The Role of Prolactin in the Cellular Response to DNA Damaging Agents

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2015-09-30 The Role of Prolactin in the Cellular Response to DNA Damaging Agents

Karayazi Atici, Odul

Karayazi Atici, O. (2015). The Role of Prolactin in the Cellular Response to DNA Damaging Agents (Unpublished doctoral thesis). University of Calgary, Calgary, AB. doi:10.11575/PRISM/28347 http://hdl.handle.net/11023/2567 doctoral thesis

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The Role of Prolactin in the Cellular Response to DNA Damaging Agents

Odul Karayazi Atici

A THESIS

SUBMITTED TO THE FACULTY OF GRADUATE STUDIES

IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE

DEGREE OF DOCTOR OF PHILOSOPHY

GRADUATE PROGRAM IN BIOLOGICAL SCIENCES

CALGARY, ALBERTA

SEPTEMBER, 2015

High serum levels of the peptide hormone prolactin are associated with increased breast cancer risk and poor prognosis. Prolactin is also involved in breast cancer resistance to different chemotherapeutics. The overall goal of this project is to investigate potential pathways involved in prolactin induced resistance to DNA damaging agents with the hypothesis that the cross-talk between the prolactin pathway and the DNA damage response is important in the mechanism.

We previously identified that one isoform of heat shock protein-90 (HSP90), Hsp90alpha, is a prolactin-Janus kinase-2 (Jak2)-signal transducer and activator of transcription-5 (Stat5) regulated gene in breast cancer cells. We have now observed that prolactin increased the viability of breast cancer cells to DNA damaging chemotherapeutics, and Hsp90 inhibitors, 17AAG and

BIIB021, abrogated the effect of prolactin, indicating the mechanism of enhanced viability involves the master cancer chaperone Hsp90. The stability of Jak2 and both the total ataxia- telangiectasia mutated protein (ATM) and phospho-ATM appear to be dependent on functional

Hsp90. Inhibition of Jak2 and ATM, with highly selective inhibitors (G6 and KU55933, respectively), abrogated prolactin enhanced viability, suggesting their role in prolactin induced cell viability. Drug combination experiments with Hsp90 inhibitor BIIB021 and doxorubicin, and ATM inhibitor KU55933 and doxorubicin, showed drug synergism between doxorubicin and both KU55933 and BIIB021 in MCF7 breast cancer cells. Interestingly, in orthotopic xenograft studies, autocrine prolactin from human breast cancer cells increased the tumor latency of doxorubicin induced DNA damaged cells in SCID mice compared to untreated or prolactin or doxorubicin alone. We hypothesize that this is in part due to the cross-talk of the prolactin and

DNA damage response pathways that may be affecting the tumor microenvironment.

ii Acknowledgements

I would like to extend my gratitude and appreciation to my supervisor, Dr. Carrie S.

Shemanko, for her guidance, patience and support during my research in her laboratory. Working in the Shemanko lab has given me invaluable and unforgettable experience. I would also like to thank the members of my committee, the past member Dr. Sung-Woo Kim and the current members Dr. Susan Lees-Miller and Dr. Hamid Habibi, who have provided me with valuable ideas and advices. I would like to extend my gratitude to Dr. David Hansen and Dr. Karl

Riabowol for attending in my candidacy exam and to Dr. Catherine Too and Dr. Aru Narendran for attending in my defence exam.

Thank you to all the past and the present members of the Shemanko lab. To Dr. Christian

Perotti and the graduate students Anna Urbanska and Ashley Sutherland, for their guidance in the laboratory. I would like to thank Lin Su, for her endless help and valuable scientific discussions. I am highly grateful to Amanda Forsyth, for her friendship and support. I would especially like to thank to undergraduate students who worked directly on this project with me including Erin Marie Bell, Sara Mirzaei, Emilija Malogajski and Colin Stewart.

Thank you to the supervisors and students of the Hansen, Habibi, Cobb, Moorhead, Buret and Chan laboratories for all their help and the use of their equipments.

Thank you to all LESARC members for their support. I would especially like to thank to

Dawn Martin and Mike Collier for their endless help during animal experiments.

I would like to acknowledge the financial support of University of Calgary, Natural

Sciences and Engineering Research Council of Canada (NSERC), Queen Elizabeth II

Scholarships. The constant helps of the administrative staff Karen Barron and Sophia George are highly appreciated.

iii Special thanks to all my family and friends. To my parents for always believing and supporting me. Very special thanks to my husband, Mehmet Atici, for his never-ending support and encouragement and, to my daughter and unborn son for passing me their infinite positive energy.

iv Dedication

To my aunts Yurdagul Uzun and Tulay Aktas, who could not win their battle with breast cancer.

v Table of Contents

Abstract...... ii! Acknowledgements...... iii! Table of Contents...... vi! List of Figures and Illustrations ...... xii! List of Symbols, Abbreviations and Nomenclature...... xvi!

! 1.1 Breast cancer statistics...... 1! CHAPTER(ONE:1.2 The mammary(INTRODUCTION gland and mammary...... gland microenvironment...... 1! 1.2.1 Mammary gland overview...... 1! 1.2.2 The mammary gland microenvironment ...... 3! 1.3 The hormone prolactin and its function in the mammary gland...... 6! 1.3.1 Prolactin...... 6! 1.3.2 Prolactin Receptor (PRLR)...... 11! 1.3.3 Prolactin signalling pathways...... 16! 1.3.3.1 Jak2- Stat5 Pathway...... 16! 1.3.3.2 Ras-Raf-MAPK pathway...... 19! 1.3.3.3 PI3K/ Akt pathway ...... 19! 1.4 Breast cancer and hormonal influence...... 22! 1.4.1 Breast cancer classification ...... 22! 1.4.2 Treatment of breast cancer ...... 23! 1.4.3 The role of estrogen in breast cancer...... 24! 1.4.4 The role of progesterone in breast cancer...... 26! 1.4.5 The role of prolactin in breast cancer...... 27! 1.4.5.1 Epidemiologic studies highlight the role of prolactin in breast cancer..28! 1.4.5.2 In vivo studies implicating the role of prolactin in tumor formation.....30! 1.4.5.3 The role of prolactin in resistance to chemotherapy agents...... 31! 1.4.5.4 Prolactin receptor antagonists...... 32! 1.5 Heat shock protein 90 (Hsp90) ...... 34! 1.5.1 Chaperone mechanism of HSP90...... 35! 1.5.2 The role of Hsp90 in DNA damage response...... 39! 1.5.3 The role of HSP90 in cancer and chemotherapy resistance ...... 40! 1.5.4 Targeting Hsp90 in cancer treatment ...... 42! 1.6 DNA damage response ...... 43! 1.6.1 DNA damage response overview ...... 43! 1.6.2 DNA repair mechanism...... 44! 1.6.2.1 Base excision repair (BER)...... 44! 1.6.2.2 Mismatch repair (MMR)...... 44! 1.6.2.3 Nucleotide excision repair (NER)...... 45! 1.6.2.4 Double-strand break repair (DSB repair)...... 45! 1.6.3 ATM (Ataxia Telangiectasia mutated protein )...... 47! 1.6.4 The working mechanism of topoisomerase II poision, doxorubicin, and its role in DNA damage ...... 49! 1.7 Cellular mechanisms of Autophagy and Senescence ...... 50! 1.7.1 Autophagy overview ...... 50! vi 1.7.2 Senescence and the senescence-associated secretory phenotype (SASP)...... 51! 1.7.3 Senescence...... 51! 1.7.4 Senescence-associated secretory phenotype (SASP) ...... 52!

! 2.1 Specific aims and objectives...... 53! CHAPTER(TWO:(HYPOTHESIS(AND(SPECIFIC(AIMS...... 53 ! 3.1 Cell lines ...... 55! CHAPTER(THREE:3.2 Breast cancer(MATERIALS(AND(METHO cell culture and maintenanceDS...... 5555! 3.2.1 PEI transfection ...... 56! 3.3 Chemotherapy agents...... 57! 3.3.1 17- (Allylamino)-17-demethoxygeldanamycin (17-AAG) (1000 nM) ...... 57! 3.3.2 Doxorubicin (100 mM)...... 57! 3.3.3 BIIB021 (1000 nM)...... 57! 3.3.4 KU55933 (100 mM)...... 57! 3.3.5 G6 (NSC33994) (5 mM) ...... 58! 3.3.6 NU7441 (KU7788) (5 mM)...... 58! 3.4 Hormones...... 58! 3.4.1 Insulin (5 mg/ml)...... 58! 3.4.2 Ovine prolactin (1 mg/ml)...... 58! 3.4.3 Human recombinant prolactin (100 µg/ml)...... 58! 3.4.4 Δ1-9-G129R-hPRL receptor angatonist (1 mg/ml) ...... 58! 3.4.5 17β-estradiol (1 mg/ml)...... 59! 3.4.6 LFA 102...... 59! 3.5 WST-1 cell viability assay...... 59! 3.5.1 WST-1 cell viability assay with different cell numbers ...... 60! 3.5.1.1 Slope and intercept calculation from WST-1 assay...... 60! 3.5.2 WST-1 cell viability assay with prolactin and doxorubicin treatments ...... 60! 3.5.3 WST-1 cell viability assay with estrogen, prolactin and doxorubicin...... 61! 3.5.4 WST-1 cell viability assay with Hsp90 inhibitor, BIIB021 ...... 61! 3.5.5 WST-1 cell viability assay with ATM inhibitor, KU55933...... 62! 3.5.6 WST-1 cell viability assay with Jak2 inhibitor, G6 ...... 62! 3.6 Combination Index Calculations ...... 63! 3.7 Clonogenic cell survival assay...... 64! 3.7.1 Clonogenic cell survival assay with prolactin and doxorubicin...... 64! 3.8 Polymerase Chain Reactions ...... 64! 3.8.1 RNA extration and quantification ...... 64! 3.8.2 Complementary DNA Synthesis ...... 65! 3.8.3 Quantitative polymerase chain reaction ...... 65! 3.8.4 Endpoint PCR...... 66! 3.8.5 ATM Gene Expression...... 66! 3.8.6 JAK2 Gene Expression...... 66! 3.8.7 GAPDH Gene Expression ...... 66! 3.8.8 Sh-ble Gene Expression ...... 67! 3.8.9 YWHAZ Gene Expression...... 67!

vii 3.8.10 Visualization of PCR Products...... 67! 3.9 Protein Immunobloting...... 68! 3.9.1 Whole cell lysate extract with NP-40 buffer without sonication ...... 68! 3.9.2 Whole cell lysate extract with 1XSDS Buffer...... 69! 3.9.3 Secreted protein extraction from conditioned media...... 69! 3.9.4 Nuclear lysate extract ...... 69! 3.9.5 Measuring protein concentration...... 70! 3.9.6 Separation of proteins by SDS-Page electrophoresis ...... 71! 3.9.7 Immunoblotting for p-ATM ...... 73! 3.9.8 Immunoblotting for ATM...... 74! 3.9.9 Immunoblotting for Hsp90α ...... 74! 3.9.10 Immunoblotting for GRB2 ...... 74! 3.9.11 Immunoblotting for Beclin-1...... 75! 3.9.12 Immunoblotting for Jak2 ...... 75! 3.9.13 Immunoblotting for p-Stat5...... 75! 3.9.14 Immunoblotting for Stat5 ...... 76! 3.9.15 Immunoblotting for Histone- H3...... 76! 3.9.16 Immunoblotting for Prolactin...... 76! 3.9.17 Visualization of Proteins ...... 77! 3.9.18 Quantifying Western blot data...... 77! 3.10 Xenograft Animal Models ...... 78! 3.10.1 Xenograft Animal Model with Endocrine Prolactin Delivery ...... 78! 3.10.2 Xenograft Animal Model with Autocrine Prolactin Delivery...... 79! 3.10.3 Serum Estradiol levels measurement from SCID mice...... 81! 3.10.4 Immunohistochemistry for Ki-67 and Hsp90α ...... 81! 3.10.4.1 Image Quantification and Statistical Analysis of Ki-67 Immunohistochemisty Stains ...... 83! 3.10.5 Soluble Senescence-Associated β-galactosidase activity assay (ONPG assay)83! 3.10.6 Autophagy assay (Determination of Beclin-1 levels) ...... 84! 3.10.7 Cytokine Array ...... 85! 3.10.8 ELISA for SDF-1 alpha and beta ...... 85! 3.11 Statistical analysis...... 86!

! 4.1 Assess the Mechanism of Prolactin Mediated Cellular Response to DNA Damaging CHAPTER(FOUR:Agents in vitro(RESULTS(I...... 8888! 4.1.1 Prolactin increases the viability of breast cancer cells treated with DNA damaging agents ...... 89! 4.1.2 Comparison of cell viability measured by WST-1 assay with cell number ....97! 4.1.3 Prolactin increases clonogenic survival of breast cancer cells treated with doxorubicin ...... 102! 4.1.4 Prolactin and Estrogen increase the viability of breast cancer cells treated with doxorubicin ...... 104! 4.1.5 The role of Hsp90 in prolactin- increased cell viability against DNA damaging agents ...... 107!

viii 4.1.5.1 The inhibition of Hsp90 abrogates with prolactin- mediated increase in cell viability ...... 107! 4.1.5.2 Synergistic activity of doxorubicin with Hsp90 inhibitor BIIB021 ....113! 4.1.6 Stability of ATM and Jak2 are dependent on Hsp90 ...... 119! 4.1.6.1 The stability of ATM and/or p-ATM is dependent upon Hsp90 in breast cancer cells...... 119! 4.1.6.2 The stability of Jak2 is dependent upon Hsp90 in breast cancer cells.124! 4.1.7 The involvement of ATM in mechanism of prolactin-mediated cell viability130! 4.1.7.1 ATM is involved in the prolactin–induced increase cell viability against doxorubicin in breast cancer cells...... 130! 4.1.7.2 Synergistic inhibition of doxorubicin with ATM inhibitor KU55933.136! 4.1.8 The involvement of Jak2 in the mechanism of prolactin induced increase in cell viability ...... 142!

! 5.1 Assess the Role of Prolactin on Tumorigenicity and Tumor Volume in Response to CHAPTER(FIVE:DNA Damaging(RESULTS(II Agents...... in vivo ...... 154154! 5.1.1 The effect of endocrine prolactin on tumorigenicity and tumor volume in response to DNA damaging agents...... 155! 5.1.1.1 To investigate the effect of endocrine prolactin in tumorigenicity, latency, tumor size of breast cancer cells in a xenograft animal model (SCID-B- EoPRL) in response to DNA damaging agents...... 155! 5.1.1.2 To investigate the effect of endocrine prolactin on proliferation using immunohistochemistry analysis of the xenograft tumors (SCID-B-EoPRL) ...... 162! 5.1.2 The effect of autocrine prolactin on tumorigenicity and tumor volume in response to DNA damaging agents...... 169! 5.1.2.1 Preparation and confirmation of autocrine prolactin secreting MCF7 cell line...... 169! 5.1.2.2 To investigate the effect of autocrine prolactin in tumorigenicity, latency, tumor size of breast cancer cells in a xenograft animal model (SCID- AhPRL-500K-60D) in response to DNA damaging agents...... 172! 5.1.2.3 To investigate the effect of autocrine prolactin on proliferation using immunohistochemistry analysis of the xenograft tumors (SCID-AhPRL- 250K-120D) ...... 187! 5.1.2.4 The serum estradiol levels from SCID mice...... 190! 5.2 Assess the Mechanism of Prolactin Mediated Cellular Response to DNA Damaging Agents ...... 193! 5.2.1 The effect of prolactin, in the presence of DNA damage, on autophagy ...... 193! 5.2.2 The effect of prolactin in the presence of DNA damage on cellular senescence...... 196! 5.2.3 To investigate if prolactin regulates the Senecence-Associated Secretory Phenotype in breast cancer cells ...... 199! 5.2.4 To investigate if prolactin regulates secretion of Stromal derived factor-1 (SDF-1) in the presence of DNA damage in breast cancer cells...... 205!

CHAPTER(SIX:(DISCUSSION ...... ix ...... 221 6.1 Prolactin increases the viability and clonogenic survival of breast cancer cells treated with DNA damaging agents...... 225! 6.2 Prolactin and Estrogen increase the viability of breast cancer cells treated with DNA damaging agent, doxorubicin...... 229! 6.3 Hsp90 is involved in the mechanism of prolactin-enhanced viability of breast cancer cells treated with DNA damaging agents...... 232! 6.3.1 Synergism between BIIB021 and doxorubicin...... 233! 6.4 The Hsp90-dependent stability of ATM and/or p-ATM and Jak2 and their involvement in mechanism of prolactin-mediated cell viability...... 234! 6.4.1 The stability of ATM and/or p-ATM is dependent on Hsp90 in breast cancer cells ...... 235! 6.4.2 The involvement of ATM in mechanism of prolactin-mediated cell viability237! 6.4.3 Synergism between KU55933 and doxorubicin...... 237! 6.4.4 The stability of Jak2 is dependent on Hsp90 in breast cancer cells ...... 238! 6.4.5 The involvement of Jak2 in mechanism of prolactin-mediated cell viability239! 6.5 The role of prolactin on tumorigenicity and tumor volume in response to DNA damaging agents in vivo...... 241! 6.5.1 The effect of endocrine prolactin on tumorigenicity and tumor volume in response to DNA damaging agents in vivo...... 241! 6.5.2 The effect of autocrine prolactin on tumorigenicity and tumor volume in response to DNA damaging agents in vivo...... 243! 6.5.3 Estrogen pellets: their release and potential side effects...... 245! 6.6 The effect of prolactin on senescence, autophagy and senescence-associated-secreting- phenotype...... 246! 6.6.1 The effect of prolactin on autophagy in the presence of DNA damage ...... 246! 6.6.2 The effect of prolactin on cellular senescence and the senescence-associated secretory phenotype in the presence of DNA damage...... 247!

! 7.1 Future Directions ...... 249! CHAPTER(SEVEN:7.2 Overall Significance(FUTURE(DIRECTIONS(A...... ND(SIGNIFICANCE...... 249249!

! A.1. Clonogenic cell survival assay with ATM siRNA and mock transfected cells ...271! APPENDIX(A:(SUPPLEMEA.1.1. Knockdown withNTARY(METHODS siRNA ...... 271271! A.1.2. ΔΔCq and Percent Knockdown Calculations ...... 272! A.2. Whole cell lysate extract with NP-40 buffer with sonication...... 272! A.2.1. Immunoblotting for p-KAP1 ...... 273! A.2.2. Immunoblotting for KAP-1 ...... 273! A.2.3. Immunoblotting for p-Chk2...... 273! A.2.4. Immunoblotting for Chk2...... 274!

! B.1. To investigate if the kinase activity of ATM is dependent upon prolactin...... 275! APPENDIX(B:(SUPPLEMEB.2. To investigate the involvementNTARY(RESULTS of ATM...... in prolactin- mediated...... cell viability...... with275 clonogenic assay using short inferring RNA (siRNA) against ATM ...... 289!

x List of Tables

Table 1. Breast Cancer Cell Lines Characteristics...... 55!

Table 2. Combination Index Values from Drug Combination Studies of Doxorubicin with BIIB021...... 117!

Table 3. Combination Index Values from Drug Combination Studies of Doxorubicin with KU55933...... 140!

Table 4. Abnormalities seen in xenograft experiment (SCID-AhPRL-500K-60D) ...... 180!

xi List of Figures and Illustrations

Figure 1. Mammary gland development...... 5!

Figure 2. Prolactin protein structure demonstrating locations of disfulfide bonds, posttranslational modifications and heparin binding site...... 9!

Figure 3. Diagram of human prolactin gene...... 10!

Figure 4. PRLR activation by prolactin-induced dimerization...... 14!

Figure 5. Prolactin receptor isoforms...... 15!

Figure 6. Prolactin-Jak2-Stat5 pathway...... 18!

Figure 7. Prolactin and prolactin receptor (PRLR) signalling pathways...... 21!

Figure 8. ATPase cycle of Hsp90...... 37!

Figure 9. Chaperone cycle of Hsp90...... 38!

Figure 10. Doxorubicin dose response curve in MCF7 cells...... 90!

Figure 11. Human recombinant prolactin dose response curve in MCF7 cells...... 92!

Figure 12. Prolactin enhanced viability in MCF7 cells treated with doxorubicin...... 93!

Figure 13. Prolactin enhanced viability in SKBR3 cells treated with doxorubicin. e...... 94!

Figure 14. Prolactin enhanced viability in T47D cells treated with doxorubicin...... 96!

Figure 15. Comparison of cell viability measured by WST-1 assay and cell number in untreated cells...... 100!

Figure 16 Comparison of cell viability measured by WST-1 assay and cell number in doxorubicin treated cells...... 101!

Figure 17. Prolactin increased clonogenic cell survival of MCF7 cells treated with doxorubicin...... 103!

Figure 18. Estrogen and prolactin increases viability of MCF7 cells treated with doxorubicin...... 106!

Figure 19. BIIB021 dose curve in MCF7 cells...... 109!

Figure 20. Hsp90 inhibition abrogates the prolactin-mediated increase in cell viability...... 112!

Figure 21. Reduced cell viability with doxorubicin and BIIB021 combination treatment. n..... 116!

xii Figure 22. Synergism Between Doxorubicin and BIIB021...... 118!

Figure 23. The stability of p-ATM and/or ATM are dependent on Hsp90...... 122!

Figure 24. ATM mRNA levels are not affected from 17AAG, prolactin and doxorubicin treatments. )...... 123!

Figure 25. Jak2 stability is dependent on Hsp90...... 128!

Figure 26. Jak2 mRNA levels are not affected from 17AAG, prolactin and doxorubicin treatments...... 129!

Figure 27. ATM inhibition abrogated the prolactin increased cell viability in MCF7 cells...... 133!

Figure 28. ATM inhibition abrogated the prolactin increased cell viability in SKBR3 cells. ... 135!

Figure 29. Reduced cell viability with doxorubicin and KU55933 combination treatment...... 139!

Figure 30. Synergism Between Doxorubicin and KU55933...... 141!

Figure 31. Dose response curves of Jak2 inhibitor, G6...... 146!

Figure 32. Western blot dose response of Jak2 inhibitor, G6...... 147!

Figure 33. Jak2 inhibition abrogates with prolactin increased cell viability in MCF7 cells. .... 150!

Figure 34. Jak2 inhibition abrogates with prolactin increased cell viability in SKBR3 cells. . . 153!

Figure 35. The schematic of SCID-beige mouse recurrence model (SCID-B-EoPRL) to understand the role of endocrine prolactin in latency and tumorigenicity...... 156!

Figure 36. The effect of endocrine prolactin on tumor formation in SCID- beige mice (SCID- B-EoPRL)...... 161!

Figure 37. Ki-67 immunohistochemistry results from xenograft tumors (SCID-B-EoPRL) assessing the role of endocrine prolactin on cell proliferation...... 164!

Figure 38. Hsp90α expression in primary xenograft tumors (SCID-B-EoPRL) of human MCF7 breast cancer cells in the mouse mammary gland...... 166!

Figure 39. Survival of SCID-beige mice without xenografts over 60 days...... 168!

Figure 40. Confirmation of MCF7hprl and MCF7pcDNA3.1 cell lines...... 171!

Figure 41. The schematic of the SCID mouse recurrence model (SCID-AhPRL-500K-60D) to understand the role of autocrine prolactin in latency and tumorigenicity...... 173!

Figure 42. The effect of autocrine prolactin on tumor formation in SCID mice (500,000 cell injection)...... 177!

xiii Figure 43. The effect of autocrine prolactin on tumor formation in SCID mice (SCID- AhPRL-250K-120D)...... 183!

Figure 44. Primary tumors, perineum lesions and observed abnormalities in xenograft experiment (SCID-AhPRL-250K-120D)...... 186!

Figure 45. Ki-67 immunohistochemistry results from primary xenograft tumors (SCID- AhPRL-250K-120D) assessing the role of autocrine prolactin on cell proliferation (250,000 cell injection)...... 188!

Figure 46. Ki-67 immunohistochemistry results from perineum lesions (SCID-AhPRL-250K- 120D)...... 189!

Figure 47. Serum estradiol level measurements in SCID mice and estrogen-pellet side effect. 192!

Figure 48. Beclin-1 levels are not affected from prolactin and/or doxorubicin treatments...... 195!

Figure 49. Determining the effect of doxorubicin and prolactin on senescence by ONPG assay in MCF7 cells...... 198!

Figure 50. The effect of autocrine prolactin on secreted cytokines in the presence of DNA damage from doxorubicin...... 204!

Figure 51. SDF-1 alpha levels (pg/ml) measured from MCF7pcDNA3.1 control cells in the presence or absence or DNA damage from doxorubicin ...... 207!

Figure 52. SDF-1 alpha levels (pg/ml) measured from MCF7hprl breast cancer cells in the presence or absence or DNA damage from doxorubicin...... 209!

Figure 53. SDF-1 alpha levels measured from SKBR3 and T47D breast cancer cells in the presence or absence or DNA damage from doxorubicin...... 212!

Figure 54. SDF-1 beta levels measured from MCF7pcDNA3.1 and T47D breast cancer cells in the presence or absence or DNA damage from doxorubicin...... 214!

Figure 55. SDF-1 alpha and beta levels measured from breast cancer cells in the presence or absence or DNA damage from doxorubicin (Second ELISA experiment)...... 220!

Figure 56. The proposed model...... 223!

Figure S1. The effect of prolactin on ATM target proteins...... 280!

Figure S2. The effect of prolactin on ATM target proteins in the presence of DNAPK inhibitor, NU7441...... 285!

Figure S3. The effect of prolactin, 17AAG and NU7741 on ATM target proteins in the absence of DNA damage...... 288!

Figure S4. siRNA-mediated silencing of GAPDH and ATM genes...... 290!

xiv Figure S5. siRNA-mediated silencing of ATM abrogated with prolactin increased clonogenic cell survival...... 292!

xv List of Symbols, Abbreviations and Nomenclature

Symbol Definition Δ1-9-G129R-hPRL pure human prolactin receptor antagonist

17AAG 17-allylamino-17-demethoxygeldanamycin

17DMAG 17-(2-dimethylaminoethyl)amino-17-

demethoxygeldanamycin

A-T Ataxia-telangiectasia

AC-TH doxorubicin, cyclophosphamide, paclitaxel and

trastuzumab

AIs aromatase inhibitors

APE1 Apurinic/apyrmidinic endonuclease 1

ATCC American Type Culture Collection

Atg AuTophaGy-related

ATM ataxia-telangiectasia mutated kinase protein

ATR ATM and Rad3-related

BER base excision repair

BRCA1 Breast cancer gene 1

BSA Bovine serum albumin

Cdk Cyclin dependent kinase

CHK1 Checkpoint kinase 1

CHK2 Chekcpoint kinase 2

CMF cyclophosphamide, methotrexate, and fluororuracil

DMEM Dulbecco’s Modified Eagle Medium

xvi DNA-PK DNA-dependent protein kinase

DSB Double-strand break

DTT Dithiothreitol

ECD Extracellular domain

ECM extracellular matrix

EGF Epidermal growth factor

ELISA Enzyme-linked immunosorbant assay

EMT Epithelial mesenchymal transition

ER Estrogen receptor

ERK Extracellular signal-regulated protein kinase

Exo1 Exonuclease 1

FAC flouroruracil, doxorubicin and cyclophoshamide

FBS Fetal bovine serum g-PAK p-21 activated protein kinase

GAS Gamma interferon-activated sites

GM geldanamycin

GRB2 growth factor receptor-bound protein 2

GST Glutathione-S-transferase

HER2 Human epidermal growth-factor 2

HIF 1a Hypoxia-inducible transcription factor

HIP Hsp70-interacting protein

HOP HSP70/90 organizing protein

HP1 heterochromatin protein 1

xvii HR Homologous recombination

Hsp90 Heat shock protein 90

ICD Intercellular domain

IHC Immunohistochemistry

IP Immunophilins

ISH In situ hybridization

Jak Janus kinase

KAP1 Krüppel-associated box (KRAB)-associated

protein 1

MAPK Mitogen-activated protein kinase

MEK Mitogen-activated protein kinase kinase

MMP metalloproteinase

MMR Micmatch repair

MRN complex Mre11-Rad50-Nbs1

MT Metallothionein

NER Nucleotide excision repair

NHEJ non-homologous end-joining

NP-40 Nonidet-P-40

NRL Neu-related lipocalin

PEI Polyethyleminine

PH pleckstrin-homology

PI3K phosphoinisitide 3 kinase

PIP2 phosphatidylinositol (3,4,5)-triphosphate from

xviii phosphatidylinositol (4,5)-biphosphate

Pit-1 Pituitary-specific positive transcription factor-1

PR Progesterone receptor

PRLR Prolactin receptor

PRLRBP Prolactin receptor binding protein

RPA Replication protein A

RPMI Roswell Park Memorial Institute

SASP senescence-associated secretory phenotype

SDF-1 Stromal cell-derived factor-1

SDS Sodium dodecyl sulfate

SERM Selective estrogen receptor modulators

SH3 Src-homology 3

Shp2 Src-homology 2 domain-containing phosphatase 2

TAC docetaxel, doxorubicin and cyclophosphamide

TCH docetaxel, cyclophosphamide and trastuzumab

TEB Terminal end bud

TEMED Tetramethylethylenediamine

TM Transmembrane domain

TOPII Topoisomerase II

TRAP1 tumor necrosis factor receptor-associated protein 1

TRP tetratricopeptide repeat

Tween-20 Polyoxyethylenesorbitan monolaurate

Tyk Tyrosine kinase

xix ULK unc-51 like autophagy activating kinase

XRCC1 X-ray repair cross-complementing protein 1

YWHAZ Tyrosine 3-monooxygenase/tryptophan 5-

monooxygenase activation protein

D1-9-G129R-hPRL pure human prolactin receptor antagonist

17AAG 17-allylamino-17-demethoxygeldanamycin

17DMAG 17-(2-dimethylaminoethyl)amino-17-

demethoxygeldanamycin

Chapter One: INTRODUCTION

1.1 Breast cancer statistics

Breast cancer is the most common cancer in women worldwide. In 2012, 1.7 million new breast cancer cases were diagnosed that represents about 25% of all cancers in women and the highest incidence of breast cancer was found in North America (World Cancer Research Fund

International, 2012). According to the Canadian Cancer Society, 25,000 women will be diagnosed with breast cancer in 2015 and 5,000 will die of it. Based on statistics, one in nine

Canadian women is predicted to develop breast cancer during their lifetime and one in thirty will die from it (Canadian Cancer Society, 2015). The breast cancer incidence rate increased in the early 1990s but decreased in the early 2000s due to improvements in screening programs. The treatment options for breast cancer includes surgery, radiation therapy, chemotherapy and hormonal therapy, however resistance to therapies remains a problem.

1.2 The mammary gland and mammary gland microenvironment

1.2.1 Mammary gland overview

The unique development of the mammary gland begins during embryogenesis and continues during puberty, pregnancy, lactation and post-lactation. This unique development makes the mammary gland an important organ in which to investigate cell fate specification, proliferation, differentiation, survival and death of cells, as well as the influence of the microenvironment on the gland. The studies on mammary gland development provide critical understanding of dysregulated signalling pathways and the processes in breast cancer progression.

The mammary gland is composed of two main tissue compartments; the epithelium and the stroma. There are multiple epithelial cell types in the mammary gland. Most epithelial cells

are luminal cells (secretory cells) that form ducts and milk-producing alveolar cells [reviewed in

(Hennighausen and Robinson, 2005, Inman et al., 2015, Musumeci et al., 2015)]. The luminal

cells are enclosed by basally oriented and contractile myoepithelial cells which are in contact

with a specialized layer of extracellular matrix (ECM), named the basement membrane. The

stroma, which is also called the mammary fat pad is composed of fat-filled adipocytes,

fibroblasts, immune, lymphatic and vascular cells, and surrounds the ductal and alveolar systems.

The mammary gland development occurs through neonatal stage, puberty, pregnancy,

lactation and involution (Figure 1) [reviewed in (Hennighausen and Robinson, 2005, Musumeci

et al., 2015)]. The rudiment of the mammary gland is present at birth and remains quiescent until

puberty. During puberty, the ductal growth is promoted by the production of ovarian estrogen

and progresterone. Specialized epithelial cells, such as cap cells and body cells, form

proliferative terminal end buds (TEBs) which elongate and branch through the fat pad. In mature

mammary glands, the primary and secondary ducts fill the entire fat pad and the epithelium

proliferates and apoptoses during each estrus cycle. During pregnancy, prolactin, placental

lactogens, progesterone and estrogen increase cell proliferation and the luminal cells functionally

differentiate into milk-secreting alveoli. During lactation, luminal cells synthesize and secrete milk into lumen of alveoli, and contractile myoepithelial cells transport milk through the ducts to the nipple. At the end of lactation, a remodelling program begins which is called involution. In involution, the processes involve cell death, remodelling of epithelial compartments and extracellular matrix, the collapse of alveoli, returning the mammary gland into resting adult state.

Prolactin contributes to mammary gland maturation from ductal system to fully mature gland in adult. Proliferation and maturation of the alveolobular system are mainly controlled by prolactin and placental lactogens during pregnancy. Prolactin stimulates milk protein gene expression

during lactation and the loss of prolactin after weaning leads to death of luminal cells during

involution (Horseman, 1999, Hennighausen and Robinson, 2005).

Due to extensive changes and remodelling in mammary gland, there is an increased

possibility of DNA damage occurrence during mammary gland development. During pregnancy,

increased cell proliferation and DNA replication, as well as rapid alterations in the

microenvironment are most likely to increase DNA damage (Davis and Lin, 2011).

The regenerative potential of epithelial cells during mammary gland development

suggests that there are different cell lineages and mammary epithelial stem cells in mammary

gland epithelium [reviwed in (Hennighausen and Robinson, 2005, Inman et al., 2015). Recent

studies showed that there are both stem and progenitor cells in the prepubertal mammary gland.

Unipotent progenitor cells which originate from bipotent stems cells generate both luminal and

myoepithelial cells. Studies also demonstrated that ductal homeostasis and remodelling are

controlled by bipotent stem cells (Rios et al., 2014). However, the progenitor progeny of luminal,

alveolar and myoepithelial cells are not well understood. Although there are studies using cell

surface maker sorting and lineage-tracing assays to understand stem and progenitor cells and

their differentiation in mammary gland, those studies provide conflicting evidence and therefore

most of the mechanisms are not well understood yet.

In addition to the described mechanisms, the development and homeostasis of mammary gland is also regulated by the tissue microenvironment.

1.2.2 The mammary gland microenvironment

The mammary gland development is regulated by endocrine signals and signals from the microenvironment, such as growth factors, paracrine/autocrine factors, cytokines, stromal-

derived growth factors and signalling from ECM molecules [reviewed in (Musumeci et al., 2015,

Inman et al., 2015)]. The signalling network is accomplished in the mammary gland via direct

contact of myoepithelial cells with other epithelial cells (such as other myoepithelial cells and

luminal cells via desmosomes) and the basement membrane (via hemidesmosomes) (Adriance et

al., 2005, Pandey et al., 2010). In addition to cell-cell contact, myoepithelial cells and stromal

cells are known to produce ECM-degrading enzymes and the ECM components, such as

laminins, collogens, fibronection and proteoglycans, which are essential for tissue architecture,

signalling and the cell fate decision of stem and progenitor cells in mammary gland. As an

example of tissue architecture, the laminin-111 secreted from myoepthelial cells was shown to be

required for luminal cell apical-basal polarity (Inman, 2011) and collogen-I fiber is crucial for

mammary branch orientation during mammary gland development (Brownfield et al., 2013).

Proteases, such as metalloproteinases (MMPs), are important for remodelling the ECM

and stroma, providing growth factors and cytokines for signalling and mammary gland

development. Proteases can also promote the epithelial mesenchymal transition (EMT), which

occurs at some degree at the TEB by providing required signalling molecules [reviewed in

(Inman et al., 2015).

Although the microenvironment is crucial for mammary gland development, as reviewed

by Dr. Mina Bissell (Bissell and Labarge, 2005, Bissell et al., 2003), defects and genetic manipulation of stromal cells can increase cancer risk. The microenvironment can protect the mammary gland from cancer with its tumor suppressor activity or promote cancer even in normal tissue with its oncogene signalling.

Figure 1. Mammary gland development. A. Mammary gland development starts during embryogenesis and continues during puberty, pregnancy, lactation and involution. In neonatal period, the ductal system remains quiescent. During puberty, the growth and ductal branching are regulated by estrogen and progesterone. In pregnancy, estrogen, progesterone, prolactin and placental lactogens promote alveolar development. During lactation, prolactin regulates the secretory state of alveoli. After laction, the mammary gland involutes and returns back to its mature state. The figure is adapted from (Musumeci et al., 2015). B. Whole-mount staining of mature Balb/c mammary gland showing the ductal system that fills the entire fat pad.

1.3 The hormone prolactin and its function in the mammary gland

1.3.1 Prolactin

Prolactin is a 23-kDa peptide hormone that controls proliferation, differentiation, survival and motility by activating several signalling pathways upon binding to its receptor. Prolactin, which is mostly recognised as a lactogenic hormone, has multiple functions and is also involved in tumorigenesis. In addition to being secreted from the pituitary gland, prolactin is also secreted from extrapituitary sites where it behaves as a paracrine/autocrine signalling molecule.

Based on structural and biological features [reviwed in (Ben-Jonathan et al., 2008, Bole-

Feysot et al., 1998), prolactin belongs to a large cytokine superfamily which includes growth hormone and placental lactogens. As other family members, prolactin has a bundle of four antiparallel α helices in an up-up-down-down configuration in its tertiary structure and binds to a single pass, transmembrane receptor which is described in detail below (1.3.2). The single polypeptide chain of prolactin has 3 disulfide bridges (Figure 2), while growth hormone and placental lactogens have only 2 disulfide bridges. Although prolactin has only 21-22% homology with growth hormone and placental lactogens, all three lactogens can bind to the prolactin receptor.

The prolactin gene is located on chromosome 6 and consists of five exons separated by four introns [reviwed in (Bole-Feysot et al., 1998, Ben-Jonathan et al., 2008, Muthuswamy,

2012)]. The prolactin gene is expressed from the pituitary and also from various extrapituitary sites. As a secretory protein, the prolactin gene has an N-terminal signal peptide. Prolactin gene expression in the pituitary is driven by proximal promoter and it is activated by Pituitary-specific positive transcription factor-1 (Pit-1), which is regulated by dopamine. The transcription of prolactin mRNA in extrapituitary sites is regulated by a superdistal promoter, which is located

5.8 kb upstream of the initiation site used in the pituitary and it is not dependent on Pit-1. The presence of a superdistal promoter results in an alternative transcriptional start site, exon 1a, which is spliced into exon 1b and since the novel exon is in a 5’ untranslated region, the transcripts are identical in the pituitary and extrapituitary sites (Figure 3). Prolactin secreted from the pituitary gland and extrapituitary sites have the same primary, secondary and tertiary structure and they both bind to the same receptor.

The prolactin protein can go through several posttranslational modifications, such as proteolytic cleavage, polymerization, glycosylation and phosphorylation which overall affect the stability, biological activity, half-life and receptor binding ability of prolactin [reviewed in (Bole-

Feysot et al., 1998, Ben-Jonathan et al., 2008)]. There are few prolactin forms detected in human serum in addition to 23 kDa prolactin. The 100 kDa macroprolactin, which is composed of monomeric prolactin and IgG, is known to have a longer half-life, low bioactivity and its high levels are detected in patients with hyperprolactinemia. Big prolactin with a size of 40-60 kDa is dimerized prolactin with unknown properties. The proteolytic cleavage of prolactin results in a

16 kDa prolactin which was shown to have antiangiogenic feature and involved in tumorigenesis.

30% of total human pituitary prolactin is N-glycosylated on Asn 31 (Sinha, 1995).

Glycosylated prolactin has reduced binding affinity to the prolactin receptor, and therefore its activity and tissue distribution are altered. It is not stored in pituitary secretory vesicles and is constitutively secreted (Pellegrini et al., 1990). It is mostly found in serum, milk and amniotic fluid (Sinha, 1995).

Prolactin can be phosphorylated on Ser 179 and phoshorylation of prolactin affects biological function and activity of prolactin (Wang and Walker, 1993). Phosphorylation of prolactin was shown to occur in the secretory vesicles of lactotrophs just before exocytosis

(Greenan et al., 1989). There are different protein kinases that are responsible for prolactin phosphorylation such as; p-21 activated protein kinase (γ-PAK) (Tuazon et al., 2002), casein kinase I, protease-activated kinase I, calcium/phospholipid-dependen kinase, cAMP-dependent protein kinase (Oetting et al., 1986). Phoshorylated protein has lower biological activity, antagonizes the growth promoting effect of non-phosphorylated prolactin (Wang and Walker,

1993) and regulates prolactin secretion from lactotrophs (Ho et al., 1989). The phosphorylation status of pituitary released prolactin changes during the estrous cycle and pregnancy (Ho et al.,

1993), and phospho-prolactin was found in high levels in human during lactation (Huang et al.,

2008).

The heparin-binding domain in human prolactin protein differs prolactin from growth hormone and placental lactogens, and having this binding domain increases tissue concentration of prolactin and enhances its cytokine efficiency (Khurana et al., 1999). Figure 2 presents cleavage, phosphorylation, glycosylation and heparin binding sites of prolactin.

Prolactin is released from lactotrophs in the anterior pituitary gland by calcium- dependent exocytosis [reviewed in (Ben-Jonathan et al., 2008)]. However its secretion from the pituitary gland is not controlled by hypothalamus positive stimulation and target tissue negative feedback, and prolactin does not have target organs as do the other hormones. The pituitary prolactin secretion is regulated by neuropeptides, steroid and growth factors and inhibited by dopamine.

Figure 2. Prolactin protein structure demonstrating locations of disfulfide bonds, posttranslational modifications and heparin binding site. The native protein is composed of 199 amino acids with three disulfide bonds. The N-terminus and C-terminus of the protein are identified and the disulfide bonds are indicated in red color between the indicated amino acids (C= cysteine). Prolactin is mainly glycosylated at N31 and phosphorylated at S179 in human. Amino substitution from S to D mimics phosphorylation (S179D). The proteolytic cleavage of prolactin at 145-149 results in a 16 kDa prolactin. Two heparin binding domains specific to prolactin are also demonstrated in the figure. Figure is modified from (Ben-Jonathan et al., 2008).

Superdistal promoter Proximal promoter -900 -5800 -5100 1000 250 0

Exon 1a

Superdistal promoter Pit-1 binding domain Proximal promoter Exon 1a

Enhancer

Figure 3. Diagram of human prolactin gene. The superdistal promoter in extrapituitary prolactin and its start site located 5.8 kb upstream of the the pituitary start site. The proximal promoter is present on both extrapituitary and pituitary prolactin and the Pit-1 binding domain only exists on the proximal promoter which is responsible for dopamine binding. Figure modified from (Ben-Jonathan et al., 2008).

Prolactin is also secreted from extrapituitary sites [reviwed in (Ben-Jonathan et al., 1996,

Muthuswamy, 2012)] which include; decidua, brain, endometrium, myometrium, adipose tissue, immune system, skin and mammary, sweat, lacrimal and prostate glands. There is limited information on extrapituitary secreted prolactin since its release does not have a uniform mechanism and it is tissue specific. There are no storage granules available in the cells which results in constitutive release of prolactin from extrapituitary sites.

Prolactin has a broad function including reproduction, metabolism, immunoregulation, osmoregulation and behaviour. Studies on mice showed that homozygous deletion of prolactin causes loss of lobular formation in virgin mammary glands, however no effect was observed on normal ductal networks. During pregnancy, prolactin -/- mice did not have proper lobuloalveolar development indicating the important role of prolactin during the differentiation process in mammary gland development (Bole-Feysot et al., 1998).

1.3.2 Prolactin Receptor (PRLR)

Prolactin receptor (PRLR) is a 90 kDa protein that belongs to the cytokine-type 1 receptor family (reviwed in (Ben-Jonathan et al., 2008, Bole-Feysot et al., 1998, Clevenger et al.,

2003). PRLR does not have protein kinase activity but can be phoshorylated by cytoplasmic proteins. Upon binding of prolactin to its receptor, several signalling pathways are activated such as Jak2-Stat5, mitogen-activated protein kinase (MAPK) and the phosphoinositide 3 kinase

(PI3K) pathways.

The human PRLR (hPRLR) gene is located on chromosome 5 and consists of 11 exons.

Alternative promoters regulate the transcription of hPRLR in a tissue-specific manner, therefore there are several hPRLR isoforms present.

PRLR protein has three domains: an extracellular domain (ECD), a transmembrane

domain ™ and an intracellular domain (ICD). The ECD is a ligand-binding domain with type III fibronectin-like motifs and contains an amino terminal and a membrane-proximal region. There are two pairs of disulfide bonds in amino terminal and linked cysteines which are involved in ligand binding. The WSXWS-motif (Trp-Ser-x-Trp-Ser) in the membrane-proximal region is essential for correct receptor folding and cellular trafficking. The ICD does not have any intrinsic kinase activity but is required for signal transduction. A proline-rich region in the ICD, named the Box1 motif, has a Src-homology 3 (SH3) binding domain that is recognized by signal transducers and is known as the docking site for Jak2. There is also a variable Box2 motif in ICD with unknown function (Goffin et al., 2005, Ben-Jonathan et al., 2008, Clevenger et al., 2003,

Bole-Feysot et al., 1998).

Signal transduction starts with PRLR dimerization. However there are controversial models suggested for PRLR dimerization, and it is not clear if ligand-binding induces dimerization or whether ligand binds to the dimerized receptor. It is only known that in order to bind to the two receptors, prolactin requires two binding sites with different affinities (Figure 4)

(Bole-Feysot et al., 1998, Muthuswamy, 2012).

In studies, cells are treated with different species of prolactin, however recent studies from Utama et al. (Utama et al., 2006, Utama et al., 2009) showed that hPRLR is insensitive to mouse and bovine prolactin and ovine prolactin is 10-fold less effective than human prolactin.

More structural studies are required to understand PRLR activation by different lactogens and different species prolactin.

Based on the length of the ICD, several PRLR isoforms have been identified which are generally classified as long, intermediate and short forms. In humans there are six isoforms: long

(90 kDa), intermediate (50 kDa), ΔS1 (70 kDa), short 1a (56 kDa) and short 1b (42 kDa) and

soluble PRLRBP (prolactin receptor binding protein) (Figure 5) (Ben-Jonathan et al., 2008).

However, 5 more variants were identified based on the length and composition of ECD or ICD

domain: Δ4/6 S1a, Δ4-S1b, S1c, Δ7/11 and Δ4-Δ7/11. Δ7/11 and Δ4-Δ7/11 are soluble isoforms generated by proteolytic cleavage of membrane anchored receptors (Atlas of Genetics, 2009).

