Decoding MUC1 and AR axis in a radiation-induced neuroendocrine prostate cancer cell-subpopulation unveils novel therapeutic targets

Decoding MUC1 and AR axis in a radiation-induced neuroendocrine prostate cancer cell-subpopulation unveils novel therapeutic targets | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (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],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Decoding MUC1 and AR axis in a radiation-induced neuroendocrine prostate cancer cell-subpopulation unveils novel therapeutic targets Carmen Jerónimo, Catarina Macedo-Silva, Ângela Castro, Iris Carriço, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5614729/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 03 Jul, 2025 Read the published version in Cell Death Discovery → Version 1 posted You are reading this latest preprint version Abstract Despite initial efficacy of radiotherapy (RT), with or without concurrent androgen-deprivation, in prostate adenocarcinoma (PCa), neuroendocrine prostate cancer (NEPC) emerging from disease progression is a highly aggressive malignancy for which standard therapies are mostly ineffective. Although oncogenic MUC1-C is a leading driver of NEPC and of PCa lineage plasticity, its putative role in response to RT, including RT-induced neuroendocrine transdifferentiation (tNED), has not been explored. We thus aimed to explore the interplay between androgen receptor (AR) signaling and MUC1 in PCa progression to NEPC. Firstly, using a radioresistant PCa cell line (22Rv1-RR) we demonstrated that epigenetic suppression of AR signaling caused MUC1/MUC1-C upregulation, which seems to be activated through γSTAT3. MUC1 activation positively associated with increased expression of neuroendocrine-related markers, including CD56, chromogranin A, synaptophysin and INSM transcriptional repressor 1 (INSM1). In NEPC tissues and comparing to prostate adenocarcinoma, MUC1 was upregulated and negatively correlated with AR, which was suppressed. Finally, proteomic analyses revealed that MUC1 activation upon RT selective pressure led to acquisition of stemness features, induction of epithelial to mesenchymal transition, and enhancement of basal cell-like traits. Notably, MUC1 knockdown (KD) significantly boosted response to RT in both 22Rv1-RR and DU145 cell lines. Moreover, AR-induced overexpression in PC3 cell lines entailed MUC1 downregulation, resulting in attenuated neuroendocrine (NE) traits and radioresistance, as well as impaired cell migration and invasion capabilities. Collectively, these results highlight MUC1 as a promising radiosensitization target and may ultimately help overcome therapy resistance and NEPC progression. Health sciences/Diseases/Cancer/Tumour heterogeneity Biological sciences/Molecular biology/Epigenetics Biological sciences/Cell biology/Cell adhesion/Focal adhesion Prostate cancer radioresistance MUC1 neuroendocrine differentiation histone modification EMT Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Considering that nearly 90% of prostate cancers (PCa) are diagnosed as organ-confined disease, the long-standing challenge remains to overcome therapeutic resistance and disease progression to more, uncurable advanced stages. Although neuroendocrine prostate cancers (NEPC) represent a rare histological type, accounting for only 1% of all diagnosed PCa, focal treatment-induced neuroendocrine differentiation (NED) occurs in approximately 30% of advanced PCa ( 1 – 3 ). Likewise, de novo neuroendocrine transdifferentiation (tNED) represents a common progression route for PCa, consistently associated with resistance to androgen deprivation (ADT) and/or radiotherapy (RT) ( 4 , 5 ). NEPC encompasses traits such as (i) loss of androgen receptor (AR) axis accompanied by low or absent levels of prostate-specific antigen (PSA), (ii) heightened stemness and (iii) increased cell aggressiveness associated with epithelial-mesenchymal transition (EMT) induction ( 6 ). Tumors are complex ecosystems to which heterogeneity adds another level of challenge for dissection of the underlying mechanisms. As a transcription factor, AR plays pivotal roles in PCa growth and progression. Currently, neoadjuvant or concurrent ADT combined with RT is an established standard-of-care for intermediate/high risk organ-confined PCa patients ( 7 ). Despite the initial success of ADT in tumor reduction, PCa may rapidly relapse and adapt to progress for more advanced stages ( 8 , 9 ). The role of AR in PCa radioresistance is controversial and little has been explored in this field. AR has been shown to exhibit contrasting biological functions, initially acting as oncogenic driver and later as tumor suppressor ( 10 ). Upon disease progression, AR reduction could be associated with a variety of adaptive resistance mechanisms, including enriched gene mutations and/or epigenetic modulation. Indeed, we have previously shown evidence of the involvement of epigenetic mechanisms in AR gene promoter regulation ( 11 ). We found that although the AR negative cell line DU145 disclosed heterogeneity in AR methylation signature, AR re-expression was not successfully accomplished upon 5-aza-2-deoxycytidine (DAC) and Trichostatin A (TSA) exposure, due to the increase in repressive histone markers acting as alternative protective mechanism fostering cancer cell survival ( 11 ). In clinical practice, AR heterogeneity and variations represents a serious concern that hampers PCa eradication. Hence, finding alternative oncogenic signaling pathways activated in the absence of AR is key to disclose new therapeutic avenues for advanced PCa, particularly in the context of RT resistance and treatment-induced NED. Remarkably, AR signaling suppression has been associated with oncogenic MUC1 overexpression, and with its cell surface functional fraction -MUC1-C ( 12 ). Indeed, MUC1-C has been shown to drive NEPC progression, promoting self-renewal capacity and tumorigenicity ( 12 ). Despite being mostly considered an oncogene, overexpressed in multiple solid tumors, MUC1 is also involved in metabolic reprograming of cancer cells, contributing to pancreatic cancer radioresistance ( 13 ). Furthermore, MUC1 overexpression was also shown to confer radioresistance in head and neck cancer cells and hepatocellular carcinomas ( 14 , 15 ). Additionally, MUC1 was involved in important functions enabling cancer spread and overall cell aggressiveness ( 16 ). Nonetheless, a putative role in acquisition of PCa radioresistance has not been studied, yet. Hence, we sought to explore the AR-MUC1 signaling axis in a PCa radiation-resistant cell line which we previously established ( 17 ), to dissect PCa radioresistant mechanisms and uncover novel therapeutic targets. We found that, in RR PCa cells, overexpression of MUC1-C due to AR abrogation led to increased invasiveness and aggressiveness of neoplastic cells. Overall, MUC1 emerged as a promising target for radiosensitization, particularly in patients with advanced androgen-insensitive and unresponsive PCa. Results Mucin 1 (MUC1) is increased in neuroendocrine-like PCa cells exhibiting radioresistance We first screened wild-type PCa cell lines for AR and MUC1 expressions (Supplementary Fig. 1A, B). PC3 and DU145, which are AR-negative PCa cell lines (Supplementary Fig. 1A), derived from castration resistant NE-like tumors, disclosed the highest MUC1 mRNA expression levels contrarily to the less aggressive cell lines C4-2 and 22Rv1 (Supplementary Fig. 1B). C4-2 and 22Rv1, which express high levels of AR, depicted reduced levels of MUC1 (Supplementary Fig. 1A, B). In PCa tissues, MUC1 protein was significantly overexpressed in advanced tumors with NED, compared to localized hormone-naïve prostate adenocarcinomas (Fig. 1 A), while showing decreased AR immunoscore (Fig. 1 B). A significant negative correlation between these two proteins was depicted [r=-0.54, p = 0.0041] (Fig. 1 C)]. This data agrees with in-silico data from The Cancer Genome Atlas (TCGA) (Supplementary Fig. 1C). Additionally, in silico MUC1 expression positively correlates with classical NED markers, such as NCAM1 (CD56), neuron specific enolase ( NSE) and CHGA (chromogranin A) (supplementary Fig. 1D-F). Thus, we focused on elucidating the role of AR and MUC1 in a radioresistance model. Upon single-dose irradiation (SD-IR) with 2, 4, 6 and 8 Gy, the survival fraction (SF) of PC3 and DU145 cell lines (AR-/MUC1+) was significantly higher compared to C4-2 and 22Rv1 (AR+/MUC1-) (Supplementary Fig. 1G). Because MUC1 can mediate PCa lineage plasticity ( 12 ), we questioned whether AR-MUC1 dynamics might alter RT-induced NED. Firstly, we found that prolonged exposure to fractionated RT led to an increase of CD56, INSM1, CGA and Synaptophysin (Syn) in radioresistant (RR) cells – 22Rv1-RR - that we previously established ( 17 ) (Fig. 1 D). Contrarily, in a gain-of-function model, we found that forced AR overexpression in PC3 cells (PC3-AR) (Fig. 1 E, F) led to a decrease of NED-related markers (Fig. 1 G). Moreover, PC3-AR cells showed significantly reduced MUC1 and MUC1-C protein and mRNA levels (Fig. 1 H, I). Together, these results suggest that MUC1, contrarily to AR, is overexpressed in CRPC NE-like cells, rendering them less responsive to RT. AR abrogation induces MUC1 expression upon prolonged RT exposure in PCa cells Although parental 22Rv1 cell lines (22Rv1-P) expressed high AR mRNA and protein levels, upon prolonged exposure to fractionation IR (FIR), both protein and transcript AR expression was dramatically lost (Fig. 2 A, B), while MUC1/MUC1-C was expressed de novo (Fig. 2 C). Indeed, AR protein levels were completely abrogated upon 10 fractions of IR (Fig. 2 A). Next, we investigated possible MUC1 regulators by analyzing a protein-protein interaction network, using in silico platforms -TRRUST version 2 (Fig. 2 D). As anticipated, AR surfaced as a transcriptional suppressor of MUC1 expression. Conversely, STAT3, STAT1 and GATA3 stood out as relevant transcription factors that positively regulate MUC1 (Supplementary table 1 ). Remarkably, STAT3 transcript levels (Fig. 2 E) and its active phosphorylated form, ySTAT3, were significantly upregulated in 22Rv1-RR cells compared to parental cells (Fig. 2 F). No significant differences were found, however, for STAT1 and GATA3 transcription levels (Supplementary Fig. 2). Remarkably, STAT3 was that with more overlapping targeted genes with AR , comparing to the other two genes (Supplementary table 2 ). Epigenetic modulation of AR gene promoter in 22Rv1-RR cells To dissect the molecular mechanism by which RT triggers AR suppression to facilitate the expression of MUC1 and NED in PCa, we further focused on investigating the impact of epigenetic modifications on the transcriptional modulation of AR. We found a significant and impressive increase in the occupancy of histone repressive marks – H3K27me3, H3K9me2 and H4K20me3 - within AR gene promoter region domain in 22Rv1-RR cells, comparatively with the parental cells (Fig. 3 A-C). Conversely, constitutively active chromatin domains – H3K4me3, H3K36me2 and global lysine acetylation – were significantly downregulated at the same promoter region, except for H3K27ac which increased AR occupancy (Fig. 3 D-G). This epigenetic regulation of AR is also in line with our previous findings ( 11 ). As a proof-of-concept, complete abrogation of RNA polymerase II binding was observed at AR promoter region in 22Rv1-RR cells, which supports the lack of AR expression in these cells (Fig. 3 H). Overall, we showed that AR is epigenetically regulated in PCa cells upon radioresistance. AR and STAT3 as transcriptional regulators of MUC1 Our data suggested that AR acts as a negative transcriptional regulator of MUC1 in radioresistant PCa cells. We then investigated whether STAT3 might function as an enhancer of MUC1 transcription in the absence of AR. Chromatin immunoprecipitation (ChIP) assay indicated that AR binds MUC1 promoter region in 22Rv1 parental cells (Fig. 4 A). Likewise, there was no binding in PC3 parental cells, which are AR-negative, but when AR was overexpressed, binding of AR to MUC1 promoter region was depicted (Fig. 4 B). We, then, hypothesized whether in the absence of AR, STAT3 might emerge as replacement, i.e., a positive regulator. Indeed, both in 22Rv1-RR and in PC3-NC cells, a significant positive binding of STAT3 to MUC1 gene promoter was observed, compared to the 22Rv1-P and PC3-AR, respectively (Fig. 4 C, D). The schematic panel in Fig. 4 E illustrates the interaction of AR and STAT3 with MUC1 gene promoter in both studied models – 22Rv1-P/RR and PC3-NC/AR. Our data thus suggests that MUC1 is transcriptionally activated in the presence of STAT3, whereas AR, when present, acts as a transcriptional repressor, entailing MUC1 silencing. MUC1 silencing attenuates radioresistance of PCa cells All previously described results suggest that prolonged exposure to RT may promote NED in PCa cells via alteration of the AR/MUC1/ STAT3 axis, leading to therapy resistance. To delve deeper into how response to RT depends on AR/MUC1 expression, we accomplished AR downregulation in 22Rv1-P cells, which closely mimics what we found to occur during prolonged RT exposure (Fig. 5 A). Notably, AR knockdown ( AR - KD) significantly increased MUC1/MUC1-C expression in 22Rv1-P cells, at least with the siRNA AR 13.1 oligo sequence (Fig. 5 B, C). Furthermore, 22Rv1 cells with AR downregulation disclosed significantly increased clonogenicity when exposed to SD-IR from 0 to 8Gy, which is indicative of a more resistant behavior (Fig. 5 D). Next, we knocked down MUC1 expression ( MUC1-KD ) in both 22Rv1-RR and DU145 cell lines (Fig. 5 E-H). Contrarily to AR knockdown, AR levels in MUC1-KD cells were not significantly altered (Fig. 5 G, H and supplementary Fig. 3). Nonetheless, a slight increase of AR protein levels was observed in MUC1-KD -22Rv1-RR, but not in DU145 cells (Fig. 5 G). Remarkably, both 22Rv1-RR- and DU145- MUC1-KD cells depicted significantly reduced clonogenicity upon SD-IR exposure (Fig. 5 I, J). Moreover, PC3-AR cells, which express low levels of MUC1 (Fig. 1 I, J), disclosed significant radiosensitivity compared to the negative control (Fig. 5 K). Noteworthy, 22Rv1- MUC1-KD cells displayed a radiosensitivity profile closer to the original 22Rv1-P - yellow arrow (Supplementary Fig. 4). Altogether, these results suggest that MUC1-KD may enhance RT response even when AR levels are absent or residual. Indeed, MUC1, rather than AR, seems to act as a master radioresistant driver in our cell model. Mass spectrometry-based quantitative proteomics discloses significant differences between parental and RR cells. To unveil the most relevant altered molecular pathways linking MUC1/MUC1-C with a radioresistant-induced NED phenotype in PCa, mass spectrometry-based quantitative proteomics was performed in 22Rv1-P and RR cells. The results revealed significant differences in expression of a considerable number of peptides (Fig. 6 A-C). Specifically, 300 proteins were significantly upregulated, whereas 