Local androgen and 11-oxyandrogen metabolism and signaling emerges as a novel prognostic and therapeutic axis in high-grade serous ovarian cancer

Local androgen and 11-oxyandrogen metabolism and signaling emerges as a novel prognostic and therapeutic axis in high-grade serous ovarian cancer | 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 Research Article Local androgen and 11-oxyandrogen metabolism and signaling emerges as a novel prognostic and therapeutic axis in high-grade serous ovarian cancer Marija Gjorgoska, Klemen Pečnik, Nika Marolt, Janez Plavec, Tea Lanišnik Rižner This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8132276/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background High-grade serous ovarian cancer (HGSOC) is the most lethal gynaecological malignancy, exhibiting marked heterogeneity that complicates treatment. The implications of intratumoral androgen metabolism and signalling, particularly involving 11-oxygenated androgens, in HGSOC remains poorly understood. Methods We analysed expression of key androgen-metabolizing enzymes and androgen receptor (AR) in relation to tumor site, chemotherapy response, and survival using two independent public HGSOC cohorts. Quantitative gene expression and steroid metabolism assays upon incubation with classic and 11-oxyandrogen precursors were performed in six HGSOC cell lines and one normal ovarian epithelial cell line. Untargeted transcriptomic and metabolomic profiling were performed to assess cellular responses to potent classic and 11-oxygenated androgens in the AR -positive OVSAHO cell line. Results Differential expression of steroid-metabolizing enzymes and AR were observed between primary and metastatic tumors and chemo-sensitive and chemo-resistant tumors. Higher intra-tumoral expression of HSD11B2 , HSD17B2 , and AR correlated with improved survival, whereas elevated PAPSS1/2 and HSD17B4 predicted poorer outcomes. In vitro, classic androgen precursors showed limited conversion to bioactive androgens and did not generate 11-oxyandrogens. In contrast, 11-oxyandrogen precursors were efficiently converted to the potent AR agonist 11-keto-testosterone (11KT) in chemo-sensitive HGSOC cell lines, but not in chemo-refractory or control lines. Potent classic and 11-oxyandrogens triggered stress-adaptive and proliferative transcriptional programs, with 11-keto-dihydrotestosterone (11KDHT) additionally driving widespread metabolic reprogramming, including depletion of amino acids, glutathione, and nucleotide sugar metabolites. These changes were associated with a trend toward reduced cell proliferation. Conclusions Our findings offer mechanistic insight into local androgen and 11-oxyandrogen metabolism and signalling in HGSOC and reveal steroid-induced cellular vulnerabilities that may synergize with chemotherapy or molecularly targeted therapies. These results further support the therapeutic potential of modulating steroid pathways in HGSOC. high-grade serous ovarian cancer intracrinology 11-oxyandrogens transcriptomics metabolomics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Ovarian cancer (OC) is the eighth most common cancer in women and a leading cause of gynecologic cancer mortality, with incidence and mortality rates projected to increase by over 25% globally by 2035 [ 1 , 2 ] . Epithelial OC accounts for ~ 90% of cases, with high-grade serous ovarian cancer (HGSOC) representing the most common and lethal subtype (50–60%) [ 3 , 4 ] . HGSOC is characterized by aggressive growth, frequent late-stage diagnosis primarily due to nonspecific symptoms, high recurrence rates, and poor overall survival [ 3 , 5 ] . The current standard of care combines surgical debulking with platinum- and taxane-based chemotherapy, supplemented with targeted therapies such as poly (ADP-ribose) polymerase inhibitors (PARPIs) and anti-vascular endothelial growth factor (anti-VEGF) agents [ 5 ] . Immunotherapies, including immune checkpoint inhibitors [ 6 , 7 ] are also under investigation, but clinical benefits in OC remain modest [ 8 – 10 ] . Recurrence after platinum-based chemotherapy, therapy resistance, and immune evasion continue to challenge treatment. A major contributing factor to this is the molecular, cellular, and microenvironmental heterogeneity of HGSOC. Indeed, transcriptomic analyses of HGSOC tumors have identified four molecular subtypes, immunoreactive, differentiated, proliferative, and mesenchymal, from which the immunoreactive subtype was associated with most favorable outcome whereas the mesenchymal subtype with the poorest prognosis [ 11 , 12 ] . This tumor heterogeneity can be exploited for targeted therapies tailored to the tumor profile. Steroid hormones are emerging as key modulators of the tumor microenvironment and anti-tumor immunity. Recent reviews have highlighted their impact on immune cell populations and relevance in response to immunotherapy [ 13 , 14 ] . HGSOC tumors and tumor-adjacent stroma express steroid-metabolizing enzymes [ 15 – 20 ] and receptors [ 18 , 21 – 24 ] , supporting local steroid interconversion that may influence intra-tumoral signaling. Our previous work demonstrated that HGSOC cell lines can convert estrone-sulfate into active estrogens, a process more pronounced in chemo-sensitive than in chemo-resistant models [ 16 , 17 ] . In this study, we focused on androgens and their lesser-known 11-oxygenated derivatives (11-oxyandrogens), a class of bioactive androgens increasingly recognized in female physiology [ 25 ] but not yet explored in HGSOC. We investigated the expression of key enzymes involved in androgen and 11-oxyandrogen metabolism in HGSOC tissues in relation to tumor site, chemotherapy response and survival using two public datasets. Using six well-characterized HGSOC cell lines and one normal ovarian epithelial line, we examined steroid-metabolizing enzyme and androgen receptor (AR) expression, local metabolism of classic and 11-oxyandrogen precursors, and the transcriptomic and metabolomic changes induced by these steroids, alongside effects on cell proliferation. Understanding androgen and 11-oxyandrogen function in HGSOC may reveal new therapeutic avenues that exploit steroid signaling within the tumor microenvironment. 2. Materials and methods 2.1. Tissue analysis - public cohorts The cohort from Chowdhury et al. [ 26 ] included treatment-naïve tissue specimens from patients with stage III-IV disease who underwent primary debulking surgery followed by platinum/taxane adjuvant therapy and categorized with respect to response to adjuvant chemotherapy. The cohort consisted of 75 primary, 83 metastatic and 10 mixed HGSOC tissues with available proteomic data. In terms of chemotherapy response status, 93 were chemo-sensitive, and 75 were refractory. Transcriptomic data was available for 42 primary and 59 metastatic HGSOC tissues. Of them 55 were chemo-sensitive, 46 chemo-refractory. Baseline and detailed characteristics of patients can be found in the original article [ 26 ] . Data was retrieved from the Proteomic Data Commons (PDC000358, PDC000360). The cohort from the Cancer Genome Atlas – ovarian cancer (TCGA-OV) [ 11 ] involved 489 treatment-naïve, clinically annotated stage II-IV HGSOC samples. Chemotherapy response status was available for 211 samples, data on molecular tumor classification for 365 samples, proteomics data was available for 202 samples. Baseline and detailed characteristics of patients can be found in the original article [ 11 ] . The data was accessed through the University of California San Francisco Xena browser (UCSC Xena). For survival analysis, optimal cut points for log2-transformed FPKM-upper quartile (uq)-normalized RNA-seq data were determined using maximally selected rank statistics with the maxstat package in R studio [ 27 ] . Survival plots were generated using the Kaplan Meier method. Uni and multivariate cox proportional hazards models were fitted to estimate hazard ratios. P-values were two-sided, confidence intervals were calculated at the 95% level, and significance was predefined as < 0.05. 2.2. Cell lines In our study we used Caov-3, OVSAHO, Kuramochi, COV362, OVCAR-3 and OVCAR-4 as HGSOC models, and HIO-80 cell line as control. The immortalized HIO-80 cell line (CVCL_E274) were originally established from normal ovarian surface epithelium [ 28 ] , and were kindly obtained from Andrew K. Godwin (University of Kansas Medical Center, USA) as passage 72. Cells in passage + 10 were authenticated by short tandem repeat (STR) profiling performed by ATCC on 22 February 2019. HIO-80 cells were grown in a RPMI (R5586; Sigma-Aldrich, USA), with 10% FBS (F9665; Sigma–Aldrich) and 2 mM L-glutamine (G7513; Sigma–Aldrich). The Caov-3 cell line (CVCL_0201) is a primary HGSOC cell line isolated from a 54-year-old, white patient [ 29 ] . Caov-3 was purchased from ATCC (ATCC-HTB-75, lot 70026036) on December 2, 2021. STR profiling was performed by ATCC. Caov-3 cells were grown in DMEM (D5671; Sigma-Aldrich, USA), supplemented with 10% FBS (F9665; Sigma-Aldrich, USA) and 2 mM L-glutamine. The OVSAHO (CVCL_3144) cell line was established from a serous papillary adenocarcinoma from a metastatic site in the abdomen [ 30 ] of a 56-year-old woman and was purchased from JCRB (JCRB1046; lot 04062015) on June 4, 2018. STR profiling authentication was performed by JCRB. OVSAHO cells were grown in RPMI (R5886; Sigma-Aldrich, USA), supplemented with 10% FBS (F9665; Sigma-Aldrich, USA) and 2 mM L-glutamine. The Kuramochi (CVCL_1345) cell line was originally established from HGSOC from a metastatic site in the ascites [ 31 ] and was purchased from JCRB (JCRB0098, lot 06302015) on October 23, 2017. STR profiling authentication was performed by JCRB. Kuramochi cells were grown in RPMI (R5886; Sigma-Aldrich, USA), supplemented with 10% FBS (F9665; Sigma-Aldrich, USA) and 2 mM L-glutamine. The COV362 (CVCL_2420) cell line was originally established from a high-grade ovarian serous adenocarcinoma derived from a metastatic site in pleural effusion [ 32 ] . It was purchased from ECACC (ECACC 07071910) on October 13, 2017. STR profiling authentication was performed by ECACC. COV362 cells were grown in DMEM (D5546; Sigma-Aldrich, USA), supplemented with 10% FBS (F9665; Sigma-Aldrich, USA) and 2 mM L-glutamine. The OVCAR-3 (CVCL_0465) cell line is a HGSOC cell line established from the ascitic fluid of a 60-year-old patient with cisplatin-refractory OC [ 33 ] . It was purchased from ATCC (HTB-161, lot 70018483) on December 2, 2021. STR profiling authentication was performed by ATCC. OVCAR-3 cells were grown in modified RPMI (A10491–01; Gibco™, Thermo Fisher Scientific, USA), supplemented with 20% FBS (F7524; Sigma-Aldrich, USA) and 2 mM L-glutamine. The OVCAR-4 (CVCL_1627) cell line is a HGSOC cell line established from the ascites of a 42-year-old patient with a cisplatin-refractory OC, and resistant to multiple chemotherapeutic agents [ 34 ] . It was purchased from Sigma-Aldrich, USA on June 18, 2021 (SCC258). STR profiling authentication was performed by Sigma-Aldrich, USA. OVCAR-4 cells were grown in RPMI (R5586; Sigma-Aldrich, USA), supplemented with 10% FBS (F9665; Sigma–Aldrich) and 2 mM L-glutamine. All cell lines were grown in a cell incubator at 37°C and 5% CO 2 . Passages up to a maximum of p + 20 were used in this study. All cell lines were regularly tested and were negative for mycoplasma infection using the MycoAlert™ Mycoplasma Detection Kit (LT07–418, Lonza, Basel, Switzerland). Chemo-sensitivity status was previously reported in Pavlič et al. and Marolt et al. [ 16 , 17 ] . 2.3. RNA isolation Total RNA extraction was carried out using a Macherey-Nagel kit (#740933.5, Macherey-Nagel GmbH&Co, Germany), according to the manufacturer’s instructions. Samples of total RNA (10 µg) were transcribed into cDNA using the SuperScript® VILO™ cDNA Synthesis kit (#11754050, Invitrogen, USA) according to the manufacturer’s instructions. The cDNA samples were stored at -20°C. 2.4. Quantitative PCR For quantitative PCR analysis we first cultured cells in complete culture media under standard conditions until 80% confluence. Quantitative PCR was performed using TaqMan® Fast Advanced Master Mix (Applied Biosystems; Foster City, CA, USA) and TaqMan® probes ( STS, SULT2B1, SULT2A1 , PAPSS1, PAPSS2 , CYP11B1 , HSD11B2 , HSD11B1 , H6PD , SRD5A1 ; SRD5A2 ; SRD5A3 ; HSD3B1 , HSD3B2 , AKR1C3 ; all from Thermo Fisher Scientific, USA; catalogue numbers are given in Supplementary File 1, section S2.4), and SYBR Green I Master (Roche, Basel, Switzerland) and probes: AR-A , AR-B (Sigma Aldrich) and GPRC6A (Integrated DNA Technologies, USA); primer sequences are given in Supplementary File 1, section S2.4. Some of these genes were evaluated previously by our group [ 16 , 17 ] , and were reused for this study. Quantitative PCR was performed using the Applied Biosystems® ViiA™ 7 Real-Time PCR System (Thermo Fisher Scientific, Waltham, MA, USA); see Supplementary File 1, section S2.4. Each sample’s normalization factor was calculated based on the geometric mean of two housekeeping genes, POLR2A (#Hs00172187_m1) and HPRT1 (#Hs99999909_m1). The relative expressions of the genes of interest were calculated in each sample from the quantification cycle (Cq), as E − Cq , divided by the normalization factor. The guidelines of the Minimum Information for Publication of Quantitative Real-Time PCR Experiments were followed when performing qPCR and interpreting the results [ 35 ] . Gene expression was compared using one-way ANOVA with Tukey’s Honestly Significant Difference (HSD) post-hoc test or Kruskal-Wallis with Dunn’s multiple comparisons as appropriate. Differences with p values < 0.05 were considered statistically significant. 2.5. Steroid metabolism studies Cell lines were seeded in 6-well plates in complete culture media at a cell density required to reach 70% confluence in 24h. After 24h, the cells were washed with DPBS (#D8537, Sigma Aldrich), and the medium was replaced with phenol red-free, FBS-free medium (DMEM medium (#D5921, Sigma-Aldrich, USA) for Caov-3 and COV362; RPMI 1640 medium (#11835, Sigma-Aldrich, USA) for OVCAR-4 and RPMI 1640 medium for OVSAHO, OVCAR-3 and Kuramochi (#R7509, Sigma-Aldrich, USA). Cells were incubated with 1.6 µM dehydroepiandrosterone sulphate (DHEAS, #D5297, Sigma Aldrich GmbH), 15 nM dehydroepiandrosterone (DHEA, #A8500-00, Sigma Aldrich GmbH), 3 nM androstenedione (A4, #A6030-000, Steraloids), 15 nM 11β-hydroxy-androstenedione (11OHA4, #A-3009, Sigma Aldrich GmbH), or 3 nM 11-keto-androstenedione (11KA4, #284998, Sigma Aldrich GmbH), for 4, 8, 24, 48, and 72 h. All steroid precursors were prepared in ethanol (#1.11727, Supelco). After each point, the culture media were collected in Eppendorf tubes and stored at -80°C until steroid extraction. Three independent experiments were performed, each in technical duplicates. 2.6. Steroid extraction Sample preparation involved liquid-liquid extraction with methyl- tert -butyl ether (MTBE, #1634-04-4, Sigma Aldrich GmbH) as described previously [ 36 , 37 ] . Briefly, 1 mL of culture media was thawed and mixed with an internal standard, [ 13 C 3 ]-T (#730610, Sigma Aldrich GmbH). Next, 750 µL of MTBE/sample was added, and the samples were shaken for 10 min in an Eppendorf thermomixer (#5382000031, Eppendorf). After phase separation, the organic layer was collected in a separate tube; this was repeated thrice, after which samples were dried under vacuum at 45°C. Prior to LC-MS/MS analysis, samples were reconstituted in 70 µL of 70% methanol (#34966, Honeywell/Riedel-de Haen) in water (#1.15333, Supelco) (v/v) with 0.2 mM NH 4 F (#52481, Honeywell/Fluka). For DHEAS, we performed solid-phase extraction (SPE) on C18 columns (#8B-S001-EAK, Phenomenex) as previously described [ 36 ] . We used 100 µL of culture media, to which we added internal standard, DHEAS-d 5 (#D-066, Cerilliant). SPE included: column conditioning with 1 mL of methanol, equilibration with 1 mL of water, sample loading, column drying for 10 min, and elution with 1.5 mL methanol. Subsequently, samples were evaporated under vacuum at 45°C and reconstituted in 150 µL of 70% methanol with 0.2 mM NH 4 F before LC-MS/MS. 2.7. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) Steroids were analyzed as described previously [ 36 , 37 ] . DHEA, A4, testosterone (T, #B6500, Sigma Aldrich GmbH), 5α-dihydrotestosterone (DHT, #A2579-000, Steraloids), 11OHA4, 11KA4, 11β-hydroxy-testosterone (11OHT, #A5760-000, Steraloids), 11-keto-testosterone (11KT, #K8250, Sigma Aldrich GmbH), and 11-keto-dihydrotestosterone (11KDHT, #A2375-000, Steraloids) were profiled in a single run in positive electrospray ionization (ESI) mode. DHEAS was analyzed separately in ESI-negative mode. Compound- and instrument-specific parameters can be found in the original article by Gjorgoska et al. [ 36 ] . Chromatographic separation was performed on a Shimadzu Nexera XR HPLC system (Shimadzu Corporation, Kyoto, Japan) with a Kinetex 2.6 µm XB-C18 (100 × 4.6 mm) column (#00D-4496-E0, Phenomenex). Mobile phase A (5% methanol in H 2 O, 0.2 mM NH 4 F) and B (methanol, 0.2 mM NH 4 F) were used in both methods, but with a different gradient elution profile (described in Gjorgoska et al. [ 36 ] ). The column temperature was set to 45°C for the ESI-positive mode method and 38°C for DHEAS. In both methods, the total solvent flow was set at 0.5 mL/min, the injection volume was 25 µL. The MS analysis was performed on a Sciex 3500 Triple Quadrupole system (AB Sciex Deutchland GmbH, Darmstadt, Germany). The lower limit of quantification for each analyte can be found in the original article [ 36 ] . Data acquisition and analysis were performed using the Analyst 1.6 software. Calibrators ranging from 5 pg/mL to 250 ng/mL (or in the case of DHEAS, 5 pg/mL to 500 ng/mL) were prepared in cell culture media and extracted as samples. 