Combinatorial Targeting of the Androgen for

Prostate Cancer Therapy

A thesis submitted to the University of Adelaide in the fulfilment of the requirements for

the degree of Doctor of Philosophy

By

Sarah Louise Carter B.BiomolChem.(Hons)

Dame Roma Mitchell Cancer Research Laboratories

School of Medicine

The University of Adelaide and

The Hanson Institute

March 2015

Contents Chapter 1: General Introduction ...... 1 1.1 Background ...... 2 1.2 Androgens and the Prostate ...... 3 1.3 Androgen Signalling through the ...... 4 1.3.1 The androgen receptor (AR) ...... 4 1.3.2 Androgen signalling in the prostate ...... 6 1.4 Current Treatment Strategies for Prostate Cancer ...... 8 1.4.1 Diagnosis ...... 8 1.4.2 Localised disease ...... 10 1.4.3 Relapse and metastatic disease ...... 13 1.4.4 Failure of hormonal therapy ...... 15 1.5 Mechanisms Underlying Development of Castrate-Resistant Prostate Cancer ...... 17 1.5.1 Increased AR levels ...... 19 1.5.2 AR mutations ...... 19 1.5.3 Alterations in AR coregulators ...... 20 1.5.4 Adrenal and intra-tumoural androgen biosynthesis ...... 20 1.5.5 Non-canonical activation of the AR ...... 21 1.5.6 AR splice variants ...... 22 1.6 Combinatorial AR Targeting as an Approach to Avoid -Therapy Mediated Selection Pressure ...... 22 1.6.1 inhibitors ...... 23 1.6.2 inhibitors ...... 24 1.7 Summary ...... 28 1.8 Objectives of this Thesis ...... 30 Chapter 2: General Materials and Methods ...... 31 2.1 Materials ...... 32 2.1.1 Chemicals and general reagents ...... 32 2.1.2 Drugs ...... 36 2.1.3 Antibodies ...... 37 2.1.4 Primers ...... 38

2.1.5 Mice ...... 40 2.1.6 Equipment ...... 40 2.1.7 Software ...... 41 2.2 Buffers and Solutions ...... 42 2.3 General Methods ...... 46 2.3.1 culture ...... 46 2.3.2 Drug treatments and proliferation/death assays ...... 48 2.3.3 Western blotting ...... 49 2.3.4 Quantitative real-time polymerase chain reaction (qRT-PCR)...... 50 2.3.5 Statistical analysis ...... 52 Chapter 3: NFKBIA (IBα) mediates prostate cancer cell death induced by combination treatment with vorinostat and bicalutamide ...... 53 Chapter 4: The efficacy of the combination therapy in vivo ...... 117 4.1 Introduction ...... 118 4.2 Materials and Methods ...... 118 4.2.1 Inoculation of male nude mice with LNCaP cells ...... 119 4.2.2 Drug treatments and calculation of tumour volume ...... 119 4.2.3 ...... 120 4.2.4 Video assisted scoring of nuclear ki67 and cleaved caspase 3 staining ...... 121 4.3 Results ...... 123 4.3.1 Change in tumour volume over time with combination treatment ...... 123 4.3.2 Fold change in tumour volume over time with combination treatment ...... 123 4.3.3 End-point and survival analysis ...... 127 4.3.4 Analysis of tumour growth and apoptosis using immunohistochemical markers ...... 132 4.3.5 Tolerability and toxicity...... 136 4.4 Discussion ...... 139 Chapter 5: Combining 17-AAG with androgen receptor modulating agents enhances cell death and minimises the heat shock response in prostate cancer cells ...... 142 Chapter 6: General Discussion...... 184

6.1 Targeting the AR for treatment of prostate cancer ...... 185 6.2 Major findings of this thesis ...... 186 6.2.1 Combination therapy for prostate cancer ...... 188 6.3 Future directions ...... 190

Abstract Prostate cancer is one of the most commonly diagnosed cancers in Australian men and is the second leading cause of death from cancer. Since the advent of prostate specific antigen (PSA) testing, more men are being diagnosed with early-stage or organ-confined prostate cancer. At this stage of the disease, surgical removal of the prostate and/or radiotherapy is potentially curative. However, approximately 10-30% of men will progress with metastatic disease despite an initial diagnosis of organ-confined cancer, and 5-10% of men are diagnosed in the first instance with metastatic disease. Given that prostate cancer is dependent on androgens for growth and survival, the current standard of treatment for these men is androgen deprivation therapy (ADT). Despite an initial positive response to this treatment, it is not curative and relapse generally occurs within 5 years. At this stage of the disease, further hormonal manipulations or chemotherapy do not typically significantly prolong survival. It is now well accepted that this relapse is due to mechanisms by which the prostate cancer continues to rely on androgen signalling through the androgen receptor, despite the efficacy of androgen deprivation. Our laboratory and others have shown that clinical agents and molecular methods that target the androgen receptor (AR), as opposed to the androgen, are effective at suppressing growth and inducing death in prostate cancer cells.

The objective of this thesis was to characterise the effects of combining clinically different drugs that modulate levels and/or activity of the AR. The histone deacetylase inhibitor vorinostat and the hsp90 inhibitor 17-AAG were investigated in combination with bicalutamide, an AR antagonist currently in clinical use. Both combinations proved to be significantly effective at synergistically suppressing growth and inducing death in prostate cancer cells in vitro, using concentrations of the drugs that are individually sub-effective. Due to factors beyond control, in vivo testing did not result in a definitive answer regarding efficacy in a mouse model of prostate cancer.

Microarray profiling revealed a mechanism for the synergistic interaction between vorinostat and bicalutamide, implicating loss of the NFKBIA as a cause of prostate cancer cell death. Furthermore, microarray analysis showed that combining 17-AAG with bicalutamide reduces the characteristic and undesirable heat shock response associated

with 17-AAG, but also implicated NFKBIA in the prostate cancer cell death caused by this combination. These insights provide a basis for further investigation into the role that manipulation of NFKBIA could play in future therapeutics, and the potential for the use of 17-AAG in a clinical setting despite the development of new generation hsp90 inhibitors. Overall, the information presented in this thesis builds on the pre-clinical characterisation of two different combinations targeting the AR for prostate cancer treatment, and facilitates clinical testing of these treatment options.

Declaration

I certify that this work contains no material which has been accepted for the award of any other degree or diploma in my name, in any university or other tertiary institution and, to the best of my knowledge and belief, contains no material previously published or written by another person, except where due reference has been made in the text. In addition, I certify that no part of this work will, in the future, be used in a submission in my name, for any other degree or diploma in any university or other tertiary institution without the prior approval of the University of Adelaide and where applicable, any partner institution responsible for the joint-award of this degree.

I give consent to this copy of my thesis when deposited in the University Library, being made available for loan and photocopying, subject to the provisions of the Copyright Act 1968.

The author acknowledges that copyright of pubished works contained within this thesis resides with the copyright holder(s) of those works.

I also give permission for the digital version of my thesis to be made available on the web, via the University’s digital research repository, the Library Search and also through web search engines, unless permission has been granted by the University to restrict access for a period of time.

Sarah Carter

March 2015

Acknowledgements

First and foremost, I would like to thank my principal supervisor, A/ Prof. Lisa Butler. Basically, without you, this thesis never would have happened – you certainly put the “super” in “supervisor”. Thank you for your never ending determination to see me through, thank you for your kindness and encouragement, thank you for your friendship over the years, and thank you for instilling in me an overwhelming distate for unjustified text in documents. To Dr. Luke Selth, thank you so much for signing on to be my co-supervisor halfway through. I suspect it couldn’t have been easy, but you always gave excellent advice and really inspired me to be a better scientist and student. A shout out to my semi supervisor Dr. Maggie Centenera – you really helped me throughout the later years of my PhD and I am grateful for all of your guidance and advice. Finally, to Prof. Wayne Tilley, thank you so much for giving me the opportunity to study in your laboratory, I learned heaps and I have you to thank for helping me see the big picture.

Thank you to my fellow PhD students – the originals who were with me from the start and the ones I picked up along the way – I’m not naming names because I’m sure I’ll forget someone and feel mega guilt for the rest of my life, but you guys made doing a PhD so much fun, and I’m so glad I’ve met you all and am chuffed to have you as friends. I’m sorry to say that my ability to quote the Simpsons has gone downhill since I’ve left, but I’ll always be up for a game of hungry hungry hippos whenever you’re bored of the waiting game. Infinite thanks also to the rest of the staff at the DRMCRL – many of you spent loads of time helping me out, teaching me something, or just being great friends, and I thank you so much for all you’ve done for me.

Thanks to my friends, I’m so grateful that you’ve all been there for me through a lot of tough times, and I can’t thank you all enough for the amazing support network over the years. Special thanks to my Mum, who has given me so much support, whether it was words of encouragement or sneaking into my house to wash a pile of dishes or put on a load of laundry. I don’t know if I can ever express the gratitude I feel (especially for doing the dishes!) and I’m so lucky to have such a great Mum. A dedication of sorts to my Dad –

you had to out before I started my PhD, but I know how proud you were that I’d been accepted and I know how proud you would have been that I finished it. I would also like to thank my fantastic “in-laws” – I couldn’t have asked for a better second family and I’m so grateful for the love and support I get from you guys. I’m glad I can finally tell you “it’s done!” when you ask how the thesis is going! To my partner in crime, Tony – words can’t describe how thankful I am for you. You’ve been with me the whole way through, you’ve seen me at my best and you’ve seen a lot of me at my worst, and you’re still here... I suspect that means you’re somewhat crazy, but anyway I’m so blessed I found you and I thank you so much for all the cooked dinners, the pick ups from the lab, hanging out with me for midnight sample collection... etc etc. I was trying to keep this short and sweet but I’ve already gone over into two pages so I better wrap it up.

TL;DR – Thanks everyone, you’re all great 

Chapter 1:

General Introduction

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1.1 Background

Prostate cancer is the most commonly detected cancer in Western men, with approximately 19,000 new cases diagnosed each year in Australia alone (AIHW, 2012). Close to 3,300 of these men will die from the disease in a year, making prostate cancer the second leading cause of death from cancer in Australian men, after lung cancer. Although the aetiology of prostate cancer remains unclear, increasing age and a familial history of prostate cancer are the two most significant risk factors that increase a man’s chances of developing the disease.

Therapeutic options for prostate cancer vary depending on the stage at which the disease is diagnosed. For the majority of men, prostate cancer is diagnosed at an early stage or “organ-confined” stage, where surgical removal of the tumour and/or radiation therapy is potentially curative. For men who progress after this therapy, or for those men who present in the first instance with metastatic prostate cancer, hormonal manipulation is the mainstay of treatment. Prostate cancers are reliant on male sex called androgens for growth and survival. Thus, manipulations to decrease levels of androgens in the body or prevent androgens from binding to the cellular mediator of action, the androgen receptor (AR), are initially effective at reducing symptoms and tumour burden in almost all patients. However, within 1-5 years, the majority of men undergoing this treatment experience a relapse and their disease progresses. At this stage of the disease, further hormonal manipulations or chemotherapeutics are essentially palliative.

It is now accepted that despite various hormonal manipulations, the AR continues signalling throughout prostate cancer progression and remains the key therapeutic target. This chapter outlines the role of androgens and the AR in prostate cancer, the currently available clinical agents that either directly target the AR or modulate the AR, and the potential for combining these agents to more effectively suppress AR signalling and improve treatment options.

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1.2 Androgens and the Prostate

The prostate is an exocrine gland, approximately the size and shape of a walnut, and is part of the male reproductive system. It is located within the pelvic region, surrounding the urethra just below the bladder. The primary function of the prostate is to secrete a fluid containing simple sugars and enzymes into the lumen of the ducts, which carry the fluid to the urethra where it makes up over half of the volume of the ejaculate and functions to protect and enrich the spermatozoa. The prostate is formed at approximately 10 weeks gestation by the outgrowth of the urogenital sinus epithelium into the urogenital sinus mesenchyme (Cunha et al., 1987). The mature prostate is organised into three distinct zones: the peripheral zone, the central zone, and the transitional zone, with the peripheral zone making up the majority of the prostate (~70%) (McNeal, 1980).The prostate consists of highly branched epithelial ducts, which can be divided into two compartments – the epithelial compartment, consisting of secretory luminal cells and neuroendocrine cells upon a basal cell layer, which separates the epithelial compartment from the stromal compartment, comprised mostly of smooth muscle.

Male sex hormones, known as androgens, are crucial for the growth, development and maintenance of the prostate. The main androgen circulating in the male bloodstream is testosterone (T), which, in the prostate, is converted via an enzyme called 5-α reductase to the androgen dihydrotestosterone (DHT), which, although similar in structure, has a higher dissociation constant for the androgen receptor and is up to ten times more potent than testosterone (Kaufman and Pinsky, 1983, Deslypere et al., 1992, Wilson and French, 1976). Production of testosterone by the testes is controlled by the hypothalamic-pituitary- gonadal axis (Figure 1.1). Low blood levels of androgen stimulate the hypothalamus to produce gonadotropin-releasing hormone (GnRH), which in turn stimulates the pituitary to release luteinising hormone (LH) and follicle stimulating hormone (FSH) to stimulate production of testosterone from the Leydig cells in the testes. Testosterone is transported through the blood to its target tissues bound to a plasma protein called sex hormone binding globulin (SHBG). These target tissues also include the hypothalamus and pituitary, which acts as a negative feedback loop to tightly control blood levels of testosterone. Depending on factors such as age, fitness or other health issues, testosterone levels in the 3

average human male can be anywhere between 300 and 1,000 nanograms per decilitre (Swerdloff, 2011). A small amount of androgen is also produced by the adrenal glands, predominantly in the form of dehydroepiandrosterone (DHEA) and androstenedione. It is important to note that both DHEA and androstenedione can be converted peripherally to testosterone, a process that is clinically relevant for men undergoing hormonal therapy for prostate cancer (see section 1.5.4)

1.3 Androgen Signalling through the Androgen Receptor

Androgens exert their effects throughout the body by binding to the androgen receptor (AR), a ligand inducible factor that is critical not only for the growth and maintenance of the normal prostate, but also the development and progression of prostate cancer.

1.3.1 The androgen receptor (AR) The AR is a member of the nuclear superfamily and shares a similar three-dimensional structure to many other hormone receptors. The AR gene is located on the X at Xq11-12, and consequently presents as a single copy in males, allowing for phenotypic mutations in the absence of a second wild-type allele. Eight make up the AR gene, which is translated to a protein of 919 amino acids and a molecular weight of 110 kDa.

Like other hormone receptors, the exons of the AR code for functionally distinct regions of the protein. 1 codes for the amino (NH2) terminal transactivation domain (NTD), which plays a key role in transactivation, dimerisation, and recruitment of coregulators involved in transcriptional function. Exons 2 and 3 code for a central DNA-binding domain (DBD), and exons 4 – 8 code for a carboxyl (COOH) terminal domain (CTD) containing the ligand binding domain (LBD) (Claessens et al., 2008). A short hinge region exists between the DBD and the LBD.

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Figure 1.1

Figure 1.1 - Hypothalamic-pituitary-gonadal axis in men. Low blood levels of androgen stimulate the hypothalamus to produce gonadotropin-releasing hormone (GnRH), which in turn stimulates the release of luteinising hormone (LH) and follicle stimulating hormone (FSH) from the pituitary gland. LH and FSH act on the Leydig cells present in the testes to stimulate testosterone production. The testosterone then acts via a negative feedback loop on the hypothalamus and pituitary to inhibit the release of GnRH, LH and FSH.

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1.3.2 Androgen signalling in the prostate As a , ligand-bound AR regulates the expression of target , including prostate specific antigen (PSA), that promote growth, survival and differentiation of both the normal and malignant prostate. A schematic representation of this process is outlined in Figure 1.2, and described below.

AR mRNA is transcribed from the DNA, and translated in the cytoplasm to the immature, unbound protein, where it remains complexed with molecular chaperones, including heat shock 40 and 70 (Hsp40 and ). Heat shock proteins induce the correct folding of the AR protein and maintain it in a stable conformation via an ATP-dependent mechanism (Pratt and Toft, 2003, Marcelli et al., 2006). Prior to ligand (androgen) binding, Hsp interacting protein (hip) binds to stabilise the Hsp40/Hsp70/AR complex. This is followed by binding of Hsp70/Hsp90 organising protein (hop), which recruits an Hsp90 dimer, inducing a conformational change to facilitate ligand binding. Finally, binding of the small protein p23 stabilises this ligand accepting conformation while dissociating Hsp70, hip, and hop. Upon ligand binding, the AR translocates to the nucleus with the assistance of tetratricopeptide repeat TPR chaperone proteins, where AR dissociates from its chaperone complex, is phosphorylated, dimerises through the DBD, and then binds to specific DNA sequences called androgen response elements (AREs). AREs are found in the promoters and enhancers of androgen-regulated genes, and AR binding can either activate or repress transcription (Claessens et al., 2001). Coregulators and transcriptional machinery are subsequently recruited to AR-bound , leading to transcriptional regulation of the target gene. Chromatin-associated AR can be released from the DNA and translocate back to the cytoplasm, sans ligand, where it can be re-bound by ligand and mediate several cycles of gene regulation.

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Figure 1.2

Figure 1.2 - Schematic representation of androgen signalling in the prostate. Serum testosterone enters the cytoplasm of the prostate cell where it is converted to dihydrotestosterone (DHT) by the enzyme 5α-reductase. AR normally resides in the cytoplasm as part of a chaperone heterocomplex. Once the chaperone protein 90 (Hsp90) induces a conformational change in the androgen receptor, allowing it to bind DHT and translocate to the nucleus, the AR is phosphorylated and forms a homodimer. The AR dimer binds to specific androgen response elements in the promoters of target genes, recruits co-factors and other transcriptional machinery (TM) and activates transcription of genes associates with growth, differentiation, and survival.

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1.4 Current Treatment Strategies for Prostate Cancer

The choice of treatment for prostate cancer is highly dependent on the stage of the disease upon diagnosis, and the grade of the tumour (Figure 1.3). Other factors that can influence the treatment include the age of the patient, which takes into consideration the life expectancy and risk involved with invasive treatment, and the personal preference of the patient.

1.4.1 Diagnosis Given that one of the major risk factors associated with development of prostate cancer is increasing age, it is recommended in Australia that men over the age of 50 include testing for prostate cancer as part of their annual health check-up as very few men with early stage or clinically localised prostate cancer experience obvious urological symptoms (PCFA, 2015). For men with the additional risk of familial cancer, where more than one close relative developed prostate cancer before the age of 65, the recommended age at which to start testing is lowered to 40 (Smith et al., 2013). However, it is up to each individual man to discuss these options with his doctor to determine the appropriate level of testing for him. Prostate cancer screening is conducted through two main tests – a blood test and a digital rectal examination (DRE).

Blood testing for prostate cancer measures the amount of a protein called prostate specific antigen (PSA) in the serum. PSA is the protein product of an androgen regulated gene under direct transcriptional control by the AR, and is secreted by both the normal and malignant prostate at levels that are proportionate to the size of the gland. PSA is normally secreted into the ejaculate via the urethra, where plays a role in aiding sperm motility. Under normal circumstances, very little is able to enter the bloodstream and a range of 0 – 4 ng/ml is considered normal (Stamey et al., 1987).

Prostate cancer, as well as non-malignant conditions such as inflammation of the prostate or benign enlargement of the prostate (benign prostatic hyperplasia, BPH), can cause the breakdown of the cellular architecture of the prostate, liberating more PSA than usual into the bloodstream. 8

Figure 1.3

Figure 1.3 - Therapeutic options at different stages of prostate cancer. Depending on the stage of progression at the time of diagnosis, there are several current options for prostate cancer therapy. For clinically localised disease, depending on the age/frailty of the patient and the grade of the disease as determined by a tissue biopsy, there are three primary treatment options – active surveillance for men who are old, and/or have low grade cancer, and may not benefit from intensive and invasive therapy, and prostatectomy and/or radiotherapy. These therapies can be administered with or without neoadjuvant or adjuvant hormonal therapy. Androgen deprivation therapy (ADT), also known as hormonal therapy, is the therapeutic option for metastatic disease. Once the disease has progressed to castrate-resistant disease, second line hormonal therapy or chemotherapy are the options.

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Therefore, an elevated or rising level of PSA in the blood can be an indicator of prostatic disease, but can also be a result of normal situations such as ejaculation or bicycle riding. As such, PSA testing can often return false positives and so it is used in conjunction with the DRE. The DRE involves a clinician inserting a finger into the anus of the patient, where it is possible to palpate the back of the prostate to feel for any unusual areas that are irregular, hard, or lumpy, as these characteristics are indicative of cancer.

Upon an elevated PSA level in the blood (> 4 ng/ml (Smith et al., 2013)) and/or a suspicious DRE result, tissue biopsies are taken from the prostate and assessed by a pathologist. The pathologist will then report the number of biopsy cores positive for prostate cancer, and the Gleason grade of the tumour if cancer is found. Gleason grade is determined by microscopic examination of the histological properties of the sample, and scores from 1 – 5 are assigned to different tumour morphological patterns as described in Figure 1.4 (Gleason and Mellinger, 1974).

The final Gleason grade (between 2 and 10) is the sum of two separate values determined by the scoring system above: the first number is the score of the most common tumour pattern observed in the samples, and the second number is the score of the second most common tumour pattern observed. Gleason grade gives a measure of how differentiated the carcinoma is – poorly differentiated carcinomas are, in general, more aggressive. Gleason grade is also used in conjunction with the evaluation of the degree of spread of the primary tumour (T stage), regional lymph node status (N stage), and the presence of metastases (M stage), which, collectively, provide an overall prognosis of the cancer.

1.4.2 Localised disease Men diagnosed with low grade, localised prostate cancer may be placed under ‘active surveillance’, where the disease is closely monitored through regular blood testing, DREs and biopsies, but no active treatment is administered unless there is evidence of disease progression. In many cases, men with low grade prostate cancer who are placed under active surveillance will not progress to advanced stages of prostate cancer and so do not have to undergo unnecessary treatments. This is particularly relevant for elderly or frail men who may not be able to tolerate intensive surgical or hormonal treatments.

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Figure 1.4

Figure 1.4 – Gleason grading system for prostate carcinoma biopsy samples. Upon confirmation of prostate cancer in a patient’s prostate biopsy, the cancer is examined under a microscope and graded according to the patterns based upon the original drawings made by Dr. Donald Gleason in 1974 shown in this figure. The different grades are representative of the extent of differentiation observed in the carcinoma sample.

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Younger, otherwise healthy men diagnosed with localised disease, or older men diagnosed with a high grade but localised cancer that are healthy enough to tolerate treatment can opt for surgical removal of the prostate or radiation therapy.

1.4.2.1 Radical prostatectomy and radiotherapy Radical prostatectomy is the surgical removal of the prostate through an incision made either in the lower abdomen or the perineum. Radiotherapy is administered via either external beam radiotherapy or brachytherapy, which is the implantation of a radioactive isotope into the prostate. Although radical prostatectomy is more invasive than radiotherapy, it delivers a slightly higher prostate-cancer specific survival rate, and is generally the preferred treatment at this stage of disease (Degroot et al., 2013, Abdollah et al., 2012, Boorjian et al., 2011). These treatments are curative for many men with organ- confined disease; however, a subset of men (10 – 30%) will subsequently relapse with metastatic prostate cancer (Catalona and Smith, 1994, Pound et al., 1999, Zietman et al., 2004). While incomplete tumour removal at the time of surgery may explain some cases of recurrent prostate cancer, some studies suggest that high-risk patients, such as those with very high PSA levels prior to treatment or a high grade of prostate cancer, may relapse due to undetectable micro-metastases at the time of diagnosis (Soloway and Roach, 2005, Deguchi et al., 1997).

1.4.2.2 Neoadjuvant and adjuvant hormonal therapy As prostate cancer is an androgen-dependent cancer, hormonal manipulations to either reduce circulating levels of androgen or block androgen action can be offered to patients with localised or locally advanced disease, as either a neo-adjuvant (prior to therapy) or adjuvant (in conjunction with therapy) treatment. It is often offered to men with locally advanced disease, where the cancer has escaped the capsule of the prostate but has not spread beyond the structures surrounding the prostate. Hormonal therapy offered in the neo-adjuvant setting significantly reduces the likelihood of positive tumour margins following surgery (Witjes et al., 1997); however, it does not consistently confer a clear survival benefit (Scolieri et al., 2000). The use of hormonal therapy in the adjuvant setting targets residual tumour cells that may have been missed in either of the radical treatments

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and, as opposed to neo-adjuvant treatment, appears to confer a survival benefit following radiotherapy (Bolla et al., 1997, Messing et al., 1999, Lawton et al., 2001).

1.4.3 Relapse and metastatic disease About 5-10% of men with prostate cancer are diagnosed in the first instance with metastatic disease, and 10-30% of men diagnosed with early stage prostate cancer will develop metastases despite previous radical prostatectomy and/or radiotherapy. Metastases occur when cancer cells from the prostate migrate away from the original tumour and spread through the lymph or blood to other sites in the body, and are the main cause of death from prostate cancer. Prostate cancer commonly metastasises to the bone, but can also spread to soft tissues such as the lymph nodes, liver, or lungs. For men with metastatic prostate cancer, the current mainstay of treatment is androgen deprivation therapy (ADT), also known as hormonal therapy. ADT aims to lower or reduce the levels of circulating androgen in the patient in order to inhibit the pro-survival action of androgens in prostate cancer cells.

1.4.3.1 Surgical castration (orchidectomy) Orchidectomy, or surgical castration, is the removal of the testicles in order to eliminate the main source of androgen production in the male body. Once the testicles are removed, the level of testosterone in the bloodstream falls very quickly, and in approximately 90% of cases there is a favourable response, characterised by shrinking of the tumour and relief of symptoms such as pain and difficulty passing urine. Although once considered the ‘gold standard’ of androgen deprivation therapy, men with metastatic disease now only rarely consider orchidectomy as castrate levels of testosterone are also achievable using medicinal hormonal agents (see section 1.4.3.2). However, despite being irreversible, some men prefer the single treatment of orchidectomy compared to ongoing injections required with hormonal therapy.

1.4.3.2 Medical castration Medical castration involves the administration of agents that block endogenous production of testosterone by manipulating the hypothalamic-pituitary-gonadal axis. GnRH

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antagonists (abarelix, degarelix, cetrorelix, ganirelix) suppress the activity of GnRH and prevent the release of LH. GnRH agonists (leuprolide, goserelin, buserelin, nafarelin, histrelin, deslorelin) act in the opposite way to increase the release of LH, which initially causes a spike in testosterone levels, which then exerts a negative feedback loop to suppress testosterone production, causing a marked decrease in circulating testosterone to almost undetectable levels.

1.4.3.3 AR antagonists (anti-androgens) AR antagonists, also known as anti-androgens, are clinical agents that function as competitive inhibitors of androgen action by binding to and blocking access to the LBD of the AR. Currently used AR antagonists include steroidal (cyproterone acetate) and nonsteroidal antagonists (bicalutamide, flutamide, nilutamide and more recently, enzalutamide). Non-steroidal agents are more commonly used in Australia and the USA, as they do not interact with other steroid receptors and function as relatively pure AR antagonists. However, it should be noted that flutamide and nilutamide have some weak agonist activity at high concentrations. Of the original nonsteroidal AR antagonists, bicalutamide has the highest affinity for the AR and the longest half-life and so it is the most commonly administered AR antagonist. Despite this, the affinity of bicalutamide for the AR is approximately 50-fold lower than that of DHT, highlighting the need for more potent AR antagonists. Enzalutamide (MDV-3100) is one such recently developed AR antagonist with increased potency (Tran et al., 2009, Scher et al., 2010).

AR antagonists are somewhat less effective than castration but can be preferred by some patients and their doctors due to the potential for fewer side effects. However, clinical use of AR antagonists can result in ‘anti-androgen withdrawal syndrome’, in which a subset (up to 30%) of patients experience a improvement in symptoms, such as pain caused by tumour growth, after cessation of treatment with anti-androgen. This is generally associated with a switch from antagonistic to agonistic activity of these anti-androgens in some tumours, which can be caused by mutations in the AR that cause receptor promiscuity. Anti-androgen withdrawal syndrome is more often observed with flutamide usage, but can also occur with the more commonly used bicalutamide (Scher, 1993, Kelly, 1998, Wirth, 1997, Small, 1994, Small, 1995). 14

1.4.3.4 Combined androgen blockade Testicular androgens comprise approximately 60% of the androgen present in the prostate, while the remaining 40% is synthesised from adrenal precursors by the prostate itself (Labrie et al., 2005). Consequently, in order to effectively reduce levels of circulating androgen and block the action of any remaining non-testicular androgens, combined androgen blockade (CAB) was developed, where surgical or medical castration is used in conjunction with non-steroidal AR antagonists (Eisenberger et al., 1998). While clinical trials comparing castration alone to CAB have demonstrated a more rapid response with CAB as determined by PSA levels, a meta-analysis of the multiple trials showed that the addition of AR antagonists to castration only slightly improves survival (in the order of 2%) (PCTCG, 2000). This small benefit of CAB is generally considered to be outweighed by the extra cost and increased side effects of CAB, and so it is not widely used for the treatment of metastatic prostate cancer.

1.4.4 Failure of hormonal therapy ADT is initially very effective, characterised by a marked decrease in serum PSA levels. However, ADT typically fails within 5 years of the initial treatment, and the cancer returns as castrate-resistant prostate cancer (CRPC). Further treatments for CRPC are essentially palliative, and thus, there is a considerable interest in developing new and more potent agents for treatment at this stage of the disease.

1.4.4.1 Chemotherapy and second line hormonal treatments Once prostate cancer has progressed to castrate-resistant disease, further hormonal manipulations or chemotherapies do not substantially extend the life expectancy of most patients. Docetaxel and cabazitaxel are the currently approved chemotherapies for use in CRPC patients. Both of these drugs are tubulin-binding taxanes that show a benefit to CRPC patients, however, while some patients can experience a significant response, on average the benefits are minimal (Tannock et al., 2004, Petrylak et al., 2004).

Consequently, new and more potent agents that target androgen signalling have been developed in order to extend life expectancy. One such agent is enzalutamide (MDV- 15

3100), a novel AR antagonist that has demonstrated clinical activity in men who have failed both ADT and docetaxel-based chemotherapy (Scher et al., 2010). AR antagonists targeting the NTD, as opposed to the LBD, are also in development; one example is EPI- 001, which inhibits AR transactivation and cofactor recruitment (Andersen et al., 2010). Abiraterone acetate is another recently developed agent, which targets cytochrome P450- c17 (CYP17) enzymes required for adrenal and intratumoral androgen biosynthesis. Phase III clinical trials demonstrated that enzalutamide and abiraterone extend median survival of men with advanced CRPC by several months and thus both have now received FDA approval (de Bono et al., 2011). Despite these advances, resistance to both enzalutamide and abiraterone invariably occurs, and on average they provide a modest overall survival advantage of 4-6 months.

New non-hormonal therapies, such as sipuleucel T and alpharadin, have been developed and are showing promise for the treatment of prostate cancer. Sipuleucel T (Provenge) is an immunotherapy produced by incubating the patient’s blood with an artificial recombinant fusion protein comprising prostatic acid phosphatase (PAP), an antigen predominantly expressed on prostate epithelial cells, and granulocyte-macrophage colony- stimulating factor (GM-CSF), an immunostimulatory cytokine (So-Rosillo and Small, 2006). When blood cultured in this way is transfused back into the patient, it enhances the activity of the patients’ immune response to be cytotoxically active against PAP. Clinical trials of sipuleucel T showed that it conferred a survival advantage to men with CRPC (Small et al., 2006, Higano et al., 2009, Kantoff et al., 2010), and it has recently received FDA approval in the post-docetaxel setting. Alpharadin is a bone-targeting therapy composed of the alpha-emitting isotope radium-223. It was developed to help reduce the severity of skeletal-related events caused by prostate cancer spreading to the bone, given that over 80% of patients with CRPC develop bone metastases. Radium-223 is a calcium mimetic, and naturally accumulates in the bone mineral hydroxyapatite that accumulates around metastatic deposits. As radium-223 is an alpha emitter, it exposes a small area around the isotope to radiation, and thus it is able to reasonably specifically target the metastatic lesions (Henriksen et al., 2002). Clinical trials in CRPC patients have shown promising results, with an increase in overall survival and extension of time to a skeletal 16

related event (Nilsson et al., 2007, Parker et al., 2013). Based on these initial results, radium-223 has also recently been FDA-approved (Kluetz et al., 2013). It remains to be seen whether these new, non-hormonal agents will avoid the issues of resistance observed with hormonal therapies.

1.5 Mechanisms Underlying Development of Castrate-Resistant Prostate Cancer

Despite the relative successes of enzalutamide and abiraterone acetate, the average increase in life expectancy achieved in patients is modest and, much like first-line hormonal therapy, relapse will inevitably occur. Historically it was believed that resistance to hormonal therapies was due to prostate cancer cells gradually losing their dependence on androgen signalling through the AR. It is now established that throughout various hormonal manipulations, castrate-resistant prostate cancers not only continue to express the AR (Sadi and Barrack, 1991, Tilley et al., 1994, Hobisch et al., 1995, Hobisch et al., 1996, van der Kwast et al., 1991), but remain reliant on androgen signalling for growth and survival. There are numerous mechanisms shown to enable AR signaling despite castrate levels of circulating androgen, and these are outlined in Figure 1.5 and the following sections.

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Figure 1.5

Figure 1.5 - Schematic representation of mechanisms employed by castrate- resistant prostate cancer for continued androgen signalling. Despite low levels of circulating testicular androgen, castrate-resistant prostate cancer maintains androgen signalling through the androgen receptor through one or more of the following mechanisms: 1. Increased levels of AR mRNA or protein, increasing sensitivity to low levels of androgen, 2. AR mutations allowing for activation by non-classical ligands, 3. Increased levels of AR co-activators, 4. Production of androgens by the adrenal glands and the prostate itself, 5. Non-canonical activation of the AR in the absence of ligand through 18 phosphorylation, and 6. AR splice variants lacking a LBD, making them constitutively active and resistant to AR antagonists.

1.5.1 Increased AR levels Amplification and increased expression of the AR gene is observed more frequently in castrate-resistant prostate cancers than in primary tumours, with amplification occurring in approximately 20-30% of recurrent (local or metastatic) prostate cancers compared to fewer than 2% of primary tumours (Visakorpi et al., 1995, Linja et al., 2001, Koivisto et al., 1997, Bubendorf et al., 1999, Edwards et al., 2003). Furthermore, an increase in the expression of AR mRNA is reported as one of the few consistent changes when prostate cancer progresses through to CRPC (Chen et al., 2004). AR gene amplification can also result as an adaptive response to high dose AR antagonist therapy, which may explain the finding that overexpression of wild type AR changes bicalutamide from an AR antagonist to an agonist (Chen et al., 2004). Amplification of the AR gene accounts for most instances of increased AR protein expression; however, increased expression of the AR protein can be found without any associated AR gene amplification (Edwards et al., 2003). Regardless of mechanism, the increased expression of AR protein can allow for greater sensitivity to very low levels of androgen and facilitate androgen signalling throughout ADT.

1.5.2 AR mutations Up to 50% of CRPC cases are found to have mutations in the AR (Tilley et al., 1996), and to date over 85 AR mutations have been found, most of which collocate in distinct regions within the NTD, hinge or LBD (Buchanan et al., 2001a). AR mutations not only permit increased sensitivity of the AR to classical ligands, but can also broaden receptor specificity to allow for growth during ADT. Furthermore, studies have shown that extended treatment with AR antagonists can cause selective pressure for mutations that enhance AR activity, indicating that AR mutations could be an adaptive mechanism (Taplin et al., 2003, Hara et al., 2003, Yoshida et al., 2005). Mutations in the LBD affect the ligand binding pocket, and can broaden the spectrum of AR agonists to a wider range of steroid hormones, such as and progestogens, and even AR antagonists (Taplin et al., 1995, Fenton et al., 1997, Culig et al., 1993, Tan et al., 1997). Of particular interest is the point mutation found at codon 877 (T877A), which is one of the more frequently reported mutations in the AR in castrate-resistant prostate cancers (Gaddipati et al., 1994). 19

It was initially characterised in the LNCaP prostate cancer cell line and confers the ability for the AR to be activated by estrogens, progestogens and the anti-androgen hydroxyflutamide (Veldscholte, 1990). AR mutations clustering in the N-terminal region may affect interactions with coactivators or subcellular localisation of the AR (Gregory et al., 2001b, Chen et al., 2005a, Jagla et al., 2007), whereas mutations in the hinge region bordering the DBD and LBD can affect interaction with , not only reducing the effect of AR antagonists but also increasing the sensitivity of the AR to ligand interactions (Buchanan et al., 2001b).

1.5.3 Alterations in AR coregulators Coregulators are proteins that act to either enhance (coactivator) or suppress () transcriptional activity of transcription factors (McKenna et al., 1999). Altered levels of coregulators that interact with the AR can cause aberrant AR signaling. For example, increased levels of AR coactivators can make the AR more sensitive to lower levels of androgen (Culig et al., 2005) or make it responsive to a wider range of androgens (Gregory et al., 2001a). Increased tumoural expression of p300 or members of the p160 family (SRC1, TIF2) has been detected in patients with CRPC (Debes et al., 2003, Gregory et al., 2001a). Overexpression of coactivators can also permit activation of the AR by non- classical ligands such as estradiol, or hydroxyflutamide, an AR antagonist (Yeh et al., 1999). Conversely, decreased levels of AR corepressors results in increased AR signaling, as coactivators and coregulators compete to bind the AR and facilitate or suppress AR transcriptional activity. Recruitment of corepressors is believed to be a key component in antagonist mediated inhibition of the AR, which suggests that loss of corepressors could allow the prostate cancer cells to grow even in the presence of AR antagonists (Miyamoto et al., 2004). Therefore, the ratio of coactivators to corepressors present in the cell determines the relative transcriptional activity of the AR, and perturbations to this ratio may increase AR activity independent of androgen concentration.

