Claessens, Frank

Abstract Background Prostate cancer is a heterogeneous disease comprising diverse cell types within the tumor microenvironment that support cancer cell survival. Using single cell and spatial transcriptomics, we previously identified subpopulations of treatment-persistent cancer cells exhibiting regenerative properties and a distinct molecular signature. Herein, we aimed to characterize the locked ‘Persist’ signature (PS) and its association with outcomes in a large cohort of non-metastatic prostate cancer tumors. Methods Patients with non-metastatic prostate cancer that underwent Decipher prostate genomic classifier and whole-transcriptome profiling on biopsy (BX, n=146,941) and radical prostatectomy (RP, n=55,329) specimens between October 2016 and February 2024 were included (Veracyte Inc., San Diego, CA, USA). Baseline clinical and pathological data from deidentified patients as well as signature scores were retrieved from the Decipher Genomic Resource for Intelligent Discovery (GRID) registry (Clinical Trials identifier: NCT02609269). The PS score based on the expression of 17 genes was calculated for all samples. Results PS was significantly associated with adverse clinical and pathological variables. In biopsy sample, PS was higher in cases with worse T3/4 clinical stage, Grade group (GG4-5), and high/very high NCCN risk (p<0.001 for all). In RP sample, PS was significantly higher in patients with GG4-5, extraprostatic extension, seminal vesicle invasion and lymph node involvement (p<0.001 for all). PS was also higher in patients with very high Decipher (>0.85) in both BX and RP (p<0.001 for both). When evaluating the prognosis of PS in multiple retrospective cohorts, PS in biopsy was prognostic of adverse pathology features at RP. PS was also prognostic of distance metastasis (DM) in multiple RP cohorts with long-term follow up (META855). In the ARNEO trial testing apalutamide in addition to ADT as neo-adjuvant treatment prior RP, PS was prognostic of early recurrence. In the Stampede trial, PS was prognostic of progression free survival, PCSM and OS in both non-metastatic and metastatic disease. When associating PS with other GRID signatures, PS was higher in samples with homologous recombination deficiency, TP53 mutations, Luminal B and luminal proliferating subtypes. On the other hand, PS was lower in samples with low androgen receptor-activity, RB-loss and neuroendocrine-like tumors. Finally, PS was positively correlated with radiation sensitivity signatures. Conclusions Persist is a novel molecular signature associated with more aggressive tumor characteristics. Future studies aim to further examine beyond this signature potential to identify patients at risk of aggressive prostate cancer, and understand what the optimal treatments for those with treatment-persistent prostate cancer cells are. Citation Format: Francesco Ballesio, Sinja Taavitsainen, Mohammed Alshalalfa, Antti Kiviaho, Mazlina Ismail, Reetta Nätkin, Alexander Giesen, Thomas Munro, James A. Proudfoot, Hyunnam Ryu, Elai Davicioni, Matthew C. Cooperberg, R. Jeffrey. Karnes, Ruochen Jiang, Steven Joniau, Gerhardt Attard, Frank Claessens, Matti Nykter, Alfonso Urbanucci. Persist: a molecular signature for prostate cancer patients with preexisting treatment-persistent cells associated with aggressive disease [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Innovations in Prostate Cancer Research and Treatment; 2026 Jan 20-22; Philadelphia PA. Philadelphia (PA): AACR; Cancer Res 2026;86(2_Suppl):Abstract nr B079.

Abstract Molecular drivers of metastasis in patients with high-risk localized prostate cancer are poorly understood. Therefore, we aim to study molecular drivers of metastatic progression in patients with high-risk prostate cancer. A retrospective matched case-control study of two clinico-pathologically identical groups of patients with high-risk prostate cancer was undertaken. One group developed metastatic recurrence (n = 19) while the other did not (n = 25). The primary index tumor was identified by a uro-pathologist, followed by DNA and RNA extraction for somatic copy-number aberration (SCNA) analysis and whole-transcriptome gene expression analysis. In vitro and in vivo studies included cell line manipulation and xenograft models. The integrative CNA and gene expression analyses identified an increase in Antizyme Inhibitor 1 (AZIN1) gene expression within a focal amplification of 8q22.3, which was associated with metastatic recurrence of patients with high-risk prostate cancer in four independent cohorts. The effects of AZIN1 knockdown were evaluated, due to its therapeutic potential. AZIN1 knockdown effected proliferation and metastatic potential of prostate cancer cells and xenograft models. RNA sequencing after AZIN1 knockdown in prostate cancer cells revealed upregulation of genes coding for collagen subunits. The observed effect on cell migration after AZIN1 knockdown was mimicked when exposing prostate cancer cells to bio-active molecules deriving from COL4A1 and COL4A2. Our integrated CNA and gene expression analysis of primary high-risk prostate cancer identified the AZIN1 gene as a novel driver of metastatic progression, by altering collagen subunit expression. Future research should further investigate its therapeutic potential in preventing metastatic recurrence. Implications: AZIN1 was identified as driver of metastatic progression in high-risk prostate cancer through matrikine regulation.

