INHIBITION OF MITOCHONDRIAL TRANSLATION AS A

THERPEUTIC STRATEGY FOR ACUTE MYELOID LEUKEMIA

by

Marko Škrtić

A thesis submitted in conformity with the

requirements for the degree of

Doctor of Philosophy (Ph.D.), Graduate Department of the

Institute of Medical Sciences,

University of Toronto,

©Copyright by Marko Škrtić. 2012.

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Inhibition of mitochondrial translation as a therapeutic strategy

for acute myeloid leukemia

Marko Škrtić

Doctor of Philosophy

Institute of Medical Science

University of Toronto

2012

Abstract

Intro: Acute myeloid leukemia (AML) therapies have remained unchanged for 20 years,

and thus new therapies are needed.

Objective: To identify FDA-approved agents with anti-leukemia stem cell activity, we

performed a screen and identified the antimicrobial tigecycline (TIG).

Methods: Primary AML mononuclear cells were isolated by Ficoll centrifugation from

peripheral blood. Flow cytometry dye; JC-1, Carboxy-H2DCFDA, Mitotracker GreenFM.

Leukemia stem cell activity was assayed by human AML engraftment in NOD/SCID

mice.

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Results: TIG induced cell death in primary AML patient samples (LD50, 3-6µM n=14),

preferentially over normal hematopoietic cells. Likewise, in colony assays, TIG (5µM)

reduced the clonogenic growth of AML samples (n=7) by 93%, demonstrating an effect

on leukemia progenitor cells, but not normal hematopoietic cells (34% reduction, n=5). A

yeast genome-wide screen identified mitochondrial translation inhibition as the

mechanism of tigecycline-mediated cell death in eukaryotic cells. TIG decreased the

expression of mitochondrial peptides, enzyme activity and membrane potential

preferentially in AML cells over normal hematopoietic cells. ShRNA knockdown of

TuFM mitochondrial translation factor in leukemia cells reproduced TIG anti-leukemia

target effects previously described. We discovered that primary AML CD34+/CD38-

stem cells have greater mitochondrial mass (3-fold, n=5) than normal CD34+ cells (n=4).

Higher baseline mitochondrial mass in primary AML samples was predictive for

tigecycline sensitivity in vitro (r=-0.71, p<0.05). We assessed the effect of TIG on

primary AML stem cells defined by their ability to initiate leukemic engraftment in vivo.

NOD/SCID mice treated with TIG had decreased human AML engraftment (n=3 AML

patients) compared to control.

Conclusions: We identified mitochondrial translation inhibition as a novel therapeutic

strategy for AML. Currently, a Phase I clinical trial of tigecycline in hematological

malignancies is underway.

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Acknowledgements

On the long highways of the journey of life, we encounter many turns, hills, forks,

and plains. The following content represents one of these major roads where I have spent

my empowering youth; challenging myself daily to continue on the path of righteousness.

This story has been influenced and improved by many individuals around me in the

Schimmer laboratory, social circle and loving family home.

Foremost, I am indebted to my supervisor, Dr. Aaron D. Schimmer who provided

me the opportunity to grow and develop as a scientist under his guidance, mentorship and

constructive feedback. I have fond memories of our early morning meetings where we

discussed with enthusiasm the theoretical and practical questions of our research. I hope

to continue these morning meetings and collaboration in my future career.

Over the course of my research time in the laboratory two individuals have had a

significant impact on my growth and development as a scientist, person and lab member.

The Schimmer lab managers Rose Hurren and Marcela Gronda have collectively been the

most valuable resource a graduate student and hopeful independent researcher can ever

hope to have. I will always cherish our many moments of laughter and joy. The

completion of this body of work in the set time period would never have been possible

without you. There will be many future social visits where I hope we can continue to

build our friendship.

Many other individuals in the Schimmer laboratory have been extremely helpful

in the development of this scientific work. Furthermore, I thank you all for your patience

and understanding in the context of my loud and distractive demeanor in the workplace.

A special thank you goes to Nazir Jamal whose technical expertise in primary human

culture, and loving friendship will always be cherished. I also wish to extend many

thanks to all of our collaborators in Toronto and across Canada who have helped with the

project; this would not have been possible without you.

Furthermore, my committee members Drs. Mark Minden and Fei-Fei Liu have

provided invaluable feedback and advice towards my development as a future clinician

scientist. Our frequent meetings were always a highlight of my academic year. I am

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especially indebted to Dr. Minden whose clinical teaching in adult leukemia over the

course of two years has left a significant imprint in my growth as a clinician.

The Canadian Institute for Health Research, Leukemia and Lymphoma Society,

American Society of Hematology, Division of Hematology/Toronto, Institute of Medical

Science, Department of Medicine/Toronto should be congratulated for the financial

support I received and their continued assistance to young researchers in Canada.

The mentioned individuals have provided me with orientation and help on this

scientific journey. But it is my family; father Vid, mother Marija, and sister Annamaria

who are the concrete foundation of the road, and the fuel in my tank during this trip.

Their unconditional patience and understanding during my long hours of work is

appreciated. Of course, my weekly trips to Hamilton were also highlighted by the

gourmet food, which travelled back to Toronto with me every Sunday evening

throughout these years.

Into  my  heart  an  air  that  kills  From  yon  far  country  blows:  What  are  those  blue  remembered  hills,  What  spires,  what  farms  are  those?    That  is  the  land  of  lost  content,  I  see  it  shining  plain,  The  happy  highways  where  I  went  And  cannot  come  again.    A.E.  Housman  (1859-­‐1936)    

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Tables of Contents

Acknowledgements...……………..……………..……………..……………..……….….iv

Tables of Contents….……………..……………..……………..……………..……….…vi

List of Abbreviations…….……..………………..………………..………………………x

List of Figures. ..………………....………………....………………....……………...….xii

Chapter 1: Introduction …………….……………..……………..……………………1

1.1.1. Acute myeloid leukemia………..………..……………..……………..…..1

1.1.2. Cancer Stem cells……………..……………..……………………...4

1.1.3. Drug Repositioning……………..……………..……………………5

1.1.4. Tigecycline……………..……………..……………..……………...7

1.2.1 Mitochondria in Cancer……………..……………..………………….…..9

1.2.2. Mitochondrial DNA Replication and Maintenance………….…..12

1.2.3. Mitochondrial DNA Transcription……………..…………………14

1.3.1 Mitochondrial Translation……………..……………..………………….19

1.3.2. Mitochondrial Ribosomes……………..……………..……………20

1.3.3. Translation Initiation……………..……………..…………………21

1.3.4. Translation Elongation……………..……………..……………….23

1.3.5. Translation Termination……………..……………..……………..25

1.3.6. Translation Modulation……………..……………..………………26

1.3.7. Recent insights: mitochondrial gene expression…………………..30

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Chapter 2: Rationale and Hypothesis……………..……………..…………………..33

2.1.1. General Rationale……………..……………..…………………………..33

2.2.1. Tigecycline……………..……………..……………..…………………..34

Chapter 3: Methods……………..……………..……………..………………………37

Chapter 4: Results……………..……………..……………..………………………..50

PART I: Tigecycline – a novel anti-leukemia compound…………………….....50

4.1.1. Chemical screen for compounds targeting leukemic cells identifies the

antimicrobial tigecycline……………..……………..……………..……….….…50

4.1.2. Validation dose-response curves……………..…………………………..52

4.1.3. Tigecycline activity in malignant cell lines……………..……………….52

4.1.4. Tigecycline kills primary AML bulk more effectively than normal

hematopoietic cells..………………...………………...…………………………56

4.1.5. Tigecycline kills AML progenitors and stem cells more effectively than the

normal equivalent cells………………...………………...………………...........59

4.1.6. Tigecycline shows anti-AML activity in xenograft models of human

leukemia………...………………...………………...………………...…………62

4.1.7. Tigecycline shows activity in humanized xenotransplantation models of

leukemia.………………...…..………………...…..………………...…………..65

4.1.8. Tigecycline shows synergy in combination with standard AML

chemotherapy……...…..…………...…..…………...…..…………...…..………67

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PART II: Tigecycline inhibits mitochondrial translation in leukemia cells…….71

4.2.1. Haplo-Insufficiency Profiling in S. Cerevisiae identifies mitochondrial

translation as target of tigecycline in eukaryotic cells…………………………..71

4.2.2. Tigecycline inhibits mitochondrial translation in established and primary

leukemia cells. .…………...…..…..…………...…..…..…………...…..……….75

4.2.3. Tigecycline decreases activity of the oxidative phosphorylation cascade.78

4.2.4. Tigecycline collapses mitochondrial membrane potential in leukemia…..81 4.2.5. Tigecycline does not increase reactive oxygen species in leukemia……..83 4.2.6. Anti-leukemia activity of tigecycline is oxygen-dependent………….…..86 4.2.7. Anti-leukemia activity of tigecycline is dependent on baseline

mitochondrial mass..…………... ..…………... ..…………... ..…………...........88

PART III: Broad inhibition of mitochondrial translation has anti-leukemia

activity…………….………….………….………….………….……………..…90

4.3.1. Genetic inhibition of mitochondrial translation displays anti-leukemia

properties………….…………………….…………………….………………....90

4.3.2. Chemical inhibition of mitochondrial translation displays anti-leukemia

properties….………………….………………….………………….…………...97

PART IV: Mitochondrial Characteristics of leukemia cells………………100

4.4.1. Mitochondrial membrane potential of leukemia and normal hematopoietic

cells…………….…………………….…………………….…………………100

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4.4.2. Primary human AML cells have higher mitochondrial biogenesis than

normal hematopoietic cells……………….………………….…………………102

4.3.3. Base-line mitochondrial mass is predictive for tigecycline sensitivity in

vitro…….……………………….……………………….……………………...106

Chapter 5: Discussion……………..………………………………………………..108

5.1 PART I: Tigecycline, a novel anti-leukemia compound………………...…108

5.2 PART II, III: Tigecycline inhibits mitochondrial translation in

leukemia………………………………………………………………………...110

5.3. PART IV: Mitochondrial characteristics of leukemia versus normal cells..112

5.4. Preclinical Signifance.. ……………………………………………………114

5.5 Conclusion………………………………………………………………….115

5.6 Significance……………………………………………………………….115

5.7. Future Directions…………………………………………………………..116

REFERENCES…………………………………………………………………………119

Appendix I: The anti-parasitic agent ivermectin induces chloride-dependent membrane

hyperpolarization and cell death in leukemia cells…………….……………………….143

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List of Abbreviations

APL: Acute Promyelocytic leukemia

AML: Acute Myeloid Leukemia

AUC: Area under curve

CAP: Chloramphenicol

Complex IV: Cytochrome c oxidase

CSC: Cancer Stem Cell

CR: Complete Remission

CT: Threshold cycle of amplification

E site: Exit site

EF-Tu: Elongation factor Tu (mitochondrial)

FITC: Fluoroscein isothiocyanate

G-CSF: Granulocyte colony-stimulating factor

GSEA: Gene-set enrichment analysis

GO: Gene Ontology

H: Heavy

HGB: Human globulin

IF3: Initiation factor 3 (mitochondrial)

IGF-1: Insulin-like growth factor

L: Light

LIN: Linezolid

Lin- CB: Lineage-depleted human cord blood cells

LRP130: Leucine-rich protein 130

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LSC: Leukemia Stem-cells

MDS: Myelodysplastic syndrome

MFI: Median fluorescene intensity

MN1: Meningioma 1

mtDNA: Mitochondrial DNA

mtRPOL: Mitochondrial RNA polymerase

mtRRF: Mitochondrial recycling factor

NOD-SCID: Non-obese diabetic-severe combined immunodeficient

NRF: Nuclear Respiratory factor

PGC-1α: Proliferator-activated receptor gamma co-activator 1α

PI: Propidium Iodide

PNC1: Pyrimidine nucleotide carrier 1

POL γ: DNA polymerase γ

ROS: Reactive oxygen species

SCF: Stem-cell factor

SCID: Severe combined immune deficiency

SDS: Sodium dodecyl sulphate

shRNA: Short hair-pin RNA

TFA: Mitochondrial transcription factor A

UPR: Unfolded protein response

  xii  

List of Figures

Figure 1A. Schematic representation of the mitochondrial respiratory chain…………..11

Figure 1B. Schematic representation of the mitochondrial translation control…………29

Figure 2. Chemical screen for compounds targeting leukemic cells identifies the

antimicrobial tigecycline…………………………………………………………………51

Figure 3. Validation dose-response curves……………………………………………....53

Figure 4. Tigecycline activity in malignant cell lines……………………………………54

Figure 5. Tigecycline activity in malignant cell lines……………………………….…...55

Table 1. AML Patient Characteristics………………………………………………....…57

Figure 6. Tigecycline kills AML cells preferentially over normal hematopoietic cells…58

Figure 7. Tigecycline kills AML progenitors preferentially over normal hematopoietic

progenitors……………………………………………………………………………….60

Figure 8. Tigecycline kills AML stem cells preferentially over normal hematopoietic

stem cells…………………………………………………………………………………61

Figure 9. Tigecycline plasma concentration after single administration in SCID mice…63

Figure 10. Tigecycline has in vivo activity in models of human leukemia in mice……...64

Figure 11. Tigecycline shows activity in xenotransplantation models of leukemia….….66

Figure 12. Tigecycline has synergistic activity with daunorubicin in vitro………….…..68

Figure 13. Tigecycline has synergistic activity with cytarabine (Ara C) in vitro…….….69

Figure 14. Tigecycline has synergistic activity with Ara C and daunorubicin in vivo…..70

Figure 15. S. Cerevisiae grown in respiratory media exhibits enhanced sensitivity to

tigecycline compared to standard glycolytic conditions…………………………………72

Figure 16. HIP assays with drugs in S. Cerevisiae………………………………….……74

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Figure 17. Tigecycline decreases mitochondrially-translated proteins in leukemia cells.75

Figure 18. Tigecycline increases mRNA expression of mitochondrially-translated

proteins in leukemia cells………………………………………………………………...77

Figure 19. Tigecycline decreases enzyme activity of complexes I and IV……………....79

Figure 20. Tigecycline decreases oxygen consumption in leukemia cells………………80

Figure 21. Tigecycline collapses mitochondrial membrane potential in leukemia cells..82

Figure 22. Tigecycline does not increase reactive oxygen species in leukemia cells…...84

Figure 23. Tigecycline’s inhibition of respiratory complexes is functionally distinct from

respiratory chain inhibitors in terms of ROS production..………………………………85

Figure 24. Anti-leukemia activity of tigecycline is oxygen-dependent…………………87

Figure 25. Anti-leukemia activity of tigecycline is dependent on mitochondrial mass…89

Figure 26. EF-Tu, but not IF-3 knockdown decreases viability of TEX cells…………..92

Figure 27. EF-Tu inhibits mitochondrial translation in TEX cells…………………...…93

Figure 28. EF-Tu knockdown decreases mitochondrial membrane potential…………..94

Figure 29. EF-Tu knockdown doesn’t alter reactive oxygen species in TEX cells……..95

Figure 30. EF-Tu knockdown decreases oxygen consumption rate in TEX cells………96

Figure 31. Chloramphenicol and linezolid inhibit proliferation of TEX cells…………..98

Figure 32. Chloramphenicol inhibits clonogenic growth of primary AML cells………..99

Figure 33. Mitochondrial membrane potential of malignant and normal cells………...101

Figure 34. Mitochondrial DNA copy number of primary AML and normal cells……..103

Figure 35. Mitochondrial mass of primary AML and normal hematopoietic cells…….104

Figure 36. Resting oxygen consumption of primary AML and normal cells………….105

Figure 37. Base-line mitochondrial mass is predictive for tigecycline sensitivity……..107

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CHAPTER 1: INTRODUCTION

1.1.1. ACUTE MYELOID LEUKEMIA

Acute Myeloid Leukemia (AML) is a heterogeneous group of aggressive

hematological neoplasms of the myeloid lineage characterized by clonal proliferation of

myeloid precursors, with a reduced capacity to differentiate into mature cellular

components (Löwenberg et al., 1999). As a result, there is a loss of hematopoietic

function due to the lack of mature granulocytes and monocytes as well as decreased red

blood cell and platelet production. These abnormal precursor cells are capable of

proliferation and cell division, but lack the capacity to differentiate.

AML is the most common form of acute leukemia in adults and makes up about

80 percent of cases in this group (Yamamoto and Goodman, 2008). While there have

been advances in treatment of certain hematological malignancies, the prognosis of AML

remains grim. Patients older than 60 mostly have a poor prognosis, with a 2-year survival

probability of less than 10 percent (Löwenberg et al., 1998). Moreover, the therapy of

AML has remained essentially unchanged for over 20 years. Thus, further research is

warranted into developing novel therapeutic strategies for the treatment of this disease.

Most cases of AML arise abruptly and randomly, most likely due to the

acquisition of somatic mutations in hematopoietic progenitors that confer survival,

proliferation, and lack of differentiation(Hoffman, 2005). However, exposure to certain

environmental agents has been associated with AML pathogenesis. Any exposure source

that results in DNA strand breaks, such as ionizing radiation or DNA alkylating agents

  2  

can produce point mutations or chromosomal translocations that are associated with

hematopoietic cell transformation(Levine and Bloomfield, 1992). Less commonly,

chemical exposure to organic solvents such as benzene has been linked to higher risk for

development of AML. Therapy-associated AML occurs when either radiation or

chemotherapy treatment used for curative therapy of a malignancy such as Hodgkin

lymphoma inadvertently causes distinct mutations that alter hematopoiesis and result in

AML development. Common therapy-associated AML genetic abnormalities seen are

associated with chromosome 5, 7, and 11q23(Thirman et al., 1993; Thirman and Larson,

1996). Also, there is concerning evidence about the development of myelodysplastic

syndrome (MDS) and/or AML post intensive therapy and/or autologous stem cell

transplantation in lymphoma and breast cancer(Miller et al., 1994; Stone et al., 1994).

Patients with MDS have a 30% life-time risk of developing AML.

AML is a very heterogeneous disease due to it’s differing acquired mutations;

both chromosomal structural aberrations, and submicroscopic mutations and gene

expression changes(Mrozek and Bloomfield, 2006). Both of these genetic alterations are

used for predicting clinical outcome at the time of diagnosis. The most common

chromosome alterations are involved with DNA-binding activity or regulatory function of

transcription factors(Look, 1997). This creates a fusion protein, which results in a

dominant increased function of the wild-type protein. Risk stratification of AML patients

is organized into three groups based on cytogenetics. Patients with t(15;17)(q22;q12-21),

t(8;21)(q22;q22) or inv(16)(p13q22)/t(16;16)(p13;q22) have a favourable

prognosis(Mrozek and Bloomfield, 2006). Alternatively, patients with a complex

karyotype and inv(3)(q21q26)/ t(3;3)(q21;q26), and at least 5 chromosomal aberrations

  3  

are in the unfavorable risk group. The intermediate group consists of patients with a

normal karyotype and t(9;11)(p22;q23), del6q, del9q, del11q, or del20q. Patients with

cytogenetically normal status can also be risk-stratified in terms of genetic alterations.

NPM1 and CEBPA mutations indicate a favourable prognosis, while FLT3-ITD, MLL-

PTD mutation, BAALC, or ERG overexpression are associated with a favorable

prognosis. While cytogenetic analyses of bone marrow is standard for newly diagnosed

AML patients, the development of other molecular technologies such as fluorescence in

situ hybridization, and RT-PCR is changing guidelines for AML diagnosis and therapy

selection.

The main course treatment of AML has remained unchanged for the last three

decades. Younger patients (age < 65) with AML receive the “7+3” regimen where

cytarabine (100-200 mg/m2 per day) continuous infusion is given for seven days and a

bolus infusion of an anthracycline is administered for the first three days (usually

daunorubicin). Although other anthracycline such as doxorubicin or idarubicin may be

used, there is no evidence highlighting one anthracycline being more advantageous over

another(Kolitz, 2006). After induction chemotherapy, 70-80% of younger AML patients

or roughly 50% of older patients (age >65) will achieve complete remission (CR) of

AML, defined primarily as normal neutrophil, platelet counts, independence from red

blood cell transfusion and the presence of less than 5% non-leukemic blasts in the bone

marrow(Lowenberg et al., 1999). After CR, younger AML patients can proceed with

three options: allogeneic bone marrow transplantation, autologous bone marrow

transplantation or maintenance chemotherapy. However, the majority of patients (over

  4  

50%) who achieve CR will relapse within 3 years(Kell, 2006) while 10-15% of de novo

AML patients will never achieve CR and will have primary refractory disease.

Relapsed AML is a challenging disease-state based on several patient-centered

and disease-related factors(Litzow, 2007). Patient factors include co-morbidities, time

since CR, age, and previous therapies, while disease factors are cytogenetics, AML

subtype and disease burden at time of relapse. Options for relapsed AML patients include

re-induction chemotherapy, bone marrow transplantation, myeloablative or palliative

chemotherapy depending on the mentioned factors in a evidence-based algorithm(2003).

Unfortunately, treatment of relapsed AML remains a challenge due to efficacy of

chemotherapy regimens and the lack of available bone marrow transplant donors.

Therefore, the generation of novel targeted therapies for both de novo and relapsed AML

warrants further investigation.

1.1.2. Cancer Stem Cells

Today’s most challenging aspect of cancer therapy is perhaps the cancer stem cell

(CSC). Stem cells were first described in 1961 by Till and McCulloch(TILL and

McCULLOCH, 1961), and are generally defined by their potential for self-renewal and

differentiation ability into diverse cell types. Cancer stem-cells, which comprise a

minority component of tumours, are believed to have the capacity to initiate and sustain

the tumourigenic process. It is difficult to eradicate them completely during treatment,

and therefore they have become an intriguing target for cancer therapy.

  5  

Much of the evidence for the cancer stem-cell hypothesis has come from studies

in hematologic malignancies. Studies by Dick and colleagues(Lapidot et al., 1994) found

leukemia stem-cells (LSC) in a small compartment from the peripheral blood for Acute

Myeloid Leukemia (AML) patients. They were then able to successfully engraft these

LSCs into the bone marrow of non-obese diabetic-severe combined immunodeficient

(NOD-SCID) mice where these human cells proliferated and disseminated a phenotype

similar to that in the original patients. As a result, the current functional standard of a

LSC is the successful engraftment into NOD-SCID mice.

Although LSCs have the capacity for self-renewal and differentiation, evidence

has shown that a substantial number of LSCs are found in a quiescent Go phase(Guzman

et al., 2001). This could provide a possible reason for the failure of chemotherapeutics to

eliminate LSCs as they commonly target rapidly cycling populations. Other reasons for

LSC resistance to drugs and toxins could be the expression of ATP-associated

transporters(Dean et al., 2005), or resistance to apoptotic stimuli(Konopleva et al., 2002).

Therefore, it would be beneficial to identify novel therapeutic compounds that can

directly affect the viability of leukemia stem cells. To rapidly advance therapeutics that

can target leukemia stem cells into clinical trials, a drug repositioning strategy was

adapted.

1.1.3. Drug repositioning as a strategy to rapidly advance novel therapeutic agents

into clinical trials

  6  

Drug repositioning is a strategy to rapidly advance new therapeutic options into

clinical trial and has been shown to have clinical efficacy. The repositioning of

thalidomide as a therapeutic agent for the treatment of myeloma and myelodysplasia is

one of the best-known examples of this strategy, but there have been multiple other

successes. For example, the broad-spectrum antiviral ribavirin was found to suppress

oncogenic transformation by disrupting the function and subcellular localization of the

eukaryotic translation initiation factor eIF4E (Kentsis et al., 2004; Tan et al., 2008). As

such, ribavirin was recently evaluated in a phase I dose escalation study in patients with

relapsed or refractory M4/M5 acute myeloid leukemia (AML). In this study of 13

patients treated with ribavirin, there was 1 complete remission, and 2 partial remissions.

Thus, ribavirin might be an efficacious agent for the treatment of AML (Assouline et al.,

2009). Likewise, the anti-fungal ketoconazole inhibits the production of androgens from

the testes and adrenals in rats. Given this finding, ketoconazole was rapidly advanced into

clinical trials for patients with prostate cancer where it displayed clinical efficacy in early

studies (Sella et al., 1994; Small et al., 2004).

Recently, we determined that the anti-parasitic clioquinol exhibited preclinical

activity against leukemia and myeloma in vitro and in vivo. Mechanistically, it was

demonstrated that this compound inhibits the proteosome through both copper-dependent

and independent mechanisms (Mao et al., 2009). Thus, our pre-clinical data suggest that

this antiparasitic could be repurposed for the treatment of hematological malignancies.

As an oral formulation of clioquinol was not available, we partnered with the generic

pharmaceutical company PharmaScience, who formulated and manufactured oral

clioquinol tablets for our study. We then leveraged the prior pharmacology and

  7  

toxicology data on this compound to rapidly initiate a Phase I study to evaluate the dose-

limiting toxicity, maximum tolerated dose, and recommended Phase II dose of clioquinol

in patients with relapsed or refractory hematologic malignancies (ClinicalTrials.gov

Identifier: NCT00963495).

1.1.4. Tigecycline

To identify compounds active against leukemia stem cells, a library of on-patent

and off-patent drugs (n = 312) with well-characterized pharmacokinetics and toxicology

and a wide therapeutic window was compiled. This library was then screened to identify

agents that reduced the viability of TEX and M9-ENL1 cells. TEX and M9-ENL1 cells

were derived from lineage-depleted human cord blood cells (Lin- CB) transduced with

TLS-ERG or MLL-ENL oncogenes respectively, and displayed properties of stem cells

including hierarchal differentiation and marrow repopulation(Barabé et al., 2007; Warner

et al., 2005). The TLS-ERG oncogene is in a subset of acute myeloid leukemia where the

NH terminal region of TLS (translocation liposarcoma) is fused to COOH terminal

domain of ERG (ets related gene) via the t(16;21) translocation(Ichikawa et al., 1994).

The MLL-ENL oncogene occurs when mixed lineage leukemia (MLL) fuses with eleven

nineteen leukemia (ENL) protein via the t(11;19) translocation. This translocation is

equally prevalent in myeloid and lymphoid/mixed lineage leukemias(Zeisig et al., 2003).

In this screen, TEX and M9-ENL1 cells were treated with differing concentrations of

drugs with viability determined by the MTS assay. From this screen, tigecycline was

identified as a potential agent.

  8  

Tigecycline is a recently characterized anti-microbial agent of the novel

glycylcycline class that is active against a range of gram-positive and gram-negative

bacteria, particularly drug-resistant pathogens(Stein and Craig, 2006) and FDA-approved

in the treatment of complicated gram positive and negative infections. Tigecycline was

developed synthetically as an analogue to minocycline with the addition of a tert-butyl-

glycylamido side chain to the tetracycline backbone(Garrison et al., 2005)

(Supplementary Figure 1). This modification decreased drug resistance effects mediated

by efflux pumps, and improved its affinity for the ribosome. Consistent with its design,

tigecycline has been shown to inhibit bacterial protein synthesis by 3 and 20-fold greater

efficacy compared to minocycline or tetracycline respectively(Olson et al., 2006).

