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Kamlesh K. Yadav^1, Khader Shameer^

2,3 Ben Readhead

2,3, Shalini S. Yadav

1, Li Li

2,3, Andrew Kasarskis

3,

Ashutosh K. Tewari1,* and Joel T. Dudley

2,3,4,*

1Department of Urology, Icahn School of Medicine at Mount Sinai, New York, New York, USA; 2Harris Center for Precision Well-ness, Icahn School of Medicine at Mount Sinai, New York, New York, USA; 3Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA; 4Department of Health Evidence and Policy, Icahn School of Medi-cine at Mount Sinai, New York, New York, USA

A R T I C L E H I S T O R Y

Received: March 10, 2016

Accepted: May 25, 2016

DOI: 10.2174/1381612822666160513145924

Abstract: Background: Prostate cancer is the most commonly diagnosed cancer in men.

More than 200,000 new cases are added each year in the US, translating to a lifetime risk

of 1 in 7 men. Neuroendocrine prostate cancer (NEPC) is an aggressive and treatment-

resistant form of prostate cancer. A subset of patients treated with aggressive androgen

deprivation therapy (ADT) present with NEPC. Patients with NEPC have a reduced 5-year

overall survival rate of 12.6%. Knowledge integration from genetic, epigenetic, biochemi-

cal and therapeutic studies suggests NEPC as an indicative mechanism of resistance devel-

opment to various forms of therapy.

Methods: In this perspective, we discuss various experimental, computational and risk

prediction methodologies that can be utilized to identify novel therapies against NEPC. We

reviewed literature from PubMed and computationally analyzed publicly available genomics data to present dif-

ferent possibilities for developing systems medicine based therapeutic and curative models to understand and

target prostate cancer and NEPC.

Results: We discuss strategies including gene-set analyses, network analyses, genomics and phenomics aided

drug development, microRNA and peptide-based therapeutics, pathway modeling, drug repositioning and can-

cer immunotherapies. We also discuss the application of cancer risk estimations and mining of electronic medi-

cal records to develop personalized risk predictions models for NEPC. Preemptive stratification of patients who

are at risk of evolving NEPC phenotypes using predictive models could also help to design and deliver better

therapies.

Conclusion: Collectively, understanding the mechanism of NEPC evolution from prostate cancer using systems

biology approaches would help in devising better treatment strategies and is critical and unmet clinical need.

Keywords: Prostate cacner, precision medicine, systems biology, neuroendocrine prostate cancer.

INTRODUCTION

An estimated 2.9 million people are living with prostate cancer (PCa) in the US. Nearly 27,500 patients will die this year due to complications arising from the disease, making it the second lead-ing cause of cancer-related deaths in American men [1]. The SEER database also suggests PCa to be the most commonly diagnosed cancer in American men; more than 220,000 new cases diagnosed each year with a lifetime risk of 1 in 7. Localized prostate cancer is usually treated by either surgical excision (Radical Prostatectomy) and/or radiotherapy, both of which cure a majority of the patients. However, in a small proportion, recurrence and metastasis devel-ops. The metastatic disease is routinely treated using androgen dep-rivation therapy (ADT) but invariably progresses to a castration-resistant prostate cancer (CRPC) phase with poor prognosis. Re-cently approved AR-signaling inhibitors Enzalutamide and Abi-raterone have been shown to be effective in more than 50% of the CRPCs [2-7]. However, 50% of the responders develop resistance

*Address correspondence to these authors at the Department of Urology

(AKT, Email: [email protected]) and Department of Genetics and Genomic Sciences (JTD: E-mail: [email protected]), Mount Sinai

Health System, New York, NY, USA

^ Equally contributed to this work.

to the new treatments within two years [8-11]. Analysis of tumors from these patients suggests that nearly 25% of them present with an aggressive and treatment resistant form of the disease called neuroendocrine prostate cancer (NEPC) [12, 13]. There is a recent interest in understanding the mechanism of differentiation of CRPC to a neuroendocrine form and developing better diagnostic and precision therapies to manage these lethal and drug resistant tu-mors. In this comprehensive perspective, we discuss the cancer biology of NEPC, various strategies to identify potential biomarkers of NEPC, opportunities to characterize potential drug targets and develop precision therapy using systems medicine-based ap-proaches.

NEUROENDOCRINE PROSTATE CANCER

The prostate parenchyma is made up of prostatic acini sur-rounded by stromal, fibroblast, nerve and endothelial tissues. The acini are comprised of basal and luminal cells and few interspersed neuroendocrine cells around the basal cells that are AR-negative (Fig. 1). These cells secrete neuropeptides such as bombesin, neuro-tensin, serotonin and calcitonin that have been postulated to support the growth of both normal and malignant prostate tissues [13, 14]. The diagnosis and sub-classification of NEPC is principally carried out using the evidence gathered from cellular morphology, distribu-

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REVIEW ARTICLE

Systems Medicine Approaches to Improving Understanding, Treatment,and Clinical Management of Neuroendocrine Prostate Cancer

Systems Medicine Approaches for Targeting Neuroendocrine Prostate Cancer Current Pharmaceutical Design, 2016, Vol. 22, No. 34 5235

tion and expression of well-known neuronal markers, such as neu-ron-specific enolase (NSE, expressed from ENO2), chromogranins (CHGA/CHGB), synaptophysin (SYP) and CD56 [15, 16]. NEPC can be divided into five subclasses: a) adenocarcinomas with focal NE differentiation, b) adenocarcinomas with Paneth-cell-like NE differentiation, c) carcinoid tumors d) large cell carcinoma and e) small carcinoma. Of these, small cell carcinomas are the most common representative of the cellular phenotype [17].

The percentage of neuroendocrine (NE) cells has been shown to increase with disease aggressiveness, suggesting their role in either supporting the growth of cancer cells or bringing about resistance to therapy. Whereas PCa primarily arises primarily from the acinar cells [18], newly diagnosed NEPC, which comprises less than 1-2% of all prostate cancers, arise from the NE cells [17]. The concomi-tant increase in the NE cells with aggressive disease progression has been purported as a mechanism of resistance development, and termed as treatment induced neuroendocrine prostate cancer (tNEPC) [14, 19]. Indeed in vitro studies with PCa cell lines sug-gest NEPC development as a mode of resistance to ADT and radia-tion therapy [20-22].

In the human phenotype ontology, prostate neoplasm and endo-crine neoplasm are described using separate term hierarchies (Fig. 2) and the transition of PCa phenotype to NEPC as part of the che-motherapy resistance mechanism combines the pathological fea-tures of both phenotypes. In the tumor evolution setting (where ADENOCARCINOMA � CRPC � NEPC; Figs. 1 & 3) a series of changes are brought about not only to promote the tumor prolif-eration and migration to visceral organs, but also to initiate signal-ing alterations that drive neuronal gene expression [23, 24].

NEURONAL CORRELATIONS OF PROSTATE CANCER

Several lines of evidence indicate a high correlation between neuronal structures and prostate cancer. PCa involved with perineu-ral invasion show more aggressive and proliferative phenotype (Fig. 3) [25]. Studies in mice models suggest the involvement of the autonomic nervous system in the initiation (sympathetic nervous system) and progression (parasympathetic system) of PCa [26]. Furthermore, epidemiological studies have identified an inverse correlation between PCa and nervous system disorders such as pa-tients with spinal cord disorders have small prostate sizes and lower chances of developing PCa [27], and patients with central nervous system (CNS) diseases including schizophrenia, Parkinson’s and Alzheimer’s disease have a lower likelihood of developing PCa [28]. Finally, interleukin-6 (IL6) not only plays a significant role in PCa progression and NE differentiation [29-32] but also has a

pathological role in CNS disorder schizophrenia [33-35]. Thus, emerging evidence suggests that PCa has a predisposition towards developing into a neuronal phenotype due to the shared signaling and genetic cues and may explain the observation of anti-psychosis drugs having a suppressive effect on PCa development [36, 37].

EXPERIMENTAL MODEL FOR NEPC

To understand the processes underlying the differentiation of ADENOCARCINOMA � CRPC � NEPC, in vitro models using prostate cell-lines have been developed. Upon transformation, the cells display dramatic morphological changes associated with neu-rons such as branched neurite formation and expression of neuronal markers (Fig. 2). One of the first such studies identified cAMP as a ligand that brings about differentiation of LNCaP (an AR respon-sive adenocarcinoma cell line model derived from lymph node me-tastasized PCa) cells in vitro [38]. Another study identified andro-gen deprivation leads to neuronal differentiation of LNCaP cells as well [39, 40]. Thereafter, several other studies demonstrated the use of other factors such as IL6 [41], neuropeptide bombesin [42], WNT-11 [43], radiation [22], valporic acid [44], genistein [45], vasoactive intestinal peptides [46, 47], Jolkilonide B, [48], and COX-2 inhibitor [49] to bring about NE differentiation. However, most of these studies have been limited to using the LNCaP adeno-carcinoma cell line model, which does not reflect clinical observa-tion of ADENOCARCINOMA � CRPC � NEPC. A ligand capa-ble of converting prostate cancer cell lines in to the NE form, irre-spective of AR status, would be crucial in understanding the pro-gressive mechanism of prostate tumor evolving towards a neuroen-docrine phenotype. Unfortunately, the in vitro transformed cells undergo post-mitotic arrest preventing their use as a model system to test drug efficacy studies.

The poor availability of a purely NEPC cell line has limited drug screening and testing efforts. The PC3 cells (an AR negative CRPC line derived from a bone metastasized PCa) have been shown to share characteristics of NEPC and express markers such as NSE and Chromogranin [50]. However, the NCI-H660 cell-line, which was initially attributed as a small cell lung cancer cell line [51], and later confirmed to be of prostate origin using cytogenetic studies [52], exhibits all characteristics of NEPC. It also harbors the prominent TMPRSS2-ERG fusion that is present in more than 50% of all prostate cancers as well as a mutated form of TP53 [53, 54]. Importantly, genomic and biochemical studies have identified it to be a suitable model of NEPC, expressing all the neuronal markers, and has been successfully utilized to test small-molecule drug against NEPC [23].

Fig. (1). Evolution of Neuroendocrine prostate cancer (NEPC).

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5236 Current Pharmaceutical Design, 2016, Vol. 22, No. 34 Yadav et al.

SYSTEMS MEDICINE APPROACHES FOR TARGETING NEPC

Systems medicine is defined as an interdisciplinary approach to study a disease at the systems level of the human body by consider-ing it as part of an integrated complex system with biochemical, physiological, and environmental interactions [55]. NEPC is treated primarily with chemotherapy, especially a combination of docetaxel with etoposide and cisplatin. However, resistance development is very rapid, and survival is less than 1.5 years [56, 57]. Pre-clinical success with zoledronic acid [58], plumbagin [59] and danusertib [23] has also been reported. The prevalence of high-throughput sequencing and phenotyping is revolutionizing design, development and delivery of precision medicine for various cancers including prostate [60]. Kidd et al. illustrated an example of a case-study on how data-driven precision therapy could guide in designing patient-specific therapy [61]. While personalized molecular profiling and individualized treatment strategies have conflicting outcome reports [62] – personalized, data-driven recommendation and its integration with predictive models could provide a better outcome for patients with NEPC.

