Phenotypic Screening of Parthenolide Derivatives Reveals The

Home , MMB (cipher), Parthenolide

PHENOTYPIC SCREENING OF PARTHENOLIDE DERIVATIVES REVEALS THE

CHEMOPROTECTIVE ROLE OF GALECTIN-1 IN ACUTE MYELOID LEUKEMIA

JESSICA NICOLE PONDER

B.S. Truman State University, 2010

M.S. University of Colorado, 2013

A thesis submitted to the

Faculty of the Graduate School of the

University of Colorado in partial fulfillment

of the requirements for the degree of

Doctor of Philosophy

Toxicology Program

2018

This thesis for the Doctor of Philosophy degree by

Jessica Nicole Ponder

has been approved for the

Toxicology Program

David Ross, Chair

Craig Jordan, Advisor

Peter Crooks

Brad Bendiak

Kristopher Fritz

Date: May 18, 2018

Ponder, Jessica Nicole Ph.D., Toxicology

Phenotypic Screening of Parthenolide Derivatives Reveals the Chemoprotective Role of

Galectin-1 in Acute Myeloid Leukemia

Thesis directed by Professor of Medicine Craig T. Jordan

ABSTRACT

Despite decades of investigation, the diagnosis of acute myeloid leukemia still carries with it a bleak prognosis, primarily as a result of the failure of clinical chemotherapy to target the leukemic stem cell population. In order to overcome this obstacle a library of derivatives of the small molecule parthenolide, a leukemic stem cell targeting natural product, were screened against acute myeloid leukemia (AML). Using the M9-ENL cell line as a high-throughput screening model for leukemic stem cells, over four hundred novel parthenolide derivatives were screened by flow cytometry for apoptotic activity. Using this method, more than forty compounds with improved efficacy against primary AML were identified, but the most remarkable compounds were those that were dimers of melampomagnolide B (MMB dimers), which had virtually no measurable toxicity to healthy blood stem and progenitor cells.

Despite their dramatic improvement in therapeutic index, these derivatives were only weakly associated with the leukemic stem cell targeting mechanism of the parent compound.

Pharmacological investigation utilizing a chemoproteomic approach reveals that, unlike parthenolide, these bivalent MMB dimers potently and rapidly induce the depletion of nuclear galectin-1, a chemoprotective protein that is found to be overexpressed by more than two orders of magnitude in leukemic stem cells at diagnosis and relapse. In primary AML, nuclear monomeric galectin-1 is depleted within fifteen minutes of exposure to a 4 µM dose of MMB dimer JVM 3-88A. This discovery is significant both because galectin-1 has not previously been

iii demonstrated to play a role in acute myeloid leukemia, and because to date, no clinical inhibitors of this emerging cancer target yet exist. Future investigations will seek to further understand the mechanism by which galectin-1 protects leukemic stem cells from apoptosis and how these compounds are able to alter its nuclear localization.

The form and content of this abstract are approved. I recommend its publication.

Approved: Craig T. Jordan

Jai Guru Deva 

ACKNOWLEDGEMENTS

The author would like to acknowledge Shanshan Pei, Nabilah Khan, Mohd Minhajuddin, and Biniam Adane of the Jordan lab for their invaluable contributions to this work, intellectually and materially. Venumadhav Janganati, Bommagani Shobanbabu, and Narsimha Reddy

Penthala, led by Peter Crooks at University of Arkansas for Medical Science, deserve the clear majority of credit for synthesis of the small molecules screened, as well as labeled small molecules used for chemoproteomic target identification. A few compounds synthesized by

Suresh Kuarm Bowroju and Soma Shekar Dachavaram are also included and compound initials

(i.e. JVM, BS, PNR, BSK, or DSS) indicate the identity of the organic chemist for each molecule. From the very first chapter and throughout the body of the text, the author hopes to have made clear to the reader the significance of the contributions of these synthetic chemists to this project. Additional credit goes to Monika Dzieciatkowska, of the University of Colorado

AMC Proteomic Mass Spectrometry Core and Michael Becker of the University of Rochester.

Specific experimental credits are embedded within the text where appropriate.

Additionally, this work was made possible by the financial contributions of the National

Institutes of Health R01CA158275. Additional funds were provided through the generous contribution of the UC Denver Anschutz Medical Campus Graduate School, in no small part due to recommendations by former Dean Barry Shur and professors Brad Bendiak and Vasilis

Vasiliou. The author is grateful for this financial support and the endorsement implied, without which this work would surely not have been possible.

Ultimately, it would be an indefensible oversight to neglect the leukemia patients and clinical research team responsible for maintaining the primary sample tissue bank used in this project. Without the willingness of patients and the dedication of physicians, nurses, and other

vi clinical and research staff to provide these samples and care for them, in many cases long after the donor has deceased, as researchers we would not have the ability to truly understand this tragic disease and how to fight it, no matter how advanced our technological resources. So, it is with humble but sincere gratitude that the author would like to recognize the patients as well as the clinical and research staff who provide this priceless resource. Only by partnering together will we ultimately be able to develop a cure for cancer.

vii

TABLE OF CONTENTS

CHAPTER

I. ACUTE MYELOID LEUKEMIA: CHALLENGES AND OPPORTUNITIES ...... 1

Introduction ...... 1

The Challenge of Acute Myeloid Leukemia ...... 4

Clinical acute myeloid leukemia ...... 4

Leukemic stem cells ...... 6

The Opportunity of Parthenolide...... 8

Summary ...... 10

II. HIGH-THROUGHPUT SCREENING OF PARTHENOLIDE DERIVATIVES ...... 11

High-Throughput Screening Platform ...... 11

High-Throughput Screening Results ...... 12

Dose-response curves and structures of derivatives ...... 12

Quantitative results ...... 14

Discussion ...... 14

Quantitative structure-activity relationships ...... 14

Simple combinations ...... 17

Trans-annular cyclized (TAC) derivatives ...... 18

C13-substituted (C13) derivatives ...... 18

C14-substituted (C14) derivatives ...... 18

Dimer derivatives ...... 26

viii

Conclusions ...... 29

Inactive modifications ...... 29

Potent modifications ...... 30

Methods ...... 30

Library handling and storage ...... 30

M9-ENL cell culture ...... 30

Apoptosis assay ...... 31

Quantitative results ...... 31

III. DISCOVERY OF AML-SELECTIVE MMB DIMERS ...... 33

Therapeutic Index Screening ...... 33

LSC-Selective Mechanism of Action ...... 34

Therapeutic Index Screening Results ...... 35

Primary acute myeloid leukemia ...... 35

Determination of therapeutic index ...... 36

AML comparison ...... 38

Antileukemic Mechanism of Action ...... 39

NF-κB inhibition...... 39

Oxidative stress...... 40

Discussion ...... 42

Primary AML and therapeutic index ...... 42

Selective drug mechanism ...... 44

Conclusions ...... 46

Methods ...... 47

Primary cell culture ...... 47

Apoptosis assay ...... 48

Flow cytometric labeling of primitive cell populations ...... 48

Western blot and electrophoretic mobility shift assay (EMSA) ...... 48

Quantification of total glutathione content ...... 49

Measurement of reactive oxygen species ...... 49

Quantitative real-time PCR ...... 50

IV. MMB DIMERS TARGET GALECTIN-1 TO OVERCOME CHEMORESISTANCE IN ACUTE MYELOID LEUKEMIA ...... 51

Chemoproteomic Screening in Drug Discovery ...... 51

Galectin-1: Structure, Function and Role in Cancer ...... 52

Structure and function of Galectin-1 ...... 53

Emerging role of Galectin-1 in cancer ...... 55

Results ...... 56

Identification of in situ protein binding targets ...... 56

Affinity tag selection...... 56

Affinity purification of protein binding targets ...... 58

Galectin-1 expression in AML and hematopoiesis ...... 59

shRNA knockdown of Galectin-1 ...... 63

Culture with rGalectin-1 ...... 64

MMB dimers induce rapid loss of Galectin-1 protein ...... 65

Discussion ...... 67

Identification of MMB dimer protein binding targets ...... 67

MMB dimers target Galectin-1 ...... 70

Conclusions ...... 76

Methods ...... 77

Cell culture and apoptosis assay ...... 77

Western blot and cellular fractionation ...... 77

Affinity purification of MMB dimer binding targets ...... 78

Binding target identification ...... 78

Sample preparation for mass spectrometric analysis ...... 78

Mass Spectrometry...... 79

Database searching, protein identification ...... 80

Bioinformatic filtration of binding targets ...... 81

shRNA knockdown of LGALS1 ...... 81

Quantitative Real-Time PCR ...... 82

V. SUMMARY AND CONCLUSIONS ...... 83

Phenotypic Screening ...... 83

Targeting Galectin-1 ...... 85

Conclusions ...... 86

REFERENCES ...... 89

APPENDIX ……………………………………………………………………………………...99

A. HTS OF PARTHENOLIDE DERIVATIVES ...... 99

B. MMB DIMER BINDING TARGETS IN AML IDENTIFIED BY PROTEOMIC MS .... 207

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CHAPTER I

ACUTE MYELOID LEUKEMIA: CHALLENGES AND OPPORTUNITIES

Introduction

Life, in its infinite complexity, can perhaps be described most simply as a series of challenges and opportunities. And it seems that whatever trivial effect we can have on choosing which challenges we will face, there remain plentiful opportunities to face them. This is rather fortunate in the field of medical research, given how often we must be able and willing to exhaust a copious number of those chances to overcome even the smallest of hurdles. The following work certainly illustrates this familiar phenomenon. But it also, if humbly so, endeavors to accelerate the ultimate clinical translation of knowledge gained through implementing a few crucial strategies during the earliest stages of the drug discovery process.

Historically, the process of drug discovery has faithfully followed advances in spectroscopic technology from the scale of the organism and the organ down to tissue levels and then delving into the minutiae of molecular interactions within cells and organelles. These advances have given us an unprecedented advantage in our ability to directly analyze the mechanisms of drugs and the diseases they treat on a molecular level. However, in an enthusiasm to utilize modern technology to its fullest, the field has in some ways lost touch with the fundamental purpose of our inquiry, i.e. the treatment of disease and alleviation of human suffering.

This is evident in the traditional process of rational drug design, in which efforts are focused on improving the physicochemical interactions of a drug and its purported target at the expense of addressing the multitude of biological barriers that exist in the cell, let alone the human patient. To be sure, technological advances in the structural analysis of proteins through

XRAY crystallography and NMR spectroscopy have delivered us into a magnificent age, in

which we can examine with fine detail the precise angles of the various physical attractions of the individual atoms that allow a disease to progress or a drug to function and use computational models to describe the dynamics of such interactions. These tools are certainly paramount to our understanding of the pharmacological mechanisms of medicine. But there is a more rewarding rate of return on effort spent when attention is also duly paid to the biochemical challenges of drug delivery sooner rather than later in the drug development process. In the field of oncology, especially, both patients’ unmet needs and high drug attrition rates present practical challenges, with a meager five percent of clinical trials in the United States ending in an FDA-approved therapy.1 If the failure rate for clinical trials is measured at ninety-five percent, we can reasonably assume that the failure rate of preclinical studies is at least that high. It therefore behooves all stakeholders to make a dedicated effort to address this failure, and that includes not only policy makers and pharmaceutical companies, but basic researchers as well. As costs increase rapidly with each additional phase of drug development, it follows that strategic adjustments on the bench can pay off most generously in terms of time, money, and if we are diligent, in quality of life for patients. To this end, this drug discovery project has implemented interdisciplinary deviations from the modern model of rational drug design to generate a body of knowledge that not only advances basic science but does so with improved clinical relevance.

Not least of which involves a return to classical pharmacology through investment in modern phenotypic screening.

Regardless of their ultimate route of administration in vivo, all anticancer therapeutics can be expected to encounter cellular membranes and their transporters. While much effort can be duly expended in maximizing the molecular attractions of a molecule and its intended target, quantitating binding constants and dissociation enthalpies from kinetic analyses, doing so

without proper regard to drug deliverability leads to consumption of already strained resources with little to no measurable benefit in the clinic. The phenotypic screening method of drug discovery begins in cells and as such, all downstream efforts are focused on cellularly active compounds. Moreover, the cellular disease models chosen for such screening efforts must balance demands for minimal resource consumption of high throughput screening with the maintenance of clinical relevance. For better or for worse, cell lines remain a necessary evil in drug discovery and the choice of which cell line to use can have a drastic impact on any drug screen. Clonal selection over time leads to genotypic and phenotypic drift in immortalized cells, so it is essential to carefully select a cell line that represents the biology of interest to the greatest degree possible. In the best-case scenario, data obtained from cell lines will be promptly verified in primary cells and/or clinical data at many stages of investigation to prevent straying from the disease to study artifacts of cell culture systems. The cell line utilized in this study for high- throughput screening of a library of parthenolide derivatives was selected for its ability to remain stem-cell-like in culture with human plasma and cytokines, resulting in a high transferability rate from cell line data to primary cells. Additionally, the cytotoxicity of compounds identified by the cell line screen is verified in primary cells and mechanistic investigations are conducted in primary cells as well, to prevent expending resources on pathways that occur exclusively in immortalized cell lines.

This method of analysis, while straightforward in reasoning, is not trivial in practice and relies upon an uninterrupted pipeline from the clinic to the bench. From clinicians who order diagnostic testing and the patients that provide the biological samples and consent necessary to advance the study of disease, to the researchers that handle clinical records and cryopreserve human samples for future studies into tissue banks, a high degree of cooperation and

communication between all the staff from bedside to bench is necessary to achieve a clinical tissue bank program that can successfully further our knowledge of human disease. This project has relied heavily on such coordination, and the discoveries made herein owe a great deal to the many hands involved in generating such an invaluable resource. As such, the results of this project underscore the necessity of high-quality primary tissue banks in basic and preclinical drug research.

The Challenge of Acute Myeloid Leukemia

Leukemia is the result of unchecked hematopoietic proliferation in the bone marrow, and can be classified as acute or chronic in presentation, as well as myeloid or lymphoid in origin.

Leukemia affects more than 1 in every 100 people at some point in their life.2 Among the four most common types of leukemia, acute myeloid leukemia (AML) stands out due to its bleak prognosis in adult patients.2-3 While treatment advances have made great strides for younger patients, a retrospective analysis reports that among 4000 patients over age 65 diagnosed with

AML between 1997 and 2007, those receiving chemotherapy had a one-year survival rate of

30%. For those who only received supportive care, survival was only 5% in the same time frame. Median survival in these groups was only 7 and 1.5 months, respectively. Overall, the total healthcare cost associated with these outcomes was over $96,000 per patient.4

Clinical acute myeloid leukemia

AML, like other leukemias, develops from aberrant hematopoiesis. The accumulation of immature hematopoietic cells, termed “blasts,” characterizes both acute myeloid leukemia and acute lymphoblastic leukemia. This overpopulation can be observed in peripheral blood as well as bone marrow. Immunophenotyping and cytogenetics are used to confirm the AML diagnosis, which represents approximately 75% of adult acute leukemias.3 While the etiology of AML

remains unknown, epidemiological studies have demonstrated DNA damage to be a significant risk factor in many forms. Genetic syndromes such as Down syndrome,5 occupational and medical radiation exposure,6-8 and occupational chemical exposure9-10 as well as exposure to

DNA-damaging chemotherapy6-8 are all known to increase the risk of AML development.

Certain chromosomal translocations and genetic mutations are also implicated in the development of AML. The “two-hit” model of leukemogenesis involves a theory in which two separate mutation events are required to initiate AML by activating proliferation (class I mutations, e.g. FLT3) and inhibiting hematopoietic differentiation (class II mutations, e.g.

NPM1).11 However, a new class of epigenetic mutations (e.g. IDH) have since emerged which affect both proliferation and differentiation, suggesting the genetic background of AML is more complex than the two-hit model.12-13

Patients with AML are usually recognized by symptoms directly related to the accumulation of blasts in the bone marrow and peripheral blood, including high white cell count, low platelet count, and anemia, as well as fatigue and anorexia.2 These symptoms escalate so quickly that, without treatment, patients are likely to succumb to infection or bleeding as a result of bone marrow failure within a few months of their initial diagnosis.2, 4 AML is a complex disease with a marked amount of heterogeneity both across patients and over time within the same patient, especially after they are exposed to chemotherapy.14 Treatment regimens are selected based on the patient’s individual history and risk factors, with as much as half of patients over the age of sixty-five not meeting criteria for chemotherapy.2, 15 The French-

American-British (FAB) classification system, introduced in 1976, was recently replaced by the

World Health Organization (WHO) scheme in 2016 which classifies AML into six broad categories based on specified features of genetics, morphology, immunophenotype, and clinical

presentation.13 The choice of chemotherapy, radiation, allogenic hematopoietic stem cell transplant, and use of targeted agents is highly dependent on the understanding of a patient’s prognosis through a full characterization of these descriptors. For eligible patients, induction therapy (typically a “7 + γ” regimen combining seven days of cytarabine as a continuous infusion with three days of anthracycline) usually achieves the clinically defined complete remission (CR, < 5% blasts in bone marrow), but fails to ablate the remaining disease, requiring consolidation therapy (high dose chemotherapy and/or stem cell transplant) to maintain the remission.13 Unfortunately, CR periods are typically short in duration (months) and relapses are so commonplace that clinicians refer to the first complete remission as CR1, the second as CR2, and so on. These almost inevitable relapses are attributed to the failure of chemotherapy to affect the population responsible for giving rise to the blast cells, known as leukemic stem cells

(LSCs).

Leukemic stem cells

Many decades of research have relied on bulk tumor burden as the sole disease model, leading to the poor clinical outcomes we have today. The “dandelion” theory of LSCs likens this population to the roots of a dandelion.16 Like chemotherapy for AML, mowing a field of dandelions may temporarily satisfy the eye by removing the obvious flower heads, but this method has an inherent flaw. Leaving the roots behind will only result in more flowerheads, and following each mowing event the flowerheads will only become more numerous, much like the brief CRs observed in clinical practice. More recently, dedicated investment into characterizing this population in our laboratory and elsewhere has demonstrated that there are a number of factors that add to the challenge of effectively treating this elusive population. However, we are also beginning to enter a new era of opportunity in cancer research where the targetable

differences between leukemic stem cells and hematopoietic stem cells are being described and beginning to show promise in clinical trials.17

While immunophenotyping LSCs based on surface markers has been shown to help delineate LSC-enriched populations, the LSC population is defined functionally by the ability to initiate leukemia when transplanted into immunodeficient mouse models.18 The proportion of

LSCs in a primary sample at diagnosis corresponds to poor prognosis, emphasizing the unmet clinical need of LSC-targeting agents.19 Further, the frequency of this population can increase by one or two orders of magnitude during relapses that follow administration of chemotherapy, and perhaps worse, the cell surface markers of the emboldened LSCs no longer correspond to those observed at diagnosis.14 Additionally, LSCs are believed to have the ability to escape chemotherapy by sharing more characteristics with hematopoietic stem cells (HSCs) than rapidly growing bulk tumor cells and by residing in the bone marrow. We know that their resistance to conventional chemotherapy stems from their quiescence, in that they are not actively replicating, they are not metabolically active, and they maintain a low level of total reactive oxygen species, contrary to bulk tumor analyses. To make matters worse, they have constitutively active antiapoptotic defenses, particularly through NF-κB activation and overexpression of the apoptosis inhibitor Bcl-2.18, 20 For these reasons, we believe that clinical advancement will ultimately depend on the development of agents that can exhibit efficacy against this elusive population. Therefore the focus of our laboratory is understanding the targetable properties of leukemic stem cells so that treatment regimens can ultimately be designed to address this population adequately during induction therapy in order to prolong CR and ultimately prevent relapse. Thus, it was with great excitement that the discovery of parthenolide as a LSC-targeting natural compound was reported.21-22

The Opportunity of Parthenolide

Parthenolide is a small molecule natural product isolated from Feverfew (Tanacetum parthenium, L.), a common flowering plant (Asteraceae) that derives its common name from its fever-reducing properties (the Latin name febrifugia translates to “drive away fever”). Feverfew was prescribed by the first century physician Dioscorides for “all hot inflammations,” a list that has historically included headache and migraine, arthritis, allergies, gynecological disorders, skin disorders, and gastrointestinal disorders, quite astonishingly with no reports of serious side effects.23-25 Some nineteen centuries later, we are beginning to unravel the molecular basis for the remarkable medicinal properties of this unassuming, daisy-like flower that blooms in clusters along roadsides and fields.

Parthenolide is a sesquiterpene lactone, more specifically a germacranolide, featuring a ten-carbon ring fused to an α-methylene--lactone functionality.26 Sesquiterpene lactones are known to exert their biological effects by binding cellular thiols including small molecules such as glutathione as well as cysteine residues of proteins.27-30 Cellular target binding is attributed to

Michael addition reactions in which nucleophilic attack by thiolates at the carbon (C1γ) result in alkylated thiols that can no longer participate in redox homeostasis (in the case of glutathione) or destabilized proteins with altered function (in the case of cysteine residues).31-32 The consistent dependence of biological activity on this functionality has been demonstrated by quantitative structure activity analyses both on reduction and substitution of the methylene as well as among other sesquiterpene lactones isolated from natural sources.28, 31-35 However, potency of α-methylene--lactones in in vitro cytotoxicity studies varies from high nM to high

µM and direct correlations between measured cysteine addition rates and efficacy are not always evident, suggesting that cysteine alone is a poor surrogate for the specificity of these molecules

among the complex landscape of biological thiols.31-32 Additional features of parthenolide which enhance its activity among the α-methylene--lactones are both the 4,5-epoxide and the 1(10)-(E) double bond, hypothesized to explain parthenolide’s exceptionally high activity when viewed as a monofunctional sesquiterpene lactone due to the ability to generate a second alkylating center through trans-annular cyclization under acidic conditions.28, 34, 36

In AML, the efficacy of parthenolide has been attributed to two major biological mechanisms, which make it uniquely able to simultaneously target bulk AML and LSCs. First, parthenolide and other sesquiterpene lactones are known to potently inhibit NF-κB transcription thereby eliminating an antiapoptotic defense mechanism upon which LSCs rely. 21, 37-39

Secondly, our work with parthenolide has revealed that inducing oxidative stress through the rapid depletion of intracellular glutathione pools by covalently binding both free reduced glutathione as well as the cysteine residues of multiple glutathione pathway proteins is a separate selective mechanism targeting LSCs, where normal HSCs can recover from this insult over time but LSCs cannot.22

To further our understanding of the LSC-targeting ability of sesquiterpene lactones, we have developed a collaboration with Peter Crooks at the University of Arkansas for Medical

Sciences, one of the leaders in the medicinal chemistry of sesquiterpene lactones. Through this partnership we have screened over 400 parthenolide derivatives throughout the course of five years, derived structurally from dimethlyaminoparthenolide, (DMAPT), micheliolide (MCL), or melampomagnolide B (MMB). Dimethlyaminoparthenolide is a water-soluble and bioavailable prodrug of PTL when formulated as a fumarate salt.40-41 Melampomagnolide B and micheliolide are oxidized and trans-annularly cyclized forms of parthenolide. This library forms the fundamental basis of this work, which sequentially presents a quantitative structure-activity

relationship analysis of the antileukemic activity of parthenolide derivatives, followed by phenotypic screening to determine therapeutic index between AML and LSCs and an initial pharmacological characterization of the most promising hits discovered through this analysis and the discovery of a novel drug target in AML.

Summary

Acute myeloid leukemia is an aggressive, devastating blood malignancy that is difficult to treat clinically because of the failure of conventional chemotherapy to target the leukemic stem cell population. This unmet clinical need can only be addressed by fully characterizing this elusive population so that we can understand how to target it effectively during induction therapy. An interdisciplinary approach to targeting AML, utilizing an LSC-selective natural product pharmacophore, is uniquely poised to address this unmet clinical need by taking advantage of an evolutionarily designed bioactive scaffold that is not yet fully understood. The utilization of small molecules with differential activity to elucidate disease biology represents a powerful approach that serves to identify targetable pathways and the means to target them in concert. The success of this work demonstrates the efficacy of interdisciplinary collaboration between natural product medicinal chemistry efforts and translational modeling of the biology of human disease to advance our understanding of the molecular mechanisms of both.

CHAPTER II

HIGH-THROUGHPUT SCREENING OF PARTHENOLIDE DERIVATIVES

High-Throughput Screening Platform

For the purpose of screening a sizeable library of derivatives, the M9-ENL cell line was selected for its superior ability to represent leukemia and leukemic stem cells relative to traditional leukemia cell lines such as K562, Kasumi-1, or HL-60. The M9-ENL cell line has several well-established advantages over both traditional leukemia cell lines and primary leukemic stem cells that have been thoroughly described previously.42-43 Briefly, primary acute myeloid leukemia and particularly leukemic stem cells represent precious samples that are not readily available enough to warrant their consumption in a high-throughput format. The transduction of the oncogenic fusion between Mixed Lineage Leukemia (MLL) and eleven nineteen leukemia (ENL) into lineage-depleted human cord blood generates a powerful cell line model of human leukemia stem cells, and as such, M9-ENL cells show a wide variety of stem and progenitor characteristics including the ability to generate a rapid pro-B cell acute lymphoblastic leukemia when injected into immune-deficient mice.42-43

Additionally, the use of flow cytometry for high-throughput screening is particularly ideal for cells cultured in suspension, but also represents a more robust measure of cytotoxicity than traditional tetrazolium viability assays such as MTT or MTS. The major general drawback of these viability assays is that, being based on metabolic activity, they cannot readily separate cytostatic compounds from cytotoxic compounds. For our purpose, the enzymatic reduction of tetrazolium represents a problematic assay readout that can be sensitive to redox conditions.44-46

With a library of potent oxidative stress inducers, non-enzymatic redox effects may convolute the interpretation of these results. Instead, the use of a fluorescent conjugate to Annexin V

allows for the quantification of cell-surface-expressed phosphatidylserine, an early hallmark of apoptotic cell death. When used in conjunction with membrane-impermeable DNA-staining fluorescent molecules such as 7-aminoactinomycin D (7-AAD), we can distinguish healthy, viable cells (Annexin V-, 7-AAD-) from those undergoing early (Annexin V +) or late (Annexin

V+, 7-AAD+) apoptosis, and necrosis (Annexin V-, 7-AAD+). This analysis is appreciably more informative than a tetrazolium assay and less prone to generate false positives through cytostatic effects or assay interference.

Presented within this Chapter are the comprehensive results of the high-throughput screen of 413 parthenolide derivatives. The observed structure-activity relationships are described for major structural classes (e.g. C14 modifications relative to C13 modifications) as well as subclasses of these broader categories. The major potential obstacle of phenotypic screening is the inability to distinguish the source of a failure, whether from decreased interactions with cellular targets, a loss of cellular permeability, or instability in aqueous solution. Additionally, it is rather complicated to determine the difference between compounds that behave as prodrug delivery system of a potent drug versus a potent derivative with direct drug activity (or some hybrid of the two). However, contextual clues can sometimes favor one of these explanations over another. Where possible, an attempt to rationalize these observations has been made, with the caveat that additional experiments are necessary to tease out these subtleties and fully define a mechanism of action.

High-Throughput Screening Results

Dose-response curves and structures of derivatives

The dose-response curves for the parent compounds parthenolide (PTL), melampomagnolide B (MMB), dimethylaminoparthenolide (DMAPT), and micheliolide (MCL)

Figure 1. Dose-response curves of parent compounds parthenolide (PTL), dimethylaminoparthenolide (DMAPT), melampomagnolide B (MMB), and micheliolide (MCL) against the M9-ENL cell line. Error bars represent standard deviation from the mean (n = 3). are shown below in Figure 1. Parthenolide was included in all screens as a positive control. The

EC50 and inter-day assay variation for parthenolide was 6 ± 1 µM. The parent compounds

DMAPT, MMB, and MCL exhibited EC50 values of 7.5, 15, and 15 µM against the M9-ENL cell line, respectively. All dose-response curves from the M9-ENL high-throughput screen of parthenolide derivatives are presented in Appendix A alongside the corresponding molecule structures. Each figure represents a separate experiment, including parthenolide as a positive control. Figures and structures are reported together by the analysis batch code (ranging from

Batch 0 through Batch 26) given in Appendix A. Batch number 0 denotes experiments performed at the University of Rochester, while Batches numbering 1 through 26 were analyzed at the University of Colorado Anschutz Medical Campus.

Quantitative results

The determined EC50 values for each compound along with their corresponding 95% confidence intervals are reported for 413 parthenolide derivatives in Appendix A. The table is organized by compound name for ease of reference, so each compound’s Batch number is also denoted for correspondence to the following figures. Often, more than one experiment was required to accurately determine the EC50 for each compound in a given Batch. In several cases, the compound was re-shipped and new aliquots were prepared for a subsequent Batch analysis.