Mechanistically, tigecycline reversibly binds to the 30S subunit of the bacterial ribosome,

blocking the aminocyl-tRNA from entering the A site(Doan et al., 2006), thereby

inhibiting elongation of the peptide chain and protein synthesis.

Tigecycline is routinely administered as 50 mg intravenously every 12 hours

without significant toxicity, but higher doses have also been used safely. For example,

intravenous doses of 300 mg are well tolerated except for mild nausea, resulting in a

Cmax of 2.82ug/mL (5µM)(Muralidharan et al., 2005), a concentration within the range

required for anti-leukemic effects. Toxicology studies in animals also suggest a potential

for anti-leukemic activity. Rats receiving > 30 mg/kg/day for 2 weeks developed

reversible anemia, thrombocytopenia, and leucopenia with a hypocellular bone

marrow(Wyeth-Canada, 2007). The dose of 30 mg/kg translates to 250 mg of drug in

humans based on scaling for body surface area and weight, and is within 3 times the

antimicrobial dose of the drug. However, these higher concentrations of tigecycline are

  9  

not used in the treatment of infection, potentially explaining why anti-cancer activity has

not been previously reported with the drug. Further supporting the potential of

tigecycline as an anti-leukemic agent, animal studies have demonstrated that the drug

accumulates in tissues such as the bone and bone marrow with ratios to the plasma as

high as 19:1.

1.2 MITOCHONDRIA IN CANCER

Over the last decade, there have been multiple advances in understanding the role of

mitochondria in cancer physiology (Gogvadze et al., 2008). Mitochondria are the

powerhouse of the cell, enabling energy production and therefore are important for

survival of eukaryotic cells (Fulda et al., 2010). Mitochondria have also recently been

classified as being key regulators for various cell death pathways (Garrido et al., 2006;

Kroemer et al., 2007). Studies have exploited these aberrant cell death pathways to

develop novel therapeutic agents targeting apoptotic pathway elements (Konopleva et al.,

2006; Schimmer et al., 2004). Focus has also shifted to understanding the role of

mitochondrial gene expression in tumorigenesis. Studies have shown that mutations in

mitochondrial DNA (mtDNA) can increase the risk of developing breast, and prostate

cancer (Canter et al., 2005; Petros et al., 2005). In hematological malignancies, it has

been noted that B-cell Chronic lymphocytic leukemia cells (CLL) have higher mtDNA

copy number, and increased mitochondrial biogenesis than normal lymphocytes (Carew

et al., 2004).

  10  

Eukaryotic cells have two separate genomes; nuclear DNA organized in

chromosomes, and the circular mitochondrial DNA located within the mitochondria.

Mitochondrial DNA (mtDNA) is comprised of a double-stranded circular genome 16.6

kb in length, without introns (Lang et al., 1999). It encodes two rRNAs, 22 t-RNAs and

13 of the 90 proteins in the mitochondrial respiratory chain. The remaining proteins of

the respiratory chain are nuclear-encoded, imported into the mitochondria and assembled

into the functional complexes of the electron transport chain. The 13 mt-DNA encoded

proteins are translated by mitochondrial ribosomes within the mitochondrial matrix.

Mitochondrial ribosomes differ from bacterial and eukaryotic cytosolic ribosomes in their

structure, and chemical properties (O'Brien, 2003). Although mitochondrial ribosomes

differ structurally from cytoplasmic and bacterial ribosomes, they function similarly. In

addition, mitochondrial and bacterial ribosomes use similar elongation initiation

machinery (Gaur et al., 2008; Hunter and Spremulli, 2004; Zhang and Spremulli, 1998b).

Interestingly, the 13 mtDNA-encoded subunits of the electron transport chain are

important for functional regulation of oxidative phosphorylation (see Figure 1A) (Fukuda

et al., 2007). Therefore, the role of human mitochondrial gene expression in the context

of cancer metabolism should be explored.

                       

  11  

     

             

               

Figure  1A.  Schematic representation of the mitochondrial respiratory chain. Peptides encoded by mitochondrial DNA and translated by mitochondrial ribosomes are shown on the bottom for each relevant complex enzyme.  

  12  

 1.2.2. Mitochondrial DNA Replication and Maintenance Mitochondria usually contain 2-10 mtDNA copies, and subsequently there are 103-104

mitochondria per cell. Unlike nuclear DNA, mtDNA is constantly being replicated

throughout the cell cycle, and also in non-replicated cells such as cardiomyocytes(Smits

et al.). In eukaryotic cells, mtDNA replication and transcription are regulated by a series

of nuclear-encoded factors in a complex biological process(Shadel and Clayton, 1997).

The relative abundance of mtDNA in the specific cell type is controlled by these

regulatory systems. MtDNA lacks introns, and the only long, non-coding region of the

genome contains control elements for DNA replication and transcription(Asin-Cayuela

and Gustafsson, 2007). The strands of mtDNA are designated as heavy (H) and light (L)

because of their differential buoyant densities as determined by a cesium chloride

gradient. H-strand transcription is initiated at two regulated sties, HSP1 (H1) and HSP2

(H2), whereas L-strand transcription is initiated at one single promoter (LSP)(Montoya et

al., 1982). These sites are located in the non-coding region of the genome termed the D –

loop. The process of mtDNA replication is temporally and spatially coupled to RNA

transcription(Kelly and Scarpulla, 2004). After RNA cleavage occurs in the D-loop

region, these sites are also used for the initiation of DNA synthesis.

There is one polymerase used in mtDNA synthesis termed DNA polymerase γ

(POL γ)(Stumpf and Copeland). POL γ consists of a single 140 kDa catalytic subunit and

a 55 kDa accessory subunit that forms a tight dimer. This polymerase is responsible for

multiple aspects of mtDNA synthesis, including replication, recombination and DNA

repair. Efficient mtDNA maintenance is not solely dependent on POL γ activity, but also

  13  

a host of nuclear genes(Lee and Wei, 2005). Also, mtDNA replication is not directly

associated with growth and proliferation of organelles, therefore also including

mitochondria(Shadel and Clayton, 1997). Depending on the tissue and it’s metabolic

requirements, different human cells will have varying levels of mtDNA(Moraes, 2001).

The exact mechanisms of this differential regulation are still being investigated.

In the context of disease, POL γ mutations have been most commonly associated

with disorders of the nervous and muscle systems ranging from infantile cerebrohepatic

disease to a progressive external opthalmoplegia that occurs later in life(Cohen and

Naviaux; Milone and Massie). The role of POL γ mutations in cancerogenesis is not as

well established. Recent work has shown that POL γ mutations may be associated with

increased tumorigenesis in primary breast cancer cells(Singh et al., 2009). However, the

association of mtDNA mutations with increased cancerogenesis is more studied, with

mtDNA mutations increasing the risk of developing breast, and prostate cancer (Canter et

al., 2005; Petros et al., 2005).

Targeting POL γ for anti-cancer strategies remains a relatively uncharacterized

strategy. Sasaki and colleagues have shown that vitamin K compounds display

cytotoxicity to a wide-range of cancer cell types by a mechanism of direct inhibition of

POL γ(Sasaki et al., 2008). This POL γ inhibition was associated with apoptosis induction

by way through superoxide species generation at higher concentrations. Recently, it has

been determined that DNA polymerase, including POL γ may be therapeutic targets for

cancers deficient in DNA mismatch repair proteins(Martin et al.). SiRNA-mediated

knockdown of POL γ decreased survival in human colon cancer cells with deficient DNA

mismatch repair proteins, along with decreased mtDNA content, and increased oxidative

  14  

DNA lesions. This study highlighted the potential benefit of DNA polymerase targeted

treatment as only malignant colonic cells have mutations in these defective DNA repair

pathways, and therefore would provide potential malignant-selective future therapies in

colon cancer.

One major benefit of inhibiting POL γ directly in cancer therapy is that you can

target aberrant mtDNA gene expression by directly targeting one enzyme. This enables

more feasible small molecular drug design, in terms of both pharmacokinetic and

pharmacodynamic efficacy. Target validation is easily achievable, as standard in vitro

assays exist to functionally assess the activity of POL γ. Also, mtDNA replication and

repair are early processes in the mitochondrial gene expression cascade and therefore

effective targeting of POL γ can bypass any aberrant regulatory systems present in the

mitochondrial machinery.

1.2.3. Mitochondrial DNA Transcription

Mitochondria contain a single subunit RNA polymerase enzyme (mtRPOL) distinct form

nuclear polymerases that displays sequence similarity to RNA polymerase of the T-odd

lineage of bacteriophages(Masters et al., 1987; Tiranti et al., 1997). The existence of this

mitochondria-specific RNA polymerase was first reported in S. Cerevisiae and then

subsequently in human cells. Importantly, mtRPOL requires a group of transcription

factors for initiation of transcription; mitochondrial transcription factor A (TFA) and one

of mitochondrial transcription factors B TFB1M, or TFB2M(Asin-Cayuela and

Gustafsson, 2007). Also, mtRPOL has primase activity required to form RNA primers

  15  

used in the initiation of mtDNA synthesis at the origin of H strand DNA

replication(Wanrooij et al., 2008).

Most research to date on mtDNA transcription has focused on the functional

complex of mtRPOL and it’s accessory factors during the transcription process. TFB1M

and TFB2M mRNA levels are found to be highly expressed in tissues with high

metabolic demand such as liver, heart and skeletal muscle(Asin-Cayuela and Gustafsson,

2007). Both TFB1m and TFB2M have rRNA methyltransferase activity in vivo(Cotney

and Shadel, 2006), but recent work has demonstrated that TFB2M is the more active

transcription factor, while TFB1M is mainly responsible for the methyltransferase

activity(Rantanen et al., 2003). Subsequent studies in D. melanogaster have shown that

TFB1 may be more pivotal in regulating mitochondrial protein synthesis than mtDNA

transcription(Matsushima et al., 2005). It is unclear which of these factors has the

primary role of promoter activity within the transcription cascade. Most recent studies

have agreed that mtRPOL has a significant role in detecting sequence specificity of

initiation at the mtDNA promoter(Gaspari et al., 2004) This is conditional on the binding

of mtRPOL in a 1:1 complex with TFB1 or TFB2, TFA being bound to the promoter at a

specific distance from the initiation site, and the distortion of DNA configuration

allowing for transcription to proceed(Bonawitz et al., 2006).

As the outcome of mitochondrial gene expression is the coding of subunits

required for oxidative phosphorylation and metabolic needs, the control of mtDNA

transcription needs to be regulated depending on the given cell’s metabolic requirements.

Early work examining the transcriptional regulation of the cytochrome c oxidase

(Complex IV) genes revealed certain nucleus-encoded transcription factors were

  16  

responsible for promoter activity and transcriptional control(Virbasius and Scarpulla,

1991). Subsequently, it was revealed that the two nuclear-encoded regulatory factors

implicated in this regulation are nuclear respiratory factors 1 and 2 (NRF-1 and NRF-2);

regulators of the TFAM gene, encoding TFA(Virbasius and Scarpulla, 1994). Both NRF-

1 and NRF-2 have been linked to the transcriptional control of many genes involved in

mitochondrial biogenesis and function including TFB1, TFB2, Complex I, II, II, IV, and

V subunits, and protein import proteins such as TOM20 and TOM70(Kelly and

Scarpulla, 2004). The key element to coordinating the transcriptional control of

mitochondrial biogenesis by regulating the mentioned factors was found to be the

transcriptional co-activator PGC-1α(Wu et al., 1999). PGC-1α stimulated NRF-1 and

NRF-2 gene expression, and also bound and co-activated NRF-1 transcription factor on

the promoter region of the TFAM gene. Overexpression studies in transgenic mice have

provided additional support for the role of PGC-1α in mitochondrial biogenesis as

cardiac specific overexpression lead to massive mitochondrial proliferation(Lehman et

al., 2000), while PGC-1α overexpression in skeletal muscle triggered mitochondrial

proliferation and formation of type-I oxidative muscle fibers(Lin et al., 2002). Therefore,

the nuclear transcriptional control systems of mtDNA transcription provide opportunities

for nucleus-mitochondrion signaling interactions that may determine mitochondrial gene

expression patterns depending on the cell’s specific metabolic needs.

MtDNA transcriptional regulation hasn’t been explored in the context of potential

cancer therapeutics. However, there has been recent work showing associations between

mtDNA transcription factor TFA and tumorigenesis. Analysis of the TFAM gene in

colorectal cancer cell lines revealed frameshift mutations associated with microsatellite

  17  

instability(Guo et al.). This was accompanied by decreased mtDNA copy number and

increased tumorigenesis in vivo. Subsequent studies have shown that there are different

isoforms of the TFAM gene in different normal tissues and specific tumor cells(De

Virgilio et al.). Alternative splicing most likely plays an important role in regulating

differential mtDNA transcription in different tissues and in malignant states. Recently, it

was discovered that TFA can be located in the nucleus as well as mitochondria, where it

can regulate the expression of nuclear genes(Han et al.). Targeted knockdown of TFAM

gene by siRNA resulted in decreased tumor cell growth; contrary to previous reports.

Therefore, the exact function of mtDNA transcription regulatory factors such as TFA in

tumorigenesis remains to be elucidated. As mitochondrial gene expression is central to

the cell metabolic life cycle, the regulatory pathways of transcription are complex and

highly regulated.

There are several advantages in targeting components of mitochondrial

transcriptional control for anti-cancer potential. As there are many levels of regulatory

control, there should be an established understanding of the relative abundance of

different factors in differing tissues states (malignant versus normal). This can then

provide the possibility of preferential selectivity for malignant cells over normal tissues.

For example, early studies focused on the absolute expression levels of TFB1 and TFB2

in different tissues, but it later became evident that the ratio of TFB1 and TFB2

expression in tissues may be the more intuitive output. They are both highly expressed in

the energy-demanding tissues such as heart, and skeletal muscle, but in placenta, kidney

and pancreas, TFB2 is expressed at significantly higher levels than TFB1(Bonawitz et al.,

2006). This level of differential expression can allow for complex temporal and spatial

  18  

control of mitochondrial gene expression, which can be exploited for selective anti-

cancer therapeutics targeting cancer cells, while protecting energy-demanding normal

tissues. Additionally, specific anti-malignant design can be elucidated by studying the

role of alternative splicing in transcriptional control, as was previously mentioned in the

case of TFA. Once isoforms of mtDNA transcriptional regulators are identified in

subsets of cancer cells, calculated approaches can be taken to efficaciously target the

required regulatory factors.

  19  

1.3.1. MITOCHONDRIAL TRANSLATION

Translation Machinery

Although the classical understanding of mitochondrial protein synthesis is related

to the organelle’s evolutionary history of endosymbiosis, it has become clear that

mitochondrial translation remains unique in several aspects of machinery and regulatory

process compared to prokaryotic or eukaryotic cytosolic translation. Aside from the

rRNA and tRNAs encoded by mtDNA, mitochondrial translation depends on nuclear-

encoded (i) mitochondrial ribosomal proteins; (ii) initiation, elongation and termination

factors; (iii) mitochondrial amino-acyl tRNA synthetases, and tRNAs(Smits et al.).

The central structures of mitochondrial translation are the mitochondrial

ribosomes composed of rRNA and mitochondrial ribosomal proteins. These will be

discussed in the next section. While bacterial translation processes have three initiation

factors, so far two initiation factors have been identified in mitochondria; IF2 and

IF3(Spremulli et al., 2004b). During the elongation phase of translation, three factors are

responsible for the coordination of translocation; EFTu, EFTs, and EFG. After translation

is complete, mitochondria use two release factors, mtRF1 and mtRF1a, and a recycling

factor (mtRRF) to coordinate the termination process(Rorbach et al., 2008;

Soleimanpour-Lichaei et al., 2007; Zhang and Spremulli, 1998a). Mitochondrial tRNAs

differ from cytosolic and bacterial tRNAs as they are shorter, have variations in D and T

loops, and are missing conserved nucleotides involved with creating the L-shape due to

tertiary interactions(Smits et al.). Also, post-translational modification of tRNAs is

  20  

necessary for maintenance of the tertiary structure of mitochondrial tRNAs more so than

cytosolic tRNAs(Helm, 2006). There are 19 mitochondrial tRNA synthetases that have

been characterized, with the exception of glutaminyl-tRNA synthease(Bonnefond et al.,

2005). Similar to bacterial translation, methionyl-tRNA synthetases need to be

formylated by methionyl-tRNA transformylase prior to initiation of mitochondrial

translation.

1.3.2. Mitochondrial Ribosomes

Mitochondrial ribosomes differ from bacterial and eukaryotic cytosolic ribosomes in their

structure, and chemical properties(O'Brien, 2003). While bacterial and cytosolic

ribosomes have sedimentation coefficients of 70S and 80S, respectively, mitochondrial

ribosomes have sedimentation coefficients of 55S. The small and large sub-ribosomal

particles have sedimentation coefficients of 28S and 39S respectively(O'Brien, 1971).

However, they are larger than bacterial ribosomes in terms of molecular mass and

dimensions(Pietromonaco et al., 1991). Compared to bacterial ribosomes, mitochondrial

ribosomes have approximately half as much rRNA and over twice the amount of

protein(O'Brien, 2003). Mitochondrial ribosomal proteins are encoded by nuclear genes

and translated in the cytosol. Once translated, these proteins are imported into the

mitochondria where they join two rRNA molecules to form the functional ribosomes of

the mitochondria. Many of these mitochondrial ribosomal proteins have no similar

analogues in bacterial or cytosolic ribosomes. Similar numbers of proteins are in both

mitochondrial and cytosolic ribosomes(Pietromonaco et al., 1991). Uniquely, human

  21  

mitochondrial ribosomes have an intrinsic GTP binding protein in the small subunit with

GTPase activity, a feature not observed in any other translation systems. Although

mitochondrial ribosomes differ structurally from cytoplasmic and bacterial ribosomes,

they function similarly. In addition, mitochondrial and bacterial ribosomes use similar

translation initiation and elongation machinery(Gaur et al., 2008; Hunter and Spremulli,

2004; Zhang and Spremulli, 1998b).

1.3.3. Translation Initiation

Similar to cytosolic processes, mitochondrial translation has three stages of

translation; initiation, elongation and termination. The regulation of the initiation phase

of translation has mostly been characterized on the basis of studies in prokaryotic

translation. However, it has been recognized that there are unique differences in

mitochondrial mRNA processing, as mitochondrial mRNA lacks the Shine-Dalgarno

sequence observed in prokaryoties and the 7-methylguanylate cap found in eukaryotic

cytosolic mRNA used for ribosome binding during translation initiation(Smits et al.).

Also, the mitochondrial mRNA contain few noncoding sequences upstream of the 5’-

terminal initiation codon, and are therefore termed leaderless mRNAs(Jones et al., 2008;

Temperley et al.). The initiation process in mitochondria depends on two factors for

coordination; IF-2 promotes the binding of fMet-tRNA to the ribosome(Liao and

Spremulli, 1990)and IF-3 stimulates initiation complex formation by causing dissociation

of the 55S mitochondrial ribosomes(Koc and Spremulli, 2002). Recent work from

Christian and Spremulli(Christian and Spremulli) has added further evidence to develop

  22  

a working model of the initiation process. Initially, IF-3 binds to the 55S ribosome

causing dissociation of the 39S and 28S subunits. Then mRNA in tandem with IF-2 binds

to the entrance gate on the 28S subunit. With the help of IF-2:GTP, fMet-tRNA binds and

the resulting codon-anti-codon interaction stabilizes the initiaion complex. The large 39S

subunit then joins the intitiation complex, hydrolyzes GTP to GDP, and the IF-2 and IF-3

factors are released so that the elongation phase of translation may commence.

There haven’t been studies examining the implications of targeting mitochondrial

translation initiation in cancerogenesis and cancer progression. If the desired therapeutic

end result is translation inhibition, strategies could be employed to selectively inhibit

either of the initiation factors; IF-2, and IF-3. This would presumably result in decreased

mitochondrial translation, resulting in less oxidative phosphorylation metabolic output in

the cell. This is similar to other strategies discussed here aiming to inhibit some aspect of

the mitochondrial translation machinery. It should be noted that this area of

mitochondrial translation is still being investigated, and the functional redundancy of

these regulatory factors is not fully characterized. Therefore, the resulting effect of

specific inhibition of either IF-2 or IF-3 on mitochondrial translation should be assessed

prior to further therapy development. In terms of developing a selective malignant

therapy, it would be advantageous to study the differential levels of the initiation factors

in malignant versus normal tissues. This could provide further insight into how to best

alter the initiation process for a therapeutic benefit. There may also be unidentified

regulatory factors involved with functional control of the initiation process whose

targeting may provide additional benefits in regulating aberrant mitochondrial gene

expression.

  23  

1.3.4. Translation Elongation

Elongation of the mitochondrial translation machinery is aided by three identified

elongation factors; EF-Tu, EF-Ts, and EF-G1. The basic steps of elongation are similar to

prokaryotic translation, and further work characterizing the sequence has been influenced

by prokaryotic studies(Spremulli et al., 2004b). In the first step, EF-Tu brings the

appropriate aminoacyl-tRNA to the decoding site (A site) on the ribosome in the from of

a ternary complex with GTP. Once codon:anticodon interactions are formed, GTP is

hydrolyzed, and EF-Tu is released as EF-Tu:GDP from the complex. EF-ts will then

catalyze the exchange of GDP for GTP so that EF-Tu can continue binding aminoacyl-

tRNA and bringing it to the A site. Peptide bond formation occurs between the peptidyl-

tRNA in the P site of the ribosome and the amino acid of the aa-tRNA in the A site

through the peptidyl transferase activity of the ribosome. Subsequently, EF-G1 catalyzes

the translocation step of translation where the deacylated tRNA in the P site is moved to

the E site (exit site), the peptidyl-tRNA to the P site, and the entire ribosome shifts one

codon (3 nucleotides) relative to the mRNA, exposing a new codon in the A site so that

the cycle can be repeated.

Most work characterized the role of mitochondrial elongation factors in disease

have examined mutational analysis and their subsequent pathological effects. Two

siblings with fatal hepatoencaphalopathy were found to have mutations in the GTP

domain of EF-G1(Coenen et al., 2004). The result was reduced respiratory chain

complexes containing mtDNA-encoded subunits and decreased translation of all

  24  

mitochondrially-translated subunits in vitro. Subsequent studies have found similar

phenotypes of encephalopathy and myopathy in infants with mutations in EF-Ts

(Smeitink et al., 2006) and EF-Tu(Valente et al., 2007). Therefore, the proper functioning

of these elongation factors is critical for human development and functioning, particularly

in high-oxygen demanding tissues such as brain and muscle. It has been shown that the

relative EF-Tu:EFTs ratios in different tissues are related to the differential respiratory

chain activity seen in different tissues(Antonicka et al., 2006). Potential therapies

targeting these elongation factors in cancers with aberrant mitochondrial gene expression

should explore the relative dependency on tumor compared to normal tissue to avoid

adverse effects in highly oxidative phosphorylation-dependent tissues.

The role of differentiation in cancer therapy has been studied in

neuroblastoma(Walton et al., 2004) and acute promyelocytic leukemia (APL)(Chen et

al., 1991; Schlenk et al., 2004). Recently, it has been shown that differentiation of the

APL cell-line HL-60 by 12-0-tetradecanoyl-1-phorbol-13-acetate was associated with

decreased protein expression of elongation factors EF-Tu(Takeuchi and Ueda, 2003).

Pulse labeling experiments showed that this decrease of EF-Tu was concurrent with

decreased mitochondrial translation activity during cell differentiation. Future studies

were postulated to explore the potential of decreased mitochondrial translation causing

apoptosis induction in APL cells during differentiation.

Alternatively, it has been shown that EF-Tu has chaperone-like activity in mitochondria

aside from it’s documented role of elongation control(Suzuki et al., 2007). During heat

stress conditions, EF-Tu prevented thermal aggregation of proteins and enhanced protein

folding in vitro. This points to a possible role for EF-Tu regulating protein quality control

  25  

of mis-folded newly synthesized mitochondrial peptides. Therefore, protein folding and

chaperone associations of mitochondrially-encoded peptides should be explored in the

context of tumorigenesis. The therapeutic anti-cancer potential of decreasing

mitochondrial elongation factors should be approached with caution as shown by

functional mutation studies done in human cases previously discussed. However, it

appears that elongation factors are functionally important in the translation machinery

and as such are interesting points of studying in mitochondrial gene expression-related

cancerogenesis studies. From the differentiation studies, it appears that mitochondrial

translation elongation control is related to the process of cell lineage fate determination,

and also likely cell transformation.

1.3.5. Translation Termination

The last stage of mitochondrial translation occurs when a stop codon (UAG,

UAA AGA, or AGG) is present in the A site(Smits et al.). This stop codon interaction is

most likely sensed by a mitochondrial release factor, either mtRF1(Zhang and Spremulli,

1998a) or mtRF1a(Soleimanpour-Lichaei et al., 2007) and results in the newly formed

peptide in the P site being released from the tRNA (A site) after ester bond hydrolyzation.

The recycling factor mtRRF then allows for the dissociation of the translation machinery

(mRNA, tRNA, ribosome subunits) in a GTP dependent mechanism(Rorbach et al.,

2008). A new cycle of translation can again occur with all the dissociated machinery in

conjunction with the appropriate regulatory control.

  26  

There haven’t been direct studies pursuing the role of termination factors in

cancer, but the previous studies mentioned which characterized the roles of these

termination factors explored their functional dependence in cancer cells. Depletion

experiments of mtRRF in HeLa cells resulted in decreased cell viability, proliferation and

mitochondrial dysfunction(Rorbach et al., 2008). This was associated with increased

reactive oxygen species (ROS) generation and mitochondrial dysmorphism. Similarly,

siRNA-mediated depletion of mtRF1a in HeLa cells resulted in decreased proliferation,

increased reactive oxygen species, but was not associated with a gross defect in

mitochondrial translation(Soleimanpour-Lichaei et al., 2007). It is clear that depletion of

these termination factors can have anti-cancer effects in the absence of mitochondrial

translation inhibition. One possibility is that the mechanisms of decreased cell

proliferation are related to the observed increased ROS generation. Future studies should

assess the functional importance of these various termination factors, as this outcome

may bias the resulting experiments examining targeted depletion of these factors. Also,

the potential interplay between mitochondrial translation and mitochondrial chaperone

activity can be provide interesting insights into the effects of deregulated mitochondrial

protein synthesis in the context of cancer.