1/x weighing, and linear least squares regression was used to produce standard curves. Groups were compared by time points with one-way ANOVA with Tukey’s HSD post-hoc test or Kruskal-Wallis with Dunn’s multiple comparisons as appropriate. Differences with p values < 0.05 were considered statistically significant. 2.8. mRNA sequencing OVSAHO cells were seeded at cell density 2x10 6 /well in 6-well plates in complete culture media. After 24h, the culture media was replaced with phenol-red free, steroid-free culture media with L-glutamine as a supplement. Following incubation, cells were washed with DPBS, and lysates were collected using the NucleoSpin RNA isolation protocol, immediately snap-frozen and stored at − 80°C. Total RNA was extracted using the NucleoSpin RNA kit. RNA integrity was assessed using an Agilent 2100 Bioanalyzer and was > 9.5 for all sequenced samples. mRNA sequencing was performed by Novogene Inc. Briefly, mRNA was enriched using poly-T oligo-attached magnetic beads and fragmented. First-strand cDNA synthesis was performed using random hexamer primers, followed by second-strand synthesis using dTTP. Libraries were pooled and sequenced on an Illumina NovaSeq X platform using paired-end 150 bp (PE150) reads (approximately 12 Gb raw data/sample). Raw reads were processed with fastp software, clean reads were aligned to the reference genome (GRCh38) using HISAT2 v2.0.5. Gene-level counts were generated using featureCounts v1.5.0-p3, and gene expression was quantified as Fragments Per Kilobase per Million mapped fragments (FPKM). Differential gene expression analysis on raw gene counts was performed using the DeSeq2 package in R Studio [ 38 ] . P-values were adjusted for multiple testing using the Benjamini Hochberg (BH) method. Differentially expressed genes were identified based on a fold change threshold greater than 2 (absolute value) and an adjusted p-value < 0.01. Pathway activity scores were calculated using single-sample gene set enrichment analysis (ssGSEA) implemented in the GSVA R package [ 39 , 40 ] , and gene sets from the Reactome database. Differences in pathway activity scores between groups were analyzed by moderated t-test with BH correction for multiple testing; adjusted p values less than 0.01 were considered significant. Three independent experiments were performed. 2.9. Untargeted metabolomics with NMR OVSAHO cells were seeded at cell density 2x10 6 /well in 6-well plates in complete culture media. After 24h, the culture media was replaced with phenol-red free, steroid-free culture media with L-glutamine as a supplement. Cells were treated with 10 nM bioactive androgens (T and DHT), 10 nM bioactive 11-oxyandrogens (11KT and 11KDHT) or vehicle (ethanol) for 72h. After the incubation, cells were washed with PBS three times, then 300 µL deuterated water D 2 O/well was added and cells were scraped from the surface. The cell lysates were collected in an Eppendorf tube and briefly sonicated on ice. Sonicated lysates were stored at -80°C until NMR analysis. For NMR analysis, cell lysates were thawed and transferred into Teflon tube liner (wilmad cat. no. 6005), which was inserted into a 5 mm NMR tube containing 150 µL D 2 O solution with 1 mM 3-(Trimethylsilyl)propionic-2,2,3,3-d 4 acid sodium salt (TMSP-d 4 ) used for reference. NMR spectra were acquired on a Bruker Avance NEO 600 MHz NMR spectrometer equipped with a 5 mm BBO probe at 298 K. Spectra were processed with TopSpin 4.0.8 (Bruker). Metabolite signals were assigned using 2D NMR spectra and the Human Metabolome Database [ 41 ] . Full details of NMR acquisition parameters, pulse sequences, and processing are provided in Supplementary File 1, section S2.9. Processed 1D ¹H-CPMG NMR spectra were imported into Python (version 3.12) using nmrglue library [ 42 ] . The spectra were manually bucketed with variable width to ensure no signal cutting. The integrated signal intensities of these spectral buckets were exported to a TXT file for statistical analysis in R studio version 4.3.0. Differential analysis in metabolite levels between treatment groups was performed with unpaired t-test with BH correction for multiple comparisons; fold changes greater than 1.5 and adjusted p values lower than 0.01 were considered statistically significant. 2.10. Cell proliferation assay OVSAHO cells were seeded in 96-well plates (5000 cells/well) in complete culture media. After 24h, the culture media was replaced with phenol-red free, FBS-free culture media supplemented with L-glutamine and treated with 10 and 100 nM concentration of T, DHT, 11KT, 11KDHT or ethanol as control for a total of 72h, after which cell viability was assessed with Alamar Blue HS reagent (#A50100, Thermo Fisher Scientific, USA), following manufacturer instructions. Viability was normalized to control condition. 2.11. Statistical analysis Data analysis was performed in GraphPad Prism software for Windows, version 10 (San Diego, CA, USA) and on R studio version 4.3.0 or higher. Data is expressed as median and interquartile range (IQR), unless otherwise stated. The statistical tests used are specified in the appropriate method section, and in figure legends. 3. Results 3.1. Primary and metastatic HGSOC tumors differ in expression of several androgen- and 11-oxyandrogen-metabolizing enzymes We analyzed the gene and protein expression of key enzymes involved in androgen and 11-oxyandrogen metabolism in primary and metastatic HGSOC tumors using data from Chowdhury et al. cohort [ 26 ] . The position of these enzymes in the steroid biosynthesis pathways is given in Supplementary Fig. 1. Among enzymes involved in the metabolism of classic androgens (STS, SULT2A1, SULT2B1, PAPSS1, PAPSS2, HSD3B1/2), only PAPSS2 , which encodes the synthase generating 3'-phosphoadenosine-5'-phosphosulfate (PAPS), the universal sulfate donor to sulfotransferases [ 43 ] , was differentially expressed between primary and metastatic tumors, being higher in the latter (Fig. 1 A). Transcriptomic data were not available for HSD3B1 , suggesting low or absent expression. No significant differences were observed at the protein level for STS, SULT2B1, and PAPSS1/2 with respect to tumor site; proteomic data were missing for SULT2A1 and HSD3B1/2. Among enzymes involved in 11-oxyandrogen metabolism (CYP11B1, HSD11B2/1, and H6PD), CYP11B1 , required for 11β-hydroxylation of classic androgens (A4 and T) into 11-oxygenated androgens (11OHA4 and 11OHT, respectively), was not expressed in either primary or metastatic tumors, suggesting that 11-oxyandrogens do not form in situ from classical ones. HSD11B2 and HSD11B1 showed significantly higher gene expression in primary tumors compared to metastatic ones (Fig. 1 B-C), while H6PD gene expression was similar across tumor sites. However, protein levels of HSD11B1/2 and H6PD were comparable between primary and metastatic tumors. Among enzymes involved in pre-receptor regulation of androgen action (AKR1C3, HSD17B2, HSD17B4, SRD5A1/2), only SRD5A2 gene expression differed significantly between tumor sites, being higher in primary compared to metastatic tumors (Fig. 1 D). At a protein level, no significant differences were found in AKR1C3 and HSD17B4 levels across tumor sites, whereas no data was available for HSD17B2, and the SRD5A isoforms. 3.2. Chemo-sensitive and chemo-resistant HGSOC tumors differ in expression of several androgen- and 11-oxyandrogen-metabolizing enzymes We next examined gene and protein expression of key enzymes involved in androgen and 11-oxyandrogen metabolism in chemo-sensitive versus chemo-refractory HGSOC tumors, using data from the Chowdhury et al. cohort [ 26 ] , as well as gene expression data from primary HGSOC tumors in the TCGA-OV cohort [ 11 ] . In the Chowdhury cohort, no significant differences were observed in the gene expression of enzymes involved in classic androgen metabolism ( STS, SULT2A1, SULT2B1, PAPSS1/2, HSD3B2 ), 11-oxyandrogen metabolism ( HSD11B1, H6PD ), or pre-receptor androgen regulation ( AKR1C3, HSD17B2/4, SRD5A1/2 ) between chemo-sensitive and chemo-refractory tumors. The only exception was HSD11B2 , which showed significantly higher expression in chemo-sensitive tumors (Fig. 2 A). At the protein level, expression of STS, SULT2B1, PAPSS1/2, HSD11B2/1, H6PD, AKR1C3, and HSD17B4 did not differ between the two groups. In primary HGSOC tumors from the TCGA-OV cohort, gene expression of STS (Fig. 2 C) and HSD17B2 (Fig. 2 D) was significantly higher in chemo-sensitive tumors. Similarly, HSD11B2 expression was also elevated in chemo-sensitive tumors compared to chemo-refractory ones, though this difference did not reach statistical significance (Fig. 2 B). Consistent with findings in the Chowdhury cohort, CYP11B1 expression was absent in primary tumors from the TCGA-OV cohort. Proteomics data were not available for this cohort. 3.3 PAPSS1/2 , HSD3B1 , HSD11B2 , H6PD , and HSD17B2/4 are associated with survival outcomes in patients with primary HGSOC We assessed the association between the expression of steroid-metabolizing enzymes and clinical outcomes in patients with primary HGSOC from the TCGA-OV cohort; survival data was not yet available for the Chowdhury cohort [ 26 ] . Among enzymes involved in classic androgen metabolism ( PAPSS1, PAPSS2, HSD3B1, STS, SULT2A1, SULT2B1, HSD3B2 ), higher intra-tumoral PAPSS1, PAPSS2 , and HSD3B1 gene expression was associated with worse prognosis in terms of disease-specific survival (DSS) and disease-free interval (DFI) (Table 1 ). STS, SULT2A1, SULT2B1 , and HSD3B2 expression were not significantly associated with survival. For enzymes involved in 11-oxyandrogen metabolism ( HSD11B2, HSD11B1, H6PD ), higher gene expression of HSD11B2 was associated with improved DSS (HR: 0.73, 95% CI: 0.55–0.97, p = 0.03) and DFI (HR: 0.75, 95% CI: 0.58–0.96, p = 0.02), whereas higher H6PD expression predicted worse DSS (HR: 1.66, 95% CI: 1.25–2.22, p < 0.001) and DFI (HR: 1.30, 95% CI: 1.00–1.68, p = 0.048). HSD11B1 expression was not significantly associated with outcomes. Among enzymes involved in pre-receptor regulation ( AKR1C3, HSD17B2, HSD17B4, SRD5A1, SRD5A2 ), higher gene expression of HSD17B2 correlated with improved survival outcomes, whereas elevated HSD17B4 was associated with worse DSS and DFI (Table 1 ). AKR1C3 showed a trend toward improved DSS (HR: 0.68, 95% CI: 0.46–1.01, p = 0.056), though not statistically significant. Finally, SRD5A1 and SRD5A2 expression were not associated with survival outcomes. Table 1 Association of intra-tumoral expression of steroid-metabolizing enzymes with survival of patients with primary HGSOC from the TCGA-OV cohort [ 11 ] . Gene (high vs low expression) DSS DFI HR 95% CI P value HR 95% CI P value PAPSS1 1.37 1.04–1.80 0.027 1.45 1.13–1.85 0.003 PAPSS2 1.33 1.00-1.78 0.054 1.28 0.99–1.66 0.055 HSD3B1 1.33 1.00-1.76 0.049 1.27 0.99–1.62 0.065 HSD11B2 0.73 0.55–0.96 0.026 0.75 0.58–0.96 0.020 H6PD 1.66 1.25–2.22 0.001 1.30 1.00-1.68 0.048 AKR1C3 0.68 0.46–1.01 0.056 0.79 0.57–1.11 0.181 HSD17B2 0.66 0.50–0.87 0.003 0.70 0.55–0.89 0.004 HSD17B4 1.37 1.02–1.84 0.035 1.46 1.13–1.89 0.004 Total number of participants, 371. PAPSS1 -low, n = 222, PAPSS1 -high, n = 149; PAPSS2 -low, n = 244; PAPSS2 -high, n = 127; HSD11B2 -low, n = 160, HSD11B2 -high, n = 211; H6PD -low, n = 242, H6PD -high, n = 129; AKR1C3 -low, n = 313, AKR1C3 -high, n = 58; HSD17B2 -low, n = 171, HSD17B2 -high, n = 200; HSD17B4 -low, n = 129, HSD17B4 -high, n = 242. The optimal cut-off value was estimated using Maximally Selected Rank Statistics [ 27 ] . Abbreviations: CI, confidence interval; DFI, disease-free interval; DSS, disease-specific survival; HR, hazard ratio. 3.4. Androgen receptor expression correlates with better chemo-sensitivity and better survival We evaluated the expression of AR in primary and metastatic HGSOC from both the Chowdhury cohort [ 26 ] and the TCGA-OV cohort [ 11 ] . The gene and protein expression of AR were significantly higher in primary compared to metastatic tumors (Fig. 3 A-B). The gene and protein expression were also higher in chemo-sensitive compared to chemo-resistant tumors from the Chowdhury et al. cohort (Fig. 3 C-D). In contrast, no significant differences were observed between chemo-sensitive and chemo-refractory tumors in the TCGA-OV cohort. AR expression also varied between primary tumors of different molecular subtypes from the TCGA-OV cohort (Supplementary Fig. 2). Specifically, the differentiated subtype showed the highest AR expression, with significantly higher levels than those in the immunoreactive and mesenchymal subtypes. At protein level, both differentiated and proliferative subtypes exhibited significantly higher AR expression than the immunoreactive and mesenchymal subtypes. Clinically, high AR protein expression was associated with better overall survival compared to low AR expression in patients with primary HGSOC (HR: 0.71, 95% CI: 0.55–0.92, p = 0.01) (Fig. 3 E). 3.5 Classic androgen precursors are a limited source of bioactive androgens and do not contribute to 11-oxyandrogen production in HGSOC cell lines We assessed the expression of key steroid-metabolizing enzymes in HGSOC cell lines by qPCR (Fig. 4 ). CYP11B1 expression was absent in all lines, indicating that local 11β-hydroxylation of A4 and T cannot occur. Next, we incubated cells with near-physiological concentrations of classic androgen precursors (DHEAS (1.6 µM), DHEA (15 nM), and A4 (3 nM)) for 72 h, and the resulting metabolites were quantified by LC-MS/MS (DHEAS, DHEA, A4, T, DHT, 11OHA4, 11KA4, 11OHT, 11KT, 11KDHT) (Fig. 5 A). No 11-oxyandrogens were detected in any cell line from any classic androgen precursor, consistent with the absence of CYP11B1 . DHEAS, the most abundant androgen precursor, was moderately metabolized across all cell lines, primarily to DHEA (Fig. 5 B-C, Supplementary Table 1), a conversion regulated by STS and sulfotransferases (primarily SULT2A1 and SULT2B1). All cell lines expressed STS and SULT2B1 , while SULT2A1 was absent (Fig. 4 ) [ 16 , 17 ] . OVCAR-3 had the lowest STS but high SULT2B1 , resulting in a low STS/SULT2B1 ratio; OVCAR-4 and Caov-3 also had high SULT2B1 . Accordingly, DHEA formation was lowest in OVCAR-3 and Caov-3. Downstream metabolites A4 and T formed at very low levels (< 1% of starting DHEAS), reflecting limited HSD3B1/2 expression. A4 and T formation was most prominent in HIO-80 and Caov-3, which had low HSD3B1 , while OVSAHO and Kuramochi formed less A4. DHT was below detection in all lines. Cells were also incubated with 15 nM DHEA, and OVSAHO and Caov-3 showed the highest conversion rates (Fig. 5 F, Supplementary Table 1). A4 formed in HIO-80 and Caov-3, consistent with HSD3B1 expression, while other lines produced minimal A4 (Fig. 5 G). Low T levels were detected only in Caov-3 (Fig. 5 H); in the remaining lines, A4 and T accounted for ~ 3% and 0.3% of DHEA, respectively, reflecting limited HSD3B1/2 expression. A substantial fraction of DHEA remained unaccounted for: Caov-3 (93.7%), OVSAHO (76.2%), OVCAR-4 (41.5%), COV362 (40%), OVCAR-3 (24.6%), HIO-80 (17.3%), and Kuramochi (15.6%) (Supplementary Table 1). This suggests DHEA conversion to other unmeasured metabolites, such as 5α-androstenediol (via HSD17B) [ 44 , 45 ] or C7/C16-hydroxylated derivatives (via CYP3A4/5/7) [ 46 ] , which were not included in our assay. Regarding A4, all cell lines metabolized the precursor, with Caov-3, OVSAHO, and OVCAR-3 showing the highest conversion (Fig. 5 I, Supplementary Table 1). However, T formation remained low (< 5% of A4) in all cell lines, being altogether highest in COV362 consistent with its high AKR1C3 expression, however, the high HSD17B2 levels in this cell line potentially limited net T accumulation (Fig. 4 ). As with DHEA, a large fraction of A4 remained unaccounted for, being most pronounced in OVSAHO (98.5%), OVCAR-3 (96.7%), and Caov-3 (95.4%), suggesting conversion to alternative metabolites such as 5α-androstenedione (via SRD5A) or estrone (via CYP19A1). All lines expressed high SRD5A1/3 levels, with Caov-3 and OVCAR-3 also expressing SRD5A2 . This supports the formation of 5α-reduced metabolites from A4. In terms of estrogen formation from A4, we showed previously CYP19A1 expression to be generally low in all cell lines [ 16 , 17 ] with COV362 showing the highest levels, indicating possible estrone formation. 3.6. 11-oxyandrogen precursors serve as an important source of bioactive 11KT in chemo-sensitive HGSOC cell lines We next incubated HGSOC cell lines with 15 nM 11OHA4 for 72 hours and measured the formation of downstream 11-oxyandrogens, including 11KA4, 11OHT, 11KT, and 11KDHT (Fig. 6 D). Among all cell lines, Kuramochi showed the most efficient conversion of 11OHA4 to 11KA4 (Fig. 6 A-B, Supplementary Table 1), which aligns with its high expression of HSD11B2 (Fig. 4 ). This chemo-sensitive cell line also produced the highest levels of the bioactive androgen 11KT, followed by the chemo-sensitive OVSAHO cell line (Fig. 6 C), indicating efficient conversion of 11KA4 to 11KT via AKR1C3. Notably, 11OHT was undetectable in all cell lines, suggesting that 11OHA4 is preferentially oxidized to 11KA4 via HSD11B2 rather than reduced to 11OHT (by reductive HSD17B enzymes like HSD17B3 [ 47 ] ). 