Development of Combination Therapy for Prostate Cancer

DEVELOPMENT OF COMBINATION THERAPY FOR PROSTATE CANCER A Dissertation Presented to the Faculty of the Weill Cornell Graduate School of Medical Sciences in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy by Alexander R. Root July 2017 © 2017 Weill Cornell Graduate School of Medical Sciences ALL RIGHTS RESERVED DEVELOPMENT OF COMBINATION THERAPY FOR PROSTATE CANCER Alexander R. Root, Ph.D. Cornell University 2017 There is an unmet need in prostate cancer for effective therapies to prevent the emergence of resistance. Combinations of small molecules targeting key pathways are a promising strategy. I investigated how populations of the early metastatic prostate cancer cell line LNCaP respond at the proteomic and phenotypic levels to six clinically-relevant, one-drug treatments and their 15 pairs of two-drug combinations, administered simultaneously to treatment-naive cells. After 24 hours of drug addition for all 21 drug treatments I measured 52 total- proteins by selected-reaction monitoring mass-spectrometry based proteomics (SRM), 20 phospho-proteins, and 50 total-proteins by reverse-phase protein ar- rays (RPPA). I measured phenotypic effects on cell proliferation and apoptosis in all conditions using phase-contrast and fluorescence microscopy. Network analysis identified (phospho)-proteins with large responses to drug treatments that are druggable with FDA-approved drugs or have nearest-neighbors that are druggable. A total of ten drugs targeting these nearest responder (phospho)-proteins were tested in single, double, and triple combinations. I found that 7 out of 10 triple combinations co-targeting androgen receptor and PI3K pathways were no more effective than the two-drug combination at the doses tested: PRKC (en- zastaurin), MAPK (losmapimod), STAT3 (napabucasin), HDAC (panobinostat), SRC (saracatinib), casein kinase (silmitasertib), MAPK (ulixertinib). I was un- able to determine the relative efficacy of aurora kinase B (barasertib), 14-3-3 in- hibor (BV02) and their combinations. For one drug tested (dinaciclib, CDK1/2 inhibitor) I found increased efficacy with PI3K signaling inhibition (MK2206, AKT1/2 inhibitor). I propose a simple model where blocking PI3K signaling in LNCaP cells causes a partial inhibition of the cell cycle G1/S transition through hypo- phosphorylation of RB and hypo-phosphorylation of CDK2 at the G2/M transition. These effects can be enhanced by co-targeting PI3K signaling and drivers of the cell cycle using the CDK1/2 inhibitor dinaciclib. I also investigated how proteins that interact with the androgen receptor change in response to perturbation in a prostate cancer context (VCaP cell line). I identified 111 proteins with affinity purification mass spectrometry (AP-MS). These proteins have diverse functions and many have not been reported in the literature. I found that treatment with an antagonist (enzalutamide) has mini- mal effects on the interactors. Interactors such as metadherin (MDTH) and chro- mosome transmission fidelity factor 8 (CHTF8) are recurrently altered in patient cohorts. Metadherin is recurrently amplified and may represent a promising drug target. BIOGRAPHICAL SKETCH My undergraduate major was in biomedical engineering, which included an additional specialization, sub-major in tissue engineering. As an undergraduate, I performed research in the laboratory of Eduardo Macagno on mechanisms of neuronal growth and arbor formation using a quantitative approach. During the 2nd Iraq and Afghanistan wars I pursued public service as a mathematics teacher and worked in public schools where all students came from households below the federal poverty line. My thesis research in the laboratory of Chris Sander involved preclinical systems pharmacology and systems biology. I worked on the development of combination therapy for early stage metastatic prostate cancer using a combined wet- and dry-lab approach. My thesis research included training and practice in experimental methods and mathematical modeling. I performed the majority of work in steps 1-5 with the exception of the targeted proteomics, which was performed by collaboration with Ruedi Aebersold’s laboratory. In addition to my graduate coursework, I received training in cell culture and pharmacology assays from members of the Sander laboratory and the Molecular Cytology and Microscopy core facility; training in targeted proteomics from a rotation in the laboratory of Paul Tempst and several visits to the laboratory of Ruedi Aeber- sold; and training in network modeling from members of the Sander laboratory. I am interested in continuing research on the development of combination therapy in post-doctoral work along several lines: (1) use of newer model systems such prostate organoids; (2) use of