Identifying critical pathways governing disease progression is essential for accurate prognosis and effective therapy. We developed a broadly applicable and novel systems‐level gene discovery strategy. This approach focused on constitutively active androgen receptor (AR) splice variant‐driven pathways as representative of an intractable mechanism of prostate cancer (PC) therapeutic resistance. We performed a meta‐analysis of human prostate samples using weighted gene co‐expression network analysis combined with experimental AR variant transcriptome analyses. An AR variant‐driven gene module that is upregulated during human PC progression was identified. We filtered this module by identifying genes that functionally interacted with AR variants using a high‐throughput synthetic genetic array screen in Schizosaccharomyces pombe . This strategy identified seven AR variant‐regulated genes that also enhance AR activity and drive cancer progression. Expression of the seven genes predicted poor disease‐free survival in large independent PC patient cohorts. Pharmacologic inhibition of interacting members of the gene set potently and synergistically decreased PC cell proliferation. This unbiased and novel gene discovery strategy identified a clinically relevant, oncogenic, interacting gene hub with strong prognostic and therapeutic potential in PC.

Introduction Combining computational systems‐level analyses of patient samples with rigorous experimental high‐throughput approaches is a powerful, unbiased strategy to identify novel biomarkers and therapeutic targets in cancer. Such approaches generate gene co‐expression modules that permit researchers to uncover disease‐relevant, dynamic gene networks (e.g., Lamb et al, 2006; Zhu et al, 2016). Identified gene clusters (co‐expression modules) often represent genes involved in common biological or pathologic functions, genes that define cell‐type differences, and/or genes governed by a common regulatory mechanism (e.g., driven by a specific transcription factor). Identifying the components of gene modules that control key pathways of cancer progression and therapeutic resistance permits pathway targeting, as opposed to single oncogene targeting, which readily leads to therapeutic resistance (Kreeger & Lauffenburger, 2009). We applied such an approach to PC, which is the second‐leading cause of cancer‐related death in U.S. men (Siegel et al, 2016). Androgen deprivation therapy (ADT) is the standard of care for non‐organ confined advanced PC, leading to tumor regression. However, PC inevitably recurs as castration‐resistant prostate cancer (CRPC; e.g., Knudsen & Penning, 2010; Karantanos et al, 2013), an incurable form of the disease with limited therapeutic options (Antonarakis et al, 2014). A proposed major driver of CRPC progression is constitutively active AR variants (AR‐Vs) that lack the C‐terminal ligand‐binding domain (LBD) of the full‐length AR, but retain the potent transactivating N‐terminal domain (NTD) and the DNA‐binding domain (DBD). These AR‐Vs are thought to constitutively promote transcription of an oncogenic program resulting in therapeutic resistance (Zhang et al, 2011; Li et al, 2013; Antonarakis et al, 2017; Ho & Dehm, 2017). AR‐V7 (also termed AR3 or AR1/2/3/CE3) is the best‐characterized AR splice variant in human specimens (Hörnberg et al, 2011). AR‐V7 homodimerizes and heterodimerizes with full‐length AR resulting in constitutive, unregulated AR activity (Watson et al, 2010; Chan et al, 2015; Xu et al, 2015). Additionally, the expression of AR‐V7 is linked to poor prognosis, epithelial–mesenchymal transition (EMT; Kong et al, 2015), and resistance to current treatments (Qu et al, 2015; Antonarakis et al, 2017). Even though AR‐V7 is considered a plausible target for CRPC therapy, the design of specific high‐affinity compounds is a major challenge due in large part to the lack of the LBD (reviewed in Imamura & Sadar, 2016). Gene networks downstream of AR‐V7 drive its oncogenic signaling and disease progression (e.g., Hu et al, 2012; Cao et al, 2014; Shafi et al, 2015; Xu et al, 2015). Identifying disease‐relevant AR‐V7‐driven genes is challenging, in part because of the extensive overlap with the full‐length AR transcriptome (e.g., Watson et al, 2010; Chan et al, 2012; Hu et al, 2012; reviewed in Lu et al, 2015). Components of this network may not only underlie AR‐V7 oncogenicity but possess the dual feature of enhancing AR activity, since positive feedback loops are common in endocrine cancers (including PC). Such reciprocally acting proteins have the strongest potential to be exploited therapeutically (e.g., Chia et al, 2011; Karacosta et al, 2012; Goodwin et al, 2015). Moreover, there is a need for prognostic biomarkers of tumor aggressiveness that can stratify patients at the time of PC diagnosis and thereby guide clinical management accordingly by predicting the patient's therapeutic response to treatment (Bjartell et al, 2011; Prensner et al, 2012). We utilized a large cohort of publicly available gene microarray data to identify gene clusters associated with PC and CRPC and then combined this information with rigorous experimental approaches in different model systems to identify disease‐relevant genes. With this gene discovery strategy, which is broadly suitable, for example, for any disease driven by a transcription factor, we defined a novel gene set that served as a prognostic marker for PC and revealed a new drug combination that potently and synergistically inhibited CRPC growth.