11KDHT was below the limit of quantification in all cell lines. Of note, at the end of the 72-hour incubation, the total measured 11-oxyandrogens (11OHA4, 11KA4, 11OHT, and 11KT) accounted for only 75.1% of the initial 11OHA4 dose in OVCAR-3 and 83.0% in Caov-3, suggesting the formation of additional, unmeasured metabolites (Supplementary Table 1). The likely candidates include 11β-hydroxy-5α-androstanedione (from 11OHA4) and 11-keto-5α-androstanedione (from 11KA4), formed via SRD5A isoforms. This is supported by SRD5A1-3 expression in both OVCAR-3 and Caov-3 (Fig. 4 ). In a separate experiment, we incubated cell lines with 3 nM 11KA4 for 72 hours. Caov-3 and OVSAHO showed the highest conversion of 11KA4 to downstream products, while the control cell line HIO-80 had the lowest conversion rate (Fig. 6 E). Similarly, 11KT levels were lowest in HIO-80, followed by the chemo-refractory cell lines OVCAR-3 and OVCAR-4 (Fig. 6 F). No 11OHA4, 11OHT, or 11KDHT were detected in any cell line. At the end of this incubation, detected 11-oxyandrogens accounted for 78.7% of the starting 11KA4 in OVCAR-3 and 90.0% in Caov-3, again pointing to the formation of unmeasured metabolites. A likely product is 11-keto-5α-androstanedione, formed via SRD5A isoforms. Altogether, we found the formation of bioactive 11KT from both 11-oxyandrogen precursors, 11OHA4 and 11KT, to be highest in the chemo-sensitive cell lines Kuramochi and OVSAHO, followed by the cell line of primary HGSOC, Caov-3 (Fig. 6 G). Total 11KT levels were lowest in the control cell line, HIO-80, followed by the chemo-refractory cell lines OVCAR-3 and OVCAR-4. 3.7. Potent androgens and 11-oxyandrogens promote a stress-adapted proliferative state in AR -expressing, chemo-sensitive HGSOC model For evaluating the transcriptomic and metabolomic effects of potent androgens and 11-oxyandrogens, we selected the OVSAHO cell line, which exhibits the highest expression of AR , including both the full-length variant AR-B and the shorter AR-A variant (Supplementary Fig. 3A-B). Notably, expression of GPRC6A , which encodes a membrane receptor responsive to a broad range of ligands including androgens, was undetectable in all tested cell lines. We incubated OVSAHO cells with 10 nM of bioactive classic androgens (T or DHT) and 11-oxyandrogens (11KT or 11KDHT) for 72 hours to investigate transcriptomic changes induced by these compounds. Across all four treatments, we observed modest transcriptomic convergence, with only 23 genes (4%) commonly upregulated and 492 genes (7%) commonly downregulated (Fig. 7 A-B), indicating a shared androgen-responsive transcriptional program. However, a more pronounced overlap was seen between DHT, 11KT, and 11KDHT, with 195 genes (33%) commonly upregulated and 3,910 genes (55%) commonly downregulated. The list of altered genes is given in Supplementary Table 2. Pathway enrichment analysis using the Reactome database revealed that all four treatments activated key cellular stress response mechanisms and perturbed cell cycle regulation (Fig. 7 C). Specifically, stress-related pathways such as NFE2L2 (NRF2) signaling and the unfolded protein response were upregulated, suggesting an adaptive antioxidant response to oxidative and proteotoxic stress. In parallel, upregulation of anaphase-promoting complex/cyclosome (APC/C):Cdc20-mediated degradation of mitotic regulators pointed to enhanced turnover of cell cycle proteins and accelerated mitotic progression. Consistently across all androgen treatments, the Rho-GTPase cycle, which regulates actin polymerization, cell polarity, and migration, was the only pathway commonly downregulated. The list of altered cellular pathways is given in Supplementary Table 3. Interestingly, upon incubation with DHT, 11KT or 11KDHT transcriptomic effects were even more pronounced (Fig. 7 D). Apart from upregulated cellular stress response mechanisms and perturbed cell cycle regulation, there were other alterations, including upregulation of additional cell cycle-related pathways, immune response and antigen processing, protein processing and trafficking pathways, as well as downregulation of processes involved in regulation of translation, RNA quality control, metabolic pathways. At the metabolomic level, incubation with 11KDHT caused distinct intracellular changes, notably reducing levels of amino acids such as glutamine, glutamate, aspartate, and threonine, along with lower glutathione levels compared to control (Fig. 7 E). Multiple intracellular glucose NMR signals, corresponding to α- and β-anomers, were also reduced. In addition, several uridine diphosphate-N-acetyl-glucosamine (UDP-GlcNAc)-related NMR peaks were depleted, indicating a broader reduction in nucleotide sugar pools. These findings, derived from the analysis of 1D ¹H-CPMG NMR spectra (Supplementary Fig. 4) with metabolite assignments confirmed using 2D NMR, align with transcriptomic data showing increased turnover of mitotic proteins and downregulated protein translation. Similar changes were seen with DHT treatment, but these were less pronounced, with most falling below the 1.5-fold change threshold, whereas T and 11KT did not induce significant metabolomic alterations (Supplementary Fig. 5). The complete list of significantly altered metabolites is provided in Supplementary Table 4. Finally, we examined the effects of bioactive classic androgens (T and DHT) and 11-oxyandrogens (11KT and 11KDHT) at 10 nM and 100 nM on cell proliferation on OVSAHO cells. Although treatment with any of the androgens led to a general reduction in cell proliferation compared to control, these differences did not reach statistical significance (Supplementary Fig. 3C-D). Discussion This study provides new insights into androgen signaling in HGSOC through three key findings: (1) differential expression of steroid-metabolizing enzymes and AR between primary and metastatic tumors, with associations to chemotherapy response and survival; (2) heterogeneous local metabolism of classic and 11-oxygenated androgens in HGSOC cell models; and (3) distinct transcriptomic and metabolomic responses to bioactive androgens and 11-oxyandrogens in an AR-expressing HGSOC line. To our knowledge, this is the first comprehensive study to examine 11-oxyandrogen-related enzymes in HGSOC, their local metabolism, and their downstream functional effects. Current knowledge of the steroid-metabolizing potential of HGSOC is limited, although the expression of several steroid-metabolizing enzymes, such as CYP17A1, AKR1C3, CYP19A1, HSD17B1 as well as AR has been previously reported [ 18 ] . Our previous work also confirmed AKR1C3 expression in HGSOC tumors, but with no links to survival outcomes [ 19 ] . More recently, our single-cell RNA-sequencing analysis of HGSOC tumors revealed differential expression of multiple steroidogenic and steroid-metabolizing enzymes, including AKR1C family members, SRD5A isoforms, and steroid-conjugating enzymes, across epithelial, stromal, and immune cell compartments [ 15 ] . In this study, we identified several prognostic biomarker candidates among steroid-metabolizing enzymes in HGSOC tumors. Notably, higher expression of PAPSS1/2 , enzymes involved in steroid sulfation, was associated with worse survival in patients with primary HGSOC. This contrasts with previous reports where increased expression of SULT1A1 [ 17 ] and SULT1E1 [ 48 ] , also involved in steroid sulfation, correlated with better outcomes in HGSOC and epithelial ovarian cancer, respectively, while elevated STS, which reverses sulfation, predicted poor prognosis in epithelial ovarian cancer [ 49 , 50 ] . These findings on STS and SULTs suggest that limiting the availability of active steroids through increased sulfation or decreased desulfation may improve survival. However, this does not explain the association of PAPSS1/2 with poorer prognosis. Furthermore, we found HSD11B2 expression to be higher in chemo-sensitive vs chemo-resistant tumors and to corelate with better survival outcomes. These effects may reflect the role of HSD11B2 in glucocorticoid metabolism, where it inactivates cortisol to cortisone, thereby reducing ligand availability for GR, which has been linked to poorer survival outcomes in OC [ 13 , 46 , 51 – 54 ] . However, we found no differences in NR3C1 expression, which encodes GR, between primary and metastatic tumors or between chemo-sensitive and chemo-refractory tumors in the Chowdhury et al. cohort. Similarly, no differences were observed between chemo-sensitive and chemo-resistant primary tumors in the TCGA-OV cohort, and NR3C1 expression was not significantly associated with patient survival. Apart from HSD11B2 , we also found H6PD , another key enzyme in glucocorticoid and 11-oxyandrogen metabolism [ 55 – 58 ] , to be associated with worse prognosis. Therefore, a metabolic profile characterized by high HSD11B2 and low H6PD expression may result in higher local 11KT levels and thus enhanced AR signaling, as well as lower intra-tumoral cortisol levels and consequently attenuated GR signaling, altogether leading to improved survival outcomes. We also found that HSD17B2 and HSD17B4 , both involved in pre-receptor steroid regulation, were associated with opposite survival outcomes: HSD17B2 with better, and HSD17B4 with worse prognosis. HSD17B2 inactivates estradiol and T [ 59 ] , reducing both estrogen and androgen signaling. It also converts 20α-hydroxyprogesterone to active progesterone [ 59 ] , potentially enhancing progesterone receptor signaling, previously linked to better survival and better chemosensitivity in HGSOC [ 21 , 23 , 24 ] . These combined effects may explain the survival advantage associated with high HSD17B2 expression. HSD17B4 also reduces estradiol levels but does not affect androgens [ 60 ] , potentially tipping the balance toward AR signaling. The impact of this shift remains unclear, as estrogen receptor (ER)-related survival data are inconsistent [ 21 , 22 , 61 ] , and studies on AR are limited. However, one study linked higher intra-tumoral AR expression with improved survival and platinum sensitivity [ 21 ] , consistent with our findings. Apart from steroid metabolism, HSD17B4 is involved in peroxisomal beta-oxidation pathway for fatty acids [ 62 ] , therefore the association of HSD17B4 with survival outcomes in patients with HGSOC could be associated with alterations in fatty acid metabolism apart from steroid metabolism. Furthermore, we observed distinct differences in the local steroid-metabolizing capacity of classic and 11-oxyandrogens among chemo-sensitive, chemo-refractory, and control HGSOC cell lines. More specifically, classic androgen precursors did not give rise to 11-oxyandrogens, likely due to the absence of CYP11B1. Consistently, CYP11B1 expression was absent in HGSOC tumors in both clinical cohorts, further supporting the conclusion that in situ conversion of classic androgens to 11-oxyandrogens is unlikely in HGSOC. To our knowledge, ectopic expression of CYP11B1 in non-adrenal tissues has been reported primarily in colon carcinoma cell lines [ 63 ] , and in tumor-associated monocyte-macrophage lineage cells isolated from colon carcinoma in mice [ 64 ] . Moreover, classic androgen precursors were poor sources of bioactive androgens in HGSOC cell lines. DHEAS was moderately converted to DHEA, but further metabolism to A4 and T was limited, consistent with low or absent HSD3B1/2 expression, reflected also in HGSOC tumors in both clinical cohorts. This aligns with Poschner et al., who found minimal T formation in HGSOC cell lines from DHEA even at high micromolar doses [ 65 ] . These data suggest that DHEA may be shunted into alternative metabolites (e.g., 5α-androstenediol, oxidized DHEA derivatives) or rapidly converted to A4 and subsequently to T, followed by T conjugation. Similarly, A4 conversion to T was limited, with the highest levels observed in AKR1C3 -high COV362 cells. Nevertheless, A4 was metabolized in all cell lines, likely via 5α-androstenedione formation, supported by SRD5A1-3 expression, or through rapid T formation followed by conjugation. These poorly characterized alternative routes may critically affect AR ligand availability and signaling in HGSOC. In contrast, conversion of 11-oxyandrogen precursors to bioactive 11KT was most pronounced in the chemo-sensitive, HSD11B2 -high Kuramochi cell line, followed by the chemo-sensitive OVSAHO, moderate in Caov-3, and lowest in non-malignant HIO-80 and chemo-refractory OVCAR-3 and OVCAR-4. The higher conversion rates in chemo-sensitive lines could point to a functional link between 11-oxyandrogen signaling and treatment response. Active 11KT generation supports AR signaling [ 66 ] , which is associated with better survival and chemosensitivity in HGSOC [ 21 ] . Alternatively, local steroid metabolism may reflect broader metabolic competence, which is diminished in chemo-resistant cells, which are less differentiated and with mesenchymal features [ 67 ] . Notably, this pattern is consistent with previous findings of differential estrogen metabolism between chemo-sensitive and chemo-resistant models [ 16 , 17 , 65 ] . Finally, we characterized the functional effects of androgen and 11-oxyandrogen signaling. We found that the incubation of AR -expressing OVSAHO cells with potent androgens and 11-oxyandrogens activates a shared androgen-responsive program, characterized by increased proliferative signaling alongside cellular stress responses. The metabolomic effects were milder, with only 11KDHT inducing depletion of amino acids, glutathione, and nucleotide sugar metabolites. Taken together, these findings suggest that potent classic and 11-oxygenated androgens elicit stress-adaptive programs, which may sensitize cells to additional stressors, such as chemotherapy. This could explain the observed antiproliferative effects of androgens and 11-oxyandrogens and the positive association between intra-tumoral AR expression, chemotherapy response, and overall survival, both in our study and in prior work by Tan and colleagues [ 21 ] . Our study has several limitations. Most notably, we investigated local steroid metabolism and the transcriptomic and metabolomic effects of bioactive androgens and 11-oxyandrogens using cell lines, which are suboptimal as they represent only the epithelial cell population of the highly complex tumor microenvironment. Other cell populations also express steroid-metabolizing enzymes and steroid receptors [ 15 ] . Therefore, the integrated effects of steroid signaling in the tumor itself may differ significantly from those observed in epithelial cells alone. Nevertheless, our study also has important strengths. We employed six well-characterized HGSOC cell lines derived from both primary tumors and metastatic sites to capture the molecular diversity and heterogeneity of real-world HGSOCs. We also exposed these cells to physiologically relevant concentrations of steroid precursors to study their local metabolism. Collectively, our findings provide mechanistic insight into local androgen and 11-oxyandrogen metabolism and signaling in HGSOC with therapeutical implications. Conclusion We identified distinct androgen and 11-oxyandrogen metabolism patterns across tumor sites and chemotherapy status in HGSOC, along with steroid-induced cellular vulnerabilities that may enhance responses to chemotherapy or molecularly targeted therapy. These findings highlight the therapeutic potential of modulating steroid pathways in HGSOC and warrant further investigation. Abbreviations 11KA4 11-keto-androstenedione 11KDHT 11-keto-dihydrotestosterone 11KT 11-keto-testosterone 11OHA4 11β-hydroxy-androstenedione 11OHT 11β-hydroxy-testosterone A4 Androstenedione AKR1C3 Aldo- keto reductase family 1 member C3 ANOVA Analysis of variance AR Androgen receptor ATCC American Type Culture Collection BH Benjamini Hochberg CI Confidence interval CPMG Carr-Purcell-Meiboom-Gill Cq Quantification cycle CYP11B1 Cytochrome P450 11β-hydroxylase CYP19A1 Aromatase CYP3A Cytochrome P450 Family 3 Subfamily A DFI Disease-free interval DHEA Dehydroepiandrosterone DHEAS Dehydroepiandrosterone sulfate DHT 5α-dihydrotestosterone DMEM Dulbecco's Modified Eagle Medium DPBS Dulbecco's phosphate-buffered saline DSS Disease-specific survival dTTP Deoxythymidine triphosphate ECACC European Collection of Authenticated Cell Cultures ER Estrogen receptor ESI Electrospray ionization FBS Fetal Bovine Serum FPKM Fragments Per Kilobase per Million mapped fragments Glc Glucose Gln Glutamine Glu Glutamate H6PD Hexose-6-phosphate dehydrogenase HGSOC High-grade serous ovarian cancer HPLC High performance liquid chromatography HR Hazard ratio HSD Honestly Significant Difference test HSD11B 11β-hydroxysteroid dehydrogenase HSD17B 17β-hydroxysteroid dehydrogenase HSD3B 3β-hydroxysteroid dehydrogenase IQR Interquartile range JCRB Japanese Collection of Research Bioresources LC-MS/MS Liquid chromatography-tandem mass spectrometry Leu Leucine MTBE Methyl-tert-butyl ether NH4F Ammonium fluoride NMR Nuclear magnetic resonance NRF2 Nuclear factor erythroid 2-related factor 2 OC Ovarian cancer PAPSS Phosphoadenosine 5'-phosphosulfate synthase PARPI Poly (ADP-ribose) polymerase inhibitors qPCR Quantitative polymerase chain reaction RPMI Roswell Park Memorial Institute medium SPE Solid phase extraction SRD5A Steroid 5α-reductase ssGSEA Single-sample gene set enrichment analysis STR Short Tandem Repeat STS Sulfatase SULT Sulfotransferase TCGA The Cancer Genome Atlas T Testosterone Thr Threonine TMSPA 3-(Trimethylsilyl)propionic-2,2,3,3-d4 acid sodium salt TPM Transcripts per million UDP-GlcNAc Uridine diphosphate-N-acetyl-glucosamine VEGF Vascular endothelial growth factor Declarations Supplementary Information Supplementary data related to this article can be found in the online version of the article. Acknowledgements: We thank Ms. Lea Štrum and Ms. Špela Kos for help with metabolism studies. Credit authorship contribution statement: Marija Gjorgoska: Conceptualization, Data curation; Formal analysis, Investigation; Methodology, Visualization, Writing - original draft, Writing - review & editing. Klemen Pečnik, Investigation, Methodology, Formal analysis; Writing - review and editing. Nika Marolt: Writing - review and editing. Janez Plavec, Writing – review and editing. Tea Lanišnik Rižner: Conceptualization, Funding acquisition, Supervision, Writing - review & editing. Funding: This study was funded by the Slovenian Research and Innovation Agency, grant number J3-2535 and program funding P3-0449, both to T.L.R., and P1-242 to J.P. Data availability: All data related to this article can be found in supplementary materials. The raw and processed RNA-seq data were deposited on Gene Expression Omnibus (GEO) database ID number: GSE302881. 