The long form is the major form of the receptor and it transmits signals and stimulates cell proliferation. ΔS1 has reduced ligand affinity and the intermediate form has minor effects on cell proliferation. The short isoforms have dominant-negative effects on prolactin transcriptional responses when they are co-expressed with the long form, suggesting that they may have a protective role against overstimulation by prolactin (Bole-Feysot et al., 1998, Ben-Jonathan et al., 2008).

According to PRLR knockout studies in mice, heterozygous PRLR (+/-) females did not show any defect in fertility or pregnancy, however lactation was absent in their first pregnancy and it was not efficient in later pregnancies. In homozygous PRLR (-/-) female reproduction was impaired due to decreased ovulation rates and oocyte maturation, no pseudopregnancy and implantation and delayed fertility. In the mammary gland, there was reduced ductal branching and no alveolar formation (Bole-Feysot et al., 1998).

PRL$ 1$ 2$ PRL$ PRL$ 1$ 2$ 1$ 2$

PRLR Inactive Active

Figure 4. PRLR activation by prolactin-induced dimerization. Prolactin (PRL) interacts with the receptor with its binding site 1 and forms an inactive receptor-protein complex, followed by binding of prolactin to a second receptor with its binding site 2, which leads to dimerization and activation of the PRLR. Figure is modified from (Bole-Feysot et al., 1998)

!!L!!!!!!!!!!!ΔS1!!!!!!!!!!!!I!!!!!!!!!!!!!!!S1a!!!!!!!!!!!!!S1b!!!!!!!!!!!BP!

Disulfide bond Box 1

WS Motif Box 2

Figure 5. Prolactin receptor isoforms. Receptors are classified based on the length of the intracellular domains as; long (L), intermediate (I), and short (S). The receptor with missing extracellular domain (ΔS1) and a receptor with only extracellular domain (BP). The intracellular domain contains Box 1 and Box 2 motifs, which are required for binding to signalling molecules. Figure modified from (Clevenger et al., 2003, Ben-Jonathan et al., 2008)

1.3.3 Prolactin signalling pathways

The main signalling pathways that are activated by the prolactin receptor include: Jak2-

Stat5, Ras-Raf-MAPK, PI3K/Akt, Tec-Vav2-Nek3-Rac and Src kinase signalling pathways.

Recently Jak2-dependent activation of PAK1 signalling has also been identified (Hammer and

Diakonova, 2015).

1.3.3.1 Jak2- Stat5 Pathway

The prolactin receptor is characterized by its ability to activate Janus kinases (JAKs) and the STATs transcription factors [reviewed in (Ben-Jonathan et al., 2008, Clevenger et al., 2003,

Bole-Feysot et al., 1998).

Jak2 is a member of the Jak family that consists of Jak1, Jak2, Jak3 and Tyrosine kinase 2

(Tyk2) (Han et al., 1997, Tan and Nevalainen, 2008, Bole-Feysot et al., 1998). Jak2 is the

primary Jak protein that activates Stat5a/b and the major Janus kinase activated by the prolactin

receptor in mammary epithelial cells (Clevenger et al., 2003, Tan and Nevalainen, 2008). The

Stat family contains eight members that are all encoded by separate genes: Stat1 (a and b), Stat2,

Stat3, Stat4, Stat5a, Stat5b, Stat6 (or IL-4 Stat) (Tan and Nevalainen, 2008, Bole-Feysot et al.,

1998). Although Stat 1, 3, and 5 can be activated upon prolactin binding, Stat5a/b is particularly

important for mammary gland development and function.

Jak2 is known to be constitutively associated with Box1 motif of the PRLR. Upon

prolactin binding and PRLR dimerization, receptor-associated Jak2 is rapidly phosphorylated

(within 1 minute) (Clevenger et al., 2003). Active Jak2 then phosphorylates tyrosine residues on

the PRLR and also Stat proteins. The phosphorylated Stat proteins dissociate from PRLR and

form hetero- or homodimers via SH2 phoshotyrosine interactions. The dimer then translocates to

the nucleus and binds to GAS (γ interferon activated site) elements to promote transcription of

target genes (Figure 6).

Members of the suppressors of cytokine/cytokine-inducible inhibitor of signalling

(SOCS/CIS) family terminate the signalling. Following receptor dimerization and pathway

activation, SOCS/CIS can interact with the PRLR or Jak2 and compete with Stat proteins

binding and inhibit further signalling. SOCS proteins can also target interacting proteins for

degradation (Anderson et al., 2006, Clevenger et al., 2003). In addition to the SOCS/CIS family,

the peptide inhibitors of activated Stat (PIAS) family can inhibit DNA binding of STATs and

terminate the signalling (Chung et al., 1997, Liu et al., 1998a).

In studies on the role of Jak2 in mammary gland development, mammary gland transplants were used since Jak2 loss resulted in embryonic lethality (Clevenger et al., 2003, Xie et al., 2002). The studies demonstrated that Jak2 is responsible for development of the secretory epithelium of the mammary gland during pregnancy (Shillingford et al., 2002, Clevenger et al.,

2003).

Studies demonstrated that Stat5 is fundamental for cell proliferation and differentiation

and is a survival factor of differentiated alveolar epithelium in the mammary gland during

pregnancy (Miyoshi et al., 2001, Shillingford et al., 2002, Cui et al., 2004). Although Stat5a and

Stat5b share 96% sequence homology at the amino acid level (Liu et al., 1997), some studies

suggested that they have different functions (Tan and Nevalainen, 2008). While Stat5a was

indicated to have a role in viability and survival of differentiated alveolar mammary epithelial

cells, Stat5b was specified to mediate the effects of growth hormone (GH). Also, it was

suggested that Stat5b can compensate for the lack of Stat5a in mammary gland (Liu et al.,

1998b).

Figure 6. Prolactin-Jak2-Stat5 pathway. Upon prolactin (PRL) binding and PRLR dimerization, Jak2 is phosphorylated. Active Jak2 then phosphorylates PRLR and Stat5. The phosphorylated Stat5 dissociate from the PRLR and form dimers. The dimer translocate to nucleus and binds to GAS element. Figure is modified from (Goffin et al., 2005).

Although Jak2-Stat5 pathway is the main signalling pathway used by prolactin, there are other pathways that are important for prolactin signalling.

1.3.3.2 Ras-Raf-MAPK pathway

The Ras-Raf-MAPK pathway is the second major pathway that is activated by prolactin

and it is involved in regulation of proliferation. The adaptor proteins (Shc, GRB2 and SOS) are

recruited to the PRLR by phoshoryated Jak2 which eventually activates a small GTP-binding

protein, Ras. Active GTP-Ras then activates Raf which is followed by consecutive

phosphorylation and activation of Mitogen-activated protein kinase kinase (MEK) and

extracellular signal-regulated protein kinase (ERK). Inhibition of MEK was shown to abrogate

prolactin-mediated mitogensis (Camarillo et al., 1997) and since MEK inhibition did not have

any effect on milk protein B-casein synthesis, this pathway is suggested to have a role in

proliferation.

1.3.3.3 PI3K/ Akt pathway

Upon signal from G-protein-coupled receptors, tyrosine kinases or cytokine receptors, the

activation of phosphoinositide 3-kinase (PI3K) induces production of phosphatidylinositol

(3,4,5)-triphosphate (PIP3) from phosphatidylinositol (4,5)-biphosphate (PIP2). PIP3, a

secondary messenger, serves as a docking site in the plasma membrane for proteins with

pleckstrin-homology (PH) domains. PI3K has two subunits, the p85 adaptor and the p110

catalytic subunit, that upon binding of p85 to a phosphorylated tyrosine kinase residue, allows

the p110 subunit phosphorylates PIP2. Although the interaction has not been established well,

p85 was shown to interact with the PRLR after prolactin binding (Yamauchi et al., 1998) and

activates the pathway. In the plasma membrane, PDK1 (which has an SH domain) is activated by

PIP3 which follows with phoshorylation of AKT and activation of the downstream pathway. The prolactin-activated PI3K/AKT pathway has been shown to be involved in survival of cells.

Prolactin has been shown to interact with Src family members, such as Fyn, Shp2 (Src- homology 2 domain-containing phosphatase 2), which results in activation of the PI3K pathway, as well as cross-talk with other pathways [reviewed in (Martín-Pérez et al., 2015)]. The Tec-Vav pathway has been shown to be activated by direct interaction with Jak2 or by cross-talk with

PI3K and Src signalling. The Tec-Vav pathway activation results in GDP-GTP exchange on Rho family members, such as Rac1, and this pathway is suggested to be involved in prolactin- mediated migration and motility of cells. Recently Jak2 has been shown to phosphorylate PAK1 to facilitate cell motility. Jak2 phosphorylated PAK1 can form a paxillin/GIT1/βPIX/pTyr-PAK1 complex that phosphorylates filamin A and increases actin-regulated cell motility. In a second mechanism, which is dependent on MAPK, Jak2 phosphorylated PAK1 can induce MMP-1 and

MMP-3 (Hammer and Diakonova, 2015).

Prolactin and PRLR signalling pathways are presented in Figure 7.

Figure 7. Prolactin and prolactin receptor (PRLR) signalling pathways. Upon binding of prolactin to the PRLR, several signalling pathways are activated. The main pathway is the Jak2- Stat5 pathway. The MAPK pathway is another important pathway that is activated by prolactin and its receptor. The signalling transduction includes Shc/Grb2Sos/Ras/Raf, MEK1/2 and ERK12 proteins. Src kinase activation is involved in PI3K/AKT and Tec/Vav/Rac pathway activation. Prolactin pathways are involved in proliferation, survival, differentiation and motility of the cells. Figure modified from (Goffin et al., 2005, Clevenger et al., 2003).

1.4 Breast cancer and hormonal influence

1.4.1 Breast cancer classification

According to histological classification, breast cancer can be classified as either in situ or invasive carcinoma. In situ carcinoma is further subdivided into lobular and ductal carcinoma where ductal in situ carcinoma is more common than lobular carcinoma [reviewed in (Malhotra et al., 2014)]. Although histological classification provides valuable information, further categorization tools are necessary for prognosis and therapies.

The status of molecular biomarkers such as estrogen receptor (ER), progesterone receptor

(PR) and human epidermal growth factor-2 (HER2) has been guiding clinical decisions on given therapies to patients (Maughan et al., 2010). Approximately 65-75% of breast cancer patients are hormone receptor (ER and PR) positive and the numbers are suggested to rise which emphasizes the importance of the biomarkers in diagnosis and prognosis.

Microarray based analysis has generated molecular classification which currently includes 7 group: basal-like, luminal A, luminal B, HER2+, normal breast-like, luminal ER-

/Androgen receptor (AR)+ and claudin low. The basal-like breast cancer which is also known as

triple negative breast cancer is an ER-/ PR- /HER2 – breast cancer that is present in 15-20% of patients and it has the worst outcome with the shortest survival rate among all breast cancer subtypes (Sorlie et al., 2001, Sorlie et al., 2003). Most Breast Cancer 1, Early Onset (BRCA1) breast cancers are included in this group (Young et al., 2009). In addition to receptor status, the

Ki-67 proliferation marker is critical to categorize luminal subtypes (Goldhirsch et al., 2011).

Luminal A is an ER+ and HER2low breast cancer. It is the most common breast cancer that is

present in approximately 40% of patients and it has the best prognosis among all other subtypes

(Voduc et al., 2010). Luminal B is ERlow, HER2low and Ki-67high breast cancer and is seen in

approximately 20% of patients. HER2+ is HER2+ and ER- breast cancer which is detected in 20-

25% of patients, and normal breast-like cancer has an adipose tissue gene signature. Claudin low

breast cancer is a recent subtype and it is often triple negative, however it is characterized by the

expression of cell junction proteins such as claudin-, vimentin+ and E-cadherinlow. Luminal ER-

/AR+ is another subtype that was recently identified (Lehmann et al., 2011).

1.4.2 Treatment of breast cancer

Current therapies are designed based on summarized molecular breast cancer subtypes and surgery followed by adjuvant treatment has been standard for breast cancer treatment for decades [reviewed in (Miller et al., 2014)].

Anthracycline-based therapy and CMF (cyclophosphamide, methotrexate, and fluororuracil) regimen are two common therapies that have been used for breast cancer patients for a long time. The chemotherapy drugs in the CMF regimen overall interfere with DNA replication. Cyclophosphamide is an alkylating agent that interferes with DNA replication, resulting from the formation of intrastrand and interstrand DNA crosslinks (Emadi et al., 2009).

Methotrexate is an antifolate drug that inhibits folate-dependent enzymes, which are essential in de novo synthesis of nucleotides for DNA replication (Nagar, 2010). Fluororuracil is a nucleoside analogue that interferes with DNA replication (Damaraju et al., 2003). Although those treatments were used alone in early years starting from the 1970s, the combination of antracycline, particularly doxorubicin, with the CMF regimen has been commonly used in adjuvant settings.

Another group of chemotherapy agents, taxane which is an antimicrotubule agent which[stabilizes microtubules and inhibits mitosis (Chang et al., 2003), is also used in

combination with other treatments, such as TAC (docetaxel, doxorubicin and cyclophosphamide]

or FAC [flouroruracil, doxorubicin and cyclophoshamide]. Importantly, doxorubicin is the main

chemotherapy agent that is used in all combinations in adjuvant therapy settings for breast cancer

patients.

Patients with ER+ breast cancer receive endocrine therapy which is in two main categories: selective estrogen receptor modulators (SERMs) and aromatase inhibitors (AIs).

SERMs competitively bind to estrogen receptor and interfere with the estrogen signalling pathway. Tamoxifen is the most common SERM that is used for patients.

HER2+ breast cancer is observed in approximately 20-25% of patients and trastuzumab is the first monoclonal antibody developed as anti-cancer treatment for patients with HER2 overexpression. Trastuzumab treatment alone or in combination [such as, AC-TH (doxorubicin, cyclophosphamide, paclitaxel and trastuzumab) or TCH (docetaxel, cyclophosphamide and trastuzumab)] with other chemotherapy agents is still the standard regimen used for HER2+

breast cancer patients.

Although chemotherapy treatments provide survival advantage for breast cancer patients,

resistance to chemotherapy agents still remains a challenge for breast cancer.

1.4.3 The role of estrogen in breast cancer

The steroid hormone estrogen and its nuclear receptors Estrogen Receptor-α (ERα) and

Esrogen Receptor-β (ERβ) play important roles in several cellular processes, as well as in tumor

progression (Herynk and Fuqua, 2007).

In mammary gland development, knockout mice studies demonstrated that while ERα is

required for proper mammary gland development particularly involved in ductal elongation and

outgrowth during puberty, ERβ is involved in normal lobuloalveolar development during

lactation but is not required for ductal development (Forster et al., 2002, Krege et al., 1998).

Dysregulated estrogen signalling has been implicated in breast cancer (Russo & Russo

1998) and the role of ERα has been well studied in normal mammary gland development.

Approximately 75% of breast cancers are defined as ERα-positive (Osborne, 1998, Osborne et al., 2000).

Studies suggest that estrogen and ERs use several different pathways to regulate biological processes (Hall et al., 2001). In the classical action of nuclear genomic ER activity, upon binding of estrogen to ER, dimerized ER directly binds to hormone response elements located in the promoter region of target genes (Heldring et al., 2007, Schiff et al., 2005). This binding is believed to recruit co-activator proteins with histone acetyl transferase (HAT) activity and consequently increase transcriptional activity (Herynk and Fuqua, 2007).

In the second ligand-dependent pathway, upon binding of estrogen to the small portion of

ERs located near the plasma membrane, the cellular ERs interact with growth factor tyrosine kinase receptors. This interaction activates tyrosine kinase receptors and signalling pathways such as MAPK and PI3K/AKT pathways. Additionally, ligand-bound ER is suggested to behave as a G-protein-coupled receptor and activate c-Src, which subsequently activates human epidermal growth factor receptor (HER) and downstream , MAPK and PI3K/AKT pathways.

Those activations of downstream kinases phosphorylate nuclear ER and its co-activators, which lead to overexpression of tyrosine kinases, growth factor and signalling intermediate molecules that in turn activate growth factor tyrosine kinase receptors (Schiff et al., 2005). This pathway is believed to be involved in hormone resistance of breast cancer cells.

Although tamoxifen is the most commonly used and effective therapy for the majority of

ER+ breast cancer patients, hormone resistance develops in most of the tumors that initially respond to tamoxifen therapy and leads to relapse of the disease in five years (Herynk and

Fuqua, 2007). One of the mechanisms of this resistance is suggested to involve the second pathway described above where tyrosine kinases and growth factors and their signalling pathways are activated by estrogen and its receptor.

1.4.4 The role of progesterone in breast cancer

The ovarian steroid hormone progesterone and its receptors are also involved in normal mammary gland development and breast cancer progression [reviewed in (Lange and Yee,

2008)]. The expression of progesterone receptor isoforms is regulated by ERα-mediated transcriptional events, which makes it difficult to distinguish the effects of progesterone from estrogen. EGF is another factor that is involved in regulation of expression of progesterone and progesterone receptors.

There are three progesterone receptor (PR) isoforms: PR-A, PR-B and PR-C. They are ligand-activated transcription factors and members of the steroid hormone receptor family.

Progesterone and PR isoforms are involved in TEB development and alveoli formation during mammary gland development. PR-A is involved in uterine development and reproductive function and PR-B is essential for normal mammary gland development. PR-A and PR-B are usually expressed together in tissues. PR-C has been shown to induce progesterone activity in breast cancer cells and also it can function as an inhibitor of PR-B in the uterus (Mulac-Jericevic et al., 2003, Jacobsen et al., 2002).

Progesterone has similar signalling mechanisms as estrogen in that upon binding of progesterone to its receptor, the receptor undergoes conformational changes, dimerization and it disengages from heat shock proteins, which are required for the stability of the unbound form of the receptor. The activated receptor translocates into the nucleus, associates with co-regulatory molecules and directly binds to progesterone response elements in the promoter region of target

genes and induces transcriptional activity. Progesterone can also activate c-Src, MAPK and PI3K

pathways (Richer et al., 1998).

In in vitro studies, progesterone was shown to be involved in proliferation, prosurvival

and differentiation of breast cancer cells. High serum progesterone level is also related to poor

prognosis in breast cancer (Moore et al., 2006, Sartorius et al., 2005).

Several progesterone receptor modulators were designed to reduce progesterone effects in

breast cancer however in clinical trials those modulators failed in that some modulators showed

cross activity with growth hormone receptor (Allan et al., 2006).

1.4.5 The role of prolactin in breast cancer

In addition to normal mammary epithelial cells, prolactin is highly secreted from

malignant mammary epithelial cells (Clevenger and Plank, 1997, Ginsburg and Vonderhaar,

1995). Examination of normal and malignant breast tissues revealed that cancerous breast cells have increased prolactin levels and prolactin receptor expression when compared with normal mammary cells (McHale et al., 2008), and 98% of human breast cancers were shown to express the prolactin receptor (Mertani et al., 1998, Ormandy, 1997).

Prolactin promotes survival, differentiation, proliferation and motility of cells by interacting with several pathways (Nevalainen et al., 2002). Increased levels of autocrine prolactin secretion from breast cancer cells were shown to induce proliferation of cancer cells, enhance PRLR expression and accelerate tumor growth (Liby et al., 2003). Autocrine prolactin has also been shown to promote cell viability via the Jak-Stat5a/b signalling pathway in prostate cancer cells (Dagvadorj et al., 2007).

Prolactin was shown to be a potential survival factor against apoptosis (Perks et al.,

2004) and the its proliferative effect was shown to be due to Jak2/MAPK activation (Yamauchi

et al., 2000). Prolactin enhanced cell survival was also demonstrated to be through Stat5 regulation of caboxypeptidase-D and PI3K/AKT signalling (Abdelmagid and Too, 2008). A splice variant of Stat5a has been demonstrated to be expressed at higher ratios from invasive ductal carcinoma. This variant has been implicated in increased proliferation of cancer cells and promoting ductal carcinoma formation in mice. Prolactin activated Tec/Vav/Rac pathway (Miller et al., 2005) is involved in increased motility of cancer cells.

1.4.5.1 Epidemiologic studies highlight the role of prolactin in breast cancer

There are several epidemiological studies that emphasize the association between high prolactin levels and increased breast cancer risk. Most of the studies measure serum prolactin levels where serum prolactin levels greater than 20-25 ng/ml are accepted as high serum levels of prolactin and diagnosed as hyperprolactenemia [reviewed in (Serri et al., 2003)]. During pregnancy and lactation periods, the levels of prolactin can increase from 20 ng/ml to over 200 ng/ml (Biswas and Rodeck, 1976), which is required for alveolar development and milk production.

In early studies, patients diagnosed with prolactinomas were suggested to have increased risk of breast cancer (Theodorakis et al., 1985, Strungs et al., 1997). One of the epidemiological studies implicated uncertainty of this association due to limited sample sizes (Dekkers et al.,

2010). However, a recent population based cohort study and meta-analysis of literature revealed that there is no association between increased breast cancer risk and prolactinoma (Dekkers et al., 2015).

In a study by Wu et al. (Wu et al., 2011), mRNA and protein expression of growth hormone and prolactin was investigated mammary tumors of 19 patients, endometrial tumors of

102 patients and normal tissues by using in situ hybridization and immunohistochemistry

analysis. Prolactin expression was found to be related to lymph node metastasis and, higher

tumor grade and tumor stage in breast tumors. In endometrial cancer prolactin expression was

associated with myometrial invasion. There was no significant association between the

expression of growth hormone or prolactin and the expression of estrogen or progesterone

receptors in breast tumor samples. The study demonstrated that the expression of both prolactin

and growth hormone, individually and combined, are related to worse relapse-free-survival and

overall survival in breast and endometrial cancer patients.

There are several epidemiological studies that have been done by Tworoger et al. and

they emphasized that high serum prolactin levels are related to increased risk of breast cancer.

Their studies in 2004 (Tworoger et al., 2004) indicated that high plasma prolactin levels are

associated with increased risk of postmenopausal ER+/PR+ breast cancer. However, in their

publication in 2006 (Tworoger et al., 2006), prolactin was shown to increase risk among

premenopausal women. In 2007 (Tworoger et al., 2007), the group indicated that the risk is not

related to menopausal and ER status in another cohort study. In 2013 (Tworoger et al., 2013),

they have evaluated over 2000 breast cancer cases and compared them with over 4000 controls

in a 20-year prospective study. According to their results, high serum levels of prolactin were

related to increased breast cancer risk in premenopausal women and the risk was strongest for

ER+ breast cancer and lymph node-positive tumors. In their 2014 epidemiological study

(Tworoger et al., 2014), prolactin was indicated to provide predictions of invasive breast cancer

in postmonapausal women. In the latest study in 2015 (Tworoger et al., 2015), Tworoger et al.

investigated bioactive prolactin and the bioactivity was measured by using Nb2 lymphoma cell bioassay. According to their study 1329 cases and 1329 controls were investigated, there was no significant relation between bioactive prolactin levels and risk for breast cancer, however there

was a suggestive association for breast cancer risk for postmenopausal women. There was a significantly stronger association between the bioassay and the risk of breast cancer occurring shortly after blood collection, which indicated involvement of autocrine prolactin secretion from tumor. According to the study, nulliparous women had higher bioactive prolactin when compared with parous women and women with family history of breast cancer had higher bioactive prolactin levels.

A recent European EPIC cohort study in 307 in situ breast cancer cases and 307 controls showed significant positive association between high levels of serum prolactin with risk of in situ breast cancer among all pre- and postmenopausal women (Tikk et al., 2015). There was not a significant association based on the menopausal status of women evaluated. However, in their previous study in 2014 (Tikk et al., 2014), Tikk et al. observed significant association between high serum levels of prolactin and invasive breast cancer in postmenopausal women. According to a 2015 study, the association was strongest among women diagnosed with breast cancer less than 4 years after a blood donation and also among nulliparous women.

Overall all studies indicate that high levels of prolactin a related to increased risk for breast cancer.

1.4.5.2 In vivo studies implicating the role of prolactin in tumor formation

In early studies, prolactin was found to induce spontaneous tumors in rodents. Pituitary isografts to mice and systemic prolactin treatment induced spontaneous tumor formation in mice

(Boot, 1962, Muhlbock and Boot, 1959) and the rat tumors formed with chemical carcinogens were promoted by prolactin (Welsch, 1977). Dopamine agonist (bromocriptine) treatment was shown to inhibit prolactin secretion from the pituitary gland and it decreased mammary tumor formation in mice (Welsch and Gribler, 1973). The first transgenic mouse model was designed to

overexpress rat prolactin by using a rat prolactin transgene under the control of metallothionein

(MT) promoter. All transgenic virgin mice developed mammary tumors (Wennbo et al., 1997).

Dr. Linda A. Schuler’s laboratory generated a transgenic mouse model that overexpresses prolactin within mammary epithelial cells under the control of the neu-related lipocalin (NRL) promoter, which mimicked autocrine prolactin effects (Arendt and Schuler, 2008b). NRL-PRL transgenic mice were shown to develop invasive ER+ and ER- mammary tumors (Rose-

Hellekant et al., 2003). In a following study using NRL-PRL transgenic mice, autocrine prolactin induced carcinomas were shown to be diverse with respect to histotype and ERα/PR expression.

However they were demonstrated to mostly have luminal characteristics (Arendt et al., 2011).

1.4.5.3 The role of prolactin in resistance to chemotherapy agents

Prolactin mediated cytotoxic resistance to various chemotherapy treatments has been implicated in different studies.

In a study using ovarian cancer cells with PRLR, prolactin treatment inhibited cisplatin- induced cell death and decreased apoptosis (Asai-Sato et al., 2005). Prolactin was shown to be a potent survival factor against C2-Ceramide induced apoptosis in breast cancer cell lines (Perks et al., 2004). In another in vitro study, the effect of “pure” prolactin receptor antagonist, Δ1-9-

G129R-hPRL, was investigated in breast cancer cells. The inhibition of prolactin signalling by the receptor antagonist sensitized breast cancer cells to doxorubicin and paclitaxel treatments.

In a recent study (LaPensee et al., 2009), a mechanism of prolactin mediated cytotoxic resistance against cisplatin was identified. Prolactin significantly increased viability of breast cancer cells (MDA-MB-468 and T47D) against various chemotherapy agents such as taxol, vinblastine, doxorubicin and cisplatin. When the cytotoxic resistance against cisplatin was investigated in detail, prolactin was shown to prevent cisplatin-induced G2/M cell cycle arrest

and apoptosis, as well as prolactin decreased cisplatin binding to DNA and cisplatin mediated

DNA damage. The mechanism was shown to include a detoxification enzyme, glutathione-S-

transferase (GST). Prolactin increased GST activity and therefore inhibited cisplatin entry into

the nucleus.

Prolactin mediated chemotherapeutic resistance was also observed in clinical studies

(Lissoni et al., 2002, Lissoni et al., 2000, Lissoni et al., 2001). In the studies, an abnormal high level of serum prolactin was identified in metastatic breast cancer patients, and the high levels were suggested to have increased due to autocrine prolactin secretion from tumors or treatment related stress in patients. When the patients were treated with bromocriptine, serum prolactin levels decreased and patients showed better response to taxotere (analog of taxol) treatment.

1.4.5.4 Prolactin receptor antagonists

Given the importance of prolactin in the risk of breast cancer, several approaches have been taken to target both endocrine and autocrine prolactin. One of the early PRLR antagonists

(Goffin et al., 2005, Goffin et al., 2006), G129R-hPRL, was designed based on growth hormone receptor antagonist where the helix 3 glycine was replaced with arginine on human prolactin protein. Glycine 129, which is suggested to serve as a docking site for large residues of PRLR, was replaced by arginine on the protein’s N-terminus. However when tested in Nb2 cell proliferation assay, G129R-hPRL did not antagonize prolactin activated cell proliferation, it showed intrinsic mitogenic activity and behaved as a weak prolactin agonist. Although it failed in bioassay studies, several in vitro and in vivo experiments were done with this antagonist. In one of the studies, prolactin was shown to upregulate bcl-2 protein and mRNA expression in breast cancer cell lines and G129R-hPRL antagonist completely inhibited bcl-2, it decreased cell proliferation and slowed the growth rate of tumors in mice.

Following this study, G129R-hPRL was fused with an angiogenesis inhibitor (endostatin)

and the fused G129R-endostatin antagonist was shown to inhibit prolactin signal transduction,

human umbilical vein endothelial cell proliferation in vitro, disturbed endothelial tube structure

formation and decreased tumor formation in mice. In a following in vivo experiment, G129R-

endostatin was shown to limit angiogenesis, delay tumor recurrence, reduce lung metastasis and

decrease blood vessel density.

S179D is another antagonist designed by Walker’s group as a molecule that mimics

phosphorylated prolactin (Bernichtein et al., 2001, Bernichtein et al., 2003). Serine 179 is the major phosphorylation site for prolactin and it was replaced with aspartate during the antagonist design. In the studies it was able to antagonize prolactin mediated mitogenic activity but was also shown to activate prolactin downstream pathways such as Jak2/Stat5 and MAPK pathways.

The second generation of PRLR antagonist (Δ1-9-G129R-hPRL) was from Goffin group

(Bernichtein et al., 2003) and it was based on the first designed G129R-hPRL antagonist.

However, while designing the second antagonist, they tried to eliminate agonistic effects of the

G129R-hPRL by removing the first nine residues from the N-terminus of prolactin and combined this mutation with the first G129R mutation. Glycine replacement disturbed the binding process to the PRLR, however, when combined with the second deletion mutation the antagonist was shown to responsed in all bioassays. Although Δ1-9-G129R-hPRL prolactin receptor antagonist has been shown to inhibit prolactin signalling and function, this receptor antagonist has low affinity (10-fold lower than human prolactin) and shows residual agonistic activity in sensitive bioassays. Additionally it is not proper for in vivo experiments that several mice treatments failed with the Δ1-9-G129R-hPRL prolactin receptor antagonist.

Recently, Novartis designed a neutralizing monoclonal antibody against human PRLR,

LFA 102 (Damiano et al., 2013). LFA 102 is a humanized IgG1-kappa antibody derived from

mouse hybridoma and was shown to inhibit biological functions of both endocrine and autocrine

prolactin. This neutralizing monoclonal antibody antagonized prolactin and PRLR signalling and

inhibited prolactin biological functions in vitro, as well as reduced the growth of prolactin

sensitive tumors in vivo. A clinical phase one trial with LFA 102 for prolactin receptor-positive

castration-resistant prostate cancer or prolactin receptor-positive metastatic breast cancer has recently closed (NCT01338831).

1.5 Heat shock protein 90 (Hsp90)

The functional protein and the efficient gene expression require native conformation of polypeptide chains, as well as their localization in or out of the cell. Most of the polypeptides need another machinery, which is called molecular chaperones or heat shock proteins (HSPs), to keep their native folded conformation (Smith et al., 1998). In mammalian cells six major families of heat shock proteins are classified based on their molecular size: Hsp100, Hsp90, Hsp70,

Hsp60, Hsp40 and small heat shock proteins including Hsp27 (Jolly and Morimoto, 2000).

Hsp90 is a highly conserved protein family in all eukaryotic cells (Kim and Kim, 2011) and known as the master chaperone in cancer (Khalil et al., 2011). There are two main isoforms of Hsp90: Hsp90α and Hsp90β (Neckers, 2002), which are encoded by different genes, but have

76% similarity (Csermely et al., 1998). Some reviews (Sreedhar et al., 2004, Kim and Kim,

2011) also consider three more isoforms in the family such as glucose-regulated protein 94

(GRP94) in endoplasmic reticulum, mitochondrial tumor necrosis factor receptor-associated protein 1 (TRAP1/ HSP75), and membrane-associated HSP90N. Although those isoforms are located in different cellular compartments, their structures and chaperone functions are suggested

to be similar due to their common cyclic conformational change mechanism (Kim and Kim,

2011).

Hsp90 is involved in many important functions such as cell cycle progression, apoptosis, mitotic signalling, telomere maintenance, protein degradation, and transportation of the proteins

(Hartl et al., 2011). Hsp90 accomplishes these functions by folding, assembly, maturation and stabilization of more than 200 client proteins. Some of the client proteins include cell cycle regulators (Cdk4, Cdk6), steroid receptors (androgen, estrogen receptors), various cell signalling proteins (AKT, CRAF, BRAF, IKK, p53, BCR-ABL, SRC, vascular endothelial growth factor receptor (VEGFR), tyrosine kinases (HER2, epidermal growth factor receptor (EGFR), MET and insulin-like growth factor-1 receptor (IGF-1R), transcription factor hypoxia inducible factor-1α

(HIF-1α), and telomerase (Kamal et al., 2004, Kim and Kim, 2011). Importantly most of the proteins are involved in the various mechanisms of cancer.

1.5.1 Chaperone mechanism of HSP90

Hsp90, which is present as a homodimer, functions as dimer subunits in the cell (Kim and

Kim, 2011, Hartl et al., 2011). Each subunit is composed of three domains: N-terminal ATPase domain which binds and hydrolyze ATP, middle domain which is suggested to be the client binding domain, and the C-terminal domain that provides protein-protein interaction and dimerization (Prodromou and Pearl, 2003, Hartl et al., 2011). Hsp90 chaperone activity requires both ATP binding and hydrolysis (Prodromou et al., 1997). The ATPase cycle and chaperone cycle of Hsp90 are presented in Figure 8 and Figure 9, respectively. The C-terminal domain of

Hsp90 recruits co-chaperones, which mostly use the tetratricopeptide repeat (TRP) domain

(Wandinger et al., 2008). During Hsp90 chaperone and ATPase cycle, both TRP proteins such as

HOP and non-TRP protein such as CDC37, p23 and Aha1 have important roles. For example,

HOP is known to provide a direct connection between HSP70 and HSP90.

As demonstrated in Figure 8, upon ATP binding, the N-terminal domains are dimerized which results in molecular clamp and compaction of the Hsp90 dimer. The middle domain binds to client protein and also interacts with AHA1, which induces ATP hydrolysis. After hydrolysis,

Hsp90 monomers separate from the N-terminal domain. During this cycle, CDC37 recruits kinase substrates to Hsp90 and inhibits ATPase activity, HOP inhibits dimerization of the N- terminal and p23 stabilizes dimer HSP90 before hydrolysis (Hartl et al., 2011).

During the overall chaperone cycle of Hsp90 (Figure 9), which also includes the ATPase cycle, client proteins bind to Hsp90 in a complex with Hsp70, Hsp40, HIP and HOP, which is named the intermediate complex. After ATP binding and hydrolysis, the mature complex is formed with Hsp90, p23, CDC37 and immunophilins (IP) which activate the client protein

(Kamal et al., 2004). Figure 8 also illustrates the binding of Hsp90-inhibitor, geldanamycin

(GM) to the N-terminal of HSP90 which leads to inhibition of ATP binding and hydrolysis. The binding of geldanamycin to Hsp90 results in ubiquitination and proteasomal degradation of the client protein.

Inactive Substrate

ND ATP ATP ATP Inactive MD Substrate

ADP+Pi HOP

CDC37 AHA1

p23 ATP ATP ADP ADP Inactive Substrate

Active Substrate

Figure 8. ATPase cycle of Hsp90. Clockwise from top left; Upon ATP binding to the N- terminal ATPase domain (ND) of Hsp90, a conformational change occurs and the ATP lid closes. Subsequently, Hsp90 forms a dimer and the dimer triggers ATP hydrolysis. Following hydrolysis, NDs dissociate and the inactive substrate interacts with Hsp90 from the middle domain (MD). The substrate is activated through Hsp90 ATPase cycle. The co-factors involved in this process are: CDC37, HOP, AHA1 and p23 which regulates the steps of the cycle. Figure is modified from (Hartl et al., 2011).

HIP HOP Hsp70 HOP HOP HIP Hsp40 Hsp90 Hsp70 GM Hsp40 Hsp90 GM Hsp90 Client Intermediate complex

HIP

Hsp70 ATP HIP Hsp40 HOP Hsp70 Client

Hsp40 IP Client

p23 Client cdc37 cdc37 Hsp90 cdc37 Hsp90 IP p23 p23 IP Client

Mature complex Proteasome

Figure 9. Chaperone cycle of Hsp90. The client protein binds to Hsp90 intermediate complex, which involves co-chaperones HSP70, HSP40, HIP and HOP. Following ATP binding and hydrolysis, Hsp90 forms a mature complex with p23, p50/ cdc37 and immunophilins (IP). This complex leads to conformational maturation of client protein. Upon binding of Hsp90- inhibitors, such as geldanamycin (GM), to the N-terminal ATP binding domain of Hsp90, the inhibitor inhibits ATP binding and hydrolysis, and dissociates the intermediate complex. The client protein is ubiquitinated and targeted to proteasomal degradation. Figure is modified from (Kamal et al., 2004).

1.5.2 The role of Hsp90 in DNA damage response

Hsp90 stabilizes more than 200 proteins and some of those proteins are involved in DNA damage repair. Some studies are summarized below to underline the important role of Hsp90 in the DNA damage response.

Upon DNA damage and replication stress, activation of the ATR-Chk1 signalling pathway induces a DNA damage response, which leads to the inhibition of S cell-cycle progression and stabilization of stalled replication forks. Previous studies demonstrated that

Chk1 is a Hsp90 client protein (Arlander et al., 2006), such that Hsp90 inhibition destabilized

Chk1 and disturbed Chk1 signalling. In addition, the disruption of Chk1 activation by Hsp90 inhibition, sensitized cancer cells to an S phase-specific nucleoside analogue chemotherapy agent, gemcitabine (Arlander et al., 2003). In a following study, ATR and Chk1 were shown as client proteins of Hsp90, and Hsp90 inhibition reduced the protein levels and the activation of

ATR and Chk1, as well as reduced ATR and Chk1 accumulation at the DNA damage site. Hsp90 inhibitor pre-treatment followed by ionizing radiation (IR) inhibited ATR-Chk1 dependent DNA damage repair, and sensitized cancer cells to IR (Ha et al., 2011).

The involvement of Hsp90 in base excision repair (BER) was implicated showing that in response to DNA damage and cellular proliferation, Hsp90-mediated regulation of Polymerase β and X-ray repair cross-complementing protein 1 (XRCC1) complex affects the architecture of

DNA repair complex and regulates repair pathway choice in the context of cell cycle progression and genome surveillance (Fang et al., 2014).

DNA-dependent protein kinase (DNA-PK) dependent phosphorylation of Hsp90α at Thr-

7 residue in response to double stranded DNA breaks was shown to cause the accumulation of phoshorylated Hsp90α at the DNA damage site and formation of repair foci with slow kinetics,

which correlated with phoshorylated histone H2AX levels and foci formation. This indicated that

Hsp90α is directly involved in DNA repair and a potential biomarker for genomic instability

(Quanz et al., 2012).

The importance of Hsp90 in double stranded DNA break repair has been implicated in a cooperation with a nuclear tumor suppressor, BRCA1, within mechanisms involving homologous repair, non-homologous end joining and G2/M activation. Although BRCA1 is a tumor suppressor, its high or restored expression is associated with resistance to chemotherapy and poor prognosis. Hsp90 inhibition was shown to decrease BRCA1 levels, abolish BRCA1- dependent DSB repair and sensitize cancer cells to IR and platinum based treatments (Stecklein et al., 2012).

1.5.3 The role of HSP90 in cancer and chemotherapy resistance

As described in detail in previous parts, Hsp90 is involved in folding, disaggregating, and maturing many proteins under different stress conditions such as environmental, chemical and pathophysiological stress that includes acute and chronic stress (Landriscina et al., 2010).

Consequently, in many cancer types, Hsp90 is overexpressed which causes poor prognosis and poor response to therapies in many patients (Ciocca and Calderwood, 2005). There are few suggested molecular mechanisms of Hsp90 in chemotherapy resistance such as: (i) in response to cytotoxic drug exposure, increasing stability of damaged proteins, (ii) interacting with apoptotic pathways by inhibiting apoptotic mechanisms and inducing cell survival (Beere, 2005), (iii) preventing microvasculature in the cancerous tissue (Ciocca et al., 2003) and (iv) increasing

DNA repair (Mendez et al., 2003).

The overexpression of Hsp90 has been demonstrated in several studies. In a breast cancer study (Yano et al., 1999), Hsp90 levels were measured by using immunohistochemistry (IHC),

in situ hybridization (ISH) and RT-PCR, and compared between breast tumors and normal

tissues. The results demonstrated that 89.5% of invasive ductal carcinomas are Hsp90 positive.

There was increased expression of both Hsp90α and Hsp90β in tumor tissues when compared

with the normal tissues.

In the concept of drug resistance, there are different studies implicating the involvement

of Hsp90 family in resistance to cancer therapies. Hsp90 was shown to be involved in resistance

to cisplatin (Wang and Lippard, 2005) in ovarian cancer cells and the resistant cells were shown

to be sensitized upon administration of Hsp90 inhibitor, geldanamycin.

The role of Hsp90 has also been implicated in HER2 positive breast cancers. HER2 is

one of the client proteins of Hsp90 (Basso et al., 2002) and Hsp90 inhibitors were shown to be

effective in HER2-driven xenograft model (Munster et al., 2001). There are different suggestions

about the resistance mechanism to anti-HER2 agents, one of which involves phosphatidylinositol

3-kinase (PI3K) pathway, which is downstream signalling of the HER2 receptor (Xing et al.,

2008). The key protein of PI3K pathway, AKT, is known as one of the client proteins of Hsp90

(Kim and Kim, 2011), which indicates the involvement of Hsp90 in the drug resistance mechanism of HER2 positive cancers. In the second mechanism of anti-HER2 drug resistance, it is believed that the truncated HER2 receptor, p95HER2 does not have any binding domain for trastuzumab, which makes the trastuzumab therapy inefficient in those patients. Truncated HER2 is believed to be one of the client proteins of HSP90 and studies showed that inhibitors of HSP90 block p95HER2 signalling in resistant tumors and suppress their growth (Chandarlapaty et al.,

2010).

The role of HSP90 in cancer and drug resistance has been investigated in the studies of the Hsp90 client proteins and the mechanism of HPS90 involvement in these processes has been

demonstrated in these studies. Considering over 200 client proteins of Hsp90, it would be difficult to evaluate several suggested roles of HSP90 in cancer and drug resistance in this short assay.

1.5.4 Targeting Hsp90 in cancer treatment

The overexpression of HSP90 was indicated in various cancers and was found to be related to increased cell proliferation, poor prognosis and resistance to chemotherapeutic agents

(Khalil et al., 2011). As a major cancer chaperone, all those outcomes are correlated with its client proteins that are involved in many cellular events, such as cell cycle, cell proliferation, apoptosis and many others, which are also important mechanisms in cancer progression. Various inhibitors of Hsp90 have shown efficient antitumor activity in different cancer types (Taldone et al., 2008). There are approximately 14 drug candidates for Hsp90, most of which are in clinical trials. For example, geldanamycin, which is a well-known ansamycin antibiotic, is one of the inhibitors that targets the N-terminal ATP-binding domain of Hsp90. Binding of geldanamycin to Hsp90 competitively inhibits ATPase activity (Stebbins et al., 1997, Prodromou et al., 1997), disturbs Hsp90-client protein interaction and decreases the activity of client protein by leading to its proteosomal degradation (Kamal et al., 2004). The antifungal agent, Radicicol, is another inhibitor of Hsp90, which binds to the N-terminal domain and inhibits chaperone activity of

Hsp90 and suppresses transformation by the Src and Ras oncogenes (Sharma et al., 2012). There are also other drugs that are in clinical trials such as 17-allylamino-17-demethoxygeldanamycin

(17AAG) which is in Phase 3 clinical trials for multiple myeloma, leukemia, kidney and breast cancers, IPI-504 is in Phase 2 clinical trials for patients with HER2- positive breast cancer,

BIIB021 is in Phase 2 clinical trials for breast cancer patients and there are many others that are in clinical trials for different types of cancers (Porter et al., 2010). As described in the role of

Hsp90 in chemotherapy resistance, HSP90 inhibitors are also suggested as a therapy for resistant cancers by targeting different client proteins, which might be involved in chemoresistance mechanism.

1.6 DNA damage response

1.6.1 DNA damage response overview

DNA damage is a common event that occurs due to endogenous or exogenous factors in the cell and may lead to genomic instability and cancer. In order to overcome the harmful effects of DNA damage, special DNA repair mechanisms are activated based on the type of DNA damage. There are five main DNA repair mechanisms such as base excision repair (BER), mismatch repair (MMR), nucleotide excision repair (NER) and double-strand break repair that includes homologous recombination (HR) and non-homologous end-joining (NHEJ) [reviewed in (Dexheimer, 2013, Sancar et al., 2004).

DNA damage might be the result of endogenous factors. The simplest DNA damage occurs due to spontaneous DNA base hydrolysis and the abasic sites that form as results of hydrolytic nucleobase loss. The abasic sites can also be produced during base excision repair.

Chemical modifications by reactive molecules, such as reactive oxygen species (ROS), produced during normal cellular metabolism can lead to different DNA adducts and DNA damage. The

DNA repair process may also be error prone and generate DNA damages, which may cause A-G mismatch, T-C mismatch, deletion and insertion. In addition to endogenous factors, exogenous factors such as UV, polycyclic aromatic hydrocarbons, ionizing radiation and DNA-damaging chemotherapy agents can induce DNA damage.

1.6.2 DNA repair mechanism

1.6.2.1 Base excision repair (BER)

This mechanism is mostly responsible for the repair of damaged bases [reviewed in

(Hitomi et al., 2007, Zharkov, 2008)]. The damage that occurs due to ROS, X-rays, alkylating reagents or spontaneous reactions is mostly repaired with BER. The repair is initiated by a DNA glycosylase. A damaged base is removed by a DNA glycosylase to form an abasic site.

Apurinic/apyrmidinic endonuclease 1 (APE1) cleaves the phosphodiester backbone and creates a

DNA strand break. This break is repaired through a DNA synthesis/ligation step which is divided into short-patch and long-patch BER. During short-patch BER, where a single nucleotide is incorporated at the DNA break, APE1 recruits Pol β to fill the break. In long-patch BER, APE1 recruits the RFC/PCNA-Polδ/ε complex which carries out repair synthesis (Fortini and Dogliotti,

2007).

1.6.2.2 Mismatch repair (MMR)

MMR is an essential mechanism to repair mismatched bases which occurs as a result of replication errors [reviewed in (Li, 2008, Larrea et al., 2010, Modrich, 2006)]. MMR deficient cells mostly characterized by their elevated mutation frequency and germline mutation in MMR genes is seen in many hereditary cancer types. MMR involves three steps. The first step is the recognition step that is accomplished by MutSα and MutSβ in mammalian cells, which is followed by the excision step where the mismatched strand is cleaved by Exonuclease 1 (Exo1) recruited by the Muts/MutL/DNA complex. The repair is filled by DNA resynthesis which is accomplished by the DNA Polymerase δ/ PCNA/Replication protein A complex and the remaining nick is sealed by DNA ligase I.