1.5.4 Adrenal and intra-tumoural androgen biosynthesis Prostate cancer cells can overcome androgen deprivation by altering ligand availability through increased intracrine androgen synthesis. In locally recurrent prostate cancer after

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ADT, intratumoural levels of DHT reached levels sufficient to activate the AR, despite the low testosterone level in the bloodstream (Mohler et al., 2004a). High intratumoural levels of DHT can be caused by an increase in the expression of enzymes involved in androgen biosynthesis, resulting in activation of these synthetic pathways (Stanbrough et al., 2006, Holzbeierlein et al., 2004, Montgomery et al., 2008, Mohler et al., 2004b, Nishiyama et al., 2004, Mostaghel et al., 2007, Locke et al., 2008). The efficacy of abiraterone acetate (section 1.4.4.1) further implicates intumoural synthesis of androgens as a key mechanism of resistance to ADT.

1.5.5 Non-canonical activation of the AR Re-activation of the AR in CRPC independent of androgen can also occur by cross-talk with proteins involved in other cellular pathways, such as growth factors and cytokines. Insulin-like growth factor 1 (IGF-1), as well as keratinocyte growth factor (KGF) and epidermal growth factor (EGF) have been shown to independently activate the AR in the absence of DHT, in a mechanism that is thought to involve downstream phosphorylation of either the AR or its associated proteins (Gregory et al., 2004, Culig et al., 1994). Further implicating IGF-1, it has also been shown that inhibition of IGF-1 suppresses prostate cancer cell growth (Burfeind, 1996). Forskolin, an activator of A (PKA), can also activate the AR through the PKA signalling pathway (Sadar, 1999, Ikonen, 1994, Nazareth, 1996). Interleukin-6 (IL-6) has also been shown to activate the AR in DU-145 and LNCaP prostate cancer cells (Hobisch, 1998) by phosphorylation of the coactivator SRC1, which increases the interaction between the AR-NTD and SRC1 (Ueda et al., 2002). Furthermore, elevated levels of IL-6 have been associated with prostate cancer progression and metastasis (Adler et al., 1999, Shariat et al., 2001, Nakashima et al., 2000). Finally, receptor tyrosine kinases such as Her-2/Neu can also activate the AR independently in the absence or synergistically in the presence of androgen (Yeh, 1999, Craft et al., 1999, Wen et al., 2000, Mellinghoff et al., 2004) via AR phosphorylation, and are associated with aggressive tumours and CRPC (Craft et al., 1999, Signoretti et al., 2000).

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1.5.6 AR splice variants Another mechanism of castration resistance is the production of constitutively active variants of the AR that signal independently of androgen ligand. Truncated splice variants of the AR were first reported in 2002, in a study demonstrating that the 22Rv1 prostate cancer cell line contained two separate AR proteins of different sizes (Tepper et al., 2002). Characterisation of this variant showed that it contained the NTD and DBD regions, and was able to bind DNA without ligand. Several other AR splice variants have been characterised, and while the structures can vary the majority of variants contain an intact NTD but lack part or all of the LBD (reviewed in (Dehm and Tindall, 2011)). Androgen receptors lacking the LBD are intrinsically resistant to AR antagonists that target the LBD or therapies that reduce levels of androgen (Sun et al., 2010, Dehm and Tindall, 2011, Guo et al., 2009, Hu et al., 2009). While the precise role and significance of AR variants is an area of intense research interest, it has so far been shown that two of the most commonly found AR splice variants, AR-V7 and ARv567es, can be induced by castration or AR antagonist treatment, and their expression is associated with progression of CRPC and resistance to therapy (Watson et al., 2010, Dehm et al., 2008).

1.6 Combinatorial AR Targeting as an Approach to Avoid Hormone-Therapy Mediated Selection Pressure

It is clear that prostate cancer maintains and continues to rely on signalling through the AR, despite castrate levels of circulating androgen and the use of AR antagonists. Unfortunately, while advances are being made for better and more potent AR antagonists, the average survival benefit of these agents is still modest (in the order of months, not years). The selection pressure exerted by individual androgen or AR targeting therapies results in adaptive changes to AR signalling in prostate cancer cells, including AR gene amplification, AR mutations and AR splice variants. Therefore, we propose that by using AR antagonists in combination with other classes of anti-cancer agents that also exhibit anti-AR effects, we can target AR signalling at several different stages of the signalling pathway – such as stability/folding of the protein, or transcriptional regulation. This approach may thereby circumvent the adaptive responses to a single AR-targeting therapy

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and prevent selection pressure. Two such classes of anti-cancer agents are at our disposal – Hsp90 inhibitors, and histone deacetylase inhibitors. These two classes of agents inhibit proteins in the cell that have a number of target proteins, but importantly in prostate cancer research, the AR is included among the targets.

1.6.1 Hsp90 inhibitors Heat shock proteins are ATP-dependent molecular chaperones responsible for regulation of folding and stabilisation of a wide range of proteins, termed ‘client’ proteins. Chaperone proteins function to fold newly translated proteins, re-fold denatured proteins, prevent protein misfolding, stabilise proteins prior to ready for ligand binding and assist with intracellular protein transport (Taipale et al., 2010). Heat shock proteins are upregulated in response to cellular stress, and assist with maintaining normal cellular function upon stress or insult. Given that tumour environments are often associated with stresses such as genomic instability, reduced oxygen levels and the need to prevent destruction by the immune system, inappropriate activation or use of heat shock proteins can facilitate malignant transformation of cells (Dai et al., 2007, Min et al., 2007).

Heat shock protein 90 (Hsp90) is ubiquitously expressed throughout the body, and is the most abundant heat shock protein, accounting for 1-2% of the total protein found in cells (Taipale et al., 2010). Hsp90 is an ATP-dependent chaperone protein, which, in concert with a co-chaperone complex including Hsp70, Hsp40, HOP and p23, regulates the folding and maturation of over 200 client proteins (a detailed list of Hsp90 client proteins can be found at http://www.picard.ch/downloads/Hsp90interactors.pdf). These client proteins impact a wide array of cellular functions, and the list includes transcription factors, protein kinases and steroid receptors. Hsp90 is implicated in oncogenesis due to its ability to stabilise pro-tumourigenic proteins, as well playing an important role in stabilising mutant proteins. Examples of pro-tumourigenic proteins include proteins that can be exploited by cancer to gain a survival advantage, such as survival-signalling kinases (e.g. Akt or the PI3 kinases), as well as such as v-Src, Bcr/Abl, Raf-1, ErB-2, HER2 and mutated (Blagosklonny, 2002, Taipale et al., 2010). In the context of prostate cancer, Hsp90 is particularly important, as it is often over-expressed in prostate cancer (Cardillo and

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Ippoliti, 2006) and the AR is a client protein. Taken together, this evidence marks Hsp90 as an attractive target for prostate cancer therapy.

Inhibition of Hsp90 results in degradation of its client proteins. One of the first discovered Hsp90 inhibitors is the naturally occurring antibiotic geldanamycin (Whitesell et al., 1994), which functions by displacing ATP and competing for the ATP binding pocket present on the chaperone (Roe et al., 1999). When bound by an inhibitor, Hsp90 cannot perform its usual chaperone function and client proteins are degraded by the proteasome pathway (Esser et al., 2004). Unfortunately, geldanamycin showed unacceptable levels of hepatotoxicity and low bioavailability in in vivo models, and so the derivative 17- allylamino-17-demethoxygeldanamycin (17-AAG, also known as tanespimycin) was developed and became the first in-class agent for inhibiting Hsp90.

Pre-clinical testing of 17-AAG showed promising anti-tumour activity and degradation of client proteins in vitro and in vivo (Neckers, 2002), and in prostate cancer treatment with 17-AAG resulted in degradation of AR and significant growth inhibition of xenograft tumours (Solit et al., 2002a, Williams et al., 2007). However, thus far, no Hsp90 inhibitors have been FDA approved for treatment of cancer. This is due mostly to high toxicity, low oral availability and low anti-tumour efficacy, mechanisms that can be attributed to, in part, the induction of a heat shock response by other heat shock proteins (Hsp70) upon inhibition of Hsp90 (Zou et al., 1998). Due in part to these undesirable responses, the use of Hsp90 inhibitors in combination with other drugs has gained considerable interest.

1.6.2 Histone deacetylase inhibitors Dimethyl sulphoxide (DMSO) was initially described as an agent that could induce differentiation of murine erythroleukaemia cells (Friend et al., 1971), though its intracellular target was unknown. Drugs with similar structures and greater potencies were developed for use as anti-cancer agents (Richon et al., 1989, Yoshida et al., 1990, Richon et al., 1998), causing growth arrest, differentiation and apoptosis. Further investigation into vorinostat, a hydroxamic acid, showed that these anti-cancer agents inhibited histone deacetylase (HDAC) enzymes. HDACs, along with histone acetyl transferases (HATs), are

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most well known as regulators of acetylation of histone tails, but are now known to regulate acetylation of other cellular proteins.

In humans, HDAC enzymes have been identified and categorised into four main classes. Class I includes HDAC 1, 2, 3 and 8, class II includes HDAC 4, 5, 7 and 9, class IV includes HDAC 11. Class III HDACs are the sirtuins, which are structurally unrelated to class I, II and IV, and do not have histones as their primary target. A wide range of chemical structures have now been identified that inhibit class I, II and IV HDACs (Johnstone, 2002, Marks et al., 2001). Most can be divided into five chemical classes: hydroxamic acid derivatives (vorinostat, belinostat (PXD-101), panobinostat (LBH-589)), carboxylates (valproate, phenylbutyrate), benzamides (entinostat (MS-275), mocetinostat), electrophilic ketones and cyclic peptides (romidepsin (FK-228), depsipeptide) (Miller et al., 2003). Most of these chemical classes work equally well for class I, II and IV HDACs, although the cyclic peptide depsipeptide and the benzamides are notable examples of class I specific inhibitors. However there are far fewer specific inhibitors than pan-HDAC inhibitors, and even fewer that are capable of inhibiting a particular HDAC within a class (Park et al., 2004, Mai et al., 2003, Heltweg et al., 2004, Haggarty et al., 2003). At present, there is little evidence that inhibiting any particular class of HDACs improves therapeutic potential; therefore, in terms of cancer treatment, investigation into pan-HDAC inhibitors is ongoing.

HDAC inhibition results in the accumulation of acetyl groups on histones and other proteins. Accumulation of acetyl groups reduces the positive charge on the histone tails, resulting in a weakened attraction between the negatively charged DNA and the histone. This produces a more open chromatin structure, allowing access for transcription factors and other cellular machinery involved in transcription. The mechanism of action of HDAC inhibitors is thought to involve the transcriptional modulation of molecules involved in cancer cell proliferation and apoptosis, such as downregulation of cell cycle proteins through the upregulation of upstream repressors, or upregulation of tumour suppressor proteins. HDAC inhibitors can also cause accumulation of acetyl groups on proteins other than histones; studies conducted in three human cell lines (MV4-11, A549 and Jurkat) have shown that approximately 3600 acetylation sites exists across 1750 human proteins. 25

These sites exist on proteins involved in a wide range of cellular processes including chromatin remodelling, splicing, cell cycle, and nuclear transport (Choudhary et al., 2009). In addition, HDAC inhibitors can affect transcription by modulating the acetylation status and therefore the activation of important transcription factors in cancer, such as p53 (Gu and Roeder, 1997, Barlev et al., 2001), BCL6 (Bereshchenko et al., 2002), and STAT3 (Wang et al., 2005).

Defects in HDAC enzymes have been reported in a number of cancers, including over- expression (Choi et al., 2001, Halkidou et al., 2004a, Spurling et al., 2008, Jin et al., 2008, Chang et al., 2009), aberrant recruitment to oncogenic transcription factors (Dhordain et al., 1998, Grignani et al., 1998), and, less frequently, mutations (Hanigan et al., 2008). These defects can cause changes in chromatin structure, as well as deregulation of genes involved in cell cycle progression, differentiation, and apoptosis, giving cancer cells a survival advantage over normal cells. Fortunately, this increased dependence on aberrant HDAC activity also makes cancer cells much more sensitive to HDAC inhibition than their non-malignant counterparts (Johnstone, 2002, Atadja, 2004, Ungerstedt et al., 2005, Nebbioso et al., 2005), making HDAC inhibitors an attractive therapeutic option for cancer. This is particularly true in the context of prostate cancer, given that the majority of prostate cancers express HDACs 1, 2 and 3 at a higher level than the non-malignant prostate (Halkidou et al., 2004b, Nakagawa et al., 2007, Weichert et al., 2008). In addition, it has been shown that Hsp90 is a non-histone target of HDAC enzymes, which, when acetylated, loses its chaperone function (Bali et al., 2005a), resulting in the degradation of its client proteins, including proteins of significance in prostate cancer such as the AR, Her-2, p53 and Raf-1 (Fuino et al., 2003, Nimmanapalli et al., 2003, Bali et al., 2005b, Chen et al., 2005b).

Since their discovery, HDAC inhibitors have shown significant promise in pre-clinical testing, causing growth arrest and apoptosis of a variety of cancer cells, including prostate cancer cells, in vitro (Butler et al., 2000, Butler et al., 2002, Huang and Pardee, 2000, Richon et al., 1998, Richon et al., 1996, Vrana et al., 1999, Zhang et al., 2005, Munster et al., 2001, Eyupoglu et al., 2005, Kumagai et al., 2007, Ruefli et al., 2001, Sakajiri et al., 2005). HDAC inhibitors have also been successful at inhibiting tumour growth without 26

significant toxicity effects in a number of in vivo rodent models, including carcinogen induced tumours (Cohen et al., 1999, Cohen et al., 2002, Desai et al., 2003), human xenograft models (Butler et al., 2000, Spiller et al., 2006, Fazzone et al., 2009, Campbell et al., 2010), and genetic mouse models of cancer (Spiller et al., 2006, He et al., 2001, Lindemann et al., 2007). Preclinical testing conducted in human xenograft (Kulp et al., 2006, Xia et al., 2006, Lai et al., 2008) or genetic mouse models of prostate cancer (Sargeant et al., 2008) have shown similar efficacy. Despite the promising preclinical results, to date, HDAC inhibitors have not reported to be effective in the clinic for the majority of solid malignancies. One notable exception is cutaneous t-cell (CTCL), for which two HDAC inhibitors have been FDA approved (Olsen et al., 2007, Campas-Moya, 2009). Vorinostat and romidepsin are both used clinically for treatment of CTCL that has progressed after other systemic therapies, but thus far, dose-limiting toxicities appear to limit the potential of these agents as monotherapies in solid tumours, such as prostate cancer (Bradley et al., 2009, Molife et al., 2010).

Vorinostat, formerly known as suberoylanilide hydroxamic acid (SAHA), is one of the most extensively studied HDAC inhibitors in many laboratories, including our own. Vorinostat, like other HDAC inhibitors, has been shown to induce growth arrest and apoptosis in human prostate cancer cell lines in vitro and in vivo, with remarkable specificity and lack of significant toxicity (Butler et al., 2000). Studies by our laboratory and others have also demonstrated that treatment of prostate cancer cells with histone deacetylase inhibitors, including vorinostat, results in downregulation of the AR at the mRNA level (Chen et al., 2005b, Marrocco et al., 2007b, Welsbie et al., 2009). Given that vorinostat shows promise as an AR modulating agent, is readily available and approved for use in humans but not clinically effective as a monotherapy in prostate cancer, it was selected for further investigation in this thesis as part of a combinatorial treatment strategy.

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1.7 Summary

Clinical efficacy of AR antagonists still only provides, on average, a modest survival benefit due to the therapy-mediated selection pressure evident when resistance to these agents inevitably occurs. Therefore, we aim to circumvent the adaptation of prostate cancer cells to this selection pressure by combining AR antagonists with Hsp90 inhibitors, or HDAC inhibitors, in order to target different aspects of the androgen signalling pathway simultaneously (Figure 1.6). In addition, efficacy of other AR-modulating agents such as vorinostat and 17-AAG in the clinic has been modest at best, due to dose-limiting toxicity or limited bioavailability, providing further incentive to use these agents in combination therapy. Given that bicalutamide is currently in use for prostate cancer therapy, is easily obtained and remains an antagonist for most mutated ARs, it was selected for use in the combination therapies investigated in this thesis.

Our laboratory has previously shown that vorinostat and bicalutamide synergise to arrest growth and induce apoptosis in prostate cancer cells at low, individually sub-effective doses (Marrocco et al., 2007b). The doses used in vitro are readily achievable in patients, and while this is a promising result in terms of overcoming the issues facing vorinostat in the clinic, the mechanism of the synergy between the two drugs is largely unknown. Mechanistic studies not only aid in defining potential clinical issues, but may also reveal biomarkers for use in the clinic, and other, more specific cellular targets involved in prostate cancer cell death.

One of the major issues with 17-AAG in the clinic is its lack of bioavailability and anti- tumour efficacy. It demonstrates clinical issues similar to vorinostat, in that it is difficult to achieve the high doses needed for efficacy in patients. Given that the combination of two AR modulating agents, vorinostat and bicalutamide, showed promise as a way to achieve efficacy with significantly lower doses, 17-AAG plus bicalutamide is also a feasible combination. Indeed, preliminary studies in our laboratory show that a similar synergy exists between 17-AAG and bicalutamide. However, similar to the vorinostat combination, the mechanism of the synergy between these two drugs is unknown, as is the magnitude of any heat shock response that may occur. Collectively, these issues need to be resolved in order to facilitate this combination therapy into use for patients. 28

Figure 1.6

Figure 1.4 – Depiction of targets for AR antagonists, Hsp90 inhibitors and HDAC inhibitors on the androgen signalling pathway. AR antagonists prevent ligand (DHT) binding to the AR by competitively binding. Hsp90 inhibitors prevent the conformational change and stability caused by binding of a Hsp90 dimer, which normally allows the AR to bind ligand. HDAC inhibitors cause transcriptional repression of the AR mRNA, and also cause degradation of the protein due to acetylation of Hsp90. 29

1.8 Objectives of this Thesis

The overall hypothesis of this thesis is that combining androgen receptor modulating agents will cause synergistic prostate cancer cell death via suppression of the compensatory androgen signalling pathways associated with castrate-resistant prostate cancer. To address this hypothesis, the aims of this thesis are:

1. To investigate the mechanism of the synergy between vorinostat and bicalutamide. The following methods will be utilised to achieve this objective:

a. Cell culture: growth assays, RT-PCR, and western blotting, to characterise the conditions required for synergy.

b. Microarray profiling to identify key molecules involved, potential biomarkers and facilitate development of the combination for use in the clinic.

c. Treatment of nude mice bearing human LNCaP xenograft tumours, to determine in vivo efficacy and toxicity.

2. To characterise the effect of combining 17-AAG and bicalutamide on prostate cancer cell growth and viability. The following methods will be utilised to achieve this objective:

a. Cell culture methods, as for the vorinostat and bicalutamide combination, to determine if the combination is synergistic and to characterise any additive/synergistic mechanisms that may arise. Outcomes important for clinical application, such as heat shock response, will also be investigated.

b. Microarray profiling to identify key molecules involved, potential biomarkers and facilitate development of the combination for use in the clinic.

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Chapter 2:

General Materials and Methods

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2.1 Materials

2.1.1 Chemicals and general reagents Suppliers of all materials used in this thesis are listed in this section.

Reagent Supplier

Acrylamide/bis solution 40% Bio-Rad Laboratories (Hercules, CA, USA)

APS (ammonium persulphate) Sigma-Aldrich (St. Louis, MO, USA)

Avidin-biotin blocking kit Invitrogen (Carlsbad, CA, USA)

Bio-Rad Protein Assay reagent Bio-Rad Laboratories (Hercules, CA, USA)

Bromophenol blue Sigma-Aldrich (St. Louis, MO, USA)

BSA (bovine serum albumin) Sigma-Aldrich (St. Louis, MO, USA)

Caspase inhibitor (z-VAD-fmk) Calbiochem (Alexandra, NSW, Australia)

Cells – Human prostate cancer cell lines

LNCaP

VCaP American Type Culture Collection (ATTC)

PC3 (Rockville, MD, USA)

Nalgene Nunc International (Rochester, NY, Cell scrapers USA)

Asia Pacific Specialty Chemicals (Seven Hills, Charcoal NSW, Australia)

Chloroform Sigma-Aldrich (St. Louis, MO, USA)

Citric acid monohydrate Sigma-Aldrich (St. Louis, MO, USA)

Complete protease inhibitor cocktail Roche Applied Sciences (Penzberg, Germany)

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4-12% Criterion XT precast gel Bio-Rad Laboratories (Hercules, CA, USA)

Cycloheximide Sigma-Aldrich (St. Louis, MO, USA)

DAB (3, 3’-diaminobenzidine) Sigma-Aldrich (St. Louis, MO, USA)

Developer and fixer AGFA (Mortsel, Belgium)

Amersham Biosciences (Buckinghamshire, Dextran 70 England)

DHT (5α-dihydrotestosterone) Sigma-Aldrich (St. Louis, MO, USA)

Dulbecco’s Modified Eagle Medium (DMEM) Sigma-Aldrich (St. Louis, MO, USA)

BDH Laboratory Supplies (Kilsyth, VIC, DMSO (dimethyl sulfoxide) Australia)

DNA ladder (100 bp) New England Biolabs (Beverly, MS, USA)

DPX mounting medium Chemsupply (Gilman, SA, Australia)

DTT (dithiothreitol) Sigma-Aldrich (St. Louis, MO, USA)

ECLTM chemiluminescence detection Amersham Biosciences (Buckinghamshire, kit England)

EDTA (Ethylenediaminetetraacetic acid) Sigma-Aldrich (St. Louis, MO, USA)

Essential amino acids Sigma-Aldrich (St. Louis, MO, USA)

Ethidium bromide Sigma-Aldrich (St. Louis, MO, USA)

Ethanol (molecular grade) Sigma-Aldrich (St. Louis, MO, USA)

Filter paper Whatman (Kent, England)

Foetal bovine serum (FBS) Sigma-Aldrich (St. Louis, MO, USA)

Formalin (10% neutral buffered) Fronine Laboratory Supplies (Riverstone, NSW,

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Australia)

Glycerol Sigma-Aldrich (St. Louis, MO, USA)

Glycine Sigma-Aldrich (St. Louise, MO, USA)

GlycoBlueTM Ambion Inc. (Austin, TX, USA)

Goat serum Sigma-Aldrich (St. Louis, MO, USA)

Hybond-C Extra nitrocellulose Amersham Biosciences (Buckinghamshire, membrane England)

Hydrochloric acid Ajax Chemicals (Sydney, NSW, Australia)

Asia Pacific Specialty Chemicals (Seven Hills, Hydrogen peroxide NSW, Australia)

Hyperfilm GE Healthcare (Buckinghamshire, England)

2x iQ SYBR green supermix Bio-Rad Laboratories (Hercules, CA, USA) iScriptTM cDNA synthesis kit Bio-Rad Laboratories (Hercules, CA, USA)

Veterinary Companies of Australia (Kings Park, Isofluorane NSW, Australia)

Isopropanol (molecular grade) Sigma-Aldrich (St. Louis, MO, USA)

L-glutamine Sigma-Aldrich (St. Louis, MO, USA)

Lillie-Mayer haematoxylin Australian Biostain (Taralgon, VIC, Australia)

Lipofectamine 2000 Life Technologies (Carlsbad, CA, USA)

Matrigel BD Biosciences (Franklin Lakes, NJ, USA)

Methanol Ajax Chemicals (Sydney, NSW, Australia)

MOPS buffer Bio-Rad Laboratories (Hercules, CA, USA)

NFKBIA lentiviral overexpression construct Genecopoeia (Rockville, MD, USA)

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PBS (phosphate buffered saline) Sigma-Aldrich (St. Louis, MO, USA)

Phenol red free (PRF) RPMI 1640 medium Sigma-Aldrich (St. Louis, MO, USA)

JRH Biosciences (Lenexa, KS, USA)

RPMI 1640 media (with l-glutamine) Sigma-Aldrich (St. Louise, MO, USA)

SDS (sodium dodecyl sulphate) Sigma-Aldrich (St. Louis, MO, USA)

SeeBlueTM Plus pre-stained protein standard Invitrogen (San Diego, CA, USA)

Skim milk powder (instant) Diploma (Melbourne, VIC, Australia)

Small interfering RNA (siRNA)

ON-TARGETplus non-targeting siRNA #1

ON-TARGETplus siRNA human NFKBIA pool of four - #1, #2, #3, #4 Dharmacon (Lafayette, CO, USA)

Sodium chloride Ajax chemicals (Sydney, NSW, Australia)

Sodium hydroxide Ajax chemicals (Sydney, NSW, Australia)

Sodium molybdate Sigma-Aldrich (St. Louis, MO, USA)

Sodium pyruvate Sigma-Aldrich (St. Louis, MO, USA)

Strepdavidin, HRP-conjugated Dako (Botany, NSW, Australia)

Superfrost-plus glass slides Thermo Scientific (Waltham, MA, USA)

SYBR green supermix (2x) Bio-Rad Laboratories (Hercules, CA, USA)

TE buffer Ambion Inc. (Austin, TX, USA)

TEMED Sigma-Aldrich (St. Louis, MO, USA)

Testosterone pellets (60-day release) Innovative Research of America (Sarasota, FL,

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USA)

Tissue embedding cassette LOMB Scientific (Taren Point, NSW, Australia)

Tris Sigma-Aldrich (St. Louis, MO, USA)

Triton X-100 Sigma-Aldrich (St. Louis, MO, USA)

Trizol Sigma-Aldrich (St. Louis, MO, USA)

Trypan blue Sigma-Aldrich (St. Louis, MO, USA)

Trypsin EDTA Sigma-Aldrich (St. Louis, MO, USA)

TURBO DNA-freeTM Ambion, Inc (Austin, TX, USA)

Tween 20 Sigma-Aldrich (St. Louis, MO, USA)

Water – sterilised reverse osmosis (RO) Filtered and autoclaved within laboratory

Baxter Baxter Healthcare (Old Toongabbie, NSW, Australia)

Nuclease free Applied Biosystems (Foster City, CA, USA)

Xylene Ajax chemicals (Sydney, NSW, Australia)

2.1.2 Drugs

Drug Solvent Supplier

Vorinostat

(Suberoylanilide hydroxamic DMSO Merck & Co. Inc. (New Jersey, NY, USA) acid (SAHA), Zolinza)

Astra Zeneca (London, England, UK) Bicalutamide (Casodex) ethanol Sigma

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17-AAG

(17-N-Allylamino-17- National Cancer Institute (Bethesda, MD, DMSO demethoxygeldanamycin, USA) Tanespimycin)

NVP-AUY992 DMSO Novartis (Basel, Switzerland)

2.1.3 Antibodies

Working Antibody Type Supplier dilution

1:1000 Santa Cruz Biotechnology (Santa AR (N-20) Rabbit polyclonal (WB) Cruz, CA, USA)

Santa Cruz Biotechnology (Santa PSA Goat polyclonal 1:500 (WB) Cruz, CA, USA)

Mouse 1:1000 Cell Signaling Technology Inc IκBα monoclonal (WB) (Danvers, MA, USA)

Mouse 1:1000 Merck Millipore (Billerica, MA, α-tubulin monoclonal (WB) USA)

Santa Cruz Biotechnology (Santa Hsp90 Rabbit polyclonal 1:500 (WB) Cruz, CA, USA)

Santa Cruz Biotechnology (Santa Raf-1 (C-20) Rabbit polyclonal 1:500 (WB) Cruz, CA, USA) c-erb2/ Mouse Labvision Corporation (Fremont, 1:500 (WB) Her2/Neu monoclonal CA, USA)

Ki67 Rabbit polyclonal 1:200 (IHC) DAKO (Botany, NSW, Australia)

Cleaved caspase 3 Rabbit polyclonal 1:200 (IHC) DAKO (Botany, NSW, Australia)

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Goat anti-rabbit IgG/biotinylated 1:400 DAKO (Botany, NSW, Australia)

Rabbit anti-mouse IgG/biotinylated 1:400 DAKO (Botany, NSW, Australia)

IgG/HRP- Goat anti-rabbit 1:2000 DAKO (Botany, NSW, Australia) conjugated

IgG/HRP- Rabbit anti-mouse 1:1000 DAKO (Botany, NSW, Australia) conjugated

Donkey anti- IgG/HRP- 1:1000 DAKO (Botany, NSW, Australia) sheep/goat conjugated

2.1.4 Primers All primers were made by Geneworks (Australia)

Gene Primer Sequence (5’-3’)

Forward CCTGGCTTCCGCAACTTACAC AR Reverse GGACTTGTGCATGCGGTACTCA

Forward GGTGGCTGTGTACAGTCATGGAT KLK2 Reverse TGTCTTCAGGCTCAAACAGGTTG

Forward ACCAGAGGAGTTCTTGACCCCAAA KLK3 Reverse CCCCAGAATCACCCGAGCAG

Forward GGCACTGGTCATGGAAAACGA KLK4 Reverse TCAAGACTGTGCAGGCCCAGCC

Forward GACCAAGAACAATGACATTGCG TMPRSS2 Reverse GTTCTGGCTGCAGCATCATG

IGF1R Forward TTACTTCTGCTCAGATGCTCCAA

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Reverse TCGATGAGCCCCATGAAGTT

Forward GTCTGCACGGTCCTTTGCTC PMEPA1 Reverse CGTTGCGCCCTGCAGATCCT

Forward ACA CTGTGACTTGCGTCTGTCA TMEFF2 Reverse CTGTTTGCATGCAGCCTGTC

Forward AGCCACAACTCCCAGAGGTTT C1orf116 Reverse TCGTCCTTGCTGAGTGATGG

Forward CGTCTGGGTACCTGAACGAGGTG TP53INP1 Reverse GGAGATTAAAGTGCACAGGGTGCTT

Forward ATGTGGACGACCGCCACGACA NFKBIA Reverse ATGGCCAAGTGCAGGAACGAGTC

Forward CTG GCA GAG ACC GAG CCA GAA AG NKX3-1 Reverse AGC GCT TCT GCG GCT GCT TAG

Forward GGG CAG TGG CTC CGC TGG GAT AA PGM2L1 Reverse CTGCCCCCATGGCAGAACGAAGT

Forward GAAGAGTGGGTGGCTGAAGCCATAC STEAP1 Reverse ATGCTGGTCTCTCCCGTGTCCTTA

Forward TGGACCTGGAGACTCTCAGGGTCG CDKN1A Reverse TTAGGGCTTCCTCTTGGAGAAGATC

Forward ATAAAAGCCCAGGGGCAAGCG HSP70 Reverse TTCGCTCTGGGAAGCCTTGG

Forward TTTAAAGGACAAGCCCCACAA HSP40 Reverse TTCACTGTGCAGCCACACA

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Forward TGACGGTCAAGACCAAGGAT Reverse TATTTCCGCGTGAAGCACC

Forward CGCAAGACACTGCTCAGCAA Reverse ACACTCCTGGGAGCTCCTT

Forward GTTATGGCGACCCGCAG HPRT Reverse ACCCTTTCCAAATCCTCAGC

Forward TGCCAGTGGAAAAATCAGCCA RPL19 Reverse CAAAGCAAATCTCGACACCTTG

Forward CGTCCCACCTAGAATCTGCT GUSB Reverse TTGCTCACAAAGGTCACAGG

2.1.5 Mice Male Balb/c nude mice (nu/nu) 5-6 weeks of age were obtained from Perth Animal Resource Centre (Perth, WA, Australia) and housed in a barrier facility at the Institute of Medical and Veterinary Science (IMVS) animal house. Animals were housed in filter top cages on a 12 hour light/dark cycle, with food and water ad libitum. Animal ethics approval was obtained from the University of Adelaide and IMVS ethics committees prior to initation of animal studies.

2.1.6 Equipment

Equipment Supplier

Biological safety cabinet class II Clyde-Apac (Woodville, SA, Australia)

C1000 thermocycler – CFX384 Bio-Rad Laboratories (Hercules, CA, USA)

Criterion cell gel apparatus Bio-Rad Laboratories (Hercules, CA, USA)

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Decloaker Biocare Medical (Concord, CA, USA)

ProSciTech Pty Ltd (Kirwan, QLD, Neubauer improved haemocytometer Australia)

Olympus America, Inc. (Melville, NY, Microscope USA)

Leica Biosystems (North Rhyde, NSW, Microtome RM2235 Australia)

Amersham Biosciences (Buckinghamshire, Mini tank transfer unit for electrotransfer England)

Mini trans-blot cell Bio-Rad Laboratories (Hercules, CA, USA)

NanoDrop Technologies (Wilmington, DE, NanoDrop spectrophotometer ND1000 USA)

Nanozoomer Hamamatsu Corporation (Shizuoka, Japan)

Power supply units for electrophoresis Bio-Rad Laboratories (Hercules, CA, USA)

Refrigerated microcentrifuges Eppendorf (Hamburg, Germany)

SDS-PAGE mini-vertical electrophoresis Amersham Biosciences (Buckinghamshire, unit England)

Ultra microplate reader EL808 Crown Scientific (NSW, Australia)

2.1.7 Software

Software Supplier

Bio-Rad Laboratories (Hercules, CFX Manager Software (Version 1.6) CA, USA)

bonsai.hgc.jp/~mdehoon/software Cluster 3.0 /cluster/software.htm

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Database for Annotation, Visualisation, and Integrated National Institute of Allergy and Discovery (DAVID) http://david.abcc.ncifcfr.gov/ Infectious Diseases, NIH (Bethesda, MD, USA)

Definiens Tissue Studio Definiens (München, Germany)

Gene Set Enrichment Analysis (GSEA) Broad Institute (Cambridge, MA, http://genepattern.broadinstitute.org/gp/pages/index.jsf USA)

Graphpad Software (San Diego, Graphpad Prism 5 CA, USA)

Ingenuity Systems (Redwood Ingenuity Pathway Analysis (v9.0) City, CA, USA)

Java TreeView jtreeview.sourceforge.net/

KC4 plate reader software BioTek (Winooski, VT, USA)

NanoDrop Technologies NanoDrop ND 1000 2.2 (Wilmington, DE, USA)

Hamamatsu Corporation NDP nanozoom image viewer (Shizuoka, Japan) qBase geNORM Biogazelle (Ghent, Belgium)

2.2 Buffers and Solutions

All buffers and solutions were made up in RO water and stored at room temperature unless otherwise specified.

10% APS

1 g APS in 10 ml H2O Stored at -20 °C

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Citrate Buffer

10 mM citric acid monohydrate (C6H8O7), pH 6.5 1.05 g of citrate acid dissolved in approximately 450 ml H2O. pH adjusted to 6.5. Final volume made up to 500 ml with H2O. Citrate buffer was made fresh before use.

DAB solution 105 ml Tris buffer, 100 mg Isopac of DAB. Stored at – 20 °C

Dextran coated charcoal (DCC) in Tris/EDTA buffer 0.5% charcoal, 55 nM dextran, 20% glycerol Charcoal 5 g Dextran 0.5 g Glycerol 100 ml Made up to 1 litre in Tris/EDTA buffer and stirred overnight at room temperature

Freezing mix, for mammalian cells 40% DMSO, 40% FBS, 20% RPMI media

Hydrogen peroxide (H2O2) solution

0.12% H2O2 in Tris buffer Stored at -20 °C

10x PBS (pH 7.3)

1.46 M NaCl, 822 mM K2HPO4, 184 KH2PO4 NaCl 425 g

K2HPO4 71.5 g

KH2PO4 12.5 g

Made up to 5 litres in RO-H2O, pH to 7.3

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1x PBS 10x PBS 500 ml

Made up to 5 litres in RO H2O

6x protein load dye 0.35 M Tris-Cl, 10.4% SDS, 30% glycerol, 0.6 M DTT, 0.012% bromophenol blue 4 x Tris-Cl/SDS was prepared:

3.025 g Tris-Cl dissolved in 20 ml H2O, adjusted to pH 6.8,

2 g of SDS added with RO H2O to a final volume of 50 ml

4x Tris-Cl/SDS 7 ml Glycerol 3 ml SDS 1 g DTT 0.93 g Bromphenol blue 1.2 mg

RIPA lysis buffer 10 mM Tris (pH 7.4), 150 mM NaCl, 1 mM EDTA, 1% triton X-100 A complete protease inhibitor cocktail tablet was dissolved into 50 ml of lysis buffer before use. Aliquotted and stored at -20 °C

10x SDS-PAGE running buffer Tris base 75.75 g Glycine 360 g SDS 25 g

Made up to 2.5 litres in RO H2O

1x SDS-PAGE running buffer

250 ml 10x running buffer, made up to 2.5 litres in RO H2O

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SDS-PAGE separating gel (10%) 1 M Tris (pH 8.8) 3.75 ml Acrylamide/Bis 2.5 ml 20% SDS 50 µl 10% APS 50 µl TEMED 5 µl Water 3.65 ml

SDS-PAGE stacking gel (4%) 1 M Tris (pH 6.8) 625 µl Acrylamide/Bis 500 µl 20% SDS 50 µl 10% APS 25 µl Water 3.7 ml

10x SDS-PAGE transfer buffer 20 mM Tris, 192 mM glycine 77.5g Tris 360g Glycine

Made up to 2.5 litres in RO H2O

1 x SDS-PAGE transfer buffer 10x transfer buffer 400 ml Methanol 800 ml

Made up to 4 litres in RO H2O

10x TBS (pH 7.4) 0.5 M Tris, 1.5 M NaCl Tris 121.1 g NaCl 58.4 g

Made up to 2.5 litres in RO H2O, pH to 7.4

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TBS + Tween®20 (TBST) 10x TBS 400 ml Tween®20 5 ml

Made up to 4 litres in RO H2O

Tris/EDTA buffer

0.01M Tris, 1.5 mM EDTA, 0.01 M Na2MO4, 10% glycerol Tris base 1.211 g EDTA 0.588 g

Na2MO4 2.42 g Glycerol 100 ml

Made up to 1 litre in RO H2O, pH to 7.4

Trypan blue 0.01% trypan blue dissolved in PBS

Weak Lillie-Mayer haematoxylin Concentrated solution was filtered and diluted 1:5 freshly before each use.