1.3.6. Translation Modulation

While mtDNA transcription regulation factors have been characterized

extensively, the knowledge of translational activation and regulation factors in human

mitochondria is lacking. In S. Cerevisiae, translational activators have been discovered

  27  

that regulate translation by binding to 5’-UTR regions of mRNA(Naithani et al., 2003).

However, human mitochondrial mRNA lacks these 5’-UTR regions, and therefore other

mechanisms of translational activation presumably exist(Montoya et al., 1981). Early

insight into human translational activation was provided by studying genome data sets of

patients with Leigh syndrome (French-Canadian type), a human cytochrome c oxidase

deficiency marking to chromosome 2(Mootha et al., 2003). They identified the LRPPRC

(leucine-rich pentatricopeptide repeat-containing protein) gene having mutations

associated with this syndrome, which encodes for an mRNA-binding protein involved

with mtDNA transcript processing. Subsequent studies discovered that the LRPPRC

mutation in Leigh syndrome resulted in decreased COX1 and COXIII (subunits of

cytochrome C oxidase) mRNA, and COX1 subunit translation(Xu et al., 2004). Also,

they found that LRPPRC mRNA was levels were highest in skeletal and muscle tissue,

opposite in strength of the cytochrome oxidase effect. Now it is understood that LRPPRC

is important for the expression of all mitochondrial DNA-encoded mRNAs, but not

nuclear-encoded subunits of mitochondrial proteins(Gohil et al.). Further analysis of

Leigh syndrome using genome-wide linkage analysis revealed a mutation on

chromosome 17q encoding for the protein TACO1, a translational activator of COX1

subunit located in the mitochondrial matrix(Weraarpachai et al., 2009). TACO1 had a

similar elution pattern to elongation factor EF-Ts, and most likely affects mitochondrial

translation through interaction with these elongation factors.

In S. Cerevisiae, it has been shown that the mitochondrial AAA protease is

responsible for processing of the mitochondrial ribosomal protein MRPL32(Nolden et al.,

2005). MRPL32 defect in murine mitochondria was associated with impaired

  28  

mitochondrial translation, and coupled with it’s location on the inner mitochondrial

membrane, it is plausible that MRPL32 is an important factor for the regulation of

mitochondrial ribosomal assembly at this location in cells. The mitochondrial

transcription factor TFB1, which we previously described, has also been found to have a

regulatory role in mitochondrial translation in Drosophila(Matsushima et al., 2005).

RNAi knockdown of TFB1 reduced mitochondrial protein synthesis, but not transcription

or mtDNA copy number, highlighting a potential role for translation regulation when

TFB1 acts alone, opposed to it’s transcriptional regulation when functioning in

conjunction with TFB2.

There is yet to be a clear understanding of the activation and regulation of

mitochondrial translation in human cells. It is evident from these early sentinel studies

that the regulation of translation processes in mitochondria are highly regulated, likely

due to the important effects that these pathways have on a cell’s metabolism. Recently, a

leucine-rich protein 130 (LRP130) was found to regulate the expression of apoptosis-

related genes in hepatocarcinoma cells(Michaud et al.) providing an initial link of

translation regulation and malignancy. Knockdown of LRP130 in hepatocarcinoma cells

functionally reduced cytochrome c oxidase activity, as well as altered apoptosis-related

genes involved in apoptosis resistance. The role of leucine-rich proteins in cancer should

be explored in the context of altering LRP mRNA levels in different tissues as previously

discussed(Xu et al., 2004). This could yet provide another plausible approach to

understanding undesired therapeutic side-effects of targeting mitochondrial gene

expression for anti-malignancy.

  29  

Figure   1B.   Schematic representation of the mitochondrial translation control. The four major categories of cell processes, and their underlying targets that can be targeted for anti-cancer therapeutic strategy.  

  30  

1.3.7. Recent insights: Mitochondrial gene expression related findings in cancer

Thus far, we have been highlighting direct methods of targeting various aspects of

the mitochondrial transcription and translation-related machineries for the purpose of

anti-cancer therapeutics (Figure 1B). However, there has been recent work identifying in-

direct methods of altering mitochondrial gene expression or biogenesis in caner cells.

One example is a study by Favre et al. where IGF-1 (insulin-like growth factor)-related

mitochondrial pyrimidine nucleotide carrier 1 (PNC1) reduction altered mitochondrial

biogenesis and the invasive phenotype of cancer cells(Favre et al.). PNC1 was required

for mitochondrial function by controlling mtDNA replication and copy number.

Reduction of PNC1 resulted in oxidative phosphorylation defects and epithelial-

mesenchymal transition. It wasn’t clearly determined how PNC1 overexpression or

suppression effected the aggressiveness of the cancer phenotype. Therefore, although

targets such as PNC1 may play a role in regulating mitochondrial replication, their effects

on cancerogenesis should be studied closely. Another study has explored the role of Pim

kinases in metabolism and cell growth(Beharry et al.). These serine/threonine kinases are

overexpressed in solid and hematologic malignances(Allen et al., 1997; Fujii et al., 2005;

Li et al., 2006; Mikkers et al., 2002), and promote increased cancer proliferation and

survival. Pim kinase expression increased c-Myc and peroxisome proliferator-activated

receptor gamma coactivator 1α (PGC-1α), enzymes regulating glycolysis and

mitochondrial biogenesis. We have previously discussed the role of PGC-1α in

mitochondrial co-activational transcription. They also developed a novel Pim kinase

  31  

inhibitor, which may be used for anti-cancer indications in the future. Importantly, it is

now clear that the anti-cancer modulation of Pim kinases in these varied malignancies is

most likely due to it’s in-direct effects on downstream mitochondrial transcription and

biogenesis.

So far, we have focused on mitochondrial machineries, which produce the end

result of protein subunits comprising various components of the oxidative

phosphorylation chain. However, after protein translation is complete, it is important to

note that polypeptides have to be properly folded in order to gain appropriate function

within the cell. The unfolded protein response (UPR) in the context of cancer has been

studied extensively. Mitochondrial UPR aims to maintain protein homeostasis within

mitochondria of both mtDNA and nuclear-encoded proteins(Haynes and Ron). The

molecular chaperones and proteases are imported into the mitochondria from the cytosol

where they can promote proper protein folding, and quality control. Recently, the role of

UPR in mitochondria of cancer cells has been determined as being functionally important

for cell survival(Siegelin et al.). Using a small molecule, they inhibited Hsp90 (heat

shock protein-90) chaperones in mitochondria, which triggered compensatory autophagy

and UPR responses. This UPR response enhanced tumor cell apoptosis and inhibited

glioblastoma in vivo. Therefore, the targeting of not only dysfunctional mitochondrial

protein machineries, but also the subsequent protein folding sequela can have anti-tumor

potential in cells with aberrant mitochondrial gene expression.

Another possibility that hasn’t been discussed is that current standard

chemotherapy regimens may have uncharacterized effects on mitochondrial gene

expression as a part of their anti-tumor mechanism of action. Doxorubicin is a widely

  32  

used chemotherapeutic agent, which is limited in use because of its

cardiotoxicity(Takemura and Fujiwara, 2007). While studying the mechanistic basis of

these adverse cardiac effects, it was discovered that doxorubicin decreased ATP

production, mitochondrial membrane potential, mitochondrial respiratory chain

complexes, and altered mtDNA-encoded mRNA and protein expression(Pointon et al.). It

is plausible that a part of doxorubicin’s anti-tumor mechanism is based on this targeting

of mitochondrial gene expression. Other standard chemotherapy agents should be

explored for similar mitochondrial effects, particularly DNA-targeting agents. Therefore,

although many genetically heterogeneous malignancies are presenting a challenge for

therapy, there may exist common aberrant pathways such as mitochondrial gene

expression that can provide effective therapeutic targets for cancer.  

                                                 

  33  

CHAPTER 2:   RATIONALE AND HYPOTHESIS

2.1.1. General Rationale       While there have been recent advances in the treatment of some hematological

malignancies, the therapy of AML has remained a clinical challenge. For patients

diagnosed when older than 60, the prognosis is particularly poor, with a 2-year survival

probability of less than 10 percent (Löwenberg et al., 1998). Thus, further research is

warranted into developing novel therapeutic strategies for the treatment of this disease.

One approach to the development of more effective therapies for AML is to identify

agents able to induce the death of leukemia stem cells (LSCs). These represent a rare

subset of cells in the clone that share many properties with normal hematopoietic stem

cells, including an extensive self-renewal ability, a slow turnover and resistance to many

standard chemotherapeutic drugs. As a result, LSCs are often not killed by available

treatments, leading to eventual disease relapse (Wang, 2007). Thus, it is crucial to

develop therapeutic agents that can effectively target LSCs in AML.

  To address this challenge, we compiled a custom library of FDA-approved

compounds with known toxicology and pharmacology in order to perform high-

throughput screen focused on developing novel anti-leukemia agents.

i) AIM 1: To perform a screen of FDA-approved compounds targeting

viability and proliferation of leukemia cell lines with LSC characteristics

of self-renewal and differentiation

  34  

We hypothesize that this screen will identify agents with previously un-recognized anti-

cancer activity. In particular, these agents will possibly be active against leukemia cells

with differentiation potential, which we will investigate in subsequent experiments.

2.2.1. Tigecycline

We chose to pursue the antibiotic tigecycline among six from the screen that had

no previously recognized anti-cancer activity. In our preliminary studies, it became

evident that tigecycline had preferential activity towards leukemia cell lines over

myeloma and solid tumour cell lines. Subsequently, tigecycline showed a specific

toxicity towards primary AML cells over normal hematopoietic cells in vitro highlighting

a possible therapeutic window. The following pre-clinical studies will provide the

rationale and data to support an initial clinical trial of tigecycline in patients with

leukemia.

ii) AIM 2: To determine the mechanism of action of tigecycline as an anti-

leukemic agent

We hypothesize that tigecycline will cause leukemia cell death due to inhibition of

mitochondrial protein synthesis, as similar antimicrobial bacterial protein synthesis

inhibitors have been shown to have these off-target effects(McKee et al., 2006; Nagiec et

al., 2005). The functional consequence of this will be decreased activity of oxidative

phosphorylation as several protein subunits of the respiratory chain are mitochondrially-

  35  

encoded (Figure 1). There will be decreased mitochondrial membrane potential, and ATP

production resulting in cell death due to decreased metabolic output.

iii) AIM 3: To explore the differences in mitochondrial characteristics in

leukemic, and normal hematopoietic cells

We hypothesize that the preferential anti-leukemia activity of tigecycline is due to

intrinsic differences in mitochondrial metabolism between leukemic and normal

hematopoietic cells. Therefore, we will examine mitochondrial mass, oxygen

consumption and membrane potential to better understand the selective action of

tigecycline in AML.

iv) AIM 4: To explore the action of tigecycline against leukemia stem cells

Our initial screen was performed in leukemia cell lines displaying characteristics of

LSCs, including self-renewal and differentiation. We postulate that tigecycline will have

activity against LSCs, but are uncertain as to whether this activity will be preferential or

not to bulk leukemia cells. We will utilize tigecycline treatment in colony-formation

assays to study leukemia progenitors, as well as the gold-standard functional assays of

LSC: engraftment of human AML cells into immune-compromised NOD-SCID

mice(Lapidot et al., 1994).

  36  

v) AIM 5: To identify drugs that synergize with tigecycline and enhance its

cytotoxicity

Standard debulking agents used in AML treatment such as daunorubicin and cytarabine

are successful in decreasing initial leukemia burden, but many patients will relapse at a

later time point. Tigecycline will most likely be used in combination with these standard

agents in AML treatment. We will test different combinations of tigecycline with AML

standard agents and other commonly used neoplastic agents to identify synergistic

activity that will allow for lower drug dosages with less undesired side-effects.

                                       

  37  

CHAPTER 3: METHODS  

Cell lines

TEX leukemia cells were maintained in IMDM (Iscove’s modified Dulbecco’s medium),

15% FCS, 2mM L-glutamine, 1%, penicillin-streptomycin, 20 ng/mL SCF (stem cell

factor), 2 ng/mL IL-3. M9-ENL1 cells were maintained in alpha-MEM (Minimum

Essential Eagle Medium), 20% FBS (fetal bovine serum) 2mM L-glutamine, 5% human

plasma, 1% penicillin-streptomycin, 100 ng/ml SCF, 10 ng/ml IL-3, 5 ng/ml IL-7, 5

ng/ml FLT3L. Murine leukemia cell lines were derived from mouse bone marrow with

various inducers of pre-leukemic and leukemic phenotypes. 3ND13pac pSF91 cells are

representative of a pre-leukemic model, which can induced to AML with secondary hits

(Meis1, MN1). 9MN1 cells are transduced with the oncogene meningioma 1 (MN1)

(Heuser et al., 2007), and are capable of aggressive AML induction in mouse models.

ND13pac MN1 cells are engineered to express both MN1 and ND13 oncogenes. Both

9MN1 and ND13pac MN1 cells maintain high frequencies of leukemic stem cells.

HoxA9neo Meis1 cells co-express HOXA9 and Meis1 oncogenes, and are capable of

transplantable AML induction in mouse models (Pineault et al., 2003). Human leukemia

(OCI-AML2, U937), prostate cancer (PC3), lung cancer (A549), ovarian cancer

(OVCAR) cell lines were maintained in RPMI 1640 medium. Myeloma (LP-1, KMS11,

8226 JJN3, OPM2) cell lines were maintained in IMDM medium. Media was

supplemented with 10% FCS, 100 µg/mL penicillin and 100 units/mL of streptomycin

(all from Hyclone, Logan, UT). All cells were incubated at 37oC in a humidified air

  38  

atmosphere supplemented with 5% CO2. For hypoxia experiments, cells were transferred

to hypoxic culture chambers (MACS VA500 microaerophilic workstation, H35

HypoxyWorkStation; Don Whitley Scientific). The atmosphere inside the chambers

consisted of 5% H2 5% CO2, 0%, 0.2% 1% or 5% O2 and residual N2.

Chemical screen to identify drugs cytotoxic to leukemia cells.

The compounds in the chemical library were purchased from Sequoia Research Products

(Pangbourne, United Kingdom). M9-ENL1 cells and TEX cells were plated in 96-well

plates. After plating, cells were treated with aliquots of compounds (10 and 1 µM) with a

final DMSO concentration of 0.25%. Seventy-two (TEX) and forty-eight (ENL-1) hours

after drug addition, cell growth and viability were measured by the MTS assay. Liquid

handling was performed by a Biomek FX Laboratory Automated Workstation (Beckman

Coulter Fullerton, CA).

Primary AML and normal hematopoietic cells

Primary human AML samples were isolated from peripheral blood samples from

consenting patients with AML, who had at least 80% malignant cells among the low –

density cells isolated by Ficoll density centrifugation. Primary low-density normal

hematopoietic cells were similarly obtained from healthy consenting volunteers donating

peripheral blood stem cells for allogeneic stem cell transplantation after G-CSF

mobilization. Primary cells were cultured at 37°C in IMDM, supplemented with 20%

fetal bovine serum (FBS), and appropriate antibiotics. The collection and use of human

  39  

tissue for this study were approved by the University Health Network institutional review

board and Review Ethics Board of the University of British Columbia.

Cell proliferation and viability assays

Cell proliferation and viability was assessed by the MTS assay (Promega, Madison, WI)

according to the manufacturer’s instructions. Cell death was measured by Annexin V-

fluoroscein isothiocyanate (FITC) and Propidium Iodide (PI) (Biovision Research

Products, Mountain View, CA) staining using flow cytometry according to the

manufacturer’s instructions. To identify CD34+ cells, AML and normal PBSC samples

were co-stained with PE–anti-CD34+ (Beckman Coulter, Marseille France), and APC–

anti-CD45 (Becton Dickenson, San Jose CA).

To assess clonogenic growth, primary AML cells or granulocyte colony-stimulating

factor (G-CSF) mobilized PBSCs (4 x 105/mL) were plated by equal volume in duplicate

GF H4434 medium (StemCell Technologies, Vancouver, BC) containing 1%

methycellulose in IMDM, 30% FCS, 1% bovine serum albumin, 3 U/mL of recombinant

human erythropoietin, 10−4 M of 2-mercaptoethanol, 2 mM of L-glutamine, 50 ng/mL of

recombinant human SCF, 10 ng/mL of GM-CSF, and 10 ng/mL of rh IL-3. MethoCult

GF H4434 medium contained either DMSO control or tigecycline (final concentration 5

µM). Seven days (AML samples) or 14 days (normal PBCS) after plating, the number of

  40  

colonies containing 10 or more cells for AML or over 100 cells for normal samples was

counted as previously described (Xu et al., 2010)

Yeast genomic screen

To identify the primary mechanism of drug action, HIP in yeast was used to profile the

fitness of ~6000 heterozygous deletion strains (Giaever et al., 2004; Smith et al., 2010) in

the presence of our compounds. Because wild-type yeast growth was more sensitive in

respiratory media, the yeast heterozygous deletion pools were grown in YP media

supplemented with 2% glycerol and 1% ethanol. The fitness assay on the deletion strains

was performed as described (Pierce et al., 2006) with the following modifications: 1) for

barcode amplification, 0.2µg of genomic DNA was used in a 50µl PCR reaction

containing 1uM mix of up- or down-tag primers and 82% (v/v) of High Fidelity

Platinum PCR Supermix (Invitrogen, Carlsbad, California); 2) 34 amplification cycles

were used for the PCR using an extension temperature of 68° C for 2 minutes except for a

final 10 minutes in the last cycle 3) after 10-16 hours of hybridization the arrays were

washed in a GeneChip Fluidic Station 450 (Affymetrix, Santa Clara, CA) using the

GeneFlex_Sv3_450 protocol with one additional wash cycle before the staining. The

Affymetrix GeneChip Command Console Software was used to extract the intensity

values from the arrays and the fitness defects were calculated for each deletion strains as

log2 ratios (mean signal intensity of control/ mean signal intensity of drug).

Yeast growth rate measurements

  41  

The growth rate of wild-type yeast(Giaever et al., 2002) was determined by measuring

the ratio of the area under the curve (AUC) after 20 generations of growth plus drug to

AUC in vehicle alone (2% DMSO).

Immunoblotting

Total cell lysates were prepared from cells as described previously (Schimmer et

al., 2006). Briefly, cells were washed twice with phosphate buffered saline pH 7.4 and

suspended in lysis buffer (1.5% n-dodecyl β-maltoside (Sigma Aldrich, St. Louis, MO))

containing protease inhibitor tablets (Complete tablets; Roche, IN). Protein

concentrations were measured by the DC Protein assay (Bio Rad, Hercules, CA) Equal

amounts of protein were subjected to sodium dodecyl sulphate (SDS)-polyacrylamide

gels followed by transfer to nitrocellulose membranes. Membranes were probed with

anti-Cox-1 1:1000 (Santa Cruz Biotechnology Inc), anti-Cox-2 1:500 (Santa Cruz

Biotechnology Inc), anti-Cox-4 1:2000 (Santa Cruz Biotechnology Inc), anti-grp78

1:1000 (Sigma Aldrich, St. Louis, MO), anti-XIAP 1:500 (BD Biosciences), anti-TUFM

(EF-Tu) 1:1000 (Abcam, Cambridge, MA), anti-MTIF3 1:1000 (IF-3) (Sigma-Aldrich,

St. Louis, MO) anti-α-tubulin 1:2000 (Sigma Aldrich, St. Louis, MO), anti-β-actin

1:1000 (Cell signaling Technology), and secondary antibodies from GE Health (IgG

peroxidase linked species-specific whole antibody). Anti c-myc was graciously supplied

by Dr. Linda Penn (Toronto, Canada). Detection was performed by the enhanced

chemical luminescence method (Pierce, Rockford, IL).

  42  

Quantitative real-time polymerase chain reaction

The cDNAs encoding Cox-1, Cox-2, Cox-4, EF-Tu, IF-3 and 18s were amplified

using the following primer pairs: (Cox-1F) 5’-CTATACCTATTATTCGGCGCATGA-

3’, (Cox-1R) 5’- CAGCTCGGCTCGAATAAGGA-3’, (Cox-2F) 5’-

CTGAACCTACGAGTACACCG-3’, (Cox-2R) 5’- TTAATTCTAGGACGATGGGC-3’,

(Cox-4F) 5’- GCCATGTTCTTCATCGGTTTC-3’, (Cox-4R) 5’-

GGCCGTACACATAGTGCTTCTG-3’, (EF-TuF) 5’- ATTGGCACCGGTCTAGTCAC-

3’, (EF-TuR) 5’- TGTCCATCTAGCTGCCCTCT-3’, (IF-3F) 5’-

GCACCGAGCAAATGTGATTA-3’, (IF-3R) 5’- CTTTCTCAGGGTTGGTCCAG-3’,

(18sF) 5’- AGGAATTGA CGGAAGGGCAC-3’, (18sR) 5’-

GGACATCTAAGGGCATCACA-3’. Equal amounts of cDNA for each sample were

added to a prepared master mix (SYBR Green PCR Master mix; Applied Biosystems,

Foster City, CA). Quantitative reverse-transcriptase polymerase chain reaction (qRT-

PCR) reactions were performed on an ABI Prism 7900 sequence detection system

(Applied Biosystems, Foster City, CA) as described previously (Schimmer et al., 2006).

The relative abundance of a transcript was represented by the threshold cycle of

amplification (CT), which is inversely correlated to the amount of target RNA/first-strand

cDNA being amplified. To normalize for equal amounts of the latter, we assayed the

transcript levels of 18s gene. The comparative CT method was calculated per the

manufacturer's instructions. The expression level of Cox-1 relative to the baseline level

  43  

was calculated as 2–ΔCT

(Cox-1), where ΔCT is (average Cox-1 CT – average 18s CT) and is CT

(average CT-treated sample – average CT-untreated sample.

Mitochondrial enzymatic assays

After the appropriate treatment, cells were centrifuged, and cell pellets were

frozen at – 80 oC for future assessment of mitochondrial complex activity. Complex I

activity was detected by monitoring rotenone-sensitive 2,6-dichloroindophenol reduction

by electrons accepted from decylubiquinol reduced after oxidation of NADH by complex

I (Janssen et al., 2007). Complex II activity was measured by monitoring malonate-

sensitive reduction of 2,6-dichloroindophenol when coupled to complex II-catalyzed

reduction of decylubiquinol(Jung et al., 2000). Complex IV activity was measured by

KCN-sensitive oxidation of ferrocytochrome c (Trounce et al., 1996). Ferrocytochrome c

was prepared by reducing cytochrome c with sodium ascorbate followed by dialysis for

24 hours (Zheng et al., 1989). The method of citrate synthase activity was based on the

chemical coupling of CoASH, released from acetyl-CoA during the enzymatic synthesis

of citrate to DTNB (Ellman’s reagent, 5,5’-dithiobis(2-nitrobenzoic acid), and the release

of the absorbing mercaptide ion was monitored at 412 nm (Kaplan and Colowick, 1955).

The enzyme activity was of Complexes I, II, and IV was normalized to citrate synthase

activity, and notated as nmol/min/mg / citrate synthase activity.

Determination of mitochondrial membrane potential and ROS generation

  44  

To measure mitochondrial membrane potential, cells were washed twice with PBS and

incubated with 2 μM of 5,5',6,6'-tetrachloro-1,1',3,3'-tetraethyl

benzimidazolylcarbocyanine iodide (JC-1, Invitrogen) for 20 minutes at 37°C. Each

sample was then washed with 1 mL PBS and resuspended in 500 µL PBS prior to being

read on a BD FACS Calibur. Samples were excited at 488 nm and emission was

collected at 526 nm (FL1) and 595 nm (FL2). Analysis was conducted using FlowJo

version 7.7.1 (TreeStar, Ashland, OR). To obtain the mitochondrial membrane potential

(FL2/FL1), emission from the red channel was divided by emission from the green

channel.

Intracellular Reactive oxygen species (ROS) were detected by staining cells with

Carboxy-H2DCFDA (final concentration 5 μM) or dihyodroethidium (10 μM) and flow

cytometric analysis as previously described (Schimmer et al., 2006). Cells were stained

with Carboxy-H2DCFDA in PBS buffer at 37°C for 30 minutes, and then re-suspended in

PBS with propidium iodide to identify viable cells and assess their reactive oxygen

intermediate levels. Data were analyzed with FlowJo version 7.7.1 (TreeStar).

Oxygen Consumption Rate

Measurement of oxygen consumption was performed using a Seahorse XF96 analyzer

(Seahorse Bioscience, North Billerica, MA, USA). Suspension cells were cultured in

their usual growth medium with or without a specified treatment and were then

centrifuged and washed. Cells were resuspended with un-buffered medium and seeded at

1 x 105 cells/well (TEX cells) or 1 x 106 cells/well (primary AML/normal cells) in XF96

  45  

plates. Cells were equilibrated to the un-buffered medium for 45 min at 37oC in a CO2-

free incubator before being transferred to the XF96 analyzer. We measured the basal

Oxygen Consumption Rate (OCR), and then sequentially injected 1.2 μM (final

concentrations) oligomycin, and 50 mM 2-deoxy-D-glucose.

shRNA knockdown of EF-Tu and IF-3

Construction of hairpin-pLKO.1 vectors (carrying a puromycin antibiotic resistance gene)

containing shRNA sequences and production of short hairpin RNA viruses has been

described previously in detail (REF). The shRNAs targeting the EF-Tu (Accession No.

NM_003321) coding sequence are as follows: EF-TushRNA1 5’-

CAGCCAATGATCTTAGAGAAA-3’, EFTushRNA2 5’-

GCTCACCGAGTTTGGCTATAA-3’. The shRNAs targeting the IF-3 (Accession No.

NM_152912) coding sequences are as follows: IF-3shRNA1: 5’-

CCCAAGACTCTCCTTCCTAAT-3’, IF-3shRNA2: 5’-

GTATCAGCTCATGACAGGATT-3’.

Lentiviral infections were performed essentially as described (Xu et al., 2010). Briefly,

cells (5 x 106) in suspension culture were centrifuged and re-suspended in 10 mL media

containing protamine sulfate (5 µg/mL), 3 mL of virus cocktail was added, followed by

overnight incubation (37 oC, 5% CO2) without removing the virus. The following day,

cells were centrifuged, washed and fresh media with puromycin (1µg/mL) was added.

Six days later, equal numbers of live cells in each condition were plated for viability and

growth assays. The remaining cells were used for all other assays as described in Results.

Mitochondrial mass measurements

  46  

To assess mitochondrial DNA (mtDNA) copy number, genomic DNA was

extracted from primary cells using the DNeasy Blood and Tissue kit (Qiagen MD, USA).