In these cases, EC50 values from the newer aliquots may have replaced the prior values if a disagreement was observed between the experiments. This was done in several cases in order to reflect the EC50 value that was found to be directly comparable between compounds and reproducible in the current location, with the current instrument, on the current passage of the cell line, in the hands of the current technician.

Discussion

Quantitative structure-activity relationships

The use of a phenotypic screen does not allow for direct analysis of structure-activity relationships in terms of molecular interactions with a purported biological target. Instead, the structure-activity relationships observed necessarily take into account a more relevant biological system (the whole cell, with its physiological barriers intact) that simultaneously eliminates compounds without target efficacy, compounds without cellular permeability, and compounds without sufficient stability for delivery in aqueous media. Conclusions cannot be drawn, therefore, from cellular efficacy values alone which of these factors caused a particular molecule to fail to produce a cytotoxic result. However, from the perspective of accelerated drug development, it ultimately doesn’t matter which of these reasons contributed to a failure- so for

our purposes it is sufficient to classify certain modifications as inactive and move on to a more pertinent analysis of the structures that do show efficacy.

Indeed, this was precisely the purpose of the high-throughput screen implemented. The decision to use a cell line to model primary human disease was not primarily to identify structures that are active in the primary disease (which will be discussed in the following section) but to eliminate structures from the compound set that were unlikely to have efficacy before testing on primary AML. The use of the M9-ENL cell line for this purpose, while imperfect, can be considered justified as the rate of transfer for both inactive and potent compounds was high.

Only four of forty-six compounds which showed efficacy values below 2 µM in the cell line were ultimately inactive against primary AML, while only one of thirteen compounds which showed efficacy values at or above 10 µM in the cell line had a mean EC50 below 10 µM against

AML. Somewhat comfortingly, this singular exception (JVM 3-83) was not overwhelmingly effective (mean AML EC50 = 8.9 ± 8.6, N = 2), although it does provide evidence of false negatives generated from the high-throughput screen. Still, the utility of the M9-ENL cell line

(and its relatively intensive culture requirements) as a high-throughput screening model for acute myeloid leukemia is clearly demonstrated by this dataset.

In context of the low attrition rate of this model, we can begin to sort through the structure-activity relationships observed in the efficacy values presented in Appendix A with some certainty of their relevance. In the broadest terms, each of the 413 derivatives contained one or more of the following modifications: transannular cyclization (as in MCL), C14 substitution (as in MMB), C13 substitution (as in DMAPT), and dimerization of two parthenolide scaffolds (each containing one or more of these modifications). The distribution of compound modifications is shown below in Figure 2A. If we compare the EC50 values

determined from these broad classes (see Figure 2B), there is a dramatic trend demonstrating the

relative potency of C14 substitutions, relative to both C13 substitutions and trans-annular

cyclized (TAC) compounds. Compounds bearing both C13 and C14 substitutions were potent in

a few select cases (five of thirty-eight demonstrating EC50 values < 5 µM, limited exclusively to

dimethylamino substitutions) but otherwise were mostly inactive. Taken together, this strongly

supports the hypothesis that the antileukemic activity of parthenolide derivatives is driven by the

alkylating ability of the α-methylene--lactone, although we cannot over-generalize the results of

this analysis as the distribution of modifications included in each class was neither systematically

controlled nor randomly populated.

Dimers were also quite successful as a broad class, which contained combinations of all

three modification types. Dimers of parthenolide moieties were created by linking two

molecules together through C13 or C14, in the presence and absence of additional structural

Figure 2. A) Distribution of derivatives among structural classes. B) Distribution of efficacy values among derivative classes. Dotted line indicates the EC50 value of parthenolide. C14 substitutions and dimers yielded more potent derivatives. For this analysis, an estimated EC50 value of 100 was assigned to inactive molecules whose efficacy could not be determined.

modifications. Necessarily, we will examine the subtler structure-activity relationships of each of these classes separately.

Simple combinations

As mentioned, the vast majority of derivatives represented one or more structural modifications based synthetically on DMAPT, MMB, and/or MCL. While not considered a true separate structural class as presented in Figure 2, a noteworthy anecdote about these modifications in combination is worth mentioning before exploring those classes deeper.

Interestingly, while the parent molecules all had measurable activity, and more complex modifications of these structures were often quite potent, the simplest combinations of these fundamental modifications were inactive, as were aminoparthenolides missing one or both methyl groups. None of the tested combinations of these fundamental structures (Figure 3) showed any appreciable toxicity at high doses (10 – 20 µM). Whether this is a result of target interactions (or a lack thereof) or a permeability or stability issue is not clear.

Figure 3. Simple combinations of parent compound structures MMB, DMAPT, and MCL are found to be inactive against M9-ENL cells.

Trans-annular cyclized (TAC) derivatives

Micheliolide (MCL), while having modest activity as a parent compound (EC50 = 15

µM), was eventually abandoned as a scaffold after only one of fourteen MCL derivatives

(including an amine-linked MCL dimer) showed any efficacy, and this sole exception was not more potent than MCL itself (PNR 6-25, EC50 = 15 µM). Notably, all of the TAC derivatives analyzed were modified at C1γ, including modifications not sensitive to -elimination. MCL derivatives with an intact C13 alkylating center (i.e. the α-methylene--lactone) were not tested and may represent an active structural class, despite these results.

C13-substituted (C13) derivatives

Molecules featuring C13 substitutions were also, as a general rule, inactive. Most of these compounds featured N-linked substitutions carrying various aromatic heterocycles. Non- aromatic N-linked heterocycles, as well as aromatic alkene substituents were also explored. A striking majority of these compounds were completely inactive (52/65), and none of the active compounds showed improved cytotoxicity over parthenolide (EC50 = 6 µM). In fact, the only potent C13-substituted molecules analyzed whatsoever were dimethylamino-modified versions of C14 derivatives and their fumarate salt formulations (“C1γ+C14” structural class in Figure 2), and in 80% of these cases (4 out of 5 compounds) the dimethylamino modification reduced potency relative to the C14 modification alone.

C14-substituted (C14) derivatives

A total of 224 (non-dimeric) C14-substituted derivatives were screened, with only 15 inactive compounds. As seen in Figure 2B, this set contained some of the most potent compounds tested, with 120 compounds showing potent efficacy values (EC50 < 5 µM), including six compounds with sub-micromolar efficacy values. There were only fifteen inactive

compounds, including ester, carbonate ester, carbamate, triazole, imine, and conjugated diketone linkages, encompassing most of the linkages studied. Substituents included both aromatic and non-aromatic heterocycles, as well as hydrocarbon chains with and without unsaturated elements. However, these fifteen inactive compounds included four out of four of the derivatives featuring a free carboxylic acid, even though other analogs with similar structures were quite potent. The presence of an ionized carboxylate could easily interfere with cell permeability, although additional analyses would be necessary to determine if that is the case.

While nearly all of the C14-substituted derivatives were active, suggesting that no particular subset of C14-substituents should be abandoned (as could be the case with TAC modifications and C13 substitutions), we can still explore subsets to see if any appear to edge out others in terms of potency. Most broadly speaking, the C14 substitutions can be classified as aromatic and non-aromatic heterocycles connected through an extensive variety of heteroatom functional groups, including esters, amides, carbamates, carbonate esters, α-keto esters, imines.

Derivatives tethered to known cytotoxic agents and bone-targeting agents are also included.

Additionally, several molecules feature “tags” such as a biotin, alkyne, or azido moiety designed for in situ tracing.

However, it is not the purpose of this Chapter to describe the finer details of each modification; for that purpose, a structural account is presented within Appendix A. Given the abundance of screening hits generated, here we are primarily concerned with exploring the most potent structures to take forward. To that end, a total of twenty-eight C14-modified compounds had efficacy values at or below 2 µM (by 95% CI), representing the most potent 12.5% of this class of derivatives. Among these, ten featured a substituted indole (or benzimidazole) linked via amide, ester, carbamate, or α-keto ester, six were substituted phenyltriazoles, and an

additional three were indoles connected via triazole. Other compounds included two diazole esters, two naphthyl substituents, four tethered molecules (two carrying benzyl thiadiazolidine diones, one carrying doxorubicin and another carrying a benzothiophene acrylonitrile), and a single halogenated derivative (Figure 4). Below, we will explore the structure-activity relationships of each of these top hits within the context of their respective structural families.

Figure 4. Most potent C14 derivative substituents. Shown here are the twenty-eight of 224 (12.5%) C14-substituted compounds tested which had efficacy values at or below 2.0 uM in the high-throughput screen.

Allyl halides. The halogenated derivative, an allyl iodide (BS 3-37), is particularly interesting in terms of drug mechanism as it adds an alkylating center. Primary iodides are significantly more reactive alkylating agents than bromides or chlorides due to the stability of the leaving group ion. Allyl iodides are especially effective enolate alkylators. Comparing the efficacy values of BS 3-37 (EC50 = 1.5 ± 0.1 µM) with the allyl bromide BS 2-60 (EC50 = 2.8 ±

0.1 µM) and the allyl chloride JVM 3-25 (EC50 = 2.4 ± 0.1 µM), alkylating efficiency seems like a reasonable mechanistic explanation of this trend, but a 1 µM difference is arguably not meaningful in the context of this assay.

Tethered molecules. The potency of the four tethered molecules can be most easily attributed to cooperation between delivery of parthenolide and the borne substituent, however, follow-up experiments to verify this are yet to be completed. Doxorubicin was a potent C14 substituent both with an amide linkage (JVM 3-97, EC50 = 3.0 µM) and even more so with a carbamate linkage (JVM 3-96, EC50 = 1.0 µM). Doxorubicin is a DNA-intercalating, topoisomerase-inhibiting anthracycline antibiotic that has clinical efficacy against cancer through induction of DNA damage and oxidative stress.47 Similarly, both thiadiazolidine diones tested

(BSK 1-40 and BSK 1-87) were quite potent, whether bearing an N-chloroethyl or an N- iodoethyl substituent. Thiadiazolidine diones (TDZD) have been shown to selectively target leukemias and leukemic stem cells relative to hematopoietic stem cells through glycogen synthase kinase 3 (GSK-3) inhibition.48-49 GSK-3 is known to regulate leukemogenic Homeobox

(HOX) transcription, representing an attractive antileukemic target.50 Finally, BS 4-10, bearing a benzothiophene acrylonitrile substituent designed as a combretastatin analog, was a potent derivative representing the only compound in its class. Combretastatins are antitublin agents known as “vascular disruptors” that target both leukemic blasts as well as the neovasculature that

supports them, another selective mechanism in AML.51 While the sample set is small, together, these molecules represent a 100% success rate in terms of improving potency of parthenolide through the conjugation of known antileukemic agents, an encouraging sign that this class of derivatives should be expanded and studied further. Whether these conjugated delivery relationships were synergistic or merely additive was not explored.

Diazoles and naphthyls. As C13 substituents, both diazoles and naphthyls generated inactive compounds, but as C14 substituents they were quite effective. A total of six naphthyl

C14 substituents were analyzed, bearing 1-naphthyl or 2-naphthyl sulfonamides, esters, or amides (namely JVM 3-36, JVM 3-38, JVM 4-34, JVM 4-36, JVM 4-78, and JVM 5-88). These six C14 naphthyl derivatives had efficacy values ranging from 1.6 to 3.1 µM, while three C13 naphthyl derivatives (2-naphthyl amide JVM 4-10, 2-naphthyl sulfonamide JVM 3-14, 2- naphthyl ether BS 4-46) were inactive. Diazoles were quite potent as simple C14 esters (JVM 5-

11, JVM 5-20, and JVM 5-21) with efficacy values from 0.9 – 2.1 µM, less so as a simple C14 thioether (JVM 1-66, EC50 = 13 µM, and its dimethylamino JVM 2-49, EC50 = 4.2 µM), and inactive as longer-chain substituents (JVM 1-58, JVM 1-73). Twenty different C13 diazole substitutions were explored, all of which were inactive. In light of this consistency, it seems probable that these activity relationships reflect target interactions rather than stability or permeability issues.

The majority of the top C14-modified derivatives featured indole and/or phenyltriazole heterocycles. The indole-bearing derivatives could be further classified according to the type of linkage functionality used: α-keto ester, amide, ester, carbamate, or triazole (Figure 5).

Interestingly, there were no apparent trends favoring one of these linkage functionalities over any other, but trends could be observed within each of these types.

Figure 5. Observed efficacy of selected C14 heterocycle substituents. Dotted line indicates the EC50 value of parthenolide. Indoles were quite potent using a variety of linkage systems. Phenyltriazoles were also among the most potent C14 derivatives. Open symbols represent dimethylamino modifications. Half-filled symbols represent bioisosteres. For this analysis, an estimated EC50 value of 100 was assigned to inactive molecules whose efficacy could not be determined.

Alpha-keto ester indoles. A total of sixteen MMB derivatives with indoles linked via an

α-keto ester functionality at the 3 position were analyzed. The most potent indoles in this subset were 5-substituted with bromine, chlorine, or methoxy groups. 5-bromo indoles were the most effective, while cyano indoles were by far the least. The addition of an N-methyl substituent to the indole resulted in improved potency in six out of six cases, whether the indole featured a halogen, methoxy, or cyano substituent. This improvement ranged from modest (δ= 0.2 µM) for the unsubstituted and 5-fluoro indoles to nearly ten-fold in the 5-methoxy indoles (8 µM to 0.97

µM). By contrast, N-benzyl and N-p-chlorobenzyl substitutions resulted in decreased potency.

Both dimethylamino fumarate salts were found to significantly decrease potency (over five-fold increase in efficacy values). The remarkable consistency of substituent effects within this set suggests a target interaction may be at play.

Amide indoles. Amide indoles represent the smallest subset with only six representative compounds. All six of the amide indoles were potent (EC50 ≤ 5 µM). The most potent compounds were JVM 4-28, an N-methyl substituted 3-indole, and JVM 4-33, a 5-methoxy substituted 2-indole. The small size of this subset did not allow for robust structural comparisons.

Ester indoles. Unlike the α-keto ester indoles, simple ester indoles varied widely at the location of indole attachment. 2-indoles and 3-indoles were well represented, but the 5-indole

BS 2-31 and 6-indole BS 2-05 were also shown to be potent. Also in contrast, N-methyl substitution drastically reduced potency in the only case examined (BS 4-61, EC50 = 2.0 µM and

N-methyl BS 4-60, EC50 = 7.2 µM). 5-methoxy and 5-chloro substituents were somewhat detrimental to the 2-indole scaffold of BS 1-28 (EC50 = 1.1 µM), but dimethylamino modification was well-tolerated (dimethylamino BS 2-81, EC50 = 2.1 µM, and its fumarate salt

BS 3-18, EC50 = 2.8 µM). The 3-indole BS 2-04 was not directly conjugated to the indole as in

BS 4-61, but instead featured a 3-indolylacrylic acetate, and this was the most potent in the subset (EC50 = 0.72 µM).

Carbamate indoles. Ten carbamate-linked indole substituents were analyzed, six of which constituted 3-methylindoles and three of which were 3-ethylindoles. The tenth, a 2- methylindole, was the most potent of this subset (EC50 = 1.5 µM). All of the compounds were quite potent, with efficacy values of 4 µM or better. Three closely related structures, DSS 1-137

(a 3-methyl indazole), JVM 5-18 (a 2-methyl benzimidazole) and JVM 5-19 (a 2-benzimidazole)

had similar potencies, demonstrating that carbamate indazoles and carbamate benzimidazoles are effective bioisosteres for the carbamate indoles.

Phenyltriazoles. A small library of 1,2,3-triazole derivates were explored, affording 4- substituted triazoles bearing alkanes or aromatic groups. JVM 4-42, bearing a carboxylic acid, was the only completely inactive triazole (Figure 5). Compounds bearing simple non-aromatic triazole substitutions were moderately potent (e.g. JVM 4-20, a 4-butyl-substituted triazole, EC50

= 4.3 µM) to weakly active (JVM 3-75, a 4-hydroxymethyl-substituted triazole, EC50 = 38 µM).

By contrast, 4-phenyl triazole substituents (excepting JVM 4-42) were extremely potent, with eight of thirteen of these having EC50 values ≤ 2 µM. The best of these was the 3,5- bis(trifluoromethyl)phenyltriazole JVM 4-29, which was extraordinarily potent (EC50 = 0.56

µM). The dimethylamino and fumarate salt formulations JVM 4-29B and JVM 4-29C were three- to four-fold less potent, but given the sub-micromolar potency of JVM 4-29 these compounds were still quite potent (EC50 values ≤ 2 µM). Unsubstituted phenyl JVM 3-74 had an

EC50 = 1.9 µM, while p-fluoro- (JVM 4-26, JVM 4-27), m-chloro- (JVM 4-25), and difluorophenyltriazole (JVM 4-48) also proved to be quite potent phenyl substitutions.

Triazole indoles. Following these successful series, a new series combining indole substitutions with the 1,2,3-triazole was first introduced in Batch 25, and expanded in Batch 26.

Thus far, these compounds have been successful, with nine of twelve demonstrating potency

(EC50 ≤ 5 µM). As yet, they have not reached the sub-micromolar potency observed both in the indoles and phenyltriazoles. Still, they represent a promising new series of derivatives that may share some of the pharmacological features of one or both of the parent series. Additional compounds with this linkage will help to further develop this series.

Dimer derivatives

A total of 72 dimers featuring two parthenolide-like moieties were assembled with a similar variety of linkages as the sets of monomeric derivatives. We can most easily classify the dimers by their linkage points of each monomeric unit, with three permutations: C1γ,C1γ’;

C1γ,C14’ or C14,C14’. Somewhat complicating matters, these subsets may include additional modifications, such as dimethylamino-modified dimers that are C14, C14’ linked (DMA-

C14,C14’). The distribution of linkages is shown in Figure 6A and their corresponding efficacy values are presented in Figure 6B.

Figure 6. A) Substructural distribution of dimerized compounds. B) Efficacy values of substructures. Dotted line indicates the EC50 value of parthenolide. Open triangles indicate dimethylamino-modified dimers (DMA-C14,C14’). For this analysis, an estimated EC50 value of 100 was assigned to inactive molecules whose efficacy could not be determined.

When comparing dimers this way, we quickly see a similar relationship between C14 and

C1γ subclasses to that evident in the monomers. Only one of seven C1γ,C1γ’-linked dimers was active at all (JVM 2-63A, an N-linked simple dimer of aminoparthenolide), and only weakly so

(EC50 = 19 µM). Three C1γ,C14’ dimers were also analyzed, all of which had measurable efficacy, namely JVM 3-71, JVM 3-55B, and JVM 3-73 in order of decreasing potency.

Whether these compounds have innate activity or act as prodrug delivery systems of parthenolide or MMB is not clear, as the sample size was extraordinarily small and one included an additional

C14 triazole substitiuent beyond the simple linkage functionality.

The majority of synthetic effort was therefore ultimately dedicated to producing dimers of MMB (C14,C14’ dimers) and dimethylamino-modified versions of these dimers (DMA-

C14,C14’). The first potent MMB dimer analyzed, JVM β-76, was included in Batch 1, the first batch of derivatives screened at the University of Colorado. This dimer had submicromolar potency in the M9-ENL cell line. If we consider the dimers in a strictly additive model, we might expect that MMB dimers would have roughly double the potency of parthenolide or

MMB, bearing twice as many alkylating groups on a mole-per-mole basis (plus or minus delivery and stability effects). However, with an EC50 value of 0.56 µM (95% CI 0.53 – 0.62

µM), this dimer was found to be not two-fold but ten-fold more potent than parthenolide and thirty-fold more than MMB, prompting the synthesis of an ever-expanding set of MMB dimers.

Indeed, to date, despite an additional five years of synthetic effort yielding another three hundred or so compounds since their introduction, dimers of MMB remain among the most potent (and arguably most intriguing, as we shall ultimately see) antileukemic parthenolide derivatives examined. The first generation of MMB dimers (first appearing in Batch 1 and 2) included potent carbamate-linked dimers connected via alkyl chains and succinic ester amide linkages, which were generally not effective. Also in Batch 2, JVM 3-55, an MMB dimer linked with a singular carbonate ester was found to be quite potent.

Second-generation MMB dimers introduced in Batches 4 and 6 included carbonate ester, amide, and ester linkages with similar alkyl chain lengths as the carbamate linkages. Excluding the MMB dimers featuring hydrolytically labile ester linkages, not only were many of these dimers quite potent, they began to evidence a chain-length dependence with a local minimum

EC50 value occurring at a similar alkyl chain length, regardless of the linkage functionality. In addition to the powerful synergy observed by ligating two MMB moieties together, this observation also supported the hypothesis that MMB dimers may have a specific antileukemic mechanism. Third-generation MMB dimers (first appearing in Batch 8) featured triazole, phenyl, and pyridine heterocycles and fourth-generation (first appearing in Batch 23) were esters featuring one or more thioethers or a disulfide. These sets, bearing more complex and in some cases rigid linking chains, were not subjected to a structural analysis based on chain length, as these sets did not vary based on chain length. Preliminary analysis suggests that triazoles combined with esters or hydrophobic spacers and esters linked with a single thioether are effective, but these conclusions are based on very small sets (N = 5) in both cases.

These results are displayed semi-quantitatively in Figure 7A below. Overall, MMB dimers connected via carbamate esters and were the most potent, but both ester-linked and heterocycle-linked MMB dimers were able to reach sub-micromolar efficacy values. Excluding esters and heterocycles, and the mono-linked carbamate ester JVM 3-55 and amide JVM 5-45, a local minimum efficacy value was observed with 10 – 11 atoms in the connection between C14 of the first MMB moiety and C14’ of the second (Figure 7B). This observation, which seemed unlikely to be explained by cell permeability, first piqued our interest in understanding the antileukemic mechanism of MMB dimers. Ultimately, with the exception of the succinic ester

amides, all of these subsets demonstrated improved potency relative to parthenolide, some as much as ten-fold, and were selected for further analysis.

Figure 7. A) Relative potencies of MMB dimers, classified by linkage functional groups. B) Chain-length dependence of EC50 values in MMB dimers. Dotted line indicates the EC50 value of parthenolide. For this analysis, an estimated EC50 value of 100 was assigned to inactive molecules whose efficacy could not be determined.

Conclusions

Inactive modifications

Excepting dimethylamino modifications and their fumarate salt formulations (as applied to potent C14 modifications), C13 modifications failed to improve the observed potency of derivatives in every case (N = 72). Similarly, C13-substituted trans-annularly cyclized derivatives of micheliolide were not active, though this sample set was smaller (N = 14).

Together, this provides very strong support for the hypothesis that the antileukemic mechanism relies on the alkylating ability of the α-methylene--lactone. Additionally, whether on an alkyl or an aromatic backbone, the presence of a carboxylic acid rendered the molecule inactive. This was most dramatic in the case of JVM 4-42, the only completely inactive phenyltriazole (Figure

5). In the broadest terms, all other classes of compounds were able to produce active molecules, to varying degrees.

Potent modifications

Of the 413 compounds analyzed, ten had EC50 values below 1 µM: five MMB dimers, and five heterocyclic C14 derivatives. Among the heterocycles, α-keto indoles, amide indoles, simple ester indoles, and phenyltriazoles were the most potent. Similarly, MMB dimers with a variety of linkage functionalities were among the most potent, including carbamates, carbonate esters, and a carbonate ester triazole combination. As a result, each of these subclasses of C14- modified derivatives warrant follow-up screening in primary samples to verify their efficacy against human acute myeloid leukemia. This exploration, as well as an investigation into their potential mechanisms of action, will be the focus of Chapter III.

Methods

Library handling and storage

Upon receipt, each compound was dissolved in DMSO to a concentration of 10 mM and stored in aliquots at -20 ºC. DMSO aliquots were permitted up to three freeze-thaws before discarding. In the case of salt formulations, compounds were dissolved in purified water and stored at -80 ºC as single-use aliquots.

M9-ENL cell culture

M9-ENL cells were cryopreserved in a freezing medium made up of alpha-MEM, fetal bovine serum (FBS), and DMSO and cultured in alpha-MEM supplemented with 5% human plasma, 20% FBS, pen/strep, and the cytokines SCF, IL-3, IL-7, and FLT3 ligand. Cells were maintained below 5x106 cells/mL and passaged for up to four weeks before a new aliquot was thawed. Culture beyond this point resulted in a significant drift of observed EC50 values.

Apoptosis assay

For quantitative analysis of apoptosis using flow cytometry, cells were plated in fresh media at a density of 106 cells/mL in 96-well plates and incubated for an hour before drug addition. Drugs were diluted from a DMSO stock into PBS such that the final concentration of

DMSO was constant and did not exceed 0.5%. Cells were incubated overnight in a humidified incubator (37 ºC, 5% CO2). The following day, flow cytometric analysis was performed by co- staining with Annexin V antibody and 7-AAD or DAPI, according to manufacturer specifications, to identify the percentage of non-apoptotic cells, defined as the population with negative staining for both labels. This percentage of viable cells observed was normalized to that of the vehicle control for each assay. Three or four doses of each compound were used in serial two-fold dilution, with concentrations chosen depending on the potency of the compound.

Analyses were conducted in triplicate except where indicated. In some cases, a broad screen at a single dose (10 µM) was implemented to identify and exclude inactive compounds before a dose-response experiment was conducted. In these cases, bar graphs are presented in lieu of dose-response curves.

Quantitative results

Where possible, a nonlinear fit was applied to each dose-response curve using GraphPad

Prism’s variable slope model for normalized data to determine the EC50 value for cytotoxicity, defined as the concentration of drug required to induce apoptosis in 50% of cells, according to the following equation:

� = log ��50−�∗�� using the method of least squares. In +the normalized model, Y = 50 is used to determine the

EC50. The calculated HillSlope values typically ranged from -2 to -10, justifying the use of the

variable slope model. Dose-response curves and structures are comprehensively presented together by analysis Batch in Appendix A. For the purpose of a semi-quantitative structure- activity relationship analysis, an estimated EC50 value of 100 was assigned to inactive molecules whose efficacy could not be determined, however in Appendix A these are reported with EC50 values “>>10 µM” with 95% confidence intervals not determined (nd).

CHAPTER III

DISCOVERY OF AML-SELECTIVE MMB DIMERS

Therapeutic Index Screening

While theoretically straightforward, it is too often impractical to incorporate primary clinical samples or healthy tissue into a meaningful screening assay. However, our laboratory has dedicated a substantial amount of effort to maintaining close-knit relationships with clinicians and clinical trials at the University of Colorado Hospital and curating a successful clinical AML tissue bank. This invaluable resource provides our team with the opportunity to directly study mechanisms of human disease in vitro, rather than relying on surrogate measurements of cell line data that must ultimately be verified in primary samples. While the

M9-ENL cell line is ideally suited for our high-throughput screen, efficacy in this cell line was not sufficient for identifying the best pre-clinical drug candidates, and potent compounds are subjected for a secondary analysis in primary AML samples.

Another notable consideration of our drug-screening method design that is often neglected is the use of healthy hematopoietic stem and progenitor cells as a control for nonspecific toxicity. In the field of rational drug design, specificity usually describes drug affinity in terms of relative binding constants for an objective target relative to some other structurally similar non-target. This method can and has been used successfully to generate compounds with tolerable clinical profiles, but the high attrition rate of novel small molecules in discovery can at least be attributed in part to the fact that off-target effects cannot possibly be limited to the small handful of enzymes or other drug targets that can be functionally quantified.

To address this shortcoming, we incorporate a counter-screen using healthy hematopoietic stem cells isolated from human umbilical cord blood, such that compounds that are equipotent in

AML and the cells responsible for repopulating the hematopoietic system following chemotherapy, we can eliminate the compounds which have a high failure risk sooner, rather than later, and focus our efforts on the best drug candidates.

LSC-Selective Mechanism of Action

As derivatives of parthenolide, the LSC-selective sesquiterpene lactone, the phenotype we are screening for is selectivity for leukemia relative to healthy hematopoiesis. Work from our laboratory has shown that the mechanism by which parthenolide is able to potently induce apoptosis in AML and LSCs relies not only on inhibition of NF-κB,39 but also the introduction of an intolerable oxidative stress insult through the rapid depletion of intracellular glutathione pools.22 LSCs appear to be particularly sensitive to both of these mechanisms, suggesting that parthenolide’s potency is owed to the synergy of achieving them simultaneously. It seems probable that derivatives of parthenolide will share these mechanistic characteristics, provided that the structural feature giving rise to this activity (namely, the α-methylene--lactone) is left unaltered, which is true for the MMB dimers. However, the very basis of a structure-activity- relationship analysis presupposes that small changes to a molecule can have dramatic and unpredictable effects on its binding affinities. Therefore, compounds showing a promising in vitro therapeutic index were compared to parthenolide to verify whether they inherited these activities of the parent compound.