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Cancers . 2020;12(2). doi:10.3390/cancers12020279 Pretorius E, Africander DJ, Vlok M, et al. 11-Ketotestosterone and 11-Ketodihydrotestosterone in Castration Resistant Prostate Cancer: Potent Androgens Which Can No Longer Be Ignored. PLoS One . 2016;11(7):e0159867. doi:10.1371/journal.pone.0159867 Toledo B, González-Titos A, Hernández-Camarero P, et al. A Brief Review on Chemoresistance; Targeting Cancer Stem Cells as an Alternative Approach. International Journal of Molecular Sciences . 2023;24(5). doi:10.3390/ijms24054487 Additional Declarations No competing interests reported. Supplementary Files SupplementaryTablesCHAPTER7.xlsx Graphicalabstract.docx Cite Share Download PDF Status: Posted 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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1","display":"","copyAsset":false,"role":"figure","size":142359,"visible":true,"origin":"","legend":"\u003cp\u003eExpression of key enzymes involved in androgen and 11-oxyandrogen metabolism in primary versus metastatic HGSOC tumors. Data are from the Chowdhury et al. cohort \u003csup\u003e[26]\u003c/sup\u003e: primary HGSOC tumors (n=42) and metastatic HGSOC tumors (n=59). Gene expression in Transcripts per million (TPM). Data is shown as median with interquartile range (IQR). Statistical analysis using Mann-Whitney \u003cem\u003eU\u003c/em\u003e test, *, p\u0026lt;0.05, **, p\u0026lt;0.01. Abbreviations: HGSOC, high-grade serous ovarian cancer.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8132276/v1/bd80c7418eaa8002dcbddc0c.png"},{"id":96919626,"identity":"f60c8451-5fe9-4c55-9921-813a1c5b0992","added_by":"auto","created_at":"2025-11-27 14:14:14","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":145783,"visible":true,"origin":"","legend":"\u003cp\u003eExpression of key enzymes involved in androgen and 11-oxyandrogen metabolism in HGSOC tumors in relation to chemotherapy response. Data are shown from two cohorts: Chowdhury et al. cohort \u003csup\u003e[26]\u003c/sup\u003e: chemo-sensitive (n = 55), chemo-refractory (n = 46). TCGA-OV cohort \u003csup\u003e[11]\u003c/sup\u003e: chemo-sensitive (n = 148), chemo-refractory (n = 63). Gene expression in Transcripts per million (TPM) for the Chowdhury cohort, and log\u003csub\u003e2\u003c/sub\u003e(FPKM+1) for the TCGA-OV cohort. Data is shown as median with interquartile range (IQR). Statistical analysis using Mann-Whitney \u003cem\u003eU\u003c/em\u003e test, *, p\u0026lt;0.05. Abbreviations: HGSOC, high-grade serous ovarian cancer; FPKM, fragments per kilobase per million mapped fragments; TCGA, The Cancer Genome Atlas.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8132276/v1/fed795e0667713d5a9d8b98f.png"},{"id":96878093,"identity":"e531e2ee-a616-48b1-9646-074c37a6d4aa","added_by":"auto","created_at":"2025-11-27 06:10:09","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":125028,"visible":true,"origin":"","legend":"\u003cp\u003eAR association with tumor site, chemotherapy response, and survival in HGSOC. (A-B) Gene and protein expression of AR in primary and metastatic HGSOC tumors. (C-D) Gene and protein expression of AR in chemo-sensitive and chemo-refractory HGSOC tumors. (E) Association of AR protein expression with overall survival in patients with primary HGSOC tumors. Data in panels A-D are from the Chowdhury et al. cohort \u003csup\u003e[26]\u003c/sup\u003e: primary HGSOC (n = 42), metastatic HGSOC (n = 59); chemo-sensitive HGSOC (n = 55), chemo-refractory HGSOC (n = 46). Data in panel E is from the TCGA-OV cohort \u003csup\u003e[11]\u003c/sup\u003e: Gene expression is represented as Transcripts per million (TPM), and protein expression is shown as the average peak area ratios of the peptides in the protein. Data is shown as median with interquartile range (IQR). Statistical analysis using Mann-Whitney U test (A-E), *, p\u0026lt;0.05, **, p\u0026lt;0.01; ****, p\u0026lt;0.0001. The optimal cut-off value in relation to survival was estimated using Maximally Selected Rank Statistics \u003csup\u003e[27]\u003c/sup\u003e. Abbreviations: CI, confidence interval; HGSOC, high-grade serous ovarian cancer; TCGA, The Cancer Genome Atlas.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8132276/v1/25be00110972ba0744f71d3f.png"},{"id":96878098,"identity":"f7166f6d-91d4-46ba-a9b7-857afdf4b772","added_by":"auto","created_at":"2025-11-27 06:10:09","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":125644,"visible":true,"origin":"","legend":"\u003cp\u003eNormalized gene expression of key steroid-metabolizing enzymes in HGSOC cell lines, including Caov-3 (primary tumor), OVSAHO, Kuramochi, and COV362 (metastatic tumors), OVCAR-3 and OVCAR-4 (chemo-refractory, metastatic tumors), and the control cell line HIO-80. Data is shown as mean ± standard deviation (SD). Data is from three independent biological replicates. Statistical test used One-Way ANOVA with Tukey’s HSD post hoc test or Kruskal-Wallis test with Dunn’s correction; *, p\u0026lt;0.05; **, p\u0026lt;0.01; ***, p\u0026lt;0.001; ****, p\u0026lt;0.0001.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8132276/v1/b7ef7ccb617fbdf0893ca457.png"},{"id":96878104,"identity":"cc8240fd-07e5-4303-b45b-ee005e312711","added_by":"auto","created_at":"2025-11-27 06:10:09","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":251991,"visible":true,"origin":"","legend":"\u003cp\u003eMetabolism of classic androgen precursors in HGSOC cell lines. (A) Schematic overview of the experimental design. (B) Metabolism of 1.6 µM DHEAS in the control cell line HIO-80 and HGSOC cell lines: Caov-3 (primary tumor), OVSAHO, Kuramochi, COV362 (metastatic tumors), and OVCAR-3, OVCAR-4 (chemo-refractory, metastatic tumors). (C–E) Levels of downstream metabolites A4 and T formed from DHEAS after 72 hours of incubation. (F) Metabolism of 15 nM DHEA in HIO-80 and HGSOC cell lines. (G–H) A4 and T levels formed from DHEA after 72 hours. (I) Metabolism of 3 nM A4 in HIO-80 and HGSOC cell lines. (J) T levels formed from A4 after 72 hours. Data is shown as median with interquartile range (IQR). Statistical test used Kruskal-Wallis with Dunn’s post hoc test. *, p\u0026lt;0.05. Abbreviations: A4, androstenedione; DHEA, dehydroepiandrosterone; DHEAS, DHEA sulfate; DHT, 5α-dihydrotestosterone; HGSOC, high-grade serous ovarian cancer; LC-MS/MS, liquid chromatography–tandem mass spectrometry; T, testosterone.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8132276/v1/48f4682f96e6edcd5d267713.png"},{"id":96920558,"identity":"c48c08fd-8fba-4e6d-898b-e4cfc57af1b7","added_by":"auto","created_at":"2025-11-27 14:15:16","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":266409,"visible":true,"origin":"","legend":"\u003cp\u003eMetabolism of 11-oxyandrogen precursors in HGSOC cell lines. (A) Metabolism of 15 nM 11OHA4 in the control cell line HIO-80 and HGSOC cell lines: Caov-3 (primary tumor), OVSAHO, Kuramochi, COV362 (metastatic HGSOC), and OVCAR-3, OVCAR-4 (chemo-refractory, metastatic HGSOC). (B) Levels of 11KA4 formed from 11OHA4 after 72 hours of incubation. (C) Levels of 11KT formed from 11OHA4 after 72 hours of incubation. (D) Schematic overview of the experimental design. (E) Metabolism of 3 nM 11KA4 in HIO-80 and HGSOC cell lines. (F) 11KT levels formed from 11KA4 after 72 hours. (G) Total amount of bioactive 11KT formed from 11-oxyandrogen precursors (11OHA4 and 11KA4). Data is shown as median and interquartile range (IQR). Statistical analysis performed for the 72h time point using Kruskal-Wallis with Dunn’s post hoc test; *, p\u0026lt;0.05. Abbreviations: 11OHA4, 11β-hydroxy-androstenedione; 11OHT, 11β-hydroxy-testosterone; 11KA4, 11-keto-androstenedione; 11KT, 11-keto-testosterone; 11KDHT, 11-keto-dihydrotestosterone; HGSOC, high-grade serous ovarian cancer; LC-MS/MS, liquid chromatography–tandem mass spectrometry.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8132276/v1/70dd8d61ed44f825b8fbf223.png"},{"id":96920548,"identity":"6df54a37-dec6-46af-ae79-da137f48467f","added_by":"auto","created_at":"2025-11-27 14:15:15","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":179984,"visible":true,"origin":"","legend":"\u003cp\u003eTranscriptomic and metabolomic effects of potent androgens and 11-oxyandrogens. (A-B) Upregulated and downregulated genes, respectively upon incubation with 10 nM levels of classic androgens (T and DHT) and 11-oxyandrogens (11KT and 11KDHT) for 72 hours in OVSAHO cell line. (C) Commonly altered cellular pathways upon incubation with classic (T and DHT) and 11-oxyandrogens (11KT and 11KDHT) in OVSAHO cells. (D) Commonly altered cellular pathways upon incubation with DHT, 11KT and 11KDHT in OVSAHO cells. (E) Volcano plot showing metabolomic changes in OVSAHO cells upon incubation with 10 nM 11KDHT for 72 hours; the dashed vertical lines correspond to a Log₂ Fold Change threshold of ±1.5, and the dashed horizontal line marks the adjusted p-value threshold of 0.01. Red dots in C-D indicate upregulation; blue dots indicate downregulation. Blue dots in E represent significantly altered metabolites with reduced intracellular levels, while gray dots indicate metabolites that did not meet the combined criteria for significance (i.e., they were either not below the adjusted p-value threshold of 0.01 or not outside the fold change threshold of 1.5). Statistical test used in C-D: moderated t-test with BH correction for multiple testing; adjusted p values lower than 0.01 were considered significant; in E: moderated t-test with BH correction for multiple testing; fold changes greater than 1.5 and adjusted p values lower than 0.01 were considered significant. Three independent experiments were performed for both transcriptomic and metabolomic data. Abbreviations: 11KT, 11-keto-testosterone; 11KDHT, 11-keto-dihydrotestosterone; Asp, aspartate; DHT, 5α-dihydrotestosterone; Glc, glucose; Gln, glutamine; Glu, glutamate; Leu, leucine; T, testosterone; Thr, threonine; UDP-Glc-NAc, uridine diphosphate N-acetylglucosamine.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-8132276/v1/8b9c4e64963a5b8dc5af370c.png"},{"id":97801359,"identity":"01a3627f-567d-4cf8-84c2-c8fadab295ec","added_by":"auto","created_at":"2025-12-09 13:53:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2408747,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8132276/v1/b7ca7032-3acf-45ed-9640-a4f18ca76822.pdf"},{"id":96878097,"identity":"c2e809fa-02b4-4a27-bf47-f8bc33e1abb9","added_by":"auto","created_at":"2025-11-27 06:10:09","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1682908,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTablesCHAPTER7.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8132276/v1/2027b252ce0959f711bdfba7.xlsx"},{"id":96878095,"identity":"2c731d3a-da10-40a4-869e-628aa8fec42d","added_by":"auto","created_at":"2025-11-27 06:10:09","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":179022,"visible":true,"origin":"","legend":"","description":"","filename":"Graphicalabstract.docx","url":"https://assets-eu.researchsquare.com/files/rs-8132276/v1/6d45de617de9fa12805a4bbe.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Local androgen and 11-oxyandrogen metabolism and signaling emerges as a novel prognostic and therapeutic axis in high-grade serous ovarian cancer","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eOvarian cancer (OC) is the eighth most common cancer in women and a leading cause of gynecologic cancer mortality, with incidence and mortality rates projected to increase by over 25% globally by 2035 \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Epithelial OC accounts for ~\u0026thinsp;90% of cases, with high-grade serous ovarian cancer (HGSOC) representing the most common and lethal subtype (50\u0026ndash;60%) \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. HGSOC is characterized by aggressive growth, frequent late-stage diagnosis primarily due to nonspecific symptoms, high recurrence rates, and poor overall survival \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe current standard of care combines surgical debulking with platinum- and taxane-based chemotherapy, supplemented with targeted therapies such as poly (ADP-ribose) polymerase inhibitors (PARPIs) and anti-vascular endothelial growth factor (anti-VEGF) agents \u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Immunotherapies, including immune checkpoint inhibitors \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e are also under investigation, but clinical benefits in OC remain modest \u003csup\u003e[\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. Recurrence after platinum-based chemotherapy, therapy resistance, and immune evasion continue to challenge treatment. A major contributing factor to this is the molecular, cellular, and microenvironmental heterogeneity of HGSOC. Indeed, transcriptomic analyses of HGSOC tumors have identified four molecular subtypes, immunoreactive, differentiated, proliferative, and mesenchymal, from which the immunoreactive subtype was associated with most favorable outcome whereas the mesenchymal subtype with the poorest prognosis \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. This tumor heterogeneity can be exploited for targeted therapies tailored to the tumor profile.\u003c/p\u003e\u003cp\u003eSteroid hormones are emerging as key modulators of the tumor microenvironment and anti-tumor immunity. Recent reviews have highlighted their impact on immune cell populations and relevance in response to immunotherapy \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. HGSOC tumors and tumor-adjacent stroma express steroid-metabolizing enzymes \u003csup\u003e[\u003cspan additionalcitationids=\"CR16 CR17 CR18 CR19\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e and receptors \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan additionalcitationids=\"CR22 CR23\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e, supporting local steroid interconversion that may influence intra-tumoral signaling. Our previous work demonstrated that HGSOC cell lines can convert estrone-sulfate into active estrogens, a process more pronounced in chemo-sensitive than in chemo-resistant models \u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. In this study, we focused on androgens and their lesser-known 11-oxygenated derivatives (11-oxyandrogens), a class of bioactive androgens increasingly recognized in female physiology \u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e but not yet explored in HGSOC.\u003c/p\u003e\u003cp\u003eWe investigated the expression of key enzymes involved in androgen and 11-oxyandrogen metabolism in HGSOC tissues in relation to tumor site, chemotherapy response and survival using two public datasets. Using six well-characterized HGSOC cell lines and one normal ovarian epithelial line, we examined steroid-metabolizing enzyme and androgen receptor (AR) expression, local metabolism of classic and 11-oxyandrogen precursors, and the transcriptomic and metabolomic changes induced by these steroids, alongside effects on cell proliferation. Understanding androgen and 11-oxyandrogen function in HGSOC may reveal new therapeutic avenues that exploit steroid signaling within the tumor microenvironment.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Tissue analysis - public cohorts\u003c/h2\u003e\u003cp\u003eThe cohort from Chowdhury et al. \u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e included treatment-na\u0026iuml;ve tissue specimens from patients with stage III-IV disease who underwent primary debulking surgery followed by platinum/taxane adjuvant therapy and categorized with respect to response to adjuvant chemotherapy. The cohort consisted of 75 primary, 83 metastatic and 10 mixed HGSOC tissues with available proteomic data. In terms of chemotherapy response status, 93 were chemo-sensitive, and 75 were refractory. Transcriptomic data was available for 42 primary and 59 metastatic HGSOC tissues. Of them 55 were chemo-sensitive, 46 chemo-refractory. Baseline and detailed characteristics of patients can be found in the original article \u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. Data was retrieved from the Proteomic Data Commons (PDC000358, PDC000360).\u003c/p\u003e\u003cp\u003eThe cohort from the Cancer Genome Atlas \u0026ndash; ovarian cancer (TCGA-OV) \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e involved 489 treatment-na\u0026iuml;ve, clinically annotated stage II-IV HGSOC samples. Chemotherapy response status was available for 211 samples, data on molecular tumor classification for 365 samples, proteomics data was available for 202 samples. Baseline and detailed characteristics of patients can be found in the original article \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. The data was accessed through the University of California San Francisco Xena browser (UCSC Xena).