Discussion Integrative approaches, such as those used here, transform one‐dimensional cancer signatures into multidimensional networks of connecting modules (Rhodes & Chinnaiyan, 2005), which can facilitate more optimal therapeutic strategies. We identified a novel AR‐V7‐related gene set with prognostic and therapeutic value for PC using an integrated and unbiased data mining and experimental strategy (Fig 5), which could be readily applied to other cancer types. As just one example, this approach could be adopted for cancers that are also driven by transcription factors, such as c‐Myc, KIT, and ER. Our approach included meta‐analyses of gene expression profiles from human prostate tumors to derive gene modules, whose expression coincides across disease states. These modules were integrated with data obtained from human PC cells that identified AR‐V7‐regulated genes and with data from an AR‐V7 functional network, constructed through a powerful model genetic system. This multifaceted approach, which does not use any filtering or a priori assumptions, resulted in the identification of disease‐relevant genes that were regulated by AR‐V7 and that reciprocally enhanced AR ligand‐independent activity. We performed extensive intervalidation with independent patient datasets and extended findings using cell‐based experimentation. Figure 5. Schematic of pipeline developed, validated, and applied We performed a meta‐analysis of microarray data on clinical PC samples, including 375 samples from eight different datasets (obtained from the same type of array so gene expression measurements could be directly compared) and encompassing six different phenotypes/disease stages. The large number of samples provided robustness to the module definition, as well as power in the ability to identify relevant modules. The gene members of the green module had expression levels significantly associated with and upregulated upon cancer onset and progression to CRPC, as well as Gleason score. This green module contained 60 genes (nearly 75%) regulated by AR‐V7. Notably, AR variants regulating all the 60 genes was confirmed by RNA‐seq data of He et al (2018). However, only a few of the genes in the green module (6%) are full‐length AR‐regulated genes. Ligand‐activated AR is well recognized as a regulator of the cyclin D‐RB axis in prostate cancer (reviewed in Balk & Knudsen, 2008). However, our findings suggest that AR variants, in particular AR‐V7, may be intricately related to G2‐M phase cellular dynamics, consistent with Hu et al (2012), who demonstrated AR‐V7 regulation of several genes involved in mitosis. An important implication is that the seven genes represent a vulnerability, especially but not exclusively, for AR‐V7‐driven CRPC and could provide possible approaches for overcoming androgen deprivation therapy and taxane resistance in CRPC patients. There is a critical need to identify gene signatures that robustly predict PC aggressiveness and that can inform active surveillance disease management decisions (Cooperberg & Carroll, 2015). We found that patients with tumors exhibiting higher expression levels of the seven genes had an elevated risk of relapse after primary therapy and a greater risk of death. Thus, this seven‐gene set has the potential for use in stratifying patients and guiding treatment according to clinical risk. Despite having established roles in cell cycle and cell division, which are hallmarks of all cancers, the seven‐gene set did not predict patient survival metrics in other types of cancer from TCGA dataset cohorts. This finding supports a PC‐specific role of this gene set, especially for cells that depend on AR‐V7 signaling. In fact, six out of the seven genes encoded proteins that enhanced AR ligand‐independent activity in cell‐based assays. This finding may help to explain why the seven genes are selectively associated with PC progression. Moreover, another cell cycle progression (CCP) signature has shown prognostic value in PC patients (Cuzick et al, 2011), supporting the importance of cell cycle genes in predicting PC patient outcome. However, the 31‐gene CCP signature contains only three of the seven genes identified here, suggesting that the seven‐gene set is distinct. The seven‐gene set was derived using a strong biological rationale and a systems‐biology approach and is a smaller signature, easier to apply and obtain significant predictive and survival metrics. Lastly, our signature also provided the rationale for a promising combination therapy to be explored for castration‐resistant prostate cancer. Since full‐length AR is more readily targeted therapeutically than AR‐V7, this work focused on AR‐V7, which may promote PC therapeutic resistance and disease progression (reviewed in Luo et al, 2017). Most PC patient tissue and cell lines that express AR‐V7 also express full‐length AR (Guo et al, 2009; Hörnberg et al, 2011), and as AR‐V7 can heterodimerize with full‐length AR (Xu et al, 2015), any actions of AR‐V7 likely occur in the context of full‐length AR. The extent to which the full‐length AR transcriptome overlaps with AR‐V7 (or heterodimers of full‐length AR and AR variants) is not fully understood (Watson et al, 2010; Hu et al, 2012; Cao et al, 2014; Xu et al, 2015). However, we found that ligand‐activated full‐length AR did not regulate the expression of any of the seven genes. Further, full‐length AR mRNA levels did not correlate with the expression of the gene set in PC patients. The seven genes we identified could interact with and promote AR‐V7 transcriptional activity in various ways, and it is likely that they also participate in full‐length AR networks. In fact, two members of the seven‐gene set are known to regulate or enhance full‐length AR activity (Chen et al, 2006; Yu et al, 2014; Schaefer‐Klein et al, 2015). Li et al, 2015 showed that TOP2A inhibition reduces full‐length AR and AR‐V7 transcriptional activity, through decreased AR recruitment to target gene promoters and reduced nuclear localization. In addition, Chen et al (2006) showed that CCNB1/CDK1 phosphorylates the AR amino‐terminus, stabilizes, and increases the transcriptional activity of full‐length AR. We found that at least two members of the gene set (including TOP2A) reduced full‐length AR mRNA levels upon depletion. Moreover, depletion of KIF20A as well as BUB1B decreased AR‐V7 mRNA levels. CCNB2 was the one of the seven genes that upon knockdown increased ligand‐independent AR activity and increased AR‐V7 and full‐length AR mRNA levels. Because depletion of the seven genes decreased proliferation but did not uniformly decrease ligand‐independent AR transcriptional activity (as shown for CCNB2 depletion), the effects on AR activity are not likely to be secondary to reduced cell proliferation. Since the seven genes are interrelated and highly connected, it is possible that depletion of CCNB2 increased the expression of select members of the gene set in a compensatory way, which could drive the increase in AR ligand‐independent transcriptional activity as well as AR‐V7 and full‐length AR mRNA levels. Thus, the seven identified genes, while not being regulated by full‐length AR, participate at least in some settings in enhancing full‐length AR. Indeed, as discussed below, LNCaP and C4‐2B cells, which are not thought to be driven by AR variants, were growth inhibited by the combination of nanomolar doses of doxorubicin and N‐9. These findings support the importance of the seven‐gene signature in a broader PC context beyond AR‐V7‐driven tumors. Because the seven genes belong to highly interconnected pathways and networks that control each other's expression and/or activities, there is a strong likelihood that inhibition of any two of these genes would provide significantly enhanced antitumor effects. Indeed, we used two known chemotherapeutic agents as a proof of principle: doxorubicin (targeting the activity of TOP2A) and N9‐isopropylolomoucine (N‐9; targeting CCCNB1/CDK1 activity, and indirectly affecting three other genes). The drugs, used within the nanomolar range, provided synergistic suppression of CRPC growth in 22Rv1 cells, which express AR‐V7 (Peacock et al, 2012) and C4‐2B, which are also highly reliant on AR signaling (Liu et al, 2014). In contrast, the two compounds had no effect on non‐tumorigenic or AR‐null cells, and affected the androgen‐dependent LNCaP cell line to a much lesser extent. The effects of the drug combination on LNCaP and C4‐2B, which do not express AR‐V7, could likely be due to effects on full‐length AR. Indeed, DOX and N‐9 target TOP2A and CCNB1, respectively. As discussed above, we showed here that the depletion of TOP2A decreased full‐length AR mRNA levels, and Li et al (2015) and Chen et al (2006) reported that TOP2A and CCNB1 enhance full‐length AR transcriptional activity. CRPC cells may possess a unique dependency on these seven genes for growth and survival since these genes are not only targets of AR‐V7 but also enhance ligand‐independent AR activity. In summary, we developed and used an integrative and unbiased data mining and experimental strategy to define a new AR‐V7‐related gene set with prognostic and therapeutic value for PC. These findings support future in vivo and possibly clinical studies in which combinations of these seven gene products are inhibited in PC. Additionally, this seven‐gene set should be explored in prospective studies of PC to determine their prognostic capacity in different clinical risk settings.