Presented within this Chapter is the comparative mechanistic analysis from the MMB dimers, the only structural class displaying a tremendous improvement in the therapeutic index screen. Interestingly, the improved potency of MMB dimers in AML was not explained by improved efficacy in the mechanisms known to provide selectivity for AML and leukemic stem cells to the parent compound.

Therapeutic Index Screening Results

Primary acute myeloid leukemia

Compounds showing significantly improved potency in the cell line screen relative to parthenolide (or a representative set, if numerous potent compounds in a given Batch were structurally similar) were selected from each Batch and tested in the same apoptosis assay. A total of six primary samples were used for this purpose, with varying degrees of sensitivity to parthenolide, with EC50 values ranging from 2.5 µM to 16 µM (mean ± sd: 7 ± 5 µM, N = 6). In most cases, compounds were tested against at least two of the six primary AML samples to determine a mean primary AML EC50 value.

Because clinical acute myeloid leukemia has a variety of subtypes and associated cytogenetics, and because parthenolide has no known associations with clinically monitored genetic characterization (e.g. FLT3-ITD), the six clinical samples utilized throughout the project were not selected based on these factors. Instead, derivatives were screened against AML specimens with relatively high viability (45- 70%) twenty-four hours after thawing, to allow for reliable quantification of cytotoxicity. A small set of primary samples (N = 6) was used throughout the project to maintain a high degree of intercomparability in the absence of screening each compound against the complete set. The set of AMLs used in this work are described in Table 1 below:

Table 1. Characteristics of primary AML samples used to screen parthenolide derivatives.

Sample Overnight viability PTL EC50 (µM) Notes AML01 65% 8 AML02 45% 5 NPM1 mut AML03 45% 2.5 IDH2 R140Q Relapse, FLT3-ITD, complex karyotype AML04 70% 16 including del(5q13) and t(7;22) AML05 70% 7 Relapse, FLT3-ITD, NPM1 mut AML06 45% 4 FLT3 mut, NPM1 mut, IDH1 mut

A total of eighty-seven compounds were screened against both the cell line and AML.

Linear regression evidenced a positive correlation between EC50 values determined in the cell line and those determined in primary specimens. Using the seventy-two compounds for which

EC50s could be quantified, the best-fit line was determined:

� ��50 = 4.9 ��50 −

Figure 8. Correlation between EC50 values in the M9-ENL cell line and primary AML samples. A slope of 4 is observed, demonstrating that, overall, compounds were four-fold less potent in AML than the cell line.

This slope was significantly nonzero (p < 0.0001) and quantified the general observation that compounds were less potent in primary samples relative to the cell line.

Determination of therapeutic index

For the purpose of determining an in vitro quantitative therapeutic index, compounds showing promising efficacy in at least two primary AML samples were screened against healthy stem and progenitor cells isolated from fresh (less than 72 hours old) human umbilical cord samples from consenting volunteers. Stem and progenitor cells were defined by their surface

expression of CD34 and CD45 (CD34+CD45dim). To date, a total of nineteen compounds have been screened against hematopoietic stem and progenitor cells.

The data obtained for both screens are combined together in Figure 9 below for facility of comparison. While several classes showed improved efficacy in AML relative to parthenolide, including a wide variety of indoles and triazoles, only the carbamate and carbonate ester linked MMB dimers demonstrated a dramatic increase in the in vitro therapeutic index calculated according to the following equation:

� ��50 = � ��50

Figure 9. Efficacy of structural classes in primary AML and HSCs. A) The comparative efficacy of compounds against AML (filled symbols) and CD34+ cells from human umbilical cord blood (X symbols). B) MMB dimers, examined by the linkage functionality. A dotted line indicated the mean efficacy of parthenolide in AML (N=6). A dramatic increase in therapeutic index is readily observed for both the carbamate and carbonate ester MMB dimers, relative to parthenolide and the other potent classes.

AML comparison

This striking property led us to investigate the MMB dimers across our panel of AML

samples to determine if any trends in sensitivity could be observed in our samples. Interestingly,

while our panel is small, no such trends could be observed. However, when comparing MMB

dimer potency to that of parthenolide, we notice a striking difference in efficacy for AML04, a

relapsed refractory sample that is resistant to parthenolide. A similar lack of resistance is also

observed for AML01, and AML05, also a relapse sample. The lack of congruence between the

AML EC50 values of MMB dimers and the parent compound suggests that MMB dimers may not

share its mechanism of action.

Figure 10. Efficacy of MMB dimers on AML samples. Parthenolide EC50 values are shown as black circles. EC50 values for MMB dimers on AML01 - AML06 demonstrates no observable trends across AML samples. Notably, MMB dimers do not lose potency in AML04, a relapsed refractory sample that shows resistance to parthenolide. Bars represent mean ± SEM.

Antileukemic Mechanism of Action

To understand how MMB derivatives were able to induce selective cytotoxicity in AML relative to HSCs, they were compared to parthenolide to determine if they had inherited the mechanism of action of the parent compound. Assays to measure inhibition of NF-κB and effects on glutathione depletion and oxidative stress were implemented to test whether the improved TI observed was a result of improved efficacy in either (or both) of these mechanisms.

NF-κB inhibition

In most of our primary samples, the constitutive activation of NF-κB by phosphorylation at serine residue 536 of the p65 subunit is readily measurable using a primary antibody specific for the presence of this phosphorylation event. Additionally, differences in electrophoretic mobility can be used to functionally assess inhibition via quantitative displacement of the NF-

κB transcription complex from target oligonucleotides. Both of these assays were utilized to understand the relative efficacy of MMB dimers to parthenolide in this LSC-selective drug mechanism (Figure 11).

Figure 11. Inhibiton of NF-κB by MMB dimers. Constitutive activation of NF-κB transcription in primary AML is readily measurable by Western blot (A) and electrophoretic mobility shift assay (B). Parthenolide potently inhibits NF-κB transcription, resulting in potent cytotoxicity. MMB dimers are able to inhibit NF-κB but not more than parthenolide, despite being five- to ten-fold more potent in AML cells.

Oxidative stress

As mentioned, the oxidative stress insult generated by parthenolide is attributed to rapid and potent depletion of intracellular glutathione pools. MMB dimers, by contrast, do not seem to have this ability at pharmacologically relevant doses (Figure 12). Comparing the abilities of parthenolide and MMB dimers to induce apoptosis relative to their abilities to deplete glutathione, we find that while the cytotoxicity of parthenolide clearly shows correspondence, for

MMB dimers, overall these two measures do not correspond, and moreover, potent cytotoxicity can be observed in the absence of glutathione depletion.

Figure 12. Glutathione depletion in AML. Parthenolide (10 µM) potently depletes glutathione, leading to cytotoxicity. By contrast, MMB dimers JVM 2-76 and JVM 3-64 (5 µM) achieve increased cytotoxicity with little to no effect on glutathione content. Symbol shapes represent individual AML samples: ● AML01, ▲ AML02, ▼AML03, and ■ AML04. Error bars represent standard error of the mean.

Figure 13. Effect of MMB dimers on oxidative stress. A) Total reactive oxygen species were measured by flow cytometry using CellROX after two hours of drug exposure. B) HMOX1 protein and C) mRNA levels were measured as a way to compare oxidative stress. MMB dimer JVM 3-88A induces HMOX1 gene expression at low doses and disable this response near the EC50, even as oxidative stress increases. Error bars indicate standard deviation from the mean.

Despite this loss of the ability to deplete glutathione pools, MMB dimers were found to potently induce oxidative stress, as measured by the general reactive oxygen species fluorescent probe CellROXTM Deep Red, which emits a strong fluorescence at 665 nm when oxidized.

Comparing the fluorescence intensity of cells treated with parthenolide or MMB dimer (Figure

13A), it was found that MMB dimer JVM 3-88A induces more oxidative stress as the dose increases from subtoxic (0.625 µM) to cytotoxic (5 µM) doses, with a distinct inflection point near the EC50 value (2.5 µM) . As another measure of global oxidative stress, Heme oxygenase

1 (HMOX1) levels were measured after exposure to parthenolide MMB dimers (protein levels in

Figure 13B and mRNA levels in Figure 13C). Typically, oxidative stress leads to the

upregulation of HMOX1 at the transcriptional level as a means to neutralize reactive oxygen species. Indeed, with parthenolide, we see a dramatic increase in HMOX1 protein and mRNA levels that parallel the measured oxidative stress by CellROXTM. However, MMB dimers show a curious ability to induce HMOX1 expression at doses below the EC50 value but above this value, this response is muted, and the accumulation of reactive oxygen accelerates.

Discussion

Primary AML and therapeutic index

Overall, the data showed a four-fold increase in EC50 values when moving from the cell line to the AML model, suggesting the necessity of a lower cutoff value in the cell line to generate hits of a desired potency in AML. However, it is apparent from Figure 8 that most of the more potent compounds (efficacy values below 3 µM in the cell line) fall below this line, approximating the line of equivalence (M9 EC50 = AML EC50) suggesting that most compounds were more or less equipotent, but the fit line is skewed upward by the compounds that failed to demonstrate efficacy in AML. This relationship between the efficacy values observed in the cell line and the efficacy values observed in primary samples validates the M9-ENL model yet underscores the necessity of primary samples as the primary screening endpoint for efficacy in human disease to prevent wasted efforts on false positives generated from cell lines, which still remain despite our best efforts in cell line selection.

Comparing the structural classes individually in Figure 9, the simple combinations (see

Figure 3) inactive in the cell line were also inactive in AML. Similarly, an assortment of heterocycles with mediocre efficacy in the cell line were also mediocre in AML. Naphthyls, diazoles, tethered molecules, and allyl halides were moderately effective in AML but, with exceptions, were generally not more potent than parthenolide (dotted line). The most potent

compounds overall were the indoles, phenyltriazoles, and MMB dimers. No clear trends could be observed in the different indole linkages, suggesting that they may share characteristics of drug stability, membrane permeability, and drug targets. This was not true for the MMB dimers, as seen in Figure 9B. For unknown reasons, amide-linked MMB dimers were inactive in AML which cannot readily be attributed to hydrolytic stability since the ester-linked MMB dimers were quite potent.

When considering relative toxicities, a dramatically consistent observation emerges of structural classes with no discernable differences in cytotoxicity to hematopoietic stem cells, as measured by the screen against CD34+ cord blood cells, excepting the MMB dimers. While other individual MMB derivatives showed some discretionary toxicity, even the best of these

(namely indole ester BS 1-28 and JVM 4-29) had modestly favorable in vitro TI values (TI < 2).

By contrast, calculated TI values for the carbamate and carbonate ester MMB dimers tested were

Figure 14. Toxicity of MMB dimers to HSCs. The population of HSCs in human umbilical cord blood (CD34+CD45dim) is sensitive to 16 µM treatment of parthenolide (PTL) but not MMB dimer JVM 2-76.

in most cases not possible to obtain, because so little toxicity was observed in HSC samples.

Although the relatively low frequency of CD34+ cells in umbilical cord samples is dose-limiting for each assay, the screen was repeated several times using additional cord blood samples, each time with increasing doses until the highest dose reached 16 µM (Figure 14). At this dose, the

MMB dimer JVM 2-76 affected 10-20% of the CD34+ population, while parthenolide eliminated more than 70%. The same MMB dimer showed a mean AML EC50 value (N = 5) of 1.7 µM ±

0.6, resulting in a TI > 10.

This represents more than five-fold improvement in TI compared to parthenolide as determined from the same set of AML and CB samples. Because these MMB dimers elicited the very phenotype we were hoping to find, i.e. being simultaneously more toxic to primary AML and less toxic to HSCs than the parent compound, they represented an exciting new class of parthenolide derivatives that were selected for pharmacological analysis to understand their mechanism of action.52

Selective drug mechanism

Sesquiterpene lactones, broadly speaking, have long been known to have anti- inflammatory activity in a wide variety of contexts, attributed to inhibition of the transcriptional activity of NF-κB.38, 53 Monofunctional sesquiterpene lactones can directly inhibit DNA binding by alkylating Cys38 of the p65 subunit, which is located in the DNA binding region.28-29, 35

Bifunctional sesquiterpene lactones can cross-link Cys38 to Cys120, resulting in a conformational change that no longer has affinity for DNA, resulting in improved potency.28

However, our bifunctional MMB dimers were not remarkably effective at inhibiting either the phosphorylation of Ser536 or the binding of the NF-κB transcriptional complex to DNA in AML

(Figure 11). Of the three AMLs analyzed for phosphorylated p65, MMB dimers were more

potent only in AML03, while by EMSA MMB dimers were only more potent in AML02.

Considering AML04, in which the MMB dimers were ten-fold more potent, significant phosphorylation remained at 5 µM for the MMB dimer JVM 3-64 (AML04 EC50 = 1.6 µM) while parthenolide fully inhibited this event at the same dose (AML04 EC50 = 16 µM).

Moreover, if we consider the relative efficacies in the three AMLs measured by EMSA (the more relevant measure of inhibitory strength), we find that we cannot explain the improved cytotoxicity of MMB dimers (increasing potency: AML02, AML01, AML03) using their relative efficacies at NF-κB inhibition (increasing potency: AML0γ, AML01, AML0β). While we clearly see that MMB dimers can inhibit NF-κB at high doses, based on the AML samples studied we were unable to use this mechanism to account for their improved cytotoxicity and continued to look elsewhere for explanation of this behavior.

The next most logical explanation for improved cytotoxicity was oxidative stress. As previously mentioned, work from our laboratory has shown that parthenolide has a unique ability to rapidly deplete the intracellular pools of glutathione, inducing an oxidative stress insult that is toxic in LSCs but tolerated in HSCs. This striking loss of the intracellular glutathione pool has been attributed both to direct alkylation of the thiol-bearing tripeptide itself but also to the cysteine-rich proteins of the glutathione pathway that helps to maintain redox homeostasis.22

These redox homeostasis proteins were found to be dysregulated in LSCs relative to HSCs, rendering LSCs particularly vulnerable to the cytotoxic effect of glutathione depletion. Thus, it was much to our surprise to learn that our MMB dimers failed to inherit this mechanism of action (Figure 12). Despite being as much as ten-fold more cytotoxic to AML relative to parthenolide, they were as much as ten-fold less effective at depleting intracellular glutathione.

Perplexed by this result, we looked to other measures of oxidative stress. In contrast to the glutathione data, we found that MMB dimers are able to induce oxidative stress at or better than parthenolide at cytotoxic doses (Figure 13) as measured by total reactive oxygen species

(ROS) or by the biological induction of HMOX1 expression. However, as doses increased beyond the EC50, this biological response was silenced, and as a result total ROS generation accelerated. This result was especially intriguing, as there is no precedent in the literature for this biphasic HMOX1 response from a small molecule, suggesting that MMB dimers may induce potent oxidative stress at low doses and then achieve cytotoxicity by simultaneously eliminating the ability of cells to mount an antioxidant response. This effect, readily measurable on both the mRNA and protein level suggests that MMB dimers affect the gene transcription or translation of

HMOX1 rather than through direct interaction with the protein.

Conclusions

Taking the most potent hits from the high throughput screen, we find that, with the exception of the naphthyls and diazoles, allyl halides, and amide-linked dimers, improved potency in the HTS faithfully predicted improved potency in primary AML. However, of these classes, only MMB dimers linked by carbamates or carbonate esters also demonstrated a profound reduction in toxicity to HSCs. Faced with a set of molecules with improved toxicity to the target cells and reduced off-target toxicity to healthy stem cells, we attempted to understand this favorable pharmacological improvement in both the numerator and the denominator of the calculation of therapeutic index using our understanding of the mechanism of action of the parent compound.

Unexpectedly, investigating these mechanisms only further confounded our understanding. While MMB dimers do have the ability to inhibit NF-κB and deplete glutathione

at supra-cytotoxic doses, they are clearly able to induce cell death in the absence of either of these activities, and while our sample set of AML is small, no clear trend could be observed in which improved efficacy in either mechanism could be correlated with improved cytotoxicity.

Instead, we find that in stark contrast to parthenolide, MMB dimers have the curious ability to potently induce oxidative stress at low doses, and as the dose approaches and exceeds cytotoxic levels, the ability of cells to mount this antioxidant response is silenced and total reactive oxygen species continue to accumulate.

Taken together, it was concluded that whatever mechanism of action the MMB dimers utilize, not only does this mechanism show over ten-fold discrimination between AML and

HSCs, but it clearly does not depend on the known LSC-selective mechanisms of parthenolide.

Additionally, we do not observe similar efficacy trends between MMB dimers and parthenolide across AML samples, and AML04 fails to show resistance to MMB dimers. As a result, it was hypothesized that MMB dimers represent an exciting new class of sesquiterpene lactones that may have a novel selective mechanism that can circumnavigate the mechanism of drug resistance in AML04. The potential reward of better understanding their mechanism of action represented not only the discovery of potent antileukemic small molecules but also the possibility of discovering a previously unknown targetable difference in the biology of these populations. Our efforts therefore turned to elucidating the binding targets of MMB dimers, as presented in Chapter IV.

Methods

Primary cell culture

For primary AML specimens, cells were obtained from volunteer donors. Informed consent was obtained in accordance with the Declaration of Helsinki. Human umbilical cord

blood (CB) samples were obtained from the National Disease Research Interchange (NDRI).

Live mononuclear cells were isolated from the samples using Ficoll-Paque density gradient separation. Primary AML cells were cryopreserved in freezing medium of Iscove-modified

Dulbecco’s medium (IMDM), fetal bovine serum, and dimethylsulfoxide (DMSO) or in

CryoStor CS-10 (VWR,West Chester, PA). All primary cells were cultured in serum-free medium (SFM), prepared with Iscove's MDM supplemented with 20% BIT 9500 serum substitute (StemCell Technologies), LDL, and beta-mercaptoethanol.

Apoptosis assay

Quantitative analysis of apoptosis using flow cytometry was performed as described in the previous chapter.

Flow cytometric labeling of primitive cell populations

To identify the primitive cell populations in human umbilical cord blood, flow cytometric analysis was performed by combining staining with fluorescent antibodies recognizing the surface markers CD34 and CD45 according to manufacturer specifications, with the apoptosis assay staining method described above. The percentage of non-apoptotic hematopoietic progenitor cells was defined as the population with negative staining for Annexin V and/or DAPI but positive staining for CD34 (and CD45 where applicable). In some cases, the CD34+ population of cells was enriched via magnetic isolation to accelerate analysis using the MACS®

CD34 MicroBead kit, according to manufacturer instructions. All analyses using primary cells were conducted in triplicate.

Western blot and electrophoretic mobility shift assay (EMSA)

For analysis of protein content, cell samples were washed three times and collected after the indicated treatments for four hours and lysed in RIPA buffer (50mM Tris (pH 7.4), 150mM

NaCl, 1% deoxycholic acid, 1% Triton X-100, 0.25mM EDTA, 5mM NaF) with freshly added protease inhibitor cocktail (Sigma-Aldrich) and clarified at 13000 RPM for 15 minutes at 4 ºC.

Electrophoresis loading was normalized to sample cell counts in lieu of protein concentration measurements. Blots were probed first with primary antibody for phosphorylated-NF-κB,

Actin, GAPDH, or HMOX1 followed by an HRP-conjugated secondary antibody and detection using the automated Gel Doc XR+ system equipped with Image Lab software (Bio-Rad

Laboratories). Nuclei from AML samples treated as indicated were extracted for EMSA and analyzed by Mohd Minhajuddin, PhD, as previously described.54

Quantification of total glutathione content

Total glutathione (GSH + GSSG) was quantified by colorimetric detection of the rate of production of 5′-thio-2-nitrobenzoic acid (TNB) from 5,5′-dithiobis-(2-nitrobenzoic) acid

(DTNB), measured at 405 nm, by an established method.55 AML cells were thawed and incubated for an hour before a four hour exposure to drug or vehicle. Compounds were dissolved in DMSO and added to media such that the final concentration of DMSO was held constant at 0.1%. Total glutathione was normalized to protein content for each sample as measured by the Bradford assay to correct for any variances in sample collection. Total glutathione expressed as nmol/mg total protein for each experimental sample was then normalized to the mean of four vehicle treated samples. The experimental assay was conducted in duplicate, each replicate of which was measured in triplicate for glutathione and protein content.

Measurement of reactive oxygen species

To measure the total reactive oxygen species content, cells were stained with CellROX®

Deep Red (Thermo Fisher) reagent according to manufacturer’s instructions. This proprietary

cell permeable reagent acquires fluorescence upon oxidation by reactive oxygen species with emission maxima around 665 nm. AML cells were treated with parthenolide or MMB dimer

JVM 3-88A for two hours at the indicated concentrations. Samples were counter-stained with

DAPI to exclude dead cells from the analysis. Compounds were dissolved in DMSO and added to media such that the final concentration of DMSO was held constant at 0.1%. The mean fluorescence intensity of untreated cells (X0) was subtracted from the mean fluorescence intensity (X) of each sample. All measurements were conducted in triplicate.

Quantitative real-time PCR

RNA was extracted from AML cells using Qiagen’s RNeasy Plus Mini kit. RNA concentrations were quantified on a NanoDrop spectrophotometer based on the A260/280 and

A260/230 ratios. Quantitative Real-Time PCR (qPCR) was performed on a LightCycler480

(Roche) using SYBR Green (Thermo Fisher) Master Mix reagent according to the manufacturer’s instructions. The quantified mRNA expression was first internally normalized to

GAPDH and then normalized to the control mean for comparison. Primer sequences HMOX1-F:

5’-AAGACTGCGTTCCTGCTCAAC-γ’, HMOX1-R: 5’-AAAGCCCTACAGCAACTGTCG-

γ’, GAPDH-F: 5’-TGCACCACCAACTGCTTAGC-γ’, GAPDH-R: 5’-

GGCATGGACTGTGGTCATGAG-γ’ were obtained from Integrated DNA Technologies and dissolved in molecular biology grade water. The experimental assay was conducted in triplicate, each replicate of which was measured in triplicate by qPCR.

CHAPTER IV

MMB DIMERS TARGET GALECTIN-1 TO OVERCOME CHEMORESISTANCE IN

ACUTE MYELOID LEUKEMIA

Chemoproteomic Screening in Drug Discovery

Alkylating agents have their own unique challenges when it comes to understanding pharmacology. In the case of parthenolide, which readily reacts with small molecule thiols as easily as cysteine residues in solution, understanding its efficacy for a given enzymatic target using traditional in vitro enzymatic inhibition assays is prohibited. For example, the purported inhibition of GPx22 cannot be quantified using traditional enzymatic assays, because while parthenolide may directly inhibit GPx activity, this cannot be decoupled from its reaction with the glutathione substrate. As a result, our understanding of parthenolide’s drug mechanism is primarily limited to what can be understood from cellular assays, and studying the relative efficacy of parthenolide derivatives suffers from the same limitations. However, while there may be many informative assays that are not suitable for alkylating agents, there is one in which they have a unique advantage: chemoproteomic target identification. Chemoproteomic screens, in which an affinity label applied to the small molecule of interest is used to purify their protein targets for identification by mass spectrometry, are gaining traction for their ability to identify the true milieu of in situ binding targets.56-57 While not limited to alkylating agents, the isolation of covalently-bound targets is more specific, greatly simplifying the interpretation of the isolated binding targets. This method, while powerful, is limited by the ability to correctly identify proteins small and large by the presence of peptides produced by trypsin digestion, inherently depending on the number of peptides produced, their relative ability to “fly” under the conditions chosen, and the ability of software algorithms to correctly identify the protein giving rise to each

identified peptide. As a result, the data generated is not quantitative and is susceptible both to false negatives and false positives, and as such must be interpreted with caution.

Despite these caveats, which become less significant as technology advances, previous work in our laboratory has successfully utilized this approach to characterize the glutathione- targeting mechanism of parthenolide using a biotin tag to identify cellular binding targets in

AML. Our initial attempts to replicate this success using a biotin-tagged MMB dimer were not successful, which we ultimately attributed to their large molecular weight which was not tolerant to the addition of the biotin tag. However, the use of “click chemistry” copper-catalyzed azido- alkyne cycloaddition allowed us to incorporate the biotin tag in situ, allowing us to generate a list of the protein binding targets of MMB dimers from AML, the results of which are presented in the first half of this Chapter. Analysis of these targets led us to the realization that MMB dimers target Galectin-1, an emerging target in cancer progression.

Galectin-1: Structure, Function and Role in Cancer

Galectins are an evolutionarily conserved family of -galactoside recognizing proteins carrying one or more carbohydrate recognition domains. Eleven human galectins have been identified to date. Structures vary within the family; galectins with a single carbohydrate recognition domain exist as “prototypic” homodimers (Galectin-1, -2, -7, -10, -13, -14),

“chimeric” oligomers (Galectin-3), while others carry two distinct carbohydrate recognition domains (“tandem repeat” Galectin-4, -8, -9 and -12).58-59 The galectins also vary in their affinity for oligosaccharides, allowing for specificity among the family in a given biological context such as stage of tissue development or disease state. Galectins represent a unique class of lectins that are fully distributed throughout the cell from the nucleus and cytoplasm to the plasma membrane and extracellular matrix, despite lacking transmembrane domains or classical

secretory sequences.60 They are known to regulate immune and inflammatory responses and can induce pro- or anti-inflammatory responses depending on the context studied. Galectin-361-63 and Galectin-964-66 have recently garnered interest as targets in AML, but to the author’s knowledge, Galectin-1 has not yet been shown to play a significant role in this disease.

Structure and function of Galectin-1

The four exons of the LGALS1 gene, located on chromosome 22q12, together encode for a 135 amino acid, 14 kD protein whose initial methionine residue is post-translationally removed. Expression of LGALS1 is highly sensitive to epigenetic methylation status and is enhanced by demethylation of the promoter.67 The association of Galectin-1 units into a non-

68 covalent homodimer has been shown to be concentration dependent in solution (Kd = 7 µM).

Each monomeric carbohydrate recognition unit is folded into a jellyroll of two anti-parallel - sheets, a motif characteristic of the family (Figure 15).69-70 The monomers are held together where the N- and C-termini meet by -sheet interactions at the interface and the resulting formation of a common hydrophobic core.69 Like other galectins, Galectin-1 is widely distributed throughout the cell and extracellular matrix. Depending on the context, monomeric and dimeric forms are believed to have different functions that are also redox-sensitive.71-73 In the homodimer, bivalent carbohydrate recognition occurs on either end, opposite the N- and C- terminal binding interface. This lectin activity is considered necessary for extracellular function, but not for intracellular functions which are attributed primarily to protein-protein interactions.69

Dimerized Galectin-1 has been shown to have ten-fold increased binding affinity for complex multiantennary repeating chains of lactosamine disaccharides over straight-chain poly-N- lactosamine.74

Of particular interest for our purposes is not only the general structure of Galectin-1 but also the location and functionality of its cysteine residues. Each monomer of

Galectin-1 bears six cysteine residues, two of which are buried within the hydrophobic core and are not accessible. The solvent-accessible cysteines, namely Cys2, Cys16, Cys88, and Cys130, are labeled in Figure 15B. Cys16 and Cys88 have been shown to generate disulfides under oxidative conditions.71 Cys2, also redox sensitive,71 is situated at the flexible N-terminus, freely

Figure 15. Solution structure of Galectin-1 homodimer (PDB ID: 2KM2). A) Jellyroll assembly of antiparallel beta sheets is the characteristic structure of galectins. Ribbons are colored blue to red from N- to C-terminus. B) Solvent-accessible surface of the dimeric Galectin-1 structure. Surface is color-coded by atom, with yellow indicating the location of sulfur atoms. Cysteine residues are labeled.

accessible in the native dimer or monomer state and is the most probable candidate for interactions with MMB dimers.

As one might expect, given the remarkable structural complexity of this 14 kD protein, the reported functions of Galectin-1 are similarly highly variable and context dependent. Their lectin function is primarily connected with extracellular signaling and cell-cell recognition.

Protein-protein interactions within the cell do not appear to be dependent on carbohydrate binding ability. Intracellularly, galectin-1 is involved in pre-mRNA splicing, and nuclear extracts depleted of Galectin-1 were shown to lose splicing activity.75-79 Extracellular Galectin-1 has been shown to have a biphasic effect on cell proliferation, with mitogenic activity at low (1 nM) doses and growth inhibition observed at high (1 µM) doses.69 Galectin-1 is known to bind with integrins in the plasma membrane, regulating cell adhesion and motility by modulating anchorage in the extracellular matrix.80-83 Galectin-1 is also a major regulator of maternal-fetal immune tolerance in placental mammals.72, 84 While the many functions of Galectin-1 continue to be elucidated, for our purposes the clinical significance of Galectin-1 in cancer is the most relevant.

