Quantitative Modeling and Analysis of Drug Screening Data for Personalized Cancer Medicine
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UNIVERSITY OF HELSINKI FACULTY OF MEDICINE QUANTITATIVE MODELING AND ANALYSIS OF DRUG SCREENING DATA FOR PERSONALIZED CANCER MEDICINE Lenalidomide Momelotinib Imiquimod Tacedinaline Tofacitinib Temsirolimus Ruxolitinib Roscovitine Tofacitinib Entinostat Gefitinib RoscovitineRuxolitinib Refametinib Pimasertib Sirolimus Selumetinib Tandutinib EverolimusNilotinib PF−04691502 NVP−BEZ235 Trametinib Palbociclib Melphalan Belinostat CUDC−101 AZD8055 Erlotinib Gefitinib Momelotinib Tacedinaline Cediranib SNS−032 Levamisole PF−04691502 Motesanib Panobinostat NVP−AUY922 Palbociclib OSI−027 Erlotinib OSI−027 Axitinib BIIB021 Imatinib Alvespimycin Masitinib TemsirolimusVorinostat Ponatinib Sunitinib Dasatinib Tanespimycin Melphalan Sorafenib Masitinib Pazopanib Sirolimus NVP−SNSAUY922−032 Vatalanib Dasatinib Regorafenib Everolimus Sunitinib MGCD−265 Panobinostat Tivozanib AZD8055 Foretinib NVP−BEZ235 Regorafenib Entinostat Prednisolone Tivozanib Dexamethasone Vincristine Cediranib Belinostat Methylprednisolone Pazopanib CUDC100−101 100 Vinblastine 100 100 Refametinib −40−20 0 20 40 Vatalanib Vorinostat Pimasertib MK1775 Sorafenib MGCD−80265 80 Trametinib ABT−751 80 80 Vandetanib Logistic Selumetinib Axitinib Alvespimycin DSS Triethylenemelamine S−trityl−L−cysteine 60 function3 60 DSS / sDSS 60 AA 60 Canertinib Tanespimycin Mechlorethamine Camptothecin Crizotinib Ponatinib Mitomycin C Paclitaxel Fingolimod 40 IC50 40 Lapatinib Calculation 40 40 Afatinib Imiquimod Patupilone AddictionDrug response score 100 Slope Chlorambucil DSS Tandutinib BIIB021 40 Uracil mustard Topotecan Nilotinib 20 Rmin 20 20 20 Selective DSS Irinotecan Nitrogen mustard Motesanib Relative inhibition (%) Relative inhibition (%) R Pipobroman Irinotecan Relative inhibition (%) Relative inhibition (%) Response in patient sample max Low High Lapatinib Topotecan Thiotepa Docetaxel Dovitinib 0 Streptozocin0 AT9283 0 0 Response in control 80 Vincristine Cyclophosphamide Chlorambucil −9−8 −7 −6−5 20 −9−8 −7 −6−5 −9−8 −7 −6−5 −9−8 Alvocidib−7 −6−5 Patupilone Daunorubicin Mitomycin C Drug Concentration (log M) Mitoxantrone Drug Concentration (log M) Vinorelbine Drug Concentration (logM) Drug Concentration (logM) Paclitaxel Valrubicin Triethylenemelamine Teniposide LevamisoleBI 2536 Mechlorethamine DocetaxelAT9283 Idarubicin Uracil mustard Drug dose response Doxorubicin Curve fitting and Pipobroman 60 Vandetanib Drug Sensitivity scoreMitoxantrone (DSS) and Amonafide Barasertib Barasertib Idarubicin BI 2536 0 Imatinib Etoposide Teniposide MK1775 Danusertib Etoposide Valrubicin Daunorubicin Vinorelbine Doxorubicin δ profiling in a sample score parameter extraction Selective DSS calculation Foretinib Vinblastine Danusertib 40 ABT−751 Alvocidib Camptothecin Dovitinib Afatinib −20 Crizotinib Thiotepa Canertinib Fingolimod Prednisolone Dexamethasone Streptozocin Amonafide Methylprednisolone Lenalidomide 20 SelectiveTriethylenemelamine DSS S−trityl−L−cysteine −40 Relative inhibition (%) Response in patient sample Cyclophosphamide Nitrogen mustard Addiction score Doxorubicin Selective DSS 0 10 Response inMitomycin control C Valrubicin 2500 Dovitinib Lenalidomide Melphalan −9−8 −7 −6−5 Low High Sensitive Insensitive Sensitive Afatinib 625 Drug ConcentrationAmonafideCrizotinib (logM) cdk inhibitors 0.8 5 Vandetanib Cyclophosphamide Everolimus (nM) Daunorubicin 156.2 Idarubicin Fingolimod Teniposide Imiquimod 50 Foretinib JAK inhibitors Mitoxantrone Canertinib Sunitinib Sorafenib 12.5 Streptozocin Cediranib 39.1 0 Patupilone MEK inhibitors3.1 NitrogenChlorambucil mustard Vincristine Docetaxel Paclitaxel9.8 0.8 0.6 Barasertib HDAC inhibitors Lapatinib Danusertib 0.2 Selective DSS −Pipobroman5 Etoposide Ibrutinib (nM) Insensitive S−trityl−L−cysteine Mitotic inhibitors Selumetinib Irinotecan Pimasertib Vinorelbine HSP90 inhibitors Methylprednisolone 391 032 866 027 ABT−751 − 0980 − − − − Vinblastine AT9283 BIIB021 0.4 PX Alisertib BEZ235 AUY922 Indibulin Dovitinib Sunitinib KX2 OSI Dexamethasone Alvocidib − AZD8055 − Tivozanib SNS Idarubicin RDEA119 04691502 Entinostat AZD 7762 AT9283 Obatoclax Alkylating agents Daporinad Vincristine Vincristine Mepacrine Decitabine Plicamycin Plicamycin Vinblastine Fingolimod Vinorelbine GDC − VER 155008 Mocetinostat Camptothecin Alvespimycin Dactinomycin GSK2126458 Tanespimycin Prednisolone EMD1214063 NVP PF NVP Controls Patient samples Galiellalactone Topotecan MGCD−265 OSI−027 Immunomodulators MotesanibSelective drugs for a samplepIC50 PonatinibSelective drug response profiles Rand index Selective targets Tofacitinib BI 2536 ErbB family inhibitors Tandutinib for samples for a sample Levamisole Palbociclib 0.2 Nilotinib Abl/III/VEGFR inhibitors MechlorethamineMK1775 Ruxolitinib Thiotepa Roscovitine Topoisomerase I inhibitors