Materials and Methods Microarray dataset preprocessing Microarray datasets were downloaded from the Gene Expression Omnibus (GEO, National Center for Biotechnology Information) or ArrayExpress (European Bioinformatics Institute). Microarrays on PC samples were selected if they used the Affymetrix Human Genome U133 plus 2.0 array and had clinical metadata for each sample (Varambally et al, 2005; Data ref: Varambally et al, 2005; Traka et al, 2008; Data ref: Traka et al, 2008; Arredouani et al, 2009; Data ref: Arredouani et al, 2014; Satake et al, 2010; Data ref: Satake et al, 2013; Jia et al, 2011; Data ref: Wang, 2009; Vaarala et al, 2012 ; Data ref: Vaarala et al, 2012; Rands et al, 2013; Data ref: Roth, 2008; Mortensen et al, 2015; Data ref: Mortensen & Dyrskjøt, 2015) (Appendix Fig S1A and B). In total, 375 samples met our inclusion criteria. These represent eight studies performed at different institutes. The arrays were assigned to one of the following groups: normal, benign hyperplasia, high‐grade prostatic intraepithelial neoplasia (basement membrane intact; HGPIN), or cancerous. The cancerous group contained nested subsets for CRPC and metastatic samples. Microarray data were read into R and preprocessed using the “Affy” package. Preprocessing was performed as previously described (Chandran et al, 2016). Briefly, the “expresso” function was used to perform MAS5 preprocessing on each array. The correlation of gene expression between samples was calculated, and samples with mean correlations more than two to three standard deviations below average were excluded. Filtered samples were combined, annotated, and quantile normalized. Clinically relevant metadata were constructed from sample annotations. Construction of prostate cancer gene co‐expression networks The weighted gene co‐expression network analysis (WGCNA) package was used to construct consensus modules containing highly connected nodes present across different PC datasets (Langfelder & Horvath, 2008). Modules were constructed with a minimum module size of 30 genes, and highly similar modules were combined using a dissimilarity threshold of 0.25. The Pearson correlation was first calculated between gene pairs. A weighting parameter, β, was applied to the correlation matrix, with β satisfying scale‐free topology criteria (Chandran et al, 2016). The weighted correlation matrix was used to calculate a topological overlap matrix and node dissimilarity. Genes were hierarchically clustered using the distance measure, and dynamic tree‐cutting algorithm was used to define modules (Zhang & Horvath, 2005). Batch effect was minimized by using only U133plus2 arrays and removing outlier samples. All genes probed on this platform were used for the analysis. The resulting modules represent sets of highly connected nodes across the PC datasets. The first principle component of each module was correlated with the clinical data to identify module–disease state relationships. Gene ontology analysis was performed using the “GOenrichmentAnalysis” function of the “WGCNA” R package. Cell culture and chemical reagents The human PC cell lines LNCaP, 22Rv1, PC‐3, and RWPE‐1 were obtained from American Type Culture Collection (Manassas, VA) and cultured in RPMI‐1640 (Cellgro by Mediatech, Inc.), supplemented with 100 IU/ml penicillin, 100 μg/ml streptomycin, 2 mM l‐glutamine (Life Technologies, Inc.), and 10% fetal bovine serum (FBS; Atlanta Biologicals) or charcoal‐stripped serum (CSS) to deplete endogenous steroid hormones. The human PC cell line C4‐2B was a generous gift from Dr. Conor Lynch (Moffitt Cancer Center, Tampa, FL), and cells were cultured in DMEM (Cellgro by Mediatech, Inc.), under the same conditions as mentioned above. R1881 (methyltrienolone) was purchased from PerkinElmer Life and