Emerging role of Galectin-1 in cancer

The most striking property of Galectin-1 with respect to oncology is perhaps its seemingly universal correspondence with poor prognosis in a wide variety of solid tumors, including colorectal,85 head and neck squamous cell86 and renal cell87 carcinomas, gastric,80, 82 lung,83 brain,81, 88 pancreatic,89 cervical,90-91 ovarian,92-93 breast,94-95 and prostate cancers.96

Consistently, over-expression of Galectin-1 is associated with advanced tumor stages, epithelial- to-mesenchymal transition and metastasis as well as endothelial cell proliferation and migration necessary for tumor-sustaining angiogenesis.97-98 Of particular interest for understanding the role

of Galectin-1 in hematological malignancies, however, are two key observations about Galectin-

1: first, that gene silencing of LGALS1 has been demonstrated to reduce engraftment potential in murine xenograft models of invasive tumors,99 and second, that Galectin-1 confers T cell immune privilege to tumors.100 If Galectin-1 is ultimately found to play a similar role in AML, these observations suggest it may represent a targetable feature of leukemic stem cells, as well as a means to enhance natural T cell antitumor responses or sensitize cells to T cell based immunotherapies.

Presented in this Chapter are the results of the chemoproteomic identification of binding targets of the MMB dimers, the analysis of which led us to the realization that MMB dimers are potent Galectin-1 inhibitors which induce the rapid degradation of this protein. This degradation event is found to be specific to membrane and nuclear compartments, implicating cell membrane signaling and spliceosome functions of Galectin-1 in the progression of AML. While a single report exists in which Galectin-1 knockdown has been shown to sensitize OCI-AML3 cells to

BCL2 inhibition in a study of the Galectin-3 inhibitor GCS-100,63 to our knowledge there have been no prior studies addressing the role of Galectin-1 in AML. Further, while many groups are working to develop Galectin-1 inhibitors through rational design around the active lectin site, there are no FDA-approved Galectin-1 inhibitors. Future studies will seek to illuminate both the role of Galectin-1 in leukemic stem cells and the inhibitory mechanism of MMB dimers.

Results

Identification of in situ protein binding targets

Affinity tag selection

Our understanding of parthenolide’s LSC-selective mechanism of action is largely based on a study characterizing the covalent binding targets of a biotin-tagged derivative. This method

was implemented in our laboratory to elucidate parthenolide’s cellular protein binding partners,

so we sought to reproduce that process using a biotin labeled MMB dimer. While the biotin-

tagged MMB derivative BS 1-99 was sufficiently effective to identify a large number of putative

binding targets of parthenolide, a similar biotin tag applied to the structure of JVM 3-88A was

not effective enough to be used for this purpose (Figure 16). The azido-tagged MMB dimer BS

4-70, while similarly potent to parthenolide, was significantly less potent than the alkyne-tagged

MMB dimer JVM 5-61. This alkyne-tagged compound had similar potency in AML relative to

Figure 16. Affinity tag selection. A) Structures of tagged derivatives of the MMB dimer JVM 3- 88A: biotin-tagged BS-438A, azido-tagged BS 4-70, and alkyne-tagged JVM 5-61. B) Cytotoxicity of biotin-tagged MMB BS 1-99 and tagged MMB dimers relative to parthenolide and MMB in the M9-ENL cell line. C) Efficacy of the alkyne-tagged JVM 5-61 in AML relative to parent compounds parthenolide (PTL) and MMB, and MMB dimers.

lead MMB dimers JVM 2-76, JVM 3-88A, and JVM 3-64 (Figure 16C). As a result JVM 5-61

was ultimately chosen as the tagged compound for chemoproteomic screening in AML.

Affinity purification of protein binding targets

Copper-catalyzed azide-alkyne cycloaddition101 was performed in whole cells using the

mild copper reducing agent tris(2-carboxyethyl)phosphine (TCEP) and the ion-stabilizing

tris(benzyltriazolyl)methyl amine (TBTA) in phospho-buffered saline (Figure 17). Reaction

conditions to generate a successful cycloaddition were first tested using a fluorescent azide and

Figure 17. Biotin labeling reaction scheme. Cells treated with the alkyne-tagged MMB dimer JVM 5-61 were incubated in PBS containing a biotin azide in the presence of reagents to catalyze the cycloaddition of the biotin tag to covalent protein targets.

monitored by flow cytometry (data not shown). Optimized reaction conditions leading to

specific biotin-labeling were as follows: of 1 mM Cu(II)SO4, 1 mM TCEP, 0.1 mM TBTA, and

200 µM biotin PEG3 azide in PBS. Primary AML cells were treated for fifteen minutes with

JVM 5-61 before being subjected to the cycloaddition labeling reaction. Cells were then washed,

lysed in 1% sodium dodecyl sulfate (SDS), and biotin-labeled proteins were isolated from these

cell lysates using streptavidin affinity beads. Under these conditions, a large quantity of biotin-

labeled proteins could be detected by Western Blot by probing with HRP-conjugated streptavidin

(Figure 18). A separate aliquot of this sample was submitted for trypsin digest and proteomic

mass spectrometry identification.

Disregarding trypsin and streptavidin, a total of 770 putative MMB dimer target proteins

were identified by more than one peptide from primary AML. This list, rank-ordered by the

number of peptides identified, is presented in full in Appendix B. While we were optimistic that

Figure 18. Visualization of biotin-tagged proteins. Cells subjected to biotin-labeling of MMB dimer binding targets via cycloaddition and affinity purification were identified by HRP- conjugated streptavidin (left) or Simply BlueTM gel stain (right). The gel shown at right was cut into four sections and destained for trypsin digest and proteomic mass spectrometry identification. Arrows indicate nonspecific detection of streptavidin monomers.

the novel activity of MMB dimers might indicate less promiscuous binding relative to

parthenolide, this was unfortunately not the case. Thus, additional analysis was necessary to

interpret the assortment of binding targets.

Galectin-1 expression in AML and hematopoiesis

While nearly eight hundred proteins were identified as potential binding targets,

suggesting that MMB dimers have little binding specificity in AML cells, we also know that,

whatever their binding partners, MMB dimers exert selective cytotoxicity in AML while remaining nontoxic to HSCs. We reasoned that if the binding target(s) responsible for this differential activity could be located within this list, they could reasonably be expected to significantly differ in expression between AML and HSCs. We therefore examined these 770 hits in terms of their relative gene expression data using whole transcriptome RNA sequencing

(unpublished data) of functionally-defined LSCs from primary AML at diagnosis (dLSC, N=9), relapse (rLSC, N = 15) and HSCs from normal bone marrow (HSC, N = 6).14 Using the student’s t test, a total of β51 of these were found to be significantly (P < 0.05) different between

LSCs and HSCs, and seven of these 251 genes were found to be more than 20-fold overexpressed in LSCs relative to HSCs. Two of these genes, namely LGALS1 and ANXA2, had more than 100-fold expression differences. The top ten genes, as ranked by fold-change in expression between HSCs and LSCs at diagnosis are shown in Table 2 below. The top three genes stand out from this analysis with over fifty-fold higher expression in both LSCs and non-

LSC AML (dAML) cells at diagnosis, as well as LSCs from primary samples at relapse.

Table 2.. Gene expression of significantly over-expressed MMB dimer targets.

T-test (p value) Fold change HSC v HSC v HSC v HSC v HSC v HSC v dLSC dAML rLSC dLSC dAML rLSC LGALS1 0.003 0.093 0.0004 138.2 59.5 103.3 ANXA2 0.009 0.147 0.007 102.2 56.7 117.9 SAMHD1 0.004 0.132 0.004 59.6 68.4 83.7 TLR2 0.020 0.126 0.001 31.7 28.4 17.3 PRKCD 0.0002 0.067 0.000002 23.5 23.4 8.6 STAB1 0.042 0.140 0.005 20.5 17.9 14.5 ITGB2 0.006 0.088 0.001 20.0 18.0 11.2 APOBR 0.004 0.101 0.002 14.3 13.9 12.2 IQGAP1 0.004 0.110 0.002 10.8 9.2 8.7 FKBP5 0.017 0.002 0.004 6.5 3.3 4.0

To understand the clinical relevance of these top hits, Kaplan-Meier survival curves and relative gene expression values were generated using the Bloodspot database.102 The results from this analysis are presented in Figure 19 for LGALS1 and in Figure 20 for ANXA2 and

SAMHD1.

Figure 19. LGALS1 in AML. A) Relative gene expression of LGALS1 in hematopoiesis and AML. B) Overall survival of AML patients with high (N = 85, red line) and low (N = 87, blue dotted line) LGALS1 expression.

Comparing the top hits this way, it becomes clear that LGALS1 has a number of features that make it an ideal drug target in AML. In terms of specificity, the relative expression profile of LGALS1 in hematopoiesis suggests that targeting this gene or its product would be well- tolerated clinically. Among the genetic AML subtypes studied, there also appears to be a strong association with t(11q23)/MLL AML. Further, overall survival of AML patients is quite dramatically (p = 0.00075) correlated with LGALS1 gene expression, in strong agreement with our gene expression data of Galectin-1 LSCs at diagnosis and relapse.

Figure 20. ANXA2 and SAMHD1 in AML. A) Relative gene expression of ANXA2 and C) SAMHD1 in hematopoiesis and AML. B) Overall survival of AML patients with high (N = 91, red line) and low (N = 81, dotted blue line). D) Overall survival of AML patients with high (N = 86, red line) and low (N = 86, dotted blue line).

By contrast, other top hits ANXA2 and SAMHD1 (Figure 20) showed higher expression in normal hematopoiesis (ANXA2, Figure 20A) or lower expression in AML (Figure 20C).

Additionally, these hits were not significantly associated with overall survival (ANXA2, Figure

20B) or not strongly associated (SAMHD1 p = 0.03, Figure 20D). Comparable results were found for the other top hits presented in Table 2 (data not shown). Covalent binding between

MMB dimers and the Galectin-1 protein was verified by probing the affinity-purified proteins for

Galectin-1 protein (data not shown; result reproduced in Figure 23). LGALS1 was therefore selected as the lead potential drug target for pharmacological analysis. shRNA knockdown of Galectin-1

Figure 21. Knockdown of Galectin-1. A) Western Blot showing Galectin-1 protein levels eight days post-infection. B) Expression of LGALS1 mRNA as measured by qPCR. C) Cytotoxicity of PTL (10 µM) and MMB dimers (2.5 µM).

Knockdown of the Galectin-1 gene was successfully achieved in the Molm-13 (MLL-

AF9) leukemia cell line using short hairpin RNA (shRNA). The level of Galectin-1 protein in these cells was indeed quite high as measured by Western Blot and full knockdown of Galectin-1 protein took eight days following infection (Figure 21). At this time, Molm-13 cells infected with the Galectin-1 targeting shRNA (shGal1-e and shGal1-f) as well as the Scramble control vector were counted and plated for drug treatment with parthenolide or MMB dimer. The experiment was first conducted using MMB dimer JVM 3-88A and repeated using MMB dimer

JVM 2-76 (Figure 21C). No differences in cytotoxicity were observed using shGal1-e, but significant sensitization to the cytotoxicity of MMB dimers was observed in both experiments for shGal1-f. While no differences were observed for parthenolide in the first experiment, the second experiment showed sensitization to the parent compound, negating the ability to draw any conclusion about parthenolide without repeating the experiment. However, comparing the two, this could potentially be explained by penetrance of the knockdown, as the second experiment showed a more significant effect for MMB dimers as well.

Culture with rGalectin-1

Because the Galectin-1 protein also exists as an extracellularly secreted signal that is known to promote tumor aggressiveness, we sought to measure whether cells exposed to extracellular Galectin-1 could develop resistance to MMB dimers. By culturing leukemia cells in the presence of recombinant human Galectin-1 protein (1 µg/mL, ≈ 70 nM), significant resistance to MMB dimers, but not parthenolide was observed (Figure 22). This resistance was so robust that even cells who were exposed to drug after removal of rGalectin-1 from the culture media remained resistant to MMB dimers.

Figure 22. Protective effect of rGalectin-1. Molm-13 cells were cultured with or without recombinant Galectin-1 protein (rGal1, 1 µg/mL) for one week. After this period, cells were exposed to parthenolide (PTL, 20 µM) or MMB dimer JVM 3-88A (2.5 µM). Cells previously cultured with Galectin-1 were resistant to MMB dimers but not parthenolide.

MMB dimers induce rapid loss of Galectin-1 protein

While the pathological function of Galectin-1 in AML has yet to be elucidated, by analyzing Galectin-1 levels over time following MMB dimer treatment, we find that extending the exposure to the bivalent MMB dimers induces rapid loss of recoverable Galectin-1 protein

(Figure 23). Primary AML cells were treated with the alkyne-labeled MMB dimer JVM 5-61 (2

µM) for 15 minutes to 6 hours, followed by biotin labeling and affinity purification. Probing for

Galectin-1 protein shows a rapid loss of recovery of Galectin-1 that is preceded by the detection of Galectin-1 dimers as well as specific higher molecular weight conjugates (left). A control blot of the same samples, probed for total biotinylated protein, shows that this effect was specific for

Galectin-1 until around the four to six hours (right).

Figure 23. MMB dimers induce rapid depletion of Galectin-1 protein. AML cells treated with alkyne-labeled MMB dimer show rapid loss of recovered Galectin-1 protein. Probing with HRP- conjugated streptavidin demonstrates this effect was specific to Galectin-1 until four to six hours.

To confirm that this depletion could be observed in cells treated with unlabeled MMB dimers, AML cells were treated with MMB dimer JVM 3-88A for a similar timecourse.

However, total Galectin-1 was only moderately affected in the same time frame observed for

JVM 5-61 (data not shown). Reasoning that depletion must occur in a specific subset of the total

Galectin-1 protein, the experiment was repeated, with lysates separated into subcellular fractions representing cytoplasm, membrane (and organelles), and nucleus (and cytoskeleton). The results of this cellular subfractionation are shown in Figure 24. While the effect on whole cell

Galectin-1 was minor, both the membrane and nuclear compartments showed a dramatic loss of

Galectin-1. Importantly, this loss cannot be contributed more generally to the effects of

oxidative stress or apoptosis, as there was no effect on total Galectin-1 protein levels when treated with parthenolide.

Figure 24. MMB dimers induce depletion of monomeric Galectin-1 in the membrane and nucleus. AML06 cells treated with MMB dimer JVM 3-88A (4 µM) show loss of Galectin-1 in the membrane and the nucleus. Cross-linked Galectin-1 dimers also accumulate, as well as higher molecular weight complexes. By contrast, parthenolide (PTL, 10 µM) does not reduce Galectin-1 in the membrane or nucleus. Control lanes are shown at right.

Discussion

Identification of MMB dimer protein binding targets

Although attempts were made to identify biotinylated drug targets following treatment with the biotin-tagged (BS 4-38A) and azido-tagged (BS 4-70), these were ultimately unfruitful and were most likely a result of poor delivery in the case of BS 4-38A (molecular weight = 1163 amu) and reduction of the azide functionality in the case of BS 4-70. The alkyne-tagged JVM 5-

61, by contrast, successfully reproduced the potency of the MMB dimers in the cell line and primary AML (Figure 16). For proteomic analysis, primary AML cells were treated with a high

dose (10 µM) of this dimer to increase the potential of the “net” cast to successfully capture the most important protein target(s). This exposure was limited to fifteen minutes in order to minimize the capture of late binding events that might happen secondary to the initial drug action.

Although we had been cautiously optimistic that the improved specificity of MMB dimers relative to parthenolide would result in a simpler list of binding targets, this was not the case. Previous work in our laboratory identified 312 putative protein binding targets using a biotin-labeled MMB derivative.22 Using the copper-catalyzed azide-alkyne cycloaddition to biotin-label MMB dimer binding targets for affinity purification, we identified 770 putative protein targets (Appendix B). While these quantities aren’t straightforward to compare, having been conducted too far apart in time, space, and advancement of proteomic mass spectrometry, and not confirmed for the vast majority of proteins listed, it is worth mentioning that exactly half of the 312 protein targets of parthenolide appear in our list of MMB dimer targets, representing one-fifth of the total proteins in this list. Among these shared targets are many of the proteins involved in the known mechanism of action of parthenolide, including glutathione peroxidase

(GPX1), glutathione reductase (GSR), thioredoxin (TXN), and thioredoxin reductase (TXNRD1) as well as inhibitor of nuclear factor kappa b kinase subunit beta (IKK). However, absent knowing whether the improved therapeutic index of MMB dimers relative to parthenolide was a result of differential affinity for shared binding targets or from differential target identities, we could not readily interpret these two qualitative data sets further other than to validate the method. A large proportion (20 – 50%) of overlap between the two suggests that the affinity purification and proteomic identification methods utilized are both highly robust.

Examining the putative MMB dimer targets that appear in the list of 653 protein targets exclusive to the bivalent MMB dimers, an assortment of pathways known to be dysregulated in

AML are represented, including multiple JAK-STAT and (additional) BCL-2 signaling pathway members. Prominently featured are numerous ribosomal, splicing factor, nuclear, ribonuclear, and epigenetic proteins, potentially suggesting MMB dimers may affect gene transcription and/or translation. However, even if we assume these putative binding targets are truly representative of MMB dimer binding partners, it remains to be determined which of those binding events have any biological consequence whatsoever, let alone antileukemic effects. Moreover, while any number of these binding targets (and others not identified yet as well) may all play a role in the antileukemic mechanism of action of MMB dimers and deserve further investigation, we wanted to understand which of these were the most relevant for preclinical development of MMB dimers. Specifically, we were most interested in understanding the portion of their mechanism of action responsible for improved therapeutic index.

To incorporate this strategic element into our filtering of the list of MMB dimer binding targets, an independent whole-transcriptome profile of HSCs and LSCs at diagnosis and relapse was utilized. A simple student’s t-test evidenced that nearly a third of the 770 protein targets were significantly different in their expression between LSCs at diagnosis and healthy HSCs from bone marrow, so these were rank-ordered by the fold-change between these populations, the top ten of which are shown in Table 2. Gene expression differences between these populations, while more incriminating as the fold-change increases, are not sufficient to demonstrate a significant role in the disease. So, to better characterize how these top hits affect

AML patients, an independent informatics database was used to analyze the role of these differentially expressed MMB dimer targets to determine which were most likely to be involved

in the mechanism of action of MMB dimers. The Bloodspot database in an online resource housing a wealth of gene expression data from AML and normal hematopoiesis from human studies derived from oligonucleotide arrays.102 This valuable and intuitive tool allows researchers to visualize not only the relative gene expression in various genetic forms of AML, but also to compare these to normal blood cell development by simply entering the gene name.

More significant, perhaps, to the bench scientist, is the ability to instantly generate Kaplan-Meier survival curves using the clinically annotated gene expression data for 183 AML patients from the Cancer Genome Atlas research network. The LGALS1 gene expression was not only significantly different between normal HSCs and AML but it was also very strongly associated

(p < 0.001) with poor survival in the clinically annotated data (Figure 19). Since none of the other top hits of Table 2 shared both of these characteristics, LGALS1 was chosen as the lead

MMB dimer binding target for further investigation.

MMB dimers target Galectin-1

Validating the ability of MMB dimers to covalently bind Galectin-1 with or without the alkyne tag was facilitated by the bivalent nature of these thiol alkylators. Treating cells for short time periods (10-15 minutes) allows for detection of cross-linked Galectin-1 homodimers by

Western Blot (Figure 23 and Figure 24). While Galectin-1 is known to function both as a monomer and a homodimer, only the monomer can be observed under the denaturing conditions of SDS-PAGE unless the monomers are covalently linked. The presence of higher MW complexes picked up by probing for Galectin-1 following treatment with MMB dimers, but not parthenolide, demonstrate that binding Galectin-1 (and cross-linking it to itself and to other proteins) happens within minutes of exposure to MMB dimers.

Knowing with certainty that MMB dimers can bind Galectin-1, and given that Galectin-1

is associated with aggressive cancer phenotypes such as drug resistance, the epithelial-to- mesenchymal transition associated with metastasis, and increased engraftment potential in animal models of solid tumors, and further realizing that Galectin-1 is overexpressed in the LSC population that is difficult to target clinically, and that patients with higher levels of Galectin-1 succumb to the disease much earlier, with measurable differences in overall survival just months from diagnosis, we hypothesized that targeting Galectin-1 plays a significant role in the drug mechanism of MMB dimers.

The chemoprotective role of Galectin-1 has been demonstrated in numerous other cancer models, but not in AML. Thus, we first sought to determine whether Galectin-1 overexpression was protecting AML from MMB dimer induced cytotoxicity. And in fact, we see this is the case, both from the standpoint of sensitizing cells to MMB dimer treatment using shRNA gene silencing (Figure 21), or rendering them resistant by culturing cells in the presence of exogenous rGalectin-1 (Figure 22). The absence of Galectin-1 protein did not affect the viability or growth rate of Molm-13 cells in culture, supporting the hypothesis that Galectin-1 is chemoprotective rather than critical for the survival of cancer cells. This observation is important not only in terms of understanding the role of Galectin-1 in AML but also in terms of understanding the cytotoxic mechanism of MMB dimers.

Having observed the Galectin-1 dimers and higher molecular weight conjugates in the control blots, we opted to perform the biotin-labeling and affinity purification of binding targets after exposing cells to the alkyne-labeled dimer for increasing periods of time. Contrary to our expectations, we actually saw a loss of Galectin-1 recovery over time in this experiment that was clearly not a result of global protein recovery loss, which did not occur until sometime after four hours (Figure 23). Comparing these two blots, we could only rationalize that binding of MMB

dimers to Galectin-1 protein must cause its depletion, whether through degradation, cellular export, or conjugation to other proteins. To test this hypothesis, we treated AML cells with

MMB dimer JVM 3-88A for increasing periods of time. In this experiment, while a small loss of total Galectin-1 monomer could be observed after six hours of MMB dimer exposure, the change was underwhelming when compared to the affinity purified lysates (data not shown).

The simplest way to resolve this apparent contradiction was that MMB dimers must cause degradation (or export or conjugation) in a specific subset of the total cellular Galectin-1 protein. We know that Galectin-1 is widely distributed throughout the nucleus, cytoplasm, and is associated with glycosylated surface proteins as well as carrying out functions as an extracellularly secreted signal in the ECM, and a review of the literature suggests that its functions are highly dependent on its cellular location, as well as its relative existence as a dimer or monomer. We thus reasoned that probing for Galectin-1 levels after subcellular fractionation of cell lysates could be informative in helping us understand the interaction between MMB dimers and Galectin-1 protein levels. And ultimately, this was the case.

The effect of MMB dimer JVM 3-88A on the total Galectin-1 monomer in the cell

(Figure 24) after four hours was not significant, although dimers and additional conjugates accumulate over the same time period. Minimal quantities of oxidized Galectin-1 are detected when cells are treated with an equipotent dose of parthenolide. Notably, beyond the Galectin-1 dimers appearing around 28 kD, higher molecular weight bands identified by anti-Galectin-1 antibody are not shared between MMB dimers and parthenolide, suggesting MMB dimers have effects on Galectin-1 beyond general oxidative stress. Cytoplasmic results were similar to the whole cell results, suggesting that most of the intracellular Galectin-1 in AML is housed in the

cytoplasm. Parthenolide appears to cause a slight increase in cytoplasmic Galectin-1 levels but at this extraordinarily high level of expression it is difficult to judge.

Both the membrane and nuclear compartments, however, showed distinct losses of

Galectin-1. Interestingly, the membrane (and organelle) fraction demonstrates a consistent increase in membrane-bound Galectin-1 at one hour for both MMB dimer JVM 3-88A and parthenolide, suggesting that oxidative stress may induce a brief recruitment of Galectin-1 to the membrane. However, for JVM 3-88A, a distinct loss of Galectin-1 is observed after four hours without accumulation of Galectin-1 dimers, consistent with our observation after treatment with

JVM 5-61, which cannot be contributed to general oxidative stress as Galectin-1 levels did not drop after treatment with parthenolide. Much more dramatically, JVM 3-88A depleted nuclear

Galectin-1, while parthenolide had no effect on this compartment. After just 15 minutes of treatment, nuclear Galectin-1 monomers are depleted and large MW conjugates containing

Galectin-1 begin to appear after an hour. Monomeric nuclear Galectin-1 is barely detectable after four hours of exposure to JVM 3-88A. The isolation of high molecular weight (> 250 kD) complexes specific to the nucleus, which appear at one and four hours after drug treatment, in light of what we know about Galectin-1’s role in mRNA splicing suggests that Galectin-1’s cytoprotective role could be a result of this function.

Indeed, if we look back at the table of 770 putative MMB dimer binding targets

(Appendix B), we see that there are over 100 nuclear and translational proteins, including thirteen nuclear pore complex proteins, fifteen splicing factors and RNA-binding proteins, as well as over fifty ribosomal proteins, together encompassing more than 20% of the proteins identified by this method. Notably, SMN1, Gemin5, SNRNP200, as well as numerous RNA helicases and heterogenous nuclear ribonuclearproteins as well as several RNA-binding proteins

are among the proteins identified as putative binding targets. All of these are known components of the tightly-bound Survival of Motor Neuron (SMN) spliceosomal complex which Galectin-1 has been shown to regulate.75-76 The high MW complexes (> 250 kD) that appear in the nuclear fractions after an hour of MMB dimer treatment could potentially represent this complex.

Together with the observed rapid nuclear depletion of Galectin-1, this strongly implicates inhibition of Galectin-1’s splicing function in the mechanism of the bivalent MMB dimers.

However, we know that oxidative stress plays an intriguing role in their mechanism as well, as shown in Figure 13. The transient membrane recruitment of Galectin-1 seen an hour after exposure to both MMB dimers and parthenolide suggests this may be a response to general oxidative stress, as induced by sesquiterpene lactones. Interestingly, the transient increase is also observed in nuclear Galectin-1, but only for JVM 3-88A. Additionally, Galectin-1 is known to interact with Integrin Subunit Beta 2 (ITGB2) in the cell membrane, which was the seventh gene in the list of over-expressed binding targets in Table 2. Given our results, understanding this interplay between membrane-bound and nuclear Galectin-1, and whether these are related to the transcriptional control of oxidative stress response, may also be enlightening in terms of unraveling the chemoprotective mechanism of Galectin-1.

Taken together, these results demonstrate that nuclear and/or membrane-bound Galectin-

1 are responsible for the cytoprotective effects of Galectin-1 in AML, and that MMB dimers can rapidly and potently deplete Galectin-1 in both of these compartments, within minutes in the case of nuclear fractions. This nuclear effect, combined with the observation that nuclear and ribonuclear proteins, as well as numerous other components of the SMN assembly complex populate the list of proteins identified by the chemoproteomic screen, strongly implicate

Galectin-1’s role in pre-mRNA processing in the antileukemic mechanism of action of MMB

dimers. Interestingly, while most of the cellular Galectin-1 in our primary cells apparently resides in the cytoplasm, which theoretically separates the plasma membrane and organelles from the nucleus, we see only minor effects on cytosolic Galectin-1 in the same time frame, suggesting the involvement of a rapid nuclear shuttling mechanism. It is worth noting that the blast cells that characterize AML have very little cytoplasm separating the nucleus and plasma membrane, yet here the concentrations of Galectin-1 are high and very little effect on Galectin-1 levels is observed. However, the results of the affinity purification demonstrated quite convincingly that the consequence of MMB dimer binding to Galectin-1 results in its immediate and steady depletion from the cell. Whether this loss of nuclear Galectin-1 represents export, degradation, or some other complex trafficking mechanism remains unclear. It stands to reason that if the Galectin-1 loss observed in the nuclear compartment (or membrane) in Figure 24 was merely a consequence of intercompartmental shuttling, not only would we expect an increase in the cytoplasmic Galectin-1, which may be the case, moreover, we would have expected to see an increase in total Galectin-1 (or at least a plateau) isolated with increasing exposure to JVM 5-61 in Figure 23, in which we see the precise opposite. Further, although MMB dimer binding to

Galectin-1 could potentially explain the loss of signal over time by blocking antibody access to the epitope which recognizes a peptide near the N-terminus (which houses Cys2, the most likely candidate for MMB dimer binding) we see that this loss of signal is specific to the nuclear and membrane compartments, and we also see the accumulation of higher molecular weight conjugates, demonstrating that this binding event is both specific to the nuclear and membrane compartments and also has some functional consequence, at least for nuclear Galectin-1.

The specific loss of Galectin-1 protein relative to other biotinylated protein targets recovered in this assay is therefore most readily attributable to degradation or cellular export of

nuclear Galectin-1. The rapid effect on nuclear Galectin-1 levels, measurable after just fifteen minutes of exposure to JVM 3-88A, appears to be primarily responsible for the dramatic loss observed after binding to JVM 5-61. The mechanism by which MMB dimers cause the rapid loss of Galectin-1 monomer in the nucleus, the interplay between membrane-bound and nuclear

Galectin-1, and the mechanism by which Galectin-1 is able to exert a chemoprotective effect in

AML, have yet to be elucidated.

