NUAK2 is a therapeutically tractable regulator of RNA splicing and tumor progression in neuroendocrine prostate cancer

NUAK2 is a therapeutically tractable regulator of RNA splicing and tumor progression in neuroendocrine prostate cancer | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results NUAK2 is a therapeutically tractable regulator of RNA splicing and tumor progression in neuroendocrine prostate cancer Umar Mehraj , Uran Maimekov , Shaista Manzoor , Emily Cordova , Manasiben Patel , Ikeer Y. Mancera-Ortiz , Stefanie Howell , Zachary W. Davis-Gilbert , Mu-En Wang , Ming Chen , Jung Wook Park , Yuzhuo Wang , Andrew J. Armstrong , Jiaoti Huang , David H. Drewry , Antonina Mitrofanova , Everardo Macias doi: https://doi.org/10.1101/2025.11.12.687734 Umar Mehraj 1 Department of Pathology, Duke University School of Medicine , Durham, NC, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Uran Maimekov 2 Department of Health Informatics, Rutgers School of Health Professions , Newark, NJ 07107, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Shaista Manzoor 1 Department of Pathology, Duke University School of Medicine , Durham, NC, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Emily Cordova 3 Department of Biology, Colorado State University Pueblo , Colorado, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Manasiben Patel 4 Department of Molecular Genetics and Microbiology Duke University , Durham, NC, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ikeer Y. Mancera-Ortiz 5 Structural Genomics Consortium and Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill , Chapel Hill, NC 27599, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Stefanie Howell 5 Structural Genomics Consortium and Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill , Chapel Hill, NC 27599, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Zachary W. Davis-Gilbert 5 Structural Genomics Consortium and Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill , Chapel Hill, NC 27599, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mu-En Wang 1 Department of Pathology, Duke University School of Medicine , Durham, NC, USA 6 Duke Cancer Institute, Center for Prostate and Urologic Cancers, Duke University , Durham, North Carolina, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ming Chen 1 Department of Pathology, Duke University School of Medicine , Durham, NC, USA 6 Duke Cancer Institute, Center for Prostate and Urologic Cancers, Duke University , Durham, North Carolina, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jung Wook Park 1 Department of Pathology, Duke University School of Medicine , Durham, NC, USA 6 Duke Cancer Institute, Center for Prostate and Urologic Cancers, Duke University , Durham, North Carolina, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Yuzhuo Wang 7 Vancouver Prostate Centre, Vancouver General Hospital and Department of Urologic Sciences, The University of British Columbia , Vancouver, British Columbia, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site Andrew J. Armstrong 6 Duke Cancer Institute, Center for Prostate and Urologic Cancers, Duke University , Durham, North Carolina, USA 8 Department of Medicine, Division of Medical Oncology, Duke University School of Medicine , Durham, North Carolina, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jiaoti Huang 1 Department of Pathology, Duke University School of Medicine , Durham, NC, USA 6 Duke Cancer Institute, Center for Prostate and Urologic Cancers, Duke University , Durham, North Carolina, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site David H. Drewry 5 Structural Genomics Consortium and Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill , Chapel Hill, NC 27599, USA 9 UNC Lineberger Comprehensive Cancer Center, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill , Chapel Hill, NC 27599, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Antonina Mitrofanova 2 Department of Health Informatics, Rutgers School of Health Professions , Newark, NJ 07107, USA 10 Rutgers Cancer Institute of New Jersey , New Brunswick, NJ, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Everardo Macias 1 Department of Pathology, Duke University School of Medicine , Durham, NC, USA 6 Duke Cancer Institute, Center for Prostate and Urologic Cancers, Duke University , Durham, North Carolina, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: everardo.macias{at}duke.edu Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract Prostate cancer (PC) remains the second leading cause of cancer-related mortality in men. The emergence of treatment-emergent neuroendocrine prostate cancer (NEPC) arising from androgen receptor (AR) pathway inhibition poses a significant clinical challenge. Here, we report that NUAK family kinase 2 (NUAK2) is an actionable therapeutic target in NEPC. NUAK2 expression is markedly elevated in NEPC patient specimens and preclinical models, and its genetic or pharmacologic inhibition suppresses NEPC tumor growth. The FDA-approved CDK4/6 inhibitor trilaciclib exerts potent inhibition of NUAK2, leading to marked tumor suppression alone and enhanced efficacy in combination with carboplatin. Integrated phospho-target and interactome analyses demonstrate that NUAK2 engages core spliceosome components to regulate pre-mRNA splicing. As proof of principle, we validated that NUAK2 inhibition perturbs pre-mRNA splicing of EZH2 and TTK leading to reduced translation. Collectively, these findings establish NUAK2 as a clinically actionable regulator of RNA splicing and tumor progression in NEPC, revealing a novel mechanism by which trilaciclib exerts antitumor activity in NEPC. Download figure Open in new tab Introduction Since their initial FDA approval nearly two decades ago, androgen receptor (AR) signaling inhibitors such as abiraterone acetate and enzalutamide have become established first-line treatments for men with metastatic castration-resistant prostate cancer (mCRPC) ( 1 , 2 ). With more frequent and earlier use of AR pathway inhibitors (ARPIs) in hormone sensitive and non-metastatic settings, a significant number of post-ARPI CRPC patients have tumors exhibiting lineage plasticity, evolving into aggressive variant prostate cancer (AVPC) including double-negative prostate cancer (DNPC) and NEPC ( 3 , 4 ). The emergence of NEPC from adenocarcinoma following ARPIs poses a significant clinical challenge. Meta-analyses estimate that ∼16% (9–24%) of patients receiving ARPIs develop NEPC. Rapid autopsy studies further show that up to 25% of PC deaths involve tumors with neuroendocrine or AR-negative profiles, with a median survival of ∼9.6 months in the mCRPC setting ( 5 – 7 ). Approximately 90% of NEPC cases exhibit loss of PTEN, and RB1 , often concurrent with TP53 mutations ( 8 – 12 ). NEPC is also characterized by genomic amplifications of MYCN , AURKA , and BCL2 family genes, alongside overexpression of transcriptional and epigenetic regulators such as SOX2 , EZH2 , FOXA2 , and ASCL1 ( 11 – 14 ). Recent studies have further implicated RNA splicing factors and alternatively spliced transcripts in treatment resistance and the transdifferentiation of prostate adenocarcinoma to NEPC ( 15 – 17 ). In addition to NEPC, DNPC lacking both AR and neuroendocrine markers has emerged as another aggressive, therapy-resistant PC subtype ( 18 ). DNPC shares several molecular characteristics with NEPC, including loss of AR signaling, PTEN and TP53 mutations, and activation of stemness and epithelial–mesenchymal transition pathways, suggesting overlapping mechanisms driving lineage plasticity and resistance to AR-targeted therapies ( 19 ). Despite progress in understanding the genetic and molecular landscape of NEPC or DNPC, viable molecular therapeutics are still lacking, underlining the critical need to uncover actionable molecular targets. NUAK2 (also known as sucrose nonfermenting-like kinase, SNARK) is a member of the AMPK kinase family and remains poorly characterized, as highlighted by the Illuminating Druggable Genome Program ( 20 – 22 ). Emerging evidence implicates NUAK2 as an oncogenic driver in several cancers, including melanoma, liver, breast, skin, and glioblastoma ( 23 – 27 ). We previously demonstrated that NUAK2 expression increases progressively from normal prostate tissue to localized PC and is further elevated in lethal mCRPC ( 28 , 29 ). In the Durham VA cohort, elevated NUAK2 expression in radical prostatectomy specimens was associated with a threefold increased risk of metastasis, and phosphoproteomic analyses of rapid autopsy of PC specimens revealed heightened NUAK2 kinase activity in metastatic lesions ( 28 ). Interestingly, NUAK2 mRNA was found to be significantly upregulated in NEPC, underscoring its potential role in PC adenocarcinoma disease progression ( 28 ). Here, we investigate the role of NUAK2 in NEPC and its potential as a therapeutic target. Using genetic depletion and overexpression models, we demonstrate that NUAK2 drives NEPC tumor growth. Furthermore, our results establish that pharmacological inhibition of NUAK2 using both preclinical and clinical-grade inhibitors effectively suppresses NEPC growth in vitro and in vivo and exhibited enhanced efficacy in combination with standard chemotherapeutic agents. While NUAK2 is frequently associated with oncogenic activity through a positive feedback loop with YAP/TAZ, downregulation of YAP1 in NEPC suggests that NUAK2 functions through a YAP1-independent mechanism in NEPC ( 24 , 25 , 30 – 32 ). Integrating multi-omics revealed NUAK2 interacts with core spliceosome proteins and plays a crucial role in pre-mRNA splicing. Collectively, our findings establish NUAK2 as an actionable target in NEPC and a key regulator of RNA splicing. Methods Clinical datasets NUAK2 expression analysis and pseudotime enrichment across prostate cancer progression were performed using data obtained from the Prostate Cancer Atlas, which integrates multi-omics and single-cell transcriptomic datasets spanning various PC disease states ( 33 ). Expression trends of NUAK2 were evaluated across normal prostate, primary adenocarcinoma, mARPC, and DNPC and NEPC samples. Pseudotime trajectory analysis was used to infer the dynamic changes in NUAK2 expression during tumor evolution and lineage plasticity. All plots and statistical analyses were generated using GraphPad Prism. Transcriptomic data from the Beltran et al. and Labrecque et al. (GSE126078) was assessed for NUAK2 expression levels across PC subtypes ( 9 , 34 ). Transcriptomic data from the LuCaP PDX series (GSE199596), Living Tumor Laboratory (LTL) PDX models ( https://www.livingtumorlab.com/searchform.php ) and LTL-331 PC to NEPC transdifferentiation model (GSE59986) were also interrogated ( 35 , 36 ). For mRNA analysis