 1 (Pdk-1) Inhibitors for Androgen-Independent Prostate Caner
    FROM CELEBREX TO A NOVEL CLASS OF PHOSPHOINOSITIDE-DEPENDENT KINASE 1 (PDK-1) INHIBITORS FOR ANDROGEN-INDEPENDENT PROSTATE CANER DISSERTATION Presented in Partial Fulfillment of the requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University By Jiuxiang Zhu, M.S. * * * * * The Ohio State University 2005 Dissertation Committee: Approved by Professor Ching-Shih Chen, Advisor Professor Robert Brueggemeier Advisor Professor Pui-Kai (Tom) Li Graduate Program in Pharmacy Professor Matthew D. Ringel ABSTRACT Celebrex, a nonsteroidal anti-inflammatory drug (NSAID, cyclooxygenase-2 inhibitor), was reported to induce apoptosis in the prostate cancer cell line PC-3 at 50µM. Early research from our laboratory demonstrated that this apoptotic inducing effect was independent of its COX-2 inhibitory activity. Further investigation showed that PDK-1 was a major protein targeted by celebrex in PC-3 cells to induce apoptosis. However, celebrex was very weak in inhibiting PDK-1 with IC50 of 48µM. To improve its potency, two series of analogs were designed and synthesized. In the first series of 24 compounds, the 5-position methylphenyl moiety of celebrex was replaced by various aromatic ring systems to explore the optimal hydrophobic group. OSU02067 (IC50=9µM) with phenanthrene at the 5-position was the best inhibitor among this series and was selected as the lead compound for further modification. Enzyme kinetics study of PDK-1 inhibition by celebrex indicated that it competed with ATP for binding. Docking of OSU02067 into the ATP binding domain of PDK-1 showed that the sulfonamide moiety hydrogen bonded to hinge region residue Ala162.
    [Show full text]
  • Tyrosine Kinase Inhibitor, Vatalanib, Inhibits Proliferation and Migration of Human Pterygial Fibroblasts
    BASIC INVESTIGATION Tyrosine Kinase Inhibitor, Vatalanib, Inhibits Proliferation and Migration of Human Pterygial Fibroblasts Hong Kyu Kim, MD,* Ji-Young Choi, PhD,† Sang Min Park, PhD,‡ Chang Rae Rho, MD, PhD,§ Kyong Jin Cho, MD, PhD,¶ and Sangmee Ahn Jo, PhD‡k b significantly reduced, but there was no alteration in p53 protein Purpose: Vatalanib is a small-molecule tyrosine kinase inhibitor. levels in HPFs. We investigated the effects of vatalanib on the proliferation and migration of cultured human pterygial fibroblasts (HPFs). Conclusions: These results indicate that vatalanib significantly suppressed the proliferation and migration of HPFs by decreasing Methods: Pterygium tissues were obtained after pterygium exci- vascular endothelial growth factor and transforming growth factor-b. sion surgery and subjected to primary culture. HPFs were treated Vatalanib showed less toxicity than that of MMC. Based on these with vatalanib at various concentrations. Mitomycin C (MMC) was results, vatalanib may potentially serve as a new adjuvant treatment used as a positive control. Cell proliferation and migration assays after pterygium excision surgery. were used to investigate the effects of vatalanib. Cell death was measured using flow cytometry analysis. Western blot analysis was Key Words: tyrosine kinase inhibitor, vatalanib, mitomycin C, performed to identify signaling molecules associated with the pterygium, cell growth response to vatalanib. (Cornea 2017;36:1116–1123) Results: Vatalanib inhibited both proliferation and migration of HPFs in a dose-dependent manner. Cell proliferation was signif- m terygium is one of the most common ocular surface icantly suppressed by vatalanib (10 and 100 M) and MMC diseases. It is characterized by remodeling, proliferation, (0.004% and 0.04%) treatments.