THERAPEUTIC STRATEGIES TO TARGET NEPC

Designing experimental and computational strategies for the systematic targeting of pathways, genes or genetic variations in-volved in PCa or NEPC could help to find better therapies for NEPC. Such methods include gene-set-based target discovery, net-work medicine approaches, genotype-phenotype aided drug discov-ery, development of protein or peptide drugs, miRNA-informed therapy, targeting gene or transcript-fusion events, pathway-based drug development, high-throughput screening of small molecules and drug repositioning approaches.

MOLECULAR ARCHITECTURE OF PCA AND NEPC

Multiple gene sets are currently available to perform systematic analyses of PCa and NEPC. From the first report in the late ‘90s on the role of BMX (Etk/BMx) in NEPC [41] till 2015 more than 30 additional genes, such as (DEK [63], PEG10 [64], CREB, IL-6 [32, 65-67], EGF [68], ASH-1 [69] Midkine [70], Mash1 expression [71], AKT/hnRNPK/AR/beta-catenin [72], MnSOD [73], Wnt-11 [43], SNAI1 transcription factor [74], SOD2 [75], CD44 [76], Neu-roD1 [77], Cystatin C [78], IL8 [79], Granin A and Calcitonin [80], Ps2 protein [80], BCL2 [81]), and their expression pattern have been studied using in vitro and in vivo model systems. Next genera-tion studies (NGS) over the last few years, including the recent NGS-report from the TCGA, have provided extensive catalog of genes and subtypes associated with PCa and its subtypes including NEPC [20, 23, 82, 83]. Observation of factors previously identified in adenocarcinomas (such as TMPRSS2-ERG translocation [84]), strongly suggests the progressive development of NEPC from ade-nocarcinomas through the CRPC phase [85] . Specific oncogenic drivers (such as MYCN [86, 87], MYCC [88], PLK1 [89], and AURKA [23] are activated, and tumor suppressors such as TP53 [90], RB1 [91], along with transcription factor, RE1-Silencing Transcription Factor (REST1, alias Neural-Restrictive Silencing Factor [24]), are lost or downregulated. Upregulation of epigenetic regulators such as EZH2, H2S/Cav3.2 [92], Secretogranin II [93], Orexin type 1 [94], AGR2 [95] have also been identified as key players of NEPC pathology. Selective inhibition of driver genes, such as AURKA, has been shown to alleviate the NEPC phenotype [23]. Several canonical gene sets are reported in the literature indi-cating involvment in NEPC and pleiotropic mechanisms where one gene influences multiple traits have been implicated in PCa [96]. Cancer sub-type specific gene-sets from whole-exome, genome and transcriptome studies are reported as part of TCGA studies. A re-

Fig. (2). Taxonomy of prostate neoplasm and neuroendocrine neoplasm using human phenotype ontology.

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cent study also reported the expression quantitative trait loci (eQTLs) associated with genetic variants involved in PCa [97]. Such lists of genes, transcripts and proteins associated with PCa and NEPC are continuously expanding. Thus, designing drugs that can reverse the validated expression signature could aid in developing new therapies for NEPC.

NETWORK ANALYSES OF GENE-SETS TO FIND KEY HUBS DRIVING CANCER PATHOLOGIES

Network analyses of the candidate, or curated genes discovered from NGS profiling can be used to not only find driver and passen-ger genes but also genes that are vital to the disease pathways [98]. Deriving network analyses based connectivity metrics of PCa or NEPC genes can be useful to pinpoint key players of pathophysi-ological mechanisms. Understanding these fundamental mecha-nisms could help to identify new targets and new therapies. A re-cent study explored the key proteins associated with ovarian cancer by converging nanobiotechnology and network analyses [99]. Briefly, nanoparticles were used as therapeutic agents to quench the growth of ovarian cancer cell lines. Using gold-nanoparticle tagging and mass spectrometry, the proteins involved at various progressive phases were identified. The proteins were then used to derive puta-tive functional networks. These networks were then prioritized using connectivity indices like degree, radiality and eccentricity. Developing similar network driven approaches using canonical gene-lists or patient-derived gene sets and combining clinical and biological relevance would help to find key hubs that drive aggres-sive cancer pathologies of the prostate.

Using a similar computation method, we used the NEPC gene sets from a recent study [23] and compiled a canonical network (Fig. 4). This network can be further perturbed using network met-rics and synthetic lethality [100] to find key hubs.

GWAS (GENOME-WIDE ASSOCIATION STUDIES) AND PheWAS (PHENOME-WIDE ASSOCIATION STUDIES)-

DRIVEN DRUG DISCOVERY

Whereas GWAS studies aim to find genetic variants associated with a given phenotype using large case and control cohorts, Phe-WAS is a unique analysis that aims to find the enriched phenotypes associated with genetic variants of a primary phenotype such as phenotypes associated with PCa. PheWAS is generally possible when GWAS patient cohorts are derived from electronic medical record (EMR)-linked biorepositories. GWAS studies have identi-fied several genetic variants and genes associated with PCa, albeit with low risk penetration. Analyzing the compendium of genetic variants, genes and the phenotypes enriched across patients harbor-ing risk variants could help to develop better therapies. The high-quality data captured during the clinical trial or clinical operations are in the silos of clinical registries or internal databases of

healthcare or pharma organizations and not readily available for data mining. Current PheWAS are leveraging EMR data as the primary data source that provides the breadth of the phenotypes suitable for high-throughput human phenomics studies.

Genotypes and Genes Associated with PCa

GWAS data is currently being explored to find new targets for a variety of diseases including cancers and autoimmune disease [101]. While there is no phenotype specific GWAS on NEPC, a total of 73 genetic variants have been identified from GWAS in case-control populations of different ancestries with PCa (Fig. 5). These genetic variants were reported or mapped to 511 genes. While a majority of these genes have known biochemical or com-putationally derived functional roles and 7.4% of these genes are orphan genes with no validated functional or biochemical roles. Exploring the functional or regulatory role of these genetic variants and genes [102] in NEPC could aid in developing GWAS-aided therapies for NEPC patient population.

Phenomic Correlations of Patients with PCa Genotypes

To understand the phenotype-genotype correlation of PCa and other clinical phenotypes, we have compiled the phenome-wide association datasets from PheWAS database (https: //phewas.mc.vanderbilt.edu/). We compiled 73 variants associated with PCa and their associations with 1218 phenotypes. Peyronie's disease was the most significant association but with a small num-ber of sample size (n=44, rs2735839, P= 4.18E-06; See Fig. 6). This association would need further investigation as Peyronie’s disease is an outcome associated with men that have undergone radical prostatectomy. The genetic variant rs2735839 is an intronic variant of KLK3, a crucial prostate cancer gene and may play a mechanistic role in NEPC. The phenotype “adverse effects of antir-heumatics” was the phenotype with the lowest odds ratio (n=56, rs10187424, P= 4.16E-05, OR= 0.3765). The phenotype “cardiac conduction disorders” had a lower risk in patients harboring vari-ants for PCa (n=2685, rs1983891, P= 0.0008882, OR=0.8627). The variant rs1983891 is localized to the intronic region of FOXP1, which negatively regulate PCa [103]. Hepatic cancer was the phe-notype with highest odds ratio among the 1645 tested phenotypes (n= 30, rs7837688, P= 0.0001333). The variant rs7837688 is local-ized to intergenic regions with CASC8 and LOC105375754; and proximal to MYC, a known PCa and NEPC gene. Intronic variants identified from GWAS have been implicated in driving pathologi-cal mechanism in various diseases including myocardial infarction [104, 105], and exploring the expression, function and regulatory mechanisms of these variants or their eQTLs may lead to discovery of new mechanisms underlying PCa and NEPC.

PROTEIN AND PEPTIDE DRUGS FOR NEPC

Protein drugs are evolving as a competing therapeutic domain to improve or augment small-molecule based therapies. Peptides

Fig. (3). Morphological characteristics of adenocarcinoma and transformed neuroendorcine cells. The androgen positive adenocarcinoma cell line LNCaP

(left) was serum starved for 3 weeks to mimic androgen depravation.

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have been shown to be effective in inhibiting previously ‘un-druggable’ target such as Ras [106] and transcription factors such as NOTCH [107]. NEPC has phenotypes regulated by peptides and developing specific protein or peptide drugs to modulate these pep-tides could be of high therapeutic value. For example, NEPC re-lated peptides promote IGF-1R signaling and promote NEPC [108]. Neuropeptides 26RFa [109] and Manserin [110] were implicated in NEPC. Midkine a heparin binding growth factor [70], recombinant human IFN-beta [111] and vasoactive intestinal peptide (VIP) [112] have been shown to have a role in NEPC phenotype development and growth. Inhibiting these peptides using stable peptidomimetics could aid in finding therapies for NEPC.

TARGETING FUSION TRANSCRIPTS ASSOCIATED WITH

NEPC Chromosomal translocations are regarded as a cytogenetic fea-ture of various tumors including PCa [113]. NGS technologies have revealed a large number of fusion genes or fusion transcripts and their putative role in diseases including various cancers, cancer resistance and relapse [114]. For example TMPRSS2-ERG, SLC45A3-BRAF, SLC45A3-ELK4 and ESRP1-RAF1 fusion tran-scripts have been implicated in PCa [115-117]. Targeting fusion transcripts could pave way towards development of better therapy, similar to strategies utilized to target BCR-Abl with imatininb for CML (chronic myelogenous leukemia) [118].

Fig. (4). Functional biochemical network of NEPC gene list compiled from TCGA

Network reconstructed using data from STRING database. Nodes are proteins and edges are biochemical actions represented as an interaction. Edge color

legend: activation=green; inhibition=red, protein-protein binding=blue; phenotype=cyan, enzyme catalysis=purple, post-translational modification=violet,

reaction=light blue; coexpression=yellow.