The relative mitochondrial DNA copy number was determined by a real-time polymerase

chain reaction (qPCR), and compared relative to nuclear DNA as previously described

(Xing et al., 2008). The primer sequences were forward primer (ND1-F), 5′-

CCCTAAAACCCGCCACATCT-3′; reverse primer (ND1-R), 5′-

GAGCGATGGTGAGAGCTAAGGT-3′, forward primer (HGB-1), 5′-

GTGCACCTGACTCCTGAGGAGA-3′; reverse primer (HGB-2), 5′-

CCTTGATACCAACCTGCCCAG-3′.

To determine mitochondrial mass, cells were stained with 50 nM of Mitotracker

Green FM (Invitrogen, Carlsbad, CA) in PBS buffer at 37°C for 30 minutes, and then re-

suspended in PBS. Samples were analyzed on a BD FACS Calibur. The median

fluorescence intensity in the FL1 channel was divided by the Forward scatter (FSC)

measurement as an estimate of mitochondrial mass. The lowest mitochondrial mass

sample in each experiment was given a value of 1.0, and all other data points points were

presented relative to that value as relative mitochondrial mass. Data were analyzed with

FlowJo version 7.7.1 (TreeStar).

Assessment of tigecycline’s anti-leukemia activity in mouse models of human

leukemia

OCI-AML2 human leukemia cells (1 x 106) were injected subcutaneously into the flanks

of SCID mice (Ontario Cancer Institute, Toronto, ON). Seven days after the injection of

  47  

these cells and the appearance of a palpable tumor, the mice were treated with tigecycline

twice daily (50 m/kg or 100 mg/kg by i.p. injection ) or vehicle control (n= 10 per group)

for 14 days. Tumor volumes were calculated 3 times per week based on caliper

measurements of tumor length and width (volume= tumor length x width2 x 0.5236).

Twenty-one days after injection of cells, mice were sacrificed, tumors excised and the

volume and mass of the tumors were measured.

To assess tigecycline in mouse models of primary AML engraftment, a frozen

aliquot of AML cells was thawed, counted and re-suspended in PBS and 1-2 x 106 viable

trypan blue-negative cells were injected into the right femur of 10 week-old female

NOD-SCID mice that had been irradiated 24 hours previously with 208 rad from a 137Cs

source, and injected with 200 µg anti-mouse CD122. Similarly, engraftment of normal

hematopoietic cells was assessed by the injection of 1 x 105 Lin- CD34+ enriched human

cord blood cells into equivalent mice. Three weeks after injection of AML or Lin- CD34+

enriched human cord blood cells cells, mice were treated with tigecycline (100 mg/kg by

i.p. injection) daily or vehicle control (n=10 per group) for three weeks. Mice were then

sacrificed, and the cells were flushed from the femurs. In order to assess effects on the

production in the primary mice of new AML stem cells, equal numbers of viable human

AML cells from control and tigecycline-treated mice bone marrow of primary

engraftment studies were injected intra-femorally into a new generation of irradiated

NOD-SCID mice (200 µg anti- mouse CD122) Six weeks after injection, mice were

sacrificed, and the cells were flushed from the femurs. Engraftment of human AML or

normal myeloid cells into the marrow was assessed by enumerating the percentage of

  48  

human CD45+CD33+CD19- cells by flow cytometry using the BD FACS Calibur. Data

were analyzed with FlowJo version 7.7.1 (TreeStar).

All animal studies were carried out according to the regulations of the Canadian Council

on Animal Care and with the approval of the local ethics review board.

Tigecycline plasma concentration determination by HPLC

Tigecycline was assayed in 50-µL mouse plasma by HPLC with UV detection (350nm)

from 6.25 to 100 µg/mL. Plasma proteins were precipitated by addition of 20 µL 100%-

trichloroacetic acid containing 200 µg/mL minocycline as an internal standard. Plasma

samples were centrifuged at 16000 rpm for 15min, and then the aqueous phase was

loaded on Symmetry C18 column (3.9*150 mm, 5 µm). Tigecycline and minocycline

were separated by 25:75 (v/v) acetonitrile-phosphate buffer (0.023 M, pH 3.0) containing

4 mM 1-octanesulfonic acid.

Drug combination studies

The combination index (CI) was used to evaluate the interaction between tigecycline and

daunorubicin as previously described (Eberhard et al., 2009). OCI-AML2 and TEX cells

were treated with increasing concentrations of tigecycline and daunorubicin or

cytarabine. Seventy-two hours after incubation cell viability was measured by the MTS

assay. The Calcusyn median effect model was used to calculate the CI values and

evaluate whether the combination of tigecycline with daunorubicin or cytarabine was

synergistic, antagonistic or additive. CI values of <1 indicate synergism, CI =1 indicate

additivity and CI>1 indicate antagonism (Chou and Talalay, 1984).

  49  

In order to assess the drug combination of tigecycline and daunorubicin or cytarabine in

vivo, the OCI-AML2 xenograft model was used. OCI-AML2 human leukemia cells (1 x

106) were injected subcutaneously into the flanks of SCID mice (Ontario Cancer Institute,

Toronto, ON). Six days after injection, once tumours were palpable, mice were treated

with tigecycline daily (50 m/kg mg/kg by i.p. injection ), daunorubicin (0.65 mg/kg

3x/week), cytarabine (10 mg/kg daily i.p.) combined tigecycline and daunorubicin,

combined tigecycline and cytarabine or vehicle control (n= 10 per group) for 14 days.

Tumor volume (tumor length x width2 x 0.5236) was measured three times a week using

calipers. Twenty days after injection of cells, mice were sacrificed, tumors excised and

the volume and mass of the tumors were measured.

Statistical Analysis

All data are expressed as mean and standard devation (SD) to indicate data

variability. Statistical analyses were performed by unpaired student’s t test, one-way

ANOVA and post-hoc Tukey’s test, as indicated. Differences were considered

statistically significant at p <0.05.

                           

  50  

CHAPTER 4: RESULTS

NB. All experiments in all figures were designed, interpreted, and analyzed by Marko

Skrtic. All experiments were completed by Marko Skrtic with/without technical

assistance except Figures: 7, 9, 10, 14, 15, 16, 18, 19, 20(partial), 30, 32, 34, 36 where

unique technical skills were required.

PART 1: Tigecycline – a novel anti-leukemia compound

4.1.1. Chemical screen for compounds targeting leukemic cells identifies the

antimicrobial tigecycline

Because of their known toxicology and pharmacology, off- and even on-patent drugs can

be rapidly repurposed for new indications. To search among such compounds for those

with potential anti-human AML activity, we compiled a library of 312 such drugs

focused mainly on anti-microbials and metabolic regulators with well-characterized

pharmacokinetics and toxicology, and wide therapeutic windows. We then screened this

library to identify agents that reduced the viability of cells from two human AML cell

lines, TEX and M9-ENL1, that display features of leukemia stem cells (Figure 2).These

cell lines were originally derived from lineage-depleted human cord blood cells (Lin- CB)

transduced with TLS-ERG or MLL-ENL oncogenes respectively. These two lines were

chosen for our first screen because of their stem cell properties including hierarchal

differentiation and self-renewal(Barabé et al., 2007; Warner et al., 2005).

  51  

Figure  2.  Chemical screen for compounds targeting leukemic cells identifies the antimicrobial tigecycline Outline and schematic of assessment of the toxicity of 312 drugs on M9-ENL1 cells and TEX cells in 96-well plates with drugs added to the wells (5 µL per well) at final concentrations of 10 (shown) and 1 µM. Cell viability and proliferation was measured by MTS assay and results shown are relative to values for DMSO-control cells.  

  52  

4.1.2. Validation dose-response curves

Figure 3 shows dose-response curves for 5 compounds that did not have any previously

recognized anti-cancer activity but displayed some anti-leukemic activity against at least

one of these 2 cell lines after a 72 hour period of exposure. Interestingly, salinomycin

was recently shown to have specific activity against breast cancer stem cells (Gupta et al.,

2009). The screen contained known anti-neoplastic agents doxorubicin, imatinib,

bortezomib, and cytarabine which caused cell death at both concentrations of 10 and 1

µM. The second most active drug was tigecycline, which we then chose to analyze

further.

4.1.3. Tigecycline activity in malignant cell lines

To determine the effect of tigecycline on a broader spectrum of malignant cell

lines, a panel of human and murine leukemia, myeloma and solid tumor cells were

similarly treated with increasing concentrations of tigecycline. IC50s ranging from 3 to 8

µM were obtained for the various leukemia cell lines (Figure 4). Tigecycline-induced cell

death was confirmed by Annexin V/PI staining (Figure 5). Of note, although tigecycline

is a structural analogue of minocycline and tetracycline, TEX cells were not sensitive to

either minocycline or tetracycline at concentrations up to 25 µM. Tigecycline was less

cytotoxic to myeloma and solid tumor cells lines with IC50s of >10 µM.

  53  

0.1 1 10 1000

25

50

75

100

125ENL-1TEX

Halometasone (µM)

% G

row

th a

nd V

iabi

lity

0.1 1 10 1000

25

50

75

100

125ENL-1TEX

Mupirocin (µM)

% G

row

th a

nd V

iabi

lity

0.1 1 10 1000

50

100

150ENL-1TEX

Zalcitabine (µM)

% G

row

th a

nd V

iabi

lity

0.1 1 10 1000

25

50

75

100

125 ENL-1TEX

Salinomycin (µM)

% G

row

th a

nd V

iabi

lity

0.1 1 10 1000

25

50

75

100

125ENL-1TEX

Tigecycline (µM)%

Gro

wth

and

Via

bilit

y

Figure   3.   Validation dose-response curves Dose-response validation of representative hits on TEX and M9-ENL1 cells. Drugs were added to TEX and ENL-1 cells (3 experiments each). Proliferation and viability of cells present after 72 hours of exposure were determined by MTS staining and the results expressed as a percent of matching DMSO-treated controls  

  54  

Figure  4.  Tigecycline activity in malignant cell lines Comparison of toxicity of tigecycline, minocycline and tetracycline on leukemia cells and of tigecycline on cells from various human and murine leukemia, myeloma, and solid tumor lines. Cells were incubated in triplicate experiments with drugs at concentrations shown for 72 hours in 96-well plates and then proliferation and viability were determined by MTS (human cells) or ViaCount (murine cells) staining and results expressed as a percent of results for untreated cells.  

0.1 1 10 1000

25

50

75

100

125TigecyclineMinocyclineTetracycline

Tigecycline (µM)

% G

row

th a

nd V

iabi

lity

Leukemia

0.1 1 10 1000

25

50

75

100

125

ENL-1TEXAML-2U937

3ND13pac pSF919MN14ND13pac MN1HoxA9neo MIY Meis

Human

Murine

HL-60

Tigecycline (µM)

% G

row

th a

nd V

iabi

lity

Tigecycline

Myeloma

0.1 1 10 1000

25

50

75

100

125

Tigecycline (µM)

% G

row

th a

nd

Via

bili

ty LP-18226JJn3KMS11OP-M2

Solid Tumour

0.1 1 10 1000

25

50

75

100

125

Tigecycline (µM)

% G

row

th a

nd

Via

bili

ty

OVCARPC3A549HELA

  55  

0 5 10 5 10 5 10 5 10 5 10 5 100

10

20

30

40

50

60

TIG (µM)

Time (hr) 3 6 9 12 24 48

% A

nnex

in V

Pos

itive

Figure  5.  Tigecycline activity in malignant cell lines Time course study of the death of TEX cells induced by exposure to 5 or 10 µM of tigecycline using Annexin V and PI staining and flow cytometry to discriminate viable cells. Data represent the mean + SD of Annexin V and PI negative cells from a representative experiment (n=3).  

  56  

4.1.4. Tigecycline kills primary AML bulk more effectively than normal

hematopoietic cells

We next compared the ability of tigecycline to kill cells from 20 primary AML samples

(18 from newly diagnosed patients and 2 from patients with relapsed, treatment-

refractory disease, see Table 1) and normal human hematopoietic cells within 48 hours of

exposure in vitro. Bulk low-density cells from 5 G-CSF-mobilized normal donors

showed an LD50 of at least 10 µM, including the CD34+ cells isolated from 2 of these

samples (Figure 6). Cells from 7 of the 20 AML patients studied displayed a similar

sensitivity to tigecycline (LD50 >10 µM) but, in the other 13 cases, a much greater

sensitivity to tigecycline was observed (LD50 <5 µM). Notably, no differences in

cytogenetic risk or disease status were evident between the sensitive and insensitive

groups and both of the samples from relapsed, treatment-refractory patients were

sensitive to tigecycline.

  57  

Gender Age FAB subtype Cytogenetic risk CytogeneticsSensitive M 77 M4 intermediate Normal

In vitro LD50 < 5 µM F 62 M4 intermediate NormalF 31 M2 good t(8:21) (q22;q22)F 31 M1 intermediate NormalM 59 M4 intermediate NormalM 87 M4 poor -7M 34 M1 poor -7M 42 M4 intermediate NormalF 68 M2 intermediate InconclusiveF 60 M1 intermediate NormalM 45 M4 intermediate NormalF 80 M4 not done Not doneM 84 M4 poor del(5) (q13q33), +5, +8,+21M 53 M1 intermediate NormalM 51 M4 intermediate NormalF 50 M4 poor inv (3) (q21q26), -7M 40 M4 intermediate NormalF 47 M6 intermediate NormalM 41 M4 intermediate NormalF 81 M5 intermediate Normal

Resistant M 55 M4 intermediate NormalIn vitro LD50 > 5 µM M 74 M4 intermediate Normal

M 42 M4 intermediate NormalM 42 M4 intermediate NormalF 59 M4 good inv(16) (p13.1q22)M 67 M1 intermediate NormalM 70 M5 intermediate NormalM 34 M4 poor inv (3) -7

Table  1.  AML Patient Characteristics AML patient samples were stratified in terms of in vitro sensitivity to tigecycline. Sensitivity was defined as tigecycline LD50 < 5 µM, while resistance was LD50 > 5 µM. Gender, age, FAB sup-type, and cytogenetic risk are shown.  

  58  

NORMAL  

 AML  

0 5 10 15 200

20

40

60

80

100

120

Tigecycline (µM)

Rel

ativ

e V

iabi

lity

0 5 10 15 200

20

40

60

80

100

120

Tigecycline (µM)

Rel

ativ

e V

iabi

lity

0 5 10 15 200

20

40

60

80

100

120

PBSC (n = 3)

CD34+ (n = 2)

Tigecycline (µM)

Rel

ativ

e V

iabi

lity

Figure   6.   Tigecycline kills AML bulk cells preferentially over normal hematopoietic cells Toxicity of tigecycline on leukemic blasts, as compared to normal hematopoietic cells. Primary AML cells (n=20) and normal hematopoietic cells (n=5) were treated with increasing concentrations of tigecycline for 48 hours. The proportion of viable cells was measured by Annexin-PI flow cytometry and these values were then used to calculate the yield of viable cells shown as a percent of the yield of DMSO-treated cells in the same experiment (n=7).  

  59  

4.1.5. Tigecycline kills AML progenitors and stem cells more effectively than the

normal equivalent cells

To compare the effect of tigecycline on functionally defined subsets of

primitive human AML and normal hematopoietic cell populations, additional

experiments were performed. Incorporation of 5 µM tigecycline into the assay medium

reduced the clonogenic growth of primary AML patient samples (n=7) by 93±4% (Figure

7). In contrast, tigecycline had only a minimal effect on the clonogenic growth of normal

hematopoietic cells assayed using the same protocol (n=5). To assess the effects of

tigecycline on AML and normal hematopoietic stem cells, we treated primary AML or

normal Lin- CD34+-enriched human cord blood cells with 5 µM tigecycline or DMSO (as

a control) for 48 hours in vitro and then compared the number of human cells produced

after 6 weeks in NOD/SCID mice transplanted with these variously treated cells. This

tigecycline treatment protocol reduced the repopulating ability of the primary AML cells

tested, but had no effect on the repopulating activity of normal hematopoietic cells

(Figure 8).

Thus for a majority of AML patients, including some with treatment refractory

disease, tigecycline effectively targets all compartments of leukemic cells including the

leukemia stem cells and does so at concentrations that appear pharmacologically

achievable and that do not have a similar negative effect on normal hematopoietic cells.

           

  60  

     

0

25

50

75

100

125

Clo

noge

nic

grow

th (

% c

ontro

l) CFU-LCFU-GMBFU-E

AML Normal  

                             

     

Figure   7.   Tigecycline kills AML progenitors preferentially over normal hematopoietic progenitors Sensitivity of colony formation by cells from 7 primary AML patient samples as compared to normal hematopoietic cells (5 individuals) to 5 µM tigecycline included in the Methocult. Values shown are the percent of colonies obtained compared to DMSO-treated cells.  

  61  

       

                         

       

 AML NORMAL

Control Tigecycline0

25

50

75

100***

% C

D45

+ C

D33

+ C

D19

-

Control Tigecycline0

2

4

6

8

10

12

14 N.S.

% C

D45

+ C

D33

+ C

D19

-

Figure   8.   Tigecycline kills AML stem cells preferentially over normal hematopoietic stem cells Effect of in vitro tigecycline treatment of primary AML versus normal cells on their subsequent in vivo repopulating activity. Cells from an AML patient and Lin- CD34+ enriched human cord blood cells were treated with 5 µM of tigecycline or DMSO for 48 hours in vitro and then injected directly into the femur of irradiated NOD/SCID mice preconditioned with anti-CD122. Six weeks later, the percent of human CD45+CD19-CD33+ cells in the femur was measured by FACS. ***P < 0.0001, N.S. not significant P > 0.05 as determined by the unpaired student’s t test.  

  62  

4.1.6. Tigecycline shows anti-AML activity in xenograft models of human leukemia

To assess the in vivo anti-leukemia efficacy of tigecycline using xenograft models, we

first evaluated the pharmacokinetics of tigecycline in mice (Figure 9). Based on these

studies, we chose a treatment schedule of twice daily intraperitoneal (i.p.) injections. In a

first experimental design, OCI-AML2 cells were transplanted subcutaneously into severe

combined immune deficiency (SCID) mice and treatment was started 7 days later when

tumors were already palpable. Compared to the vehicle control, tigecycline significantly

delayed tumor growth and showed equivalent or greater potency than daunorubicin or

bortezomib at their maximally tolerated doses (Figure 10). Treatment with tigecycline did

not alter the appearance or behavior of the mice. Moreover, at the conclusion of the

experiment 3 weeks post transplant, there were no gross changes to the organs at

necropsy.

  63  

Figure   9.   Tigecycline plasma concentration after single administration in SCID mice. Mice were injected with 50 mg/kg tigecycline by i.v. or i.p. administration and then plasma was collected at the indicated intervals. Plasma tigecycline concentration was determined by HPLC as described in supplemental experimental procedures.  

  64  

Figure   10.  Tigecycline has in vivo activity in models of human leukemia in mice Human leukemia (OCI-AML2) cells were injected subcutaneously into the flank of SCID mice. Seven days later, when tumors were palpable, mice were treated with tigecycline (50 mg/kg or 100 mg/kg twice daily by i.p. injection), bortezomib (1 mg/kg t.i.w.), daunorubicin (0.65 mg/kg t.i.w.) or vehicle control (n = 10 per group). Three weeks after injection of cells, mice were sacrificed, tumors excised and the volume and mass of the tumors were measured. The tumor mass and the mean volume + SD are shown. **P < 0.005, as determined by Tukey’s test after one-way ANOVA analysis.  

0 3 6 9 12 15 18 21 240

500

1000

1500 ControlBortezomib 1 mg/kg t.i.w.

Time (day)

Tum

our v

olum

e (m

m3 )

0 3 6 9 12 15 18 210

250

500

750

1000

1250

1500

Time (day)

Tum

our v

olum

e (m

m3 ) Control

Daunorubicin 0.65 mg/kg t.i.w.

0 3 6 9 12 15 18 21 240

500

1000

1500ControlTigecycline 50mg/kg b.i.d.Tigecycline 100 mg/kg b.i.d.

**

Time (day)

Tum

our v

olum

e (m

m3 )

0 50 1000

500

1000

1500

****

Tigecycline (mg/kg/day bid)

Tum

our m

ass

(mg)

  65  

4.1.7. Tigecycline shows activity in humanized xenotransplantation models of

leukemia

Xenotransplantation in NOD/SCID mice is a robust model system to assay stem

cells and test the efficacy of new anti-leukemia therapies in vivo(Bonnet and Dick, 1997;

Jin et al., 2006; Lapidot et al., 1994). We transplanted pre-conditioned NOD/SCID mice

intra-femorally with primary AML cells from 3 patients and Lin- CD34+ enriched human

cord blood cells, and then evaluated the effects of a 3-week course of tigecycline started 3

weeks post-transplant. Tigecycline-treated mice had significantly lower levels of

leukemic engraftment compared to control-treated mice without evidence of toxicity

(Figure 11). Importantly, leukemic cells harvested from the bone marrow of tigecycline-

treated primary mice generated smaller leukemic grafts in untreated secondary mice,

compared to cells harvested from control-treated primary mice, indicating that tigecycline

was active against AML stem cells. In contrast, tigecycline treatment in vivo did not

reduce engraftment of normal myeloid cells, indicating preferential activity against LSCs

over normal hematopoietic cells.

  66  

Figure   11.   Tigecycline shows activity in humanized xenotransplantation models of leukemia Primary cells from 3 AML patients and Lin- CD34+ enriched human cord blood cells (Normal) were injected intra-femorally into irradiated female NOD/SCID mice. Three weeks after injection, the mice were treated with tigecycline (100 mg/kg by i.p. injection daily) or vehicle control (n = 10 per group) for three weeks. Following treatment, human leukemia cell engraftment in the femur was measured by flow cytometric analysis of human CD45+CD19-CD33+

cells. Cells from mice transplanted with one AML patient experiment were used to assess secondary engraftment in a second generation of NOD/SCID mice. Equal numbers of viable leukemia cells from the bone marrow of control and tigecycline-treated mice were pooled and aliquots injected into irradiated NOD/SCID mice, which were not treated with tigecycline. Six weeks later, human leukemia cell engraftment in the femur was measured by flow cytometric analysis for human CD45+CD19-CD33+ cells. Data represent median of engrafted human cells. *P < 0.05; **P < 0.005, N.S. not significant P > 0.05 as determined by student’s t test.  

AML  (secondary)

AML  (primary)

NORMAL

Control Tigecycline0

5

10

15

20

25

30

35**

% C

D45

+ C

D33

+ C

D19

-

Control Tigecycline0

10

20

30 **

% C

D45

+ C

D33

+ C

D19

- Control Tigecycline

0

25

50

75

100

**

% C

D45

+ C

D33

+ C

D19

- Control Tigecycline

0

5

10

15

35 N.S.

% C

D45

+ C

D33

+ C

D19

-

Control Tigecycline0

1020304050607080 *

% C

D45

+ C

D33

+ C

D19

-

E

  67  

4.1.8. Tigecycline shows synergy in combination with standard AML chemotherapy

Standard AML debulking agents are often ineffective against leukemia stem cells,

as evidenced by the high frequency of relapse following cytoreductive therapy. We

evaluated the efficacy of tigecycline in combination with daunorubicin or cytarabine, 2

standard chemotherapeutic agents used for the treatment of AML. TEX and OCI-AML2

leukemia cells were treated in vitro with increasing concentrations of tigecycline alone or

in combination with daunorubicin or cytarabine, and growth and viability were assessed

(Figure 12, 13). Data were analyzed using the Calcusyn median effect model, where the

combination index (CI) indicates synergism (CI<0.9), additivity (CI=0.9-1.1) or

antagonism (CI>1.1). Tigecycline and daunorubicin added together showed an additive

or synergistic effect (CI=0.75 – 1.0). However, when tigecycline was added either before

or after daunorubicin, the combination was clearly synergistic (CI values at ED50 < 0.8).

Treatment with tigecycline in combination with cytarabine was additive or synergistic

(CI=0.75 – 1.3) regardless of drug sequence. We then tested the efficacy of the

tigecycline/daunorubicin and tigecycline/cytarabine combinations in the OCI-AML2

xenograft model. Mice treated with the 2 drug combinations showed reduced tumor

growth by comparison to those receiving single agents (Figure 14). These results suggest

that combination therapy with tigecycline may enhance the anti-leukemic efficacy of

standard chemotherapeutic agents in patients.

       

  68  

       

                             

TEXTigecycline + Daunorubicin

0.00 0.25 0.50 0.75 1.000.0

0.5

1.0

1.5

2.0

Fraction Affected

Com

bina

tion

inde

x

TEXDaunorubicin → Tigecycline

0.00 0.25 0.50 0.75 1.000.0

0.5

1.0

1.5

2.0

Fraction Affected

Com

bina

tion

inde

x

TEXTigecycline → Daunorubicin

0.00 0.25 0.50 0.75 1.000.0

0.5

1.0

1.5

2.0

Fraction Affected

Com

bina

tion

inde

x

OCI-AML2Tigecycline + Daunorubicin

0.00 0.25 0.50 0.75 1.000.0

0.5

1.0

1.5

2.0

Fraction Affected

Com

bina

tion

inde

x

OCI-AML2Daunorubicin → Tigecycline

0.00 0.25 0.50 0.75 1.000.0

0.5

1.0

1.5

2.0

Fraction Affected

Com

bina

tion

inde

xOCI-AML2

Tigecycline → Daunorubicin

0.00 0.25 0.50 0.75 1.000.0

0.5

1.0

1.5

2.0

Fraction Affected

Com

bina

tion

inde

x

Figure  12.  Tigecycline has synergistic activity with daunorubicin in vitro The effect of a 72 hour exposure of TEX and OCI-AML2 cells to different concentrations of tigecycline in combination with daunorubicin on the viability of the cells was measured by MTS assay after 72 hours of incubation. Data were analyzed with Calcusyn software to generate a Combination index versus Fractional effect plot showing the effect of the combination of tigecycline with daunorubicin. CI < 1 indicates synergism. Representative isobolograms of experiments performed in triplicate are shown.  

  69  

       

                       

     

TEXTigecycline + Ara C

0.00 0.25 0.50 0.75 1.000.0

0.5

1.0

1.5

2.0

Fraction Affected

Com

bina

tion

inde

x

TEXAra C → Tigecycline

0.00 0.25 0.50 0.75 1.000.0

0.5

1.0

1.5

2.0

Fraction Affected

Com

bina

tion

inde

x

TEXTigecycline → Ara C

0.00 0.25 0.50 0.75 1.000.0

0.5

1.0

1.5

2.0

Fraction Affected

Com

bina

tion

inde

x

OCI-AML2Tigecycline + Ara C

0.00 0.25 0.50 0.75 1.000.0

0.5

1.0

1.5

2.0

Fraction Affected

Com

bina

tion

inde

x

OCI-AML2Ara C → Tigecycline

0.00 0.25 0.50 0.75 1.000.0

0.5

1.0

1.5

2.0

Fraction Affected

Com

bina

tion

inde

xOCI-AML2

Tigecycline → Ara C

0.00 0.25 0.50 0.75 1.000.0

0.5

1.0

1.5

2.0

Fraction Affected

Com

bina

tion

inde

x

Figure  13.  Tigecycline has synergistic activity with cytarabine (Ara C) in vitro The effect of a 72 hour exposure of TEX and OCI-AML2 cells to different concentrations of tigecycline in combination with cytarabine on the viability of the cells was measured by MTS assay after 72 hours of incubation. Data were analyzed with Calcusyn software to generate a Combination index versus Fractional effect plot showing the effect of the combination of tigecycline with cytarabine. CI < 1 indicates synergism. Representative isobolograms of experiments performed in triplicate are shown.  