179 were significantly downregulated in 22Rv1-RR cells compared to the parental cells (Fig. 6 B). As anticipated, classical NED markers such as CGA ( CHGA ) and enolase 2 (ENO2) were upregulated in the RR group (Supplementary Fig. 5A). Conversely, classical prostate gland markers that are lost in NEPC, such as AR and PSA ( KLK3 ), were found downregulated (Supplementary Fig. 5A). Interestingly, CD49f, NECTIN1 and KRT14, which are commonly associated with basal cell-like traits, were found upregulated in the RR model, also suggesting increased cell aggressiveness with the loss of luminal cell identity (Supplementary Fig. 5A). Interestingly, the transition to invasive NED PCa cells upon prolonged exposure to IR was also associated with progressive changes in the structure and morphology of 22Rv1-RR cells (Supplementary Fig. 6A, B). Indeed, the established RR cells disclose significant morphometric changes in comparison with the parental fraction. Specifically, 22Rv1-RR cells are larger, with increased circularity, roundness, and solidity, while displaying reduced skewness (Supplementary Fig. 6C-H), further supporting that RT-induced AR-STAT3-MUC1-MUC1-C axis drives cell plasticity and aggressiveness in advanced PCa. Overall, gene ontology analysis revealed significant differences in biological processes related to the structure and organization of actin filaments, cell-cell junction, focal adhesion, and cytoskeleton organization and response to interferon gamma (Fig. 6 D). In particular, analysis of Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathways emphasized gene sets related to focal adhesion and cell adhesion molecules, highlighting those that were up (red)- or down (green)-regulated in 22Rv1-RR group comparing with 22Rv1-P samples (Supplementary Fig. 5B, C). Moreover, upon analysing the data using Human MSigDB Hallmark Gene Set we found that epithelial mesenchymal transition (EMT) was the most significant molecular signaling pathway altered in our RR model, followed by apical junction, interferon gamma and response to UV radiation (Fig. 6 E). Subsequently, we confirmed that a high number of proteins involved in EMT pathway were upregulated in 22Rv1-RR replicates compared with 22Rv1-P (Fig. 6 F). Among them, p-cadherin (CDH3) and vimentin (VIM), both constituting relevant peptides involved in EMT, were prominently and significantly upregulated in 22Rv1-RR cells (Supplementary Fig. 5D). Using WB, we further confirmed VIM and PCAD to be considerably upregulated in 22Rv1-RR cells compared to parental fraction (Supplementary Fig. 5E). Conversely, VIM expression was decreased in PC3-AR cells compared with the negative control (Supplementary Fig. 5F), whereas PCAD was absent in PC3 cells ( data not shown ). Interestingly, analysis of the top enriched molecules revealed that 22Rv1-RR were similar to DU145 cells (Supplementary Fig. 5G), although significantly different from the original 22Rv1 cell line, as well as from normal adult prostate tissue (Supplementary Fig. 7A, B). Likewise, many AR network-related molecules were downregulated in 22Rv1-RR compared to 22Rv1-P cells (supplementary Fig. 7C). In summary, 22Rv1-RR cells exhibited a more mesenchymal phenotype characterized by traits associated with aggressiveness, including basal cell-like features, stemness, and neuroendocrine differentiation. These changes were accompanied by a reduction in AR signalling. AR/MUC1 loop disruption leads to abnormal cancer cell migration and invasion. Next, we performed migration and invasion assays using radioresistant and AR overexpressing cells. A more pronounced wound healing ability was observed in 22Rv1-RR cells compared to the parental fraction, with statistically significant differences (Fig. 7 A). In PC3-AR cells, overexpressing AR and low MUC1, the opposite was observed, with a significant decrease in cell migration (Fig. 7 B). Furthermore, the RR fraction (AR-/MUC1+) showed significantly enhanced cell invasion capability (Fig. 7 C, E). Conversely, in PC3-AR cells (AR+/MUC1-), invasion capacity decreased by more than half compared with control cells (Fig. 7 D, F), supporting a role for MUC1 in orchestrating an aggressive phenotype in PCa cells. Discussion and Conclusions PCa remains a major health concern, having a significant impact on patient survival and quality of life ( 21 ). Intra-tumoral heterogeneity, as well as divergent molecular and biological signatures are puzzling features of PCa, contributing to primary treatment failure and consequent disease progression ( 22 , 23 ). The versatility of AR throughout disease progression and its consequent depletion in more aggressive forms, like NEPC, poses significant challenges, especially considering that most primary therapies target this receptor, aiming to reduce prostate-specific tumor proliferation ( 24 , 25 ). Exploring the effects of androgen signaling suppression and further identifying new therapeutic targets for those patients at risk, may improve the effectiveness of first-line cell-killing therapies, such as RT. Earlier studies suggested that ADT and/or radiotherapy might drive treatment-induced NED, resulting in lack of response to therapy ( 4 , 18 – 20 ). The phenomenon of tNED, stemming from primary therapy, may also be associated with the molecular and functional reprogramming of the tumor cells. The underlying mechanisms, however, have not been completely elucidated, thus far. Herein, the generation of a radioresistant in vitro model – 22Rv1-RR cells – allowed us to uncover a previously unrecognized role for MUC1-C oncoprotein in driving PCa radioresistance, disclosing evidence of the existence of a regulatory axis involving AR and MUC1 which drives PCa lineage switching towards NE-like characteristics. We found that 22Rv1-RR cells exhibited de novo expression of MUC1/MUC1-C coupled with suppression of AR axis signaling. Besides 22Rv1-RR cells, we also showed that PC3 and DU145, which are metastatic castration-resistant PCa cells, constitutively express MUC1/MUC1-C oncoprotein, while expressing only residual to undetectable levels of AR ( 12 ). Early findings demonstrated that those cells lines harbor NED traits, evidenced through expression of well-established NE cell markers ( 26 ). Interestingly, MUC1 has been recognized as a driver of PCa lineage plasticity and NED ( 12 ), with MUC1 expression associating with NEPC score, in in silico datasets from TCGA ( 12 ). Here, in a cohort of localized prostate adenocarcinomas and NEPC tissues, we confirmed AR downregulation and MUC1 overexpression, with a negative correlation. Moreover, we observed that prolonged irradiation exposure led to AR suppression, MUC1 upregulation and acquisition of NED features in PCa cells. Indeed, the increase of MUC1 expression in 22Rv1-RR cells was accompanied by increased NE markers expression, including CD56, INSM1, CGA and SYP. Interestingly, NE markers expression decreased when AR was overexpressed in PC3 cells, with a concomitant reduction of MUC1 expression, further emphasizing the negative correlation between the expression of those two proteins – AR and MUC1. Previous studies substantiated the crosstalk between MUC1 and STAT3 as a downstream target in an auto-inductive regulatory loop ( 27 – 29 ). In our study, we further demonstrated that AR functions as a transcriptional regulator of MUC1. Indeed, in the absence of AR, a significant occupancy of STAT3 at the MUC1 promoter in both 22Rv1-RR and PC3 cells, which are AR-negative cell lines, was disclosed. Interestingly, MUC1-C and JAK-1 were reported as intermediators of STAT3 phosphorylation ( 28 ). In the same vein, we found an upregulation of γ-STAT3 in 22Rv1-RR compared to the parental cells. Overall, these findings substantiate the existence of a regulatory mechanism involved in PCa radioresistance, in which epigenetic silencing of AR leads to MUC1 expression via STAT3 activation, which in turn drives tumor cell reprogramming towards a more mesenchymal phenotype, with stemness and NED traits. Remarkably, mass spectrometry data revealed differential expression patterns between the parental lineage and the RR fraction. Among them, cell-cell adhesion and dynamics on actin filaments were the most common cellular alterations found in 22Rv1-RR cells, which are suggestive of evolution to an EMT phenotype, a recognized hallmark of cancer progression ( 30 ). Moreover, the increased roundness observed in 22Rv1-RR cells has been considered to indicate a superior ability to invade the extracellular matrix ( 30 ), which is aligned with the observed increase in cell migration and invasion capacity of 22Rv1-RR. Interestingly, NE (CHGA and ENO2) and basal (cytokeratin 14, KRT14 and CD49f) cell markers were also found upregulated in 22Rv1-RR compared to 22Rv1-P cells, according to the proteomic analysis. It has been previously documented that CD49f positive cell populations overlapped with genes expressed in basal, stem and neuroendocrine cells and were associated with EMT, thus influencing cell invasion and migration( 31 ). Our results further confirm those observations and provide an additional explanation for the biological aggressiveness disclosed by 22Rv1-RR cells. In conclusion, we demonstrated in this study that prolonged radiotherapy entails loss of AR and upregulation of MUC1 in a PCa subpopulation, driving NED and fostering EMT, materialized in a phenotypic and morphological switch, which ultimately translates into resistance to RT. In the past, attempts to restore AR expression through epigenetic inhibition have been unsuccessful due to tumor cell plasticity ( 11 ). Herein, we show that MUC1 knockdown in 22Rv1-RR and DU145 cells significantly boosts RT response, even without restored AR expression, indicating the MUC1, per se , has an important role in driving radioresistance, independently of the MUC1/AR axis. Interestingly, MUC1 was previously associated with radioresistance in other models, but not in PCa ( 13 – 15 ). Despite the well-known evidence that AR is a preponderant factor for PCa progression, in a radioresistance scenario in which more aggressive phenotypes emerge and typically lose AR expression, other targets should be considered for alternative therapeutic strategies, among which MUC1 cell surface oncoprotein seems of major interest. Materials and Methods Cell culture PCa cell lines, including hormone-sensitive lineages C4-2 and 22Rv1 and hormone-insensitive PC-3 and DU145, were selected for this study. Furthermore, previously generated 22Rv1-RR cells were used as a model of radioresistance ( 17 ). All cell lines were cultured with RPMI 1640 supplemented with 10% of fetal bovine serum (FBS), 100 IU/mL penicillin and 100 µg/mL streptomycin. Optimal cell culturing was maintained at 37ºC in a humidifier incubator with 5% CO 2 . Ionizing radiation Cells were irradiated as previously described for in vitro assays ( 17 , 32 ). A radioresistant (RR) 22Rv1 cell line subpopulation was generated from the respective parental lineage using the same fractionation scheme, as previously depicted ( 17 ). PC3- AR overexpressing cells Cell transfection was carried out by pEZ-Lv105 (GeneCopoeiaTM, Rockville, MD, USA) using FuGENE® HD Transfection Reagent (Promega, Madison, WI, USA), following manufacturer’s recommendations. Briefly, cells were plated at an optimized density (2x10 4 cells/mL), one day before transfection, in a 6-well culture plates. In vitro cell transfection was performed when the cells had reached at least 30–50% of confluence. 2µg of oligo molecules were diluted in Opti-MEM™ medium (GIBCO®) to a final volume of 100µL. Additionally, 4µL of transfection reagent were added in a proportion of 2:1 FuGENE® HD Transfection Reagent:DNA ratio. Then, transfection mixtures were incubated for 15 minutes at room temperature and then added to the cells in cell growth medium (RPMI-1640). Transfection of the cells with scramble DNA oligos was performed, serving as negative controls (NC). After 48h of transfection, stable clones with the vector were selected with Puromycin dihydrochloride (cat. 631306, Clontech Laboratories Inc.) at a cytotoxic tested concentration of 0.5µg/mL. Stable transfected cells were used for further experiments. AR overexpression was confirmed by western blot and real time quantitative polymerase chain reaction (RT-qPCR). siRNA transfection for MUC-1 and AR gene silencing Trifecta dicer subtracts containing three different siRNA sequences were used to knockdown MUC1 (hs.Ri.MUC1.13.1 to hs.Ri.MUC1.13.3) or AR (hs.Ri.AR.13.1 to hs.Ri.AR.13.3) (Integrated DNA technologies (IDT), USA). Also, a negative control siRNA sequence (DsiRNA, 1nmol), was used (Integrated DNA technologies (IDT)). Silencing of MUC1 and AR gene expression by in vitro siRNA transfection was performed with Lipofectamine® 3000 reagent (Invitrogen, USA), according to manufacturer instructions. Dye labeled reagent was used for assessing cell transfection success, as confirmed by the red dots present inside the cells (supplementary Fig. 8). Gene silencing was confirmed for every experiment after 48h of siRNA transfection, a reference time point for cell irradiation (0h). Clonogenicity assay PCa transfected cells (control, knockdown or overexpressing cells) were used for colony formation assay (CFA). Briefly, cells were plated in 24-well plates 48h before IR exposure, at an adjusted density, upon siRNA transfection. Then, 1000 or 2000 cells per well were used for DU145 and PC3 or for 22Rv1-P and -RR, respectively, for colony formation in 6-well plates. All cells were maintained in low densities for 7 days after ionization exposure in a range of 0, 2, 4, 6 and 8Gy. Following colony formation, cells were stained using 1% Crystal Violet reagent in 20% methanol solution. Each colony was considered for the count if composed of at least 50 cells. Colony counting was performed using a stereomicroscope Olympus S2X16 at 7x amplification. Radiobiological cell survival curves were constructed using linear quadratic (LQ) model [S = e – (αD + βD2)], as previously described ( 17 ). The plating efficiency (PE) of each independent experiment was calculated according to the initial number of cells seeded. PE=% (number of colonies counted in the control/number of cells plated). Then, the survival fraction was calculated taking into account the PE [SF = number of colonies counted/(number of cells plated*(PE/100))]. SF values were introduced in GraphPad Prism software version 9.1.1 to assess cell survival curves through LQ model. Protein extraction, SDS-PAGE Western Blot Total protein extraction was performed as previously described ( 32 ). Protein quantification was made by colorimetric detection using PierceTM BCA Protein Assay kit, according to manufacturer instructions. Western blot was performed using 50µg of total protein extract. Anti-AR monoclonal antibody (AR 441, MA5-13426, Invitrogen, USA), anti-MUC1 antibody (VU4H, sc-7313, Santacruz Biotechnology), and anti-MUC1-C (D5K9I, Cell Signalling technology), were used. NED-related protein expression was evaluated using monoclonal antibodies