additional model systems to assess drug combination toxicity, such as primary human hepatocytes; (3) better selection and measurement of protein targets through methods development; (4) mea- suring drug response over multiple time points; (5) extending network model- iii ing approaches to incorporate this temporal data; (6) greater use of automation technology to improve sample throughput. I anticipate the greatest challenges and unmet needs are (3) better selection and measurement of protein targets through methods development in targeted proteomics. iv Dedicated to Paul Dyckman for his inspiring teaching. v ACKNOWLEDGEMENTS Chris Sander for mentorship, training, and inspiration. H. Alexander Ebhardt for training. Members of the Sander and Aebersold laboratories for helpful questions and comments. Thesis committee members including Mark Rubin and Paul Tempst for training and constructive criticism. Physiology, Biophysics, and Systems Biology department members for education. Laboratory rotation supervisors Chris Sander, Paul Tempst, Joan Massague, Francesca Demichelis, and Olivier Elemento for training and mentorship. vi TABLE OF CONTENTS Biographical Sketch . iii Dedication . .v Acknowledgements . vi Table of Contents . vii List of Abbreviations . ix List of Symbols . ix List of Tables . .x List of Figures . xii 1 Introduction: Designing Combination Therapy for Prostate Cancer 1 1.1 Why combination therapy? . .1 1.2 The perturbation biology approach . .2 1.2.1 Hypothesis . .4 1.2.2 Aims . .4 2 Methods - Design of Perturbations, Measurements and Analysis 7 2.1 Perturbations . .7 2.1.1 LNCaP cell line model . .7 2.1.2 Drug selection and treatments . .8 2.2 Measurement methods: phenotypic and proteomics quantitation of drug responses . 10 2.2.1 Microscopy methods for phenotypic responses to drugs . 10 2.2.2 Targeted proteomics methods for drug effects on proteins and phosphosites . 11 2.3 Network analysis methods, predictions, and assessment . 18 2.3.1 Nominating novel drug combinations with network analysis . 18 2.3.2 Methods for testing network analysis predictions with phenotypic measurements of cell death . 19 2.3.3 Methods for testing network analysis predictions with molecular measurements by discovery proteomics . 21 3 Results - Quantifying Molecular and Phenotypic Responses to Drug Perturbations 24 3.1 Establishment of SRM assays . 24 3.2 Phenotypic and proteomic responses to perturbations . 26 3.3 Network analysis and predictions . 26 4 Results & Discussion - Strategy 1: Co-targeting PI3K Signaling and Cell Cycle 30 4.1 Testing predictions uncovers a PI3K signaling & cell cycle inhibition strategy . 30 4.2 Discussion on PI3K signaling & cell cycle inhibition strategy . 30 vii 5 Results & Discussion - Strategy 2: Co-targeting 14-3-3 Proteins in Com- bination with AKT and AR 34 5.1 Testing predictions uncovers a 14-3-3 PI3K, and AR inhibition strategy . 34 5.2 Discussion on 14-3-3, PI3K, and AR inhibition strategy . 35 6 Alternative Approach: Identifying Androgen Receptor Interacting Proteins 43 6.1 Introduction - AR-interacting proteins may be good drug targets 43 6.2 Methods - identification of AR-interacting proteins in a cellular context relevant to prostate cancer with AP-MS . 45 6.2.1 Cell culture and drug treatments . 45 6.2.2 Affinity Purification Mass Spectrometry . 47 6.3 Results - identification of AR interactors and effects of drug perturbations . 50 6.4 Discussion . 55 7 Conclusion & Future Directions for Prostate Cancer Combination Therapy 59 7.1 Targeted proteomics and perturbation biology are a promising approach to develop combination therapy . 59 7.2 A diversity of approaches to develop combination therapy should continue to be pursued . 61 A Appendix - Review Article Reprint: Applications of Targeted Pro- teomics in Systems Biology and Translational Medicine 64 B Appendix - Note on collaborations and authorship 81 C Appendix - Mathematical modeling of drug effects 82 C.1 Partial least-squares regression model . 83 C.1.1 Introduction & Methods . 83 C.1.2 Results . 85 C.2 Undirected network model with partial correlations . 87 C.3 Coupled-system of nonlinear ordinary differential equations . 88 Bibliography 98 viii LIST OF ABBREVIATIONS Instruments & techniques AP affinity purification MS mass spectrometry or mass spectrometer AP-MS affinity-purification mass spectrometry GEM genetically engineered mice MS/MS tandem mass spectrometry SRM selected reaction monitoring RPPA reverse-phase protein array SVD singular value decomposition PLSR partial least-squares regression BP belief propagation First perturbation drug set DAS dasatinib, SRC inhibitor DMSO dimethyl sulfoxide, drug vehicle DOC docetaxel, blocks microtubule depolymerization ENZ enzalutamide, AR inhibitor MDV MDV3100, AR

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