2.3 General Methods

General experimental procedures used in two or more chapters in this thesis are described in this section, with specific experimental procedures described in each relevant chapter.

2.3.1 Cell culture

2.3.1.1 Maintenance of cell lines The human prostate cancer cell lines LNCaP, PC3, and VCaP were obtained from the American Type Culture Collection (ATCC, MD, USA). LNCaP cells, which express a mutated AR, were maintained as an adherent monolayer in RPMI medium supplemented with 10% FBS. PC3 cells, which do not express a functional AR, were maintained as an

46

adherent monolayer in RPMI medium supplemented with 5% FBS. VCaP cells, which express an amplified wild type AR, were maintained as an adherent monolayer in DMEM supplemented with 10% FBS, sodium pyruvate (1/100), essential amino acids (1/100), and

0.1 nM DHT. All cells were cultured at 37 °C in 5% CO2 atmosphere. Cells were maintained until ~90% confluence, and were then passaged or seeded as required. When required for passaging or seeding, cells grown in T75 flasks were washed with warm 1x PBS, and then incubated at 37 °C in 3 ml of /EDTA for three minutes or until the cells had detached from the surface. Fresh media appropriate to the cell line was then added to neutralise the trypsin, and the cell suspension centrifuged at 1500 rpm for 5 minutes. The supernatant was removed, and the cell pellet resuspended in fresh media. A small aliquot of cell suspension was taken to determine cell density as required by haemocytometer, using trypan blue dye exclusion to ensure that cells counted were viable. The appropriate cell density was then calculated for further passaging or seeding. All cell culture experiments were performed under aseptic conditions in a laminar flow hood. Routine mycoplasma testing was carried out in the laboratory to ensure all cell lines were free of contamination.

2.3.1.2 Freezing of cell lines Cells grown in a T75 flask were harvested as per the above protocol, and resuspended in 1.5 ml of media. A 2x freezing mix was prepared containing 40% DMSO, 40% FBS and 20% culture medium. 1.5 ml of the freezing mix was added drop wise to the cell suspension. One ml of the cell suspension/freezing mix was then added to cryovials, which were then placed into a freezing apparatus (“Mr. Frosty”) containing isopropanol, which allowed the cells to be frozen slowly at -1 °C/min until reaching -80 °C. For long term storage, cryovials were then transferred to liquid nitrogen.

2.3.1.3 Thawing of cell lines Cryovials of cells were thawed rapidly in a water bath set to 37 °C, and then placed immediately into 10 ml of pre-warmed culture media. The cell suspension was then centrifuged at 1500 rpm for 5 minutes. Supernatant was aspirated, and the cell pellet was resuspended in 10 ml of the appropriate culture media, which was then placed into a T25

47

tissue culture vessel. Cells were allowed to attach and grow undisturbed over three days, at which point they were passaged and required one further passage before experimental use.

2.3.1.4 Charcoal stripping of foetal bovine serum (DCC-FBS) Dextran coated charcoal in Tris/EDTA buffer was added to 50 ml tubes and centrifuged at 4000 rpm for 30 minutes at 4 °C. Supernatant was discarded and 50 ml of FBS was added to the charcoal pellets, which were resuspended by shaking, and then rotated at room temperature for two hours. The serum-charcoal mix was then centrifuged at 4000 rpm for 30 minutes and the serum supernatants were transferred to new tubes containing new dextran coated charcoal pellets, and the process repeated. Following centrifugation, the serum supernatant was filter sterilised and stored in 25 ml aliquots at -20 °C.

2.3.2 Drug treatments and proliferation/death assays Vorinostat, 17-AAG and AUY992 were dissolved in DMSO and stored as 10 mM aliquots at -20 °C. Bicalutamide was dissolved in 100% molecular grade ethanol at 5 mM and stored at -20 °C. Cells were seeded in 24-well plates (2 x 104 cells/well) and allowed to attach overnight at 37 °C in 5% CO2 atmosphere before treatment. Drugs were prepared freshly for each treatment, and serially diluted in the appropriate growth medium. Growth medium was removed from the cells and very carefully replaced with medium containing vehicle or drug treatment, with three wells per treatment. All procedures were carried out under aseptic conditions in a laminar flow hood.

At the time points outlined in each specific chapter, cells were harvested and assessed for viability using the trypan blue exclusion method. Culture medium from each well was placed into a 15 ml sterile tube. Cells were washed with 1 ml PBS/well, which was again aliquoted into the corresponding tube. Cells were detatched using 0.5 ml trypsin incubated at 37 °C for ~3 minutes, and 1 ml of media added to neutralise the trypsin. The cells/trypsin/media mixture was transferred to the corresponding tube. The wells were rinsed with another 1 ml of media to pick up any residual cells, which were transferred to the corresponding tube. Tubes were centrifuged for 5 minutes at 1500 rpm, after which the supernatant was very carefully aspirated and the cell pellet resuspended in 100 – 1000 μl

48

media depending on cell confluency. Cell suspension for each sample was added to 1x trypan blue in a 1:1 dilution, mixed carefully, and counted using a haemocytometer.

2.3.3 Western blotting

2.3.3.1 Protein harvest Cells were grown under conditions specified in the relevant materials and methods section of each chapter. In general, cells and all protein lysates were kept on ice at all times. Culture medium was removed from the plate, and the cells washed twice with ice-cold PBS. Ice-cold RIPA buffer containing protease inhibitors was then added to the cell layer, and cell scrapers were used to lift the cells from the plate. A 26G needle and a 1 ml syringe was used to collect the lysates, and lysates were transferred to a clean microcentrifuge tube where they were passed through the syringe several times before centrifugation at 10,000 rpm for 10 minutes to pellet cell debris. Supernatant containing the protein lysate was then transferred to a fresh microcentrifuge tube, which was then stored at -80 °C.

2.3.3.2 Bradford protein assay For each protein sample, 2 µl of sample was added to 158 µl baxter water and 40 µl of Bio-Rad protein assay reagent contained in a 96 well tissue culture plate. A standard curve was prepared in conjunction, using increasing concentrations of BSA (1 – 8 µg/µl). Standards and protein samples were all prepared in duplicate. Once all components were placed into the 96 well plate, it was well mixed using a plate shaker for approximately 10 seconds. The plate was then left at room temperature for 5 minutes before reading on a microplate reader at 595 nm, using KC4 software.

2.3.3.3 Immunoblotting Using the protein concentration determined in the Bradford assay (2.3.2.2), 20 µg of protein was made up to a consistent volume using baxter water, and the appropriate volume of 6x loading buffer added. Proteins were denatured at 95 °C for 5 minutes, and then added to each lane of either a 10% SDS-PAGE gel, or a Bio-Rad Criterion 4-12% pre- cast gel. A SeeBlueTM pre-stained standard was added to the leftmost lane of each gel. 10% polyacrylamide gels were run using the Amersham mini tank unit. Proteins were 49

electrophoresed through the stacking layer at 15 mA and then at 30 mA through the separating layer in 1x running buffer. Separated proteins were then transferred to Hybond- C nitrocellulose membrane at 250 mA for 1.5 hours in cold 1x transfer buffer. Criterion gels were run using the Bio-Rad gel apparatus. Proteins were electrophoresed at a constant 150 V in 1x MOPS buffer for approximately one hour, or until proteins had separated sufficiently according to the visible standard. Separated proteins were then transferred to nitrocellulose membrane in cold 1x transfer buffer at 400 mA for 1.5 hours using the Bio- Rad transfer apparatus.

Membranes were blocked for either one hour at room temperature, or overnight at 4 °C, in 3% skim milk powder prepared in TBST. The membrane was then probed with primary antibody diluted in 3% skim milk powder overnight at 4 °C, or at room temperature for one hour. Membranes were then washed 3 x 10 minutes in TBST, followed by incubation at room temperature for 30 minutes with the appropriate secondary horseradish peroxidase- conjugated antibody (goat anti-rabbit (1/2000), rabbit anti-mouse (1/2000) or rabbit anti- goat (1/1000) diluted in 3% skim milk powder in TBST). Immunoreactive proteins, i.e. secondary antibodies bound to primary antibodies, were detected using chemiluminescence according to the manufacturer instructions (Amersham Biosciences, England). Primary antibodies detecting either α-tubulin or Hsp90 were used as protein loading controls.

2.3.4 Quantitative real-time polymerase chain reaction (qRT-PCR)

2.3.4.1 RNA extraction of cell lines with Trizol Cells were grown under conditions specified in the relevant materials and methods section of each chapter. In general, treatment medium was aspirated, and 1 ml of Trizol added to each well (6-well plate). The Trizol was pipetted up and down several times to help lyse the cells, which were then added to a clean RNA/DNA free eppendorf tube. Tubes were incubated at 37 °C for 15 minutes, and then 200 µl chloroform added. Samples were shaken vigorously and left at room temperature for 3 minutes, and then centrifuged at 12,000 g for 15 minutes at 4 °C. The top aqueous phase for each sample was transferred to a new tube, to which 2 µl of GlycoBlueTM and 500 µl isopropanol was added. Samples were inverted several times to mix, and then left to precipitate at room temperature for 10 50

minutes. RNA was pelleted by centrifugation at 4 °C for 10 minutes at 12,000 g. Supernatant was carefully removed and the RNA pellet washed with 500 μl of 75% ethanol, vortexed and then centrifuged at 12,000 g for 5 minutes at 4 °C. The ethanol supernatant was very carefully removed and the RNA pellet was allowed to dry at room temperature. RNA was dissolved in 20 μl of nuclease free water, and dissolution was facilitated by incubation at 55 °C for 10 minutes, flick mixing and pulse spinning twice. After the pellet was dissolved, the RNA was kept on ice, and 1 μl taken for RNA quantification on the Nanodrop spectrometer.

2.3.4.2 DNAse treatment and iScript reverse transcription The TURBO DNA-free kit and its manufacturer’s protocol was used. Approximately 1 μg of RNA was treated for each sample. DNAse treated RNA was transferred to a fresh RNA/DNA free eppendorf tube, and 2 μl GlycoBlueTM and 50 μl 75% isopropanol added to each sample. Samples were incubated overnight at -80 °C, and then centrifuged at maximum speed (13,200 rpm) for 20 minutes at 4 °C. The supernatant was discarded, and the RNA pellet washed once with 500 μl 75% ethanol. The ethanol supernatant was very carefully removed and the RNA pellet was allowed to dry at room temperature. RNA was dissolved in 20 μl of TE buffer, and dissolution was facilitated by incubation at 55 °C for 10 minutes, flick mixing and pulse spinning twice. After the pellet was dissolved, the RNA was kept on ice, and 1 μl taken for RNA quantification on the Nanodrop spectrometer. Approximately 500 ng of DNAse-treated RNA was treated with the iScriptTM cDNA synthesis kit as per the manufacturer’s instructions. cDNA was diluted 1:5 with nuclease free water before use, and stored at 4 °C. Two control samples – one lacking RNA (no template control) and one lacking reverse transcriptase (no RT control) were included in the synthesis reactions.

2.3.4.3 Quantitative real time PCR To test newly designed primers, 5 μl from all cDNA samples were pooled and serially diluted to create a standard curve. Using the CFX software, an efficiency of between 90 – 51

110% was considered acceptable. For regular qRT-PCR, 1 μl of cDNA was used per well on a 384-well reaction plate, with iQ SYBR green master mix and 5 pmol of sense and antisense primers listed in 2.1.4. Reference genes for each set of samples were determined by running a panel of housekeeping genes, and then using geNORM software to determine how many and which genes were suitable for use as reference genes. All genes were analysed using biological triplicates and technical triplicates, and run on the same plate with the following cycling parameters: 95 °C for 3 min, followed by 40 cycles of 95 °C for 15 sec, 55 °C for 15 sec, and 72 °C for 30 sec, and finally a melt curve from 55 °C to 95 °C at a rate of 0.5 °C per 10 sec. Data were analysed using CFX manager software.

2.3.5 Statistical analysis Statistical analyses were performed where indicated in the figure legends or specific methods sections utilising Graphpad Prism software.

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Chapter 3:

NFKBIA (IBα) mediates prostate cancer cell death induced by combination treatment with vorinostat and bicalutamide

This chapter contains a submitted manuscript, along with supplementary figures and tables, comprising the majority of the work undertaken during this PhD. Extra data not included in the final submission of the manuscript for the sake of brevity are included at the end of this chapter as additional figures, with discussion included in the figure legends. Microarray data can be found at http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE56188.

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Title: NFKBIA (IBα) mediates prostate cancer cell death induced by combination treatment with vorinostat and bicalutamide

Authors: Sarah L. Carter, Margaret M. Centenera, Wayne D. Tilley, Luke A. Selth and Lisa M. Butler.

Affiliation: Dame Roma Mitchell Cancer Research Laboratories, Adelaide Prostate Cancer Research Centre and Freemason’s Foundation Centre for Men’s Health, School of Medicine, University of Adelaide and Hanson Institute, Adelaide, South Australia 5000, Australia.

Running Title: Combinatorial AR targeting is mediated by NFKBIA in prostate cancer cells

Corresponding Author: A/Prof. Lisa M. Butler, Dame Roma Mitchell Cancer Research Laboratories, School of Medicine, University of Adelaide, Adelaide, SA 5000, Australia. Tel: (+61) 8 8222 3225; Fax: (+61) 8 8222 3217; Email: [email protected]

Abbreviations: Androgen receptor (AR), androgen deprivation therapy (ADT), castrate- resistant prostate cancer (CRPC), Database for Annotation, Visualization and Integrated Discovery (DAVID), dihydrotestosterone (DHT), histone deacetylase inhibitor (HDACI), Ingenuity Pathway Analysis (IPA).

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Abstract

Background

Combining different clinical agents to target multiple pathways in prostate cancer cells, including androgen receptor (AR) signaling, is potentially an effective strategy to improve outcomes for men with metastatic disease. We have previously demonstrated that sub- effective concentrations of an AR antagonist, bicalutamide, and the histone deacetylase inhibitor, vorinostat, act synergistically when combined to cause death of AR-dependent prostate cancer cells.

Methods

In this study, expression profiling of human prostate cancer cells treated with bicalutamide or vorinostat, alone or in combination, was employed to determine the molecular mechanisms underlying this synergistic action. Cell viability assays and quantitative real time PCR were used to validate identified candidate genes.

Results

A substantial proportion of the genes modulated by the combination of bicalutamide and vorinostat were androgen regulated. Independent pathway analysis identified further pathways and genes, most notably NFKBIA (encoding IBα, an inhibitor of NF-κB and p53 signaling), as targets of this combinatorial treatment. Depletion of IBα by siRNA knockdown enhanced apoptosis of prostate cancer cells, while ectopic overexpression of IBα markedly suppressed cell death induced by the combination of bicalutamide and vorinostat.

Conclusion

These findings implicate IBα as a key mediator of the apoptotic action of this combinatorial AR targeting strategy and a promising new therapeutic target for prostate cancer.

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Keywords

Prostate cancer, androgen receptor, combination therapy, IBα

Background

Prostate cancer is the most commonly diagnosed cancer, and the second leading cause of cancer-related death, in men in the developed world (Jemal et al., 2010). Since Huggins, Stevens and Hodges (1941) demonstrated that prostate epithelial cells require androgens for growth and survival, the mainstay of treatment for men with metastatic prostate cancer has been suppression of testosterone production by surgical or medical castration, a strategy termed androgen deprivation therapy (ADT). Whilst these treatment modalities are initially effective (reviewed in (Klotz, 2008)), most patients eventually relapse with castrate-resistant prostate cancer (CRPC), which is incurable and the primary cause of mortality associated with this disease. It is now well established that the mediator of androgen action, the androgen receptor (AR), plays a key role in the progression of prostate cancer following ADT, despite castrate levels of circulating testosterone. A number of mechanisms, including increased levels of the AR mRNA or protein (Visakorpi et al., 1995, Koivisto et al., 1997, Linja et al., 2001, Edwards et al., 2003, Chen et al., 2004), mutation of the AR gene to produce more active or promiscuous forms of the receptor (Taplin et al., 1995, Tilley et al., 1996, Fenton et al., 1997, Marcelli et al., 2000, Buchanan et al., 2001b, Han et al., 2005), altered levels of AR coregulators (reviewed in (Chmelar et al., 2007)), the expression of constitutively active AR splice variants (Dehm et al., 2008, Guo et al., 2009, Watson et al., 2010), and adrenal and intratumoral biosynthesis of androgens (Stanbrough et al., 2006, Montgomery et al., 2008, Mohler et al., 2004b, Nishiyama et al., 2004, Mostaghel et al., 2007), explain continued AR signaling during ADT. As many of these mechanisms are refractory to conventional ADT, there is considerable impetus to develop new and more potent agents targeting the androgen signaling axis. Two such agents are enzalutamide (MDV-3100), a novel AR antagonist that has demonstrated clinical activity in men who have failed both ADT and docetaxel-based chemotherapy (Scher et al., 2010), and 56

abiraterone acetate, which targets an enzyme required for adrenal and intratumoral androgen biosynthesis. Phase III clinical trials demonstrated that these agents extend median survival of men with advanced CRPC by several months and both have received FDA approval (de Bono et al., 2011). Despite the success of enzalutamide and abiraterone, it is accepted that treatment with these agents remains essentially palliative, and that combinatorial treatment strategies targeting multiple cellular pathways in addition to androgen signaling are more likely to improve outcomes for men with CRPC. One such combination therapy comprises the AR antagonist bicalutamide and the histone deacetylase (HDAC) inhibitor vorinostat, which act synergistically together to cause death of cell line models of prostate cancer (Marrocco et al., 2007b). Vorinostat has a global effect on the acetylation of histones and other proteins within the cell but also reduces AR levels and activity and thereby directly targets androgen signaling (Marrocco et al., 2007b). The aim of this study was to interrogate the molecular mechanisms underlying the synergistic action of bicalutamide and vorinostat in prostate cancer. Through expression profiling and functional studies, we identified NFKBIA (IBα) as a critical mediator of this therapy, and in doing so provided novel insight into AR signalling and how this might be effectively targeted in prostate cancer.

Materials and Methods

Cells and Reagents LNCaP (mutant AR) human prostate cancer cells were purchased from the American Type Culture Collection (ATCC, Rockville, MD, USA) and maintained in RPMI 1640 supplemented with 10% fetal bovine serum (FBS). VCaP (wild-type AR) human prostate cancer cells were purchased from the ATCC and maintained in DMEM supplemented with sodium pyruvate, non-essential amino acids and 10% fetal bovine serum. Vorinostat was obtained from Merck (New Jersey, USA) and dissolved in DMSO. Bicalutamide was obtained from Astra Zeneca (London, UK) and dissolved in ethanol. Cycloheximide was obtained from Sigma (St. Louis, MO, USA) and dissolved in DMSO. Anti-AR (N-20), anti-prostate specific antigen (PSA; C-19) and anti-hsp90 (H-114) antibodies were

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obtained from Santa Cruz Biotechnology (Santa Cruz, CA, USA). Anti-IκBα antibody was obtained from Cell Signaling Technology Inc (Danvers, MA, USA). Anti-αtubulin antibody was obtained from Merck Millipore (Billerica, MA, USA). Horseradish peroxidase conjugated anti-rabbit, anti-mouse, and anti-sheep/goat secondary antibodies were obtained from DAKO (Botany, NSW, Australia). Non-specific, scrambled siRNA and ON-TARGETplus siRNAs targeting NFKBIA were purchased from Dharmacon (Lafayette, CO, USA) and the NFKBIA-IRES-eGFP lentiviral ORF plasmid was purchased from GeneCopoeia (Rockville, MD, USA). The pLV410 eGFP lentiviral ORF plasmid was kindly provided by Dr. Philip Gregory (University of Adelaide, Adelaide, Australia).

Cell Viability Assays LNCaP or VCaP cells were seeded in triplicate in 24-well plates, and allowed to attach overnight before the growth medium was replaced with medium containing vehicle control, 1 µM vorinostat, 2.5 µM bicalutamide, or the two agents in combination. Cells were counted every two days using a hemocytometer and cell viability was assessed using Trypan blue dye exclusion. For sequential treatments, cells were treated with drug one for 24 hours, at which point the treatment medium was removed and replaced with drug two for 48 hours. To assess the effect of cycloheximide, cells were pre-treated with 10 µM cycloheximide for one hour, which was then removed and replaced with treatment medium. Wash out experiments were performed by allowing the treatment medium to remain on the cells for 1, 2, 4, 6, 8, 16, or 24 hours, at which point it was removed and replaced with drug-free medium. At each end-point cells were counted using a hemocytometer and viability assessed as above.

Microarray Analysis LNCaP cells were cultured with vehicle control, 1 µM vorinostat, 5 µM bicalutamide, or the two agents in combination for 6 hours. A higher dose of bicalutamide compared to the initial cell viability assays was necessary to ensure consistency in terms of cell death between the two experiments. Total RNA was extracted from the cells using Trizol reagent (Life Technologies, Carlsbad, CA, USA), and RNA integrity was analyzed on an Agilent Systems Bioanalyzer. RNA from cells treated with the combination of vorinostat and 58

bicalutamide was compared with RNA from cells treated with either vehicle control or either of the agents individually using Affymetrix Human GeneChip ST 1.0 arrays at the Adelaide Microarray Centre, as described previously (Moore et al., 2012). Differential was assessed by ANOVA with the p-value adjusted using a step-up multiple test correction to control the false discovery rate (FDR) (Benjamini Y, 1995). Adjusted p-values < 0.05 were considered to be significant.

Quantitative Real-Time PCR Independent RNA samples used to validate the microarray data were generated by culturing LNCaP cells with vehicle control, 1 µM vorinostat, 2.5 µM bicalutamide or the two agents in combination for 3, 6, 9 and 12 hours. Total RNA (1 µg) was DNAse treated with Turbo DNA Free (Ambion, Austin, TX, USA), and then reverse transcribed using an iScript cDNA Synthesis Kit (Bio-Rad, Hercules, CA, USA). qRT-PCR was performed with a 1:10 dilution of the cDNA using SYBR green (Bio-Rad) on a CFX Real-Time System (Bio-Rad). geNORM analysis was used to determine appropriate housekeeper genes for each sample set. Microarray RNA was normalized to HPRT1 and RPL19, and the independent sample set was normalized to GUSB and HPRT1. Primer sequences are listed in Supplementary Table 1.

Immunoblotting LNCaP cells cultured with 2.5 µM bicalutamide or 1 µM vorinostat, individually and in combination, were lysed in radioimmunoprecipitation assay lysis buffer (10mM Tris-HCl, 125mM NaCl, 1 mM EDTA, 1% Triton X-100) supplemented with protease inhibitor cocktail (Roche, Mannheim, Germany). Lysates (20 µg) were electrophoresed through 7.5–15% SDS-polyacrylamide gels (PAGE) and transferred onto nitrocellulose membranes (GE Healthcare, Buckinghamshire, UK). Membranes were blocked overnight (4°C) in 3% non-fat milk powder in Tris-buffered saline containing 0.05% Tween-20 (TBST). Immunodetection was performed overnight at 4°C in 3% non-fat milk powder in TBST using an anti-AR (1:1000) rabbit polyclonal antibody, anti-IκBα (1:1000) mouse monoclonal antibody, or anti-PSA (1:500) goat polyclonal antibody. Antibodies against HSP90 (1:1000, rabbit polyclonal) and α-tubulin (1:1000, mouse monoclonal) were used to 59

assess loading. Proteins were detected with horseradish peroxidase-conjugated secondary antibodies and visualized on autoradiography film using enhanced chemiluminescence detection (GE Healthcare).

Pathway analysis Enriched gene pathways were identified using Ingenuity Pathway Analysis (IPA), the Database for Annotation, Visualization and Integrated Discovery (DAVID), and Gene Set Enrichment Analysis. Significantly regulated genes (p<0.05) were uploaded into IPA software v9.0 (Ingenuity Systems, CA, USA) in separate lists for the combination vs. bicalutamide alone and the combination vs. vorinostat alone. Each gene was mapped to its corresponding molecule in the Ingenuity pathways knowledge base, and core analysis identified enriched pathways and networks in the dataset against a background of the Affymetrix Human GeneChip ST 1.0 array. Lists of genes significantly regulated by the combination compared to either vehicle control or either of the individual agents were uploaded to the Functional Annotation Tool available through DAVID (http://david.abcc.ncifcrf.gov/; (Dennis et al., 2003, Huang da et al., 2009)), converted to DAVID default IDs, and analyzed against a background of the microarray platform. Genes significantly regulated by the combination, either uniquely or when compared with the individual agents, were analyzed against a background of all genes significantly regulated by the combination over vehicle control. Enriched (GO) biological processes and KEGG pathways were identified using DAVID. Gene Set Enrichment Analysis (Subramanian et al., 2005) was implemented using the Broad Institute’s public GenePattern server (http://genepattern.broadinstitute.org/gp/pages/index.jsf), with default parameters.

Transfection of siRNA and Expression Constructs For siRNA transfection, LNCaP or VCaP cells were seeded directly into transfection medium containing phenol red free (PRF) RPMI 1640, lipofectamine 2000 (Life Technologies), and reconstituted scrambled siRNA control or siRNA targeting NFKBIA at a concentration of 10 nM. Four siRNAs were tested, and #1 and #4 were found to be the most effective at achieving knockdown. After 4 h of culture, additional PRF-RPMI 60

medium containing FBS and L-glutamine was added to the LNCaP transfection mixture, while DMEM containing FBS, L-glutamine and non-essential amino acids was added to the VCaP transfection mixture. Cells were harvested for counting and assessment of cell death using Trypan blue dye exclusion, and then lysed in RIPA buffer for immunoblot analysis three days post-treatment (LNCaP) or six days post-treatment (VCaP).

For transient transfection of lentiviral constructs expressing green fluorescent protein (GFP) or co-expressing NFKBIA and GFP, LNCaP cells were seeded at ~40% confluency and allowed to attach overnight. Growth medium was removed and replaced with transfection medium containing PRF-RPMI, lipofectamine 2000, and 1.5 µg plasmid DNA. As for siRNA transfection, additional medium containing FBS, L-glutamine and either vehicle control or combination therapy was added to the transfection mix after 4 h of culture. At three days post-treatment, fluorescent cells were visualized using a fluorescent microscope, and transfection efficiency was estimated at between 40 and 50%. Cells were then harvested and assessed for death using Trypan blue dye exclusion, after which they were lysed in RIPA buffer for subsequent immunoblot analysis.

Results

The combination of bicalutamide and vorinostat commits AR-dependent prostate cancer cells to death within 8 hours of culture Combining the AR antagonist bicalutamide with the HDAC inhibitor vorinostat induces synergistic cell death of prostate cancer cells, with multiple features characteristic of apoptosis [25]. In order to accurately tailor the design of microarray studies, the timing and cellular requirements for cell death induced by the combination were determined in AR- dependent prostate cancer cells. A basal level of cell death of approximately 10% in LNCaP cells and 15-30% in VCaP cells was observed when cells were treated with vehicle control and low doses of bicalutamide (2.5 μM) or vorinostat (1 μM) alone (Fig. 1A). A significant increase in cell death was observed in LNCaP (up to 30%) and VCaP (up to 50%) cells treated with the combination of bicalutamide and vorinostat (Fig. 1A). To determine whether the synergy was due to one agent sensitizing the cells to the other agent, 61

LNCaP cells were treated sequentially with each individual agent alone and subsequently assessed for viability. Maximal cell death was only achieved when both agents were simultaneously present in culture, not when agents were used sequentially (Fig. 1B). Pre- treatment with the protein synthesis inhibitor cycloheximide indicated that cell death induced by the combination is at least partially reliant on de novo protein synthesis. High dose vorinostat, known to rely on de novo protein synthesis for induction of cell death, was included as a positive control (Fig. 1C). Finally, drug wash out studies determined the timing of molecular events leading to prostate cancer cell death. A percentage of death comparable to a full three days of combination therapy (~30%) was observed when the cells had been cultured with the drugs for at least 8 hours or more before drug washout and subsequent culture with standard culture media for 3 days (Fig. 1D), indicating that molecular events that induce irreversible cell death occur within 6-8 hours of treatment.

Gene expression profiles in LNCaP cells treated with bicalutamide and vorinostat Gene expression profiling of LNCaP cells treated with vehicle control, bicalutamide or vorinostat alone and a combination of the agents was carried out to identify the molecular mechanisms underlying combination-induced cell death. A treatment time of 6 hours was selected based on the wash out study (Fig. 1D). A total of 7,497 genes were significantly modulated (p < 0.05) by the agents alone or in combination when compared with vehicle control (Fig. 2A). Of the 5,873 genes modulated by the combination, ~70% were also modulated by vorinostat alone, ~2% were also modulated by bicalutamide alone, and ~7% were modulated by both of the individual agents. A total of 1,209 genes (~20%) were uniquely regulated by the combination. As expected of an HDAC inhibitor, which increases histone acetylation and thereby promotes chromatin accessibility and transcriptional activation, vorinostat induced significantly more genes than it repressed (Fig. 2B). In contrast, bicalutamide alone or the combination induced and repressed approximately equal proportions of genes.

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Figure 1

Figure 1. Characterization of cell death caused by the combination of bicalutamide and vorinostat. LNCaP cells (2 x 104 cells per well in 24-well plates) were cultured in triplicate wells with either vehicle control [VEH] bicalutamide [BIC] (2.5 µM) or vorinostat [VOR] (1 µM), individually and in combination [BIC+VOR], in RPMI 1640 supplemented with 10% FBS. VCaP cells (5 x 104 cells per well in 24-well plates) were cultured in triplicate wells with bicalutamide (1.25 µM) or vorinostat (0.5 µM), individually and in combination, in DMEM supplemented with sodium pyruvate, non-essential amino acids and 10% FBS. (A) LNCaP cells were counted at 2, 4 and 6 days of culture with bicalutamide and vorinostat, while VCaPs were counted at 4, 6 and 8 days of culture. (B) LNCaP cells were cultured with treatment one (1.) for 24 h, which was removed and replaced with treatment two (2.) for 48 h. (C) LNCaP cells were pre-treated for 1 h with cycloheximide (10 µM), and then cultured for 3 d with indicated treatments (HIGH BIC = 50 µM, HIGH VOR = 7.5 µM). (D) LNCaP cells were cultured with indicated treatments, and at 1, 2, 4, 6, 8, 16, or 24 h treatment medium was removed and replaced with normal culture medium not containing either agent until a total of 72 h of culture. At each end point, cells were counted using a haemocytometer, and assessed for viability using trypan blue dye exclusion. Cell death is expressed as a percentage of total cell number. Values indicated are the mean of triplicate wells ± SEM, and are representative of three independent experiments. * = p < 0.05 using one-way ANOVA with Bonferroni post-hoc test. 63

Three genes –PMEPA1, PGM2L1 and STEAP1- were randomly selected as representative examples of different expression patterns and quantified by qRT-PCR in the microarray samples and an independently generated sample set. All three genes showed a comparable pattern of regulation in both the microarray and the validation RNA sets (Supplementary Figure 1), indicating that the microarray data was robust.

Combined bicalutamide and vorinostat treatment antagonizes the expression of androgen- regulated genes As both bicalutamide and vorinostat have been shown to modulate AR levels and activity, it was feasible that cell death induced by the combination results from enhanced blockade of androgen signaling and the consequent antagonism of androgen-regulated genes required for growth and survival. To assess this possibility, genes modulated by the combination therapy were compared with LNCaP androgen-regulated genes defined in a previous study by Wang and colleagues (Wang et al., 2009). Approximately half of the androgen-regulated genes in LNCaP cells were significantly altered by the combination treatment (Fig. 3A). As expected, gene set enrichment analysis (GSEA) revealed that the genes upregulated by androgen treatment were generally repressed by the combination treatment (Fig. 3B). A high proportion of the genes significantly regulated by the combination were also regulated to a similar magnitude by bicalutamide or vorinostat treatment alone. However, as induction of cell death was restricted to the combination treatment, we focused on the genes that showed selective alteration with the combination compared to the individual agents. Combination treatment altered 412 genes compared to vorinostat alone and 6,035 genes compared to bicalutamide alone. We refined these lists to include only the genes that were significantly altered (p < 0.05; ANOVA with FDR adjustment) by the combination when compared to both of the individual agents, which yielded 216 genes (Fig. 3C; full list shown in Supplementary Table 2).

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Figure 2

Figure 2. Summary of genes expression changes after treatment with bicalutamide, vorinostat, and the combination. LNCaP cells were treated with vehicle control, bicalutamide, vorinostat and bicalutamide and vorinostat in combination, and RNA was extracted at 6 hours of treatment. Microarray analysis was performed on Affymetrix Human GeneChip 1.0 ST Arrays, with 6 biological replicates per sample. (A) Venn diagram of genes with mRNA levels significantly changed by vorinostat (light grey), bicalutamide (dark grey) or the doses of vorinostat and bicalutamide in combination (white), when compared with vehicle control. Circles represent both significantly up-regulated and significantly down-regulated genes over vehicle control, and sizes are proportional to the number of genes. (B) Numbers of genes significantly up- regulated and significantly down-regulated by each treatment when compared with vehicle control (collapsed to gene level).

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Interestingly, approximately half of the genes in this set have been reported to be androgen-regulated (Fig. 3D), suggesting that enhanced blockade of androgen signaling by the combination was likely to be a mechanism of cell death. To test this hypothesis, six known androgen regulated genes, KLK2, KLK3 (PSA), NKX3-1, IGF1R, NFKBIA and C1orf116, that exhibited significantly greater regulation by the combination in the microarray compared with individual agents alone were analyzed by qRT-PCR in an independent sample set. Each of these genes are frequently used to assess androgen receptor signaling (31). All six genes were significantly down regulated (> 2-fold) after 6 hours of treatment with the combination when compared with vehicle control (Fig. 4A). For KLK2, KLK3 (PSA), NKX3-1, IGF1R, and C1orf116, the effect of the combination therapy largely paralleled that of bicalutamide treatment alone; comparable activity of both treatments was also observed at the protein level for PSA (KLK3) (Fig 4B). Interestingly, NFKBIA, an androgen regulated gene that encodes an inhibitor of the NF-κB complex (IκBα), was significantly down regulated by the combination compared to individual treatments of both bicalutamide and vorinostat at 6 hours of culture. This observation suggested that prostate cancer cell death induced by the combination may not be occurring solely due to more complete blockade of androgen signaling, and other pathways involving a subset of androgen-regulated and non-androgen-regulated genes are likely to be involved.

Loss of NFKBIA contributes to cell death induced by the combination of bicalutamide and vorinostat To investigate other cellular pathways that potentially mediate the effects of the combination therapy, three independent gene annotation enrichment programs were utilized: Ingenuity Pathway Analysis (IPA), the Database for Annotation, Visualization and Integrated Discovery (DAVID), and GSEA. Due to the significant overlap between pathways enriched by the combination and the individual agents (Supplementary Table 3), we restricted our analysis to the set of 216 genes (Fig. 3C) that were significantly regulated by the combination treatment when compared to the effects of the individual agent treatments.

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Figure 3

Figure 3. The overlap between gene expression changes after treatment with the combination therapy and gene expression changes after treatment with androgen. (A) Venn diagram of the overlap between genes significantly changed by the combination of bicalutamide and vorinostat compared with vehicle control (white) and genes significantly changed by treatment with 100 nM DHT for 16 h from the Wang (2009) dataset (grey). Circles represent both up-regulated and down-regulated genes and are proportional to the number of genes. (B) Genes regulated by treatment with the combination of bicalutamide and vorinostat (compared with vehicle control) are negatively correlated with genes induced by DHT in prostate cancer cells, as assessed by GSEA. Sextuplicate sets of expression profiles were compared between LNCaP cells treated with vehicle control, vorinostat, bicalutamide or the combination of vorinostat and bicalutamide. Probe sets in the data were collapsed to gene level, assigned a score based on a signal-to-noise ratio algorithm and rank-ordered by this score. DHT-induced genes in the ordered data set are shown as black lines (middle), and the running enrichment score is plotted (bottom). The change in expression of each gene in response to the combination treatment is shown as a heatmap (top). (C) Flowchart of the refinement process involved in determining genes involved in the cell death caused by the combination. The refined list included the 216 genes that were significantly altered (p < 0.05; ANOVA with FDR adjustment) by the combination when compared to both of the individual agents. (D) Venn diagram of genes with mRNA levels significantly changed by the combination of vorinostat and bicalutamide [BIC+VOR vs individual agents] when compared with vehicle control, bicalutamide and vorinostat individually (white) and genes regulated by 100 nM DHT from the Wang (2009) dataset (grey). Circles represent both up-regulated and down-regulated genes and are proportional to the number of genes.