\u003c/p\u003e\u003cp\u003eFor survival analysis, optimal cut points for log2-transformed FPKM-upper quartile (uq)-normalized RNA-seq data were determined using maximally selected rank statistics with the maxstat package in R studio \u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. Survival plots were generated using the Kaplan Meier method. Uni and multivariate cox proportional hazards models were fitted to estimate hazard ratios. P-values were two-sided, confidence intervals were calculated at the 95% level, and significance was predefined as \u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Cell lines\u003c/h2\u003e\u003cp\u003eIn our study we used Caov-3, OVSAHO, Kuramochi, COV362, OVCAR-3 and OVCAR-4 as HGSOC models, and HIO-80 cell line as control.\u003c/p\u003e\u003cp\u003eThe immortalized HIO-80 cell line (CVCL_E274) were originally established from normal ovarian surface epithelium \u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e, and were kindly obtained from Andrew K. Godwin (University of Kansas Medical Center, USA) as passage 72. Cells in passage\u0026thinsp;+\u0026thinsp;10 were authenticated by short tandem repeat (STR) profiling performed by ATCC on 22 February 2019. HIO-80 cells were grown in a RPMI (R5586; Sigma-Aldrich, USA), with 10% FBS (F9665; Sigma\u0026ndash;Aldrich) and 2 mM L-glutamine (G7513; Sigma\u0026ndash;Aldrich).\u003c/p\u003e\u003cp\u003eThe Caov-3 cell line (CVCL_0201) is a primary HGSOC cell line isolated from a 54-year-old, white patient \u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. Caov-3 was purchased from ATCC (ATCC-HTB-75, lot 70026036) on December 2, 2021. STR profiling was performed by ATCC. Caov-3 cells were grown in DMEM (D5671; Sigma-Aldrich, USA), supplemented with 10% FBS (F9665; Sigma-Aldrich, USA) and 2 mM L-glutamine.\u003c/p\u003e\u003cp\u003eThe OVSAHO (CVCL_3144) cell line was established from a serous papillary adenocarcinoma from a metastatic site in the abdomen \u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e of a 56-year-old woman and was purchased from JCRB (JCRB1046; lot 04062015) on June 4, 2018. STR profiling authentication was performed by JCRB. OVSAHO cells were grown in RPMI (R5886; Sigma-Aldrich, USA), supplemented with 10% FBS (F9665; Sigma-Aldrich, USA) and 2 mM L-glutamine.\u003c/p\u003e\u003cp\u003eThe Kuramochi (CVCL_1345) cell line was originally established from HGSOC from a metastatic site in the ascites \u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e and was purchased from JCRB (JCRB0098, lot 06302015) on October 23, 2017. STR profiling authentication was performed by JCRB. Kuramochi cells were grown in RPMI (R5886; Sigma-Aldrich, USA), supplemented with 10% FBS (F9665; Sigma-Aldrich, USA) and 2 mM L-glutamine.\u003c/p\u003e\u003cp\u003eThe COV362 (CVCL_2420) cell line was originally established from a high-grade ovarian serous adenocarcinoma derived from a metastatic site in pleural effusion \u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. It was purchased from ECACC (ECACC 07071910) on October 13, 2017. STR profiling authentication was performed by ECACC. COV362 cells were grown in DMEM (D5546; Sigma-Aldrich, USA), supplemented with 10% FBS (F9665; Sigma-Aldrich, USA) and 2 mM L-glutamine.\u003c/p\u003e\u003cp\u003eThe OVCAR-3 (CVCL_0465) cell line is a HGSOC cell line established from the ascitic fluid of a 60-year-old patient with cisplatin-refractory OC \u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. It was purchased from ATCC (HTB-161, lot 70018483) on December 2, 2021. STR profiling authentication was performed by ATCC. OVCAR-3 cells were grown in modified RPMI (A10491\u0026ndash;01; Gibco\u0026trade;, Thermo Fisher Scientific, USA), supplemented with 20% FBS (F7524; Sigma-Aldrich, USA) and 2 mM L-glutamine.\u003c/p\u003e\u003cp\u003eThe OVCAR-4 (CVCL_1627) cell line is a HGSOC cell line established from the ascites of a 42-year-old patient with a cisplatin-refractory OC, and resistant to multiple chemotherapeutic agents \u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e. It was purchased from Sigma-Aldrich, USA on June 18, 2021 (SCC258). STR profiling authentication was performed by Sigma-Aldrich, USA. OVCAR-4 cells were grown in RPMI (R5586; Sigma-Aldrich, USA), supplemented with 10% FBS (F9665; Sigma\u0026ndash;Aldrich) and 2 mM L-glutamine.\u003c/p\u003e\u003cp\u003eAll cell lines were grown in a cell incubator at 37\u0026deg;C and 5% CO\u003csub\u003e2\u003c/sub\u003e. Passages up to a maximum of p\u0026thinsp;+\u0026thinsp;20 were used in this study. All cell lines were regularly tested and were negative for mycoplasma infection using the MycoAlert\u0026trade; Mycoplasma Detection Kit (LT07\u0026ndash;418, Lonza, Basel, Switzerland). Chemo-sensitivity status was previously reported in Pavlič et al. and Marolt et al. \u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. RNA isolation\u003c/h2\u003e\u003cp\u003eTotal RNA extraction was carried out using a Macherey-Nagel kit (#740933.5, Macherey-Nagel GmbH\u0026amp;Co, Germany), according to the manufacturer\u0026rsquo;s instructions. Samples of total RNA (10 \u0026micro;g) were transcribed into cDNA using the SuperScript\u0026reg; VILO\u0026trade; cDNA Synthesis kit (#11754050, Invitrogen, USA) according to the manufacturer\u0026rsquo;s instructions. The cDNA samples were stored at -20\u0026deg;C.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4. Quantitative PCR\u003c/h2\u003e\u003cp\u003eFor quantitative PCR analysis we first cultured cells in complete culture media under standard conditions until 80% confluence. Quantitative PCR was performed using TaqMan\u0026reg; Fast Advanced Master Mix (Applied Biosystems; Foster City, CA, USA) and TaqMan\u0026reg; probes (\u003cem\u003eSTS, SULT2B1, SULT2A1\u003c/em\u003e, \u003cem\u003ePAPSS1, PAPSS2\u003c/em\u003e, \u003cem\u003eCYP11B1\u003c/em\u003e, \u003cem\u003eHSD11B2\u003c/em\u003e, \u003cem\u003eHSD11B1\u003c/em\u003e, \u003cem\u003eH6PD\u003c/em\u003e, \u003cem\u003eSRD5A1\u003c/em\u003e; \u003cem\u003eSRD5A2\u003c/em\u003e; \u003cem\u003eSRD5A3\u003c/em\u003e; \u003cem\u003eHSD3B1\u003c/em\u003e, \u003cem\u003eHSD3B2\u003c/em\u003e, \u003cem\u003eAKR1C3\u003c/em\u003e; all from Thermo Fisher Scientific, USA; catalogue numbers are given in Supplementary File 1, section S2.4), and SYBR Green I Master (Roche, Basel, Switzerland) and probes: \u003cem\u003eAR-A\u003c/em\u003e, \u003cem\u003eAR-B\u003c/em\u003e (Sigma Aldrich) and \u003cem\u003eGPRC6A\u003c/em\u003e (Integrated DNA Technologies, USA); primer sequences are given in Supplementary File 1, section S2.4. Some of these genes were evaluated previously by our group \u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e, and were reused for this study.\u003c/p\u003e\u003cp\u003eQuantitative PCR was performed using the Applied Biosystems\u0026reg; ViiA\u0026trade; 7 Real-Time PCR System (Thermo Fisher Scientific, Waltham, MA, USA); see Supplementary File 1, section S2.4. Each sample\u0026rsquo;s normalization factor was calculated based on the geometric mean of two housekeeping genes, \u003cem\u003ePOLR2A\u003c/em\u003e (#Hs00172187_m1) and \u003cem\u003eHPRT1\u003c/em\u003e (#Hs99999909_m1). The relative expressions of the genes of interest were calculated in each sample from the quantification cycle (Cq), as E\u003csup\u003e\u0026minus;\u0026thinsp;Cq\u003c/sup\u003e, divided by the normalization factor. The guidelines of the Minimum Information for Publication of Quantitative Real-Time PCR Experiments were followed when performing qPCR and interpreting the results \u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eGene expression was compared using one-way ANOVA with Tukey\u0026rsquo;s Honestly Significant Difference (HSD) \u003cem\u003epost-hoc\u003c/em\u003e test or Kruskal-Wallis with Dunn\u0026rsquo;s multiple comparisons as appropriate. Differences with p values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered statistically significant.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5. Steroid metabolism studies\u003c/h2\u003e\u003cp\u003eCell lines were seeded in 6-well plates in complete culture media at a cell density required to reach 70% confluence in 24h. After 24h, the cells were washed with DPBS (#D8537, Sigma Aldrich), and the medium was replaced with phenol red-free, FBS-free medium (DMEM medium (#D5921, Sigma-Aldrich, USA) for Caov-3 and COV362; RPMI 1640 medium (#11835, Sigma-Aldrich, USA) for OVCAR-4 and RPMI 1640 medium for OVSAHO, OVCAR-3 and Kuramochi (#R7509, Sigma-Aldrich, USA).\u003c/p\u003e\u003cp\u003eCells were incubated with 1.6 \u0026micro;M dehydroepiandrosterone sulphate (DHEAS, #D5297, Sigma Aldrich GmbH), 15 nM dehydroepiandrosterone (DHEA, #A8500-00, Sigma Aldrich GmbH), 3 nM androstenedione (A4, #A6030-000, Steraloids), 15 nM 11β-hydroxy-androstenedione (11OHA4, #A-3009, Sigma Aldrich GmbH), or 3 nM 11-keto-androstenedione (11KA4, #284998, Sigma Aldrich GmbH), for 4, 8, 24, 48, and 72 h. All steroid precursors were prepared in ethanol (#1.11727, Supelco). After each point, the culture media were collected in Eppendorf tubes and stored at -80\u0026deg;C until steroid extraction. Three independent experiments were performed, each in technical duplicates.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6. Steroid extraction\u003c/h2\u003e\u003cp\u003eSample preparation involved liquid-liquid extraction with methyl-\u003cem\u003etert\u003c/em\u003e-butyl ether (MTBE, #1634-04-4, Sigma Aldrich GmbH) as described previously \u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e. Briefly, 1 mL of culture media was thawed and mixed with an internal standard, [\u003csup\u003e13\u003c/sup\u003eC\u003csub\u003e3\u003c/sub\u003e]-T (#730610, Sigma Aldrich GmbH). Next, 750 \u0026micro;L of MTBE/sample was added, and the samples were shaken for 10 min in an Eppendorf thermomixer (#5382000031, Eppendorf). After phase separation, the organic layer was collected in a separate tube; this was repeated thrice, after which samples were dried under vacuum at 45\u0026deg;C. Prior to LC-MS/MS analysis, samples were reconstituted in 70 \u0026micro;L of 70% methanol (#34966, Honeywell/Riedel-de Haen) in water (#1.15333, Supelco) (v/v) with 0.2 mM NH\u003csub\u003e4\u003c/sub\u003eF (#52481, Honeywell/Fluka).\u003c/p\u003e\u003cp\u003eFor DHEAS, we performed solid-phase extraction (SPE) on C18 columns (#8B-S001-EAK, Phenomenex) as previously described \u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. We used 100 \u0026micro;L of culture media, to which we added internal standard, DHEAS-d\u003csub\u003e5\u003c/sub\u003e (#D-066, Cerilliant). SPE included: column conditioning with 1 mL of methanol, equilibration with 1 mL of water, sample loading, column drying for 10 min, and elution with 1.5 mL methanol. Subsequently, samples were evaporated under vacuum at 45\u0026deg;C and reconstituted in 150 \u0026micro;L of 70% methanol with 0.2 mM NH\u003csub\u003e4\u003c/sub\u003eF before LC-MS/MS.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.7. Liquid chromatography-tandem mass spectrometry (LC-MS/MS)\u003c/h2\u003e\u003cp\u003eSteroids were analyzed as described previously \u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e. DHEA, A4, testosterone (T, #B6500, Sigma Aldrich GmbH), 5α-dihydrotestosterone (DHT, #A2579-000, Steraloids), 11OHA4, 11KA4, 11β-hydroxy-testosterone (11OHT, #A5760-000, Steraloids), 11-keto-testosterone (11KT, #K8250, Sigma Aldrich GmbH), and 11-keto-dihydrotestosterone (11KDHT, #A2375-000, Steraloids) were profiled in a single run in positive electrospray ionization (ESI) mode. DHEAS was analyzed separately in ESI-negative mode. Compound- and instrument-specific parameters can be found in the original article by Gjorgoska et al. \u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eChromatographic separation was performed on a Shimadzu Nexera XR HPLC system (Shimadzu Corporation, Kyoto, Japan) with a Kinetex 2.6 \u0026micro;m XB-C18 (100 \u0026times; 4.6 mm) column (#00D-4496-E0, Phenomenex). Mobile phase A (5% methanol in H\u003csub\u003e2\u003c/sub\u003eO, 0.2 mM NH\u003csub\u003e4\u003c/sub\u003eF) and B (methanol, 0.2 mM NH\u003csub\u003e4\u003c/sub\u003eF) were used in both methods, but with a different gradient elution profile (described in Gjorgoska et al. \u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e). The column temperature was set to 45\u0026deg;C for the ESI-positive mode method and 38\u0026deg;C for DHEAS. In both methods, the total solvent flow was set at 0.5 mL/min, the injection volume was 25 \u0026micro;L.\u003c/p\u003e\u003cp\u003eThe MS analysis was performed on a Sciex 3500 Triple Quadrupole system (AB Sciex Deutchland GmbH, Darmstadt, Germany). The lower limit of quantification for each analyte can be found in the original article \u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. Data acquisition and analysis were performed using the Analyst 1.6 software. Calibrators ranging from 5 pg/mL to 250 ng/mL (or in the case of DHEAS, 5 pg/mL to 500 ng/mL) were prepared in cell culture media and extracted as samples. 1/x weighing, and linear least squares regression was used to produce standard curves.\u003c/p\u003e\u003cp\u003eGroups were compared by time points with one-way ANOVA with Tukey\u0026rsquo;s HSD \u003cem\u003epost-hoc\u003c/em\u003e test or Kruskal-Wallis with Dunn\u0026rsquo;s multiple comparisons as appropriate. Differences with p values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered statistically significant.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.8. mRNA sequencing\u003c/h2\u003e\u003cp\u003eOVSAHO cells were seeded at cell density 2x10\u003csup\u003e6\u003c/sup\u003e/well in 6-well plates in complete culture media. After 24h, the culture media was replaced with phenol-red free, steroid-free culture media with L-glutamine as a supplement. Following incubation, cells were washed with DPBS, and lysates were collected using the NucleoSpin RNA isolation protocol, immediately snap-frozen and stored at \u0026minus;\u0026thinsp;80\u0026deg;C. Total RNA was extracted using the NucleoSpin RNA kit. RNA integrity was assessed using an Agilent 2100 Bioanalyzer and was \u0026gt;\u0026thinsp;9.5 for all sequenced samples.\u003c/p\u003e\u003cp\u003emRNA sequencing was performed by Novogene Inc. Briefly, mRNA was enriched using poly-T oligo-attached magnetic beads and fragmented. First-strand cDNA synthesis was performed using random hexamer primers, followed by second-strand synthesis using dTTP. Libraries were pooled and sequenced on an Illumina NovaSeq X platform using paired-end 150 bp (PE150) reads (approximately 12 Gb raw data/sample). Raw reads were processed with fastp software, clean reads were aligned to the reference genome (GRCh38) using HISAT2 v2.0.5. Gene-level counts were generated using featureCounts v1.5.0-p3, and gene expression was quantified as Fragments Per Kilobase per Million mapped fragments (FPKM).\u003c/p\u003e\u003cp\u003eDifferential gene expression analysis on raw gene counts was performed using the DeSeq2 package in R Studio \u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e. P-values were adjusted for multiple testing using the Benjamini Hochberg (BH) method. Differentially expressed genes were identified based on a fold change threshold greater than 2 (absolute value) and an adjusted p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01. Pathway activity scores were calculated using single-sample gene set enrichment analysis (ssGSEA) implemented in the GSVA R package \u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e, and gene sets from the Reactome database. Differences in pathway activity scores between groups were analyzed by moderated t-test with BH correction for multiple testing; adjusted p values less than 0.01 were considered significant. Three independent experiments were performed.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e2.9. Untargeted metabolomics with NMR\u003c/h2\u003e\u003cp\u003eOVSAHO cells were seeded at cell density 2x10\u003csup\u003e6\u003c/sup\u003e/well in 6-well plates in complete culture media. After 24h, the culture media was replaced with phenol-red free, steroid-free culture media with L-glutamine as a supplement. Cells were treated with 10 nM bioactive androgens (T and DHT), 10 nM bioactive 11-oxyandrogens (11KT and 11KDHT) or vehicle (ethanol) for 72h. After the incubation, cells were washed with PBS three times, then 300 \u0026micro;L deuterated water D\u003csub\u003e2\u003c/sub\u003eO/well was added and cells were scraped from the surface. The cell lysates were collected in an Eppendorf tube and briefly sonicated on ice. Sonicated lysates were stored at -80\u0026deg;C until NMR analysis.\u003c/p\u003e\u003cp\u003eFor NMR analysis, cell lysates were thawed and transferred into Teflon tube liner (wilmad cat. no. 6005), which was inserted into a 5 mm NMR tube containing 150 \u0026micro;L D\u003csub\u003e2\u003c/sub\u003eO solution with 1 mM 3-(Trimethylsilyl)propionic-2,2,3,3-d\u003csub\u003e4\u003c/sub\u003e acid sodium salt (TMSP-d\u003csub\u003e4\u003c/sub\u003e) used for reference. NMR spectra were acquired on a Bruker Avance NEO 600 MHz NMR spectrometer equipped with a 5 mm BBO probe at 298 K. Spectra were processed with TopSpin 4.0.8 (Bruker). Metabolite signals were assigned using 2D NMR spectra and the Human Metabolome Database \u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e. Full details of NMR acquisition parameters, pulse sequences, and processing are provided in Supplementary File 1, section S2.9.