across PC cell line models, CCLE dataset was analyzed. Cell lines and cell culture Cell lines were authenticated at the Duke University Cell Culture Facility through short tandem repeat (STR) profiling and screened for mycoplasma contamination using the MycoAlert™ Plus Mycoplasma Detection Kit (Lonza). NCI-H660 and PARCB1 cells were maintained in Advanced Dulbecco’s Modified Eagle Medium (Advanced DMEM; Thermo Fisher Scientific) supplemented with L-glutamine, B-27 supplement, recombinant human EGF (10Cng/mL), and recombinant human FGF2 (10Cng/mL). HEK293T and DU-145 cells were cultured in DMEM with 10% fetal bovine serum (FBS), while PC-3 cells were cultured in RPMI-1640 medium with 10% FBS. All cultures were incubated at 37°C in a humidified atmosphere with 5% CO₂. Generation of stable knockdown, knockout, and overexpression cell lines Gene knockdown, knockout, and overexpression cell line models were generated using lentiviral transduction. Lentiviral particles were generated by co-transfecting HEK293T cells with overexpression or gene-silencing constructs with the packaging plasmid psPAX2 and envelope plasmid pMD2.G, using the jetPRIME transfection reagent (Polyplus-transfection, Illkirch, France). The viral supernatants were collected at 48- and 72-hr post-transfection, passed through a 0.22 µm filter to remove debris, and then combined in equal parts with fresh complete medium containing 8 µg/mL Polybrene (Millipore Sigma). Target cells were exposed to the viral-containing medium for 14 to 16 hr, after which the medium was replaced with fresh growth medium. Selection of transduced cells was performed using antibiotics according to the respective resistance markers, with puromycin at 0.5–2 µg/mL, hygromycin B at 150–200 µg/mL, or blasticidin at 10 µg/mL. Knockdown, knockout, or overexpression was confirmed by immunoblotting. Generation of inducible knockdown and overexpression cell lines Inducible NUAK2 knockdown cell lines were established using the shERWOOD-UltramiR lentiviral doxycycline-inducible shRNA platform (Skyang Bio, Huntsville, AL, USA). For overexpression experiments, a NUAK2 cDNA construct along with an empty control vector were obtained from Transomic Technologies. The NUAK2 kinase-dead mutant (NUAK2_K81R) was generated via site-directed mutagenesis employing the Quik-change II XL Kit (Agilent Technologies, Santa Clara, CA). Mutagenesis primers used are listed in supplementary data (see Table S7). Generation of inducible NUAK2 knockout NCI-H600 cell line To generate a doxycycline-inducible CRISPR-Cas9 NCI-H660 cell line, cells were sequentially transduced with lentiviral vectors encoding Tet-On transactivator and doxycycline-inducible Cas9 (Tet3G and pLVX-Cas9; VectorBuilder, Chicago IL, USA). Following transduction, cells were selected with puromycin (0.5 µg/mL) and blasticidin (10 µg/mL) over 15 days. Cas9 expression was validated by immunoblotting after a 72-hour induction with doxycycline (0.5 µg/mL). Subsequently, Cas9-expressing cells were infected with lentiviral vectors carrying either a non-targeting control sgRNA or one of two NUAK2-targeting sgRNAs (VectorBuilder, Chicago, IL, USA). Infected cells were selected using hygromycin (150 µg/mL), and NUAK2 knockout efficiency was confirmed by immunoblot analysis 96 hr post-doxycycline induction. In vitro phenotypic assays For drug response assays, cells were seeded in 96-well plates. After 24 hrs, cells were treated with DMSO (vehicle), 2X drug concentrations, or doxycycline (for TRE3G shRNAs, and inducible-Cas9 system) in 100CµL of fresh medium and monitored for 5–10 days. Cell confluence was monitored using the Incucyte S3 live-cell imaging system (Sartorius, Ann Arbor, MI, USA), while cell viability was assessed with the Cell Counting Kit-8 (CCK-8, Cat# K1018, Apex Bio, Houston, TX). For 3D spheroid assays, cells were seeded at a density of 1,000 cells per well in 96-well ultra-low attachment U-bottom plates (Prime Surface, S-BIO, Hudson, NH, USA) with 100CµL of medium. After 48 hr, spheroids were treated with the indicated compounds or doxycycline at 2X concentration in an additional 100CµL of medium. Spheroid growth was quantified using the Incucyte Spheroid Software Module. For clonogenic assays, cells were plated in 12-well plates at a density of 500–750 cells per well and exposed to the specified inhibitors, doxycycline, or vehicle control. Treatments were maintained for approximately two weeks, with medium replacement every three days. At endpoint, colonies were fixed with 3.7% paraformaldehyde for 15 min and stained with 0.05% (w/v) crystal violet for 30 min at RT. Plates were then washed three times with water, air-dried, and imaged. To quantify colony formation, the crystal violet stain was solubilized in 10% acetic acid and absorbance was measured at 570Cnm. Western blotting Cells or tissues were lysed using RIPA buffer with protease inhibitor cocktail set 1 and phosphatase inhibitor cocktails 2 and 3 (MilliporeSigma, St. Louis, MO, USA). Protein concentration was determined with DC Protein Assay (Bio-Rad, Hercules, CA, USA). For immunoblot analysis, 40–80Cµg of total protein was resolved by SDS-PAGE and transferred onto 0.22Cµm or 0.45Cµm nitrocellulose membranes. Primary antibodies were used at a 1:1000 to 1:5000 dilution. Membranes were developed using Pierce ECL Western (#32106), SuperSignal West Pico PLUS (#34580), SuperSignal West Dura (#34075), or SuperSignal West Femto (#34096) chemiluminescent substrates (Thermo Fisher Scientific, Waltham, MA, USA). Signal detection was performed via film exposure and scanning. Band intensities were quantified using ImageJ software, and target protein levels were normalized to loading controls by calculating the ratio of net band intensities. Cellular Thermal Shift Assay (CETSA) DU-145 cells were seeded in 150 mm plates at approximately 80% confluency. The next day, cells were treated with DMSO, HTH-02-006 (25 µM), or G1T-28 (5 µM) for 2 hr. Cellular thermal shift assay (CETSA) was conducted as described previously with minor modifications ( 37 ). After 2h treatment, cells were trypsinized, collected in PBS, washed twice, and resuspended in PBS containing protease inhibitor cocktail. The cell suspension was aliquoted equally into PCR tube strips, and subjected to a temperature gradient on a thermocycler from 42°C to 47.5°C for 3 min. A room temperature sample served as the control. Following thermal exposure, cells were frozen on dry ice for 10 min, and samples were lysed using freeze-thaw for 10 min, and repeated 3X. Lysates were collected for soluble protein analysis. Immunoblotting was performed as above, using antibodies against NUAK2 and GAPDH. Band intensities were quantified using ImageJ and normalized to GAPDH and plotted to generate protein thermal melting curves. Multiplexed Inhibitor Bead-Mass Spectrometry (MIB/MS) MIB/MS was performed as described previously with minor modifications ( 38 ). Briefly, PARCB-1 cell lysates (≥5 mg protein in 4.0 mL MIB lysis buffer) were treated with DMSO or G1T-28 (100 nM, 1 µM, 10 µM) for 1 h on ice. Lysates were then applied to Bio-Rad Poly-Prep Columns (Cat# 731-1550) packed with 350 µL of a 50% slurry of multiplexed inhibitor bead (MIB) matrix containing Shokat, PP58, Purvalanol B, and UNC-21474 (14% each) and VI-16832 and Ctx-0294885 (22% each) on ECH Sepharose 4B. Columns were equilibrated in high-salt MIB wash buffer (5 mM HEPES, 1M NaCl, 0.5% Triton X-1, 1 mM EDTA, 1 mM EGTA, pH 7.5), loaded with lysates adjusted to 1 M NaCl, allowing flow through to pass. Columns were sequentially washed with high-salt, low-salt MIBs wash buffer (5 mM HEPES, 15 mM NaCl, 0.5% Triton X-1, 1 mM EDTA, 1 mM EGTA, pH 7.5), and 0.1% SDS in low salt MIB wash buffer. Bound kinases were eluted with SDS/Tris buffer at 95C°C, reduced (DTT), alkylated (iodoacetamide), concentrated on 10-kDa filters (Cat# UFC801008), and precipitated by methanol/chloroform extraction. Protein pellets were dried, reconstituted in 50 mM HEPES buffer, and digested overnight with sequencing-grade trypsin. Peptides were washed with ethyl acetate, desalted using C18 spin columns (Pierce Cat# 89852)), dried, and resuspended in 2% acetonitrile/0.1% formic acid. LC-MS/MS analysis was performed with a Thermo Scientific Vanquish Neo UHPLC system coupled to a Thermo Scientific Orbitrap Astral Mass Spectrometer equipped with Easy-Spray Ion Source. Samples were chromatographically separated using an IonOpticks Aurora Ultimate TS series UHPLC C18 column (75 µm ID X 25cm, 1.7 µm particle size; IonOpticks) over a 30-minute gradient. Separation was achieved with a gradient of 5-45% mobile phase B at a 300 nl/min flow rate, where mobile phase A was 0.1% formic acid in water and mobile phase B consisted of 80% acetonitrile, 0.1% formic acid. The Astral was operated in product ion scan mode for Data Independent Acquisition (DIA). A full MS scan (380-980 m/z) was collected on the Orbitrap with a 240,000 resolution, a maximum injection time of 5ms, and 500% normalized AGC target. The Astral detector acquired the MS2 scans over a scan range of 150-2000 m/z with a cycle time of 0.6 s, and a 3 m/z isolation window. The normalized AGC target was set to 500 % with a maximum injection time of 3 ms, and the normalized higher collision dissociation (HCD) energy was 25%. Raw data files were processed using Spectronaut (v20.0.250606.94229; Biognosys) and searched against the Uniprot reviewed human database (UP000005640, containing 20,422 reviewed sequences) appended to a common cell culture contaminants database (370 entries). The following settings were used: enzyme specificity set to trypsin, up to two missed cleavages allowed, cysteine carbamidomethylation set as a fixed modification, methionine oxidation and N-terminal acetylation set as variable modifications. A false discovery rate (FDR) of 1% was used to filter all data. Imputation was disabled, and single hit proteins were retained. Cross-run normalization was performed within Spectronaut. Kinases were identified by cross-referencing an inhouse kinome database (Lee Graves Lab, UNC Chapel Hill). A minimum of two unique peptides was required for label-free quantitation (LFQ) using the LFQ intensities. Log2 fold change (FC) ratios were calculated by subtracting the log2-transformed protein abundances of the drug-treated sample from those of the DMSO control (See Raw and Normalized Data, Table S1). TiO 2 enriched phosphoproteomics Proteomic studies were conducted in collaboration with the Duke Metabolomics and Proteomics Core facility. NCI-H660 cells were seeded in T75 flasks at a density of 5C×C10C cells/ml. After 24 hrs, cells were treated with DMSO, HTH-02-006 (5CµM), or G1T-28 (5CµM) for 1Chr, with three biological replicates per condition. Cells were pelleted, washed twice