against CHGA (DAK-A3, DAKO), INSM1 (sc-271408, Santacruz Biotechnology) and CD56 (CD56-504-L-CE, Leica). All primary antibodies were diluted in TBS-T solution with 5% of bovine serum albumin (BSA) at the manufacturer recommended dilutions and inoculated with nitrocellulose membranes overnight at 4ºC. Anti-β-Actin antibody (A1978, Sigma Aldrich) was used as loading normalizer. All the original, uncropped western blot images are compiled in Supplementary Fig. 9. In silico studies and online databases TCGA PanCancer database for prostate adenocarcinoma, derived from a large cohort of 488 PCa patients, was used to assess MUC1, AR and NED-related genes mRNA expression levels. Correlations were evaluated by Pearson or Spearmen statistical analysis. Data were downloaded from cBioPortal “For Cancer Genomics” online platform ( https://www.cbioportal.org ). TRRUST v2: an expanded reference database of human and mouse transcriptional regulatory interactions. Nucleic Acids Research 26 Oct, 2017 ( https://www.grnpedia.org/trrust/ ) was used to unveil transcriptional regulatory networks of MUC1 gene and to identify putative transcriptional repressors. Chromatin immunoprecipitation-ChIP (RT-qPCR) ChIP- qRT-PCR was performed as previous detailed ( 17 ). Specifically, to evaluate the binding affinity of histone modifications on AR gene promoter region, four pairs of primer sequences were used: P1, Forward: 5’AAATTTGGTGAGTGCTGGCCT 3’; Reverse: 5’AGGACCCCTGCTTCCTGAATA 3’; P2, Forward: 5’GGAGCTATTCAGGAAGCAGGG 3’; Reverse: 5’ TGGCTTTGGAGAAACAAGTGC 3’; P3, Forward: 5’CTCCAAAGCCACTAGGCAGG 3’; Reverse: 5’ GGTGGAGAGCAAATGCAACA 3’; P4, Forward: 5’TGTTGCATTTGCTCTCCACCT 3’; Reverse: 5’CCTTTTTCCCTCTGTCGCCT 3’. Primer annealing temperature was 62ºC for all the sequences, except for AR-P1 which was set at 60ºC. Furthermore, three different sequences over MUC1 transcription starting site (TSS) were interrogated for AR and STAT3 binding – P1: Forward: 5’ TTGTCACCTGTCACCTGCTC 3’; Reverse: 5’ GGGCAGAACAGATTCAGGCA 3’; P2: Forward: 5’ AGCTGGAGAACAAACGGGTA 3’; Reverse: 5’ CCTCCCCTACCTCCTACCTCT 3’; P3: Forward: 5’ CTAGCTGGCTTTGTTCCCCA 3’; Reverse 5’ CCTTTCACCAACCACTCCCT 3’. Primer annealing temperature was 60ºC for P1 and P2 and 64ºC for P3. Total RNA isolation, cDNA synthesis and RT-qPCR Total RNA extracts were obtained from all cell lines at each independent condition, using Trizol reagent-based extraction method, followed by cDNA synthesis of 1000ng of RNA using RevertAid RT kit (Thermo Fisher Scientific Inc., Waltham, MA, USA), according to manufacturer’s instructions. Relative gene transcription levels were calculated using GUSB as housekeeping gene. Primer sequences and the respective optimized annealing temperature are listed in Supplementary table 3 . Immunohistochemistry in formalin-fixed paraffin embedded (FFPE) tissues Anti-AR monoclonal antibody (AR 441, MA5- 13426, Invitrogen) and anti-MUC1 (VU4H, sc-7313, Santacruz Biotechnology) antibodies were used to assess protein expression by immunohistochemistry (IHC), using a NovoLinkTM Max Polymer Detection System (Leica Biosystems, Germany). Briefly, 4µm sections were deparaffinized and rehydrated in a serial dilution of alcohol. Then, antigen retrieval was performed in a microwave oven at 800 W for 20 minutes in citrate 1x for MUC1, or in 95–100ºC water-bath in EDTA 1x for 30 minutes for AR, followed by 10min cooling at room temperature. Next, tissue slides were incubated with 3% H 2 O 2 in methanol (GRiSP, Portugal) solution for 10min, at room temperature. Additionally, the slides were blocked with horse serum (Vector Laboratories, USA) diluted at 1:50, for 20min, and subsequently incubated overnight with the primary antibody, at 1:250 for AR and 1:300 for MUC1. The day after, slides were incubated with post-primary block followed by polymer to boost the signal, for 30min each. Next, 3,3-diaminobenzidine (DAB) (Sigma-AldrichTM, Germany) was used as chromogen, for 10min, at room temperature. Finally, slides were counterstained with hematoxylin (Leica Biosystems, Germany) for microscopic visualization. AR nuclear staining was evaluated using a quantitative method from GenASIS software (Applied Spectral Imaging, ASI), considering the percentage of positive cells and the intensity of immunostaining, providing a continuous variable: IHC score [1 × (% of cells stained weakly) + 2 × (% of cells stained intermediately) + 3 × (% of cells stained strongly)]. MUC1 staining was assessed by a dedicated uropathologist. Staining intensity was categorized between 0–3 (0 = negative, 1 = weak, 2 = moderate and 3 = strong). Then, the percentage of positive cells were categorized as < 5% followed by 10% interval categories. Extension score was defined from 0 to 9, according to the category of positivity percentage. Lastly, IHC score was calculated by multiplying intensity with extension scores. IHC images were acquired using an Olympus BX41 microscope equipped with a digital camera (Olympus U-TV0.63XC) and CellSens software (version V0116, Olympus). Cell migration (“ wound healing ”) assay Briefly, 6x10 5 cells were seeded into 6-well plates in 2mL of complete RPMI culture medium and grown until confluence was reached. Next, in each well, two parallel vertical wounds were manually performed, followed by a washing step with PBS 1x and medium refilling. Then, for easier photographing, two parallel straight lines were drawn at the bottom of the plate to intersect the “wounds”. Then, 4 specific vertices were generated to photograph four “wound” areas over the time. For PC3 cell lines, PC3 NC condition healed the wound after 20h. Instead, 22Rv1-RR cells reached the same effect only after completing 24h. All experiments were photographed using Olympus IX51 inverted microscope equipped with Olympus XM10 Digital Camera System. The relative migration distance was computed as relative migration distance (%) = (A – B) / C × 100, where A represents the initial width of the cell wound formation, B represents the width of the cell wound after a 20h or 24h, for PC3 and 22Rv1 cells, respectively, and C represents the mean width of the initial cell wound. beWound—Cell Migration Tool (Version 1.5) was used to conduct the analysis of relative migration distances, based on results from at least three independent experiments. Transwell in vitro cell invasion assay 24-well BD Biocoat Matrigel Invasion Chambers (BD Biosciences) were used to address PCa cell invasion capabilities. Each chamber was conserved at -20ºC before using. Then, for cell plating, BD Matrigel Chambers were re-hydrated with complete RPMI culture medium at 37ºC for 30 minutes. Subsequently, cells were seeded inside the inserts at a density of 2.5x10 4 cells per well and incubated at 37ºC in 5% CO 2, in a humidified chamber for 48h. Lastly, two days after cells platting, non-invading cells were removed by cotton swab and the invading cells were fixed with cold methanol during 20 min, followed by staining with 1% Cristal Violet diluted in 20% methanol. Invaded membranes were captured using Olympus SZx16 stereomicroscope (16x), and the percentage of invading cells were computed using Image J software (version 1.41; NIH). At least three independent experiments were performed. Mass spectrometry-based quantitative proteomic analysis Using 22Rv1-P and RR cell line protein extracts, a comprehensive proteomic analysis was performed. Briefly, around 30µg of total protein per sample was enzymatically digested with trypsin/LysC. Protein identification and quantification was then performed by nanoLC-MS/MS using a Vanquish neoliquid chromatography system coupled to an Eclipse Tribrid QuadrupoleIon trap Orbitrap mass spectrometer together with a High-field asymmetric waveform ion mobility spectrometry – FAIMS equipment (Thermo Scientific). The raw data was processed using Proteome Discoverer software (Thermo Scientific) and searched against the UniProt database for the Homo sapiens proteome. A common protein contaminant list search was performed for further discrimination. To gain insights into the statistically significant under- or over-expressed proteins in 22Rv1-RR cells across multiple biological sample sources, exploratory and protein label free quantification - LFQ analyses were performed. Functional Enrichment analysis, including Over-Representation Analysis and Gene Set Enrichment Analysis were also carried out to further understand the functional roles of identified proteins. Thus, protein data was queried to Gene Ontology and pathway functional databases, including KEGG and Reactome. Statistics All data were analyzed using GraphPad Prism software version 9.1.1. Normality tests (Shapiro-Wilk) were applied to all datasets to define the adequate subsequent statistical analysis: parametric tests (two-way ANOVA or Student’s t-test) for data following a normal distribution, and non-parametric tests (Mann-Whitney or Kruskal–Wallis, to compare two or more groups, respectively) for non-normal data distribution. Clonogenicity cell survival curves were constructed based on the linear quadratic model SF = e – (αD + βD2) and global differences among curves were computed using least squares regression fitting model and the comparison method of extra sum-of-squares F test, selecting both alpha and beta parameters of the equation. P value less than 0.05 was considered statistically significant. Non-parametric Spearman correlation was performed between AR and MUC1 IHC-variables, previously transformed by the function of Y = Ln (1 + Y). Simple linear regression was computed to find the linear relationship between the two described variables. Declarations Conflict of Interest: There are no competing financial interests related to the work described. Author Contributions: CM-S performed the major experiments and wrote the first draft of the manuscript. AA-C, IC$ and VMG assisted in experimental procedures. JL* was responsible for irradiation source handling. IC@ was responsible for tissue slides processing; JL# revised tissue slides and IHC staining. MPC, LA, RH and CJ supervised the work and revised the manuscript. All authors have read and approved the final version of the article. *Joana Lencart; # João Lobo; $ Iris Carriço; @ Isa Carneiro. Author Contributions to each figure: CM-S was responsible for constructing all figure panels under the supervision of the co-authors. The arrangement and design of the panels were collectively discussed and agreed upon by all authors. Ethics Approval and Consent to Participate: This study used PCa biopsy specimens as FFPE tissue samples. For that purpose, this study was approved by the institutional review board (Comissão de Ética para a Saúde) of IPO Porto, Portugal (CES-238/020). Funding: CJ Research is funded by Research Center of Portuguese Institute of Porto (BF.CBEG CI-IPOP-27-2016) and EpiParty PI 159-CI-IPOP-152-2021). LA research is funded by Epi-MS under the VALERE 2019 Program; V:ALERE 2020—“CIRCE”; Campania Regional Government Technology Platform 2038 Lotta alle Patologie Oncologiche iCURE-B21C17000030007; Campania Regional Government FASE2: IDEAL; MIUR, Proof of Concept POC01_00043; POR Campania FSE 2014-2020 ASSE III; PON RI 2014/2020 “Dottorati Innovativi con caratterizzazione industrial”; Horizon EU: CAN-SERV BBMRI; EPI-MET MISE 2022; Bando giovani ricercatori D.R. n.834 del 30/09/2022 Università Vanvitelli project: Miranda; National Plan for NRRP Complementary Investments – Law Decree May 6, 2021, n. 59, converted and modified as to Law n. 101/2021Research initiatives for technologies and innovative trajectories in the health and care sectors: project ANTHEM (AdvaNced Technologies for Human-centrEd Medicine). CM-S and IC* were funded by 2020-FETOPEN-2018-2020 “MindGAP” and Fundação para a Ciência e Tecnologia (10.54499/2022.05135.PTDC), respectively. AA-C is a research fellow funded by Liga Portuguesa Contra o Cancro- Núcleo Regional do Norte. VM-G holds a Junior researcher position UIDP/00776/2020–3C funded through CI-IPOP Programmatic funding 2020–2023 (reference UIDP/00776/2020) from FCT. MPC was funded by FCT—Fundação para a Ciência e Tecnologia (CEECINST/00091/2018). * Iris Carriço. Data Availability Statement: The authors confirm that the data supporting the findings of this study are available within the article and in Supplementary material file. Raw data that support the findings of this study are available from the corresponding author, upon reasonable request. References Yao J, Liu Y, Liang X, Shao J, Zhang Y, Yang J, et al. Neuroendocrine Carcinoma as an Independent Prognostic Factor for Patients With Prostate Cancer: A Population-Based Study. Front Endocrinol (Lausanne). 2021;12:778758. Conteduca V, Oromendia C, Eng KW, Bareja R, Sigouros M, Molina A, et al. Clinical features of neuroendocrine prostate cancer. 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Epigenetic regulation of TP53 is involved in prostate cancer radioresistance and DNA damage response signaling. Signal Transduct Target Ther. 2023;8(1):395. Luo J, Wang K, Yeh S, Sun Y, Liang L, Xiao Y, et al. LncRNA-p21 alters the antiandrogen enzalutamide-induced prostate cancer neuroendocrine differentiation via modulating the EZH2/STAT3 signaling. Nat Commun. 2019;10(1):2571. Deng X, Liu H, Huang J, Cheng L, Keller ET, Parsons SJ, et al. Ionizing radiation induces prostate cancer neuroendocrine differentiation through interplay of CREB and ATF2: implications for disease progression. Cancer Res. 2008;68(23):9663-70. Gopalan A, Al-Ahmadie H, Chen YB, Sarungbam J, Sirintrapun SJ, Tickoo SK, et al. Neuroendocrine differentiation in the setting of prostatic carcinoma: contemporary assessment of a consecutive series. Histopathology. 2022;81(2):246-54. Lee CH, Akin-Olugbade O, Kirschenbaum A. Overview of prostate anatomy, histology, and pathology. Endocrinol Metab Clin North Am. 2011;40(3):565-75, viii-ix. Taira AV, Merrick GS, Butler WM, Galbreath RW, Fiano R, Wallner KE, et al. Time to failure after definitive therapy for prostate cancer: implications for importance of aggressive local treatment. J Contemp Brachytherapy. 2013;5(4):215-21. Agarwal PK, Sadetsky N, Konety BR, Resnick MI, Carroll PR. Treatment failure after primary and salvage therapy for prostate cancer: likelihood, patterns of care, and outcomes. Cancer. 2008;112(2):307-14. Fujita K, Nonomura N. Role of Androgen Receptor in Prostate Cancer: A Review. World J Mens Health. 2019;37(3):288-95. Heinlein CA, Chang C. Androgen receptor in prostate cancer. Endocr Rev. 2004;25(2):276-308. Leiblich A, Cross SS, Catto JW, Pesce G, Hamdy FC, Rehman I. Human prostate cancer cells express neuroendocrine cell markers PGP 9.5 and chromogranin A. Prostate. 2007;67(16):1761-9. Bose M, Sanders A, Handa A, Vora A, Cardona MR, Brouwer C, et al. Molecular crosstalk between MUC1 and STAT3 influences the anti-proliferative effect of Napabucasin in epithelial cancers. Scientific Reports. 2024;14(1):3178. Ahmad R, Rajabi H, Kosugi M, Joshi MD, Alam M, Vasir B, et al. MUC1-C oncoprotein promotes STAT3 activation in an autoinductive regulatory loop. Sci Signal. 