MicroRNA-BASED THERAPY FOR NEPC

Micro RNAs (miRNAs) are non-coding RNAs that regulate expression of genes post-transcriptionally. Regulation of several androgen-induced genes has been observed by repressing three miRNAs: hsa-mir-125b-1, hsa-mir-125b-2 and hsa-mir-99a [119]. In another study, dysregulation of hsa-mir-145, which targets and regulates expression of ERG [120], and, hsa-mir-183, which targets DKK3 and SMAD4, have been implicated in PCa [121]. Collec-tively these microRNAs inhibit a set of genes (PRNP, S1PR1, TACSTD2, NR3C1, SOD2, ZNF682, WDR12, CDNF, BMPR2, COX20, IGF1R, ART4, PLEKHA1, AMOTL1, DAZAP2, BCL2L11, GMFB, BTG2, RPL9, UBN2, TNRC6C, LHFPL2, VAV3, HNRNPA1, ZNF529, WEE1, PTMA, SLC38A1). While a specific role or function of miRNAs in NEPC has not been elucidated, per-forming miRNA sequencing and analyses of downstream targets could help in the identification of new therapeutic targets for NEPC.

PATHWAY-DRIVEN DRUG DISCOVERY TO TARGET PATHWAY CROSS-TALKS IN NEPC

Biological pathways act in concert to perform a variety of functions in the “normal” and “disease” states [122]. Emerging evidence from systems-biology studies suggests that pathway cross-talks plays an important role in imparting pathophysiology of mulitple diseases and the switch between normal and disease states can be modulated through pathways with regulatory activities [123, 124]. Such approaches can use biological pathway evidences, including pathway cross-talk, and can help in the identification of new indications for existing compounds [125]. There are multiple computational experimental strategies to find new pathway modulators or targets from pathways. For example, vascular endothelial growth factor signaling pathway is targeted by a diverse set of drugs (n=67; see: http: //www.genome.jp/dbget-bin/www_bget?pathway+hsa04370) (VEGF, See Fig. 7). VEGF is a critical pathway composed of 61 genes and six organic compounds [126]. VEGF has also been implicated to play a key role in NEPC

[127]. Drugs targeting VEGF have a variety of activities including analgesic, anti-inflammatory, anti-neoplastic and anti-rheumatic. Specific disease phenotypes associated with drugs targeting VEGF pathway includes solid tumors, atherosclerosis, neuropathic pain, Acute respiratory distress syndrome (ARDS), major depressive disorder, acute coronary syndrome, Crohn’s disease, Psoriasis and age-related macular degeneration. VEGF is an example of a pathway that can be activated or inhibited by multiple compounds for therapeutic interventions. Identification of similar pathways and their cross-talks using systems biology approaches including quantitative modeling and pathway analtyics could help to reposition drugs for new indications [128-136]. Pathways such as Notch signaling [137] and cAMP signaling [138] have established role in NEPC and pathway-based drug development can be used to improve available therapies that can potentially target NEPC.

Targeting Signal Transduction Events of NEPC

Biochemical modulation of pathways or disease mechanisms by inhibition or activation of post-translational modifications including phosphorylation events can act as therapeutic strategies for NEPC. Phosphorylation of protein kinase A by RhoA is reported as a sig-naling event associated with NEPC [139]. Hormone signaling by paracrine factors [140] has been implicated in NEPC, although the precise roles of these modulations are yet to be ascertained. In-flammation, immune response and activation of macrophages [141] have also been indicated as a mechanistic basis of NEPC. Under-standing key modifiers of proteins and pathways of PCa and their role in recurrence is key to understand signaling pathways driving the progression of PCa to NEPC.

Metabolic Pathway Modeling to Identify Drug Targets for NEPC

Targeting metabolic pathways by leveraging computational modeling and simulation offers an effective method to identify novel or better drug targets [142-144]. Computational methods, that can model metabolic pathways, simulate enzyme kinetic activities and perform in-silico gene knockouts, could help in identification and prioritization of new drug targets [145]. Modeling techniques

Fig. (5). Genetic variants associated with prostate cancer mapped to human chromosomes. (Highlighted a blacke dot).


like flux balance analysis [146] and its derivatives use genome scale reconstructions of metabolic models to predict and prioritize the role of single, pair or multiple genes using gene or metabolic reaction perturbations. Briefly, the method involves five steps: 1) construction of a draft model using automated genome annotation pipelines, 2) refinement of draft models by leveraging high quality annotations using bio-curation, 3) generation of a computable model and definition of model constrains and parameters for simulations, 4) evaluation of model and simulation of gene or reaction knockout experiments, 5) interpretation of results and experimental validation.

Genome-scale metabolic models for target prioritization can be enhanced by integrating data from transcriptomics, metabolomics, proteomics, biochemical studies and predictive algorithms [147-152]. A list of software packages (See: http: //sbml.org/ SBML_Software_Guide) and detailed protocols for performing genome-scale reconstruction and modeling is available here [153, 154]. Metabolic modeling has been used to identify drug targets for infectious diseases ( tuberculosis [155] and multidrug-resistant Staphylococcus aureus [156]), cardiovascular diseases [157-159], neurodegenerative disease [160], cancer [161], diabetes, and obe-sity [162]. Metabolic models have also been employed to predict biochemical or physiological response including drug toxicity evaluations as part of drug discovery investigations [163].

HIGH-THROUGPUT SCREENING TO FIND COMPOUNDS TARGETING NEPC

Target based drug discovery relies on experimental screening of the compounds using a large number of compounds from custom or commercial ligand libraries. The compounds are tested using high-throughput screening (HTS) [164] platforms that enable rapid

testing of a large collection of compounds and their agonistic or antagonistic perturbations using cell line based screening assays or specific protein-ligand binding assays (LBA) [165]. Such experi-mental approaches can be optimized using computational screening method that simulate the screening using virtual-ligand screening method [166]. Virtual ligand screening methods help to reduce the number of compounds that need experimental evaluation and thus help in optimizing the cost of drug discovery investigations. Briefly, the virtual ligand screening method comprise of protein modeling, ligand modeling and structure optimization, protein-ligand docking and assessment of docked complexes using a variety of chemoinformatic parameters [167-170]. Compounds can be assessed using Quantitative Structure-Activity Relationships (QSAR) [171] that provides a quantitative inference of how well the ligand can perturb the target, pharmacophore properties, adsorption, distribution, metabolism and excretion and toxicity (ADMET) profiling and other chemoinformatic parameters that are relevant to identifying the optimal ligand for a given target based on disease biology, physicochemical properties and binding kinetics. Computational evaluation of drug-target interactions would help in focusing downstream functional studies and clinical trials on a subset of compounds and thus help in improving the process of drug discovery by reducing the cost and time to translate therapies from bench to the clinical setting [172, 173].

DRUG REPOSITIONING FOR NEPC

Drug repositioning comprises the use of computational and experimental analyses to identify the suitability of an existing, ap-proved or investigational compound for a new indication. This ap-proach has been used as a therapeutic strategy for more than 250 drugs and spans multiple indications from all classes of diseases

Fig. (6). Phenome-wide associations of a prostate cancer genetic variant (rs2735839, mapped to KLK3).

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(RepurposeDB, Manuscript in press). Since drug repositioning can help identify therapeutic compounds for complex, chronic or or-phan diseases using molecular information derived from a single patient, family members with rare clinical manifestation or case-control cohorts, it can prove to be useful in increasing the repertoire of currently limited therapeutic options available to treat NEPC, Fig. (8). Successful indications have been reported using drug repo-sitioning across different therapeutic domains including inflamma-tory bowel disease and non-small cell lung cancer ([174, 175].). Publicly available resources such as mRNA transcriptional profiles in databases like The Cancer Genome Atlas (TCGA) [176-179], cBioPortal [180], ArrayExpress (https: //www.ebi.ac.uk/ arrayex-press/) and Gene Expression Omnibus (GEO, http: //www.ncbi.nlm.nih.gov/geo/) can be integrated to design a global expression map of NEPC. This map can be matched in-silico using bioinformatics resource like Connectivity Map (CMap, http: //www.broadinstitute.org/cmap/) and LINCS (http: //www.lincsproject.org/) to discover drugs that can potentially re-verse the direction of gene expression phenotypes. A detailed over-view of computational and experimental strategies and translational bioinformatics resources available for drug repositioning is dis-cussed elsewhere (See Shameer et al. [181] and Readhead et.al [182]). Drug repositioning methods enables rapid testing of existing compound libraries against molecular profiles to modulate clinical phenotypes of a patient with the rare disease [183, 184].

CANCER IMMUNOTHERAPY FOR NEPC

Cancer immunotherapy is the emerging field of research to design and develop therapeutic interventions, which can activate the immune system for recognition and killing of cancer cells. Immu-notherapy was shown to be effective for complex diseases including asthma and melanoma [185-188]. Immunotherapy for cancer com-prises of the various strategies employed to either activate the im-

mune effector functions (both innate as well as adaptive) or antago-nizing immune inhibitory functions, or both [189-192]. Examples of strategies for activating immune effector functions include adop-tive cell therapy (ACT), which introduces immune cells directly into the patient [193, 194], vaccination with tumor antigens [195], cytokine treatment (such as IL-2) [196], induction of antigen pres-entation (dendritic cell pulsing and administration) [197] and anti-bodies targeting co-stimulatory molecules for T-cell activation (OX40) [198]. Examples of inhibitory strategies include antibodies that deplete regulatory T cells (anti-CD25) [199] and antibodies targeting immune checkpoints (cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) and programmed cell death- 1(PD-1) [200]. Several of the above strategies that are in various phases of clinical trails for PCa include; Sipuleucel-T which is a dendritic cell-based vaccine [201, 202], Ipilimumab a fully humanized anti-CTLA-4 monoclonal antibody based immunotherapy [201, 203], Prostavac-VF (also known as PSA-TRICOM) an attenuated recombinant Vac-cinica/Fowlpox viral-vector based vaccine [202] and GVAX, which is a PCa cell-line based vaccine [204, 205].

PCa and NEPC is ideally suited for immunotherapy as it has a long natural history, thus providing ample time for immune effector functions to develop and act against the tumor. It is also rich in several neo-antigens such as PSA and PSMA, thereby providing an excellent repertoire of antigens for targeted immunotherapy. Impor-tantly, the above ongoing-clinical studies suggest immunotherapies to be more efficacious when carried out at an earlier stage where tumor burden is low. Thus, vaccination either alone or in combina-tion with androgen deprivation therapy (ADT) or radiotherapy in pre-surgical disease settings is essential as it will serve three impor-tant purposes; 1) define true response criteria to immunotherapies which generally elicit slower responses, 2) help identify markers of disease progression by providing an opportunity to sample tumor and blood both before and after treatment, and 3) provide an oppor-

Fig. (7). Pathway based drug targeting: an example using VEGF signaling pathway.

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tunity to identify neo-antigens which could be harnessed for per-sonalized immunotherapy regimes. Although no immunotherapy-based clinical trials are currently focused on NEPC, the advent of new genomics based-computational prowess that can predict, map and reposition drug that can modulate the immune system holds immense promise in the field [206]. In the future, such systems-based approaches and clinical trials of vaccines in pre-surgical set-tings would pave the ways towards designing immunotherapies targeting NEPC.