with ice-cold PBS, and lysed in 100CµL of 8CM urea in 50CmM ammonium bicarbonate. Lysates were sonicated, quantified, and 120Cµg of total protein per sample was reduced, alkylated, and digested with sequencing-grade trypsin. Peptides were resuspended in 80% acetonitrile and 1% TFA, and phosphopeptides were enriched using TiO ₂ tips (GL Biosciences) according to the manufacturer’s instructions. LC-MS/MS was performed on 4CµL of each sample using a Vanquish Neo UPLC system (Thermo Fisher Scientific) coupled to a Thermo Orbitrap Astral high-resolution mass spectrometer. Peptides were first trapped on a Symmetry C18 pre-column (20Cmm × 180Cµm; 5CµL/min, 99.9/0.1 water/acetonitrile), followed by analytical separation on a 1.5Cµm EvoSep C18 column (150Cµm ID × 8Ccm) with a 30-minute gradient of 5–30% acetonitrile (0.1% formic acid) at 500CnL/min and 50C°C. Data were acquired in data-independent acquisition (DIA) mode with a resolution of 240,000 at m/z 200 over a scan range of m/z 380–1080 (AGC target: 4C×C10C). Raw data from LC-MS/MS runs were processed in Spectronaut (Biognosys) using a library-free approach. Files were aligned based on precursor mass and retention time. Peptide identification was performed against the Homo sapiens SwissProt database (August 2022), a common contaminant database, and reversed-sequence decoys. Search parameters included carbamidomethylation (C) as a fixed modification and oxidation (M) and phosphorylation (S/T/Y) as variable modifications. Trypsin cleavage specificity was used with precursor and fragment mass tolerances of 10Cppm and 20Cppm, respectively. Peptides were filtered at a 1% false discovery rate (FDR). Phosphopeptide abundance was determined from MS2 fragment ion intensities. Technical reproducibility was assessed by calculating the average coefficient of variation (CV): 40.6% for SPQC and 44.3% across treatment groups. Differential phosphorylation was evaluated using log ₂ -transformed intensities and two-tailed heteroscedastic t-tests comparing HTH-02-006 vs. DMSO and G1T-28 vs. DMSO Supplementary Table (see raw and normalized data, Table S2). For gene ontology (GO) analysis Metascape ( 39 ) online tool was used, and plots were generated using SR plotter ( 40 ). Gene ontology networks were further analyzed and visualized with cystoscope ( 41 ). NUAK2-miniTurboBioID proximity labeling NCI-H660 cells stably express NUAK2-V5-miniTurbo (mTb) (Addgene #189910) or HcRed-V5-miniTurbo(mTb) (Addgene #135237) were treated with 0.5 mM biotin for 4h, collected, washed twice with ice-cold PBS, and lysed with Pierce™ IP Lysis Buffer (Thermo Fisher) supplemented with protease and phosphatase cocktail inhibitors as aforementioned. Samples were cleared by centrifugation at 10,000g for 15 min and protein quantified. Streptavidin affinity pulldown was performed using Pierce™ MS-Compatible Magnetic IP Kit (Thermo Fisher, Catalog Number-90408), following manufacturer’s instructions and samples were eluted in 50 μl elution buffer. Eluted samples were spiked with 1 or 2 pmol bovine casein as an internal quality control. Next, samples were reduced, alkylated and digested using sequencing grade trypsin (Promega). Quantitative LC-MS/MS was performed using an EvoSep One UPLC coupled to a Thermo Orbitrap Astral high resolution accurate mass tandem mass spectrometer (Thermo). Data collection on the Orbitrap Astral mass spectrometer was performed in a data-independent acquisition (DIA) mode of acquisition with a r=240,000 (@ m/z 200) full MS scan from m/z 380-980 in the OT with a target AGC value of 4e5 ions. Fixed DIA windows of 4 m/z from m/z 380 to 980 DIA MS/MS scans were acquired in the Astral with a target AGC value of 5e4 and max fill time of 6 ms. Following UPLC-MS/MS analyses, data were imported into Spectronaut (Biognosis) and individual LC-MS data files were aligned. For technical reproducibility, average % coefficient of variation (%CV) was calculated for each protein within each unique group; 11.7% (SPQC) and 22.5% or the other two groups. Fold changes values were calculated between NUAK2-V5-mTb and HcRed-V5-mTb groups based on protein expression values and calculated two-tailed heteroscedastic t-test on log2-transformed data. Raw data file is attached as supplementary data (Table S3). For validation, samples were run on SDS-PAGE gels after streptavidin affinity pulldown and transferred onto 0.45- or 0.22-μm nitrocellulose membrane and probed for candidate NUAK2-V5-mTb biotinylated proteins. For gene Ontology (GO) analysis, Metascape online tool was used, and plots were generated using SR plotter ( 39 , 40 ). Gene ontology networks were analyzed and visualized with cystoscope ( 41 ). Co-immunoprecipitation DU-145 and NCI-H660 cells were seeded at 80% confluency onto 15 cm plates or T75 flasks, respectively. Cells were washed with PBS, and lysed in 1Cml of IP lysis buffer for 30 min. Cell lysates were clarified by centrifugation at 12,000 g for 15Cmin at 4C°C. For immunoprecipitation, 25 µl of Protein G Dynabeads were pre-washed and incubated in 1 ml of lysis buffer with either 5 µg mouse anti-NUAK2 antibody (Novus Biologicals, H00081788-M04) or 5 µg normal mouse IgG (Sigma-Aldrich, 12-371) for 3h at 4 °C. Clarified protein lysates (10,000 × g, 10 min) were incubated with antibody-bound Dynabeads overnight at 4 °C. Beads were washed three times with lysis buffer, and bound proteins were eluted with 40 µl of 3x Laemmli buffer by boiling for 5 min. Elutes were resolved by SDS–PAGE, followed by standard immunoblotting to assess candidate NUAK2-interacting proteins. RNA sequencing and pre-mRNA splicing analysis NCI-H660 cells were seeded in T75 flasks at a density of 5 × 10C cells per flask. Next day, cells were treated with DMSO (vehicle) or G1T-28 (5CµM) for 24h. Cells were harvested, washed twice with ice-cold PBS, and total RNA was extracted using the RNeasy Mini Kit (Qiagen) following the manufacturer’s protocol. RNA samples were subjected to DNase treatment and quality assessment prior to sequencing. High-quality RNA was processed for 150Cbp paired-end RNA sequencing on an Illumina NovaSeq X Plus platform (Genewiz, Azenta Life Sciences City, NJ). Raw sequencing reads underwent quality control using FastQC v0.12.1 to assess base quality scores and detect potential sequencing artifacts. Adapter sequences and low-quality bases were trimmed using AdapterRemoval v2.2.2 Trimmed reads were aligned to the human reference genome (GRCh38/hg38) using STAR v2.7.11a with default parameters. Differential gene expression analysis was conducted using DESeq2( 42 ) ( 43 ) ( 44 ). P values were adjusted for multiple testing using the Benjamini–Hochberg method, and genes with an adjusted P ≤ 0.01 were considered significantly differentially expressed (Supplementary Table 4). Gene ontology (GO) enrichment analysis was performed using the enrichGO function from the clusterProfiler R package (v4.14.6) ( 45 ). Major expressed transcript isoforms were identified as previously described ( 46 , 47 ). Transcript abundance was quantified using Salmon (v1.10.0) ( 48 ), and for each gene, the major isoform was defined as the transcript with the highest mean transcripts per million (TPM) across all samples, provided it accounted for ≥70% of the gene’s total expression. Isoforms located on sex chromosomes (X, Y) or mitochondrial DNA (MT), as well as those overlapping other genes, were excluded. The final reference annotation comprised 8,518 major isoforms containing 87,752 exons and 65,487 introns. Splicing ratios were computed using an exon-based splice junction analysis, as previously described ( 46 ). Only exons from major isoforms with first exons >100 base pairs were included; single-exon isoforms were excluded. A ±4Cbp window around the exon–intron junction (2Cbp in the exon and 2Cbp in the intron) was defined. Reads overlapping ≥3Cbp of this window were considered unspliced, and those overlapping ≥2Cbp were considered total junctional reads. Spliced reads were determined by subtracting unspliced read counts from total reads. Splicing ratios were calculated as the proportion of spliced reads relative to the total reads per junction. Read overlap was computed using the findOverlaps function from the GenomicRanges R package (v1.58.0) ( 49 ). Multivariate Analysis of Transcript Splicing rMATS-turbo (v4.1.2) was used to quantify five major splicing events: exon skipping (SE), intron retention (RI), alternative 5′ splice sites (A5SS), alternative 3′ splice sites (A3SS), and mutually exclusive exons (MXE) ( 50 ). Representative splicing events were visualized using rmats2sashimiplot ( https://github.com/Xinglab/rmats2sashimiplot ), using rMATS output .txt files as input and attached as Supplementary Table (Table S6). The “inc level” in sashimi plots generated by rmats2sashimiplot refers to the exon or intron inclusion level—essentially measuring how often a specific exon or intron is present in the mature mRNA, based on sequencing reads. Semi-quantitative RT-PCR NCI-H660 cells were treated with either DMSO or G1T-28 (5CµM) for 24h. For NUAK2 knockdown experiments, NCI-H660 cells stably expressing doxycycline-inducible shRNAs (shNT, shNUAK2-1, and shNUAK2-2) were treated with doxycycline (1Cµg/mL) for 72 hr. Total RNA was extracted as described above and 1Cµg of total RNA was reverse transcribed using random hexamer primers and SuperScript II Reverse Transcriptase (Thermo Fisher Scientific). Semi-quantitative PCR was performed in a 20CµL reaction containing 5Cng of cDNA. Amplified products were resolved on a 1.8% agarose gel, stained with Safe DNA Gel Stain (A8743, ApexBio), and visualized using a Li-COR imaging system. Band intensities were quantified using ImageJ software (v1.53f51), and percent splice-in (PSI) values were calculated to quantify alternative splicing events. Primer sequences used for splicing analysis are listed in Supplementary Table (Table S7). In vivo xenograft studies All animal experiments were performed in accordance with ethical regulations and approved protocols from the Institutional Animal Care and Use Committee (IACUC) of Duke University (Protocol: A190-23-09 and A034-25-04) and the Department of Defense Animal Care and Use Review Office (ACURO). Male NOD-SCID IL2Rγ^null (NSG) mice were used for all studies and housed in a pathogen-free facility under standard environmental conditions (65–75C°F; 40–60% humidity). For cell line xenograft experiments, 1C×C10C cells were suspended in a 1:1 mixture of Matrigel and Advanced DMEM/F12 (Thermo Fisher) and injected into the right flank of NSG mice. For TRE3G-shRNA or TRE3G-Cas9 studies, mice bearing tumors derived from NCI-H660 or PARCB1 cells expressing inducible constructs were switched to and maintained on doxycycline-containing (200Cmg/kg) chow (Bio-Serv, Flemington, NJ, USA) once