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Figure 4

Figure 4. Quantitative analysis of androgen regulated genes altered by the combination therapy. (A) Quantitative real-time PCR analysis of an independent RNA sample set, generated by treatment of LNCaP cells with vehicle control [VEH], 1 µM vorinostat [VOR], 2.5 µM bicalutamide [BIC] or the combination of 1 µM vorinostat and 2.5 µM bicalutamide [BIC+VOR] in triplicate for 6 h. The expression of KLK2, KLK3, NKX3-1, IGF1R, NFKBIA and C1orf116 was normalized to GUSB and HPRT1. Fold changes are expressed relative to vehicle control. Values indicated are the mean of technical and biological replicates ± SEM, and are representative of three independent experiments. * = p < 0.05 using one-way ANOVA with Bonferroni post-hoc test, compared with bicalutamide. # = p < 0.05 compared with vorinostat. (B) Western blot analysis of lysates from LNCaP cells cultured with either vehicle control [VEH], 1 µM vorinostat [VOR], 2.5 µM bicalutamide [BIC] or the combination of 1 µM vorinostat and 2.5 µM bicalutamide [BIC+VOR] for 12, 48, or 96 h. Steady state levels of PSA (KLK3) are shown, and hsp90 is shown as a loading control.

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This refined gene set was enriched for a number of pathways potentially important in the synergistic action of the combination therapy, including pathways involved in cell cycle, cell death and cell motility, such as p53 signaling, Jak-STAT signaling, and axon guidance (Table 1). Interestingly, the androgen-regulated gene NFKBIA was present in many of these pathways, implying that it may be a key regulator of the combination therapy. Supporting this concept, the network tool in IPA identified NFKBIA or its associated genes as central molecules in the top two significantly enriched networks (Supplementary Figure 2, Supplementary Table 4). Furthermore, when scrutinizing the list of 216 genes with enhanced regulation by the combination, NFKBIA was one of the most significantly down- regulated genes in response to combination treatment (>1.5 fold change) when compared to both of the individual agents alone (Supplementary Table 2). These results collectively implicated a role for NFKBIA in the molecular action of the combination treatment. Down-regulation of NFKBIA by the combination treatment was validated in a set of RNA samples generated from an independent time-course experiment (Fig. 5A). NFKBIA mRNA was repressed within 3 hours by the combination compared to both vehicle (approximately 4-fold down-regulated) and to each agent individually (approximately 2- fold). This repression was maintained for at least 6 hours of culture, but returned to levels similar to bicalutamide alone by 12 hours. Western blot analysis showed that protein levels of the corresponding protein, IκBα, were also reduced by the combination (Fig. 5B). The significant and rapid modulation of NFKBIA suggested that it might be an upstream initiator of processes involved in cell death mediated by the combination therapy. NFKBIA is a known regulator of the NF-B and p53 signaling pathways (Chang, 2002), and p53 signaling was identified as a highly enriched pathway in cells treated with the combination (Table 1).

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Table 1 Pathway analysis for genes significantly modulated by the combination compared to individual agents alone. Ingenuity Pathway Analysis DAVID (KEGG) Pathway Analysis IGF1R, LAMA1, FYN, PMAIP1, Molecular Mechanisms of LAMA3, KLK3, NKX3- NFKBIA, GNA15, Pathways in cancer Cancer 1, NFKBIA, ZBTB16, RHOU, PLCB1 PIAS1, MMP1 SYNJ1, SGK1, IGF1R, LAMA1, Inositol Phosphate PLCB1, MAK Focal adhesion LAMA3, FYN, THBS1, Metabolism VCL PMAIP1, TP53INP1, SOCS2, LIFR, PIAS1, p53 Signalling Jak-STAT signalling THBS1, SNAI2, IL6R pathway TNFRSF10B NFKBIA, NKX3-1, Prostate Cancer Signalling LAMA1, LAMA3, KLK3 Small cell lung cancer NFKBIA, PIAS1

SGK1, PLCB1, Aldosterone Signalling in IGF1R, KLK3, NKX3-1, DNAJB14 Prostate cancer Epithelial Cells NFKBIA

IGF1R, FYN, SNAI2, Glioma Signalling IGF1R Adherens junction VCL Biosynthesis of Steroids HMGCR NRP1, FYN, NTNG1, Axon guidance EFNA5 NRF2-mediated Oxidative GSTM2, MAF, ECM-receptor LAMA1, LAMA3, Stress Response DNAJB14, ABCC4 interaction THBS1 Xenobiotic Metabolism GSTM2, MAF, IGF1R, GUCY1A3, Long-term depression Signalling CYP3A5 PLCB1 Adipocytokine signalling NFKBIA, ACSL3, TWEAK Signalling NFKBIA pathway CAMKK2 TUBB, GUCY1A3, TNFR1 Signalling NFKBIA Gap junction PLCB1 TNFRSF10B, PMAIP1, TNFR2 Signalling NFKBIA p53 signalling pathway THBS1 Role of CHK Proteins in T cell receptor signalling Cell Cycle Checkpoint HUS1 CD8B, FYN, NFKBIA pathway Control Receptor POLE2, GUCY1A3, NFKBIA Purine metabolism Signalling PDE9A Cytokine-cytokine TNFRSF10B, LIFR, Prolactin Signalling FYN, SOCS2 receptor interaction IL6R Hematopoietic cell PI3K/AKT Signalling NFKBIA CD8B, IL6R lineage Insulin Receptor FYN, SGK1 ABC transporters ABCC4, ABCG1 Signalling NFKBIA, Death Receptor Signalling Drug metabolism CYP3A5, NAT1 TNFRSF10B Growth Hormone SOCS2, IGF1R Sphingolipid metabolism PPAP2A, DEGS1 Signalling Metabolism of Assembly of RNA BDP1 xenobiotics by GSTM2, CYP3A5 Polymerase III Complex cytochrome P450 70

Given this association between NFKBIA (IκBα) and p53 signaling, the expression of two p53 inducible genes, TP53INP1 and CDKN1A (p21) was measured. At early time points of 3 and 6 hours, neither TP53INP1 nor CDKN1A mRNA was altered by combination therapy or either of the agents individually. However, at 12 hours of culture, the combination induced expression of both genes by approximately four-fold when compared with vehicle control, and two-fold when compared with bicalutamide or vorinostat (Fig. 5C). The up- regulation of TP53INP1 and CDKN1A occurred after the decrease in NFKBIA, and at a similar time point to the observed loss of IκBα protein (Fig. 5B), supporting the hypothesis that the combination mediates NFKBIA-dependent activation of p53 signaling.

Specific knockdown or overexpression of NFKBIA alters LNCaP cell viability and response to the combination of bicalutamide and vorinostat To investigate the functional significance of NFKBIA for prostate cancer cell viability and the observed drug responses, siRNA-mediated knockdown of NFKBIA was performed in both LNCaP and VCaP cells. Transfection with two different siRNAs targeting NFKBIA resulted in a marked reduction in IκBα protein levels at three days post-treatment for LNCaPs, and six days post-treatment for VCaPs (Fig. 6A). Depletion of IκBα with siRNA #1 or #4 caused a significant induction of cell death in LNCaP cells (23-25% compared to the basal level of 10% observed with non-specific siRNA; p<0.05; Fig. 6B) and VCaP cells (25% compared to the basal level of 15% observed with non-specific siRNA; p<0.05; Fig. 6B). This data indicated that IκBα is an important regulator of prostate cancer cell viability in two separate AR-positive cell lines. Moreover, IκBα depletion by siRNA #4 caused cell death of a similar magnitude to that observed with combination treatment in the presence of the non-specific siRNA control, and significantly enhanced cell death caused by the combination (approximately 47% cell death with the NFKBIA siRNA compared to 32% observed with non-specific siRNA; Supplementary Figure 3).

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Figure 5

Figure 5. The effect of combination therapy on NFKBIA levels and p53 signaling. (A) The expression of NFKBIA over a timecourse was normalized to GUSB and HPRT1. Relative expression is the fold change expressed relative to vehicle control. Values indicated are the mean of technical and biological replicates ± SEM, and are representative of three independent experiments. * = p < 0.05 using one-way ANOVA with Bonferroni post-hoc test, compared with bicalutamide or vorinostat individually. (B) Western blot analysis of lysates from LNCaP cells cultured with either vehicle control [VEH], 1 µM vorinostat [VOR], 2.5 µM bicalutamide [BIC] or the combination of 1 µM vorinostat and 2.5 µM bicalutamide [BIC+VOR] for 12, 48, or 96 h. Steady state levels of IκBα are shown, and tubulin is shown as a loading control. (C) mRNA expression levels of TP53INP1 and CDKN1A are shown as for NFKBIA. * = p < 0.05 using one-way ANOVA with Bonferroni post-hoc test

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To directly assess the importance of IκBα down-regulation in combination therapy- mediated cell death, we tested whether ectopic over-expression of NFKBIA altered response to the combination treatment. Transfection efficiency of the NFKBIA expression construct, which co-expresses GFP, was estimated at between 30-50% by flow cytometry and fluorescent microscopy (data not shown). Increased levels of IκBα protein after transfection, compared to cells transfected with a control GFP-only expression vector, were confirmed by immunoblotting (Fig. 6C). Treatment of cells overexpressing GFP with the combination of vorinostat and bicalutamide induced approximately 34% cell death which, as expected, was significantly greater than in vehicle-treated controls. By contrast, in cells overexpressing IκBα, cell death caused by the combination was significantly reduced to ~17%, which was not significantly different to the basal cell death observed with vehicle control treatment. This finding indicates that downregulation of IκBα is an essential requirement for synergistic cell death induced by the combination of bicalutamide and vorinostat.

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Figure 6

Figure 6. NFKBIA is a critical mediator of death induced by combinatorial AR targeting. LNCaP cells (1 x 105 cells per well in 12-well plates) or VCaP cells (2 x 105 cells per well in 12- well plates) were transfected in triplicate wells with 10 nM of either non-specific, scrambled siRNA [N.S.] or specific siRNAs [siRNA #1 and siRNA #4] targeting NFKBIA. LNCaP cells were transfected in triplicate wells with 1.5 µg of plasmid expressing either GFP or NFKBIA+GFP, for four hours. The transfection medium was then overlaid with vehicle control [VEH], or the combination [BIC + VOR] to give a final concentration of 2.5 µM bicalutamide and 1 µM vorinostat in either PRF-RPMI 1640 supplemented with 10% FBS and 1% l-glutamine (LNCaP) or DMEM supplemented with non-essential amino acids, 1% l-glutamine and 10% FBS (VCaP). (A) Western blot analysis of lysates from LNCaP and VCaP cells transfected with either non-specific [N.S.] or specific [#1 and #4] NFKBIA siRNA. Steady state levels of IκBα at day three (LNCaP) or day six (VCaP) of treatment are shown, with hsp90 as a loading control. (B) Cells were counted at three days (LNCaP) or six days (VCaP) of vehicle control +/- siRNA treatment, and assessed for viability using trypan blue dye exclusion. Cell death is expressed as a percentage of total cell number. Values indicated are the mean of triplicate wells ± SEM, and are representative of three independent experiments. (C) Western blot analysis of lysates from LNCaP cells transfected with plasmids expressing GFP only [GFP] or NFKBIA and GFP [NFKBIA], and then treated with vehicle control [VC] or combination [BIC+VOR]. Steady state levels of IκBα at day three of treatment are shown, with hsp90 as a loading control. (D) Cells were counted at three days of treatment, and assessed for viability using trypan blue dye exclusion. Cell death is expressed as a percentage of total cell number. Values indicated are the mean of triplicate wells ± SEM, and are representative of three independent experiments. * = p < 0.05 using t-test.

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Discussion Metastatic prostate cancer inevitably becomes resistant to current hormonal therapies and, consequently, combinatorial therapeutic approaches that may be more efficacious and less prone to resistance-associated failure have garnered significant interest. We have previously shown that combining the AR antagonist bicalutamide with the HDAC inhibitor vorinostat synergistically induces growth arrest and cell death in prostate cancer cells. Importantly, this combination approach uses doses of both agents that are individually sub- effective (Marrocco et al., 2007b), implying that it would minimize dose-related toxicity. The current study provides new insight into the molecular mechanism by which these disparate agents interact synergistically to induce death of prostate cancer cells, and implicates IκBα, a regulator of the NF-κB and p53 pathways, as a critical factor for prostate cancer cell viability and treatment response.

Given that vorinostat and bicalutamide both target AR, we initially hypothesized that the combination of these two agents would enhance blockade of androgen signaling, a pathway that promotes growth and survival of prostate cancer cells. This hypothesis was reinforced by previous work from our laboratory demonstrating that the combination treatment induced death only in cells with a functional AR signaling axis, and that addition of excess dihydrotestosterone (DHT) to the system prevented cell death (Marrocco et al., 2007b). The genome-wide microarray expression data generated in the current study also supported this hypothesis. Specifically, many androgen-regulated genes were significantly altered by the combination compared to individual agents, and pathway analysis demonstrated deregulation of androgen signaling and prostate cancer networks by the combination treatment. Interestingly, for six known androgen regulated genes, we did not observe consistently greater downregulation by the combination therapy in an independent set of RNA samples. This could suggest that enhanced androgen blockade is not the substantive mechanism by which the combination exerts its effect. However, this subset of genes only represents approximately 10% of the genes significantly regulated by both DHT and the combination (when compared with the individual agents), and 1% of the total genes significantly regulated by both DHT and the combination (when compared with vehicle control). It is possible that small changes to a large number of androgen regulated 75

genes is an important factor in the mechanism of action of the combination therapy, or that these cumulative changes are able to sensitize the prostate cancer cells to HDAC inhibition.

While the importance of enhanced blockade of androgen signaling by the combination treatment remains ambiguous, the expression data revealed that this treatment also modulates a multitude of other critical cellular processes. For example, p53 signaling and other pathways involved in cell cycle arrest and cell death were highly enriched in genes modulated by the combination. In considering potential mediators of the synergistic interaction between vorinostat and bicalutamide, we noted that our pathway analyses consistently implicated an inhibitor of NF-κB signaling, NFKBIA, in this phenomenon. Interestingly, NFKBIA is an androgen regulated gene, and the protein encoded by this gene, IκBα, is best known as an inhibitor of NF-κB signaling. However, IκBα can also inhibit p53 signaling and thereby functions dichotomously to either block p53-mediated cell death or NF-κB-mediated cell growth (Ryan et al., 2000, Chang, 2002, Fujioka et al., 2004, Puszynski et al., 2009), with the final phenotypic outcome likely depending on the relative levels of NF-κB and p53 within a given cell. At a mechanistic level, IκBα sequesters NF-κB or p53 in the cytoplasm in an inactive complex; following various stimulatory events, IκBα is phosphorylated and targeted for degradation via the ubiquitin- proteasome pathway, relieving inhibition of these factors. With respect to prostate cancer cell death, we observed rapid downregulation of NFKBIA in cells treated with the combination of bicalutamide and vorinostat that was associated with increased expression of TP53INP1 and CDKN1A, two commonly known p53-inducible genes, and induction of cell death. Taken together, this data suggests that, in the context of the combination therapy, loss of IκBα results in cell death, which may be facilitated by the induction of p53 signaling. This hypothesis is supported by the observation that knockdown of NFKBIA in the absence of drug treatment resulted in a similar level of cell death to that observed with the combination in two independent prostate cancer cell lines. Moreover, co-treatment with NFKBIA siRNA and the combination therapy caused significantly more cell death than either individual treatment. Importantly, overexpression of NFKBIA almost completely negated the effect of the combination treatment on cell death. 76

Two other observations arising from the current study are worth noting for their clinical ramifications. First, the combination of bicalutamide and vorinostat worked equally well in models with a mutant (LNCaP) or amplified wild-type (VCaP) AR gene. Given that many clinical prostate cancers are characterized by aberrant AR signaling, and that intra-tumoral heterogeneity may result in foci that each potentially have structurally different androgen receptors, this is a promising feature of the combination therapy. Second, both vorinostat and bicalutamide are required simultaneously in culture for induction of cell death, indicating that if sensitization is happening it occurs rapidly. This finding indicates that future clinical testing will require the agents to be dosed together and not sequentially.

Conclusion

In summary, we have defined a novel mechanism of action by which bicalutamide and vorinostat, when used in combination, mediate death of prostate cancer cells. While enhanced blockade of androgen signaling is potentially important, we have demonstrated that other cellular pathways also play critical roles. Specifically, IκBα was identified as a critical regulator of therapy-mediated cell death; this factor may have potential either as a new therapeutic target and/or a marker of drug response. The ability to monitor molecular markers of apoptotic response to such therapeutic strategies will aid in the clinical development of this combinatorial approach for treatment of prostate cancer.

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Competing Interests

The authors declare that they have no competing interests.

Author Contributions

SLC carried out all of the cell biology and molecular studies, conducted the IPA and DAVID microarray analyses, participated in the design of the study and drafted the manuscript. MMC prepared samples for the microarray and participated in manuscript drafting. WDT participated in the design of the study and manuscript drafting. LAS performed the GSEA analysis, provided assistance with the other microarray analyses, and contributed to the design of the study and manuscript drafting. LMB conceived of the study, designed the study and participated in manuscript drafting. All authors read and approved the final manuscript.

Acknowledgements This work was supported by the National Health and Medical Research Council of Australia (627185 to WDT & LMB), Cancer Australia (627229 and 1012337 to LMB & WDT), the Royal Adelaide Hospital Research Committee (to MMC, LMB & WDT), an infrastructure grant from the Prostate Cancer Foundation of Australia (2011/0452 to WDT), Cancer Council of South Australia Senior Research Fellowship (to LMB), a Future Fellowship from the Australian Research Council (to LMB), Young Investigator grants from the Prostate Cancer Foundation of Australia (LAS, YI 0810; MMC, YI 0412), a Young Investigator Award from the Prostate Cancer Foundation (the Foundation 14 award; LAS) and the Lions Medical Research Foundation Post-Graduate Scholarship (SLC). We thank Ms Natalie Ryan for her assistance in generating material for the microarray study, Ms Joanna Gillis for her assistance in generating material for the VCaP knockdown studies, Ms Heather Armstrong for technical assistance, and Dr Paul Neilsen for expert advice on p53 signaling.

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Supplementary Figure 1

Supplementary Figure 1. Microarray validation by RT-PCR

Validation of the microarray results, using three of the most markedly changed genes: PMEPA1 was down-regulated by all three treatments, PGM2L1 was up-regulated by vorinostat and the combination but not bicalutamide, and STEAP1 was down-regulated uniquely by the combination. Expression values taken from the microarray, qRT-PCR on three of the biological replicates used in the microarray, and qRT-PCR on an independently generated sample set were compared for these three genes. Three technical replicates for each biological replicate were analysed, and the expression normalised to RPL32 and GUSB. Fold change was determined over vehicle control. Values indicated are the mean of technical and biological replicates ± SEM.

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Supplementary Figure 2

Supplementary Figure 2. Enriched networks in list of 216 genes with enhanced regulation by the combination. The network tool in Ingenuity Pathway Analysis (IPA) identified the networks of Cell Morphology, Cellular Movement and Cell Signaling (A), as well as Cellular Movement, Drug Metabolism, Endocrine System Development and Function (B), as the two most significantly enriched by the combination treatment when compared to the individual agents (list of 216 genes). Colors indicate the regulation by the combination when compared to both of the individual agents – green is downregulation, red is upregulation and half green half red means that the combination upregulated the gene compared to one treatment, and downregulated compared to the other. The lines in between the genes represent a network connection.

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Supplementary Figure 3

Supplementary Figure 3. Knockdown of NFKBIA enhances the cell death effect observed with the combination therapy.

(A) Western blot analysis of lysates from LNCaP cells transfected with either non-specific [N.S.] or specific [siRNA #1 and siRNA #4] NFKBIA siRNA and then treated with vehicle control [VC], 2.5 µM bicalutamide [BIC], 1 µM vorinostat [VOR], or the combination of 2.5 µM bicalutamide and 1 µM vorinostat [BIC+VOR]. Steady state levels of IκBα at day three of treatment are shown, with hsp90 as a loading control. (B) Cells were counted at three days of treatment, and assessed for viability using trypan blue dye exclusion. Cell death is expressed as a percentage of total cell number. Values indicated are the mean of triplicate wells ± SEM, and are representative of three independent experiments. * = p < 0.05 using t-test.

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Supplementary Table 1 Primer sequences Primer name Primer Sequence PMEPA1 forward GTCTGCACGGTCCTTTGCTC PMEPA1 reverse CGTTGCGCCCTGCAGATCCT PGM2L1 forward GGG CAG TGG CTC CGC TGG GAT AA PGM2L1 reverse CTGCCCCCATGGCAGAACGAAGT STEAP1 forward GAAGAGTGGGTGGCTGAAGCCATAC STEAP1 reverse ATGCTGGTCTCTCCCGTGTCCTTA KLK2 forward GGTGGCTGTGTACAGTCATGGAT KLK2 reverse TGTCTTCAGGCTCAAACAGGTTG KLK3 forward ACCAGAGGAGTTCTTGACCCCAAA KLK3 reverse CCCCAGAATCACCCGAGCAG NKX3-1 forward CTGGCAGAGACCGAGCCAGAAAG NKX3-1 reverse AGCGCTTCTGCGGCTGCTTAG IGF1R forward TTACTTCTGCTCAGATGCTCCAA IGF1R reverse TCGATGAGCCCCATGAAGTT NFKBIA forward ATGTGGACGACCGCCACGACA NFKBIA reverse ATGGCCAAGTGCAGGAACGAGTC C1orf116 forward AGCCACAACTCCCAGAGGTTT C1orf116 reverse TCGTCCTTGCTGAGTGATGG TP53INP1 forward CGTCTGGGTACCTGAACGAGGTG TP53INP1 reverse GGAGATTAAAGTGCACAGGGTGCTT CDKN1A forward TGGACCTGGAGACTCTCAGGGTCG CDKN1A reverse TTAGGGCTTCCTCTTGGAGAAGATC HPRT1 forward GTTATGGCGACCCGCAG HPRT1 reverse ACCCTTTCCAAATCCTCAGC RPL19 forward TGCCAGTGGAAAAATCAGCCA RPL19 reverse CAAAGCAAATCTCGACACCTTG GUSB forward CGTCCCACCTAGAATCTGCT GUSB reverse TTGCTCACAAAGGTCACAGG

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Supplementary Table 2 List of 216 genes significantly regulated by combination therapy vs individual agents.

COMBINATION vs COMBINATION vs VORINOSTAT BICALUTAMIDE

fold Gene name Refseq p value fold change p-value change NKX3-1 NM_006167 -2.85263 4.03917E-15 -1.71618 3.01878E-09 C1orf116 NM_023938 -2.29797 1.00377E-13 -1.2955 0.000131532 SNAI2 NM_003068 -2.26569 1.37528E-08 2.44675 1.53482E-10 KLK2 NM_001002231 -2.18714 4.32992E-17 -1.67071 3.45062E-13 ZBTB16 NM_006006 -2.06001 1.80277E-15 -1.18913 0.000464414 NFKBIA NM_020529 -1.99927 2.89549E-12 -1.73419 7.52679E-11 SLC45A3 NM_033102 -1.94909 1.62206E-11 -1.40117 1.90529E-06 PMEPA1 NM_020182 -1.94777 3.04781E-13 -1.19992 0.00105158 THBS1 NM_003246 -1.94256 1.62206E-11 1.19777 0.00516974 GREB1 NM_014668 -1.93631 2.02681E-11 -1.66296 8.09975E-10 KLK3 NM_001030047 -1.88542 3.03362E-11 -1.4109 9.25534E-07 IGF1R NM_000875 -1.81509 2.5694E-06 1.28255 0.010904 FAM105A NM_019018 -1.65066 6.59152E-08 1.61776 9.73339E-09 ERRFI1 NM_018948 -1.63259 1.76756E-09 1.15713 0.0119312 C19orf48 NM_199249 -1.61513 1.77213E-06 -1.42754 1.68933E-05 GSTM2 AK299482 -1.60376 0.00443709 -1.4744 0.00258998 MAF NM_001031804 -1.54978 8.57574E-06 1.21777 0.0124486 KCNN2 NM_021614 -1.53408 8.22921E-09 -1.24277 8.65643E-05 DEFB132 NM_207469 -1.52821 0.000332638 -1.55176 1.32244E-05 MTMR9 NM_015458 -1.51593 1.00358E-08 1.23868 8.53063E-05 ANKRD11 NM_013275 -1.51363 0.0233201 -1.246 0.00261579 PMAIP1 NM_021127 -1.48284 7.61257E-06 -1.38986 9.60643E-06 GUCY1A3 NM_000856 -1.46278 4.88019E-11 -2.09312 6.9488E-19

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PMCHL2 NR_003922 -1.46033 0.000166825 -1.4456 1.63806E-05 LOC440905 BC144438 -1.44438 0.000518842 -1.63341 3.13904E-07 SLC16A6 NM_004694 -1.44186 1.19851E-06 -1.58817 6.54705E-10 PAK1IP1 NM_017906 -1.43754 2.96653E-07 -1.22247 0.000258585 EHF NM_012153 -1.43242 0.000118337 -1.16633 0.0468649 MAK NM_005906 -1.43061 5.81896E-05 1.24288 0.00246284 RHOU NM_021205 -1.42853 8.54787E-09 1.54736 7.90368E-12 PRAGMIN NM_001080826 -1.4272 0.000394532 1.18041 0.0461671 TMEFF2 NM_016192 -1.42413 1.59171E-06 -1.27494 4.67987E-05 RBM24 NM_001143942 -1.42369 0.000182169 -1.28177 0.00116033 POTEF NM_001099771 -1.42199 0.000017682 -1.14076 0.00815964 CDC42EP3 NM_006449 -1.4116 0.000619201 1.45215 1.31468E-05 POTEE NM_001083538 -1.4075 1.75241E-05 -1.36046 6.6062E-06 STEAP1 NM_012449 -1.40644 0.0299445 -1.63868 2.97397E-05 PCOTH NM_001014442 -1.39672 0.000780485 -1.31468 0.000668641 C11orf92 NM_207429 -1.38919 7.61257E-06 -1.17762 0.00507725 CYP3A5 NM_000777 -1.38189 0.00813625 1.32818 0.00241973 ADAM7 NM_003817 -1.37649 0.000017682 -1.5865 1.08094E-09 NRP1 NM_003873 -1.36951 6.17256E-06 -1.7328 4.37988E-12 C2orf14 NR_023391 -1.36558 2.5694E-06 -1.57738 8.2715E-11 KLK15 NM_017509 -1.36448 3.57777E-06 -1.132 0.0205857 SLC26A2 NM_000112 -1.36339 0.00498382 1.20191 0.0353808 TIGD6 NM_030953 -1.36243 0.00126287 -1.6648 1.91442E-08 FAM65B NM_014722 -1.3601 0.00103024 -1.53107 3.83991E-07 KCNRG NM_173605 -1.35861 0.0168558 -1.38481 0.000696497 SLC36A1 NM_078483 -1.35667 0.000460334 1.38383 1.21549E-05 POTEH NM_001136213 -1.3528 7.84713E-05 -1.13923 0.0389797 POTEG NR_027480 -1.3517 4.23205E-05 -1.16338 0.0276417 GRHL2 NM_024915 -1.35168 4.83588E-07 -1.12894 0.0088105 ZBTB10 NM_001105539 -1.35026 4.95954E-05 1.34762 3.5931E-06 STAG3L4 NM_022906 -1.34978 0.00770934 -1.60766 7.03512E-07

84

NSMAF NM_003580 -1.34298 1.21493E-05 -1.84715 2.18716E-13 ARHGAP6 NM_013427 -1.34097 0.000234727 -1.50337 5.33969E-08 ELK4 NM_001973 -1.33268 1.12727E-05 -1.32879 8.63354E-07 CENPN NM_001100624 -1.32956 0.000248414 -1.54311 9.81429E-09 RASSF3 NM_178169 -1.32943 0.00697337 -1.2492 0.0060122 PPFIBP1 NM_003622 -1.32768 1.70814E-05 1.11902 0.037655 LRRC63 ENST00000446175 -1.32554 0.00477314 1.39475 3.73919E-05 KLK4 NM_004917 -1.32366 1.70814E-05 -1.1705 0.00261079 TNFAIP8 NM_014350 -1.32192 0.00323789 1.33784 0.000128873 ZFX NM_003410 -1.31983 0.0189417 -2.19773 4.09816E-11 IFIT5 NM_012420 -1.31619 0.0150013 -1.26417 0.00549174 SLC6A8 NM_005629 -1.31535 0.0168558 -1.29532 0.00239821 LOC388152 NR_027001 -1.3133 2.55963E-06 1.13484 0.00555988 LRRFIP2 NM_006309 -1.30955 0.000332638 1.21473 0.00137211 TBC1D8 NM_001102426 -1.30923 0.000229382 1.26004 0.000131386 FLJ39632 AK096951 -1.30909 0.0172272 -1.24091 0.0104091 LOC80154 NR_026811 -1.30735 1.31582E-06 1.1601 0.000648975 ZNF649 NM_023074 -1.30631 0.00477314 -1.57161 9.11615E-08 ELL2 NM_012081 -1.30432 1.70814E-05 1.22982 5.44546E-05 SLC41A1 NM_173854 -1.30335 0.000303451 1.32864 6.97019E-06 PHACTR2 NM_014721 -1.30058 0.00231945 -1.2224 0.00311677 HOMER2 NM_199330 -1.30011 0.00753275 -1.18537 0.0257886 C11orf82 NM_145018 -1.29855 0.000166825 1.17739 0.00347285 TRIM13 NM_213590 -1.29689 0.011402 -1.37265 0.00007642 PTGER4 NM_000958 -1.2968 0.00391548 1.31152 0.000154434 DNAJB14 NM_001031723 -1.29486 0.0221605 1.14474 0.0311174 DTX3L NM_138287 -1.29121 0.0106551 -2.74342 5.21202E-15 CENPL NM_001127181 -1.2898 0.00512119 -1.16591 0.0324195 KIAA1731 NM_033395 -1.28888 4.57265E-05 -1.18555 0.000756414 FGD4 NM_139241 -1.28885 0.00559844 1.26182 0.0010652 KIAA0040 NM_014656 -1.28504 0.0440293 1.42889 7.60315E-05

85

NAT1 NM_001160170 -1.28428 0.00559844 1.23406 0.00257767 SNRPD1 NM_006938 -1.28364 0.0495629 -1.3947 0.000231525 NTNG1 NM_001113226 -1.28259 0.0136633 -1.49772 1.21165E-06 IL6R NM_000565 -1.28216 0.000802121 -1.73353 2.10954E-11 PDE9A NM_002606 -1.28092 0.000189173 -1.15636 0.0066834 SOCS2 NM_003877 -1.28048 0.00165702 1.19372 0.00450061 NPC1 NM_000271 -1.28038 1.94242E-06 1.4947 5.45659E-12 C1orf21 NM_030806 -1.28031 0.000676027 -1.12758 0.0454704 ABHD2 NM_007011 -1.27833 2.51813E-09 -1.15052 7.85656E-06 SGEF NM_015595 -1.27724 0.0242508 -1.31422 0.00063356 ORC5L NM_002553 -1.27649 0.00010039 1.18115 0.00105469 CAMKK2 NM_006549 -1.27503 0.00218893 -1.22391 0.00128355 MICAL1 NM_022765 -1.27454 0.0292598 1.32794 0.000475872 SPSB1 NM_025106 -1.27369 0.00760768 1.33716 5.32521E-05 PDLIM5 NM_006457 -1.27335 0.000470672 1.265 4.70538E-05 STK17B NM_004226 -1.27082 0.0106963 1.5394 1.22363E-07 POLE2 NM_002692 -1.2699 0.0088152 -1.26746 0.000732675 ARHGAP28 NM_001010000 -1.2693 0.0114981 1.23976 0.00259073 SYNJ1 NM_003895 -1.26753 0.0193701 1.23436 0.00495445 LRCH1 NM_015116 -1.26055 0.0361916 1.49526 2.4626E-06 FBXO38 NM_205836 -1.25961 0.000518842 1.22145 0.000241306 CYB5A NM_148923 -1.25737 0.0203522 -1.17528 0.028904 SNORD82 NR_004398 -1.25229 0.0239256 -1.62732 1.8431E-08 SNX24 NM_014035 -1.25128 0.0436037 1.24039 0.00582121 FAM13A NM_014883 -1.25086 0.00468399 1.86376 2.87891E-12 CDYL2 NM_152342 -1.25074 0.0121503 -1.18028 0.0143575 PCTP NM_001102402 -1.25073 0.0434289 1.27471 0.00184999 RLN1 NM_006911 -1.24954 0.0038226 -1.71038 4.70142E-11 KIF20A NM_005733 -1.24773 0.0136247 -1.36293 1.35973E-05 PPM1K NM_152542 -1.246 0.0141688 1.38777 5.41003E-06 EFNA5 NM_001962 -1.24454 0.0256367 -1.23061 0.00356593

86

LOC388692 NR_027002 -1.24437 0.046137 -1.22108 0.00952941 SGK1 NM_001143676 -1.2443 0.00226988 1.33403 0.000002526 HMGCR NM_000859 -1.24328 0.00679091 1.36278 3.41534E-06 LIFR NM_002310 -1.24308 0.020547 1.84466 5.8966E-11 LAMA3 NM_198129 -1.24125 1.75703E-06 -1.16703 2.91644E-05 PPAP2A NM_003711 -1.23907 0.00403973 -1.27339 5.67999E-05 WAC NR_024557 -1.23689 0.0487296 -1.261 0.00211193 TAF5L NM_014409 -1.23512 0.00565963 -1.32068 9.23375E-06 ACSL3 NM_004457 -1.23374 1.28578E-05 1.20724 5.18208E-06 DEGS1 NM_003676 -1.23296 0.0179251 -1.14515 0.0444441 PLCB1 NM_182734 -1.23276 0.0366559 -1.17957 0.0218053 CEP120 NM_153223 -1.23224 0.000454345 1.16282 0.00168347 FNBP1L NM_001024948 -1.23211 0.0130111 1.32246 2.47439E-05 TNFRSF10B NM_003842 -1.22932 0.00217589 -1.146 0.0105049 TRIM49 NM_020358 -1.22902 0.0171721 1.14227 0.0296197 STEAP2 NM_152999 -1.22745 0.00441367 -1.48198 6.64615E-09 DPY30 NM_032574 -1.2245 0.0160576 -1.14123 0.0395834 UBE2D1 NM_003338 -1.22019 0.0382366 1.37082 1.25117E-05 ZNF608 NM_020747 -1.21987 0.0307706 1.37637 6.45117E-06 SLC38A2 NM_018976 -1.21979 0.000619201 -1.21754 4.74618E-05 UNQ9368 NR_003542 -1.21858 0.0283178 1.15068 0.034808 C16orf53 NM_024516 -1.21702 0.0179251 -1.87725 2.38198E-12 FAM111B NM_198947 -1.21678 0.0026419 1.21201 0.000235082 LOC285550 NM_001145191 -1.21602 0.0475263 -1.42379 2.47409E-06 CHD7 NM_017780 -1.21437 0.00474847 1.6144 4.29346E-11 ZC3H12A NM_025079 -1.21272 0.0193701 -1.49045 2.762E-08 CENPO NM_024322 -1.21256 0.00678114 -1.29311 9.5216E-06 SETMAR NM_006515 -1.21104 0.00480239 -1.82389 2.18716E-13 ARHGAP1 NM_004308 -1.20884 0.0186786 -1.45814 5.54669E-08 JAG1 NM_000214 -1.2087 0.0088152 1.2731 2.94141E-05 MTAP NM_002451 -1.20853 0.0104781 -1.18678 0.00229679

87

ADAMTS1 NM_006988 -1.20813 0.000158831 1.19496 2.50011E-05 RAD51AP1 NM_001130862 -1.20773 0.01297 -1.13886 0.0247796 SLC4A4 NM_001098484 -1.20713 0.0121503 -2.28298 7.3824E-16 CLU NM_001831 -1.20606 0.00252481 1.41409 5.63014E-09 PIAS1 NM_016166 -1.20581 0.0147346 1.33082 3.84152E-06 TRPM8 NM_024080 -1.2048 0.03315 -1.1684 0.0133436 SLC25A37 NM_016612 -1.20433 0.00152472 -1.23799 1.44455E-05 DNAH5 NM_001369 -1.2031 0.0118843 -1.52086 1.65706E-09 MAP9 NM_001039580 -1.198 0.000260986 1.26344 2.35426E-07 ABCC4 NM_005845 -1.1976 0.0193701 -1.16294 0.00819867 ASAP1 NM_018482 -1.19624 0.0216666 -1.47661 1.34963E-08 HEATR6 NM_022070 -1.19442 0.0452897 -1.57445 3.4437E-09 POLK NM_016218 -1.19437 0.0452897 1.27388 0.000144607 HUS1 NM_004507 -1.19315 0.00745052 -1.58995 2.51043E-11 ZFHX3 NM_006885 -1.19175 0.034665 -1.86273 1.31423E-12 C2orf3 NM_003203 -1.19098 0.0372785 -1.15922 0.0136335 THRB NM_001128176 -1.18919 0.0381349 -1.53812 3.73598E-09 FOXA1 NM_004496 -1.18912 0.00202986 -1.66655 1.78559E-13 TEX2 NM_018469 -1.18457 0.0172272 1.18506 0.00135089 PNMA1 NM_006029 -1.18296 0.0129595 1.32546 6.18498E-07 VCL NM_014000 -1.18266 0.0484857 1.36669 1.55222E-06 MKI67 NM_002417 -1.18264 0.0238298 -1.30865 4.06673E-06 AKAP1 NM_003488 -1.17966 9.43564E-05 -1.15191 6.08622E-05 LIMCH1 NM_014988 -1.17844 0.0079617 -1.28749 1.19732E-06 HJURP NM_018410 -1.17647 0.0484857 -1.26786 6.53543E-05 FASN NM_004104 -1.17567 0.0374548 -1.15132 0.011091 TPT1 NM_003295 -1.17344 0.0026419 -1.28656 1.37452E-07 BDP1 NM_018429 -1.17082 0.00247934 -1.21714 7.15654E-06 PHF8 NM_015107 -1.16806 0.024751 -1.1125 0.0389841 USP46 NM_022832 -1.16762 0.0345012 -1.27752 1.03017E-05 LAMA1 NM_005559 -1.16542 0.00768003 1.20896 0.000035093