    [Show full text]
  • Prognostic and Predictive Role of Lactate Dehydrogenase 5 ( LDH5) Expression in Colorectal Cancer Patients Treated with PTK787/Z
    Author Manuscript Published OnlineFirst on June 1, 2011; DOI: 10.1158/1078-0432.CCR-10-2918 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. 1 Prognostic and predictive role of lactate dehydrogenase 5 ( LDH5) expression in colorectal cancer patients treated with PTK787/ZK 222584 (Vatalanib) anti- angiogenic therapy. Short title: LDH5 and PTK/ZK in colorectal cancer 1 1 1 Michael I. Koukourakis, Alexandra Giatromanolaki, Efthimios Sivridis, 2 3 4 Kevin C. Gatter, Tanja Trarbach Gunnar Folprecht 5 5 6 7 Michael M. Shi , David Lebwohl , Tarja Jalava , Dirk Laurent 8 2 Gerold Meinhardt , Adrian L. Harris For the ‘Tumour and Angiogenesis Research Group’ 1 Departments of Pathology, and Radiotherapy/Oncology, Democritus University of Thrace, Alexandroupolis 68100, Greece Tel/Fax 0030-2551-31522, Email: [email protected] 2 Cancer Research UK, Molecular Oncology Laboratories, Weatherall Institute of Molecular Medicine and Nuffield Department of Clinical Laboratory Sciences, John Radcliffe Hospital, Oxford, OX3 9DS, UK; Tel -44-1865-222457 Fax: -44-1865- 222431 E-mail: [email protected] 3 University of Essen, West German Cancer Center, Essen, Germany; [email protected] 4 University Hospital Dresden, Dresden, Germany; [email protected] 5 Novartis Pharmaceuticals Co, East Hanover, NJ, USA; [email protected] 6 ; Bayer Schering Pharma Oy, Espoo, Finland [email protected] 7 Bayer Healthcare Pharmaceuticals, Berlin, Germany; [email protected] 8 Bayer Healthcare Pharmaceuticals, NJ, USA, [email protected] Address for correspondence Michael I. Koukourakis, MD Department of Radiotherapy – Oncology Democritus University of Thrace Alexandroupolis 68100, Greece Tel; 25510-74628 Fax: 25510-30329 Email: [email protected] Downloaded from clincancerres.aacrjournals.org on September 23, 2021.
    [Show full text]
  • Individualized Systems Medicine Strategy to Tailor Treatments for Patients with Chemorefractory Acute Myeloid Leukemia
    Published OnlineFirst September 20, 2013; DOI: 10.1158/2159-8290.CD-13-0350 RESEARCH ARTICLE Individualized Systems Medicine Strategy to Tailor Treatments for Patients with Chemorefractory Acute Myeloid Leukemia Tea Pemovska 1 , Mika Kontro 2 , Bhagwan Yadav 1 , Henrik Edgren 1 , Samuli Eldfors1 , Agnieszka Szwajda 1 , Henrikki Almusa 1 , Maxim M. Bespalov 1 , Pekka Ellonen 1 , Erkki Elonen 2 , Bjørn T. Gjertsen5 , 6 , Riikka Karjalainen 1 , Evgeny Kulesskiy 1 , Sonja Lagström 1 , Anna Lehto 1 , Maija Lepistö1 , Tuija Lundán 3 , Muntasir Mamun Majumder 1 , Jesus M. Lopez Marti 1 , Pirkko Mattila 1 , Astrid Murumägi 1 , Satu Mustjoki 2 , Aino Palva 1 , Alun Parsons 1 , Tero Pirttinen 4 , Maria E. Rämet 4 , Minna Suvela 1 , Laura Turunen 1 , Imre Västrik 1 , Maija Wolf 1 , Jonathan Knowles 1 , Tero Aittokallio 1 , Caroline A. Heckman 1 , Kimmo Porkka 2 , Olli Kallioniemi 1 , and Krister Wennerberg 1 ABSTRACT We present an individualized systems medicine (ISM) approach to optimize cancer drug therapies one patient at a time. ISM is based on (i) molecular profi ling and ex vivo drug sensitivity and resistance testing (DSRT) of patients’ cancer cells to 187 oncology drugs, (ii) clinical implementation of therapies predicted to be effective, and (iii) studying consecutive samples from the treated patients to understand the basis of resistance. Here, application of ISM to 28 samples from patients with acute myeloid leukemia (AML) uncovered fi ve major taxonomic drug-response sub- types based on DSRT profi les, some with distinct genomic features (e.g., MLL gene fusions in subgroup IV and FLT3 -ITD mutations in subgroup V). Therapy based on DSRT resulted in several clinical responses.