PREDICTIVE STRATEGIES FOR NEPC PATIENT STRATIFICATION

Therapeutic interventions are important in cancer management to provide positive patient outcomes including improvement in the quality of life of patient and reduction of symptom burden. NEPC is primarily a treatment induced, aggressive form of cancer that occur in a subset of patients with poor response to treatment. In this sce-nario, developing predictive models by combining deep profiling data from tumors and pharamacogenes with phenomic data could aid in developing risk models to see which patients are at risk for NEPC. Identifying the variable to develop such predictive models often need large-scale clinical trials. With the advent of automated data mining techniques and phenotyping algorithms, this is chang-ing. Algorithms can be designed to precisely phenotype PCa or NEPC and these algorithms can be used to compile a large patient population retrospectively. Data can also be assembled from cancer registries and public biomedical and healthcare databases. There are multiple ways to develop such predictive models; here we highlight two cases using EMR mining and risk model development.

MINING OF EHR AND CLINICAL TRIAL DATABASES TO

FIND PCa AND NEPC TRAJECTORIES Systematic analyses of data generated as part of the care-delivery of PCa or NEPC patient populations using data compiled in electronic health records (EHR) would also help to understand clinical implications of NEPC and the evolution of patients from PCa to NEPC or how the patients responds to NEPC treatments.

EHR based data mining and digital epidemiology studies are cost-effective, efficient and provide clinical insights more quickly than clinical trials. EHR-linked biorepositories have been used to do phenomics, phenome-wide enrichment analyses and discovering genetic variants associated red blood cell traits, platelet traits and diseases including peripheral arterial diseases [207-210]. Mining multiple data types from EHR has shown to be an important way to find patient clusters that can be treated with an intervention or ther-apy. For example, Li et al. (See: http://stm.sciencemag.org/content/7/311/311ra174) have shown that utilizing clinical characteristics including patient demographics, lab measures, and drug/s taken could help to segregate Type-2 diabetes population as three subtypes. Individualized or population scale therapies can now be designed to target these subtypes and improve outcomes. Similar approaches can be adapted for PCa and NEPC cohort and could help to determine patient clusters based on genetic variation, phenotypes or treatment response rates.

RISK PREDICTION MODELS FOR RESPONSE TO PROS-

TATE CANCER TREATMENT AND NEPC

Developing predictive models to assess the treatment responses (no response, partial response or full response) will be a valuable clinical aid in managing PCa patients and understanding the demo-graphics of NEPC patient. GWAS enabled the availability of ge-netic variants and genes mediating complex disease, and risk esti-mation models are now being built using genetic data as part of their parameters [86, 211]. Pharmacogenomics risk models are currently explored in the setting of neurodegenerative diseases [212]. Candidate-gene-set-based risk estimation using genetic vari-ants in pro-inflammatory genes and PCa genes have also been sug-gested [213], but some of these recommendations were not repli-cated when tested using whole-genome or exome-wide approaches. Nevertheless, combining large patient population and mapping sub-population specific risk would help to delineate pharmacogenomic variants and genetic predisposition to chemotherapy responses.

Fig. (8). Drug repositioning of cancer drugs mapped across different disease groups across International Classification of Disease – v9 coding system. Dis-

eases categories are labeled using green font, name of medications are labeled in black.

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DISCUSSION

Delivering the right drug for the right patient in the right dose at the right time via the right route is a critical step in implementing precision therapy in a clinical setting. Recommending cancer treat-ments by leveraging variations in pharamacogenes (genes associ-ated with metabolism of drugs) and mutation landscape of tumor would help to develop precision therapy. NEPC is an aggressive cancer phenotype that evolves via complex and largely unknown mechanism and patient communities can benefit from such ap-proaches. Typically, NEPCs do not express AR, and subsequently prostate-specific antigen (PSA) used for PCa screening. Thus, early diagnosis of NEPC is challenging, under-reported, and. by the time diagnosis is made, therapeutic interventions and treatment stratifi-cation are limited. Furthermore, since PSA is also used to monitor biochemical recurrence during and after treatment, development of treatment-induced NEPC is under appreciated. Recent data suggests that upwards of 25% of patients treated with new generation anti-androgen signaling therapies targeting signaling modules progress towards NEPC [205, 214]. Thus, there is an unmet and urgent clini-cal need to not only develop better biomarkers for detecting NEPC early, but also for treatment strategies to overcome NEPC devel-opment. With an increasing number of diagnoses, limited treatment options, and high mortality rates, NEPC patient populations need better treatment modalities faster than the typical drug development processes deliver them. We discussed a variety to approaches to tackle NEPC, but other integrated approaches can also be used to understand and treat the NEPC. For example, designing RNA inter-ference based single molecule or gene-set based inhibition could help to find key players of NEPC. Drug screening using viability assays and chemical genomics of NEPC cell lines would then help to find primary candidates for NEPC. Developing better cell lines, patient-derived xenograft (PDX) models and other mouse models, and leveraging high-throughput sequencing could help to find new targets, processes and pathways of NEPC.

CONCLUSION

With the advent of precision phenotyping and personalized risk assessment, we see the rapid evolution of cancers and cancer sub-types aresistant to standard treatment methods. Better therapies that consider the dynamic characteristics of tumor evolution and tumor volatility to evolve, and emerge as metastatic cancer phenotypes needs to be addressed. Studies are needed on systems scale using deep profiling techniques to understand the disease trajectory of the evolution of NEPC from PCa. Computational approaches including systematic drug repurposing, drug-modeling, metabolic modeling, developing protein or peptide therapies, and the use of nanoparti-cles could play a significant role in discovering and developing effective therapies to target NEPC. In addition to the curative strategies, developing predictive models could aid to understand the individual risk of patients to develop chemotherapy resistance and NEPC. An integrated pharmacological risk assessment that uses pharmacogenomic profiling with tumor profiling would also be helpful to predict response to therapies. Developing personalized risk estimates using genetics, family history, diet, environmental exposure and behavioral end points would help to develop precision risk models. To conclude, developing precision risk models that use pharmacogenomic profiling, lifestyle data (including exercise, diet, and environmental exposure) coupled with deep molecular profiling using genomics, transcriptomics, metabolomics and proteomics will elaborate a more comprehensive, systems-wide understanding of NEPC and ultimately enable predictive and therapeutic strategies.

AUTHORS’ CONTRIBUTION

KKY and KS wrote the first draft manuscript. BR, SSY, LL and AK provided critical inputs. KS compiled the data and performed the empirical analyses. KKY and SSY performed in vitro assays and model of NEPC differentiation. AT and JTD contributed to the

overall planning of the analyses and the manuscript. All authors read and approved the final manuscript.

CONFLICT AND FINANCIAL INTEREST

KKY, KS, SSY: None declared;

AKT has received research grants from Intuitive Surgicals, Inc (Sunnyvale, CA), Prostate Cancer Foundation (PCF) and National Institutive of Bioimaging and Bioengineering. JTD has received consulting fees or honoraria from Janssen Pharmaceuticals, GSK, AstraZeneca, and Hoffman-La Roche. JTD holds equity in NuMedii Inc, Ayasdi, Inc. and Ontomics, Inc. No writing assistance was utilized in the production of this manuscript.

ACKNOWLEDGEMENTS

This work was funded by the following grants from National Institutes of Health: National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK, R01-DK098242-03); National Can-cer Institute (NCI, U54-CA189201-02) to JTD and National Center for Advancing Translational Sciences (NCATS, UL1TR000067) Clinical and Translational Science Award (CTSA) grant to JTD and KS. KKY and SSY thank the Prostate Cancer Foundation for Young Investigator Awards. Authors would like to thank Harris Center For Precision Wellness (http: //www.precisionwellness.org/), Icahn Institute for Genomics and Multiscale Biology (http: //icahn.mssm.edu/departments-and-institutes/genomics), and Deane Prostate Health of Mount Sinai Health System for infrastructural support.

REFERENCES

[1] SEER Stat Fact Sheets: Prostate Cancer. USA: NIH 2015. [2] Chen CD, Welsbie DS, Tran C, et al. Molecular determinants of

resistance to antiandrogen therapy. Nat Med 2004; 10(1): 33-9. [3] Watson PA, Arora VK, Sawyers CL. Emerging mechanisms of

resistance to androgen receptor inhibitors in prostate cancer. Nat Rev Cancer 2015; 15(12): 701-11.

[4] Robinson D, Van Allen EM, Wu YM, et al. Integrative clinical genomics of advanced prostate cancer. Cell 2015; 161(5): 1215-28.

[5] Tran C, Ouk S, Clegg NJ, et al. Development of a second-generation antiandrogen for treatment of advanced prostate cancer.

Science 2009; 324(5928): 787-90. [6] Barrie SE, Potter GA, Goddard PM, Haynes BP, Dowsett M, Jar-

man M. Pharmacology of novel steroidal inhibitors of cytochrome P450(17) alpha (17 alpha-hydroxylase/C17-20 lyase). J Steroid

Biochem Mol Biol 1994, 50(5-6): 267-73. [7] Pinto-Bazurco Mendieta MA, Negri M, et al. Synthesis, biological

evaluation, and molecular modeling of abiraterone analogues: novel CYP17 inhibitors for the treatment of prostate cancer. J Med Chem

2008; 51(16): 5009-18. [8] Scher HI, Fizazi K, Saad F, et al. Increased survival with enzalu-

tamide in prostate cancer after chemotherapy. N Engl J Med 2012; 367(13): 1187-97.

[9] Scher HI, Beer TM, Higano CS, et al. Antitumour activity of MDV3100 in castration-resistant prostate cancer: a phase 1-2 study.

Lancet 2010; 375(9724): 1437-46. [10] de Bono JS, Logothetis CJ, Molina A, et al. Abiraterone and in-

creased survival in metastatic prostate cancer. N Engl J Med 2011; 364(21): 1995-2005.

[11] Ryan CJ, Smith MR, de Bono JS, et al. Abiraterone in metastatic prostate cancer without previous chemotherapy. N Engl J Med

2013; 368(2): 138-48. [12] Vlachostergios PJ, Papandreou CN. Targeting neuroendocrine

prostate cancer: molecular and clinical perspectives. Front Oncol 2015; 5: 6.

[13] Grigore AD, Ben-Jacob E, Farach-Carson MC. Prostate cancer and neuroendocrine differentiation: more neuronal, less endocrine?

Front Oncol 2015; 5: 37. [14] Abrahamsson PA. Neuroendocrine cells in tumour growth of the

prostate. Endocr Relat Cancer 1999; 6(4): 503-19. [15] Yao JL, Madeb R, Bourne P, et al. Small cell carcinoma of the

prostate: an immunohistochemical study. Am J Surg Pathol 2006; 30(6): 705-12.