1.6.2.3 Nucleotide excision repair (NER)

The damages produced by UV, such as pyrimindine dimers, or cisplatin-DNA adducts can be repaired by NER. As seen in previous repair mechanisms, NER involves three steps:

DNA damage recognition, local DNA opening and repair synthesis/ligation [reviewed in (Shuck et al., 2008, Costa et al., 2003)]. Defects in NER leads to genetic disorders, cancer, immunological defects and several disorders (Cleaver et al., 2009). NER is divided into global genome-NER and transcription-coupled-NER based on the differences in the first recognition step. Overall, the damage is recognized by the RPA, XPA and XPC-TFII complex. TFIIH- associated helicases XPB and XPD unwind the DNA helix which gives access to XPA to the damaged site. RPA binding completes extension and stabilization, and endonucleases such as

XPG and XPF/ERCC1 cleaves the damaged DNA. The gap is filled by DNA Polymerase δ and

DNA ligase seals the remaining nick.

1.6.2.4 Double-strand break repair (DSB repair)

Double-strand breaks can be induced by ionizing radiation, reactive oxygen species or

DNA-damaging chemotherapy agents and they can be biologically deleterious. An imprecise double-strand break repair can lead to genomic instability and cancer. There are two main DSB repair mechanism such as homologous recombination (HR) and non-homologous recombination

(NHEJ) (Khanna and Jackson, 2001).

1.6.2.4.1 Homologous recombination (HR)

During the HR mechanism, genetic information is used from the undamaged sister chromatid and this mechanism is implicated as an error-free mechanism. HR mechanism is limited to late-S and G2 phases (Li and Heyer, 2008, Dexheimer, 2013, Sancar et al., 2004).

The HR mechanism involves three main steps such as strand invasion, branch migration and Holliday junction formation. MRN complex (Mre11-Rad50-Nbs1) initiates 5’ to 3’ resection. Following Rad52 end-binding, Rad51 is recruited to the conjunction and forms Rad51- coated single-strand DNA tail (Rad51 nucleoprotein filament). Strand invasion and branch migration are initiated by Rad51 which is essential for DNA sequence homology search. Rad51 initiates strand invasion and branch migration after the homologous DNA is identified (Forget and Kowalczykowski, 2010). DNA synthesis is accomplished by DNA polymerase η and ligation is accomplished by DNA ligase I to form a four-way recombination intermediate which is known as the Holliday junction (Ip et al., 2008). The junction is resolved by symmetrical cleavage leading to error-free repair of DSB.

1.6.2.4.2 Non-homologous end-joining (NHEJ)

NHEJ is known be the predominant DSB repair mechanism in mammalian cells during all cell cycle phases. Since it eliminates the DSB by direct ligation, it is in generally an error prone process (Lieber, 2010, Sancar et al., 2004, Dexheimer, 2013).

During the first recognition step, Ku70/Ku80 (Ku) heterodimer binds to the two ends of a double strand break and recruits DNA-dependent protein kinase (DNA-PKcs), which binds to the

DNA termini after Ku translocation (Walker et al., 2001, Yoo and Dynan, 1999). Binding of

DNA-PKcs promotes synapsis and DNA-PKcs autophosphorylation. The resynthesis of missing nucleotides is accomplished by DNA Polymerase µ and λ or alternatively NHEJ-specific nuclease Artemis (Jeggo and O'Neill, 2002). Following the proper process, DNA ligase IV-

XRCC4 promotes DNA ligation.

1.6.3 ATM (Ataxia Telangiectasia mutated protein )

The ataxia-telangiectasia mutated kinase protein (ATM) plays a critical role in the

cellular response to DNA damage. The loss of ATM causes progressive neurodegeneration,

immunodeficiency and radiation sensitivity in a rare human disease called ataxia-telangiectasia

(A-T) (Shiloh, 2003).

ATM is a relatively large protein (350 kDa) and a member of the phosphatidylinositol 3-

kinase-like family of serine/threonine protein kinase (PIKKs) (Shiloh, 2003). ATM, as other

members of this protein kinase family (ATR and DNA-PK), phosphorylates its substrates on

serine or threonine followed by glutamine (Shiloh, 2003).

ATM has three main domains: Focal adhesion kinase (FAT domain), a phosphotinosite

3,4 kinase (PI3K) domain and a FAT carboxyl-terminal (FAT-C) domain. Electron microscope

analysis also revealed that ATM has a large ‘head’ domain (Llorca et al., 2003) and the amino

side contains Huntington, Elongation Factor 3, PR65/A, TOR (HEAT) repeats (Perry and

Kleckner, 2003). Upon DNA damage from irradiation or chemotherapy agents (such as doxorubicin, etoposide), Serine 1981 in ATM FAT domain is phosphorylated (Bakkenist and

Kastan, 2003). ATM is normally present as an inactive dimer in the cells and during its phosphorylation from serine 1981 it becomes a monomer (Bakkenist and Kastan, 2003). Once active, ATM phosphorylates H2AX on Serine 139 (Burma et al., 2001).

During DSB repair, the MRN complex acts as a damage sensor (Stracker and Petrini,

2011) and it is required for optimal ATM activation and recruitment of ATM to the damaged side. Activation of ATM leads to the phosphorylation of various downstream targets that are involved in several cellular processes such as DNA damage recognition, regulation of cell cycle check points (G1, intra-S, G2/M) and apoptosis (Kurz and Lees-Miller, 2004, Shiloh, 2003). In

the signal transduction pathway of ATM, a number of targets of ATM have been identified.

These substrates include p53, Brca1, Chk2. Nbs1 and SMC1 (Kurz et al., 2004).

CHK2 is a known as tumor suppressor gene. The protein encoded by the gene is Chk2 protein, which is a serine/threonine protein kinase and involved in the DNA damage response upon phosphorylation by ATM. Phosphorylated Chk2 can activate a variety of substrates including; Cdc25, p53, PML, E2F1 and Brca1. Chk2 is involved in multiple roles such as repair of DSBs and base modification upon Brca1 and Brca2 phosphorylation, heterochromatin relaxation mediated by Chk2 dependent KAP1 phosphorylation on Serine 473, cell cycle arrest at

G1/S and G2/M cell cycle phases, apoptosis upon irreparable DNA damage, senescence and regulation of senescence-associated secretory phenotype (SASP). In addition to its role in DNA damage repair, Chk2 is also required for the mitotic spindle assembly and chromosomal stability during mitosis (Stolz et al., 2011, Zannini et al., 2014).

The study from (Goodarzi et al., 2008) has implicated that less then 25% of DSBs require

ATM and ATM signalling for repair. DSBs in heterochromatin regions are repaired slower than euchromatin DSBs, and ATM and the ATM target KAP1 (Krüppel-associated box (KRAB)- associated protein 1) protein were shown to be involved in heterochoromatin DSB repair. KAP1 is a nuclear protein that is involved in co-repression of gene transcription in a complex with heterochromatin protein 1 (HP1). In response to DSBs, KAP1 is phosphorylated on Serine 824 in an ATM-dependent manner. Phosphorylated KAP1 spreads throughout the chromatin with

HP1 and decondenses heterochromatin. The switch between sumo-KAP1 and phospho-KAP1 regulates decondensation of condenstation function of KAP1. After DNA is repaired, phospho-

KAP1 returns back to sumo-KAP1 state and controls heterochromatin formation (Ziv et al.,

2006).

In an epidemiological study, ATM mutations that are seen in A-T patients were shown to increase their susceptibility to breast cancer (Ahmed and Rahman, 2006). In another study investigating the clinicohistopahtalogic effect of ATM and Chk2 expression on sporadic breast cancer, low nuclear levels of ATM were shown to be associated with aggressive breast cancer and low ATM protein levels were significantly related poor survival in ER- breast cancer patients who received adjuvant therapy (Abdel-Fatah et al., 2014).

Since ATM loss in A-T patients increases DNA damage sensitivity, ATM inhibitors have been developed for potential anti-cancer therapies. The recently developed inhibitor, KU55933, is a ATM-competitive inhibitor that inhibits the kinase activity of ATM. KU55933 was shown to sensitize cells to irradiation and chemotherapy agents, such as doxorubicin, etoposide, and campotothecin (Hickson et al., 2004). In a separate study, KU55933 was shown to reduce cell proliferation by inhibiting Akt phosphorylation and its downstream signalling (Li and Yang,

2010).

Overall, ATM plays a key role in DNA damage response that upon its phosphorylation several damage pathways and cell check points are activated. Proficient ATM signalling network is therefore required for coordination of DNA repair, cell cycle progression and apoptosis in response to DNA damage.

1.6.4 The working mechanism of topoisomerase II poision, doxorubicin, and its role in DNA damage

Topoisomerase II complex, a DNA cleavage complex, is known to regulate winding of

DNA double helix and resolve supercoils and tangles. It requires ATM for catalytic activity and creates double-stranded DNA break while passing an intact DNA helix.

Doxorubicin, a topoisomerase II poison and anthracycline drug, do not affect catalytic function of topoisomerase II complex, rather poison and increase stready-state of the DNA cleavage complex. This action results in high levels of permanent DSBs (Froelich-Ammon and

Osheroff, 1995). In addition, during drug metabolism process, doxorubicin is oxidized to an unstable metabolite and converted back to doxorubicin which leads to release of reactive oxygen species and therefore DNA damage (Thorn et al., 2011). Upon DNA damage created by doxorubicin, ATM is known to be activated by phosphorylation of Ser1981 (Kurz et al., 2004).

In a recent study (Alvarez-Quilon et al., 2014), a novel ATM-dependent repair pathway has been identified that involves DSBs created by topoisomerase II poisons. Doxorubicin and etoposide are known to generate DSBs by covalent peptide blockage of the 5’-ends. A recently identified tyrosyl-DNA phosphodiesterase 2 (Tdp2) has been shown to repair trapped topoisomerase II- DNA complexes and unblock the DNA ends induced by topoisomerase II poisons (Pommier et al., 2014). In the study of Alvarez-Auilon (Alvarez-Quilon et al., 2014),

ATM has been demonstrated to have function in the rejoining of blocked DSBs and mediate repair of DSBs with blocked DNA ends.

1.7 Cellular mechanisms of Autophagy and Senescence

1.7.1 Autophagy overview

Autophagy is a lysosomal degradation pathway that is required for cellular homeostasis, degradation of long-lived proteins and turnover of damaged or non-functional organelles. During autophagy intracellular materials are digested and recycled back to the cytoplasm in order to provide nutrition to the cells and limit metabolic stress (Meijer and Codogno, 2009). During autophagy, the cytoplasmic compartment containing target protein or molecule is surrounded by a double membrane to form autophagosome. The formation of autphagosomes includes four

steps as: initiation, nucleation, elongation, closure. The activation of unc-51 like autophagy activating kinase 1 and 2 (ULK1 and ULK2) complex, which is inhibited by mTOR, initiates the process. A complex including Beclin-1 protein is involved in nucleation. Following nucleation, elongation of phagophore is controlled by ATG16L complex and several Atg (AuTophaGy- related) proteins. After forming a double membrane autophagosome, maturation occurs under the control of the Beclin-1 protein and the autophagosome is fused with endosomes and lysosomes

(Kang et al., 2011a). The autolysosome degrades the material and the producst return to the cytosol via specific permeases (Meijer and Codogno, 2009). Autophagy is regulated by several pathways, such as mTOR, Erk1/2 and p38 signaling pathways. There is increasing evidence of the involvement of autophagy in cancer (Gong et al., 2013, Xue et al., 2010).

1.7.2 Senescence and the senescence-associated secretory phenotype (SASP)

1.7.3 Senescence

When the cells encounter oncogenic events, they can undergo permanent cell-cycle arrest, which is named as senescence. During senescence, cells stop their proliferation, resist apoptosis and undergo changes in gene expression including alterations in cell-cycle inhibitors or activators. Senescent cells cannot normally be stimulated to proliferate, therefore their growth arrest is permanent (Campisi and d'Adda di Fagagna, 2007), however they are metabolically active and can express or secrete proteins (Coppe et al., 2010).

Senescence markers are quite limited and β-galactosidase is one of the most common markers used to detect senescent cells. Senescence-associated β-galactosidase derives from lysosomal β-galactosidase and it indicates increased lysosomal biogenesis, which is typically occurs in senescent cells (Dimri, 2005). The protein, p16, which is a regulator of senescence, is also used as marker for senescence.

DNA-damage can lead to senescence and DNA-damage induced senescence is dependent

on p53 and p21 in the cells (Herbig et al., 2004, Di Leonardo et al., 1994). Cancer cells with

wild-type p53, as opposed to mutant p53, mostly undergo senescence in response to chemotherapy agents (te Poele et al., 2002) .

1.7.4 Senescence-associated secretory phenotype (SASP)

Senescence, which results in cell-cycle and growth arrest, is suggested as a potential

tumor-suppressive mechanism. However recent studies indicate that senescent cells can have

alterations in their secretory activity which can lead to changes in the microenvironment and therefore can promote tumorigenesis (Krtolica et al., 2001, Coppe et al., 2008). Senescent cells can be both beneficial and harmful, that the SASP presents the deleterious side of the senescence.

Characterization of SASP has been done from endothelial, mammary gland, colon, pancreas and prostate cells [reviewed in (Coppe et al., 2010)]. According to those studies, the

SASP includes several soluble and insoluble factors. Overall they can be summarized as; soluble factors (interleukins, chemokines, cytokines, inflammatory cytokines, growth factors), secreted proteases and secreted insoluble proteins/ ECM components. The factors that are secreted from senescent cells can bind cell-surface receptors and activate several signal transductions. The studies also demonstrated that not all factors are upregulated and not all factors are secreted at the same time which indicates that the SASP are under control of different cellular mechanisms.

Based on the secreted factors, SASPs can promote cell proliferation, cell motility and cell differentiation in breast cancer and other cancer cells.

Chapter Two: HYPOTHESIS AND SPECIFIC AIMS

In previously published studies, prolactin has been shown to increase the viability of breast cancer cells against DNA damaging chemotherapy agents, however, the mechanism of prolactin-mediated cytotoxic resistance is poorly understood. The overall hypothesis of this thesis is that prolactin will contribute to increased cell viability to DNA damaging drugs in a mechanism that involves HSP90 and its potential client proteins Jak2 and ATM, and therefore contribute to disease progression.

2.1 Specific aims and objectives

My research focuses on the following aims and objectives.

1 Assess the molecular and cellular mechanism of the prolactin-mediated cellular response to

DNA damaging agents in vitro

1.1 To confirm the prolactin-mediated cellular resistance to DNA damaging agents

using breast cancer cell lines

1.2 To test the role of HSP90, Jak2 and ATM as part of the mechanism in the

prolactin-mediated cellular response to DNA damaging agents.

2 Test the role of prolactin on tumorigenicity and tumor volume in response to DNA damaging agents in vivo

2.1 To test the effect of endocrine prolactin on tumorigenicity, latency, tumor size of

human breast cancer cell derived tumors in xenograft animal models in response to

DNA damaging agents, and investigate the mechanism of prolactin on survival and

proliferation using immunohistochemistry analysis of the xenograft tumors.

2.2 To test the effect of autocrine prolactin on tumorigenicity, latency, tumor size of

human breast cancer cell derived tumors in xenograft animal models in response to

DNA damaging agents, and investigate the mechanism of prolactin on survival and proliferation using immunohistochemistry analysis of the xenograft tumors.

2.3 To test the mechanism of prolactin-mediated cellular response to DNA damaging agents by investigating autophagy, senescence, and the senescence-associated secretory phenotype.

Chapter Three: MATERIALS AND METHODS

3.1 Cell lines

The human breast cancer cell lines MCF7 (HTB-22) and SKBR3 (HTB-30) were obtained from the American Type Culture Collection (ATCC, Manssas, VA, USA). The T47D human breast cancer cell line (ATCC, HTB-133) was a kind gift from Dr. Manfred Lohka

(University of Calgary, AB, Canada). Cells from the ATCC that were frozen and used within 6 months of revival were not STR (Short tandem repeat analysis) tested. Cells were not used after passage 30. The T47D cell line and the transfected MCF7 cell line (MCF7hprl) were authenticated by Genetica DNA Laboratories (Burlington, NC, USA) using STR analysis. The characteristics of each cell line are presented in Table 1.

Table 1. Breast Cancer Cell Lines Characteristics Cell line Estrogen Progesterone HER2 status p53 status PRLR status receptor receptor (Holliday! and! (Huovinen! et! (Peirce! et! al.,! status status Speirs,!2011) al.,!2011) 2001) (Holliday! and! (Holliday! and! Speirs,!2011) Speirs,!2011) MCF7 Positive Positive Negative Wild type Positive

SKBR3 Negative Negative Positive Mutated Positive

T47D Positive Positive Negative Mutated Positive

3.2 Breast cancer cell culture and maintenance

MCF7 and SKBR3 breast cancer cells were maintained in Dulbecco’s Modified Eagle

Medium (DMEM; Invitrogen, Burlington, ON, Canada). T47D cells were maintained in Roswell

Park Memorial Institute (RPMI) medium. Both DMEM and RPMI were supplemented with 10%

fetal bovine serum (FBS; PAA Laboratories Inc., Etobicoke, ON, Canada, from 2009 to 2014)

(FBS; Invitrogen, Burlington, ON, Canada, from 2014 to 2015), 100 µg/ml streptomycin

(Invitrogen), 100 units/ml penicillin (Invitrogen) and 2mM L-glutamine (Invitrogen). MCF7 and

T47D cells were also supplemented with 10 µg/ml bovine insulin (Sigma-Aldrich Canada Co.,

Oakville, ON, Canada).

All cell lines were maintained in 10 cm cell culture plates (Corning, Tewksbury, MA,

USA) at 37°C and 5% CO2. In order to passage the cells, they were washed twice with phosphate buffered saline (PBS; pH 7.4, containing 1.37 M NaCl, 26.8 mM KCl, 0.1 M Na2HPO4.7H20 and

0.027 mM KH2P04) and were detached from the plate using 0.05% Trypsin-EDTA (Invitrogen) for 4 minutes at 37°C. Cells were collected with media and centrifuged at 400 rcf for 4 minutes at 22°C. Approximately 1.2 x 106 cells were plated in a 10 cm plate with 8 ml media. Cells were split every 4 days.

3.2.1 PEI transfection

In order prepare stable MCF7hprl and MCF7pcDNA3.1 cell lines, Polyethyleminine

(PEI) transfection was used. The polyethyleminine (PEI) MW25K (Polyscience Inc.,

Warrington, Pa, USA) was dissolved in water at 80°C to final concentration of 1 mg/ml and after cooling, aliquots were stored at -80°C.

The human prolactin plasmid was received as a gift from our collaborator, Dr. Vincent

Goffin (University Paris Decartes, Paris, France). The hPRL construct was in pcDNA3.1/Zeo(+)

mammalian expression vector driven by CMV promoter. Empty pcDNA3.1/Zeo(+) plasmid was

prepared as control for the human prolactin plasmid.

The day before transfection, 4.5x105 cells were plated into 6 cm cell culture plates

(Corning) with 3 ml media and an hour before transfection, media was refreshed. To prepare the

transfection solution, 3 µg DNA was mixed with 300 µl of serum free media. A volume of 12 µl

of PEI was mixed into DNA/media by pipetting and incubating it 10-15 minutes at room 56

temperature. The transfection mixture was gently added into the media with a total volume of 3

ml and cells were cultured with transfection media for 24-48 hours. The stably transfected cells

carrying the Zeocin antibiotic resistance gene (Sh-ble) were selected with Zeocin (800 µg/ml)

(Invivogen, San Diego, CA, USA). Following 10-15 days of incubation in the presence of

Zeocin, the colonies were selected with Cloning cylinders (Fisher Scientific) using Dow Corning high-vacuum grease (Fisher Scientific). The colonies were cultured in Zeocin (800 µg/ml)

containing media until use.

3.3 Chemotherapy agents

3.3.1 17- (Allylamino)-17-demethoxygeldanamycin (17-AAG) (1000 nM)

500 µg of 17AAG (Sigma) was dissolved in 850 µl of sterile DMSO (Sigma), 10 µl aliquots were stored at -20°C and protected from light.

3.3.2 Doxorubicin (100 mM)

10 mg of doxorubicin (Sigma) was dissolved in 172.4 µl of sterile DMSO, 2 µl aliquots were prepared to avoid multiple freeze/thaw cycles and stored at -20°C. The doxorubicin stocks, aliquots and dilutions were protected from light.

3.3.3 BIIB021 (1000 nM)

5 mg of BIIB021 (Selleck Chemicals, Houston, TX, USA) was dissolved in 15.58 ml of sterile DMSO, 1 ml and 50 µl aliquots were stored at -20°C and protected from light.

3.3.4 KU55933 (100 mM)

10 mg of KU55933 (Tocris, Minneapolis, MN, USA) was dissolved in 252.85 µl of sterile DMSO, 10 µl aliquots were stored at -20°C and protected from light.

3.3.5 G6 (NSC33994) (5 mM)

10 mg of G6 (Tocris) was dissolved in 4.5 ml of sterile DMSO, 100 µl of aliquots were

stored at -20°C and protected from light.

3.3.6 NU7441 (KU7788) (5 mM)

10 mg of NU7441 was dissolved in 4.8 ml of sterile DMSO, 100 µl of aliquots were

stored at -20°C and protected from light.

3.4 Hormones

3.4.1 Insulin (5 mg/ml)

50 mg of insulin from bovine pancreas (Sigma) was dissolved in 9900 µl of double

distilled water (ddH2O) and 100 µl of glacial acetic acid on a rocker at 4°C for 3 hours. After filter sterilization (Durapore (PVDF) filter, 0.45 µm pore size, EMD Millipore), 500 µl sterile aliquots were snap frozen in liquid nitrogen and stored at -80°C.

3.4.2 Ovine prolactin (1 mg/ml)

10 mg of ovine prolactin (Sigma) was dissolved in 10 ml of 1 x phosphate buffered saline

(1XPBS, pH 7.4). After filter sterilization, 500 µl sterile aliquots were snap frozen in liquid nitrogen and stored at -80°C.

3.4.3 Human recombinant prolactin (100 µg/ml)

100 µg of prolactin (a kind gift from Dr. Vincent Goffin, Paris, France) was dissolved in

1 ml sterile PBS (pH 7.4) and 25 µl aliquots were stored at -20°C.

3.4.4 Δ1-9-G129R-hPRL receptor angatonist (1 mg/ml)

1 mg of Δ1-9-G129R-hPRL receptor angatonist (a kind gift from Dr. Vincent Goffin,

Paris, France) was dissolved in 1 ml sterile PBS (pH 7.4) and stored in 2 µl aliquots at -20°C.

3.4.5 17β-estradiol (1 mg/ml)

1 mg of β-Estradiol was dissolved in 100% Ethanol (Commercial Alcohols, Brampton,

ON, Canada) and stored at -20°C.

3.4.6 LFA 102

10 mg/ml and 1 mg/ml dilutions were prepared from 28.4 mg/ml and 29.5 mg/ml stocks provided by Novartis (Emeryville, CA, USA) and stored at 4°C.

3.5 WST-1 cell viability assay

WST-1 is a colorimetric assay for cellular proliferation, viability and cytotoxicity. This

96-well plate format experiment measures amount of formazan cleaved from tetrazolium salts in metabolically active cells. Live cells produce more formazan and therefore give higher optical density in plate reader.

Overall, cells were seeded at a specific density into 96-well plates at least 12 hours before the experiment. After required treatments, 1/10th volume of WST-1 reagent (Roche, Laval, QC,

Canada) was added directly on cells in culture medium. After 4 hours incubation time, the optical density was read using a plate reader at 490 nm with a reference wavelength of 650 nm

(Spectramax M4 Microplate Reader, Dr. Buret’s Laboratory, University of Calgary). Blank

(media alone) optical density was automatically subtracted from results by the plate reader.

During normalization and percent survival calculations, the optical density of prolactin treated cells were divided by the optical density of untreated cells. The optical density of doxorubicin or other drugs were divided by the optical density of DMSO (vehicle) treated cells. The DMSO volume was calculated separately for each drug or drug combination and the same time frame was used for DMSO and drug treatments.

WST-1 experiments were excluded from the thesis if the cells were contaminated with

mycoplasma or no decrease was observed with doxorubicin treatments.

3.5.1 WST-1 cell viability assay with different cell numbers

MCF7 or SKBR3 cells were seeded in increasing cell numbers (1000, 2000, 3000, 4000,

5000 or up to 10,000 cells) into 96-well plates with 6 internal and 3 external replicates. 24 hours after plating cell viability was determined using a WST-1 assay. The optical density from 1000 cell was accepted as 1 and the optical density of increasing cell numbers were divided to the optical density of 1000 cells to calculate fold increase.

3.5.1.1 Slope and intercept calculation from WST-1 assay

Calculated fold change or percent cell survival from WST-1 assay was entered into

GraphPad Software, best-fit values of slope and intercept were calculated with linear regression model and p values were calculated from an F-test.

3.5.2 WST-1 cell viability assay with prolactin and doxorubicin treatments

In order to investigate the effect of prolactin on cell viability of doxorubicin-treated cells,

5000 MCF7 or SKBR3 cells were plated into 96-well plates. The next day, cells were pre-treated with human recombinant prolactin (25 ng/ml) or ovine prolactin (5 µg/ml) for 24 hours, followed by 2 hours with the indicated concentrations of doxorubicin or appropriate volumes of DMSO vehicle control treatment. Doxorubicin and DMSO were removed from the cells and the cells were allowed to recover for 48 hours. Prolactin was kept on the cells during the doxorubicin treatment and recovery time. The cell viability was assessed using a WST-1 assay.

In order to test prolactin mediated viability of doxorubicin-treated T47D cells, 5000 cells

were plated into 96-well plate and the next day cells were treated with ovine prolactin (5 µg/ml)

for 24 hours followed by 4 days of doxorubicin treatment in low (ng/ml) concentrations in the

presence or absence of prolactin according to the protocol adapted from the literature (LaPensee et al., 2009). The cell viability was determined with WST-1 assay at the end of 4 days treatment.

3.5.3 WST-1 cell viability assay with estrogen, prolactin and doxorubicin

MCF7 cells were adapted to phenol-free media (DMEM, Invitrogen) supplemented with

10% charcoal (Sigma)-stripped FBS (Invitrogen) a week before the experiment and phenol-free, charcoal-stripped medium was used during the experiment. A total of 5000 cells were plated into

96-well plates and the next day cells were treated with human recombinant prolactin (25 ng/ml) and/or β-Estradiol (0.1 ng/ml and 1 ng/ml) for 24 hours. Appropriate ethanol dilutions were used as vehicle controls for β-Estradiol treatments. Following 24 hours treatment, cells were treated with doxorubicin (1 µM) or DMSO (10 µl/ml) control for 2 hours in the presence or absence of prolactin and/or β-Estradiol. Doxorubicin and DMSO were removed and cells recovered for 48 hours with or without prolactin and/or β-Estradiol. Cell viability was determined using a WST-1 assay.

3.5.4 WST-1 cell viability assay with Hsp90 inhibitor, BIIB021

In order to test the cytotoxicity of BIIB021 on MCF7 cells, 5000 cells were plated into

96-well plates and the next day treated with the indicated increasing concentrations of BIIB021 and appropriate dilutions of DMSO for 26 hours, including 2 hours of doxorubicin treatment time, similar to that exposure used in the main experiment. Cells recovered in the absence of

BIIB021 and DMSO for 48 hours and cell viability was determined with a WST-1 assay.

In order to test the involvement of Hsp90 in prolactin increased cell viability, 5000

MCF7 cells were plated into 96 well plate and treated with human recombinant prolactin (25 ng/ml) and/or BIIB021 (800 nM) for 24 hours followed by 2 hours of doxorubicin treatment with or without prolactin and/or BIIB021. Drugs were removed and cells were recovered with or

without prolactin for 48 hours and cell viability was determined with WST-1 reagent. Separate

vehicle controls were used for doxorubicin and BIIB021 such that appropriate DMSO dilutions

for each doxorubicin and BIIB021 concentration were prepared and used in same time frame as

the drugs.

3.5.5 WST-1 cell viability assay with ATM inhibitor, KU55933

MCF7 and SKBR3 were treated with 10 µM ATM inhibitor, KU55933, a concentration

chosen from the literature (Hickson et al., 2004, Rainey et al., 2008, Li and Yang, 2010).

In order to test the involvement of ATM in prolactin increased cell viability, 5000 cells

were plated into 96-plates and the next day treated with human recombinant prolactin (25 ng/ml)

and/or KU55933 (10 µM) for 24 hours followed by 2 hours of doxorubicin treatment with or

without prolactin and/or KU55933. Doxorubicin was removed and cells recovered for 48 hours

with or without prolactin and/or KU55933. Separate vehicle controls were used for doxorubicin

and KU55933 such that appropriate DMSO dilutions for each doxorubicin concentration and

KU55933 were prepared and used in same time frame as the drugs. At the end of the recovery

time, cell viability was determined using a WST-1 assay.

3.5.6 WST-1 cell viability assay with Jak2 inhibitor, G6

In order to test the cytotoxicity of G6 on breast cancer cells, 5000 cells were plated into

96- well plates. The next day cells were treated 3 hours, 6 hours, 12 hours or 24 hours with G6, followed by 4 hours recovery time in the absence of G6. Appropriate dilutions of DMSO were used as vehicle control. Cell viability was determined using a WST-1 assay at the end of the recovery time.

In order to test the involvement of Jak2 in prolactin increased cell viability, 5000 MCF7 or SKBR3 cells were plated into 96-well plates and the next day cells were pre-treated 24 hours

with human recombinant prolactin (25 ng/ml) and/or 12 hours with G6 (25 µM), followed by 2

hours of doxorubicin treatment with or without prolactin and/or G6. DMSO dilutions were

separately prepared for doxorubicin concentrations and G6 and the same time frame was used as

the drugs. Doxorubicin, G6 and DMSO were removed from cells and 48 hours recovery was

used with or without prolactin followed by cell viability detection with a WST-1 assay.

3.6 Combination Index Calculations

MCF7 cells (5000) were plated into 96-well cell culture plates. Cells were plated in

triplicate for doxorubicin and BIIB021 alone or combination treatments, or doxorubicin and

KU55933 alone or combination treatments. Cells were pre-treated with KU55933 or BIIB021 for

24 hours followed by 2 hours of doxorubicin treatment in the presence of inhibitors. Fixed

concentrations of BIIB021 (400 nM and 800 nM) and KU55933 (10 µM and 20 µM) were used with 3-fold increasing concentrations of doxorubicin during drug combination treatments. In order to test doxorubicin concentrations alone for sensitivity, only 2-hour doxorubicin treatment was used. All drugs were removed from the media except KU55933 during 48-hour recovery time. The cell viability was assessed using a WST-1 assay and the results were read with the plate reader. All treatments were normalized to the vehicle controls.

The cell viability results from drug treatments alone and in combination were entered into the CompuSyn Program of Chou in order to evaluate the synergism, additive effect or antagonism between combined drugs. The computer program used Chou’s Median-Effect

Equation which is described as “fa/fu= D/Dm” (fa= affected cell fraction, fu= unaffected cell fraction, D= dose of the drug, Dm= median effect dose, m= slope of the median effect curve)

(Chou, 2006). The Combination Index (CI) calculated from the equation indicates synergism if

CI<1, additive effect is CI =1 and antagonism if CI>1. According to detailed description CI

value less than 0.1 indicates very strong synergism, CI value between 0.1 and 0.3 indicates strong synergism, CI value between 0.3 and 0.7 indicates synergism, CI value between 0.7 and

0.85 indicates moderate synergism, CI value between 0.85 and 0.90 indicates slight synergism and the value between 0.9 and 1.1 indicates nearly additive effect.

3.7 Clonogenic cell survival assay

3.7.1 Clonogenic cell survival assay with prolactin and doxorubicin

In order to test clonogenic cell survival, 2000 MCF7 cells were plated into 6 well plates and the next day treated with 25 ng/ml human recombinant prolactin for 24 hours followed by 2 hours of doxorubicin treatment. Doxorubicin was removed from the cells and the media was refreshed with or without prolactin. Cells were allowed to form colonies over 10 days.

When the colonies formed, plates were gently washed twice with phosphate-buffer saline

(PBS, pH 7.4) and 5 minutes stained with 0.5% (w/v) gention violet (Fisher Scientific, Pittsburg,

PA, USA) prepared in Methanol (EMD Millipore, Temecula, CA, USA). The stain was washed gently under tap water and plates were dried overnight. The colonies with more than 50 cells were counted under a light microscope. The numbers from prolactin treatment were normalized to untreated cells and the numbers from doxorubicin treatment were normalized to DMSO control.

3.8 Polymerase Chain Reactions

3.8.1 RNA extration and quantification

RNA was extracted from breast cancer cells using a Qiagen RNEasy Mini Kit (Qiagen

Inc., Mississauga, ON, Canada) or NucleoSpin RNA Kit (Macherey-Nagel Inc., Bethlehem, PA,

USA) according to the supplied protocols. The extracted RNA was DNAse treated on-column

with RNase-free DNAse provided by suppliers. RNA was eluted with provided RNase-free

ddH2O and stored at -80 °C until use.

Quantity and purity of the RNA was determined using NanoDrop 100 Spectrophotometer

(Thermo Scientific, Wilmington, DE, USA; Hansen Lab, University of Calgary). The ratio of

absorbance at 260 nm and 280 nm is used to assess the purity. A ratio of ∼2.0 is accepted as

“pure”.

3.8.2 Complementary DNA Synthesis

Complimentary DNA (cDNA) was synthesized from 2 µg of RNA using Superscript II

Reverse Transcriptase kit (Invitrogen). According to the protocol, the DNase treated RNA was

incubated with 0.2mM dNTPs (Fermantas, Burlington, ON, Canada) and 2.5 x 103 µg/ml oligo-

dT primer (5’-TTTTTTTTTTTTTTTTTTTTV-3’) at 65 °C for 5 minutes. The reaction was

mixed with 5 x First Strand Buffer (250 mM Tris-HCl, pH 8.3; 375 mM KCL; 15 mM MgCl2),

0.1 M DTT and Superscript II Reverse Transcriptase followed by 50 minutes incubation at 42

°C. The reaction was stopped with heat inactivation at 70°C for 15 minutes. cDNA was stored at

-20°C until use.

3.8.3 Quantitative polymerase chain reaction

Quantitative polymerase chain reactions (qPCR) were carried out using iTaq Universal

SYBR Green Supermix (Biorad, Mississauga, ON, Canada) with 1 µl of each forward and reverse primer (final primer concentration of 200 mM) and 1 µl of cDNA and brought up to 20

µl with ddH2O. Each reaction was performed in triplicate, pipetted into 96-well PCR plate

(BioRad) and sealed with Optical Sealing Tape (Biorad). Reactions were cycled on the BioRad

iQ5 Real-Time PCR Detection System (Hansen Lab, University of Calgary).

All primers were designed using the NCBI Primer Blast-program. Operon Oligo Analysis

tool was used to detect possible primer dimmers and self-complementation was identified with

IDT Oligo Analyzer. The desired primers were obtained from University of Calgary DNA

Synthesis Lab (Calgary, AB, Canada).

3.8.4 Endpoint PCR

The PCR experiments, which did not require real time data, were performed in 0.2 ml

thin wall PCR tubes (Axygen, Corning) and the genes were amplified in the BioRad MJMini

Opticon Real-Time PCR System.

3.8.5 ATM Gene Expression

Primer sequences for ATM were as follows: forward primer 5’-

CTGTGCAGCGAACAATCCCA-3’ and reverse primer 5’-TAACCAGTTGCCACAAACCCT-

3’ with an expected amplicon of 70 base pairs. ATM amplification was performed according to

the following protocol: 95 °C for 2 minutes, 40 cycles of 95 °C for 10 seconds, 58 °C for 30

seconds, 78 °C for 20 seconds, and final extension step of 72 °C for 10 minutes.

3.8.6 JAK2 Gene Expression

Primer sequence for JAK2 were designed as follows: forward primer 5’-

TACCTCTTTGCTCAGTGGCG-3’ and reverse primer 5’-ACTGCCATCCCAAGACATTC -3’

with an expected amplicon of 95 base pairs. JAK2 amplification was performed according to the

following protocol: 95 °C for 2 minutes, 40 cycles of 95 °C for 10 seconds, 58 °C for 30 seconds, 78 °C for 20 seconds, and final extension step of 72 °C for 10 minutes.

3.8.7 GAPDH Gene Expression

Glyceraldehyde 3-phospate dehhydrogenase (GAPDH) primers were designed as follows: forward primer 5’-GTCTCCTCTGACTTCAACAGCG-3’ and reverse primer 5’-

ACCACCCTGTTGCTGTAGCCAA-3’ with an expected amplicon of 131 base pairs. The

following protocol was performed for amplification of GAPDH: 95 °C for 2 minutes, 40 cycles

of 95 °C for 10 seconds, 60 °C for 30 seconds, 78 °C for 20 seconds, and final extension step of

72 °C for 10 minutes.

3.8.8 Sh-ble Gene Expression

Steptoalloteichus hindustanus bleomycin (Sh-ble), Zeocin resistance gene primers were

as follows: forward primer 5’-AAGTTGACCAGTGCCGTTCC-3’ and reverse primer 5’-

CTCCTCGGCCACGAAGTG-3’ with an expected amplicon of 360 base pairs. The following

protocol was performed for amplification of Sh-ble gene: 95 °C for 2 minutes, 40 cycles of 95 °C

for 5 seconds, 60 °C for 30 seconds, 78 °C for 20 seconds, and final extension step of 72 °C for

10 minutes.

3.8.9 YWHAZ Gene Expression

Tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein (YWHAZ) primers were used as a reference gene for all PCR experiments. YWHAZ primers were previously designed in the laboratory and the sequences are as follows: forward primer 5’-

AGTCGTACAAAGACAGCACGTAA-3’ and reverse primer 5’-

AGGCAGACAAAGGTTGGAAGG-3’ with an expected amplicon of 138 base pairs. The

following protocol was used for amplification of YWHAZ gene: 95 °C for 2 minutes, 40 cycles

of 95 °C for 10 seconds, 60 °C for 30 seconds, 78 °C for 20 seconds, and final extension step of

72 °C for 10 minutes.

3.8.10 Visualization of PCR Products

All PCR products were visualized on 1% agarose gel (Ultrapure agarose, Invitrogen) in a

buffer containing 1X Tris-borate-EDTA (TBE; 89 mM Tris-base, 89 mM boric acid, 2 mM

Na2EDTA) with 0.1% ethidium bromide (Fisher Scientific). To prepare the samples, 9 µl PCR

product was mixed with 1 µl of 10X DNA Buffer containing 4.5 µM Tris, 2.5 mg/ml

bromophenol blue and 0.6% glycerol in ddH2O. The samples were loaded into an agarose gel in

TBE buffer and DNA was resolved by electrophoresis at 80-120 mA for 45 minutes. DNA was visualized using ultraviolet light on a Biorad GelDoc 2000 and Quantity One software

(Urbanska, 2015).

3.9 Protein Immunobloting

3.9.1 Whole cell lysate extract with NP-40 buffer without sonication

This protocol was used to extract phosphorylated ATM (p-ATM), ATM, Hsp90, growth factor receptor-bound protein 2 (GRB2) (Results section- 4.1.6.1) and Beclin-1 proteins. SKBR3 or MCF7 cells were pre-treated or not with prolactin and/or 17AAG followed by 2 hours of doxorubicin treatment and protein extraction. Following the treatments, media was removed and the plates were washed twice with 1XPBS. Cells were scraped using rubber policeman in 1 ml cold 1XPBS and transferred into 1.5 ml microcentrifuge tubes. Following centrifugation at

12000 rpm for 10 minutes at 4 °C using Eppendorf 5415R microcentrifuge, the supernatant was removed. The pellet was dissolved in 30-50 µl of 1% NP-40 lysis buffer containing 1% Nonidet-

P-40 (Sigma), 50 mM Tris-HCl pH 7.5, 5 mM EGTA and 200 mM NaCl. Protease and phosphatase inhibitors were freshly added and are as follows: 1 mM sodium vanadate (Sigma-

Aldrich), 20 nM phenyllarsine oxide (PAO; EMD Millipore), 1 µg/ml leupeptin (EMD

Millipore), 0.5 µg/ml aprotinin (Sigma-Aldrich), 100 µM phenylmethylsulfonyl fluoride (PMSF;

Sigma-Aldrich), 1 µM dithiothretiol (DTT; Fermentas) and 1 µg/ml pepstatin (Sigma-Aldrich).

The cells were incubated for 20 minutes at 4°C followed by centrifugation at 12,000 rpm for 10

min at 4°C. The supernatant was transferred into a new tube and protein concentration was measured. Proteins were snap frozen and stored at -80 °C until use.

3.9.2 Whole cell lysate extract with 1XSDS Buffer

The protocol was used for extraction of Jak2 and prolactin proteins. In order to detect

Jak2, cells were pre-treated 24 hours with human recombinant prolactin and/or 17AAG or

BII021 followed by 2 hours of doxorubicin treatment if indicated. To determine prolactin levels from whole cell extracts, MCF7hprl cells were plated and cultured 7 days before protein extraction. The media was aspirated at the end of doxorubicin treatment and the cells were washed twice with 1XPBS. Cells were directly scraped in 1XSDS buffer containing 2% (w/v)

SDS, 10% (w/v) glycerol, 100 mM DTT, 0.02% (w/v) bromophenol blue, 1M Tris-HCl pH 6.8, followed by sonication three times for 5 seconds with 5 seconds intervals at #5 (on dial) (Fisher

Scientific 60 Sonic Dismembrator, Moorhead Laboratory, University of Calgary) on ice. Protein concentration was determined and proteins were snap frozen to keep at -80 °C until use

(Urbanska, 2015).

3.9.3 Secreted protein extraction from conditioned media

To investigate secreted prolactin levels from MCF7hprl cells, 1x 106 cells were plated into 10 cm plates and cultured for 7 days. A total of 1 ml conditioned media was removed from the cells and transferred into microcentrifuge tubes and centrifuged at 4°C at 13,200 rpm for 15 minutes. Centrifuged conditioned media containing secreting proteins was transferred into a new cold microcentrifuge tube, snap frozen and stored at -80 °C until use (Howell et al., 2008).

3.9.4 Nuclear lysate extract

The nuclear lysate extract was used to extract p-Stat5, Stat5 and Histone-H3 proteins. In order to determine p-Stat5 levels, cells were plated and the next day treated 30 minutes with

human recombinant prolactin. To investigate the effect of Jak2 inhibition on p-Stat levels, cells

were plated and the next day treated 12 hours with Jak2 inhibitor, G6, followed by 30 minutes

human recombinant prolactin treatment. MCF7hprl cells were plated and cultured during the

time of MCF7 or MCF7pcDNA3.1 cells treatments. Plates were washed twice with 1XPBS after

treatments and the cells were scraped in cold 1XPBS. Cells were transferred into microcentrifuge

tubes and centrifuged at 2600 rpm for 5 min at 4°C. Supernatant was removed and pellets were

resuspended in “Buffer A” containing 20 mM HEPES pH 7.9, 10 mM KCl, 1 mM EDTA, 0.2%

(v/v) NP-40, 10% (v/v) glycerol, and freshly added protease and phosphatase inhibitors. Cells

were incubated on ice for 5 minutes in “Buffer A”, followed by centrifugation for 5 minutes at

13,200 rpm at 4°C. The supernatant containing cytoplasmic proteins was transferred to new

microcentrifuge tube and the pellet was resuspended in “Buffer B” containing 420 mM NaCl,

20% (v/v) glycerol, 20 mM HEPES pH 7.9, 10 mM KCl, 1mM EDTA, and freshly added

protease and phosphatase inhibitors. Cells were incubated for 30 minutes at 4°C with gentle

agitation, followed by 5 minutes centrifugation at 13,200 rpm at 4°C. The supernatant containing

nuclear protein extract was transferred into new microcentrifuge. Protein concentration was

determined from cytoplasmic and nuclear protein extracts and proteins were snap frozen and

stored at -80 °C.

3.9.5 Measuring protein concentration

The Bio-Rad Protein Assay was used to determine protein concentrations from all protein

samples. In order to prepare a standard curve, bovine serum albumin (BSA, Fisher Scientific)

dilutions were prepared in ddH2O. Protein samples (1 µl) were added into disposable cuvette containing 799 µl ddH20 and 200 ddH2O Bio-Rad reagent (Bio-Rad). The absorbance of each

samples was determined using spectrophotometer at a wavelength of 595 nm. The cuvette 70

without protein was set up as blank during reading and the concentration of proteins was

determined based on the standard curve.

3.9.6 Separation of proteins by SDS-Page electrophoresis

Whole cell extracts of p-ATM, ATM, Hsp90, GRB2, p-KAP1, KAP-1, p-Chk2, Chk2

were resolved on triple gels containing 15% acrylamide resolving gel (3.75 ml 30% (v/v)

acrylamide: 0.8% (w/v) bis-acrylamide (National Diagnostics, Atlanta, GA, USA), 2.8 ml 1M

Tris-HCl pH 8.8, 963 µl ddH2O, 38 µl 20% SDS, 25 µl 10% ammonium persulfate (APS) and 10

µl tetramethylethylenediamine (TEMED, Bio-Rad), 8% low-bis acrylamide resolving gel (1.5

ml 30% low-bis acrylamide (VWR), 200 µl 2% bis-acrylamide (Becton- Dickinson Biosciences),

2.1 ml 1M Tris-HCl pH 8.8, 1528 µl ddH2O, 28 µl 20% SDS, 20 µl 10% APS, 2.6 µl TEMED)

and 5 % acrylamide stacking gel (835 µl 30% (v/v) acrylamide: 0.8% (w/v) bis-acrylamide, 625

µl 1M Tris-HCl pH 6.8, 25 µl 20% SDS, 3.525 ml ddH2O, 25 µl 10% APS and 5 µl TEMED

(Kurz et al., 2004, Urbanska, 2015).

Nuclear proteins (p-Stat5, Stat5, Histone-H3) and Jak2 proteins were resolved on triple

gel containing a 15% resolving acrylamide gel (3.75 ml 30% (v/v) acrylamide: 0.8% (w/v) bis-

acrylamide, 2.8 ml 1M Tris-HCl pH 8.8, 963 µl ddH2O, 38 µl 20% SDS, 25 µl 10% APS and 10

µl TEMED), an 8% acrylamide resolving gel (3.75 ml 30% (v/v) acrylamide: 0.8% (w/v) bis-

acrylamide, 2.5 ml 1M Tris-HCl pH 8.8, 4.6 ml ddH2O, 50 µl 20% SDS, 100 µl 10% APS and 6

µl TEMED) and 5% acrylamide stacking gel (835 µl 30% (v/v) acrylamide: 0.8% (w/v) bis- acrylamide, 625 µl 1M Tris-HCl pH 6.8, 25 µl 20% SDS, 3.525 ml ddH2O, 25 µl 10% APS and

5 µl TEMED.