tumors became palpable (∼50Cmm³). Tumor volume was measured using calipers and calculated using the formula: (length × width 2 )/2. Mice were euthanized once control tumors reached an approximate volume of 1,500Cmm³. Tumors were excised, weighed, and a portion flash-frozen and other portions fixed in 10% neutral-buffered formalin for histopathological analysis. For pharmacological efficacy studies, mice were randomized into treatment groups once tumors reached a palpable volume (∼50-100Cmm³). HTH-02-006 was administered by intraperitoneal (i.p.) injections twice daily in 90% of 5% dextrose solution with 10% DMSO at 5 or 10 mg/kg. For G1T-28 studies, mice were randomized to vehicle (10CmM citrate buffer, pH 4), 50- or 100Cmg/kg G1T-28, administered once daily by oral gavage. For patient-derived xenograft (PDX) studies, freshly excised LTL-352 tumor tissues (∼20Cmg) were implanted subcutaneously into the flank of NSG male mice under sterile conditions. Once tumors reached an average volume of 40–50Cmm³, mice were randomized into control or treatment groups and treated as described above with vehicle or HTH-02-006 or G1-T28. For the orthotopic study, PC3_Luc cells were transduced with lentiviral vectors encoding pLX304 (empty vector), pL-NUAK2, or pL-NUAK2-KD and selected with blasticidin (10 µg/mL) for 7 days. Subsequently, 2.5 × 10 5 cells were suspended in a 1:1 mixture of Matrigel and RPMI medium (total volume 20 µL) and orthotopically implanted into the dorsal prostatic lobe of 6–8-week-old male SCID/Beige mice (Jackson Laboratory) as described previously ( 51 ). For bioluminescence imaging, mice were injected intraperitoneally with 150 µL of D-luciferin (30 mg/mL). Tumor burden and metastatic dissemination were measured and quantified using the IVIS Spectrum In Vivo Imaging System (PerkinElmer, Waltham, MA). For complete blood count (CBC) analysis, whole blood was collected into K2EDTA-containing tubes following the final dose of vehicle, G1T28, carboplatin, or combination. CBC measurements were performed using the HESKA elemental HT5 at the Duke VDL core facility. Histology, and immunohistochemistry For tumor xenografts, liver sections, and patient samples used for immunohistochemistry (IHC), tissues were fixed in 10% neutral-buffered formalin for 24 hours and subsequently paraffin-embedded at the Duke BioRepository & Precision Pathology Center (BRPC). Tissues were sectioned in 5 μm-thickness. After deparaffinization and rehydration, antigen retrieval with antigen retrieval buffer (vector Laboratories) for 15 min. The sections were washed twice followed by blocking for endogenous peroxidase (3% H2O2 in PBS) for 20 min. Next the sections were blocked with block buffer (5% goat serum in in TBS/0.1%BSA) for 20 min at room temperature. The sections were incubated with primary antibodies, anti NUAK2 antibody (NBP1-81880, 1:200) and anti ki67 (D2H10 cell signaling, 1:400) at 4°C overnight. Next, the sections were incubated with secondary antibodies at room temperature for 1 h. After washes as above, the sections were incubated with ABC Elite (Vector Laboratories, Newark, CA, USA) for 30 mins. The sections were then washed with TBS followed by Incubation with ImmPACT DAB EqV (Vector Laboratories) and deactivation with water. After 1 min incubation in 50% hematoxylin stain, the samples were dehydrated and coverslipped. Statistical analysis Comparisons between two groups were performed using two-sided Student’s t tests unless otherwise specified. For multiple-group comparisons, one-way or repeated measures ANOVA was applied, followed by appropriate post hoc tests. P values ≤0.05 were considered statistically significant. Quantification of NUAK2 expression in prostate cancer (PC) versus NEPC or cytoplasmic versus nuclear staining in immunohistochemistry (IHC) was performed using H-scores by a contract pathologist blinded to the experimental conditions. All statistical analyses and data visualization were conducted using GraphPad Prism (version 10.6.0). Results NUAK2 mRNA and protein expression is elevated in NEPC We previously reported that elevated NUAK2 expression is associated with an increased risk of developing metastasis in radical prostatectomy patients and progressively increased with disease progression including NEPC ( 28 ). To extend these observations, we analyzed the PC Atlas dataset and observed a stepwise increase in NUAK2 expression across prostate cancer disease states ( Fig. 1A ) ( 33 ). Compared with normal prostate tissue, NUAK2 levels were modestly elevated in primary adenocarcinoma and significantly higher in DNPC and NEPC samples ( Fig. 1A ). Pseudotime trajectory analysis revealed a pronounced enrichment of NUAK2 expression in tumors occupying later pseudotime states, characterized by diminished AR activity and features consistent with either DNPC or NEPC phenotypes ( Fig. 1B ). Consistent with this, NUAK2 expression was significantly upregulated in NEPC compared to castration-resistant PC in the Beltran et al. dataset (Fig. S1A) ( 9 ). This pattern was corroborated in the Labrecque dataset, where NUAK2 mRNA levels were markedly elevated in AR ⁻/ NE ⁺ tumors relative to other subtypes including the DNPC phenotype (Fig. S1B) ( 34 ). Additionally, NUAK2 expression positively correlated with increased expression of NEPC-associated markers(SYP, CHGA) and was inversely associated with AR and RB1 expression loss (Fig. S1C). Download figure Open in new tab Figure 1. NUAK2 mRNA and protein is upregulated in NEPC (A) Analysis of the Prostate Cancer Atlas dataset showing NUAK2 expression across normal prostate, primary adenocarcinoma, mARPC (AR + ), DNPC, and NEPC samples. (B) Pseudotime trajectory analysis of RNA-seq data demonstrating enrichment of NUAK2 in pseudotime states. (C) Longitudinal expression profiles of NUAK2 and NEPC marker SYP during progression from adenocarcinoma to NEPC in the LTL331 model, illustrating concurrent induction of NUAK2 expression with neuroendocrine differentiation. (D-E) Heatmaps illustrating mRNA expression levels of NUAK2, SYP, CHGA, YAP1, AR, and RB1 across adenocarcinoma-CRPC and NEPC in (D) LTL PDX and (E) LuCaP PDX series. (F) Representative IHC images of NUAK2 expression in adenocarcinoma-CRPC (n=50) and NEPC (n=10) tissue samples. Scale bar= 100Cµm. (G) Quantification of NUAK2 protein expression from IHC images using H-score analysis. (H) Quantification of nuclear-localized NUAK2 IHC staining in adenocarcinoma-CRPC versus NEPC, also represented by H-score. (I) NAUK2 expression profiles across prostate cancer cell lines, highlighting elevated levels in advanced PC models in CCLE dataset. (J) Immunoblot analysis of NUAK2 and key proteins, in adenocarcinoma and NEPC PDX tumors. p- values were determined by one-way ANOVA test in (A), and unpaired two-tailed Student’s t -test for (G, and H). In the LTL-331 PC to NEPC transdifferentiation PDX model, NUAK2 expression was significantly elevated in relapsed NEPC tumors compared to treatment-naive adenocarcinomas, coinciding with increased SYP levels ( Fig. 1C ) ( 36 ). Likewise, NUAK2 expression in the Living Tumor Laboratory (LTL) PDX series was also elevated in NEPC PDXs ( Fig. 1D , Fig. S1D) ( 36 ). In the LuCaP PDX series, NUAK2 expression was significantly elevated in models with NEPC features ( Fig. 1E , Fig. S1E) ( 35 ). NUAK2 expression coincided with classical neuroendocrine markers such as SYP and CHGA , while inversely correlating with key markers of luminal identity, including AR, RB1, and YAP1 , in both the LTL and LuCaP PDX models ( Fig. 1D-E ). Immunohistochemical (IHC) analysis on bioarchived formalin-fixed paraffin-embedded (FFPE) PC (GS 6-7) and NEPC tissues showed elevated NUAK2 expression in NEPC compared to PC (Fig.C1F). Quantitative assessment demonstrated that NUAK2 H-scores were significantly elevated in NEPC specimens compared to PC (Fig.C1G). Notably, NUAK2 exhibited predominant nuclear localization in NEPC tissues, in contrast to PC ( Fig. 1H ). In commonly used PC cell line models, mRNA analysis showed elevated NUAK2 levels in CRPC, DNPC or NEPC models compared to adenocarcinoma counterparts ( Fig. 1I ). Compared to the adenocarcinoma PDX model LTL-484, immunoblot analysis revealed elevated NUAK2 expression in the NEPC PDXs LTL-352 and LTL-545 ( Fig. 1J ). As anticipated, NEPC PDXs exhibited loss of AR and downregulation of YAP1, along with upregulation of NEPC markers NKX2-1 and SYP ( Fig. 1J ). Collectively, these findings indicate that NUAK2 expression progressively increases during PC progression and is markedly upregulated in NEPC. Functional characterization of NUAK2 reveals Its oncogenic role in NEPC To investigate the functional role of NUAK2 in NEPC, we generated three stable cell lines with doxycycline-inducible NUAK2 knockdown (NCI-H660, PARCB-1, and the DNPC cell line DU-145 cells), as well as inducible CRISPR/Cas9-mediated NUAK2 knockout systems in NCI-H660 and PARCB-1 cells ( Fig. 2A , and Fig. S2A-B). Genetic depletion of NUAK2 significantly suppressed cell proliferation across all three cell lines ( Fig. 2B–D ). Consistently, inducible CRISPR-mediated knockout of NUAK2 in NCI-H660 and PARCB-1 cells resulted in a marked reduction in cell proliferation (Fig. S2C-D). In DU-145 cells, NUAK2 depletion markedly suppressed spheroid growth kinetics and clonogenic potential ( Fig. 2E-H ). Download figure Open in new tab Figure 2. NUAK2 depletion inhibits NEPC cell proliferation and tumor growth in vivo (A) Representative Immunoblot analysis of doxycycline-inducible NUAK2 knockdown in stable NCI-H660, PARCB-1, and DU-145 treated with doxycycline for 72h. (B-D) Cell proliferation assays for NCI-H660 (B) and PARCB-1 (C), and cell confluence assay for DU-145 cells (D) following doxycycline-induced NUAK2 knockdown with two independent shRNAs (shNUAK2-1 and shNUAK2-2) compared to a non-targeting control (shNT). Data are presented as mean ± SD. (E-F) Representative images (E) and quantification (F) of spheroid growth kinetics in DU-145 cells after NUAK2 knockdown versus shNT control upon doxycycline treatment. Data represent mean ± SEM, n = 6-7 biological replicates. (G-H) Representative images (G) and quantification (H) of colony formation assay of DU-145 cells with either shNT or NUAK2 knockdown upon doxycycline treatment. Data showing mean ± SD of n = 10 biological replicates. (I, K) Tumor growth kinetics and representative endpoint images of NCI-H660 (I) and PARCB-1 (K) xenografts in male NSG mice. Xenografts stably expressing shNT, shNUAK2-1, or shNUAK2-2 following doxycycline induction. Data are means ± SEM. (J, L) Endpoint tumor weight quantification of NCI-H660 (J) and PARCB-1 (L) xenografts from (I) and (K). Data represent mean ± SD.; n = 6–8 mice per group. (M, N) Growth kinetics (M) and