[16] Wang W, Epstein JI. Small cell carcinoma of the prostate. A mor-

phologic and immunohistochemical study of 95 cases. Am J Surg Pathol 2008; 32(1): 65-71.

[17] Nadal R, Schweizer M, Kryvenko ON, Epstein JI, Eisenberger MA. Small cell carcinoma of the prostate. Nat Rev Urol 2014; 11(4):

213-9. [18] Wang ZA, Toivanen R, Bergren SK, Chambon P, Shen MM. Lumi-

nal cells are favored as the cell of origin for prostate cancer. Cell Rep 2014; 8(5): 1339-46.

[19] di Sant'Agnese PA, Cockett AT. Neuroendocrine differentiation in prostatic malignancy. Cancer 1996; 78(2): 357-61.

[20] Terry S, Maille P, Baaddi H, et al. Cross modulation between the androgen receptor axis and protocadherin-PC in mediating neu-

roendocrine transdifferentiation and therapeutic resistance of pros-tate cancer. Neoplasia 2013; 15(7): 761-72.

[21] Shen R, Dorai T, Szaboles M, Katz AE, Olsson CA, Buttyan R. Transdifferentiation of cultured human prostate cancer cells to a

neuroendocrine cell phenotype in a hormone-depleted medium. Urol Oncol 1997; 3(2): 67-75.

[22] Deng X, Elzey BD, Poulson JM, et al. Ionizing radiation induces neuroendocrine differentiation of prostate cancer cells in vitro, in

vivo and in prostate cancer patients. Am J Cancer Res 2011; 1(7): 834-44.

[23] Beltran H, Rickman DS, Park K, et al. Molecular characterization of neuroendocrine prostate cancer and identification of new drug

targets. Cancer Discov 2011; 1(6): 487-95. [24] Lapuk AV, Wu C, Wyatt AW, et al. From sequence to molecular

pathology, and a mechanism driving the neuroendocrine phenotype in prostate cancer. J Pathol 2012; 227(3): 286-97.

[25] Ayala GE, Dai H, Ittmann M, et al. Growth and survival mecha-nisms associated with perineural invasion in prostate cancer. Cancer

Res 2004; 64(17): 6082-90. [26] Magnon C, Hall SJ, Lin J, Xue X, Gerber L, Freedland SJ, Frenette

PS. Autonomic nerve development contributes to prostate cancer progression. Science 2013; 341(6142): 1236361.

[27] Patel N, Ngo K, Hastings J, Ketchum N, Sepahpanah F. Prevalence of prostate cancer in patients with chronic spinal cord injury. PM R

2011; 3(7): 633-6. [28] Ibanez K, Boullosa C, Tabares-Seisdedos R, Baudot A, Valencia A.

Molecular evidence for the inverse comorbidity between central nervous system disorders and cancers detected by transcriptomic

meta-analyses. PLoS Genet 2014; 10(2): e1004173. [29] Giri D, Ozen M, Ittmann M. Interleukin-6 is an autocrine growth

factor in human prostate cancer. Am J Pathol 2001; 159(6): 2159-65.

[30] Lou W, Ni Z, Dyer K, Tweardy DJ, Gao AC. Interleukin-6 induces prostate cancer cell growth accompanied by activation of stat3 sig-

naling pathway. Prostate 2000; 42(3): 239-42. [31] Mori S, Murakami-Mori K, Bonavida B. Interleukin-6 induces G1

arrest through induction of p27(Kip1), a cyclin-dependent kinase inhibitor, and neuron-like morphology in LNCaP prostate tumor

cells. Biochem Biophys Res Commun 1999; 257(2): 609-14. [32] Spiotto MT, Chung TD. STAT3 mediates IL-6-induced neuroendo-

crine differentiation in prostate cancer cells. Prostate 2000; 42(3): 186-95.

[33] Kim YK, Myint AM, Verkerk R, Scharpe S, Steinbusch H, Leonard B. Cytokine changes and tryptophan metabolites in medication-

naive and medication-free schizophrenic patients. Neuropsychobi-ology 2009; 59(2): 123-9.

[34] Kalmady SV, Venkatasubramanian G, Shivakumar V, et al. Rela-tionship between Interleukin-6 gene polymorphism and hippocam-

pal volume in antipsychotic-naive schizophrenia: evidence for dif-ferential susceptibility? PLoS One 2014; 9(5): e96021.

[35] Lin CC, Chang CM, Chang PY, Huang TL. Increased interleukin-6 level in Taiwanese schizophrenic patients. Chang Gung Med J

2011; 34(4): 375-81. [36] Fond G, Macgregor A, Attal J, et al. Antipsychotic drugs: pro-

cancer or anti-cancer? A systematic review. Med Hypotheses 2012; 79(1): 38-42.

[37] Zhou W, Chen MK, Yu HT, et al. The antipsychotic drug pimozide inhibits cell growth in prostate cancer through suppression of

STAT3 activation. Int J Oncol 2016; 48(1): 322-8. [38] Bang YJ, Pirnia F, Fang WG, et al. Terminal neuroendocrine dif-

ferentiation of human prostate carcinoma cells in response to in-creased intracellular cyclic AMP. Proc Natl Acad Sci USA 1994;

91(12): 5330-34.

[39] Nouri M, Ratther E, Stylianou N, Nelson CC, Hollier BG, Williams

ED. Androgen-targeted therapy-induced epithelial mesenchymal plasticity and neuroendocrine transdifferentiation in prostate cancer:

an opportunity for intervention. Front Oncol 2014; 4: 370. [40] Lipianskaya J, Cohen A, Chen CJ, et al. Androgen-deprivation

therapy-induced aggressive prostate cancer with neuroendocrine differentiation. Asian J Androl 2014; 16(4): 541-4.

[41] Qiu Y, Robinson D, Pretlow TG, Kung HJ. Etk/Bmx, a tyrosine kinase with a pleckstrin-homology domain, is an effector of phos-

phatidylinositol 3'-kinase and is involved in interleukin 6-induced neuroendocrine differentiation of prostate cancer cells. Proc Natl

Acad Sci USA 1998; 95(7): 3644-49. [42] Levine L, Lucci JA, 3rd, Pazdrak B, et al. Bombesin stimulates

nuclear factor kappa B activation and expression of proangiogenic factors in prostate cancer cells. Cancer Res 2003; 63(13): 3495-502.

[43] Uysal-Onganer P, Kawano Y, Caro M, et al. Wnt-11 promotes neuroendocrine-like differentiation, survival and migration of pros-

tate cancer cells. Mol Cancer 2010; 9: 55. [44] Sidana A, Wang M, Chowdhury WH, et al. Does valproic acid

induce neuroendocrine differentiation in prostate cancer? J Biomed Biotechnol 2011; 2011: 607480.

[45] Pinski J, Wang Q, Quek ML, et al. Genistein-induced neuroendo-crine differentiation of prostate cancer cells. Prostate 2006; 66(11):

1136-43. [46] Gutierrez-Canas I, Juarranz MG, Collado B, et al. Vasoactive intes-

tinal peptide induces neuroendocrine differentiation in the LNCaP prostate cancer cell line through PKA, ERK, and PI3K. Prostate

2005; 63(1): 44-55. [47] Collado B, Sanchez MG, Diaz-Laviada I, Prieto JC, Carmena MJ.

Vasoactive intestinal peptide (VIP) induces c-fos expression in LNCaP prostate cancer cells through a mechanism that involves

Ca2+ signaling. Implications in angiogenesis and neuroendocrine differentiation. Biochim Biophys Acta 2005; 1744(2): 224-33.

[48] Liu WK, Ho JC, Qin G, Che CT. Jolkinolide B induces neuroendo-crine differentiation of human prostate LNCaP cancer cell line. Bio-

chem Pharmacol 2002; 63(5): 951-7. [49] Meyer-Siegler K. COX-2 specific inhibitor, NS-398, increases

macrophage migration inhibitory factor expression and induces neuroendocrine differentiation in C4-2b prostate cancer cells. Mol

Med 2001; 7(12): 850-60. [50] Tai S, Sun Y, Squires JM, Zhang H, Oh WK, Liang CZ, Huang J.

PC3 is a cell line characteristic of prostatic small cell carcinoma. Prostate 2011; 71(15): 1668-79.

[51] Ohsaki Y, Yang HK, Le PT, Jensen RT, Johnson BE. Human small cell lung cancer cell lines express functional atrial natriuretic pep-

tide receptors. Cancer Res 1993, 53(13): 3165-71. [52] Johnson BE, Whang-Peng J, Naylor SL, et al. Retention of chromo-

some 3 in extrapulmonary small cell cancer shown by molecular and cytogenetic studies. J Natl Cancer Inst 1989, 81(16): 1223-8.

[53] van Bokhoven A, Varella-Garcia M, Korch C, et al. Molecular characterization of human prostate carcinoma cell lines. Prostate

2003; 57(3): 205-25. [54] Mertz KD, Setlur SR, Dhanasekaran SM, et al. Molecular charac-

terization of TMPRSS2-ERG gene fusion in the NCI-H660 prostate cancer cell line: a new perspective for an old model. Neoplasia

2007; 9(3): 200-6. [55] Federoff HJ, Gostin LO. Evolving from reductionism to holism: is

there a future for systems medicine? JAMA 2009; 302(9): 994-6. [56] Culine S, El Demery M, Lamy PJ, Iborra F, Avances C, Pinguet F.

Docetaxel and cisplatin in patients with metastatic androgen inde-pendent prostate cancer and circulating neuroendocrine markers. J

Urol 2007; 178(3 Pt 1): 844-848; discussion 848. [57] Aparicio AM, Harzstark AL, Corn PG, et al. Platinum-based che-

motherapy for variant castrate-resistant prostate cancer. Clin Cancer Res 2013; 19(13): 3621-30.

[58] Hashimoto K, Masumori N, Tanaka T, et al. Zoledronic acid but not somatostatin analogs exerts anti-tumor effects in a model of

murine prostatic neuroendocrine carcinoma of the development of castration-resistant prostate cancer. Prostate 2013; 73(5): 500-11.

[59] Hafeez BB, Zhong W, Mustafa A, Fischer JW, Witkowsky O, Verma AK. Plumbagin inhibits prostate cancer development in

TRAMP mice via targeting PKCepsilon, Stat3 and neuroendocrine markers. Carcinogenesis 2012; 33(12): 2586-92.

[60] Cancer Genome Atlas Research Network. Electronic address scmo, Cancer Genome Atlas Research N. The molecular taxonomy of

primary prostate cancer. Cell 2015; 163(4): 1011-25.


[61] Kidd BA, Readhead BP, Eden C, Parekh S, Dudley JT. Integrative

network modeling approaches to personalized cancer medicine. Personalized Med 2015; 12(3): 245-57.