Analytical Sciences (Boston, MA) and used at 0.1 or 1 nM. Cells transduced with the different pLKO.1 plasmids were selected in 2.5 μg/ml of puromycin for 3 days and then maintained at 400 ng/ml of puromycin. Doxycycline was used at 100 ng/ml. Doxorubicin (DOX; from Sigma‐Aldrich, D1515) was dissolved in distilled water and used at 100 ng/ml. N9‐Isopropylolomoucine (N‐9) was purchased from Santa Cruz (CAS 158982‐15‐1), dissolved in DMSO, and used at 200 ng/ml. 22Rv1 and C4‐2B cells were kept in their corresponding media with 10% CSS (androgen‐depleted). For the experiments in which the effects of DOX and N‐9 on cell proliferation were examined, those wells that at time zero had a cell confluence of the mean ± 1.5 times the standard deviation were excluded from analysis. The combination index (CI) was calculated using the software Compusyn, by Ting Chao‐Chou and Nick Martin (http://www.combosyn.com/feature.html), based on Chou‐Talalay's Combination Index Theorem (Chou & Talalay, 1984). All cell lines were authenticated in February 2016 using STR (Genetica) and tested for mycoplasma contamination every 6 months using the Mycoplasma PCR Detection Kit (Sigma, St. Louis, MO; MP0035‐1KT). All cell lines used were negative for mycoplasma, bacteria, and fungi contamination. Plasmids and gene depletion The MMTV and ΔGRE/ARE luciferase plasmids were provided by Dr. Mona Nemer (University of Ottawa, Canada). The pLKO.1 shGFP and the doxycycline‐induced tet‐pLKO shGFP were provided by Dr. Priya Rai (University of Miami) and tet‐pLKO shAR‐V7 were from Dr. Yun Qiu (University of Maryland School of Medicine, Maryland). The following constructs were purchased from Sigma‐Aldrich (first construct against 3′UTR, second construct against coding region): pLKO.1 shKIF20A (TRCN0000290278, TRCN0000290348), pLKO.1 shKIF23 (TRCN0000296388, TRCN0000296327), pLKO.1 shTOP2A (TRCN000049278, TRCN000049279), pLKO.1 shCCNB1 (TRCN0000293917, TRCN000045291), pLKO.1 shCCNB2 (TRCN000045193, TRCN000045197), pLKO.1 shBUB1 (TRCN0000288618, TRCN0000288618), and pLKO.1 shBUB1B (TRCN0000197142, TRCN0000194741). Microarray Three independent 22Rv1 cell isolates were derived following transduction of tet‐pLKO shGFP and tet‐pLKO shAR‐V7. Cells were grown in androgen‐depleted conditions (10% CSS), plus or minus doxycycline for 1–3 days. Knockdown was evaluated via Western blot using an AR‐specific antibody [rabbit polyclonal AR (N‐20), Santa Cruz Biotechnology, Cat. sc‐816] from a parallel protein harvest. A short‐term, doxycycline‐inducible knockdown system was utilized. After 48 h, RNA from the 12 samples was sent to the University of Miami Genetics Core for RNA Integrity Number (RIN) evaluation. gcRMA package was used for the analysis. Analysis was performed by examining changes in mRNA levels upon doxycycline treatment. Of the 25,293 transcripts examined, 4,273 genes exhibited significant alteration in expression following AR‐V7 depletion (P‐value < 0.05). Genes whose expression levels were significantly regulated in the shGFP control upon doxycycline treatment (628; compared to the non‐induced control gene set) were removed from the list of potential AR‐V7‐regulated genes. This analysis identified 3,645 genes solely regulated following AR‐V7 depletion (P‐value < 0.05). Yeast synthetic genetic array (SGA) To create the query strain, AR‐V7 was cloned into a pENTR/D‐TOPO vector (Life Technologies, Cat. K2400‐20) from pcDNA3.1 AR‐V7, following the manufacturer's protocols. The following primers were used: forward primer: 5′‐atggaagtgcagttagggct‐3′; reverse primer: 5′‐tcagggtctggtcattttgag‐3′, and the genetic insertion was confirmed by sequencing (GENEWIZ). The AR‐V7 gene was transferred into a destination vector using LR Gateway reactions, according to the manufacturer's protocol, to