2011;4(160):ra9. Gao J, McConnell MJ, Yu B, Li J, Balko JM, Black EP, et al. MUC1 is a downstream target of STAT3 and regulates lung cancer cell survival and invasion. Int J Oncol. 2009;35(2):337-45. Wu JS, Jiang J, Chen BJ, Wang K, Tang YL, Liang XH. Plasticity of cancer cell invasion: Patterns and mechanisms. Transl Oncol. 2021;14(1):100899. Ellis L, Loda M. Advanced neuroendocrine prostate tumors regress to stemness. Proc Natl Acad Sci U S A. 2015;112(47):14406-7. Macedo-Silva C, Miranda-Gonçalves V, Lameirinhas A, Lencart J, Pereira A, Lobo J, et al. JmjC-KDMs KDM3A and KDM6B modulate radioresistance under hypoxic conditions in esophageal squamous cell carcinoma. 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Additional Declarations There is no duality of interest Supplementary Files Supplementaryfigurescaptions.docx Supplementary figure captions SupplementaryTables.docx Supplementary tables Supplementaryfigure1.tif Supplementary figure 1 Supplementaryfigure2.tif Supplementary figure 2 Supplementaryfigure3.tif Supplementary figure 3 Supplementaryfigure4.tif Supplementary figure 4 Supplementaryfigure5.tif Supplementary figure 5 Supplementaryfigure6.tif Supplementary figure 6 Supplementaryfigure7.tif Supplementary figure 7 Supplementaryfigure8.tif Supplementary figure 8 Supplementaryfigure9.tif Supplementary figure 9 Cite Share Download PDF Status: Published Journal Publication published 03 Jul, 2025 Read the published version in Cell Death Discovery → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5614729","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":390905172,"identity":"2db5d17b-5042-44a2-9302-8b797742f976","order_by":0,"name":"Carmen 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08:41:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5614729/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5614729/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41420-025-02597-4","type":"published","date":"2025-07-03T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":75629090,"identity":"e262c74c-4570-4861-bc30-8b43dab9e689","added_by":"auto","created_at":"2025-02-06 13:33:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1099257,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eMucin 1 (MUC1) is increased in NEPC and inversely correlated with androgen receptor (AR) expression.\u003c/strong\u003e\u003c/em\u003e A) AR and B) MUC1 IHC score values comparing prostate adenocarcinomas (n=18) with NEPC (n=8) tissue samples, represented by scatter plot graphs with bars. C) Scatter plot with a superimposed linear regression line. Each value was transformed using Ln (y+1). The regression line shows a statistically significant negative correlation between AR and MUC1 (R² = -0.54, \u003cem\u003ep \u0026lt; 0.01\u003c/em\u003e). D) Total protein levels of CD56 (95kDa), INSM1 (58kDa), CGA (48kDa) and SYN (38kDa) for 22Rv1-P (AR+/MUC1-) and RR (AR-/MUC1+) cells. E) Total protein levels of AR (110kDa), for PC3-NC (negative cell transfection control) and PC3-AR (overexpressing AR). F) Relative mRNA expression levels of \u003cem\u003eAR\u003c/em\u003e for PC3-NC and PC3-AR cells, normalized by \u003cem\u003eGUSB\u003c/em\u003egene expression levels (housekeeping). Results are presented as mean ± SD of at least 3 independent experiments. *** p value \u0026lt;0.001. G) Total protein levels of CD56 (95kDa), INSM1 (58kDa), CGA (48kDa) and SYN (38kDa) for PC3-NC and PC3-AR cells. H) Total protein levels of MUC1 (130kDa) and MUC1-C (25kDa) for PC3-NC and PC3-AR cells. b-actin (42kDa) was used as loading control for all western blots (D, E, F, G) and the images were acquired by Chemidoc detection system (Biorad, Berkeley, California). I) Relative mRNA expression levels of \u003cem\u003eMUC1\u003c/em\u003e for PC3-NC and PC3-AR cells, normalized by \u003cem\u003eGUSB \u003c/em\u003egene expression levels (housekeeping). Results are presented as mean ± SD of at least three independent experiments. \u003cem\u003ens\u003c/em\u003e, not significant.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5614729/v1/7f4644d4c647ba12fb8d8f47.png"},{"id":75628629,"identity":"e4b5835c-ce6c-41c8-b068-c0be203887d1","added_by":"auto","created_at":"2025-02-06 13:25:14","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":823023,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eAR abrogation, MUC1-C de novo expression and STAT3 activation in 22Rv1-RR cells. \u003c/strong\u003e\u003c/em\u003eA)\u003cem\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/em\u003eTotal protein levels of AR full length-FL (110kDa), MUC1 (130kDa) and MUC1-C (25kDa) in 22Rv1-P and -RR cells submitted to 5, 10, 15 and 20 fractions of 2.5Gy. b-actin (42kDa) was used as loading control. Relative mRNA expression levels of (B) \u003cem\u003eAR-FL\u003c/em\u003e and (C) \u003cem\u003eMUC1\u003c/em\u003e in 22Rv1-P and -RR cells. Results are presented as mean ± SD of at least three independent experiments. \u003cem\u003eGUSB\u003c/em\u003ewas used as reference gene for normalization. D) Protein-protein interaction of MUC1 regulatory network. Data was retrieved from TRRUST v2 online platform (\u003ca href=\"https://www.grnpedia.org/trrust/\"\u003ehttps://www.grnpedia.org/trrust/\u003c/a\u003e). E) Relative mRNA expression levels of \u003cem\u003eSTAT3 \u003c/em\u003ein 22Rv1-P and -RR cells. Results are presented as mean ± SD of at least three independent experiments. GUSB was used as reference gene for normalization. F) Total protein levels of g-STAT3 (86kDa) in 22Rv1-P and -RR subpopulations. b-actin (42kDa) was used as loading control. Western blot images (A, F) were acquired by Chemidoc detection system (Bio-Rad, USA).\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5614729/v1/29430deee8266aec060c80ec.png"},{"id":75627672,"identity":"368c7f1f-bb48-4756-bd89-407ebd2fc5f1","added_by":"auto","created_at":"2025-02-06 13:17:14","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":687976,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eAR gene promoter epigenetic modulation via histone methylation dynamics in 22Rv1-RR cells.\u003c/strong\u003e\u003c/em\u003eH3K27me3 (A), H3K9me2 (B), H4K20me3 (C), H3K4me3 (D), H3K36me2 (E), H3K27ac (F), global acetylated lysine (G) and RNA polymerase II (H) % input values at \u003cem\u003eAR\u003c/em\u003egene promoter in four different regions above transcription starting site (TSS) for 22Rv1-RR compared to 22Rv1-P cells. Graphs were represented by mean ± SD values. \u003cem\u003eNs\u003c/em\u003e, not significant, **, p value\u0026lt;0.01, *** p value\u0026lt;0.001; **** p value\u0026lt;0.0001.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-5614729/v1/9d59233e5fc38546d578b767.png"},{"id":75627674,"identity":"69291d73-8fa9-4847-8f69-74be57cd13fe","added_by":"auto","created_at":"2025-02-06 13:17:14","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":688001,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eMUC1 gene promoter modulation by AR and STAT3. \u003c/strong\u003e\u003c/em\u003eAR (A-B) and g-STAT3 (C-D) % input values at \u003cem\u003eMUC1\u003c/em\u003e gene promoter in three different regions above transcription starting site (TSS) for 22Rv1-RR (radioresistant) compared to 22Rv1-P (parental) cells and for PC3-NC and PC3-AR (AR overexpressing cells), respectively. Graphs were represented by mean ± standard deviation (SD) values. *, p value\u0026lt;0.05, ** p value\u0026lt;0.01; **** p value\u0026lt;0.0001. E) Representative scheme of MUC1 gene promoter regulation by AR (Blue) and g-STAT3 (green) binding. AR expression in 22Rv1-P and PC3-AR leads to MUC1 transcriptional repression, while AR downregulation in 22Rv1-RR and PC3-NC leads to MUC1 transcriptional activation.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-5614729/v1/777f7b7031c531ce936428cb.png"},{"id":75627675,"identity":"1941c8ba-46c1-4c42-b46a-e3b6d753ac8b","added_by":"auto","created_at":"2025-02-06 13:17:14","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1418630,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eMUC1 Knockdown (KD) leads to the mitigation of radioresistant phenotype in 22Rv1-RR and DU145 cells. \u003c/strong\u003e\u003c/em\u003eRelative mRNA expression levels of (A) \u003cem\u003eAR \u003c/em\u003eand (B) \u003cem\u003eMUC1\u003c/em\u003e in 22Rv1-P cells upon AR siRNA silencing with three different oligos (13.1 to 13.3) and the respective negative control. Results are presented as mean ± standard deviation (SD) of at least three independent experiments. \u003cem\u003eGUSB\u003c/em\u003e was used as reference gene for normalization. C) Total protein levels of AR (110 KDa), MUC1 (130-175kDa) and MUC1-C (25kDa) in 22Rv1-P cells upon AR siRNA silencing with three different oligos (13.1 to 13.3) and the respective negative control. b-actin (42kDa) was used as loading control. Images were acquired by Chemidoc detection system (Bio-Rad, USA). D) Cell survival fraction (SF) of 22Rv1-P upon AR siRNA silencing with three different oligos (13.1 to 13.3) and the respective negative control, represented through linear-quadratic model (LQ = (S=e \u003csup\u003e– (xD + BD2)\u003c/sup\u003e)), ***,\u003cem\u003e p value \u0026lt;0.001\u003c/em\u003e **** \u003cem\u003ep value \u0026lt;0.0001\u003c/em\u003e. Relative mRNA expression levels of \u003cem\u003eMUC1\u003c/em\u003e in 22Rv1-RR cells (E) and DU145 (F) cells upon MUC1 siRNA silencing with three different oligos (13.1 to 13.3) and the respective negative control. Results are presented as mean ± SD of at least three independent experiments. \u003cem\u003eGUSB\u003c/em\u003e was used as reference gene for normalization. Total protein levels of AR (110 KDa), MUC1 (130-175kDa) and MUC1-C (25kDa) in 22Rv1-RR cells (G) and DU145 (H) cells upon MUC1 siRNA silencing with three different oligos (13.1 to 13.3) and the respective negative control. b-actin (42kDa) was used as loading control. Images were acquired by Chemidoc detection system (Bio-Rad, USA). Cell survival fraction (SF) of 22Rv1-RR (I) and DU145 (J) cells upon MUC1 siRNA silencing with three different oligos (13.1 to 13.3) and the respective negative control, represented through linear-quadratic model (LQ = (S=e \u003csup\u003e– (xD + BD2)\u003c/sup\u003e)), **,\u003cem\u003e p value \u0026lt;0.01\u003c/em\u003e **** \u003cem\u003ep value \u0026lt;0.0001\u003c/em\u003e. K) Cell survival fraction (SF) of PC3 negative control (NC) and PC3-AR (AR overexpressing) cells, represented through linear-quadratic model (LQ = (S=e \u003csup\u003e– (xD + BD2)\u003c/sup\u003e)), **,\u003cem\u003e p value \u0026lt;0.01\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-5614729/v1/722d7ed0d803cbc9ba2359fc.png"},{"id":75627677,"identity":"94eb1574-aeb6-4386-b67a-2f6ba7190222","added_by":"auto","created_at":"2025-02-06 13:17:14","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2224342,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eIdentification of 22Rv1-RR cell traits and relevant signaling pathways by Mass spectrometry.\u003c/strong\u003e\u003c/em\u003e A) Heatmap representing the relative abundance of peptides identified in 22Rv1-P (parental) and -RR (radioresistant) samples using mass spectrometry (LC-MS) analysis. Rows represented the detected peptides and columns represent each analyzed samples, 22Rv1-P in light blue and 22Rv1-RR in light red, in independent triplicates. The color intensity of each square reflects the relative abundance of a specific proteins. The range is indicated by the scale bar with red indicating higher abundance, blue indicating lower abundance and white representing no abundance detected. B) Volcano plot depicting log\u003csub\u003e2\u003c/sub\u003e scale fold change versus negative log\u003csub\u003e10\u003c/sub\u003e-transformed \u003cem\u003ep-value\u003c/em\u003e for all identified peptides. Light Red dots indicate peptides with statistically significant up-regulation (p-value \u0026lt; 0.05, fold change \u0026gt; 2), and light blue dots indicate peptides with statistically significant down-regulation (p-value \u0026lt; 0.05, fold change \u0026lt; -2). Sample RR (22Rv1-RR cells in triplicates) was compared with samples P (22Rv1-P cells in triplicates). C) Scatter plot representing the distribution of 2Rv1-P and -RR samples in the first two principal components (PC1 and PC2). Each point represents a sample.\u0026nbsp;The axes represented by PC1 and PC2 explain 67.5% and 14.4% of the total variance in the data,\u0026nbsp;respectively. D) Cytoscape network visualization (cNETplot) depicting the relationships between differentially expressed peptides following irradiation exposure in 22Rv1-RR cells. Nodes represent genes, and edges represent significant co-expression relationships between them. Node size corresponds to the absolute value of the log2 fold change. Node colour indicates the direction of fold change (red for up-regulated, blue for down-regulated). Edges are coloured based on the correlation coefficient between gene expression profiles. Colour legends are represented in the figure. Source: SRplot online platform. E) Bar chart showing significantly enriched pathways in 22Rv1-RR cells identified through the proteomic analysis for the differential expressed peptides between 22Rv1-P (parental) and -RR (radioresistant) cells. Higher bar heights indicate stronger enrichment. Source: Online Enricher library. F) Heatmap representing the relative abundance of peptides identified in 22Rv1-P (parental) and -RR (radioresistant) samples using Gene set enrichment analysis (GSEA), molecular signature database (MsigDB) for “hallmark of epithelial-mesenchymal-transition” Human gene set. Rows represented the detected peptides and columns represent each analyzed samples, 22Rv1-P in light blue and 22Rv1-RR in light red, in independent triplicates.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-5614729/v1/323810bdc5c0c407311748d3.png"},{"id":75627679,"identity":"7c0a3092-21f1-493a-a048-19e08764739d","added_by":"auto","created_at":"2025-02-06 13:17:14","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":2182942,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eMUC1 overexpression led to higher PCa cell migration and invasion. \u003c/strong\u003e\u003c/em\u003eCell migration differences between 22Rv1-P and RR cells and between PC3 negative control (PC3-NC) and AR expressing (PC3-AR) cells, A and B, respectively, during 24 h after \u003cem\u003ewound\u003c/em\u003e formation. Results are represented by mean ± standard deviation (SD) in scatter plot with bars. \u003cem\u003eyy\u003c/em\u003e axis represent the % of cell migration regarding the control timepoint of wound formation (0h). *** \u003cem\u003ep value\u0026lt;0.001\u003c/em\u003e, **** \u003cem\u003ep value\u0026lt;0.0001\u003c/em\u003e. C, D) Global changes in the percentage of invasive cells in transwell inserts and the respective plots with dots and lines confirming the evolution of each individual replicate between 22Rv1-P (parental) and -RR (radioresistant) cells. E, F) Global changes in the percentage of invasive cells in transwell inserts and the respective plots with dots and lines confirming the evolution of each individual replicate between PC3-NC (negative control) and PC3-AR (AR overexpressing) cells. *,\u003cem\u003e p value \u0026lt;0.05\u003c/em\u003e. C) and E) results are represented as truncated violine plots with median ± standard deviation (SD) values. G, H) Representative images of cell invasion membranes in 22Rv1-P and RR (upper) and PC3-NC and PC3-AR (down), taken by a stereomicroscope Olympus S2X16 at 7x amplification. Violet dots are invasive cells.