Cellular and secreted prolactin, and Beclin-1 proteins were resolved in a double gel

system containing a 15% acrylamide gel and a 5% acrylamide stacking gel.

The 15% acrylamide gel was first poured and allowed to polymerize for 20-30 minutes at

room temperature followed by the 8% gel polymerized for 1 hour at room temperature or

overnight at 4°C. The 5% stacking gel was prepared and poured an hour before the protein

samples were loaded.

To prepare loading samples, 30-50 µg whole cell extract proteins or 15 µg nuclear protein or 30 µl conditioned media was mixed with 4XSDS buffer (2% (w/v) SDS, 10% (w/v) glycerol, 100 mM DTT, 0.02% (w/v) bromophenol blue, 1 M Tris-HCl pH 6.8) and ddH2O to same final volume and boiled at 95 °C for 5 minutes. Protein samples and protein standard (Pre- stained Precision Plus Protein Standard, Bio-Rad) were loaded into columns formed in 5% stacking gel. The western blot was run in SDS buffer containing 0.1% SDS, 25 mM Tris-Base,

0.91 M glycine and the protein electrophoresis was carried out 1-1.5 hour at 25 mA per gel, until the SDS sample buffer reached the bottom of the gel.

Whole cells extract proteins were transferred to nitrocellulose membrane (VWR-Pall Life

Sciences) using the Bio-Rad mini Trans-Blot wet transfer system at 100 volts for 1-2 hours using transfer buffer containing 250 mM Tris-base, 1.92M glycine, 20% (v/v) methanol and 0.0016

(w/v) SDS. Gels containing only small proteins (Beclin-1, Prolactin) and nuclear proteins were transferred to Nitrocellulose or PVDF membrane (GE Healthcare, Baie-D’Urfe, QC, Canada) using semi-dry transfer system which involved sandwiching the gel and the membrane between

Whatman filter papers soaked in transfer buffers. The first 3 filter papers were soaked in cathode buffer containing 2.5 mM Tris-base, 20% (v/v) methanol and 0.5% (v/v) caproic acid at pH 9.4 and the gel placed “upside-down” on cathode papers with the standards on the right. Membrane

and 2 pieces of filter papers were soaked in Anode II buffer containing 2.5mM Tris- base, 20%

(v/v) methanol at pH 10.4 and placed on the gel. The PVDF membrane was wet in methanol before soaking in Anode II buffer. The last two pieces of filter papers were soaked in Anode I buffer containing 0.3M Tris-base and 20% (v/v) methanol at pH 10.4 and placed on the top. The semi-dry transfer was run at 200 mA for 1 hour and each transferred membrane was cut to separate proteins according to their molecular sizes.

3.9.7 Immunoblotting for p-ATM

In order to detect p-ATM, the membrane was blocked 1 hour in blocking buffer containing 10% (w/v) dried skim milk in Tris buffered saline (TBS, pH 7.5, containing 24.8 mM

Tris base, 150.6 mM NaCl, 2.7 mM KCl) and 0.1% polyexyethylenesorbitan monolaurate

(Tween-20) (VWR, Edmonton, AB, Canada) (TBST 0.1%). The membrane was washed three times for 10 minutes each in TBST 0.1% and incubated over night at 4°C with rabbit anti-ATM protein kinase pS1981 (Epitomics, clone EP1890Y, Burlingame, CA, USA) primary antibody diluted for a final concentration of 1:1000 in 0.1% gelatin in TSBS 0.1%. The membrane washed

3 times for 10 minutes each in TBST 0.1% and incubated 1 hour at room temperature with horse radish peroxidase (HRP)-conjugated goat anti rabbit (Bio-Rad) secondary antibody (Bio-Rad) diluted at 1:10000 in 10% skim milk in TBST 0.1%. The blot was washed three times for 10 minutes each in TBST 0.1% (Kurz et al., 2004).

After p-ATM immunoblotting, the membrane was stripped to remove primary and secondary antibodies incubating the membrane in stripping buffer containing 100 mM 2- mercaptoethanol, 2% (w/v) SDS, 62.5 mM Tris-HCl pH 6.8) at 60 °C for 30 minutes with gentle agitation. Membranes washed several times with TBST 0.1%.

3.9.8 Immunoblotting for ATM

In order to detect total ATM, the membrane was blocked for 1 hour with 10% dried skim

milk in TBST 0.1% and then washed three times for 10 minutes each in TBST 0.1%. The

membrane was incubated in mouse-anti-ATM-2C1 primary antibody (GeneTex, San Antonio,

TX, USA) diluted 1:3000 in 1% BSA in TBST 0.1% overnight at 4°C. Following three times

washes 10 minutes each, the blot was incubated in HRP conjugated goat anti mouse (Bio-Rad)

secondary antibody diluted at 1:10000 in 10% dired skim milk in TBST 0.1% for 1 hour. The

membrane washed three times 10 minutes each with TBST 0.1% following secondary antibody

incubation.

3.9.9 Immunoblotting for Hsp90α

In order to detect Hsp90α, the membrane was blocked in 15% dried skim milk in TBST

0.05% for 2 hours, followed by 3 washes (10 minutes each) with TBST 0.05%. The membrane was incubated in rabbit-anti Hsp90α primary antibody (StressGen, Victoria, BC, Canada) diluted

1:5000 in 1% BSA in TBST 0.05% overnight at 4°C. The blot was washed three times (10 minutes each) and incubated in HRP conjugated goat anti rabbit secondary antibody (1:10000 dilution) in 15% dried skim milk in TBST 0.05% for 1 hr. The blot was washed three times (10 minutes) with TBST 0.05% after secondary antibody incubation.

3.9.10 Immunoblotting for GRB2

In order to detect GRB2 (Perotti et al., 2008), the membrane was blocked in 10% dried skim milk in TBST 0.05% for 2 hours and then washed three times (10 minutes each) with TBST

0.05%. The membrane was incubated with mouse anti-GRB2 primary antibody (BD

BioSciences) (1:5000 dilution) in 3% BSA in TBST 0.05% overnight at 4°C. Following three times washes (10 minutes each) with TBST 0.05%, the blot was incubated in HRP conjugated

goat anti mouse secondary antibody (1:10000 dilution) in 10% dried skim milk in TBST 0.05% for 1 hour and then washed three times for 10 minutes each with TBST 0.05%.

3.9.11 Immunoblotting for Beclin-1

In order to detect Beclin-1, the membrane was blocked in 10% dried skim milk in TBST

0.05% for 2 hours and then washed three times (10 minutes each) with TBST 0.05%. The membrane was incubated with mouse anti-Beclin primary antibody (BD BioSciences) (1:5000 dilution) in 3% BSA in TBST 0.05% overnight at 4°C. Following three times washes (10 minutes each) with TBST 0.05%, the blot was incubated in HRP conjugated goat anti mouse secondary antibody (1:10000 dilution) in 10% dried skim milk in TBST 0.05% for 1 hour and then washed three times for 10 minutes each with TBST 0.05%.

3.9.12 Immunoblotting for Jak2

In order to detect Jak2, the membrane was blocked in 5% dried skim milk in TBST

0.05% for 1 hour and then washed three times (10 minutes each) with TBST 0.1%. The membrane was incubated with rabbit anti-Jak2 (D2E12) XPTM monoclonal primary antibody

(Cell Signaling Techology) (1:5000 dilution) in 5% BSA in TBST 0.1% overnight at 4°C.

Following three times washes (10 minutes each) with TBST 0.1%, the blot was incubated in

HRP conjugated goat anti rabbit secondary antibody (1:10000 dilution) in 10% dried skim milk in TBST 0.1% for 1 hour and then washed three times for 10 minutes each with TBST 0.1%.

3.9.13 Immunoblotting for p-Stat5

In order to detect p-Stat5, the PVDF membrane was wet with methanol and blocked in

5% dried skim milk in TBST 0.05% for 1 hour and then washed three times (10 minutes each) with TBST 0.05%. The membrane was incubated with rabbit anti-STAT5 (phospho Y694) primary antibody [E208] (Abcam) (1:1000 dilution) in 5% BSA in TBST 0.05% overnight at

4°C. Following three times washes (10 minutes each) with TBST 0.05%, the blot was incubated

in HRP conjugated goat anti rabbit secondary antibody (1:10000 dilution) in 5% dried skim milk in TBST 0.05% for 1 hour and then washed three times for 10 minutes each with TBST 0.05%.

3.9.14 Immunoblotting for Stat5

In order to detect Stat5, the PVDF membrane was wet with methanol and blocked in 5% dried skim milk in TBST 0.05% for 1 hour and then washed three times (10 minutes each) with

TBST 0.05%. The membrane was incubated with mouse STAT5 primary antibody (Transduction

Laboratories, BD BioSciences) (1:1000 dilution) in 5% BSA in TBST 0.05% overnight at 4°C.

Following three times washes (10 minutes each) with TBST 0.05%, the blot was incubated in

HRP conjugated goat anti mouse secondary antibody (1:10000 dilution) in 5% dried skim milk in

TBST 0.05% for 1 hour and then washed three times for 10 minutes each with TBST 0.05%.

3.9.15 Immunoblotting for Histone- H3

In order to detect Histone-H3, the PVDF membrane was wet with methanol and blocked in 3% dried skim milk in PBS overnight at 4°C. The next day the membrane was washed three times for 10 minutes each with TBST 0.05%. The membrane was incubated with rabbit anti-H3 primary antibody (Millipore, Billerica, MA, USA) (1:1000 dilution) in 3% dried skim milk in

PBS overnight at 4°C. The next day the blot was washed three times for 10 minutes each with

TBST 0.05% and incubated in HRP conjugated goat anti rabbit secondary antibody (1:10000 dilution) in 3% dried skim milk in PBS for 1 hour and then washed three times for 10 minutes each with TBST 0.05%.

3.9.16 Immunoblotting for Prolactin

In order to detect prolactin, the membrane was blocked in 3% dried skim milk in TBST

0.1% for 2 hours and then washed three times 5 minutes each with TBST 0.1%. The membrane

was incubated in Monoclonal Anti-human Prolactin Antibody (R&D System, Minneapolis, MN,

USA) (1:100 dilution) in 5% dried skim milk in TSBT 0.1% overnight at 4°C. The next day the blot was washed three times for 10 minutes each with TBST 0.1% and incubated in HRP conjugated goat anti-mouse secondary antibody (1:10000 dilution) in 5% dried skim milk in

TBST 0.1% for 30 minutes. The blot was washed three times for 10 minutes each with TBST

0.1% after secondary antibody incubation.

3.9.17 Visualization of Proteins

Enhanced chemoluminescence (ECL) method was used to visualize proteins during western blot experiments. The ECL solution was prepared with 250 mM luminol (Sigma-

Aldrich), 90 mM p-coumeric acid solution (Sigma- Aldrich), 1M Tris-HCl pH 8.5 and 30% hydrogen peroxide (Sigma-Aldrich). After incubating blots 5 minutes in ECL solution, Kodak X-

Omat AR film (VWR, Edmonton, AB, Canada) was exposed to the blot for 30 seconds to 3 hours according to specific antibody and the film was developed in a dark room.

3.9.18 Quantifying Western blot data

In order to compare the density of protein bands on western blots, ImageJ software

(http://rsb.info.nih.gov/ij/index.html) was used according to the online instructions

(http://lukemiller.org/index.php/2010/11/analyzing-gels-and-western-blots-with-image-j/). To calculate the fold change of protein levels, the integrated density of target proteins were divided by the integrated density of loading control, GRB2.

3.10 Xenograft Animal Models

3.10.1 Xenograft Animal Model with Endocrine Prolactin Delivery

All animal procedures were carried out under strict accordance with University of

Calgary and government guidelines. Protocols were approved by the Ethics Committee of the

University of Calgary.

The 9 week-old SCID-Beige female mice were purchased from Charles River

Laboratories (Montreal, QC, Canada) and 5 mice were used per experimental group. In order to

deliver prolactin and estrogen to animals, the ovine prolactin pellets (3 mg/ pellet, 30 day release,

Cat. No. SX-999), Placebo for ovine prolactin pellets (3 mg/pellet, 30 day release, Cat. No. SC-

111) and 17β-Estradiol pellets (0.72 mg/pellet, 60 day release, Cat. No. SE-121) were obtained from Innovative Research of America (IRA, Sarasota, Florida, USA).

The pellets were inserted subcutaneously during a small surgery carried out under sterile conditions. Mice were anesthetised with isofluorine and a small incision was created with a scissors. A small hole was expanded under the skin in order to place the pellets and after pellets were inserted, the incision was closed with a surgical stapler. Mice were also numbered using ear notching system based on experimental groups. The mice recovered in a space blanket. A second ovine prolactin pellet was inserted after 30 days.

Three days after the estrogen pellet insertion, mice were injected with breast cancer cells in their mammary fat pads (4th mammary fat pad).

Four experimental groups were used as follows, MCF7, MCF7+dox, MCF7+prl,

MCF7+dox+prl. In order to prepare the cells for experimental groups, 1 x 106 MCF7 cells were plated into 10 cm cell culture plates (Corning) and the next day MCF7+prl and MCF7+prl+dox plates were pre-treated with ovine prolactin (5 µg/ml) for 24 hours followed by 2 hours of

doxorubicin treatment (1 µM) for MCF7+dox and MCF7+dox+prl plates. Media was changed

from all plates and cells recovered for 48 hours. Prolactin was kept on cells in MCF7+prl and

MCF7+dox+prl plates during the recovery time. Cells were washed twice with 1XPBS and

trypsinized. Following centrifugation at 400 rpm for 4 minutes cells were resuspended in media

and counted in order to obtain 1 x 106 cells per mouse. These cells were centrifuged at 400 rpm for 4 minutes at 4°C one and the pellets were washed with PBS followed by the last centrifugation step at 400 rpm for 4 minutes at 4°C. The washed pellets were resuspended in 100

µl cold PBS and 100 µl Cultrex Basement Membrane Extract (Cultrex BME) (Trevigen,

Gaithersburg, MD, Germany) mixture. Cells were injected using cold syringes into the 4th

mammary fat pad of mice based on experimental groups.

Following breast cancer cell injection, mice were followed every other day for their health, weight and tumor formation. The palpable tumors were confirmed by a technician or

laboratory member who was blind to the experiment. The mammary tumors were measured with

a calliper when they reached a certain size (over 2-3 mm). The mice with a poor health condition

as specified in animal use and ethics protocol were euthanized before the end of the experiment.

All animals were euthanized by cervical dislocation at the end of the experiment (60 days) and

the tumor samples were flash frozen in O.C.T Embedding Compound (Tissue-Tek, Sakura, The

Netherlands) on dry ice and kept at -80°C for future histological experiments.

3.10.2 Xenograft Animal Model with Autocrine Prolactin Delivery

In this experiment, 9 week-old female SCID mice were used (Charles River

Laboratories). Five mice were used per experimental group. In order to deliver estrogen to the

mice, 17β-Estradiol pellets (0.72 mg/pellet, 60 day release, Cat. No. SE-121) were obtained from

Innovative Research of America (IRA, Sarasota, Florida, USA). The pellets were inserted

subcutaneously all mice with a small surgery carried out in sterile conditions as described in

3.10.1.

Three days after pellet insertion, breast cancer cells were injected into the the 4th mammary fat pads. The contralatreal mammary fat pads were injected with PBS and Cultrex

BME mixture.

Four experimental groups were used as follow, MCF, MCF7+dox, MCF7hprl,

MCF7hprl+dox. MCF7 and MCF7hprl cells were plated and the following day indicated groups were treated with doxorubicin (1 µM) for 2 hours and recovered for 48 hours. Cells were washed twice with 1XPBS and trypsinized. Following centrifugation at 400 rpm for 4 minutes cells were resuspended in media and counted in order to obtain 1x 106 cells per mouse. The counted cells were centrifuged at 400 rpm for 4 minutes at 4°C one more time and the pellets were washed with 1XPBS followed by the last centrifugation step at 400 rpm for 4 minutes at 4°C. The washed pellets were resuspended in 100 µl cold PBS and 100 µl Cultrex BME mixture. Cells were injected into 4th mammary fat pad of mice based on the experimental groups.

Two experiments were carried out with autocrine prolactin delivery, and the only differences were the injected cells numbers and the total days of the experiments. In the first experiment, 500,000 cells were injected into mammary fat pad and the mice were followed over

60 days. In the second experiment, 250,000 cells were injected and the mice were followed over

120 days. Since the 17β-Estradiol pellets had a 60-day release of the hormone, the second pellet was inserted after 60 days in the 120 day-experiment.

Following breast cancer cell injection, the mice were followed every other day as described in 3.10.1.

3.10.3 Serum Estradiol levels measurement from SCID mice

Four SCID female mice were purchased from Charles River Laboratories and

approximately 200 µl of blood was taken from the saphenous vein from each mouse before the

pellet surgery. 17β-Estradiol pellets (0.72 mg/pellet, 60 day release, Cat. No. SE-121) were

obtained from Innovative Research of America (IRA, Sarasota, Florida, USA). Five days after

the saphenous bleed, the pellets were inserted subcutaneously all mice with a small surgery

carried out in sterile conditions as described in 3.10.1. Ten days after the surgery, blood samples

were obtained. Blood was collected into microtubes and allowed to clot at room temperature for

30 minutes followed by centrifugation at 2000 x g for 15 minutes at 4°C. After centrifugation,

the serum was transferred into a clean micro tube and stored at -80°C until the ELISA. The blood

samples were obtained every 10-15 days over a 75 day period.

Serum samples were thawed on ice and estradiol was extracted using ethyl acetate and

evaporated with nitrogen stream (Ro Lab, University of Calgary). The ELISA was carried out

according to the supplier’s protocols. The absorbance was read on plate reader at 450 nm

(Spectramax M4 Microplate Reader, Buret laboratory, University of Calgary). Data was

analyzed using GraphPad Prism software. Three internal replicates were used for each

measurement.

3.10.4 Immunohistochemistry for Ki-67 and Hsp90α

Frozen tissue blocks in O.C.T Embedding compound were sectioned at 8 µm thickness at

-30°C using a cryostat (human breast cancer cell xenograft tumors were sectioned at Dr. Jennifer

Chan’s laboratory, University of Calgary; mouse tissues were sectioned at Dr. John Cobb’s laboratory, University of Calgary). Sectioned tissues were mounted on coated slides (Fisher

Scientific, Ontario, Canada) and then sections were dried at room temperature overnight. The

slides were stored at -80°C until staining.

The slides were thawed at room temperature for 15 minutes prior to

immunohistochemistry staining using Envision+ Dual Link System-HRP (DAB+) kit (DAKO,

Carpinteria, CA, USA). Following fixation in 95% ethanol, the slides were washed three times 5

minutes each in wash buffer containing 0.05M Tris-HCl pH 7.6, 0.15M NaCl, 0.25% Tween-20.

The slides were blocked in Dual Endogenous Enzyme Block (DAKO) at room temperature and

then washed three times 5 minutes each in wash buffer, followed by permeabilization in 0.25%

Triton-X 100 in wash buffer and washing three times 5 minutes each in wash buffer. To prevent

nonspecific binding, sections were blocked in 5% BSA in wash buffer for 10 minutes and

washed three times three minutes each in wash buffer. Slides were incubated in a dark

humidified slide chamber in rabbit monoclonal anti-Ki67 antibody (1:50 dilution in 1% BSA in

wash buffer) ( Thermo Scientific, Ontario, Canada) or rabbit polyclonal Hsp90α antibody (1:200

dilution in 1%BSA in wash buffer) (Stressgen Bioreagents) for 2 hours at room temperature or

overnight at 4°C. A slide from an adjacent section was incubated in 1% BSA in wash buffer to

use as negative control for staining. The slides were washed in wash buffer three times 5 minutes

each after primary antibody incubation, followed by 30 minutes incubation (in dark and room

temperature) in HRP Polymer (DAKO). Following three times wash 5 minutes each in wash

buffer, sections were blocked in DAB substrate-chromogen solution containing 1 ml substrate

buffer and 1 drop of DAB chromogen (DAKO) for 10 minutes followed by rising with dH2O for

5 minutes. Slides were finally stained with 1:10 dilution of Harris Hematoxylin (Fisher

Scientific) and washed in tap water for 10 minutes, followed by decolorization in 1% acid

alcohol for 1 second and washing in tap water for 5 minutes. Slides were dehydrated in an

ethanol series (95%, 100%) and mounted with Permount (Fisher Scientific).

The tumor section were imaged at 32X magnification using a Nikon Coolpix 4500

camera mounted on a Zeiss Axiovert 100 bright field microscope.

3.10.4.1 Image Quantification and Statistical Analysis of Ki-67 Immunohistochemisty Stains

Image quantification of Ki-67 stains were done using the ImageJ64 online program.

Images were converted to 8-bit greyscale and the tumor field was outlined. In order to select total

nuclei (the hematoxylin counterstained nuclei) in the field, a broad range threshold with a

minimum value of 13 was applied to the image and the number of pixels from the threshold

mask were recorded. The ratio of Ki-67 positive nuclei to total nuclei was calculated in

Microsoft Excel using the recorded data.

The mean and standard deviation was calculated for each tumor section. Three

experimental replicates of Ki-67 stainings were pooled for statistical analysis. Statistical

significance was assessed using Kruskal-Wallis test with a Dunnett’s post hoc analysis. Results

were considered significant when p value was lower than 0.05 (p< 0.05).

3.10.5 Soluble Senescence-Associated β-galactosidase activity assay (ONPG assay)

MCF7 cells were plated into 10 cm cell culture plates in 1x 106 cell number and the

following day treated or not with ovine prolactin (5 µg/ml) for 24 hours. Cells were treated or

not with doxorubicin (0.2 µM) for 2 hours and recovered 6 days with or without prolactin. In

order to prevent confluency induced senescence, cells were split once during this 6 days recovery

period. Cells were related in 1x 106 cell number at the 4th day of recovery.

Media was aspirated at the end of 6 days and cells were washed twice with PBS and trypisized for 4 minutes at 37°C. Cells were collected within media and centrifuged at 800 rpm at

22°C for 4 minutes, the pellet was washed in PBS and centrifuged one more time at 800 rpm at

22°C for 4 minutes. After removing the supernatant, the pellets were resuspended in 25 µl of 0.2

M phosphate buffer at pH 6.0 (87.7 ml of 0.4M sodium phosphate monobasicm 12.3 ml of 0.4 M

sodium phosphate dibasic heptahydrate, 100 ml ddH2O) and subjected to three cycles of freeze and thaw cell lysis in liquid nitrogen and 37°C water bath. Following lysis cycles, cells were centrifuged at 12,000 rpm at 4°C for 5 minutes and supernatant containing protein was transferred into a new microcentrifuge tube. The protein concentration was measured with

BioRad protein assay and equal protein values were used for all experimental groups. The protein samples were incubated in 275 µl assay buffer containing 2 mM MgCl2, 100 mM β- mercaptoethanol, 1.3 mg/ml ONPG (from previously prepared 4 mg/ml 2-Nitrophenyl β-D- galactopyranoside in 0.2M phosphate buffer pH 6.0), 0.2 M phosphate buffer pH 6.0 for 4 to 9 hours at 37°C. The reaction was stopped with 500 µl of 1M Na2CO3. Three aliquots of each reaction (200 µl) were transferred into 96-well culture plate and absorbance was measured at 420 nm (Spectramax M4 Microplate Reader, Buret laboratory, University of Calgary). The optical densities at OD 420 were averaged for internal and experimental replicates and standard deviations were calculated. Student’s t-test (two-tailed) was performed for statistical analysis and results were considered significant when p<0.05.

3.10.6 Autophagy assay (Determination of Beclin-1 levels)

MCF7 cells, 1x 106, were plated into 10 cm cell culture plates and the following day treated or not with ovine prolactin (5 µg/ml) for 24 hours. Cells were then treated or not with doxorubicin (0.2, 1 and 2 µM) for 2 hours in the presence or absence of prolactin. Whole cell lysates were prepared with NP-40 lysis buffer. 50 µg proteins were used for western blots and

Beclin-1 and GRB2 levels were determined from three independent experiments. Beclin-1 levels were quantified with ImageJ analysis.

3.10.7 Cytokine Array

MCF7hprl cells and the media containing 10% FBS were plated into 6 cm plates. Three independent experimental replicates were used for two treatment groups

(MCF7hprl+doxorubicin, MCF7hprl+ Δ1-9-G129R-hPRL receptor angatonist+doxorubicin), and serum control. Cells were treated or not with Δ1-9-G129R-hPRL receptor angatonist for 24 hours followed by 2 hours doxorubicin (1 µM) treatment. Cells were allowed to recover for 6 days with our without Δ1-9-G129R-hPRL receptor antagonist and media was collected at the end of recovery period. Following centrifugation at 3000 rpm for 10 minutes at 4°C, conditioned media was snap frozen in liquid nitrogen and stored at -80°C. The cytokine array was performed by Eve

Technologies Corporation (Calgary, AB, Canada) using Luminex Technology for the Human 65- plex cytokine/chemokine panel.

3.10.8 ELISA for SDF-1 alpha and beta

Enzyme-linked immunosorbant assays (ELISA) were perfomed to determine SDF-1 alpha and SDF-1 beta levels in breast cancer cell conditioned media using CXCL12alpha/SDF-1 alpha and CXCL12/SDF-1 beta ELISA kits (Abnova, Taoyuna City, Taiwan).

Two ELISAs were carried out. MCF7pcDNA3.1 empty vector cells, MCF7hprl, SKBR3 and T47D breast cancer cells were used in the first experiment. Cells were plated into 6 cm plates and MCF7pcDNA3.1, SKBR3 and T47D cells were pre-treated or not with 25 ng/ml human recombinant prolactin or Δ1-9-G129R-hPRL receptor angatonist. MCF7hprl plates were pre-treated with Δ1-9-G129R-hPRL receptor angatonist for 24 hours prior to 2 hours doxorubicin

(1 µM) treatment as indicated. Doxorubicin was removed from cells and prolactin or Δ1-9-

G129R-hPRL receptor angatonist treatment was continued in indicated experimental groups.

Cells recovered 2, 4, 6 and 8 days and conditioned media collected from plates at 2, 4, 6 and 8 days recovery periods.

In the second ELISA experiment, only MCF7pcDNA3.1 and MCF7hprl cells were used and the same experimental protocol was used from below. In addition to Δ1-9-G129R-hPRL receptor angatonist, a second prolactin receptor antagonist, LFA 102 (Novartis, Cambridge, MA,

USA), was used in the experiments. Conditioned media was collected at 6 and 8 recovery days.

Following conditioned media collection at the indicated days, the microtubes containing conditioned media were centrifuged at 2000 rpm for 10 minutes at 4°C and stored at -80°C. The

ELISAs were carried out according to the supplier’s protocols. The absorbance was read on a plate reader at 450 nm (Spectramax M4 Microplate Reader, Buret laboratory, University of

Calgary). Data was analyzed using nonlinear regression in GraphPad Prism software. Three experimental replicates were used for each experimental group.

3.11 Statistical analysis

A One-way ANOVA followed by Bonferroni post-tests (StatPlus Statistic program) were applied to determine significance between treatment samples in WST-1 and SDF-1 ELISAs. The statistical difference in Jak2 and ATM qPCR data was analyzed using a One-way ANOVA followed by Bonferroni post-tests (GraphPad Statistics) by Sara Mirzaei. Statistical comparison of tumor volumes between treatment groups was accomplished using Kruskal-Wallis ANOVA followed by Mann-Whitney U tests (p<0.05). Log-rank (Mantel-Cox) and Gehan-Breslow

Wilcoxon tests were used for statistical analysis of the tumor latency. In immunohistochemistry assays by Erin Marie Bell, a Shapiro-Wilk test was performed to assess normality of data set, as a parametric assumption (with normal distribution being assumed at p>0.05). The statistical

significance was determined using a non-parametric Kruskal-Wallis test with a Dunnett’s post hoc analysis (p<0.05). Colin Stewart used Tukey’s test to compare statistical difference between treatment groups in indicated immunohistochemistry assays. The statistical data was analyzed using Student’s t-test in senescence experiments by Emilija Malogajski.

Chapter Four: RESULTS I

4.1 Assess the Mechanism of Prolactin Mediated Cellular Response to DNA Damaging

Agents in vitro

Prolactin contributes to the formation and progression of breast cancer through its interactions with the prolactin receptor. Recent epidemiological studies have clearly demonstrated that high serum levels of prolactin are correlated with increased risk for breast cancer (Tworoger et al., 2014, Tworoger et al., 2013, Tworoger et al., 2015, Tikk et al., 2015).

Cancerous cells have been demonstrated to have increased prolactin production when compared with normal mammary cells (McHale et al., 2008), and 98% of human breast cancers were shown to express the prolactin receptor (Mertani et al., 1998, Ormandy, 1997). There are also studies that indicate a link between the presence of tumor autocrine prolactin and poor prognosis in breast, endometrial and prostate cancers (Li et al., 2004, Wu et al., 2011).

Increasing numbers of studies point out the involvement of prolactin in breast cancer promoting resistance to a variety of different cytotoxic chemotherapeutic drugs (Perks et al.,

2004, Howell et al., 2008, LaPensee et al., 2009, Lissoni et al., 2001). A recent study from our laboratory has also demonstrated prolactin-enhanced cell viability against DNA damaging chemotherapeutic agents, such as doxorubicin and etoposide (Urbanska, 2011). Anna Urbanska’s studies, which were confirmed with my experiments, identified a potential molecular mechanism involving the prolactin-regulated gene, HSP90α, and the Hsp90 complex in vitro.

This chapter presents in vitro experiments to understand the detailed mechanism of the prolactin-mediated cellular response to DNA damaging agents.

In order to confirm the prolactin-enhanced cell viability against DNA damaging agents,

MCF7, SKBR3 and T47D cell lines were tested using the WST-1 cell viability assay. The effect of prolactin was also examined on clonogenic survival, in the presence of DNA damage. The involvement of Hsp90 in the mechanism was confirmed by using the Hsp90 inhibitor, BIIB021, in the WST-1 cell viability assay. Since Hsp90 inhibitors cause proteasomal degradation of

Hsp90 client proteins, I investigated the Hsp90-dependent stability of two potential Hsp90 clients, Jak2 and ATM, and tested their involvement in prolactin-enhanced cell viability using highly selective inhibitors. The overall hypothesis tested in this chapter is that prolactin increases the viability of breast cancer cells when challenged with DNA damage, using a potential molecular mechanism involving Hsp90 and its client proteins ATM and Jak2.

4.1.1 Prolactin increases the viability of breast cancer cells treated with DNA damaging agents

In order to confirm previous findings on prolactin-mediated cytotoxic resistance from our laboratory (Urbanska, 2011) and the literature (Howell et al., 2008, LaPensee et al., 2009), I performed WST-1 cell viability assays on breast cancer cells with a topoisomerase II poison, doxorubicin. The concentrations of doxorubicin used in MCF7 cells were determined based on a dose response curve. MCF7 cells were plated into 96-well plates and treated with doxorubicin for 2 hours. The results were read after 48 hours recovery time and all treatments were normalized to vehicle (DMSO) control. According to a representative dose response curve, significant loss of cell viability was observed starting with 3.24 µM and higher concentrations

(Figure 10). Based on the results, five concentrations were chosen (0.405 µM, 0.81 µM, 1.62

µM, 3.24 µM and 6.48 µM) for future experiments.

# # #

p-values Dox 3.24µM! 6.48µM! 12.96µM! 25.92µM! concentrations! ?8 ?11 ?12 DMSO control 0.0038 1.5x10 8.34x10 3.46x10 to Dox

Figure 10. Doxorubicin dose response curve in MCF7 cells. MCF7 cells were plated into 96 well plates and treated with doxorubicin for two hours. After 48 hours recovery time, WST-1 assay results were read using a plate reader. All treatments were normalized to vehicle control. Results represent 3 internal replicates, and 3 experimental replicates that are pooled (n=9). Data was analyzed using a One-way ANOVA followed by Bonferroni post-tests (Significant p values are indicated in the chart). Statistically significant analysis tested between the vehicle control and doxorubicin treated samples: (*) denotes p<0.05, (#) denotes p<0.00001. Error bars represent standard deviation from the mean.

The concentrations of prolactin required to activate the Prolactin-Jak2-Stat5 pathway were chosen based on phospho-Stat5 levels determined with western blot experiments (Figure

11). The concentrations reflect high, non-pregnant, serum prolactin levels in human (Serri et al.,

2003). Stat5 phosphorylation was detected at 25 ng/ml and increased with increased recombinant prolactin concentrations. Since the lowest dose 25 ng/ml activated the pathway and is considered as high serum prolactin level, 25 ng/ml human prolactin was used in the experiments unless otherwise specified.

In order to investigate the effect of prolactin on cell viability in the presence of DNA damage, MCF7 and SKBR3 cells were pre-treated with 25ng/ml human recombinant prolactin for 24 hours followed by doxorubicin treatment for 2 hours. After a 48 hours recovery time, the cell viability was determined with WST-1 cell proliferation assay. The time frames for treatments and recovery were chosen based on previous laboratory experiments that demonstrated that 2 hours doxorubicin treatment resulted in ATM phosphorylation, and 48 hours recovery time was sufficient for cells to respond to the DNA damage while going through at least one cell cycle (Urbanska, 2011). According to the results (Figure 12), prolactin increased MCF7 cell viability significantly across all five doxorubicin concentrations. Upon normalizing values to the vehicle control, prolactin + doxorubicin treatment was compared with doxorubicin alone treatment within the same concentration and the viability of MCF7 cells increased by 26%

(0.405 µM dox), 16% (0.81 µM dox), 9% (1.62 µM dox), 8% (3.24 µM dox), and 12% (6.48 µM dox). In SKBR3 cells, the viability increased by 9% (0.405 µM dox) and 5% (1.27 µM dox)

(Figure 13).

hPRL 25 ng 50 ng 100 ng 150 ng

P-Stat5 92 kDa

Histone H3 17 kDa

Figure 11. Human recombinant prolactin dose response curve in MCF7 cells. MCF7 cells were plated into 6 cm plates and treated with human recombinant prolactin or not for 30 minutes followed by nuclear and cytoplasmic protein extraction. Nuclear extracts were resolved by western blot in order visualise phospho-Stat5 and Histone H3 (loading control) levels.

* * * **

p-values Dox 0.405µM! 0.81µM! 1.62µM! 3.24µM! 6.48µM! concentrations! Dox to 6.9 x 10-5 0.0095 0.01 0.0039 0.0003 Dox+Prl

Figure 12. Prolactin enhanced viability in MCF7 cells treated with doxorubicin. MCF7 cells were plated into 96 well plates and pre-treated with 25 ng/ml human recombinant prolactin for 24 hours followed by 2 hours doxorubicin treatment. After 48 hours recovery time in the presence or absence of prolactin, the WST-1 assay reagent was added into plate wells and the results were read with a plate reader. All treatments were normalized to vehicle control. The results represent 6 internal replicates and 3 external replicates that are pooled (n=18). Data was analyzed using a One-way ANOVA followed by Bonferroni post-tests (Significant p values are indicated in the chart). The statistical analysis indicates the comparison between PRL+Dox (prolactin+ doxorubicin) and Dox within each concentration. Statistically significant analysis (*) denotes p<0.05, (**) denotes p<0.001, (#) denotes p<0.00001. Error bars represent standard deviation from the mean .

p-values Dox 0.405µM! 1.27µM! 3.24µM! concentrations! Dox to 0.01 0.0039 NS Dox+Prl

Figure 13. Prolactin enhanced viability in SKBR3 cells treated with doxorubicin. SKBR3 cells were plated into 96 well plates and pre-treated with 25ng/ml human recombinant prolactin for 24 hours followed by 2 hours doxorubicin treatment. After 48 hours recovery time, WST-1 assay reagent was added into plate wells and the results were read with ELISA plate reader. All treatments were normalized to vehicle (DMSO) control. The results represent 6 internal replicates and 3 external replicates that were pooled (n=18). Data was analyzed using a One-way ANOVA followed by Bonferroni post-tests (Significant p values are indicated in the chart, NS= Not Significant). The statistical analysis indicates the comparison between PRL+Dox (prolactin+ doxorubicin) and Dox within each concentration. Statistically significant analysis (*) denotes p<0.05. Error bars are smaller than symbol size, hence are not visible.

In order to confirm the literature findings (LaPensee et al., 2009) on the effect of prolactin on T47D cell viability in the presence of DNA damage, the experiment was repeated from the literature using same concentrations and the time frame (LaPensee et al., 2009). T47D cells are similar to MCF7 cells as they are reported as ER+, PR+, HER2- (Holliday and Speirs,

2011), PRLR+, however they are different from other cell lines with their characteristics of synthesizing and secreting biologically active prolactin (Ginsburg and Vonderhaar, 1995). For the cytotoxicity assay, T47D cells were plated into a 96 well plate and treated with 5 µg/ml ovine prolactin for 24 hours followed by 4 days of doxorubicin treatment in low concentrations. Cell viability was determined with the WST-1 cell viability assay. The values obtained from WST-1 assay were normalized to vehicle control and the viability of prolactin + doxorubicin treated cells were compared with doxorubicin alone treated cells within the same concentration. According to the results showed in Figure 14, prolactin increased cell viability significantly across all three doxorubicin treatments as follow; 23% (1 ng/ml dox), 9% (5ng/ml dox), 16% (25 ng/ml dox).

Together, these studies confirm that prolactin increases the cell viability of MCF7, SKBR3 and

T47D breast cancer cell lines against the DNA damaging agent, doxorubicin.

p-values Dox 1 ng/ml! 5 ng/ml! 25 ng/ml! concentrations ! Dox!to!Dox+Prl 2.06 x 10-8 0.0007 1.7 x 10-9

Figure 14. Prolactin enhanced viability in T47D cells treated with doxorubicin. 5000 cells were plated into 96 well plates and pre-treated with 5 ug/ml ovine prolactin for 24 hours followed by 4 days doxorubicin treatment with low concentrations. The cell viability was determined with WST-1 cell proliferation reagent and the results were read with ELISA plate reader. All treatments were normalized to vehicle control. The results represent 6 internal replicates and 2 extrernal replicates that were pooled (n=12). Data was analyzed using a One- way ANOVA followed by Bonferroni post-tests (Significant p values are indicated in the chart). The statistical analysis indicates the comparison between PRL+Dox (prolactin+ doxorubicin) and Dox within each concentration. Statistically significant analysis (*) denotes p<0.05, (#) denotes p<0.00001. Error bars represent standard deviation from the mean.

4.1.2 Comparison of cell viability measured by WST-1 assay with cell number

WST-1 assay is one of the cell proliferation assays that uses tetrazolium salts.

Mitochondrial enzymes cleave tetrazolium salts to formazan in metabolically active cells and

formazan is quantified by spectrophotometer (plate reader) at the end of the assay (Berridge et

al., 2005). In order to test if the absorbance read from the cell viability experiments directly

correlates with cell number, I set up two experiments.

In the first experiment, MCF7 and SKBR3 cells were plated at 2-fold increasing cell

numbers and after 24 hours the cells were assayed using either the WST-1 method to quantify

cell viability, or trypan blue-negative viable cells were counted under the light microscope. Fold

change was calculated from each quantification based on the values obtained from 1000 cells

(the lowest cell number seeded). In MCF7 cells (Figure 15A), fold changes were consistent with

the fold change of plated cells and there was no statistical significance between the slopes of

WST-1 assay (1.14 x 10-3) and the cell count (1.18 x 10-3). However, in SKBR3 cells (Figure

15B) there was statistically significant difference (p< 0.0001) between the slopes of WST-1 assay (2.5 x 10-4) and the cell count (6.5 x 10-4). The experiment with SKBR3 cells were confirmed with a second experiment where there was again statistically significant difference (p<

0.0001) between the slopes of WST-1 assay (0.0001795) and the cell count (0.0009144) (Figure

15C). WST-1 assays values may change based on the growth stage, growth rate, metabolic activity and the size of different cell lines (Berridge et al., 2005).

In the second experiment, the effect of doxorubicin treatment on cell viability and cell number was investigated. MCF7 and SKBR3 cells were plated, incubated 24 hours and followed by 2 hours doxorubicin treatment. Cells were recovered in the absence of doxorubicin for 48

hours, cell viability was determined with the WST-1 assay or alternatively the cells were counted under light microscope. In both MCF7 cells (Figure 16A) and SKBR3 cells (Figure 16B) the slopes of WST-1 assay and cell count were not statistically different. In MCF7 cells, the slopes were -15.81 (doxorubicin) for WST-1 assay and -7.53 (doxorubicin) for cell count. In SKBR3 cells, the slopes were -8.06 (doxorubicin) for WST-1 assay and -8.62 (doxorubicin) for cell count. Although there was no statistical difference between the slopes, when normalized to vehicle controls the percent control survival was lower by cell count than the WST-1 assay in both SKBR3 and MCF7 cells. The studies suggest that the trend seen in WST-1 readings represents the trend in cell number at least for the longer-term cytotoxicity assays.

Figure 15. Comparison of cell viability measured by WST-1 assay and cell number in untreated cells. Cells were plated at 2-fold increasing cell number and after 24 hours cell viability was determined with WST-1 assay or the cells were counted under light microscope. A) MCF7 cell results. The slopes of the lines are: 1.14 x 10-3 (WST-1 assay) and 1.18 x 10-3 (cell count). No statistical difference between two groups. B) SBKR3 cell results. The slopes of the lines are: 2.5 x 10-4 (WST-1 assay) and 6.5 x 10-4 (cell count). C) SKBR3 results with more cell number. The slopes of the lines are: 0.0001795 (WST-1 assay) and 0.0009144 (cell count). n=3. Error bars are smaller than symbol size, hence are not visible.

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Figure 16 Comparison of cell viability measured by WST-1 assay and cell number in doxorubicin treated cells. 5000 cells were plated into 96 well plates. After 24 hours prolactin untreated incubation, cells were treated with doxorubicin for 2 hours and recovered in the absence of doxorubicin for 48 hours. Cell viability results were determined with WST-1 assay or cells were counted under light microscope. A) MCF7 cells results. B) SKBR3 cells results. n=3. Error bars represent standard deviation or they are smaller than symbol size, hence are not visible.

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4.1.3 Prolactin increases clonogenic survival of breast cancer cells treated with doxorubicin

The clonogenic cell survival assay is an in vitro assay that determines the reproductive

ability of a single cell to proliferate and form a large colony. This assay is used to investigate the

effect of cytotoxic chemotherapy drugs or radiation on reproductive integrity and reproductive

viability of cancer cells (Munshi et al., 2005). In order to investigate the effect of prolactin on the clonogenic survival of breast cancer cells in the presence of DNA damage from doxorubicin, low numbers of MCF7 cells were plated into 6 well plates and pre-treated with 25ng/ml human recombinant prolactin for 24 hours followed by 2 hours of doxorubicin treatment. The cell number of plated cells and doxorubicin concentrations were determined according to preliminary experiments, which are not shown. After 2 hours of doxorubicin treatment, media was changed to fresh media with or without prolactin, and cells were allowed to form colonies over 10 days.

Cell clusters were stained with gentian violet staining, which stains the nuclear DNA, and colonies were counted under a light microscope. Clusters with at least 50 cells were considered

as colonies (Munshi et al., 2005). The treatments were normalized to the vehicle control and the

clonal cell survival was compared between prolactin + doxorubicin and doxorubicin alone

treated cells within same concentration. According to the results (Figure 17), prolactin increased

clonogenic cell survival significantly by 10.39 % (0.01 µM dox) and 19.15 % (0.03 µM dox) in

MCF7 cells. Therefore prolactin not only increased cell viability and cell numbers of

doxorubicin-treated cells across a wide dose of drug concentrations, in MCF7, SKBR3 and T47D

cells, but also increased the clonal cell survival of doxorubicin-treated MCF7 cells at lower, but

not at higher drug concentrations.

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* *

p-values Dox! 0.01!µM! 0.03!µM! concentrations Dox to 0.035 0.015 Dox+Prl

Figure 17. Prolactin increased clonogenic cell survival of MCF7 cells treated with doxorubicin. 2000 cells were plated into 6 well plates and pre-treated with 25ng/ml human recombinant prolactin for 24 hours followed by 2 hours doxorubicin treatment. Media was changed with fresh media with or without prolactin and cells were allowed to form colonies over 10 days. After gentian violet staining, colonies with at least 50 cells were counted with light microscope. All treatments were normalized to vehicle control. The results represent 3 internal replicates and 2 external replicates that were pooled (n= 6). Data was analyzed using a One-way ANOVA followed by Bonferroni post-tests (Significant p values are indicated in the chart). The statistical analysis indicates the comparison between PRL+Dox (prolactin+ doxorubicin) and Dox within each concentration. Statistically significant analysis (*) denotes p<0.05. Error bars represent standard deviation from the mean.

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Following the confirmation of the results using different experimental approaches, I used

the WST-1 assay in the following experiments, since it provided the opportunity of processing

large numbers of samples and replicates in a short time.

4.1.4 Prolactin and Estrogen increase the viability of breast cancer cells treated with

doxorubicin

Epidemiologic studies demonstrate that high serum levels of prolactin are related to

increased risk for estrogen receptor alpha positive breast cancer (Tworoger and Hankinson,

2008, Tworoger et al., 2013, Tworoger et al., 2014). The crosstalk between prolactin and

estrogen pathways has been well demonstrated. Estrogen induces prolactin receptor

transcription, and prolactin phosphorylates ERα receptor and increases its transcription

(Swaminathan et al., 2008, Ormandy, 1997, Barcus et al., 2015). In in vitro experiments,

prolactin enhanced estrogen-induced growth of ER positive T47D and MCF7 breast cancer

cells (Rasmussen et al., 2010, Sato et al., 2013).

In our in vitro experiments, we use regular phenol red media to culture breast cancer cells and the fetal bovine serum supplement specifically tested for prolactin and many other growth factors (such as EGF and insulin), however it should be noted that estrogen was not stripped from the fetal bovine serum in our experiments. On the other hand, in order to form xenograft tumors with ER positive MCF7 breast cancer cells in in vivo experiments (Chapter Five:

RESULTS II), we use estradiol pellet supplements in SCID mice. Giving the potential prolactin- estrogen cross-talk and the experimental conditions, the effect of prolactin independent of estrogen and of estrogen alone on viability of MCF7 cells treated with doxorubicin was investigated.

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MCF7 cells were adapted to charcoal stripped serum containing phenol red-free media one week before the experiment and same media was used during the experiment. The estradiol concentrations chosen from the literature (Sato et al., 2013, Rasmussen et al., 2010) also covered the range used in in vitro and in vivo experiments. MCF7 cells were pre-treated with estradiol

(0.1 ng/ml and 1 ng/ml) and/ or prolactin (25 ng/ml) for 24 hours, followed by 2 hours doxorubicin treatment (1 µM) with or without estradiol and/or prolactin. Doxorubicin was removed and cells recovered 48 hours in fresh media with or without estradiol and/or prolactin.