Objective Long-term androgen deprivation therapy (ADT) negatively influences bone. The short-term effects on bone and mineral homeostasis are less known. Therefore, we aimed to investigate the early effects of ADT on calcium/phosphate homeostasis and bone turnover. Design Prospective cohort study. Methods Eugonadal adult, male sex offenders, who were referred for ADT to the endocrine outpatient clinic, received cyproterone acetate. Changes in blood markers of calcium/phosphate homeostasis and bone turnover between baseline and first follow-up visit were studied. Results Of 26 screened patients, 17 were included. The median age was 44 (range 20–75) years. The median time interval between baseline and first follow-up was 13 (6–27) weeks. Compared to baseline, an 81% decrease was observed for median total testosterone (to 3.4 nmol/L (0.4–12.2); P < 0.0001) and free testosterone (to 0.06 nmol/L (0.01–0.18); P < 0.0001). Median total estradiol decreased by 71% (to 17.6 pmol/L (4.7–35.6); P < 0.0001). Increased serum calcium (P < 0.0001) and phosphate (P = 0.0016) was observed, paralleled by decreased PTH (P = 0.0156) and 1,25-dihydroxyvitamin D3 (P = 0.0134). The stable calcium isotope ratio (δ44/42Ca) decreased (P = 0.0458), indicating net calcium loss from bone. Bone-specific alkaline phosphatase and osteocalcin decreased (P < 0.0001 and P = 0.0056, respectively), periostin tended to decrease (P = 0.0500), whereas sclerostin increased (P < 0.0001), indicating suppressed bone formation. Serum bone resorption markers (TRAP, CTX) were unaltered. Conclusions In adult men, calcium release from the skeleton occurs early following sex steroid deprivation, reflecting early bone resorption. The increase of sclerostin and reduction of bone formation markers, without changes in resorption markers, suggests a dominant negative effect on bone formation in the acute phase.

Abstract Background: Whether associations between circulating metabolites and prostate cancer are causal is unknown. We report on the largest study of metabolites and prostate cancer (2,291 cases and 2,661 controls) and appraise causality for a subset of the prostate cancer–metabolite associations using two-sample Mendelian randomization (MR). Methods: The case–control portion of the study was conducted in nine UK centers with men ages 50–69 years who underwent prostate-specific antigen screening for prostate cancer within the Prostate Testing for Cancer and Treatment (ProtecT) trial. Two data sources were used to appraise causality: a genome-wide association study (GWAS) of metabolites in 24,925 participants and a GWAS of prostate cancer in 44,825 cases and 27,904 controls within the Association Group to Investigate Cancer Associated Alterations in the Genome (PRACTICAL) consortium. Results: Thirty-five metabolites were strongly associated with prostate cancer (P &lt; 0.0014, multiple-testing threshold). These fell into four classes: (i) lipids and lipoprotein subclass characteristics (total cholesterol and ratios, cholesterol esters and ratios, free cholesterol and ratios, phospholipids and ratios, and triglyceride ratios); (ii) fatty acids and ratios; (iii) amino acids; (iv) and fluid balance. Fourteen top metabolites were proxied by genetic variables, but MR indicated these were not causal. Conclusions: We identified 35 circulating metabolites associated with prostate cancer presence, but found no evidence of causality for those 14 testable with MR. Thus, the 14 MR-tested metabolites are unlikely to be mechanistically important in prostate cancer risk. Impact: The metabolome provides a promising set of biomarkers that may aid prostate cancer classification.

125 Background: Men with lymph node involvement (LNI) at prostatectomy (RP) are at high risk of dying from prostate cancer. However, survival following RP is highly variable, with some men apparently cured. Nomograms developed for men with LNI have been based on series where all or the vast majority of men received adjuvant treatment. Because administration of adjuvant treatment is not universal, even for LNI, we evaluated whether the Decipher genomic classifier (GC) can improve upon clinical models to predict metastasis within 5 years (MET5) and prostate cancer specific mortality within 10 years (PCSM10) in a cohort of LNI patients, the majority of whom did not receive adjuvant treatment. Methods: 141 patients from 4 institutions (Johns Hopkins, Mayo Clinic, Leuven, MD Anderson) had LNI at RP, had adequate tissue and clinical data for analysis of MET5, and 86 were analyzed for PCSM10. 43% of men received adjuvant therapy. RP tumor tissue was analyzed by Affymetrix Human Exon 1.0 ST GeneChip; the GC was calculated based on 22 genes in the previously trained and validated algorithm. Logistic regression evaluated whether the GC, dichotomized as high risk (GC score > 0.6) vs low-intermediate risk (≤0.6), improved prediction of MET5 and PCSM10 beyond that achieved with established clinical prognostic factors. Results: 62 men (43%) developed MET5, and 35 (41%) developed PCSM10. For both MET5 and PCSM10 CAPRA-S, number of positive lymph nodes, and age were significant; adjuvant therapy was not significant. GC was a significant independent strong prognostic factor when added to the clinical model for prediction of MET5, odds ratio = 4.04 (95% CI: 1.48, 11.02), p = 0.006, and prediction of PCSM10, odds ratio = 6.71 (95% CI: 2.01, 22.38), p = 0.002. Addition of GC to the clinical model improved the AUC from 0.77 to 0.79 for MET5, and from 0.65 to 0.74 for PCSM10. Conclusions: The Decipher GC significantly improves upon clinical variables to predict metastasis and prostate cancer specific mortality in men at high risk due to LNI. This is the first study to show a genomic classifier predicts outcomes in men with LNI; validation is needed to determine if Decipher can improve treatment decisions in men with LNI.