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-5614729/v1/1707293af26939e0f67f9638.png"},{"id":85999393,"identity":"70de2194-81a8-4949-9441-8a389ed96f0d","added_by":"auto","created_at":"2025-07-04 07:11:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":10816411,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5614729/v1/bf626d11-6e2e-4429-abde-e6fec9ad1d25.pdf"},{"id":75627670,"identity":"dc93b590-0747-42f0-9fd6-aeff6082ffc8","added_by":"auto","created_at":"2025-02-06 13:17:14","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":17853,"visible":true,"origin":"","legend":"Supplementary figure captions","description":"","filename":"Supplementaryfigurescaptions.docx","url":"https://assets-eu.researchsquare.com/files/rs-5614729/v1/763edf50549fa481a11f715f.docx"},{"id":75628630,"identity":"30e0c8d4-9769-481d-8596-3275472167f6","added_by":"auto","created_at":"2025-02-06 13:25:14","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":18838,"visible":true,"origin":"","legend":"Supplementary tables","description":"","filename":"SupplementaryTables.docx","url":"https://assets-eu.researchsquare.com/files/rs-5614729/v1/5fdd3d2d5248495ac995f85f.docx"},{"id":75627685,"identity":"5cc47ac8-0991-43c1-9fec-915c37f8f424","added_by":"auto","created_at":"2025-02-06 13:17:15","extension":"tif","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":15963664,"visible":true,"origin":"","legend":"Supplementary figure 1","description":"","filename":"Supplementaryfigure1.tif","url":"https://assets-eu.researchsquare.com/files/rs-5614729/v1/36b782dedc251fcec67ae7dd.tif"},{"id":75628631,"identity":"e9180b87-4efc-4f4e-829b-9f45c4678fd6","added_by":"auto","created_at":"2025-02-06 13:25:14","extension":"tif","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":3159312,"visible":true,"origin":"","legend":"Supplementary figure 2","description":"","filename":"Supplementaryfigure2.tif","url":"https://assets-eu.researchsquare.com/files/rs-5614729/v1/f33712091b2a92d6f9eb4c70.tif"},{"id":75627683,"identity":"06697444-dac7-41e4-bb72-1e3c87483d41","added_by":"auto","created_at":"2025-02-06 13:17:15","extension":"tif","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":3544640,"visible":true,"origin":"","legend":"Supplementary figure 3","description":"","filename":"Supplementaryfigure3.tif","url":"https://assets-eu.researchsquare.com/files/rs-5614729/v1/3abca9d4d0a45d40d7415385.tif"},{"id":75627680,"identity":"d6f8f564-cc20-44e8-ae2f-dd1d40c9134e","added_by":"auto","created_at":"2025-02-06 13:17:14","extension":"tif","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":6727372,"visible":true,"origin":"","legend":"Supplementary figure 4","description":"","filename":"Supplementaryfigure4.tif","url":"https://assets-eu.researchsquare.com/files/rs-5614729/v1/910e9825aa780623cf0f1e9c.tif"},{"id":75627688,"identity":"da2588bd-f3ec-4438-baa7-612db9caa497","added_by":"auto","created_at":"2025-02-06 13:17:16","extension":"tif","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":51578096,"visible":true,"origin":"","legend":"Supplementary figure 5","description":"","filename":"Supplementaryfigure5.tif","url":"https://assets-eu.researchsquare.com/files/rs-5614729/v1/703fc3c1e09c7da7e14fb0f6.tif"},{"id":75627682,"identity":"e891c941-9d9e-4073-95c7-193682c57907","added_by":"auto","created_at":"2025-02-06 13:17:15","extension":"tif","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":19960688,"visible":true,"origin":"","legend":"Supplementary figure 6","description":"","filename":"Supplementaryfigure6.tif","url":"https://assets-eu.researchsquare.com/files/rs-5614729/v1/9dc0a1dc4874f23c02709e4d.tif"},{"id":75627686,"identity":"7d5dc9bd-d1d3-4d93-8a91-affc972d82f7","added_by":"auto","created_at":"2025-02-06 13:17:15","extension":"tif","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":22967364,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary figure 7\u003c/p\u003e","description":"","filename":"Supplementaryfigure7.tif","url":"https://assets-eu.researchsquare.com/files/rs-5614729/v1/7ad8a86cf8153de36219cd35.tif"},{"id":75628632,"identity":"15d12774-e0c7-4553-a1fa-ca41d1542907","added_by":"auto","created_at":"2025-02-06 13:25:15","extension":"tif","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":14351012,"visible":true,"origin":"","legend":"Supplementary figure 8","description":"","filename":"Supplementaryfigure8.tif","url":"https://assets-eu.researchsquare.com/files/rs-5614729/v1/44c08dea3cb445dc368e9817.tif"},{"id":75627687,"identity":"b83c2234-32d3-4470-8e4e-47cbad476467","added_by":"auto","created_at":"2025-02-06 13:17:16","extension":"tif","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":57533924,"visible":true,"origin":"","legend":"Supplementary figure 9","description":"","filename":"Supplementaryfigure9.tif","url":"https://assets-eu.researchsquare.com/files/rs-5614729/v1/f38eea12293443867d1bdc38.tif"}],"financialInterests":"There is no duality of interest","formattedTitle":"Decoding MUC1 and AR axis in a radiation-induced neuroendocrine prostate cancer cell-subpopulation unveils novel therapeutic targets","fulltext":[{"header":"Introduction","content":"\u003cp\u003eConsidering that nearly 90% of prostate cancers (PCa) are diagnosed as organ-confined disease, the long-standing challenge remains to overcome therapeutic resistance and disease progression to more, uncurable advanced stages. Although neuroendocrine prostate cancers (NEPC) represent a rare histological type, accounting for only 1% of all diagnosed PCa, focal treatment-induced neuroendocrine differentiation (NED) occurs in approximately 30% of advanced PCa (\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Likewise, \u003cem\u003ede novo\u003c/em\u003e neuroendocrine transdifferentiation (tNED) represents a common progression route for PCa, consistently associated with resistance to androgen deprivation (ADT) and/or radiotherapy (RT) (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). NEPC encompasses traits such as (i) loss of androgen receptor (AR) axis accompanied by low or absent levels of prostate-specific antigen (PSA), (ii) heightened stemness and (iii) increased cell aggressiveness associated with epithelial-mesenchymal transition (EMT) induction (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTumors are complex ecosystems to which heterogeneity adds another level of challenge for dissection of the underlying mechanisms. As a transcription factor, AR plays pivotal roles in PCa growth and progression. Currently, neoadjuvant or concurrent ADT combined with RT is an established standard-of-care for intermediate/high risk organ-confined PCa patients (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Despite the initial success of ADT in tumor reduction, PCa may rapidly relapse and adapt to progress for more advanced stages (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). The role of AR in PCa radioresistance is controversial and little has been explored in this field. AR has been shown to exhibit contrasting biological functions, initially acting as oncogenic driver and later as tumor suppressor (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Upon disease progression, AR reduction could be associated with a variety of adaptive resistance mechanisms, including enriched gene mutations and/or epigenetic modulation. Indeed, we have previously shown evidence of the involvement of epigenetic mechanisms in \u003cem\u003eAR\u003c/em\u003e gene promoter regulation (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). We found that although the AR negative cell line DU145 disclosed heterogeneity in \u003cem\u003eAR\u003c/em\u003e methylation signature, AR re-expression was not successfully accomplished upon 5-aza-2-deoxycytidine (DAC) and Trichostatin A (TSA) exposure, due to the increase in repressive histone markers acting as alternative protective mechanism fostering cancer cell survival (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn clinical practice, AR heterogeneity and variations represents a serious concern that hampers PCa eradication. Hence, finding alternative oncogenic signaling pathways activated in the absence of AR is key to disclose new therapeutic avenues for advanced PCa, particularly in the context of RT resistance and treatment-induced NED. Remarkably, AR signaling suppression has been associated with oncogenic MUC1 overexpression, and with its cell surface functional fraction -MUC1-C (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Indeed, MUC1-C has been shown to drive NEPC progression, promoting self-renewal capacity and tumorigenicity (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Despite being mostly considered an oncogene, overexpressed in multiple solid tumors, MUC1 is also involved in metabolic reprograming of cancer cells, contributing to pancreatic cancer radioresistance (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Furthermore, MUC1 overexpression was also shown to confer radioresistance in head and neck cancer cells and hepatocellular carcinomas (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Additionally, MUC1 was involved in important functions enabling cancer spread and overall cell aggressiveness (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Nonetheless, a putative role in acquisition of PCa radioresistance has not been studied, yet.\u003c/p\u003e \u003cp\u003eHence, we sought to explore the AR-MUC1 signaling axis in a PCa radiation-resistant cell line which we previously established (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e), to dissect PCa radioresistant mechanisms and uncover novel therapeutic targets. We found that, in RR PCa cells, overexpression of MUC1-C due to AR abrogation led to increased invasiveness and aggressiveness of neoplastic cells. Overall, MUC1 emerged as a promising target for radiosensitization, particularly in patients with advanced androgen-insensitive and unresponsive PCa.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eMucin 1 (MUC1) is increased in neuroendocrine-like PCa cells exhibiting radioresistance\u003c/h2\u003e \u003cp\u003eWe first screened wild-type PCa cell lines for AR and MUC1 expressions (Supplementary Fig.\u0026nbsp;1A, B). PC3 and DU145, which are AR-negative PCa cell lines (Supplementary Fig.\u0026nbsp;1A), derived from castration resistant NE-like tumors, disclosed the highest MUC1 mRNA expression levels contrarily to the less aggressive cell lines C4-2 and 22Rv1 (Supplementary Fig.\u0026nbsp;1B). C4-2 and 22Rv1, which express high levels of AR, depicted reduced levels of MUC1 (Supplementary Fig.\u0026nbsp;1A, B). In PCa tissues, MUC1 protein was significantly overexpressed in advanced tumors with NED, compared to localized hormone-naïve prostate adenocarcinomas (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA), while showing decreased AR immunoscore (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). A significant negative correlation between these two proteins was depicted [r=-0.54, p = 0.0041] (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC)]. This data agrees with \u003cem\u003ein-silico\u003c/em\u003e data from The Cancer Genome Atlas (TCGA) (Supplementary Fig.\u0026nbsp;1C). Additionally, \u003cem\u003ein silico\u003c/em\u003e MUC1 expression positively correlates with classical NED markers, such as \u003cem\u003eNCAM1\u003c/em\u003e (CD56), neuron specific enolase (\u003cem\u003eNSE)\u003c/em\u003e and \u003cem\u003eCHGA\u003c/em\u003e (chromogranin A) (supplementary Fig.\u0026nbsp;1D-F). Thus, we focused on elucidating the role of AR and MUC1 in a radioresistance model. Upon single-dose irradiation (SD-IR) with 2, 4, 6 and 8 Gy, the survival fraction (SF) of PC3 and DU145 cell lines (AR-/MUC1+) was significantly higher compared to C4-2 and 22Rv1 (AR+/MUC1-) (Supplementary Fig.\u0026nbsp;1G). Because MUC1 can mediate PCa lineage plasticity (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e), we questioned whether AR-MUC1 dynamics might alter RT-induced NED. Firstly, we found that prolonged exposure to fractionated RT led to an increase of CD56, INSM1, CGA and Synaptophysin (Syn) in radioresistant (RR) cells – 22Rv1-RR - that we previously established (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). Contrarily, in a gain-of-function model, we found that forced AR overexpression in PC3 cells (PC3-AR) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE, F) led to a decrease of NED-related markers (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eG). Moreover, PC3-AR cells showed significantly reduced MUC1 and MUC1-C protein and mRNA levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eH, I). Together, these results suggest that MUC1, contrarily to AR, is overexpressed in CRPC NE-like cells, rendering them less responsive to RT.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAR abrogation induces MUC1 expression upon prolonged RT exposure in PCa cells\u003c/h3\u003e\n\u003cp\u003eAlthough parental 22Rv1 cell lines (22Rv1-P) expressed high AR mRNA and protein levels, upon prolonged exposure to fractionation IR (FIR), both protein and transcript AR expression was dramatically lost (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, B), while MUC1/MUC1-C was expressed \u003cem\u003ede novo\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Indeed, AR protein levels were completely abrogated upon 10 fractions of IR (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Next, we investigated possible MUC1 regulators by analyzing a protein-protein interaction network, using \u003cem\u003ein silico\u003c/em\u003e platforms -TRRUST \u003cem\u003eversion 2\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). As anticipated, AR surfaced as a transcriptional suppressor of MUC1 expression. Conversely, STAT3, STAT1 and GATA3 stood out as relevant transcription factors that positively regulate MUC1 (Supplementary table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Remarkably, \u003cem\u003eSTAT3\u003c/em\u003e transcript levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE) and its active phosphorylated form, ySTAT3, were significantly upregulated in 22Rv1-RR cells compared to parental cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF). No significant differences were found, however, for \u003cem\u003eSTAT1\u003c/em\u003e and \u003cem\u003eGATA3\u003c/em\u003e transcription levels (Supplementary Fig.