According to the WST-1 assay results (Figure 18), increased cell viability was observed in the following groups when compared with doxorubicin alone treatment; prolactin + doxorubicin

(7.1%), 0.1 ng/ml estrogen + doxorubicin (12.87%), 0.1 ng/ml estrogen + prolactin + doxorubicin (16.71%), 1ng/ml estrogen + doxorubicin (16.44%), 1 ng/ml estrogen + prolactin + doxorubicin (20.14%). Estrogen and prolactin combination also increased the cell viability by

12.25% (0.1ng/ml estrogen) and 17.95% (1ng/ml estrogen) when compared with vehicle control.

Estrogen treatment at 1 ng/ml concentration statistically increased the cell viability of prolactin + doxorubicin treated cells by 13%, however the increase was not significant at 0.1 ng/ml estrogen concentration. The results demonstrated that estrogen treatment alone increased the viability of

DNA damaged breast cancer cells, however this increase was more prominent when prolactin and estrogen were combined in the treatments.

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** 140 # * * * 120 *

100 *

80 *

60 % Control Survival % Control 40

Prl Dox Dox+prl 1ng/ml E2 0.1ng/ml E2 1ng/ml E2+prl1ng/ml E2+dox 0.1ng/ml E2+prl0.1ng/ml E2+dox 1ng/ml E2+dox+prl 0.1ng/ml E2+dox+prl Treatments

p-values Dox+prl 0.1ng/ml 0.1ng/ml! 1ng/ml! 1ng/ml! E2+dox E2+prl+dox E2+dox E2+prl+dox Dox 0.03 0.02 0.003 3.06x10-5 0.0003 Dox+Prl ? NC NS NC 0.009

p-values 0.1ng/ml E2+prl 1ng/ml E2+prl Vehicle control 0.01 0.001

Figure 18. Estrogen and prolactin increases viability of MCF7 cells treated with doxorubicin. MCF7 cells were plated into 96 well plate in phenol-red free media and charcoal stripped serum. Cells were pre-treated with estrogen (0.1 ng/ml or 1 ng/ml) and/or human recombinant prolactin (25 ng/ml) for 24 hours followed by 2 hours of doxorubicin treatment (1 µM) in the precence or absence of hormones. Media was refreshed with media containing estrogen and/or prolactin and cells recovered for 48 hours. Cell viability was determined with a WST-1 assay using a plate reader. All treatments were normalized to vehicle control. The results represent 6 internal replicates and 3 external replicates that were pooled (n=18). Data was analyzed using a One-way ANOVA followed by Bonferroni post-tests (Significant p values are indicated in the chart, NC= Not Compared, NS= Not Significant). Statistically significant analysis (*) denotes p<0.05, (**) denotes p<0.001, (***) denotes p<0.0001, (#) denotes p<0.00001. Error bars represent standard deviation from the mean.

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4.1.5 The role of Hsp90 in prolactin- increased cell viability against DNA damaging agents

4.1.5.1 The inhibition of Hsp90 abrogates with prolactin- mediated increase in cell viability

The Hsp90 chaperone protein is ubiquitously expressed in the cells and involved in cell

signalling, survival and proliferation by stabilizing many client proteins in those cellular

processes. The inhibition of Hsp90 leads to proteasomal degradation of its client proteins

involved in all six hallmark pathways of cancer (Holzbeierlein et al., 2010). Although Hsp90α is

believed to chaperone more cancer related proteins, both Hsp90α and Hsp90β isoforms are

sensitive to HSP90 inhibitors and isoform selective inhibitors are not available.

HSP90A was identified as prolactin-Jak2-Stat5 regulated gene by our lab and it was

shown to promote survival of breast cancer cells (Perotti et al., 2008). Given that both prolactin

and its downstream target HSP90 act as survival factors (Perotti et al., 2008), the inhibition of

Hsp90 was investigated in prolactin- enhanced cell viability after DNA damage (Urbanska

2011). In that study, the Hsp90 inhibitor, 17-(Allylamino)-17-demethoxygeldanamycin

(17AAG), was shown to abrogate prolactin-enhanced viability of DNA damaged cells and it

supported our hypothesis that Hsp90 is involved in prolactin-mediated cytotoxic resistance of

breast cancer cells.

In order to confirm the previous results in the lab from A. Urbanska (Urbanska, 2011) , I

used a second HSP90 inhibitor, BIIB021 (formerly known as CNF2024), which is a synthetic

inhibitor of Hsp90 that binds to the ATP-binding pocket of Hsp90, similar to 17AAG (Lundgren et al., 2009).

The dose range tested in the experiments was chosen from the literature (Lundgren et al.,

2009). MCF7 cells were treated with BIIBI021 for 26 hours and allowed to recover in the

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absence of the inhibitor for 48 hours. The time frame of the dose response of BIIB021 was based

on cell viability experiments (Urbanska, 2011) where cells were treated with Hsp90 inhibitors for

24 hours followed by 2 hours of doxorubicin treatment in the presence of the inhibitor and 48

hours recovery period in the absence of the inhibitor.

According to the dose response studies (Figure 19), a significant decrease in cell viability was observed at 800 nM and higher concentrations. 800 nM, which caused a 20% reduction in viability in breast cancer cells was chosen for subsequent experiments. The reason of using approximately 20% reduced viability from BIIB021 was to see the biological effect of the inhibitor rather than its overwhelming toxic effect when combined with doxorubicin.

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120

100 * * * 80 * ** 60

40 Control % Survival Control 20

0 25 50 100 200 400 800 1000 1200 1400 1600 BIIB021 concentrations (nM)

p-values BIIB021 800 1000 1200 1400 1600 Concentrations! nM! nM! nM! nM! nM! DMSO control 0.04 0.009 0.03 0.009 0.0005 to BIIB021

Figure 19. BIIB021 dose curve in MCF7 cells. MCF7 cells were plated into 96 well plates and the next day treated with BIIB021 for 26 hours followed by 48 hours recovery period without the inhibitor. Cell viability was determined with WST-1 assay using a plate reader. All treatments were normalized to vehicle control. Results represent 3 internal replicates , and 2 experimental replicates that are pooled (n=6). Data was analyzed using a One-way ANOVA followed by Bonferroni post-tests (Significant p values are indicated in the chart).The statistical comparison was done between vehicle control and BIIB021 treated samples. Statistically significant analysis (*) denotes p<0.05, (**) denotes p<0.001. Error bars represent standard deviation from the mean.

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In order to investigate if Hsp90 is involved in prolactin-enhanced cell viability against

doxorubicin, MCF7 cells were first pre-treated with 25 ng/ml human recombinant prolactin

and/or 800 nM BIIB021 for 24 hours followed by 2 hours of doxorubicin treatment in the

presence or absence of prolactin and/or BIIB021. After 2 hours the media was changed to fresh

media with or without prolactin and cells recovered for 48 hours. According to the cell viability

results, prolactin alone increased the viability 25.63% and BIIB021 treatment reduced viability

approximately 30% when compared with vehicle controls (Figure 20A). When doxorubicin

treatment was compared with prolactin plus doxorubicin treatment within each concentration,

prolactin increased the viability of doxorubicin treated cells across all five concentrations as

follows; 26.37% (0.405 µM), 16.64% (0.81 µM), 8.55% (1.62 µM), 8.12% (3.24 µM), 11.82%

(6.48%), which is statistically indicated on the graph (Figure 20B). The combination of BIIB021

and doxorubicin decreased the cell viability by 40.75 % (0.405 µM, p= 5.57 x 10-13), 47.20%

(0.81 µM, p= 8.28 x 10-11), 54.34% (1.62 µM, p= 8.63 x 10-14), 61.53% (3.24 µM, p= 2.11 x 10-

10), 62.60% (p= 4.20 x 10-10) when compared with vehicle control. The combination of BIIB021 and doxorubicin decreased the cell viability by 35.32% (0.405 µM), 29.29% (0.81 µM), 32.39%

(1.62 µM), 15.31% (3.24 µM) when compared with doxorubicin treatment within each concentration (Figure 20C).

Hsp90 inhibition with BIIB021 abrogated prolactin increased viability across all doxorubicin concentrations (Figure 20B p value table) which indicates that Hsp90 is involved in the prolactin mediated increase in cell viability.

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160 # 140 120 100 # 80 60

% Control Survival % Control 40 20 0 Prl BIIB021 A p-values Treatments! Prl! BIIB021! Vehicle!control! 6.78 x 10-5 1.16 x 10-9 to!treatments!

* * * **

B p values Dox 0.405µM! 0.81µM! 1.62µM! 3.24µM! 6.48µM! concentrations ! Dox to Dox+Prl 6.9 x 10-5 0.0095 0.01 0.0039 0.0003 Prl+Dox!to! 2.14 x 10-17 1.7 x 10-13 5.5 x 10-12 0.0001 0.0001 BI+Dox+Prl

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# # #

C p-values Dox! 0.405µM! 0.81µM! 1.62µM! 3.24µM! 6.48µM! concentrations Dox to Dox+BI 9.5 x 10-8 2.88 x 10-5 1.47 x 10-9 0.0006 NS

Figure 20. Hsp90 inhibition abrogates the prolactin-mediated increase in cell viability. MCF7 cells were pre-treated with 25 ng/ml human recombinant prolactin, or 800 nM Hsp90 inhibitor BIIB021 or a combination of the two for 24 hours followed by 2 hours doxorubicin treatment with or without prolactin and/or BIIB021, and a 48 hours recovery period with or without prolactin. Cell viability was determined with a WST-1 assay and all treatments were normalized to vehicle controls. A. Viability of cells treated with prolactin and BIIB021. B. Combination treatments with prolactin, doxorubicin and/or BIIB021. Statistical analysis denotes the comparison between prolactin + doxorubicin and doxorubicin alone treatment within each concentration. The statistical comparison of prolactin + doxorubicin and prolactin + doxorubicin + BIIB021 within each concentrations is presented in p-value table. C. Comparison of doxorubicin alone treatment with BIIB021 and doxorubicin combination treatment within each concentration. All results represent 3 external replicates and 6 internal replicates that are pooled (n=18). Data was analyzed using a One-way ANOVA followed by Bonferroni post-tests (Significant p values are indicated in the chart, NS= Not Significant). Statistically significant analysis (*) denotes p<0.05, (**) denotes p<0.001, (***) denotes p<0.0001, (#) denotes p<0.00001. Prl= Prolactin, dox= doxorubicin, BI= BIIB021. Error bars represent standard deviation from the mean.

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4.1.5.2 Synergistic activity of doxorubicin with Hsp90 inhibitor BIIB021

The combination of doxorubicin and BIIB021 treatment reduced the cell viability

significantly when compared with vehicle control or doxorubicin alone treatment as mentioned

above. Hsp90 and topoisomerase II were reported to be part of a complex and the inhibition of

Hsp90 was shown to sensitize cells to topoisomerase II poisons, which caused a synergistic

increase in apoptosis (Barker et al., 2006).

Based on our WST-1 cell viability assays with doxorubicin and the Hsp90 inhibitor

BIIB021, we have evaluated the nature of interaction between the drugs in the concentration

range used in my experiments. I used the CompuSyn computer program to calculate median

effect and combination index according to the method of Chou and Talalay (Chou, 2006). Based

on this method, combinations of doxorubicin with BIIB021 were evaluated for synergism,

additive effects or antagonism. In the first set of experiments, the individual cytotoxicity of

doxorubicin and BIIBI021 was tested on MCF7 breast cancer cells using WST-1 cell viability

assay and IC50 values were calculated as 5 µM for doxorubicin and 55 µM for BIIB021 using

Compusyn computer program. The concentrations of BIIB021 (400 nM and 800 nM) that reduced cell viability approximately 20-25% were chosen to use as separate fixed concentrations in calculations where doxorubicin was used in three fold increasing concentrations (Figure 21A).

The cell viability with doxorubicin treatment and combination with BIIB021 is shown in Figure

21B. The combination of doxorubicin with 400 nM and 800 nM BIIB021 decreased the viability when compared with doxorubicin treatment alone as follows: 34.4% (400 nM BIIB021+ 0.27

µM doxorubicin), 31.55 % (400 nM BIIB021+ 0.81 µM doxorubicin), 29.27% (400 nM

BIIB021+ 2.43 µM doxorubicin), 47.42% (800 nM BIIB021+ 0.27 µM doxorubicin), 56.59%

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(800 nM BIIB021+ 0.81 µM doxorubicin), 37.86% (800 nM BIIB021+ 2.43 µM doxorubicin)

and 6.91% (800 nM BIIB021+ 7.29 µM doxorubicin). These results confirmed my previous

WST-1 findings that the combination of doxorubicin and BIIB021 decreased cell viability when compared with doxorubicin and BIIB021 treatments alone.

Table 2 and Figure 22 show the combination index values, where less than 1 (CI<1) indicates the synergy (Chou, 2006). Our results show that there is strong to slight synergism between doxorubicin and BIIB021 in a range of drug doses used in our experiments.

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100 90 80 70 60 50 40

%"Control"Survival" 30 20 10 0 400nM 800nM BIIBI021"concentra5ons" A

p-values Dox! 0.27µM ! 0.81µM! 2.43µM! 7.29µM ! concentrations Dox to Dox + 400 6.87x10-6 1.05x10-6 0.00035 NS nM BI Dox to Dox + 800 3.49 x 10-6 3.10 x 10-6 3.50 x 10-6 0.001 nM BI

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Figure 21. Reduced cell viability with doxorubicin and BIIB021 combination treatment. MCF7 cells were pre-treated with BIIB021 (400nM or 800nM) for 24 hours followed by 2 hours of doxorubicin treatment. Drugs were removed with a media change and cells recovered for 48 hours. Cell viability was determined with WST-1 cell viability assay and the treatments were normalized to vehicle control. Results represent 3 internal and 3 external experiments that are pooled (n=9). Data was analyzed using a One-way ANOVA followed by Bonferroni post-tests (Significant p values are indicated in the chart, NS= Not Significant). A. BIIB021 alone treatments (400 nM and 800 nM) B. The comparison of BIIB021 and doxorubicin combination treatment with doxorubicin alone treatment. Error bars represent standard deviation from the mean.

116

Table 2. Combination Index Values from Drug Combination Studies of Doxorubicin with BIIB021 Fixed Inhibitor Doxorubicin Combination index Type of Synergism

Concentrations Concentration (µM) values

0.27 0.4685 ± 0.1864 Synergism

BIIB021 (400nM)

0.81 0.37933 ± 0.1237 Synergism

2.43 0.47691 ± 0.1750 Synergism

7.29 0.88603 ± 0.0990 Slight Synergism

0.27 0.1584 ± 0.171 Strong Synergism

BIIB021 (800nM)

0.81 0.15167 ± 0.1488 Strong Synergism

2.43 0.3889 ± 0. 104 Synergism

7.29 0.65919 ± 0.039 Synergism

Values are mean ± SD of three internal and three external replicates.

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Synergism Between Doxorubicin and BIIB021 1.5! Antagonism

1! AddiMve!Eﬀect!! Moderate!Synergism!!

400nM BIIB021 0.5! 800nM BIIB021 !Synergism!! Combition Index (CI) Values Values Combition Index (CI)

0! 0! 2! 4! 6! 8! 10!

Doxorubicin concentration (uM)

Figure 22. Synergism Between Doxorubicin and BIIB021. Combination Index (CI) Values for Doxorubicin and BIIB021 combination treatments in MCF7 cells. CI value: <1 Synergism; =1 Additive Effect; and >1 Antagonism. n=9. Error bars are smaller than symbol size, hence are not visible.

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4.1.6 Stability of ATM and Jak2 are dependent on Hsp90

Hsp90 stabilizes and chaperones more than 200 proteins from growth factor receptors

(HER-2, IGF-1R), signalling kinases (Akt, Raf-1), cell cycle regulators (cdk4), nuclear steroid receptors (AR, ER), to transcription factors (mutant p53, HIF-1α) (Neckers, 2002, Kamal et al.,

2004) and DNA repair proteins (DNA-PKcs (Falsone et al., 2005), and the MRN complex (Dote

et al., 2006). Based on previous preliminary laboratory findings, the dependency of p-

ATM/ATM and Jak2 upon Hsp90 for their stability was investigated with Hsp90 inhibitors in

breast cancer cell lines.

4.1.6.1 The stability of ATM and/or p-ATM is dependent upon Hsp90 in breast cancer cells

Radiation-induced activation of ATM was shown to be reduced with the Hsp90 inhibitor,

17DMAG, in human pancreatic carcinoma cell lines (Dote et al., 2006). According to previous

studies from our laboratory (Urbanska, 2011), decreased levels of p-ATM and ATM were

observed in MCF7 and SKBR3 cells treated with the Hsp90 inhibitor, 17AAG, without

doxorubicin, but particularly so with doxorubicin. Overall, studies support our hypothesis that

the stability of ATM and p-ATM are dependent upon Hsp90.

In order to confirm previous findings from the laboratory, I treated SKBR3 cells with

three fold increasing concentrations of 17AAG for 24 hours followed by 2 hours of doxorubicin

treatment. Decreased ATM and p-ATM protein levels were observed by western blot when both

ATM and p-ATM levels were normalized to GRB2 loading control (Figure 23A, 23B, 23C).

When p-ATM was normalized to ATM, there was a small decrease in p-ATM levels at 9 nM, 27

nM and 81 nM 17AAG concentrations (Figure 23D). Since this experiment has been repeated

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several times in our laboratory by different people (Anna Urbanska, Ruixuan Liu), only one experimental replicate is presented.

We used ovine prolactin in the following experiment and some experiments in the following chapters. Ovine prolactin is known to be 10-fold less effective as a ligand on human prolactin receptors (Utama et al., 2009). Previous laboratory experiments confirmed that when used in certain concentrations, ovine prolactin shows equivalent responses with human recombinant prolactin (Sutherland, 2010, Urbanska, 2011). The concentration of ovine prolactin

(5 µg/ml) used in the experiments was shown to induce Jak2 activation (Bernichtein et al., 2001) and the expression of Jak2- Stat5 regulated target genes, including Hsp90α (Perotti et al., 2008) in the literature.

In addition to investigating the relative amounts of ATM protein, the expression of ATM at the mRNA level was investigated with qPCR, by the undergraduate student, Sara Mirzaei. In this experiment, MCF7 cells were pre-treated with ovine prolactin (5 µg/ml) and/or 17AAG (100 nM) for 24 hours followed by 2 hours doxorubicin treatment. According to the results no significant difference was observed between treatment groups (Figure 24) indicating that ATM gene expression was not affected by Hsp90 inhibition or prolactin treatment.

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27 nM 81 nM

- - 3 nM 9 nM 17AAG 243 nM

Dox (0.2 µM) - + + + + + +

p-ATM 320 kDa

ATM 320 kDa

Hsp90 90 kDa

GRB2 25 kDa A

ATM"ImageJ"Results"(Normalized"to"GRB2)" 1.2" 1" 0.8" 0.6" 0.4" Fold"Change" 0.2" 0"

0.2uM"dox" 3nM"17AAG" 9nM"17AAG" 27nM"17AAG" 81nM"17AAG" 243nM"17AAG" B

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0.2uM"dox" 3nM"17AAG" 9nM"17AAG" 27nM"17AAG" 81nM"17AAG" 243nM"17AAG" C

Fold"Change" 0.4" 0.2" 0"

0.2uM"dox" 3nM"17AAG" 9nM"17AAG" 27nM"17AAG" 81nM"17AAG" 243nM"17AAG" D

Figure 23. The stability of p-ATM and/or ATM are dependent on Hsp90. SKBR3 cells were treated with three fold increasing concentrations of the Hsp90 inhibitor, 17AAG, followed by 2 hours doxorubicin treatment. A. Western blot image of p-ATM (Ser1981), ATM, Hsp90 and loading control GRB2. B. ATM ImageJ results. ATM levels were normalized to GRB2. C. p- ATM ImageJ results. p-ATM levels were normalized to GRB2. D. p-ATM ImageJ results. p- ATM levels were normalized to ATM.

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0.003

a a a a 0.002 a a

0.001

0.000

Treatments

Figure 24. ATM mRNA levels are not affected from 17AAG, prolactin and doxorubicin treatments. MCF7 cells were pre-treated with ovine prolactin (5 µg/ml) and/or 17AAG for 24 hours, followed by 2 hours doxorubicin treatment (0.2 µM). ATM expression was compared by qPCR and the results were normalized to YWHAZ control (mean ± SEM; n = 5). The ΔΔCt method was used to analyze the relative changes in gene expression data from the qPCR. The letter of “a” above SEM bars denotes that there were not any statistical significant differences (One-way ANOVA followed by Bonferroni test, p <0.05).

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4.1.6.2 The stability of Jak2 is dependent upon Hsp90 in breast cancer cells

In myeloproliferative diseases, Hsp90 is important for Jak/Stat5 signalling (Schoof et al.,

2009). The activation and stability of Jak2 were suggested to be dependent on Hsp90 in leukemia cell lines (Marubayashi et al., 2010). In order to confirm if the stability of Jak2 is dependent on

Hsp90 in breast cancer cells, MCF7 cells were pre-treated with three fold increasing concentrations or 100 nM of 17AAG and/or ovine prolactin (5 µg/ml) for 24 hours, followed by

2 hours doxorubicin treatment where indicated. In order to keep experimental conditions consistent between the stability experiments (ATM and Jak2), doxorubicin treatment was also used in some of the Jak2 experiments.

According to western blot results, when cells were treated with prolactin and three fold increasing concentrations of 17AAG, decreased levels of Jak2 were observed at 9 nM and higher

17AAG concentrations (Figure 25A). A similar response was observed in a separate experiment, in the absence of prolactin but in the presence of doxorubicin, where cells were treated with doxorubicin and three fold increasing concentrations of 17AAG (Figure 25B). Decreased levels of Jak2 were seen at 9 nM and higher concentrations. In the last experiment of the series,

‘prolactin, 17AAG and doxorubicin treatments were combined and decreased protein levels of

Jak2 were detected where cells were treated with 17AAG alone or in combination with prolactin and/or doxorubicin (Figure 25C). The results with two Hsp90 inhibitors indicate that Jak2 stability is dependent on Hsp90 in breast cancer cells. The trend of increased prolactin levels with prolactin treatment (Figure 25B and 25C) suggested that prolactin may increase Jak2 stability in breast cancer cells.

In order to confirm that the effect of Hsp90 inhibition was on Jak2 protein levels, mRNA levels of Jak2 were examined with qPCR, by project student Sara Mirzaei. In this experiment,

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same experimental conditions were followed where MCF7 cells were pre-treated with 100 nM of

17AAG and/or ovine prolactin (5 µg/ml) for 24 hours, which is followed by 2 hours doxorubicin.

According the results, statistical significance was not observed between treatment groups indicating that the effect of Hsp90 inhibitors was at the protein level and that prolactin and doxorubicin treatments also do not have any effect on Jak2 mRNA expression (Figure 26).

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9 nM 3 nM 81 nM

- - 243 nM 17AAG - 27 nM - Prolactin - + + + + + + - DMSO - + - - - - -

Jak2 125 kDa

GRB2 25 kDa

1.4" 1.2" 1" 0.8"

Fold change 0.6" 0.4" 0.2" 0" Prl Control DMSO

3nM17AAG+prl9nM17AAG+prl 27nM17AAG+prl81nM17AAG+prl 243nM17AAG+prl A

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81 nM

- 9 nM 3 nM - - 27 nM 17AAG 243 nM - - + + + + + + Dox (0.2 µM) - + ------DMSO

Jak2 125 kDa

GRB2 25 kDa

1.2" 1" 0.8" 0.6" Fold change 0.4" 0.2" Control DMSO Dox 3nM 9nM 27nM 81nM 243nM 0" 17AAG 17AAG 17AAG 17AAG 17AAG + dox + dox + dox + dox + dox B

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17AAG (100nM) - - - - + + +

Dox (0.2 µM) - - + + - - +

Prolactin (5µg/ml) - + - + - + +

Jak2 125 kDa

GRB2 25 kDa

1.4" 1.2" 1" 0.8"

Fold change 0.6" 0.4" 0.2"

0" Prl dox Control dox +prl 17AAG 17AAG + prl 17AAG+prl+dox C

Figure 25. Jak2 stability is dependent on Hsp90. MCF7 cells were pre-treated with ovine prolactin (5µg/ml) and/or 17AAG for 24 hours followed by 2 hours doxorubicin treatment (0.2 µM). Jak2 and GRB2 protein levels were examined with western blot. GRB2 is used as loading control. A. The effect of Hsp90 inhibition in the presence of prolactin on Jak2 protein levels. Jak2 and GRB2 western blot results and ImageJ calculation where Jak2 levels were normalized to GRB2. Western blot results represent two independent experiments (n=2). B. The effect of Hsp90 inhibition with doxorubicin on Jak2 protein levels. Jak2 and GRB2 western blot results and ImageJ calculation (Jak2 levels were normalized to GRB2). Western blot results represent two independent experiments (n=2). C. The effect of Hsp90 inhibition in the presence of prolactin and or prolactin on Jak2 protein levels. Jak2 and GRB2 western blot results and ImageJ calculation (Jak2 levels were normalized to GRB2). Western blot and ImageJ results represent three independent experiments (n=3). Error bars represent standard deviation from the mean.

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0.0035 a 0.0030

0.0025 a a a 0.0020 a a 0.0015

0.0010

0.0005

0.0000

Treatments

Figure 26. Jak2 mRNA levels are not affected from 17AAG, prolactin and doxorubicin treatments. MCF7 cells were pre-treated with ovine prolactin (5 µg/ml) and/or 17AAG for 24 hours, followed by 2 hours doxorubicin treatment (0.2 µM). Jak2 expression was compared by qPCR and the results were normalized to the YWHAZ control (mean ± SEM; n = 5). The ΔΔCt method was used to analyze the relative changes in gene expression data from the Real-Time PCR. The letter of “a” above SEM bars denotes that there were not any statistical significant differences (One-way ANOVA followed by Bonferroni test, p < 0.05).

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4.1.7 The involvement of ATM in mechanism of prolactin-mediated cell viability

Our previous studies and those in this thesis, show that ATM is stabilized, indirectly or

directly, by HSP90. I tested the involvement of ATM in prolactin mediated cell viability against

DNA damaging agents using ATM inhibitor KU55933 (Tocris) and MCF7 cells transfected with short interfering RNA (siRNA) directed against ATM, GAPDH (positive control) or with scrambled siRNA (negative control).

4.1.7.1 ATM is involved in the prolactin–induced increase cell viability against doxorubicin in breast cancer cells

In order to investigate if ATM is involved in prolactin enhanced cell viability, MCF7 and

SKBR3 were treated with 10 µM ATM inhibitor, KU55933, a concentration chosen from the literature where tumor cell lines (such as, HeLa, PC-3, MDA-MB-463, etc.) were tested in conjunction with DNA damaging chemotherapy agents (doxorubicin, etoposide, etc.) (Hickson et al., 2004, Rainey et al., 2008, Li and Yang, 2010).

In this thesis, cells were pre-treated with 25 ng/ml human recombinant prolactin and/or

10 µM KU55933 for 24 hours followed by 2 hours doxorubicin treatment. Cells recovered in the absence of doxorubicin and the presence of KU55933 and/or prolactin consistent with the first treatment conditions for 48 hours.

In MCF7 cells, prolactin alone increased the cell viability 20.26% and KU55933 decreased cell viability 10.94% when compared with their vehicle controls (Figure 27A). When the viability of doxorubicin treated cells were compared with prolactin + doxorubicin treated cells within same concentrations, prolactin increased the viability of doxorubicin treated cells across all five concentrations as follows; 20.47% (0.405 µM), 26.51% (0.81 µM), 16.82% (1.62

µM), 6.7% (3.24 µM). The statistical significance was indicated on the cell viability graph and p-

130

values are presented in the table (Figure 27B). KU55933 and doxorubicin combination decreased

the viability by 12.9% (0.405 µM, p= 0.004), 22.71% (0.81 µM, p=1.6 x 10-9), 33.76% (1.62

µM, p= 1.02 x 10-9), 46.67% (3.24 µM, p= 1.44 x 10-11), 63.68% (6.48 µM, p= 1.16 x 10-14) when compared with vehicle control. The combination of the two drugs decreased cell viability by 11.74% (0.405 µM,), 20.26% (1.62 µM), 22.22% (3.24 µM), 12.69% (6.48 µM) when compared with doxorubicin alone (Figure 27B and 27C). Prolactin did not increase the viability of doxorubicin and KU55933 treated cells across all concentrations (Figure 26B). The results showed that ATM inhibition strongly reduces prolactin increased viability across all doxorubicin concentrations (p-values are presented in the table of Figure 27B) , which suggests that ATM

may be involved in the mechanism of prolactin mediated increase in cell viability.

In SKBR3 cells, prolactin alone increased the cell viability by 17.35 % and KU55933

decreased the viability by 11.96 when compared with their vehicle controls (Figure 28A). When

doxorubicin treatment was compared with prolactin + doxorubicin within same concentration,

prolactin increased viability of doxorubicin treated cells by 9.25% (0.405 µM dox) and 5.18%

(1.27 µM dox) but there was no significant increase at highest concentration (3.24 µM dox). The

statistical difference is indicated on the graph (Figure 28B). Doxorubicin and KU55933

combination decreased cell viability 63.60% (0.405 µM dox, p= 4.5 x 10-12), 63.38% (1.27 µM dox, p= 3.3 x 10-12) and 65.08% (3.24 µM dox, p= 2.1 x 10-10) when compared with vehicle

control. The combination of the two drugs decreased cell viability 37.70 % (0.405 µM dox) and

10.60% (1.27 µM dox) when compared with only doxorubicin treated cells (Figure 28B and

28C). Prolactin treatment did not increase cell viability of doxorubicin and KU55933 treated

cells indicating that in both MCF7 and SKBR3 cells, the inhibition of ATM abrogates the

prolactin increased cell viability of DNA damaged cells (Figure 28 B) 131

160 140 * 120 100 * 80 60

% Control Survival % Control 40 20 0 Prl KU55933 A

p-values Treatments Prl KU55933 Vehicle control to treatments 0.001 0.009 B

p values ** ** **

Dox! 0.405µM 0.81µM 1.62µM 3.24µM 6.48µM concentrations Dox!to!Dox+Prl 0.0007 0.0006 0.0002 0.02 NS ?6 ?6 ?7 ?7 ?5 Dox+Prl!tp! 5.3!x!10 6.4!x!10 1.7!x!10 4.04!x!10 2.24!x!10 Dox+KU+Prl

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* * #

p-values Dox! 0.405µM 0.81µM 1.62µM 3.24µM 6.48µM concentrations Dox to Dox+KU 0.002 NS 0.001 3.5 x 10-6 0.004

Figure 27. ATM inhibition abrogated the prolactin increased cell viability in MCF7 cells. MCF7 cells were pre-treated with 25ng/ml human recombinant prolactin, or 10µM ATM inhibitor KU55933 or a combination of the two for 24 hours. This was followed by 2 hours doxorubicin treatment with or without prolactin and/or KU55933 and a 48 hour recovery period with or without KU55933 and/or prolactin consistent with the first treatment conditions. Cell viability was determined with a WST-1 assay and all treatments were normalized to vehicle controls. A. Viability of cells treated with prolactin or KU55933. B. Combination treatments with prolactin, doxorubicin and/or KU55933. Statistical analysis denotes the comparison between prolactin + doxorubicin and doxorubicin alone treatments within each concentration. The statistical comparison of prolactin + doxorubicin and prolactin + doxorubicin + KU within each concentrations is presented in p-value table. C. Comparison of doxorubicin alone with KU55933 and doxorubicin combination treatment. All results represent 3 external replicates and 6 internal replicates that are pooled (n=18). Data was analyzed using a One-way ANOVA followed by Bonferroni post-tests (Significant p values are indicated in the chart, NS= Not Significant). Statistically significant analysis (*) denotes p<0.05, (#) denotes p<0.00001. Prl= Prolactin, dox= doxorubicin, KU= KU55933. Error bars represent standard deviation from the mean.

133

160 140 * 120 100 * 80 60 40 % Control Survival % Control 20 0 Prl KU55933 A

p-values Treatments! Prl! KU55933! Vehicle control to treatments 0.01 0.02

p-values Dox!concentrations! 0.405µM! 1.27µM! 3.24µM! Dox to Dox+Prl 0.01 0.004 NS Prl+Dox!to!Ku+Dox+Prl! 8.3!x!10?8! 6.49!x!10?5! NS!

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p-values Dox! 0.405µM! 1.27µM! 3.24µM! concentrations! Dox to 2.49 x 10-11 0.02 NS KU+Dox

Figure 28. ATM inhibition abrogated the prolactin increased cell viability in SKBR3 cells. SKBR3 cells were pre-treated with 25ng/ml human recombinant prolactin, or 10µM ATM inhibitor KU55933 or a combination of the two for 24 hours. This was followed by 2 hours doxorubicin treatment with or without prolactin and/or KU55933 and 48 hours recovery period with or without KU55933 and/or prolactin consistent with the first treatment conditions. Cell viability was determined with a WST-1 assay and all treatments were normalized to vehicle controls. A. Viability of cell treated with prolactin and KU55933. B. Combination treatments with prolactin, doxorubicin and/or KU55933. Statistical analysis denotes the comparison between prolactin + doxorubicin and doxorubicin alone treatments within each concentration. The statistical comparison of prolactin + doxorubicin and prolactin + doxorubicin + KU55933 within each concentrations is presented in the p-value table. C. Comparison of doxorubicin alone with KU55933 and doxorubicin combination treatment. All results represent 3 external replicates and 6 internal replicates that are pooled (n=18). Data was analyzed using a One-way ANOVA followed by Bonferroni post-tests (Significant p values are indicated in the chart, NS= Not Significant). Statistically significant analysis (*) denotes p<0.05, (**) denotes p<0.001, (***) denotes p<0.0001, (#) denotes p<0.00001. Prl= Prolactin, dox= doxorubicin, KU= KU55933. Error bars are smaller than symbol sizes, hence are not visible.

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4.1.7.2 Synergistic inhibition of doxorubicin with ATM inhibitor KU55933

The combination of doxorubicin and KU55933 treatment reduced the cell viability

significantly when compared with vehicle control or doxorubicin alone treatment as mentioned

above (Figure 27B and 27C, Figure 28B and 28C) . KU55933 has been shown to sensitize cells

to topoisomerase II inhibitors etoposide, doxorubicin and amsacrine in Hela cells, which

supports our finding (Hickson et al., 2004).

Based on our WST-1 cell viability assays with doxorubicin and the ATM inhibitor

KU55933, I have evaluated the nature of the interaction between the drugs in the concentration

range used in my experiments. The CompuSyn computer program was used to calculate the

median effect and combination index according to the method of Chou and Talalay (Chou,

2006). Combinations of doxorubicin with KU55933 were evaluated for synergism, additive

effects or antagonism. The cytotoxicity of doxorubicin or KU55933 was tested on MCF7 breast

cancer cells using a WST-1 cell viability assay and the IC50 values were calculated as 5 µM for doxorubicin and 30 µM for KU55933. Based on these dose response curves, two concentrations of KU55933 (10 and 20 µM) that reduced cell viability approximately 20-30% were chosen to use as fixed concentration in calculations where doxorubicin was used in three fold increasing concentrations (Figure 29A). The cell viability with doxorubicin alone and in combination with

KU55933 is shown in Figure 29B. The combination of doxorubicin with 10 µM and 20 µM

KU55933 decreased the viability when compared with doxorubicin treatment alone as follow;

27.05% (10 µM KU55933+ 0.27 µM doxorubicin), 37.60 % (10 µM KU55933+ 0.81 µM doxorubicin), 33.30 % (10 µM KU55933+ 2.43 µM doxorubicin), 13.99 % (10 µM KU55933+

7.29 µM doxorubicin), 72.16 % (20 µM KU55933 + 0.27 µM doxorubicin), 75.14 % (20 µM

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KU55933 + 0.81 µM doxorubicin, p= 4.47 x 10-7), 54.62 % (20 µM KU55933 + 2.43 µM doxorubicin) and 15.77% (20 µM KU55933 + 7.29 µM doxorubicin).

Table 3 and Figure 30 show the combination index values, where less than 1 (CI<1) indicates the synergy. Our results show that there is slight, moderate and full synergism between doxorubicin and KU55933 in a range of drug doses used in our experiments.

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120

100

%"Control"Survival" 40

0 10uM 20uM KU55933"concentra5ons" A

p-values Dox! 0.27µM ! 0.81µM ! 2.43µM ! 7.29µM ! concentrations Dox to Dox + 10 0.0003 0.0001 0.0005 0.0007 µM KU Dox to Dox + 20 2.72 x 10-7 4.47 x 10-7 1.69 x 10-6 0.0001 µM KU

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Figure 29. Reduced cell viability with doxorubicin and KU55933 combination treatment. MCF7 cells were pre-treated with KU55933 (10 µM and 20 µM) for 24 hours followed by 2 hours of doxorubicin treatment. Doxorubicin was removed and cells recovered for 48 hours with or without KU55933 and/or prolactin. Cell viability was determined with a WST-1 cell viability assay and the treatments were normalized to vehicle control. Results represent 3 internal and 3 external experiments that are pooled (n=9). Data was analyzed using a One-way ANOVA followed by Bonferroni post-tests (Significant p values are indicated in the chart, NS= Not Significant). A. KU55933 alone treatment (10 µM and 20 µM). B. Comparison of doxorubicin treatment alone or with KU55933 as indicated in the table below. Error bars represent standard deviation from the mean.

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Table 3. Combination Index Values from Drug Combination Studies of Doxorubicin with KU55933 Fixed Inhibitor Doxorubicin Combination index Type of Synergism

Concentrations Concentration (µM) values

0.27 0.75952 ± 0.1629 Moderate Synergism

KU55933 (10µM)

0.81 0.65443 ± 0.1445 Synergism

2.43 0.7633 ± 0.1650 Moderate Synergism

7.29 0.71625 ± 0.0551 Moderate Synergism

0.27 0.61971 ± 0.1229 Synergism

KU55933 (20µM)

0.81 0.55185 ± 0.1125 Synergism

2.43 0.70035 ± 0.093 Moderate Synergism

7.29 0.82405 ± 0.0459 Slight Synergism

Values are mean ± SD of three internal and three external replicates.

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Synergism Between Doxorubicin and KU55933 1.5!

Antagonism

1! AddiMve!Eﬀect!!

Moderate!Synergism!! 10uM!KU55933! 0.5! 20uM!KU55933! !Synergism!! CombinaAon!Index!(CI)!Values!

0! 0! 2! 4! 6! 8! 10! Doxorubicin!concentraAon!(uM)!

Figure 30. Synergism Between Doxorubicin and KU55933. Combination Index (CI) values for doxorubicin and BIIB021 combination treatments in MCF7 cells. CI value: <1 Synergism; =1 Additive Effect; and >1 Antagonism. n=9. Error bars are smaller than symbol size, hence are not visible.

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4.1.8 The involvement of Jak2 in the mechanism of prolactin induced increase in cell viability

The stability of Jak2 was confirmed to be dependent on Hsp90 in breast cancer cells

(section 4.1.6.3). Jak2 has been implicated in pathological cell growth in leukemia and myeloma

and selective Jak2 inhibitors were suggested as potential therapeutic options in specific cancer types (Majumder et al., 2011). G6 (NSC33994) is an inhibitor of Jak2 that binds to the ATP-

binding pocket of the Jak2 kinase domain and has an inhibitory effect on Jak2 kinase activitiy.

The central stillbenoid core of G6 is suggested to mediate specific Jak2 inhibitory potential

(Majumder et al., 2010, Kiss et al., 2009).

In order to test if prolactin-mediated cell viability involves a Jak2-based mechanism, I

have tested G6 in cell viability assays.

Dose response studies over 24 hours were first conducted based on the concentrations

chosen from the literature (Majumder et al., 2011). Although 25 µM and higher concentrations

were recommended in the literature to inhibit Jak2 kinase activity, without high toxicity of G6,

we noticed that MCF7 cells were very sensitive to 24 hours of G6 treatment at concentrations of

25 µM and higher. As seen in Figure 31A, G6 decreased cell viability by 75.60% (25 µM) and

80.75% (50µM). Due to the dramatic decrease in cell viability, the dose curve experiments were repeated at 3 hours, 6 hours and 12 hours time points with two fold increasing concentrations of

G6. According to the results; cell viability decreased by 10.50% (25 µM), 33.14% (50 µM) and

74 20% (100 µM) at 3 hours (Figure 31B), 24.72% (25 µM), 52.51% (50 µM) and 78.58% (100

µM) at 6 hours (Figure 31C), 14.34% (12.5 µM), 23.85% (25 µM), 56.17% (50 µM) and 68.52%

(100 µM) at 12 hours (Figure 31D).

Using the conditions established in the cell viability assays, the inhibition of Jak2 kinase activity was confirmed with western blot experiments at 12 hours by looking at P-Stat5 levels. 142

The results showed a decrease in P-Stat5 levels starting at 12.5µM and almost complete inhibition at 25µM and higher concentrations (Figure 32).

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24 hours G6 dose curve 140 120 100 80 60

40 # % Control survival % Control # 20 0 0.1 uM 0.5uM 1 uM 5 uM 25 uM 50 uM G6 concentrations A

G6 Concentrations 25 µM 50 µM Vehicle control to G6 1.8 x 10-10 2.5 x 10-10

3 hours G6 dose curve 140 120 100 * 80 # 60 40 # % Control Survival % Control 20 0 1.56 uM 3.125 uM 6.25 uM 12.5 uM 25uM 50uM 100uM G6 Concentrations B

G6 Concentrations 25 µM 50 µM 100 µM! Vehicle control to 0.04 8.7 x 10-6 6.8 x 10-11! G6

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6 hours G6 dose curve 140 120 100 ** 80 60 # 40 #

% Control Survival % Control 20 0 1.56 uM 3.125 uM 6.25 uM 12.5 uM 25uM 50uM 100uM G6 Concentrations C

G6 Concentrations 25 50 µM 100 µM µM Vehicle control to G6 0.0002 4.47 x 10-8 1.8 x 10-10

12 hours G6 dose response 140 120 * 100 # 80 60 # # 40 % Control Survival % Control 20 0 1.56 uM 3.125 uM 6.25 uM 12.5 uM 25uM 50uM 100uM G6 concentrations D

G6 Concentrations 12.5 µM 25 µM 50 µM 100 µM! Vehicle control to G6 0.03 7.78 x 10-5 8.13x 10-9 1.56x 10-11!

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Figure 31. Dose response curves of Jak2 inhibitor, G6. MCF7 cells were treated with G6 at indicated time points and concentrations. The cell viability was determined with WST-1 cell viability assay. Treatments were normalized to vehicle control. All results represent 3 external and 6 internal replicates that are pooled (n=18). A. 24 hours G6 dose curve. B. 3 hours G6 dose curve. C. 6 hours G6 dose curve. D. 12 hours G6 dose curve. Data was analyzed using a One- way ANOVA followed by Bonferroni post-tests (Significant p values are indicated in the chart). The statistical comparison was done between vehicle control and G6 treated samples. Statistically significant analysis (*) denotes p<0.05, (**) denotes p<0.001, (***) denotes p<0.0001, (#) denotes p<0.00001. Error bars represent standard deviation.

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G6 (µM) 0 0 DMSO 1.56 3.12 6.25 12 25 50 100 Prolactin - + + + + + + + + + p-Stat5 92 kDa

Stat5 92 kDa

HistoneH3 17 kDa

Figure 32. Western blot dose response of Jak2 inhibitor, G6. MCF7 cells were treated with two fold increasing concentrations of G6 for 12 hours. p-Stat5 levels were determined by Western blot assay from nuclear protein extracts. Results are representative of 2 independent experimental replicates.

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Based on the dose response curves and western blot results, 25 µM G6 for 12 hours was

chosen for the following experiments. In order to investigate if Jak2 is involved in the prolactin

increased cell viability against DNA damaging agents, MCF7 and SKBR3 cells were pre-treated

for 24 hours with 25 ng/ml human recombinant prolactin and 12 hours with 25 µM G6 which is

followed by 2 hours of doxorubicin treatment. Cell viability was determined by a WST-1 assay

after 48 hours recovery time with or without prolactin. According to the results in MCF7,

prolactin treatment alone increased viability by 21.16% and G6 treatment alone decreased cell

viability by 30.66% compared to vehicle controls (Figure 33A). When doxorubicin treatment

was compared with prolactin + doxorubicin within the same doxorubicin concentration, prolactin

increased cell viability across four doxorubicin concentrations as follows: 9.56% (0.405 µM),

21.30% (0.81 µM), 19.94% (1.62 µM) and 12.87% (3.24 µM). The statistical significance is

indicated on the graph and p-values are presented (Figure 33B). G6 and doxorubicin combination

treatment decreased cell viability 31.59% (0.405 µM, p= 4.9 x 10-6), 31.97% (0.81 µM, p= 1.10 x 10-8), 45.07% (1.62 µM, p= 1.98 x 10-8), 57.90% (3.24 µM, p= 1.10 x 10-9) and 71.78% (6.48

µM, p= 3.25 x 10-11) when compared with vehicle controls and the cell viability was decreased

41.46% (0.405 µM), 38.78% (0.81 µM), 40.06% (1.62 µM), 39.58% (3.24 µM) and 42.25%

(6.48 µM) when compared with doxorubicin alone (Figure 33B and 33C). Prolactin treatment

only increased the cell viability of 6.48 µM doxorubicin and G6 treated cells by 3.02% (p= 0.04)

(Figure 33B) . The results with MCF7 cells showed that prolactin increased the cell viability

across all treatments with doxorubicin, and this increase was abrogated with Jak2 inhibition

(Figure 33B).

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160 140 * 120 100 80 # 60 40 % Control Survival % Control 20 0 Prl G6 A

p-values Treatments Prl! G6! Vehicle control to treatments 0.001 2.9 x 10-6

** * *

p-values Dox!concentrations 0.405!µM 0.81!µM 1.62!µM 3.24!µM 6.48!µM Dox!to!Dox+Prl! 0.04! 0.004! 0.02! 0.03! 0.03! Dox+Prl to 7.8 x 10-10 1.6 x 10-7 1.12 x 10-7 6.01!x!10-9! 1.4!x!10?9! Dox+Prl+G6

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# # # # #

p-values Dox! 0.405 µM! 0.81 µM! 1.62 µM! 3.24 µM! 6.48 µM! concentrations Dox to Dox+G6 9.49 x 10-8 8.16 x 10-6 2.74 x 10-6 7.85 x 10-10 9.37 x 10-7

Figure 33. Jak2 inhibition abrogates with prolactin increased cell viability in MCF7 cells. MCF7 cells were pre-treated with 25 ng/ml prolactin for 24 hours and/or 25 µM Jak2 inhibitor, G6, for 12 hours followed by 2 hours doxorubicin treatment with or without prolactin and/or G6 and 48 hours recovery period in the presence or absence of prolactin. Cell viability was determined with WST-1 assay and all treatments were normalized to vehicle controls. A. Viability of cells treated with prolactin or G6. The statistical comparison was done between vehicle control and prolactin or G6 treated samples. B. The effect of combination treatments with prolactin, doxorubicin and/or G6 on MCF7 cell viability. Statistical analysis denotes the comparison between prolactin + doxorubicin and doxorubicin alone treatment within each concentration. The statistical comparison of prolactin + doxorubicin and prolactin + doxorubicin + G6 within each concentrations is presented in the p-value table. C. Comparison of doxorubicin alone treatment with G6 and doxorubicin combination treatment within each concentration. All results represent 6 internal and 3 external replicates that are pooled (n=18). Data was analyzed using a One-way ANOVA followed by Bonferroni post-tests (Significant p values are indicated in the chart). Statistically significant analysis (*) denotes p<0.05, (**) denotes p<0.001, (***) denotes p<0.0001, (#) denotes p<0.00001. Error bars represent standard deviation from the mean. 150

In SKBR3 cells, prolactin treatment alone increased cell viability by 12.55% and G6

treatment decreased cell viability by 51.24% when compared with vehicle controls (Figure 34A).