    [Show full text]
  • (12) United States Patent (10) Patent No.: US 8,945,572 B2 Chant Et Al
    USOO8945572B2 (12) United States Patent (10) Patent No.: US 8,945,572 B2 Chant et al. (45) Date of Patent: Feb. 3, 2015 (54) METHODS AND COMPOSITIONS FOR THE FOREIGN PATENT DOCUMENTS DAGNOSIS AND TREATMENT OF CANCER WO WO 1999/44062 9, 1999 WO OOf 46343 8, 2000 (75) Inventors: John Chant, Millbrae, CA (US); WO O3,024987 A1 3, 2003 Anthony S. Guerrero, San Francisco, WO 2005/066211 A2 7/2005 CA (US); Peter Haverty, San Francisco, WO WO 2005,115363 12/2005 CA (US); Cynthia Honchell, San WO 2006/110581 A2 10, 2006 Francisco, CA (US); Kenneth Jung, San WO 2007 134210 A2 11/2007 Francisco, CA (US); Thomas Wu, San OTHER PUBLICATIONS Francisco, CA (US) Gura (Science, 1997, 278: 1041-1042).* (73) Assignee: Genentech, Inc., South San Francisco, Kaiser (Science, 2006, 313: 1370).* CA (US) Gura (Science, 1995, 270:575-577).* Tockman et al (Cancer Res., 1992, 52:271 1s-2718s).* (*) Notice: Subject to any disclaimer, the term of this Pritzker (Clinical Chemistry, 2002, 48: 1147-1150).* patent is extended or adjusted under 35 Bai et al. (Cancer Res., 2010, 70: 7630-9).* Otte et al. (European Journal of Clinical Investigation, 2000, 30, U.S.C. 154(b) by 902 days. 222-229).* Yoshino et al. (Oncology Reports 2005, 13: 247-252).* (21) Appl. No.: 12/300,156 Eswarakumar et al., “Cellular signaling by fibroblast growth factor receptors' Cytokine Growth Factor Rev. 16(2): 139-49 (Apr. 2005). (22) PCT Filed: May 11, 2007 Miki et al., “Determination of ligand-binding specificity by alterna tive splicing: two distinct growth factor receptors encoded by a single (86).
    [Show full text]
  • Piperazine Are Toll-Like Receptor 4 Antagonists, Inhibit NF-Κb Activation, and Decrease TNF-Alpha Secretion in Primary Microglia
    Journal Pre-proof Mitoxantrone, pixantrone and mitoxantrone (2-hydroxyethyl)piperazine are toll-like receptor 4 antagonists, inhibit NF-κB activation, and decrease TNF-alpha secretion in primary microglia Maiju Rinne Conceptualization; Data curation; Formal analysis; Investigation; Methodology; Supervision; Validation; Visualization; Roles/Writing - original draft; Writing - review & editing , Kert Matlik¨ Conceptualization; Data curation; Formal analysis; Investigation; Methodology; Supervision; Writing - review & editing , Tiina Ahonen Formal analysis; Validation; Writing - review & editing , Fabio Vedovi Conceptualization; Data curation; Formal analysis; Visualization; Roles/Writing - original draft; Writing - review & editing , Giovanni Zappia Conceptualization; Funding acquisition; Supervision; Writing - review & editing , Vaniaˆ M. Moreira Conceptualization; Funding acquisition; Supervision; Writing - review & editing , Jari Yli-Kauhaluoma Conceptualization; Funding acquisition; Project administration; Supervision; Writing - review & editing , Sakari Leino Formal analysis; Investigation; Visualization; Writing - review & editing , Outi Salminen Formal analysis; Investigation; Visualization; Writing - review & editing , Eija Kalso Conceptualization; Funding acquisition; Project administration; Supervision; Writing - review & editing , Mikko Airavaara Conceptualization; Funding acquisition; Investigation; Methodology; Project administration; Resources; Supervision; Visualization; Roles/Writing - original draft; Writing - review &
    [Show full text]
  • Precision Medicine in Pediatric Neurooncology: a Review
    UCLA UCLA Previously Published Works Title Precision Medicine in Pediatric Neurooncology: A Review. Permalink https://escholarship.org/uc/item/6tv2b43d Journal ACS chemical neuroscience, 9(1) ISSN 1948-7193 Authors Mochizuki, Aaron Y Frost, Isaura M Mastrodimos, Melina B et al. Publication Date 2018 DOI 10.1021/acschemneuro.7b00388 Peer reviewed eScholarship.org Powered by the California Digital Library University of California HHS Public Access Author manuscript Author ManuscriptAuthor Manuscript Author ACS Chem Manuscript Author Neurosci. Author Manuscript Author manuscript; available in PMC 2019 July 24. Published in final edited form as: ACS Chem Neurosci. 