\u0026nbsp;2). Remarkably, \u003cem\u003eSTAT3\u003c/em\u003e was that with more overlapping targeted genes with \u003cem\u003eAR\u003c/em\u003e, comparing to the other two genes (Supplementary table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eEpigenetic modulation of AR gene promoter in 22Rv1-RR cells\u003c/h3\u003e\n\u003cp\u003eTo dissect the molecular mechanism by which RT triggers AR suppression to facilitate the expression of MUC1 and NED in PCa, we further focused on investigating the impact of epigenetic modifications on the transcriptional modulation of AR. We found a significant and impressive increase in the occupancy of histone repressive marks – H3K27me3, H3K9me2 and H4K20me3 - within \u003cem\u003eAR\u003c/em\u003e gene promoter region domain in 22Rv1-RR cells, comparatively with the parental cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA-C). Conversely, constitutively active chromatin domains – H3K4me3, H3K36me2 and global lysine acetylation – were significantly downregulated at the same promoter region, except for H3K27ac which increased \u003cem\u003eAR\u003c/em\u003e occupancy (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD-G). This epigenetic regulation of AR is also in line with our previous findings (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). As a proof-of-concept, complete abrogation of RNA polymerase II binding was observed at \u003cem\u003eAR\u003c/em\u003e promoter region in 22Rv1-RR cells, which supports the lack of AR expression in these cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eH). Overall, we showed that \u003cem\u003eAR\u003c/em\u003e is epigenetically regulated in PCa cells upon radioresistance.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eAR and STAT3 as transcriptional regulators of MUC1\u003c/h3\u003e\n\u003cp\u003eOur data suggested that AR acts as a negative transcriptional regulator of MUC1 in radioresistant PCa cells. We then investigated whether STAT3 might function as an enhancer of \u003cem\u003eMUC1\u003c/em\u003e transcription in the absence of AR. Chromatin immunoprecipitation (ChIP) assay indicated that AR binds \u003cem\u003eMUC1\u003c/em\u003e promoter region in 22Rv1 parental cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Likewise, there was no binding in PC3 parental cells, which are AR-negative, but when AR was overexpressed, binding of AR to \u003cem\u003eMUC1\u003c/em\u003e promoter region was depicted (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). We, then, hypothesized whether in the absence of AR, STAT3 might emerge as replacement, i.e., a positive regulator. Indeed, both in 22Rv1-RR and in PC3-NC cells, a significant positive binding of STAT3 to \u003cem\u003eMUC1\u003c/em\u003e gene promoter was observed, compared to the 22Rv1-P and PC3-AR, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC, D). The schematic panel in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE illustrates the interaction of AR and STAT3 with \u003cem\u003eMUC1\u003c/em\u003e gene promoter in both studied models – 22Rv1-P/RR and PC3-NC/AR. Our data thus suggests that \u003cem\u003eMUC1\u003c/em\u003e is transcriptionally activated in the presence of STAT3, whereas AR, when present, acts as a transcriptional repressor, entailing MUC1 silencing.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eMUC1 silencing attenuates radioresistance of PCa cells\u003c/h3\u003e\n\u003cp\u003eAll previously described results suggest that prolonged exposure to RT may promote NED in PCa cells via alteration of the AR/MUC1/ STAT3 axis, leading to therapy resistance. To delve deeper into how response to RT depends on AR/MUC1 expression, we accomplished AR downregulation in 22Rv1-P cells, which closely mimics what we found to occur during prolonged RT exposure (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Notably, AR knockdown (\u003cem\u003eAR\u003c/em\u003e-\u003cem\u003eKD)\u003c/em\u003e significantly increased MUC1/MUC1-C expression in 22Rv1-P cells, at least with the siRNA AR 13.1 oligo sequence (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB, C). Furthermore, 22Rv1 cells with \u003cem\u003eAR\u003c/em\u003e downregulation disclosed significantly increased clonogenicity when exposed to SD-IR from 0 to 8Gy, which is indicative of a more resistant behavior (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). Next, we knocked down MUC1 expression (\u003cem\u003eMUC1-KD\u003c/em\u003e) in both 22Rv1-RR and DU145 cell lines (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE-H). Contrarily to AR knockdown, AR levels in \u003cem\u003eMUC1-KD\u003c/em\u003e cells were not significantly altered (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eG, H and supplementary Fig.\u0026nbsp;3). Nonetheless, a slight increase of AR protein levels was observed in \u003cem\u003eMUC1-KD\u003c/em\u003e-22Rv1-RR, but not in DU145 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eG). Remarkably, both 22Rv1-RR- and DU145- \u003cem\u003eMUC1-KD\u003c/em\u003e cells depicted significantly reduced clonogenicity upon SD-IR exposure (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eI, J). Moreover, PC3-AR cells, which express low levels of MUC1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eI, J), disclosed significant radiosensitivity compared to the negative control (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eK). Noteworthy, 22Rv1-\u003cem\u003eMUC1-KD\u003c/em\u003e cells displayed a radiosensitivity profile closer to the original 22Rv1-P - \u003cem\u003eyellow arrow\u003c/em\u003e (Supplementary Fig.\u0026nbsp;4). Altogether, these results suggest that \u003cem\u003eMUC1-KD\u003c/em\u003e may enhance RT response even when AR levels are absent or residual. Indeed, MUC1, rather than AR, seems to act as a master radioresistant driver in our cell model.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eMass spectrometry-based quantitative proteomics discloses significant differences between parental and RR cells.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo unveil the most relevant altered molecular pathways linking MUC1/MUC1-C with a radioresistant-induced NED phenotype in PCa, mass spectrometry-based quantitative proteomics was performed in 22Rv1-P and RR cells. The results revealed significant differences in expression of a considerable number of peptides (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA-C). Specifically, 300 proteins were significantly upregulated, whereas 179 were significantly downregulated in 22Rv1-RR cells compared to the parental cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). As anticipated, classical NED markers such as CGA (\u003cem\u003eCHGA\u003c/em\u003e) and enolase 2 (ENO2) were upregulated in the RR group (Supplementary Fig.\u0026nbsp;5A). Conversely, classical prostate gland markers that are lost in NEPC, such as AR and PSA (\u003cem\u003eKLK3\u003c/em\u003e), were found downregulated (Supplementary Fig.\u0026nbsp;5A). Interestingly, CD49f, NECTIN1 and KRT14, which are commonly associated with basal cell-like traits, were found upregulated in the RR model, also suggesting increased cell aggressiveness with the loss of luminal cell identity (Supplementary Fig.\u0026nbsp;5A).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eInterestingly, the transition to invasive NED PCa cells upon prolonged exposure to IR was also associated with progressive changes in the structure and morphology of 22Rv1-RR cells (Supplementary Fig.\u0026nbsp;6A, B). Indeed, the established RR cells disclose significant morphometric changes in comparison with the parental fraction. Specifically, 22Rv1-RR cells are larger, with increased circularity, roundness, and solidity, while displaying reduced skewness (Supplementary Fig.\u0026nbsp;6C-H), further supporting that RT-induced AR-STAT3-MUC1-MUC1-C axis drives cell plasticity and aggressiveness in advanced PCa.\u003c/p\u003e \u003cp\u003eOverall, gene ontology analysis revealed significant differences in biological processes related to the structure and organization of actin filaments, cell-cell junction, focal adhesion, and cytoskeleton organization and response to interferon gamma (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). In particular, analysis of Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathways emphasized gene sets related to focal adhesion and cell adhesion molecules, highlighting those that were up (red)- or down (green)-regulated in 22Rv1-RR group comparing with 22Rv1-P samples (Supplementary Fig.\u0026nbsp;5B, C). Moreover, upon analysing the data using Human MSigDB Hallmark Gene Set we found that epithelial mesenchymal transition (EMT) was the most significant molecular signaling pathway altered in our RR model, followed by apical junction, interferon gamma and response to UV radiation (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE). Subsequently, we confirmed that a high number of proteins involved in EMT pathway were upregulated in 22Rv1-RR replicates compared with 22Rv1-P (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF). Among them, p-cadherin (CDH3) and vimentin (VIM), both constituting relevant peptides involved in EMT, were prominently and significantly upregulated in 22Rv1-RR cells (Supplementary Fig.\u0026nbsp;5D). Using WB, we further confirmed VIM and PCAD to be considerably upregulated in 22Rv1-RR cells compared to parental fraction (Supplementary Fig.\u0026nbsp;5E). Conversely, VIM expression was decreased in PC3-AR cells compared with the negative control (Supplementary Fig.\u0026nbsp;5F), whereas PCAD was absent in PC3 cells (\u003cem\u003edata not shown\u003c/em\u003e).\u003c/p\u003e \u003cp\u003eInterestingly, analysis of the top enriched molecules revealed that 22Rv1-RR were similar to DU145 cells (Supplementary Fig.\u0026nbsp;5G), although significantly different from the original 22Rv1 cell line, as well as from normal adult prostate tissue (Supplementary Fig.\u0026nbsp;7A, B). Likewise, many AR network-related molecules were downregulated in 22Rv1-RR compared to 22Rv1-P cells (supplementary Fig.\u0026nbsp;7C).\u003c/p\u003e \u003cp\u003eIn summary, 22Rv1-RR cells exhibited a more mesenchymal phenotype characterized by traits associated with aggressiveness, including basal cell-like features, stemness, and neuroendocrine differentiation. These changes were accompanied by a reduction in AR signalling.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAR/MUC1 loop disruption leads to abnormal cancer cell migration and invasion.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eNext, we performed migration and invasion assays using radioresistant and AR overexpressing cells. A more pronounced \u003cem\u003ewound healing\u003c/em\u003e ability was observed in 22Rv1-RR cells compared to the parental fraction, with statistically significant differences (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). In PC3-AR cells, overexpressing AR and low MUC1, the opposite was observed, with a significant decrease in cell migration (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). Furthermore, the RR fraction (AR-/MUC1+) showed significantly enhanced cell invasion capability (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC, E). Conversely, in PC3-AR cells (AR+/MUC1-), invasion capacity decreased by more than half compared with control cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD, F), supporting a role for MUC1 in orchestrating an aggressive phenotype in PCa cells.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e "},{"header":"Discussion and Conclusions","content":"\u003cp\u003ePCa remains a major health concern, having a significant impact on patient survival and quality of life (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Intra-tumoral heterogeneity, as well as divergent molecular and biological signatures are puzzling features of PCa, contributing to primary treatment failure and consequent disease progression (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). The versatility of AR throughout disease progression and its consequent depletion in more aggressive forms, like NEPC, poses significant challenges, especially considering that most primary therapies target this receptor, aiming to reduce prostate-specific tumor proliferation (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Exploring the effects of androgen signaling suppression and further identifying new therapeutic targets for those patients at risk, may improve the effectiveness of first-line cell-killing therapies, such as RT. Earlier studies suggested that ADT and/or radiotherapy might drive treatment-induced NED, resulting in lack of response to therapy (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e–\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). The phenomenon of tNED, stemming from primary therapy, may also be associated with the molecular and functional reprogramming of the tumor cells. The underlying mechanisms, however, have not been completely elucidated, thus far. Herein, the generation of a radioresistant \u003cem\u003ein vitro\u003c/em\u003e model – 22Rv1-RR cells – allowed us to uncover a previously unrecognized role for MUC1-C oncoprotein in driving PCa radioresistance, disclosing evidence of the existence of a regulatory axis involving AR and MUC1 which drives PCa lineage switching towards NE-like characteristics.\u003c/p\u003e\u003cp\u003eWe found that 22Rv1-RR cells exhibited \u003cem\u003ede novo\u003c/em\u003e expression of MUC1/MUC1-C coupled with suppression of AR axis signaling. Besides 22Rv1-RR cells, we also showed that PC3 and DU145, which are metastatic castration-resistant PCa cells, constitutively express MUC1/MUC1-C oncoprotein, while expressing only residual to undetectable levels of AR (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Early findings demonstrated that those cells lines harbor NED traits, evidenced through expression of well-established NE cell markers (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Interestingly, MUC1 has been recognized as a driver of PCa lineage plasticity and NED (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e), with MUC1 expression associating with NEPC score, in \u003cem\u003ein silico\u003c/em\u003e datasets from TCGA (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Here, in a cohort of localized prostate adenocarcinomas and NEPC tissues, we confirmed AR downregulation and MUC1 overexpression, with a negative correlation. Moreover, we observed that prolonged irradiation exposure led to AR suppression, MUC1 upregulation and acquisition of NED features in PCa cells. Indeed, the increase of MUC1 expression in 22Rv1-RR cells was accompanied by increased NE markers expression, including CD56, INSM1, CGA and SYP. Interestingly, NE markers expression decreased when AR was overexpressed in PC3 cells, with a concomitant reduction of MUC1 expression, further emphasizing the negative correlation between the expression of those two proteins – AR and MUC1.