When doxorubicin treatment was compared with prolactin + doxorubicin within same

concentrations, prolactin increased the viability of doxorubicin treated cells by 9.05% (0.405

µM) and 7.38% (1.27 µM). The statistical significance and p-values are presented in Figure

34B.. G6 and doxorubicin combination treatments decreased cell viability by 77.17% (0.405 µM,

p= 1.08 x 10-12), 82.42% (1.27 µM, p= 1.64 x 10-12) and 83.83% (3.24 µM, p= 1.28 x 10-12) when compared with vehicle controls. The combination of the two decreased cell viability by 52.05%

(0.405 µM), 32.47% (1.27 µM) and 27.47% (3.24 µM) when compared with doxorubicin alone

(Figure 34B and 34C). Prolactin increased the cell viability of doxorubicin and G6 treated cells only by 1.27% (p= 0.0008) at 3.24 µM doxorubicin concentration (Figure 34B). The results with

SKBR3 cells demonstrate that the prolactin increased cell viability was abrogated with Jak2 inhibition (Figure 34B).

Overall the results indicate that Jak2 is likely involved in a mechanism mediated by prolactin in breast cancer cells and the inhibition of Jak2 with G6 abrogates the effect of prolactin on DNA damaged cells.

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140

120 *

100

60 #

%"Control"Survival" 40

0 Prl G6 A

p-values Treatments!! Prl! G6! Vehicle control to treatments 0.001 9.9 x 10-11

p-values Dox concentrations 0.405!µM 1.27!µM 3.24!µM Dox to Dox+Prl! 0.03! 0.04! NS! ?10 ?10 ?10 Prl+Dox!to! 6.12!x!10 2.39!x!10 1.5!x!10 G6+Dox+Prl

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# #

p-values Dox! 0.405!µM 1.27!µM 3.24!µM concentrations Dox to Dox+G6 1.21 x 10-7 6.98 x 10-11 1.93 x 10-10

Figure 34. Jak2 inhibition abrogates with prolactin increased cell viability in SKBR3 cells. SKBR3 cells were pre-treated with 25ng/ml prolactin for 24 hours and/or 25 µM Jak2 inhibitor, G6, for 12 hours followed by 2 hours doxorubicin treatment with or without prolactin and/or G6 and 48 hours recovery period in the presence or absence of prolactin. Cell viability was determined with Wst-1 assay and all treatments were normalized to vehicle controls. A. Viability of cells treated with prolactin or G6. The statistical comparison was done between vehicle control and prolactin or G6 treated samples. B. The effect of combination treatments with prolactin, doxorubicin and/or G6 on MCF7 cell viability. Statistical analysis denotes the comparison between prolactin + doxorubicin and doxorubicin alone treatment within each concentration. The statistical comparison of prolactin + doxorubicin and prolactin + doxorubicin + G6 within each concentrations is presented in the p-value table. C. Comparison of doxorubicin alone treatment with G6 and doxorubicin combination treatment within each concentration. All results represent 6 internal and 3 external replicates that are pooled (n=18). Data was analyzed using a One-way ANOVA followed by Bonferroni post-tests (Significant p values are indicated in the chart, NS= Not Significant). Statistically significant analysis (*) denotes p<0.05, (**) denotes p<0.001, (***) denotes p<0.0001, (#) denotes p<0.00001. Error bars represent standard deviation from the mean.

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Chapter Five: RESULTS II

5.1 Assess the Role of Prolactin on Tumorigenicity and Tumor Volume in Response to DNA Damaging Agents in vivo

Prolactin is known to decrease apoptosis (Perks et al., 2004) and act as a survival factor in in vitro studies (Perks et al., 2004, Abdelmagid and Too, 2008, Ginsburg and Vonderhaar,

1995). It should be noted that there are limited numbers of in vivo studies investigating the role of prolactin on tumorigenicity and tumor volume and there is no in vivo study in literature on the role of prolactin in response to DNA damaging agents.

Linda A. Shuler’s laboratory generated a transgenic mouse model (NRL-PRL) where prolactin expression was targeted to mammary epithelial cells and mimicked the local prolactin

production (Arendt and Schuler, 2008b). Prolactin was shown to induce mammary tumors in

mice after a long latency (Arendt and Schuler, 2008b) and the formed tumors were diverse but

mostly luminal breast cancers (Arendt et al., 2011). Nira Ben-Jonathan’s laboratory engineered

MDA-MB-435 breast cancer cells to overexpress human prolactin and when the cells were

injected into mammary fad pad of nude mice, tumors formed earlier than in the control groups without prolactin expression (Liby et al., 2003).

In order to investigate the role of prolactin on tumorigenicity and tumor volume in

response to DNA damaging agents, we used two delivery methods of prolactin, endocrine and

autocrine, which will be described in detail below.

A novel model of breast cancer recurrence was used to investigate the role of autocrine and endocrine prolactin on tumorigenicity and tumor volume in the present experiments. Breast

cancer cells were treated with the DNA damaging agent doxorubicin in cell culture in the

presence or absence of prolactin, and after a specified recovery time, the cells were injected into

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the mammary fat pad of immune-deficient mice. It should be noted that there was no initial tumor formation before recurrence and we did not treat mice with chemotherapy after tumor formation in the recurrence model.

5.1.1 The effect of endocrine prolactin on tumorigenicity and tumor volume in response to DNA damaging agents

5.1.1.1 To investigate the effect of endocrine prolactin in tumorigenicity, latency, tumor size of breast cancer cells in a xenograft animal model (SCID-B-EoPRL) in response to DNA damaging agents

MCF7 cells were pre-treated or not with 5 µg/ml ovine prolactin followed by 2 hours doxorubicin (0.2 µM) treatment where indicated (Figure 35). Untreated cells were used as a control group. After doxorubicin treatment, cells were allowed to recover for 48 hours without doxorubicin in the presence or absence of prolactin, and 1x106 cells were counted and injected into the mammary fad pad of SCID-beige mice. Since human breast cancer cells do not respond efficiently to mouse prolactin (Utama et al., 2006), we supplemented with ovine prolactin, via insertion of ovine prolactin release-pellets (3 mg/pellet, 30 day release (Peeva et al., 2006)) subcutaneously. Ovine prolactin has been shown to 10-fold less potent than human prolactin therefore high levels of hormone were used in the experiment (Utama et al., 2009). Control animals were inserted with placebo for ovine prolactin pellets (3 mg/ pellet, 30 day release). A second prolactin or placebo pellet was implanted after 30 days. In order to form xenograft tumors with ER positive MCF7 breast cancer cells (Subik et al., 2010), a 17β-Estradiol pellet (0.72 mg/ pellet, 60 days release) was subcutaneously inserted into all mice. Tumorigenicity, latency to tumor formation, and tumor size were followed over 60 days. This model will be referred to as

SCID-B-EoPRL (SCID-beige-Endocrine-ovine Prolactin)

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Doxorubicin Prolactin Doxorubicin Prolactin

MCF7 cells MCF7 cells MCF7 cells MCF7 cells

Recovery for 48 hrs

Estrogen pellet Estrogen pellet Estrogen pellet Estrogen pellet & & Prolactin pellet Prolactin pellet

Figure 35. The schematic of SCID-beige mouse recurrence model (SCID-B-EoPRL) to understand the role of endocrine prolactin in latency and tumorigenicity. MCF7 cells were pre-treated or not with 5 µg/ml ovine prolactin followed by doxorubicin treatment and 48 hours recovery time in the presence or absence of prolactin. After 48 hours of recovery time from doxorubicin treatment,1 x 106 cells were collected and injected into the #4 mammary fat pad of SCID-beige mice. All mice had a subcutaneous 17β-Estradiol pellet, and those animals in prolactin group had an additional ovine prolactin pellet inserted subcutaneously 3 days prior to cell implantation.

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The results for latency to tumor formation are presented using a Kaplan- Meier (Figure

36A) curve. All animals were tumor free (100%) at the beginning of experiment and tumor formation is indicated as percent decrease calculated based on 5 mice in each group. All control group animals injected with untreated MCF7 cells formed a mammary gland tumor 18 days after the cell injection. Although no statistically significant results were found, several interesting trends were observed. Doxorubicin delayed latency to tumor formation; the first palpable tumor was detected 18 days after cell injection and by 35 days, all mice developed a mammary gland tumor. Doxorubicin treatment of the cells did not prevent tumor formation in SCID-beige mice.

Prolactin was demonstrated to initially decrease latency in mice injected with prolactin alone and prolactin and doxorubicin treated cells. The first tumor formation was noted at day 10 from both group of mice injected with prolactin-treated and prolactin and doxorubicin treated cells. In the group of mice injected with prolactin-treated cells, two more mice acquired a mammary gland tumor on day 18, and 2 mice remained tumor-free for the duration of the experiment. In the group of mice injected with prolactin and doxorubicin treated cells, there was one more animal that developed a tumor at 18 day but after this point the remaining mice stayed tumor-free.

Although prolactin decreased latency in both treatment groups, and possibly offered a protective effect long-term, there no statistical difference was observed between experimental groups.

Untreated MCF7 cells formed the largest tumors over 60 days, followed by doxorubicin treated MCF7 cells. The tumors formed in mice injected with prolactin or prolactin and doxorubicin treated cells did not appear to grow in size during the experimental period.

According to data analysis using Kruskal-Wallis ANOVA followed by Mann-Whitney U Tests, the tumors formed with MCF7 cell injection were significantly larger then the tumors formed with prolactin and doxorubicin treated cells over 60 days (p= 0.02) (Figure 36B, 36C). When

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tumors at the experimental endpoint were compared, there was statistical difference between

following groups: MCF7 to MCF7+dox+prl (p= 0.03), MCF7+dox to MCF7+dox+prl (p= 0.02),

MCF7 toMCF7+prl (p= 0.04) (Figure 36D).

We observed some abnormalities in mice in the single treatment groups of doxorubicin

and prolactin in the SCID-B-EoPRL experiment. In the group of mice injected with doxorubicin- treated cells, 4 mice acquired estrogen-pellet related perineum lesions (Gakhar et al., 2009) and one of those 4 mice had ovary and uterus tumors, which caused death 38 days after cell injection.

In the group of mice injected with prolactin-treated cells, one animal acquired perineum lesions and also one more tumor was detected on the back of animal under the skin. There were no non- orthotopic tumors observed in the mice injected with MCF7 or prolactin and doxorubicin treated

MF7 cells.

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1200

1000 ) 3 800

600 MCF7 MCF7+dox 400 MCF7+prl MCF7+dox+prl

Tumor volume (mm Tumor 200

0 0 28 39 50 60 -200 The day after injection

159

700.00

600.00

500.00 ) 3 400.00

Box 300.00 Mean line Mild outliers 200.00

Tumor volume (mm Tumor Extreme outliers

100.00

0.00

-100.00 MCF7 MCF7+dox MCF7+PRL MCF7+dox+PRL C

2000.00 *

1500.00 n=4 * )

3 1000.00 * n=5

n=3 500.00 Box$ n=2 Mean$line$ Mild$outliers$

Tumor volume (mm Tumor 0.00

-500.00

-1000.00 MCF7 MCF7+dox MCF7+PRL MCF7+dox+PRL

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Figure 36. The effect of endocrine prolactin on tumor formation in SCID- beige mice (SCID-B-EoPRL). A. Tumor latency in SCID-beige mice after injection of cells that are untreated or treated with ovine prolactin or doxorubicin or prolactin and doxorubicin. Kaplan- Meier curve shows percent of mice without a mammary gland tumor. B. Comparison of average tumor volume at all times between treatment groups over 60 days. C. Comparison of accumulated tumor volumes between treatment groups using box-and-whisker plots. D. Comparison of final tumor volumes between treatment groups using box-and-whisker plots. Data was analyzed using Kruskal-Wallis ANOVA followed by Mann-Whitney U tests (p<0.05).

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5.1.1.2 To investigate the effect of endocrine prolactin on proliferation using immunohistochemistry analysis of the xenograft tumors (SCID-B-EoPRL)

In order to determine if prolactin increases cell proliferation as previously reported in vitro (Yamauchi et al., 2000, Liby et al., 2003) and in vivo (Lu et al., 2014), the Ki-67 proliferation marker was investigated in the xenograft tumors using immunohistochemistry

(IHC) analysis. Undergraduate student, Erin Marie Bell, worked on this project by using the tumor samples from the above described in vivo study in which SCID-beige mice were injected with MCF7 cells treated or not with prolactin and/or doxorubicin, in the presence of the ovine prolactin pellet.

According to the Ki-67 IHC results, the tumors formed with prolactin treated cells had the highest proliferative index followed by tumors formed with doxorubicin-treated, untreated and doxorubicin and prolactin treated cells. High proliferative index was expected from prolactin samples since the increasing effect of prolactin on proliferation has been well established

(Clevenger et al., 2003, Lu et al., 2014, Liby et al., 2003) However, statistical analysis showed no difference between the tumors formed with prolactin-treated, untreated, and doxorubicin- treated cells, but the tumors formed with doxorubicin and prolactin treated cells had significantly less Ki-67 positive cells when compared with all other groups (doxorubicin + prolactin and control, p<0.01; doxorubicin + prolactin and doxorubicin, p< 0.01; doxorubicin + prolactin and prolactin, p<0.001) (Figure 37A and 37B). It was also determined that there was no significant correlation between Ki-67 positive cells and the tumor volume (Figure 37C).

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Experiment 1 Experiment 2

B 163

PRL + DOX PRL DOX CONT Mean Ratio of Ki67 Positive Nuclei

Tumor Volume (mm3) C

Figure 37. Ki-67 immunohistochemistry results from xenograft tumors (SCID-B-EoPRL) assessing the role of endocrine prolactin on cell proliferation. A. Representative images from MCF-7 xenograft tumors SCID-beige mice with Ki-67 immunohistochemisty staining. The upper panels (A-L) are images from sections incubated with 1% BSA (negative control). The lower panes (A’-L’) are incubated with 1:50 anti-rabbit- Ki-67 antibody. Ki-67 was detected with HRP-DAB immunohistochemistry (brown) and nuclear hematoxylin counterstain (light purple). 32X magnification, scale bars= 0.1mm. B. Statistical analysis of Ki-67 staining. The mean ratio of Ki-67 positive nuclei for three experiments were compared between ovine prolactin + doxorubicin (PRL + DOX), control (CONT), doxorubicin (DOX) and ovine prolactin (PRL) treated MCF7 human breast cancer cells used to produce a xenografted tumor in mouse mammary fat pads. In each experiment, between 2-60 fields were imaged and analyzed— representing the entire tumor. Values represent the mean of three tumors (PRL, DOX), the mean of 3 sections of the same tumor (PRL +DOX), or the mean of two sections from one tumor and one section from another tumor (CONT). Sections incubated with Ki-67 antibody are represented in dark purple; sections incubated with 1% BSA (negative control) are represented in light purple. Error bars represent standard deviation. Data were found to be significant (Kruskal- Wallis, p<0.001, n=3). (Dunnett’s test, ** p<0.01, *** p<0.001, ns = not significant). C. The comparison between tumor volume and proliferative index in primary xenograft tumors of human MCF7 breast cancer cells. Tumors were removed from the mouse after 60 days and the volume was measured and correlated to the Ki-67 proliferative index of that individual tumor. Each point represents the mean proportion of Ki-67 positive cells detected in a single tumor section, n=1 (Pearson’s correlation, R2 = 0.01432, p>0.05). Colors indicate the treatment group from wherein the individual tumor formed. Results are from Erin Marie Bell.

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In order to investigate if increased Hsp90alpha protein levels in primary xenograft tumors

(SCID-B-EoPRL) is associated with prolactin stimulation, a rabbit polyclonal Hsp90alpha

antibody was used in IHC staining. Hsp90a levels were high in all tumor samples (Figure 38), as could be expected, since Hsp90a is very abundant in cells and present in nucleus, cytoplasm and extracellular area (McCready et al., 2006). Immunohistochemical detection of Hsp90a was not informative, since the difference in Hsp90alpha expression could not be detected between the tumor samples of different treatment groups.

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Figure 38. Hsp90α expression in primary xenograft tumors (SCID-B-EoPRL) of human MCF7 breast cancer cells in the mouse mammary gland. Each experiment represents a single section from one primary xenograft tumor of human MCF7 breast cancer cells in the mouse (SCID-beige) mammary gland in each of the four treatment groups: control, oPRL, doxorubicin, and doxorubicin + oPRL; a different tumor is used in each experiment except for the doxorubicin + oPRL groups which are represented by different sections from the same tumor. The upper panels (A-H) represent sections incubated with 1% BSA in replace of primary antibody as a negative control; the lower panels represent two representative images from different fields within a single tumor section (A’-H’ and A’’-H’’). Hsp90α was detected with HRP-DAB immunohistochemistry and a hematoxylin counterstain (light purple nuclei). The bright- field image was taken at 32X magnification; scale bars represents 0.1mm.

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During the above xenograft experiment (SCID-B-EoPRL), we observed death in the groups that received prolactin pellets, starting as early as 18 days after the first pellet surgery

(3/5 dead mice over a 30 day period) (Figure 39A). In MCF7 injected mice, there was one cannibalism related death at day 18 and one likely tumor related death at day 45. In the group of mice injected with doxorubicin-treated cells, there was one death at day 38 likely due to non- orthotopic tumors formed in different parts of the body. However, the deaths in groups that received prolactin pellets were not related with primary tumor formation or potential metastatic spread. In order to test if ovine prolactin pellets have any effect on survival in combination with estradiol pellets, we designed a control experiment. Three control animals were subcutaneously inserted with placebo for ovine prolactin pellets (3 mg/ pellet, 30 day release) and 17β- Estradiol pellets (0.72 mg/ pellet, 60 days release) and the second group of three animals was inserted with ovine prolactin pellets (3 mg/ pellet, 30 day release) and 17β-estradiol pellets (0.72 mg/ pellet,

60 days release) and their mammary fat pad was injected with the combination of 1X PBS and

Cultrex BME that was used for cell injection. A second placebo or prolactin pellet was inserted after 30 days and survival of the mice was followed over 60 days. On day 40, 2 of the 3 animals with the prolactin pellet had died while there were no deaths in the control group over 60 days

(Figure 39B). There was no statistical difference between experimental groups due to small sample size. Due to complications with prolactin delivery, in further experiments we investigated the effect of human autocrine prolactin production instead of endocrine ovine prolactin. It was not economically feasible to prepare recombinant human prolactin pellets nor experimentally feasible to use small pumps for delivery, as the animals would have required multiple surgeries.

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Figure 39. Survival of SCID-beige mice without xenografts over 60 days. A. Percent survival of SCID-beige mice used in the endocrine prolactin experiment. MCF7 cell injected group was inserted with placebo pellet, prolactin groups were inserted with ovine prolactin pellets. All groups had 17β-estradiol pellets. B. Percent survival of SCID- beige mice used in control survival experiment. Control animals were subcutaneously inserted with placebo for ovine prolactin and 17β-estradiol pellets. Animals in the prolactin group received subcutaneous ovine prolactin and 17β-estradiol pellets. Survival was assessed for 60 days.

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5.1.2 The effect of autocrine prolactin on tumorigenicity and tumor volume in response to DNA damaging agents

Due to complications with endocrine prolactin delivery, we tested the effect of autocrine

prolactin on tumorigenicity and tumor volume using MCF7 cells genetically engineered to secret autocrine prolactin. The details are presented below.

5.1.2.1 Preparation and confirmation of autocrine prolactin secreting MCF7 cell line

A human prolactin expression (hPRL) plasmid was received as a gift from our collaborator Dr. Vincent Goffin (Paris) (Liby et al. 2003). MCF7 cells were stably transfected with the plasmid (Chapter 3.2.1). Prolactin levels were evaluated from whole cell extractions

(Figure 40A). Protein levels were confirmed from 3 colonies (Colony 1, 3, 5) and very low levels were detected from other two colonies. Prolactin secretion from transfected colonies was also evaluated from conditioned media (Figure 40B and 40C) using the protocol described in Chapter

3.10.4. Prolactin secretion was confirmed from all colonies, although the amounts differed.

According to the results, two colonies (Colony 1 and Colony 5) were shown to have relatively high levels of prolactin (55 and 47ng/ml, respectively) when the number was compared with high serum concentration of prolactin (25ng/ml). After confirming the expression in the cell lines, MCF7hprl colonies 1 and 5 were combined for following future and in vivo studies.

A vector control was created using MCF7 cells stably transfected with the empty pcDNA3.1 vector. After the selection process, MCF7pcDNA3.1 (empty vector) colonies and the

MCF7hprl cell lines were confirmed using qPCR for the expression of the zeocin resistance gene

(Sh-ble). All transfected colonies and MCF7hprl cell lines were shown to express the Sh-ble resistance gene (Figure 40D).

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In order to confirm that the autocrine prolactin is successfully activating the prolactin receptor of the transfected MCF7 cells, we examined phospho-Stat5 (p-Stat5) versus total Stat5 levels as a read-out of PRL-Jak2-Stat5 pathway activation in the parental MCF7,

MCF7pcDNA3.1 vector control and MCF7hprl cells. Parental MCF7 and MCF7pcDNA3.1 cells demonstrated increased p-Stat5 levels with increasing concentrations of human prolactin (25 ng/ml, 50 ng/ml, 100 ng/ml and 150 ng/ml) (Figure 40E), confirming that the recombinant human prolactin we use can activate the Jak2 pathway even at 25 ng/ml, which although reflects a high serum concentration, is not easily seen by researchers in the field. MCF7hprl cell lines were shown to have similar levels of p-Stat5 with the parental cell lines treated with 50 ng/ml prolactin concentrations, which is consistent with their calculated daily prolactin secretion. The experimental results confirmed that the stably transfected cell lines (MCF7hprl and

MCF7pDNA3.1) do express the zeocin resistance gene. The MCF7hprl cell lines were also confirmed to secrete prolactin and the secreted prolactin can activate PRL-Jak2-Stat5 pathway.

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MCF7hprl colonies

MCF7 Col 1 Col2 Col3 Col4 Col5

Prolactin (25 kDa) A

Recombinant hprl (ng) MCF7hprl colonies 10 20 40 80 150 1 2 3 4 5 MCF7

Prolactin (25 kDa) B

Recombinant hprl (ng) MCF7hprl colonies 2 5 10 20 40 1 2 3 4 5

Prolactin (25 kDa)

Daily prl secretion ng/ml 55" 0" 28" 45" 47" C

MCF7pcDNA3.1 colonies

MCF7 MCF7hrpl 1 2 3 4 5 6 7 8 9 349 pb Sh-ble

138 pb YWHAZ D

MCF7 MCFhprl Pcdna 3.1 0 25ng 50ng 100ng 150ng 0 25ng 50ng 100ng 150ng

p-Stat5 92 kDa

Stat5 92 kDa

E HistoneH3 17 kDa

Figure 40. Confirmation of MCF7hprl and MCF7pcDNA3.1 cell lines. A. Prolactin levels measured from whole cell extraction of MCF7 parental cells and MCF7hprl colonies. B. C. Prolactin secretion compared between human recombinant prolactin standard and MCF7hprl colonies. The daily secretion from colonies were calculated based on human prolactin standard using ImageJ software analysis. D. PCR data showing Sh-ble zeocin resistance gene from MCF7hprl and MCF7pcDNA3.1 colonies. YHWAZ was used as house-keeping gene. E. p-Stat5 and total Stat5 and Histone-H3 (loading control) levels from MCF7 parental and MCF7pcDNA3.1 cells treated with increasing concentrations of human prolactin, and from MCF7hprl cells.

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5.1.2.2 To investigate the effect of autocrine prolactin in tumorigenicity, latency, tumor size of breast cancer cells in a xenograft animal model (SCID-AhPRL-500K-60D) in response to DNA damaging agents

In order to test the effect of autocrine prolactin, MCF7hprl cell lines secreting autocrine prolactin were used in in vivo experiment, however, since the empty vector control was not ready at the time of experiments, MCF7 parental cells were used as control group. The SCID mouse model was chosen rather than SCID-beige, as the beige mutation did not provide sufficient estrogen that was required for tumorigenicity, as had been suggested by others in the field.

SCID mice were subcutaneously inserted each with one 17β- Estradiol pellet (0.72 mg/ pellet, 60 days release) by surgery before cell injection. MCF7 and MCF7hprl cells were treated for 2 hours with doxorubicin (1µM) followed by 48 hours recovery time without doxorubicin.

Cells were then collected and 500,000 cells were injected into the number 4 mammary fat pad.

The experimental design is described in Figure 41. Tumorigenicity, latency to tumor formation and tumor size were followed over 60 days. This model is referred to as SCID-AhPRL-500K-

60D (SCID mice-Autocrine human Prolactin-500000 cells-60 days).

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Doxorubicin High autocrine prolactin Doxorubicin

MCF7 cells MCF7 cells MCF7hprl cells MCF7hprl cells

Recovery for 48 hrs

Estrogen pellet Estrogen pellet Estrogen pellet Estrogen pellet

Figure 41. The schematic of the SCID mouse recurrence model (SCID-AhPRL-500K-60D) to understand the role of autocrine prolactin in latency and tumorigenicity. MCF7 and MCF7hprl cells were treated with doxorubicin. After 48 hours recovery time, cells were collected and injected into mammary fat pad of SCID mice. All mice were subcutaneously inserted with 17β-Estradiol pellets.

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According to the latency results (Figure 42A), tumor formation was observed around 10 days after cell injection in group of mice injected with MCF7hprl cells and doxorubicin-treated

MCF7 cells, is followed by tumor formation in mice injected with MCF7 cells. Eighty percent of mice carrying MCF7hprl or MCF7 cells formed a palpable tumor after 20 days post injection and

80% of mice injected with doxorubicin-treated MCF7 cells formed a palpable tumor at day 35.

However, the first tumor from the group of mice injected with doxorubicin-treated MCF7hprl cells formed a palpable tumor 25 days after cell injection but with no other detectable tumor formation observed over 60 days in this group. According to Log-rank and Gehan-Breslow

Wilcoxon statistical analyses, there was no difference in latency when all groups were compared, however there was statistical significant difference between the mice injected with MF7hprl cells and the mice injected with doxorubicin-treated MCF7hprl cells (Log-rank p= 0.039, Gehan-

Breslow Wilcoxon p= 0.0282). The results showed that the latency had a similar trend in mice injected with MCF7 cells as the mice injected with doxorubicin-treated MCF7 cells or MCF7hprl cells. The latency to tumor formation was delayed for doxorubicin-treated MCF7hprl cells particularly when compared with MCF7hprl cells when injected into SCID mice.

According to the trend seen from the tumor volume graphs (Figure 42B, 42C and 42D), mice injected with control MCF7 cells had the largest tumors, followed by the mice injected with

MCF7hprl cells. The mice injected with doxorubicin-treated MCF7 cells formed smaller tumors than both MCF7 and MCF7hprl injected mice and the mice injected with doxorubicin-treated

MCF7hprl formed the smallest tumors when compared with all groups, however, according to data analysis using Kruskal-Wallis ANOVA and Mann-Whitney U Test there was no statistical difference between tumor volumes.

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Log rank p= 0.0386

n=5 per group A

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Figure 42. The effect of autocrine prolactin on tumor formation in SCID mice (500,000 cell injection). A. Tumor latency in SCID mice after injection of MCF7 or MCF7hprl cells untreated or treated doxorubicin. Kaplan- Meier curve shows percent of mice without tumor. Log-rank (Mantel-Cox) and Gehan-Breslow Wilcoxon tests were used for statistical analysis. B. Comparison of accumulated tumor volumes between treatment groups over 60 days. C. Comparison of accumulated tumor volumes between treatment groups using box-and-whisker plots. D. Comparison of final tumor volumes between treatment groups using box-and-whisker plots. Data was analyzed using Kruskal-Wallis ANOVA followed by Mann-Whitney U tests. (p<0.05).

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Some abnormalities were also observed in this experiment. Two mice from each

experimental group acquired perineum lesions and one mouse injected with doxorubicin-treated

MCF7hprl cells formed an oesophageal tumor. The oesophagel tumor caused poor health and the

mouse was euthanized. Detailed information on the observations is presented in Table 4.

In order to confirm our results and provide an opportunity to observe if the apparent

protective effect would extend over time, we repeated the above experiment, but reduced the

number of injected cells to 250,000 and the increased the observation days to over 120 days

(SCID-AhPRL-250K-120D). According to the results for the latency to tumor formation (Figure

43A and 43B), MCF7hprl injected mice formed palpable mammary tumors starting from day 10

after cell injection, 80% of the mice formed a tumor within 35 days and all animals acquired a

mammary tumor by 100 days. The group of mice injected with MCF7 cells had similar results, in

that early tumor formation was detected at 10 days post cell injection, but then 80% of the mice

formed a tumor within 50 days and all animals had a tumor within 60 days post cell injection.

Latency was slightly delayed in the group injected with doxorubicin-treated MCF7 cells, such

that although early tumor formation started around 10 days after cell injection, 80% of the

grouped formed tumor over 60 days and all the animals acquired a tumor in the group by the end

of the experimental time (120 days). Consistent with first experiment, increased latency was also

observed in the mice injected with doxorubicin-treated MCF7hprl cells, as tumor formation started 30 days post cell injection, 40% of the mice formed tumor over 60 days and all the animals acquired a tumor in the group by the end of the experimental time (120 days). There was no statistical significance across these groups, however there was a clear trend, consistent with the previous experiment, that the latency to tumor formation was increased in mice injected with

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doxorubicin-treated MCF7 cells secreting human PRL, although the treatments were not completely protective.

When the groups were compared over 120 days for latency, there was no statistical difference. However there was statistical difference between the group of mice injected with

MCF7hprl and doxorubicin-treated MCF7hprl cells at the shorter time point of 60 days (Log- rank p= 0.0419, Gehan-Breslow Wilcoxon p= 0.039) (Figure 43A).

The tumor volume measurements demonstrated a trend that parental MCF7 cells formed the largest tumors, followed by MCF7hprl, doxorubicin-treated MCF7 cells (Figure 43C, 43D,

43E and Figure 44). The tumors generated by doxorubicin-treated MCF7hprl cells were very small in volume. The trend was consistent with the previous experiment using autocrine prolactin secreting MCF7hprl cells. According to the data analysis using Kruskal-Wallis ANOVA and

Mann-Whitney U Tests, there was a statistical difference between tumors formed with MCF7 cells and doxorubicin-treated MCF7hprl cells at the 120 day endpoint (Figure 43E).

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Table 4. Abnormalities seen in xenograft experiment (SCID-AhPRL-500K-60D) Experimental group/ mouse Primary tumor volume Observation number (mm3) MCF7 (5) 38.67 Perineum lesion observed at day 28 and spread to hind legs on day 38. MCF7 (2) 37.11 Perineum lesion spread to hind legs over the dorsum of the tail on day 41. MCF7 + doxorubicin (5) 19.65 Small perineum lesion appeared on day 28. MCF7 +doxorubicin (5) 37.89 Perineum lesion appeared on day 28, started to spread on day 36. Lesion spread to hind legs over the dorsum of the tail and to forelegs on day 49. MCF7hprl (4) 26.89 Perineum lesion spread was reported on day 36 and the lesion spread to hind legs over the dorsum of the tail and to forelegs on day 49. MCF7hprl (5) 93.73 The perineum lesion was reported as severe on day 49. MCF7hprl + doxorubicin (3) No primary tumor The mouse was ill on day 24 and euthanized on day 39. Oesophageal tumor was removed.

MCF7hprl + doxorubicin (4) 42.48 Spread of perineum lesion was detected on day 49.

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Log rank p= 0.0419

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Tumor volume (mm Tumor 1000.00 Extreme outliers n= 5 n= 5 n= 5

500.00

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Figure 43. The effect of autocrine prolactin on tumor formation in SCID mice (SCID- AhPRL-250K-120D). A (60 days) and B (120 days) Tumor latency in SCID mice after injection of parental cells or hPRL-secreting cells that are untreated or treated with doxorubicin. Kaplan- Meier analysis shows the percentage of mice without a tumor. Log-rank (Mantel-Cox) and Gehan-Breslow Wilcoxon tests were used for statistical analysis. C. Comparison of accumulated tumor volumes between treatment groups over 120 days. D. Comparison of accumulated tumor volumes between treatment groups using box-and-whisker plots. E. Comparison of final tumor volumes between treatment groups using box-and-whisker plots. Data was analyzed using Kruskal-Wallis ANOVA followed by Mann-Whitney U tests. (p<0.05).

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Some abnormalities were observed in this experiment (Figure 44). Perineum lesions were observed in 3 mice injected with parental MCF7 cells, 3 mice injected with doxorubicin-treated

MCF7 cells, 3 mice injected with MCF7hprl cells and 4 mice injected with doxorubicin-treated

MCF7hprl cells (n= 5 mice per group). The lesions were also investigated by Faculty

Veterinarian, Stefanie Anderson (University of Calgary) and Pathologist Erin Locke (Antech

Diagnostics, ON, Canada). Based on histology, they observed ulceration and inflammation in the tissues. In addition to the perineum lesions, a thymus tumor was observed in 2 mice injected with doxorubicin-treated MCF7 cells and one mouse injected with MCF7hprl cells. Shoulder wounds were observed from 2 of doxorubicin-treated MCF7 cells and one of MCF7hprl cells injected mice. Undissolved estradiol pellets were found in the contralateral uninjected mammary gland of mammary gland of 2 mice injected with MCF7 cells and one mouse injected with MCF7hprl cells.

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Figure 44. Primary tumors, perineum lesions and observed abnormalities in xenograft experiment (SCID-AhPRL-250K-120D).

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5.1.2.3 To investigate the effect of autocrine prolactin on proliferation using immunohistochemistry analysis of the xenograft tumors (SCID-AhPRL-250K-120D)

In order to investigate if prolactin has any effect on cell proliferation in vivo, the xenograft tumors were investigated by immunohistochemistry analysis using the Ki-67 proliferation marker. Undergraduate student, Colin Stewart, worked on this project by testing both the primary tumors and perineum lesions from autocrine prolactin experiment in which

SCID mice were injected with 250,000 MCF7 and MCF7hprl cells treated or not with doxorubicin.

According to the Ki-67 IHC results from the primary tumors (Figure 45A), those formed from MCF7 (parental Control) or MCF7hprl (PRL) cells had the highest proliferative index, followed by tumors formed from doxorubicin-treated MCF7 (DOX) cells and doxorubicin- treated MCF7hprl (PRL + DOX) cells (Figure 45B). According to the statistical analysis

(Tukey’s test), tumors formed with doxorubicin-treated MCF7hprl cells had the lowest proliferative index when compared with tumors formed with MCF7 cells (Control) (p= 0.029) and MCF7hprl (PRL) (p= 0.02) groups. There was no statistical difference between the other groups.

In order to investigate if any treatment used in the experiment had any effect on the perineum lesions, the lesions were examined using Ki-67 IHC analysis. According to the Ki-67

IHC results (Figure 46A), the lesions in the control group and the PRL group had the highest proliferative index followed by lesions in the DOX and PRL + DOX groups (Figure 46B). There was, however, no statistical difference between experimental groups.

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B Figure 45. Ki-67 immunohistochemistry results from primary xenograft tumors (SCID- AhPRL-250K-120D) assessing the role of autocrine prolactin on cell proliferation (250,000 cell injection). A. Representative images from SCID mice tumors with Ki-67 immunohistochemisty staining. The upper panels are images from sections incubated with 1% BSA (negative control). The lower panes are incubated with 1:50 anti-rabbit- Ki-67 antibody. Ki-67 was detected with HRP-DAB immunohistochemistry (brown) and nuclear hematoxylin counterstain (light purple). 32X magnification, scale bars= 0.1 mm. B. Statistical analysis of Ki- 67 staining. The mean ratio of Ki-67 positive nuclei for three experiments were compared between MCF7hprl + doxorubicin (PRL + DOX), MCF7 (Control), MCF7 + doxorubicin (DOX) and MCF7hprl (PRL) injected experimental groups. In each experiment, between 3-16 fields were imaged and analyzed—representing the entire tumor. Values represent the mean of three tumors. Sections incubated with Ki-67 antibody are represented in blue; sections incubated with 1% BSA (negative control) are represented in red. Error bars represent standard deviation. Data were found to be significant (Tukey’s test, * p<0.05). The figures were provided by Colin Stewart.

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Figure 46. Ki-67 immunohistochemistry results from perineum lesions (SCID-AhPRL- 250K-120D). A. Representative images from SCID mice lesions with Ki-67 immunohistochemisty staining. The upper panels are images from sections incubated with 1% BSA (negative control). The lower panes are incubated with 1:50 anti-rabbit- Ki-67 antibody. Ki-67 was detected with HRP-DAB immunohistochemistry (brown) and nuclear hematoxylin counterstain (light purple). 32X magnification, scale bars= 0.1mm. B. Statistical analysis of Ki- 67 staining. The mean ratio of Ki-67 positive nuclei for three experiments were compared between MCF7hprl + doxorubicin (PRL + DOX), MCF7 (Control), MCF7 + doxorubicin (DOX) and MCF7hprl (PRL) injected experimental groups. In each experiment, between 4-20 fields were imaged and analyzed—representing the entire tumor. Values represent the mean of three lesions. Sections incubated with Ki-67 antibody are represented in blue; sections incubated with 1% BSA (negative control) are represented in red. Error bars represent standard deviation. Data were found to be significant (Tukey’s test, * p<0.05). The figures were provided by Colin Stewart.

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5.1.2.4 The serum estradiol levels from SCID mice

Estrogen-pellet implantation is commonly used to support the growth of estrogen-receptor

positive mammary tumors in xenograft mice models. Commercially available slow-release

estrogen pellets can be found in different dose ranges and 0.72 mg pellets are commonly used to

form mammary tumors with MCF7 breast cancer cells (Lewis and Porter, 2009). The serum

estrogen levels of normal mice can range between 10 pg/ml and 60 pg/ml (Lewis and Porter,

2009) and 0.72 mg estrogen pellets should achieve 300-400 pg/ml estrogen according to

manufacturer suggestions and the literature (Lewis and Porter, 2009, Gupta et al., 2007), which is the level equivalent to levels in women in midcycle. However recently, the side effects of estrogen pellets have been documented in the literature and the serum levels were shown to be in different range during measurements after estrogen pellet implantation. In addition it was implicated in the literature that the 60-day release pellets caused high serum levels after the pellet expiry date (Gakhar et al., 2009).

In order to investigate the estrogen levels in our experiments, 4 SCID mice were purchased

and before pellet insertion serum samples were obtained from all mice. 0.72 mg estrogen pellets

(60-day release) were inserted with small pellet surgery and serum samples were obtained every

10-15 days over 75 days period. The serum estradiol levels were measured using ELISA. The

graph shows estradiol levels of each mouse over 75 days.

According to the results (Figure 47A), the mice had average of 18 pg/ml serum estradiol

before pellet insertion, with the lowest value of 10 pg/ml and the highest value of 31 pg/ml. The

estradiol levels could not be detected in one of four mice. After pellet insertion, the estradiol

levels increased to average of 252 pg/ml after 10 days and the mean level was 236 pg/ml after 24

days with the lowest value of 30 pg/ml and the highest value of 335 pg/ml. The average value

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was 133 pg/ml after 38 days with the range of 35 to 240 pg/ml estradiol levels and the estradiol levels decreased to 95 pg/ml after 50 days with the range of 10 to 131 pg/ml estradiol. Due to cage flooding two mice died at day 59 and the serum estradiol levels were measured from two mice at day 60 with the average of 54 pg/ml (19 pg/ml and 95 pg/ml). The serum estradiol levels were also measured 15 days after pellet expiry date and surprisingly average of 82 pg/ml estradiol was detected. The results showed that the estrogen pellet release is not consistent between mice and the estradiol levels increased dramatically after 10 and 20 days after pellet insertion. The levels started to decrease after 30 days and surprisingly increased after pellet expiry date. We can not rule out the fact that this may have affected tumorigenicity in our experiments.

One of two remaining mice had also a perineum lesion, which started to turn into necrotic tissue and spread through the hind leg (Figure 47B), which indicates that the perineum lesions observed in this thesis might be estrogen-pellet related.

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Serum Estradiol Levels 400

350

300

250

200 SCID1 SCID2 150 SCID3

Estradiol level (pg/ml) SCID4 100

0 Day 0 10 days 24 days 38 days 50 days 60 days 75 days Days after pellet surgery

Figure 47. Serum estradiol level measurements in SCID mice and estrogen-pellet side effect. A. Serum estradiol levels determined from 4 SCID mice serum using ELISA. Three internal replicates were pooled and graphed. The error bars represent standard deviation. B. Perineum lesion detected after estrogen-pellet implantation.

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5.2 Assess the Mechanism of Prolactin Mediated Cellular Response to DNA Damaging Agents

In order to investigate the mechanism behind the prolactin mediated response to DNA

damaging agents, we investigated cellular mechanisms such as autophagy and senescence as well

as the secreted cytokines from senescent cells.

5.2.1 The effect of prolactin, in the presence of DNA damage, on autophagy

Autophagy is a lysosomal degradation pathway where intracellular materials are digested

and recycled back to cytoplasm in order to provide nutrition to cells, limit metabolic stress and

clean damaged organelles in the cells (Meijer and Codogno, 2009). The ULK1 complex and a

class II phosphoinositide 3- kinase complex including Beclin-1 is involved in initial membrane

nucleation which initiates autophagy (Kang et al., 2011a). After forming a double membrane

autophagosome, maturation occurs by fusion with endosomes and lysosomes, and the

autolysosome degrades the material (Meijer and Codogno, 2009). Autophagy has been

demonstrated to delay apoptotic cell death in breast cancer cells following DNA damage (Abedin

et al., 2007) and Beclin-1 was implicated as anti-apoptotic protein that the decreased levels of

Beclin-1 lead to increased apoptosis (Kang et al., 2011a).

According to our in vivo assays where prolactin increases latency and deceases tumor volume in response to DNA damaging agents, we hypothesized that there was an decrease in autophagy in the presence of prolactin and doxorubicin. We tested our hypothesis by investigating Beclin-1 levels by western blot in an in vitro experimental setting.

The experiment was performed by undergraduate student, Emilija Malogajski. Based on time points used in in vivo experiments before cell injection, MCF7 cells were pre-treated or not

for 24 hours with ovine prolactin (5 µg/ml), followed by 2 hours of treatment with doxorubicin.

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Whole cell protein extracts were prepared after 48 hours recovery time. Beclin-1 and GRB2

levels were determined with western blot. Beclin-1 levels were not affected by prolactin and/or doxorubicin treatment according the results of three independent experiments (Figure 48).

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0.2 0.2 1 1 2 2 Doxorubicin _ _ µM µM µM µM µM µM _ _ _ _ Prolactin + + + + (5µg/ml)

Beclin-1 60 kDa

GRB2 25 kDa

Figure 48. Beclin-1 levels are not affected from prolactin and/or doxorubicin treatments. MCF7 cells were pre-treated with ovine prolactin (5µg/ml) for 24 hours, followed by 2 hours doxorubicin (0.2, 1, 2 µM) treatment, and after a 48 hours recovery period in the presence or absence of prolactin, protein samples were resolved and tested by western blot. Beclin-1 and GRB2 (loading control) proteins were detected. Figure represents n=3 independent experiments. The experiments were performed by Emilija Malogajski.

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5.2.2 The effect of prolactin in the presence of DNA damage on cellular senescence.

According to A. Urbanska’s and my in vitro experimental results, we observed that prolactin significantly increases viability in cells treated with DNA damaging agents. However in in vivo experiments, prolactin treatment increased latency and showed a trend for decreased tumor volume with doxorubicin-treated cells. We investigated if prolactin had any effect on cellular senescence which may play a role in vitro or in vivo. Cellular senescence is a tumor suppressive mechanism that occurs as the permanent arrest of the cells in the cell cycle in response to excessive extracellular or intracellular stress in order to prevent further damage to the next cell generation and prevent the risk for malignant transformation (Campisi and d'Adda di

Fagagna, 2007, Coppe et al., 2010). DNA damage with doxorubicin was shown to induce senescence in tumor cells (Chang et al., 2002). In order to test the role of prolactin in senescence, an undergraduate student, Erin Marie Bell, optimized in vitro senescence experiments that detect senescence-associated β-galactosidase staining and I and another undergraduate student, Emilija

Malogajski, finalized the study. This assay is based on the idea that the β-galactosidase activity is increased and detectable at pH 6.0 in senescent cells due to increased lysosome number and size (Lee et al., 2006).

MCF7 breast cancer cells were tested for senescence in the presence or absence of prolactin and/or DNA damage with doxorubicin. As suggested in the literature (Lee et al., 2006), senescent cells were detected after a recovery period (6 days) following doxorubicin treatment.

In the experiment, ortoho-nitrophenyl-β-galactophyranoside (ONPG) is used as the artificial substrate that in the presence of active β-galactosidase produces chromogenic yellow ortho- nitrophenol that is measured by a spectrophotometer (Li et al., 2012). Doxorubicin induced

senescence significantly in the presence or absence or prolactin when compared with control and

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prolactin treated cells, and prolactin did not have any significant effect on doxorubicin-induced senescence over this short time period (Figure 49).

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Senescence ONPG Assay Average Absorbances of Both Triplicates 0.3 * * 0.25 * *

0.2 CON oPRL 0.15 DOX DOX + oPRL 0.1 Absorbance (420 nm)

0.05

Figure 49. Determining the effect of doxorubicin and prolactin on senescence by ONPG assay in MCF7 cells. MCF7 cells were pre-treated with ovine prolactin (5µg/ml) for 24 hours which is followed by 2 hours doxorubicin treatment (0.2 µM). The cells recovered in the presence or absence of prolactin for 6 days. ONP levels were measured by spectrophotometer. Graph represents 6 independent experiments. The experiment repeated by Odul Karayazi Atici and Emilija Malogajski.