endpoint tumor weights (N) for NCI-H660 xenografts expressing inducible sgNT, sgNUAK2-1, or sgNUAK2-2 after doxycycline induction in male NSG mice. Data shown as mean ± SEM; n = 7–8 mice per group. (O) Immunoblot analysis of individual NCI-H660 xenograft tumors expressing inducible sgNT, sgNUAK2-1 or sgNUAK2-2 constructs to confirm NUAK2 knockout in vivo . For statistical analysis, one-way ANOVA with Tukey’s multiple-comparison test (B, C, H, J, L and N) and two-way ANOVA and Tukey’s post hoc test (D, F, I, K and M), ****P < 0.0001, ***P < 0.001, **P < 0.01, *P < 0.05. To determine if NUAK2 is essential for NEPC tumor growth in vivo , we implanted NCI-H660 and PARCB-1 cells stably expressing inducible shRNA (shNT, shNUAK2-1, and shNUAK2-2) into male NSG mice. In both xenograft models, NUAK2 knockdown led to a pronounced suppression of tumor growth kinetics and significantly reduced tumor weights at endpoint ( Fig. 2I-L ). Consistently, CRISPR/Cas9-mediated NUAK2 knockout in NCI-H660 xenografts markedly impaired tumor growth, resulting in over a 50% reduction in tumor volume compared with controls and significantly lower tumor weights at endpoint, despite incomplete knockout efficiency ( Fig. 2M–O ). To further define the functional role of NUAK2, we ectopically expressed an empty vector (pLX-304), wild-type NUAK2 (pLX-NUAK2), or NUAK2 kinase-dead (NUAK2 K81R mutant, pLX-NUAK2-KD) in NCI-H660, PC-3, and DU-145 cells ( Fig. 3A , Fig. S3A). Overexpression of wild-type NUAK2 significantly enhanced cell proliferation in the NEPC cell line NCI-H660, as well as in the DNPC cell lines PC-3 and DU145 ( Fig. 3B–C ; Fig. S3B). In contrast, pLX-NUAK2-KD failed to promote proliferation in most contexts, although a modest increase was observed in PC-3 cells, suggesting that the proliferative effects of NUAK2 are largely kinase-dependent, with potential cell type–specific contributions in PC-3 cells. Clonogenic assays further demonstrated that wild-type NUAK2 robustly increased colony-forming capacity of DU-145 and PC-3 cells, whereas expression of pLX-NUAK2-KD markedly impaired clonogenicity compared to controls ( Fig. 3D-E , Fig. S3C-D). Extending these findings in vivo , subcutaneous implantation of NCI-H660 cells expressing pLX-NUAK2 led to accelerated tumor growth, with tumor volumes increasing by 43% and 30% compared to empty vector and pLX-NUAK2-KD groups respectively ( Fig 3F ). Correspondingly, endpoint tumor weights were higher in the pLX-NUAK2 group relative to both control and pLX-NUAK2-KD groups ( Fig. 3G ). Download figure Open in new tab Figure 3. NUAK2 overexpression enhance NEPC growth in a kinase dependent manner (A) Validation immunoblot analysis of NUAK2 expression in NCI-H660 and PC-3 cells following ectopic expression of empty vector control (pLX304), wild-type NUAK2 (pLX-NUAK2), or kinase-dead NUAK2 (pLX-NUAK2-KD). (B–C) Proliferation assays of (B) NCI-H660 and (C) PC-3 cells stably expressing pLX304, pLX-NUAK2, or pLX-NUAK2-KD. Data are shown as mean ± SD. (D–E) Representative images (D) and quantification (E) of colony formation assay in PC-3 cells expressing pLX304, pLX-NUAK2, or pLX-NUAK2-KD. Data represent mean ± SD. (F–G) Tumor growth kinetics (F) and endpoint tumor weights (G) of NCI-H660 xenografts stably expressing pLX304, pLX-NUAK2, or pLX-NUAK2-KD. Data represent mean ± SEM for n = 6 mice per group. (H–I) Representative bioluminescence images (H) and quantification (I) of primary tumor burden in PC-3_Luc cells expressing pLX304, pLX-NUAK2, or pLX-NUAK2-KD. Data represent mean ± SEM, pLX304 n = 8, pLX-NUAK2 n = 9, and pLX-NUAK2-KD n = 8. (J) Quantification of prostate wet weights at endpoint from PC-3_Luc tumor-bearing mice expressing pLX304, pLX-NUAK2, or pLX-NUAK2-KD. pLX304 n = 7, pLX-NUAK2 n = 9, and pLX-NUAK2-KD n = 7. (K) Representative hematoxylin and eosin (H&E), human-specific Ki67 and NUAK2 immunohistochemistry (IHC) images of metastatic tumor foci in mouse livers. (L) Quantification of metastatic tumor burden based on Ki67-positive area. pLX304 n = 7, pLX-NUAK2 n = 6, or pLX-NUAK2-KD n = 8. Statistical analyses were performed using one-way ANOVA with Tukey’s multiple-comparison test for panels (B, C, E, and G), two-way ANOVA for panels (F, and I) and Kruskal-Wallis test with Dunn’s multiple-comparison test for panel (J and L). To further assess the role of NUAK2 in promoting tumor growth and metastatic dissemination, we employed the PC-3-Luc intraprostatic orthotopic xenograft model. Bioluminescence imaging and quantitative analyses revealed that tumors expressing wild-type NUAK2, as well as those in the control group, exhibited significantly greater tumor burden compared to the pLX-NUAK2-KD group, whereas no significant difference was observed between the wild-type and control groups ( Fig. 3H–I ). Endpoint analysis of prostate tumor wet weights demonstrated a significant increase in tumor mass in the wild-type NUAK2 group relative to both the control and pLX-NUAK2-KD groups ( Fig. 3J ). Histological analyses, including H&E staining and immunohistochemistry for NUAK2 and human-specific Ki67, confirmed the presence of metastatic foci in the livers of mice across all three groups. Although the NUAK2 wild-type group exhibited a higher number of hepatic lesions compared to control, this increase did not reach statistical significance. In contrast, NUAK2-KD tumors showed a significant reduction in both the number of liver metastases and overall tumor proliferation, as indicated by Ki67 staining ( Fig. 3K–L ). Collectively, these results establish NUAK2 as a central driver of tumor growth and metastatic progression in NEPC and DNPC models, with its kinase activity governing tumor growth, clonogenicity, and dissemination. These findings nominate NUAK2 as a compelling therapeutic target in aggressive prostate cancer. NUAK2 inhibition with preclinical and FDA-approved compounds suppresses tumor growth and enhances chemotherapy response To evaluate the therapeutic potential of NUAK2, we first examined the efficacy of the semi-selective NUAK2 inhibitor HTH-02-006 in vitro ( 24 ). Treatment with HTH-02-006 induced robust growth inhibition in NCI-H660, PARCB-1, and DU-145 cells ( Fig. 4A ). In DU145-RFP spheroid assays, HTH-02-006 significantly suppressed spheroid growth and dose-dependently reduced clonogenic potential of DU-145 cells ( Fig. 4B–C ; Fig. S4A–B). Download figure Open in new tab Figure 4. Repurposing G1T-28 for selective NUAK2 inhibition (A) Dose response curves of NCI-H660, PARCB-1, and DU-145 upon treatment with the NUAK2 inhibitor HTH-02-006. Data represent mean ± SD from three independent biological replicates. (B–C) Representative images (B) and quantification (C) of spheroid growth kinetics in DU-145-RFP cells following HTH-02-006 treatment. Data are shown as mean ± SEM from three biological replicates; significance assessed by two-way ANOVA; P < 0.001. (D) Dose response of NCI-H660, PARCB-1, and DU-145 following treatment with the repurposed NUAK2 inhibitor G1T-28. Data represent mean ± SD from three biological replicates. (E–F) Representative images (E) and quantification (F) of spheroid growth kinetics in DU-145-RFP cells treated with G1T-28. Data represent mean ± SEM from three biological replicates; significance determined by two-way ANOVA; P < 0.001. (G–H) Representative images (G) and quantification (H) of DU-145 colony formation assay after G1T-28 treatment. Data represent mean ± SD from three biological replicates. (I) Dose response curves of NCI-H660, PARCB-1, and DU-145 upon treatment with ribociclib. Data represent mean ± SD from three independent biological replicates. (J) NanoBRET intracellular kinase assay demonstrating dose-dependent displacement of a tracer from NUAK2-NanoLuc fusion protein by G1T-28, indicating direct intracellular target engagement. No displacement was observed for NUAK1-NanoLuc. (K–M) Cellular thermal shift assay (CETSA) assessing NUAK2 stability in DU-145 cells treated with DMSO, HTH-02-006, or G1T-28. Immunoblot analysis of cell lysates following CETSA (K), NUAK2 melting curves after treatment with (L) HTH-02-006 and (M) G1T-28. (N) Schematic overview of the multiplexed-inhibitor bead (MIB) competition assay workflow performed on PARCB-1 cell lysates coupled with mass spectrometry analysis. Schematic was generated using canva online tool. (O) Log2FC dose-dependent displacement of NUAK2 from MIBs by G1T-28 at 100 nM, 1 μM, and 10 μM, demonstrating high-affinity and selective target engagement. IC 50 and melting temperature were calculated using non-linear regression (curve fit) for panels (A, D, I, J, L and M). Statistical analyses were performed using two-way ANOVA for panels (C), and (F); one-way ANOVA for (H). To identify FDA-approved inhibitors with reported off-target activity against NUAK2, we queried the Pharos database, aiming to repurpose clinically available compounds for rapid therapeutic evaluation in NEPC (Fig. S3C) ( 21 ). Among the identified candidates, the CDK4/6 inhibitors trilaciclib (G1T-28) and lerociclib (G1T-38) demonstrated high binding affinity and notable selectivity for NUAK2, as confirmed by KINOMEscan (DiscoveRx) profiling, which revealed >90% inhibition of NUAK2 at 100 nM concentration (Fig. S3D-E) ( 52 , 53 ). We prioritized G1T-28 given its FDA approval, although G1T-38 remains under clinical development. In NCI-H660, PARCB-1, and DU145 cells, G1T-28 elicited a dose-dependent suppression of cell proliferation despite RB deficiency ( Fig. 4D ) and markedly impaired DU145-RFP spheroid growth in vitro ( Fig. 4E–F ). G1T-28 treatment also significantly reduced colony-forming capacity of DU-145 cells ( Fig. 4G–H ). In contrast, ribociclib, a selective CDK4/6 inhibitor with minimal NUAK2 activity, did not suppress growth in NEPC or DU-145 cells at lower doses, consistent with their RB-deficient status and intrinsic resistance to CDK4/6 inhibition ( Fig. 4I ). Comparable effects on cell viability and clonogenicity were observed with G1T-38 (Fig. S4F–H), supporting this inhibitor class as a potential strategy for targeting NUAK2. To confirm intracellular engagement of G1T-28 with NUAK2, we performed a NanoBRET kinase assay using a NUAK2-NanoLuc fusion construct. G1T-28 efficiently displaced the NanoBRET tracer, with an estimated IC 50 of 3.14uM, while showing no detectable activity against NUAK1–NanoLuc ( Fig. 4J ). Next, we conducted a cellular thermal shift assay (CETSA), using the established NUAK2 inhibitor HTH-02-006 as a positive control and G1T-28 ( Fig. 4K–M ). HTH-02-006 induced thermal stabilization of NUAK2, reflected by a ΔTm (change in melting temperature) of 2.56°C, consistent with intracellular target engagement. Similarly, treatment with G1T-28 resulted in a ΔTm of 2.01°C, confirming direct binding and thermal stabilization