\u003c/p\u003e\u003cp\u003eProcessed 1D \u0026sup1;H-CPMG NMR spectra were imported into Python (version 3.12) using nmrglue library \u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e. The spectra were manually bucketed with variable width to ensure no signal cutting. The integrated signal intensities of these spectral buckets were exported to a TXT file for statistical analysis in R studio version 4.3.0. Differential analysis in metabolite levels between treatment groups was performed with unpaired t-test with BH correction for multiple comparisons; fold changes greater than 1.5 and adjusted p values lower than 0.01 were considered statistically significant.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e2.10. Cell proliferation assay\u003c/h2\u003e\u003cp\u003eOVSAHO cells were seeded in 96-well plates (5000 cells/well) in complete culture media. After 24h, the culture media was replaced with phenol-red free, FBS-free culture media supplemented with L-glutamine and treated with 10 and 100 nM concentration of T, DHT, 11KT, 11KDHT or ethanol as control for a total of 72h, after which cell viability was assessed with Alamar Blue HS reagent (#A50100, Thermo Fisher Scientific, USA), following manufacturer instructions. Viability was normalized to control condition.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e2.11. Statistical analysis\u003c/h2\u003e\u003cp\u003eData analysis was performed in GraphPad Prism software for Windows, version 10 (San Diego, CA, USA) and on R studio version 4.3.0 or higher. Data is expressed as median and interquartile range (IQR), unless otherwise stated. The statistical tests used are specified in the appropriate method section, and in figure legends.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1. Primary and metastatic HGSOC tumors differ in expression of several androgen- and 11-oxyandrogen-metabolizing enzymes\u003c/h2\u003e\n \u003cp\u003eWe analyzed the gene and protein expression of key enzymes involved in androgen and 11-oxyandrogen metabolism in primary and metastatic HGSOC tumors using data from Chowdhury et al. cohort \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. The position of these enzymes in the steroid biosynthesis pathways is given in Supplementary Fig.\u0026nbsp;1.\u003c/p\u003e\n \u003cp\u003eAmong enzymes involved in the metabolism of classic androgens (STS, SULT2A1, SULT2B1, PAPSS1, PAPSS2, HSD3B1/2), only \u003cem\u003ePAPSS2\u003c/em\u003e, which encodes the synthase generating 3\u0026apos;-phosphoadenosine-5\u0026apos;-phosphosulfate (PAPS), the universal sulfate donor to sulfotransferases \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/sup\u003e, was differentially expressed between primary and metastatic tumors, being higher in the latter (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA). Transcriptomic data were not available for \u003cem\u003eHSD3B1\u003c/em\u003e, suggesting low or absent expression. No significant differences were observed at the protein level for STS, SULT2B1, and PAPSS1/2 with respect to tumor site; proteomic data were missing for SULT2A1 and HSD3B1/2.\u003c/p\u003e\n \u003cp\u003eAmong enzymes involved in 11-oxyandrogen metabolism (CYP11B1, HSD11B2/1, and H6PD), \u003cem\u003eCYP11B1\u003c/em\u003e, required for 11\u0026beta;-hydroxylation of classic androgens (A4 and T) into 11-oxygenated androgens (11OHA4 and 11OHT, respectively), was not expressed in either primary or metastatic tumors, suggesting that 11-oxyandrogens do not form in situ from classical ones. \u003cem\u003eHSD11B2\u003c/em\u003e and \u003cem\u003eHSD11B1\u003c/em\u003e showed significantly higher gene expression in primary tumors compared to metastatic ones (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB-C), while \u003cem\u003eH6PD\u003c/em\u003e gene expression was similar across tumor sites. However, protein levels of HSD11B1/2 and H6PD were comparable between primary and metastatic tumors.\u003c/p\u003e\n \u003cp\u003eAmong enzymes involved in pre-receptor regulation of androgen action (AKR1C3, HSD17B2, HSD17B4, SRD5A1/2), only \u003cem\u003eSRD5A2\u003c/em\u003e gene expression differed significantly between tumor sites, being higher in primary compared to metastatic tumors (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eD). At a protein level, no significant differences were found in AKR1C3 and HSD17B4 levels across tumor sites, whereas no data was available for HSD17B2, and the SRD5A isoforms.\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2. Chemo-sensitive and chemo-resistant HGSOC tumors differ in expression of several androgen- and 11-oxyandrogen-metabolizing enzymes\u003c/h2\u003e\n \u003cp\u003eWe next examined gene and protein expression of key enzymes involved in androgen and 11-oxyandrogen metabolism in chemo-sensitive versus chemo-refractory HGSOC tumors, using data from the Chowdhury et al. cohort \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e, as well as gene expression data from primary HGSOC tumors in the TCGA-OV cohort \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003eIn the Chowdhury cohort, no significant differences were observed in the gene expression of enzymes involved in classic androgen metabolism (\u003cem\u003eSTS, SULT2A1, SULT2B1, PAPSS1/2, HSD3B2\u003c/em\u003e), 11-oxyandrogen metabolism (\u003cem\u003eHSD11B1, H6PD\u003c/em\u003e), or pre-receptor androgen regulation (\u003cem\u003eAKR1C3, HSD17B2/4, SRD5A1/2\u003c/em\u003e) between chemo-sensitive and chemo-refractory tumors. The only exception was \u003cem\u003eHSD11B2\u003c/em\u003e, which showed significantly higher expression in chemo-sensitive tumors (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA). At the protein level, expression of STS, SULT2B1, PAPSS1/2, HSD11B2/1, H6PD, AKR1C3, and HSD17B4 did not differ between the two groups.\u003c/p\u003e\n \u003cp\u003eIn primary HGSOC tumors from the TCGA-OV cohort, gene expression of \u003cem\u003eSTS\u003c/em\u003e (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eC) and \u003cem\u003eHSD17B2\u003c/em\u003e (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eD) was significantly higher in chemo-sensitive tumors. Similarly, \u003cem\u003eHSD11B2\u003c/em\u003e expression was also elevated in chemo-sensitive tumors compared to chemo-refractory ones, though this difference did not reach statistical significance (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB). Consistent with findings in the Chowdhury cohort, \u003cem\u003eCYP11B1\u003c/em\u003e expression was absent in primary tumors from the TCGA-OV cohort. Proteomics data were not available for this cohort.\u003c/p\u003e\u003cspan\u003e\n \u003cp\u003e\u003cstrong\u003e3.3 PAPSS1/2\u003c/strong\u003e, \u003cstrong\u003eHSD3B1\u003c/strong\u003e, \u003cstrong\u003eHSD11B2\u003c/strong\u003e, \u003cstrong\u003eH6PD\u003c/strong\u003e, \u003cstrong\u003eand\u003c/strong\u003e \u003cstrong\u003eHSD17B2/4\u003c/strong\u003e \u003cstrong\u003eare associated with survival outcomes in patients with primary HGSOC\u003c/strong\u003e\u003c/p\u003e\n \u003c/span\u003e\n \u003cp\u003eWe assessed the association between the expression of steroid-metabolizing enzymes and clinical outcomes in patients with primary HGSOC from the TCGA-OV cohort; survival data was not yet available for the Chowdhury cohort \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. Among enzymes involved in classic androgen metabolism (\u003cem\u003ePAPSS1, PAPSS2, HSD3B1, STS, SULT2A1, SULT2B1, HSD3B2\u003c/em\u003e), higher intra-tumoral \u003cem\u003ePAPSS1, PAPSS2\u003c/em\u003e, and \u003cem\u003eHSD3B1\u003c/em\u003e gene expression was associated with worse prognosis in terms of disease-specific survival (DSS) and disease-free interval (DFI) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). \u003cem\u003eSTS, SULT2A1, SULT2B1\u003c/em\u003e, and \u003cem\u003eHSD3B2\u003c/em\u003e expression were not significantly associated with survival.\u003c/p\u003e\n \u003cp\u003eFor enzymes involved in 11-oxyandrogen metabolism (\u003cem\u003eHSD11B2, HSD11B1, H6PD\u003c/em\u003e), higher gene expression of \u003cem\u003eHSD11B2\u003c/em\u003e was associated with improved DSS (HR: 0.73, 95% CI: 0.55\u0026ndash;0.97, p\u0026thinsp;=\u0026thinsp;0.03) and DFI (HR: 0.75, 95% CI: 0.58\u0026ndash;0.96, p\u0026thinsp;=\u0026thinsp;0.02), whereas higher \u003cem\u003eH6PD\u003c/em\u003e expression predicted worse DSS (HR: 1.66, 95% CI: 1.25\u0026ndash;2.22, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and DFI (HR: 1.30, 95% CI: 1.00\u0026ndash;1.68, p\u0026thinsp;=\u0026thinsp;0.048). \u003cem\u003eHSD11B1\u003c/em\u003e expression was not significantly associated with outcomes.\u003c/p\u003e\n \u003cp\u003eAmong enzymes involved in pre-receptor regulation (\u003cem\u003eAKR1C3, HSD17B2, HSD17B4, SRD5A1, SRD5A2\u003c/em\u003e), higher gene expression of \u003cem\u003eHSD17B2\u003c/em\u003e correlated with improved survival outcomes, whereas elevated \u003cem\u003eHSD17B4\u003c/em\u003e was associated with worse DSS and DFI (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). \u003cem\u003eAKR1C3\u003c/em\u003e showed a trend toward improved DSS (HR: 0.68, 95% CI: 0.46\u0026ndash;1.01, p\u0026thinsp;=\u0026thinsp;0.056), though not statistically significant. Finally, \u003cem\u003eSRD5A1\u003c/em\u003e and \u003cem\u003eSRD5A2\u003c/em\u003e expression were not associated with survival outcomes.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAssociation of intra-tumoral expression of steroid-metabolizing enzymes with survival of patients with primary HGSOC from the TCGA-OV cohort \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eGene\u003c/p\u003e\n \u003cp\u003e(high vs low expression)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eDSS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eDFI\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePAPSS1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.04\u0026ndash;1.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.027\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.13\u0026ndash;1.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePAPSS2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00-1.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.99\u0026ndash;1.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHSD3B1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00-1.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.99\u0026ndash;1.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHSD11B2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.55\u0026ndash;0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.026\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.58\u0026ndash;0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.020\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eH6PD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.25\u0026ndash;2.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00-1.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.048\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAKR1C3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.46\u0026ndash;1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.57\u0026ndash;1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.181\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHSD17B2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.50\u0026ndash;0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.55\u0026ndash;0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.004\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHSD17B4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.02\u0026ndash;1.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.035\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.13\u0026ndash;1.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.004\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eTotal number of participants, 371. \u003cem\u003ePAPSS1\u003c/em\u003e-low, n\u0026thinsp;=\u0026thinsp;222, \u003cem\u003ePAPSS1\u003c/em\u003e-high, n\u0026thinsp;=\u0026thinsp;149; \u003cem\u003ePAPSS2\u003c/em\u003e-low, n\u0026thinsp;=\u0026thinsp;244; \u003cem\u003ePAPSS2\u003c/em\u003e-high, n\u0026thinsp;=\u0026thinsp;127; \u003cem\u003eHSD11B2\u003c/em\u003e-low, n\u0026thinsp;=\u0026thinsp;160, \u003cem\u003eHSD11B2\u003c/em\u003e-high, n\u0026thinsp;=\u0026thinsp;211; \u003cem\u003eH6PD\u003c/em\u003e-low, n\u0026thinsp;=\u0026thinsp;242, \u003cem\u003eH6PD\u003c/em\u003e-high, n\u0026thinsp;=\u0026thinsp;129; \u003cem\u003eAKR1C3\u003c/em\u003e-low, n\u0026thinsp;=\u0026thinsp;313, \u003cem\u003eAKR1C3\u003c/em\u003e-high, n\u0026thinsp;=\u0026thinsp;58; \u003cem\u003eHSD17B2\u003c/em\u003e-low, n\u0026thinsp;=\u0026thinsp;171, \u003cem\u003eHSD17B2\u003c/em\u003e-high, n\u0026thinsp;=\u0026thinsp;200; \u003cem\u003eHSD17B4\u003c/em\u003e-low, n\u0026thinsp;=\u0026thinsp;129, \u003cem\u003eHSD17B4\u003c/em\u003e-high, n\u0026thinsp;=\u0026thinsp;242. The optimal cut-off value was estimated using Maximally Selected Rank Statistics \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. Abbreviations: CI, confidence interval; DFI, disease-free interval; DSS, disease-specific survival; HR, hazard ratio.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4. Androgen receptor expression correlates with better chemo-sensitivity and better survival\u003c/h2\u003e\n \u003cp\u003eWe evaluated the expression of AR in primary and metastatic HGSOC from both the Chowdhury cohort \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e and the TCGA-OV cohort \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. The gene and protein expression of AR were significantly higher in primary compared to metastatic tumors (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA-B). The gene and protein expression were also higher in chemo-sensitive compared to chemo-resistant tumors from the Chowdhury et al. cohort (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eC-D). In contrast, no significant differences were observed between chemo-sensitive and chemo-refractory tumors in the TCGA-OV cohort.\u003c/p\u003e\n \u003cp\u003eAR expression also varied between primary tumors of different molecular subtypes from the TCGA-OV cohort (Supplementary Fig. 2). Specifically, the differentiated subtype showed the highest \u003cem\u003eAR\u003c/em\u003e expression, with significantly higher levels than those in the immunoreactive and mesenchymal subtypes. At protein level, both differentiated and proliferative subtypes exhibited significantly higher AR expression than the immunoreactive and mesenchymal subtypes. Clinically, high AR protein expression was associated with better overall survival compared to low AR expression in patients with primary HGSOC (HR: 0.71, 95% CI: 0.55\u0026ndash;0.92, p\u0026thinsp;=\u0026thinsp;0.01) (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eE).\u003c/p\u003e\u003cspan\u003e\n \u003cp\u003e\u003cstrong\u003e3.5 Classic androgen precursors are a limited source of bioactive androgens and do not contribute to 11-oxyandrogen production in HGSOC cell lines\u003c/strong\u003e\u003c/p\u003e\n \u003c/span\u003e\n \u003cp\u003eWe assessed the expression of key steroid-metabolizing enzymes in HGSOC cell lines by qPCR (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). \u003cem\u003eCYP11B1\u003c/em\u003e expression was absent in all lines, indicating that local 11\u0026beta;-hydroxylation of A4 and T cannot occur. Next, we incubated cells with near-physiological concentrations of classic androgen precursors (DHEAS (1.6 \u0026micro;M), DHEA (15 nM), and A4 (3 nM)) for 72 h, and the resulting metabolites were quantified by LC-MS/MS (DHEAS, DHEA, A4, T, DHT, 11OHA4, 11KA4, 11OHT, 11KT, 11KDHT) (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA). No 11-oxyandrogens were detected in any cell line from any classic androgen precursor, consistent with the absence of \u003cem\u003eCYP11B1\u003c/em\u003e.\u003c/p\u003e\n \u003cp\u003eDHEAS, the most abundant androgen precursor, was moderately metabolized across all cell lines, primarily to DHEA (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eB-C, Supplementary Table 1), a conversion regulated by STS and sulfotransferases (primarily SULT2A1 and SULT2B1). All cell lines expressed \u003cem\u003eSTS\u003c/em\u003e and \u003cem\u003eSULT2B1\u003c/em\u003e, while \u003cem\u003eSULT2A1\u003c/em\u003e was absent (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e) \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. OVCAR-3 had the lowest \u003cem\u003eSTS\u003c/em\u003e but high \u003cem\u003eSULT2B1\u003c/em\u003e, resulting in a low \u003cem\u003eSTS/SULT2B1\u003c/em\u003e ratio; OVCAR-4 and Caov-3 also had high \u003cem\u003eSULT2B1\u003c/em\u003e. Accordingly, DHEA formation was lowest in OVCAR-3 and Caov-3. Downstream metabolites A4 and T formed at very low levels (\u0026lt;\u0026thinsp;1% of starting DHEAS), reflecting limited \u003cem\u003eHSD3B1/2\u003c/em\u003e expression. A4 and T formation was most prominent in HIO-80 and Caov-3, which had low \u003cem\u003eHSD3B1\u003c/em\u003e, while OVSAHO and Kuramochi formed less A4. DHT was below detection in all lines.\u003c/p\u003e\n \u003cp\u003eCells were also incubated with 15 nM DHEA, and OVSAHO and Caov-3 showed the highest conversion rates (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eF, Supplementary Table 1). A4 formed in HIO-80 and Caov-3, consistent with \u003cem\u003eHSD3B1\u003c/em\u003e expression, while other lines produced minimal A4 (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eG). Low T levels were detected only in Caov-3 (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eH); in the remaining lines, A4 and T accounted for ~\u0026thinsp;3% and 0.3% of DHEA, respectively, reflecting limited \u003cem\u003eHSD3B1/2\u003c/em\u003e expression. A substantial fraction of DHEA remained unaccounted for: Caov-3 (93.7%), OVSAHO (76.2%), OVCAR-4 (41.5%), COV362 (40%), OVCAR-3 (24.6%), HIO-80 (17.3%), and Kuramochi (15.6%) (Supplementary Table 1). This suggests DHEA conversion to other unmeasured metabolites, such as 5\u0026alpha;-androstenediol (via HSD17B) \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/sup\u003e or C7/C16-hydroxylated derivatives (via CYP3A4/5/7) \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/sup\u003e, which were not included in our assay.