\u003c/p\u003e\u003cp\u003ePrevious studies substantiated the crosstalk between MUC1 and STAT3 as a downstream target in an auto-inductive regulatory loop (\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e–\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). In our study, we further demonstrated that AR functions as a transcriptional regulator of MUC1. Indeed, in the absence of AR, a significant occupancy of STAT3 at the \u003cem\u003eMUC1\u003c/em\u003e promoter in both 22Rv1-RR and PC3 cells, which are AR-negative cell lines, was disclosed. Interestingly, MUC1-C and JAK-1 were reported as intermediators of STAT3 phosphorylation (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). In the same vein, we found an upregulation of γ-STAT3 in 22Rv1-RR compared to the parental cells. Overall, these findings substantiate the existence of a regulatory mechanism involved in PCa radioresistance, in which epigenetic silencing of AR leads to MUC1 expression via STAT3 activation, which in turn drives tumor cell reprogramming towards a more mesenchymal phenotype, with stemness and NED traits.\u003c/p\u003e\u003cp\u003eRemarkably, mass spectrometry data revealed differential expression patterns between the parental lineage and the RR fraction. Among them, cell-cell adhesion and dynamics on actin filaments were the most common cellular alterations found in 22Rv1-RR cells, which are suggestive of evolution to an EMT phenotype, a recognized hallmark of cancer progression (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Moreover, the increased roundness observed in 22Rv1-RR cells has been considered to indicate a superior ability to invade the extracellular matrix (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e), which is aligned with the observed increase in cell migration and invasion capacity of 22Rv1-RR. Interestingly, NE (CHGA and ENO2) and basal (cytokeratin 14, KRT14 and CD49f) cell markers were also found upregulated in 22Rv1-RR compared to 22Rv1-P cells, according to the proteomic analysis. It has been previously documented that CD49f positive cell populations overlapped with genes expressed in basal, stem and neuroendocrine cells and were associated with EMT, thus influencing cell invasion and migration(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Our results further confirm those observations and provide an additional explanation for the biological aggressiveness disclosed by 22Rv1-RR cells.\u003c/p\u003e\u003cp\u003eIn conclusion, we demonstrated in this study that prolonged radiotherapy entails loss of AR and upregulation of MUC1 in a PCa subpopulation, driving NED and fostering EMT, materialized in a phenotypic and morphological switch, which ultimately translates into resistance to RT. In the past, attempts to restore AR expression through epigenetic inhibition have been unsuccessful due to tumor cell plasticity (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Herein, we show that MUC1 knockdown in 22Rv1-RR and DU145 cells significantly boosts RT response, even without restored AR expression, indicating the MUC1, \u003cem\u003eper se\u003c/em\u003e, has an important role in driving radioresistance, independently of the MUC1/AR axis. Interestingly, MUC1 was previously associated with radioresistance in other models, but not in PCa (\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e–\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Despite the well-known evidence that AR is a preponderant factor for PCa progression, in a radioresistance scenario in which more aggressive phenotypes emerge and typically lose AR expression, other targets should be considered for alternative therapeutic strategies, among which MUC1 cell surface oncoprotein seems of major interest.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eCell culture\u003c/h2\u003e \u003cp\u003ePCa cell lines, including hormone-sensitive lineages C4-2 and 22Rv1 and hormone-insensitive PC-3 and DU145, were selected for this study. Furthermore, previously generated 22Rv1-RR cells were used as a model of radioresistance (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). All cell lines were cultured with RPMI 1640 supplemented with 10% of fetal bovine serum (FBS), 100 IU/mL penicillin and 100 \u0026micro;g/mL streptomycin. Optimal cell culturing was maintained at 37\u0026ordm;C in a humidifier incubator with 5% CO\u003csub\u003e2\u003c/sub\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eIonizing radiation\u003c/h2\u003e \u003cp\u003eCells were irradiated as previously described for \u003cem\u003ein vitro\u003c/em\u003e assays (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). A radioresistant (RR) 22Rv1 cell line subpopulation was generated from the respective parental lineage using the same fractionation scheme, as previously depicted (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003ePC3-\u003c/b\u003e \u003cb\u003eAR\u003c/b\u003e \u003cb\u003eoverexpressing cells\u003c/b\u003e\u003c/p\u003e \u003cp\u003eCell transfection was carried out by pEZ-Lv105 (GeneCopoeiaTM, Rockville, MD, USA) using FuGENE\u0026reg; HD Transfection Reagent (Promega, Madison, WI, USA), following manufacturer\u0026rsquo;s recommendations. Briefly, cells were plated at an optimized density (2x10\u003csup\u003e4\u003c/sup\u003e cells/mL), one day before transfection, in a 6-well culture plates. \u003cem\u003eIn vitro\u003c/em\u003e cell transfection was performed when the cells had reached at least 30\u0026ndash;50% of confluence. 2\u0026micro;g of oligo molecules were diluted in Opti-MEM\u0026trade; medium (GIBCO\u0026reg;) to a final volume of 100\u0026micro;L. Additionally, 4\u0026micro;L of transfection reagent were added in a proportion of 2:1 FuGENE\u0026reg; HD Transfection Reagent:DNA ratio. Then, transfection mixtures were incubated for 15 minutes at room temperature and then added to the cells in cell growth medium (RPMI-1640). Transfection of the cells with scramble DNA oligos was performed, serving as negative controls (NC). After 48h of transfection, stable clones with the vector were selected with Puromycin dihydrochloride (cat. 631306, Clontech Laboratories Inc.) at a cytotoxic tested concentration of 0.5\u0026micro;g/mL. Stable transfected cells were used for further experiments. AR overexpression was confirmed by western blot and real time quantitative polymerase chain reaction (RT-qPCR).\u003c/p\u003e \u003cp\u003e \u003cb\u003esiRNA transfection for\u003c/b\u003e \u003cb\u003eMUC-1\u003c/b\u003e \u003cb\u003eand\u003c/b\u003e \u003cb\u003eAR\u003c/b\u003e \u003cb\u003egene silencing\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTrifecta dicer subtracts containing three different siRNA sequences were used to knockdown MUC1 (hs.Ri.MUC1.13.1 to hs.Ri.MUC1.13.3) or AR (hs.Ri.AR.13.1 to hs.Ri.AR.13.3) (Integrated DNA technologies (IDT), USA). Also, a negative control siRNA sequence (DsiRNA, 1nmol), was used (Integrated DNA technologies (IDT)). Silencing of \u003cem\u003eMUC1\u003c/em\u003e and \u003cem\u003eAR\u003c/em\u003e gene expression by \u003cem\u003ein vitro\u003c/em\u003e siRNA transfection was performed with Lipofectamine\u0026reg; 3000 reagent (Invitrogen, USA), according to manufacturer instructions. Dye labeled reagent was used for assessing cell transfection success, as confirmed by the red dots present inside the cells (supplementary Fig.\u0026nbsp;8). Gene silencing was confirmed for every experiment after 48h of siRNA transfection, a reference time point for cell irradiation (0h).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eClonogenicity assay\u003c/h2\u003e \u003cp\u003ePCa transfected cells (control, knockdown or overexpressing cells) were used for colony formation assay (CFA). Briefly, cells were plated in 24-well plates 48h before IR exposure, at an adjusted density, upon siRNA transfection. Then, 1000 or 2000 cells per well were used for DU145 and PC3 or for 22Rv1-P and -RR, respectively, for colony formation in 6-well plates.\u003c/p\u003e \u003cp\u003eAll cells were maintained in low densities for 7 days after ionization exposure in a range of 0, 2, 4, 6 and 8Gy. Following colony formation, cells were stained using 1% Crystal Violet reagent in 20% methanol solution. Each colony was considered for the count if composed of at least 50 cells. Colony counting was performed using a stereomicroscope Olympus S2X16 at 7x amplification. Radiobiological cell survival curves were constructed using linear quadratic (LQ) model [S\u0026thinsp;=\u0026thinsp;e \u0026ndash; (αD\u0026thinsp;+\u0026thinsp;βD2)], as previously described (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). The plating efficiency (PE) of each independent experiment was calculated according to the initial number of cells seeded. PE=% (number of colonies counted in the control/number of cells plated). Then, the survival fraction was calculated taking into account the PE [SF\u0026thinsp;=\u0026thinsp;number of colonies counted/(number of cells plated*(PE/100))]. SF values were introduced in GraphPad Prism software version 9.1.1 to assess cell survival curves through LQ model.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eProtein extraction, SDS-PAGE Western Blot\u003c/h2\u003e \u003cp\u003eTotal protein extraction was performed as previously described (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Protein quantification was made by colorimetric detection using PierceTM BCA Protein Assay kit, according to manufacturer instructions. Western blot was performed using 50\u0026micro;g of total protein extract. Anti-AR monoclonal antibody (AR 441, MA5-13426, Invitrogen, USA), anti-MUC1 antibody (VU4H, sc-7313, Santacruz Biotechnology), and anti-MUC1-C (D5K9I, Cell Signalling technology), were used. NED-related protein expression was evaluated using monoclonal antibodies against CHGA (DAK-A3, DAKO), INSM1 (sc-271408, Santacruz Biotechnology) and CD56 (CD56-504-L-CE, Leica). All primary antibodies were diluted in TBS-T solution with 5% of bovine serum albumin (BSA) at the manufacturer recommended dilutions and inoculated with nitrocellulose membranes overnight at 4\u0026ordm;C. Anti-β-Actin antibody (A1978, Sigma Aldrich) was used as loading normalizer. All the original, uncropped western blot images are compiled in Supplementary Fig.\u0026nbsp;9.\u003c/p\u003e \u003cp\u003e \u003cb\u003eIn silico\u003c/b\u003e \u003cb\u003estudies and online databases\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTCGA PanCancer database for prostate adenocarcinoma, derived from a large cohort of 488 PCa patients, was used to assess MUC1, AR and NED-related genes mRNA expression levels. Correlations were evaluated by Pearson or Spearmen statistical analysis. Data were downloaded from cBioPortal \u0026ldquo;For Cancer Genomics\u0026rdquo; online platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cbioportal.org\u003c/span\u003e\u003cspan address=\"https://www.cbioportal.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). TRRUST v2: an expanded reference database of human and mouse transcriptional regulatory interactions. Nucleic Acids Research 26 Oct, 2017 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.grnpedia.org/trrust/\u003c/span\u003e\u003cspan address=\"https://www.grnpedia.org/trrust/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to unveil transcriptional regulatory networks of \u003cem\u003eMUC1\u003c/em\u003e gene and to identify putative transcriptional repressors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eChromatin immunoprecipitation-ChIP (RT-qPCR)\u003c/h2\u003e \u003cp\u003eChIP- qRT-PCR was performed as previous detailed (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Specifically, to evaluate the binding affinity of histone modifications on \u003cem\u003eAR\u003c/em\u003e gene promoter region, four pairs of primer sequences were used: P1, Forward: 5\u0026rsquo;AAATTTGGTGAGTGCTGGCCT 3\u0026rsquo;; Reverse: 5\u0026rsquo;AGGACCCCTGCTTCCTGAATA 3\u0026rsquo;; P2, Forward: 5\u0026rsquo;GGAGCTATTCAGGAAGCAGGG 3\u0026rsquo;; Reverse: 5\u0026rsquo; TGGCTTTGGAGAAACAAGTGC 3\u0026rsquo;; P3, Forward: 5\u0026rsquo;CTCCAAAGCCACTAGGCAGG 3\u0026rsquo;; Reverse: 5\u0026rsquo; GGTGGAGAGCAAATGCAACA 3\u0026rsquo;; P4, Forward: 5\u0026rsquo;TGTTGCATTTGCTCTCCACCT 3\u0026rsquo;; Reverse: 5\u0026rsquo;CCTTTTTCCCTCTGTCGCCT 3\u0026rsquo;. Primer annealing temperature was 62\u0026ordm;C for all the sequences, except for AR-P1 which was set at 60\u0026ordm;C. Furthermore, three different sequences over \u003cem\u003eMUC1\u003c/em\u003e transcription starting site (TSS) were interrogated for AR and STAT3 binding \u0026ndash; P1: Forward: 5\u0026rsquo; TTGTCACCTGTCACCTGCTC 3\u0026rsquo;; Reverse: 5\u0026rsquo; GGGCAGAACAGATTCAGGCA 3\u0026rsquo;; P2: Forward: 5\u0026rsquo; AGCTGGAGAACAAACGGGTA 3\u0026rsquo;; Reverse: 5\u0026rsquo; CCTCCCCTACCTCCTACCTCT 3\u0026rsquo;; P3: Forward: 5\u0026rsquo; CTAGCTGGCTTTGTTCCCCA 3\u0026rsquo;; Reverse 5\u0026rsquo; CCTTTCACCAACCACTCCCT 3\u0026rsquo;. Primer annealing temperature was 60\u0026ordm;C for P1 and P2 and 64\u0026ordm;C for P3.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eTotal RNA isolation, cDNA synthesis and RT-qPCR\u003c/h2\u003e \u003cp\u003eTotal RNA extracts were obtained from all cell lines at each independent condition, using Trizol reagent-based extraction method, followed by cDNA synthesis of 1000ng of RNA using RevertAid RT kit (Thermo Fisher Scientific Inc., Waltham, MA, USA), according to manufacturer\u0026rsquo;s instructions. Relative gene transcription levels were calculated using \u003cem\u003eGUSB\u003c/em\u003e as housekeeping gene. Primer sequences and the respective optimized annealing temperature are listed in Supplementary table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eImmunohistochemistry in formalin-fixed paraffin embedded (FFPE) tissues\u003c/h2\u003e \u003cp\u003eAnti-AR monoclonal antibody (AR 441, MA5- 13426, Invitrogen) and anti-MUC1 (VU4H, sc-7313, Santacruz Biotechnology) antibodies were used to assess protein expression by immunohistochemistry (IHC), using a NovoLinkTM Max Polymer Detection System (Leica Biosystems, Germany). Briefly, 4\u0026micro;m sections were deparaffinized and rehydrated in a serial dilution of alcohol. Then, antigen retrieval was performed in a microwave oven at 800 W for 20 minutes in citrate 1x for MUC1, or in 95\u0026ndash;100\u0026ordm;C water-bath in EDTA 1x for 30 minutes for AR, followed by 10min cooling at room temperature. Next, tissue slides were incubated with 3% H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e in methanol (GRiSP, Portugal) solution for 10min, at room temperature. Additionally, the slides were blocked with horse serum (Vector Laboratories, USA) diluted at 1:50, for 20min, and subsequently incubated overnight with the primary antibody, at 1:250 for AR and 1:300 for MUC1. The day after, slides were incubated with post-primary block followed by polymer to boost the signal, for 30min each. Next, 3,3-diaminobenzidine (DAB) (Sigma-AldrichTM, Germany) was used as chromogen, for 10min, at room temperature. Finally, slides were counterstained with hematoxylin (Leica Biosystems, Germany) for microscopic visualization. AR nuclear staining was evaluated using a quantitative method from GenASIS software (Applied Spectral Imaging, ASI), considering the percentage of positive cells and the intensity of immunostaining, providing a continuous variable: IHC score [1 \u0026times; (% of cells stained weakly)\u0026thinsp;+\u0026thinsp;2 \u0026times; (% of cells stained intermediately)\u0026thinsp;+\u0026thinsp;3 \u0026times; (% of cells stained strongly)]. MUC1 staining was assessed by a dedicated uropathologist. Staining intensity was categorized between 0\u0026ndash;3 (0\u0026thinsp;=\u0026thinsp;negative, 1\u0026thinsp;=\u0026thinsp;weak, 2\u0026thinsp;=\u0026thinsp;moderate and 3\u0026thinsp;=\u0026thinsp;strong). Then, the percentage of positive cells were categorized as \u0026lt;\u0026thinsp;5% followed by 10% interval categories. Extension score was defined from 0 to 9, according to the category of positivity percentage. Lastly, IHC score was calculated by multiplying intensity with extension scores. IHC images were acquired using an Olympus BX41 microscope equipped with a digital camera (Olympus U-TV0.63XC) and CellSens software (version V0116, Olympus).