create an N‐terminal HA‐tagged AR‐V7 under the control of the nmt1 promoter (LR Clonase II from Life Technologies, Cat. 11791020). This expression vector was then integrated into an h−leu1‐32 ura4‐D18 Ade6‐M210 S. pombe strain (PN572) to create an AR‐V7 query strain (h− integrated pjk148‐nmt13X‐HA‐ARV7‐nmt1term leu1‐32 ura4‐D18 Ade6‐M210). Strains containing AR‐V7 (query strain) were grown in PMG media (Sunrise Scientific, Cat. 2060), and the expression of HA‐tagged AR‐V7 was induced by removing thiamine from the media after washing cells with sterile water. AR‐V7 induction was confirmed by immunoblotting using an AR‐V7‐specific antibody (mouse monoclonal Precision antibody, Cat AG10008). Growth conditions and genetic manipulations were previously described (Moreno et al, 1991). The query strain was crossed to the S. pombe haploid deletion library (Bioneer, version 4.0 equivalent), utilizing a modified SGA procedure (Dixon et al, 2008). This procedure is described in detailed in Wiley et al (2014). Briefly, each cross was grown in four replicates under AR‐V7‐inducing (without thiamine) or AR‐V7‐non‐inducing (with thiamine) conditions. Colony growth was monitored for 3 days utilizing a flatbed scanner, and plates were analyzed for “hits” (i.e., a strain with a deleted gene that when AR‐V7 is expressed caused a significant growth defect or growth enhancement in comparison with the same deletion strain under non‐induced conditions, P‐value < 0.05). Essential genes were identified as hits based on the criteria that they interact with at least two primary hits in a S. pombe protein network (STRING, high confidence of 0.7). The identified S. pombe genes were converted into human orthologs using Homologene (http://www.ncbi.nih.gov/homologene; build 67), INPARANOID (http://inparanoid.sbc.su.se/cgi-bin/index.cgi), OrthoMCL (http://orthomcl.org/orthomcl/; version 5), and Pombase (www.pombase.org; build 2014‐03‐17‐v1). Network maps were then generated in STRING at high confidence (0.7) using either experimental data (BIND, DIP, GRID, HPRD, IntAct, MINT, and PID) or experimental data and database data (Biocarta, BioCyc, GO, KEGG, and Reactome). Gene co‐expression, Gleason score, pathologic stage, and MRI evidence of extraprostatic lesions analyses The TCGA Prostate Adenocarcinoma provisional dataset (n = 499) was used. Gene co‐expression analyses were performed using cbioportal.org with a Fisher's exact test, where a P‐value < 0.05 denotes a significant association between the genes (Cerami et al, 2012; Gao et al, 2013). For the other analyses, the dataset with the clinical information was downloaded from UCSC Xena: http://xena.ucsc.edu and analyzed. Kaplan–Meier curves for disease‐free survival (DFS) and death The TCGA Prostate Adenocarcinoma provisional (n = 465) and the Prostate Adenocarcinoma MSKCC, Cancer Cell 2010 (n = 123) datasets were downloaded from FireBrowse (http://gdac.broadinstitute.org) and analyzed using the R package “survival”. The z‐score threshold was ≤ 1.96. Reporter gene assays and transfections A dual‐plasmid mouse mammary tumor virus (MMTV)‐luciferase system was used in which one plasmid encodes wild‐type MMTV promoter, while the control plasmid lacks androgen/glucocorticoid response elements (∆GRE/ARE). Non‐AR‐driven transcriptional activity and transfection efficiency can be accounted for by utilizing the ∆GRE plasmid as a baseline control. Transfection of luciferase constructs was performed using Lipofectamine (Invitrogen Life Technologies) and PLUS reagent (Invitrogen Life Technologies), according to the manufacturer's instructions. 