of endogenous NUAK2 in cells. We further performed a multiplexed inhibitor bead (MIB) competition assay coupled with mass spectrometry using PARCB-1 NEPC cell lysates to identify the cellular binding targets of G1T-28. ( Fig. 4N ). In control samples, MIBs loaded with broad-spectrum kinase inhibitors bound 412 kinases from NEPC cell lysates (Table S1). Compared to DMSO, G1T-28 resulted in dose-dependent competitive displacement of NUAK2 from the beads, with log fold changes (LogFC) of –1.65, –2.36, and –2.98 at 100 nM, 1 μM, and 10 μM, respectively, indicating high-affinity engagement ( Fig. 4O ). Consistent with NanoBRET results, no engagement of G1T-28 with NUAK1 was detected in the MIB assay. G1T-28 exhibited remarkable selectivity, displacing only 9 kinases with ≥1 –LogFC at the 100 nM concentration. Notably, although CDK4/6 kinases in NEPC lysates were bound by MIBs in control samples, they were not displaced by G1T-28. However, in this assay G1T-28 consistently engaged other reported off-target kinases, including PRKD1/2/3, GAK, and SLK (Fig. S4I). Together, these findings confirm NUAK2 is a direct target of G1T-28. To determine whether NUAK2 inhibition confers therapeutic benefit in vivo, we tested both the preclinical NUAK2 inhibitor HTH-02-006 and the FDA-approved compound G1T-28 across three preclinical NEPC xenograft models (Fig. S5A). In NEPC cell line-derived xenografts (CDXs), HTH-02-006 treatment significantly inhibited tumor growth compared to vehicle controls ( Fig. 5A-D ). Notably, in NCI-H660 and PARCB-1 xenografts, HTH-02-006 delayed tumor growth kinetics and resulted in significantly reduced tumor weights at the study endpoint ( Fig. 5B, D ). Consistent with results observed in NEPC CDX models, HTH-02-006 significantly suppressed tumor growth in LTL-352 PDX tumors ( Fig. 5E-F ). HTH-02-006 was well tolerated, with no significant changes in body weight observed over the course of treatment (Fig. S5B). Download figure Open in new tab Figure 5. Pharmacologic inhibition of NUAK2 suppresses tumor growth and potentiates chemotherapy response in NEPC models (A–D) Longitudinal tumor volume measurements and endpoint tumor weight quantifications in NCI-H660 (A-B) and PARCB-1 (C-D) CDX models treated with the NUAK2 inhibitor HTH-02-006 (5mpk or 10mpk) or vehicle control. (E-F) Tumor growth kinetics and endpoint tumor weights in the LTL-352 PDX model following treatment with HTH-02-006 (10mpk) or vehicle. (G–J) Longitudinal tumor volume kinetics and endpoint tumor weights in NCI-H660 (G-H) and PARCB-1 (I-J) CDX models treated with G1T-28 (50mpk or 100mpk) or vehicle. (K–L) Tumor growth kinetics and endpoint tumor weights in the LTL-352 PDX model treated with G1T-28 (100mpk) or vehicle. (M-O) Longitudinal tumor growth kinetics (M), endpoint tumor weights (N), and representative image of NCI-H660 CDX models treated with vehicle, G1T-28 (50mpk), carboplatin (40mpk), or combination of carboplatin plus G1T-28. (P-R) Longitudinal tumor volume kinetics (P), endpoint tumor weights and (R) representative image of PARCB-1 CDX models treated with vehicle, G1T-28 (50mpk), carboplatin (20mpk), or combination of carboplatin plus G1T-28. mpk = mg/kg. Statistical analyses were performed using two-way ANOVA for panels for A, C, E, G, I, K, M and P, and one-way ANOVA for panels B, D, F, H, J, L, N and Q. In NCI-H660 xenografts, G1T-28 treatment significantly reduced tumor volume, with vehicle-treated tumors reaching a mean volume of 1607.7 mm³, compared to 931.4 mm³ and 990.6 mm³ in the 50 mg/kg and 100 mg/kg G1T-28 groups, respectively. PARCB-1 xenografts exhibited even greater sensitivity, showing >75% tumor growth inhibition in treated groups relative to controls ( Fig. 5G–J ). Endpoint tumor weights from both CDX studies further confirmed a marked reduction in tumor burden in G1T-28–treated mice relative to vehicle controls ( Fig. 5H, J ). Consistent effects were observed in the LTL-352 PDX model, where G1T-28 administration completely suppressed tumor growth ( Fig. 5K–L ). G1T-28 was well tolerated, as indicated by stable body weights throughout treatment (Fig. S5C). Given the strong antitumor activity of G1T-28 monotherapy, we next evaluated its efficacy in combination with carboplatin. G1T-28 synergized robustly with carboplatin, significantly enhancing growth inhibition relative to either agent alone, with mean Bliss synergy scores of 12.5 in DU-145 and 10.89 in NCI-H660 cells (Fig. S5D–G). Similar synergistic effects were observed with G1T-28 plus docetaxel in both cell lines (Fig. S5H–K). To validate these interactions in vivo, we performed a four-arm xenograft study comparing vehicle, G1T-28, carboplatin, and the combination. The G1T-28 + carboplatin combination treatment resulted in complete reduction in tumor growth and endpoint tumor weights compared to either monotherapy or control. Specifically, G1T-28 alone reduced tumor growth by 51.3%, carboplatin by 72.3%, while the combination achieved a 91.9% reduction ( Fig. 5M–O ). Consistent results were observed in the PARCB-1 xenograft model, where the combination treatment resulted in increased tumor growth inhibition relative to single-agent treatments ( Fig. 5P–R ). Notably, PARCB-1 tumors displayed greater sensitivity to G1T-28 than NCI-H660 xenografts, mirroring earlier in vivo results ( Fig. 5G–J ). Collectively, these findings demonstrate that pharmacological NUAK2 inhibition, alone or in combination with standard chemotherapies, potently suppresses NEPC tumor growth in preclinical models. NUAK2 phospho-proteomic and proximity ligation highlight role in pre-mRNA splicing To elucidate the molecular mechanisms underlying NUAK2 function in NEPC, we combined differential label-free phosphoproteomic profiling with a biotin proximity–ligation (mini-turbo-BioID)–based NUAK2 interactome analysis ( Fig. 6A ). Phosphoproteomics profiling was conducted in NCI-H660 cells following 1h treatment with DMSO, G1T-28 (5 mM) or HTH-02-006 (5 mM). Comparative analysis demonstrated substantial concordance in the phospho-proteomic profiles between G1T-28 and HTH-02-006–treated cells, indicating NUAK2-specific effects ( Fig. 6B-C , Table S2). Enrichment analysis of differentially phosphorylated peptides (ΔPPs; |Log₂FC| ± 5) upon NUAK2 inhibition identified significant enrichment of pathways related to chromatin organization, RNA metabolism, DNA damage response, cell cycle regulation, and neurodevelopmental processes ( Fig. 6D-E ). Download figure Open in new tab Figure 6. Mapping the NUAK2 substrate and interactome landscape uncovers links to RNA metabolism and splicing. (A) Schematic of the experimental strategy integrating label-free phosphoproteomic profiling with miniTurbo-based BioID proximity labeling to investigate NUAK2 function in NEPC. (B, C) Volcano plots showing differential phosphopeptides in NCI-H660 cells treated with the NUAK2 inhibitors HTH-02-006 or G1T-28 compared with DMSO. (D, E) Enrichment analysis of overlapping differentially phosphorylated peptides (DPP) with significantly altered phosphorylation (|log₂FC| ≥ 5) in G1T-28- and HTH-02-006–treated cells relative to DMSO. (F) Bubble plot showing proteins significantly biotinylated by mTb-NUAK2 compared to control(v5-mTb-HcRed). Each bubble represents a distinct protein, with the size of the bubble corresponding to the extent of biotinylation enrichment (number of peptides). (G) Enrichment analysis of proteins identified by NUAK2-dependent biotin labeling reveals significant enrichment of RNA metabolism, RNA splicing, and chromatin remodeling pathways. Enrichment significance determined by hypergeometric test; FDR C<C0.05 considered significant. (H) Venn diagram depicting overlap of proteins differentially phosphorylated (≤ –5 Log2FC, p = 0.01) and significantly biotinylated (≥ 2-fold, p = 0.01) relative to control, identifying 273 candidate NUAK2-associated substrate proteins. (I) Gene set enrichment analysis of overlapping proteins highlights mRNA metabolic processes, cytoskeletal organization, mRNA transport, and chromosome organization. (J) Protein–protein interaction network of the 273 overlapping genes constructed from STRING ( 54 ), BioGrid ( 55 ), OmniPath ( 56 ), and InWeb_IM ( 57 ) databases, restricted to experimentally validated interactions and analyzed using Molecular Complex Detection (MCODE) to identify densely connected sub-networks and visualized in Cytoscape. (K) Immunoblot validation of NUAK2 proximity interactions in NCI-H660 cells expressing mTb-NUAK2 or mTb-HcRed controls. Cells were treated with 0.5 mM biotin for 4 hr, followed by streptavidin affinity pulldown to enrich biotinylated proteins. (L-M) Representative immunoblots of co-immunoprecipitations (Co-IP) validating the association of endogenous NUAK2 with core splicing factors in DU-145 and NCI-H660 cells. Schematic in (A) was generated using BioRender. To identify NUAK2 interacting proteins, we employed the miniTurbo (V5-mTb) proximity labeling system (Fig. S6A-B). Optimization of biotin labeling in NCI-H660 cells stably expressing NUAK2–V5-mTb or HcRed–V5-mTb (control) indicated that a 4 h labeling period was sufficient to achieve maximal proximity biotinylation (Fig. S6C). Based on these results, streptavidin-based enrichment followed by LC-MS/MS was performed to define the NUAK2 proximal proteome (Fig. S6D). We identified 1414 proteins significantly biotinylated by NUAK2-v5-mTb with a fold change of ≥ 2, p < 0.01, compared to the HcRed-v5-mTb control ( Fig. 6F ). Enrichment analysis of the biotinylated proteins showed significant enrichment in RNA splicing, chromatin remodeling, and RNA processing pathways ( Fig. 6G ). To prioritize candidate NUAK2-binding proteins and kinase-substrates, we integrated the phosphoproteomic and proximity-labeling datasets, focusing on proteins that were differentially phosphorylated (fold change ≤ −5 Log₂FC, p ≤ 0.01) and biotinylated (fold change ≥ 2 Log₂FC, p ≤ 0.01). Applying these stringent criteria, we identified 273 overlapping proteins that exhibited both reduced phosphorylation and direct biotinylation by NUAK2 ( Fig. 6H , Table. S4). Enrichment analysis of these prioritized NUAK2 interactors/substrates revealed strong overrepresentation of pathways related to mRNA metabolism, regulation of mRNA processing, cytoskeletal organization, and mRNA transport ( Fig. 6I ). Remarkably, over 100 of the overlapping proteins were linked to mRNA processing, RNA splicing, transesterification-based splicing, spliceosome function, and protein–RNA complex organization. To delineate functional relationships among enriched terms, a representative subset of up to 15 terms per cluster (250 total) was visualized using Cytoscape v5, with edges connecting terms sharing a similarity score >0.3. The resulting network revealed