[62] Le Tourneau C, Delord JP, Goncalves A, et al. Molecularly targeted therapy based on tumour molecular profiling versus conventional

therapy for advanced cancer (SHIVA): a multicentre, open-label, proof-of-concept, randomised, controlled phase 2 trial. Lancet On-

col 2015; 16(13): 1324-34. [63] Lin D, Dong X, Wang K, et al. Identification of DEK as a potential

therapeutic target for neuroendocrine prostate cancer. Oncotarget 2015; 6(3): 1806-20.

[64] Akamatsu S, Wyatt AW, Lin D, et al. The placental gene PEG10 promotes progression of neuroendocrine prostate cancer. Cell Rep

2015; 12(6): 922-36. [65] Chang PC, Wang TY, Chang YT, et al. Autophagy pathway is

required for IL-6 induced neuroendocrine differentiation and chemoresistance of prostate cancer LNCaP cells. PLoS One 2014;

9(2): e88556. [66] Ge D, Gao AC, Zhang Q, Liu S, Xue Y, You Z. LNCaP prostate

cancer cells with autocrine interleukin-6 expression are resistant to IL-6-induced neuroendocrine differentiation due to increased ex-

pression of suppressors of cytokine signaling. Prostate 2012; 72(12): 1306-16.

[67] Wang Q, Horiatis D, Pinski J. Inhibitory effect of IL-6-induced neuroendocrine cells on prostate cancer cell proliferation. Prostate

2004; 61(3): 253-59. [68] Cortes MA, Cariaga-Martinez AE, Lobo MV, et al. EGF promotes

neuroendocrine-like differentiation of prostate cancer cells in the presence of LY294002 through increased ErbB2 expression inde-

pendent of the phosphatidylinositol 3-kinase-AKT pathway. Carcinogenesis 2012; 33(6): 1169-77.

[69] Rapa I, Volante M, Migliore C, et al. Human ASH-1 promotes neuroendocrine differentiation in androgen deprivation conditions

and interferes with androgen responsiveness in prostate cancer cells. Prostate 2013; 73(11): 1241-9.

[70] Nordin A, Wang W, Welen K, Damber JE. Midkine is associated with neuroendocrine differentiation in castration-resistant prostate

cancer. Prostate 2013; 73(6): 657-67. [71] Gupta A, Yu X, Case T, et al. Mash1 expression is induced in neu-

roendocrine prostate cancer upon the loss of Foxa2. Prostate 2013; 73(6): 582-9.

[72] Ciarlo M, Benelli R, Barbieri O, et al. Regulation of neuroendo-crine differentiation by AKT/hnRNPK/AR/beta-catenin signaling in

prostate cancer cells. Int J Cancer 2012; 131(3): 582-590. [73] Quiros-Gonzalez I, Sainz RM, Hevia D, Mayo JC. MnSOD drives

neuroendocrine differentiation, androgen independence, and cell survival in prostate cancer cells. Free Radic Biol Med 2011; 50(4):

525-36. [74] McKeithen D, Graham T, Chung LW, Odero-Marah V. Snail tran-

scription factor regulates neuroendocrine differentiation in LNCaP prostate cancer cells. Prostate 2010; 70(9): 982-92.

[75] Quiros I, Sainz RM, Hevia D, et al. Upregulation of manganese superoxide dismutase (SOD2) is a common pathway for neuroen-

docrine differentiation in prostate cancer cells. Int J Cancer 2009; 125(7): 1497-504.

[76] Palapattu GS, Wu C, Silvers CR, et al. Selective expression of CD44, a putative prostate cancer stem cell marker, in neuroendo-

crine tumor cells of human prostate cancer. Prostate 2009; 69(7): 787-98.

[77] Cindolo L, Franco R, Cantile M, et al. NeuroD1 expression in hu-man prostate cancer: can it contribute to neuroendocrine differentia-

tion comprehension? Eur Urol 2007; 52(5): 1365-73. [78] Jiborn T, Abrahamson M, Gadaleanu V, Lundwall A, Bjartell A.

Aberrant expression of cystatin C in prostate cancer is associated with neuroendocrine differentiation. BJU Int 2006; 98(1): 189-96.

[79] Huang J, Yao JL, Zhang L, et al. Differential expression of inter-leukin-8 and its receptors in the neuroendocrine and non-

neuroendocrine compartments of prostate cancer. Am J Pathol 2005; 166(6): 1807-15.

[80] Deftos LJ. Granin-A, parathyroid hormone-related protein, and calcitonin gene products in neuroendocrine prostate cancer. Prostate

Suppl 1998; 8: 23-31. [81] Segal NH, Cohen RJ, Haffejee Z, Savage N. BCL-2 proto-oncogene

expression in prostate cancer and its relationship to the prostatic neuroendocrine cell. Arch Pathol Lab Med 1994, 118(6): 616-8.

[82] Prostate Adenocarcinoma [http: //cancergenome.nih.gov/ cancersse-

lected/prostatecancer] [83] Baca SC, Prandi D, Lawrence MS, et al. Punctuated evolution of

prostate cancer genomes. Cell 2013; 153(3): 666-77. [84] Mounir Z, Lin F, Lin VG, et al. TMPRSS2: ERG blocks neuroen-

docrine and luminal cell differentiation to maintain prostate cancer proliferation. Oncogene 2015; 34(29): 3815-25.

[85] Guo CC, Wang Y, Xiao L, Troncoso P, Czerniak BA. The relation-ship of TMPRSS2-ERG gene fusion between primary and metas-

tatic prostate cancers. Hum Pathol 2012; 43(5): 644-9. [86] Beltran H, Eng K, Mosquera JM, et al. Whole-exome sequencing of

metastatic cancer and biomarkers of treatment response. JAMA Oncol 2015; 1(4): 466-74.

[87] Otto T, Horn S, Brockmann M, et al. Stabilization of N-Myc is a critical function of Aurora A in human neuroblastoma. Cancer Cell

2009; 15(1): 67-78. [88] Wang J, Kim J, Roh M, et al. Pim1 kinase synergizes with c-MYC

to induce advanced prostate carcinoma. Oncogene 2010; 29(17): 2477-87.

[89] Deeraksa A, Pan J, Sha Y, et al. Plk1 is upregulated in androgen-insensitive prostate cancer cells and its inhibition leads to necropto-

sis. Oncogene 2013; 32(24): 2973-83. [90] Chen H, Sun Y, Wu C, et al. Pathogenesis of prostatic small cell

carcinoma involves the inactivation of the P53 pathway. Endocr Re-lat Cancer 2012; 19(3): 321-31.

[91] Tan HL, Sood A, Rahimi HA, et al. Rb loss is characteristic of prostatic small cell neuroendocrine carcinoma. Clin Cancer Res

2014; 20(4): 890-903. [92] Fukami K, Sekiguchi F, Yasukawa M, et al. Functional upregula-

tion of the H2S/Cav3.2 channel pathway accelerates secretory func-tion in neuroendocrine-differentiated human prostate cancer cells.

Biochem Pharmacol 2015; 97(3): 300-9. [93] Courel M, El Yamani FZ, Alexandre D, et al. Secretogranin II is

overexpressed in advanced prostate cancer and promotes the neu-roendocrine differentiation of prostate cancer cells. Eur J Cancer

2014; 50(17): 3039-49. [94] Alexandre D, Hautot C, Mehio M, et al. The orexin type 1 receptor

is overexpressed in advanced prostate cancer with a neuroendocrine differentiation, and mediates apoptosis. Eur J Cancer 2014; 50(12):

2126-33. [95] Kani K, Malihi PD, Jiang Y, et al. Anterior gradient 2 (AGR2):

blood-based biomarker elevated in metastatic prostate cancer asso-ciated with the neuroendocrine phenotype. Prostate 2013; 73(3):

306-15. [96] Panagiotou OA, Travis RC, Campa D, et al. A genome-wide plei-

otropy scan for prostate cancer risk. Eur Urol 2015; 67(4): 649-57. [97] Thibodeau SN, French AJ, McDonnell SK, et al. Identification of

candidate genes for prostate cancer-risk SNPs utilizing a normal prostate tissue eQTL data set. Nat Commun 2015; 6: 8653.

[98] Barabasi AL, Gulbahce N, Loscalzo J. Network medicine: a net-work-based approach to human disease. Nat Rev Genet 2011;

12(1): 56-68. [99] Giri K, Shameer K, Zimmermann MT, et al. Understanding protein-

nanoparticle interaction: a new gateway to disease therapeutics. Bioconjug Chem 2014; 25(6): 1078-90.

[100] Jacunski A, Dixon SJ, Tatonetti NP. Connectivity homology en-ables inter-species network models of synthetic lethality. PLoS

Comput Biol 2015; 11(10): e1004506. [101] Sanseau P, Agarwal P, Barnes MR, et al. Use of genome-wide

association studies for drug repositioning. Nat Biotechnol 2012; 30(4): 317-20.

[102] Karczewski KJ, Dudley JT, Kukurba KR, et al. Systematic func-tional regulatory assessment of disease-associated variants. Proc

Natl Acad Sci USA 2013; 110(23): 9607-12. [103] Takayama K, Horie-Inoue K, Ikeda K, et al. FOXP1 is an andro-

gen-responsive transcription factor that negatively regulates andro-gen receptor signaling in prostate cancer cells. Biochem Biophys

Res Commun 2008; 374(2): 388-93. [104] Harismendy O, Notani D, Song X, et al. 9p21 DNA variants associ-

ated with coronary artery disease impair interferon-gamma signal-ing response. Nature 2011; 470(7333): 264-8.

[105] Visel A, Zhu Y, May D, et al. Targeted deletion of the 9p21 non-coding coronary artery disease risk interval in mice. Nature 2010;

464(7287): 409-12. [106] Patgiri A, Yadav KK, Arora PS, Bar-Sagi D. An orthosteric inhibi-

tor of the Ras-Sos interaction. Nat Chem Biol 2011; 7(9): 585-7.


[107] Moellering RE, Cornejo M, Davis TN, et al. Direct inhibition of the

NOTCH transcription factor complex. Nature 2009; 462(7270): 182-8.

[108] DaSilva JO, Amorino GP, Casarez EV, Pemberton B, Parsons SJ. Neuroendocrine-derived peptides promote prostate cancer cell sur-

vival through activation of IGF-1R signaling. Prostate 2013; 73(8): 801-12.

[109] Alonzeau J, Alexandre D, Jeandel L, et al. The neuropeptide 26RFa is expressed in human prostate cancer and stimulates the neuroen-

docrine differentiation and the migration of androgeno-independent prostate cancer cells. Eur J Cancer 2013; 49(2): 511-9.