22Rv1 were plated at a density of 3.0 × 105 cells in 35‐mm dishes 24 h before transfection. Immediately before transfection, media were replaced with unsupplemented DMEM. After a 6‐h incubation period, the media were removed and cells were kept in RPMI 1640 supplemented with 2% CSS. After 48 h, cells were harvested, lysed, and assessed for luciferase activity blindly using the Promega luciferase assay kit (Promega Corp.) and a luminometer. Cell growth assays Cells were plated in 96‐well plates at 5,000 cells/well (for RWPE‐1) or 7,500 cells/well (for 22Rv1, C4‐2B, PC3), in 6–12 replicates. 22Rv1 cell lines with stable gene depletions (see Plasmids and gene depletion section) were also transfected with 2% v/v of non‐perturbing nuclear restricted green fluorescent label (IncuCyte NucLight Green BacMam 3.0, Essen Bioscience). After 2 h, cells were incubated in an Incucyte Zoom (Essen Bioscience), acquiring phase (and green fluorescent images when appropriate) at 10× every 2 h. The Incucyte Zoom software was used to analyze and graph the results blindly. Each well measurement was normalized to the number of cells at the initial time and then normalized to the control (shGFP or vehicle treatment accordingly). RNA isolation and reverse transcriptase quantitative RT–qPCR Total RNA was collected using TRIzol according to the manufacturer's protocol (Life Technologies) and isolated using Direct‐zol RNA MiniPrep Plus (Zymo Research, Catalog number R2072). Total RNA was reverse‐transcribed using a cDNA Reverse Transcription Kit (Applied Biosystems, Catalog number 4368814) as per the manufacturer's protocol. TaqMan probes from Applied Biosystems for FKBP5 (Hs01561006_m1), UBE2C (Hs00964100_g1), KIF20A (Hs00993573_m1), KIF23 (Hs00370852_m1), TOP2A (Hs01032137_m1), CCNB1 (Hs01030099_m1), CCNB2 (Hs01084593_g1), BUB1 (Hs01557695_m1), BUB1B (Hs01084828_m1), and GAPDH (Hs02786624_g1) were used. Statistical analysis Data were graphed and analyzed using Prism 7 (GraphPad) and Statistica 8.0 (Statsoft). Data were tested for normality (Shapiro–Wilk test) and homogeneity of variances (Levene's test). When assumptions were met, data were tested for significance (P < 0.05) using a two‐tailed Student's t‐test (two groups) or analysis of variance (ANOVA; three or more groups). Otherwise, Welch's correction or non‐parametric statistical analyses were used: Mann–Whitney's test (two groups) and Kruskal–Wallis (three or more groups).

Data availability The microarray data that support the findings of this study are available in the following databases: Microarray data: Gene Expression Omnibus GSE104572 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE104572).

Acknowledgements We are grateful to Drs. Sawsan Khuri, Tan Ince, Enrique Mesri, Irina Agoulnik, Sandra Lemmon, Priyamvada Rai, and Nagi Ayad for helpful advice; to Mr. Dimitri Nader for assistance with experiments; to Ms. Meenakkshy Manoharan for help with the manuscript preparation; and to Ms. Ann M. Greene for editing the manuscript. This research was conducted using the resources of the University of Miami Center for Computational Science (CCS) and the Onco‐genomics Shared Resource of Sylvester Comprehensive Cancer Center. Research performed in this manuscript was supported by NIH Grant CA132200 (KLB), NIH predoctoral fellowship F30AG038275 (SOP), Women's Cancer Association (KLB), and by developmental and shared equipment funds from the Sylvester Comprehensive Cancer Center.

Author contributions FM contributed to the design of experiments, data acquisition, data analysis, and writing of the manuscript. ERB contributed to the design of experiments, data acquisition, and data analysis. VAC, MJM, NZ, LH, and SOP contributed to data acquisition. DJW contributed to data acquisition and data analysis. GD'U contributed to data analysis and provided reagents and equipment. KLB contributed to the design of experiments, data analysis, and writing of the manuscript and provided reagents and equipment.