distinct clusters associated with RNA metabolism, RNA splicing, transcription, and protein localization/folding (Fig. S6E). Notably, several spliceosome-associated factors, including SF1, SRRM2, RBM15, SNW1, and U2AF2, were among the top candidates, showing significant differential phosphorylation and interaction with NUAK2 (Fig. S6F–G). We further performed protein–protein interaction (PPI) enrichment analysis on the 273 overlapping genes using the Metascape platform, integrating curated databases including STRING ( 54 ), BioGrid ( 55 ), OmniPath ( 56 ), and InWeb_IM ( 57 ). To ensure data robustness, only experimentally validated physical interactions from STRING ( 54 ), and BioGrid ( 55 ) were included. The resulting PPI network was analyzed using the Molecular Complex Detection (MCODE) algorithm to identify densely connected sub-networks, which were subsequently visualized with Cytoscape ( Fig. 6J ). Consistent with gene ontology findings, the integrated PPI network analysis highlighted a central role for NUAK2 in coordinating mRNA metabolism, with predominant focus on mRNA processing and RNA splicing pathways. Building on our integrated omics analyses that highlighted NUAK2’s interaction with and regulation of RNA splicing factors, we next examined the association of NUAK2 with core spliceosome components by proximity-immunoblot labeling assay. Streptavidin affinity pulldown of NCI-H660 cells stably expressing NUAK2–V5–mTb or HcRed–V5–mTb following 4 h biotin treatment revealed robust biotinylation and confirmed proximity of NUAK2 with key splicing factors, including SF3B1, CDC40 (PRP17), SNW1, RBM15, and RBM17 ( Fig. 6K ). To further substantiate these findings, endogenous NUAK2 was immunoprecipitated from both NCI-H660 and DU-145 cells, followed by immunoblot analysis, which confirmed physical interactions with core spliceosome proteins SF1, SF3B1, and CDC40 ( Fig. 6 L-M). Together, these results establish a direct association between NUAK2 and the spliceosome machinery, supporting a pivotal role for NUAK2 in RNA splicing and processing. NUAK2 inhibition impairs pre-mRNA splicing Transcriptome-wide RNA sequencing (RNA-seq) of NCI-H660 cells treated with G1T-28 or DMSO for 24h resulted in the significant differential expression of genes with 3611 genes upregulated and 1907 genes downregulated (±Log₂FCC2, p≤C0.01) ( Fig. 7A , Table S5). Gene set enrichment analysis (GSEA) identified pathways related to RNA splicing, oxidative phosphorylation, morphogenesis, regulation of neuron differentiation and DNA methylation as significantly affected by NUAK2 inhibition ( Fig. 7B-C ). Download figure Open in new tab Figure 7. NUAK2 inhibition induces widespread pre-mRNA splicing alterations in NEPC. (A) Volcano plot of differentially expressed genes in NCI-H660 cells treated with G1T-28 (5 µM), or DMSO for 24 hr. (B, C) Gene set enrichment analysis (GSEA) of differentially expressed genes in NCI-H660 cells following 24Ch G1T-28 treatment, showing significantly downregulated pathways (B) and upregulated pathways (C). (D) Box plots of spliced-to-total read ratios stratified by intron position (first, middle, last) in RNA-seq of NCI-H660 cells treated with DMSO or G1T-28 for 24Ch, across 8,518 isoforms. Statistical significance was assessed by Wilcoxon signed-rank test (P < 2.2 × 10⁻¹C). (E) Summary pie chart of total differential splicing events identified by rMATS-turbo in the NCI-H660 cells treated with DMSO vs G1T-28 after filtering out events supported by <10 reads in either sample group, as well as events with IncLevel Difference (average IncLevel of DMSO) – (average IncLevel of G1T-28) ±0.3. (F) Gene set enrichment analysis (GSEA) of genes exhibiting splicing alterations upon G1T-28 treatment in NCI-H660 cells, as identified by rMATS. (G-H) Correlation between TTK and NUAK2 expression (z-scores) in Beltran et al, 2016 (G) and Labrecque et al, 2019 (H) datasets. (I) Correlation between EZH2 and NUAK2 expression (z-scores) in Beltran et al, 2016 dataset. (J) Correlation between EZH2 signature scores and NUAK2 expression (z-scores) in Beltran et al, dataset. (K-L) Integrative Genomics Viewer (IGV) tracks showing RNA-seq read coverage across the TTK and EZH2 locus in NCI-H660 cells treated with G1T-28 or DMSO (control) for 24Ch. Building on our observation that NUAK2 associates with key RNA splicing factors, we examined RNA-seq data for global splicing alterations upon NUAK2 perturbation, as described previously ( 47 ). Calculation of splicing ratios for 5′ and 3′ splice sites revealed a marked accumulation of unspliced intronic reads across the transcriptome upon NUAK2 inhibition, particularly when introns were stratified by position within transcripts (first, middle, or last) (Wilcoxon signed-rank test, p < 2.2 × 10⁻¹C) ( Fig. 7D , Fig. S7B). These results demonstrate that G1T-28–mediated NUAK2 inhibition globally impairs pre-mRNA splicing efficiency. Replicate Multivariate Analysis of Transcript Splicing (rMATS) of RNA-seq data revealed widespread and statistically significant alterations in pre-mRNA splicing patterns upon NUAK2 inhibition (Fig. S7C). Substantial changes were observed across all major splicing categories, including alternative 5′ splice sites (A5SS) and alternative 3′ splice sites (A3SS), skipped exons (SE), mutually exclusive exons (MXE), and intron retention (IR) events (Fig. S7C, Table S6). Using a cutoff of |ΔIncLevel| ± 0.3, we identified 19,966 events between DMSO- and G1T-28–treated samples, with the majority corresponding to SE events (76.3%), followed by MXE (10.45%) and IR events (5.01%) ( Fig. 7E ). Gene Ontology (GO) enrichment analysis of genes exhibiting NUAK2-dependent splicing alterations revealed overrepresentation of biological processes related to mitotic cell cycle progression, DNA repair, microtubule cytoskeleton organization, RNA splicing, and DNA metabolic processes ( Fig. 7F ). Complementary KEGG pathway analysis highlighted enrichment in pathways associated with neurodegeneration, cell cycle regulation, and the mRNA surveillance pathway (Fig. S7D). GO enrichment of molecular functions (MF) among genes with NUAK2-dependent splicing alterations highlighted catalytic activity on nucleic acids, ribonucleoside triphosphate phosphatase activity, kinase activity, chromosome segregation, and chromatin binding (Fig. S7E). Together, these results indicate that NUAK2 inhibition induces global splicing defects, predominantly affecting genes essential for cell division, chromatin regulation, and RNA metabolism, thereby linking its splicing role to transcriptional control and oncogenic signaling. NUAK2 inhibition results in intron retention and skipped exon in EZH2 and TTK Building on our transcriptome-wide analyses and global splicing assessment, we examined genes with NUAK2-dependent splicing alterations that were enriched in GO and KEGG pathways related to cell cycle progression, mitosis, and chromatin modification—processes central to tumor growth and lineage plasticity. Within these functionally enriched categories, we prioritized TTK and EZH2 for validation as key nodes. TTK, a mitotic checkpoint kinase, was among the top genes in the “mitotic cell cycle” and “spindle organization” terms, whereas EZH2, a core histone methyltransferase of the PRC2 complex, was enriched in “chromatin modification” and “epigenetic regulation of gene expression.” Both genes are strongly implicated in cancer progression and therapy resistance, with EZH2 particularly relevant to NEPC development ( 58 , 59 ). Supporting these findings, transcriptomic correlation analyses across independent patient-derived NEPC datasets revealed strong positive associations between NUAK2 expression and both EZH2 and TTK mRNA levels. In the Beltran and Labrecque cohorts, NUAK2 expression correlated tightly with TTK (R² = 0.709 and 0.63, p < 0.05) and EZH2 (R² = 0.762 and 0.66, p < 0.05) ( Fig. 7G–I , Fig. S7F) ( 9 ) ( 34 ). Moreover, EZH2 activity scores were positively correlated with NUAK2 expression in both Beltran and Labrecque cohorts (R² = 0.684 and 0.593; p < 0.05; Fig. 7J , Fig. S7G) ( 59 ). Manual inspection of TTK and EZH2 transcripts in the Integrative Genomics Viewer (IGV) confirmed the splicing alterations including retention of introns alongside exon skipping events upon NUAK2 inhibition with G1T-28 ( Fig. 7K-L ). Consistent with the global splicing alterations identified by rMATS, these transcript-level analyses confirmed that NUAK2 inhibition disrupts exon inclusion and intron removal in key oncogenic targets. Using Sashimi plots to visualize and quantify splicing changes, NUAK2 inhibition led to pronounced exon 15 skipping in TTK transcripts (average IncLevel = 0.17) compared with vehicle control (average IncLevel = 1.00) ( Fig. 8A ). Similarly, EZH2 transcripts exhibited significant exon 8 skipping (average IncLevel = 0.22 vs. 1.00 in control) accompanied by retention of intron 10 ( Fig. 8B–C ). Download figure Open in new tab Figure 8. NUAK2 inhibition disrupts pre-mRNA splicing of oncogenic targets EZH2 and TTK. (A-C) Sashimi plots illustrating splicing abnormalities in key regulatory transcripts TTK (exon 15 skipping) and EZH2 (exon 8 skipping and intron 10 retention) following NUAK2 inhibition with G1T-28. (D) RT–PCR validation of splicing alterations in NCI-H660 cells treated with G1T-28 or doxycycline for NUAK2 knockdown, confirming exon-skipping and intron-retention events in TTK and EZH2 observed in RNA-seq analyses. (E-F) Immunoblot analysis of NCI-H660 (E) and PARCB1 (F) cells treated with G1T-28 with indicated doses for 96h and 72 h respectively and probed for TTK and EZH2. (G) Immunoblot analysis of DU-145 cells with time course NUAK2 knockdown probed for EZH2, TTK and NUAK2. (H) Immunoblot analysis of DU-145 and NCI-H660 cells treated with G1T-28 (1 µM) at indicated time points. (I) Schematic model illustrating the proposed mechanism of NUAK2 function in NEPC, highlighting its interaction with spliceosome components, regulation of pre-mRNA splicing, and downstream effects on EZH2 and TTK proteins. Schematic was generated using canva online tool. RT-PCR analysis in NCI-H660 cells treated with G1T-28 validated the induction of alternative splicing events in TTK and EZH2 ( Fig. 8D ). To confirm that these splicing alterations were specifically mediated by NUAK2, we employed a TRE3G-inducible NUAK2 knockdown system in NCI-H660 cells. NUAK2 depletion recapitulated the splicing defects observed with G1T-28 treatment in both TTK and EZH2 pre-mRNAs, reinforcing a direct role for NUAK2 in regulating pre-mRNA splicing ( Fig. 8D ). Quantitative assessment of the RT-PCRs using the percent spliced in (PSI) metric for affected SE and RI events demonstrated significant increase (Fig. S8A). NUAK2 inhibition led to TTK intron