NVP−AUY922Gefitinib Tacedinaline Erlotinib Entinostat Panobinostat Masitinib Topoisomerase II inhibitors Trametinib Dasatinib 0 Refametinib SNS−032 Momelotinib Alvocidib Uracil mustard DSS3 AA pIC50 Regorafenib mTOR/PI3K kinase inhibitors Axitinib Tivozanib Pazopanib Imatinib Vatalanib CUDCBelinostat−101 VorinostatBIIB021 Sirolimus Tanespimycin Alvespimycin AZD8055 Temsirolimus Everolimus PF−04691502 NVP−BEZ235 BHAGWAN YADAV Helsinki 2017 ISBN 978-951-51-2965-9 Institute for Molecular Medicine Finland, FIMM Doctoral School in Health Sciences (DSHealth) Doctoral Program in Biomedicine (DPBM) University of Helsinki, Finland Quantitative modeling and analysis of drug screening data for personalized cancer medicine Bhagwan Yadav ACADEMIC DISSERTATION To be presented, with the permission of the Faculty of Medicine, University of Helsinki, for public examination in Lecture Hall 2, Biomedicum Helsinki on Friday, March 24, 2017, at 12 o’clock noon. HELSINKI, 2017 Supervised by: Prof. Tero Aittokallio, Ph.D. FIMM-EMBL Group Leader Institute for Molecular Medicine Finland, FIMM University of Helsinki Helsinki, Finland and Dr. Krister Wennerberg, Ph.D. FIMM-EMBL Group Leader Institute for Molecular Medicine Finland, FIMM University of Helsinki Helsinki, Finland Reviewed by: Dr. Jaakko Hollmén Aalto University School of Science Department of Computer Science Espoo, Finland and Dr. Petri Auvinen Institute of Biotechnology University of Helsinki Helsinki, Finland Official Opponent: Prof. Mats Gustafsson, Ph.D. Department of Medical Sciences Uppsala University Uppsala, Sweden © Bhagwan Yadav Cover layout by Bhagwan Yadav Cover image by Bhagwan Yadav ISBN 978-951-51-2965-9 (paperback) ISBN 978-951-51-2966-6 (PDF) http://ethesis.helsinki.fi/ Helsinki 2017 Coming together is a beginning; keeping together is progress; working together is a success. – Henry Ford Do the difficult things while they are easy and do the great things while they are small. A journey of a thousand miles must begin with a single step. –Laozi Dedicated to my parents and family “Family is the most important thing in the world.” – Princess Diana TABLE OF CONTENTS ABBREVIATIONS ..................................................................................................... 5 ORIGINAL PUBLICATIONS .................................................................................... 7 ABSTRACT ................................................................................................................ 8 INTRODUCTION .................................................................................................... 10 LITERATURE REVIEW .......................................................................................... 13 1. Cancer as a complex disease ............................................................................ 13 2. Personalized medicine: The future of cancer medicine .................................... 15 3. Functional compound screening ....................................................................... 16 4. Quantification of compound activity ................................................................ 17 5. Computational target deconvolution ................................................................ 21 6. Compound synergy scoring .............................................................................. 24 AIMS OF THE STUDY ........................................................................................... 29 MATERIALS AND METHODS .............................................................................. 30 7. Study specimens ............................................................................................... 30 8. Drug sensitivity and resistance testing (DSRT) ............................................... 32 9. Development of drug response models ............................................................ 33 9.1. Drug sensitivity scoring (DSS) ...................................................................... 33 9.2. Target addiction scoring (TAS) ...................................................................... 34 9.3. Drug combination scoring ............................................................................. 35 RESULTS .................................................................................................................. 37 10. Drug sensitivity scoring (DSS) ...................................................................... 38 11. Target addiction scoring (TAS) ...................................................................... 42 12. Delta scoring of drug combinations ............................................................... 44 13. Application to AGCT data ............................................................................. 47 DISCUSSION ..........................................................................................................