Conclusions

This study demonstrates several novel findings, not least of which is the discovery that

MMB dimers are potent inhibitors of Galectin-1. While other inhibitors of this elusive target have been hard sought after, none rival the potency of MMB dimers in vitro either in terms of cytotoxicity or inhibition of Galectin-1. Further, this is the first report demonstrating that

Galectin-1 plays a significant role in AML. The chemoprotective effects of Galectin-1, found to be more than 100-fold overexpressed in the difficult-to-treat leukemic stem cell population both at diagnosis and at relapse, may explain the striking correlation between its gene expression and prognosis in these patients. Together these results strongly support the need for additional preclinical development of MMB dimers as Galectin-1 targeting agents. Preliminary pharmacokinetic assays are planned to determine whether the water-soluble dimethylamino fumarate salt of JVM 3-88A (namely JVM 3-88C) has appreciable bioavailability.

Because Galectin-1 exists both extracellularly and intracellularly in every compartment and is known to have a wide variety of functions depending on the biological context and cellular localization, none of which play a clearly defined role in the biology of AML, we sought to clarify which compartment was affected by MMB dimers. Surprisingly, a dramatic loss of

Galectin-1 monomer was observed first in the nuclear fraction, and then the membrane, but not

the cytosolic fraction. This suggests that Galectin-1 can move from the membrane to the nucleus directly without relying on passive diffusion through the cytoplasm, potentially through endocytosis. Additionally, this implicates two major functions of Galectin-1 in AML: membrane bound activity that may reflect cell surface glycoprotein recognition, and nuclear activity that may reflect spliceosome function. Given the overexpression of Galectin-1 observed in our own functionally-defined LSCs at diagnosis and relapse, and the dramatic correspondence of

Galectin-1 with survival in AML patients, future studies elucidating how these functions may play a role in LSC biology are our main priority moving forward.

Methods

Cell culture and apoptosis assay

M9-ENL and primary AML cells were cultured as described in the previous chapters.

Molm-13 cells were cultured in RPMI 1640 supplemented with 10% FBS and pen/strep. For culture experiments with recombinant Galectin-1, cells were cultured in the presence of 1 μg/mL recombinant human Galectin-1 (abcam) by addition to the media during each subculture event throughout the course of a week before harvesting cells for analysis in the cytotoxicity assay.

Apoptosis assays were conducted as described in Chapter II.

Western blot and cellular fractionation

AML cells were treated as described before washing and collecting for Western Blot as described in Chapter III. For analysis of subcellular fractions, a commercially available kit and associated control antibodies (Cell Signaling Technology) were used according to the manufacturer’s specifications. Proteins were separated using TGX® gradient gels or Stain-

FreeTM TGX® precast gradient gels (BioRad). Primary antibodies against human Galectin-1,

AIF, MEK1/2, and Histone H3 were obtained from Cell Signaling Technology. Biotin-labeled proteins were detected using HRP-conjugated streptavidin (Thermo Fisher).

Affinity purification of MMB dimer binding targets

Copper-catalyzed azide-alkyne cycloaddition101 was used to conjugate a biotin label to the binding targets of the alkyne-labeled carbonate ester MMB dimer JVM 5-61 in situ. For proteomic analysis, primary AML cells were incubated for 15 minutes with 10 µM JVM 5-61, then collected and washed three times before the addition of cycloaddition reagents in PBS (1 mM CuSO4, 1 mM TCEP, 0.1 mM TBTA) with 200 µM azido-biotin (Lumiprobe). Cells were agitated on ice for one hour to allow the labeling reaction to complete. Following the labeling, cells were washed three times with PBS and lysed by sonication in 1% sodium dodecyl sulfate solution in PBS followed by enrichment using PierceTM streptavidin agarose beads in an end- over-end rotator overnight. Beads were vigorously washed three times each with high salt (500 mM NaCl, 0.09 M NaOAc, pH 5.0) and glycine (0.1 M, pH 2.8) wash solutions to remove any nonspecific binding proteins before washing with TBS to remove residual glycine. Binding targets were eluted from the washed beads directly into PAGE loading buffer for Western Blot or proteomic analysis by boiling for twenty minutes.

Binding target identification

Identification of affinity-purified proteins was performed by Monika Dzieciatkowska,

PhD, as follows:

Sample preparation for mass spectrometric analysis

Samples were loaded onto a 1.5 mm thick NuPAGE Bis-Tris 4−1β% gradient gel

(Invitrogen). The BenchMark™ Protein Ladder (Invitrogen) was used as a protein molecular mass marker. The electrophoretic run was performing by using MES SDS running buffer, in an

X-Cell II mini gel system (Invitrogen) at 200 V, 120 mA, 25 W per gel for 30 minutes. The gel was stained using SimplyBlue™ SafeStain (Invitrogen, Carlsbad, CA) stain and de-stained with water according to the manufacturer’s protocol. Each lane of the gel was divided into 4 equal- sized bands, and proteins in the gel were digested as follows. Gel pieces were destained in 200

µL of 25 mM ammonium bicarbonate in 50 % v/v acetonitrile for 15 min and washed with 200

µL of 50% (v/v) acetonitrile. Disulfide bonds in proteins were reduced by incubation in 10 mM dithiothreitol (DTT) at 60 °C for 30 min and cysteine residues were alkylated with 20 mM iodoacetamide (IAA) in the dark at room temperature for 45 min. Gel pieces were subsequently washed with 100 µL of distilled water followed by addition of 100 µL of acetonitrile and dried on SpeedVac (Savant ThermoFisher). Then 100 ng of trypsin was added to each sample and allowed to rehydrate the gel plugs at 4 °C for 45 min and then incubated at 37 °C overnight. The tryptic mixtures were acidified with formic acid up to a final concentration of 1%. Peptides were extracted two times from the gel plugs using 1% formic acid in 50% acetonitrile. The collected extractions were pooled with the initial digestion supernatant and dried on SpeedVac (Savant

ThermoFisher). Samples were desalted on Thermo Scientific Pierce C18 Tip.

Mass spectrometry

Samples were analyzed on a Q Exactive quadrupole orbitrap mass spectrometer (Thermo

Fisher Scientific, Waltham, MA, USA) coupled to an Easy nLC 1000 UHPLC (Thermo Fisher

Scientific) through a nanoelectrospray ion source. Peptides were separated on a self-made 15 cm

C18 analytical column (100 µm x 10 cm) packed with 2.7 µm Phenomenex Cortecs C18 resin.

After equilibration with 3µL 5% acetonitrile 0.1% formic acid, the peptides were separated by a

70 min a linear gradient from 4% to 30% acetonitrile with 0.1% formic acid at 400nL/min. LC mobile phase solvents and sample dilutions used 0.1% formic acid in water (Buffer A) and 0.1%

formic acid in acetonitrile (Buffer B) (Chromasolv LC–MS grade; Sigma-Aldrich, St. Louis,

MO). Data acquisition was performed using the instrument supplied Xcalibur™ (version γ.0) software. The mass spectrometer was operated in the positive ion mode, in the data–dependent acquisition mode. The full MS scans were obtained with a range of m/z 300 to 1800, a mass resolution of 60,000 at m/z 200, and a target value of 1.00E+06 with the maximum injection time of 50 ms. HCD collision was performed on the 15 most significant peaks, and tandem mass spectra were acquired at a mass resolution of15,000 at m/z 200 and a target value of 1.00E+05 the maximum injection time of 100 ms. Isolation of precursors was performed with a window of

2 Th. The dynamic exclusion time was 20s. The normalized collision energy was 30.

Database searching, protein identification

MS/MS spectra were extracted from raw data files and converted into mgf files using

MassMatrix (Cleveland, OH). These mgf files were then independently searched against mouse

SwissProt database using an in-house Mascot™ server (Version β.5, Matrix Science). Mass tolerances were +/- 15 ppm for MS peaks, and +/- 20 ppm for MS/MS fragment ions. Trypsin specificity was used allowing for 1 missed cleavage. Met oxidation, protein N-terminal acetylation, peptide N-terminal pyroglutamic acid formation were allowed for variable modifications while carbamidomethyl of Cys was set as a fixed modification.

Scaffold (version 4.4.6, Proteome Software, Portland, OR, USA) was used to validate

MS/MS based peptide and protein identifications. Peptide identifications were accepted if they could be established at greater than 95.0% probability as specified by the Peptide Prophet algorithm. Protein identifications were accepted if they could be established at greater than

99.0% probability and contained at least two identified unique peptides.

Bioinformatic filtration of binding targets

To better understand which binding targets might be able to explain the observed in vitro therapeutic index of MMB dimers, the results from a gene expression profile of leukemic stem cells relative to hematopoietic stem cells from normal bone marrow was introduced as a means to filter out the nonspecific binding targets identified by the proteomic approach. Whole transcriptome profiling of leukemic stem cells at diagnosis, relapse, and normal bone marrow was conducted in collaboration with University of Rochester (unpublished data) as part of a wider study, published recently.14 From this dataset, the gene expression of the 770 binding targets identified by proteomics were extracted for analysis. Comparing the gene expression of normal bone marrow stem cells (N = 6) to leukemic stem cells as diagnosis (N = 9) and relapse

(N = 15), a total of seven genes in the list of binding targets were found to be significantly (two- tailed student’s t test, P < 0.05) and dramatically (over twenty-fold) overexpressed in LSCs at diagnosis and relapse. Gene expression data and overall survival data from BloodSpot capturing healthy and malignant hematopoiesis was used to further characterize these seven genes in terms of putative drug mechanism(s). shRNA knockdown of LGALS1

Knockdown of the Galectin-1 protein in Molm-13 cells was achieved using lentiviral infection according to an established procedure.103 The coding sequence of LGALS1 was effectively targeted by the following shRNA sequences: shGAL1-e: 5’-

CCTGAATCTCAAACCTGGAGA-γ’ and shGAL1-f: 5- CAACCTGTGCCTGCACTTCAA-γ’.

These sequences were constructed into the pLKO.1-GFP vector, which contains a human U6 promoter to drive shRNA expression and an IRES-GFP to label shRNA-expressing cells. Five days post-infection, live (DAPI-) GFP-positive cells were sorted by FACS on a BD FACSAria

and analyzed for mRNA expression of LGALS1. Protein expression was significantly reduced by eight days post-infection, at which time knockdown cells and those transfected with a scrambled control sequence were treated with parthenolide or MMB dimers for analysis in the apoptosis assay.

Quantitative Real-Time PCR

RNA purification and qPCR were performed as described in Chapter III. Primer sequences Gal1-F: 5’- CTGTGCCTGCACTTCAACC-γ’, and Gal1-R: 5’-

CATCTGGCAGCTTGACGGT-γ’ were obtained from Integrated DNA Technologies and dissolved in molecular biology grade water.

CHAPTER V

SUMMARY AND CONCLUSIONS

Phenotypic Screening

The high-throughput screen of parthenolide derivatives yielded several consistent observations. C13 substitutions, excepting dimethylamino modifications and their fumarate salts, reduced the potency of the parent compound, regardless of whether the scaffold was that of parthenolide, MMB, or micheliolide, supporting the hypothesis that its antileukemic mechanism relies on the alkylating ability of the α-methylene--lactone. Of the 413 compounds analyzed, ten had EC50 values below 1 µM: five MMB dimers, and five heterocyclic C14 derivatives. This observation was characteristic of the relative efficacies of the many derivative classes analyzed.

Among the heterocycles, α-keto indoles, amide indoles, simple ester indoles, and phenyltriazoles were the most potent. Similarly, MMB dimers with a variety of linkage functionalities were among the most potent, including carbamates, carbonate esters, and a carbonate ester triazole combination.

In primary AML, a high degree of similarity was observed between the HTS data and our

AML samples, validating the use of this surrogate as a measure of AML toxicity. Naphthyls, diazoles, and allyl halides were effective but not moreso than parthenolide, while with one exception the amide-linked MMB dimers were inactive for unknown reasons. Unfortunately, though we identified a substantial number of potent antileukemic agents that could induce apoptosis in 90% or more primary AML cells overnight at low (< 5 µM) doses, most of these classes exhibited a similar toxic effect in HSCs, suggesting they may have no therapeutic index in which delivering nontoxic doses could generate an antileukemic effect in vivo. However, the

MMB dimers, which were nontoxic to HSCs at doses ten times higher than their AML EC50

(JVM 2-76, AML EC50 = 1.7 ± 0.6 µM (N = 5), HSC EC50 > 16 µM) were a remarkable exception. Additionally, we found that while MMB dimers were ten-fold more potent than parthenolide in the M9-ENL cell line, the relative potency varied in primary AML from two-fold to ten-fold. In the results from AML04, a relapsed and treatment refractory sample that shows resistance to parthenolide (AML04 EC50 = 16 µM), we find no such resistance for MMB dimers

(Figure 10). This suggests that the mechanism of drug resistance in this sample is not effective against these compounds, and further, that MMB dimers and parthenolide do not share cytotoxic mechanisms. Indeed, instead of inhibiting NF-κB and depleting glutathione, we find that MMB dimers induce oxidative stress at low doses, and as the dose increases the ability of cells to mount an antioxidant response is silenced and total reactive oxygen species continue to accumulate.

Together, these results drove us to the conclusion that MMB dimers have a novel mechanism of action that is more toxic to AML and less toxic to HSCs, that is not sensitive to mechanisms of drug resistance. We immediately began a quest to elucidate their cellular binding targets in the hopes of understanding this mechanism. Efforts utilizing a directly applied biotin tag were unsuccessful, as was the use of an azido tag, but the alkyne-tagged MMB dimer was ideal for our purposes as it maintained the potency of MMB dimers in M9-ENL and AML cells and could readily be biotin-labeled using the copper catalyzed cycloaddition reaction with a biotin azide. This biotin label was used to affinity-purify MMB dimer targets which were identified by proteomic mass spectrometry, resulting in a list of 770 potential cellular protein targets. Comparing what we know about these putative targets in terms of relative gene expression from shotgun transcriptome sequencing in LSCs and HSCs, we find that Galectin-1 has the highest relative expression between LSCs and HSCs at diagnosis. Additionally, the

relative expression between HSCs and progenitor cells in the Cancer Genome Atlas dataset, which includes gene expression values from microarrays using cell-surface defined hematopoietic populations (N = 183) agrees with our smaller, but functionally verified comparison using data generated from RNAseq. Finally, Galectin-1 expression is found to be very strongly (p < 0.001) correlated with patient survival, suggesting a high degree of unmet clinical need in the form of Galectin-1 targeting agents. Together, these observations led us to investigate the role of Galectin-1 in the antileukemic mechanism of MMB dimers.

Targeting Galectin-1

Similar to the results observed in other cancers, we find that Galectin-1 is chemoprotective in AML, especially for MMB dimers JVM 2-76 and JVM 3-88A. Using a shRNA knockdown of Galectin-1 we can sensitize cells to MMB dimers, and possibly parthenolide. Further, culturing cells with rGalectin-1 renders cells quite resistant to MMB dimer treatment, a finding that is not observed for parthenolide. These results conclusively demonstrate that Galectin-1 plays a chemoprotective role in the antileukemic mechanism of

MMB dimers. However, this does not explain how or why Galectin-1 exerts this function, or how MMB dimers are able to subdue it. In the hopes of beginning to understand this mechanism, we measured Galectin-1 levels after increasing exposure to MMB dimers, with and without affinity purification.

When MMB dimer targets are affinity purified, a dramatic loss of recovered Galectin-1 is observed that is specific to Galectin-1. Given the nature of this method, in which intact cells are subjected to the cycloaddition reaction after increasing exposure to an alkyne-labeled MMB dimer, we can only reason that this result shows that the consequence of binding to MMB dimers is the loss of Galectin-1 protein, potentially through intracellular degradation. By measuring

Galectin-1 levels in the membrane, cytosol, and nucleus, we find that this event is specific to the nucleus and membrane. This suggests that Galectin-1 can move from the membrane to the nucleus directly without requiring diffusion through the cytoplasm. This implicates two major functions of Galectin-1 in AML: membrane-associated activity that may relate to cell surface glycoprotein recognition, and nuclear activity that may relate to spliceosome function. The mechanism by which MMB dimers cause the rapid loss of Galectin-1 protein in the nucleus, the interplay between membrane-bound and nuclear Galectin-1, and the mechanism by which

Galectin-1 is able to exert a chemoprotective effect in AML, have yet to be elucidated.

Conclusions

This work simultaneously presents the discovery of MMB dimers as potent Galectin-1 inhibitors alongside the discovery that this target plays a chemoprotective role in AML. MMB dimers were discovered from a high-throughput screen of over 400 derivatives of parthenolide.

MMB dimers are two-fold to ten-fold more potent in primary AML than parthenolide (JVM 2-76

AML EC50 = 1.7 ± 0.6, JVM 3-88A AML EC50 = 1.7 ± 0.7, N = 5) and show almost no toxicity to HSCs at ten times this dose. MMB dimers can exert potent cytotoxic effects in the absence of both NF-κB inhibition and glutathione inhibition, the two major hallmarks of parthenolide’s

LSC-selective mechanism, suggesting these derivatives have a novel mechanism of action.

Combining the informatic power of chemoproteomic screening using an in-situ applied biotin label to identify protein binding targets with transcriptomic sequencing of functionally defined

LSCs and HSCs as well as clinically annotated gene expression data of AML patients, we find that MMB dimers potently deplete nuclear and membrane-bound Galectin-1. Nuclear depletion of Galectin-1 is especially rapid, occurring within minutes of MMB dimer exposure, while using lentiviral delivery of short hairpin RNA, it takes eight days or more to deplete total cellular

Galectin-1 (which is not overtly cytotoxic). This points to the role of Galectin-1 as a chemoprotective signal rather than playing a cytotoxic role, in agreement with findings in other cancer models. If Galectin-1 is chemoprotective, that would imply that the actual cytotoxic event exerted by MMB dimers lies elsewhere, which is indeed in keeping with the data observed from the shRNA knockdown in which MMB dimers are more cytotoxic in the absence of

Galectin-1, not less.

The success of this work belies the efficacy of the strategies used herein. This project would not have been possible without the combination of state of the art medicinal chemistry and disease biology. The library of derivatives utilized was not a plate of known compounds pulled from a shelf, but a novel library synthesized around a natural pharmacophore with the phenotype of interest, namely the ability to selectively target leukemic stem cells. The medicinal chemistry effort involved is evident not only in the sheer number of compounds screened, but in their sequential increase in potency. Similar expertise was exercised in the selection of in vitro screening models that balance the need for high-throughput analysis with the dedication to truly representative disease models. The ability to identify a clinically relevant disease target heretofore unknown in AML through a small molecule discovery project must also be attributed to the clinical relevance of the in vitro models utilized. In particular, the counter-screen against

HSCs was ultimately the deciding factor between dozens of potential small molecule leads with improved potency against AML. Further, the data from our relatively small sets of primary

AML samples for Galectin-1 was found to be in agreement with a completely independent, publicly available gene expression dataset of clinically annotated AML as well as hierarchical hematopoietic differentiation, allowing us to identify a significant prognostic indicator with

differential expression between HSCs and LSCs while simultaneously identifying a way to target the same.

The chemoprotective effects of Galectin-1, found herein to be more than 100-fold overexpressed in the difficult-to-treat leukemic stem cell population both at diagnosis and at relapse, may explain the striking correlation with prognosis in these patients. The survival curves observed in the Cancer Genome Atlas dataset strongly suggest that serum Galectin-1 levels can inform prognosis in the clinic, as has already been posited for other cancers, including lymphoma. Unfortunately, there are no clinical agents that target Galectin-1, though many groups are working toward these and at the time of this publication, one has reached Phase I clinical trial.98 Unlike other Galectin-1 inhibitors in development, MMB dimers are also inherently cytotoxic, making them unique in that they exert their own inherent synergy against

AML by disarming this defense mechanism. Considering the challenges of unmet clinical need with respect to Galectin-1 inhibitors in a wide variety of cancers and the dearth of potent

Galectin-1 targeting compounds, these results demonstrate that the preclinical development of

MMB dimers as first-in-class antileukemic Galectin-1 targeting agents may represent a remarkable opportunity in anticancer therapeutics.

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APPENDIX A

HTS OF PARTHENOLIDE DERIVATIVES

Presented here are the high-throughput screening results, including dose-response curves, from parthenolide derivatives screened against the M9-ENL cell line. All compounds presented in this Appendix were synthesized by Venumadhav Janganati, PhD (JVM), Narsimha Reddy

Penthala, PhD (PNR), Shobanbabu Bommagani, PhD (BS), Suresh Kuarm Bowroju, (BSK), or

Soma Shekar Dachavaram (DSS), in Peter Crooks’ laboratory in the Department of

Pharmaceutical Sciences at the University of Arkansas for Medical Sciences from 2012 – 2017.

Contents

ACUTE MYELOID LEUKEMIA: CHALLENGES AND OPPORTUNITIES ...... 1

Introduction ...... 1

The Challenge of Acute Myeloid Leukemia ...... 4

Clinical acute myeloid leukemia ...... 4

Leukemic stem cells ...... 6

The Opportunity of Parthenolide ...... 8

Summary ...... 10

HIGH-THROUGHPUT SCREENING OF PARTHENOLIDE DERIVATIVES ...... 11

High-Throughput Screening Platform ...... 11

High-Throughput Screening Results...... 12

Dose-response curves and structures of derivatives ...... 12

Quantitative results ...... 14

Discussion ...... 14

Quantitative structure-activity relationships ...... 14

Simple combinations ...... 17

Trans-annular cyclized (TAC) derivatives ...... 18

C13-substituted (C13) derivatives ...... 18

C14-substituted (C14) derivatives ...... 18

Dimer derivatives ...... 26

Conclusions ...... 29

Inactive modifications ...... 29

Potent modifications ...... 30

Methods...... 30

Library handling and storage...... 30

M9-ENL cell culture ...... 30

Apoptosis assay ...... 31

Quantitative results ...... 31

DISCOVERY OF AML-SELECTIVE MMB DIMERS ...... 33

Therapeutic Index Screening ...... 33

LSC-Selective Mechanism of Action ...... 34

Therapeutic Index Screening Results ...... 35

Primary acute myeloid leukemia ...... 35

Determination of therapeutic index ...... 36

AML comparison ...... 38

100

Antileukemic Mechanism of Action ...... 39

NF-κB inhibition ...... 39

Oxidative stress ...... 40

Discussion ...... 42

Primary AML and therapeutic index ...... 42

Selective drug mechanism ...... 44

Conclusions ...... 46

Methods...... 47

Primary cell culture ...... 47

Apoptosis assay ...... 48

Flow cytometric labeling of primitive cell populations ...... 48

Western blot and electrophoretic mobility shift assay (EMSA) ...... 48

Quantification of total glutathione content...... 49

Measurement of reactive oxygen species ...... 49

Quantitative real-time PCR ...... 50

MMB DIMERS TARGET GALECTIN-1 TO OVERCOME CHEMORESISTANCE IN

ACUTE MYELOID LEUKEMIA ...... 51

Chemoproteomic Screening in Drug Discovery ...... 51

Galectin-1: Structure, Function and Role in Cancer ...... 52

Structure and function of Galectin-1 ...... 53

Emerging role of Galectin-1 in cancer ...... 55

101

Results ...... 56

Identification of in situ protein binding targets ...... 56

Affinity tag selection ...... 56

Affinity purification of protein binding targets ...... 58

Galectin-1 expression in AML and hematopoiesis...... 59

shRNA knockdown of Galectin-1 ...... 63

Culture with rGalectin-1...... 64

MMB dimers induce rapid loss of Galectin-1 protein...... 65

Discussion ...... 67

Identification of MMB dimer protein binding targets ...... 67

MMB dimers target Galectin-1 ...... 70

Conclusions ...... 76

Methods...... 77

Cell culture and apoptosis assay ...... 77

Western blot and cellular fractionation ...... 77

Affinity purification of MMB dimer binding targets ...... 78

Binding target identification...... 78

Sample preparation for mass spectrometric analysis ...... 78

Mass Spectrometry ...... 79

Database searching, protein identification ...... 80

Bioinformatic filtration of binding targets ...... 81

102

shRNA knockdown of LGALS1 ...... 81

Quantitative Real-Time PCR ...... 82

SUMMARY AND CONCLUSIONS ...... 83

Phenotypic Screening...... 83

Targeting Galectin-1 ...... 85

Conclusions ...... 86

REFERENCES ...... 89

103

Quantitative Results

The determined EC50 values for each compound along with their corresponding 95% confidence intervals are reported below. The table is organized by compound name for ease of reference, so each compound’s corresponding Batch number is also denoted. With few exceptions, more than one experiment was required to accurately determine the EC50 for each potent compound in a given Batch. In some cases, the compound was re-shipped and new aliquots were prepared for a subsequent Batch analysis for the purposes of publication. In these cases, EC50 values from the newer aliquots may have replaced the prior values if a disagreement was observed between the experiments.

