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Bioinformatic Identification of Disease Driver Networks Using Functional Profiling Data

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Bioinformatic Identification of Disease Driver Networks Using Functional Profiling Data Institute for Molecular Medicine Finland, FIMM University of Helsinki, Helsinki, Finland Doctoral Programme in Biomedicine (DPBM) BIOINFORMATIC IDENTIFICATION OF DISEASE DRIVER NETWORKS USING FUNCTIONAL PROFILING DATA Agnieszka Szwajda ACADEMIC DISSERTATION To be presented, with the permission of the Faculty of Medicine, University of Helsinki, for public examination in lecture hall 3, Biomedicum Helsinki, Haartmaninkatu 8, on Friday 2nd of February 2018, at 12 noon. Helsinki, 2018 Supervised by: Professor Tero Aittokallio, Ph.D. Institute for Molecular Medicine Finland, FIMM University of Helsinki Helsinki, Finland Krister Wennerberg, Ph.D. Institute for Molecular Medicine Finland, FIMM University of Helsinki Helsinki, Finland Reviewed by: Associate Professor Harri Lähdesmäki, D.Sc. Department of Computer Science Aalto University School of Science Espoo, Finland Associate Professor Henri Xhaard, Ph.D. Division of Pharmaceutical Chemistry and Technology University of Helsinki Helsinki, Finland Opponent: Professor Matti Nykter, Ph.D. Institute of Biosciences and Medical Technology University of Tampere Tampere, Finland Custos: Professor Samuli Ripatti, Ph.D. Institute for Molecular Medicine Finland, FIMM University of Helsinki Helsinki, Finland © Agnieszka Szwajda Cover layout by Anita Tienhaara ISBN 978-951-51-3959-7 (paperback) ISBN 978-951-51-3960-3 (PDF) ISSN 2342-3161 (print) ISSN 2342-317X (online) Unigrafia Oy, Helsinki 2018 2 Table of contents ABBREVIATIONS ....................................................................................................................................... 5 LIST OF ORIGINAL PUBLICATIONS ............................................................................................................ 6 AUTHOR’S CONTRIBUTIONS .................................................................................................................... 7 ABSTRACT ................................................................................................................................................ 8 1. INTRODUCTION ................................................................................................................................. 10 2. REVIEW OF LITERATURE .................................................................................................................... 12 2.1. Methods for the identification of disease drivers (II, III) ............................................................ 12 2.2. Oncogene addiction and other molecular vulnerabilities in cancer (II) ..................................... 13 2.3. Functional proteomics using mass spectrometry (III, IV) ........................................................... 14 2.3.1. Quantitative MS ................................................................................................................... 15 2.3.2. Affinity Purification (AP) coupled with MS .......................................................................... 15 2.3.3. Limitation of MS-approaches .............................................................................................. 15 2.4. Protein-protein interaction networks (III, IV) ............................................................................. 16 2.5. Protein phosphorylation and nitrosylation (II, IV) ...................................................................... 17 2.6. Chemical and genetic perturbation screening (II) ...................................................................... 18 2.7. Quantitative scoring of single and combinatorial drug responses (II) ........................................ 20 2.8. Compound-target selectivity profiling (I, II) ............................................................................... 21 2.9. Background of diseases used in the studies (II, IV) .................................................................... 23 2.9.1. Breast cancer ....................................................................................................................... 23 2.9.2. Alzheimer’s disease ............................................................................................................. 24 3. AIMS OF THE STUDY .......................................................................................................................... 25 4. MATERIALS AND METHODS ............................................................................................................... 26 4.1. Comparison of kinase inhibitor profiling datasets (I) ................................................................. 26 4.2. Evaluation of reproducibility and reliability of kinase inhibitor profiling datasets (I) ................ 26 4.3. Integration of the target bioactivity measurements (I) .............................................................. 27 4.4. Kinase inhibition sensitivity score (KISS) for prediction of molecular cancer addictions (II) ..... 27 4.5. Combinatorial KISS for prediction of co-essential target pairs and synergistic drug combinations (II) ...................................................................................................................................................... 27 4.6. Compound sensitivity testing (II) ................................................................................................ 28 4.7. Compound-target mappings (II) ................................................................................................. 28 4.8. Comparison of driver de-convolution methods (II) .................................................................... 29 4.9. Independent compound and siRNA testing to validate KISS predictions (II).............................. 30 4.10. Construction of addiction networks (II) .................................................................................... 30 4.11. Generation of PME-1 interactome using MS/AP (III) ................................................................ 31 4.12. Relevance rank platform (RRP) for predicting functional similarity (III) ................................... 31 3 4.13. Identification of nitrosylated proteins (IV) ............................................................................... 33 4.14. Functional analysis of the proteomic data (IV) ......................................................................... 33 5. RESULTS ............................................................................................................................................. 34 5.1. Comparative evaluation and integration of target selectivity profiles (I) ................................... 34 5.1.1. Comparative analysis of the compound bioactivity studies ................................................ 34 5.1.2. Evaluation of the reliability of the compound bioactivity studies ....................................... 34 5.1.3. Integration of kinase inhibitor bioactivity data ................................................................... 35 5.2. KISS for prediction of single and combinatorial cancer drivers (II)............................................. 35 5.2.1. Comparison of KISS with other driver deconvolution approaches...................................... 36 5.2.2. Comparison of KISS and transcriptomic profiles ................................................................. 37 5.2.3. KISS-predicted kinase addictions showed consistency between independent drug collections ...................................................................................................................................... 37 5.2.4. KISS-based prediction of pharmacologically actionable drivers in breast cancer cell lines 37 5.2.5. Predicted signal addictions form cell line specific driver networks .................................... 38 5.2.6. Combinatorial KISS prediction of co-essential target pairs and synergistic drug combinations ................................................................................................................................. 39 5.3. RRP analysis of PIN1 and PME-1 interactome (III) ...................................................................... 39 5.3.1. Functional similarity ranking of PIN1 interactome .............................................................. 39 5.3.2. Functional similarity ranking of AP-MS-derived PME-1 interactome .................................. 41 5.4. Functional analysis of S-nitrosylated proteins (IV) ..................................................................... 42 6. DISCUSSION AND CONCLUSIONS....................................................................................................... 44 7. ACKNOWLEDGEMENTS...................................................................................................................... 49 8. REFERENCES....................................................................................................................................... 51 4 ABBREVIATIONS AD – Alzheimer’s disease AP – affinity purification AP-MS – affinity purification coupled with mass spectrometry APP-amyloid precursor protein CML- chronic myeloid leukemia DSS – drug sensitivity score EC50 - half-maximal effective concentration ER – estrogen receptor GSEA – gene set enrichment analysis HSA - highest singe agent IC50 – half-maximal inhibitory concentration Kd – dissociation constant Ki – inhibition constant KIBA - kinase inhibitor bioactivity KISS - kinase inhibition sensitivity score KS – Kolmogorov-Smirnoff MS-mass spectrometry NGS – next generation sequencing NO – nitrogen oxide PPI- protein-protein interaction
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