88

RAB6C NM_032144 -1.16276 0.0265154 -1.33007 2.87615E-07 KIAA1244 NM_020340 -1.13492 0.00165702 -1.35123 1.46722E-11 PRUNE2 NM_015225 -1.13403 0.0307473 -1.4197 1.94907E-10 UAP1 NM_003115 -1.12791 0.0172347 1.23959 3.5231E-07 HEBP2 NM_014320 -1.11861 0.0256562 -1.11445 0.00299574 APPBP2 NM_006380 -1.11813 0.0374571 1.15617 0.000191647 SORD NM_003104 -1.10795 0.00537706 -1.10625 0.000459683 TUBB NM_178014 -1.07757 0.0282495 -1.13917 1.36199E-06 GUSBL1 BC171739 1.11327 0.0171929 -1.16655 3.57553E-06 SMA5 AK289851 1.12009 0.00134603 -1.14772 5.01879E-06 GUSBL2 NR_003660 1.13571 0.00120797 -1.13162 0.000117385 MMP1 NM_002421 1.15926 0.0304391 1.10598 0.0462673 TRIM42 NM_152616 1.16259 0.0361803 1.14018 0.0105706 CACNA2D1 NM_000722 1.16731 0.0215678 1.1314 0.0129334 PLEKHH2 NM_172069 1.17777 0.045188 1.26206 0.000080112 TFAP2B NM_003221 1.18459 0.0287645 1.14901 0.0134207 EPB41L4A NM_022140 1.18684 0.0223049 1.17029 0.00420387 CYTH2 NM_017457 1.18754 0.0131972 -1.27475 0.00024265 CD8B NM_172099 1.19557 0.039108 1.13767 0.0404713 FAM167A NM_053279 1.20637 0.00760768 1.28625 0.000010588 SIDT1 NM_017699 1.21484 0.03315 1.38019 4.50727E-06 C12orf60 NM_175874 1.22378 0.0484857 1.2154 0.00643523 GNA15 NM_002068 1.22852 0.0383984 1.19226 0.0131736 TP53INP1 NM_033285 1.23274 0.000706435 1.18673 0.000622981 FYN NM_002037 1.2355 0.00273241 2.5358 2.08151E-17 SYTL2 NM_206927 1.2466 0.000626777 1.15691 0.0048956 AVIL NM_006576 1.25662 0.0422237 1.22889 0.00945533 IL1F6 NM_014440 1.26047 0.00568403 1.15234 0.0313232 LOC143188 NR_015409 1.26395 0.0452897 1.23658 0.0098958 LIPH NM_139248 1.27535 0.0374548 2.08284 5.9782E-11 GSDMB NM_001042471 1.27842 0.00477314 2.77277 3.17093E-16

89

KIAA1324 NM_020775 1.30532 0.00692554 1.41758 0.000013169 BAMBI NM_012342 1.31377 0.00882871 1.31373 0.000678913 ABCG1 NM_207627 1.316 0.00403973 1.29473 0.000579727 SLC7A11 NM_014331 1.32953 9.86063E-06 -1.42286 1.14536E-08 HBEGF NM_001945 1.33382 0.0141688 1.62228 7.18267E-07 CHAC1 NM_024111 1.33858 0.0374548 -1.81066 1.63379E-07 DDIT3 NM_004083 1.34439 0.00669707 -1.33003 0.000724264

90

Supplementary Table 3 Comparison of pathway analyses of bicalutamide, vorinostat, or the combination versus vehicle control, using KEGG pathways in the Database for Annotation, Visualization and Integrated Discovery (DAVID), Gene Set Enrichment Analysis (GSEA) and Ingenuity Pathway Analysis (IPA)

Bicalutamide vs Vehicle Control Vorinostat vs Vehicle Control Combination vs Vehicle Control KEGG Pathway Term p-value KEGG Pathway Term p-value KEGG Pathway Term p-value hsa03022: Basal transcription hsa00562: Inositol phosphate hsa05222: Small cell lung cancer 0.0022 8.45E-06 7.25E-07 factors metabolism hsa00512: O-Glycan biosynthesis 0.0069 hsa04210: Apoptosis 5.50E-05 hsa04210: Apoptosis 1.83E-06 hsa04070: Phosphatidylinositol hsa05200: Pathways in cancer 0.0075 hsa04520: Adherens junction 7.48E-05 1.23E-05 signaling system hsa04130: SNARE interactions in hsa05215: Prostate cancer 0.0104 hsa04115: p53 signaling pathway 9.47E-05 1.66E-04 vesicular transport hsa04920: Adipocytokine signaling hsa00562: Inositol phosphate 0.0194 4.00E-04 hsa04110: Cell cycle 1.78E-04 pathway metabolism hsa00500: Starch and sucrose 0.0253 hsa05200: Pathways in cancer 5.33E-04 hsa00310: Lysine degradation 2.97E-04 metabolism hsa04960: Aldosterone-regulated 0.0253 hsa04070: Phosphatidylinositol 7.63E-04 hsa05223: Non-small cell lung 7.99E-04

91

sodium reabsorption signaling system cancer hsa00053: Ascorbate and aldarate 0.0259 hsa04110: Cell cycle 0.0011 hsa04144: Endocytosis 0.0012 metabolism hsa00040: Pentose and glucuronate hsa04130: SNARE interactions in hsa03022: Basal transcription 0.0305 0.0021 0.0014 interconversions vesicular transport factors hsa04730: Long-term depression 0.0633 hsa05210: Colorectal cancer 0.0027 hsa04520: Adherens junction 0.0017 hsa05120: Epithelial cell signaling in hsa04012: ErbB signaling pathway 0.0660 0.0031 hsa00100: Steroid biosynthesis 0.0023 Helicobacter pylori infection hsa04360: Axon guidance 0.0833 hsa05213: Endometrial cancer 0.0032 hsa04115: p53 signaling pathway 0.0024 hsa04914: - hsa05221: Acute myeloid leukemia 0.0840 hsa05212: Pancreatic cancer 0.0044 0.0026 mediated oocyte maturation hsa04662: B cell receptor signaling 0.0872 hsa05215: Prostate cancer 0.0046 hsa03410: Base excision repair 0.0026 pathway hsa04120: Ubiquitin mediated hsa00983: Drug metabolism 0.0927 0.0055 hsa04710: Circadian rhythm 0.0030 proteolysis hsa05223: Non-small cell lung 0.0059 hsa05211: Renal cell carcinoma 0.0031 cancer hsa00310: Lysine degradation 0.0074 hsa04910: Insulin signaling 0.0032

92

pathway hsa00280: Valine, leucine and 0.0074 hsa05215: Prostate cancer 0.0034 isoleucine degradation hsa04666: Fc gamma R-mediated hsa04330: Notch signaling pathway 0.0082 0.0037 phagocytosis hsa05211: Renal cell carcinoma 0.0083 hsa05200: Pathways in cancer 0.0062 hsa05220: Chronic myeloid 0.0091 hsa05212: Pancreatic cancer 0.0067 leukemia hsa00100: Steroid biosynthesis 0.0106 hsa05222: Small cell lung cancer 0.0077 hsa04722: Neurotrophin hsa05222: Small cell lung cancer 0.0181 0.0149 signaling pathway hsa04120: Ubiquitin mediated hsa04142: Lysosome 0.0200 0.0172 proteolysis hsa00450: Selenoamino acid hsa03040: Spliceosome 0.0201 0.0182 metabolism hsa05120: Epithelial cell hsa00450: Selenoamino acid 0.0209 signaling in Helicobacter pylori 0.0192 metabolism infection

93

hsa05216:Thyroid cancer 0.0239 hsa05214: Glioma 0.0228 hsa00860: Porphyrin and 0.0265 hsa03040: Spliceosome 0.0237 chlorophyll metabolism hsa04012: ErbB signaling pathway 0.0313 hsa00510: N-Glycan biosynthesis 0.0238 hsa04914: Progesterone-mediated hsa05220: Chronic myeloid 0.0369 0.0242 oocyte maturation leukemia hsa04012: ErbB signaling hsa00600: Sphingolipid metabolism 0.0404 0.0248 pathway hsa04010: MAPK signaling hsa03020: RNA polymerase 0.0410 0.0268 pathway hsa04960: Aldosterone-regulated hsa04910: Insulin signaling pathway 0.0461 0.0281 sodium reabsorption hsa05110: Vibrio cholerae infection 0.0474 hsa00920: Sulfur metabolism 0.0313 hsa05219: Bladder cancer 0.0362 hsa04114: Oocyte meiosis 0.0391 hsa05110: Vibrio cholerae 0.0401 infection hsa04150: mTOR signaling 0.0486

94

pathway hsa05213: Endometrial cancer 0.0486 hsa05130: Pathogenic 0.0491 Escherichia coli infection

Bicalutamide vs Vehicle Vorinostat vs Vehicle Control Combination vs Vehicle Control Control

GSEA DOWN GSEA UP GSEA UP GSEA DOWN

HSA01030: GLYCAN HSA01032: GLYCAN HSA01030: GLYCAN HSA00150: ANDROGEN HSA04730: LONG TERM STRUCTURES STRUCTURES STRUCTURES AND DEPRESSION BIOSYNTHESIS 1 DEGRADATION BIOSYNTHESIS 1 METABOLISM (p-value 0) (p-value 0) (p-value 0.031621) (p-value 0) (p-value 0.003861) HSA04930: TYPE II HSA04530: TIGHT JUNCTION HSA04012: ERBB HSA00534: HEPARAN

DIABETES MELLITUS (p-value 0) SIGNALING PATHWAY SULFATE BIOSYNTHESIS

95

(p-value 0.00198) (p-value 0.034908) (p-value 0.003846) HSA05120: EPITHELIAL HSA04630: JAK STAT CELL SIGNALING IN HSA05110: CHOLERA HSA00330: ARGININE AND SIGNALING PATHWAY HELICOBACTER PYLORI INFECTION PROLINE METABOLISM (p-value 0.00206) INFECTION (p-value 0.0355) (p-value 0.007937) (p value 0.005906) HSA05120: EPITHELIAL HSA00641: 3 HSA00533: KERATAN CELL SIGNALING IN CHLOROACRYLIC ACID HSA04360: AXON GUIDANCE SULFATE BIOSYNTHESIS HELICOBACTER PYLORI DEGRADATION (p-value 0.006036) (p-value 0.04208) INFECTION (p-value 0.0125) (p-value 0.01004) HSA04660: T CELL HSA04520: ADHERENS HSA00720: REDUCTIVE RECEPTOR SIGNALING HSA04530: TIGHT JUNCTION JUNCTION CARBOXYLATE CYCLE PATHWAY (p-value 0.01417) (p-value 0.018) (p-value 0.04365) (p-value 0.009747) HSA00980: METABOLISM HSA05130: PATHOGENIC HSA04910: INSULIN HSA04360: AXON GUIDANCE OF XENOBIOTICS BY ESCHERICHIA COLI SIGNALING PATHWAY (p-value 0.020243) CYTOCHROME P450 INFECTION EHEC (p-value 0.04536)

96

(p-value 0.02692) (p-value 0.009921) HSA00760: NICOTINATE HSA05131: PATHOGENIC HSA04670: LEUKOCYTE HSA00534: HEPARAN AND NICOTINAMIDE ESCHERICHIA COLI TRANSENDOTHELIAL SULFATE BIOSYNTHESIS METABOLISM INFECTION EPEC MIGRATION (p-value 0.04573) (p-value 0.03) (p-value 0.009921) (p-value 0.02439) HSA05130: PATHOGENIC HSA05215: PROSTATE HSA00251: GLUTAMATE HSA00511: N GLYCAN ESCHERICHIA COLI CANCER METABOLISM DEGRADATION INFECTION EHEC (p-value 0.03107) (p-value 0.01) (p-value 0.04609) (p-value 0.02729) HSA00220: UREA CYCLE HSA04070: HSA05131: PATHOGENIC HSA05218: MELANOMA AND METABOLISM OF PHOSPHATIDYLINOSITOL ESCHERICHIA COLI

(p-value 0.03306) AMINO GROUPS SIGNALING SYSTEM INFECTION EPEC (p-value 0.010267) (p-value 0.04619) (p-value 0.02729) HSA04660: T CELL HSA05210: COLORECTAL HSA00330: ARGININE AND HSA00510: N GLYCAN RECEPTOR SIGNALING CANCER PROLINE METABOLISM BIOSYNTHESIS PATHWAY (p-value 0.03861) (p-value 0.024291) (p-value 0.04647) (p-value 0.033333)

HSA05010: ALZHEIMERS HSA04070:

97

DISEASE PHOSPHATIDYLINOSITOL (p-value 0.026) SIGNALING SYSTEM (p-value 0.037657) HSA00600: SPHINGOLIPID HSA05110: CHOLERA METABOLISM INFECTION (p-value 0.031068) (p-value 0.038462) HSA00720: REDUCTIVE CARBOXYLATE CYCLE (p-value 0.044898)

Bicalutamide vs Vehicle Control Vorinostat vs Vehicle Control Combination vs Vehicle Control

Ingenuity canonical pathway (p-value) Ingenuity canonical pathway (p-value) Ingenuity canonical pathway (p-value)

TNFR2 Signaling 1.45E-03 Molecular Mechanisms of Cancer 7.41E-08 Inositol Phosphate Metabolism 2.75E-08 Type II Diabetes Mellitus Signaling 1.70E-03 Hereditary Breast Cancer Signaling 2.82E-05 p53 Signaling 3.39E-08 Nicotinate and Nicotinamide 4.79E-03 Prostate Cancer Signaling 7.59E-05 Hereditary Breast Cancer 8.91E-06

98

Metabolism Signaling MIF Regulation of Innate Immunity 8.13E-03 Cyclins and Cell Cycle Regulation 9.12E-05 Molecular Mechanisms of Cancer 1.15E-05 O-Glycan Biosynthesis 1.45E-02 RANK Signaling in Osteoclasts 1.58E-04 Death Receptor Signaling 1.70E-05 Regulation of eIF4 and p70S6K Death Receptor Signaling 1.48E-02 Signaling 1.70E-04 ATM Signaling 4.47E-05 Neuregulin Signaling 1.58E-02 p53 Signaling 2.95E-04 RANK Signaling in Osteoclasts 5.13E-05 Germ Cell-Sertoli Cell Junction Induction of Apoptosis by HIV1 1.78E-02 Inositol Phosphate Metabolism 3.09E-04 Signaling 8.51E-05 TNFR1 Signaling 2.00E-02 Signaling 3.63E-04 CD27 Signaling in Lymphocytes 8.51E-05 Role of CHK Proteins in Cell Cycle mTOR Signaling 2.63E-02 Small Cell Lung Cancer Signaling 3.72E-04 Checkpoint Control 8.91E-05 Role of BRCA1 in DNA Damage p53 Signaling 2.69E-02 ATM Signaling 4.68E-04 Response 1.15E-04 Assembly of RNA Polymerase II April Mediated Signaling 2.88E-02 Complex 5.50E-04 Prostate Cancer Signaling 1.48E-04 B Cell Activating Factor Signaling 3.47E-02 Non-Small Cell Lung Cancer Signaling 6.31E-04 TWEAK Signaling 1.86E-04

99

Growth Hormone Signaling 3.55E-02 HGF Signaling 7.76E-04 Small Cell Lung Cancer Signaling 3.02E-04 Glycine, Serine and Threonine Metabolism 3.55E-02 TWEAK Signaling 7.76E-04 Induction of Apoptosis by HIV1 3.47E-04 Non-Small Cell Lung Cancer RAR Activation 3.72E-02 Chronic Myeloid Leukemia Signaling 9.77E-04 Signaling 5.13E-04 4-1BB Signaling in T Lymphocytes 4.17E-02 Induction of Apoptosis by HIV1 1.05E-03 Estrogen Receptor Signaling 5.25E-04 IL-12 Signaling and Production in Macrophages 4.17E-02 VEGF Signaling 1.23E-03 Insulin Receptor Signaling 5.75E-04 Inositol Phosphate Metabolism 4.57E-02 Death Receptor Signaling 1.26E-03 SAPK/JNK Signaling 7.08E-04 Pentose and Glucuronate Germ Cell-Sertoli Cell Junction Aldosterone Signaling in Interconversions 4.68E-02 Signaling 1.29E-03 Epithelial Cells 7.59E-04 Production of Nitric Oxide and Reactive Oxygen Species in Polyamine Regulation in Colon Lymphotoxin β Receptor Macrophages 4.90E-02 Cancer 1.51E-03 Signaling 8.71E-04 LPS-stimulated MAPK Signaling 4.90E-02 Pancreatic Adenocarcinoma Signaling 1.82E-03 Apoptosis Signaling 1.00E-03 Prolactin Signaling 4.90E-02 CD27 Signaling in Lymphocytes 2.14E-03 Mitotic Roles of Polo-Like Kinase 1.05E-03

100

PI3K/AKT Signaling 2.29E-03 HGF Signaling 1.23E-03 Renal Cell Carcinoma Signaling 2.29E-03 Glioma Signaling 1.35E-03 Role of BRCA1 in DNA Damage Response 2.51E-03 14-3-3-mediated Signaling 2.14E-03 2.82E-03 GNRH Signaling 2.14E-03 NRF2-mediated Oxidative Stress Mitotic Roles of Polo-Like Kinase 2.95E-03 Response 2.19E-03 TNFR1 Signaling 3.02E-03 B Cell Receptor Signaling 2.34E-03 Protein Ubiquitination Pathway 3.39E-03 TNFR1 Signaling 2.63E-03 Pancreatic Adenocarcinoma Mediated Apoptosis Signaling 4.07E-03 Signaling 2.75E-03 Role of PKR in Interferon Apoptosis Signaling 4.90E-03 Induction and Antiviral Response 3.09E-03 Chronic Myeloid Leukemia Aryl Hydrocarbon Receptor Signaling 6.31E-03 Signaling 3.16E-03 ERK/MAPK Signaling 6.46E-03 TR/RXR Activation 3.16E-03 Breast Cancer Regulation by SAPK/JNK Signaling 6.61E-03 Stathmin1 3.39E-03

101

Endometrial Cancer Signaling 6.92E-03 AMPK Signaling 3.39E-03 Prolactin Signaling 6.92E-03 PPARα/RXRα Activation 3.39E-03 Regulation of eIF4 and p70S6K Type I Diabetes Mellitus Signaling 8.32E-03 Signaling 4.07E-03 Role of NFAT in Cardiac Ceramide Signaling 9.33E-03 Hypertrophy 4.07E-03 Cell Cycle: G2/M DNA Damage Checkpoint Regulation 0.010233 Renal Cell Carcinoma Signaling 4.07E-03 Breast Cancer Regulation by Stathmin1 0.010471 FAK Signaling 4.07E-03 Role of CHK Proteins in Cell Cycle Checkpoint Control 0.010471 Cyclins and Cell Cycle Regulation 4.07E-03 IL-6 Signaling 0.010715 Granzyme B Signaling 4.27E-03 Erythropoietin Signaling 0.01122 p70S6K Signaling 4.27E-03 Assembly of RNA Polymerase III Complex 0.011482 ERK/MAPK Signaling 5.01E-03 Synthesis and Degradation of Ketone Bodies 0.011482 CD40 Signaling 5.25E-03

102

0.012303 LPS-stimulated MAPK Signaling 5.75E-03 Cell Cycle: G1/S Checkpoint Regulation 0.012303 Prolactin Signaling 5.75E-03 Antiproliferative Role of TOB in T Cell Signaling 0.012303 Glutamate Metabolism 6.61E-03 Methionine Metabolism 0.015136 RAR Activation 6.92E-03 Type I Diabetes Mellitus IGF-1 Signaling 0.015849 Signaling 6.92E-03 NRF2-mediated Oxidative Stress Myc Mediated Apoptosis Response 0.016596 Signaling 7.41E-03 Production of Nitric Oxide and Reactive Oxygen Species in Macrophages 0.016982 Huntington's Disease Signaling 7.76E-03 Hypoxia Signaling in the Retinoic acid Mediated Apoptosis Cardiovascular System 0.018621 Signaling 8.13E-03 Insulin Receptor Signaling 0.020893 NF-κB Signaling 8.91E-03 Cell Cycle Regulation by BTG Family Nicotinate and Nicotinamide Proteins 0.02138 Metabolism 9.55E-03

103

Type II Diabetes Mellitus 0.02138 Signaling 9.55E-03 Sphingolipid Metabolism 0.02138 Erythropoietin Signaling 9.77E-03 DNA Methylation and Transcriptional Repression Signaling 0.02138 Melatonin Signaling 9.77E-03 Tight Junction Signaling 0.021878 Signaling in Neurons 0.01 Assembly of RNA Polymerase III TR/RXR Activation 0.023442 Complex 0.010715 Synthesis and Degradation of LPS-stimulated MAPK Signaling 0.023442 Ketone Bodies 0.010715 Rac Signaling 0.024547 Growth Hormone Signaling 0.012303 Production of Nitric Oxide and Reactive Oxygen Species in PTEN Signaling 0.024547 Macrophages 0.01349 EGF Signaling 0.024547 Methionine Metabolism 0.013804 Assembly of RNA Polymerase II TNFR2 Signaling 0.026303 Complex 0.015488 mTOR Signaling 0.026915 EIF2 Signaling 0.015488

104

0.02884 Protein Ubiquitination Pathway 0.015849 AMPK Signaling 0.029512 PI3K/AKT Signaling 0.016218 Glioma Signaling 0.030903 p38 MAPK Signaling 0.020417 ILK Signaling 0.030903 Rac Signaling 0.020893 Signaling 0.030903 PTEN Signaling 0.020893 EIF2 Signaling 0.031623 N-Glycan Biosynthesis 0.02138 OX40 Signaling Pathway 0.031623 IL-3 Signaling 0.023988 Cytotoxic T Lymphocyte-mediated Estrogen-Dependent Breast Apoptosis of Target Cells 0.034674 Cancer Signaling 0.024547 14-3-3-mediated Signaling 0.035481 Xenobiotic Metabolism Signaling 0.029512 p70S6K Signaling 0.036308 Melanoma Signaling 0.031623 DNA Double-Strand Break Repair by Homologous Recombination 0.037154 Cardiac Hypertrophy Signaling 0.033113 IL-10 Signaling 0.038019 mTOR Signaling 0.033884 Melanocyte Development and Glutamate Metabolism 0.038019 Pigmentation Signaling 0.033884 PKCθ Signaling in T Lymphocytes 0.039811 Pyrimidine Metabolism 0.034674 0.040738 DNA Double-Strand Break Repair 0.034674

105

by Homologous Recombination Role of PKR in Interferon Induction and Antiviral Response 0.041687 IGF-1 Signaling 0.037154 Cdc42 Signaling 0.042658 Angiopoietin Signaling 0.038905 Neurotrophin/TRK Signaling 0.044668 VEGF Signaling 0.039811 Cell Cycle Control of Lymphotoxin β Receptor Signaling 0.045709 Chromosomal Replication 0.041687 B Cell Receptor Signaling 0.045709 EGF Signaling 0.043652 Ceramide Signaling 0.044668 Docosahexaenoic Acid (DHA) Signaling 0.045709 CCR3 Signaling in Eosinophils 0.046774 Endometrial Cancer Signaling 0.047863

106

Supplementary Table 4 Ingenuity Pathway Analysis (IPA) network analysis for genes regulated by the combination when compared to the individual doses of bicalutamide and vorinostat. Combination vs. Vorinostat Focus Score Top Functions Molecules in Network Molecules ABCG1,ADAMTS1,AKAP1,BAMBI,Collagen type I,Collagen type Cell IV,Collagen(s),CYTH2,EHF,Fibrinogen,GNA15,HJUR Morphology, P,IL36A,KLK4,LAMA1,LAMA3,Laminin,LDL,MKI67, 46 24 Cellular MMP1 (includes EG:300339),NFkB Movement, Cell (complex),NPC1,Nr1h,PAK1IP1,Pdgf Signaling (complex),PDGF BB,PMAIP1,PMEPA1,RLN1/RLN2,SNAI2,Tgf beta,THBS1,TNFAIP8,TNFRSF10B,ZC3H12A Cellular Movement, Alpha Drug catenin,ARHGAP1,ARHGAP6,Calpain,CAMKK2,ELL2 Metabolism, ,ERK1/2,FSH,GTPASE,hCG,HOMER2,IL6R,JAK,Lh,LI 40 22 Endocrine FR,MAF,NFKBIA,NRP1,PIAS1,Pias,PLC,PLCB1,PPAP System 2A,SGK1,SLC4A4,SOCS2,STAT,STAT5a/b,STEAP1,T Development BC1D8,TMEFF2,VCL,Vegf,ZBTB10,ZBTB16 and Function Cell Cycle, DNA 26s Proteasome,AMPK,Ap1,CLU,Cytochrome Replication, c,EFNA5,ERRFI1,Estrogen Receptor,F Recombination, Actin,FASN,HMGCR,Hsp27,Hsp70,IGF1R,Insulin,KIF 36 20 and Repair, 20A,KLK2,KLK3,KLK15,NKX3-1,p85 (pik3r),PI3K Cellular Growth (complex),PLC gamma,POLK,Ras and homolog,RHOU,SLC38A2,SLC6A8,SYNJ1,THRB,TPT Proliferation 1,Trypsin,TUBB,Ubiquitin,ZFHX3 ABCC4,ACSL3,ANKRD11,C16orf53,C1orf21,CDC42E Lipid P3,CHD7,CYB5A,dihydrotestosterone,DPY30,EGFR Metabolism, ligand,FADS2,GFPT1,HPGD,HPGDS,KISS1R,LEP,ML 27 16 Small Molecule L4,MT-CO3,MT-CYB,MT-ND4,Mup1 (includes Biochemistry, others),NCOA1,NCOA4,PDE9A,PDLIM5,PMEPA1,PP Cancer ARG,PTGER4,Relaxin,SDPR,SORD,TGFB1,TRPM8,U AP1 ABHD2,alpha-estradiol,BAMBI,beta- Embryonic estradiol,Ca2+,CTNNB1,CYP3A5,FAM105A,GREB1, Development, GRHL2,GUCY1A3,GUCY2E,GUCY2F,HPGD,KCNN2,K 24 15 Organismal LK5,LRRFIP2,MAK,mir- Development, 124,MYOF,NRP2,Pbsn,POLE2,PPM1K,PTP4A1,PTP4 Cancer A2,REST,SEMA3C,SFRP2,SLC26A2,SMARCA4,STK1 7B,SYTL2,TCF7L1,WNT3 Gene APPBP2,AR,CDKN2A,CENPN,CPS1,Cyp2c40 Expression, (includes 22 14 Infectious others),DDC,DDIT4,EHF,EID3,EPHX1,FNBP1L,FOXA Disease, Lipid 1,FOXD3,HUS1,HUS1B,hydrogen 107

Metabolism peroxide,KLK4,NAT1 (includes EG:116632),NR3C1,NSMAF,ONECUT1,ORC4,ORC5, Pbsn,PMEPA1,POU5F1,PPFIBP1,PTP4A2,Rad9- Rad1- Hus1,RAD9A,RAD9B,SCAP,SLC45A3,TP53INP1 Cell Death, ANKRA2,BDP1,BLOC1S1,CCP110,CENPO,EAF1,EWS Cellular R1,FAM167A,FXR2,HEATR6,HNF4A,KCNRG,KDM5 Development, C,KRR1,Mediator,MTMR9,NECAB2,PNMA1,POTEG/ 20 13 Respiratory POTEM,PRUNE2,PTPN4,PTPN11,RB1,RNF40,SLC16 System A6,TADA1,TAF5L,TERT,TFAP2B,TFIIB,TIGD6,TNN Development T1,TRIP11,UBP1,UXT and Function Akt,ASAP1,AVIL,C2orf3,DDIT3,DEGS1,ELK4,ERK,FG D4,Focal adhesion Cellular kinase,FYN,Gpcr,GSTM2,HBEGF,Histone h3,IgG,IKK Movement, Gene 20 14 (complex),IL1,Immunoglobulin,Interferon Expression, Cell alpha,Jnk,Mapk,Mmp,Nfat (family),P38 Morphology MAPK,Pka,Pkc(s),Rac,Ras,SETMAR,SLC25A37,SNRP D1,SPSB1,SRC,TCR Genetic Actin,APP,ARHGEF26,ATP2B2,CACNA2D1,Caspase, Disorder, DLG4,DNAH1,DNAH5,DNAH9,DNAH10,DNAH14,D Respiratory NAI1,DNALI1,FAM65B,heparin,HOMER2,HTT,ICAM 19 13 Disease, Cellular 1,IL5,LIPH,NSF,OSTF1,PHF8,PRDX4,PTPN5,RAB6C Assembly and /WTH3DI,RASSF3,RASSF4,SAV1,SGK223,SLC7A11, Organization SNX24,STK4,WAC Molecular C11orf82,CHAC1,CMIP,CRYAA,DDR2,DNAJA3,DNAJ Transport, B14,DNAJC3,DNAJC7,DTX3L,GABA,GBP1,GRASP,HS Nucleic Acid P,HSPA1L,IFIT5,IFNG,IREB2,LRCH1,MICAL1,PARP9 18 12 Metabolism, ,PCTP,PDE4A,PHACTR2,PRDX2,SLC36A1,SRC,TRIM Small Molecule 8,TRIM25,TRPC4,UBE2D1,UBE2R2,VLDLR,ZFP36,Z Biochemistry FX DNA ALDH9A1,Calmodulin,CCNH,Ck2,EIF2S2,ELK3,HEB Replication, P2,JAG1,KCNN1,KCNN2,KCNN3,LIMCH1,MFAP2,mi Recombination, R-124,miR-21/miR-590- 14 10 and Repair, 5p,MTAP,PHLPP1,PHLPP2,PODXL,PPFIBP1,RAD51 Gene AP1,RNA polymerase Expression, Cell II,RPRM,SNAI2,SPHK2,THBS2,TOP1,TP53,TRIM13, Signaling TRIM29,Troponin t,TRPV4,Tubulin,USP39,USP46 Cell Death, Gene Expression, 2 1 LOC100287275/PCOTH,TAF1B Cellular Development Behavior, Skeletal and Muscular 2 1 System C1orf116,HOMER1 Development and Function, Cell Signaling 2 1 Cell Cycle, CEP120,SPICE1 108

Cellular Assembly and Organization, DNA Replication, Recombination, and Repair Genetic Disorder, Neurological 2 1 Disease, ADAM7,ITM2B Organismal Injury and Abnormalities Neurological Disease, Psychological 2 1 Disorders, Cell- RBFOX1,RBM24 To-Cell Signaling and Interaction Cellular Assembly and Organization, Nervous System 2 1 LRRC4C,NTNG1 Development and Function, Psychological Disorders Cellular Assembly and Organization, Cellular 2 1 Compromise, FBXO38,KLF7,USP7 DNA Replication, Recombination, and Repair

Combination vs. Bicalutamide Focus Score Top Functions Molecules in Network Molecules ABCG1,ADAMTS1,AKAP1,BAMBI,Collagen type Cell I,Collagen type Morphology, IV,Collagen(s),CYTH2,EHF,Fibrinogen,GNA15,HJUR 46 24 Cellular P,IL36A,KLK4,LAMA1,LAMA3,Laminin,LDL,MKI67, Movement, MMP1 (includes EG:300339),NFkB Cell Signaling (complex),NPC1,Nr1h,PAK1IP1,Pdgf (complex),PDGF 109

BB,PMAIP1,PMEPA1,RLN1/RLN2,SNAI2,Tgf beta,THBS1,TNFAIP8,TNFRSF10B,ZC3H12A Cellular Movement, Alpha Drug catenin,ARHGAP1,ARHGAP6,Calpain,CAMKK2,ELL2, Metabolism, ERK1/2,FSH,GTPASE,hCG,HOMER2,IL6R,JAK,Lh,LIF 40 22 Endocrine R,MAF,NFKBIA,NRP1,PIAS1,Pias,PLC,PLCB1,PPAP2 System A,SGK1,SLC4A4,SOCS2,STAT,STAT5a/b,STEAP1,TB Development C1D8,TMEFF2,VCL,Vegf,ZBTB10,ZBTB16 and Function Cell Cycle, DNA 26s Proteasome,AMPK,Ap1,CLU,Cytochrome Replication, c,EFNA5,ERRFI1,Estrogen Receptor,F Recombination, Actin,FASN,HMGCR,Hsp27,Hsp70,IGF1R,Insulin,KIF 36 20 and Repair, 20A,KLK2,KLK3,KLK15,NKX3-1,p85 (pik3r),PI3K Cellular (complex),PLC gamma,POLK,Ras Growth and homolog,RHOU,SLC38A2,SLC6A8,SYNJ1,THRB,TPT1 Proliferation ,Trypsin,TUBB,Ubiquitin,ZFHX3 ABCC4,ACSL3,ANKRD11,C16orf53,C1orf21,CCR10,C Energy DC42EP3,CHD7,CYB5A,dihydrotestosterone,DPY30, Production, EGFR Lipid ligand,EHHADH,HPGD,HPGDS,KISS1R,LEP,LIFR,ML 29 17 Metabolism, L4,MT-CO3,MT-CYB,Mup1 (includes Small Molecule others),MYF6,NCOA1,NCOA4,PDE9A,PDLIM5,PMEP Biochemistry A1,PPARG,PTGER4,Relaxin,SORD,TGFB1,TRPM8,UA P1 ABHD2,BAMBI,beta- Nucleic Acid estradiol,Ca2+,Calbindin,CTNNB1,CYP3A5,FAM105 Metabolism, A,GC,GREB1,GRHL2,GUCY1A3,GUCY2D,GUCY2E,GU Small Molecule 24 15 CY2F,HPGD,KCNN2,LRRFIP2,MAK,mir- Biochemistry, 124,MYOF,NRP2,Pbsn,POLE2,PPM1K,REST,SEMA3C Embryonic ,sGC,SLC26A2,SMARCA4,SPN,STK17B,SYTL2,TCF7L Development 1,WNT3 ALDOB,APPBP2,AR,CDKN2A,CENPN,CPS1,Cyp2c40 (includes Gene others),DDC,EHF,EID3,EPHX1,FNBP1L,FOXA1,FOXD Expression, 3,HUS1,HUS1B,hydrogen peroxide,KLK4,LIG1,NAT1 22 14 Infectious (includes Disease, Lipid EG:116632),NR3C1,NSMAF,ONECUT1,ORC5,Pbsn,P Metabolism MEPA1,POU5F1,PPFIBP1,PTP4A2,RAD1,Rad9- Rad1-Hus1,RAD9A,RAD9B,SLC45A3,TP53INP1 ANKRA2,BDP1,BLOC1S1,CCP110,CENPO,CNN1,EWS Gene R1,FAM167A,FLOT1,FXR2,HEATR6,HNF4A,KCNRG, Expression, KDM5C,KRR1,Mediator,MTMR9,NECAB2,PNMA1,P 20 13 Cell Death, OTEG/POTEM,PRUNE2,PTPN4,PTPN11,RB1,RNF40 Cellular ,SLC16A6,TADA1,TAF5L,TERT,TFAP2B,TFIIB,TIGD6 Development ,TNNT1,TRIP11,UBP1 Cellular Akt,ASAP1,AVIL,C2orf3,DDIT3,DEGS1,ELK4,ERK,FG Movement, D4,Focal adhesion 20 14 Gene kinase,FYN,Gpcr,GSTM2,HBEGF,Histone h3,IgG,IKK Expression, (complex),IL1,Immunoglobulin,Interferon 110

Cell alpha,Jnk,Mapk,Mmp,Nfat (family),P38 Morphology MAPK,Pka,Pkc(s),Rac,Ras,SETMAR,SLC25A37,SNRP D1,SPSB1,SRC,TCR Gene ABL2,AGRN,C11orf82,CHAC1,CMIP,CRYAA,DDR2,D Expression, NAJB14,DNAJC3,DNAJC7,DTX1,DTX3L,EVL,GABA,HS 18 12 P,IFIT5,IFNG,IREB2,LRCH1,MICAL1,PARP9,PCTP,PD Metabolism, E4A,PDE4D,PHACTR2,SLC36A1,SRC,TRIM8,TRIM25 Molecular ,TRPC4,UBE2D1,UBE2E2,UBE2R2,ZFP36,ZFX Transport Gastrointestina Actin,APP,ARC,ARHGEF26,CACNA2D1,Caspase,DLG l Disease, 4,DNAH1,DNAH5,DNAH6,DNAH8,DNAH9,DNAH10, Genetic DNAH14,DNAI1,DNALI1,FAM65B,heparin,HTT,ICA 17 12 Disorder, M1,IL5,LIPH,NSF,OSTF1,PHF8,PRDX4,PTPN5,RAB6 Respiratory C/WTH3DI,RASSF3,SGK223,SLC7A11,SNX24,STK4, Disease THY1,WAC DNA ALDH9A1,Calmodulin,Ck2,ELK3,HEBP2,JAG1,KCNN Replication, 1,KCNN2,KCNN3,LIMCH1,MAP2,MED14,MFAP2,mi Recombination, R-124,miR-21/miR-590- and Repair, 14 10 5p,MTAP,PHLPP2,PODXL,PPFIBP1,PTPRA,RAD51A Gene P1,RNA polymerase Expression, II,RPRM,S100B,SNAI2,SPHK2,THBS2,TOM1L1,TP53, Cellular TRIM13,Troponin t,TRPV4,Tubulin,USP46,XRCC1 Movement Cell Death, Gene 2 1 Expression, LOC100287275/PCOTH,TAF1B Cellular Development Behavior, Skeletal and Muscular 2 1 System C1orf116,HOMER1 Development and Function, Cell Signaling Cell Cycle, Cellular Assembly and Organization, 2 1 CEP120,SPICE1 DNA Replication, Recombination, and Repair Genetic Disorder, Neurological 2 1 Disease, ADAM7,ITM2B Organismal Injury and Abnormalities 111

Neurological Disease, Psychological 2 1 Disorders, Cell- RBFOX1,RBM24 To-Cell Signaling and Interaction Cellular Assembly and Organization, Nervous 2 1 System LRRC4C,NTNG1 Development and Function, Psychological Disorders Cellular Assembly and Organization, Cellular 2 1 Compromise, FBXO38,KLF7,USP7 DNA Replication, Recombination, and Repair

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Additional Figure 1

Additional Figure 1. Quantitative real-time PCR analysis of androgen regulated genes altered by the combination therapy – additional data to Figure 4. Quantitative real-time PCR analysis of an independent RNA sample set, generated by treatment of LNCaP cells with vehicle control, 1 µM vorinostat, 2.5 µM bicalutamide, or the combination of 1 µM vorinostat and 2.5 µM bicalutamide in triplicate for 3, 6, 9, or 12 h. The expression of AR, KLK2, KLK3, KLK4, PMEPA1, TMPRSS2, PTEN, TMEFF2, IGF1R, C1orf116 and NFKBIA was normalized to GUSB and HPRT1. Fold changes are expressed relative to vehicle control. Values indicated are the mean of technical and biological replicates ± SEM, and are representative of three independent experiments. * = p < 0.05 using one-way ANOVA with Bonferroni post-hoc test, compared with both bicalutamide and vorinostat individually. This additional figure shows the analysis of the full panel of androgen regulated genes investigated over an extended timecourse, which was not included in the manuscript submission.