\u003c/p\u003e\n \u003cp\u003eRegarding A4, all cell lines metabolized the precursor, with Caov-3, OVSAHO, and OVCAR-3 showing the highest conversion (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eI, Supplementary Table 1). However, T formation remained low (\u0026lt;\u0026thinsp;5% of A4) in all cell lines, being altogether highest in COV362 consistent with its high \u003cem\u003eAKR1C3\u003c/em\u003e expression, however, the high \u003cem\u003eHSD17B2\u003c/em\u003e levels in this cell line potentially limited net T accumulation (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). As with DHEA, a large fraction of A4 remained unaccounted for, being most pronounced in OVSAHO (98.5%), OVCAR-3 (96.7%), and Caov-3 (95.4%), suggesting conversion to alternative metabolites such as 5\u0026alpha;-androstenedione (via SRD5A) or estrone (via CYP19A1). All lines expressed high \u003cem\u003eSRD5A1/3\u003c/em\u003e levels, with Caov-3 and OVCAR-3 also expressing \u003cem\u003eSRD5A2\u003c/em\u003e. This supports the formation of 5\u0026alpha;-reduced metabolites from A4. In terms of estrogen formation from A4, we showed previously \u003cem\u003eCYP19A1\u003c/em\u003e expression to be generally low in all cell lines \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e with COV362 showing the highest levels, indicating possible estrone formation.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003e3.6. 11-oxyandrogen precursors serve as an important source of bioactive 11KT in chemo-sensitive HGSOC cell lines\u003c/h2\u003e\n \u003cp\u003eWe next incubated HGSOC cell lines with 15 nM 11OHA4 for 72 hours and measured the formation of downstream 11-oxyandrogens, including 11KA4, 11OHT, 11KT, and 11KDHT (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eD). Among all cell lines, Kuramochi showed the most efficient conversion of 11OHA4 to 11KA4 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eA-B, Supplementary Table\u0026nbsp;1), which aligns with its high expression of \u003cem\u003eHSD11B2\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). This chemo-sensitive cell line also produced the highest levels of the bioactive androgen 11KT, followed by the chemo-sensitive OVSAHO cell line (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eC), indicating efficient conversion of 11KA4 to 11KT via AKR1C3. Notably, 11OHT was undetectable in all cell lines, suggesting that 11OHA4 is preferentially oxidized to 11KA4 via HSD11B2 rather than reduced to 11OHT (by reductive HSD17B enzymes like HSD17B3 \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e]\u003c/sup\u003e). 11KDHT was below the limit of quantification in all cell lines.\u003c/p\u003e\n \u003cp\u003eOf note, at the end of the 72-hour incubation, the total measured 11-oxyandrogens (11OHA4, 11KA4, 11OHT, and 11KT) accounted for only 75.1% of the initial 11OHA4 dose in OVCAR-3 and 83.0% in Caov-3, suggesting the formation of additional, unmeasured metabolites (Supplementary Table\u0026nbsp;1). The likely candidates include 11\u0026beta;-hydroxy-5\u0026alpha;-androstanedione (from 11OHA4) and 11-keto-5\u0026alpha;-androstanedione (from 11KA4), formed via SRD5A isoforms. This is supported by \u003cem\u003eSRD5A1-3\u003c/em\u003e expression in both OVCAR-3 and Caov-3 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eIn a separate experiment, we incubated cell lines with 3 nM 11KA4 for 72 hours. Caov-3 and OVSAHO showed the highest conversion of 11KA4 to downstream products, while the control cell line HIO-80 had the lowest conversion rate (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eE). Similarly, 11KT levels were lowest in HIO-80, followed by the chemo-refractory cell lines OVCAR-3 and OVCAR-4 (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eF). No 11OHA4, 11OHT, or 11KDHT were detected in any cell line. At the end of this incubation, detected 11-oxyandrogens accounted for 78.7% of the starting 11KA4 in OVCAR-3 and 90.0% in Caov-3, again pointing to the formation of unmeasured metabolites. A likely product is 11-keto-5\u0026alpha;-androstanedione, formed via SRD5A isoforms.\u003c/p\u003e\n \u003cp\u003eAltogether, we found the formation of bioactive 11KT from both 11-oxyandrogen precursors, 11OHA4 and 11KT, to be highest in the chemo-sensitive cell lines Kuramochi and OVSAHO, followed by the cell line of primary HGSOC, Caov-3 (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eG). Total 11KT levels were lowest in the control cell line, HIO-80, followed by the chemo-refractory cell lines OVCAR-3 and OVCAR-4.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003ch2\u003e3.7. Potent androgens and 11-oxyandrogens promote a stress-adapted proliferative state in \u003cem\u003eAR\u003c/em\u003e-expressing, chemo-sensitive HGSOC model\u003c/h2\u003e\n \u003cp\u003eFor evaluating the transcriptomic and metabolomic effects of potent androgens and 11-oxyandrogens, we selected the OVSAHO cell line, which exhibits the highest expression of \u003cem\u003eAR\u003c/em\u003e, including both the full-length variant \u003cem\u003eAR-B\u003c/em\u003e and the shorter \u003cem\u003eAR-A\u003c/em\u003e variant (Supplementary Fig.\u0026nbsp;3A-B). Notably, expression of \u003cem\u003eGPRC6A\u003c/em\u003e, which encodes a membrane receptor responsive to a broad range of ligands including androgens, was undetectable in all tested cell lines.\u003c/p\u003e\n \u003cp\u003eWe incubated OVSAHO cells with 10 nM of bioactive classic androgens (T or DHT) and 11-oxyandrogens (11KT or 11KDHT) for 72 hours to investigate transcriptomic changes induced by these compounds. Across all four treatments, we observed modest transcriptomic convergence, with only 23 genes (4%) commonly upregulated and 492 genes (7%) commonly downregulated (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eA-B), indicating a shared androgen-responsive transcriptional program. However, a more pronounced overlap was seen between DHT, 11KT, and 11KDHT, with 195 genes (33%) commonly upregulated and 3,910 genes (55%) commonly downregulated. The list of altered genes is given in Supplementary Table\u0026nbsp;2.\u003c/p\u003e\n \u003cp\u003ePathway enrichment analysis using the Reactome database revealed that all four treatments activated key cellular stress response mechanisms and perturbed cell cycle regulation (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eC). Specifically, stress-related pathways such as \u003cem\u003eNFE2L2\u003c/em\u003e (NRF2) signaling and the unfolded protein response were upregulated, suggesting an adaptive antioxidant response to oxidative and proteotoxic stress. In parallel, upregulation of anaphase-promoting complex/cyclosome (APC/C):Cdc20-mediated degradation of mitotic regulators pointed to enhanced turnover of cell cycle proteins and accelerated mitotic progression. Consistently across all androgen treatments, the Rho-GTPase cycle, which regulates actin polymerization, cell polarity, and migration, was the only pathway commonly downregulated. The list of altered cellular pathways is given in Supplementary Table\u0026nbsp;3.\u003c/p\u003e\n \u003cp\u003eInterestingly, upon incubation with DHT, 11KT or 11KDHT transcriptomic effects were even more pronounced (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eD). Apart from upregulated cellular stress response mechanisms and perturbed cell cycle regulation, there were other alterations, including upregulation of additional cell cycle-related pathways, immune response and antigen processing, protein processing and trafficking pathways, as well as downregulation of processes involved in regulation of translation, RNA quality control, metabolic pathways.\u003c/p\u003e\n \u003cp\u003eAt the metabolomic level, incubation with 11KDHT caused distinct intracellular changes, notably reducing levels of amino acids such as glutamine, glutamate, aspartate, and threonine, along with lower glutathione levels compared to control (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eE). Multiple intracellular glucose NMR signals, corresponding to \u0026alpha;- and \u0026beta;-anomers, were also reduced. In addition, several uridine diphosphate-N-acetyl-glucosamine (UDP-GlcNAc)-related NMR peaks were depleted, indicating a broader reduction in nucleotide sugar pools. These findings, derived from the analysis of 1D \u0026sup1;H-CPMG NMR spectra (Supplementary Fig.\u0026nbsp;4) with metabolite assignments confirmed using 2D NMR, align with transcriptomic data showing increased turnover of mitotic proteins and downregulated protein translation. Similar changes were seen with DHT treatment, but these were less pronounced, with most falling below the 1.5-fold change threshold, whereas T and 11KT did not induce significant metabolomic alterations (Supplementary Fig.\u0026nbsp;5). The complete list of significantly altered metabolites is provided in Supplementary Table\u0026nbsp;4.\u003c/p\u003e\n \u003cp\u003eFinally, we examined the effects of bioactive classic androgens (T and DHT) and 11-oxyandrogens (11KT and 11KDHT) at 10 nM and 100 nM on cell proliferation on OVSAHO cells. Although treatment with any of the androgens led to a general reduction in cell proliferation compared to control, these differences did not reach statistical significance (Supplementary Fig. 3C-D).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study provides new insights into androgen signaling in HGSOC through three key findings: (1) differential expression of steroid-metabolizing enzymes and AR between primary and metastatic tumors, with associations to chemotherapy response and survival; (2) heterogeneous local metabolism of classic and 11-oxygenated androgens in HGSOC cell models; and (3) distinct transcriptomic and metabolomic responses to bioactive androgens and 11-oxyandrogens in an AR-expressing HGSOC line. To our knowledge, this is the first comprehensive study to examine 11-oxyandrogen-related enzymes in HGSOC, their local metabolism, and their downstream functional effects.\u003c/p\u003e\u003cp\u003eCurrent knowledge of the steroid-metabolizing potential of HGSOC is limited, although the expression of several steroid-metabolizing enzymes, such as CYP17A1, AKR1C3, CYP19A1, HSD17B1 as well as AR has been previously reported \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. Our previous work also confirmed AKR1C3 expression in HGSOC tumors, but with no links to survival outcomes \u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. More recently, our single-cell RNA-sequencing analysis of HGSOC tumors revealed differential expression of multiple steroidogenic and steroid-metabolizing enzymes, including \u003cem\u003eAKR1C\u003c/em\u003e family members, \u003cem\u003eSRD5A\u003c/em\u003e isoforms, and steroid-conjugating enzymes, across epithelial, stromal, and immune cell compartments \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn this study, we identified several prognostic biomarker candidates among steroid-metabolizing enzymes in HGSOC tumors. Notably, higher expression of \u003cem\u003ePAPSS1/2\u003c/em\u003e, enzymes involved in steroid sulfation, was associated with worse survival in patients with primary HGSOC. This contrasts with previous reports where increased expression of \u003cem\u003eSULT1A1\u003c/em\u003e \u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e and SULT1E1 \u003csup\u003e[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]\u003c/sup\u003e, also involved in steroid sulfation, correlated with better outcomes in HGSOC and epithelial ovarian cancer, respectively, while elevated STS, which reverses sulfation, predicted poor prognosis in epithelial ovarian cancer \u003csup\u003e[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]\u003c/sup\u003e. These findings on STS and SULTs suggest that limiting the availability of active steroids through increased sulfation or decreased desulfation may improve survival. However, this does not explain the association of \u003cem\u003ePAPSS1/2\u003c/em\u003e with poorer prognosis.\u003c/p\u003e\u003cp\u003eFurthermore, we found \u003cem\u003eHSD11B2\u003c/em\u003e expression to be higher in chemo-sensitive vs chemo-resistant tumors and to corelate with better survival outcomes. These effects may reflect the role of HSD11B2 in glucocorticoid metabolism, where it inactivates cortisol to cortisone, thereby reducing ligand availability for GR, which has been linked to poorer survival outcomes in OC \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan additionalcitationids=\"CR52 CR53\" citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]\u003c/sup\u003e. However, we found no differences in \u003cem\u003eNR3C1\u003c/em\u003e expression, which encodes GR, between primary and metastatic tumors or between chemo-sensitive and chemo-refractory tumors in the Chowdhury et al. cohort. Similarly, no differences were observed between chemo-sensitive and chemo-resistant primary tumors in the TCGA-OV cohort, and \u003cem\u003eNR3C1\u003c/em\u003e expression was not significantly associated with patient survival. Apart from \u003cem\u003eHSD11B2\u003c/em\u003e, we also found \u003cem\u003eH6PD\u003c/em\u003e, another key enzyme in glucocorticoid and 11-oxyandrogen metabolism \u003csup\u003e[\u003cspan additionalcitationids=\"CR56 CR57\" citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]\u003c/sup\u003e, to be associated with worse prognosis. Therefore, a metabolic profile characterized by high \u003cem\u003eHSD11B2\u003c/em\u003e and low \u003cem\u003eH6PD\u003c/em\u003e expression may result in higher local 11KT levels and thus enhanced AR signaling, as well as lower intra-tumoral cortisol levels and consequently attenuated GR signaling, altogether leading to improved survival outcomes.\u003c/p\u003e\u003cp\u003eWe also found that \u003cem\u003eHSD17B2\u003c/em\u003e and \u003cem\u003eHSD17B4\u003c/em\u003e, both involved in pre-receptor steroid regulation, were associated with opposite survival outcomes: \u003cem\u003eHSD17B2\u003c/em\u003e with better, and \u003cem\u003eHSD17B4\u003c/em\u003e with worse prognosis. HSD17B2 inactivates estradiol and T \u003csup\u003e[\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]\u003c/sup\u003e, reducing both estrogen and androgen signaling. It also converts 20α-hydroxyprogesterone to active progesterone \u003csup\u003e[\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]\u003c/sup\u003e, potentially enhancing progesterone receptor signaling, previously linked to better survival and better chemosensitivity in HGSOC \u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. These combined effects may explain the survival advantage associated with high \u003cem\u003eHSD17B2\u003c/em\u003e expression. HSD17B4 also reduces estradiol levels but does not affect androgens \u003csup\u003e[\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]\u003c/sup\u003e, potentially tipping the balance toward AR signaling. The impact of this shift remains unclear, as estrogen receptor (ER)-related survival data are inconsistent \u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]\u003c/sup\u003e, and studies on AR are limited. However, one study linked higher intra-tumoral AR expression with improved survival and platinum sensitivity \u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e, consistent with our findings. Apart from steroid metabolism, HSD17B4 is involved in peroxisomal beta-oxidation pathway for fatty acids \u003csup\u003e[\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]\u003c/sup\u003e, therefore the association of HSD17B4 with survival outcomes in patients with HGSOC could be associated with alterations in fatty acid metabolism apart from steroid metabolism.\u003c/p\u003e\u003cp\u003eFurthermore, we observed distinct differences in the local steroid-metabolizing capacity of classic and 11-oxyandrogens among chemo-sensitive, chemo-refractory, and control HGSOC cell lines. More specifically, classic androgen precursors did not give rise to 11-oxyandrogens, likely due to the absence of \u003cem\u003eCYP11B1.\u003c/em\u003e Consistently, \u003cem\u003eCYP11B1\u003c/em\u003e expression was absent in HGSOC tumors in both clinical cohorts, further supporting the conclusion that in situ conversion of classic androgens to 11-oxyandrogens is unlikely in HGSOC. To our knowledge, ectopic expression of \u003cem\u003eCYP11B1\u003c/em\u003e in non-adrenal tissues has been reported primarily in colon carcinoma cell lines \u003csup\u003e[\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]\u003c/sup\u003e, and in tumor-associated monocyte-macrophage lineage cells isolated from colon carcinoma in mice \u003csup\u003e[\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eMoreover, classic androgen precursors were poor sources of bioactive androgens in HGSOC cell lines. DHEAS was moderately converted to DHEA, but further metabolism to A4 and T was limited, consistent with low or absent \u003cem\u003eHSD3B1/2\u003c/em\u003e expression, reflected also in HGSOC tumors in both clinical cohorts. This aligns with Poschner et al., who found minimal T formation in HGSOC cell lines from DHEA even at high micromolar doses \u003csup\u003e[\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]\u003c/sup\u003e. These data suggest that DHEA may be shunted into alternative metabolites (e.g., 5α-androstenediol, oxidized DHEA derivatives) or rapidly converted to A4 and subsequently to T, followed by T conjugation. Similarly, A4 conversion to T was limited, with the highest levels observed in \u003cem\u003eAKR1C3\u003c/em\u003e-high COV362 cells. Nevertheless, A4 was metabolized in all cell lines, likely via 5α-androstenedione formation, supported by \u003cem\u003eSRD5A1-3\u003c/em\u003e expression, or through rapid T formation followed by conjugation. These poorly characterized alternative routes may critically affect AR ligand availability and signaling in HGSOC.