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5.2.3 To investigate if prolactin regulates the Senecence-Associated Secretory Phenotype in breast cancer cells

Although senescence was suggested as a tumor suppressive mechanism, there is accumulating evidence showing the relation of senescence with resistance of cells to chemotherapy treatments or irradiation. One aspect of senescent cells is defined as a Senescence-

Associated Secretory Phenotype, which is considered to result in changes in the tissue microenvironment, creating a permissive microenvironment that promotes tumorigenesis and aggressive cancer cells. It is suggested that senescent cells secrete several soluble and insoluble factors that have the ability of promoting tumor progression (Coppe et al., 2010, Kang et al.,

2011b, Rodier et al., 2009). Based on our in vitro and in vivo experiments, we hypothesized that prolactin may affect the profile of a SASP from doxorubicin-induced senescent breast cancer cells that may induce immune clearance that reduces primary tumor formation and possibly alter tumor progression or metastasis.

In this experiment, I used autocrine prolactin-secreting MCF7 cells (MCF7hprl) to assess the SASP induced by doxorubicin within the context of prolactin treatment. There are two groups used in this study in addition to the serum control. The first group of MCF7hprl cells was treated with a prolactin receptor antagonist (Δ1-9-G129R-hPRL, a gift from Dr V. Goffin, Paris), which is followed by DNA damage with doxorubicin, and the second group was only treated with doxorubicin. Cells were allowed to recover for the same time period as the senescence assays. The conditioned media was collected from three experimental replicates for a luminex- based cytokine array and based on the list provided in Coppé et al., 2009, we chose the Human-

64 cytokine array from Eve Technologies (University of Calgary).

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Cytokines in conditioned media from MCF7hprl cells treated with the Δ1-9-G129R-

hPRL antagonist + doxorubicin, and from MCF7hprl + doxorubicin, that were above the levels

of the serum controls, were graphed and presented in Figure 50 (presented over 3 pages).

Fractalkine, PDGF-AA, IL-1a, IL-4, IL-1RA, IL-7 showed a trend of higher levels in

conditioned media from doxorubicin treated MCF7hprl cells compared with the cells that were

treated with prolactin receptor antagonist, however there was no statistical difference between

the two groups. SDF-1a+B showed trend of higher levels in conditioned media from MCF7hprl

+ doxorubicin treated cells when compared with conditioned media from MCF7hprl cells treated

with the prolactin receptor antagonist + doxorubicin, however there was no significant difference

between the two group. Since the values of SDF-1a+B in the antagonist treated group were under the detection limits, and to specifically identify which cytokine isoform was upregulated, we further investigated SDF-1alpha and beta levels with ELISAs.

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TGF5a** G5CSF* 60.00# 8.00# 7.00# 50.00# 6.00# 40.00# 5.00#

30.00# 4.00# 3.00# 20.00# Concentra)on*(pg/ml)*

Concentra)on*(pg/ml)* 2.00# 10.00# 1.00#

0.00# 0.00# MCF7hprl#+#dox# MCF7hprl#+#ant#+dox# MCF7hprl#+#dox# MCF7hprl#+#ant#+dox#

Flt53L* Fractalkine* 20.00# 60.00# 18.00# 50.00# 16.00# 14.00# 40.00# 12.00# 10.00# 30.00# 8.00# 20.00# 6.00#

Concentra)on*(pg/ml)* 4.00# Concentra)on*(pg/ml)* 10.00# 2.00# 0.00# 0.00# MCF7hprl#+#dox# MCF7hprl#+#ant#+dox# MCF7hprl#+#dox# MCF7hprl#+#ant#+dox#

MDC* PDGF5AA* 450.00# 1600.00# 400.00# 1400.00#

350.00# 1200.00# 300.00# 1000.00# 250.00# 800.00# 200.00# 600.00# 150.00#

Concentra)on*(pg/ml)* 400.00# Concentra)on*(pg/ml)* 100.00# 50.00# 200.00# 0.00# 0.00# MCF7hprl#+#dox# MCF7hprl#+#ant#+dox# MCF7hprl#+#dox# MCF7hprl#+#ant#+dox#

PDGF5BB* IL51a* 450.00# 2.50# 400.00# 2.00# 350.00# 300.00# 1.50# 250.00# 200.00# 1.00# 150.00# Concentra)on*(pg/ml)* Concentra)on*(pg/ml)* 100.00# 0.50# 50.00# 0.00# 0.00# MCF7hprl#+#dox# MCF7hprl#+#ant#+dox# MCF7hprl#+#dox# MCF7hprl#+#ant#+dox#

201

IL415* IL44* 14.00# 9.00# 8.00# 12.00# 7.00# 10.00# 6.00# 8.00# 5.00# 6.00# 4.00# 3.00# 4.00# Concentra)on*(pg/ml)* Concentra)on*(pg/ml)* 2.00# 2.00# 1.00# 0.00# 0.00# MCF7hprl#+#dox# MCF7hprl#+#ant#+dox# MCF7hprl#+#dox# MCF7hprl#+#ant#+dox#

IL41RA* IL46* 16.00# 50.00# 45.00# 14.00# 40.00# 12.00# 35.00# 10.00# 30.00# 8.00# 25.00#

6.00# 20.00# 15.00# Concentra)on*(pg/ml)*

Concentra)on*(pg/ml)* 4.00# 10.00# 2.00# 5.00# 0.00# 0.00# MCF7hprl#+#dox# MCF7hprl#+#ant#+dox# MCF7hprl#+#dox# MCF7hprl#+#ant#+dox#

IL47* IL48* 2.00# 80.00#

1.80# 70.00# 1.60# 60.00# 1.40# 1.20# 50.00# 1.00# 40.00#

0.80# 30.00# 0.60#

Concentra)on*(pg/ml)* Concentra)on*(pg/ml)* 20.00# 0.40# 0.20# 10.00# 0.00# 0.00# MCF7hprl#+#dox# MCF7hprl#+#ant#+dox# MCF7hprl#+#dox# MCF7hprl#+#ant#+dox#

IP410* MIP41a* 2500.00# 5.00# 4.50#

2000.00# 4.00# 3.50#

1500.00# 3.00# 2.50#

1000.00# 2.00# 1.50# Concentra)on*(pg/ml)* Concentra)on*(pg/ml)* 500.00# 1.00# 0.50# 0.00# 0.00# MCF7hprl#+#dox# MCF7hprl#+#ant#+dox# MCF7hprl#+#dox# MCF7hprl#+#ant#+dox#

MIP41B* 3.00#

2.50#

2.00#

1.50#

1.00# Concentra)on*(pg/ml)* 0.50#

0.00# MCF7hprl#+#dox# MCF7hprl#+#ant#+dox#

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SDF51a+B* MCP51* 70.00# 16.00#

60.00# 14.00# 12.00# 50.00# 10.00# 40.00# 8.00# 30.00# 6.00# 20.00# 4.00# Concentra)on*(pg/ml)* Concentra)on*(pg/ml)* detection limit 10.00# 2.00#

0.00# 0.00# MCF7hprl#+#dox# MCF7hprl#+#ant#+dox# MCF7hprl#+#dox# MCF7hprl#+#ant#+dox#

RANTES* TNFa* 4000.00# 12.00# 3500.00# 10.00# 3000.00# 8.00# 2500.00# 2000.00# 6.00# 1500.00# 4.00# 1000.00# Concentra)on*(pg/ml)* Concentra)on*(pg/ml)* 2.00# 500.00# 0.00# 0.00# MCF7hprl#+#dox# MCF7hprl#+#ant#+dox#

VEGF* Eotaxin53* 700.00# 35.00#

600.00# 30.00#

500.00# 25.00#

400.00# 20.00#

300.00# 15.00#

200.00# 10.00# Concentra)on*(pg/ml)* Concentra)on*(pg/ml)* 100.00# 5.00#

0.00# 0.00# MCF7hprl#+#dox# MCF7hprl#+#ant#+dox# MCF7hprl#+#dox# MCF7hprl#+#ant#+dox#

TRAIL* TARC* 6.00# 20.00# 18.00# 5.00# 16.00#

4.00# 14.00# 12.00# 3.00# 10.00# 8.00# 2.00# 6.00# Concentra)on*(pg/ml)* Concentra)on*(pg/ml)* 1.00# 4.00# 2.00# 0.00# 0.00# MCF7hprl#+#dox# MCF7hprl#+#ant#+dox# MCF7hprl#+#dox# MCF7hprl#+#ant#+dox#

SCF* IL528A* 25.00# 4500.00# 4000.00# 20.00# 3500.00# 3000.00# 15.00# 2500.00# 2000.00# 10.00# 1500.00# Concentra)on*(pg/ml)* Concentra)on*(pg/ml)* 5.00# 1000.00# 500.00# 0.00# 0.00# MCF7hprl#+#dox# MCF7hprl#+#ant#+dox# MCF7hprl#+#dox# MCF7hprl#+#ant#+dox#

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Figure 50. The effect of autocrine prolactin on secreted cytokines in the presence of DNA damage from doxorubicin. MCF7hprl cells were treated 2 hours with doxorubicin or pre- treated with a prolactin receptor antagonist followed by doxorubicin treatment. After recovery (6 days), conditioned media was analyzed with the Human-64 cytokine array by Eve Technologies. The levels of cytokines higher than serum control are graphed and presented. MCF7hprl+dox= MCF7hprl + doxorubicin, MCF7hprl + ant + dox= MCF7hprl + prolactin receptor (Δ1-9-G129R- hPRL) antagonist + doxorubicin.

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5.2.4 To investigate if prolactin regulates secretion of Stromal derived factor-1 (SDF-1) in the presence of DNA damage in breast cancer cells

Stromal derived factor-1 alpha and beta (SDF-1a+B) was found to be higher in autocrine prolactin-secreting MCF7hprl cells after DNA damage with doxorubicin and the levels were not detectable in the presence of the prolactin receptor antagonist or in the serum alone. We did not detect SDF-1 upregulation in previous cytokine array experiments in the absence of DNA damage (A Forsyth, Shemanko, unpublished results). This indicates that prolactin induces SDF-1 levels only in the context of the DNA damage response.

SDF-1 and its receptor CXCR4 have been implicated in breast cancer metastasis (Ray et al., 2015). As a chemokine, SDF-1 is constitutively secreted in bone marrow stroma. There are two isoforms of SDF-1 (a and B) that interact with the CXCR4 receptor and the both are known to act as chemoattractants and have anti-apoptotic function (Cojoc et al., 2013). SDF-1 expression from both breast cancer cells and stromal cells was shown to be related to late-stage metastasis of breast cancer cells as well as angiogenesis and invasion and survival of those tumor cells (Kang et al., 2005, Jin et al., 2012, Ray et al., 2015). In studies when breast cancer cells are genetically modified to secret SDF-1, increased invasion of breast cancer cells was also determined (Kang et al., 2005). According to Coppé et al. (2009), SDF-1 was found to be highly expressed from stromal cells as part of a Senecence-Associated Secretory Phenotype. However the studies are still limited in breast cancer.

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In order to investigate if prolactin has any effect on SDF-1 secretion from breast cancer

cells treated with doxorubicin, I used MCF7pcDNA3.1 (empty vector control cells), MCF7hprl,

SKBR3 and T47D cells. MCF7pcDNA3.1, SKBR3 and T47D cells were pre-treated with human

recombinant prolactin (25ng/ml) and/or prolactin receptor (Δ1-9-G129R-hPRL) antagonist and indicated MCF7hprl samples were pre-treated with prolactin receptor antagonist for 24 hours followed by 2 hours doxorubicin treatment in the presence or absence of prolactin or prolactin receptor antagonist. During the recovery time, conditioned media was removed at 2 days, 4 days,

6 days and 8 days and investigated with by ELISAs. SDF-1 alpha and beta levels were measured separately. Since the ELISA plate was limited to 96 samples including the experimental triplicates, only the samples treated with doxorubicin in the presence of prolactin were tested at 2 days, 4 days, 6 days and 8 days, all other samples were tested at 6 days otherwise specified.

According to the SDF-1 alpha ELISA results in MCF7pcDNA3.1 cells, the SDF-1 alpha

was significantly high in vehicle control cells (vc) when compared with prolactin treated (40.04

pg/ml, p= 0.001), doxorubicin treated (53.81 pg/ml, p= 0.03) or prolactin + doxorobucin (6 day)

treated (40.02 pg/ml, p= 0.01) experimental groups. SDF-1 alpha levels significantly increased in

the prolactin + doxorubicin treated cells from day 2 to 6 day. There was a 68.67 pg/ml difference

between day 2 and day 6 samples (p= 0.02), and the difference was 38.62 pg/ml between day 4

and day 6 (p= 0.01) (Figure 51). The results from control MCF7pcDNA3.1 cells showed the

increased levels of SDF-1 alpha in prolactin + doxorubicin treated cells over 6 days, however

high levels of SDF-1 alpha were not specific to the treatment group and the highest level was

observed in vehicle control cells.

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SDF-1 alpha (MCF7pcDNA3.1) 300 *

250 *

* 200 * * 150

100 Concentration (pg/ml)

Figure 51. SDF-1 alpha levels (pg/ml) measured from MCF7pcDNA3.1 control cells in the presence or absence or DNA damage from doxorubicin. Cells were pre-treated or not with human recombinant prolactin (25 ng/ml) and/or prolactin receptor antagonist for 24 hours. The pre-treatments were followed by 2 hours doxorubicin (1 µM) with or without prolactin and/or prolactin receptor antagonist. Cells recovered for 2 days, 4 days or 6 days and conditioned media was tested by ELISA. All results represent 3 independent replicates that are pooled (n=3). The data was analyzed using one-way ANOVA followed by Bonferroni test. Statistically significant analysis (*) denotes p<0.05. vc= vehicle control, prl= prolactin, dox= doxorubicin, ant+prl= prolactin receptor antagonist + prolactin, dox + prl= doxorubicin + prolactin, dox + prl+ ant = doxorubicin + angatonist + prolactin treated cells. The error bars represent standard deviation.

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There was significant increase in SDF-1 alpha levels in MCFhprl cells treated with doxorubicin from day 2 to day 8. The increased levels were; 92.62 pg/ml (p= 0.04) between day

2 and day 6, 211.62 pg/ml (p= 0.0002) between 2 and day 8, 70.93 pg/ml (p= 0.04) between day

4 and day 6, 211.62 pg/ml (p= 0.01) between day 4 and day 8, and 118.99 pg/ml (p= 0.03) between day 6 and day 8 (Figure 52). The results from MCF7hprl cells showed the increased levels of SDF-1 alpha in doxorubicin treated cells over 8 days. However, the levels from doxorubicin treated cells at 6 day were not significantly different when compared with vehicle control, antagonist and antagonist + doxorubicin treated cells at 6 days indicating that the increase with doxorubicin treatment in autocrine prolactin secreting MCF7hprl cells likely occurred over recovery time.

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SDF-1 alpha (MCF7hprl) * * ** 300

250 *

200 *

150

100 Concentration (pg/ml)

MCF7hprl vc (6 days) MCF7hprl dox (2 days) MCF7hprl dox (4 days) MCF7hprl dox (6 days) MCF7hprl dox (8 days) MCF7hprl ant (6 days)

MCF7hprl dox+ant (6 days)

Figure 52. SDF-1 alpha levels (pg/ml) measured from MCF7hprl breast cancer cells in the presence or absence or DNA damage from doxorubicin. MCF7hprl cells were pre-treated with or not with prolactin receptor antagonist for 24 hours followed by 2 hours doxorubicin (1 µM) with or without prolactin receptor antagonist. Cells recovered for 2 days, 4 days, 6 days or 8 days and conditioned media was tested by ELISA. All results represent 3 independent replicates that are pooled (n=3). The data was analyzed using one-way ANOVA followed by Bonferroni test. Statistically significant analysis (*) denotes p<0.05, (**) denotes p<0.001. vc= vehicle control, dox= doxorubicin, ant= prolactin receptor antagonist, ant + dox= prolactin receptor antagonist + doxorubicin. The error bars represent standard deviation.

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In p53 mutant SBR3 cells, according to the comparison of samples after 6 days recovery, doxorubicin treated cells had higher SDF-1 alpha levels (39.57 pg/ml, p= 0.008) when compared with prolactin treated cells and doxorobucin + prolactin cells higher SDF-1 alpha levels (32.59 pg/ml, p= 0.006) when compared with vehicle control cells (Figure 53A). In SKBR3 cells doxorubicin and doxorubicin + prolactin treatments appear to increase SDF-1 alpha levels, however this increase was not significant when compared with all treatment groups.

In p53 mutant T47D cells there was significant difference between prolactin and doxorubicin treated cells (77.87 pg/ml, p= 0.003) (Figure 53B). In T47D cells, prolactin only treatment appear to increase SDF-1 alpha levels and DNA damage from doxorubicin in the presence or absence of prolactin did not increase the levels of SDF-1 alpha.

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SDF-1 alpha (SKBR3) 300

250

* 200 * 150

100 Concentration (pg/ml)

SDF-1 alpha (T47D) 300 *

250

200

150

100 Concentration (pg/ml)

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Figure 53. SDF-1 alpha levels measured from SKBR3 and T47D breast cancer cells in the presence or absence or DNA damage from doxorubicin. SKBR3 and T47D breast cancer cells were pre-treated or not with human recombinant prolactin (25 ng/ml) and/or prolactin receptor antagonist for 24 hours. The pre-treatments were followed by 2 hours doxorubicin (1 µM) with or without prolactin and/or prolactin receptor angatonist. Cells recovered for 6 days and conditioned media was tested by ELISA. All results represent 3 independent replicates that are pooled (n=3). The data was analyzed using one-way ANOVA followed by Bonferroni test. Statistically significant analysis (*) denotes p<0.05. vc= vehicle control, prl= prolactin, dox= doxorubicin, dox + prl= doxorubicin + prolactin, dox + prl+ ant = doxorubicin + angatonist + prolactin treated cells. The error bars represent standard deviation.

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The same samples were tested for SDF-1 alpha and SDF-1 beta ELISAs, however SDF-1

beta was not detectable from all tested samples and only the detected values were graphed in

Figure 53. According to the results, in MCF7pcDNA3.1 cells the vehicle control cells had higher

SDF-1 beta levels when compared with doxorubicin (145.96 pg/ml, p= 0.009), antagonist + prolactin (122.66 pg/ml, p= 0.02), doxorubicin + prolactin (6 day) (134.07 pg/ml, p= 0.03) treated cells. Prolactin treated cells had higher levels of SDF-1 beta when compared with doxorubicin (136.87 pg/ml, p= 0.001), prolactin + doxorubicin (6 day) (124.98 pg/ml, p= 0.01) and antagonist + prolactin (113.57 pg/ml, p= 0.008) treated cells. Overall, SDF-1 beta levels were higher in vehicle control and prolactin treated cells when compared with all treatment groups. Doxorubicin and prolactin treatment did not increase the SDF-1 beta levels in

MCF7pcDNA3.1 cells.

In T47D cells (Figure 54), the vehicle control cells significantly had high levels of SDF-1 beta when compared with doxorubicin (1080.60 pg/ml, p= 0.001), doxorubicin + prolactin

(1083.86 pg/ml, p= 0.0009), antagonist + prolactin (1003.75 pg/ml, p= 0.001) treated cells. The prolactin treated cells had higher levels of SDF-1 beta when compared with doxorubicin (888.59 pg/ml, p= 0.001), doxorubicin + prolactin (891.85 pg/ml, p= 0.001) and antagonist + prolactin

(811.74 pg/ml, p= 0.04). SDF-1 beta levels were overall high in vehicle control and prolactin treated cells. Doxorubicin and prolactin treatment did not increase SDF-1 beta levels in T47D cells.

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* SDF-1 Beta * 1600 * *

1400 * * 1200 * 1000 * 800 * * 600 * Concentration (pg/ml) 400 *

200

Figure 54. SDF-1 beta levels measured from MCF7pcDNA3.1 and T47D breast cancer cells in the presence or absence or DNA damage from doxorubicin. Cells were pre-treated or not with human recombinant prolactin (25 ng/ml) and/or prolactin receptor antagonist for 24 hours. The pre-treatments were followed by 2 hours doxorubicin (1 µM) with or without prolactin and/or prolactin receptor angatonist. Cells recovered for 2 days, 4 days or 6 days and conditioned media was tested by ELISA. All results represent 3 independent replicates that are pooled (n=3). The data was analyzed using one-way ANOVA followed by Bonferroni test. Statistically significant analysis (*) denotes p<0.05. vc= vehicle control, prl= prolactin, dox= doxorubicin, dox + prl= doxorubicin + prolactin, dox + prl+ ant = doxorubicin + angatonist + prolactin treated cells. The error bars represent standard deviation.

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Although SDF-1 levels increased from 2 day recovery time to 8 day recovery time in the

presence of prolactin and doxorubicin in both MCF7pcDNA3.1 and MCF7hprl cells , there was

no statistical difference when compared with the experimental group treated with prolactin

receptor antagonist (day 6) indicating that the SDF-1 secretion did not increased with prolactin in

the presence of DNA damage from doxorubicin. Therefore the results were not consistent with

cytokine array result.

The SDF-1 alpha ELISA was repeated one more time since the expected results were not

seen from the first ELISA. In second experiment, I used the same conditions and treatments to

collect conditioned media with only a few changes as follows; only MCF7pcDNA3.1 and

MCF7hprl cells were used, and the conditioned media was tested from 6-day and 8-day recovery

times. The time points were chosen based on the first ELISA and cytokine array. Since cytokine

array results were based on autocrine prolactin secreting MCF7hprl cells and no significant

effect was observed with prolactin and doxorubicin treatments during first ELISA, SKBR3 and

T47D cells were not included in this experiment.

In order to confirm our results with prolactin receptor (Δ1-9-G129R-hPRL) antagonist, I used a second monoclonal antibody-based prolactin receptor antagonist for the prolactin receptor

(LFA102) (Novartis) (Damiano et al., 2013) in the experiment.

According to the results at 6 days recovery time, MCF7pcDNA3.1 vehicle control cells showed higher levels of SDF-1 alpha when compared with prolactin (11.46 pg/ml, p= 0.04), doxorubicin (50.89 pg/ml, p= 0.005), doxorubicin + prolactin (63.02 pg/ml, p= 0.007), Δ1-9-

G129R-hPRL antagonist + prolactin+ doxorubicin (Δ1-9+prl+dox) (70.51 pg/ml, p= 0.0001) and

LFA102 antagonist + prolactin + doxorubicin (LFA+prl+dox) (75.37 pg/ml, p= 0.005) treated cells. Prolactin treated cells also had higher levels of SDF-1 alpha when compared with

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doxorubicin (50.89 pg/ml, p= 0.003), doxorubicin + prolactin (51.55 pg/ml, p= 0.008), Δ1-9-

G129R-hPRL antagonist + prolactin+ doxorubicin (Δ19+prl+dox) (59.04 pg/ml, p= 0.002) and

LFA102 antagonist + prolactin + doxorubicin (LFA+prl+dox) (63.90 pg/ml, p= 0.004) treated cells. There was also significant difference between doxorubicin and LFA102 antagonist + prolactin + doxorubicin (LFA+prl+dox) treated cells (12.34 pg/ml, p= 0.01). There was no significant difference between treatment groups at 8 days recovery time (Figure 55A). Overall,

SDF-1 alpha levels were higher in vehicle control and prolacitn treated MCF7pcDNA3.1 cells at

6 days recovery time when compared with all treatment groups. However, same effect was not observed at 8 days recovery time. As consistent with first ELISA, prolactin and doxorubicin treatment did not increase SDF-1 alpha levels in MCF7pcDNA3.1 cells.

In MCF7hprl cells the LFA102 alone treated cells showed higher levels of SDF-1 alpha when compared with vehicle control (40.97 pg/ml, p= 0.009), doxorubicin (38.68 pg/ml, p=

0.03), Δ1-9-G129R-hPRL antagonist (Δ19) (27.91 pg/ml, p= 0.01) and Δ1-9-G129R-hPRL antagonist + doxorubicin (Δ19 + dox) (24.90 pg/ml, p= 0.02) treated cells. There was no significant difference between treatment groups at 8 days recovery time (Figure 55B). Based on the ELISA results, no difference was observed between autocrine and recombinant prolactin on their effects on SDF-1 secretion.

In this ELISA, I have also tested 8 days recovery conditioned media from the previous experiment and compared the SDF-1 levels between MCF7hprl cells treated with doxorubicin and doxorubicin + Δ1-9-G129R-hPRL antagonist. There was no significant difference between two experimental groups (Figure 55C) indicating that the increased SDF-1 alpha levels at 8 days during the first ELISA experiment (Figure 50) likely to occur over recovery time. Doxorubicin

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treatment in the presence of autocrine prolactin may not have significant effect on SDF-1 secretion.

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SDF-1 alpha (MCF7pcDNA3.1) * ** * * 180 * * * 160 * * 140

120 *

100

60 Concentration (pg/ml)

218

SDF-1 alpha (MCF7hprl) 180

160

140

120 * * 100 * *

Concentration (pg/ml) 60

219

SDF-1 alpha (MCF7hprl) 180

160

140

120

100

Concentration (pg/ml) 60

0 MCF7hprl dox (8 days) MCF7hprl dox+Δ19 (8 days)

Figure 55. SDF-1 alpha and beta levels measured from breast cancer cells in the presence or absence or DNA damage from doxorubicin (Second ELISA experiment). Breast cancer cells were pre-treated or not with human recombinant prolactin (25 ng/ml) and/or Δ1-9-G129R- hPRL or LFA 102 prolactin receptor antagonist for 24 hours followed by 2 hours doxorubicin (1µM) with or without prolactin and/or prolactin receptor antagonists. Cells recovered for 6 days and 8 days and conditioned media was tested by ELISA at the indicated days of recovery. A. SDF-1 alpha levels (pg/ml) from MCF7pcDNA3.1 conditioned media. B. SDF-1 alpha levels (pg/ml) levels from MCF7hprl conditioned media. C. SDF-1 alpha levels from MCF7hprl conditioned media (conditioned media from the first ELISA experiment ). All results represent 3 independent replicates that are pooled (n=3). The data was analyzed using one-way ANOVA followed by Bonferroni test. Statistically significant analysis (*) denotes p<0.05, (**) denotes p<0.001. vc= vehicle control, prl= prolactin, dox= doxorubicin, Δ19= Δ1-9-G129R-hPRL antagonist, LFA= LFA102 antagonist, Δ19 + dox + prl = Δ1-9-G129R-hPRL antagonist + doxorubicin + prolactin, LFA + dox + prl= LFA102 + doxorubicin + prolactin.

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Chapter Six: DISCUSSION

The goal of current study was to assess the contribution of the PRL-Jak2-Stat5-Hsp90 pathway to the prolactin-mediated resistance to DNA damaging agents in vitro, with regard to potential cross-talk with the DNA damage response pathway. In addition, the role of prolactin on tumorigenicity and tumor volume after DNA damage was investigated in novel in vivo mouse models. The current study overall hypothesis is that prolactin will contribute to increased cell viability to DNA damaging drugs in a mechanism that involves HSP90 and its potential client proteins Jak2 and ATM, and therefore contribute to disease progression. The hypothesis of this study was supported with in vitro experiments and the aims were accomplished. However, the in vivo experimental results did not support the hypothesis of the role of prolactin on disease progression and the results have generated new hypotheses to be tested. It was confirmed that prolactin increased the viability of breast cancer cells to the DNA damaging chemotherapeutic, doxorubicin. A new synthetic inhibitor of Hsp90, BIIB021, decreased the effect of prolactin, indicating the mechanism of enhanced viability involves the master cancer chaperone, Hsp90.

When the stability and involvement of two potential Hsp90 client proteins, Jak2 and ATM and/or p-ATM were investigated in prolactin mediated cytotoxic resistance, the stability of Jak2 and both the total ataxia-telangiectasia mutated protein (ATM) and phospho-ATM appeared to be dependent on functional Hsp90. Inhibition of Jak2 and ATM, with highly selective inhibitors

(G6 and KU55933, respectively), abrogated prolactin enhanced viability, suggesting their role in prolactin induced cell viability after treatment with doxorubicin (Figure 56). Drug combination experiments with the Hsp90 inhibitor BIIB021 and doxorubicin, and the ATM inhibitor

KU55933 and doxorubicin, showed drug synergism in MCF7 breast cancer cells. Interestingly,

in orthotopic xenograft studies, endocrine prolactin resulted in an overall possible protection, a

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trend for decreased tumor volume and a significantly decreased Ki-67 proliferation index in the

presence of DNA damage with doxorubicin in SCID-beige mice. In two additional orthotopic

xenograft studies, autocrine prolactin from human breast cancer cells significantly increased the

tumor latency, and lowered the Ki-67 proliferation index, demonstrating a trend for decreased tumor volume, of doxorubicin induced DNA damaged cells in SCID mice compared to untreated, prolactin-treated or doxorubicin-treatment alone. The findings raised the question whether there is tumor suppression, tumor clearance or indication of later tumor progression behind the in vivo mechanism. In order to investigate the possible mechanisms, senescence, autophagy and senescence associated secretory phenotype were examined. When the effect of prolactin on senescence or autophagy was investigated, no particular effect was observed.

Additionally, when a small proportion of the senescence associated secretory phenotype was investigated using Luminex technology, there was no significant change with prolactin (Figure

57). Although the mechanism has not been identified, the cross-talk between prolactin and DNA repair pathway is suggested to affect microenvironment and therefore tumorigenicity and tumor volume in the in vivo studies (Figure 56).

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Figure 56. The proposed model. Prolactin enhances breast cancer cell viability against the DNA damaging agent doxorubicin, in a mechanism that involves Hsp90 and its potential client proteins Jak2 and ATM. The cross-talk between prolactin and the DNA repair pathway is suggested to be important for prolactin-enhanced cell viability. This cross-talk may also affect the tumor microenvironment in vivo.

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Figure 57. Possible mechanisms to explain delayed and small tumor formation in the presence of prolactin and doxorubicin in vivo. The possible mechanisms to explain the in vivo results are suggested to be involved in tumor suppression, tumor clearance or indication of later tumor promotion. In this regard, cellular senescence was investigated as part of tumor suppression, although autopagy could be involved in both tumor promotion and tumor suppression. The effect of prolactin was investigated in autopahgy in the context of tumor promotion, and finally the senescence-associated secreted phenotype was investigated in the context of tumor promotion and tumor clearance.

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6.1 Prolactin increases the viability and clonogenic survival of breast cancer cells treated with DNA damaging agents

In order to investigate the role of prolactin in cytotoxic resistance, the viability and clonogenic survival of breast cancer cells were assessed in response to doxorubicin in the presence or absence of prolactin. Prolactin alone often increased cell viability statistically, however the increase in clonogenic survival was not significant in replicate experiments. When breast cancer cells pre-treated with prolactin followed by doxorubicin, prolactin enhanced the viability of MCF7, SKBR3 and T47D cells against doxorubicin treatment. The clonogenic cell survival was shown to increase with prolactin at low doses of doxorubicin, however the statistical difference was not as strong in comparison with cell viability assays. More replicates might be required to confirm the prolactin-induced clonogenic survival of breast cancer cells against doxorubicin. However, the overall results from the experiments support my hypothesis that prolactin lead to a cytotoxic resistance of breast cancer cells treated with DNA damaging chemotherapy agent.

LaPensee and colleagues also observed prolactin-enhanced cell viability in breast cancer cells against a number of chemotherapy agents including taxol, vinblastine, and doxorubicin

(LaPensee et al., 2009). In the study, MDA-MB-468 breast cancer cells were pre-treated 24 hours with 25 ng/ml human recombinant prolactin followed by 4 days doxorubicin treatment (1 to 25 ng/ml) with or without prolactin. The cell viability was determined by using MTT assay and prolactin increased the cell viability approximately 30% against doxorubicin. In the cell viability assay using T47D cells, I used the same conditions as the LaPensee group, such that the cells were pre-treated with 25 ng/ml human recombinant prolactin for 24 hours followed by 4 days doxorubicin treatment (1 to 25 ng/ml) in the presence or absence of prolactin. According to

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WST-1 assay results, prolactin enhanced cell viability approximately 13% against doxorubicin in

T47D cells that the increased cell viability detected was lower than Lapensee and colleagues

observed value. This difference may be due to the different source of cell lines used in the

experiments. Additionally, when the effect of prolactin was tested on cytotoxic resistance in

MCF7 and SKBR3 breast cancer cells, only 2 hours doxorubicin treatment was used in high

concentrations (ranging from 0.4 µM to 6.5 µM) following 24 hours pre-treatment with human

recombinant prolactin (25 ng/ml). The cell viability was determined after 48 hours recovery time

and average of 12% and 7% increase was observed in MCF7 and SKBR3 cells, respectively. The

cell viability increase in SKBR3 and MCF7 cells were approximately 5-15% lower than the

LaPensee findings which is mostly likely due to the dose and the length of doxorubicin treatment

and the different cell lines used. Although LaPensee and colleagues investigated prolactin-

mediated cyctotoxic resistance in breast cancer cells, they focused on the resistance mechanism

against cisplatin and no further investigation was done with doxorubicin.

In the study of LaPensee et al., prolactin was demonstrated to antagonize cisplatin-

induced apoptosis in a mechanism involving the detoxification enzyme glutathione-S-transferase

(GST). The transcription of GST increased through either, or both, of prolactin-activated

Jak2/Stat5 and ERK 1/2 pathways. Prolactin-mediated GST activity lead to the effusion of cisplatin from the cells via transporters and reduced the entry of cisplatin into the cancer cells, and finally decreased apoptosis.

Prolactin was also shown to increase the survival of breast cancer cells to chemotherapy agents by decreasing apoptosis. In Perks and colleagues study (Perks et al., 2004), prolactin reduced C2-ceramide induced-apoptosis in breast cancer cells and had a role as survival factor.

In the presence of prolactin-neutralizing antibody and C2-ceramide, increased apoptosis was

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observed in T47D and MCF7 cell lines. In another study, the role of prolactin on cell survival

and resistance to chemotherapy agents was shown using Δ1-9-G129R-hPRL receptor antagonist

(Howell et al., 2008). When the breast cancer cells were treated with chemotherapy agents,

doxorubicin or paxlitaxel, in the presence of Δ1-9-G129R-hPRL receptor antagonist, both cell

number and colony-forming ability of MCF7 breast cancer cells in soft-agar was reduced

significantly.

The study by Howell and colleagues is the first and the only experiment that used a soft-

agar colony formation assay to test the role of prolactin in response to chemotherapy agents. The

majority of published experiments used the mitochondrial based MTT cell viability assay (Perks et al., 2004, Liby et al., 2003, LaPensee et al., 2009). In the thesis, prolactin-enhanced cell viability was determined by the WST-1 assay, similar to the MTT assays.

The WST-1 assay has been used in different studies (Dunkern et al., 2003, Brandes et al.,

2010, Lam et al., 2010), particularly with chemotherapy agents, such as doxorubicin, etoposide and 17-AAG, and the cell viability has been shown to decrease with increasing concentrations of the drugs in breast cancer cells. Although this assay is commonly used in studies with chemotherapy agents and it provides the opportunity of processing large numbers of samples and replicates in a short time, the limitations of this assay should be considered for this thesis. The

WST-1 assay measures increased activity of mitochondrial dehydrogenases in viable cells, and it is known that mitochondrial Hsp90 is responsible for the stability of mitochondrial dehydrogenases (Chae et al., 2013). When Hsp90 inhibitors are used in the experiments, there is a possibility of disturbing stability and activity of these enzymes and therefore affecting the viability results. Additionally, this experiment only provides information on cell viability and

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cannot be informative on the DNA damage response. For example, it cannot distinguish senescent cells from normal viable cells.

It has been also shown that MCF7 and T47D breast cancer cell lines secrete their own prolactin (Ginsburg and Vonderhaar, 1995, Howell et al., 2008). In the study by Howell and colleagues, autocrine prolactin was suggested to be involved in survival mechanism of breast cancer cells and the Δ1-9-G129R-hPRL receptor antagonist inhibited the effect of autocrine prolactin, that the cell number and the clonogenicity decreased dramatically. However in this thesis, prolactin secretion was not detected from parental MCF7 breast cancer cell line, and the

Jak2-Stat5 pathway was not activated in either MCF7 and SKBR3 cell lines without addition of human recombinant prolactin. This may also explain the lower levels of prolactin-induced cell viability seen in the experiments when compared with the literature (LaPensee et al., 2009).

Transfected cells engineered to secrete autocrine prolactin were used in in vivo assays in this thesis in order to circumvent technical issues surrounding prolactin delivery.

A control experiment to test whether the absorbance read from the WST-1 cell viability experiments directly correlates with the viable cell number was performed. When different cell numbers were seeded, the WST-1 readings correlated with cell number in MCF7 cells, however they did not correlate in SKBR3 cells. According to the experiments where prolactin and doxorubicin treatments were used, the cell numbers showed similar trend with WST-1 readings, although WST-1 readings had a higher overall percent survival than the cell numbers in both

MCF7 and SKBR3 cells when the values were normalized to vehicle controls. In the Lapensee and colleagues’ study (LaPensee et al., 2009), the MTT cell viability assay was used to investigate prolactin-enhanced cell viability against chemotherapy agents and the MTT assay has a similar working mechanism as WST-1 assay (Ngamwongsatit et al., 2008). The difference seen

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between MCF7 and SKBR3 cell numbers and WST-1 slopes for cell survival may indicate that

the WST-1 assay values, which are dependent on mitochondrial activity of the cells, may change

based on the growth stage, growth rate, metabolic activity and the size of different cell lines

(Berridge et al., 2005). Additionally the results indicate that the correlation between WST-1

values and cell number may vary depending on the cell type used in the experiments. There were no differences between the slope of the lines of cell viability and cell survival (based on cell counts) in assays involving the DNA damage response.

As part of experimental limitations, it was noted that the cell viability does not go below

20% in cell viability experiments. The reason could be due to limiations of WST-1 experiment that although background absorbance is substracted from experimental absorbance, there might be a small portion that could be calculated and may affect cell viability results. Another

possibility could be due to variations during cell viability assays. Although the same

experimental conditions were used in each experiment, the cells were not synchronized during

the experiments and it is known that the drugs and the inhibitors could target specific cell cycle

of the cells. Since the cells were in mix cell cycle phases, the drugs might be able to target only a

portion of the cells that the small portion might still survive and may explain approximately 20%

viability observed in the experiments.

6.2 Prolactin and Estrogen increase the viability of breast cancer cells treated with DNA damaging agent, doxorubicin

Estrogen and prolactin are both involved in mammary gland development and have been

implicated in breast cancer progression, therefore the cross-talk between prolactin and estrogen

pathways is well demonstrated, however their roles in resistance to chemotherapy agents are not

well studied. Since estrogen was not removed from cell culture conditions in in vitro experiments

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and it was used to support tumor growth in xenograft animal experiments, and giving the potential prolactin-estrogen cross-talk, the effect of prolactin, the effect of prolactin independent of estrogen, and of estrogen alone, on viability of MCF7 cells treated with doxorubicin was investigated. When the cells treated with prolactin (25 ng/ml) and estrogen (0.1 ng/ml and 1 ng/ml) alone, the increased cell viability was not significant, however the combination of prolactin and estrogen increased cell viability significantly in MCF7 cells. When the cells were pretreated with prolactin and/or estrogen, followed by doxorubicin treatment, prolactin and estrogen alone increased viability of DNA damaged cells, however this increase was more prominent when prolactin and estrogen was combined in the experiments.

Prolactin and estrogen are different in chemical structure, receptor characteristics and signalling mechanisms. Estrogens are known to bind to its classical receptors (ERα and ERβ) and also to non-classical receptors, such as G protein-coupled receptor 30 (GPR 30) (Manavathi and Kumar, 2006), however, there is only one receptor for prolactin to bind (Swaminathan et al.,

2008). Despite the differences, both prolactin and estrogen are endocrine hormones and locally secreted from breast tissue (Ben-Jonathan et al., 2002), therefore serum levels of prolactin and estrogen do not reveal the exposure of breast tissue to these hormones. Breast tissue expresses prolactin and estrogen receptors, however, expression increases in breast tumors, such that 80-

90% of breast tumors express the PRLR (Touraine et al., 1998) and approximately 75% are

ERα- positive (Karayiannakis et al., 1996). Additionally both estrogen and prolactin receptors can bind other proteins or compounds that can activate the pathways. Steroidol and non-steroidol compounds can bind to ER (Bai and Gust, 2009) and lactogens can bind to PRLR (Goffin et al.,

2005).

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There is a bidirectional cross-talk between prolactin and estrogen that occurs in multiple ways. Prolactin can increase the expression (Frasor and Gibori, 2003) and phosphorylation of

ERα, the phoshorylation is suggested to be through MAPK and PI3K/AKT pathways (Glaros et al., 2006, Arendt and Schuler, 2008a). In MCF7 cells engineered to secrete autocrine prolactin,

ER expression and estrogen responsiveness were demonstrated to increase (Gutzman et al.,

2004). In MCF7 cells, AP-1 activity was also shown to be enhanced with estrogen and prolactin through increased phosphorylation of p38, ERK1/2 and c-Fos (Gutzman et al., 2005).

Conversely, estrogen can induce the transcription of both prolactin (Duan et al., 2008) and the

PRLR (Swaminathan et al., 2008) in breast cancer cells. Estrogen can also potentiate prolactin- mediated Stat5 activity in some mammary cells and breast cancer cell lines (Bjornstrom et al.,

2001, Wang and Cheng, 2004).

Estrogen, similar to prolactin, has been implicated in resistance to chemotherapy agents, although the studies are limited. Estrogen was shown to increase cell proliferation and decrease cell death via activation of PI3K/Akt pathway and Bcl-2 antiapoptotic proteins (Huang et al.,

1997, Rodrik et al., 2005). In a study from LaPensee and colleagues (LaPensee et al., 2010), low doses of estrogen (0.01- 10 nM) increased the viability of breast cancer cells against cisplatin treatment and decreased apoptosis. However, the study showed that the estrogen-mediated resistance is not ERα-dependent and the mechanism of resistance is not similar to prolactin- mediated cytotoxic resistance, since cisplatin was able to enter into the nucleus. In a study where estrogen was depleted from MCF7 cells (Teixeira et al., 1995), the sensitivity of cells to doxorubicin increased and estrogen depletion resulted decrease in Bcl-2.

Prolactin and estrogen was shown to act synergistically and enhance cell proliferation in breast cancer cells. Additionally, prolactin and estrogen co-treatment was demonstrated to

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regulate expression of 105 genes that were suggested the cooperative role of prolactin and estrogen in breast cancer (Rasmussen et al., 2010). In a recent study (Barcus et al., 2015), the extracellular matrix density was implicated to be important in estrogen-prolactin crosstalk. While high density/stiff matrices increased prolactin and estrogen-induced ER+ breast cancer cell growth in 3D culture in a Src family dependent manner, in low density/compliant matrices there was modest cooperation between estrogen and prolactin on the cell growth.

Overall, estrogen may have contributed to the results in this thesis, since it has not been excluded from the experiments. However, in order to test its contribution, an estrogen antagonist could be used in the experiments.

6.3 Hsp90 is involved in the mechanism of prolactin-enhanced viability of breast cancer cells treated with DNA damaging agents

HSP90α has been identified as a prolactin Jak2-Stat5 regulated gene by our laboratory using substraction hydridization in SKBR cells, and it was shown to promote survival of breast cancer cells (Perotti et al., 2008). Substraction hydridization method is a PCR based amplification and includes normalization and substraction steps, which balances the abundance of cDNA within experimental population and excludes common sequences between control and experimental populations. The method was used as a powerful technique in Perotti’s study to distinguish gene expression between prolactin-treated and prolacin-untreated cell populations.

Given that both prolactin and its downstream target Hsp90α is involved in survival, the role of

Hsp90α was indirectly investigated in prolactin-enhanced cell viability after DNA damage by using Hsp90 inhibitor, 17AAG (Urbanska, 2011). 17AAG inhibits both Hsp90α and Hsp90β isoforms and the experiments from Urbanska (2011) showed that Hsp90 inhibition abrogated prolactin-enhanced cell viability of DNA damaged cells. Since Hsp90α is the isoform induced

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by prolactin, it is more likely to be involved in the mechanism of prolactin-enhanced viability. In

this thesis, the involvement of Hsp90 in prolactin-mediated mechanism was investigated using a

second Hsp90 inhibitor, BIIB021. The addition of BIIB021 strongly reduced the prolactin-

enhanced viability in MCF7 cells, confirming that Hsp90 is involved in the prolactin-enhanced

viability of DNA damaged breast cancer cells. When BIIB021 was combined with doxorubicin,

the cell viability decreased significantly in comparison with doxorubicin alone treatment. This

decrease was further investigated for drug synergism.

6.3.1 Synergism between BIIB021 and doxorubicin

The combination of BIIB021 and doxorubicin decreased cell viability significantly when

compared with doxorubicin treatment alone and the interaction between two drugs were

investigated using the Chou-Talalay method in MCF7 cells (Chou, 2006).

Anthracyclines, such as doxorubicin and epirubicin, have been widely used in treatments

of breast cancer patients (Khasraw et al., 2012, Gianni et al., 2009). Particularly the combinations of anthracyclines with other cytotoxic drugs that inhibition different pathways, have been used clinically for cancer treatments. In this regard, the Chou and Talalay combination index analysis has been used to design clinical protocols and determine effective concentrations of drugs to combine for treatments (Chou et al., 1994, Chou and Talalay, 1984). Doxorubicin is

known as a Topoisomerase II poison, which stabilizes this nuclear enzyme that is essential for

DNA replication and leaves double strand breaks. Doxorubicin is also known to induce a DNA

damage response by inducing phosphorylation of ATM and activating downstream pathways

(Nitiss, 2009). Although doxorubicin has been combined with different cytotoxic inhibitors, this

is the first study that shows its combination with the Hsp90 inhibitor, BIIB021, in breast cancer

cells.

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BIIB021 is a novel fully synthetic inhibitor of Hsp90 and it binds to ATP binding pocket of Hsp90. It interferes with chaperone function of Hsp90 and degrades client proteins such as

HER-2, Akt (Lundgren et al., 2009).

The combination of doxorubicin with the Hsp90 inhibitor, BIIB021, synergistically reduced cell viability. When combination index was calculated based on the method of Chou and

Talalay (Chou, 2006), strong to slight synergism was observed. Strong synergism was seen at the lowest doses of doxorubicin and BIIB021.

Hsp90 was found to directly interact with Topoisomerase II using immunoprecipitation assays in human colon cancer cells and the combination of Hsp90 inhibitor (geldanamycin) with topoisomerase II poisons (etoposide, mitoxantrane) synergistically increased apoptotic cell death

(Barker et al., 2006). According to the hypothesis, inhibition of Hsp90 disrupts the interaction between Hsp90 and topoisomerase II, which leads to increased activation of topoisomerase II and therefore increased number of cleavable complexes formed in the presence of topoisomerease II poision. This leads to more DNA damage and more cell death. Although the study mentioned synergistic act of two drugs, synergism was not calculated mathematically in this study.