[110] Nishikawa K, Soga N, Ishii K, et al. Manserin as a novel histo-chemical neuroendocrine marker in prostate cancer. Urol Oncol

2013; 31(6): 787-95. [111] Angelucci C, Iacopino F, Ferracuti S, Urbano R, Sica G. Recombi-

nant human IFN-beta affects androgen receptor level, neuroendo-crine differentiation, cell adhesion, and motility in prostate cancer

cells. J Interferon Cytokine Res 2007; 27(8): 643-52. [112] Collado B, Gutierrez-Canas I, Rodriguez-Henche N, Prieto JC,

Carmena MJ. Vasoactive intestinal peptide increases vascular endo-thelial growth factor expression and neuroendocrine differentiation

in human prostate cancer LNCaP cells. Regul Pept 2004; 119(1-2): 69-75.

[113] Shan L, Ambroisine L, Clark J, et al. The identification of chromo-somal translocation, t(4; 6)(q22; q15), in prostate cancer. Prostate

Cancer Prostatic Dis 2010; 13(2): 117-25. [114] Kaye FJ. Mutation-associated fusion cancer genes in solid tumors.

Mol Cancer Ther 2009; 8(6): 1399-408. [115] Tomlins SA, Aubin SM, Siddiqui J, et al. Urine TMPRSS2: ERG

fusion transcript stratifies prostate cancer risk in men with elevated serum PSA. Sci Transl Med 2011; 3(94): 94ra72.

[116] Rickman DS, Pflueger D, Moss B, et al. SLC45A3-ELK4 is a novel and frequent erythroblast transformation-specific fusion transcript

in prostate cancer. Cancer Res 2009; 69(7): 2734-8. [117] Palanisamy N, Ateeq B, Kalyana-Sundaram S, et al. Rearrange-

ments of the RAF kinase pathway in prostate cancer, gastric cancer and melanoma. Nat Med 2010; 16(7): 793-8.

[118] Druker BJ, Tamura S, Buchdunger E, et al. Effects of a selective inhibitor of the Abl tyrosine kinase on the growth of Bcr-Abl posi-

tive cells. Nat Med 1996; 2(5): 561-566. [119] Sun D, Layer R, Mueller AC, et al. Regulation of several androgen-

induced genes through the repression of the miR-99a/let-7c/miR-125b-2 miRNA cluster in prostate cancer cells. Oncogene 2014;

33(11): 1448-57. [120] Hart M, Wach S, Nolte E, et al. et al. The proto-oncogene ERG is a

target of microRNA miR-145 in prostate cancer. FEBS J 2013; 280(9): 2105-16.

[121] Ueno K, Hirata H, Shahryari V, et al. microRNA-183 is an onco-gene targeting Dkk-3 and SMAD4 in prostate cancer. Br J Cancer

2013; 108(8): 1659-67. [122] Chan SY, Loscalzo J. The emerging paradigm of network medicine

in the study of human disease. Circulation Res 2012; 111(3): 359-74.

[123] Li Y, Agarwal P, Rajagopalan D. A global pathway crosstalk net-work. Bioinformatics 2008; 24(12): 1442-7.

[124] Natarajan M, Lin KM, Hsueh RC, Sternweis PC, Ranganathan R. A global analysis of cross-talk in a mammalian cellular signaling net-

work. Nat Cell Biol 2006; 8(6): 571-80. [125] Yamanishi Y, Kotera M, Kanehisa M, Goto S. Drug-target interac-

tion prediction from chemical, genomic and pharmacological data in an integrated framework. Bioinformatics 2010; 26(12): i246-54.

[126] Kowanetz M, Ferrara N. Vascular endothelial growth factor signal-ing pathways: therapeutic perspective. Clin Cancer Res 2006;

12(17): 5018-22. [127] Borre M, Nerstrom B, Overgaard J. Association between immuno-

histochemical expression of vascular endothelial growth factor (VEGF), VEGF-expressing neuroendocrine-differentiated tumor

cells, and outcome in prostate cancer patients subjected to watchful waiting. Clin Cancer Res 2000; 6(5): 1882-90.

[128] Ashburn TT, Thor KB. Drug repositioning: identifying and devel-oping new uses for existing drugs. Nat Rev Drug Discov 2004;

3(8): 673-83. [129] Cheng F, Liu C, Jiang J, et al. Prediction of drug-target interactions

and drug repositioning via network-based inference. PLoS Comput Biol 2012; 8(5): e1002503.

[130] Lee HS, Bae T, Lee JH, et al. Rational drug repositioning guided by

an integrated pharmacological network of protein, disease and drug. BMC Syst Biol 2012; 6: 80.

[131] Emig D, Ivliev A, Pustovalova O, et al. Drug target prediction and repositioning using an integrated network-based approach. PLoS

One 2013; 8(4): e60618. [132] Harrold JM, Ramanathan M, Mager DE. Network-based approaches

in drug discovery and early development. Clin Pharmacol Ther 2013; 94(6): 651-8.

[133] Re M, Valentini G. Network-based drug ranking and repositioning with respect to DrugBank therapeutic categories. IEEE/ACM Trans

Comput Biol Bioinformatics 2013; 10(6): 1359-71. [134] Wu C, Gudivada RC, Aronow BJ, Jegga AG. Computational drug

repositioning through heterogeneous network clustering. BMC Syst Biol 2013; 7 (Suppl 5): S6.

[135] Wu Z, Wang Y, Chen L. Network-based drug repositioning. Mol bioSystems 2013; 9(6): 1268-81.

[136] Ye H, Liu Q, Wei J. Construction of drug network based on side effects and its application for drug repositioning. PLoS One 2014;

9(2): e87864. [137] Danza G, Di Serio C, Rosati F, et al. Notch signaling modulates

hypoxia-induced neuroendocrine differentiation of human prostate cancer cells. Mol Cancer Res 2012; 10(2): 230-8.

[138] Mori R, Xiong S, Wang Q, et al. Gene profiling and pathway analy-sis of neuroendocrine transdifferentiated prostate cancer cells. Pros-

tate 2009; 69(1): 12-23. [139] Jones SE, Palmer TM. Protein kinase A-mediated phosphorylation

of RhoA on serine 188 triggers the rapid induction of a neuroendo-crine-like phenotype in prostate cancer epithelial cells. Cell Signal

2012; 24(8): 1504-14. [140] Zhang C, Soori M, Miles FL, et al. Paracrine factors produced by

bone marrow stromal cells induce apoptosis and neuroendocrine differentiation in prostate cancer cells. Prostate 2011; 71(2): 157-

67. [141] Lee GT, Kwon SJ, Lee JH, et al. Macrophages induce neuroendo-

crine differentiation of prostate cancer cells via BMP6-IL6 Loop. Prostate 2011; 71(14): 1525-37.

[142] Kim HU, Sohn SB, Lee SY. Metabolic network modeling and simulation for drug targeting and discovery. Biotechnol J 2012;

7(3): 330-42. [143] Voit EO. Metabolic modeling: a tool of drug discovery in the post-

genomic era. Drug Discov Today 2002; 7(11): 621-8. [144] Yarmush ML, Banta S. Metabolic engineering: advances in model-

ing and intervention in health and disease. Ann Rev Biomed Eng 2003; 5: 349-81.

[145] Li Z, Wang RS, Zhang XS. Two-stage flux balance analysis of metabolic networks for drug target identification. BMC Syst Biol

2011; 5 (Suppl 1): S11. [146] Orth JD, Thiele I, Palsson BO. What is flux balance analysis? Nat

Biotechnol 2010; 28(3): 245-8. [147] Cortassa S, Caceres V, Bell LN, O'Rourke B, Paolocci N, Aon MA.

From metabolomics to fluxomics: a computational procedure to translate metabolite profiles into metabolic fluxes. Biophys J 2015;

108(1): 163-72. [148] Blazier AS, Papin JA. Integration of expression data in genome-

scale metabolic network reconstructions. Front Physiol 2012; 3: 299.

[149] Radrich K, Tsuruoka Y, Dobson P, et al. Integration of metabolic databases for the reconstruction of genome-scale metabolic net-

works. BMC Syst Biol 2010; 4: 114. [150] Christian N, May P, Kempa S, Handorf T, Ebenhoh O. An integra-

tive approach towards completing genome-scale metabolic net-works. Mol BioSyst 2009; 5(12): 1889-903.

[151] Benedict MN, Mundy MB, Henry CS, Chia N, Price ND. Likeli-hood-based gene annotations for gap filling and quality assessment

in genome-scale metabolic models. PLoS Comput Biol 2014; 10(10): e1003882.

[152] Schmidt BJ, Papin JA, Musante CJ. Mechanistic systems modeling to guide drug discovery and development. Drug Discov Today

2013; 18(3-4): 116-27. [153] Thiele I, Palsson BO. A protocol for generating a high-quality ge-

nome-scale metabolic reconstruction. Nat Protocols 2010; 5(1): 93-121.

[154] Schellenberger J, Que R, Fleming RM, et al. Quantitative predic-tion of cellular metabolism with constraint-based models: the CO-

BRA Toolbox v2.0. Nat Protocols 2011; 6(9): 1290-307.


[155] Raman K, Yeturu K, Chandra N. targetTB: a target identification

pipeline for Mycobacterium tuberculosis through an interactome, reactome and genome-scale structural analysis. BMC Syst Biol

2008; 2: 109. [156] Lee DS, Burd H, Liu J, et al. Comparative genome-scale metabolic

reconstruction and flux balance analysis of multiple Staphylococcus aureus genomes identify novel antimicrobial drug targets. J Bacte-

riol 2009; 191(12): 4015-24. [157] MacLellan WR, Wang Y, Lusis AJ. Systems-based approaches to

cardiovascular disease. Nat Rev Cardiol 2012; 9(3): 172-84. [158] Lusis AJ, Weiss JN. Cardiovascular networks: systems-based ap-

proaches to cardiovascular disease. Circulation 2010; 121(1): 157-70.

[159] Stevenson JE, Wright BR, Boydstun AS. The metabolic syndrome and coronary artery disease: a structural equation modeling ap-

proach suggestive of a common underlying pathophysiology. Me-tabol Clin Exp 2012; 61(11): 1582-8.

[160] Khachaturian ZS. Models and modeling systems in Alzheimer disease drug discovery. Alzheimer Dis Assoc Disord 2002; 16

(Suppl 1): S9-S12. [161] Ros S, Schulze A. Balancing glycolytic flux: the role of 6-

phosphofructo-2-kinase/fructose 2,6-bisphosphatases in cancer me-tabolism. Cancer Metabol 2013; 1(1): 8.

[162] Varemo L, Nookaew I, Nielsen J. Novel insights into obesity and diabetes through genome-scale metabolic modeling. Front Physiol

2013; 4: 92. [163] Wilson AG, White AC, Mueller RA. Role of predictive metabolism

and toxicity modeling in drug discovery--a summary of some recent advancements. Curr Opin Drug Discov Develop 2003; 6(1): 123-8.