2018 January 17; 9(1): 11–28. doi:10.1021/acschemneuro.7b00388. Precision Medicine in Pediatric Neurooncology: A Review Aaron Y. Mochizuki∥, Isaura M. Frost∥, Melina B. Mastrodimos∥, Ashley S. Plant┴, Anthony C. Wang#, Theodore B. Moore∥, Robert M. Prins#,∇,○, Paul S. Weiss*,†,‡,§,∇, Steven J. Jonas*,†,∥,◆,¶ †California NanoSystems Institute, University of California, Los Angeles, Los Angeles, California 90095, United States ‡Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, California 90095, United States §Department of Materials Science and Engineering, University of California, Los Angeles, Los Angeles, California 90095, United States ∥Department of Pediatrics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California 90095, United States ┴Division of Pediatric Oncology, Children’s Hospital of Orange
    [Show full text]
  • Patent Application Publication ( 10 ) Pub . No . : US 2019 / 0192440 A1
    US 20190192440A1 (19 ) United States (12 ) Patent Application Publication ( 10) Pub . No. : US 2019 /0192440 A1 LI (43 ) Pub . Date : Jun . 27 , 2019 ( 54 ) ORAL DRUG DOSAGE FORM COMPRISING Publication Classification DRUG IN THE FORM OF NANOPARTICLES (51 ) Int . CI. A61K 9 / 20 (2006 .01 ) ( 71 ) Applicant: Triastek , Inc. , Nanjing ( CN ) A61K 9 /00 ( 2006 . 01) A61K 31/ 192 ( 2006 .01 ) (72 ) Inventor : Xiaoling LI , Dublin , CA (US ) A61K 9 / 24 ( 2006 .01 ) ( 52 ) U . S . CI. ( 21 ) Appl. No. : 16 /289 ,499 CPC . .. .. A61K 9 /2031 (2013 . 01 ) ; A61K 9 /0065 ( 22 ) Filed : Feb . 28 , 2019 (2013 .01 ) ; A61K 9 / 209 ( 2013 .01 ) ; A61K 9 /2027 ( 2013 .01 ) ; A61K 31/ 192 ( 2013. 01 ) ; Related U . S . Application Data A61K 9 /2072 ( 2013 .01 ) (63 ) Continuation of application No. 16 /028 ,305 , filed on Jul. 5 , 2018 , now Pat . No . 10 , 258 ,575 , which is a (57 ) ABSTRACT continuation of application No . 15 / 173 ,596 , filed on The present disclosure provides a stable solid pharmaceuti Jun . 3 , 2016 . cal dosage form for oral administration . The dosage form (60 ) Provisional application No . 62 /313 ,092 , filed on Mar. includes a substrate that forms at least one compartment and 24 , 2016 , provisional application No . 62 / 296 , 087 , a drug content loaded into the compartment. The dosage filed on Feb . 17 , 2016 , provisional application No . form is so designed that the active pharmaceutical ingredient 62 / 170, 645 , filed on Jun . 3 , 2015 . of the drug content is released in a controlled manner. Patent Application Publication Jun . 27 , 2019 Sheet 1 of 20 US 2019 /0192440 A1 FIG .
    [Show full text]
  • Phenotype-Based Drug Screening Reveals Association Between Venetoclax Response and Differentiation Stage in Acute Myeloid Leukemia
    Acute Myeloid Leukemia SUPPLEMENTARY APPENDIX Phenotype-based drug screening reveals association between venetoclax response and differentiation stage in acute myeloid leukemia Heikki Kuusanmäki, 1,2 Aino-Maija Leppä, 1 Petri Pölönen, 3 Mika Kontro, 2 Olli Dufva, 2 Debashish Deb, 1 Bhagwan Yadav, 2 Oscar Brück, 2 Ashwini Kumar, 1 Hele Everaus, 4 Bjørn T. Gjertsen, 5 Merja Heinäniemi, 3 Kimmo Porkka, 2 Satu Mustjoki 2,6 and Caroline A. Heckman 1 1Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki; 2Hematology Research Unit, Helsinki University Hospital Comprehensive Cancer Center, Helsinki; 3Institute of Biomedicine, School of Medicine, University of Eastern Finland, Kuopio, Finland; 4Department of Hematology and Oncology, University of Tartu, Tartu, Estonia; 5Centre for Cancer Biomarkers, De - partment of Clinical Science, University of Bergen, Bergen, Norway and 6Translational Immunology Research Program and Department of Clinical Chemistry and Hematology, University of Helsinki, Helsinki, Finland ©2020 Ferrata Storti Foundation. This is an open-access paper. doi:10.3324/haematol. 2018.214882 Received: December 17, 2018. Accepted: July 8, 2019. Pre-published: July 11, 2019. Correspondence: CAROLINE A. HECKMAN - [email protected] HEIKKI KUUSANMÄKI - [email protected] Supplemental Material Phenotype-based drug screening reveals an association between venetoclax response and differentiation stage in acute myeloid leukemia Authors: Heikki Kuusanmäki1, 2, Aino-Maija
    [Show full text]