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Additional Figure 2

Additional Figure 2. Quantitative real-time PCR analysis of androgen regulated genes by high and low doses of bicalutamide and vorinostat, and the combination of low doses of bicalutamide and vorinostat – additional data to Figure 4. Quantitative real-time PCR analysis of an independent RNA sample set, generated by treatment of LNCaP cells with vehicle control, high dose bicalutamide (50 µM), high dose vorinostat (7.5 µM), low dose bicalutamide (2.5 µM), low dose vorinostat (1 µM), or the combination of 1 µM vorinostat and 2.5 µM bicalutamide in triplicate for 6 h. The expression of KLK2, KLK3, KLK4, PMEPA1, TMPRSS2, PTEN, TMEFF2, IGF1R, C1orf116 and NFKBIA was normalized to GUSB and RPL19. Fold changes are expressed relative to vehicle control. Values indicated are the mean of technical and biological replicates ± SEM, and are representative of three independent experiments. * = p < 0.05 using one-way ANOVA with Bonferroni post-hoc test, compared with both bicalutamide and vorinostat individually. This additional figure shows the analysis of the full panel of androgen regulated genes investigated in the study, but with the addition of high doses of the individual agents. For all genes except C1orf116 and NFKBIA, the combination was either similar to the low doses of the agents individually, or it was not as effective as the high doses of the agents alone. C1orf116 showed a trend towards difference between the combination and the two low doses of the individual agents, but this difference was not significant. However, the combination reduced C1orf116 to a level that was similar to that achieved by the high dose of vorinostat alone, but not high dose bicalutamide. Given that the high dose of bicalutamide does not cause cell death, but the high dose of vorinostat does, this is a possible implication to investigate C1orf116 further in prostate cancer cell death. NFKBIA, on the other hand, was not only significantly further downregulated by the combination when compared to the individual low dose agents, but was downregulated to a level similar to that achieved with the high dose of vorinostat. This provides further evidence to the evidence presented in the submitted manuscript that NFKBIA plays a role in prostate cancer cell death. 114

Additional Figure 3

Additional Figure 3. Quantitative real-time PCR of NFKBIA and reversal of induction by the addition of androgen (DHT) – additional data to Figure 5. Quantitative real-time PCR analysis of an independent RNA sample set, generated by treatment of LNCaP cells with vehicle control [VEH], 2.5 µM bicalutamide [BIC], 1 µM vorinostat [VOR], or the combination of 1 µM vorinostat and 2.5 µM bicalutamide [BIC+VOR] in the presence of either ethanol or 10 nM DHT, in triplicate for 6 h. The expression of NFKBIA was normalized to GAPDH and PUM1. Fold changes are expressed relative to vehicle +ethanol. Values indicated are the mean of technical and biological replicates ± SEM and are representative of three independent experiments. Our laboratory has previously shown that the addition of excess DHT to culture medium containing the combination prevents the cell death normally seen with the combination therapy (Marrocco et al. 2007). Therefore, we reasoned that if NFKBIA was a key contributor to the cell death caused by the combination, then addition of excess DHT should prevent loss of NFKBIA caused by the combination therapy. DHT caused a ~1.6 fold increase in NFKBIA levels in the presence of vehicle control, which was expected as NFKBIA is an androgen regulated gene. In the presence of ethanol, the combination caused a ~2 fold decrease in NFKBIA mRNA levels. In the presence of excess DHT, this fold change was decreased to ~1.5 fold. Therefore, addition of DHT prevented some of the downregulation of NFKBIA by the combination, but not entirely. However, it appears that the absolute levels of NFKBIA may play a more important role, as despite the reduction in NFKBIA levels caused by the combination compared to the vehicle control in the presence of DHT, the levels remained approximately equal with that observed with vehicle+ethanol. While the level of NFKBIA appeared to correlate with conditions causing cell death – i.e. a threshold effect, this would require further investigation to be considered definitive.

115

Additional Figure 4

Additional Figure 4. Quantitative real-time PCR of p53-inducible genes – additional data to Figure 5. Quantitative real-time PCR analysis of a RNA sample set generated by treatment of LNCaP cells with vehicle control or the combination of 1 µM vorinostat and 2.5 µM bicalutamide in triplicate for 3, 6 or 12 h. The expression of TP53INP1, CDKN1A, Cyclin G, HDM2 or PMAIP1 was normalized to GUSB and HPRT1. Fold changes are expressed relative to vehicle control. Values indicated are the mean of technical and biological replicates ± SEM, and are representative of three independent experiments. * = p < 0.05 using one-way ANOVA with Bonferroni post-hoc test. This additional figure shows the entire panel of p53 regulated genes that were investigated by qRT-PCR. TP53INP1 and CDK1NA were the only genes that showed a significant regulation by the combination therapy (up to four fold change) when compared to the vehicle control, whereas other p53 regulated genes such as cyclin G, HDM2, and PMAIP1 showed a reduced level of regulation.

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Chapter 4:

The efficacy of the combination therapy in vivo

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4.1 Introduction

In this chapter, the LNCaP xenograft model was used to test the in vivo efficacy of the combination of low doses of vorinostat and bicalutamide. While 2D cell culture systems are useful for initial screens of drug or combination efficacy, they do not accurately recapitulate the complexities of the tumour environment within an organism. Furthermore, testing in vivo allows assessment of the consequences of administering these agents to an organism, such as pharmacodynamic properties, dosing levels, and toxicity.

Briefly, the LNCaP xenograft model involves the injection of LNCaP human prostate cancer cells suspended within a basement membrane matrix under the skin of immune- deficient nude (nu/nu) mice. Nude mice lack a thymus, which allows for the malignant growth of foreign cells in vivo without rejection. One advantage of using this xenograft model is the ability to see the tumour growing beneath the skin, allowing for relatively accurate monitoring of tumour growth and ease of removal at the treatment end point.

The LNCaP xenograft model of prostate cancer was chosen for several different reasons. Firstly, it is an established model for in vivo drug testing within our laboratory (D. Marrocco, L. Butler, unpublished data). Secondly, LNCaP xenografts recapitulate many important features of prostate cancer in humans; in particular, they are androgen sensitive and follow a pattern of tumour growth stasis and relapse following castration similar to that observed in human progression to CRPC (Lim et al., 1993). They also express the AR and secrete PSA into the bloodstream at levels proportional to tumour volume (Horoszewicz et al., 1983, Lim et al., 1993). Finally, LNCaP cells can be propagated both as a cell line and as a xenograft, allowing direct comparison between the in vitro studies conducted in Chapter 3 and the in vivo efficacy of the combination.

4.2 Materials and Methods

Animal ethics approvals were obtained from the Institute of Medical and Veterinary Science (IMVS), where the animals were housed, and the University of Adelaide prior to commencement of any animal studies.

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4.2.1 Inoculation of male nude mice with LNCaP cells One week prior to inoculation with the LNCaP cells, 60-day release 12.5 mg testosterone pellets (Innovative Research of America) were implanted subcutaneously behind the neck. LNCaP cells were grown in T75 flasks, trypsinised, and resuspended in RPMI containing 10% FBS. Large clumps were removed by filtration and cells were counted using a haemocytometer, with approximate viability of at least 90%. Cells were spun down and resuspended in RPMI+10% FBS to give a concentration of 4x106 cells per 100 µl. Ice-cold Matrigel basement membrane matrix (BD Biosciences) was added to the media suspension (on ice) in a 1:1 ratio to give a final concentration of 2x106 cells per 100 µl. In order to inoculate with 2x106 cells, each 7-week old mouse was injected with 100 µl of the cell/media/Matrigel solution subcutaneously on the right flank.

4.2.2 Drug treatments and calculation of tumour volume The mice were monitored until they had palpable tumours, which occurred 10 days after inoculation. Treatments (Table 4.1) were subsequently delivered by intraperitoneal injection 5 times per week (Monday – Friday), and continued for up to 8 weeks. The dose for vorinostat was chosen based on previous studies carried out by members of our laboratory (Butler et al., 2000), and a high dose physiologically relevant to doses achieved in humans was used for each drug as a positive control.

Table 4.1 – Drug doses used for LNCaP xenograft treatment Treatment groups Code Mice (n) Vehicle Control VC 9 Bicalutamide 5 mg/kg (low dose) BIC LO 9 Bicalutamide 50 mg/kg (high dose) BIC HI 9 Vorinostat 50 mg/kg (low dose) VOR LO 9 Vorinostat 150 mg/kg (high dose) VOR HI 9 Vorinostat 50 mg/kg + bicalutamide 5 mg/kg (combo) COMBO 9 Tumour growth was measured twice weekly using digital callipers. The length, width and depth of the tumours were measured. Tumour volume was calculated using the following formula: [length × width × depth × 0.5233]. This formula was modified from the standard formula of [length × width × π⁄6] to include the depth of the tumour, as many of the

119 tumours grew quite large but were very flat, which would not be accurately calculated using the original formula. Mice were weighed twice weekly throughout the duration of the experiment.

Any mice with tumours that reached 1000 mm3 or any mice displaying outward signs of severe illness before the end of the study were culled by cardiac bleed followed by cervical dislocation under isofluorane anaesthesia, in accordance with animal ethics guidelines. The rest of the mice were culled at treatment day 57. At the end point, tumours were removed and weighed, and if size allowed, dissected into three pieces – one was snap frozen, one was formalin fixed for paraffin embedding, and one piece was kept fresh for histone isolation. If the tumours were small, priority was given to formalin fixation and paraffin embedding.

4.2.3 Immunohistochemistry The expression of ki-67, a marker of cell proliferation, and cleaved caspase 3, a marker of apoptosis, was assessed by immunohistochemistry. Table 4.2 lists the concentration of primary and secondary antibodies, and the blocking solution used. For negative control slides, primary antibody was replaced with block. Blocking solution was prepared in PBS.

Table 4.2 – Concentrations of antibodies used for immunohistochemistry

Primary antibody Primary Secondary Secondary Blocking antibody antibody antibody solution concentration concentration ki-67 1:200 Goat anti-mouse 1:400 5% goat serum Cleaved caspase 3 1:200 Goat anti-rabbit 1:400 5% goat serum Tissue sections 3 µm thick were cut from paraffin blocks using a microtome and placed on superfrost-plus glass slides, which were then placed onto a hot plate at 62 °C overnight. The slides were de-waxed in xylene, and then rehydrated in ethanol, followed by PBS.

Endogenous peroxidase was blocked by treatment with 30% H2O2 prepared in PBS for 5 minutes. Antigen retrieval was performed in 10 mM citrate buffer (pH 6.5) in a Biocare Medical Decloaker with the following programming: SP1 – 125 °C 5 mins, SP2 – 90 °C 10 secs. Slides were allowed to cool, rinsed in PBS, and placed in a humid chamber.

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Avidin/biotin blocking was performed using a kit (Invitrogen), and then slides were blocked using 5% goat serum at room temperature for 30 minutes. All slides except the negative control were then incubated in primary antibody overnight at 4 °C. After a PBS wash, all slides were then incubated in biotinylated secondary antibody for one hour at room temperature. HRP-streptavidin solution was prepared approximately 30 minutes before use. Slides were washed in PBS, and then incubated with the HRP-streptavidin solution for one hour at room temperature. Slides were washed in PBS, stained with freshly mixed DAB and H2O2 for exactly 6 minutes, and counterstained with Lillie Mayer’s haematoxylin.

4.2.4 Video assisted scoring of nuclear ki67 and cleaved caspase 3 staining Ki-67 and cleaved caspase 3 staining was quantified using Definiens Tissue Studio 2.1 image analysis software (Definiens AG, Munich), which uses semi-automated discrimination of tumour cell areas based on user-defined training of the software. Briefly, a subset (sub-area) of a representative slide was chosen, and segmented by the program. If the segmentation between different cell areas was not distinctive enough, it was manually adjusted until segmentation was sufficient. Segments were then coded into three different ROIs (Regions of Interest) – epithelial cells, inflammatory cells/blood vessels, and white space (Fig 4.1a). Within the epithelial ROI, another subset was used to train the software to detect nuclei with haematoxylin stain (negative) or DAB stain (positive) (Fig. 4.1b). Software training was applied to the entirety of every slide, giving the total number of ki- 67 and cleaved caspase 3 expressing and non-expressing LNCaP epithelial cells.

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Figure 4.1

Figure 4.1 Software training for automated detection and counting. Examples of subsets used for training the Definiens software to accurately detect epithelial areas, and detect positive and negative nuclei within the epithelial areas. (A) A representative subset of the slide was chosen, and then segmented by the program. Segments were then coded into three different ROIs (Regions of Interest) – epithelial cells (light blue), inflammatory cells/blood vessels (orange), and white space (pale yellow). (B) Within the epithelial ROI, another subset was selected. Thresholds for detecting haematoxylin and DAB stains were adjusted until as many negative nuclei were detected as possible (dark blue ovals), and all definitively positive nuclei were detected (indicated by arrows, dark brown ovals).

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4.3 Results

4.3.1 Change in tumour volume over time with combination treatment Tumour volume was calculated from calliper measurements taken twice weekly throughout the course of the treatments. By approximately the fourth week of treatments, the mean growth of the tumours from the vehicle control group and each treatment group was highly variable, with no apparent correlation to the treatment administered. Neither high dose vorinostat nor high dose bicalutamide significantly altered the growth of the tumours (Fig. 4.2a). Combination treatment or low doses of the individual agents also did not alter the growth of the tumours (Fig. 4.2b). When the growth of each individual tumour was examined, very few tumours appeared to respond to any treatment and, overall, tumour growth was inconsistent (Fig. 4.3a-f). By the fourth week, in all treatment groups, several mice were culled due to tumours growing to sizes nearing ethical limits (indicated by arrows in Fig. 4.2a and 4.2b). As a consequence, all treatment groups were reduced to five mice or less. This number was reduced to 2 mice or less by the sixth week of treatment, except in the low dose vorinostat group (Fig. 4.3d). The removal of mice with large tumours explains the sudden drop in average tumour sizes at approximately four weeks of treatment, but does not explain the inconsistent growth of tumours within treatment groups when tumour growth is assessed individually.

4.3.2 Fold change in tumour volume over time with combination treatment In cell culture experiments, we observed that cell number at the time of treatment could alter the response to combination treatment. Essentially, if cells were over-confluent at the time of treatment, the treatments were less effective. Therefore, in case variable growth was occurring due to a difference in initial tumour size, we calculated the tumour volume over time as a fold change over the size of the tumour at the point of the first treatment injection.

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Figure 4.2

Figure 4.2. LNCaP xenograft tumour growth over time. Nude mice were inoculated subcutaneously on their right hind flank with 2x106 LNCaP cells suspended 1:1 in 100 µl Matrigel. Once mice had palpable tumours treatments were commenced, as indicated by the injection start arrow. Mice were injected 5x per week with: (A) vehicle control (VC), 50 mg/kg bicalutamide (BIC HI), or 150 mg/kg vorinostat (VOR HI); (B) vehicle control (VC), 5 mg/kg bicalutamide (BIC LO), 50 mg/kg vorinostat (VOR LO) or 5 mg/kg bicalutamide in combination with 50 mg/kg vorinostat (COMBO). Error is shown as ± SEM.

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Figure 4.3a-c

Figure 4.3a-c. Variability of LNCaP xenograft tumour growth within treatment groups. Nude mice were inoculated as in Figure 4.2. Graphs show the growth of each individual tumour, separated into treatment groups: (A) vehicle control (VC), (B) 50 mg/kg bicalutamide (BIC HI), and (C) 150 mg/kg vorinostat (VOR HI). 125

Figure 4.3d-f

Figure 4.3d-f. Variability of LNCaP xenograft tumour growth within treatment groups. Nude mice were inoculated as in Figure 4.2. Graphs show the growth of each individual tumour, separated into treatment groups: (A) 5 mg/kg bicalutamide (BIC LO), (B) 50 mg/kg vorinostat (VOR LO), and (C) 5 mg/kg bictalutamide + 50 mg/kg vorinostat (COMBO). 126

Represented as fold change, growth of tumours treated with the low dose of vorinostat appeared normal and consistent with previous LNCaP xenograft studies. Tumours treated with the high individual doses of each agent or the combination did not appear to grow normally and some appeared to exhibit tumour growth suppression, which was expected for these treatments. However, the negative control group (tumours treated with vehicle control) also had examples of minimal tumour growth similar to the high dose treatments and combination, which was not expected and indicates that the data cannot be readily interpreted to assess treatment efficacy.

Tumours treated with the low dose of bicalutamide grew until the sixth week of treatment and then dropped down to a mean volume of zero (Fig. 4.4). The large drop of the mean tumour volume in the low dose bicalutamide group can be explained by the lack of mouse numbers near the end of the experiment. Graphing the fold change over initial tumour volume individually for each tumour shows a similar trend to the absolute tumour volumes (Fig. 4.5). Tumour growth, even when variable initial tumour volumes were corrected for, was still highly variable – some tumours grew readily despite treatment, some tumours did not grow despite no treatment, and in one case (I2 – BIC HI treatment group) the tumour grew rapidly to a large volume and then appeared to respond to treatment (Fig. 4.5). This analysis indicates that differential tumour volume at the start of treatment did not affect the efficacy of the agents and does not explain the differences observed in tumour growth rates within treatment groups.

4.3.3 End-point and survival analysis The majority of the mice culled before the official end point of the experiment (day 57) were sacrificed due to tumour burden. Therefore, it was worth investigating whether any of the therapies were able to delay the time to excess tumour burden. Kaplan-Meier analysis and Log rank testing revealed that there was no significant difference between the treatment groups in terms of survival (Fig. 4.6). At the end point for each mouse, whether due to excessive tumour size, unexplained illness, or the end of the study, the tumours were measured, then excised and weighed.

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Figure 4.4

Figure 4.4. LNCaP xenograft tumour growth fold change over initial tumour volume. Nude mice were inoculated with LNCaP cells as in Figure 4.2. For each tumour, the fold change is expressed as the tumour volume at each time point divided by the tumour volume at the time of the first treatment. This graph shows the average fold change for each treatment group: vehicle control (VC), 50 mg/kg bicalutamide (BIC HI), 150 mg/kg vorinostat (VOR HI), 5 mg/kg bicalutamide (BIC LO), 50 mg/kg vorinostat (VOR LO) or 5 mg/kg bicalutamide in combination with 50 mg/kg vorinostat (COMBO).

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Figure 4.5a-c

Figure 4.5a-c. Variability of LNCaP xenograft fold change within treatment groups. Nude mice were inoculated as in Figure 4.2. Graphs show the fold change of each individual tumour, separated into treatment groups: (A) vehicle control (VC), (B) 50 mg/kg bicalutamide (BIC HI), and (C) 150 mg/kg vorinostat (VOR HI). 129

Figure 4.5d-f

Figure 4.5d-f. Variability of LNCaP xenograft fold change within treatment groups. Nude mice were inoculated as in Figure 4.2. Graphs show the fold change of each individual tumour, separated into treatment groups: (A) 5 mg/kg bicalutamide (BIC LO), (B) 50 mg/kg vorinostat (VOR LO), and (C) 5 mg/kg bictalutamide + 50 mg/kg vorinostat (COMBO).

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Figure 4.6

Figure 4.6. Survival of mice bearing LNCaP xenograft tumours. Nude mice bearing LNCaP xenograft tumours were culled when their tumours became close to or reached 1000 mm3. Kaplan-Meier survival analysis showed no significant difference between the treatment groups for survival (p = 0.3668, Mantel-Cox test; p = 0.8137, logrank test for trend; p = 1, Gehan-Breslow-Wilcoxon test).

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The final volumes of the tumours showed a small, non-significant decrease in the size of the tumours treated with high doses of the individual agents (Fig. 4.7a). However, there was no difference in the final volume of the tumours treated with the combination compared to the vehicle control. A small, non-significant decrease in tumour weight was also noted for low dose and high dose vorinostat compared with vehicle control tumours (Fig. 4.7b).

4.3.4 Analysis of tumour growth and apoptosis using immunohistochemical markers To determine if the treatments affected tumour proliferation or death at a cellular level, formalin fixed and paraffin embedded tissues were examined by immunohistochemistry for two well characterised markers of proliferation and apoptosis – ki67 and cleaved caspase 3, respectively.

No significant differences existed between the treatment groups for ki67 percent positivity, either by Definiens semi-automated counting (Fig. 4.8a) or by visual assessment in representative images (Fig. 4.8b). Similarly, there was no significant difference between the treatment groups for semi-automated counts of cleaved-caspase 3 positivity (Fig. 4.9a), and no apparent differences noted in representative images (Fig. 4.9a). A similar result was reported for a small subset counted by blinded manual counting (data not shown).

Overall, this data indicated that, on average, there was no discernible effect on cellular proliferation or apoptosis by any of the treatments. We then went on to investigate the correlation between the ki67 positivity or the cleaved caspase 3 positivity and the final tumour volume at the level of each individual tumour, to determine if the mice could be separated into ‘responders’ and ‘non-responders’.

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Figure 4.7

Figure 4.7. End-point LNCaP xenograft tumour volume and weight. LNCaP xenograft tumours from mice treated with vehicle control (VC), 50 mg/kg bicalutamide (BIC HI), 150 mg/kg vorinostat (VOR HI), 5 mg/kg bicalutamide (BIC LO), 50 mg/kg vorinostat (VOR LO) or 5 mg/kg bicalutamide in combination with 50 mg/kg vorinostat (COMBO) were measured at the end point using callipers and then weighed. Shown are the average tumour volumes (A) and weights (B) for each treatment group, ± SEM.

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Figure 4.8

Figure 4.8. Immunohistochemical detection of ki67 positivity in LNCaP xenograft tumours. LNCaP xenograft tumour sections derived from nude mice treated with vehicle control (VC), 50 mg/kg bicalutamide (BIC HI), 150 mg/kg vorinostat (VOR HI), 5 mg/kg bicalutamide (BIC LO), 50 mg/kg vorinostat (VOR LO) or 5 mg/kg bicalutamide in combination with 50 mg/kg vorinostat (COMBO) were assessed for ki67 immunoreactivity using a specific ki67 antibody. Representative images for each treatment group were captured at 20x magnification and are shown in part (A). Scale on each image indicates 300 µm. (B) Ki67 % positivity was calculated by quantification of positive and negative nuclei by Definiens image analysis. 134

Figure 4.9

Figure 4.9. Immunohistochemical detection of cleaved caspase-3 (CC-3) positivity in LNCaP xenograft tumours. LNCaP xenograft tumour sections derived from nude mice treated with vehicle control (VC), 50 mg/kg bicalutamide (BIC HI), 150 mg/kg vorinostat (VOR HI), 5 mg/kg bicalutamide (BIC LO), 50 mg/kg vorinostat (VOR LO) or 5 mg/kg bicalutamide in combination with 50 mg/kg vorinostat (COMBO) were assessed for CC-3 immunoreactivity using a specific cleaved caspase-3 antibody. Representative images for each treatment group were captured at 20x magnification and are shown in part (A). Scale on each image indicates 300 µm. (B) CC-3 % positivity was calculated by quantification of positive and negative nuclei by Definiens image analysis.

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In order to potentially identify any tumours that responded to any of the treatments, tumours were split up into treatment groups, and further split up into individual tumours. For each individual tumour, the ki67 positivity was plotted against the final tumour volume. Correlation analysis revealed that for all of the treatment groups, there was no correlation between ki67 positivity and the final tumour volume (Fig 4.10). This indicated that neither tumour volume nor ki67 positivity were related to tumour response; that is, high ki67 did not necessarily equate to a large final tumour volume, nor did low cleaved caspase 3 positivity.

4.3.5 Tolerability and toxicity Over the course of the experiment, each mouse was weighed and checked for signs of outward ill-health, such as a hunched appearance, loose skin and dull eyes. Mice were also weighed three times per week (Fig. 4.11). From the weights alone, the combination treated mice did not exhibit signs of toxicity, and in fact, nearing the end of the experiment they had a higher average weight than the control treated mice. Overall, the weights for each treatment group were similar over the majority of the experiment, and only in the last two weeks of the treatment course did the weights start to differ between groups. Compared to vehicle control, the two low-dose individual drug treatments showed a lower average weight of the mice. This may be attributed to the toxicity associated with large tumour burden and the low numbers of mice within these groups nearing the end of the experiment. Overall, the mice appeared to tolerate the combination therapy well. Given that the combination did not appear to have any other effects on the mice with regards to tumour volume, it cannot be said for certain that the absence of toxic effects is due to the tolerability of the drugs. However, it is important to note that the combination therapy did not cause toxicity in addition to lack of efficacy.

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Figure 4.10

Figure 4.10. Correlation between ki67 percent positivity and final tumour volume. LNCaP xenograft tumour sections derived from nude mice treated with vehicle control (VC), 50 mg/kg bicalutamide (BIC HI), 150 mg/kg vorinostat (VOR HI), 5 mg/kg bicalutamide (BIC LO), 50 mg/kg vorinostat (VOR LO) or 5 mg/kg bicalutamide in combination with 50 mg/kg vorinostat (COMBO) were assessed for ki67 immunoreactivity using a specific ki67 antibody. Ki67 percent positivity was calculated by quantification of positive and negative nuclei by Definiens image analysis. Ki67 percent positivity was analysed for correlation with the final tumour volume before the tumours were removed using Spearman correlation analysis, where r = 1 or – 1 is complete correlation and p-value < 0.05 is considered significant. 137

Figure 4.11

Figure 4.11. Mouse weights over time. All mice from the six treatment groups: vehicle control (VC), 50 mg/kg bicalutamide (BIC HI), 150 mg/kg vorinostat (VOR HI), 5 mg/kg bicalutamide (BIC LO), 50 mg/kg vorinostat (VOR LO) or 5 mg/kg bicalutamide in combination with 50 mg/kg vorinostat (COMBO) were weighed three times per week and monitored for outward signs of ill health over the entire course of the experiment. In this figure, the course of the experiment ran from the delivery of the mice (day 1), to the final end point (day 113). LNCaP cell injection occurred on day 35, and treatments were commenced on day 56.

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4.4 Discussion

A major step in the development of any new treatment regimen is to confirm initial in vitro testing with in vivo experimentation. This chapter aimed to test the efficacy of the combination in vivo, using the LNCaP human prostate cancer xenograft model. In addition to efficacy, tolerability of bicalutamide and vorinostat used individually and in combination in a biological system was also investigated by monitoring outward signs of health.

Overall, with no apparent correlation to treatment, the tumour growth was highly and unexpectedly variable – ranging from complete suppression to rapid growth in different animals. In addition, animals treated with vehicle control also showed a range of responses from suppression to regular growth, as well as animals treated with high doses of the individual agents, which were used as a positive control based on previous studies (Butler et al., 2000). This is likely due to the inconsistent growth of the LNCaP tumours throughout the experiment, or the delivery method chosen for the treatments, which will be described in more detail below.

It is well known that LNCaP xenografts can be difficult to grow in nude (nu/nu) mice. In the experiment outlined in this chapter, several steps were taken to circumvent many of the problems that occur. Firstly, athymic (nu/nu) mice have significantly less circulating testosterone than their heterozygous littermates (Rebar et al., 1982), and it has been shown that a high level of testosterone is required for LNCaP xenograft growth (Martin I. Resnick, 2000). Indeed, we have previously found that supplementation with testosterone facilitates a better take rate and more consistent tumour growth (D. Marrocco, unpublished data). Therefore, in the experiment detailed in this chapter, the mice were implanted with 60-day slow release testosterone pellets in order to maintain a higher level of testosterone and allow the tumours to grow optimally. We observed an almost 100% tumour take rate, with only two cases where a tumour did not form, indicating that testosterone levels at the start of the experiment should have been sufficient. However, the major issue within the experiment was the inconsistent growth of the tumours over time. The experiment was conducted over a total of 74 days (testosterone pellet implantation – 7 days, tumour take –

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10 days, treatment period – 57 days), which is longer than the 60-day slow release pellet time period. It is possible that near the end of the study, testosterone levels were dropping substantially, which could have altered the growth rates of some of the tumours. It is also possible that this could have occurred even before the end of the 60-day pellet period. However, this does not fully explain the inconsistencies observed, as many tumours were growing variably within treatment groups earlier in the experiment than the 60-day mark. In future, it may be worth monitoring circulating testosterone levels in the blood throughout the course of the experiment to rule out the possibility of variable testosterone levels interfering with tumour growth.

Steps were taken to ensure that the LNCaP cells were suitable for xenograft implantation. LNCaP cells were thawed from low passage stocks, and passaged twice in in vitro culture before being grown in multiple T75 tissue culture flasks in numbers sufficient for 2 x 106 cells per mouse. Cells were of a low passage and growing well when they were harvested and combined with Matrigel. It is unclear whether LNCaP cell quality or number contributed to the variable growth rates observed, as cells were growing well and in order to ensure a good take rate the number of cells injected (2 x 106) was more than previously used in our laboratory (1 x 106). However, anecdotal evidence from other laboratories working with LNCaP xenografts in nude mice have suggested that up to 5 x 106 cells per mouse gives a good tumour take rate and consistent growth. Additionally, due to the large numbers of mice inoculated on the same day, and the fact that the same person inoculated all of the mice to diminish inter-operator variation, the inoculation process took a significant amount of time (around two hours) to complete. It is therefore feasible that during this process, the LNCaP cells may have settled or formed clumps within the Matrigel mixture, which could have changed both the number of cells injected and their growth characteristics. While tubes of cells were agitated regularly during inoculation in order to avoid this, we cannot rule out the possibility as LNCaP cells clump readily.

Given the highly variable rate of tumour growth in the vehicle control group, it is highly unlikely that the intra-peritoneal drug injection resulted in a variable delivery of drug. For example, if the injection was delivered too deep or too shallow, it could have altered the concentration of the agent delivered. However, some mice did show signs of peritonitis around the injection site at the time of culling, indicating that some injections might have 140 delivered the agents improperly. Intraperitoneal injections are a standard method of drug delivery used previously in our laboratory, and are comparable to oral gavage in effect with greater ease of delivery, greater control over the amount of drug delivered, and cause less distress to the animals. While it is unlikely that the route of administration caused any major effect, it cannot be ruled out completely. Time constraints prevented comprehensive investigation into biomarkers identified in Chapter 3 (e.g. IκBα or PSA) to determine whether the drugs were able to penetrate the tumours, as immunohistochemical techniques for detecting these proteins in tumours would have required lengthy development and optimisation. However, a preliminary study on histones isolated from a small subset (n = 5) of tumours from animals treated with vehicle control, low dose vorinostat, and high dose vorinostat showed that increased histone acetylation was variable and did not occur in all tumours within the subset treated with vorinostat, nor was there a dose dependent effect (data not shown).

Overall, despite the precautions taken based on previous experience with this tumour model, the tumour growth we observed was erratic and did not correlate with treatment. Highly variable tumour growth rate was recorded for all treatments, at a gross level and at the cellular level, including the vehicle control (negative control) and the high individual doses (positive controls). Survival data did not show a higher survival rate in the combination or high dose treatment groups, and end point data did not distinguish between vehicle control and treatment. Further optimisation of this model, or use of alternative cell lines or mice strains, is essential in order to evaluate the efficacy of single or combinatorial treatments in vivo.

Therefore, we cannot conclude from this study whether bicalutamide and vorinostat, individually or in combination, are effective at suppressing the growth of LNCaP xenograft tumours. Similarly, given that we are unable to define the efficacy of the combination in vivo based on these results, we are also unable to determine if the low toxicity observed is due to a high tolerability of the drug or simply that there was little drug present in the system of the mice.

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Chapter 5:

Combining 17-AAG with androgen receptor modulating agents enhances cell death and minimises the heat shock response in prostate cancer cells

This chapter contains a draft of a submitted manuscript, comprising a portion of the work undertaken during this PhD. The draft version of the manuscript is included in this chapter, as the manuscript has since been divided into two parts to allow for expansion on the mechanism of each combination outside of the scope of this PhD. Extra data outside of the manuscript are included at the end of this chapter as additional figures, with discussion included in the figure legends. Microarray data can be found at http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE56188.

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Title:

Combining 17-AAG with AR targeting agents enhances cell death and minimizes the heat shock response in prostate cancer cells.

Authors:

Margaret M. Centenera1, Sarah L. Carter1, Wayne D. Tilley1, 2 and Lisa M. Butler1, 2

Affiliations:

1Dame Roma Mitchell Cancer Research Laboratories and Adelaide Prostate Cancer Research Centre, School of Medicine, University of Adelaide & Hanson Institute, Adelaide, Australia 5000

2Freemason’s Foundation Centre for Men’s Health, Adelaide, Australia 5000

Running Title: Combinatorial therapy in prostate cancer

Corresponding author: Lisa M. Butler, Dame Roma Mitchell Cancer Research Laboratories, University of Adelaide and Hanson Institute, PO Box 14, Rundle Mall, Adelaide, South Australia, Australia, 5000. Email: [email protected]. Phone: +61 8 8222 3270. Fax: +61 8 8222 3217

Key Words: prostate cancer, 17-AAG, vorinostat, bicalutamide, combination

Abbreviations: 17-allylamino-demethoxygeldanamycin (17-AAG), androgen receptor (AR), androgen ablation therapy (AAT), geldanamycin (GA), histone deacetylase inhibitor (HDACi), heat shock protein-90 (Hsp90), prostate-specific antigen (PSA).

Article Category: Cancer Cell Biology

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Abstract

Prostate cancer cells are initially dependent on androgens for growth and survival, and androgen deprivation therapy (ADT) is used to control tumor growth. Unfortunately, resistance to ADT inevitably occurs, and there is an urgent need for better strategies to block androgen signaling in castration-resistant prostate cancer. Combining different clinical agents to more completely inhibit pathways critical for prostate cancer cell survival, including androgen receptor (AR) signaling, is a strategy with potential to improve patient outcomes. Agents that target the molecular chaperone heat shock protein 90 (Hsp90) are particularly attractive for combinatorial strategies as many proto- oncogenes, including the AR, rely on Hsp90 for their activity. In this study, enhanced efficacy of the Hsp90 inhibitor, 17-AAG, was achieved in AR-dependent prostate cancer cells when the agent was used in combination with either an anti-androgen (bicalutamide) or a histone deacetylase inhibitor (vorinostat), both of which are inhibitors of AR function and/or expression. Furthermore, these cell death effects were observed using doses of each agent that individually have no effects. Gene expression profiling revealed enhanced inhibition of androgen signaling in cells co-treated with 17-AAG and bicalutamide but not 17-AAG and vorinostat. Importantly, the heat shock response that is elicited with therapeutic doses of 17-AAG, and is a potential mediator of resistance to Hsp90 inhibitors, was significantly reduced in cells co-treated with 17-AAG and bicalutamide. This study demonstrates that use of an Hsp90 inhibitor and AR antagonist in a combinatorial approach affords the potential to more effectively target AR signaling in prostate cancer cells while reducing treatment resistance, which could significantly improve outcomes for men with advanced prostate cancer.