\u003c/p\u003e \u003cp\u003e \u003cb\u003eCell migration (\u0026ldquo;\u003c/b\u003e \u003cb\u003ewound healing\u003c/b\u003e \u003cb\u003e\u0026rdquo;) assay\u003c/b\u003e \u003c/p\u003e \u003cp\u003eBriefly, 6x10\u003csup\u003e5\u003c/sup\u003e cells were seeded into 6-well plates in 2mL of complete RPMI culture medium and grown until confluence was reached. Next, in each well, two parallel vertical wounds were manually performed, followed by a washing step with PBS 1x and medium refilling. Then, for easier photographing, two parallel straight lines were drawn at the bottom of the plate to intersect the \u0026ldquo;wounds\u0026rdquo;. Then, 4 specific vertices were generated to photograph four \u0026ldquo;wound\u0026rdquo; areas over the time. For PC3 cell lines, PC3 NC condition healed the wound after 20h. Instead, 22Rv1-RR cells reached the same effect only after completing 24h. All experiments were photographed using Olympus IX51 inverted microscope equipped with Olympus XM10 Digital Camera System. The relative migration distance was computed as relative migration distance (%) = (A \u0026ndash; B) / C \u0026times; 100, where A represents the initial width of the cell wound formation, B represents the width of the cell wound after a 20h or 24h, for PC3 and 22Rv1 cells, respectively, and C represents the mean width of the initial cell wound. beWound\u0026mdash;Cell Migration Tool (Version 1.5) was used to conduct the analysis of relative migration distances, based on results from at least three independent experiments.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTranswell\u003c/b\u003e \u003cb\u003ein vitro\u003c/b\u003e \u003cb\u003ecell invasion assay\u003c/b\u003e\u003c/p\u003e \u003cp\u003e24-well BD Biocoat Matrigel Invasion Chambers (BD Biosciences) were used to address PCa cell invasion capabilities. Each chamber was conserved at -20\u0026ordm;C before using. Then, for cell plating, BD Matrigel Chambers were re-hydrated with complete RPMI culture medium at 37\u0026ordm;C for 30 minutes. Subsequently, cells were seeded inside the inserts at a density of 2.5x10\u003csup\u003e4\u003c/sup\u003e cells per well and incubated at 37\u0026ordm;C in 5% CO\u003csub\u003e2,\u003c/sub\u003e in a humidified chamber for 48h. Lastly, two days after cells platting, non-invading cells were removed by cotton swab and the invading cells were fixed with cold methanol during 20 min, followed by staining with 1% Cristal Violet diluted in 20% methanol. Invaded membranes were captured using Olympus SZx16 stereomicroscope (16x), and the percentage of invading cells were computed using Image J software (version 1.41; NIH). At least three independent experiments were performed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eMass spectrometry-based quantitative proteomic analysis\u003c/h2\u003e \u003cp\u003eUsing 22Rv1-P and RR cell line protein extracts, a comprehensive proteomic analysis was performed. Briefly, around 30\u0026micro;g of total protein per sample was enzymatically digested with trypsin/LysC. Protein identification and quantification was then performed by nanoLC-MS/MS using a Vanquish neoliquid chromatography system coupled to an Eclipse Tribrid QuadrupoleIon trap Orbitrap mass spectrometer together with a High-field asymmetric waveform ion mobility spectrometry \u0026ndash; FAIMS equipment (Thermo Scientific). The raw data was processed using Proteome Discoverer software (Thermo Scientific) and searched against the UniProt database for the \u003cem\u003eHomo sapiens\u003c/em\u003e proteome. A common protein contaminant list search was performed for further discrimination.\u003c/p\u003e \u003cp\u003eTo gain insights into the statistically significant under- or over-expressed proteins in 22Rv1-RR cells across multiple biological sample sources, exploratory and protein label free quantification - LFQ analyses were performed. Functional Enrichment analysis, including Over-Representation Analysis and Gene Set Enrichment Analysis were also carried out to further understand the functional roles of identified proteins. Thus, protein data was queried to Gene Ontology and pathway functional databases, including KEGG and Reactome.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eStatistics\u003c/h2\u003e \u003cp\u003eAll data were analyzed using GraphPad Prism software version 9.1.1. Normality tests (Shapiro-Wilk) were applied to all datasets to define the adequate subsequent statistical analysis: parametric tests (two-way ANOVA or Student\u0026rsquo;s t-test) for data following a normal distribution, and non-parametric tests (Mann-Whitney or Kruskal\u0026ndash;Wallis, to compare two or more groups, respectively) for non-normal data distribution. Clonogenicity cell survival curves were constructed based on the linear quadratic model SF\u0026thinsp;=\u0026thinsp;e \u0026ndash; (αD\u0026thinsp;+\u0026thinsp;βD2) and global differences among curves were computed using least squares regression fitting model and the comparison method of extra sum-of-squares F test, selecting both alpha and beta parameters of the equation. \u003cem\u003eP value\u003c/em\u003e less than 0.05 was considered statistically significant. Non-parametric Spearman correlation was performed between AR and MUC1 IHC-variables, previously transformed by the function of Y\u0026thinsp;=\u0026thinsp;Ln (1\u0026thinsp;+\u0026thinsp;Y). Simple linear regression was computed to find the linear relationship between the two described variables.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of Interest:\u0026nbsp;\u003c/strong\u003eThere are no competing financial interests related to the work described.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u0026nbsp;\u003c/strong\u003eCM-S performed the major experiments and wrote the first draft of the manuscript. AA-C, IC$ and VMG assisted in experimental procedures. JL* was responsible for irradiation source handling. IC@ was responsible for tissue slides processing; JL# revised tissue slides and IHC staining. MPC, LA, RH and CJ supervised the work and revised the manuscript. All authors have read and approved the final version of the article. *Joana Lencart; # Jo\u0026atilde;o Lobo; $ Iris Carri\u0026ccedil;o; @ Isa Carneiro.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions to each figure:\u0026nbsp;\u003c/strong\u003eCM-S was responsible for constructing all figure panels under the supervision of the co-authors. The arrangement and design of the panels were collectively discussed and agreed upon by all authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval and Consent to Participate:\u0026nbsp;\u003c/strong\u003eThis study used PCa biopsy specimens as FFPE tissue samples. For that purpose, this study was approved by the institutional review board (Comiss\u0026atilde;o de \u0026Eacute;tica para a Sa\u0026uacute;de) of IPO Porto, Portugal (CES-238/020).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eCJ Research is funded by Research Center of Portuguese Institute of Porto (BF.CBEG CI-IPOP-27-2016) and EpiParty PI 159-CI-IPOP-152-2021). LA research is funded by Epi-MS under the VALERE 2019 Program; V:ALERE 2020\u0026mdash;\u0026ldquo;CIRCE\u0026rdquo;; Campania Regional Government Technology Platform 2038 Lotta alle Patologie Oncologiche iCURE-B21C17000030007; Campania Regional Government FASE2: IDEAL; MIUR, Proof of Concept POC01_00043; POR Campania FSE 2014-2020 ASSE III; PON RI 2014/2020 \u0026ldquo;Dottorati Innovativi con caratterizzazione industrial\u0026rdquo;; Horizon EU: CAN-SERV BBMRI; EPI-MET MISE 2022; Bando giovani ricercatori D.R. n.834 del 30/09/2022 Universit\u0026agrave; Vanvitelli project: Miranda; National Plan for NRRP Complementary Investments \u0026ndash; Law Decree May 6, 2021, n. 59, converted and modified as to Law n. 101/2021Research initiatives for technologies and innovative trajectories in the health and care sectors: project ANTHEM (AdvaNced Technologies for Human-centrEd Medicine). CM-S and IC* were funded by 2020-FETOPEN-2018-2020 \u0026ldquo;MindGAP\u0026rdquo; and Funda\u0026ccedil;\u0026atilde;o para a Ci\u0026ecirc;ncia e Tecnologia (10.54499/2022.05135.PTDC), respectively. AA-C is a research fellow funded by Liga Portuguesa Contra o Cancro- N\u0026uacute;cleo Regional do Norte. VM-G holds a Junior researcher position UIDP/00776/2020\u0026ndash;3C funded through CI-IPOP Programmatic funding 2020\u0026ndash;2023 (reference UIDP/00776/2020) from FCT. MPC was funded by FCT\u0026mdash;Funda\u0026ccedil;\u0026atilde;o para a Ci\u0026ecirc;ncia e Tecnologia (CEECINST/00091/2018). * Iris Carri\u0026ccedil;o.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u003c/strong\u003e The authors confirm that the data supporting the findings of this study are available within the article and in Supplementary material file. Raw data that support the findings of this study are available from the corresponding author, upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eYao J, Liu Y, Liang X, Shao J, Zhang Y, Yang J, et al. Neuroendocrine Carcinoma as an Independent Prognostic Factor for Patients With Prostate Cancer: A Population-Based Study. Front Endocrinol (Lausanne). 2021;12:778758.\u003c/li\u003e\n\u003cli\u003eConteduca V, Oromendia C, Eng KW, Bareja R, Sigouros M, Molina A, et al. Clinical features of neuroendocrine prostate cancer. Eur J Cancer. 2019;121:7-18.\u003c/li\u003e\n\u003cli\u003ePatel GK, Chugh N, Tripathi M. Neuroendocrine Differentiation of Prostate Cancer-An Intriguing Example of Tumor Evolution at Play. Cancers (Basel). 2019;11(10).\u003c/li\u003e\n\u003cli\u003eHu CD, Choo R, Huang J. Neuroendocrine differentiation in prostate cancer: a mechanism of radioresistance and treatment failure. 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Cell Death Dis. 2020;11(12):1068.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"cell-death-discovery","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"cddiscovery","sideBox":"Learn more about [Cell Death Discovery](http://www.nature.com/cddiscovery/)","snPcode":"41420","submissionUrl":"https://mts-cddiscovery.nature.com/","title":"Cell Death Discovery","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Prostate cancer, radioresistance, MUC1, neuroendocrine differentiation, histone modification, EMT","lastPublishedDoi":"10.21203/rs.3.rs-5614729/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5614729/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDespite initial efficacy of radiotherapy (RT), with or without concurrent androgen-deprivation, in prostate adenocarcinoma (PCa), neuroendocrine prostate cancer (NEPC) emerging from disease progression is a highly aggressive malignancy for which standard therapies are mostly ineffective. Although oncogenic \u003cem\u003eMUC1-C\u003c/em\u003e is a leading driver of NEPC and of PCa lineage plasticity, its putative role in response to RT, including RT-induced neuroendocrine transdifferentiation (tNED), has not been explored. We thus aimed to explore the interplay between androgen receptor (AR) signaling and MUC1 in PCa progression to NEPC. Firstly, using a radioresistant PCa cell line (22Rv1-RR) we demonstrated that epigenetic suppression of AR signaling caused MUC1/MUC1-C upregulation, which seems to be activated through γSTAT3. MUC1 activation positively associated with increased expression of neuroendocrine-related markers, including CD56, chromogranin A, synaptophysin and INSM transcriptional repressor 1 (INSM1). In NEPC tissues and comparing to prostate adenocarcinoma, MUC1 was upregulated and negatively correlated with AR, which was suppressed. Finally, proteomic analyses revealed that MUC1 activation upon RT selective pressure led to acquisition of stemness features, induction of epithelial to mesenchymal transition, and enhancement of basal cell-like traits. Notably, MUC1 knockdown (KD) significantly boosted response to RT in both 22Rv1-RR and DU145 cell lines. Moreover, AR-induced overexpression in PC3 cell lines entailed MUC1 downregulation, resulting in attenuated neuroendocrine (NE) traits and radioresistance, as well as impaired cell migration and invasion capabilities. Collectively, these results highlight MUC1 as a promising radiosensitization target and may ultimately help overcome therapy resistance and NEPC progression.\u003c/p\u003e","manuscriptTitle":"Decoding MUC1 and AR axis in a radiation-induced neuroendocrine prostate cancer cell-subpopulation unveils novel therapeutic targets","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-06 13:17:09","doi":"10.21203/rs.3.rs-5614729/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"cell-death-discovery","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"cddiscovery","sideBox":"Learn more about [Cell Death Discovery](http://www.nature.com/cddiscovery/)","snPcode":"41420","submissionUrl":"https://mts-cddiscovery.nature.com/","title":"Cell Death Discovery","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"65a115d8-c565-493b-a69a-341604d2f65a","owner":[],"postedDate":"February 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":41661349,"name":"Health sciences/Diseases/Cancer/Tumour heterogeneity"},{"id":41661350,"name":"Biological sciences/Molecular biology/Epigenetics"},{"id":41661351,"name":"Biological sciences/Cell biology/Cell adhesion/Focal adhesion"}],"tags":[],"updatedAt":"2025-07-04T07:11:14+00:00","versionOfRecord":{"articleIdentity":"rs-5614729","link":"https://doi.org/10.1038/s41420-025-02597-4","journal":{"identity":"cell-death-discovery","isVorOnly":false,"title":"Cell Death Discovery"},"publishedOn":"2025-07-03 04:00:00","publishedOnDateReadable":"July 3rd, 2025"},"versionCreatedAt":"2025-02-06 13:17:09","video":"","vorDoi":"10.1038/s41420-025-02597-4","vorDoiUrl":"https://doi.org/10.1038/s41420-025-02597-4","workflowStages":[]},"version":"v1","identity":"rs-5614729","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5614729","identity":"rs-5614729","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}