\u003c/p\u003e\u003cp\u003eIn contrast, conversion of 11-oxyandrogen precursors to bioactive 11KT was most pronounced in the chemo-sensitive, \u003cem\u003eHSD11B2\u003c/em\u003e-high Kuramochi cell line, followed by the chemo-sensitive OVSAHO, moderate in Caov-3, and lowest in non-malignant HIO-80 and chemo-refractory OVCAR-3 and OVCAR-4. The higher conversion rates in chemo-sensitive lines could point to a functional link between 11-oxyandrogen signaling and treatment response. Active 11KT generation supports AR signaling \u003csup\u003e[\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]\u003c/sup\u003e, which is associated with better survival and chemosensitivity in HGSOC \u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. Alternatively, local steroid metabolism may reflect broader metabolic competence, which is diminished in chemo-resistant cells, which are less differentiated and with mesenchymal features \u003csup\u003e[\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]\u003c/sup\u003e. Notably, this pattern is consistent with previous findings of differential estrogen metabolism between chemo-sensitive and chemo-resistant models \u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eFinally, we characterized the functional effects of androgen and 11-oxyandrogen signaling. We found that the incubation of \u003cem\u003eAR\u003c/em\u003e-expressing OVSAHO cells with potent androgens and 11-oxyandrogens activates a shared androgen-responsive program, characterized by increased proliferative signaling alongside cellular stress responses. The metabolomic effects were milder, with only 11KDHT inducing depletion of amino acids, glutathione, and nucleotide sugar metabolites. Taken together, these findings suggest that potent classic and 11-oxygenated androgens elicit stress-adaptive programs, which may sensitize cells to additional stressors, such as chemotherapy. This could explain the observed antiproliferative effects of androgens and 11-oxyandrogens and the positive association between intra-tumoral AR expression, chemotherapy response, and overall survival, both in our study and in prior work by Tan and colleagues \u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eOur study has several limitations. Most notably, we investigated local steroid metabolism and the transcriptomic and metabolomic effects of bioactive androgens and 11-oxyandrogens using cell lines, which are suboptimal as they represent only the epithelial cell population of the highly complex tumor microenvironment. Other cell populations also express steroid-metabolizing enzymes and steroid receptors \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Therefore, the integrated effects of steroid signaling in the tumor itself may differ significantly from those observed in epithelial cells alone.\u003c/p\u003e\u003cp\u003eNevertheless, our study also has important strengths. We employed six well-characterized HGSOC cell lines derived from both primary tumors and metastatic sites to capture the molecular diversity and heterogeneity of real-world HGSOCs. We also exposed these cells to physiologically relevant concentrations of steroid precursors to study their local metabolism. Collectively, our findings provide mechanistic insight into local androgen and 11-oxyandrogen metabolism and signaling in HGSOC with therapeutical implications.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWe identified distinct androgen and 11-oxyandrogen metabolism patterns across tumor sites and chemotherapy status in HGSOC, along with steroid-induced cellular vulnerabilities that may enhance responses to chemotherapy or molecularly targeted therapy. These findings highlight the therapeutic potential of modulating steroid pathways in HGSOC and warrant further investigation.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e11KA4\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;11-keto-androstenedione\u003c/p\u003e\n\u003cp\u003e11KDHT\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;11-keto-dihydrotestosterone\u003c/p\u003e\n\u003cp\u003e11KT\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;11-keto-testosterone\u003c/p\u003e\n\u003cp\u003e11OHA4\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;11β-hydroxy-androstenedione\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e11OHT\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;11β-hydroxy-testosterone\u003c/p\u003e\n\u003cp\u003eA4\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Androstenedione\u003c/p\u003e\n\u003cp\u003eAKR1C3\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Aldo- keto reductase family 1 member C3\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eANOVA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Analysis of variance\u003c/p\u003e\n\u003cp\u003eAR\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Androgen receptor\u003c/p\u003e\n\u003cp\u003eATCC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;American Type Culture Collection\u003c/p\u003e\n\u003cp\u003eBH\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Benjamini Hochberg\u003c/p\u003e\n\u003cp\u003eCI\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Confidence interval\u003c/p\u003e\n\u003cp\u003eCPMG\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Carr-Purcell-Meiboom-Gill\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCq\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Quantification cycle\u003c/p\u003e\n\u003cp\u003eCYP11B1\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Cytochrome P450 11β-hydroxylase\u003c/p\u003e\n\u003cp\u003eCYP19A1\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Aromatase\u003c/p\u003e\n\u003cp\u003eCYP3A\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Cytochrome P450 Family 3 Subfamily A\u003c/p\u003e\n\u003cp\u003eDFI\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Disease-free interval\u003c/p\u003e\n\u003cp\u003eDHEA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Dehydroepiandrosterone\u003c/p\u003e\n\u003cp\u003eDHEAS\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Dehydroepiandrosterone sulfate\u003c/p\u003e\n\u003cp\u003eDHT\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;5α-dihydrotestosterone\u003c/p\u003e\n\u003cp\u003eDMEM\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Dulbecco's Modified Eagle Medium\u003c/p\u003e\n\u003cp\u003eDPBS\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Dulbecco's phosphate-buffered saline\u003c/p\u003e\n\u003cp\u003eDSS\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Disease-specific survival\u003c/p\u003e\n\u003cp\u003edTTP\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Deoxythymidine triphosphate\u003c/p\u003e\n\u003cp\u003eECACC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;European Collection of Authenticated Cell Cultures\u003c/p\u003e\n\u003cp\u003eER\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Estrogen receptor\u003c/p\u003e\n\u003cp\u003eESI\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Electrospray ionization\u003c/p\u003e\n\u003cp\u003eFBS\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Fetal Bovine Serum\u003c/p\u003e\n\u003cp\u003eFPKM\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Fragments Per Kilobase per Million mapped fragments\u003c/p\u003e\n\u003cp\u003eGlc\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Glucose\u003c/p\u003e\n\u003cp\u003eGln\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Glutamine\u003c/p\u003e\n\u003cp\u003eGlu\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Glutamate\u003c/p\u003e\n\u003cp\u003eH6PD\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Hexose-6-phosphate dehydrogenase\u003c/p\u003e\n\u003cp\u003eHGSOC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;High-grade serous ovarian cancer\u003c/p\u003e\n\u003cp\u003eHPLC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;High performance liquid chromatography\u003c/p\u003e\n\u003cp\u003eHR\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Hazard ratio\u003c/p\u003e\n\u003cp\u003eHSD\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Honestly Significant Difference test\u003c/p\u003e\n\u003cp\u003eHSD11B\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;11β-hydroxysteroid dehydrogenase\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHSD17B\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;17β-hydroxysteroid dehydrogenase\u003c/p\u003e\n\u003cp\u003eHSD3B\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;3β-hydroxysteroid dehydrogenase\u003c/p\u003e\n\u003cp\u003eIQR\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Interquartile range\u003c/p\u003e\n\u003cp\u003eJCRB\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Japanese Collection of Research Bioresources\u003c/p\u003e\n\u003cp\u003eLC-MS/MS\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Liquid chromatography-tandem mass spectrometry\u003c/p\u003e\n\u003cp\u003eLeu\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Leucine\u003c/p\u003e\n\u003cp\u003eMTBE\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Methyl-tert-butyl ether\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNH4F\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Ammonium fluoride\u003c/p\u003e\n\u003cp\u003eNMR\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Nuclear magnetic resonance\u003c/p\u003e\n\u003cp\u003eNRF2\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Nuclear factor erythroid 2-related factor 2\u003c/p\u003e\n\u003cp\u003eOC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Ovarian cancer\u003c/p\u003e\n\u003cp\u003ePAPSS\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Phosphoadenosine 5'-phosphosulfate synthase\u003c/p\u003e\n\u003cp\u003ePARPI\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Poly (ADP-ribose) polymerase inhibitors\u003c/p\u003e\n\u003cp\u003eqPCR\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Quantitative polymerase chain reaction\u003c/p\u003e\n\u003cp\u003eRPMI\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Roswell Park Memorial Institute medium\u003c/p\u003e\n\u003cp\u003eSPE\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Solid phase extraction\u003c/p\u003e\n\u003cp\u003eSRD5A\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Steroid 5α-reductase\u003c/p\u003e\n\u003cp\u003essGSEA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Single-sample gene set enrichment analysis\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSTR\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Short Tandem Repeat\u003c/p\u003e\n\u003cp\u003eSTS\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Sulfatase\u003c/p\u003e\n\u003cp\u003eSULT\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Sulfotransferase\u003c/p\u003e\n\u003cp\u003eTCGA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;The Cancer Genome Atlas\u003c/p\u003e\n\u003cp\u003eT\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Testosterone\u003c/p\u003e\n\u003cp\u003eThr\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Threonine\u003c/p\u003e\n\u003cp\u003eTMSPA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;3-(Trimethylsilyl)propionic-2,2,3,3-d4 acid sodium salt\u003c/p\u003e\n\u003cp\u003eTPM\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Transcripts per million\u003c/p\u003e\n\u003cp\u003eUDP-GlcNAc\u0026nbsp; \u0026nbsp;Uridine diphosphate-N-acetyl-glucosamine\u003c/p\u003e\n\u003cp\u003eVEGF\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Vascular endothelial growth factor\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eSupplementary Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupplementary data related to this article can be found in the online version of the article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Ms. Lea \u0026Scaron;trum and Ms. \u0026Scaron;pela Kos for help with metabolism studies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCredit authorship contribution statement:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMarija Gjorgoska: Conceptualization, Data curation; Formal analysis, Investigation; Methodology, Visualization, Writing - original draft, Writing - review \u0026amp; editing. Klemen Pečnik, Investigation, Methodology, Formal analysis; Writing - review and editing. Nika Marolt: Writing - review and editing. Janez Plavec, Writing \u0026ndash; review and editing. Tea Lani\u0026scaron;nik Rižner: Conceptualization, Funding acquisition, Supervision, Writing - review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by the Slovenian Research and Innovation Agency, grant number J3-2535 and program funding P3-0449, both to T.L.R., and P1-242 to J.P.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data related to this article can be found in supplementary materials. The raw and processed RNA-seq data were deposited on Gene Expression Omnibus (GEO) database ID number: GSE302881.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interest:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eFerlay J EM, Lam F, Laversanne M, Colombet M, Mery L, Pi\u0026ntilde;eros M, Znaor A, Soerjomataram I, Bray F Global Cancer Observatory: Cancer Today. 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A Brief Review on Chemoresistance; Targeting Cancer Stem Cells as an Alternative Approach. \u003cem\u003eInternational Journal of Molecular Sciences\u003c/em\u003e. 2023;24(5). doi:10.3390/ijms24054487\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"high-grade serous ovarian cancer, intracrinology, 11-oxyandrogens, transcriptomics, metabolomics","lastPublishedDoi":"10.21203/rs.3.rs-8132276/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8132276/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHigh-grade serous ovarian cancer (HGSOC) is the most lethal gynaecological malignancy, exhibiting marked heterogeneity that complicates treatment. The implications of intratumoral androgen metabolism and signalling, particularly involving 11-oxygenated androgens, in HGSOC remains poorly understood.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe analysed expression of key androgen-metabolizing enzymes and androgen receptor (AR) in relation to tumor site, chemotherapy response, and survival using two independent public HGSOC cohorts. Quantitative gene expression and steroid metabolism assays upon incubation with classic and 11-oxyandrogen precursors were performed in six HGSOC cell lines and one normal ovarian epithelial cell line. Untargeted transcriptomic and metabolomic profiling were performed to assess cellular responses to potent classic and 11-oxygenated androgens in the \u003cem\u003eAR\u003c/em\u003e-positive OVSAHO cell line.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDifferential expression of steroid-metabolizing enzymes and AR were observed between primary and metastatic tumors and chemo-sensitive and chemo-resistant tumors. Higher intra-tumoral expression of \u003cem\u003eHSD11B2\u003c/em\u003e, \u003cem\u003eHSD17B2\u003c/em\u003e, and AR correlated with improved survival, whereas elevated \u003cem\u003ePAPSS1/2\u003c/em\u003e and \u003cem\u003eHSD17B4\u003c/em\u003e predicted poorer outcomes. In vitro, classic androgen precursors showed limited conversion to bioactive androgens and did not generate 11-oxyandrogens. In contrast, 11-oxyandrogen precursors were efficiently converted to the potent AR agonist 11-keto-testosterone (11KT) in chemo-sensitive HGSOC cell lines, but not in chemo-refractory or control lines. Potent classic and 11-oxyandrogens triggered stress-adaptive and proliferative transcriptional programs, with 11-keto-dihydrotestosterone (11KDHT) additionally driving widespread metabolic reprogramming, including depletion of amino acids, glutathione, and nucleotide sugar metabolites. These changes were associated with a trend toward reduced cell proliferation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur findings offer mechanistic insight into local androgen and 11-oxyandrogen metabolism and signalling in HGSOC and reveal steroid-induced cellular vulnerabilities that may synergize with chemotherapy or molecularly targeted therapies. These results further support the therapeutic potential of modulating steroid pathways in HGSOC.\u003c/p\u003e","manuscriptTitle":"Local androgen and 11-oxyandrogen metabolism and signaling emerges as a novel prognostic and therapeutic axis in high-grade serous ovarian cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-27 06:10:04","doi":"10.21203/rs.3.rs-8132276/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5d946649-1094-483d-b092-c91125556ae6","owner":[],"postedDate":"November 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-09T13:53:13+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-27 06:10:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8132276","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8132276","identity":"rs-8132276","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}