  70  

                           

Figure   14.  Tigecycline has synergistic activity with cytarabine (Ara C) and daunorubicin in vivo In vivo effects of daunorubicin/tigecycline and cytarabine/tigecycline combinations on OCI-AML2 leukemia xenografts were also assessed. Human leukemia (OCI-AML2) cells were injected subcutaneously into the flank of SCID mice. Six days after injection, when tumors were palpable, mice were treated with tigecycline (50 mg/kg daily by i.p. injection) and/or daunorubicin (0.65 mg/kg t.i.w. by i.p. injection) and/or cytarabine (10 mg/kg daily by i.p. injection) or vehicle control (7 mice per treatment group). Another 2 weeks later, mice were sacrificed, tumors excised and the volume and mass of the tumors were measured and mean values determined. The tumor mass and the mean volume + SD are shown. *P < 0.05, ** P < 0.005, as determined by Tukey’s test after One-way ANOVA analysis.  

0 5 10 15 200

250

500

750

1000

1250

1500 ControlDaunorubicin 0.65 mg/kg t.i.w.Tigecycline 50 mg/kg dailyDaunorubicin + Tigecycline

******

Time (day)

Tum

our v

olum

e (m

m3 )

0 5 10 15 200

300

600

900

1200

1500

1800

2100 ControlAra-C 10 mg/kg dailyTigecycline 50 mg/kg dailyAra-C + Tigecycline

**

Time (day)

Tum

our v

olum

e (m

m3 )

0

500

1000

1500 ControlAra-C 10 mg/kg dailyTigecycline 50 mg/kg dailyAra-C + tigecycline

**

**Tum

our m

ass

(mg)

0

500

1000

1500 ControlDaunorubicin 0.65 mg/kg t.i.w. Tigecycline 50 mg/kg dailyDaunorubicin + tigecycline

**

****

Tum

our m

ass

(mg)

  71  

PART II: Tigecycline inhibits mitochondrial translation in leukemia cells    4.2.1. Haplo-Insufficiency Profiling in S. Cerevisiae identifies mitochondrial

translation as target of tigecycline in eukaryotic cells

Tigecycline is currently used clinically as a broad-spectrum antibiotic due to its high

affinity and potent inhibition of the bacterial ribosome (Olson et al., 2006; Stein and

Craig, 2006). To determine the mechanism of tigecycline’s activity in eukaryotic cells,

we used Haplo-Insufficiency Profiling (HIP), a well-validated chemical genomics

platform developed in the yeast S. Cerevisiae. The HIP assay allows an unbiased in vivo

quantitative measure of the relative drug sensitivity of all ~6,000 yeast proteins in a

single assay, and results in a list of candidate protein targets (Giaever et al., 1999; Hoon

et al., 2008). Under standard fermentation conditions in rich media (YP), where the

primary mode of metabolism is glycolysis, yeast growth was relatively insensitive to

tigecycline. In contrast, yeast grown in respiratory conditions that depend on oxidative

phosphorylation exhibited increased sensitivity and dose-dependent inhibition by

tigecycline (Figure 15). Because growth inhibition is a necessary criterion for the HIP

assay, all subsequent experiments were performed in respiratory media (YPGE).

  72  

0 100 200 300 400 500 600 7000

20

40

60

80

100

120

YPGEYP

Tigecycline (µM)

% V

iabi

lity

Figure   15.   S. Cerevisiae grown in respiratory media (YPGE see methods) exhibits enhanced sensitivity to tigecycline compared to standard glycolytic conditions (YPD). Yeast Data represent mean growth rate ± SD (AUCdrug / AUCvehicle).  

  73  

The rank-ordered gene list of drug sensitive strains generated from the HIP assays

was analyzed using Gene Set Enrichment Analysis (GSEA) to identify Gene Ontology

(GO) biological processes that were enriched in the tigecycline screens. The most

significantly enriched GO process was the mitochondrial ribosome (p-value <0.0001,

FDR q-value < 0.005) (Figure 16). In the HIP assay, when the target is a large complex,

no single gene in the complex stands out from the rest. For comparison, we also screened

the known mammalian mitochondrial translation inhibitors chlorampheniciol (McKee et

al., 2006) and linezolid (Nagiec et al., 2005), and the anthracycline family-member

doxorubicin, which displays broad mechanisms of anti-cancer activity (Swift et al., 2006;

Tewey et al., 1984; Wallin et al., 2010). As expected, chloramphenicol and linezolid

yielded similar results to tigecylcine, while the doxorubucin GO enrichment analysis

revealed a mechanism distinct from tigecycline (none of the doxorubicin GO terms

passed our significance filter (p-value <0.001, FDR q-value 0.1). Taken together, the

yeast genomic screens suggest that tigecycline acts to inhibit growth and viability of

eukaryotic cells through interference with mitochondrial protein translation. This finding

is consistent with the known function of tigecycline as a potent inhibitor of the bacterial

ribosome and highlights the potential for antibiotics that bind the bacterial ribosome to

cross-react with human mitochondrial ribosomes.

                   

  74  

     

                                 

 

Figure   16.   HIP assays with drugs in S. Cerevisiae. A pool of ~6000 S. Cerevisiae heterozygote mutant strains were cultured in the presence or absence of tigecycline, chloramphenicol, linezolid or doxorubicin in YPGE media and those showing altered growth responses relative to control cells identified. Top Gene Set enrichment analysis (GSEA) processes are shown. Commonly enriched genes involved in mitochondrial translation identified from this GSEA analysis are shown below in the Venn diagram and the heat map. Red color is associated with higher gene enrichment in the presence of drug relative to control cells.  

  75  

4.2.2. Tigecycline inhibits mitochondrial translation in established and primary

leukemia cells

 To determine whether the specific toxicity of tigecycline on leukemic cells is mediated

by a similar mechanism, we next asked whether their exposure to tigecycline alters

expression of proteins whose translation is known to be dependent on cytosolic and

mitochondrial ribosomes. In a first set of experiments, TEX, OCI-AML2 and cells from 2

AML patients were incubated for 48 hours in increasing concentrations of tigecycline and

effects on Cytochrome C Oxidase-1, 2 and 4 (Cox-1, 2 and 4) levels measured at the end

of that time. Cox-1 and Cox-2 are subunits of respiratory complex IV in the electron

transport chain in mitochondria and are translated by mitochondrial ribosomes (Tam et

al., 2008) (see Figure 1). Cox-4 is a component of the same respiratory complex, but is

encoded by the nuclear genome and translated by nuclear ribosomes. Tigecycline

treatment caused a preferential decrease of Cox-1 and Cox-2 as compared to Cox-4

(Figure 17). Tigecycline also did not alter the expression of other proteins translated by

cytosolic ribosomes including grp78 and the short half-life protein XIAP. The reductions

in Cox-1 and Cox-2 protein levels were associated with increases in their mRNA

expression with less change in Cox-4 mRNA levels in the same cells. (Figure 18) This

result is consistent with a previous report (Chrzanowska-Lightowlers et al., 1994) in

which inhibition of mitochondrial translation was found to be accompanied by an

increase in the expression of mitochondrially encoded mRNA. Our findings support a

tigecycline-mediated inhibition of mitochondrial translation as its anti-leukemic cell

mechanism of action.

  76  

           

                     

     

 

TEX   TEX  

AML   AML  OCI-AML2  

Figure   17.   Tigecycline decreases mitochondrially translated proteins in leukemia cells. Effects of increasing concentrations of tigecycline on protein levels of Cox-1, Cox-2, Cox-4, grp78, XIAP, actin and tubulin in TEX, OCI-AML2 and 2 AML patients’ cells treated for 48 hours. Total proteins were extracted and analyzed by immunoblotting as described in the methods.  

  77  

                                                                                           

Figure   18.   Tigecycline increases mRNA expression of mitochondrially translated proteins in leukemia cells. Effects of increasing concentrations of tigecycline on Cox-1, Cox-2, and Cox-4 mRNA expression in TEX and AML patient cells treated for 48 hours. Transcript levels were determined by quantitative RT-PCR and values normalized relative to 18s. Data is shown as mean ± SD fold change in mRNA expression compared to untreated controls (n=3).  

TEX AML

0 2.5 5 2.5 50.0

2.5

5.0

7.5

10.0

12.5

15.0

17.5 Cox-1Cox-2Cox-4

36 hr 48 hrTIG (µM)

Rel

ativ

e m

RN

A e

xpre

ssio

n

0 2.5 5 2.5 50.0

2.5

5.0

7.5

10.0

12.5

15.0

17.5 Cox-1Cox-4

36 hr 48 hrTIG (µM)

Rel

ativ

e m

RN

A e

xpre

ssio

n

  78  

4.2.3. Tigecycline decreases activity of the oxidative phosphorylation cascade

   

We next asked whether a similar exposure to tigecycline would affect the

enzymatic activity of respiratory complexes I and IV, both of which contain proteins

translated on mitochondrial ribosomes, and by comparison to the respiratory chain

complex II, which does not contain mitochondrially-encoded subunits in its sub-structure

(Ott and Herrmann, 2009). Tigecycline significantly decreased the enzyme activity of

respiratory complexes I and IV, but had less effect on the enzymatic activity of the

complex II (Figure 19). These findings were mirrored by treatment with

chloramphenicol, a known mitochondrial protein synthesis inhibitor. Consistent with its

effects on the mitochondrial respiratory chain, tigecycline decreased oxygen consumption

in TEX cells at concentrations associated with, but at times preceding cell death (Figure

20).

                                       

  79  

   

     

                                         

             

     

Figure  19.  Tigecycline decreases enzyme activity of complexes I and IV, but not complex II. Effect of increasing concentrations of tigecycline and chloramphenicol (CAP) on Complex I, II and IV enzyme activities relative to citrate synthase activity in TEX cells treated for 72 hours. Enzyme activities were determined as described in the supplementary methods. Values shown are averaged from 3 independent experiments. *P < 0.05, ** P < 0.005, as determined by Tukey’s test after One-way ANOVA analysis.  

0 2.5 5 101000

20

40

60

80

100

120

% C

ompl

ex a

ctiv

ity /

citr

ate

synt

hase

TIG (µM)

CAP (µM)

* *

0 2.5 5 101000

20

40

60

80

100

120

% C

ompl

ex a

ctiv

ity /

citra

te s

ynth

ase

TIG (µM)

CAP (µM)

*

0 2.5 5 101000

20

40

60

80

100

120

% C

ompl

ex a

ctiv

ity /

citr

ate

synt

hase

TIG (µM)

CAP (µM)

**** **

Complex I Complex II Complex IV

  80  

                                                                     

     

Figure  20.  Tigecycline decreases oxygen consumption in leukemia cells. Effect of tigecycline on oxygen consumption in TEX cells treated for 12 or 24 hours. Oxygen consumption was measured on washed cells as described in the supplementary methods. Arrow denotes addition of 1.2 µM oligomycin. Values shown are averaged from 3 independent experiments.  

TEX 12 hr

1 2 4 8 16 32 64 1280

255075

100125150175200

CTL 2.5 µM TIG 5 µM TIG 10 µM TIG

Time (min)

OC

R (p

Mol

es/m

in)

1 2 4 8 16 32 64 1280

255075

100125150175200

CTL 2.5 µM TIG 5 µM TIG 10 µM TIG

Time (min)

OC

R (p

Mol

es/m

in)

TEX 24 hr

  81  

4.2.4. Tigecycline collapses mitochondrial membrane potential in leukemia cells      

The mitochondrial respiratory chain generates an electrochemical proton gradient

that establishes the mitochondrial membrane potential (Ramzan et al., 2010) used to drive

ATP generation by complex V (ATP synthase). Therefore, we examined the effect of

tigecycline treatment on the mitochondrial membrane potential as determined by staining

with the carbocyanine dye JC-1 (Smiley et al., 1991). TEX cells and 3 different primary

AML samples showed a decreased mitochondrial membrane potential after tigecycline

treatment (5 µM), at times preceding the onset of cell death (Figure 21). In contrast, loss

of mitochondrial membrane potential was not seen in normal hematopoietic cells from

two different G-CSF mobilized normal donors after a similar in vitro incubation with

tigecycline. The preferential effect of tigecycline on collapsing the membrane potential of

leukemia cells may help explain the preferential cytotoxicity of tigecycline for AML cells

over normal cells.

                                   

  82  

   

                   

   

AML

0 12 240.0

0.2

0.4

0.6

0.8

1.0

1.2

Time (hr)

Δψ

(rel

ativ

e to

con

trol)

0 12 240.0

0.2

0.4

0.6

0.8

1.0

1.2

Time (hr)

Δψ

(rel

ativ

e to

con

trol)

0 12 240.0

0.2

0.4

0.6

0.8

1.0

1.2

Time (hr)

Δψ

(rel

ativ

e to

con

trol)

0 6 9 120.0

0.2

0.4

0.6

0.8

1.0

1.2

Time (hr)

Δψ

(rel

ativ

e to

con

trol)

0 12 240.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

Time (hr)

Δψ

(rel

ativ

e to

con

trol)

0 12 240.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

Time (hr)

Δψ

(rel

ativ

e to

con

trol)

NORMAL TEX

Figure   21.   Tigecycline collapses mitochondrial membrane potential in leukemia cells. Effect of increasing concentrations of tigecycline on mitochondrial membrane potential (Δψ) in TEX, AML patients’ and normal donors’ cells treated for 12 or 24 hours and then stained with JC-1 dye and flow cytometry. Shown are the average Red/Green ratios thus derived for tigecycline-treated cells expressed as a percent of the values measured in DMSO-treated control cells from the same experiments (n=3 per cell type).  

  83  

 4.2.5. Tigecycline does not increase reactive oxygen species in leukemia cells      

A byproduct of the mitochondrial electron transport chain is the generation of

reactive oxygen species (ROS). Respiratory chain inhibitors such as rotenone and Na

azide have been previously shown to induce rapid increases in ROS generation leading to

cell death (Li et al., 2003; Park et al., 2007; Turrens and Boveris, 1980). Therefore, we

explored the role of tigecycline on ROS generation in leukemia cells. Tigecycline did not

increase ROS generation in TEX cells at time-points up to 24 hours (Figure 22). In

contrast, various mitochondrial complex enzyme inhibitors produced rapid increases in

ROS levels (Figure 23). Therefore, we postulate that the kinetics of tigecycline-induced

inhibition of mitochondrial translation and respiratory complex activity produce

functional effects distinct from agents that inhibit or uncouple the respiratory chain.

                                         

  84  

           

0 5 10 5 10 5 10 5 100.00

0.25

0.50

0.75

1.00

1.25

6 hr 9 hr 12 hr 24 hrTIG (µM)

Rel

ativ

e R

OS

(H2-

DC

FDA

)

0 5 10 5 100.00

0.25

0.50

0.75

1.00

1.25

TIG (µM)

6 hr 24 hr

Rel

ativ

e R

OS

(DH

E)

                                     

 

Figure  22.  Tigecycline does not increase reactive oxygen species in leukemia cells. Effect of tigecycline on the generation of ROS determined by H2-DCFDA and dihydroethidium dyes and flow cytometry analysis on the same cells analyzed in Figure 21. Results are again shown relative to DMSO treated controls.

  85  

Contro

l

Tigecy

cline

6uM

Sodium

Azid

e 120

0uM

Roteno

ne 30

uM

Oligom

ycin

40uM

Antimyc

in 40

uM0

2×1005

4×1005

6×1005

8×1005

1×1006

Via

ble

cell

num

ber

Tigecy

cline

6uM

Sodium

Azid

e 120

0uM

Roteno

ne 30

uM

Oligom

ycin

40uM

Antimyc

in 40

uM0.0

0.2

0.4

0.6

0.8

1.0

1.2

Δψ

(co

ntro

l = 1

)

Tigecy

cline

6uM

Sodium

Azid

e 120

0uM

Roteno

ne 30

uM

Oligom

ycin

40uM

Antimyc

in 40

uM0

20

40

60

80

100

% V

iabi

lity

(con

trol =

100

%)

Tigecy

cline

6uM

Sodium

Azid

e 120

0uM

Roteno

ne 30

uM

Oligom

ycin

40uM

Antimyc

in 40

uM0

1

2

3

4

Rel

ativ

e R

OS

(co

ntro

l = 1

)

A

C D

B

Figure  23.  Tigecycline’s inhibition of respiratory complexes is functionally distinct from respiratory chain inhibitors in terms of ROS production. TEX cells were treated with increasing concentrations of tigecycline (6 µM), sodium azide (1200 µM), rotenone (30 µM), oligomycin (40 µM), and antimycin (40 µM). Seventy-two hours after treatment, growth was assessed by trypan blue counting (A), and apoptosis was quantified by Annexin-V/PI staining (B). (C) 6 (oligomycin) or 24 hours (all other compounds) after drug treatment, mitochondrial membrane potential was determined by staining cells with JC-1 dye, and flow cytometry analysis (Red/Green ratio). Data represents median ± S.D. Red/Green ratio relative to vehicle-treated TEX cells. (D) Twenty-four hours after drug treatment, ROS generation was determined by H2-DCFDA dye staining and flow cytometry analysis. Data represents median ± S.D FL1 (green) fluorescence relative to vehicle-treated TEX cells.

  86  

4.2.6. Anti-leukemia activity of tigecycline is oxygen-dependent      To determine whether tigecycline’s effects on the viability of leukemia cells are

dependent on inhibition of mitochondrial function, we evaluated its anti-leukemic activity

under different conditions of hypoxia (20% - 0.2% O2) and anoxia (0% O2) . Incubation

of both TEX and primary AML cells under anoxic conditions alone was toxic and in

parallel reduced the mitochondrial membrane potential in these cells (Figure 24).

However, co-exposure to tigecycline under anoxic conditions caused no further cell loss

and changes in mitochondrially-translated COX subunits I, II determined by

immunoblotting 48 hours after tigecycline treatment, were no longer evident (Figure

24D). Nevertheless, these experiments demonstrated that tigecycline remained active

against leukemia cells and primary patient AML samples under oxygen concentrations of

1-5% that are present in the bone marrow of patients with AML (Fiegl et al., 2009).

Taken together, these results demonstrate that the ability of tigecycline to kill leukemia

cells is dependent on oxygen availability and an intact mitochondrial respiratory chain.

                               

  87  

 

                     

   

0 510 0 510 0 510 0 510 0 5100

102030405060708090

100110

TIG (µM)5% 1% 0.2% 0%Oxygen 21%

Rel

ativ

e V

iabi

lity

0 5 10 0 5 10 0 5 10 0 5 100.0

0.2

0.4

0.6

0.8

1.0

1.2

5% 1% 0.2% 0%OxygenTIG (µM)

Δψ

(fol

d ch

ange

)

0 510 0 510 0 510 0 5100

10

20

30

40

50

60

21% 1% 0.2% 0%

% A

nnex

in V

pos

itive

Oxygen

TIG (µM)

A B

C D

Figure  24.  Anti-leukemia activity of tigecycline is oxygen-dependent. (A, B, C) TEX and primary AML (10 AML) cells were treated under different oxygen concentrations for 48 hours . Viability (Annexin-PI) and mitochondrial membrane potential (Δψ, Red/Green ratio of JC-1) were assessed by flow cytometry. Results are shown relative to DMSO treated control. (D) Total proteins were extracted from TEX cells and analyzed by immunoblotting for Cox1, Cox2, Cox4, and tubulin.

  88  

4.2.7. Anti-leukemia activity of tigecycline is dependent on baseline mitochondrial

mass

     

We next assessed tigecycline sensitivity of leukemic cells with genetically altered

mitochondrial biogenesis. Previous studies have shown that Myc plays an important role

in promoting mitochondrial biogenesis in a Burkitt’s lymphoma model (Li et al., 2005).

Consistent with previous reports, inducible repression of Myc in p493-6 Burkitt’s cells

resulted in decreased mitochondrial mass and mitochondrial DNA copy number (Figure

25A,B). We used these cells to evaluate the effects of reduced mitochondrial biogenesis

on the cytotoxicity of tigecycline. Tigecycline treatment reduced the growth and viability

of control p493 cells with functional Myc. In contrast, p493 cells with decreased

mitochondrial mass following Myc repression were resistant to tigecycline (Figure 25C).

These results further support the notion that tigecycline’s anti-leukemic mechanism of

action is dependent on inhibition of mitochondrial function.

                                 

  89  

   

P493

+Myc  P493 -­‐Myc  P493

+Myc -Myc0.0

0.2

0.4

0.6

0.8

1.0

1.2

*

Rel

ativ

e M

itoch

ondr

ial m

ass

+Myc -Myc0.0

0.2

0.4

0.6

0.8

1.0

1.2

**

Rel

ativ

e m

tND

1

0 2.5 5 100

20

40

60

80

100

120

140

Tigecycline (µM)

% G

row

th a

nd V

iabi

lity

0 2.5 5 100

20

40

60

80

100

120

140

Tigecycline (µM)

% G

row

th a

nd V

iabi

lity

A B

C

Figure  25.  Anti-leukemia activity of tigecycline is dependent on baseline mitochondrial mass. (A) p493 lymphoma cells carrying a tetracycline-repressible human MYC construct were cultured in the presence and absence of 0.1 µg/ml (0.22 µM) of tetracycline for 96 hours. Total proteins were extracted and analyzed by immunoblotting for myc and actin. (B) Mitochondrial mass was measured by incubating cells with mitotracker Green FM dye, and subsequent flow cytometry. Median fluorescence intensity is shown relative to wild-type p493 cells. DNA was extracted from cells and qPCR was used to measure levels of mitochondrial ND1 relative to human globulin (HGB). ND1/HGB ratio is shown relative to wild-type p493 cells. *P < 0.05, **P < 0.005 as determined by unpaired student’s t test. (C) p493 cells with or without repressed MYC were washed and then treated with increasing concentrations of tigecycline for 48 hours. After treatment, the number of viable cells was determined by trypan blue staining and cell counts. Data represent the mean + SD number of viable cells from 1 of 3 independent experiments.

  90  

PART 3: BROAD INHIBITION OF MITOCHONDRIAL TRANSLATION

HAS ANTI-LEUKEMIA ACTIVITY

   4.3.1. Genetic inhibition of mitochondrial translation displays anti-leukemia

properties

To further explore the anti-leukemic activity of mitochondrial translation inhibition, we

asked whether genetic strategies would produce similar anti-leukemic effects as seen with

tigecycline. Protein translation in mitochondria is regulated by a series of initiation and

elongation factors specific to this organelle (Spremulli et al., 2004a). Mitochondrial

Initiation Factor 3 (IF-3) plays an active role in the initiation of mitochondrial translation

(Christian and Spremulli, 2009). Mitochondrial elongation factor Tu (EF-Tu) is

responsible for bringing aminoacyl-tRNAs in complex with GTP to the decoding site on

the mitochondrial ribosome (Spremulli et al., 2004a). We evaluated the effects of

lentiviral vector-mediated shRNA knock-down of IF-3 or EF-Tu in TEX cells. Target

knockdown was confirmed by QRT-PCR and immunoblotting using 2 independent

shRNA for each gene (Figure 26). Compared to control shRNA, knockdown of EF-Tu

decreased protein expression and increased mRNA expression of Cox-1 and Cox-2

(Figure 27), but did not change Cox-4 protein or mRNA levels. Similar to tigecycline,

EF-Tu knockdown reduced the growth and viability of TEX cells (Figure 26), and was

associated with decreased mitochondrial membrane potential and oxygen consumption

(Figures 28,30), with no change in ROS production (Figure 29). In contrast to the effects

  91  

of EF-Tu knockdown, IF-3 knockdown did not alter levels of Cox-1 and Cox-2 protein

and mRNA (Figure 27), did not reduce mitochondrial membrane potential or oxygen

consumption (Figure 28, 30), and did not alter the cell growth and viability of TEX cells

(Figure 26). These results validate inhibition of mitochondrial translation as a therapeutic

strategy against human leukemic cells. These results also demonstrate that some but not

all components of the mitochondrial protein translation machinery are necessary to

maintain mitochondrial translation.

                                                   

  92  

                                 

                                 

 

Figure  26.  EF-Tu, but not IF-3 knockdown decreases viability of TEX cells. Effect of IF-3 and EF-Tu knockdown on TEX cell viability. TEX cells were infected with relevant shRNA targeting or control sequences in lentiviral vectors. Six days post-transduction, EF-Tu and IF-3 mRNA expression relative to 18s and protein expression determinations were made by qRT-PCR and immunoblotting, respectively. Viable cell numbers were measured by trypan blue staining and cell counts and evidence of cell death was made using Annexin-V staining. Data from 1 of 3 independent experiments are shown. Additional cells treated in the same way were used to measure effects on other parameters.

Tex-W

T

CTLshRNA

IF-3

shRNA1

IF-3

shRNA2

0.0

0.2

0.4

0.6

0.8

1.0

1.2

Rel

ativ

e m

RN

A e

xpre

ssio

n

EF-­‐Tu

IF-­‐3

Tex-W

T

CTLshRNA

EF-Tush

RNA1

EF-Tush

RNA20.0

0.2

0.4

0.6

0.8

1.0

1.2

Rel

ativ

e m

RN

A e

xpre

ssio

n

Tex-W

T

CTLshRNA

EF-Tush

RNA1

EF-Tush

RNA20

10

20

30

40

% A

nne

xin

V P

ositi

ve

0 1 2 30.0

2.5×1006

5.0×1006

7.5×1006

1.0×1007

CTLshRNAEF-TushRNA1EF-TushRNA2

TEX-WT

DAY

Viab

le C

ells

0 1 2 30.0

2.5×1006

5.0×1006

7.5×1006

CTLshRNAIF-3shRNA1IF-3shRNA2

DAY

Viab

le C

ells

  93  

   

                                     

                         

     

   

Figure  27.  EF-Tu inhibits mitochondrial translation in TEX cells. Effects on expression of Cox-1, Cox-2, Cox-4 and tubulin protein were determined by immunoblotting (a representative experiment is shown) and on mRNA were determined by q-RT-PCR using 18s RNA as an internal standard (1 of 3 representative experiments shown).