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Introduction

Androgen deprivation therapy (ADT) through medical or surgical castration is used to control tumor growth and metastasis in men who have failed definitive treatments for localized prostate cancer. As the majority of patients undergoing ADT will eventually relapse with castrate-resistant prostate cancer (CRPC) that is not curable by any current treatment, there is an urgent need for new therapeutic options. Extensive pre-clinical and clinical data demonstrate that androgen signaling via the androgen receptor (AR) is the major driver of CRPC cell growth and survival (Chen et al., 2008). In fact, CRPC develops as prostate cancer cells evolve to facilitate their survival in the castrate environment through a range of AR-dependent resistance mechanisms that include increased AR expression, gain-of-function AR mutations, constitutively expressed AR spliced variants, altered levels of critical AR co-regulators and local synthesis of androgens (Scher and Sawyers, 2005). What is increasingly evident is that the mechanism used by the prostate cancer cell depends on the specific therapy being received by the patient. This phenomenon, known as therapy-mediated selection pressure, is becoming more frequent in diseases where molecular targeted therapies are utilized (Ellis and Hicklin, 2009). A strategy that simultaneously targets different aspects of AR signalling therefore has the potential to prevent selection for cells with enhanced AR activity, more effectively eliminate AR-dependent prostate cancer cells, and markedly improve overall patient survival.

The functional maturation of the AR protein is a necessary step in the androgen signalling axis, which can be targeted through inhibition of the molecular chaperone heat shock protein 90 (Hsp90)(Centenera et al., 2013a). Chaperone proteins are required for the stabilization and activation of a diverse group of client proteins involved in cell signalling, growth, survival, and oncogenesis, including steroid receptors, transcription factors and protein kinases (Trepel et al., 2010). Hsp90 is necessary for folding the AR into the correct conformation for high affinity ligand binding (Pratt and Toft, 1997, Robzyk et al., 2007). The chaperone activity of Hsp90 is ATP-dependent therefore most Hsp90 inhibitors are targeted to the conserved ATP-binding site, and this results in ubiquitin-mediated

145 proteasomal degradation of over 200 client proteins (Dittmar et al., 1997, Prodromou et al., 1997, Scheibel and Buchner, 1998).

The first Hsp90 inhibitors identified were bacterial-derived antibiotics, including the ansamycin antibiotic geldanamycin (GA) and its derivatives 17-allylamino- demethoxygeldanamycin (17-AAG) and 17-(dimethylaminotheyl-amino)-17- demethoxygeldanamycin (17-DMAG). In prostate cancer cells, these agents cause degradation of the AR and other proteins implicated in oncogenesis (Her2, Akt, Bcr-Abl and Raf-1), cell cycle arrest, decreased PSA expression, and inhibition of both androgen- dependent and -independent xenograft tumour growth (Williams et al., 2007, Vanaja et al., 2002, Solit et al., 2002b). Unfortunately 17-AAG has not performed well as a single agent in patients with CRPC, due to toxicity and poor pharmacodynamic properties that prevent therapeutic doses from being achieved (Sharp and Workman, 2006, Heath et al., 2008). While more tolerable and potent inhibitors such as AUY922 are currently being developed and show promise as single agents (Centenera et al., 2012, Samuel et al., 2010), recent evidence of additive or synergistic activity between 17-AAG and cytotoxic agents or specific molecular therapeutics provides an new avenue of clinical development for this drug (Lu et al., 2012). In preclinical studies of prostate cancer, 17-AAG combined with ionizing radiation has demonstrated supra-additive reductions in cell growth and clonogenicity (Enmon, 2003, Ochel and Gademann). In a Phase I trial of 17-AAG plus docetaxel, 25% of patients with prostate cancer exhibited a PSA decline of ≥20% (Iyer et al., 2012).

The purpose of this study was to investigate whether 17-AAG would be useful in combination with agents that target different aspects of the AR signaling axis. Previously, we have demonstrated that combinations of the AR antagonist bicalutamide and the histone deacetylase inhibitor (HDACI) vorinostat act synergistically to induce apoptosis in prostate cancer cells (Marrocco et al., 2007b). We therefore tested combinatorial strategies involving 17-AAG with (i) bicalutamide, which specifically targets AR ligand binding;

146 and (ii) the histone deacetylase inhibitor vorinostat, which acts on a broad spectrum of targets including the AR.

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Materials and Methods

Cells and Reagents

LNCaP and PC-3 human prostate carcinoma cells were purchased from the American Type Culture Collection (Rockville, MD) and maintained in RPMI 1640 supplemented with 10% or 5% fetal bovine serum (FBS), respectively. 17-AAG was kindly provided by the National Cancer Institute and was dissolved in dimethylsulfoxide (DMSO). Vorinostat was provided by Merck & Co (Boston, MA) and also dissolved in DMSO. Bicalutamide was purchased from Sigma (St. Louis, MO) and dissolved in ethanol. AR (N-20) and Hsp90 antibodies were purchased from Santa Cruz Biotechnology, Inc. (Santa Cruz, CA). Horseradish peroxidase-conjugated secondary antibodies were obtained from DAKO (Carpinteria, CA). The tetrapeptide caspase inhibitor z-VAD-fmk was purchased from Calbiochem (Alexandria, New South Wales, Australia).

Measurement of Cell Viability

Cells were seeded in triplicate in 24-well plates at a density of 2.5x104 cells per well in 1 mL of RPMI medium containing 5% (PC-3) or 10% (LNCaP) FBS. Cells were allowed to attach for 24 h (PC-3) or 48 h (LNCaP) prior to incubation with medium containing 17- AAG (0, 10, 20, 40, 80 or 160 nM), bicalutamide (1.25 or 2.5 µM) or vorinostat (0.5 or 1 µM) as indicated. Cells were counted using a haemocytometer at regular intervals after initiation of treatment, and cell viability was assessed by Trypan blue dye exclusion as previously described (Marrocco et al., 2007b). Data are expressed as the mean +/- SE of triplicate wells and are representative of at least three independent experiments. For experiments using the caspase inhibitor, z-VAD-fmk, LNCaP cells were cultured as described above in the presence or absence of z-VAD-fmk (50 µM) for the duration of the experiment (5 days).

Isobole statistics

The isobole method (Berenbaum, 1981) was used to evaluate whether 17-AAG acts synergistically or additively with bicalutamide or vorinostat, as described previously (Marrocco et al., 2007b). Initially, an isobologram was plotted (Tallarida, 2001) with 17- AAG on the y-axis and bicalutamide or vorinostat on the x-axis. A and B on the graph are the doses of drug A and drug B that induce equivalent cell death effect to the 148 corresponding combination treatment (Supplementary Figure 1). The line connecting A and B is the line of additivity, wherein any combination of doses that meets on this line will produce the same (additive) effect as the doses of the individual agents at A or B. Dose pairs with a below the line are synergistic as they require lower doses of each agent, whereas dose pairs above the line are antagonistic. Combinations identified as acting in synergy were validated using isobole statistics with the equation Ac / Ae + Bc / Be = Combination Index (CI), where Ac and Bc represent the concentration of drug A and drug B used in combination, and Ae and Be represent the concentration of drug A and B that produce the same magnitude of effect when administered alone. If the CI is <1, then the drugs are considered to act synergistically. If the combination index is ≥1, then the drugs act in an antagonistic or additive manner, respectively.

Microarray & Pathway Analysis

LNCaP cells were seeded into 6-well plates, allowed to attach for 24 hours, then cultured with 17-AAG both alone and in combination with bicalutamide or vorinostat for 6 hours. Total RNA was extracted from the cells using Trizol reagent (Invitrogen), and RNA integrity was analysed on an Agilent Systems Bioanalyser. Microarray analysis was performed at the Adelaide Microarray Centre. Briefly, 300 ng of total RNA was labelled using the Affymetrix WT Sense Target labelling assay as per the manufacturer’s instructions (Affymetrix Inc, p/n 701880). Samples were hybridized to Affymetrix Human Gene 1.0 ST Arrays for 17 hours at 45oC prior to washing, staining and scanning. The array data was analyzed using Partek Genomics Suite (Partek Inc, MO). Differential gene expression was assessed by ANOVA with the p-value adjusted using step-up multiple test correction (Benjamini and Hochberg, 1995) to control the false discovery rate (FDR). Adjusted p-values < 0.05 were considered to be significant. Cluster and Treeview algorithms were used to generate self-organizing maps of the gene expression data sets (Eisen et al., 1998). Gene pathway analysis was conducted using core analysis in the Ingenuity Systems program (Ingenuity Systems, California, USA) to identify molecular and cellular functions and canonical pathways that were enriched by combination treatments. Differentially expressed genes were also analysed for enriched gene ontology (GO) groups using the Database for Annotation, Visualisation and Integrated Discovery (DAVID) (http://david.abcc.ncifcrf.gov/; (Huang da et al., 2009). Genes with a minimal 2- 149 fold change and a p<0.05 were included for analysis and the background gene set comprised of all genes on the Affymetrix Human GeneChip ST 1.0 array.

Quantitative Real-Time PCR (qPCR)

Alterations in genes identified as differentially expressed compared with vehicle control were validated by qPCR using SYBR green. One microgram of RNA was DNAse treated with Turbo DNA Free (Ambion), and then reverse transcribed using iScript cDNA Synthesis Kit (Biorad). qRT-PCR was performed with a 1:10 dilution of the cDNA using SYBR green (Biorad) on a CFX Real-Time System (Bio-Rad) with a three-step amplification for 40 cycles. geNORM analysis was used to determine appropriate housekeeper genes for this sample set. Expression of PMEPA1, NKX3.1, HSP70, HSP40, HSP27 and CLUSTERIN were normalized to GUSB and L19. Primer sequences are listed in Supplementary Table 1.

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Results

17-AAG suppresses proliferation and induces death of prostate cancer cells

Therapeutic doses of 17-AAG cannot be achieved clinically without causing toxicity (reviewed in (Centenera et al., 2013a), thus 17-AAG based combination treatments need to demonstrate benefit with the use of sub-effective drug concentrations. To identify suitable doses of 17-AAG, LNCaP and PC-3 prostate cancer cells were exposed to increasing doses of 17-AAG (10-160 nM) for up to 5 days. Sub-effective doses were defined as having partial but not complete inhibitory effects on cell proliferation compared with vehicle treated controls. The only concentration to satisfy this requisite was 40nM 17-AAG, which reduced cell proliferation by approximately 4-fold in both LNCaP and PC-3 cells (p<0.05; Figure 1A & 1B). Ineffective doses were also evaluated and defined as having no influence on LNCaP cell proliferation compared with vehicle treated controls. A concentration of 20nM 17-AAG was selected as the ineffective dose in this study. Therapeutic doses of 17- AAG were ≥80nM, which completely suppressed both LNCaP and PC-3 cell proliferation (p<0.05; Figure 1A & 1B). Although the proliferative response to 80nM 17-AAG was similar in both cell lines, LNCaP cell death reached 83% (p<0.05; Figure 1A) whereas cell death reached only 27% in PC-3 cells (p<0.05; Figure 1B).

Combining 17-AAG with an AR antagonist enhances growth suppression and cell death in AR-positive prostate cancer cells

Bicalutamide is an AR antagonist that competes with androgens for binding to the AR ligand binding domain, and thereby blocks the stimulatory effects of androgens on prostate cancer cell growth and survival (Furr and Tucker, 1996). As for 17-AAG above, sub- effective and ineffective doses of bicalutamide were selected using LNCaP dose-response curves (Supplementary Figure 2A). Co-administration of sub-effective doses of 17-AAG (40 nM) and bicalutamide (2.5 µM) completely suppressed LNCaP cell proliferation and induced a 4-fold induction of cell death compared with vehicle control (p<0.05; Figure 2A). Similar proliferative effects and a 3.5-fold induction of cell death were achieved when concentrations of 17-AAG (20nM) or bicalutamide (1.25 µM) were reduced to ineffective doses (p<0.05; Figure 2B).

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Figure 1

Figure 1. 17-AAG suppresses growth and induces death of LNCaP and PC-3 prostate cancer cells. (A) LNCaP and (B) PC-3 cells were cultured with increasing doses of 17- AAG as indicated for up to 5 days. Cells were counted at regular intervals using a haemocytometer and cell viability assessed by trypan blue dye exclusion. The number of dead cells is expressed as a percentage of the total number of cells counted. Results are representative of at least 3 independent experiments and represent the mean +/- SE of triplicate wells. * ANOVA: P<0.05 treatments versus control.

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To achieve similar effects on cell viability and death with the agents individually required 80 nM 17-AAG (Figure 1A) or 25 µM bicalutamide (Supplementary Figure 2A). When the combination experiments were replicated using AR-negative PC-3 prostate cancer cells, no effects on PC-3 cell proliferation or death were observed with the ineffective dose combination of 20n M 17-AAG and 1.25 µM bicalutamide (Figure 2C).

Combining 17-AAG with vorinostat enhances growth suppression and cell death in AR-positive prostate cancer cells

Vorinostat is a potent HDACI that blocks the catalytic activity of both Class I and II histone deacetylases (Marrocco-Tallarigo et al., 2009). Previously we have shown that at low micromolar concentrations, vorinostat decreases AR mRNA and protein levels and effectively kills AR-positive prostate cancer cells, indicating a potential AR-mediated effect of vorinostat on prostate cell viability (Butler et al., 2000, Marrocco et al., 2007b). As for 17-AAG and bicalutamide, sub-effective and ineffective doses of vorinostat were selected using LNCaP dose-response curves (Supplementary Figure 2B). Single agent treatment of LNCaP prostate cancer cells with sub-effective doses of 17-AAG (40 nM) or vorinostat (1 M) reduced LNCaP cell proliferation by approximately 1.4-fold, but did not induce cell death compared to vehicle-treated controls (Figure 3A). When the agents were co-administered at these doses, LNCaP cell growth was completely suppressed and cell death induced by up 4.6-fold compared to vehicle treated cells (p<0.05; Figure 3A). Co- administration of ineffective doses of 17-AAG (20 nM) or vorinostat (0.5 M) also resulted in complete suppression of LNCaP cell growth and induced cell death by up to 3.2-fold over vehicle treated cells (p<0.05; Figure 3B). Doses of the individual agents needed to achieve similar affects on cell viability and death were 80 nM 17-AAG (Figure 1A) or 5 µM vorinostat (Supplementary Figure 2B). The combination of 17-AAG and vorinostat did not alter PC-3 cell proliferation or induce PC-3 cell death (Figure 3C).

153 Figure 2

Figure 2. Bicalutamide enhances growth suppression and cell death induced by 17- AAG in AR-dependent prostate cancer cells. (A) LNCaP cells were cultured with sub- effective doses of 17-AAG (40 nM) or bicalutamide (2.5 µM), alone or in combination. (B) LNCaP and (C) PC-3 cells were cultured with ineffective doses of 17-AAG (20 nM) or bicalutamide (1.25 µM), alone or in combination. Cells were counted at regular intervals using a haemocytometer and cell viability was assessed by trypan blue dye exclusion. The number of dead cells is expressed as a percentage of the total number of cells counted. Results are representative of at least 3 independent experiments and represent the mean +/- SE of triplicate wells. * ANOVA: P<0.05 treatments versus control.

154 Figure 3

Figure 3. Vorinostat enhances growth suppression and cell death induced by 17- AAG in AR-dependent prostate cancer cells. (A) LNCaP cells were cultured with sub- effective doses of 17-AAG (40 nM) or vorinostat (1 µM), alone or in combination. (B) LNCaP and (C) PC-3 cells were cultured with ineffective doses of 17-AAG (20 nM) or vorinostat (0.5 µM), alone or in combination. Cells were counted at regular intervals using a haemocytometer and cell viability was assessed by trypan blue dye exclusion. The number of dead cells is expressed as a percentage of the total number of cells counted. Results are representative of at least 3 independent experiments and represent the mean +/- SE of triplicate wells. * ANOVA: P<0.05 treatments versus control.

155

17-AAG drug combinations act synergistically to induce caspase-dependent cell death

An isobologram is a graphical representation of the nature of the interaction between two drugs (Tallarida, 2001). When the sub-effective and ineffective combinations of 17-AAG and bicalutamide were plotted on an isobologram, both dose pairs displayed synergistic activity (Figure 4A). Accordingly, both dose pairs were analyzed using isobole statistics. The sub-effective dose pair of 17-AAG and bicalutamide resulted in a CI of 0.55, and the ineffective dose pair achieved a CI of 0.275, confirming synergistic interactions between the two drugs (i.e., CI<1). When 17-AAG and vorinostat dose pairs were plotted on an isobologram, the sub-effective dose pair was shown to act additively whereas the ineffective dose pair was synergistic (Figure 4B). Statistical analysis resulted in a CI of 0.35, which confirmed that the ineffective dose pairing acts synergistically. The ineffective doses were used for the rest of the manuscript, with the exception of the microarray where subeffective doses were used to ensure a robust response.

To investigate the cell death pathway that is induced in cells treated with 17-AAG based combinations, the pan-caspase inhibitor z-VAD-fmk was added to LNCaP cells treated with the agents alone or in combination. Cell death induced by 17-AAG in combination with either bicalutamide (Figure 4C) or vorinostat (Figure 4D) was completely prevented in the presence of the pan-caspase inhibitor, with the percentage of dead cells in the presence of z-VAD-fmk equivalent to those in vehicle-treated cells (10%). The synergistic cell death is therefore caspase-dependent, suggesting that 17-AAG combinations induce the intrinsic apoptotic pathway.

Global gene expression changes following treatment with 17-AAG combinations

To explore the mechanisms underlying the synergistic activity of 17-AAG drug combinations, global gene expression profiling using Affymetrix microarray was performed on LNCaP cells treated with the agents alone or combined. Single-agent bicalutamide or 17-AAG treatment significantly (p<0.05) altered a similar number of transcripts (609 and 790 genes respectively), which increased dramatically to 2,158 transcripts when the two agents were used in combination (Figure 4E). Although there was some overlap with single-agent 17-AAG (14%) or bicalutamide (16%) treatment, the majority of genes (59%) affected by the drug combination were unique.

156

Figure 4

Figure 4. 17-AAG works in synergy with bicalutamide or vorinostat to induce caspase-dependent cell death and increase global gene expression changes in LNCaP cells. Sub-effective (closed circle) and ineffective (open circle) dose pairs of (A) 17-AAG and bicalutamide or (B) 17-AAG and vorinostat plotted on an isobologram indicate that both combinations act in synergy. (C) and (D) LNCaP cells were cultured with ineffective doses of 17-AAG and bicalutamide or vorinostat, alone or in combination, in the presence or absence of the z-VAD-fmk pan-caspase inhibitor. Cells were counted after 5 days. Results are representative of at least 3 independent experiments and represent the mean +/- SE of triplicate wells. * ANOVA: P<0.001 treatments versus control. (E) and (F) Venn diagrams represent the overlap between significantly expressed genes as identified by Affymetrix gene expression profiling in treated LNCaP cells compared with vehicle treated cells (Benjamini-Hochberg adjusted p-values; p<0.05).

157

Vorinostat treatment alone (5,973 genes) or in combination with 17-AAG (5,753 genes) affected almost ten times as many genes as single-agent 17-AAG treatment (790 genes; Figure 3F). The overlap between the combination and 17-AAG treatment alone was minimal (2%) but there was substantial overlap with vorinostat treatment alone (69%).

Inhibition of AR signaling is enhanced by co-treatment with 17-AAG and bicalutamide

The finding that 17-AAG combinations are ineffective on AR-negative PC-3 cells suggests that AR signaling has a mechanistic role in the efficacy of these treatments. This idea was further investigated by cross-referencing the gene list regulated by each combination treatment with the androgen-regulated genes identified in LNCaP cells by Wang et al (Wang et al., 2009). Approximately 29% (507/1755) and 48% (850/1755) of androgen- responsive genes were significantly (p<0.05) altered by 17-AAG and bicalutamide or vorinostat, respectively, (Supplementary Figure 3). Cluster analysis further revealed that the majority of those androgen regulated genes are antagonized by the combination treatments (Figure 5A & 5B). These findings highlight that 17-AAG based combination treatments have a marked influence on androgen signaling.

For 17-AAG and bicalutamide, enhanced inhibition of androgen signaling was demonstrated by the increased number of androgen regulated genes that were affected (525 genes) compared with the single agent treatments (176 genes for 17-AAG; 312 genes for bicalutamide; Figure 5A). In contrast, the number of androgen regulated genes did not differ between vorinostat treatment alone (844 genes) or in combination with 17-AAG (851 genes) (Figure 5B). Enhanced inhibition of androgen signaling was also demonstrated through the amplified expression (≥20%) of a subset of androgen regulated genes in combination treated cells compared with and their respective single agent treatments (Figure 5C & 5D). Co-administration of 17-AAG and bicalutamide enhanced the expression of 21% (118/561) of androgen responsive genes, whereas 17-AAG and vorinostat enhanced only 9% (100/1059) of androgen responsive genes. qPCR analysis of PMEPA1 and NKX3.1 using RNA from the microarray analysis and an independently generated RNA sample set validated these findings (Figure 5E & 5F).

158 Figure 5

Figure 5. Combining 17-AAG with bicalutamide but not vorinostat enhances inhibition of AR signaling. Heatmaps represent androgen regulated genes that are significantly up-regulated (green) or down-regulated (red) by 17-AAG alone or in combination with bicalutamide (A) or vorinostat (B). Data were median centered using Cluster 3.0, and heat maps constructed using Java TreeView (http://rana.lbl.gov/eisensoftware.htm). Relative expression levels of the top 5 androgen-responsive genes in LNCaP cells treated with 17-AAG alone or in combination with bicalutamide (C) or vorinostat (D), as determined by gene expression profiling. Expression values are fold change versus vehicle control and represent the average of 6 biological replicates. (E, F) Enhanced expression of a subset of androgen regulated genes in combination treated LNCaP cells was validated by qPCR analysis of PMEPA1 and NKX3.1. Gene expression was normalized to GUSB and L19 and represents the mean ± SEM of 3 biological replicates. *ANOVA: p<0.05. 159

Expression of the AR itself was not altered by 17-AAG or bicalutamide, either alone or in combination, whereas AR inhibition was observed with vorinostat treatment alone (-1.38 fold vs vehicle, p<0.05) and combined with 17-AAG (-1.56 fold vs vehicle, p<0.05). These effects on AR were reflected at the protein level (Supplementary Figure 3). Collectively these findings suggest that using 17-AAG and bicalutamide in combination can enhance blockade of AR signaling, and although 17-AAG and vorinostat in combination inhibits androgen signaling, the combined effect is not better than using vorinostat alone.

Cell cycle pathways are enriched by 17-AAG combination treatments

To identify additional mechanisms and pathways that contribute to the combinatorial effects, Ingenuity Pathway Analysis (IPA) was employed. Cell cycle was the most highly enriched molecular and cellular function for both 17-AAG combination treatments (Figure 6A), suggesting these treatments act through inhibition of the cell cycle. The remaining top functions differed for each combination and provide insight into alternative mechanisms of action. Bicalutamide enriched functions related to drug and lipid metabolism, whereas vorinostat enriched RNA post-transcriptional modification and cellular assembly and organization (Figure 6A). When canonical pathways were evaluated using IPA the pathways ubiquitination and p53 signaling were highly enriched by both 17-AAG combination treatments (Figure 6A).

Co-treatment with bicalutamide minimizes the heat shock response

In cells treated with each combination, by far the most significantly up-regulated transcripts were those that encode members of the heat shock protein family, including HSPA1A, HSPA1B, DNAJB1, and CLU (Supplementary Table 2). In cells co-treated with 17-AAG and bicalutamide, only 13 genes were up-regulated by at least 2-fold and almost 70% of these encode heat shock proteins. In cells co-treated with 17-AAG and vorinostat, 20% of the 50 genes up-regulated by at least 2-fold encode heat shock proteins. The production of heat shock proteins in response to cellular stress is known as the heat shock response, which serves to repair damaged proteins or prepare them for proteasomal degradation via ubiquitination (Zou et al., 1998). The observed up-regulation of heat shock 160 proteins is therefore consistent with the finding that protein ubiquitination was significantly enriched by both combinations (Figure 6A). Increased expression of genes encoding heat shock proteins Hsp70, Hsp40, Hsp27 and clusterin was validated in an independently generated RNA sample set. A critical observation was that induction heat shock proteins in LNCaP cells co-treated with 17-AAG and bicalutamide was significantly lower than in cells treated with therapeutic doses (80nM) of 17-AAG alone (p<0.05, Figure 6C), despite the fact that both treatments induced cell growth suppression and death (Figure 1 & Figure 2). In contrast, divergent effects on heat shock proteins were observed in cells co-treated with 17-AAG and vorinosat. Hsp70 and Hsp27 levels were reduced whereas clusterin and Hsp40 levels far surpassed those in cells treated with high doses of 17-AAG (p<0.05, Figure 6C).

161 Figure 6

Figure 6. Both combination treatments influence cell cycle pathways, but only the combination of 17-AAG and bicalutamide reduces heat shock response. (A) Molecular and cellular functions and (B) canonical pathways affected by combination treatments as determined by Ingenuity Pathway Analysis. (C) The effect of combining 17-AAG with bicalutamide or vorinostat on genes encoding heat shock proteins was evaluated by qPCR. LNCaP cells were treated as indicated (where the first ‘17’ indicates low dose 17-AAG, the second ‘17’ indicates effective dose 17-AAG, and the combinations are using the low dose of 17-AAG) for 6h, RNA was extracted and qPCR performed. Hsp70, Hsp40, Hsp27 and CLU were normalised to GUSB and L19. Error bars represent mean ± SEM of 3 biological replicates. *ANOVA; p<0.05. 162

Discussion

Castration resistant prostate cancer develops as a result of therapy-mediated selection pressure as the cells acquire mechanisms to maintain AR signaling in the castrate environment. Combinatorial treatments that target multiple signaling pathways critical for prostate cancer, including AR signaling, may therefore provide a more effective strategy to prevent the development of CRPC and its associated mortality (Chen et al., 2008). This study demonstrates that the Hsp90 inhibitor 17-AAG works in synergy with the AR antagonist bicalutamide or the HDACI vorinostat to enhance caspase-dependent prostate cancer cell death. Importantly, these effects were demonstrated using clinically achievable and tolerable doses of 17-AAG, bicalutamide and vorinostat (Kelly et al., 2003, Solit et al., 2007, Tyrrell et al., 2006).

The molecular biology of bicalutamide bound AR makes it an attractive agent for use in combination with 17-AAG. Several studies have demonstrated that Hsp90 does not dissociate from bicalutamide bound AR as it does from agonist bound AR (Veldscholte et al., 1992, Kuil et al., 1995, Georget et al., 2002). This stabilizes the AR in a conformation unable to interact with critical coregulators. Further, it maintains the AR as a target for degradation by Hsp90 inhibitors, a concept supported by the fact that bicalutamide bound AR is highly sensitive to degradation by geldanamycin (Georget et al., 2002). In the present study, although minimal changes in AR gene expression or protein levels were seen, the effect of combining 17-AAG and bicalutamide was not observed in AR-negative PC-3 cells and enhanced effects on androgen signaling were observed in terms of both the number of androgen regulated genes that were significantly regulated and the magnitude of the gene expression changes. Furthermore, half of the top ten genes most affected by the combination of 17-AAG and bicalutamide are well known androgen regulated genes (TMPRSS2, KLK2, NKX3.1, C1orf116 and PMEPA1). The combined action of 17-AAG and bicalutamide on AR signaling must, in part, drive the cells beyond the threshold required to maintain viability and results in cell death. A mechanistic role for AR is further implicated by the fact that lipid metabolism was a highly enriched molecular and cellular function in cells co-treated with 17-AAG and bicalutamide. In prostate cancer cells, lipid metabolism is highly regulated by androgens (Swinnen et al., 1997, Swinnen et al., 1996, Swinnen et al., 2004). Bicalutamide inhibits lipid synthesis in LNCaP cells (Swinnen et al., 163

1996), and decreased levels of key lipid enzymes can be detected in CRPC patients undergoing ADT (Ettinger et al., 2004, Rossi et al., 2003).

We and others have reported that vorinostat inhibits AR at both the level of mRNA and through acetylation of AR (Marrocco et al., 2007b, Richon et al., 2009). Furthermore, HDAC6 is an Hsp90 deacetylase thus HDAC inhibition with vorinostat leads to acetylation of Hsp90 and inhibition of its chaperone activity (Bali et al., 2005a). Like 17-AAG, vorinostat has shown synergistic activity when used in combination with numerous other agents both in vitro (Phillip et al., 2012, Richon et al., 2009) and in phase II clinical trials (Garcia-Manero et al., 2012). The synergy demonstrated between 17-AAG and vorinostat in this study does not appear to be AR-mediated. Despite only having efficacy in AR- positive LNCaP cells, the number of androgen-regulated genes did not change when vorinstat was used alone or in combination with 17-AAG, and although enhanced down- regulation compared with the respective single agent treatments was observed in a subset of genes, overall, androgen regulated genes did not rank highly among genes that were differentially expressed by the combination of 17-AAG and vorinostat. At the top of this list were heat shock proteins, transcriptional regulators (GTF2IRD-2 and -2B, UBTF, HIF0) or genes involved in cellular organization (TTN, SORBS1), which is consistent with the top ranked molecular and cellular functions enriched by the combination of 17-AAG and vorinostat, ie, gene expression, RNA post-transcriptional modification and cellular assembly and organization. The reason for the different sensitivities of LNCaP and PC-3 cells to the combination of 17-AAG and vorinostat are still unclear. Previous studies have shown that the apoptotic response to 17-AAG in combination with radiation therapy is limited in cells lacking p53 (Shintani et al., 2006). It is therefore possible that the p53-null status of PC-3 cells may play a role as p53 was down-regulated and the p53 signaling pathway was highly enriched in LNCaP cells (wild type p53) treated with either combination.

The fact that protein ubiquitination was a highly enriched pathway in cells treated with both combinations can be attributed to the use of 17-AAG in both treatment regimes, as ubiquitin-mediated proteasomal degradation is the avenue through which Hsp90 inhibitors typically exert their effects (Theodoraki and Caplan, 2012). Identification of the protein ubiquitination pathway is also supported by the fact that heat shock proteins (HSP70, 164

HSP40, HSPA4L, CLU) were the most highly up-regulated genes in cells treated with 17- AAG combinations. Despite this finding, the heat shock response was significantly reduced when 17-AAG and bicalutamide were used in combination compared with effective single-agent doses of 17-AAG. This is a critical finding for combination therapy as it demonstrates that resistance mechanisms, such as the heat shock response (Whitesell et al., 2003), can be minimized with the use of low doses of the combinatorial drugs. Combining 17-AAG with vorinostat resulted in differential effects on the heat shock proteins, thus the capacity to minimize the heat shock response was not as pronounced with these agents. This can likely be attributed to the dual effect of both 17-AAG (Centenera et al., 2013a) and vorinostat (Bali et al., 2005a) on Hsp90 making the action of this combination of agents more similar to high doses of 17-AAG.

In summary, the Hsp90 inhibitor 17-AAG can synergize with bicalutamide or the HDACI vorinostat to enhance growth suppression and death of prostate cancer cells. Importantly, the combination of 17-AAG and bicalutamide demonstrated the capacity to enhance blockade of androgen signaling and minimize the potential for resistance through the heat shock response, which provides a framework for combination trials between Hsp90 inhibitors and AR antagonists in patients with prostate cancer.

165

Acknowledgements

The authors thank Ms Bronwyn Cambareri for technical assistance and Ms Joanna Gillis for helpful discussions on the manuscript.

Financial Support: This work was supported by a grant from the National Health and Medical Research Council of Australia (#453662 to W.D.T. and L.M.B), Cancer Australia (#627229 to L.M.B., W.D.T. and H.I.S.) and the US Army Medical Research and Materiel Command (W81XWH-04-0017, to WDT LMB). D.L.M.T was the recipient of a University of Adelaide Postgraduate Award and S.L.C. was the recipient of a Lion’s Medical Research Foundation Postgraduate Scholarship. L.M.B. holds a senior research fellowship from the Cancer Council of South Australia.

166

Supplementary Materials and Methods

Immunoblotting

LNCaP cells were seeded in 6-well plates at a density of 4x105 cells per well in 1 mL of RPMI medium containing 10% FBS. Cells were allowed to attach for 24 h before medium was replaced with medium containing treatments as indicated. Cells were lysed 24, 48 or 72 h after initiation of treatment by adding radioimmunoprecipitation assay lysis buffer (10 mM Tris-HCL, 150 mM NaCl, 1 mM EDTA, 1% Triton X-100) containing mini-complete protease inhibitor pellets (Roche, Mannhein, Germany). Lysates (30 µg) were electrophoresed through 7.5% polyacrylamide gels, transferred to nitrocellulose membrane (Amersham Biosciences, Buckinghamshire, England), and blocked in 3% non-fat milk powder in TBS containing 0.05% Tween20 overnight. Immunodetection was performed for 1-2 h at room temperature in primary antibody diluted in 1% non-fat milk powder in TBS containing 0.05% Tween20, according to the manufacturer’s instructions. Immune complexes were detected with horseradish peroxidase-conjugated secondary antibodies (DAKO) and visualized on Hyperfilm (GE Healthcare, Piscataway, NJ) using enhanced chemiluminescence detection (GE Healthcare) (Neckers, 2007).

167

Supplementary Figure 1

Supplementary Figure 1. Isobologram

An isobologram is a graphical representation of the nature of the interaction between two drugs for a given effect. The individual dose of drug A and drug B that give the effect are plotted as axial points on a graph. The line connecting A and B indicates the doses of each drug that will produce the effect in an additive combination. A combination of doses that falls beneath the line, such as point C, is synergistic as it requires less quantities of each drug. A combination of doses that falls above the line, such as D, is sub-additive.

168

Supplementary Figure 2

Supplementary Figure 2. Dose response curves in LNCaPs for bicalutamide and voriostat

LNCaP cells were cultured with increasing doses of (A) bicalutamide or (B) vorinostat as indicated for up to 5 days. Cells were counted at regular intervals using a haemocytometer and cell viability was assessed by trypan blue dye exclusion. The number of dead cells is expressed as a percentage of the total number of cells counted. Results are representative of at least 3 independent experiments and represent the mean +/- SE of triplicate wells. * ANOVA: P<0.05 treatments versus vehicle control.

169

Supplementary Figure 3

Supplementary Figure 3. Comparison of androgen regulated genes altered by 17-AAG combinations

Venn diagram of the overlap between genes significantly changed by the combination of (A) 17-AAG and bicalutamide or (B) 17-AAG and vorinostat compared with vehicle control, and genes significantly changed by in LNCaP cells treated with 100 nM DHT for 16 hours (Wang et al., 2007). Circles represent both up-regulated and down-regulated genes and are proportional to the number of genes.

170

Supplementary Figure 3

Supplementary Figure 4. Alterations in the steady state level of AR in LNCaP cells when treated with 17-AAG combinations Whole cell protein lysates from LNCaP cells cultured with vehicle control (C), 20 nM 17-AAG (17), 1.25 µM bicalutamide (B), 17-AAG and bicalutamide (17+B), 0.5

µM vorinostat (V), 17-AAG and vorinostat (17+V) were analyzed by immunoblotting for expression of AR. Hsp90 was used as a loading control.