104

High-throughput screening results

COMPOUND Batch EC50 (µM) 95% CICOMPOUND Batch EC50 (µM) 95% CI BS 1- 01 1.7 >>10 nd BS 3- 24A 12.1 10 nd BS 1- 02 1.7 >>10 nd BS 3- 37 12.2 1.5 (1.4 - 1.6) BS 1- 03 1.1 9.7 (9 - 10.3) BS 3- 46 12.1 >>10 nd BS 1- 08 1.7 >>10 nd BS 4- 10 14.2 1.3 (1.1 - 1.6) BS 1- 12 1.7 >>10 nd BS 4- 38A 15.3 19 (14 - 25) BS 1- 21 1.7 >>10 nd BS 4- 38B 15.3 27 (25 - 30) BS 1- 28 12.3 1.1 (0.9 - 1.4) BS 4- 46 16.1 >>10 nd BS 1- 35 7.2 >>10 nd BS 4- 60 17.1 7.2 (6.3 - 8.3) BS 1- 87 7.2 30 (10 - 140) BS 4- 61 17.0 2 (1 - 4) BS 1- 88 10.1 >>10 nd BS 4- 70 17.0 12 (9 - 17) BS 1- 93 3.2 >>10 nd BS 4- 71 17.2 >>10 nd BS 1- 94 3.2 >>10 nd BS 4- 143 19.1 3.9 (3.5 - 4.3) BS 1- 96 3.3 >>10 nd BS 4- 144 19.1 5.7 (5.3 - 6.1) BS 1- 98 3.2 17 (15 - 18) BS 5- 11 19.2 2.1 (1.9 - 2.3) BS 1- 99 3.3 10 (9 - 11) BS 5- 13 19.1 >>10 nd BS 2- 01 6.1 2 (1 - 3) BS 5- 26 21.2 16 (13 - 20) BS 2- 04 6.2 0.72 (0.69 - 0.77) BS 5- 28 21.2 2.3 (2.2 - 2.4) BS 2- 05 3.1 2.5 (2.2 - 2.7) BS 5- 37 22.1 7 (6 - 9) BS 2- 08 7.2 15 (7 - 30) BSK 1- 40 23.2 1.3 (1.2 - 1.4) BS 2- 16 7.2 >>10 nd BSK 1- 87 25.1 1.2 (1 - 1.4) BS 2- 17 7.2 >>10 nd DSS 1- 137 25.1 2.3 (2 - 2.6) BS 2- 22 7.2 50 (10 - 600) DSS 1- 140 25.1 2.8 (2.6 - 3.1) BS 2- 27 8.1 >>10 nd DSS 1- 153B 26.1 1.6 (1.2 - 1.9) BS 2- 30 17.1 4.4 (3.9 - 5) DSS 1- 153C 26.1 4.0 (3.7 - 4.3) BS 2- 31 6.1 3.0 (2.8 - 3.2) DSS 2- 01 26.1 4.6 (4.3 - 5) BS 2- 32 6.1 4.0 (3.9 - 4.1) JVM 1- 01 0.1 >>10 nd BS 2- 59 7.2 9.6 (9.1 - 10) JVM 1- 02 0.1 >>10 nd BS 2- 60 7.3 2.8 (2.7 - 2.9) JVM 1- 03 0.1 >>10 nd BS 2- 63 7.2 3.0 (2.8 - 3.2) JVM 1- 04 0.1 >>10 nd BS 2- 64 7.3 4.4 (3.9 - 4.8) JVM 1- 05 0.1 >>10 nd BS 2- 65 7.3 3.7 (3.5 - 4) JVM 1- 06 0.1 >>10 nd BS 2- 66 8.1 2.4 (1.8 - 3.2) JVM 1- 07 0.1 >>10 nd BS 2- 67 7.2 6.7 (6.4 - 7.1) JVM 1- 08 0.1 >>10 nd BS 2- 68 8.1 2.8 (2.5 - 3.2) JVM 1- 09 0.1 >>10 nd BS 2- 69 7.2 >>10 nd JVM 1- 10 0.1 >>10 nd BS 2- 71 7.3 4.3 (4.1 - 4.5) JVM 1- 11 0.1 >>10 nd BS 2- 78 8.1 6.4 (6.1 - 6.8) JVM 1- 12 0.1 >>10 nd BS 2- 81 10.3 2.1 (2 - 2.2) JVM 1- 13 0.1 >>10 nd BS 3- 14 10.3 1.1 (1 - 1.2) JVM 1- 14 0.1 >>10 nd BS 3- 18 10.3 2.8 (2.6 - 3.1) JVM 1- 15 0.2 >>10 nd BS 3- 19 12.3 7.8 (7.3 - 8.4) JVM 1- 16 0.2 >>10 nd BS 3- 24 11.1 9.0 (8.4 - 9.7) JVM 1- 17 0.2 >>10 nd

105

High-throughput screening results

COMPOUND Batch EC50 (µM) 95% CICOMPOUND Batch EC50 (µM) 95% CI JVM 1- 18 0.2 >>10 nd JVM 2- 41 0.5 >>10 nd JVM 1- 19 0.2 >>10 nd JVM 2- 44 1.1 5 (4 - 6) JVM 1- 20 0.2 17 (16 - 18) JVM 2- 45 1.2 8 (7 - 10) JVM 1- 21 0.2 >>10 nd JVM 2- 49 0.5 4.2 (3.7 - 4.7) JVM 1- 22 0.2 >>10 nd JVM 2- 50 1.2 9 (8 - 11) JVM 1- 23 0.2 >>10 nd JVM 2- 57 1.4 6 (5 - 7) JVM 1- 24 0.2 >>10 nd JVM 2- 59 1.1 4.4 (3.7 - 5.3) JVM 1- 25 0.2 >>10 nd JVM 2- 60 1.1 6.4 (5.6 - 7.2) JVM 1- 35 0.4 >>10 nd JVM 2- 62 1.4 18 (14 - 21) JVM 1- 37 10.2 6.2 (5.9 - 6.5) JVM 2- 63 1.3 >>10 nd JVM 1- 38 0.4 >>10 nd JVM 2- 63A 1.3 19 (13 - 27) JVM 1- 51 0.3 >>10 nd JVM 2- 63B 1.5 30 nd JVM 1- 52 0.3 >>10 nd JVM 2- 66 1.1 4.1 (3.6 - 4.5) JVM 1- 55 0.3 >>10 nd JVM 2- 69 1.6 5.2 (4.5 - 6) JVM 1- 57 0.3 11.5 (11 - 12) JVM 2- 70 1.1 4.1 (2 - 5.1) JVM 1- 58 0.3 >>10 nd JVM 2- 71 1.5 8 (7 - 10) JVM 1- 59 0.3 8.0 (7.4 - 8.6) JVM 2- 76 5.4 0.57 (0.53 - 0.62) JVM 1- 61 0.3 4.3 (3.5 - 4.9) JVM 2- 77 1.6 6 (5 - 9) JVM 1- 64 0.3 3.9 (3.2 - 4.3) JVM 2- 79 1.5 19 (18 - 21) JVM 1- 66 0.3 13 (13 - 14) JVM 2- 80 1.6 >>10 nd JVM 1- 66A 0.3 >>10 nd JVM 2- 80A 1.6 >>10 nd JVM 1- 67 0.3 >>10 nd JVM 2- 81 1.6 >>10 nd JVM 1- 68 0.3 >>10 nd JVM 2- 81A 1.8 >>10 nd JVM 1- 69 0.3 11 (10 - 12) JVM 2- 82 1.5 >>10 nd JVM 1- 70 1.6 >>10 nd JVM 2- 83 1.3 >>10 nd JVM 1- 72 10.1 >>10 nd JVM 2- 84 1.5 >>10 nd JVM 1- 73 0.3 >>10 nd JVM 2- 85 1.5 21 (19 - 24) JVM 1- 74 0.3 5.9 (5.6 - 6.2) JVM 2- 86 1.5 19 (15 - 23) JVM 1- 86 0.5 12 (11 - 13) JVM 2- 87 1.6 11 (9 - 12) JVM 1- 88 0.4 >>10 nd JVM 2- 89 1.6 12 (9 - 16) JVM 1- 90 0.5 12 (11 - 14) JVM 2- 91 1.6 >>10 nd JVM 1- 95 0.5 11 (10 - 13) JVM 2- 92 1.5 6.5 (6 - 7) JVM 1- 96 0.5 12 (10 - 14) JVM 2- 93 1.4 13 (11 - 17) JVM 2- 04 0.5 13 (12 - 14) JVM 2- 93A 10.1 >>10 nd JVM 2- 05 0.5 9 (8 - 10) JVM 2- 94 1.2 26 (21 - 31) JVM 2- 13 10.3 5 (4 - 6) JVM 2- 94A 10.1 >>10 nd JVM 2- 16 0.5 14 (13 - 15) JVM 2- 95 1.3 >>10 nd JVM 2- 26 0.5 5 (4 - 6) JVM 2- 97 1.6 >>10 nd JVM 2- 31 0.5 9.2 (8.7 - 9.7) JVM 3- 01 1.1 >>10 nd JVM 2- 35 0.5 8.8 (8.3 - 9.3) JVM 3- 08 10.1 >>10 nd JVM 2- 36 2.4 >>10 nd JVM 3- 09 10.1 >>10 nd JVM 2- 40 0.5 6.8 (6.3 - 7.3) JVM 3- 10 10.1 >>10 nd

106

High-throughput screening results

COMPOUND Batch EC50 (µM) 95% CICOMPOUND Batch EC50 (µM) 95% CI JVM 3- 11 10.1 >>10 nd JVM 3- 68 23.1 10 (9 - 11) JVM 3- 12 10.1 >>10 nd JVM 3- 69 3.1 >>10 nd JVM 3- 13 2.4 >>10 nd JVM 3- 70 3.1 21 (15 - 28) JVM 3- 14 10.1 >>10 nd JVM 3- 71 3.1 2.7 (2.5 - 2.9) JVM 3- 15 10.1 >>10 nd JVM 3- 73 4.0 9 (8 - 10) JVM 3- 16 10.1 >>10 nd JVM 3- 74 4.0 1.9 (1.6 - 2.1) JVM 3- 18 10.1 >>10 nd JVM 3- 75 4.0 38 (33 - 45) JVM 3- 19 10.1 >>10 nd JVM 3- 76 4.0 11 (10 - 12) JVM 3- 22 2.3 4.7 (4.6 - 4.9) JVM 3- 78 4.0 1.2 (0.7 - 1.9) JVM 3- 23 2.4 >>10 nd JVM 3- 79 4.0 >>10 nd JVM 3- 25 2.6 2.4 (2.3 - 2.5) JVM 3- 82 5.3 1.1 (1 - 1.2) JVM 3- 30 2.3 3 (2 - 5) JVM 3- 83 5.3 >>10 nd JVM 3- 31 2.4 4.2 (3.3 - 5.4) JVM 3- 84 5.1 2.2 (2.1 - 2.3) JVM 3- 31A 13.0 8.9 (8.2 - 9.6) JVM 3- 86 5.1 3.7 (3 - 4.4) JVM 3- 36 2.5 1.7 (1.5 - 1.9) JVM 3- 87 5.1 6.8 (6.1 - 7.6) JVM 3- 37 2.4 >>10 nd JVM 3- 88 5.2 6.6 (6.1 - 7.2) JVM 3- 38 2.3 2.9 (2.2 - 3.7) JVM 3- 88A 15.2 2.3 (2.2 - 2.4) JVM 3- 39 2.1 11 (10 - 13) JVM 3- 88B 15.2 6 (5 - 9) JVM 3- 40 2.4 11 (9 - 13) JVM 3- 88C 15.2 8.3 (7.6 - 8.9) JVM 3- 41 2.2 13 (12 - 15) JVM 3- 89 5.2 1.7 (1.4 - 2.1) JVM 3- 44 2.3 3.5 (2.9 - 4.1) JVM 3- 90 5.1 >>10 nd JVM 3- 45A 2.3 >>10 nd JVM 3- 91 6.1 7 (6 - 9) JVM 3- 45B 2.3 9 (8 - 11) JVM 3- 91A 6.1 1.1 (0.8 - 1.4) JVM 3- 46 2.4 28 (22 - 35) JVM 3- 92 6.1 14 (10 - 19) JVM 3- 50 2.3 4 (2 - 8) JVM 3- 96 6.1 14 (10 - 18) JVM 3- 51 2.4 12 (10 - 14) JVM 3- 97 6.1 70 (40 - 120) JVM 3- 52 2.2 8 (7 - 10) JVM 3- 98 6.1 80 (40 - 160) JVM 3- 53 2.6 2.5 (2.4 - 2.7) JVM 3- 99 6.2 0.64 (0.61 - 0.68) JVM 3- 54 5.4 1.5 (1.4 - 1.6) JVM 3- 99B 8.1 3.1 (2.9 - 3.3) JVM 3- 55 12.0 0.94 (0.85 - 1.04) JVM 3- 100 6.1 1.8 (1.5 - 2.1) JVM 3- 55A 5.4 1.0 (0.95 - 1.1) JVM 4- 02 7.1 >>10 nd JVM 3- 55B 12.2 5.1 (4.6 - 5.6) JVM 4- 03 7.1 >>10 nd JVM 3- 55C 13.0 1.9 (1.8 - 2) JVM 4- 04 7.1 >>10 nd JVM 3- 57 2.2 5.9 (5.4 - 6.4) JVM 4- 05 7.1 >>10 nd JVM 3- 58 5.4 1.1 (0.9 - 1.3) JVM 4- 06 7.1 >>10 nd JVM 3- 61 5.4 1.9 (1.8 - 2.1) JVM 4- 07 7.1 >>10 nd JVM 3- 62 5.4 2.0 (1.8 - 2.2) JVM 4- 08 7.1 >>10 nd JVM 3- 62A 16.2 2.3 (2 - 2.5) JVM 4- 09 7.1 >>10 nd JVM 3- 62B 16.1 >>10 nd JVM 4- 10 7.1 >>10 nd JVM 3- 64 5.4 1.5 (1.4 - 1.6) JVM 4- 13 8.1 3.4 (3.2 - 3.7) JVM 3- 65 5.4 2.9 (2.5 - 3.3) JVM 4- 14 10.2 3.3 (3 - 3.6) JVM 3- 67 3.1 >>10 nd JVM 4- 16 8.1 5.5 (4.9 - 6.2)

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High-throughput screening results

COMPOUND Batch EC50 (µM) 95% CICOMPOUND Batch EC50 (µM) 95% CI JVM 4- 17 8.1 5.0 (4.2 - 5.9) JVM 4- 97 18.0 15 (9 - 24) JVM 4- 18 8.3 0.71 (0.65 - 0.79) JVM 4- 100 18.0 21 (16 - 29) JVM 4- 19 10.3 3.9 (3.6 - 4.3) JVM 4- MMBA 7.1 13 (12 - 15) JVM 4- 20 10.2 4.3 (4 - 4.7) JVM 5- 06 18.0 3.7 (3.3 - 4.2) JVM 4- 24 11.2 1.4 (1.3 - 1.6) JVM 5- 07 18.0 8.1 (7.1 - 9.4) JVM 4- 25 10.3 1.6 (1.5 - 1.7) JVM 5- 08 18.0 4.8 (4.3 - 5.4) JVM 4- 26 10.3 1.8 (1.7 - 1.9) JVM 5- 11 20.2 0.9 (0.8 - 1) JVM 4- 27 11.2 1.2 (1.1 - 1.4) JVM 5- 12 20.1 8 (6 - 10) JVM 4- 28 11.2 1.6 (1.4 - 1.7) JVM 5- 13 20.1 10.0 (9.6 - 10.5) JVM 4- 29 11.2 0.56 (0.52 - 0.6) JVM 5- 14 20.1 8 (7 - 7) JVM 4- 29B 13.0 2.0 (1.7 - 2.2) JVM 5- 15 20.1 30 (20 - 40) JVM 4- 29C 13.0 1.7 (1.3 - 2.3) JVM 5- 16 20.1 3.6 (3.2 - 4.1) JVM 4- 31 12.2 3.8 (3.7 - 3.9) JVM 5- 17 20.1 10 (4 - 30) JVM 4- 32 12.2 2.1 (1.9 - 2.3) JVM 5- 18 20.1 3.9 (3.5 - 4.4) JVM 4- 33 12.2 1.1 (1 - 1.2) JVM 5- 19 20.2 1.7 (1.6 - 1.8) JVM 4- 34 12.2 2.6 (2.5 - 2.7) JVM 5- 20 21.1 2.1 (2 - 2.3) JVM 4- 36 12.2 3.1 (2.5 - 3.7) JVM 5- 21 21.1 1.4 (1.2 - 1.5) JVM 4- 37 12.2 7 (5 - 10) JVM 5- 27 20.1 8.1 (7.3 - 9) JVM 4- 38 12.2 12 (8 - 18) JVM 5- 29 21.2 0.9 (0.8 - 1) JVM 4- 41 12.3 3.2 (2.6 - 4) JVM 5- 32 21.1 7.7 (7.1 - 8.3) JVM 4- 41A 12.2 2.3 (2.2 - 2.4) JVM 5- 33 21.1 5.4 (4.9 - 6) JVM 4- 42 12.1 >>10 nd JVM 5- 34 21.1 5.5 (5.1 - 6) JVM 4- 43 12.1 10 nd JVM 5- 36 21.3 5.5 (5.3 - 5.8) JVM 4- 44 13.0 >>10 nd JVM 5- 45 22.1 4.5 (4.2 - 4.9) JVM 4- 47 13.0 3.2 (3 - 3.5) JVM 5- 47 21.3 2.6 (2.5 - 2.7) JVM 4- 48 13.0 1.56 (1.54 - 1.58) JVM 5- 52 22.1 >>10 nd JVM 4- 50 14.1 10 (7 - 14) JVM 5- 61 22.2 1.5 (1.3 - 1.6) JVM 4- 51 14.1 4.1 (3.9 - 4.3) JVM 5- 71 23.1 >>10 nd JVM 4- 52 14.1 5.5 (5.1 - 6) JVM 5- 72 23.1 >>10 nd JVM 4- 53 14.1 2.7 (2.6 - 2.8) JVM 5- 74 23.1 3.2 (3 - 3.4) JVM 4- 54 16.2 3.6 (3.3 - 3.9) JVM 5- 75 23.1 3.5 (3.2 - 3.7) JVM 4- 56 14.1 9.9 (9.6 - 10.2) JVM 5- 76 23.1 2.8 (2.4 - 3.2) JVM 4- 58 14.1 2.8 (2.5 - 3.1) JVM 5- 84 23.1 4.3 (4.1 - 4.4) JVM 4- 59 14.1 4 (3.6 - 4.4) JVM 5- 85 23.1 2.3 (2.1 - 2.6) JVM 4- 66 16.1 14 (12 - 16) JVM 5- 87 23.1 5.5 (5.2 - 5.7) JVM 4- 68 18.0 >>10 nd JVM 5- 88 23.1 2.2 (2 - 2.3) JVM 4- 69 16.1 4.9 (4.5 - 5.3) JVM 5- 96 25.1 1.0 (0.8 - 1.3) JVM 4- 70 17.2 10 (9 - 11) JVM 5- 97 25.1 3.0 (2.8 - 3.3) JVM 4- 73 16.1 7.3 (6.3 - 8.5) JVM 6- 02 25.1 1 (0.1 - 6) JVM 4- 74 17.2 6.3 (5.4 - 7.4) PNR 5- 41 9.0 >>10 nd JVM 4- 76 17.2 10.2 (9.5 - 10.9) PNR 5- 46 9.0 >>10 nd JVM 4- 78 23.2 1.6 (1.5 - 1.7) PNR 5- 53 0.2 25 (20 - 32)

108

High-throughput screening results

COMPOUND Batch EC50 (µM) 95% CICOMPOUND Batch EC50 (µM) 95% CI PNR 5- 65 0.2 29 (26 - 32) PNR 9- 96 17.2 7.1 (6.5 - 7.7) PNR 5- 66 1.7 >>10 nd PNR 9- 97 17.2 12 (9 - 16) PNR 6- 19 0.3 >>10 nd PNR 10- 42 19.1 3.7 (3.5 - 3.9) PNR 6- 24 0.4 >>10 nd PNR 10- 43 19.1 11 (9 - 12) PNR 6- 25 0.5 15 (14 - 16) PNR 10- 46 19.1 10 (9 - 12) PNR 6- 26 0.5 >>10 nd PNR 10- 69 22.1 1.7 (1.6 - 2.2) PNR 6- 27 0.5 >>10 nd PNR 10- 70 22.1 2.1 (2 - 2.2) PNR 6- 29 0.5 >>10 nd PNR 10- 72 22.1 2.5 (2.3 - 2.6) PNR 7- 44 1.7 >>10 nd PNR 10- 72A 23.1 2.5 (2.3 - 2.7) PNR 7- 45 1.7 >>10 nd PNR 10- 72B 24.0 4.0 (3.7 - 4.4) PNR 7- 46 1.7 >>10 nd PNR 10- 78 22.1 1.5 (1.4 - 1.6) PNR 7- 47 1.7 >>10 nd PNR 10- 88.0 23.1 2.5 (2.3 - 2.7) PNR 8- 19 3.2 >>10 nd PNR 10- 88A 23.1 4.9 (4.6 - 5.2) PNR 8- 88 7.1 3.2 (2.6 - 4) PNR 10- 88B 23.1 10 (9 - 12) PNR 8- 90 7.1 8 (3 - 21) PNR 10- 96A 23.1 2.1 (2 - 2.2) PNR 8- 93 7.1 6.9 (5.8 - 8.1) PNR 10- 96B 23.1 2.7 (2.6 - 2.8) PNR 8- 94 7.1 2.8 (2.2 - 3.4) PNR 10- 96C 23.1 2 (1.9 - 2.2) PNR 8- 95 7.1 3.0 (2.8 - 3.2) PNR 10- 96D 23.1 2.8 (2.5 - 3.1) PNR 8- 96 7.3 1.9 (1.8 - 2) PNR 10- 101 23.1 3.2 (3.2 - 3.4) PNR 8- 96A 8.1 11 (10 - 12) PNR 10- 106 24.0 3.3 (3 - 3.5) PNR 8- 97 7.1 30 (20 - 50) PNR 10- 106B 26.1 4.2 (3.7 - 4.7) PNR 8- 98 8.2 5.6 (5.2 - 6.1) PNR 10- 108 24.0 3.7 (3.5 - 3.9) PNR 8- 99 8.3 2.8 (2.7 - 3) PNR 10- 108B 25.1 2.3 (2.2 - 2.5) PNR 9- 01 8.2 4.3 (3.7 - 5) PNR 10- 110A 25.1 2.7 (2.5 - 3) PNR 9- 02 8.2 2.6 (2.4 - 2.9) PNR 10- 110B 25.1 1.8 (1.7 - 1.9) PNR 9- 03 8.3 1.2 (1.1 - 1.3) PNR 10- 111A 25.1 11 (9 - 14) PNR 9- 04 8.3 0.78 (0.76 - 0.8) PNR 10- 113A 25.1 1.6 (1.5 - 1.7) PNR 9- 05 8.3 0.97 (0.95 - 1) PNR 10- 113B 25.1 5.8 (5.1 - 6..7) PNR 9- 06 8.2 6.6 (6.1 - 7) PNR 10- 115A 25.1 1.8 (1.6 - 2.1) PNR 9- 8A 8.2 >>10 nd PNR 10- 115B 26.1 2.7 (2.3 - 3.1) PNR 9- 09 8.2 >>10 nd PNR 10- 116A 25.1 3.8 (3.5 - 4.2) PNR 9- 80 14.1 7.0 (6.8 - 7.2) PNR 10- 116B 25.1 10 (9 - 12) PNR 9- 84 15.1 3.2 (3 - 3.5) PNR 10- 127A 26.2 3.5 (3.1 - 3.9) PNR 9- 85 15.1 10 (9 - 12) PNR 10- 127B 26.2 3.8 (3.3 - 4.3) PNR 9- 86 15.1 5.1 (4.9 - 5.3) PNR 10- 127C 26.2 1.4 (1.3 - 1.5) PNR 9- 87 16.1 6.0 (5.2 - 6.9) PNR 9- 88 16.1 6 (5 - 7) PNR 9- 89 16.1 7.9 (7.1 - 8.7) PNR 9- 90 16.1 8.0 (7.6 - 8.4) PNR 9- 91 16.1 7.4 (6.5 - 8.3) PNR 9- 94 17.2 12 (9 - 14) PNR 9- 95 17.2 5 (4 - 6)

109

Batch 0

Batch 0 encompasses all the compound screens performed at University of Rochester

(Oct. 2012 – June 2013). Most of the compounds represent N-substituted derivatives of aminoparthenolide, with and without the hydroxy substitution of MMB. These combinations were largely inactive, even at 20 µM. Trans-annular cyclized derivatives utilizing the MCL scaffold (PNR 6-xx series) were also mostly inactive. The implementation of prodrug-like O- substitutions using esters and carbamates of the MMB scaffold generated cytotoxic compounds.

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Batch 1

Batch 1 represents the first compounds analyzed at the University of Colorado Anschutz

Medical Campus. Batches 1 – 5 include data generated by Nabilah Khan, PhD. Derivatives in

Batch 1 included an assortment of MMB derivatives and N-substituted aminoparthenolides, the most noteworthy of which included dimers of MMB, particularly the carbamate-linked JVM 2-

76. In contrast, N-linked dimers JVM 2-80A and JVM 2-81A were not active. Transannular cyclized derivatives were again inactive, as was the carboxylic acid of MMB.

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Batch 2

In Batch 2, the most promising compounds were carbamate and carbonate esters of

MMB, especially JVM 2-76 and JVM 3-55 which, when tested at lower doses, demonstrated

EC50 values less than 1 µM. JVM 3-36, a napthoic acid MMB derivative was also quite potent

(EC50 = 1.7 µM).

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Batch 3

Batch 3 included additional dimers linked by carbamates which were most potent with an internal hydrocarbon chain length of 5 – 7 carbons. Several indole derivatives of MMB were also quite effective. Two biotin-tagged MMB derivatives were also tested, BS 1-98 and BS 1-

99. The long-chain BS 1-99 was slightly more effective. JVM 3-71, featuring a carbamate linkage tethering an MMB moiety to an aminoparthenolide was also potent.

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Batch 4

Batch 4 included more carbamate-linked MMB dimers, including the 4-carbon linked

JVM 3-79 which was inactive. This Batch also included the potent phenyltriazole JVM 3-74, while other triazoles were weakly effective.

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Batch 5

Batch 5 afforded a full comparative analysis of carbamate- and amide-linked MMB dimers which showed a similar chain-length dependence, i.e. linking chains too long or too short lost potency. JVM 3-88A, a carbonate ester dimer analogous to the carbamate dimer JVM 2-76, was also quite potent while its hydrolysis product JVM 3-88 was not.

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Batch 6

Batch 6 expanded on the MMB dimer set with various carbonate ester linkages, which showed potency similar to the carbamate- and amide-linked dimers. In contrast, the alkyne- tagged carbonate ester of MMB was reasonably effective (JVM 3-97, EC50 estimated 70 µM) while the alkyne-tagged carbamate of MMB was ineffective (JVM 3-96, EC50 = 14 µM).

Additional indole esters of MMB were also quite potent, especially BS 2-04, an indoleacrylic acid MMB derivative.

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Batch 7

Batch 7 showed that various amide-linked dimers and indole substitutions were remarkably ineffective. However, indole substitutions on MMB using an alpha keto ester showed varying degrees of efficacy, the best of which was PNR 8-96 (EC50 = 1.8 µM). The acetic acid ester of MMB (BS 2-59) was significantly more potent than MMB, but the aldehyde

(JVM 4-MMBA) was not, which could suggest a prodrug relationship. Benzofuran and benzothiophene substituents on MMB were shown to be effective as bioisosteres of indole.

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Batch 8

The best compounds in Batch 8 were primarily alpha keto ester indoles, similar to those in Batch 7. This series demonstrated that in all cases, N-methylindole substituents were more potent (up to 8-fold) than their unmethylated indole counterparts. This effect was strongest for 5- cyano, and 5-methoxy indole substituents. JVM 4-18, an MMB dimer linked with both a triazole and a carbonate ester, was also quite potent (EC50 = 0.7 µM). PNR 9-08A, an alkyne-tagged secondary aminoparthenolide, had no effect at 10 µM.

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Batch 9

Batch 9 included a small series of aromatic substituted enones, all of which were ineffective at 10 µM and were not screened further.

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Batch 10

Batch 10 was also subjected to a single-dose 10 µM screen to determine which compounds would be subjected to dose-response analysis. This batch included a wide variety of

N-substituted aminoparthenolides that showed little to no activity in the initial screen. Indole carboxylate and carbamate derivatives of MMB showed potencies similar to the indole alpha keto esters explored in Batches 7 and 8. BS 3-14 (EC50 = 1.1 µM) showed that the carbamate dimers were tolerant to the addition of a central silyl ether. Halogen-substituted phenyltriazole derivatives of MMB (JVM 4-25, JVM 4-26) demonstrated strong efficacy (EC50 = 1.7 and 1.6

µM, respectively).

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Batch 11

In Batch 11, the unprotected hydroxy-modified carbamate dimer (BS 3-24, EC50 = 9 µM) was found to be approximately ten-fold less potent than the carbamate dimer JVM 2-76 or the silyl ether protected BS 3-14. Additional phenyltriazoles and amide-linked N-methylindole

MMB derivatives expanded the high-potency sets of these two structural classes.

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Batch 12

Batch 12 represents the last Batch where a 10 µM screen was implemented to eliminate inactive compounds, as the development of derivatives reached a success rate that rendered this screen uneconomical. Benzoic acid, as a phenyltriazole, was found to be inactive, as was a biotin-tagged version (BS 3-46) of the inactive aromatic enones (e.g. BS 2-27, PNR 5-41).

Eleven new compounds more potent than parthenolide were discovered, mostly including dimers and amide-linked indoles, but also an alkyne-tagged phenyltriazole (JVM 4-41).

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Batch 13

Batch 13 included more potent phenyltriazoles, as well as an amino-linked triazole dimer that was ineffective. Phenyltriazole JVM 4-29 was still potent as a dimethylamino and when formulated as a fumarate salt of the same, and JVM 3-55C, the dimethylamino-modified carbonate dimer (JVM 3-55) still potent as well.

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Batch 14

Batch 14 included a variety of potent triazoles and some moderately effective aromatic imine derivatives of MMB. JVM 4-53 (EC50 = 2.7 µM) demonstrated that thiophene was an effective bioisostere of the benzene ring in the phenyltriazole series. The hydrocarbon-linked triazole dimer JVM 4-51 was effective (EC50 = 4.1 µM) in contrast to the amine-linked dimer

JVM 4-44 which showed no activity at 10 µM. PNR 9-80, a dimethylamino fumarate salt formulation of PNR 9-05, was less effective (EC50 = 7.0 µM vs 0.97 µM). The most potent compound in the batch, BS 4-10, has a complex benzothiophene acrylonitrile substituent designed as a combretastatin analog.

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Batch 15

Batch 15 included three carbamate linked substituted ethylbenzenes with moderate efficacy. Additionally, Batch 15 included dimethylamino- and fumarate salt formulations of carbonate ester dimer JVM 3-88A, and the biotin-tagged carbonate ester dimer JVM 4-38B. The dimethylamino modification was not well tolerated, with JVM 3-88B and JVM 3-88C showing nearly three- and four-fold reductions in potency. JVM 4-38B was not effective at low doses, with an EC50 of 19 µM. JVM 4-38A, a biotin-tagged hydrolysis product of JVM 3-88A was even less effective.

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Batch 16

Batch 16 was comprised of moderately active aromatic imine and carbamate derivatives of MMB, and a napthol ether that was inactive at 10 µM.

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Batch 17

Batch 17 primarily consisted of moderately active compounds, with indole ester, imine, and carbamate derivatives of MMB. A piperazine-linked dimer, BS 4-71, had large variances in two runs, suggesting an instability or insolubility in the assay conditions. The Huisgen cycloaddition pair, azido dimer BS 4-70 and alkyne biotin BS 4-28, were designed to replace BS

4-38A. BS 4-70 was twice as potent as BS 4-38A, and BS 4-28 was inactive.

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Batch 18

Batch 18 included additional moderately active aromatic imine derivatives of MMB.

Batches 18, 19, and 20 were held for analysis on the newly acquired FACSCelesta equipped with an HTS sampler. Using the HTS, four doses of each compound were tested.

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Batch 19

Batch 19 included three derivatives featuring a bone-targeting moiety, one of which

(PNR 10-42) was more effective than parthenolide. The most potent compound was BS 5-11, an aromatic propenone MMB derivative.