Tex-W

T

CTLshRNA

EF-Tush

RNA1

EF-Tush

RNA20.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5Cox-1Cox-2Cox-4

Rel

ativ

e m

RN

A e

xpre

ssio

n

Tex-W

T

CTLshRNA

IF-3

shRNA1

IF-3

shRNA2

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5 Cox-1Cox-2Cox-4

Rel

ativ

e m

RN

A e

xpre

ssio

n

IF-­‐3

EF-­‐Tu

  94  

                                                                     

     

Figure  28.  EF-Tu knockdown decreases mitochondrial membrane potential in TEX cells. Effects on mitochondrial membrane potential (Δψ) were determined by staining cells with the JC-1 dye and then determining Red/Green ratios by flow cytometric analysis.

TEX-WT

CTLshR

NA

EF-Tus

hRNA1

EF-Tus

hRNA2

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

Δψ

(rel

ativ

e to

con

trol)

TEX-WT

CTLshR

NA

IF-3

shRNA1

IF-3

shRNA2

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

Δψ

(rel

ativ

e to

con

trol)

EF-­‐Tu IF-­‐3

  95  

               

TEX-WT

CTLshR

NA

EF-Tus

hRNA1

EF-Tus

hRNA2

H 2O 2

0

1

2

3

4

5

Rel

ativ

e R

OS

(H

2-D

CFD

A)

TEX-WT

CTLshR

NA

IF-3shR

NA1

IF-3shR

NA2H 2

O 20

1

2

3

4

5

Rel

ativ

e R

OS

(H

2-D

CFD

A)

                     

     

Figure  29.  EF-Tu knockdown doesn’t alter reactive oxygen species in TEX cells. Effects on ROS generation were determined by flow cytometric analysis H2-DCFDA-stained cells. Results are shown relative to TEX wild-type cells.

  96  

             

                                   

   

1 2 4 8 16 32 64 1280

255075

100125150175200225

TEX-WTCTLshRNAEF-TushRNA1EF-TushRNA2

Time (min)

OC

R (p

Mol

es/m

in)

1 2 4 8 16 32 64 1280

255075

100125150175200225

TEX-WTCTLshRNAIF-3shRNA1IF-3shRNA2

Time (min)O

CR

(pM

oles

/min

)

Figure  30.  EF-Tu knockdown decreases oxygen consumption rate in TEX cells. Effects on oxygen consumption were determined as described in the methods. Arrow denotes addition of 1.2 µM oligomycin. Results for 1 of 2 experiments with similar outcomes are shown.

  97  

4.3.2.  Chemical  inhibition  of  mitochondrial  translation  displays  anti-­‐leukemia  properties        

To further assess the possibility that other strategies of inhibiting mitochondrial

translation might also have anti-leukemic potential, we treated TEX leukemia cells with

increasing concentrations of chloramphenicol and linezolid, compounds known to inhibit

mitochondrial translation in mammalian cells at high concentrations in vitro (McKee et

al., 2006; Nagiec et al., 2005). A 4-day incubation with either agent decreased the

viability and proliferation of TEX cells, but at higher drug concentrations than required

for tigecycline-induced killing (Figure 31). Both drugs also reduced expression of

mitochondrially-translated Cox-1 and Cox-2 subunits but did not affect levels of Cox-4.

Furthermore, chloramphenicol (50µM) reduced the clonogenic growth of cells from 2

primary AML patient samples to a greater degree than that of normal hematopoietic

progenitors (Figure 32). These results suggest that tigecycline is a more potent inhibitor

of mammalian mitochondrial ribosomes compared to chloramphenicol or linezolid,

consistent with its more potent inhibition of bacterial protein synthesis (Contreras and

Vázquez, 1977; Olson et al., 2006; Shinabarger et al., 1997), and provide an explanation

for tigecycline’s selective anti-leukemic effects at pharmacologically achievable

concentrations. Overall, our findings in these chemical and genetic experiments validate

inhibition of mitochondrial translation as a plausible therapeutic strategy for AML.

               

  98  

 

0 500

102030405060708090

100110

CAPLIN

Drug (µM)

% G

row

th a

nd V

iabi

lity

Cox-1

Cox-2

Cox-4

Tubulin

0 50 0 50

CAP (µM) LIN (µM)

                                     

   

Figure  31.  Chloramphenicol and linezolid inhibit proliferation of TEX cells. Effects on cell viability (trypan blue staining and cell counts) upon exposure to 50 µM of chloramphenicol (CAP) or linezolid (LIN) for 96 hours. Results shown are the averaged viable cell yields from the CAP or LIN-treated groups in 3 experiments expressed as a percentage of the yields of viable cells in the corresponding untreated control cells. Also shown are Cox-1, Cox-2, Cox-4 and tubulin protein levels detected in these cells as determined by immunoblotting. Results for one of 2 similar experiments are shown.

  99  

         

 

CAP (50 µM)

0

20

40

60

80C

lono

geni

c gr

owth

(% c

ontro

l)

CFU-LCFU-GMBFU-E

AML Normal

                           

 

Figure   32.   Chloramphenicol inhibits clonogenic growth of primary AML cells. Effect of 50 µM of chloramphenicol included in the assay medium on colony formation by primary AML and normal peripheral blood progenitor cells (2 samples each). Data are expressed as a percent of colony produced from the same number of DMSO-treated control cells and shown separately for each cell sample tested.

  100  

PART IV: MITOCHONDRIAL CHARACTERISTICS OF LEUKEMIA

VERSUS NORMAL CELLS

4.4.1. Mitochondrial membrane potential of leukemia and normal hematopoietic

cells

To investigate the basis of leukemic cell hypersensitivity to mitochondrial translation

inhibition, we assessed baseline mitochondrial characteristics of malignant cell lines and

primary normal hematopoietic and AML cells. The resting mitochondrial membrane

potential of leukemia cell lines was higher than that of myeloma and solid tumor cell

lines (Figure 33), supporting our finding that leukemia cells are more sensitive to

mitochondrial translation inhibition than other malignant cell types. However, there was

no difference in resting mitochondrial membrane potential between leukemic and normal

hematopoietic progenitor cells that could account for their differential sensitivity to

tigecycline.

                       

  101  

   

       

TEXAML-2HL60U937LP-1OPM2

KMS11A549HELA PC

30.00

0.25

0.50

0.75

1.00

1.25

1.50

LEUKEMIA MYELOMA SOLIDTUMOUR

Δψ

(fol

d ch

ange

)

0

10

20

30

40

50

60

70

NormalCD34+

AMLCD34+

% d

ecre

ase

in Δψ

                                                   

Figure  33.  Mitochondrial membrane potential of malignant and normal cells. Left panel shows baseline mitochondrial membrane potential (Δψ, Red/Green ratio) measurements made on various human leukemia, myeloma, and solid tumor cell lines growing in vitro. Right panel shows baseline mitochondrial membrane potential values for AML and normal donor CD34+ cells before and after uncoupling the potential with CCCP, as determined by staining the cells with DilC1 (5).

  102  

4.4.2. Primary human AML cells have higher mitochondrial biogenesis than normal

hematopoietic cells

       

We next evaluated mitochondrial DNA copy number, which has previously been

used as an estimate of mitochondrial mass (Xing et al., 2008) and the energy demand of a

cell (Capps et al., 2003). Cells from 6 AML patients had higher mitochondrial DNA copy

number compared to mononuclear cells from the peripheral blood of 7 normal individuals

(Figure 34). Determination of mitochondrial mass using Mitotracker green FM, which

stains mitochondria regardless of resting mitochondrial membrane potential (Pendergrass

et al., 2004), again showed higher values for the AML cells (n=5) than for normal CD34+

hematopoietic cells (n=6) (Figure 35), including both the CD34+/CD38+ and

CD34+/CD38- subsets of leukemic cells. Consistent with these findings, rates of oxygen

consumption were higher in primary AML cells (n=4) compared to normal hematopoietic

cells (n=5) (Figure 36).

                               

  103  

 

0

1

2

3

4

5

6

11

AMLNormal

Rel

ativ

e m

tND

1

                                   

   

Figure   34.  Mitochondrial DNA copy number of primary AML and normal hematopoietic cells. Mitochondrial DNA copy numbers were determined in low-density blood cells obtained from 7 AML patients and 6 normal individuals. DNA was extracted from cells and qPCR was performed for mitochondrial ND1 relative to human globulin (HGB). The ND1/HGB ratio is shown relative to cells from one normal sample.

  104  

     

                                                         

         

Figure  35.  Mitochondrial mass of primary AML and normal hematopoietic cells. Left panel shows mitochondrial mass values for AML blasts, CD34+/CD38+ cells and CD34+/CD38- cells (right panel) and normal CD34+ cells obtained from G-CSF-treated normal individuals were determined by flow cytometric analysis of cells stained with Mitotracker Green FM. Median fluorescence intensity (MFI) values are shown by comparison to the MFI measured for one of the normal samples.

0

1

2

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Normal CD34+

AMLCD34+CD38+

AMLCD34+CD38-

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100874110006110102110162

0.0

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Normal CD34+

AMLblasts

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0

100

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400600

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Normal AMLblasts

                                   

       

Figure   36.   Resting oxygen consumption of primary AML and normal hematopoietic cells. Comparison of resting oxygen consumption rates of low-density primary AML cells (n=4) and normal blood cells (n=5).

  106  

4.4.3. Base-line mitochondrial mass is predictive for tigecycline sensitivity in vitro

   

To investigate whether baseline mitochondrial mass differences in AML patient

samples are related to their in vitro hypersensitivity to tigecycline, baseline mitochondrial

mass measurements were performed on the leukemic cells from 9 AML patients and

compared to their individual sensitivities to 5 and 10 µM tigecycline (Figure 37).

Mitochondrial mass was significantly negatively correlated with in vitro sensitivity to

tigecycline after 48 hours (5 µM dose, r = -0.71, p <0.05, 10 µM dose, r = -0.69, p

<0.05). Thus, samples with the greatest mitochondrial mass were most sensitive to

tigecycline treatment in vitro. Taken together, these results suggest that AML progenitors

and stem cells are more metabolically active and dependent on mitochondrial function

than are normal hematopoietic cells, and provide a mechanism to explain the observed

differential activity of mitochondrial translation inhibition in leukemic and normal

hematopoietic cells at all levels of differentiation (Figure 6).

                                   

  107  

                                                                         

   

Figure   37.   Base-line mitochondrial mass is predictive for tigecycline sensitivity in vitro. Correlation analysis of mitochondrial mass (Mitotracker Green FM staining) and in vitro toxicity to tigecycline (Annexin-V/PI staining) of primary AML cells (n=11) based on results obtained at doses of 5 and 10 µM. *P < 0.05, as determined by Pearson correlation coefficient.

0 1 2 3 4 50

10

20

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80r = -0.71p < 0.05

Relative Mitochondrial Mass

Via

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Chapter 5: DISCUSSION

5.1. Part I: Tigecycline, a novel anti-leukemia compound

Human AML therapy has remained essentially unchanged in the last 20 years and

continues to be highly unsatisfactory for most patients. New therapeutic strategies that

can improve outcome are thus needed. One approach to develop such therapies is to

target the LSCs. Here we report that the antimicrobial tigecycline has toxicity for human

AML cells at all stages of development in both in vitro and in vivo preclinical models,

while sparing normal hematopoietic cells.

In Part I, we conducted a high-throughput screen of approved drugs with

previously unrecognized anti-cancer activity to identify agents that target primary human

AML cells. To increase the likelihood of identifying agents with activity against primary

human AML, we selected TEX and M9-ENL1 cells as candidate targets for this screen

because of their shared retention of features of primary human AML clones – i.e., the

maintenance of the line by a subset of cells with hierarchal differentiation and self-

renewal(Barabé et al., 2007; Warner et al., 2005). From the results of the screen, we

identified tigecycline as a promising candidate. Tigecycline is an anti-microbial agent of

the novel glycylcycline class and is active against a range of gram-positive and gram-

negative bacteria, particularly drug-resistant pathogens (Stein and Craig, 2006).

Subsequently, we further explored the anti-leukemia activity of tigecycline by

completing additional experiments to assess the efficacy of tigecycline against

hematopoietic progenitors, and stem cells, both leukemic and normal. These experiments

helped to demonstrate the potential therapeutic window that tigecycline treatment may

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have when used to treat hematological malignancy. In colony formation assays, we

demonstrated that tigecycline preferentially inhibited leukemic progenitors over both

erythroid and granulocyte normal progenitors. Likewise, when we treated primary AML

cells and normal cord-blood cells in vitro with tigecycline, there was a preferential

decrease in leukemia intiating cells over normal hematopoietic stem cells when the

remaining cells were assessed in xenograft NOD/SCID bone marrow engraftment

experiments.

Here we fulfilled our Aim 1 by identifying tigecycline as a novel anti-leukemia

agent targeting both bulk and stem cell compartments of the leukemic hierarchy. Based

on the experimental design of high-through put screening, we postulated that due to the

large number of possible discovered agents ~ 300 ; our screen would yield successful

results completing Aim I. Therefore, the hypothesis that our screen would produce a

novel anti-leukemia agent was accepted. Although the only true functional definition of a

leukemia stem cell is the generation of a xenograft in a mouse model, this is not feasible

for a high-throughput screening methodology. Therefore, we chose the two cell lines that

we used in our screens based on their characteristics of self-renewal, differentiation, and

leukemia-initiating capabilities. Although these factors were not prevalent in every cell of

the population, our screening design seemed to be successful based on our subsequent in

vitro and in vivo experiments described here, which characterized the broad anti-

leuekemic activity of tigecycline. Furthermore, our data providing evidence that

tigecycline is active against leukemia stem cells fulfilled Aim 4 of our hypotheses. A

potential improvement to the experimental design would have been completing further

secondary engraftment studies in addition to the one experiment we presented. The lack

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of further leukemic graft growth in the secondary engraftment is the best measure of

agent efficacy against leukemic stem cells in the research lab.

In the context of potential future clinical trials, tigecycline will most likely be

used in combination with standard AML dubulking agents in order to assess anti-

leuekmia efficacy. Aim 5 was fulfilled by our finding that tigecycline has synergistic

activity with the AML agents cytarabine. The in vitro experiments demonstrated that this

synergy was evident regardless of drug sequencing, as different temporal conditions can

have effects on drug combination activity. Subsequently, the in vivo studies of drug

combination with these agents in the OCI-AML2 xenograft model demonstrated that the

combination treatment was more effective than either drug treatment alone. This will

allow the possibility to decrease drug dosages in future clinical trials in order to decrease

the likelihood of undesired drug side-effects. However, it must be noted that drug

combination efficacy can only truly be tested in patient dosage regimens in clinical trials,

as in vitro and in vivo studies are only model systems.

5.2 Part II: Tigecycline inhibits mitochondrial translation in leukemia cells

Part III: Broad inhibition of mitochondrial translation

From a subsequent screen of the effects of tigecycline on yeast mutants that

cover most of the yeast genome, we correctly identified the inhibition of mitochondrial-

based translation as the mechanism used by tigecycline to inhibit eukaryotic cells. These

results underscore the incredible power of yeast screens to reveal critical pathways that

underlie effects seen in drug screens. Using this genome-wide yeast screens, we fulfilled

AIM 2, where we proposed to determine the mechanism of tigecycline’s anti-leukemia

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activity. Also, we accept our hypothesis tigecycline causes cell death in leukemia cells

due to mitochondrial translation inhibition as similar antiomicrobial drugs that are

bacterial ribosome inhibitors have been shown to have similar mechanisms.

To further explore our hypothesis that mitochondrial translation inhibition will

functionally impair oxidative phosphorylation, we completed experiments addressing

different components of this pathway. Tigecycline treatment preferentially decreased the

expression of mitochondrially-encoded proteins over nuclear-encoded proteins of the

cytochrome c oxidase enzyme. This was associated with a loss of mitochondrial

membrane potential, which is generated partially by the function of the respiratory chain.

Furthermore, oxygen consumption was inhibited in primary AML cells, but not normal

hematopoietic cells. These experiments satisfied most of the goals in Aim 2, excluding

the ATP determination. For technical reasons, the ATP was not assessed post tigecycline

treatment, but these experiments are a part of the future plans in later follow-up studies.

To further interrogate the role of mitochondrial functions in leukemic cells and

their potential for specific anti-leukemic targeting strategies, we used a combination of

genetic, chemical, biochemical and biologic approaches. Knockdown of initiation (IF-3)

and elongation (EF-Tu) factors in leukemia cells provided genetic confirmation of the

prediction that specific inhibition of mitochondrial translation in leukemic cells would

mimic the effects of tigecycline, although IF-3 knockdown did not. Thus, in spite of

redundancy in the mitochondrial protein synthesis machinery, some factors appear more

critical than others for the integrity of this process. Accordingly, future investigations

exploring the possible role of mitochondrial translation in other cancers will also likely

need to assess the functional importance of various initiation and elongation factors.

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We have characterized a novel way to inhibit mitochondrial translation in

eukaryotic cells using the antimicrobial agent tigecycline. This form of mitochondrial

protein synthesis inhibition has displayed novel anti-cancer properties, and provided a

rationale for future similar therapeutic approaches in other forms of malignancy. There

have not been any extensive studies characterizing mitochondrial translation inhibition as

a therapeutic strategy for cancer treatment. The wide impact on broad malignancies

remains to be addressed.

5.3 Part IV: Mitochondrial characteristics of leukemia versus normal cells

The critical dependence of primitive as well as late stage primary human AML

cells on mitochondrial protein translation has not been previously recognized.

Mitochondrial DNA (mt-DNA) is composed of a double-stranded circular genome 16.6

kb in length without introns (Lang et al., 1999). It encodes two rRNAs, 22 t-RNAs and 13

of the 90 proteins in the mitochondrial respiratory chain. The 13 mt-DNA encoded

proteins are translated by mitochondrial ribosomes within the mitochondrial matrix.

(Gaur et al., 2008; Hunter and Spremulli, 2004; Zhang and Spremulli, 1998b).

Mitochondrial ribosomes differ from eukaryotic cytosolic ribosomes in their structure and

chemical properties (O'Brien, 2003). In addition they use unique protein translation

machinery including distinct initiation and elongation factors.

The impact of inhibiting mitochondrial protein synthesis and more specifically,

the oxidative phosphorylation pathway in leukemia has not been fully assessed.

Interestingly, the 13 mtDNA-encoded subunits of the electron transport chain are

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important for functional regulation of oxidative phosphorylation (Fukuda et al., 2007).

The Warburg hypothesis proposes that malignant cells rely on glycolysis and are

significantly less dependent on oxidative phosphorylation for survival (WARBURG,

1956). Yet, more recent studies indicate that some tumors are highly dependent on

oxidative phosphorylation for survival (Funes et al., 2007; Moreno-Sánchez et al., 2007;

Rodriguez-Enriquez et al., 2006). Our data suggest that LSCs are unique in their

mitochondrial characteristics, sensitivity to inhibition of mitochondrial protein synthesis

and their reliance on oxidative phosphorylation. Recently it was demonstrated that

leukemia cells have increased rates of fatty acid oxidation (Samudio et al., 2010) and

inhibition of fatty acid oxidation targeted both leukemia stem cells and their “mature”

blast progeny. These findings complement those we now report. Electrons generated

from the oxidation of fatty acids ultimately flow through the mitochondrial respiratory

chain. As such, reducing the components of the mitochondrial respiratory chain via

mitochondrial translation inhibition would limit the ability of leukemia cells to derive

energy from fatty acid oxidation thus offering an explanation of how inhibition of either

of these processes might specifically constrain the survival or growth of leukemic cells.

The differences in the mitochondrial characteristics of primary AML cells and

their normal counterparts are noteworthy. AML stem cells and their progeny had a

greater mitochondrial mass and higher rates of oxygen consumption compared to normal

hematopoietic progenitor cells as shown by multiple endpoints. Moreover, AML cells

with the highest mitochondrial mass were the most sensitive to tigecycline suggesting a

biological correlation between these two parameters. The fact that normal hematopoietic

cells have a low mitochondrial mass is consistent with this finding and may explain the

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general preferential sensitivity of AML cells to inhibition of mitochondrial protein

synthesis. Mitochondrial mass may also serve to identify potential subgroups of AML

patients most likely to respond to a therapeutic strategy that targets their functions.

We have fulfilled Aim 3 by identifying intrinsic differences in mitochondrial

metabolism between primary AML and normal hematopoietic cells as a plausible for the

differential specificity seen with mitochondrial translation inhibition treatment. There

were differences in mitochondrial mass and oxygen consumption, but notably not

mitochondrial membrane potential. This is a major contribution to the field of acute

leukemias, as AML and other similar malignancies have not previously thought to be

oxidative mitochondria-dependent diseases. As previously mentioned, this is in

conformation with recent studies that targeting of fatty acid oxidation may also be

effective in AML treatment. The electrons generated in these metabolic systems are

mutually inclusive. Therefore, future studies should examine why leukemic cells are

specifically highly dependent on oxidative metabolism in AML tumorigenesis.

5.4 Preclinical significance

Our findings highlight mitochondrial translation a potential new therapeutic

target in human AML. The robust preclinical anti-leukemia activity documented with

tigecycline using a variety of in vitro and in vivo models and its known toxicology and

pharmacology in humans and animals, support rapidly advancing this drug into clinical

trial for leukemia to evaluate proof-of-mechanism and proof-of-concept. In humans,

tigecycline plasma concentrations of 5µM (Muralidharan et al., 2005) have been safely

  115  

achieved. Importantly, animal studies have demonstrated that the drug accumulates in

tissues such as the bone and bone marrow with ratios to the plasma as high as 19:1

(Crandon et al., 2009), suggesting that effective anti-leukemic concentrations are readily

achievable in the bone marrow.

5.5 Conclusion

We have identified the inhibition of mitochondrial translation as a plausible

therapeutic strategy for the treatment of acute myeloid leukemia (AML). Initially, we

performed a chemical screen of FDA-approved agents and identified the antimicrobial

tigecycline as having activity in two leukemic cell lines with stem cell characteristics.

Subsequently, a genome-wide screen in yeast in yeast identified mitochondrial translation

inhibition as the mechanism of tigecycline-mediated lethality. Tigecycline selectively

killed leukemia stem and progenitor cells by comparison to their normal counterparts and

also showed anti-leukemic activity in mouse models of human leukemia. ShRNA-

mediated knockdown of EF-Tu mitochondrial translation factor in leukemic cells

reproduced the anti-leukemia activity of tigecycline. These effects were derivative of

mitochondrial biogenesis which, together with an increased basal oxygen consumption,

proved to be enhanced in AML versus normal hematopoietic cells and were also

important for their difference in tigecycline sensitivity.

5.6 Significance

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We believe this work is novel in that it is the first report to demonstrate the

functional importance of mitochondrial translation in leukemia. Furthermore, it is the

first report to show there is increased mitochondrial biogenesis in AML cells versus

normal cells, including those defined functionally as progenitors and stem cells. Also, we

reported the successful combination of FDA-approved chemical and yeast genome-wide

mutant screens in revealing critical pathways of leukemic pathogenesis. Given these

results and the known pharmacology and toxicology of tigecycline in humans, targeting

mitochondrial translation inhibition as a therapeutic strategy in human leukemia is

attractive. Finally, this report highlights that in spite of the genetic and biological

diversity of AML, some common biochemical pathways accessible to selective targeting

appear to still exist and await therapeutic exploitation.

5.7 Future Directions

We have highlighted mitochondrial translation as a promising therapeutic strategy

for acute myeloid leukemia. The reason for the specific activity of agents such as

tigecycline towards AML cells appears to be related to the AML cell’s increased

dependence on mitochondrial biogenesis and metabolism. This study presents data that

has opened a novel understanding in cancerogenesis, that targeting aspects of

mitochondrial protein synthesis, and in turn oxidative phosphorylation may have

advantages for targeting both bulk tumor and cancer stem cells.

In the Introduction section, we highlighted several different components of the

mitochondrial protein synthesis machinery that could be targeted for anti-malignant

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therapies. Future studies should include experiments analyzing the effect of inhibiting

mitochondrial DNA replication, and transcription by genetic approaches. Furthermore,

there are many nuclear-encoded transcription factors that have important roles in

regulating and stimulating different pathways of mitochondrial biogenesis. The

expression of these various factors in the context of various malignant states in different

tissues should be carefully studied. This will result in a better understanding of how these

co-translational factors are changing during various stages of tumorigenesis.

Subsequently, target knockdown of these specific factors associated with increased

malignant states will likely yield successful therapeutic results. As we previously

mentioned, it will be important to define the functional importance of these various co-

transcriptional factors in mitochondrial translation in the normal state. There may be

functional redundancy, and therefore knockdown of a factor regulating mitochondrial

biogenesis may not have anti-cancer activity because it solely doesn’t affect

mitochondrial protein synthesis.

Although we have demonstrated in Part IV that there are gross differences in

mitochondrial mass, and oxygen consumption between primary AML cells and normal

hematopoietic cells, the exact reason for the differential sensitivity of these cells to

tigecycline is not clear. Future experiments should explore whether there is a difference

in the intrinsic rate of mitochondrial protein synthesis between primary AML and normal

hematopoietic cells. Also, there may be differences in the functional reserve capacity of

different respiratory chain enzymes which are partially comprised of mitochondrially-

encoded protein subunits. These alternating levels of complex activities may impact the

resting mitochondrial membrane potential differently in various cell types, and reflect on

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the ATP production output. Therefore, experiments should also analyze the impact of

functional mitochondrial translation inhibition on respiratory chain enzyme activity and

relate that to mitochondrial membrane potential and ATP production.

The outlined experiments will answer further questions in understanding the

effect of mitochondrial translation on cancer cell metabolism. Interestingly, the malignant

cell’s dependence on mitochondrial biogenesis and metabolism for cell transformation

will shed new light on previously undefined aspects of tumorigenesis. It has become clear

that the role of mitochondria in cancer is still being discovered, and remains to be

carefully characterized in future studies.

                                                   

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APPENDIX

The anti-parasitic agent ivermectin induces chloride-dependent membrane

hyperpolarization and cell death in leukemia cells

This research was originally published in Blood as: Sharmeen S*, Skrtic M*, Sukhai MA*, Hurren R, Gronda M, Wang X, Fonseca SB, Sun H, Wood TE, Ward R, Minden MD, Batey RA, Datti A, Wrana J, Kelley SO, Schimmer AD. The anti-parasitic agent ivermectin induces chloride-dependent membrane hyperpolarization and cell death in leukemia cells. Blood. 2010 116(18): 3593-3603.