[164] Macarron R, Banks MN, Bojanic D, et al. Impact of high-throughput screening in biomedical research. Nat Rev Drug Discov

2011; 10(3): 188-95. [165] de Jong LA, Uges DR, Franke JP, Bischoff R. Receptor-ligand

binding assays: technologies and applications. J Chromatogr B Anal Technol Biomed Life Sci 2005; 829(1-2): 1-25.

[166] Klebe G. Virtual ligand screening: strategies, perspectives and limitations. Drug Discov Today 2006; 11(13-14): 580-94.

[167] Gilson MK, Zhou HX. Calculation of protein-ligand binding affini-ties. Ann Rev Biophys Biomol Struct 2007; 36: 21-42.

[168] Brooijmans N, Kuntz ID. Molecular recognition and docking algo-rithms. Ann Rev Biophys Biomol Struct 2003; 32: 335-73.

[169] Marrone TJ, Briggs JM, McCammon JA. Structure-based drug design: computational advances. Ann Rev Pharmacol Toxicol 1997;

37: 71-90. [170] Marti-Renom MA, Stuart AC, Fiser A, Sanchez R, Melo F, Sali A.

Comparative protein structure modeling of genes and genomes. Ann Rev Biophys Biomol Struct 2000; 29: 291-325.

[171] De Benedetti PG, Fanelli F. Multiscale quantum chemical ap-proaches to QSAR modeling and drug design. Drug Discov Today

2014; 19(12): 1921-7. [172] Tetko IV, Bruneau P, Mewes HW, Rohrer DC, Poda GI. Can we

estimate the accuracy of ADME-Tox predictions? Drug Discov To-day 2006; 11(15-16): 700-7.

[173] Horvath D. Pharmacophore-based virtual screening. Methods Mol Biol 2011; 672: 261-98.

[174] Jahchan NS, Dudley JT, Mazur PK, et al. A drug repositioning approach identifies tricyclic antidepressants as inhibitors of small

cell lung cancer and other neuroendocrine tumors. Cancer Discov 2013; 3(12): 1364-77.

[175] Dudley JT, Sirota M, Shenoy M, et al. Computational repositioning of the anticonvulsant topiramate for inflammatory bowel disease.

Sci Transl Med 2011; 3(96): 96ra76. [176] Lee H, Palm J, Grimes SM, Ji HP. The Cancer Genome Atlas Clini-

cal Explorer: a web and mobile interface for identifying clinical-genomic driver associations. Genome Med 2015; 7(1): 112.

[177] Cheng PF, Dummer R, Levesque MP. Data mining The Cancer Genome Atlas in the era of precision cancer medicine. Swiss Med

Wkly 2015; 145: w14183. [178] Robbins DE, Gruneberg A, Deus HF, Tanik MM, Almeida JS. A

self-updating road map of The Cancer Genome Atlas. Bioinformat-ics 2013; 29(10): 1333-40.

[179] Devarakonda S, Morgensztern D, Govindan R. Clinical applications of The Cancer Genome Atlas project (TCGA) for squamous cell

lung carcinoma. Oncology (Williston Park) 2013; 27(9): 899-906.

[180] Gao J, Aksoy BA, Dogrusoz U, et al. Integrative analysis of com-

plex cancer genomics and clinical profiles using the cBioPortal. Sci Signal 2013; 6(269): pl1.

[181] Shameer K, Readhead B, Dudley JT. Computational and experi-mental advances in drug repositioning for accelerated therapeutic

stratification. Curr Top Med Chem 2015; 15(1): 5-20. [182] Readhead B, Dudley J. Translational bioinformatics approaches to

drug development. Adv Wound Care 2013; 2(9): 470-89. [183] Ekins S, Williams AJ, Krasowski MD, Freundlich JS. In silico

repositioning of approved drugs for rare and neglected diseases. Drug Discov Today 2011; 16(7-8): 298-310.

[184] Blatt J, Corey SJ. Drug repurposing in pediatrics and pediatric hematology oncology. Drug Discov Today 2013; 18(1-2): 4-10.

[185] Chelladurai Y, Suarez-Cuervo C, Erekosima N, et al. Effectiveness of subcutaneous versus sublingual immunotherapy for the treatment

of allergic rhinoconjunctivitis and asthma: a systematic review. J Allergy Clin Immunol Pract 2013; 1(4): 361-369.

[186] Lin SY, Erekosima N, Kim JM, et al. Sublingual immunotherapy for the treatment of allergic rhinoconjunctivitis and asthma: a sys-

tematic review. JAMA 2013; 309(12): 1278-8. [187] Ribas A. Tumor immunotherapy directed at PD-1. N Engl J Med

2012; 366(26): 2517-9. [188] Chapman PB, D'Angelo SP, Wolchok JD. Rapid eradication of a

bulky melanoma mass with one dose of immunotherapy. N Engl J Med 2015; 372(21): 2073-4.

[189] Scott AM, Wolchok JD, Old LJ. Antibody therapy of cancer. Nat Rev Cancer 2012; 12(4): 278-87.

[190] Rosenberg SA, Yang JC, Restifo NP. Cancer immunotherapy: moving beyond current vaccines. Nat Med 2004; 10(9): 909-15.

[191] Adams JL, Smothers J, Srinivasan R, Hoos A. Big opportunities for small molecules in immuno-oncology. Nat Rev Drug Discov 2015;

14(9): 603-22. [192] Kaufman HL, Kohlhapp FJ, Zloza A. Oncolytic viruses: a new class

of immunotherapy drugs. Nat Rev Drug Discov 2015; 14(9): 642-62.

[193] June CH. Adoptive T cell therapy for cancer in the clinic. J Clin Invest 2007; 117(6): 1466-76.

[194] Yee C, Thompson JA, Byrd D, et al. Adoptive T cell therapy using antigen-specific CD8+ T cell clones for the treatment of patients

with metastatic melanoma: in vivo persistence, migration, and anti-tumor effect of transferred T cells. Proc Natl Acad Sci USA 2002;

99(25): 16168-73. [195] Rosenberg SA, Sherry RM, Morton KE, et al. et al. Tumor progres-

sion can occur despite the induction of very high levels of self/tumor antigen-specific CD8+ T cells in patients with mela-

noma. J Immunol 2005; 175(9): 6169-76. [196] Rosenberg SA, Yang JC, Topalian SL, et al. Treatment of 283

consecutive patients with metastatic melanoma or renal cell cancer using high-dose bolus interleukin 2. JAMA 1994, 271(12): 907-13.

[197] Schuler G, Schuler-Thurner B, Steinman RM. The use of dendritic cells in cancer immunotherapy. Curr Opin Immunol 2003; 15(2):

138-47. [198] Linch SN, McNamara MJ, Redmond WL. OX40 agonists and com-

bination immunotherapy: putting the pedal to the metal. Front On-col 2015; 5: 34.

[199] Rech AJ, Vonderheide RH. Clinical use of anti-CD25 antibody daclizumab to enhance immune responses to tumor antigen vaccina-

tion by targeting regulatory T cells. Ann N Y Acad Sci 2009; 1174: 99-106.

[200] Ott PA, Hodi FS, Robert C. CTLA-4 and PD-1/PD-L1 blockade: new immunotherapeutic modalities with durable clinical benefit in

melanoma patients. Clin Cancer Res 2013; 19(19): 5300-9. [201] Small EJ, Schellhammer PF, Higano CS, et al. Placebo-controlled

phase III trial of immunologic therapy with sipuleucel-T (APC8015) in patients with metastatic, asymptomatic hormone re-

fractory prostate cancer. J Clin Oncol 2006; 24(19): 3089-94. [202] Kantoff PW, Schuetz TJ, Blumenstein BA, et al. Overall survival

analysis of a phase II randomized controlled trial of a Poxviral-based PSA-targeted immunotherapy in metastatic castration-

resistant prostate cancer. J Clin Oncol 2010; 28(7): 1099-105. [203] Tse BW, Jovanovic L, Nelson CC, de Souza P, Power CA, Russell

PJ. From bench to bedside: immunotherapy for prostate cancer. Biomed Res Int 2014; 2014: 981434.

[204] Hege KM, Jooss K, Pardoll D. GM-CSF gene-modifed cancer cell immunotherapies: of mice and men. Int Rev Immunol 2006; 25(5-

6): 321-52.


[205] Dranoff G, Jaffee E, Lazenby A, et al. Vaccination with irradiated

tumor cells engineered to secrete murine granulocyte-macrophage colony-stimulating factor stimulates potent, specific, and long-

lasting anti-tumor immunity. Proc Natl Acad Sci USA 1993, 90(8): 3539-43.

[206] Kidd BA, Wroblewska A, Boland MR, et al. Mapping the effects of drugs on the immune system. Nat Biotechnol 2016; 34(1): 47-54.

[207] Ding K, Shameer K, Jouni H, et al. Genetic Loci implicated in erythroid differentiation and cell cycle regulation are associated

with red blood cell traits. Mayo Clin Proc 2012; 87(5): 461-74. [208] Jouni H, Shameer K, Asmann YW, Hazin R, de Andrade M, Kullo

IJ. Clinical correlates of autosomal chromosomal abnormalities in an electronic medical Record-Linked Genome-Wide Association

Study: A Case Series. J Investig Med 2013; 1(4). [209] Shameer K, Denny JC, Ding K, et al. A genome- and phenome-

wide association study to identify genetic variants influencing plate-let count and volume and their pleiotropic effects. Hum Genet

2014; 133(1): 95-109.

[210] Kullo IJ, Shameer K, Jouni H, et al. The ATXN2-SH2B3 locus is

associated with peripheral arterial disease: an electronic medical re-cord-based genome-wide association study. Front Genet 2014; 5:

166. [211] Kullo IJ, Jouni H, Olson JE, Montori VM, Bailey KR. Design of a

randomized controlled trial of disclosing genomic risk of coronary heart disease: the Myocardial Infarction Genes (MI-GENES) study.

BMC Med Genomics 2015; 8: 51. [212] Cacabelos R, Fernandez-Novoa L, Lombardi V, Corzo L, Pichel V,

Kubota Y. Cerebrovascular risk factors in Alzheimer's disease: brain hemodynamics and pharmacogenomic implications. Neurol

Res 2003; 25(6): 567-80. [213] Caruso C, Balistreri CR, Candore G, et al. Polymorphisms of pro-

inflammatory genes and prostate cancer risk: a pharmacogenomic approach. Cancer Immunol Immunother 2009; 58(12): 1919-33.

[214] Akhtar NH, Beltran H, Afzal MZ, Ecker C, Nanus DM, Scott T. Neuroendocrine prostate cancer (NEPC) after androgen deprivation

therapy (ADT): Clinical characteristics. Tagawa: Genitourinary Cancers Symposium 2012.

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