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Batch 20

Batch 20 introduced a series of ester-linked dimers of MMB, which showed modest efficacy, potentially due to hydrolytic instability. The imidazole JVM 5-11 and benzimidazole

JVM 5-19 were quite potent, while the bone-targeting imine JVM 5-27 was moderately effective.

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Batch 21

Batch 21 included four additional bone-targeting derivatives (JVM 5-32 through -36) with moderate activity, and two pyrrolidine linked bone-targeting derivatives. The aminoparthenolide derivative BS 4-26 was weakly effective while the MMB derivative BS 4-28 was potent (EC50 = 2.3 µM). JVM 5-29, an interesting carbonate ester dimer in which only one of the MMB moieties is dimethylamino substituted, was quite potent (EC50 = 0.9 µM).

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Batch 22

Batch 22 included two additional bone-targeting derivatives with moderate activity, as well as potent carbamate-linked indole MMB derivatives. Also included in this batch was JVM

5-61, a potent (EC50 = 1.4 µM) alkyne-tagged carbonate ester (JVM 3-88A).

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Batch 23

Batch 23 introduced a series of carbamate-linked and amide-linked indoles and phenyl groups. BSK 1-40, the first BSK compound, was a potent benzyl thiadiazolidine dione (TDZD) derivative of MMB (EC50 = 1.3 µM), potentially as a result of glycogen synthase kinase-3 (GSK-

3) inhibition properties of this substituent. Amide-linked MMB dimers were inactive, while an

MMB dimer bearing both amide and ester linkages was weakly active. In contrast, the thioether and disulfide ester-linked MMB dimers PNR 10-88 and PNR 10-88A were both effective, but the thioether (EC50 = 2.5 µM) was twice as potent as the disulfide (EC50 = 4.9 µM).

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Batch 24

Batch 24 encompassed three additional aromatic carbamate MMB derivatives, with slightly improved potency relative to parthenolide.

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Batch 25

Batch 25 primarily expanded upon the sets of carbamate- and triazole-linked indoles, with a few variations including a carbamate-linked indazole and an ester-linked phenyl acetate, most of which were potent. PNR 10-111A and PNR 10-116B stand out as weakly effective outliers, which is made more interesting by the potency of PNR 10-110A (EC50 = 2.7 µM), a structural isomer of PNR 10-111A (EC50 = 11 µM). Given the potency of this set in general, this seems to point to a specific molecular target interaction rather than a cellular permeability or stability issue. Additionally, two doxorubicin-conjugated MMB derivatives JVM 5-96 and JVM

5-97 were quite potent, as was another thiadiazolidine dione (TDZD), BSK 1-87. JVM 6-02, a fumarate-linked dimer, appeared potent but large variances in the dose-response curve suggest a possible lack of aqueous stability.

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Batch 26

Batch 26 represents the last Batch of derivatives screened at the time of this publication.

Included were four additional triazole-linked indoles, the most potent of which was PNR 10-

127C (EC50 = 1.4 µM). Also in this set were three thioether ester-linked MMB dimers. DSS 1-

53B was the most potent of these (EC50 = 1.5 µM), though the others were also potent (EC50 < 5

µM). Like the thioether and disulfide ester-linked MMB dimers PNR 10-88 and PNR 10-88A

(Batch 23), these molecules were surprisingly potent relative to their alkyl chain counterparts, the ester-linked MMB dimers JVM 5-12 through JVM 5-17 (Batch 20).

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APPENDIX B

MMB DIMER BINDING TARGETS IN AML IDENTIFIED BY PROTEOMIC MS

MW # Gene Identified Proteins (770) Peptides (kD) 2 PRKDC DNA-dependent protein kinase catalytic subunit 198 469 3 TUBB Tubulin beta chain 120 50 4 ACTG1 Actin, cytoplasmic 2 86 42 5 CPT1A Carnitine O-palmitoyltransferase 1, liver isoform 73 88 6 TUBA1B Tubulin alpha-1B chain 76 50 7 LCP1 Plastin-2 83 70 8 NUMA1 Nuclear mitotic apparatus protein 1 61 238 9 MYH9 Myin-9 69 227 10 FLNA Filamin-A 77 281 Methylcrotonoyl-CoA carboxylase subunit alpha, 11 MCCC1 67 80 mitochondrial 12 UBA52 Ubiquitin-60S ribomal protein L40 54 15 13 DSP Desmoplakin 30 332 Propionyl-CoA carboxylase alpha chain, 14 PCCA 60 80 mitochondrial 15 MPO Myeloperoxidase 52 84 16 AHNAK Neuroblast differentiation-associated protein AHNAK 50 629 17 HNRNPK Heterogeneous nuclear ribonucleoprotein K 50 51 Sarcoplasmic/endoplasmic reticulum calcium ATPase 18 ATP2A2 45 115 2 19 ADAR Double-stranded RNA-specific adenine deaminase 47 136 20 SERPINB1 Leukocyte elastase inhibitor 49 43 21 KHSRP Far upstream element-binding protein 2 41 73 22 ALB Serum albumin 37 69 23 HRNR Hornerin 21 282 24 HNRNPM Heterogeneous nuclear ribonucleoprotein M 27 78 25 TBXAS1 Thromboxane-A synthase 42 61 26 VDAC2 Voltage-dependent anion-selective channel protein 2 46 32 27 TOP2B DNA topoisomerase 2-beta 37 183 28 HNRNPH1 Heterogeneous nuclear ribonucleoprotein H 37 49 Non-POU domain-containing octamer-binding 29 NONO 31 54 protein 30 ENO1 Alpha-enolase 37 47 31 ESYT2 Extended synaptotagmin-2 44 102

207

MW # Gene Identified Proteins (770) Peptides (kD) 32 DOCK2 Dedicator of cytokinesis protein 2 35 212 34 TLN1 Talin-1 31 270 35 HIST3H3 Histone H3.1t 9 16 36 TRIM28 Transcription intermediary factor 1-beta 27 89 37 HNRNPU Heterogeneous nuclear ribonucleoprotein U 32 91 38 DDX17 Probable ATP-dependent RNA helicase DDX17 45 80 39 PCBP1 Poly(rC)-binding protein 1 30 37 Pre-mRNA-splicing factor ATP-dependent RNA 40 DHX15 26 91 helicase DHX15 41 HNRNPL Heterogeneous nuclear ribonucleoprotein L 18 64 42 VIM Vimentin 32 54 43 PGK1 Phphoglycerate kinase 1 24 45 44 VDAC3 Voltage-dependent anion-selective channel protein 3 34 31 45 IDH2 Isocitrate dehydrogenase [NADP], mitochondrial 21 51 46 P4HB Protein disulfide-isomerase 26 57 47 UBA1 Ubiquitin-like modifier-activating enzyme 1 29 118 Microtubule-actin crs-linking factor 1, isoforms 48 MACF1 16 838 1/2/3/5 49 EEF1A1 Elongation factor 1-alpha 1 24 50 50 TMPO Lamina-associated polypeptide 2, isoform alpha 26 75 51 HSP90AB1 Heat shock protein HSP 90-beta 20 83 52 PRDX1 Peroxiredoxin-1 24 22 53 HIST1H2BH Histone H2B type 1-H 22 14 54 HNRNPF Heterogeneous nuclear ribonucleoprotein F 22 46 55 TXNRD1 Thioredoxin reductase 1, cytoplasmic 30 71 56 NCL Nucleolin 29 77 57 SLC25A3 Phphate carrier protein, mitochondrial 14 40 58 MATR3 Matrin-3 17 95 59 RCC2 Protein RCC2 23 56 Dolichyl-diphphooligaccharide--protein 60 RPN1 23 69 glycyltransferase subunit 1 Leucine-rich PPR motif-containing protein, 61 LRPPRC 24 158 mitochondrial 62 PML Protein PML 27 98 63 EEF2 Elongation factor 2 30 95 64 DOCK8 Dedicator of cytokinesis protein 8 18 239 65 HCFC1 Ht cell factor 1 21 209 66 ILF3 Interleukin enhancer-binding factor 3 23 95 67 CLTC Clathrin heavy chain 1 26 192

208

MW # Gene Identified Proteins (770) Peptides (kD) 69 PTBP1 Polypyrimidine tract-binding protein 1 12 57 Methylcrotonoyl-CoA carboxylase beta chain, 70 MCCC2 23 61 mitochondrial 71 DHX9 ATP-dependent RNA helicase A 24 141 72 ANXA1 Annexin A1 18 39 Deoxynucleide triphphate triphphohydrolase 73 SAMHD1 22 72 SAMHD1 74 ACLY ATP-citrate synthase 25 121 75 MCM6 DNA replication licensing factor MCM6 17 93 76 PDCD6IP Programmed cell death 6-interacting protein 24 96 Very long-chain specific acyl-CoA dehydrogenase, 77 ACADVL 27 70 mitochondrial 78 NUP210 Nuclear pore membrane glycoprotein 210 24 205 79 IRF2BP2 Interferon regulatory factor 2-binding protein 2 4 61 80 PARP1 Poly [ADP-ribe] polymerase 1 18 113 81 HADHA Trifunctional enzyme subunit alpha, mitochondrial 21 83 82 RANBP2 E3 SUMO-protein ligase RanBP2 19 358 83 MCM3 DNA replication licensing factor MCM3 22 91 84 SNRNP200 U5 small nuclear ribonucleoprotein 200 kDa helicase 25 245 85 CORO1A Coronin-1A 23 51 87 SPTLC2 Serine palmitoyltransferase 2 17 63 88 HSPD1 60 kDa heat shock protein, mitochondrial 19 61 89 NUP155 Nuclear pore complex protein Nup155 21 155 90 SPTBN1 Spectrin beta chain, non-erythrocytic 1 15 275 91 DCK Deoxycytidine kinase 16 31 92 IFI16 Gamma-interferon-inducible protein 16 14 88 93 DOCK11 Dedicator of cytokinesis protein 11 17 238 Heterogeneous nuclear ribonucleoprotein U-like 94 HNRNPUL2 11 85 protein 2 95 HSD17B10 3-hydroxyacyl-CoA dehydrogenase type-2 22 27 96 IMPDH2 Inine-5'-monophphate dehydrogenase 2 23 56 97 NPM1 Nucleophmin 10 33 98 HUWE1 E3 ubiquitin-protein ligase HUWE1 12 482 99 SYNCRIP Heterogeneous nuclear ribonucleoprotein Q 12 70 100 HSPA8 Heat shock cognate 71 kDa protein 17 71 101 MAP4 Microtubule-associated protein 4 14 121 102 ARHGEF2 Rho guanine nucleotide exchange factor 2 10 112 103 MDN1 Midasin 15 633 105 CNN2 Calponin-2 14 34

209

MW # Gene Identified Proteins (770) Peptides (kD) 106 HMGCL Hydroxymethylglutaryl-CoA lyase, mitochondrial 18 34 107 HNRNPA2B1 Heterogeneous nuclear ribonucleoproteins A2/B1 17 37 108 SSBP1 Single-stranded DNA-binding protein, mitochondrial 12 17 109 CD74 HLA class II histocompatibility antigen gamma chain 10 34 111 NUP98 Nuclear pore complex protein Nup98-Nup96 11 198 112 ITGB2 Integrin beta-2 14 85 113 TCOF1 Treacle protein 6 152 115 DCD Dermcidin 9 11 116 MBNL1 Muscleblind-like protein 1 4 42 117 TUFM Elongation factor Tu, mitochondrial 14 50 118 ESYT1 Extended synaptotagmin-1 17 123 119 SPCS2 Signal peptidase complex subunit 2 12 25 Constitutive coactivator of PPAR-gamma-like protein 120 FAM120A 15 122 1 121 STAT3 Signal transducer and activator of transcription 3 18 88 122 HBB Hemoglobin subunit beta 19 16 123 CFL1 Cofilin-1 16 19 124 PKM Pyruvate kinase PKM 14 58 125 NOP58 Nucleolar protein 58 16 60 126 ACACA Acetyl-CoA carboxylase 1 12 266 127 FLII Protein flightless-1 homolog 17 145 128 JUP Junction plakoglobin 9 82 129 PDIA3 Protein disulfide-isomerase A3 14 57 130 ARHGAP4 Rho GTPase-activating protein 4 18 105 131 LMNA Prelamin-A/C 16 74 132 ERMP1 Endoplasmic reticulum metallopeptidase 1 17 100 133 SCCPDH Saccharopine dehydrogenase-like oxidoreductase 17 47 134 RUFY1 RUN and FYVE domain-containing protein 1 11 80 135 HSP90AA1 Heat shock protein HSP 90-alpha 9 85 136 INPP5D Phphatidylinitol 3,4,5-trisphphate 5-phphatase 1 12 133 137 RPL13A 60S ribomal protein L13a 4 24 138 DSG1 Desmoglein-1 9 114 139 LAS1L Ribomal biogenesis protein LAS1L 11 83 Acyl-CoA dehydrogenase family member 9, 140 ACAD9 18 69 mitochondrial 141 GPX1 Glutathione peroxidase 1 18 22 142 NEK9 Serine/threonine-protein kinase Nek9 15 107 143 ACO2 Aconitate hydratase, mitochondrial 14 85 144 CAND1 Cullin-associated NEDD8-dissociated protein 1 9 136

210

MW # Gene Identified Proteins (770) Peptides (kD) 145 CNOT1 CCR4-NOT transcription complex subunit 1 11 267 146 STAT5A Signal transducer and activator of transcription 5A 11 91 Dolichyl-diphphooligaccharide--protein 147 STT3B 11 94 glycyltransferase subunit STT3B 148 LGALS1 Galectin-1 6 15 149 CLIC1 Chloride intracellular channel protein 1 15 27 150 MBOAT7 Lysophpholipid acyltransferase 7 13 53 151 SRRM2 Serine/arginine repetitive matrix protein 2 9 300 Dihydrolipoyllysine-residue acetyltransferase 152 DLAT component of pyruvate dehydrogenase complex, 13 69 mitochondrial 153 EPRS Bifunctional glutamate/proline--tRNA ligase 9 171 154 DIDO1 Death-inducer obliterator 1 6 244 Sarcoplasmic/endoplasmic reticulum calcium ATPase 155 ATP2A3 11 114 3 156 ELMO1 Engulfment and cell motility protein 1 11 84 157 SMARCA2 Probable global transcription activator SNF2L2 15 181 158 XPO1 Exportin-1 12 123 159 GAPDH Glyceraldehyde-3-phphate dehydrogenase 13 36 160 DDX39B Spliceome RNA helicase DDX39B 11 49 162 MTCH2 Mitochondrial carrier homolog 2 13 33 163 LPCAT2 Lysophphatidylcholine acyltransferase 2 13 60 164 TMLHE Trimethyllysine dioxygenase, mitochondrial 9 50 165 ELAVL1 ELAV-like protein 1 10 36 166 SLC25A5 ADP/ATP translocase 2 7 33 167 PDS5A Sister chromatid cohesion protein PDS5 homolog A 12 151 168 WDFY4 WD repeat- and FYVE domain-containing protein 4 12 354 169 APOBR Apolipoprotein B receptor 10 115 170 HIST1H1B Histone H1.5 10 23 171 PSMB8 Proteasome subunit beta type-8 10 30 172 GANAB Neutral alpha-glucidase AB 5 107 Lipopolysaccharide-responsive and beige-like anchor 173 LRBA 11 319 protein 174 CAD CAD protein 9 243 175 VCP Transitional endoplasmic reticulum ATPase 18 89 176 CAT Catalase 11 60 177 H2AFY Core histone macro-H2A.1 10 40 Thioredoxin-dependent peroxide reductase, 178 PRDX3 13 28 mitochondrial

211

MW # Gene Identified Proteins (770) Peptides (kD) 179 TPM3 Tropomyin alpha-3 chain 9 33 180 GSTP1 Glutathione S-transferase P 11 23 181 TCERG1 Transcription elongation regulator 1 5 124 182 LMNB1 Lamin-B1 10 66 183 ATAD3A ATPase family AAA domain-containing protein 3A 7 71 184 IPO5 Importin-5 13 124 185 NUP153 Nuclear pore complex protein Nup153 12 154 Phphatidylinitol 3,4,5-trisphphate-dependent Rac 186 PREX1 7 186 exchanger 1 protein 187 WRNIP1 ATPase WRNIP1 12 72 189 FLG Filaggrin 3 435 190 RPL15 60S ribomal protein L15 2 24 191 SORD Sorbitol dehydrogenase 7 38 192 PCBP2 Poly(rC)-binding protein 2 15 39 193 SRSF6 Serine/arginine-rich splicing factor 6 4 40 194 CCT2 T-complex protein 1 subunit beta 10 57 195 IGHA1 Ig alpha-1 chain C region 11 38 196 PLIN3 Perilipin-3 11 47 197 ETV6 Transcription factor ETV6 6 53 198 GTF2I General transcription factor II-I 10 112 199 KDM3B Lysine-specific demethylase 3B 12 192 200 SEC63 Translocation protein SEC63 homolog 15 88 201 ZFR Zinc finger RNA-binding protein 7 117 202 ZNF638 Zinc finger protein 638 8 221 203 APEX1 DNA-(apurinic or apyrimidinic site) lyase 11 36 204 MDH2 Malate dehydrogenase, mitochondrial 11 36 205 PCCB Propionyl-CoA carboxylase beta chain, mitochondrial 8 58 206 PDIA6 Protein disulfide-isomerase A6 12 48 207 PAICS Multifunctional protein ADE2 7 47 208 SERPINB6 Serpin B6 13 43 209 C14orf159 UPF0317 protein C14orf159, mitochondrial 10 66 210 PMPCB Mitochondrial-processing peptidase subunit beta 9 54 211 HIST1H1C Histone H1.2 9 21 212 XRCC5 X-ray repair crs-complementing protein 5 10 83 213 DDX3X ATP-dependent RNA helicase DDX3X 9 73 214 USP24 Ubiquitin carboxyl-terminal hydrolase 24 7 294 215 ARHGEF6 Rho guanine nucleotide exchange factor 6 12 88 216 CHD4 Chromodomain-helicase-DNA-binding protein 4 7 218

212

MW # Gene Identified Proteins (770) Peptides (kD) Squamous cell carcinoma antigen recognized by T- 217 SART3 8 110 cells 3 218 UBR4 E3 ubiquitin-protein ligase UBR4 4 574 219 HIST1H2AD Histone H2A type 1-D 9 14 220 YWHAQ 14-3-3 protein theta 7 28 221 ACSF2 Acyl-CoA synthetase family member 2, mitochondrial 7 68 222 PRMT1 Protein arginine N-methyltransferase 1 8 42 223 RNH1 Ribonuclease inhibitor 14 50 KH domain-containing, RNA-binding, signal 224 KHDRBS1 4 48 transduction-associated protein 1 226 RTN3 Reticulon-3 9 113 227 C16orf58 RUS1 family protein C16orf58 10 51 228 SIN3A Paired amphipathic helix protein Sin3a 8 145 229 HMOX2 Heme oxygenase 2 5 36 230 SUPT5H Transcription elongation factor SPT5 5 121 231 GCN1L1 Translational activator GCN1 3 293 232 ATP13A1 Manganese-transporting ATPase 13A1 11 133 233 HM13 Minor histocompatibility antigen H13 7 41 234 MCM5 DNA replication licensing factor MCM5 7 82 235 PNPLA6 Neuropathy target esterase 11 150 236 RRBP1 Ribome-binding protein 1 10 152 237 SAFB Scaffold attachment factor B1 8 103 Structural maintenance of chromomes flexible hinge 238 SMCHD1 7 226 domain-containing protein 1 239 THOC2 THO complex subunit 2 11 183 240 XRCC6 X-ray repair crs-complementing protein 6 9 70 241 TXN Thioredoxin 5 12 242 SNW1 SNW domain-containing protein 1 12 61 243 FUBP3 Far upstream element-binding protein 3 6 62 244 FKBP8 Peptidyl-prolyl cis-trans isomerase FKBP8 12 45 245 PCYT1A Choline-phphate cytidylyltransferase A 7 42 246 RUVBL1 RuvB-like 1 12 50 247 PHGDH D-3-phphoglycerate dehydrogenase 12 57 248 TMEM173 Stimulator of interferon genes protein 14 42 249 SFPQ Splicing factor, proline- and glutamine-rich 9 76 250 ATP5A1 ATP synthase subunit alpha, mitochondrial 8 60 251 EXOSC10 Exome component 10 8 101 252 ACTN4 Alpha-actinin-4 8 105 253 BRAT1 BRCA1-associated ATM activator 1 8 88

213

MW # Gene Identified Proteins (770) Peptides (kD) 254 COPB1 Coatomer subunit beta 9 107 255 CYFIP2 Cytoplasmic FMR1-interacting protein 2 9 148 256 MSH2 DNA mismatch repair protein Msh2 6 105 257 NOP56 Nucleolar protein 56 8 66 258 NPEPPS Puromycin-sensitive aminopeptidase 11 103 259 ARHGAP25 Rho GTPase-activating protein 25 7 73 260 RTN4 Reticulon-4 9 130 261 USP7 Ubiquitin carboxyl-terminal hydrolase 7 10 128 262 USP9X Probable ubiquitin carboxyl-terminal hydrolase FAF-X 4 292 263 XPO5 Exportin-5 6 136 265 ANXA2 Annexin A2 7 39 266 ABHD16A Abhydrolase domain-containing protein 16A 12 63 267 CAPZB F-actin-capping protein subunit beta 13 31 Guanine nucleotide-binding protein subunit beta-2- 268 GNB2L1 10 35 like 1 269 RPL18A 60S ribomal protein L18a 6 21 270 AIP AH receptor-interacting protein 6 38 Lamina-associated polypeptide 2, isoforms 271 TMPO 8 51 beta/gamma 272 TAPBP Tapasin 5 48 273 AKR1B1 Alde reductase 8 36 274 COPA Coatomer subunit alpha 5 138 275 PABPC1 Polyadenylate-binding protein 1 11 71 276 SUN2 SUN domain-containing protein 2 10 80 277 ARID1A AT-rich interactive domain-containing protein 1A 5 242 278 HK1 Hexokinase-1 4 102 279 OPA1 Dynamin-like 120 kDa protein, mitochondrial 10 112 Pre-B-cell leukemia transcription factor-interacting 280 PBXIP1 10 81 protein 1 281 PRPF8 Pre-mRNA-processing-splicing factor 8 8 274 282 SUGP2 SURP and G-patch domain-containing protein 2 6 120 283 TBC1D9B TBC1 domain family member 9B 9 141 284 S100A8 Protein S100-A8 3 11 285 EEF1G Elongation factor 1-gamma 10 50 286 HARS Histidine--tRNA ligase, cytoplasmic 9 57 287 COMT Catechol O-methyltransferase 5 30 288 CORO1C Coronin-1C 10 53 289 HNRNPR Heterogeneous nuclear ribonucleoprotein R 8 71

214

MW # Gene Identified Proteins (770) Peptides (kD) Guanine nucleotide-binding protein G(i) subunit 290 GNAI2 8 40 alpha-2 291 HMGB1 High mobility group protein B1 3 25 292 CECR5 Cat eye syndrome critical region protein 5 3 46 293 PFAS Phphoribylformylglycinamidine synthase 8 145 294 USP11 Ubiquitin carboxyl-terminal hydrolase 11 8 110 295 TRIM25 E3 ubiquitin/ISG15 ligase TRIM25 8 71 296 EPS15 Epidermal growth factor receptor substrate 15 7 99 297 RNPEP Aminopeptidase B 7 73 298 ARID1B AT-rich interactive domain-containing protein 1B 8 236 299 CDKN2AIP CDKN2A-interacting protein 10 61 300 GPD2 Glycerol-3-phphate dehydrogenase, mitochondrial 11 81 301 ITPR2 Initol 1,4,5-trisphphate receptor type 2 5 308 302 NCOR1 Nuclear receptor corepressor 1 5 270 UDP-N-acetylglucamine--peptide N- 303 OGT 10 117 acetylglucaminyltransferase 110 kDa subunit 304 PTPRC Receptor-type tyrine-protein phphatase C 7 147 305 TAP1 Antigen peptide transporter 1 6 87 306 USP5 Ubiquitin carboxyl-terminal hydrolase 5 8 96 Initol hexakisphphate and diphphoinitol- 307 PPIP5K2 4 140 pentakisphphate kinase 2 308 S100A9 Protein S100-A9 2 13 309 TGM3 Protein-glutamine gamma-glutamyltransferase E 5 77 310 HNRNPD Heterogeneous nuclear ribonucleoprotein D0 8 38 Mini-chromome maintenance complex-binding 311 MCMBP 7 73 protein 312 PTPN1 Tyrine-protein phphatase non-receptor type 1 10 50 [Pyruvate dehydrogenase (acetyl-transferring)] 313 PDK1 6 49 kinase isozyme 1, mitochondrial 314 FKBP5 Peptidyl-prolyl cis-trans isomerase FKBP5 3 51 315 NAGK N-acetyl-D-glucamine kinase 3 37 316 MAP1S Microtubule-associated protein 1S 6 112 317 SUPT16H FACT complex subunit SPT16 4 120 318 GOLGB1 Golgin subfamily B member 1 3 376 Arf-GAP with coiled-coil, ANK repeat and PH domain- 319 ACAP1 8 82 containing protein 1 320 NR3C1 Glucocorticoid receptor 7 86 321 HSPA9 Stress-70 protein, mitochondrial 7 74 322 H1F0 Histone H1.0 7 21

215

MW # Gene Identified Proteins (770) Peptides (kD) 323 HP1BP3 Heterochromatin protein 1-binding protein 3 10 61 324 HSPA1A Heat shock 70 kDa protein 1A 8 70 325 MCM7 DNA replication licensing factor MCM7 5 81 326 MYO1G Unconventional myin-Ig 6 116 327 UQCRC2 Cytochrome b-c1 complex subunit 2, mitochondrial 7 48 328 RAD50 DNA repair protein RAD50 8 154 329 SMC1A Structural maintenance of chromomes protein 1A 5 143 330 STAT6 Signal transducer and activator of transcription 6 7 94 331 SYNE3 Nesprin-3 10 112 332 THNSL1 Threonine synthase-like 1 7 83 333 RPL32 60S ribomal protein L32 2 16 334 ATRX Transcriptional regulator ATRX 2 283 335 PFN1 Profilin-1 10 15 336 GLUD1 Glutamate dehydrogenase 1, mitochondrial 7 61 337 RAC2 Ras-related C3 botulinum toxin substrate 2 6 21 338 TPI1 Triephphate isomerase 7 31 339 LDHA L-lactate dehydrogenase A chain 8 37 340 RUNX1 Runt-related transcription factor 1 4 49 341 ERLIN1 Erlin-1 7 39 342 C9orf142 Protein PAXX 10 22 343 HDAC1 Histone deacetylase 1 5 55 344 RPS8 40S ribomal protein S8 8 24 345 ANXA11 Annexin A11 7 54 346 ASMTL N-acetylserotonin O-methyltransferase-like protein 4 69 347 CTBP2 C-terminal-binding protein 2 3 49 348 MTOR Serine/threonine-protein kinase mTOR 2 289 HLA class I histocompatibility antigen, Cw-2 alpha 349 HLA-C 4 41 chain 350 ATM Serine-protein kinase ATM 5 351 351 GART Trifunctional purine biynthetic protein adenine-3 7 108 352 MCM2 DNA replication licensing factor MCM2 7 102 353 NOL6 Nucleolar protein 6 6 128 354 RPS6KA3 Ribomal protein S6 kinase alpha-3 6 84 Vacuolar protein sorting-associated protein 18 355 VPS18 4 110 homolog 356 GATAD2B Transcriptional repressor p66-beta 5 65 Apoptotic chromatin condensation inducer in the 357 ACIN1 4 152 nucleus 358 LIG3 DNA ligase 3 8 113