inclusion with a PSI of 0.94, while NUAK2 knockdown showed a PSI of 0.88. Immunoblot analysis of NCI-H660, PARCB-1, and DU-145 cells treated with G1T-28 revealed a dose-dependent reduction in TTK and EZH2 protein levels, consistent with impaired transcript maturation and translation ( Fig. 8E–F , Fig. S8B). Similarly, time-course NUAK2 knockdown in DU-145 cells caused a progressive decrease in TTK and EZH2 protein levels, confirming that these effects are specifically mediated by NUAK2 ( Fig. 8G ). Concordant with reduced EZH2 levels, G1T-28 treatment (1 µM) in NCI-H660 and DU-145 cells led to a time-dependent decline in H3K27me3, a canonical marker of EZH2 enzymatic activity ( Fig. 8H , Fig. S8C). Collectively, these findings establish NUAK2 as a key regulator of pre-mRNA splicing and post-transcriptional control of critical oncogenic effectors, including TTK and EZH2, thereby linking NUAK2 activity to the maintenance of proliferative and epigenetic programs in AVPC. Discussion In recent years, the expanded use of 2 nd and 3 rd generation AR targeted therapies has contributed to the emergence of AR-independent mCRPC, encompassing aggressive variant prostate cancer (AVPC), including DNPC and NEPC phenotypes. Patients with the de novo NEPC often present with extensive metastatic disease and have a 5-year survival rate below 20%, whereas the more common treatment-emergent NEPC and DNPC phenotypes are associated with a median survival of less than one year, highlighting a major unmet clinical need ( 7 , 60 ). Currently, the primary treatment for NEPC and DNPC remains platinum-based doublet chemotherapy, which provides only short-lived responses of 6–12 months and is frequently accompanied by severe myelosuppression and reduced quality of life. Consequently, the prognosis for patients with DNPC and NEPC remains poor, underscoring the urgent need for novel, tractable molecular targets to enable more effective and durable therapeutic strategies ( 19 ). In this study, we identify the serine/threonine kinase NUAK2 as a clinically actionable target for NEPC and a subset of DNPCs. Genetic and pharmacological inhibition of NUAK2 suppressed proliferation, clonogenicity, and tumor growth, whereas NUAK2 overexpression promoted cell proliferation, colony formation and tumorigenicity in a kinase dependent manner. While emerging evidence implicates NUAK2 in multiple cancer types, it remains a relatively understudied kinase, with its direct substrates and interacting proteins poorly defined. Our integrated phospho-proteomic and proximity-labelling interactome analyses provide a foundation for identifying novel NUAK2 targets. Using this integrated approach, we identified over 250 proteins that are proximal to/interact with NUAK2 and are differentially phosphorylated, the majority of which have not been previously reported. These analyses revealed a direct connection between NUAK2 and spliceosome components (SF3B1, CDC40, and SF1) and widespread altered RNA splicing events upon NUAK2 genetic and pharmacological inhibition. Given the central role of aberrant splicing in cancer progression, therapeutic modulation of this process has garnered significant interest, with RNA splicing small-molecule inhibitors in clinical testing ( 61 , 62 ). Notably, cancers harboring mutations in splicing factors such as SF3B1, SRSF2, or U2AF1/2 have demonstrated vulnerability to these inhibitors ( 63 , 64 ). Targeting NUAK2 may represent a novel strategy to modulate RNA splicing in cancers with high NUAK2 expression that lack mutated or aberrantly expressed RNA splicing factors offering cancer-specific therapeutic opportunities. Using multiple independent approaches, our studies demonstrate that the FDA-approved CDK4/6 inhibitor G1T-28 (trilaciclib) directly targets NUAK2, providing a novel therapeutic avenue for RB1-deficient NEPC and DNPC, contexts resistant to classical CDK4/6 inhibitors ( 65 ). G1T-28 suppressed tumor growth in cell line- and patient-derived xenografts, both as a single agent and in combination with carboplatin, demonstrating additive anti-tumor effects. Clinically, G1T-28 is well tolerated in SCLC patients, mitigating chemotherapy-induced myelosuppression, and our preclinical studies indicate that daily dosing in NEPC models does not induce adverse effects, supporting its potential repurposing ( 66 ). Like SCLC, clinically NEPC is treated with platinum-based chemotherapy and G1T-28 could provide dual benefits by protecting bone marrow via CDK4 inhibition, while simultaneously suppressing tumor growth through NUAK2 inhibition. However, effective NEPC suppression may require daily dosing, raises potential concerns for hematologic toxicities, including neutropenia, a known side effect of chronic CDK4/6 inhibition ( 67 , 68 ). In our animal studies, daily G1T-28 administration for over 20 days did not affect blood counts, supporting a favorable hematologic profile (Fig. S5L). While we focused on G1T-28 due to its FDA approval, G1T-38 (lerociclib), which exhibits improved bioavailability, also potently inhibits NUAK2 and is approved in China for HR⁺/HER2⁻ breast cancer ( 69 ). Both compounds share the same core chemical scaffold, differing only by a single side chain functional group. Together, these compounds could be rapidly repurposed for NUAK2 inhibition or serve as scaffolds for developing optimized NUAK2-targeted therapies. Our study establishes NUAK2 as a therapeutic target, demonstrates its tractability with preclinical and FDA-approved inhibitors, and reveals its role in RNA splicing, yet several limitations and unanswered questions remain. The lack of phospho-specific antibodies limited detailed characterization of NUAK2-regulated spliceosome phosphorylation, and whether NUAK2 drives lineage plasticity and transdifferentiation—given its regulation of EZH2 pre-mRNA splicing, a key modulator of transdifferentiation—remain to be elucidated ( 70 ). These gaps highlight opportunities for future studies to deepen mechanistic insight and guide therapeutic exploitation. In conclusion, this study highlights NUAK2 as a clinically actionable molecular target in NEPC and other advanced mCRPC molecular subtypes. Lastly, while this study focused on NEPC/DNPC, data presented herein and previous reports ( 28 ), suggest that the therapeutic potential of NUAK2 may be applicable in a broader PC patient population. Nevertheless, the data presented provides a strong rationale for therapeutic NUAK2 inhibition DNPC and NEPC. Author contributions UM and EM wrote, revised, and edited the manuscript and performed most in vitro and in vivo experiments. SM and EC assisted with in vitro assays. Uran Mimekov and AM contributed to bioinformatics analyses. MP assisted with immunohistochemistry. IMO, SH, ZDG, and DHD contributed to in-cell NanoBRET and MIB assays. MW, MC, and YW assisted with the LTL-352 PDX model. AJA, JH, MC, and JWP provided input on study design and manuscript revisions. EM oversaw project administration. All authors reviewed and approved the manuscript. Declaration of interests A.J.A receives research support (to Duke) from the NIH/NCI, PCF, DOD, Astellas, Pfizer, Bayer, Janssen, BMS, AstraZeneca, Merck, Pathos, Amgen, Novartis. AJA declares consulting or advising relationships with Astellas, Pfizer, Bayer, Janssen, BMS, AstraZeneca, Merck, Exelixis, Novartis, Medscape, Telix, Duality Bio, MedIQ, IDEOlogy, Sumitomo, and Precede Bio. J.H is a consultant for or owns shares in the following companies: Artera, Kingmed Diagnostics, MoreHealth, OptraScan, York Biotechnology, Chimigen Bio, Sisu Pharma, ST N Technologies Limited and Sonablate Corp. Acknowledgements This work was supported by the DoD Prostate Cancer Research Program (PCRP) Grant #HT94252310125 to Everardo Macias and the Early Investigator Research Award #HT94252510622 to Umar Mehraj. We thank Erik Soderblom, the Duke Proteomics Core, Department of Pathology, the Duke Cancer Institute Shared Resources, and the Cancer Center Isolation Facility, which are supported in part by the NCI Cancer Center Support Grant #P30CA014236. We thank Aurora Cabrera, Scott Lyons, Laura E. Herring, and Lee M. Graves (UNC Lineberger, Department of Pharmacology) and the UNC Proteomics Core Facility for assistance with MIB/MS experiments. The UNC Proteomics Core is supported in part by the NCI Center Core Support Grant (2P30CA016086-45) to the UNC Lineberger Comprehensive Cancer Center. The Structural Genomics Consortium (SGC) is a registered charity (No. 1097737) supported by funding from Bayer AG, Boehringer Ingelheim, Bristol Myers Squibb, Genentech, Genome Canada through the Ontario Genomics Institute [OGI196], the EU/EFPIA/OICR/McGill/KTH/Diamond Innovative Medicines Initiative 2 Joint Undertaking [EUbOPEN grant 875510], Janssen, Merck KGaA (also known as EMD in Canada and the US), Pfizer, and Takeda. Funder Information Declared Congressionally Directed Medical Research Programs, https://ror.org/03g2zjp07 , HT94252310125 , HT94252510622 References 1. ↵ Sartor O , de Bono JS . Metastatic Prostate Cancer . N Engl J Med . 2018 ; 378 ( 7 ): 645 – 57 . OpenUrl CrossRef PubMed 2. ↵ Clarke NW , Armstrong AJ , Thiery-Vuillemin A , Oya M , Shore N , Loredo E , et al. Abiraterone and olaparib for metastatic castration-resistant prostate cancer . NEJM evidence . 2022 ; 1 ( 9 ): EVIDoa2200043 . OpenUrl 3. ↵ Rickman DS , Beltran H , Demichelis F , Rubin MA . 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Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following NUAK2 is a therapeutically tractable regulator of RNA splicing and tumor progression in neuroendocrine prostate cancer Message Subject (Your Name) has forwarded a page to you from bioRxiv Message Body (Your Name) thought you would like to see this page from the bioRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share NUAK2 is a therapeutically tractable regulator of RNA splicing and tumor progression in neuroendocrine prostate cancer Umar Mehraj , Uran Maimekov , Shaista Manzoor , Emily Cordova , Manasiben Patel , Ikeer Y. Mancera-Ortiz , Stefanie Howell , Zachary W. Davis-Gilbert , Mu-En Wang , Ming Chen , Jung Wook Park , Yuzhuo Wang , Andrew J. Armstrong , Jiaoti Huang , David H. Drewry , Antonina Mitrofanova , Everardo Macias bioRxiv 2025.11.12.687734; doi: https://doi.org/10.1101/2025.11.12.687734 Share This Article: Copy Citation Tools NUAK2 is a therapeutically tractable regulator of RNA splicing and tumor progression in neuroendocrine prostate cancer Umar Mehraj , Uran Maimekov , Shaista Manzoor , Emily Cordova , Manasiben Patel , Ikeer Y. Mancera-Ortiz , Stefanie Howell , Zachary W. Davis-Gilbert , Mu-En Wang , Ming Chen , Jung Wook Park , Yuzhuo Wang , Andrew J. Armstrong , Jiaoti Huang , David H. 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