6.4 The Hsp90-dependent stability of ATM and/or p-ATM and Jak2 and their involvement in mechanism of prolactin-mediated cell viability

Hsp90 inhibition abrogated prolactin-enhanced cell viability in breast cancer cells and since Hsp90 inhibitors destabilize Hsp90 client proteins, following confirmation of Hsp90 dependent stability of Jak2 and ATM and/or p-ATM, the involvement of ATM and Jak2 was investigated in prolactin-mediated cell viability.

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6.4.1 The stability of ATM and/or p-ATM is dependent on Hsp90 in breast cancer cells

In order to test the effect of Hsp90 inhibition on ATM and p-ATM, SKBR3 cells were treated with increasing concentrations of 17AAG in combination with doxorubicin, and the protein levels were evaluated. ATM phosphorylation increased with doxorubicin alone and total

ATM and p-ATM levels decreased in a 17AAG dose dependent manner in the presence of doxorubicin. The decrease seen in p-ATM levels were more than the decrease seen in ATM levels. Assuming that in the presence of doxorubicin, the majority of ATM is phosphorylated in the cells, the decrease in total ATM levels may represent the decrease in p-ATM levels. The experiment was replicated multiple times in our laboratory by Anna Urbanska and Rachel Liu in both SKBR3 and MCF7 cells that 17AAG resulted decreased levels of both ATM and p-ATM.

In order to test if the effect of Hsp90 inhibition was at the protein level, the results were confirmed with qPCR and the treatments were shown to have no significant effect on ATM mRNA levels.

Previous studies using Hsp90 inhibitors in combination with radiation also indicated the loss in p-ATM levels (Koll et al., 2008, Dote et al., 2006). In the study from Dote and colleagues

(Dote et al., 2006), the effect of Hsp90 inhibitor, 17-dimethylaminoethylamino-17- demethoxygeldanamycin (17DMAG), was determined on DNA repair, using two methods of

γH2AX detection and neutral comet assays. 17DMAG was shown to inhibit repair of radiation- induced DSBs by both assays. When the mechanism was investigated in detail, 16 hours of

17DMAG (50 nmol/L) pretreatment followed by irradiation at 4 Gy decreased the activation of

DNA-PKcs, but not total DNA-PKcs levels or Ku subunits in MiaPaca human pancreatic cells.

The same treatment also reduced p-ATM levels, but not total ATM levels in nuclear protein extracts. Based on immunoprecipitation assays, no interaction was found between ATM and

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Hsp90, although this interaction was not tested with p-ATM. Nbs1 was identified as a client of

Hsp90 in MiaPaca cells and 17DMAG was suggested to disrupt the interaction between Nsb1 and ATM proteins. None of the above observations was seen in a second pancreatic cell, Aspc1, line used in the experiments indicating that the cell type specific response to Hsp90 inhibitors.

The results from Dote and colleagues (Dote et al., 2006) are comparable with the thesis observations that pre-treatment with 17AAG followed by doxorubicin treatment decreased total and p-ATM levels. Dote and colleagues (Dote et al., 2006) did not observe decrease in total

ATM levels and did not find interaction between total ATM and Hsp90, however as indicated above that their results were not identical between two pancreatic cell lines and the effects seen in our experiments might be specific to the subtype of breast cancer cell lines used in the experiments.

The cell type specific response was reported in another study where DNA-PKcs was found as client of Hsp90 in HeLa cells, but not in HEK293 cells. Additionally it was noted that cytosolic but not nuclear DNA-PKcs levels were reduced in HeLa cells (Falsone et al., 2005), which overall indicates that the type of cell lines and protein extraction may alter the results. In this thesis, a whole cell protein extraction method was used, which is anticipated to include both nuclear and cytoplasmic proteins. However chromatin-bound proteins were not specifically fractionated. Hsp90 is an abundant protein, particularly in cancer cells, and it has been shown to localize to the nucleus and cytoplasm and secreted into extracellular matrix (Yano et al., 1999).

The whole cell extraction method used in the thesis is considered to be suitable for extraction of

Hsp90 and ATM proteins.

The importance of Hsp90 in the stability of phosphatidylinositol 3-kinase related protein kinases (PIKKs), was also implicated in previous studies in a relation with Tel2 (Takai et al.,

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2007). Tel2 is known to be required for stability of PIKKs and lack of Tel2 was shown to decrease protein levels of PIKKs, such as ATM. Tel2 knockdown also reduced newly synthesized ATM and mammalian target of rapamycin (mTOR) (Takai et al., 2007). In a following study by Takai and colleagues (Takai et al., 2010), a protein complex containing Tel2-

Tti1-Tti2 was shown to co-chaperone with Hsp70 and Hsp90 and stabilize PIKKs, including

ATM. The inhibition of Hsp90 with 17AAG was demonstrated to reduce the binding of ATM and other PIKKs to Tel2. Additionally, 17AAG decreased the levels of ATM and DNAPKcs.

However direct interaction of ATM was not shown with Hsp90 in the studies. Overall, which indicates that Hsp90 is important for the stability of DNA repair proteins, including ATM, however the direct interaction of Hsp90 with ATM has not been confirmed.

6.4.2 The involvement of ATM in mechanism of prolactin-mediated cell viability

To determine whether ATM, a key player in DNA damage response, plays role in prolactin-enhanced cell viability, WST-1 assays were performed using the highly selective ATM inhibitor, KU55933. The ATM inhibitor, KU55933, is known to bind to ATP binding pocket of

ATM and prevent activation and phosphorylation of ATM (Hickson et al., 2004). The KU55933 treatment in combination with doxorubicin and prolactin strongly reduced prolactin-enhanced viability of both MCF7 and SKBR3 cells, suggesting that ATM is involved in mechanism of prolactin-mediated cell viability. The combination, of KU55933 and doxorubicin, decreased cell viability significantly when compared with doxorubicin alone and this significant decrease was further investigated for drug synergism.

6.4.3 Synergism between KU55933 and doxorubicin

In order to determine the interaction between KU55933 and doxorubicin, two fixed doses of KU55933 were combined with three-fold increasing concentrations of doxorubicin. The drug

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combination was evaluated for synergism with combination index and median effect calculations using CompuSyn computer program (Chou, 2006). Doxorubicin combined with the ATM inhibitor, KU55933, synergistically reduced cell viability. According to the combination index calculated based on the method of Chou and Talalay, moderate to full synergism was found between doxorubicin and KU55933 in a range of drug doses used in the experiments.

In the study (Hickson et al., 2004), where KU55933 was identified as a novel and highly selective inhibitor of ATM, KU55933 was shown to sensitize HeLa cells to the topoismorease II poisons etoposide, doxorubicin, amsacrine and topoisomerase I inhibitor camptothecin. Hickson and colleagues’ results support this thesis’ findings on the synergism between the ATM inhibitor,

KU55933, and the topoisomerase II poison, doxorubicin.

6.4.4 The stability of Jak2 is dependent on Hsp90 in breast cancer cells

In order to test whether Jak2, a member of the Janus family of non-receptor tyrosine kinases, stability is dependent on Hsp90 in breast cancer cells, MCF7 cells were treated with the

Hsp90 inhibitor, 17AAG, in the presence and absence of prolactin and DNA damaging agent, doxorubicin, and the Jak2 protein levels were evaluated. The results demonstrated that Jak2 levels decreased in the presence of 17AAG in a dose dependent manner, indicating that the stability of Jak2 is dependent on Hsp90 in breast cancer cells. The increasing trend of Jak2 levels with prolactin treatment suggested that Jak2 stability may also be dependent on prolactin in breast cancer cells. The studies were also confirmed with qPCR, such that there were no treatment-specific effects on Jak2 mRNA levels indicating that Hsp90 inhibition and prolactin altered Jak2 protein levels.

In myeloproliferative diseases, Hsp90 is important for Jak2/Stat signalling (Schoof et al.,

2009) and the studies have been shown that the activation and stability of Jak2 is dependent on

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Hsp90, and that Jak2 is a bona fide client protein of Hsp90 chaperone complex in non-breast cell types (Marubayashi et al., 2010, Bareng et al., 2007).

In the study from Marubayashi and colleagues, Hsp90 inhibitors PU-H71 and 17DMAG

decreased Jak2 levels in a dose-dependent manner in leukemic cell lines. An

immunoprecipitation assay demonstrated that both phospho-Jak2 and total Jak2 interact with

Hsp90, overall their studies confirmed Jak2 as a client protein of Hsp90. This thesis is the first

study that confirms the stability of Jak2 is dependent on Hsp90 in breast cancer cells.

6.4.5 The involvement of Jak2 in mechanism of prolactin-mediated cell viability

To determine whether Jak2 plays role in prolactin-enhanced cell viability, WST-1 assays were performed using the highly selective Jak2 inhibitor, G6. The selective inhibitor of Jak2, G6, binds to the ATP-binding domain of Jak2 and inhibits the kinase activity of Jak2 (Majumder et al., 2010). MCF7 and SKBR3 breast cancer cells were very sensitive to 24 hours pre-treatment with high concentrations of G6, therefore, only 12 hours pre-treatment was used during the experiments. When G6 was used in combination with prolactin and doxorubicin, Jak2 inhibition abrogated prolactin-enhanced cell viability, suggesting that Jak2 is involved in the mechanism of prolactin-mediated cell viability against DNA damaging agents.

In previous studies, Jak2 has been implicated in myeloproliferative disorders, leukemia, lymphoma and myeloma. Jak2 was shown to promote cell growth and prevent apoptosis and its inhibition with G6 or other inhibitor, such as AG490, reduced cancer cell proliferation in vitro and ex vivo (Kiss et al., 2009, Majumder et al., 2011).

Although the majority of Jak2 studies were performed for myeloproliferative disorders, recently the importance of Jak2 in breast cancer has been studied. The inhibition of Jak2 demonstrated that Jak2 is required for constant activation of PRLR-Jak2-Stat3/5a/5b pathway

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and provides potent survival signal to ERα-positive breast cancer cells (Chan et al., 2014). Jak2 inhibitors are currently in clinical trials and the study from Chan and colleagues implicated that

Jak2 inhibition could be essential to the control ERα-positive breast cancers. In another study, constitutive active Jak2 was shown to increase Stat5 activation in mammary epithelial cells and lead resistance to cell death (Caffarel et al., 2012). In triple-negative-breast cancer, the

PI3K/mTOR pathway inhibition was shown to activate Jak2/Stat5 signalling and therefore cause resistance to the treatment. The inhibition of Jak2 abrogated the cellular resistance to a

PI3K/mTOR inhibitor and reduced cell number (Britschgi et al., 2012) implicating that Jak2 could be an important target for both ERα+ and triple-negative breast cancer. With respect to prolactin and the PRLR, Sakamoto and colleagues studied the importance of Jak2/Stat5 signalling during mammary tumor formation and progression in prolactin-induced mammary cancer. The researchers generated a mouse model that overexpress autocrine prolactin in mammary gland and conditionally knocked down Jak2, which showed that Jak2 is involved in initiation of prolactin-induced tumor formation however, the deficiency of Jak2 did not affect the growth and survival of mammary cancer cells in vitro and in vivo (Sakamoto et al., 2010).

As reviewed by (Barash, 2012), the nuclear localization and activation of Stat5 have been demonstrated to be associated with low invasiveness and better prognosis in breast cancer for many years (Yamashita et al., 2006, Peck et al., 2011, Sultan et al., 2005, Nevalainen et al.,

2004). However, in vivo studies revealed the multifaceted role of Stat5 showing that deregulated

Stat5 activity could convert normal mammary gland development into a dormant oncogenic process. In this respect, Stat5 is suggested to acquire its role according to microenvironmental and systemic signals that makes Stat5 important in mammary gland homeostasis and tumor phenotype and characteristics. With respect to prolactin/PRLR activated Jak2/Stat5 signalling, a

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dual role can be speculated on the pathway that the deregulated activation and signalling could be important in breast cancer formation and progression.

6.5 The role of prolactin on tumorigenicity and tumor volume in response to DNA damaging agents in vivo

To determine if the prolactin-increased cell viability after doxorubicin treatmented had a biological effect in vivo, I investigated the role of prolactin on tumorigenicity in response to

DNA damaging agents. I used two delivery methods of prolactin were used, endocrine and autocrine. A novel breast cancer recurrence model was used in the experiments, where breast cancer cells were treated with doxorubicin in the presence or absence of prolactin and after recovery period were injected into the mammary fat pad of immuno-deficient mice. The mice were not treated with doxorubicin during the experiment. In the endocrine prolactin model, mice were supplemented with ovine prolactin pellets (3 mg/pellet, 30 day slow-release) and in the autocrine prolactin model, mice were injected with genetically engineered prolactin secreting breast cancer cells.

6.5.1 The effect of endocrine prolactin on tumorigenicity and tumor volume in response to DNA damaging agents in vivo

To study the role of endocrine prolactin on tumorigenicity and tumor volume, the MCF7 breast cancer cells were pre-treated or not with ovine prolactin followed by doxorubicin treatment. After recovery, the cells were injected into the mammary fat-pad of SCID-beige mice, where mice were supplemented or not with custom-made ovine prolactin pellets. It is known from the literature that human breast cancer cells do not respond efficiently to mouse prolactin

(Utama et al., 2006), therefore ovine prolactin pellets were used in the experiment. However since ovine prolactin is 10-fold less potent than human prolactin (Utama et al., 2009), high levels of hormones were used in the experiment. In addition to ovine prolactin pellets, 17β-Estradiol

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pellets were used in order to support the growth of tumors with ER-positive MCF7 cells.

Doxorubicin showed a trend of delayed tumor formation and reduced tumor volume when compared with the group of mice injected with untreated MCF7 cells. Prolactin alone and in combination with doxorubicin treatment resulted a trend of early tumor formation, however tumor formation was not detected in all animals in both groups and the formed tumors showed limited growth particularly in the group of mice injected with doxorubicin and prolactin treated cells. The Ki-67 immunohistochemistry marker was used to investigate the proliferation in the xenograft tumors. The tumors formed with prolactin treated cells had the highest proliferative index followed by tumors formed with doxorubicin-treated and untreated cells, however the groups were not statistically different from each other. However the tumors formed with doxorubicin and prolactin-treated cells formed statistically the lowest proliferative index when compared with all groups. Although the tumor volumes were low in prolactin-treated cells injected group, the proliferative index was high as other groups. However any proliferative effect of prolactin, when combined with doxorubicin treatment, was not observed in the tumor volume and proliferative index.

This study had a limitation due to death that observed after 20 days in the groups received prolactin pellets. The control group was supplemented with placebo for ovine prolactin pellets, and pellet-related death was not observed in this group. A control experiment revealed that the ovine prolactin pellets were related to the deaths. Although the reason of the deaths is not known, it might be due to the toxic effect of high levels of ovine prolactin or the immune response activated by the prolactin hormone.

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Unfortunately there is no literature available investigating the role of endocrine prolactin on tumor formation in the presence or absence of DNA damage in order to compare or support the thesis findings.

6.5.2 The effect of autocrine prolactin on tumorigenicity and tumor volume in response to DNA damaging agents in vivo

Due to complications with endocrine prolactin delivery, the effect of autocrine prolactin was tested on tumorigenicity and tumor volume, using MCF7 cells genetically engineered to secrete autocrine prolactin. MCF7 cells were transfected with a human prolactin expression plasmid, provided as a gift from Dr. Vincent Goffin (Paris), and prolactin secretion from the transfected cells lines was confirmed by western blot assay. In in vivo experiments, parental

MCF7 cells or autocrine prolactin secreting MCF7 cells were treated or not with doxorubicin and following the standard recovery time, injected into the mammary fat pad of SCID mice. A SCID mouse model was used, rather than SCID-beige, as the beige mutation did not provide the estrogen that was required, as had been reported. In order to support the growth of tumors formed with ER-positive MCF7, mice were subcutaneously inserted with 17β-Estradiol pellets.

The experiment was performed two times using different cell numbers and observation periods.

When 500,000 cells were injected, the latency showed similar trend in mice injected with untreated and doxorubicin treated MCF7 cells and autocrine prolactin secreting MCF7 cells.

However there was significant delayed latency in the group of mice injected with doxorubicin- treated autocrine prolactin secreting cells. When 250,000 cells were injected, the autocrine prolactin secreting cells resulted in a trend of early latency followed by untreated and doxorubicin-treated cell injected groups. There was delayed latency, as similar to previous

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results, in the group of mice injected with doxorubicin-treated autocrine prolactin secreting cells.

In addition to delayed latency, reduced tumor volume was observed in this group.

The Ki-67 proliferation index was evaluated in the experiment where 250,000 cells were injected and 120 days of observation was used. The tumors formed with untreated, autocrine prolactin secreting and doxorubicin-treated cells did not have any significant difference in their

Ki-67 proliferation index, however the tumors formed with doxorubicin-treated autocrine prolactin cells had the lowest proliferation index when compared with all treatment groups. It should be noted that there was no prolactin and treatment related death seen in this experiment.

The results overall indicated that autocrine prolactin in combination with DNA damage from doxorubicin delays tumor formation and decreases tumor volume in SCID mice.

The role of autocrine prolactin has been studied in transgenic mice model that overexpress prolactin within mammary epithelial cells under control of NRL promoter (Arendt and Schuler, 2008b). Autocrine prolactin was demonstrated to induce mammary tumors in NRL-

PRL transgenic mice (Arendt et al., 2011, Rose-Hellekant et al., 2003), however, the role of prolactin was not investigated within the context of chemotherapy response in these studies. In a study from Liby and colleagues (Liby et al., 2003), MDA-MB-435 breast cancer cell lines were genetically engineered to overexpress prolactin and the cells were injected into mammary fat pad of nude mice. Prolactin secreting cells were shown to increase tumor growth when compared with parental MDA-MB-435 cells injected mice. Additionally tumors formed with prolactin secreting cells were demonstrated to metastasize to the lymph nodes. Although Liby and colleagues used a similar autocrine prolactin delivery method as this thesis, it should be noted that the cell lines and animal models used in the experiments were different. The MDA-MB-435 cell lines used in this study has been known to be derived from breast carcinoma, however recent

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STR profiling studies revealed its origin as melanoma (Rae et al., 2007). Additionally, the

cellular chemotherapeutic response was not investigated by Liby and colleagues.

6.5.3 Estrogen pellets: their release and potential side effects

In order to support the growth of ER-positive mammary tumors in xenograft mice

models, estrogen-pellet supplementation is widely used. The pellets are commercially available

in different dose ranges and they are suggested to have slow-release over 60-days. In order to

support the growth of tumors of MCF7 breast cancer cells, 0.72 mg/pellets are recommended

(Lewis and Porter, 2009). The serum level of estrogen, which is around 10-60 pg/ml in normal

mice, can be increased up to 300-400 pg/ml with 0.72 mg estrogen pellets (Lewis and Porter,

2009, Gupta et al., 2007). In this thesis, the serum levels of estrogen were investigated from 4

SCID mice before and after pellet implantation. The serum levels were between 10-30 pg/ml

before pellet implantation, as indicated in the literature (Lewis and Porter, 2009). The levels

increased to an average of 250 pg/ml and 236 pg/ml after 10 and 20 days, respectively, however,

the estrogen levels were quite variable between animals (the range was between 30 to 335

pg/ml). The estrogen levels were the highest between 10 and 20 days which coincide the dates of

the first and the majority of palpable tumor formation detected in xenograft experiments. The

levels of estrogen started to decline after 30 days and were the lowest around 50 days, however,

there were still high levels of estrogen in the serum after 60 days, which has also been reported

in the literature (Gakhar et al., 2009).

In addition to causing variable release and high serum levels of after 60-days, potential side effects of the estrogen pellets were observed, which were also identified in the literature. I observed ulcerative dermatitis in the perineum region. The lesions sometimes spread to the hindlegs, over the dorsum of the tail head and to forelegs. In recent experiments, some of the

245

lesions turned into severe necrotic lesions. In some of the mice, the urinary bladder was enlarged and observed during postmortem examination. The perineum lesions were also examined by

Faculty Veterinary, Stefanie Anderson (University of Calgary) and Pathologist Erin Locke

(Antech Diagnostics, ON, Canada). Histological observations showed ulceration and inflammation were detected from provided tissues. According to the detailed reports, irregular epidermal acanthosis with a focal area of ulceration that is covered by neutrophils was observed.

In a detailed veterinary medicine study (Gakhar et al., 2009), the complications after estrogen pellet implantation were examined in detail in nude mice. Palpable distended bladders and perineum lesions were detected in mice, and during postmortem exanimation, dilation of the urinary bladder or ureters and kidneys were observed. The side-effects seen in these experiments did not dramatically affect the experimental results, such that the mice with perineum lesions were not euthanized early due to health conditions.

6.6 The effect of prolactin on senescence, autophagy and senescence-associated-secreting- phenotype

6.6.1 The effect of prolactin on autophagy in the presence of DNA damage

To determine if prolactin has any effect on autophagy in the presence of DNA damage from doxorubicin, Beclin-1 protein levels were examined in MCF7 breast cancer cells. The cells were pre-treated with prolactin followed by doxorubicin treatment and whole cell protein extraction. When Beclin-1 levels were examined using western blot assay, no significant effect of prolactin was observed on Beclin-1 levels.

Autophagy, which is a lysosomal degradation pathway, has been also implicated in cancer, such that several tumor suppressor genes regulated autophagy and the defect in the mechanism was suggested to increase tumorigenesis and chemotherapy resistance (Gong et al.,

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2013, Xue et al., 2010). In a previous study (Karantza-Wadsworth et al., 2007), loss of Beclin-1 was shown to sensitize mammary epithelial cells to metabolic stress and increase lumen formation in mammary acini. The DNA damage response was also activated by defective autophagy that decreased apoptosis and promoted tumor formation in vivo.

In a recent study, the antagonism of prolactin was shown to increase autophagy-related cell death (Wen et al., 2014). The prolonged treatment with the G129R PRLR antagonist was demonstrated to inhibit tumor growth with ovarian cancer cells and induce autophagy. However no effect was seen on proliferation, viability or migration of ovarian cancer cells in 2D cultures upon treatment with G129R. In 3D cancer spheroids, G129R treatment increased redundant autolysosome formation and cell death. The mechanism was indicated to be beclin-1- independent. The results suggest that although prolactin did not have any effect on autophagy in

2D in vitro experiments, there might be a beclin-1 independent mechanism involved in 3D in vitro and in vivo experiments of the thesis, which was not investigated.

6.6.2 The effect of prolactin on cellular senescence and the senescence-associated secretory phenotype in the presence of DNA damage

In order to determine whether prolactin has any effect on senescence, MCF7 breast cancer cells were pre-treated with prolactin followed by doxorubicin treatment. The senescence- associated β-galactosidase was detected after 6 days of recovery time. Doxorubicin treatment, in the presence or absence of prolactin, increased senescence when compared with untreated and prolactin treated cells, however prolactin did not have a significant effect on senescence when combined with doxorubicin treatment.

In a recent study (Fleming et al., 2013), a soluble PRLR isoform, Δ7/11, was identified as a functional prolactin-binding protein and shown to inhibit prolactin-induced cell proliferation

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and alter the prolactin-induced rescue of senescence in breast epithelial cells. In the study, T47D breast cancer cells were treated with prolactin and/or Δ7/11. Prolactin was suggested to rescue cells from early senescence by inactivating p38 and increase cell proliferation. Upon treatment with Δ7/11, the prolactin-induced rescue of early onset senescene and prolactin-mediated proliferation were inhibited. However, in this study DNA damage induced senescence was not evaluated and the results were based on cell cycle analysis. No specific senescence experiment was used in the study.

Although senescence was suggested as a tumor suppressive mechanism, a new aspect of senescence cells is defined as the senescence-associated secretory phenotype (SASP) and it is implicated to promote tumorigenesis and aggressive breast cancer cells by altering the tissue microenvironment. In order to investigate prolactin has effect on SASPs, autocrine prolactin secreting MCF7 cells were pre-treated or not with Δ1-9-G129R-hPRL prolactin receptor antagonist followed by doxorubicin treatment and a recovery time. A small portion of cytokines was evaluated from conditioned media using a luminex-based cytokine array. However, prolactin did not have any significant effect on the evaluated cytokines. Since SDF-1α/β levels appeared to be affected by autocrine prolactin, further ELISAs were performed, however no significant effect of prolactin was observed on SDF-1 α and β levels. The SASPs is a novel concept that the related mechanisms have not been investigated in detail and since only a small portion of those factors were evaluated in this thesis, there might be other factors that are influenced by prolactin in breast cancer cells.

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Chapter Seven: FUTURE DIRECTIONS AND SIGNIFICANCE

7.1 Future Directions

To investigate the effect of prolactin on clonal cell survival against DNA damaging

agents, more clonogenic assay replicates should be performed. To bypass the limitations of

WST-1 assay in the presence of Hsp90 inhibitors, clonogenic assays can also be used. Since

estrogen and prolactin cross-talk and the specific effects of estrogen were not investigated in this

study, the experiments can be expanded using estrogen antagonist.

The supplementary data suggests possible effect of prolactin on ATM target proteins,

particularly KAP1. The interaction between prolactin and KAP1 should be evaluated in detail. In

the experiments, the direct effect of prolactin on ATM stability could not be shown due to

technical limitations, which requires further investigation.

The mechanism behind increased latency and reduced tumor volume in the context of

prolactin and DNA damage should be evaluated. The cross-talk between prolactin and DNA

repair pathway might affect microenvironment which needs to be investigated in detail. Only a

small portion of cytokines was evaluated in the thesis in the context of SASPs, however a larger,

and more detailed screen will be required.

7.2 Overall Significance

Overall my studies are important to provide a possible mechanism of prolactin mediated

cytotoxic resistance. The studies indicate that this mechanism involves the Hsp90 chaperone protein and its client proteins on prolactin Jak2-Stat5 pathway. In addition to in vitro studies, my in vivo studies provide a unique experimental approach to understand the role of prolactin in tumor tumorigenicity. My studies can provide important guidance for patients who will receive combination treatment of prolactin receptor antagonist and DNA damaging agents.

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APPENDIX(A: SUPPLEMENTARY(METHODS(

A.1. Clonogenic cell survival assay with ATM siRNA and mock transfected cells

2000 MCF7 cells were plated into 96 well plates and the next day transfected with ATM siRNA or mock transfected or not tranfected. 48 hours after transfection cells were pre-treated with human recombinant prolactin (25 ng/ml) for 24 hours followed by 2 hours of doxorubicin treatment with our without prolactin. After doxorubicin treatment cells were washed twice with phosphate buffer saline (PBS, ph 7.4), trypsinized and transferred to 6 well plates. The colonies were allowed to form over 10 days and stained with 0.5% gention violet as explained in 3.7.1.

Colonies with more than 50 cells were counted under the light microscope.

A.1.1. Knockdown with siRNA

ON-TARGETplus ATM SMART pool siRNA (5 nmol), ON-TARGETplus GAPD

Control siRNA Pool (5 nmol), ON-TARGET plus Non-targeting Pool (5 nmol) and DharmaFect transfection reagent were purchased from Dharmacon part of GE Healthcare (Lafayette, CO,

USA). 20 µM stock and 5 µM working dilutions of siRNAs were prepared with 1XsiRNA buffer

(Dharmacon). According to the manufacturer’s instructions, 5000 cells per 96-well plate wells and 450,000 cells per 6 cm plates were plated in antibiotic free media the day before transfection.

The tube containing 5 µM siRNA and serum free media was mixed with another tube containing

DharmaFECT reagent and serum free media. The mixture was incubated 20 minutes and added on wells or plates with antibiotic free media with a final volume of 100 µl for 96-well plates and

3 ml for 6 cm plates. Cells were incubated in transfection media for at least 48 hours and followed with treatments if indicated.

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A.1.2. ΔΔCq and Percent Knockdown Calculations

In siRNA knockdown experiment, following qPCR analysis, the expression of siRNA-

treated target gene (ATM; TAR) was normalized to reference gene (YWWAZ; REF) expression

levels within the same sample to determine ΔCq (ΔCq= Cq (TAR) – Cq (REF)) and to correct for

treatment-unrelated variations among the wells. The ΔCq values for each replicate were

- Cq exponentially transformed to the ΔCq Expression (ΔCq Expression= 2 Δ ) followed by averaging and determining the standard deviation of replicates. The average of target gene (ATM) was normalized to Non-targeting siRNA to find ΔΔCq Expression. In order to calculate the percent knockdown following equation was used: % Knockdown= (1-ΔΔCq) x 100.

A.2. Whole cell lysate extract with NP-40 buffer with sonication

This protocol was used from the lab of Dr. Susan Lees-Miller: NETN Extracts from

Human cells and used for extraction of proteins (p-ATM, ATM, p-KAP1, KAP1, p-Chk2, Chk2,

GBRB2) shown in Supplementary Result section. MCF7 cells were pre-treated or not with human recombinant prolactin (25 ng/ml) for 24 hours followed by treatment with/without 5 µM

DNA-PK inhibitor, NU7441, 1 hour before doxorubicin treatment. Cells were treated 2 hours with doxorubicin (1 µM) with or without prolactin followed by protein extraction. As described above, cells were washed with 1XPBS after aspiration of media and scraped in 1 ml of cold

1XPBS. Following centrifugation for 10 minutes at 12,000 rpm at 4°C, the pellet was resuspended in 100-200 µl of NP-40 lysis buffer (1% (v/v) NP-40, 50 mM Tris- HCl ph 7.5, 1 mM EDTA and 150 mM NaCl) containing protease and phosphatase inhibitors and incubated for

20 minutes on ice. The cell suspension was sonicated three times for 5 seconds with 5 seconds intervals at #4 (on dial) on ice and centrifuged for 10 minutes at 12,000 rpm at 4°C. The

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supernatant was transferred into a new tube and protein concentration was measured. Proteins were snap frozen and stored at -80 °C until use.

A.2.1. Immunoblotting for p-KAP1

In order to detect p-KAP1, the membrane was blocked in 10% dried skim milk in TBST

0.1% for 1 hour and then washed tree times 10 minutes each with TBST 0.1%. The membrane was incubated with rabbit anti-KAP1 (phospho S824) primary antibody (Abcam, Cambridge,

MA, USA) (1:1000 dilution) in %5 BSA in TBST 0.1% overnight at 4°C. The blot was washed three times for 10 minutes each with TBST 0.1% and the blot was incubated in HRP conjugated anti rabbit secondary antibody (1:10000 dilution) in 10% dried skim milk in TBST 0.1% for 1 hour and then washed three times for 10 minutes each with TBST 0.1%.

A.2.2. Immunoblotting for KAP-1

In order to detect KAP1, the membrane was blocked in 10% dried skim milk in TBST

0.1% for 1 hour and then washed tree times 10 minutes each with TBST 0.1%. The membrane was incubated with rabbit anti-KAP1 primary antibody (Abcam) (1: 1000 dilution) in %5 BSA in

TBST 0.1% overnight at 4°C. The blot was washed three times for 10 minutes each with TBST

0.1% and the blot was incubated in HRP conjugated anti rabbit secondary antibody (1:10000 dilution) in 10% dried skim milk in TBST 0.1% for 1 hour and then washed three times for 10 minutes each with TBST 0.1%.

A.2.3. Immunoblotting for p-Chk2

In order to detect p-Chk2, the membrane was blocked in 10% dried skim milk in TBST

0.1% for 1 hour and then washed tree times 10 minutes each with TBST 0.1%. The membrane was incubated with rabbit anti-PhosphoChk2 (Thr68) primary antibody (Cell Signaling

Technology, Beverly, MA, USA) (1:1000 dilution) in %5 BSA in TBST 0.1% overnight at 4°C.

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The blot was washed three times for 10 minutes each with TBST 0.1% and the blot was incubated in HRP conjugated anti rabbit secondary antibody (1:10000 dilution) in 10% dried skim milk in TBST 0.1% for 1 hour and then washed three times for 10 minutes each with TBST

0.1%.

A.2.4. Immunoblotting for Chk2

In order to detect Chk2, the membrane was blocked in 10% dried skim milk in TBST

0.1% for 1 hour and then washed tree times 10 minutes each with TBST 0.1%. The membrane was incubated with rabbit anti-Chk2 primary antibody (Cell Signaling Technology) (1:1000 dilution) in %5 BSA in TBST 0.1% overnight at 4°C. The blot was washed three times for 10 minutes each with TBST 0.1% and the blot was incubated in HRP conjugated anti rabbit secondary antibody (1:10000 dilution) in 10% dried skim milk in TBST 0.1% for 1 hour and then washed three times for 10 minutes each with TBST 0.1%.

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APPENDIX(B: SUPPLEMENTARY(RESULTS(

B.1. To investigate if the kinase activity of ATM is dependent upon prolactin

The stability of ATM and p-ATM was confirmed to be dependent on Hsp90 in breast

cancer cells in a previous chapter. Following those studies I investigated if the stability of ATM and/or p-ATM is dependent on prolactin in my preliminary experiments (data not shown) using a

protein biosynthesis inhibitor, cycloheximide. However due to ATM phosphorylation with

cycloheximide the data was not informative and I tested if the kinase activity of ATM is

dependent on prolactin by investigating p-ATM target proteins; p-KAP1, KAP1, p-Chk2 and

Chk2 under different treatment conditions.

In the experiments, MCF7 cells were pre-treated with prolactin (25 ng/ml) or 17AAG

(100 nM) for 24 hours followed by DNA-PK inhibitor (5 µM), NU7441, treatment for 1 hour

where indicated. Following pre-treatments, cells were treated with doxorubicin for 0 hr, 1 hr, 2

hr, 3 hr, and 4 hr, and protein lysis was performed at indicated time points after doxorubicin

treatment. Untreated cells, prolactin- or NU7441-treated cells were used as controls in the

experiments. The protein levels were investigated from whole cell protein extracts using western

blot assay followed by ImageJ analysis.

According to the results, doxorubicin treatment in the presence or absence of prolactin

led to phosphorylation ATM and prolactin treatment resulted in an increased trend of ATM

phosphorylation at 1 hour and 3 hour time points when compared with doxorubicin alone. This

increase, however, was not consistent over the 4 hour period (Figure S1A and S1B). According

to p-KAP1 results (Figure S1A and S1C), phosphorylation of KAP1 was detected with

doxorubicin treatment with or without prolactin, however there was increasing phosphorylation

over the 4 hour period and prolactin appeared to increase p-KAP1 levels when compared with

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doxorubicin alone. The p-Chk2 phosphorylation was observed with doxorubicin treatment in the presence or absence of prolactin. The levels of p-Chk2 were similar in doxorubicin treated cells over 4 hours, however in prolactin and doxorubicin treated cells, there was a decreasing trend in

Chk2 phoshorylation. Similar to p-KAP1 levels, p-Chk2 levels showed an increasing trend in the presence of prolactin treatment when compared with doxorubicin alone, with the only exception at the 4 hour time point (Figure S1A and S1D).

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Dox Dox+prl

0hr 1hr 2hr 3hr 4hr 0hr 1hr 2hr 3hr 4hr p-ATM 320 kDa

ATM 320 kDa p-KAP1 110 kDa

KAP1 110 kDa p-Chk2 62 kDa

Chk2 62 kDa

25 kDa GRB2

Dox Dox+prl

0hr 1hr 2hr 3hr 4hr 0hr 1hr 2hr 3hr 4hr

p-ATM 320 kDa

ATM 320 kDa p-KAP1 110 kDa

KAP1 110 kDa p-Chk2 62 kDa

Chk2 62 kDa

25 kDa GRB2

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p-ATM fold change 3

2.5

1.5 dox Fold change dox+prl 1

0.5

0 0 hr 1 hr 2 hr 3 hr 4 hr Time after doxorubicin treatment

278

p-Kap1 fold change 50

25 dox

Fold change 20 dox+prl 15

0 0 hr 1 hr 2 hr 3 hr 4 hr Time after doxorubicin treatment

p-Chk2 fold change 5

4.5

3.5

2.5 dox

Fold change 2 dox+prl 1.5

0.5

0 0 hr 1 hr 2 hr 3 hr 4 hr Time after doxorubicin treatment

279

Figure S1. The effect of prolactin on ATM target proteins. MCF7 cells were pre-treated or not with human recombinant prolactin (25 ng/ml) for 24 hours followed doxorubicin (1 µM) treatment for 1 hr, 2 hr, 3 hr and 4 hr and protein lysis was started right after doxorubicin treatment and proteins lysis was done at 0 hr, 1 hr, 2 hr, 3 hr and 4 hr time points. p-ATM, ATM, p-KAP1, KAP1, p-Chk2, Chk2 and GRB2 levels were determined by western blot assay (n=3). The western blot results from three independent replicates were analyzed with ImageJ program. A. Western blot images from doxorubicin and doxorubicin + prolactin treatments. The images show three independent replicates (n=3). B. p-ATM levels from doxorubicin and doxorubicin + prolactin treatments, calculated by ImageJ analysis, results represent three independent replicates. C. p-KAP1 levels from doxorubicin and doxorubicin + prolactin treatments, calculated by ImageJ analysis, results represent three independent replicates. D. p-Chk2 levels from doxorubicin and doxorubicin + prolactin treatments, calculated by ImageJ analysis, results represent three independent replicates.

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When the DNA-PK inhibitor, NU7441, was included in the experiment, slightly different results were observed from above results. ATM, KAP1 and Chk2 phoshorylation was observed in the presence of NU7441 and doxorubicin treatment with or without prolactin (Figure S2A).

ATM phoshorylation showed increased trend at 2 hour and 3 hour time points with prolactin when compared with doxorubicin and NU7441 treatment however the increasing trend was not consistent over 4 hours (Figure S2B). The p-KAP1 levels showed earlier increase over 4 hours and there was a increasing trend with prolactin treatment (Figure S2C). The p-Chk2 levels also showed a very slight increase over 4 hours and there was increasing trend in the presence of prolactin treatment when compared with doxorubicin and NU7441 treatment (Figure S2D). The results indicated that in the presence of NU7441, prolactin tend to increase p-KAP1 and p-Chk2 levels in doxorubicin treated cells.

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Dox+NU4471 Dox+Prl+NU44471 0hr 1hr 2hr 3hr 4hr 0hr 1hr 2hr 3hr 4hr p-ATM 320 kDa

ATM 320 kDa p-KAP1 110 kDa

KAP1 110 kDa p-Chk2 62 kDa

62 kDa Chk2 25 kDa GRB2

Dox+NU4471 Dox+Prl+NU44471

0hr 1hr 2hr 3hr 4hr 0hr 1hr 2hr 3hr 4hr

320 kDa p-ATM

ATM 320 kDa

110 kDa p-KAP1

KAP1 110 kDa p-Chk2 62 kDa

Chk2 62 kDa

GRB2 25 kDa

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Dox+NU4471 Dox+Prl+NU44471 0hr 1hr 2hr 3hr 4hr 0hr 1hr 2hr 3hr 4hr

p-ATM 320 kDa

ATM 320 kDa

p-KAP1 110 kDa

KAP1 110 kDa

p-Chk2 62 kDa

Chk2 62 kDa

25 kDa GRB2

p-ATM fold change 3

2.5

1.5 Nu+Dox Fold change Nu+Dox+Prl 1

0.5

0 0 hr 1 hr 2 hr 3 hr 4 hr Time after doxorubicin treatment

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p-Kap1 fold change 3

2.5

1.5 Nu+Dox Fold change Nu+Dox+Prl 1

0.5

0 0 hr 1 hr 2 hr 3 hr 4 hr Time after doxorubicin treatment

p-Chk2 fold change 3.5

2.5

1.5 Nu+Dox Fodl change Nu+Dox+Prl 1

0.5

0 0 hr 1 hr 2 hr 3 hr 4 hr Time after doxorubicin treatment

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Figure S2. The effect of prolactin on ATM target proteins in the presence of DNAPK inhibitor, NU7441. MCF7 cells were pre-treated or not with human recombinant prolactin (25 ng/ml) for 24 hours followed by 1 hour NU7441 (5 µM) treatment. Cells were treated with doxorubicin (1 µM) for 1 hr, 2 hr, 3 hr and 4 hr and protein lysis was started right after doxorubicin treatment and proteins lysis was done at 0 hr, 1 hr, 2 hr, 3 hr and 4 hr time points. p- ATM, ATM, p-KAP1, KAP1, p-Chk2, Chk2 and GRB2 levels were determined by western blot assay (n=3). The western blot results from three independent replicates were analyzed with ImageJ program. A. Western blot images from doxorubicin + NU7441 (NU) treatments. The images show three independent replicates (n=3). B. p-ATM levels from doxorubicin + NU7441 and doxorubicin + NU7441 + prolactin treatments, calculated by ImageJ analysis, results represent three independent replicates. C. p-KAP1 levels from doxorubicin + NU7441 and doxorubicin + NU7441 + prolactin treatments, calculated by ImageJ analysis, results represent three independent replicates. D. p-Chk2 levels from doxorubicin + NU7441 and doxorubicin + NU7441 + prolactin treatments, calculated by ImageJ analysis, results represent three independent replicates.

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ATM, KAP1 and Chk2 phosphorylation was not detected in the absence of doxorubicin in control experiments where cells were kept untreated or prolactin alone or NU7441 alone treated (Figure S3A and S3B). When 17AAG pre-treatment was used in the experiment, p-ATM and ATM levels decreased as expected, however no effect was observed on p-KAP1 levels. Low levels of p-Chk2 was also observed from 17AAG treated cells (Figure S3A). Total ATM, KAP1 and Chk2 proteins were detected from each experimental group as shown and quantified in

Figure S1, S2 and S3.

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Prl alone control (no dox) 17AAG+dox

0hr 1hr 2hr 3hr 4hr 0hr 1hr 2hr 3hr 4hr

p-ATM 320 kDa

ATM 320 kDa

p-KAP1 110 kDa

KAP1 110 kDa

p-Chk2 62 kDa

Chk2 62 kDa

GRB2 25 kDa

Untreated control NU7741 alone control

0hr 1hr 2hr 3hr 4hr 0hr 1hr 2hr 3hr 4hr

p-ATM 320 kDa

ATM 320 kDa

p-KAP1 110 kDa

KAP1 110 kDa

p-Chk2 62 kDa

Chk2 62 kDa

GRB2 25 kDa

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Figure S3. The effect of prolactin, 17AAG and NU7741 on ATM target proteins in the absence of DNA damage. MCF7 cells were pre-treated or not with human recombinant prolactin (25 ng/ml) for 24 hours followed by 1 hour NU7441 (5 µM) treatment. Cells were not treated with doxorubicin, proteins lysis was done at 0 hr, 1 hr, 2 hr, 3 hr and 4 hr time points. p- ATM, ATM, p-KAP1, KAP1, p-Chk2, Chk2 and GRB2 levels were determined by western blot assay (n=3). A. Western blot image from prolactin alone and 17AAG + doxorubicin treatments (n=1). B. Western blot image from untreated and NU7441 alone treatment (n=1). Error bars represent standard deviation.

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B.2. To investigate the involvement of ATM in prolactin- mediated cell viability with clonogenic assay using short inferring RNA (siRNA) against ATM

In order to confirm our findings that ATM inhibition with KU55933 abrogates the prolactin increased cell viability observed with WST-1 assays, I used short inferring RNA

(siRNA) against ATM in clonogenic assays that test reproductive capacity.

To optimize siRNA transfection and knockdown efficiency, MCF7 cells were transfected with siRNA directed against ATM (siATM), GAPDH (siGAPDH) (positive control) or with scrambled siRNA (non-targeting control). Gene expression was compared using qPCR and the

ΔΔCq method was then used to calculate relative gene expression. Target gene results were normalized to reference gene (YWHAZ) and treatment control (non-targeting control) to calculate the percent knockdown over 96 hours. According to the results, the GAPDH gene was knocked down 8.8% at 24 hours, 86.13% at 48 hours, and 63.26% at 72 hours. The gene expression increased at 96 hours (Figure S4A). According to the observations, the viability of siGAPDH-transfected cells started to decrease after 24 hours, which could have had an effect on the knockdown results. The knockdown of ATM was observed not to affect cell viability over 96 hours. The knockdown of ATM was as follows: 30.45% at 24 hours, 59.29% at 48 hours,

90.40% at 72 hours and 85.87% at 96 hours (Figure S4B).

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GAPDH 120 100 80 60 40

% Knockdown 20 0 -20 24hr 48hr 72hr 96hr Time Post-Transfection A

ATM 100

% Knockdown 20

0 24hr 48hr 72hr 96hr Time Post-Transfection B

Figure S4. siRNA-mediated silencing of GAPDH and ATM genes. MCF7 cells were transfected with siRNA targeting GAPDH (positive control) and ATM genes or scrambled siRNA as a non-targeting control. Relative gene expression was determined with ΔΔCq method from qPCR data with YWHAZ as reference gene. Percent knockdown was calculated by normalizing data to the reference gene, followed by normalizing to the non-targeting control. A. Knockdown of GAPDH gene over 96 hours. B. Knockdown of ATM gene over 96 hours. Error bars represent standard deviation.

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Based on the ATM gene knockdown results, a clonogenic assay was designed as follows:

MCF7 cells were seeded into 96 well plates and transfected the next day with siRNA or mock transfected. After 48 hours post-transfection, cells were treated with 25 ng/ml human recombinant prolactin and 72 hours post-transfection cells were treated with doxorubicin for 2 hours. Cells were then transferred to 6 well plates with fresh media, with or without prolactin, and allowed to form colonies over 10 days, based on preliminary experiments (not shown) and the clonogenic assays presented in Figure 23. Colony formation was followed in untransfected cells, mock transfected cells and siATM transfected cells. It was noted that transfection treatment itself decreased colony survival when compared with untransfected colonies, and the untransfected plates could not be counted due to high colony numbers. According to the mock transfection colony survival results, treatment with prolactin alone did not significantly increase clonogenic survival, however prolactin increased the survival of doxorubicin treated cells by

10.5% (0.03 µM, p= 0.02) and 14.28% (0.27 µM, p= 0.03) (Figure S5A). In siATM transfected plates, cells were more resistant to doxorubicin treatment when compared with mock transfected cells and prolactin did not cause any significant increase or decrease in clonogenic survival of

MCF7 cells (Figure S5B) which overall indicated that ATM inhibition may abrogate the prolactin increased clonogenic cell survival and cell viability in breast cancer cells.

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Figure S5. siRNA-mediated silencing of ATM abrogated with prolactin increased clonogenic cell survival. MCF7 cells were mock transfected or transfected with siATM which is followed by 24 hours of 25ng/ml prolactin treatment and 2 hours doxorubicin treatment. All treatments were normalized to vehicle controls of mock transfected cells. A. Clonogenic cell survival in mock transfected cells. B. Clonogenic cell survival in siATM transfected cells. Results represent 3 external replicates that are pooled (n=3). Statistically significant analysis (*) denotes p<0.05. Square ■= dox+PRL circle ●= dox, upward triangle Prl= Prolactin, dox= doxorubicin.

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