216

MW # Gene Identified Proteins (770) Peptides (kD) 359 URB1 Nucleolar pre-ribomal-associated protein 1 3 254 360 ADD1 Alpha-adducin 5 81 361 ALG1 Chitobiyldiphphodolichol beta-mannyltransferase 5 53 362 BAX Apoptis regulator BAX 8 21 Cleavage and polyadenylation specificity factor 363 CPSF6 10 59 subunit 6 364 CYB5B Cytochrome b5 type B 8 16 365 DIAPH1 Protein diaphanous homolog 1 8 141 366 GMPS GMP synthase [glutamine-hydrolyzing] 8 77 367 NUP107 Nuclear pore complex protein Nup107 4 106 368 SEC24C Protein transport protein Sec24C 9 118 369 SF3B3 Splicing factor 3B subunit 3 8 136 370 SMARCC1 SWI/SNF complex subunit SMARCC1 6 123 371 FAM208A Protein TASOR 5 189 372 TEX10 Testis-expressed sequence 10 protein 5 106 373 ZC3HAV1 Zinc finger CCCH-type antiviral protein 1 6 101 374 FLG2 Filaggrin-2 2 248 375 GSR Glutathione reductase, mitochondrial 9 56 376 TARDBP TAR DNA-binding protein 43 7 45 377 G6PD Gluce-6-phphate 1-dehydrogenase 6 59 378 RPL4 60S ribomal protein L4 6 48 379 HBA1 Hemoglobin subunit alpha 6 15 380 HAT1 Histone acetyltransferase type B catalytic subunit 2 50 381 TRIM22 E3 ubiquitin-protein ligase TRIM22 3 57 382 EDC4 Enhancer of mRNA-decapping protein 4 4 152 383 CASP14 Caspase-14 3 28 384 ETHE1 Persulfide dioxygenase ETHE1, mitochondrial 6 28 385 MPG DNA-3-methyladenine glycylase 6 33 386 TUBB4B Tubulin beta-4B chain 3 50 387 MUC5B Mucin-5B 2 596 388 ATP5B ATP synthase subunit beta, mitochondrial 8 57 389 ZYX Zyxin 2 61 390 GTF3C1 General transcription factor 3C polypeptide 1 3 239 392 CCAR2 Cell cycle and apoptis regulator protein 2 8 103 393 TBC1D5 TBC1 domain family member 5 5 89 Serine/threonine-protein phphatase 6 regulatory 394 ANKRD44 7 108 ankyrin repeat subunit B 395 IQGAP1 Ras GTPase-activating-like protein IQGAP1 4 189 396 IQGAP2 Ras GTPase-activating-like protein IQGAP2 9 181

217

MW # Gene Identified Proteins (770) Peptides (kD) 397 MTA2 Metastasis-associated protein MTA2 4 75 398 NUP205 Nuclear pore complex protein Nup205 5 228 1-phphatidylinitol 4,5-bisphphate phphodiesterase 399 PLCB2 5 134 beta-2 400 RBM12B RNA-binding protein 12B 4 118 401 POLR2B DNA-directed RNA polymerase II subunit RPB2 6 134 402 RRP12 RRP12-like protein 3 144 403 RPS13 40S ribomal protein S13 5 17 404 TRAF3IP3 TRAF3-interacting JNK-activating modulator 3 64 405 GTF3C3 General transcription factor 3C polypeptide 3 8 101 406 TRIM33 E3 ubiquitin-protein ligase TRIM33 7 123 407 UBA7 Ubiquitin-like modifier-activating enzyme 7 9 112 408 ALDOA Fructe-bisphphate aldolase A 6 39 Dihydrolipoyllysine-residue succinyltransferase 409 DLST component of 2-oxoglutarate dehydrogenase 2 49 complex, mitochondrial 410 ALDH3A2 Fatty aldehyde dehydrogenase 6 55 411 ARG1 Arginase-1 5 35 412 RAB7A Ras-related protein Rab-7a 7 23 413 MGMT Methylated-DNA--protein-cysteine methyltransferase 4 22 414 SMAD2 Mothers against decapentaplegic homolog 2 5 52 415 PRDX4 Peroxiredoxin-4 6 31 417 PPIA Peptidyl-prolyl cis-trans isomerase A 2 18 418 SACM1L Phphatidylinitide phphatase SAC1 2 67 419 HNRNPA3 Heterogeneous nuclear ribonucleoprotein A3 4 40 420 NADK2 NAD kinase 2, mitochondrial 4 49 421 NT5DC2 5'-nucleotidase domain-containing protein 2 8 61 422 DSC1 Desmocollin-1 3 100 423 MAVS Mitochondrial antiviral-signaling protein 3 57 424 GLO1 Lactoylglutathione lyase 4 21 425 NSUN2 tRNA (cytine(34)-C(5))-methyltransferase 5 86 Phphatidylinitol 4,5-bisphphate 3-kinase catalytic 426 PIK3CD 5 119 subunit delta isoform 427 NUP133 Nuclear pore complex protein Nup133 5 129 428 HEATR1 HEAT repeat-containing protein 1 4 242 429 DDX5 Probable ATP-dependent RNA helicase DDX5 4 69 430 FAM129A Protein Niban 5 103 431 PES1 Pescadillo homolog 6 68

218

MW # Gene Identified Proteins (770) Peptides (kD) Monofunctional C1-tetrahydrofolate synthase, 432 MTHFD1L 8 106 mitochondrial 433 CHERP Calcium hometasis endoplasmic reticulum protein 6 104 434 DNM1L Dynamin-1-like protein 5 82 435 HSP90B1 Endoplasmin 8 92 436 FAM98B Protein FAM98B 5 37 437 GOLGA2 Golgin subfamily A member 2 6 113 438 OSBPL8 Oxysterol-binding protein-related protein 8 6 101 439 PSME2 Proteasome activator complex subunit 2 2 27 440 RPL7 60S ribomal protein L7 5 29 441 SF3B1 Splicing factor 3B subunit 1 5 146 442 SRBD1 S1 RNA-binding domain-containing protein 1 4 112 443 STAB1 Stabilin-1 6 275 444 TBC1D15 TBC1 domain family member 15 6 79 445 TMX1 Thioredoxin-related transmembrane protein 1 4 32 446 UBA6 Ubiquitin-like modifier-activating enzyme 6 4 118 447 XRN2 5'-3' exoribonuclease 2 5 109 448 CAPN1 Calpain-1 catalytic subunit 3 82 449 AMPD2 AMP deaminase 2 2 101 450 RPS6KA5 Ribomal protein S6 kinase alpha-5 2 90 FAD-dependent oxidoreductase domain-containing 451 FOXRED1 2 54 protein 1 452 ATP5J2 ATP synthase subunit f, mitochondrial 4 11 453 METTL7A Methyltransferase-like protein 7A 6 28 454 SRSF7 Serine/arginine-rich splicing factor 7 6 27 455 LSP1 Lymphocyte-specific protein 1 4 37 cAMP-dependent protein kinase catalytic subunit 456 PRKACB 4 41 beta 457 APIP Methylthioribule-1-phphate dehydratase 5 27 458 PPA2 Inorganic pyrophphatase 2, mitochondrial 6 38 459 SPTLC1 Serine palmitoyltransferase 1 4 53 460 RPL13 60S ribomal protein L13 3 24 461 HNRNPC Heterogeneous nuclear ribonucleoproteins C1/C2 4 34 462 RBBP4 Histone-binding protein RBBP4 2 48 Fragile X mental retardation syndrome-related 463 FXR1 3 70 protein 1 464 RPL18 60S ribomal protein L18 5 22 465 L2HGDH L-2-hydroxyglutarate dehydrogenase, mitochondrial 5 50 466 PGLS 6-phphogluconolactonase 6 28

219

MW # Gene Identified Proteins (770) Peptides (kD) 467 POLDIP2 Polymerase delta-interacting protein 2 5 42 468 MKI67 Antigen KI-67 4 359 470 SQRDL Sulfide:quinone oxidoreductase, mitochondrial 3 50 471 TRIP12 E3 ubiquitin-protein ligase TRIP12 3 220 472 EP300 Histone acetyltransferase p300 2 264 474 PTPN6 Tyrine-protein phphatase non-receptor type 6 5 68 475 RASAL3 RAS protein activator like-3 4 112 476 RHOT1 Mitochondrial Rho GTPase 1 6 71 477 MSN Moesin 3 68 478 TUBGCP3 Gamma-tubulin complex component 3 3 104 479 HADHB Trifunctional enzyme subunit beta, mitochondrial 5 51 480 GAK Cyclin-G-associated kinase 4 143 481 HTATSF1 HIV Tat-specific factor 1 3 86 Mediator of RNA polymerase II transcription subunit 482 MED12 4 243 12 483 NUP214 Nuclear pore complex protein Nup214 5 214 484 OGFR Opioid growth factor receptor 5 73 485 PRPF40A Pre-mRNA-processing factor 40 homolog A 5 109 486 PITRM1 Presequence protease, mitochondrial 4 117 487 RBM14 RNA-binding protein 14 6 69 488 TNPO1 Transportin-1 5 102 489 FLNB Filamin-B 3 278 490 CD38 ADP-ribyl cyclase/cyclic ADP-ribe hydrolase 1 2 34 491 RNF213 E3 ubiquitin-protein ligase RNF213 2 591 492 DNMT3A DNA (cytine-5)-methyltransferase 3A 2 102 493 CSE1L Exportin-2 3 110 494 MGST3 Micromal glutathione S-transferase 3 4 17 495 FABP5 Fatty acid-binding protein, epidermal 2 15 496 U2AF1 Splicing factor U2AF 35 kDa subunit 7 28 497 HNRNPA1 Heterogeneous nuclear ribonucleoprotein A1 4 39 498 LDHB L-lactate dehydrogenase B chain 5 37 499 SRSF3 Serine/arginine-rich splicing factor 3 2 19 500 SERPINB12 Serpin B12 4 46 501 DEK Protein DEK 2 43 Acidic leucine-rich nuclear phphoprotein 32 family 502 ANP32A 3 29 member A 503 PLEK Pleckstrin 2 40 504 ZNF512 Zinc finger protein 512 3 65 505 CASP1 Caspase-1 5 45

220

MW # Gene Identified Proteins (770) Peptides (kD) 506 DLD Dihydrolipoyl dehydrogenase, mitochondrial 3 54 507 CCT4 T-complex protein 1 subunit delta 4 58 508 RPL27A 60S ribomal protein L27a 2 17 509 SSRP1 FACT complex subunit SSRP1 5 81 Proline synthase co-transcribed bacterial homolog 510 PROSC 4 30 protein 511 RPL5 60S ribomal protein L5 3 34 512 TLR2 Toll-like receptor 2 4 90 513 PBRM1 Protein polybromo-1 2 193 514 PRDX6 Peroxiredoxin-6 2 25 515 TBC1D2 TBC1 domain family member 2A 2 105 516 SRRT Serrate RNA effector molecule homolog 3 101 517 MCM4 DNA replication licensing factor MCM4 4 97 518 ACSL1 Long-chain-fatty-acid--CoA ligase 1 5 78 519 ADPGK ADP-dependent glucokinase 3 54 520 CORO7 Coronin-7 5 101 521 FUBP1 Far upstream element-binding protein 1 4 68 522 GUCY1A3 Guanylate cyclase soluble subunit alpha-3 5 77 523 GSN Gelsolin 4 86 Haloacid dehalogenase-like hydrolase domain- 524 HDHD3 4 28 containing protein 3 Heterogeneous nuclear ribonucleoprotein U-like 525 HNRNPUL1 5 96 protein 1 526 KPNB1 Importin subunit beta-1 4 97 527 MYO18A Unconventional myin-XVIIIa 3 233 528 NOL9 Polynucleotide 5'-hydroxyl-kinase NOL9 7 79 529 PPM1G Protein phphatase 1G 6 59 530 RARS Arginine--tRNA ligase, cytoplasmic 3 75 531 THEMIS2 Protein THEMIS2 5 72 532 TRMT1L TRMT1-like protein 6 82 533 BAZ1B Tyrine-protein kinase BAZ1B 2 171 Arf-GAP with coiled-coil, ANK repeat and PH domain- 534 ACAP2 2 88 containing protein 2 535 NUP58 Nucleoporin p58/p45 3 61 536 POLD1 DNA polymerase delta catalytic subunit 2 124 537 STAG2 Cohesin subunit SA-2 3 141 538 EIF3A Eukaryotic translation initiation factor 3 subunit A 2 167 539 NUBP2 Cytolic Fe-S cluster assembly factor NUBP2 5 29 540 HNRNPA0 Heterogeneous nuclear ribonucleoprotein A0 4 31

221

MW # Gene Identified Proteins (770) Peptides (kD) 541 TUBA1A Tubulin alpha-1A chain 6 50 542 PTDSS1 Phphatidylserine synthase 1 2 56 543 RPS26 40S ribomal protein S26 2 13 544 GNB4 Guanine nucleotide-binding protein subunit beta-4 2 38 545 ARPC1B Actin-related protein 2/3 complex subunit 1B 4 41 Mitochondrial import receptor subunit TOM40 546 TOMM40 3 38 homolog 547 EIF4A3 Eukaryotic initiation factor 4A-III 2 47 548 GHDC GH3 domain-containing protein 4 58 549 TPR Nucleoprotein TPR 3 267 Arf-GAP with Rho-GAP domain, ANK repeat and PH 550 ARAP1 4 162 domain-containing protein 1 551 PI4KA Phphatidylinitol 4-kinase alpha 2 231 552 STRIP1 Striatin-interacting protein 1 4 96 553 SMPD4 Sphingomyelin phphodiesterase 4 2 93 1-phphatidylinitol 4,5-bisphphate phphodiesterase 554 PLCG2 2 148 gamma-2 555 DDX1 ATP-dependent RNA helicase DDX1 4 82 556 FOXK1 Forkhead box protein K1 2 75 Cleavage and polyadenylation specificity factor 557 CPSF3 3 77 subunit 3 558 OSBP Oxysterol-binding protein 1 2 89 559 RAB11FIP1 Rab11 family-interacting protein 1 2 137 Brefeldin A-inhibited guanine nucleotide-exchange 560 ARFGEF1 3 209 protein 1 561 IBA57 Putative transferase CAF17, mitochondrial 3 38 562 CKAP4 Cytkeleton-associated protein 4 3 66 563 DDB2 DNA damage-binding protein 2 3 48 564 FASN Fatty acid synthase 4 273 565 KEAP1 Kelch-like ECH-associated protein 1 4 70 566 NBN Nibrin 6 85 567 NUP160 Nuclear pore complex protein Nup160 4 162 568 NUP93 Nuclear pore complex protein Nup93 4 93 Bifunctional 3'-phphoadenine 5'-phphulfate synthase 569 PAPSS1 5 71 1 570 RB1 Retinoblastoma-associated protein 3 106 571 VARS Valine--tRNA ligase 5 140 572 TOMM70A Mitochondrial import receptor subunit TOM70 3 67 573 TP53BP1 Tumor suppressor p53-binding protein 1 3 214

222

MW # Gene Identified Proteins (770) Peptides (kD) 574 BCLAF1 Bcl-2-associated transcription factor 1 2 106 Inhibitor of nuclear factor kappa-B kinase subunit 575 IKBKB 4 87 beta 576 ISYNA1 Initol-3-phphate synthase 1 3 61 577 SND1 Staphylococcal nuclease domain-containing protein 1 3 102 578 WDR43 WD repeat-containing protein 43 2 75 579 YLPM1 YLP motif-containing protein 1 3 220 Eukaryotic peptide chain release factor GTP-binding 580 GSPT1 2 56 subunit ERF3A 581 MMS19 MMS19 nucleotide excision repair protein homolog 3 113 582 RELA Transcription factor p65 2 60 583 RPL34 60S ribomal protein L34 2 13 584 RPL27 60S ribomal protein L27 3 16 585 CPT2 Carnitine O-palmitoyltransferase 2, mitochondrial 2 74 586 RPL10 60S ribomal protein L10 2 25 587 TUBA4A Tubulin alpha-4A chain 3 50 588 GGCT Gamma-glutamylcyclotransferase 3 21 589 CPPED1 Serine/threonine-protein phphatase CPPED1 2 36 590 DOK3 Docking protein 3 2 53 591 RBM4B RNA-binding protein 4B 2 40 592 TKTL1 Transketolase-like protein 1 2 65 593 RPL24 60S ribomal protein L24 4 18 594 NCAPD2 Condensin complex subunit 1 2 157 595 SPG20 Spartin 2 73 596 HDLBP Vigilin 2 141 597 CUL4B Cullin-4B 3 104 598 SRPR Signal recognition particle receptor subunit alpha 3 70 599 ANXA6 Annexin A6 5 76 600 BCR Breakpoint cluster region protein 4 143 601 BRD1 Bromodomain-containing protein 1 3 120 602 COPG1 Coatomer subunit gamma-1 4 98 603 DNMT1 DNA (cytine-5)-methyltransferase 1 5 183 604 FMNL1 Formin-like protein 1 4 122 605 INTS4 Integrator complex subunit 4 5 108 606 IPO9 Importin-9 4 116 Multiple C2 and transmembrane domain-containing 607 MCTP2 4 100 protein 2 608 IMMT MIC complex subunit MIC60 3 84 609 NAT10 RNA cytidine acetyltransferase 2 116

223

MW # Gene Identified Proteins (770) Peptides (kD) NADH-ubiquinone oxidoreductase 75 kDa subunit, 610 NDUFS1 4 79 mitochondrial 611 NOL11 Nucleolar protein 11 4 81 612 NUP88 Nuclear pore complex protein Nup88 5 84 613 PEX11B Peroxisomal membrane protein 11B 4 28 614 RABGAP1 Rab GTPase-activating protein 1 3 122 615 REL Proto-oncogene c-Rel 3 69 616 SF1 Splicing factor 1 3 68 617 AARS2 Alanine--tRNA ligase, mitochondrial 4 107 618 GTF3C5 General transcription factor 3C polypeptide 5 4 60 619 UNC13D Protein unc-13 homolog D 4 123 620 ZNF326 DBIRD complex subunit ZNF326 5 66 621 MPP1 55 kDa erythrocyte membrane protein 3 52 622 GLYR1 Putative oxidoreductase GLYR1 3 61 623 RLTPR Leucine-rich repeat-containing protein 16C 3 155 624 NASP Nuclear autoantigenic sperm protein 2 85 625 NDUFA4 Cytochrome c oxidase subunit NDUFA4 3 9 626 PELP1 Proline-, glutamic acid- and leucine-rich protein 1 3 120 627 RPL3 60S ribomal protein L3 2 46 628 PITHD1 PITH domain-containing protein 1 3 24 629 NME3 Nucleide diphphate kinase 3 2 19 630 PHB Prohibitin 3 30 631 RAD21 Double-strand-break repair protein rad21 homolog 2 72 632 TARS2 Threonine--tRNA ligase, mitochondrial 2 81 633 OSBPL5 Oxysterol-binding protein-related protein 5 3 99 634 STK10 Serine/threonine-protein kinase 10 2 112 635 GLOD4 Glyoxalase domain-containing protein 4 3 35 636 IAH1 Isoamyl acetate-hydrolyzing esterase 1 homolog 3 28 637 ADRBK1 Beta-adrenergic receptor kinase 1 2 80 638 MSH6 DNA mismatch repair protein Msh6 2 153 639 PSMD1 26S proteasome non-ATPase regulatory subunit 1 2 106 640 TARBP1 Probable methyltransferase TARBP1 2 182 641 THRAP3 Thyroid hormone receptor-associated protein 3 2 109 Sorting and assembly machinery component 50 642 SAMM50 2 52 homolog 643 APOBEC3D DNA dC->dU-editing enzyme APOBEC-3D 2 47 644 NOL8 Nucleolar protein 8 2 132 645 PTK2B Protein-tyrine kinase 2-beta 2 116 646 CRBN Protein cereblon 2 51

224

MW # Gene Identified Proteins (770) Peptides (kD) 647 FERMT3 Fermitin family homolog 3 2 76 Dol-P-Man:Man(5)GlcNAc(2)-PP-Dol alpha-1,3- 648 ALG3 2 50 mannyltransferase 649 RPL31 60S ribomal protein L31 2 14 Transformation/transcription domain-associated 651 TRRAP 3 438 protein 652 HSPA4 Heat shock 70 kDa protein 4 2 94 653 FLAD1 FAD synthase 2 65 654 CA5B Carbonic anhydrase 5B, mitochondrial 2 36 Dolichyl-diphphooligaccharide--protein 655 DAD1 2 12 glycyltransferase subunit DAD1 SWI/SNF-related matrix-associated actin-dependent 656 SMARCAL1 2 106 regulator of chromatin subfamily A-like protein 1 657 ARL1 ADP-ribylation factor-like protein 1 3 20 658 RHOA Transforming protein RhoA 2 22 Set1/Ash2 histone methyltransferase complex 659 ASH2L 4 69 subunit ASH2 660 BAG6 Large proline-rich protein BAG6 4 119 661 CHAMP1 Chromome alignment-maintaining phphoprotein 1 3 89 662 DDX47 Probable ATP-dependent RNA helicase DDX47 4 51 663 ELF1 ETS-related transcription factor Elf-1 3 67 664 NCBP1 Nuclear cap-binding protein subunit 1 4 92 665 UQCRC1 Cytochrome b-c1 complex subunit 1, mitochondrial 3 53 666 DIS3 Exome complex exonuclease RRP44 3 109 667 IARS Isoleucine--tRNA ligase, cytoplasmic 3 145 668 TRMT1 tRNA (guanine(26)-N(2))-dimethyltransferase 3 72 669 USP4 Ubiquitin carboxyl-terminal hydrolase 4 4 109 von Willebrand factor A domain-containing protein 670 VWA5A 3 86 5A 671 VPS35 Vacuolar protein sorting-associated protein 35 3 92 673 SERPINB9 Serpin B9 3 42 674 TTLL12 Tubulin--tyrine ligase-like protein 12 3 74 675 ABCF1 ATP-binding cassette sub-family F member 1 3 96 676 SYMPK Symplekin 2 141 677 KDM3A Lysine-specific demethylase 3A 2 147 Arf-GAP with GTPase, ANK repeat and PH domain- 678 AGAP2 3 125 containing protein 2 679 FARS2 Phenylalanine--tRNA ligase, mitochondrial 2 52 680 CANX Calnexin 2 68

225

MW # Gene Identified Proteins (770) Peptides (kD) 681 IKZF1 DNA-binding protein Ikar 2 58 682 PDCD11 Protein RRP5 homolog 2 209 683 WDR91 WD repeat-containing protein 91 2 83 684 TKT Transketolase 2 68 RNA polymerase II subunit A C-terminal domain 685 CTDP1 2 104 phphatase 686 BOP1 Ribome biogenesis protein BOP1 2 84 Serine/threonine-protein phphatase 6 regulatory 687 PPP6R1 2 97 subunit 1 Signal transducer and activator of transcription 1- 688 STAT1 3 87 alpha/beta 689 FHOD1 FH1/FH2 domain-containing protein 1 2 127 690 AKAP9 A-kinase anchor protein 9 2 454 691 PABPC4 Polyadenylate-binding protein 4 2 71 692 AIM1 Absent in melanoma 1 protein 2 189 693 RAB5C Ras-related protein Rab-5C 3 23 694 GANC Neutral alpha-glucidase C 3 104 695 NLRX1 NLR family member X1 2 108 696 DEF6 Differentially expressed in FDCP 6 homolog 2 74 697 TBC1D2B TBC1 domain family member 2B 2 110 698 FARSB Phenylalanine--tRNA ligase beta subunit 2 66 699 POP1 Ribonucleases P/MRP protein subunit POP1 2 115 700 ZG16B Zymogen granule protein 16 homolog B 2 23 701 RIF1 Telomere-associated protein RIF1 2 274 702 RPS18 40S ribomal protein S18 2 18 703 TBL2 Transducin beta-like protein 2 2 50 704 AKAP17A A-kinase anchor protein 17A 2 81 705 PPP1R18 Phtensin 2 68 706 DHX36 ATP-dependent RNA helicase DHX36 2 115 707 MYBBP1A Myb-binding protein 1A 2 149 708 CTCF Transcriptional repressor CTCF 2 83 709 RPL19 60S ribomal protein L19 2 23 710 NNT NAD(P) transhydrogenase, mitochondrial 2 114 711 PDCD4 Programmed cell death protein 4 2 52 712 TOR4A Torsin-4A 2 47 713 RAN GTP-binding nuclear protein Ran 2 24 714 ZC3HAV1L Zinc finger CCCH-type antiviral protein 1-like 2 33 715 DENND3 DENN domain-containing protein 3 2 136 716 ARL6IP5 PRA1 family protein 3 2 22

226

MW # Gene Identified Proteins (770) Peptides (kD) 717 IVD Isovaleryl-CoA dehydrogenase, mitochondrial 2 46 718 UBA3 NEDD8-activating enzyme E1 catalytic subunit 2 52 719 LAP3 Cytol aminopeptidase 2 56 720 ZC3H11A Zinc finger CCCH domain-containing protein 11A 3 89 721 ATP6V1A V-type proton ATPase catalytic subunit A 3 68 722 ALDH18A1 Delta-1-pyrroline-5-carboxylate synthase 2 87 723 SMN1 Survival motor neuron protein 3 32 724 AP3B1 AP-3 complex subunit beta-1 2 121 725 STAT2 Signal transducer and activator of transcription 2 2 98 726 CSTF2T Cleavage stimulation factor subunit 2 tau variant 2 64 Threonylcarbamoyladenine tRNA 727 CDKAL1 3 65 methylthiotransferase 728 URB2 Unhealthy ribome biogenesis protein 2 homolog 2 171 729 DDX21 Nucleolar RNA helicase 2 2 87 730 OGDH 2-oxoglutarate dehydrogenase, mitochondrial 2 116 731 IPO7 Importin-7 2 120 732 NUP62 Nuclear pore glycoprotein p62 3 53 733 PKN1 Serine/threonine-protein kinase N1 2 104 734 TNIP1 TNFAIP3-interacting protein 1 2 72 735 MSL1 Male-specific lethal 1 homolog 2 67 736 ACOT8 Acyl-coenzyme A thioesterase 8 2 36 737 HSPA5 78 kDa gluce-regulated protein 2 72 738 SRP68 Signal recognition particle subunit SRP68 2 71 739 RAB27A Ras-related protein Rab-27A 2 25 740 CENPB Major centromere autoantigen B 2 65 741 SUZ12 Polycomb protein SUZ12 2 83 742 PDIA4 Protein disulfide-isomerase A4 2 73 743 TMEM57 Macoilin 2 76 744 PPIB Peptidyl-prolyl cis-trans isomerase B 2 24 ATP-binding cassette sub-family B member 7, 745 ABCB7 2 83 mitochondrial 746 TMEM63A CSC1-like protein 1 2 92 747 TPP2 Tripeptidyl-peptidase 2 2 138 748 PYGB Glycogen phphorylase, brain form 3 97 749 RHOT2 Mitochondrial Rho GTPase 2 2 68 750 UBTF Nucleolar transcription factor 1 2 89 751 GEMIN5 Gem-associated protein 5 2 169 752 TARS Threonine--tRNA ligase, cytoplasmic 2 83 753 SEC23A Protein transport protein Sec23A 2 86

227

MW # Gene Identified Proteins (770) Peptides (kD) 754 PARN Poly(A)-specific ribonuclease PARN 2 73 755 ARHGEF18 Rho guanine nucleotide exchange factor 18 2 131 756 ATXN2L Ataxin-2-like protein 2 113 757 DDX20 Probable ATP-dependent RNA helicase DDX20 2 92 758 GUCY1B3 Guanylate cyclase soluble subunit beta-1 2 71 759 PABPN1 Polyadenylate-binding protein 2 2 33 760 RPL30 60S ribomal protein L30 2 13 761 RPL36 60S ribomal protein L36 2 12 762 STAT5B Signal transducer and activator of transcription 5B 2 90 763 TMEM33 Transmembrane protein 33 2 28 764 PUS1 tRNA pseudouridine synthase A, mitochondrial 2 47 765 BAK1 Bcl-2 homologous antagonist/killer 2 23 766 NCEH1 Neutral cholesterol ester hydrolase 1 2 46 767 RAP1A Ras-related protein Rap-1A 2 21 768 IGF2BP2 Insulin-like growth factor 2 mRNA-binding protein 2 2 66 Dolichyl pyrophphate Man9GlcNAc2 alpha-1,3- 769 ALG6 2 58 glucyltransferase 770 GNL3 Guanine nucleotide-binding protein-like 3 2 62 771 NACC1 Nucleus accumbens-associated protein 1 2 57 772 PARS2 Probable proline--tRNA ligase, mitochondrial 2 53 773 PTPRE Receptor-type tyrine-protein phphatase epsilon 2 81 774 ZW10 Centromere/kinetochore protein zw10 homolog 2 89 775 PDE12 2',5'-phphodiesterase 12 2 67 776 CCT8 T-complex protein 1 subunit theta 2 60 777 PRKCD Protein kinase C delta type 2 78 778 CARS Cysteine--tRNA ligase, cytoplasmic 2 85 779 QARS Glutamine--tRNA ligase 2 88 780 NARS Asparagine--tRNA ligase, cytoplasmic 2 63 781 NCSTN Nicastrin 2 78 782 DCP1B mRNA-decapping enzyme 1B 2 68 783 ERAP1 Endoplasmic reticulum aminopeptidase 1 2 107 784 COPB2 Coatomer subunit beta' 2 102 785 THOP1 Thimet oligopeptidase 2 79 Membrane-associated progesterone receptor 786 PGRMC2 2 24 component 2 787 SLC25A6 ADP/ATP translocase 3 2 33

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