* authors contributed equally

 

My  contribution  to  this  paper  included  designing  experiments,  performing  

experiments,  analyzing  data,    and  the  manuscript  itself.  Specific  figures  of  my  

contribution  include:  Figure  1B-­‐C,  Figure  3A,  Figure  7C-­‐F  

 

 

 

 

 

 

 

 

 

 

 

 

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ABSTRACT

To identify known drugs with previously unrecognized anti-cancer activity, we compiled

and screened a library of such compounds to identify agents cytotoxic to leukemia cells.

From these screens, we identified ivermectin, a derivative of avermectin B1 that is

licensed for the treatment of the parasitic infections strongyloidiasis and onchocerciasis,

but is also effective against other worm infestations. As a potential anti-leukemic agent,

ivermectin induced cell death at low micromolar concentrations in acute myeloid

leukemia cell lines and primary patient samples preferentially over normal hematopoietic

cells. Ivermectin also delayed tumor growth in three independent mouse models of

leukemia at concentrations that appear pharmacologically achievable. As an anti-

parasitic, ivermectin binds and activates chloride ion channels in nematodes, so we tested

the effects of ivermectin on chloride flux in leukemia cells. Ivermectin increased

intracellular chloride ion concentrations and cell size in leukemia cells. Chloride influx

was accompanied by plasma membrane hyperpolarization, but did not change

mitochondrial membrane potential. Ivermectin also increased reactive oxygen species

(ROS) generation that was functionally important for ivermectin-induced cell death.

Finally, ivermectin synergized with cytarabine and daunorubicin that also increase ROS

production. Thus, given its known toxicology and pharmacology, ivermectin could be

rapidly advanced into clinical trial for leukemia.

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INTRODUCTION

Antimicrobials with previously unrecognized anti-cancer activity can be rapidly

repositioned for this new indication given their extensive prior pharmacology and

toxicology testing. For example, the broad spectrum antiviral ribavirin was found to

suppress oncogenic transformation by disrupting the function and subcellular localization

of the eukaryotic translation initiation factor eIF4E(Kentsis et al., 2004; Tan et al., 2008).

As such, ribavirin was recently evaluated in a phase I dose escalation study in patients

with relapsed or refractory M4/M5 acute myeloid leukemia (AML). In this study of 13

patients treated with ribavirin, there was 1 complete remission, and 2 partial remissions.

Thus, ribavirin may be efficacious for the treatment of AML (Assouline et al., 2009).

Likewise, the anti-fungal ketoconazole inhibits the production of androgens from the

testes and adrenals in rats. Given this finding, ketoconazole was rapidly advanced into

clinical trials for patients with prostate cancer where it displayed clinical efficacy in early

studies (Sella et al., 1994; Small et al., 2004).

Recently we demonstrated that the anti-parasitic clioquinol inhibits the

proteosome and induces cell death in leukemia and myeloma cells through copper-

dependent and independent mechanisms (Mao et al., 2009). Thus, our preclinical data

suggests that this antiparasitic could be repurposed for the treatment of haematological

malignancies. Therefore, we initiated a phase I study to evaluate the dose-limiting

toxicity, maximum tolerated dose, and recommended phase II dose of clioquinol in

patients with relapsed or refractory hematologic malignancies (ClinicalTrials.gov

Identifier: NCT00963495).

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Here we used a chemical screen to identify known drugs with previously

unrecognized activity against leukemia. From this screen, we identified the anti-parasitic

agent ivermectin. Ivermectin is a derivative of avermectin B1 and licensed for the

treatment of the parasitic infections strongyloidiasis and onchocerciasis as well as other

worm infestations (e.g., ascariasis, trichuriasis and enterobiasis) but has not been

previously tested as an anti-cancer agent. As part of the development of this agent as an

antiparasitic agent, ivermectin was extensively evaluated for its pharmacology, safety and

toxicity in humans and animals. For example, the LD50 of oral ivermectin in mice, rats

and rabbits ranges from 10 to 50 mg/kg (Dadarkar et al., 2007). In humans, when used to

treat onchocerciasis, 100-200 µg/kg of ivermectin is administered as a single dose

(Brown et al., 2000). This brief and low-dose treatment is sufficient to achieve an anti-

parasitic effect, but higher doses and treatment beyond one day have been safely

administered for other conditions. For example, in patients with spinal injury and

resultant muscle spasticity, up to 1.6mg/kg of ivermectin was administered

subcutaneously at twice weekly for up to 12 weeks. In this study, no significant adverse

effects were reported (Costa and Diazgranados, 1994). Likewise, to evaluate the safety of

oral ivermectin, healthy volunteers received 30 -120 mg on days 1, 4 and 7 and then a

further dose in week 3 (Guzzo et al., 2002). Even at a dose of 120 mg (~2mg/kg) no

serious adverse effects were noted. Finally, reports of ivermectin overdoses also support

the evaluation of high doses of ivermectin in humans, as in the majority of these cases no

serious adverse events were reported (Frost, 1996).

In our current study, we demonstrated that ivermectin displayed preclinical

activity against hematological malignancies in vitro and delayed tumor growth in vivo at

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concentrations that appear pharmacologically achievable. Mechanistically, ivermectin

induced chloride influx, membrane hyperpolarization and generated reactive oxygen

species. Furthermore, ivermectin synergized with cytarabine and daunorubicin. Thus,

given its prior safety and toxicity testing, ivermectin could be rapidly advanced into

clinical trial for patients with leukemia.

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MATERIALS AND METHODS

Reagents

The compounds in the chemical library were purchased from Sigma Aldrich (St. Louis,

MO). Annexin V-FITC and Propidium Iodide (PI) were purchased from Biovision,

(Moutainview, CA). Indo-1 AM, 6-methoxy-N-(3-sulfopropyl) quinolinium (SPQ),

carboxydichlorofluorescein diacetate (Carboxy H2DCF-DA), 5,5’,6,6’-tetrachloro-

1,1’,3,3’-tetraethyl benzimidazlycarbocyanine iodide (JC1) and bis-(1,3-dibutylbarbituric

acid)trimethine oxonol (DiBAC4(3) were all purchased from Invitrogen Canada,

(Burlington, Canada).

Cell lines

Human leukemia (OCI-AML2, HL60, U937, KG1a), and prostate cancer (DU145 and

PPC-1) cell lines and murine leukemia (MDAY-D2) cells were maintained in RPMI 1640

medium. Medium was supplemented with 10% fetal bovine serum (FBS), 100 µg/mL

penicillin and 100 units/mL of streptomycin (all from Hyclone, Logan, UT). TEX human

leukemia cells were maintained in IMDM, 15% FBS, 1%, penicillin-streptomycin, 20

ng/mL SCF, 2 ng/mL IL-3. All Cells were incubated at 37oC in a humidified air

atmosphere supplemented with 5% CO2.

Primary cells

Primary human acute myeloid leukemia (AML) samples were isolated from fresh bone

marrow and peripheral blood samples of consenting patients and mononuclear cells

fractionated by Ficoll separation. Similarly, primary normal hematopoietic mononuclear

cells were obtained from healthy consenting volunteers donating peripheral blood stem

cells (PBSC) for stem cell transplantation. Primary cells were cultured at 37oC in IMDM

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supplemented with 20% FBS, and appropriate antibiotics. The collection and use of

human tissue for this study were approved by the University Health Network institutional

review board.

Chemical screen for cytotoxic compounds

HL60, KG1a, and OCI-AML2 leukemia cells were seeded into 96-well polystyrene tissue

culture plates (Nunc). After seeding, cells were treated with aliquots of the chemical

library (n=100) at increasing concentrations (3-50 µM) with a final DMSO concentration

of 0.5%. Seventy two hours after incubation, cell proliferation and viability were

measured by the MTS assay. Liquid handling was performed by a Biomek FX Laboratory

Automated Workstation (Beckman Coulter Fullerton, CA).

Cell viability assays

Cell growth, viability and clonogenic growth of primary cells was measured as described

in the supplemental methods.

Assessment of ivermectin’s anticancer activity in mouse models of leukemia

MDAY-D2 murine leukemia cells, K562 and OCI-AML2 human leukemia cells (2.5 x

105) were injected subcutaneously into the flanks of sub-lethally irradiated (3.5 Gy)

NOD/SCID mice (Ontario Cancer Institute, Toronto, ON). Four (OCI-AML2), five

(MDAY-D2), or seven (K562) days after injection, once tumors were palpable, mice

were then treated daily for 10 days (K562) or treated with 8 doses over 10 days (OCI-

AML2) with ivermectin (3 mg/kg) by oral gavage in water or vehicle control (n = 10 per

group). MDAY-D2 mice were treated similarly but dosage escalated from 3mg/kg

(4days) to 5 mg/kg (3 days) and 6 mg/kg (3 days) as the drug was well tolerated. Tumor

volume (tumor length x width2 x 0.5236) was measured three times a week using

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calipers. Fourteen (MDAY-D2), 15 (OCI-AML2) or 17 (K562) days after injection of

cells, mice were sacrificed, tumors excised and the volume and mass of the tumors were

measured.

In order to measure gene expression changes in vivo, OCI-AML2 cells (2.5 x 105) were

injected subcutaneously into the flanks of sub-lethally irradiated NOD/SCID mice. Once

tumors were established, mice were treated with ivermectin (7mg/kg) or vehicle control

intraperitoneally for 5 days. After treatment, mice were sacrificed, and tumors

harvested. mRNA was extracted and changes in STAT expression were measured by

quantitative RT-PCR (QRT-PCR). Evidence of apoptosis was measured by Tunel

staining and immunohistochemistry (Pathology Research Program, University Health

Network, Toronto, Canada).

All animal studies were carried out according to the regulations of the Canadian Council

on Animal Care and with the approval of the Ontario Cancer Institute animal ethics

review board.

Intracellular ion measurements

Intracellular chloride concentration was measured using a fluorescent indicator for

chloride, SPQ as previously described (Pilas and Durack, 1997). Upon binding halide

ions like chloride, SPQ is quenched resulting in a decrease in fluorescence without a shift

in wavelength. After treating OCI-AML2 (5X105) and DU145 (4X105) cells overnight

with ivermectin (3-10 µM), cells were incubated for 15 minutes with SPQ (5mM) at 37oC

in a hypotonic solution (HBSS/H2O 1:1) to promote the intracellular uptake of SPQ.

After 15 minutes of incubation with SPQ, cells were diluted 15:1 in HBSS and

centrifuged. The supernatant was removed, cells were resuspended in 200µL of fresh

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HBSS and incubated for 15 minutes at 37oC to allow recovery from the hypotonic shock.

Cells were then stained with propidium iodide (PI) and SPQ fluorescence was determined

in the PI negative cells using an LSR-II flow cytometer (Becton Dickinson, San Jose,

CA) (excitation 351 nm, emission 485 nm). In parallel, changes in cell size were

determined by measuring forward light scatter by flow cytometry. Results were analyzed

with FlowJo version 8.8 (TreeStar, Ashland, OR).

Changes in cytosolic calcium concentration were detected with the fluorescent dye Indo-

1 AM (final concentration 6µM) as previously described(Gurfinkel et al., 2006).

Determination of plasma and mitochondrial membrane potential and ROS

generation

Plasma and mitochondrial membrane potential were measured by staining cells with

DiBAC4(3) and JC-1 (Invitrogen), respectively as described in the supplemental methods.

Intracellular reactive oxygen species (ROS) were detected by staining cells with

Carboxy-H2DCFDA (final concentration 10 µM) and analysing with flow cytometry as

previously described (Pham et al., 2004) and as described in the supplemental methods

Gene expression studies

OCI-AML2 leukemia cells were treated with buffer control or ivermectin (3 µM) for 30

and 40 hours. After treatment, cells were harvested, total RNA was isolated and gene

expression was measured as described in the supplemental methods.

Drug combination studies

The combination index (CI) was used to evaluate the interaction between ivermectin and

cytarabine or daunorubicin as previously described (Eberhard et al., 2009). OCI-AML2

and U937 cells were treated with increasing concentrations of ivermectin, cytarabine and

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daunorubicin. Seventy-two hours after incubation cell viability was measured by the

MTS assay. The CalcuSyn median effect model was used to calculate the CI values and

evaluate whether the combination of ivermectin with cytarabine or daunorubicin was

synergistic, antagonistic or additive. CI values of <1 indicate synergism, CI =1 indicate

additivity and CI>1 indicate antagonism (Chou and Talalay, 1984).

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RESULTS A chemical screen identifies ivermectin with potential anti-cancer activity

Off-patent and on-patent drugs with previously unrecognized anti-cancer activity can be rapidly

repurposed for this new indication given their prior toxicology and pharmacology testing. To identify such

compounds, we compiled a small chemical library (n=100) focused on anti-microbials and metabolic

regulators with wide therapeutic windows and well understood pharmacokinetics. We treated OCI-AML2,

HL60, and KG1a leukemia cell lines with aliquots of this chemical library at five concentrations (ranging

from 3-50 μM). Seventy two hours after incubation, cell growth and viability were measured by the MTS

assay. From this screen, we identified ivermectin that reduced cell viability in all cell lines in the screen

with an EC50 < 10μM. The results for the screen of OCI-AML2 cells with compounds added at a final

concentration of 6 μM are shown in Figure 1A.

Ivermectin is cytotoxic to malignant cell lines and primary patient samples

Having identified ivermectin in our chemical screens, we tested the effects of

ivermectin on cell growth and viability in a panel of 5 leukemia cell lines. Cells were

treated with increasing concentrations of ivermectin and 72 hours after incubation, cell

growth and viability were assessed by the MTS assay. Ivermectin decreased the viability

of the tested leukemia cell lines with an EC50 of approximately 5µM (Figure 1B). The

loss of viability was detected at 24 hours after treatment and increased in a time

dependent manner. Cell death and apoptosis were confirmed by Annexin V and PI

staining (Figure 1C). Cell death was caspase-dependent, as co-treatment with the pan-

caspase inhibitor z-VAD-fmk abrogated cell death (Supplementary Figure 1A).

Furthermore, times and concentrations of ivermectin that preceded cell death induced G2

cell cycle arrest. (Supplemental figure 1B and data not shown).

Given the cytotoxicity of ivermectin towards leukemia cell lines, we compared

its cytotoxicity to primary normal hematopoietic cells and acute myeloid leukemia

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(AML) patient samples (n = 4 intermediate risk cytogenetics, n = 1 good risk

cytogenetics, and n = 1 unknown cytogenetics). Normal hematopoietic cells and patient

sample cells were treated for 48 hours with increasing concentrations of ivermectin.

After incubation, cell viability was measured by Annexin V and PI staining. Ivermectin

was cytotoxic to AML patient samples at low micromolar concentrations. In contrast, it

did not induce cell death in the peripheral blood stem cells (PBSC) at concentrations up

to 20µM (Figure 1C). However, when gating on the CD34+ cells from one PBSC

sample, ivermectin induced cell death with an IC50 of 10.5 + 0.6 µM. Thus, ivermectin

induced cell death in primary AML cells preferentially over normal cells, but the

therapeutic window over normal stem cells may be narrow.

Ivermectin was also evaluated in clonogenic assays in primary normal

hematopoietic and AML cells. Ivermectin (6 µM) had minimal effects on the clonogenic

growth of normal hematopoetic cells (n =3) with < 15% reduction in clonogenic growth.

In contrast, ivermectin reduced clonogenic growth by > 40% in 3/6 primary AML

samples (Figure 1D). Similar effects were noted when primary cells were directly plated

into clonogenic assays with ivermectin (Supplemental figure 2).

Ivermectin delays tumor growth in mouse models of leukemia

Given the effects of ivermectin as a potential anti-leukemic agent, we evaluated

ivermectin in mouse models of leukemia. Human leukemia (OCI-AML2 and K562) and

murine leukemia (MDAY-D2) cells were injected subcutaneously into the flank of

NOD/SCID mice. Four (OCI-AML2), five (MDAY-D2), or seven (K562) days after

injection, once tumors were palpable, mice were treated with ivermectin (3 mg/kg) by

oral gavage in water or vehicle control (n = 10 per group) for 10 days (K562) or 8 doses

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over 10 days (OCI-AML2). MDAY-D2 mice (n = 10 per group) were treated similarly

but with escalating doses (3mg/kg for 4 days, 5 mg/kg for 3 days and then 6 mg/kg for 3

days) as the drug was well tolerated. Tumor volume and mass were measured over time.

Compared to buffer control, oral ivermectin significantly (p<0.05) decreased tumor mass

and volume in all 3 models (Figure 2A-E) by up to 70% without any gross organ toxicity.

In an OCI-AML2 xenograft, we showed that ivermectin increased apoptosis in the

subcutaneous tumor as measured by Tunel staining (Supplemental Figure 3). Of note, a

dose of 3mg/kg in mice translates to a dose of 0.24 mg/kg in humans based on scaling of

body weight and surface area and appears readily achievable based on prior studies(Costa

and Diazgranados, 1994; Frost, 1996). Thus, the activity in the xenograft studies and the

in vitro studies above suggests that a therapeutic window may be achievable.

Ivermectin induces intracellular chloride flux, increase in cell size and

hyperpolarization of the plasma membrane

As an antiparasitic agent, ivermectin activates chloride channels in nematodes,

causing an influx of chloride ions into the nematode’s cells (Gonzalez Canga et al.,

2008). Thus, we investigated chloride flux after ivermectin treatment in OCI-AML2

leukemia cells where ivermectin induced cell death after 24 hours of treatment and

DU145 prostate cancer cells that were more resistant to ivermectin-induced cell death

(Figure 3A). OCI-AML2 and DU145 cells were treated with 10 µM ivermectin for 2

hours and levels of intracellular chloride were measured by staining cells with the

fluorescent dye SPQ that is quenched at high chloride ion concentrations. In OCI-AML 2

cells, ivermectin decreased SPQ fluorescence, consistent with an increase in levels of

intracellular chloride at concentrations that induced cell death but at times that preceded

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cell death, (Figure 3B and data not shown). In contrast, chloride influx was not observed

in DU145 cells that were resistant to 10 µM ivermectin (Figure 3C and data not shown).

Chloride influx can increase cell size. Therefore, we measured changes in cell

size in parallel to measuring changes in chloride flux. As measured by flow cytometry,

after 2 hours of treatment, ivermectin caused an increase in cell size in OCI-AML2 but

not in the resistant DU145 cells, consistent with its effects on chloride influx (Figure 3D,

E).

In nematodes, increases in intracellular chloride after ivermectin treatment cause

membrane hyperpolarization. Therefore, we evaluated the effects of ivermectin on

plasma and mitochondrial membrane polarization in leukemia cells. OCI-AML2 , U937,

and TEX leukemia cells sensitive to ivermectin-induced death, a primary AML patient

sample, DU145 and PPC-1 prostate cancer cells and primary normal hematopoietic cells

were treated with increasing concentrations of ivermectin. At increasing times after

incubation, plasma membrane potential was measured by staining cells with DiBAC4(3)

and flow cytometric analysis. In OCI-AML2 cells, treatment with ivermectin induced

membrane hyperpolarization in a dose dependent manner (Figure 4A) and as early as

after 1 hour of treatment (Figure 4B), consistent with the influx of intracellular chloride

and the effects observed in nematodes. Likewise, U937 and TEX leukemia cells as well

as primary AML cells sensitive to ivermectin-induced death also demonstrated plasma

membrane hyperpolarization after ivermectin treatment (Figure 4C). In contrast, DU145

and PPC-1 cells as well as primary normal hematopoietic cells that were more resistant to

ivermectin did not show changes in their plasma membrane potential when treated with

up to 6µM of ivermectin for up to 24 hours (Figure 4D).

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To determine whether the plasma membrane hyperpolarization observed after

ivermectin treatment was related to increased chloride ion flux, we measured plasma

membrane polarization after treating cells with ivermectin in buffers with and without

chloride. OCI-AML2 cells were treated for 5 hours with ivermectin in a chloride replete

buffer or a chloride-free buffer where sodium and potassium chloride were replaced with

equimolar gluconate salts of sodium and potassium. When added to cells in the chloride

replete buffer, ivermectin induced plasma membrane hyperpolarization similar to cells

treated in RPMI medium. However, when added to cells in chloride-free buffer,

ivermectin caused plasma membrane depolarization (Figure 4E). Thus, the effects of

ivermectin on plasma membrane polarization appear to be related to increased chloride

flux.

Ivermectin increases intracellular calcium but is not functionally relevant in

leukemia cells

Plasma membrane hyperpolarization can lead to calcium influx (McCarty et al., 2009).

Therefore, we tested the effects of ivermectin on calcium influx in leukemia cells. OCI-

AML2 cells were treated with ivermectin and the concentration of intracellular calcium

was measured by staining cells with the ratiometric dye, Indo-1 AM. As a positive

control, cells were treated with digoxin which is known to increase intracellular calcium

(Meral et al., 2002; Wagner et al., 1978). Similar to the effects of digoxin, ivermectin

increased intracellular calcium (Supplemental Figure 4A, B). However, the increase in

intracellular calcium did not appear sufficient to explain the cytotoxicity of ivermectin,

because chelation of intra- and extra-cellular calcium with BAPTA-AM and EDTA,

respectively, did not inhibit ivermectin -induced cell death (data not shown).

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Ivermectin increases intracellular reactive oxygen species

Manganese chloride, cobalt chloride and mercuric chloride can lead to generation

of reactive oxygen species (ROS) (Kamiya et al., 2008; Park and Park, 2007; Zhang et

al., 2007). Therefore, we tested whether ivermectin increased ROS production in

leukemia cells due to the observed chloride influx. OCI-AML2 cells were treated with

ivermectin at increasing concentrations and times of incubation. After treatment, levels

of intracellular ROS were measured by staining cells with Carboxy-H2DCFDA and flow

cytometry. Treatment with ivermectin increased ROS production at times and

concentrations that coincided with plasma membrane hyperpolarization (Figure 5A, B).

Likewise, U937 and TEX leukemia cells that were sensitive to ivermectin induced death

demonstrated increased ROS generation 2 hours after ivermectin treatment (Figure 5C).

In contrast, DU145 and PPC-1 cells that were more resistant to ivermectin did not show

changes in ROS generation. Likewise, primary AML cells, but not normal hematopoietic

cells demonstrated increased ROS generation after ivermectin treatment (Figure 5C).

To determine whether the increased ROS production was functionally important

for ivermectin-induced cell death, cells were treated simulataneously with ivermectin

along with the free radical scavenger N-acetyl-L-cysteine (NAC). NAC abrogated

ivermectin -induced cell death consistent with a mechanism of cell death related to ROS

production and keeping with its effects on plasma membrane hyperpolarization and

chloride influx (Figure 5D).

Changes in ROS production are indicative of a biological response to ivermectin,

but are very difficult to measure in the context of a clinical trial. Therefore, to identify

alterations in gene expression that are a result of ROS production and could be used as

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biomarkers in the context of a clinical trial, we undertook gene expression profiling

analysis (using Affymetrix HG U133 Plus 2.0 arrays) of RNA derived from OCI-AML2

cells treated with ivermectin for 30 hr and 40 hr (Supplemental Table 1). One hundred

and fifty genes were deregulated >4-fold at both time points (33 under-expressed; 117

over-expressed) compared to control. Among these genes dysregulated were STAT1,

which has been associated with increased ROS generation (Kim et al., 2008; Kim and

Lee, 2005; Liu et al., 2004) and the STAT1 downstream targets IFIT3, OAS1 and

TRIM22. We validated the upregulation of STAT1 and target genes IFIT3, OAS1 and

TRIM22 after ivermectin treatment by Q-RT-PCR, (Figure 6A). Likewise, U937 and

HL60 leukemia cells that were sensitive to ivermectin- induced death also demonstrated

increased STAT1 mRNA. In contrast, DU145 and PPC-1 cells that were more resistant

to ivermectin did not show changes in STAT1 expression (Figure 6B). We also

evaluated changes in STAT1 expression in tumors from a leukemia xenograft model.

Mice with OCI-AML2 subcutaneous xenografts were treated with ivermectin for 5 days.

After treatment, tumors were harvested, mRNA extracted, and STAT1 expression

measured by Q-RT PCR. STAT1 mRNA was increased in two of three tested tumors

from mice treated with ivermectin compared to STAT1 mRNA expression from tumors

harvested from mice treated with vehicle control (Figure 6C). We also demonstrated that

changes in STAT1 genes were secondary to ROS production as pre-treatment with NAC

blocked their upregulation (Figure 6D).

Of note, we also compared our array dataset to a ROS gene signature reported by

Tothova et al (Tothova et al., 2007). Of the 55 genes in the Tothova signature, 2/3 were

expressed in our dataset. Of these 36 genes, 55% (20 genes) were found to be

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differentially regulated on ivermectin treatment (fold-change of 1.25 up or down,

compared to the untreated control sample). Thus, ivermectin appears to induce genetic

changes consistent with ROS induction.

Ivermectin synergizes with cytarabine and daunorubicin

Cytarabine and daunorubicin increase ROS production through mechanisms

related to DNA damage (Figure 7A, B), and are used clinically in the treatment of AML

(Iacobini et al., 2001; Tsang et al., 2003). Therefore, we evaluated the effects of the

combination of ivermectin with cytarabine and daunorubicin on cell viabiltiy. OCI-

AML2 and U937 cells were treated with increasing concentrations of ivermectin alone

and in combination with cytarabine and daunorubicin. Cell growth and viability were

measured 72 hours after incubation using the MTS assay. Data were analyzed by the

CalcuSyn median effect model where the combination index (CI) indicates synergism

(CI<0.9), additivity (CI=0.9-1.1) or antagonism (CI>1.1). In both OCI-AML2 and U937

leukemia cells, the combination of ivermectin and cytarabine demonstrated strong

synergism with CI values at the ED25, ED50 and ED75 of 0.51, 0.58 and 0.65, respectively

in OCI AML2 cells and ED25, ED50 and ED75 of 0.55, 0.71 and 0.91 in U937 cells (Figure

7C). Likewise in OCI-AML2 cells, the combination of ivermectin and daunorubicin was

also synergistic with CI values at the ED25, ED50 and ED75 of 0.48, 0.51 and 0.54,

respectively. In contrast, the combination of ivemecrin and daunorubicin was closer to

additive in U937 with CI values at the ED25, ED50 and ED75 of 1.1, 0.98 and 0.85,

respectively (Figure 7D).

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We also tested the combination of ivermectin and cytarabine in normal

hematopoeitic cells. In contrast to the effects observed in the leukemia cell lines,

ivermectin did not enhance the cytotoxicity of cytarabine in normal cells (Figure 7E).