171

Supplementary Table 1

Sequences for primers used in this paper (5’ – 3’)

Gene name Primer Sequence TMPRSS2 F GACCAAGAACAATGACATTGCG R GTTCTGGCTGCAGCATCATG PMEPA1 F GTCTGCACGGTCCTTTGCTC R CGTTGCGCCCTGCAGATCCT KLK2 F GGTGGCTGTGTACAGTCATGGAT R TGTCTTCAGGCTCAAACAGGTTG C1orf116 F AGCCACAACTCCCAGAGGTTT R TCGTCCTTGCTGAGTGATGG NKX3.1 F CTGGCAGAGACCGAGCCAGAAAG R AGCGCTTCTGCGGCTGCTTAG GREB1 F CGCCGTTGACAAGAGGTTCT R GCCTTTCTTTCCACAGCCAA HSPA1A/HSPA1B (Hsp70) F GGACATGAAGCACTGGCCTT R TCAGCACCATGGACGAGATCT DNAJB1 (Hsp40) F TTTAAAGGACAAGCCCCACAA R TTCACTGTGCAGCCACACA HSPB1 (Hsp27) F CTCAAACGGGTCATTGCCAT R CTGCTCAGAAAAGTGCGGG CLU (Clusterin) F CGCAAGACACTGCTCAGCAA R ACACTCCTGGGAGCTCCTT GUSB F CGTCCCACCTAGAATCTGCT R TTGCTCACAAAGGTCACAGG L19 F TGCCAGTGGAAAAATCAGCCA R CAAAGCAAATCTCGACACCTTG

172

Supplementary Table 2

List of genes altered by 17-AAG combinations (≥ 2-fold)

17-AAG + bicalutamide

Gene name Fold change p-value HSPA1B 7.20 1.24 x 10-23 HSPA1A 4.50 3.23 x 10-22 HSPA7 2.94 4.03 x 10-10 DNAJB1 2.62 2.41 x 10-20 OPRK1 2.54 1.11 x 10-13 HSPA4L 2.36 1.15 x 10-17 HSPB8 2.34 7.55 x 10-10 HSPA6 2.33 5.53 x 10-8 CRYAB 2.28 1.96 x 10-11 HSPH1 2.26 1.56 x 10-14 PLD1 2.18 1.02 x 10-10 DNAJB4 2.16 7.88 x 10-11 P4HA2 2.01 9.05 x 10-16

17-AAG + vorinostat

Gene name Fold change p-value HSPA1B 8.21 4.82 x 10-24 HSPA1A 4.71 5.18 x 10-22 PGM2L1 3.67 2.84 x 10-16 DNAJB1 3.39 1.28 x 10-22 SORBS1 3.09 4.91 x 10-20 LIPH 2.98 5.75 x 10-15 GSDMB 2.89 5.01 x 10-17 CLU 2.87 6.64 x 10-21 GRK5 2.72 2.28 x 10-16 ROPN1L 2.64 7.27 x 10-11

173

TMOD2 2.60 1.75 x 10-13 HSPA7 2.58 3.68 x 10-9 MAN1A1 2.52 5.43 x 10-15 PDGFRL 2.51 8.84 x 10-12 DNAJB4 2.50 9.13 x 10-13 FAM49A 2.48 9.35 x 10-13 TSPAN5 2.46 1.80 x 10-13 MBNL2 2.44 3.69 x 10-14 ARSK 2.44 7.84 x 10-13 HSPA4L 2.42 1.87 x 10-17 CDON 2.41 1.68 x 10-14 TUFT1 2.37 5.31 x 10-13 HEG1 2.36 4.78 x 10-14 ANKRD22 2.32 4.51 x 10-11 HSPH1 2.25 3.37 x 10-14 UST 2.24 1.07 x 10-13 INSIG2 2.21 8.98 x 10-15 KBTBD3 2.20 4.18 x 10-13 ID1 2.12 1.81 x 10-5 DAPK3 2.11 1.91 x 10-11 HSD17B11 2.09 1.49 x 10-11 ST3GAL5 2.09 1.07 x 10-13 DENND2C 2.09 6.92 x 10-8 TMPRSS11E 2.08 1.77 x 10-12 DIXDC1 2.08 1.49 x 10-11 HSP90AA6P 2.07 1.23 x 10-5 BDH2 2.07 2.31 x 10-12 TMPRSS11E 2.06 2.31 x 10-12 HSPB8 2.06 1.71 x 10-8 SERPINB5 2.06 2.49 x 10-11 C9orf150 2.06 1.63 x 10-12 KITLG 2.05 1.63 x 10-10 MED12L 2.03 8.21 x 10-14 174

BTN2A2 2.01 1.59 x 10-15 IRF6 2.01 3.02 x 10-11 GRAMD1C 2.01 4.12 x 10-10 FRMD6 2.01 7.77 x 10-12 TTC33 2.01 2.78 x 10-12 FLVCR2 2.00 9.40 x 10-12 TNIK 2.00 2.11 x 10-10

175

Additional Information

Expression profiling analysis was conduction on the three different combinations in this thesis: vorinostat + bicalutamide, vorinostat + 17-AAG, and bicalutamide + 17-AAG. For each combination, there was a subset of genes altered that were unique to the combination, that is, genes not altered by either of the individual agents, only by the combination. It was therefore feasible to investigate the overlap between the unique genes altered by each combination. Given that this investigation is outside of the scope of the two manuscripts presented in this chapter and chapter three, it is included here as additional information.

176

Additional Figure 1

Additional Figure 1. Comparison of genes uniquely regulated by combinations of vorinostat, bicalutamide, or 17-AAG.

Microarray expression profiling was conducted on LNCaP cells treated with three different AR targeting agents – bicalutamide, vorinostat, or 17-AAG, either alone or in three separate combinations. For each combination, comparison of the genes significantly altered by the combination with the genes significantly altered by the individual agents yielded a list of genes uniquely regulated by the combination. These unique genes from each combination were then compared against each other, as shown in this venn diagram, to find genes commonly regulated by each combination.

177

Additional Table 1

List of 130 uniquely altered genes common to all three combination therapies, including p-value and fold change when compared to vehicle control.

Bicalutamide + 17-AAG + 17-AAG + vorinostat bicalutamide vorinostat fold fold fold Gene p-value p-value p-value change change change AAK1 2.08E-03 -1.31 1.54E-03 -1.35 1.52E-02 -1.22 ALMS1 4.76E-03 -1.21 2.53E-02 -1.18 7.96E-03 -1.20 ALPK3 2.00E-03 -1.24 1.26E-02 -1.22 5.68E-04 -1.28 ATP5D 6.84E-03 -1.28 3.17E-02 -1.25 2.94E-03 -1.31 ATXN2 1.75E-02 -1.12 4.79E-05 -1.25 6.76E-03 -1.14 B4GALT1 2.18E-04 -1.28 3.37E-03 -1.24 2.85E-04 -1.28 BCL2L1 1.45E-02 -1.15 2.76E-02 -1.15 9.87E-04 -1.20 BHLHE40 3.42E-04 -1.36 7.56E-04 -1.36 3.58E-03 -1.28 BMPR2 3.81E-02 -1.16 2.70E-03 -1.26 4.78E-03 -1.23 C9orf140 2.84E-02 -1.22 2.15E-02 -1.25 4.14E-03 -1.29 CAPZB 1.34E-02 -1.12 2.30E-03 -1.16 1.12E-03 -1.16 CCT7 6.74E-03 1.12 2.33E-05 1.22 3.04E-05 1.20 CD70 2.50E-02 1.17 2.87E-02 1.18 3.44E-02 1.16 CDC6 5.54E-03 -1.14 1.55E-02 -1.14 2.37E-05 -1.24 CDV3 2.31E-02 -1.10 2.37E-02 -1.19 5.92E-03 -1.12 CKB 3.84E-02 -1.15 2.13E-03 -1.25 1.06E-02 -1.19 CREB1 7.00E-04 -1.18 7.79E-04 -1.20 7.57E-04 -1.18 CSK 9.52E-03 -1.21 4.43E-02 -1.18 3.68E-04 -1.30 DACH1 1.18E-02 1.17 6.63E-03 1.20 2.75E-02 1.14 DAG1 1.01E-02 -1.23 2.50E-02 -1.23 4.90E-03 -1.26 DCLRE1B 6.13E-03 -1.24 3.08E-03 -1.30 1.40E-03 -1.29 DDHD2 4.42E-02 -1.09 1.58E-04 -1.20 4.71E-03 -1.13 DEGS1 2.01E-05 -1.34 1.93E-06 -1.43 1.63E-03 1.41 DEK 5.59E-04 -1.21 4.64E-03 -1.19 3.46E-04 -1.22 DEPDC1 8.69E-04 -1.40 6.63E-03 -1.35 5.92E-03 -1.32 DSN1 4.02E-04 -1.22 4.79E-05 -1.29 1.06E-03 -1.20 EBP 1.77E-02 1.26 2.18E-02 1.28 7.65E-03 1.29 EGFR 2.57E-02 -1.22 2.03E-04 -1.42 8.60E-03 -1.26 ESPL1 2.39E-03 -1.25 1.95E-02 -1.21 7.79E-04 -1.28 FANCA 1.93E-02 -1.13 4.66E-03 -1.18 7.45E-05 -1.25 FAT1 1.52E-03 -1.26 1.54E-02 -1.21 1.17E-02 -1.20 FBXL3 5.63E-05 -1.29 3.01E-02 -1.16 1.29E-02 -1.16 178

Bicalutamide + 17-AAG + 17-AAG + vorinostat bicalutamide vorinostat fold fold Gene p-value p-value Gene p-value change change FBXW11 9.91E-03 -1.13 6.39E-03 -1.15 2.47E-04 -1.19 FLOT1 2.36E-02 1.18 3.44E-02 1.19 7.30E-03 1.22 GGCT 6.92E-03 -1.24 3.83E-02 -1.21 1.04E-03 -1.31 GNAS 4.12E-02 -1.08 4.45E-03 -1.13 1.48E-02 -1.10 GSTA4 2.64E-03 -1.21 4.22E-02 -1.16 5.01E-03 -1.19 GSTM2 1.81E-05 -1.78 3.65E-03 -1.50 4.17E-03 -1.45 HDGFRP3 3.72E-03 -1.23 6.13E-03 -1.24 5.05E-03 -1.22 HSF1 2.70E-02 -1.20 4.10E-02 -1.21 3.61E-03 -1.27 IL17RC 2.57E-02 1.16 4.79E-03 1.23 1.30E-03 1.24 ITGB1BP3 4.98E-02 1.24 4.25E-02 1.28 2.66E-02 1.27 KIAA0125 2.60E-02 1.32 1.94E-02 1.38 3.29E-02 1.31 KIAA0802 3.87E-02 -1.16 1.31E-03 -1.29 1.57E-02 -1.19 KIF24 1.89E-03 -1.22 1.01E-02 -1.20 3.17E-04 -1.26 KLHL15 4.55E-02 -1.19 3.53E-02 -1.23 8.46E-03 -1.25 KLHL36 1.11E-03 -1.48 2.65E-05 -1.76 1.95E-03 -1.45 KRT74 1.67E-02 1.21 1.52E-03 1.31 1.33E-02 1.22 LHFPL2 2.17E-02 1.17 3.74E-04 1.30 2.53E-02 1.16 LIN7B 3.83E-02 -1.21 2.48E-02 -1.26 4.63E-02 -1.21 LMAN2 2.75E-02 1.15 2.24E-02 1.18 4.58E-03 1.20 MAGED2 2.41E-04 1.24 6.70E-03 1.18 6.38E-05 1.27 MARS 1.41E-03 1.15 1.08E-05 1.24 1.26E-02 1.12 MKNK2 1.75E-02 1.18 7.68E-04 1.28 3.73E-03 1.22 MMP10 1.87E-02 1.17 4.71E-02 1.17 2.12E-02 1.17 MTERFD2 1.44E-04 -1.32 4.17E-02 -1.18 6.28E-03 -1.21 MTMR2 2.59E-05 -1.27 1.62E-03 -1.21 3.56E-04 -1.22 MYBL2 7.69E-04 -1.17 2.06E-03 -1.17 1.36E-04 -1.20 MYCBP2 1.11E-03 -1.19 8.86E-03 -1.16 2.73E-02 -1.13 MYO1B 4.32E-03 -1.19 3.48E-04 -1.27 6.14E-03 -1.19 NAT9 2.93E-02 1.22 7.20E-04 1.40 6.57E-04 1.37 NCOR1 8.03E-05 -1.26 7.98E-05 -1.28 2.47E-03 -1.19 NCRNA00185 2.27E-02 1.17 3.61E-03 1.25 2.77E-02 1.16 NDUFV1 4.96E-03 1.22 1.57E-02 1.20 4.80E-03 1.22 NHSL1 3.62E-03 -1.29 6.29E-03 -1.31 2.14E-02 -1.23 NKX6-3 1.74E-02 1.26 4.09E-02 1.25 2.01E-03 1.35 NSFL1C 9.52E-04 1.19 3.46E-03 1.18 2.23E-02 1.13 OVOS/OVOS2 3.23E-03 -1.24 4.60E-03 -1.22 2.24E-03 -1.22 PARK7 3.56E-02 1.11 8.80E-04 1.18 2.06E-04 1.20 179

Bicalutamide + 17-AAG + 17-AAG + vorinostat bicalutamide vorinostat fold fold Gene p-value p-value Gene p-value change change PCNA 1.76E-03 -1.24 2.77E-03 -1.25 4.71E-03 -1.22 PCOTH 1.37E-06 -1.52 1.85E-02 -1.24 7.25E-05 -1.39 PFDN2 1.04E-02 1.24 4.68E-02 1.21 5.69E-04 1.34 PGR 2.88E-03 1.20 1.78E-03 1.23 3.02E-03 1.20 PHACTR2 3.23E-05 -1.34 4.29E-03 -1.23 4.49E-03 -1.21 PMCHL2 1.64E-07 -1.62 3.68E-03 -1.29 8.73E-04 -1.31 POLE2 1.25E-03 -1.25 2.02E-02 -1.20 2.91E-02 -1.17 POU3F1 4.05E-02 -1.20 1.69E-02 -1.26 3.30E-02 -1.20 PPCS 8.58E-03 -1.22 1.01E-02 -1.24 9.37E-03 -1.22 PPIH 1.60E-03 -1.17 2.45E-02 -1.14 3.37E-03 -1.16 PPM1A 2.28E-03 -1.22 3.36E-02 -1.17 2.31E-02 -1.16 PPP1CB 3.24E-04 -1.16 3.47E-02 -1.10 2.17E-02 -1.10 PPP1R13B 3.66E-02 -1.14 4.35E-02 -1.16 4.79E-03 -1.20 PRIM1 2.24E-02 -1.15 1.59E-03 -1.24 3.63E-03 -1.20 PSME2 8.76E-04 1.40 8.36E-03 1.34 4.54E-05 1.54 PTEN 2.11E-04 1.30 2.35E-02 1.19 4.80E-03 1.21 RAB17 4.76E-02 1.21 1.43E-04 1.48 7.97E-03 1.29 RAD51AP1 1.67E-03 -1.20 7.64E-04 -1.23 9.00E-03 -1.16 RARB 3.33E-03 -1.25 2.58E-02 -1.21 4.20E-03 -1.24 RAVER2 2.30E-05 -1.27 4.66E-03 -1.18 9.54E-05 -1.25 RFC5 3.03E-02 -1.13 4.54E-02 -1.14 4.29E-02 -1.12 RN18S1 4.04E-02 -2.18 6.59E-03 -3.04 1.49E-02 -2.50 RNF207 1.43E-03 -1.28 2.22E-02 -1.22 4.86E-03 -1.29 RNF41 7.13E-03 -1.21 1.29E-04 -1.35 4.77E-02 -1.16 RNPS1 1.05E-03 -1.18 2.87E-03 -1.18 1.45E-04 -1.22 RPL24 8.00E-03 1.25 2.68E-02 1.23 2.92E-05 1.37 RPL39 3.31E-02 1.26 2.06E-02 1.31 2.60E-04 1.48 RPLP0 2.44E-02 1.24 4.19E-02 1.21 7.17E-04 1.33 RPS25 1.91E-02 1.33 3.88E-02 1.33 3.21E-03 1.44 SCARNA17 3.75E-04 1.31 1.41E-02 1.22 1.13E-03 1.27 SIN3B 3.92E-02 -1.14 2.52E-02 -1.17 1.83E-03 -1.22 SKA1 4.22E-03 -1.23 4.00E-02 -1.19 4.33E-02 -1.16 SKA3 1.00E-03 -1.29 3.58E-03 -1.28 5.39E-04 -1.31 SLC10A3 2.61E-02 -1.15 4.63E-02 -1.15 3.45E-02 -1.14 SMCHD1 3.91E-02 -1.14 8.06E-03 -1.20 1.76E-02 -1.17 SMTN 4.15E-03 -1.27 4.65E-02 -1.21 5.08E-04 -1.34 SNORA23 3.08E-02 1.22 1.07E-02 1.29 2.36E-02 1.23 180

Bicalutamide + 17-AAG + 17-AAG + vorinostat bicalutamide vorinostat fold fold Gene p-value p-value Gene p-value change change SNORA64 2.73E-02 1.40 5.96E-03 1.58 2.24E-02 1.41 SNORA68 2.39E-02 1.43 4.65E-02 1.44 1.53E-02 1.47 SNORA71A 3.68E-02 1.28 3.50E-02 1.32 3.52E-02 1.29 STK35 5.52E-04 -1.23 2.37E-02 -1.16 1.07E-04 -1.27 TIPIN 1.99E-02 -1.17 1.27E-03 -1.26 1.71E-05 -1.35 TJP1 3.29E-03 -1.16 7.75E-04 -1.20 7.06E-04 -1.19 TLN2 3.84E-03 1.18 2.22E-02 1.16 2.87E-03 1.18 TMEM201 3.19E-04 -1.46 1.18E-02 -1.34 2.46E-04 -1.48 TOPBP1 2.83E-02 -1.12 7.29E-03 -1.16 1.82E-04 -1.21 USP31 2.22E-02 -1.15 3.50E-03 -1.21 7.92E-04 -1.22 VPS72 4.72E-03 -1.24 2.41E-02 -1.21 1.37E-02 -1.21 WASF2 1.04E-03 -1.52 2.15E-04 -1.66 4.99E-04 -1.56 WHSC1L1 2.91E-03 -1.15 3.34E-03 -1.16 1.18E-04 -1.20 WNK1 7.14E-04 -1.21 2.87E-04 -1.25 3.65E-04 -1.23 YY1AP1 1.29E-02 -1.16 1.69E-02 -1.18 3.59E-03 -1.19 ZBTB43 3.70E-02 1.21 3.70E-03 1.33 1.98E-02 1.24 ZC3H14 3.20E-02 -1.11 2.02E-04 -1.21 5.14E-03 -1.14 ZFPM1 4.65E-02 -1.18 2.78E-02 -1.22 4.39E-02 -1.18 ZMIZ1 1.17E-04 -1.41 3.35E-05 -1.50 5.27E-03 -1.28 ZNF18 2.04E-02 -1.25 4.41E-03 -1.34 1.73E-02 -1.25 ZNF354B 3.75E-03 -1.30 4.12E-02 -1.24 7.41E-03 -1.27 ZNF652 7.44E-03 -1.23 4.43E-05 -1.41 3.15E-04 -1.33 ZNF828 3.78E-02 -1.20 1.68E-02 -1.26 4.58E-03 -1.28 ZNF85 4.23E-03 -1.22 2.15E-02 -1.20 4.24E-03 -1.23

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Additional Table 2

Below is a table of the KEGG pathways and GO terms enriched in the set of 130 genes altered by all three combinations. Given that all three combinations cause death in LNCaP prostate cancer cells, it was not surprising that many of the pathways or GO terms involved cell cycle, cell death, or DNA replication/repair.

KEGG Pathway P-value Genes DNA replication 0.004034794 PCNA, POLE2, RFC5, PRIM1 Ribosome 0.043510376 RPLP0, RPS25, RPL24, RPL39

GO Term P-value Genes PPP1CB, CDC6, SKA3, EGFR, NCOR1, SKA1, RPL24, ESPL1, Cell cycle phase 9.03E-04 TIPIN, FANCA, DSN1 PPP1CB, CDC6, SKA3, EGFR, SKA1, RPL24, ESPL1, PSME2, Mitotic cell cycle 0.00158 TIPIN, DSN1 CDC6, SKA3, NCOR1, SKA1, RPL24, ESPL1, TIPIN, FANCA, M phase 0.00283 DSN1 PPP1CB, CDC6, SKA3, EGFR, NCOR1, SKA1, RPL24, ESPL1, Cell cycle process 0.00285 PSME2, TIPIN, FANCA, DSN1 Regulation of cell PPP1CB, MYCBP2, PTEN, CDC6, EGFR, PPP1R13B, RPL24, 0.00299 cycle ESPL1, TIPIN PPP1CB, PTEN, STK35, HSF1, MKNK2, MTMR2, CSK, Phosphorus 0.0048 ATP5D, PPM1A, NDUFV1, BMPR2, EGFR, ALPK3, CREB1, metabolic process AAK1, WNK1 PPP1CB, PTEN, STK35, HSF1, MKNK2, MTMR2, CSK, Phosphate 0.0048 ATP5D, PPM1A, NDUFV1, BMPR2, EGFR, ALPK3, CREB1, metabolic process AAK1, WNK1 Regulation of cell PTEN, PPP1CB, ZMIZ1, HSF1, CSK, BMPR2, PGR, CDC6, 0.00508 proliferation B4GALT1, EGFR, BCL2L1, RARB, TIPIN, FANCA Response to DNA PPP1CB, PCNA, POLE2, TOPBP1, RAD51AP1, RFC5, TIPIN, 0.00576 damage stimulus DCLRE1B, FANCA M phase of mitotic 0.00603 CDC6, SKA3, SKA1, RPL24, ESPL1, TIPIN, DSN1 cell cycle Nucleotide- 0.00684 PCNA, POLE2, RFC5 excision repair Regulation of cyclin-dependent 0.00739 PPP1CB, PTEN, CDC6, EGFR protein kinase activity DNA metabolic PCNA, CDC6, POLE2, TOPBP1, RAD51AP1, RFC5, TIPIN, 0.01075 process DCLRE1B, FANCA, PRIM1 DNA replication 0.01223 PCNA, CDC6, POLE2, RFC5, TIPIN, PRIM1 182

GO Term P-value Genes PCNA, POLE2, TOPBP1, RAD51AP1, RFC5, DCLRE1B, DNA repair 0.017 FANCA cell projection MYCBP2, PTEN, EGFR, WASF2, RPL24, CREB1, ALMS1, 0.01943 organization CAPZB chromosome 0.02205 SKA3, SKA1, ESPL1, DSN1 segregation cell division 0.02205 PPP1CB, CDC6, SKA3, SKA1, ESPL1, TIPIN, DSN1 cellular response PPP1CB, PCNA, POLE2, TOPBP1, RAD51AP1, RFC5, BMPR2, 0.02209 to stress TIPIN, DCLRE1B, FANCA nuclear division 0.02311 CDC6, SKA3, SKA1, ESPL1, TIPIN, DSN1 mitosis 0.02311 CDC6, SKA3, SKA1, ESPL1, TIPIN, DSN1 regulation of 0.02614 MYCBP2, CDC6, EGFR, RPL24, ESPL1 mitotic cell cycle PPP1CB, CDC6, SKA3, EGFR, NCOR1, SKA1, RPL24, ESPL1, cell cycle 0.02669 PSME2, TIPIN, FANCA, DSN1 organelle fission 0.0269 CDC6, SKA3, SKA1, ESPL1, TIPIN, DSN1 ZMIZ1, ZNF18, MYBL2, HSF1, NCOR1, SIN3B, VPS72, ZNF354B, ZNF85, ZFPM1, BMPR2, BHLHE40, DACH1, transcription 0.02719 PRIM1, MYCBP2, PGR, POU3F1, NKX6.3, RNPS1, ZBTB43, ZNF652, CREB1, RARB, WHSC1L1 STK35, EGFR, HSF1, ALPK3, MKNK2, CSK, ATP5D, NDUFV1, phosphorylation 0.03318 CREB1, BMPR2, WNK1, AAK1 translational 0.03783 RPLP0, RPS25, RPL24, RPL39 elongation

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Chapter 6:

General Discussion

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6.1 Targeting the AR for treatment of prostate cancer

Of all cancers diagnosed each year in Australian men, prostate cancer accounts for 30% and is the second leading cause of death from cancer, behind lung cancer (AIHW, 2012). Due to frequent testing incorporated into health check-ups, such as the prostate specific antigen blood test and/or the digital rectal exam, and awareness campaigns aimed at men over the age of 50, many men are diagnosed with early stage cancer. This, combined with the generally slow-growing nature of prostate cancer, results in an overall 92% 5-year survival rate in Australia (AIHW, 2012). However, for the men who are diagnosed in the first instance with metastatic prostate cancer, or the men who progress with metastases despite an initial diagnosis of organ-confined cancer, current therapeutic options that manipulate levels and/or activity of androgens are associated with a multitude of side effects. Morever, therapies for metastatic disease are not curative and the patient will ultimately relapse with CRPC. At this stage of the disease, further therapies, such as additional hormonal manipulations or chemotherapeutic options, generally do not significantly prolong survival.

It is now accepted that CRPC is characterised by continued or re-activated signalling through the androgen receptor (AR), despite the initial efficacy of hormonal manipulation therapy. In the non-malignant prostate, AR signalling promotes epithelial differentiation, but in prostate cancer AR also regulates a spectrum of genes that regulate cell-cycle, survival, and proliferation, leading to tumour progression (Knudsen et al., 1998, Wang et al., 2009, Xu et al., 2006, Cai et al., 2011). Many studies, including those from our laboratory, have provided evidence that specifically targeting AR levels and/or activity is sufficient to suppress growth or induce death in prostate cancer cells, and provide proof of principle that targeting the AR may be a viable treatment option for prostate cancer at any stage of the disease. These studies include the use of siRNA (Haag et al., 2005, Liao et al., 2005, Yang et al., 2005), antisense oligonucleotides (Eder et al., 2000, Eder et al., 2002, Hamy et al., 2003, Ko et al., 2004), hammerhead ribozymes (Chen et al., 1998), anti-AR antibodies (Zegarra-Moro et al., 2002) and dominant negative androgen receptors (Butler et al., 2006). However, these approaches only provide a modest overall increase in life expectancy, and despite a relatively short amount of time in clinical use, as monotherapies they often fail rapidly due to the development of resistance. At the initiation of this thesis, 185 the AR targeting treatment option for men with advanced prostate cancer was limited to the use of the AR antagonist bicalutamide during androgen deprivation therapy (ADT) and combined androgen blockade (CAB). Over the course of this thesis, other treatment options that target AR signalling have been developed, such as the CYP17 inhibitor abiraterone acetate (de Bono et al., 2011) and the new generation AR antagonist enzalutamide (Scher et al., 2010). However, these approaches only provide a modest overall increase in life expectancy, and despite a relatively short amount of time in clinical use, as monotherapies they have demonstrated issues associated with resistance and therapy-mediated selection pressure. Combination therapy has the potential to target different aspects of the AR signalling pathway, and thus this thesis has investigated the efficacy of combination therapy for prostate cancer and the molecular mechanisms by which these combinations work, in order to facilitate development of these therapeutic strategies for clinical use.

6.2 Major findings of this thesis

Two different combination therapies for the treatment of prostate cancer were investigated in this thesis: the histone deacetylase inhibitor vorinostat, or the Hsp90 inhibitor 17-AAG, in conjunction with a current prostate cancer therapeutic, the androgen receptor antagonist bicalutamide.

Investigation into the combination of vorinostat and bicalutamide revealed that NFKBIA/IκBα may be a previously unrecognised mediator of prostate cancer cell death, given that specific manipulation of this molecule by either combination therapy or molecular methods resulted in altered cell death. The cell death associated with loss of IκBα appeared to be facilitated by activation of p53, given that the induction of two common p53 genes – TP53INP1 and CDKN1A (p21) was observed after combination treatment and both cell lines use either contain wild-type p53 (LNCaP) or mutated but functional p53 (VCaP). However, not all of the p53 inducible genes tested were significantly induced by the combination. With the current data, it is unclear whether this is because the other p53 inducible genes tested are usually not as highly induced by p53 activation as TP53INP1 and CDKN1A (p21), or if the activation of TP53INP1 and

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CDKN1A (p21) was caused by p53-independent mechanisms. It has been shown that CDKN1A (p21) expression is highly induced by vorinostat treatment (Uehara et al., 2012, Yin et al., 2007, Marrocco et al., 2007a), but there is little evidence connecting bicalutamide and this gene. Furthermore, induction by vorinostat does not explain the increase in CDKN1A (p21) expression with combination treatment over and above the vorinostat treatment alone. Despite the probability that the induction of TP53INP1 and CDKN1A (p21) is likely due to p53 activation, other lines of evidence are required for more definitive proof. However, given that the activation of p53 through the loss of IκBα is due to the loss of cytoplasmic sequestering by IκBα and not via a traditional signal transduction mechanism, determination of p53 activation by more classical methods such as increased steady-state protein levels or phosphorylation may prove unreliable.

Outside of the association of loss of NFKBIA with prostate cancer cell death, microarray profiling also showed that combining AR targeting therapies causes smaller changes to many genes in the entire androgen signalling pathway, which may make these combination therapies effective for more sustainable control of prostate cancer and could potentially circumnavigate the selection pressure associated with high doses of single agents such as AR antagonists. Each combination significantly also altered several genes involved in cell death; however, upon further investigation these genes were not initiators of cell death, but were common genes involved in “end of the line” cell death pathways. This again implied that the smaller changes observed over the androgen signalling pathway and cancer cell signalling pathways are likely to cause a cumulative effect.

Despite the challenges associated with the investigation into the in vivo effects of the bicalutamide + vorinostat combination, the experiments provided some new pharmacological insights. Specifically, the mice showed no outward signs of toxicity whilst receiving the combination therapy, indicating that combining the two drugs does not cause additive toxicity. However, this was shown in the absence of any tumour suppressing effect. To move forward with clinical development, it is essential to show that the combination not only displays a favourable toxicity profile, but more importantly that it does so with improved treatment efficacy.

Finally, investigation into the combination of 17-AAG and bicalutamide revealed another promising combination for use in prostate cancer. While 17-AAG has experienced 187 difficulties in clinical use as a monotherapy, the studies in this thesis have shown that by combining 17-AAG with bicalutamide, the overall dose needed can be reduced and, importantly, can prevent the heat shock response suspected of involvement in drug resistance.

6.2.1 Combination therapy for prostate cancer Over the duration of this thesis, research interest for cancer therapy has shifted from the investigation of novel monotherapies to the development of hypothesis driven combinatorial therapies. Most prostate cancers are already treated with combination therapy, whether this is a combination of surgery, radiation therapy and/or drug therapy. However, whether it is combining surgery with hormonal drug therapy for localised prostate cancer, or combining different hormonal drug therapies for advanced/metastatic prostate cancer, the combinatorial options for prostate cancer treatment are limited and more often than not do not show a greater effect than the relevant monotherapies. Therefore, new therapeutic options for prostate cancer are of considerable interest. Combinatorial therapies are particularly attractive, due to their potential to overcome two of the main challenges facing prostate cancer therapy: the heterogeneity of prostate cancer (both intra and inter patient variability), and the compensatory mechanisms that prostate cancer can employ in response to therapies currently employed in prostate cancer treatment (therapy-mediated selection pressure, outlined in Chapter 1.5).

The inherent heterogeneity of prostate cancer implies that combination therapies may be more effective than monotherapies, where different agents can attack distinct populations of cells within a tumour. However, while combination therapy may be able to address the issue of heterogeneity, it is also likely that combination therapy will need to be a dynamic approach, where changes or alterations can be made to the treatment regimen if/when the tumour progresses. In order to employ a dynamic, personalised medicine approach, validated biomarkers will be required. The studies in Chapter 3 of this thesis have demonstrated that NFKBIA/IκBα may prove to be a useful biomarker for effective prostate cancer treatment in two of the most common AR statuses observed in the clinic – wild type AR (VCaP) and mutant AR (LNCaP). However, these studies were limited to in vitro molecular analysis and require further validation.

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Combinations of low dose treatments have the potential to result in small, but sustained changes in major cancer cell signalling pathways, meaning that therapy-mediated selection pressure may be avoided if the therapy can avoid triggering compensatory mechanisms. Microarray profiling implied that the cell death caused by the combination of bicalutamide and vorinostat could be due to the small, cumulative changes to several different cell signalling pathways, including the androgen signalling pathway. The androgen receptor remains the main oncogenic driver in castrate resistant prostate cancer throughout all stages of the disease, and so combinatorial vertical targeting (targeting the receptor and the pathway simultaneously) of the androgen signalling pathway is a logical approach. Furthermore, by using agents that not only target the androgen signalling pathway, but also have effects on many other cancer signalling pathways exploited by prostate cancer, it may be possible to overcome pathway cross-talk and compensation mechanisms that are activated by single target therapies.

In terms of toxicity, combination therapies are particularly attractive as they are often comprised of drugs that target different molecular processes. This can allow the use of lower doses of each agent, to maintain a cell death effect while reducing the likelihood of resistance and minimising overlapping drug toxicities. The in vitro cell based models used in the drug combination studies in this thesis are very useful for prioritising promising combinations and carrying out pre-clinical testing. However, in vivo studies are needed to ensure relevance to cancer in the whole animal setting, including stromal interactions, immune responses, and drug toxicity. The in vivo studies in Chapter Four of this thesis showed that the drug combination of bicalutamide and vorinostat does not produce any significant toxicity, albeit in the absence of any consistent anti-tumour effect.

Finally, combination strategies have generally focused on developing novel agents in combination with approved anti-tumour drugs, and while this may limit the number of new combinations that can be developed, the co-development of two novel investigational drugs, often from different pharmaceutical companies, may present a greater safety risk and regulatory delays. Bicalutamide is a drug that is commonly used in prostate cancer therapy, and has an established safety and toxicity profile, whereas while vorinostat is a relatively new drug, it is FDA and TGA approved for use in humans, and so many of the safety and toxicity risks with this combination can be bypassed. Despite the lack of FDA 189 or TGA approval for the use of 17-AAG as a monotherapy, combining it with a well known drug such as bicalutamide would reduce the need for extra risk management and potentially pave the way for approval of this drug for use in the clinic.

6.3 Future directions

The data characterising the cell death induced by the combination of vorinostat and bicalutamide provided evidence that the gene NFKBIA and its associated protein IκBα are significantly involved in prostate cancer cell death. A link between the combination induced cell death and the activation of p53 was hinted at by these findings, however further investigation is required to confirm this relationship. As discussed previously, it is possible that traditional markers of p53 activation may not be suitable for use in determining p53 activation by loss of IκBα, given that this is a different mechanism of activation than the normal. Therefore, the best method for determining p53 activation in this context would be to use chromatin immunoprecipitation (ChIP) to determine if p53- DNA binding is increased with loss of IκBα. It would also be useful to determine by co- immunoprecipitation (CoIP) if the association between p53 and IκBα is lost upon combination treatment.

Further experiments to determine the extent of prostate cancer dependence on NFKBIA/IκBα for growth and survival would be required before this molecule can be pursued as both a biomarker and a prostate cancer specific therapeutic target. Testing the effect of knocking down NFKBIA or administration of combination therapy in a variety of cell lines, such as the AR negative, p53 negative cell lines PC3 or DU-145, the AR variant expressing, p53 positive cell line 22RV1, or the wild type AR expressing, mutant p53 cell line LAPC-4, may help to distinguish whether AR or p53 status affect the cell death caused by either of these treatments. We already know that the combination has no effect in PC3 cells, however it is unclear is this is due to the lack of AR, the lack of p53, or both. This may be further teased out using transient or stable knockdown of p53 and determining the effect of the combination in LNCaP or VCaP cells in a p53 null, AR positive environment. In terms of use as a biomarker, NFKBIA/IκBα levels would need to be assessed in clinical

190 samples and already available clinical data, to determine if it can be related to prostate cancer progression or used as a measure of response to treatment.

There are no clear reasons as to why the tumour growth observed in the in vivo studies was so variable and non-responsive to treatment. The LNCaP mouse model of prostate cancer remains a relevant and useful tool, but in future it may require the injection of a greater number of cells, the use of a longer lasting slow release testosterone pellet, and perhaps the use of a different host mouse such as the NOD/SCID line. While LNCaP tumour xenograft models are still a useful pre-clinical model, development of therapies for prostate cancer will likely need to address the issue of immune response and heterogeneity in human tumours and the cause of resistance to prostate cancer therapy currently plaguing research and development of new treatments. The nude mice used in this thesis are immunosuppressed, and while they provide insights into whole animal effects on LNCaP tumour growth, they lack the regular immune responses present in humans with the disease. In future, the use of transgenic mice with complete immune systems may help address this issue. Intra-tumour heterogeneity is another issue that is not addressed by the growth of LNCaP xenografts, and the development of patient derived xenografts (Risbridger et al., 2014, Toivanen et al., 2013, Lawrence et al., 2013, Siolas and Hannon, 2013) can be utilised in the future to work towards more targeted prostate cancer treatment. However, while this method would provide insight into real tumour growth in a whole animal environment, it can be difficult to translate into a high throughput, broad spectrum approach, and so the use of the explant system developed by our laboratory may prove to address the issue of heterogeneity (both intra- and inter- patient) while remaining easy to use and able to cover a wide range of tumour types. Briefly, prostate explants are pieces of cancerous prostate tissue removed from patients undergoing radical prostatectomy, which are grown on gelatine sponges under cell culture conditions (Centenera et al., 2012, Centenera et al., 2013b). This allows for the treatment of heterogenous human prostate tissue with drugs and drug combinations under pre-clinical investigation, and not only gives important insight into effective drugs and drug combinations but also facilitates the validation of human biomarkers to be used in clinical personalised medicine. Use of the prostate explant system was outside of the scope of this thesis, but will certainly add to the pre-clinical characterisation of the combination therapies conducted over the course of this

191 thesis and, in particular, will allow for further investigation into the use of NFKBIA/IκBα as a biomarker or target of therapy.

Over the duration of this thesis, newer Hsp90 inhibitor compounds have been developed. These compounds, such as AUY992 and hsp990 (Novartis), have a higher potency than 17-AAG, and have a better bioavailability in the clinical setting. Whilst the work presented in Chapter Five provides proof of principle of mechanisms associated with Hsp90 inhibition, it is likely that in the course of developing these drugs for therapy these experiments will be repeated with the new generation of Hsp90 inhibitors. Nevertheless, the data in this thesis implied that the use of 17-AAG may still be viable in the clinic in the context of combination therapy. In addition, a new AR antagonist (enzalutamide) has been developed and approved for use in prostate cancer treatment. Preliminary experiments conducted in our laboratory have shown that enzalutamide is as effective in combination as bicalutamide, and it is likely that enzalutamide will be used in combination therapy investigations moving forward.

Overall, the pre-clinical studies in this thesis have provided a molecular background for the further clinical investigations of two novel drug combinations, and identified a novel factor that may prove to be a critical regulator of prostate cancer survival.

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