Drug sequencing can affect the activity of drug combinations. Therefore, we

tested the effect of drug sequencing on the synergism between ivermectin and cytarabine

or daunorubicn. In OCI-AML2 and U937 cells, the combination of ivermectin and

cytarabine remained synergistic regardless of whether the ivermectin was given with,

before or after the addition of cytarabine (Figure 7F). In contrast, in OCI-AML2 cells,

the combination of ivermectin was synergistic when given before or simultaneously with

daunorubicin. However the effects of the combination were additive when the ivermectin

was given after the addition of the daunorubicin. (Figure 7F)

We also evaluated the combination of ivermectin with the anthelmintic

albendazole as this agent synergized with ivermectin in the treatment of nematodes (Asio

et al., 2009; Demeler et al., 2009). In contrast to the synergy observed with cytarabine

and daunorubicin, albendazole antagonized the anti-leukemic effects of ivermectin with

CI values at the ED25, ED50 and ED75 of 1.59, 1.09 and 0.89, respectively (data not

shown).

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DISCUSSION

To identify known drugs with previously unrecognized anti-leukemia activity, we

compiled and screened a library of off-patent and on-patent drugs for compounds

cytotoxic to leukemia cells. From this screen we identified the anti-parasitic agent,

ivermectin, which induced cell death in leukemia cell lines at low micromolar

concentrations and delayed tumor growth in mouse models of leukemia.

As part of its development as an anti-parasitic, the pharmacology and toxicology

of ivermectin have been studied extensively in humans and animals. Humans treated for

onchocerciasis typically receive a single dose of 100-200 µg/kg of ivermectin to eradicate

the parasite. In such patients, plasma concentration of 52.0 ng/ml have been achieved in

5.2 hours with an area under the curve over 48 hours of 2852 ng.h/ml (Baraka et al.,

1996). Similar pharmacokinetics have been reported in healthy male volunteers receiving

a 14mg capsule of radiolabelled ivermectin. In these subjects, the mean Tmax was 6

hours with a half-life of 11.8 hours (Guzzo et al., 2002). These doses of ivermectin

produce plasma levels that are likely lower than the concentrations required to induce an

anti-leukemic effect, and may explain why anti-tumor effects of ivermectin have not been

previously reported in patients receiving standard doses of this drug for the treatment of

onchocerciasis. However, higher concentrations of ivermectin that may possess anti-

tumor activity have been well tolerated in both humans and animals. For example, the

LD50 of oral ivermectin is approximately 28-30 mg/kg in mice, 80 mg/kg in dogs and

above 24 mg/kg in monkeys (Baraka et al., 1996; Dadarkar et al., 2007). Humans with

spinal injury and muscle spasticity have been treated with up to 1.6mg/kg of ivermectin

subcutaneously twice weekly for up to 12 weeks without toxicity (Costa and

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Diazgranados, 1994). In addition, reports of ivermectin overdoses also support the

potential wide therapeutic window of this drug. For example, an individual who self-

administered 6g of veterinary ivermectin 30 to 50 times over the course of one year had

no evidence of toxicity from the ivermectin (Frost, 1996). Multiple other ingestion events

have also been reported, particularly in pediatric subjects who accidentally consumed

veterinary ivermectin kept in the household for the family dog. In the majority of these,

no serious adverse events were reported (Costa and Diazgranados, 1994; Frost, 1996).

While no prior clinical studies have directly evaluated ivermectin as an anti-

cancer, a case report suggests that ivermectin may have activity in the treatment of

leukemia (Yonekura et al., 2006). An adult male with T cell leukemia/lymphoma

presented with a generalized pruritic, erythrodermic rash with areas of hyperkeratosis and

was diagnosed with scabies. He received 200 µg/kg of ivermectin on days 1 and 10 with

complete resolution of the rash. While not a focus of the paper, it is possible that a

component of the patient’s rash may have been due to leukemia and this rash responded

to ivermectin. Moreover, the leukemia cells in the peripheral blood were controlled

while receiving ivermectin

Our studies suggest that ivermectin induces cell death through a mechanism

related to its known function as an activator of chloride channels. As an anti-parasitic,

ivermectin activates glutamate-gated chloride channels unique to invertebrates.

However, at higher concentrations ivermectin also activates mammalian chloride

channels(Dadarkar et al., 2007). Mammalian chloride channels broadly fall into five

classes based on their regulation: cystic fibrosis transmembrane conductance regulator

(CFTR), which is activated by cyclic AMP dependent phosphorylation; calcium-activated

  164  

chloride channels (CaCCs); voltage-gated chloride channels (ClCs); ligand-gated chloride

channels (GABA (γ-aminobutyric acid) and glycine-activated); and volume-regulated

chloride channels. These channels act in heteromeric complexes dependent upon cell

type, with many possible permutations and combinations of the subunits (Verkman and

Galietta, 2009). Currently, it is unclear which mammalian chloride channels are being

activated by ivermectin   but the complexity of their organization makes it difficult to

identify single “target” channels for ivermectin activity using standard genetic

experiments.

The short term cytotoxicity studies and the in vivo experiments support a

therapeutic window for ivermectin as an anti-leukemia agent, but the difference between

normal CD34+ cells and malignant cells was narrower as were the difference in the

clonogenic growth assays. However, it is important to note that results of these assays do

not always predict clinical toxicity. For example, cytarabine and m-AMSA are

chemotherapeutic agents routinely used in the treatment of AML, but show little or no

selectivity for malignant cells over normal cells in colony formation assays (Singer and

Linch, 1987; Spiro et al., 1981). In addition, we demonstrated that oral ivermectin

delayed tumor growth in three mouse models of leukemia without untoward toxicity,

supporting a therapeutic window. Finally, toxicology studies with ivermectin in animals

and humans did not report hematologic toxicity. Nonetheless, the small differential

sensitivity between primary AML and normal hematopoietic cells raises concerns about

the potential hematologic toxicity and its safety will have to be carefully evaluated in

phase I clinical trials.

  165  

The basis for the therapeutic window after invermectin treatment is likely

multifactorial. Chloride channels are often increased on the surface of malignant cells

compared to normal cells, potentially making them more sensitive to alterations in

chloride flux by ivermectin. For example, compared to normal neutrophils, HL60 cells

over-express the ClC-5 chloride channel that is normally expressed in renal cells (Jiang et

al., 2004). In support of this mechanism, we observed less chloride flux in cells more

resistant to ivermectin. Alterations in intracellular chloride concentrations also affect

basic homeostatic parameters, such as intracellular Ca2+ levels, pH and cell volume

(Kunzelmann, 2005) and alteration of these parameters can induce apoptosis (Lang et al.,

2005). Finally, the therapeutic window with ivermectin treatment may reflect differences

in sensitivity to ROS generation. Ivermectin increased ROS generation that appeared

functionally important for its cytotoxicity and previous studies support a mechanism of

ROS generation related to increased chloride influx (Milton et al., 2008),(Hussain et al.,

1997; Kotake-Nara and Saida, 2006; Zhang et al., 2007). Previous studies have also

demonstrated that malignant cells have higher basal levels of ROS and are less tolerant of

ROS-inducing agents compared to normal cells (Kong et al., 2000; Sawayama et al.,

2008). Future studies will help clarify the basis of the therapeutic window as well as

identify subgroups of patients most likely to respond to this therapy.

Cytarabine and daunorubicin, which are used in the treatment of AML, induce

ROS generation through a mechanism linked to DNA damage and thus a mechanism

distinct from ivermectin. Consequently, we evaluated the combination of these drugs

with ivermectin and demonstrated synergy with both of these drugs. Therefore,

  166  

ivermectin could be evaluated in combination with these agents to enhance the efficacy

of standard therapy for AML.

In summary, we have shown that ivermectin induces cell death in leukemia cells

via chloride influx, membrane hyperpolarization and increasing levels of intracellular

reactive oxygen species. Given its prior safety record in humans and animals coupled

with its pre-clinical efficacy in leukemia, a phase I clinical trial could be conducted to

determine the tolerance and biological activity of oral ivermectin in these patients.

Authorship contribution

SS designed research, analyzed data, performed research and wrote the paper. MS

designed research, analyzed data and performed research. MAS designed research,

analyzed data, and performed research. RH performed research and analyzed data. MG

performed research and analyzed data. XW performed research and analyzed data, SBF

performed research and analyzed data, HS performed research and analyzed data, TEW

designed research, analyzed data, and performed research. RW performed research and

analyzed data. MM contributed critical reagents and analyzed data, RAB designed

research and supervised research, AD designed research, preformed research and

analyzed data. JW supervised research, SK supervised research. ADS designed research,

analyzed data, supervised research and wrote the paper. All authors reviewed and edited

the paper.

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FIGURE LEGENDS Figure 1: A screen of off-patent drugs identifies the antiparasitic agent ivermectin

that reduces viability of leukemia cells.

A) OCI-AML2 cells were treated with aliquots of a small chemical library (n=100)

focused on anti-microbials and metabolic regulators. Seventy two hours after incubation,

cell growth and viability were measured by the MTS assay. Data represent the

percentage of viable OCI-AML2 cells treated with the compounds (6 µM) sorted in order

of increasing activity.

B) Leukemia cell lines were treated with increasing concentrations of ivermectin.

Seventy two hours after incubation, cell growth and viability were measured by the MTS

assay. Data represent the mean EC50 and 95% CI from 3 independent experiments.

C) Primary normal hematopoietic cells (PBSC) (n=3), primary AML patient samples

(AML) (n=3) and U937 leukemia cells were treated with increasing concentrations of

ivermectin for 48 hours. After incubation, cell viability was measured by Annexin V and

PI staining. Data represent the mean + SD percent viable cells from experiments

performed in triplicate.

D) Primary AML cell samples (AML) (n = 6) and normal hematopoietic peripheral blood

stem cell samples (PBSC) (n=3) were treated with ivermectin (6µM) for 24 hours and

then plated in a methylcellulose colony forming assay. Seven (AML) or 14 days (PBSC)

  171  

days after plating the number of colonies was counted. Data represent the mean + SD

percent colony formation compared to control treated cells.

Figure 2: Ivermectin delays tumor growth and reduces tumor mass in leukemia

mouse xenografts

Human leukemia (OCI-AML2 and K562) and murine leukemia (MDAY-D2) cells were

injected subcutaneously into the flank of sublethally irradiated NOD/SCID mice. Four

(OCI-AML2), five (MDAY-D2), or seven (K562) days after injection, once tumors were

palpable, mice were treated with ivermectin (IVM) (3 mg/kg) by oral gavage in water or

vehicle control (n = 10 per group) for 10 days (K562) or 8 doses over 10 days (OCI-

AML2). MDAY-D2 mice (n = 10 per group) were treated with escalating doses of

ivermectin (3mg/kg for 4 days, 5 mg/kg for 3 days and then 6 mg/kg for 3 days).

Fourteen (OCI-AML2), 15 (MDAY-D2) or 17 (K562) days after injection of cells, mice

were sacrificed, tumors excised and the volume and mass of the tumors were measured.

The tumor weight and the mean volume + SEM are shown. Differences in tumor volume

and weight were analyzed by an unpaired t-test: *** p<0.0001.

Figure 3: Ivermectin induces chloride influx and increases cell size in leukemia cells.

A) OCI-AML2 leukemia and DU145 prostate cancer cells were treated with increasing

concentrations of ivermectin. After 24 hours of incubation, cell growth and viability

were measured by MTS assay. Data represent the mean + SD percent viable cells from

representative experiments.

B) OCI-AML2 and C) DU145 cells were treated with 10 µM ivermectin for 1 hour and

levels of intracellular chloride were measured after staining cells with the fluorescent dye

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SPQ that is quenched by high chloride ion concentrations. Histograms from

representative experiments are shown.

D) OCI-AML2 and E) DU145 cells were treated with 6 and 10 µM ivermectin for 1 hour.

After treatment, cell size was measured by forward light scatter and flow cytometry. Data

represent mean + SD fold change in cell size compared to control from representative

experiments performed in triplicate. ** p<0.01, by unpaired t-test.

Figure 4: Ivermectin induces plasma membrane hyperpolarization dependent on

chloride influx.

OCI-AML2 cells were treated with increasing concentrations of ivermectin for 24 hours

(A) or 6 µM of ivermectin for increasing times of incubation (B). After treatment,

plasma membrane potential was measured by staining cells with DiBAC4(3) and flow

cytometric analysis. Data represent the mean + SD fold change in plasma membrane

potential compared to control treated cells. Representative experiments performed in

triplicate are shown. Differences in change of membrane potential compared to control

were analyzed by an unpaired t-test: *** p<0.001; *p<0.05.

U937 and TEX leukemia cells, a primary AML sample (AML), (C) DU145 and PPC-1

prostate cancer, and two samples of normal hematopoietic cells (D), were treated with 6

µM of ivermectin for increasing times. After treatment, plasma membrane potential was

measured as above. Data represent the mean + SD fold change in plasma membrane

potential compared to control treated cells. Representative experiments performed in

triplicate are shown. Differences in change of membrane potential compared to control

were analyzed by an unpaired t-test: *** p<0.001; *p<0.05.

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E) OCI-AML2 cells were treated with 6µM ivermectin in chloride replete and chloride-

free media for 5 hours. After incubation, plasma membrane potential was measured as

above. Data represent the mean + SD change in plasma membrane potential compared to

untreated cells in chloride-replete media. Representative experiments performed in

triplicates are shown. Differences in change of membrane potential compared to control

were analyzed by an unpaired t-test: *** p<0.001; *p<0.05.

Figure 5: Ivermectin induces generation of reactive oxygen species.

OCI-AML 2 leukemia cells were treated with increasing concentrations of ivermectin

overnight (A) or 6 µM of ivermectin for increasing incubation times (B). After

incubation, ROS was detected by staining cells with Carboxy-H2DCFDA (final

concentration 10 µM) and flow cytometric analysis. Data represent the mean + SD fold

change in ROS production compared to control. Representative experiments performed

in triplicate are shown. Differences in change of ROS compared to control were analyzed

by an unpaired t-test: *** p<0.001; **p<0.005.

C) U937 and TEX leukemia cells, DU145 and PPC-1 prostate cells were treated with

ivermectin at 6 µM for 2 hours. After treatment, ROS generation was measured as above.

Data represent the mean + SD fold change in ROS production compared to each of their

buffer treated controls. Representative experiments performed in triplicate are shown.

Differences in change of ROS compared to control were analyzed by an unpaired t-test:

*** p<0.001.

Primary AML cells (n=3) and normal hematopoietic stem cells (PBSC, n=3) were treated

with ivermectin (6µM) for 6 hours. After treatment, ROS generation was measured as

above. Data represent the mean + SD fold change in ROS production compared to each

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of their buffer treated controls for experiments performed in triplicate. Differences in

ROS production compared to control were analyzed by an unpaired t-test: *** p<0.001.

D) OCI-AML2 cells were treated simultaneously with ivermectin (3 µM), the ROS

scavenger, N-acetyl-L-Cystein (NAC) (5 µM) or the combination of NAC and

ivermectin. After 48 hours of treatment, cell growth and viability were measured by the

MTS assay. Data represent the mean + SD percent viable cells from a representative

experiment performed in triplicate. Differences in change of cell viability compared to

control were analyzed by an unpaired t-test: *** p<0.001.

Figure 6: Ivermectin increases expression of STAT1 and its target genes through a

ROS dependent mechanism

A) OCI-AML2 cells were treated with 3 µM ivermectin (IVM) for 30 hours. After

treatment, RNA was isolated, reverse transcribed and subjected to quantitative PCR using

specific primers for STAT1A, STAT1B and STAT1 target genes OAS1, TRIM22 and

IFIT3. Data represent mean + SD fold increase in gene expression normalized to 18S

expression and compared to control cells.

B) OCI AML2, U937 and HL60 leukemia, and DU145 and PPC-1 prostate cancer cells

were treated with 6 µM ivermectin for 24 hours and mRNA levels of STAT1A and

STAT1B were measured using quantitative PCR and normalized to 18S expression as (A).

Data represent mean + SD fold increase in gene expression compared to control cells.

C) OCI-AML2 cells (2.5 x 105) were injected subcutaneously into the flanks of sub-

lethally irradiated NOD/SCID mice. Once tumors were established, mice were treated

with ivermectin (7mg/kg) intraperitoneally or vehicle control for 5 days (n = 3 per

group). After treatment, mice were sacrificed, and tumors harvested. mRNA was

  175  

extracted and changes in STAT1A and 1B expression were measured by Q-RT-PCR.

Data represent mean + SD fold increase in gene expression normalized to 18S expression

compared to tumors from control treated mice.

D) OCI-AML2 cells were treated simultaneously with ivermectin (3 µM), the ROS

scavenger N-acetyl-L-cysteine (NAC) (5 µM), or both for 30 hours, and STAT1A and

STAT1B expression assessed as described for Panel A. Relative expression values

normalized to 18s are reported as fold-change + SD compared to the untreated control for

each gene.

Figure 7: Ivermectin synergizes with cytarabine and daunorubicin to induce cell

death in leukemia cells.

OCI-AML2 cells were treated with increasing concentrations of daunorubicin (A) and

cytarabine (B) overnight. After treatment, ROS was measured by staining cells Carboxy-

H2DCFDA (final concentration 10 µM) and flow cytometric analysis. Data represent the

mean + SD fold change in ROS production compared to control. Representative

experiments performed in triplicate are shown.

The effects of different concentrations of ivermectin in combination with cytarabine and

daunorubicin on the viability of OCI-AML2 and U937 cells were measured by MTS

assay after 72 hours of incubation. Data were analyzed with Calcusyn software as

described in Materials and Methods. Combination index (CI) versus Fractional effect (Fa)

plot showing the effect of the combination of ivermectin with cytarabine (C) and

ivermectin with daunorubicin (D) in OCI AML2 and U937 are illustrated in the

isobolograms. CI < 1 indicates synergism. Representative isobolograms of experiments

performed in triplicate are shown.

  176  

E) Normal hematopoietic cells (PBSC) (n = 2) were treated with T increasing

concentrations of ivermectin and cytarabine (0, 2.5 and 5 µM). After 48 hours, cell

viability was measured by Annexin V-PI staining. Data represent the mean + SD percent

of viable cells from experiments performed in triplicate.

F) OCI-AML2 (i) and U937 (ii) cells were treated with ivermectin, cytarabine or the

combination of the two drugs at varying concentrations for 72 hours.

Ivermectinàcytarabine denotes that ivermectin was added initially and cytarabine was

added for the last 48 hours of the 72 hour experiment. Cytarabineàivermectin denotes

that cytarabine was added initially and ivermectin was added for the last 48 hours of the

72 hour experiment.

OCI-AML2 (iiii) cells were treated with ivermectin, daunorubicin or the combination of

the two drugs at varying concentrations for 72 hours. Ivermectinàdaunorubicin denotes

the ivermectin was added initially and the daunorubicin was added for the last 48 hours

of the 72 hour experiment. Daunorubicinàivermectin denotes the daunorubicin was

added initially and the ivermectin was added for the last 48 hours of the 72 hour

experiment.

After treatment, cell growth and viability was measured by the MTS assay.

Representative experiments performed in triplicate are shown. Data represent mean + SD

fractional effect (cell death).

  177  

A

Ivermectin

B

4.07-7.185.41TEX

4.94-6.535.68KG1a

5.81-7.356.54U937

4.17-5.164.64HL60

4.25-4.844.54OCI AML-2

95% CI

EC5072hrs

(µM)Cell line

D

C0 20 40 60 80 100

0

20

40

60

80

100

120

Compounds ranked

Viab

ility

(% c

ontr

ol)

**

***

40

50

60

70

80

90

100

110

AML

Clo

noge

nic

grow

th(%

con

trol

)

PBSC

Figure 1

0 5 10 15 200

20

40

60

80

100

120

U937

PBSC (n=3)AML (n=3)

Ivermectin (µM)

Viab

lity

(% c

ontro

l) (4

8hrs

)

 

  178  

***

K562

0 2 5 7 10 12 15 170

100200300400500600700800

ControlIVM

Time (days)

Tum

or v

olum

e (m

m3 )

P<0.0001

Control IVM0

100

200

300

400

500

600

Tum

or w

eigh

t (m

g)

Control IVM0

50

100

150

200

250

300

350

Tum

or w

eigh

t (m

g)

OCI AML2

0 2 4 6 8 10 12 140

50100150200250300350400450

ControlIVM

Tum

or v

olum

e (m

m3 )

Time (days)

MDAY

Control IVM0

500

1000

1500

2000

Tum

or w

eigh

t (m

g)

MDAY-D2

0 2 5 7 10 12 150

500

1000

1500ControlIVM

Tum

or v

olum

e (m

m3 )

Time (days)

***

***

P<0.0001

P<0.05

Figure 2  

  179  

  A

B C

ED

Control

IVM 10µM

SPQ (RFU)0

Control

IVM 10µM

SPQ (RFU)0

SPQ (RFU)

Control

IVM 10µM

0

AML2 and DU145

242118151296300

20

40

60

80

100

120

DU145OCI AML2

Ivermectin (µM)Vi

abili

ty (

%)

OCI AML2

0 6 100

1

2

3

4

Ivermectin (µM)

Cel

l siz

e (F

old

chan

ge) DU145

0 6 100.0

0.5

1.0

Ivermectin (µM)

Cel

l siz

e (F

old

chan

ge)** **

OCI-AML2 DU 145

Figure 3

OCI-AML2 DU 145

Freq

uenc

y

Freq

uenc

y

  180  

 

C

Figure 4

* ***

Control

Iverm

ectin

Control

Iverm

ectin

-425

-400

-375

-350

-325

-300

-275

Cl +Cl -

Mem

bran

e Po

tent

ial

(mV

)

DU145

*

***BA

***

******

0 1 2 50.50

0.75

1.00

1.25

1.50

1.75

Time (hours)

Mem

bran

e po

tent

ial

(Fol

d ch

ange

)

D

U937 TEX

PBSC

AML

E

OCI AML2

0 1.5 3 60.0

0.5

1.0

1.5

2.0

2.5

Ivermectin (µM)

Mem

bran

e Po

tent

ial

(Fol

d ch

ange

)OCI AML2

PPC-1

0 1 2 50.50

0.75

1.00

1.25

1.50

1.75

Time (hours)

0 1 2 50.50

0.75

1.00

1.25

1.50

1.75

Time (hours)

*** ****

******

**

0 1 2 50.50

0.75

1.00

1.25

1.50

1.75

Time (hours)

0 1 2 50.0

0.5

1.0

1.5

2.0

Time (hours)

****

0 1 2 50.0

0.5

1.0

1.5

2.0

Time (hours)

Mem

bran

e po

tent

ial

(fold

cha

nge)

0 1 2 50.50

0.75

1.00

1.25

1.50

1.75

Time (hours)

Mem

bran

e po

tent

ial

(fold

cha

nge)

  181  

  B

Figure 5

IvermectinNAC

--

+-

-+

++

Data 1

0

20

40

60

80

100

Rel

ativ

e vi

abili

ty (%

) ***

A

**

0 4 5 60.0

0.5

1.0

1.5

2.0 OCI AML2

Ivermectin (µM)

RO

S(F

old

chan

ge)

C

D

OCI AML2

0 2 6 240.0

0.5

1.0

1.5

2.0

Time (hours)

RO

S(F

old

chan

ge) *** ****

******

**

ROS-fold change at 2 hours

0.0

0.5

1.0

1.5

2.0

TEX DU145 PPC-1U937

RO

S(F

old

chan

ge)

AML

***

ROS-fold change at 6 hours

0.0

0.5

1.0

1.5

2.0

RO

S(F

old

chan

ge)

PBSC

  182  

 A B

Changes in STAT expression

Untreated IVM NAC NAC+IVM0

5

10

15STAT1ASTAT1B

mR

NA

exp

ress

ion

leve

l(f

old

chan

ge)

Figure 6

C

Tumor 1 Tumor 2 Tumor 30

5

10

15

20STAT1ASTAT1B

mR

NA

expr

essi

on le

vel

(fol

d ch

ange

)

D

OCI AML2

OCI AML2

HL60 OCI AML2 U937 DU145 PPC-10

1

2

3

4STAT1ASTAT1B

mR

NA

Exp

ress

ion

leve

l(f

old

chan

ge)

STAT1ASTAT1B TRIM OAS IFIT0

10203040

100

200

300

400m

RN

A e

xpre

ssio

n le

vel

(fold

cha

nge)

A B

Changes in STAT expression

Untreated IVM NAC NAC+IVM0

5

10

15STAT1ASTAT1B

mR

NA

exp

ress

ion

leve

l(f

old

chan

ge)

Figure 6

C

Tumor 1 Tumor 2 Tumor 30

5

10

15

20STAT1ASTAT1B

mR

NA

expr

essi

on le

vel

(fol

d ch

ange

)

D

OCI AML2

OCI AML2

HL60 OCI AML2 U937 DU145 PPC-10

1

2

3

4STAT1ASTAT1B

mR

NA

Exp

ress

ion

leve

l(f

old

chan

ge)

STAT1ASTAT1B TRIM OAS IFIT0

10203040

100

200

300

400m

RN

A e

xpre

ssio

n le

vel

(fold

cha

nge)

  183  

 

  184  

 

 

 

 

 

 

 

Figure 7

Fi)

OCI AML2IvermectinàCytarabine Cytarabine à Ivermectin

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Ivermectin (5µM)Cytarabine (0.2µM)

--+

+++

Frac

tion

Affe

cted

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Ivermectin (7.5µM)Cytarabine (0.1µM)

--+

+++

Frac

tion

Affe

cted

U937ii)IvermectinàCytarabine Cytarabine à Ivermectin

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Ivermectin (2.5µM)Cytarabine (0.1µM)

--+

+++

Frac

tion

Affe

cted

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Ivermectin (1.9µM)Cytarabine (0.3µM)

--+

+++

Frac

tion

Aff

ecte

d

iii)OCI AML2

IvermectinàDaunorubicin Daunorubicin à Ivermectin

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Ivermectin (5µM)Daunorubicin (10nM)

--+

+++

Frac

tion

Affe

cted

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

--+

+++

Ivermectin (7.5µM)Daunorubicin (6nM)

Frac

tion

Affe

cted

Figure 7

Fi)

OCI AML2IvermectinàCytarabine Cytarabine à Ivermectin

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Ivermectin (5µM)Cytarabine (0.2µM)

--+

+++

Frac

tion

Affe

cted

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Ivermectin (7.5µM)Cytarabine (0.1µM)

--+

+++

Frac

tion

Affe

cted

U937ii)IvermectinàCytarabine Cytarabine à Ivermectin

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Ivermectin (2.5µM)Cytarabine (0.1µM)

--+

+++

Frac

tion

Affe

cted

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Ivermectin (1.9µM)Cytarabine (0.3µM)

--+

+++

Frac

tion

Aff

ecte

d

iii)OCI AML2

IvermectinàDaunorubicin Daunorubicin à Ivermectin

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Ivermectin (5µM)Daunorubicin (10nM)

--+

+++

Frac

tion

Affe

cted

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

--+

+++

Ivermectin (7.5µM)Daunorubicin (6nM)

Frac

tion

Affe

cted